pdf Kassel World Water Series 3
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pdf Kassel World Water Series 3
Water Use in Semi-arid Northeastern Brazil Modeling and Scenario Analysis Maike Hauschild . Petra Döll University of Kassel Center for Environmental Systems Research Kurt-Wolters-Straße 3 . 34125 Kassel . Germany Phone +49.561.804.3266 . Fax +49.561.804.3176 [email protected] . http://www.usf.uni-kassel.de Cover design by Maike Hauschild Cover photographs by Maike Hauschild and Petra Döll University of Kassel Center for Environmental Systems Research KASSEL WORLD WATER SERIES . REPORT No 3 Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Maike Hauschild, Petra Döll Center for Environmental Systems Research University of Kassel Water Availability and Vulnerability of Ecosystems and Society in the Northeast of Brazil Brazilian-German cooperation program financed by CNPq and BMBF May 2000 The Kassel World Water Series: Report No 1 – A Digital Global Map of Irrigated Areas Report No 2 – World Water in 2025 Report No 3 – Water Use in Semi-arid Northeastern Brazil Water Use in Semi-arid Northeastern Brazil Kassel World Water Series. Report Number 3 Report A0003, May 2000 Center for Environmental Systems Research University of Kassel • 34109 Kassel • Germany Phone +49.561.804.3266 • Fax +49.561.804.3176 [email protected] • http://www.usf.uni-kassel.de Please cite as: “Hauschild, M., Döll, P. (2000): Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis. Report A0003, Center for Environmental Systems Research, University of Kassel, 34109 Kassel, Germany.” Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Abstract If water is a scarce resource like in the semi-arid Northeast of Brazil, the water use as well as water quality must be managed in a proactive manner. In order to achieve a sustainable development of the region, water management decisions should be based on an assessment of future water use which includes the long-term effects of current activities and policies. In order to support water management decisions, we performed a scenario analysis of future water use in Piauí and Ceará in 2025 by 1. compiling, analyzing and integrating information about water use and water management in Piauí and Ceará, 2. developing the large-scale water use model NoWUM which covers the whole of Piauí and Ceará and provides sectoral water use estimates for each municipality, and, using NoWUM, 3. computing current (1996/98) water use, 4. deriving water use scenarios for the year 2025, which reflect different possible societal development paths and water demand management options, and 5. computing water scarcity indicators which show which municipalities will suffer most from water scarcity. For all sectors, the increase of water use between today and 2025 is higher in case of the Coastal Boom and Cash Crops scenario than in case of the Decentralization scenario. Due to increased water use (in 99% of the municipalities), water scarcity will become more severe in the future, even though runoff will increase in more than 50% of the municipalities due to climate change (average climate 2011-2040). The development of water use will predominantly be influenced by the development of the irrigation sector, above all the extension of irrigated areas. As a first indicator of water, scenarios of municipal nitrogen loads were computed. Future municipal nitrogen loads could be smaller than today's if 70% of the waste water in the capitals of the municipalities is subject to secondary treatment, as steep increase from today's coverage. The presented scenario analysis can form the basis for further investigating the effect of water management measures on water use, water scarcity and water quality. 3 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Contents 1 Introduction _______________________________________________________________________ 7 2 Methodology_______________________________________________________________________ 8 2.1 Large Scale Water Use Model NoWUM ____________________________________________ 8 2.2 Estimation of nitrogen loads ____________________________________________________ 10 2.3 Water use scenarios for 2025 ____________________________________________________ 11 3 Results __________________________________________________________________________ 14 3.1 Water use in 1996/98 ___________________________________________________________ 14 3.2 Water use in 2025 _____________________________________________________________ 18 3.3 Water scarcity ________________________________________________________________ 24 3.4 Water quality _________________________________________________________________ 27 4 Summary and conclusions __________________________________________________________ 28 5 References _______________________________________________________________________ 30 Appendix A: NoWUM model description A1 Overview _______________________________________________________________________ A2 Irrigation water use_______________________________________________________________ A2.1 Method_____________________________________________________________________ A2.2 Climate data input ___________________________________________________________ A2.3 Input of crop data____________________________________________________________ A2.4 Calculation of irrigation water use ______________________________________________ A3 Livestock water use _______________________________________________________________ A4 Domestic water use _______________________________________________________________ A4.1 Method_____________________________________________________________________ A4.2 Input data of population and public water supply volumes__________________________ A4.3 Computation of domestic water use _____________________________________________ A5 Industrial use____________________________________________________________________ A5.1 Methods and calculation ______________________________________________________ A5.2 Input data __________________________________________________________________ A6 Tourism water use________________________________________________________________ A6.1 Method and calculation _______________________________________________________ A6.2 Input data __________________________________________________________________ A7 Model input _____________________________________________________________________ A8 Model output ____________________________________________________________________ A9 References ______________________________________________________________________ 33 34 34 34 34 38 39 40 40 40 43 44 44 45 48 48 48 51 51 52 Appendix B: Maps and municipality values of modeled water use Municipality map of Ceará 1996 _______________________________________________________ Municipality map of Piauí 1996________________________________________________________ B1 Withdrawal water use of present state 1996/1998 _______________________________________ B2 Consumptive water use of present state 1996/1998 ______________________________________ B3 Withdrawal water use of 2025 Coastal Boom and Cash Crops scenario (RSA)_______________ B4 Withdrawal water use of 2025 Decentralization scenario (RSB)___________________________ B5 Withdrawal water use of 2025 Coastal Boom and Cash Crops intervention scenario (ISA)_____ 5 57 59 61 65 69 73 77 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 1 Introduction Scarcity of water is a major constraint for development in semi-arid Northeastern Brazil, a region characterized by recurrent droughts which are related to the El Niño phenomenon. In particular the rural population, mainly subsistence farmers, suffers from these droughts due to the loss of crops and livestock. During the last decades, irrigated agriculture has been regarded as the privileged development option for rural areas, with plans for a multitude of irrigation projects which have rarely been realized. Nevertheless, the irrigation sector has become the largest water user in Northeastern Brazil. Today, hopes are high that an extended production of irrigated fruits for export will strongly improve rural incomes. For the rural population, access to safe drinking water is difficult and becomes even more difficult during droughts, as only the urban population (or rather a part of it) is connected to the public water supply. The public water supply system has, in most cases, problems with serving the ever increasing number of urban dwellers who, given the convenience of tap water, consume relatively high amounts of water. In some areas today, and probably more in the future, industry and tourism are important water user that compete for water with the irrigation sector. Under the described conditions in Northeastern Brazil, it is necessary to manage both water supply and water demand. While the construction and proper management of water infrastructure is the necessary basis for a secure water supply, a concurrent water demand management is essential for a sustainable economic and social development of the region. Only by a proactive demand management can the scarce resource water be used efficiently. Water demand management requires an assessment of present and future water use in which water use is related to development paths and policy options. Such an assessment is preferably supported by a water use model which computes sectoral water uses at the appropriate spatial and temporal resolution. On the one hand, the water use model provides estimates of water use under current conditions, e.g. how much water is required for irrigated areas in both climatically average and drought years. On the other hand, it helps to derive scenarios of future water use which are a decision support for water managers and policy makers. In this report, we present a water use model for two states in Northeastern Brazil, Piauí and Ceará. The model computes sectoral water uses in all municipalities. We show the current water use situation and analyze water use scenarios for the year 2025 which reflect different possible development paths and water demand management options. A detailed description of the model, the data and the scenario assumptions is given in Appendix A. Taking into account the runoff 7 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis modeled by the hydrological modeling group of the WAVES project (Axel Bronstert and Andreas Güntner, Potsdam Institute for Climate Impact Research), we computed a water scarcity index in water use is compared to water availability. Water use generally leads to a degradation of water quality; as a first rough indicator for the pollution of surface waters by domestic waste water, we present scenarios of domestic nitrogen emission. Our research was conducted in the framework of the Brazilian-German cooperation program WAVES, financed by the Brazilian research ministry CNPq and the German research ministry BMBF. The goal of WAVES is to identify possible sustainable development paths for the two Brazilian states of Piauí and Ceará. Typical for Northeastern Brazil, these states are characterized by recurrent droughts that strongly affect subsistence farmers, leading to migration to the metropolitan areas of the country. Within WAVES, a dynamic systems analysis of the relationship between water availability and migration is done. A singular feature of WAVES is the development of an integrated model to simulate scenarios of the future of Piauí and Ceará. The model is created by an interdisciplinary team, in which each specific group is responsible for one submodule like, for example, our water use model or a hydrological model. The objective is to provide a tool for regional water management planning and thus to support decision makers in both states. Acknowledgments: We are grateful for the support by our Brazilian cooperation partners Prof. Dr. José Carlos de Araújo (UFC), Prof. Dr. Francisco Veloso (UFPI), Eng. Margarita López Gil (DHME), Prof. Marcos Airton Freitas (UNIFOR) and the following Brazilian institutions who provided us with the necessary data and made our work possible: in Piauí, AGESPISA, DHME, FIEPI, FUNASA, PIEMTUR, SEMAR, UFPI, and in Ceará, CAGECE, COGERH, DNOCS, FNS, IPLANCE, SDU, SEAGRI, SEMAR, SEPLAN, SETUR, SISAR, SOHIDRA, SRH, UFC. Besides, we thank the German partners of the WAVES project. 2 Methodology 2.1 Large Scale Water Use Model NoWUM The water use model NoWUM (Nordeste Water Use Model) simulates monthly values of withdrawal and consumptive water use in all 332 municipalities of Piauí and Ceará (Fig. 1). Withdrawal water use is the quantity taken from its natural location, while consumptive water use is the quantity of water lost to evapotranspiration. NoWUM is a stand-alone model as well as a module of the integrated model SIM of the WAVES project. The model distinguishes five water use sectors: irrigation, livestock, households, industry and tourism (Fig. 2). Irrigation water use is 8 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis calculated according to the methodology of FAO as a function of climate, irrigated area and type of irrigated crop (nine different crop classes). Livestock water use is calculated from the number of animals per species and a species specific water use for Northeast Brazil. We distinguish 11 different livestock species. For calculating the domestic water use, we differentiate households connected to public water supply and self-supplied households. The water use per capita in a connected household is calculated from municipality-specific data provided by the water supply agencies AGESPISA, FNS, CAGECE, SISAR in Piauí and Ceará. Future per capita domestic water use of connected households is assumed to be a function of income and water price. The economic concept of elasticities is used to compute the decreasing per capita water use in case of increasing water price (price elasticity) and the increasing per capita water use in case of increasing incomes (income elasticity). As no information on water use in unconnected households is available, a per capita withdrawal water use of 50 l/d is assumed. Industrial water use is a function of the industrial output and the industrial mix; we consider 19 industry branches and their branch-specific industrial output per municipality. Future industrial water use is also influenced by the water price. Like in the case of domestic water use, the application of watersaving technology is assumed to be driven by the water price. Tourism water use is a function of the touristic overnight stays. A detailed model description, which includes the parameterization and the data sources, can be found in Appendix A. rá a Ce í u a Pi N W E S 100 0 100 Kilometers Figure 1: The research region Piauí and Ceará, two federal states in Northeastern Brazil. 9 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis water use efficiency climate irrigated area crops water use efficiency number of livestock water use efficiency water price income population supplied water households connected to public water supply total water use per municipality (withdrawal and consumptive use) water use efficiency industrial output industrial mix water price water use efficiency overnight stays Figure 2: Scheme of the large-scale water use model NoWUM with modeled sectors and driving forces. 2.2 Estimation of nitrogen loads The return flow is the part of the withdrawn and used water that does not evapotranspirate but is returned to the environment. If the return water is of appropriate quality it can be reused. Thus, a water use model would ideally include the quality of the returned water in order to indicate the water consumption, i.e. the amount of water that cannot be used by a downstream user due to both evapotranspiration and pollution. Here, as a first indicator for pollution of surface water the nitrogen load from domestic waste is estimated for every municipality as a function of population and the existence and efficiency of waste water treatment: N − input i = Pi ∗ k N ∗(1 − Fi ∗ ws ) i: municipality of interest Pi: number of persons living in municipality i kN: N content of human waste in Europe (15 g N/p/d (de Wit and Schmoll, 1999)) Fi: fraction of cleaned waste water ws: efficiency of treatment plant (primary: minus 15% N, secondary: minus 30% N) Nitrogen loads from manure (livestock) and mineral fertilizers are not yet taken into account. 10 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 2.3 Water use scenarios for 2025 Within the WAVES Program, two reference scenarios for the year 2025 were developed. An interdisciplinary working group on scenarios developed the narrative storylines and quantified the driving forces. For a detailed description of the storylines and a complete quantification of the driving forces please consult Döll et al. (2000). Based on the general storylines and the driving forces, we derived additional driving forces that are specific to water use modeling. Then, using the driving forces as input to NoWUM, we developed scenarios of water use in the year 2025 which are consistent with the WAVES reference scenarios. We generated two reference scenarios, and an intervention scenario which simulates a higher number of households connected to the public water supply. 2.3.1 Storylines of the two reference scenarios Both reference scenarios are rooted in current societal developments. Scenario A, the Coastal Boom and Cash Crops scenario, can be regarded as the business as usual scenario; it represents a continuation of current trends and reflects general expectations of the future that exist in Piauí and Ceará. Scenario B, the Decentralization scenario, reflects the beginning development and increasing attractiveness of the medium-size towns in Piauí and Ceará, the regional centers which is caused by an improved infrastructure (e.g. universities) in these towns and a decreasing quality of live in the large metropolitan areas of the country. Scenario A: Coastal Boom and Cash Crops In Scenario A, the globalization trend is dominant. The economic, social and cultural differences between and within countries become smaller and smaller. Economic and technological growth is rapid, and the global trade strong. Environmental protection and resource conservation is done only reluctantly. In such a globalized world, there is a strong economic development in the coastal regions of Ceará and Piauí (which has a very short coastline only). The main development focus is the metropolitan area of Fortaleza, with booming commerce and industry. Fortaleza, however, is also subject to the typical problems of fast growing cities. The infrastructure is always lacking behind the needs of the population, and the favelas at the outskirts of the city grow. In addition, tourism is becoming an important economic sector along the coast. The capital of Piauí, Teresina, is only developing slowly due to its unfavorable location. In those areas of the Hinterland where water is available, the production of cash crops by large companies dominates over subsistence farming. In the remote South of Piauí, large scale commercial husbandry is performed. 11 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Scenario B: Decentralization: Integrated Rural Development In Scenario B, the world is more heterogeneous than in Scenario A. The emphasis is on local solutions to economic, social and environmental problems. Globally, the economic development is not as strong as in scenario A, whereas environmental protection and social equity more important. In such a world, the Northeast of Brazil becomes more or less decoupled from the dominating centers of Brazil in the South. In Piauí and Ceará, the development is characterized by regional centers, attractive medium-sized towns with improved infrastructure which become the markets for local and regional agricultural products. Besides, the agricultural products are processed and refined by small-scale industry in the regional centers. Economic growth is somewhat weaker than in Scenario A, and strongest in the water-rich Hinterland where local farmers, due to credits and education, have increased their productivity. The farmers are supported by the World Bank and other international institutions who wish to improve the living conditions in crisis-prone semi-arid regions. Tourism at the coast is increasing due to an increasing number of tourists from within the two states, but not as strong as in Scenario A. 2.3.2 Spatial resolution of the scenario driving forces Scenario regions are those spatial subunits of Piauí and Ceará that are assumed to develop in a homogeneous fashion. The two states were divided into eight scenario regions (Fig. 3): 1. Teresina, the capital of Piauí 2. Metropolitan area of Fortaleza, the capital of Ceará + Port of Pecém 3. Coastal region 4. Southern part of Piauí 5. Region with relatively large potential water resources in Ceará 6. Region with relatively large potential water resources in Piauí 7. Region with small potential water resources in Ceará 8. Region with small potential water resources in Piauí The scenario regions were derived by taking into account • similar (agro)economic conditions • administrative boundaries • natural conditions (sedimentary vs. crystalline subsurface, location within the river basin, precipitation) In case of conflict, the assignment of a municipality to a scenario region was based on the characteristics that appears to dominate its future development. 12 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis For each scenario region, the driving forces of the scenarios were quantified. For most driving forces, all the municipalities within a scenario region are assumed to be subject to the same percentage changes of the driving forces. Teresina Fortaleza and Pecém Coastal region Southern part of Piauí Large potential water resources in Ceará Large potential water resources in Piauí Small potential water resources in Ceará Small potential water resources in Piauí N W E S 100 0 100 Kilometers Figure 3: The eight scenario regions 2.3.3 Quantifying the driving forces The driving forces of water use are shown in Fig. 2. For each scenario region, the driving forces were quantified for both scenarios by taking into account their development in the past (data from 1991 and 1996, in most cases). All the municipalities within a scenario region are assumed to be subject to the same changes of the driving forces (an exception is the change in irrigated area where irrigation projects that are planned for certain municipalities are implemented in the model). If, for example, the annual growth of gross domestic product (GDP) is, in Scenario A, is 2.7% per year in the Coastal region, the GDP in each municipality in the coastal region increases with this rate, no matter what the current GDP of the municipality is today. Climate in 2025 is the average of the climate from 2010-2040. The climate scenario was generated by the WAVES climate group (Gerstengarbe, Potsdam Institute for Climate Impact Research) by downscaling the results of the global circulation model ECHAM4-OPYC (Röckner et al., 1996), which are based on the IS92a greenhouse gas emission scenario (Leggett et al., 1992). Downscaling was done by a cluster analysis of the different climate variables. For detailed 13 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis listings of all driving forces of water use per scenario region compare Appendix A and Döll et al. (2000). Table 1 shows the assumed changes of important driving forces of the water use scenarios between today and 2025, for both Scenario A (RSA) and Scenario B (RSB). Only the aggregate values over all eight scenario regions are shown here. The change of most driving forces is stronger in Scenario A than in Scenario B. Only the total population increase is equal but the distribution between scenario regions (in brackets) differs. The intervention scenarios assume an even larger expansion of the public water supply as already assumed in the reference scenarios. ISA represents the intervention scenario to RSA and ISB to RSB, respectively (Table 8 in Appendix A). Table 1: Assumed change of selected driving forces between today (1996/98) and 2025 for the whole or Ceará and Piauí and the two reference scenarios [%] irrigated areas population connected population industrial gross domestic product tourist overnight stays RSA: Coastal boom and cash crops + 440 + 25 (-27 to +70) + 70 + 220 + 250 RSB: Decentralization + 200 + 25 (+4 to +37) + 60 + 150 + 170 3 Results Appendix B contains tables with modeled consumptive and withdrawal water uses in each of the 332 municipalities, distinguishing the five water use sectors. Model results are for 1996/98 and 2025 (reference scenario A Coastal Boom and Cash Crops, reference scenario B Decentralization: Integrated Rural Development scenario and intervention scenario with extended public water supply). 3.1 Water use in 1996/98 3.1.1 States Table 2 shows the withdrawal and consumptive water use per sector under current conditions (1996/98), aggregated for the whole of Ceará and the whole of Piauí. In both states, the two most important water use sectors are irrigation and households. With respect to consumptive use, the irrigation sector is clearly dominating, which is due to the higher fraction of the withdrawn water that evapotranspirates during use. In Piauí, livestock water use is a larger fraction of total water 14 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis use than in Ceará. In Ceará, the industry and the tourism sector use nearly 10% of the water, their use is negligible in Piauí. The total withdrawal water use in Ceará is twice the water use in Piauí. This can be explained by the fact that the main driving forces of water use are higher in Ceará than in Piauí (Table 3). During a year, water use varies by a factor of 3 to 4 between the month with the lowest and the month with the highest water use; the seasonal variation is caused by the irrigation sector. Table 2: Withdrawal and consumptive water use per sector in 1996/98 as modeled with NoWUM. Ceará withdrawal use consumptive use [106 m3/yr] [106 m3/yr] Sector 324.0 194.4 irrigation1 81.2 81.2 livestock 225.5 45.1 households 25.6 5.1 industry option 1 46.2 9.2 industry option 2 16.7 3.3 tourism 329.1 - 333.2 minimal and maximal 673.0 - 694.0 annual value2 28.9 - 91.9 15.4 - 36.4 minimal and maximal monthly value3 [106 m3/month] Piauí withdrawal use consumptive use [106 m3/yr] [106 m3/yr] 127.5 76.5 65.1 65.1 123.6 24.7 0.9 0.2 4.1 0.8 2.1 0.4 319.1 - 322.4 166.9 - 167.6 9.6 - 41.2 5.8 - 16.1 1 calculated with irrigation areas of 1996/98 as average of irrigation requirements computed with climatic time series 1951-1980 2 minimal value with industry option 1 and maximal value with industry option 2 3 data for the month with minimum and the month with the maximum water use. Table 3: Driving forces of each water use sector in Ceará and Piauí 1996/98 Irrigated area [ha] Cows / pigs / sheep [106 m3] Population[106 m3] Industrial gross domestic product [[106 ´1995 US$/yr] Tourist overnight stays [106 m3/yr] 3.1.2 Ceará 43024 2.4 / 1.1 / 1.6 6.7 2843 36.3 Piauí 13170 1.7 / 1.4 / 1.3 2.7 363 3.8 Municipalities For water management both at the state and the drainage basin level, it is important to have spatially resolved water use information. NoWUM provides information on water use for each of the 332 municipalities in Piauí and Ceará. Fig. 4 shows the annual withdrawal water use 1996/98 in each municipality, for all water use sectors. Water use is expressed in mm/yr, i.e. it is normalized with the area of the municipality and is thus directly comparable to water availability expressed as runoff in mm/yr. The spatial pattern of irrigation water use (Fig. 4a) is explained by 15 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 1) the fraction of the area of the municipality that is irrigated, b) the percentage of crops that are grown all year round (banana, fruit trees, grass, sugar cane) and c) the interpolated climate data, which, however, have high uncertainty in the south of Piauí, in the Serra de Ibiapaba and in the Cariri. Large irrigated areas with fruit trees and vegetable cultivation occur in and around Teresina, in the Serra de Ibiapaba, in the Curu valley, along the Jaguaribe and in the Cariri region (Fig. 4a). The Sertão region of Ceará, with its high cattle density, emerges in the livestock water use map (Fig. 4b). Although the south of Piauí is economically dominated by animal husbandry, the per area livestock water use is low. The highest domestic water uses occur in the urban centers of Teresina, Parnaiba and Picos (Piauí) and in the metropolitan area of Fortaleza, Sobral, the Serra de Ibiapaba and the Cariri (Ceará) (Fig. 4c). Industrial water use also has maximum values in the urban centers. Industrial water use calculated with option 2 (industry type specific water intensities from Germany and industrial output from 19 industry types) is in most municipalities higher than option 1 simulations (direct information on industrial water use from water works) (Fig. 4d and 4e). The reality might be somewhere in between these two estimates. In 23 municipalities, option 2 provides information on industrial water use whereas it is zero under option 1. The most attractive coastal municipalities, the metropolitan area of Fortaleza and the pilgrims hot spot Juazeiro do Norte (Cariri) become evident in the tourism water use map (Fig. 4f). For all water use sectors, water use in Ceará is more intensive than in Piauí. Fig. 4g and 4h show the total withdrawal water use and the total consumptive water use of all water use sectors per municipality, respectively. 16 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis N W N E 100 0 100 Kilometers W S N E 100 0 100 Kilometers a) irrigation N 100 0 100 Kilometers W S 0 100 Kilometers E 100 0 100 Kilometers N E 100 0 100 Kilometers W S d) industry option 1 100 c) domestic N E E S b) livestock W W S S e) industry option 2 f) tourism [mm/a] 0 0.001 - 0.5 0.501 - 1 1.001 - 2 2.001 - 10 10.001 - 100 100.001 - 400 no data N W N E 100 0 100 Kilometers S g) total withdrawal water use W E 100 0 100 Kilometers S h) total consumptive water use Figure 4: Sectoral withdrawal water uses 1996/98 [mm/a] per municipality calculated with NoWUM: irrigation under average climatic conditions 1951-1980 (a), livestock (b), households (c), industry option 1 (d), industry option 2 (e), tourism (f), total withdrawal with industry option 2 (g) and total consumptive water use 1996-98 with industry option 2 (h). 17 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 3.2 Water use in 2025 3.2.1 States Fig. 5 shows the total withdrawal water use in Ceará and Piauí modeled with NoWUM, in 1996/98 and, for the two reference scenarios, in 2025. In the future we see an increase of water use in both scenarios in all sectors compared to the present state. The strongest percentage increase is found in the irrigation, the tourism and the industrial sector. For the state aggregates, there is a stronger increase in water use in A than in B. It seems in terms of water use the decentralization scenario is more favorable. The very small percentage increase of domestic water use is remarkable. Even though an additional two million people will live in the area, more households are connected to public water supply systems and people have a higher income than today, domestic water use will barely increase. This is due to the modeled effect of the pricing of water, assuming a relatively high price elasticities between -0.55 and 0.3 (decreasing with time) and a yearly increase in the water price of 6%. In the past ten years, the average annual water price increase in Ceará was 11%. Without price control the domestic water use in Ceará in the coastal boom scenario would increase from 226 ⋅ 106 m³ (1997) to 550 ⋅ 106 m³ instead of 297 ⋅ 106 m³ computed with price control. Thus, the pricing of water can contribute to water saving. Withdrawal water use [Mill. m³] 1800 CE present CE RSA 1500 CE RSB PI present 1200 PI RSA 900 PI RSB CE ISA 600 CE ISB 300 PI ISA PI ISB 0 Irrigation Livestock Domestic Industry Tourism Total Figure 5: Sectoral withdrawal water uses in Ceará (CE) and Piauí (PI) 1996/98 and 2025, for reference and intervention scenarios A and B [106 m³]. Increasing the number of households connected to the public water supply beyond the increases in RSA and RSB (intervention scenarios ISA and ISB) increases domestic water use. This is due to the fact that approx. two million people more are connected and each connected person consumes 18 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis more water per capita than a self-supplied person. In Ceará, the intervention raises domestic water use by 14% in A with regard to the reference scenario, and 17% in case of B. In Piauí, the respective numbers are 7% and 10%. The intervention scenario shows that safe drinking water for over 80% of the total population in Piauí and Ceará, realized in the intervention scenarios, only results in a small increase of domestic water use if public water supply is subject to an efficient water price policy. The increase of total water use by the intervention is insignificant, because it is dominated by the change of irrigation water use. 3.2.2 Municipalities For the two reference scenarios RSA and RSB, Figs. 6 to 11 present the difference between annual water use in 2025 and in 1996/98. Green colors mark a decrease of water use in the future, yellow and red an increase. Irrigation: Consumptive irrigation water use in 2025 is higher than today for both reference scenarios (Fig. 6c and 6d). Consumptive water use is affected by climate change, by the increase in irrigated areas, and by the crop mix. RSA and RSB only differ with respect to the irrigated area. Fig. 6b shows the impact of climate change only, which overall leads to a (rather small) decrease of water use. For the simulations we used the 30-year climate normal 1951-80 to represent present day climate conditions and the climate normal 2011-2040 for 2025 climate. In each case, 30 years of irrigation requirements were computed and then averaged. Average precipitation 2011-2040 is larger than the 1951-1980 average in the north and in large parts of Ceará (Fig. 6a). However, the effect of the extension of irrigation by far outweighs the effect of climate change. The spatial pattern of consumptive irrigation water use in both scenarios (Figs. 6c and 6d) correlates well with the maps of extended irrigated areas (Figs. 6e and 6f). The increase in irrigated area in case of Scenario B is smaller than in case of Scenario A as not all the irrigation projects planned in 1998 are assumed to be implemented and also the extension of private irrigation is smaller than in scenario B. The strongest increases in irrigation water use occur in those municipalities where new public irrigation project are implemented. Livestock: Fig. 7 shows the difference of withdrawal livestock use in future compared to the present state. The spatial pattern in both scenarios reflect the change in the distribution of (human) population, because in both scenarios the change in livestock populations is assumed to be proportional to the change in human population. Domestic: Changes of domestic water use in future compared to the present state (Fig. 8) result from 1) the change of total population per municipality, 2) an increased number of persons 19 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis connected to public water supply and 3) a decrease of per capita domestic water use of supplied persons subject to water price and income increase. Domestic water use decreases in 45% and increases in 55% of all municipalities in the Coastal Boom and Cash Crops scenario RSA (Fig. 8a). In the Decentralization scenario RSB we found a decrease in only 25% of the municipalities and increase in 75% (Fig. 6b), which is due to a less intensive concentration of population in the coastal region than in RSA. As result of the smaller GDP increase in RSB, the total increase of domestic water use on state level is less in RSB than in RSA (Fig. 5). An enforced extension of public water supply to the whole urban and 15% of the rural population in the intervention scenarios (IS), leads to an increase of domestic water use in 99% of all municipalities compared to their respective reference scenarios (Fig. 6c and 6d). However, the increase is in most municipalities very small (<= 1 mm). The strongest increase is found in Fortaleza, where the intervention increases domestic water use from 386 mm to 432 mm (RSA) and from 305 mm to 339 mm (RSB). Industry: Industrial withdrawal water use increases in future due to an increase in industrial output (expressed as IGDP) (Fig. 9). The industrial mix is assumed to remain the same. In both RSA and RSB, the strongest absolute increases occur in Teresina, municipalities in the metropolitan area of Fortaleza as well as Camocim, Itapagé, Crato and Barbalha, as these municipalities have a relatively high industrial water use today. The increase in RSA is stronger than in RSB due to the stronger increase of the industrial gross domestic product (IGDP). In both scenarios, increase of IGDP is assumed to be equal to the increase of GDP. In municipalities without industry 1996/98, no industry is assumed to be established until 2025. Tourism: Tourism water use increases in future above all in the coastal municipalities in the coastal boom scenario (Fig. 10a) and less strong in the decentralization scenario (Fig. 10b). The water use scenarios do not include establishment of tourism in non-touristic municipalities and municipalities without tourism potential (classified by EMBRATUR), because tourism water use would be negligible compared to the domestic water use there. Total withdrawal water use: In RSA, total withdrawal water use decreases in the water scarce regions of Piauí and Ceará (Fig. 11a), where population decreases. In both scenarios, the strongest water use increase occurs in the coastal region, the metropolitan area of Fortaleza and the regions of Piauí and Ceará with relatively large potential water resources (Figs. 11a and 11b). Besides, the municipalities for which irrigation projects are planned require much more water than today (comp. Fig. 6). 20 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Difference: N_2011-2040 - N_1951-1980 [mm] -250 - -51 -50 - -11 -10 - 10 11 - 50 51 - 250 251 - 350 Difference [mm] < -1 < 0 - -1 0 >0-1 > 1 - 10 > 10 - 50 > 50 no data N N W 100 E 0 100 Kilometers W E 100 0 100 Kilometers S S a) difference in annual precipitation b) climate effect (RSA) Difference [mm] < -1 < 0 - -1 0 >0-1 > 1 - 10 > 10 - 50 > 50 no data N W N E 100 0 100 Kilometers W S E 100 0 100 Kilometers S c) area and climate effect (RSA) d) area and climate effect (RSB) Increase of irrigated area [ha] 0 - 100 101-300 301-500 501-1000 1001 - 5000 5001 - 10000 10001 - 15300 no data N W N E 100 0 100 Kilometers W S e) increase of irrigated area (RSA) E 100 0 100 Kilometers S f) increase of irrigated area (RSB) Figure 6: Difference of long-term average precipitation between 2011-2040 and 1951-1980 [mm] (a). Absolute difference of consumptive irrigation water use between 2025 and 1996/98 [mm/a] for reference scenarios A (c) and B (d). The 2025 scenario was simulated with irrigated areas of 2025 and climate data 2011-2040, present state with areas of 1996/98 and climate data 1951-1980. Impact of climate change only: both simulations with irrigated area of 1996/98, but different climates (2011-2040 and 1951-1980) (b). Increase of irrigated areas from 1996-1998 to 2025 [ha] (Scenario A: e and Scenario B: f). 21 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Difference [mm] < 0 - -1 0 > 0 - 0.1 > 0.1 - 0.2 > 0.2 - 0.6 > 0.6 - 2 no data N W N E 100 0 100 Kilometers W S E 100 0 100 Kilometers S a) b) Figure 7: Difference of livestock withdrawal water use between 2025 and 1996/98 for reference scenarios A (a) and B (b). N W N E 100 0 100 Kilometers W S 100 0 100 Kilometers E 100 0 100 Kilometers S a) b) N W N E 100 0 100 Kilometers W S c) E Difference [mm] < -1 < 0 - -1 0 >0-1 > 1 - 10 > 10 - 50 > 50 no data S d) Figure 8: Difference of domestic withdrawal water use between 2025 and 1996/98 for reference scenarios A (a) and B (b). Difference of domestic withdrawal water use in 2025 between the reference and intervention scenario A (c) and between the reference and intervention scenario B (d). 22 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis N W N E 100 0 100 Kilometers W S E 100 0 100 Kilometers Difference [mm] < -1 < 0 - -1 0 >0-1 > 1 - 10 > 10 - 50 > 50 no data S a) b) Figure 9: Difference of industrial withdrawal water use, calculated with option 2, between 2025 and 1996/98 for reference scenarios A (a) and B (b). N W N E 100 0 100 Kilometers W S E 100 0 100 Kilometers Difference [mm] < -1 < 0 - -1 0 >0-1 > 1 - 10 > 10 - 50 > 50 no data S a) b) Figure 10: Difference of tourism withdrawal water use between 2025 and 1996/98 for reference scenarios A (a) and B (b). N W N E 100 0 100 Kilometers W S a) E 100 0 100 Kilometers Difference [mm] < -1 < 0 - -1 0 >0-1 > 1 - 10 > 10 - 50 > 50 no data S b) Figure 11: Difference of total withdrawal water use, with industry option 2, between 2025 and 1996/98 for reference scenarios A (a) and B (b). 23 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 3.3 Water scarcity Indicators describe quantitatively the state of a system and are therefore helpful to identify and describe changes of the system. As simple index for water scarcity, we chose the ratio of withdrawal water use and the water availability in the municipality of interest. Water availability in a municipality is defined as the total runoff in all upstream municipalities minus the consumptive water use in all municipalities upstream of the municipality of interest: Index _ water _ scarcitym = total withdrawal water usem n runoff m − ∑ consumptive water usen 1 m: municipality for which the index is calculated n: municipality upstream of m Consumptive and withdrawal water use are model results from the water use model NoWUM, while runoff per municipality is simulated by the large-scale hydrological model HYMO-WA (Bronstert and Güntner) using the 1951-80 climate time series. The higher the index, the more stress is placed on available water resources by water use. If a large part of the available water is withdrawn, the usage of water by downstream users can be impaired due to 1) the decreased streamflow and 2) the low water quality of the return flow. At this time, there is no objective basis for defining threshold values of the water scarcity index which, for example enables us to distinguish areas with high water stress from areas of low water stress. If the index is based on long-term average annual values of runoff and water use (Fig. 12a), a ratio of 0.4 and higher might indicate severe water stress (Alcamo et al., 2000). However, areas suffering from water scarcity are better identified by looking at the ratio in a typical dry year (Fig. 12b); this takes into account that in the case of two areas with the same average ratio but different interannual climate variability, the area with the higher variability suffers more from water scarcity than the area with a relatively equable climate. In a typical dry year, water scarcity occurs mainly in those areas in Ceará and Piauí with small potential water resources. Beyond this, some municipalities, for example in the Picos and the Cariri region, belong to the areas with large potential water resources and have critical water scarcity indices. These municipalities have large groundwater resources, that are not considered to calculate this index, because groundwater resources are not quantifiable, finally. 24 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis present state 1996/98 2025 coastal boom and cash crop N W 2025 decentralization N E 100 0 100 Kilometers W S N E 100 0 100 Kilometers W S E 100 0 100 Kilometers E 100 0 100 Kilometers E 100 0 100 Kilometers E 100 0 100 Kilometers S a) Long-term annual average N W N E 100 0 100 Kilometers W S N E 100 0 100 Kilometers W S S b) March N W N E 100 0 100 Kilometers W S N E 100 0 100 Kilometers W S S c) September N W N E 100 0 100 Kilometers S d) Annual value of typical dry year Water scarcity index < 0.1 0.1 - 0.4 W N E 100 0 100 Kilometers S W S 0.4 - 0.9 >= 1 withdrawal use at runoff = 0 Figure 12: Water scarcity indices: long-term annual average (a), long-term average for March (rainy season) (b) and September (dry season) (c) and annual value of typical dry year (1970 and 2037, respectively) (d) for the present state 1996/98 and for the two reference scenarios of 2025 under average climatic conditions of 1951-1980 and 2011-2040, respectively. 25 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis If long-term annual average of water availability and water use are taken into account, water scarcity (critical value > 0.4) is identified only in 5% of the municipalities (Fig. 12 a). Obviously, a water scarcity indicator based on long-term annual average cannot represent the nature of water scarcity in Northeastern Brazil. There are two reasons. First, water scarcity is a seasonal problem, and second, the high interannual climate variability leads to recurrent drought years. During the rainy season, in March (Fig. 12 b), only few municipalities have a critical water scarcity index. In the dry season, in September (Fig. 12 c), there is no rainfall and therefore no runoff in nearly all 332 municipalities. Fig. 13 shows the increasing percentage of municipalities with critical water scarcity index (> 0.4) from 4% in the rainy season to 100% in the dry season. Since the natural runoff without considering the large reservoirs is used to calculate the scarcity index, the seasonal climate variability shapes the curve. In reality, in the dry season, water is withdrawn from groundwater, large reservoirs and rivers made perennial by reservoir management. Therefore the water supply situation is less negative than shown here. Nevertheless, every dry season the governments of Ceará and Piauí need to deploy water trucks to supply persons in rural areas with drinking water. The more facilities exist to store the runoff of the wet season for the dry season, the more appropriate it is to take into account a water scarcity index based on annual averages. Such an annual index should be derived for the conditions of a typical dry year (year with an annual rainfall which is exceeded in 90% of all years) and not for long-term average climate, as water cannot be stored over many years. In the year 1970, 28% of the municipalities suffer much more from water scarcity (Fig. 12 d) than the 5% under long-term average climatic conditions. In the future, annual and monthly water scarcity increases in both scenarios compared to the present state 1996/1998 (Figs. 12 and 13). With respect to annual values, the percentage of municipalities with a critical water scarcity index increases 2025 in the case of RSA by 6% and by 1% in RSB compared to the present state (Fig. 13). With respect to the monthly water scarcity index, differences are highest in the months between the rainy and dry season. In most municipalities, the increase of runoff due to climate change cannot balance the increase of water use; water use increases in 90% of the municipalities, and runoff in only 53%. Water scarcity increases more strongly in the Coastal Boom and Cash Crops scenario than in the Decentralization scenario, which is mainly due to the different increases of irrigated areas. 26 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 110 percentage of municipalities with water scarcity index > 0.4 [%] 100 90 80 70 month_2025a month_2025b month_1996/98 year_2025a year_2025b year_1996/98 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 month Figure 13: Monthly variation and annual mean value of water scarcity for the present state 1996/98 and for the two reference scenarios of 2025 under average climatic conditions of 1951-1980 and 2011-2040, respectively. 3.4 Water quality Scenarios of future nitrogen emissions to surfaces water show the effect of an extended and improved treatment of municipal waste water. Here, we only present the results for reference scenario B. In 1997 only 11 out of 332 municipalities had waste water treatment plants, and approximately 2-44% of the waste water of this municipalities was treated. For RSB it is assumed that in 2025 all capitals of municipalities have a wastewater plant, and that 30% of their wastewater is cleaned. For the intervention scenario (ISB), the fraction is 70%. Figure 14 shows the changes of nitrogen emissions to surface water in 2025 compared to the present state. Due to the increased number of treatment plants, municipal nitrogen emissions to surface waters increase less than the number of inhabitants (Fig. 14). The emission reduction is stronger if the treatment efficiency is higher (difference between primary and secondary treatment). The effect of secondary treatment of 30% of the waste water of the capitals of the municipalities (RSB, secondary treatment) is similar to the effect of a primary treatment of 70% of the waste water (ISB, primary treatment). If 70% of the waste water undergoes secondary treatment (ISB, secondary treatment) nitrogen loads in five out of the eight regions would be smaller than today. 27 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 50 population net N input 2025RSB, primary treatment net N input 2025RSB, secondary treatment net N input 2025ISB, primary treatment net N input 2025ISB, secondary treatment 30 20 10 Piauí Ceará water poor CE water poor PI water rich CE water rich PI coast south of PI -10 Fortaleza 0 Teresina Difference towards present state [%] 40 -20 Figure 14: Differences [in % of values 1997] of population and nitrogen input in surface waters in the decentralization scenario as well as for the corresponding intervention scenario per scenario region and on state level. The elimination of nitrogen through treatment plans was calculated for 1997 for a primary treatment (minus 15% N), for the scenarios of 2025 for a primary and a secondary (minus 30% N) treatment, respectively. 4 Summary and conclusions A scenario analysis of future water use and water scarcity in the semi-arid states Piauí and Ceará in the semi-arid Northeastern Brazil was performed. The analysis was done with the help of the newly developed water use model NoWUM which computes withdrawal and consumptive water use in each of the 332 municipalities, distinguishing five water use sectors. The scenario analysis shows how water use might develop between today and the year 2025, taking into account changes of climate, population, income, industrial output and water prices as well as changes in irrigation (areas, crops, water use efficiency) and public water supply (connected households). For the first time, municipality-specific estimates of current water use are provided. For all sectors, the increase of water use between today and 2025 is higher in case of the Coastal Boom and Cash Crops scenario than in case of the Decentralization scenario. Due to increased water use (in 99% of the municipalities), water scarcity will become more severe in the future, even though runoff will increase in more than 50% of the municipalities due to climate change (average climate 2011-2040). 28 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis The development of water use will predominantly be influenced by the development of the irrigation sector, above all the extension of irrigated areas. If all the now irrigation projects planned in 1998 will be implemented by the year 2025 and private irrigation increases in a similar fashion than in the last decade (Coastal Boom and Cash Crops scenario), irrigated area will increase by a factor of 4.5 and irrigation water use by a factor of 3.8 (withdrawal water use) and 4.1 (consumptive water use). Then, irrigation would account for 68% of the total water withdrawal of 2513 km3/yr in Piauí and Ceará compared to 44% of 1016 km3/yr today. In the alternative Decentralization scenario, irrigated area increases by a factor of 2, and irrigation water use (withdrawal) by a factor of 1.7. In this scenario, the irrigation sector uses 51% of the total withdrawn water (1466 km3/yr) in 2025. The impact of climate on irrigation water use is small. Domestic water use increases where population increases but due to the high price elasticity and the increase of water price, increases are low. In an exemplary fashion, the impact of a water management measure on water use, here the extension of public water supply to a larger number of household, is shown. This measure increases domestic water use in urban centers, but does not significantly effect total water use (Fig. 5). Although industrial water use becomes more important in the future, above all in Ceará, irrigation and households remain the dominant water users. In Ceará, industrial water use will become the third most important sector, switching its rank with the livestock sector. There is a strong interdependence between water use and water quality. When water is withdrawn, used and then returned to the river, its quality usually decreases, sometimes to a degree that precludes a further use downstream. An indicator for the impact of domestic water use on water quality is the nitrogen load which can be decreased by waste water treatment. Scenarios of future nitrogen emissions to surfaces water show the effect of an extended and improved treatment of municipal waste water. If in 2025 (Decentralization scenario), 70% of the waste water produced in the capitals of the municipalities will be subject to secondary waste water treatment, nitrogen loads in five out of the eight scenario regions would even be smaller than today (even though population increases). The presented scenario analysis can form the basis for further investigating the effect of water management measures on water use, water scarcity and water quality. New intervention scenarios could explore the consequences of, for example, implementing or not certain irrigation projects, with certain areas, crop types and irrigation water use efficiencies. In order to assess the impact of to technical measures to increase water availability, e.g. new reservoirs, on water scarcity, the scenarios of future water use (or demand) must be taken into account. With respect to water 29 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis quality, it will necessary to assess not only the domestic nitrogen loads, but also the agricultural loads. Then, the interplay between land use and water quality could be analyzed. 5 References Alcamo, J.; Henrichs, T., Rösch, T. (2000): World Water in 2025 - Global modeling and scenario analysis for the World Commission on Water for the 21st Century. Report A0002, Center for Environmental Systems Research, University of Kassel. Döll, P., Fuhr, D., Herfort, J., Jaeger, A., Printz, A., Voerkelius, S. (2000): Szenarien der zukünftigen Entwicklung in Piauí und Ceará. WAVES Report, Working Group Scenarios, Center for Environmental Systems Research, University of Kassel. Leggett, J.; W.J. Pepper and R.J. Swart (1992): Emission Scenarios for the IPCC: An Update. In: IPCC, Climate Change 1992: The Supplementary Report to the IPCC Scientific Assessment. Cambridge University Press, Cambridge. Röckner, E., Arpe, K., Bengtsson, L., Christoph, M., Claussen, M., Dümenil, L., Esch, M., Giorgetta, M., Schlese, U., Schulzweida, U., (1996): The atmospheric general circulation model ECHAM-4: Model description and simulation of present day climate. MPI-Report No. 218, MPI für Meteorologie, Hamburg. 30 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Appendix A 31 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Large-scale water use model NoWUM for modeling water management in Ceará and Piauí Model description, data and scenario assumptions Version 1.0 January 2000 1 Overview The water use model NoWUM (Nordeste Water Use Model) calculates both withdrawal water use (quantity of water withdrawn from its natural location) and consumptive water use (quantity of water lost to evapotranspiration). The use quantity best suited for the assessment of water scarcity, water depletion (quantity of water used that cannot be reused), is a quantity in-between withdrawal use and consumption use, which due to the lack of information on water quality is not yet computed in the present model version 1.0. Within NoWUM, five water use sectors are distinguished: irrigation use, livestock use, domestic use, industrial use and tourism use. The irrigation water use is calculated with the methodology of FAO (FAO, 1992). Livestock water use is computed as a function of livestock-specific water use and numbers of the different livestock types. With respect to domestic water use, the specific water use of people connected to the public water supply and those not connected are differentiated. Two methods are used to calculate the industrial water use: one based on the assessment of different water sources (public supply, self-supply, raw water supply); the second multiplies the industrial gross domestic product with a water use intensity per industrial branch. Multiplying the touristic overnight stays with a touristic water use per overnight stay gives the water use in the tourism sector. Computations are performed on the municipal scale for all municipalities of Ceará and Piauí in the reference year 1996. Here, Ceará consists of 184 and Piauí of 148 municipalities. The municipalities in Ceará are numbered sequentially from 1 up to 184, those of Piauí from 185 to 332. The output of NoWUM are monthly sectoral withdrawal and consumptive water use values in each municipality. However, in the case of livestock water use and industrial water use, the monthly values are derived from annual averages, while in the case of irrigation they are the sum of 10-day-values. 33 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 2 Irrigation water use 2.1 Method The consumptive irrigation water use (net irrigation requirement) is computed following the method of CROPWAT (FAO, 1992). The crop-specific net irrigation requirement within one growing period Ic is calculated as n I c = ∑ Kc ,i E p,i − Peff ,i (F 2.1) i =1 i: n: Kc: Ep: Peff: 10-day-period number of 10-day-periods within the growing period (crop-specific) crop coefficient (crop-specific) [-] potential evapotranspiration [mm/d] effective precipitation [mm/d] Using effective precipitation instead of total precipitation P takes into account that not all the rain is available to the crops. According to the USDA Soil Conservation Service Method (as given in FAO, 1992), Peff = P(4.17 - 0.2 P) / 4.17 for P < 8.3 mm/d Peff= 4.17 + 0.1 P for P ≥ 8.3 mm/d 2.2 Climate data input NoWUM requires daily values of precipitation and potential evapotranspiration, which are provided by the WAVES-subproject Large-scale Hydrologic Model. As the FAO approach uses 10-day time steps, NoWUM aggregates daily values of precipitation and evapotranspiration into 36 time steps per year. The Integrated Model SIM of WAVES handles with 365 or 366 days per year, respectively. Hence, NoWUM sums up three times 10 days (April, June, September, November); 10, 10 and 11 days (January, March, May, July, August, October, December); in February 10, 10 and 8 or 9 days to get three time steps per month. 2.3 Input of crop data The crop data required are: 1. irrigated areas per crop in each municipality 2. crop-specific data like planting date, growing periods and crop coefficients per growing period. First, we determined nine main irrigated crops classes (Table 1). The crop-specific planting date was derived from annual schedules of DNOCS irrigation perimeters in Ceará and Piauí (DNOCS 1994a, 1995a, 1996a) and the recent Agricultural Census (IBGE 1998a, 1998b), while data on crops coefficients and length of growing period are taken from CROPWAT (FAO, 1992). 34 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Table 1: NoWUM crop classes of irrigated crops: relation of single crops to crop classes, growing period of crop classes and used crop data set (crop coefficients Kc and growing periods) per crop class from FAO (1992). class no 2.3.1 crop class 1 2 3 4 banana beans cotton fruit trees 5 6 7 8 9 grass maize rice sugar cane vegetables included crops banana beans cotton acerola, cajá, coconut, guava, graviola, jaca, orange, lemon, mango, papaw, passion fruit, urucum, uva grass maize rice sugar cane melon, onion, pumpkin, tomato, water melon growing period [days] 360 90 140 360 360 130 120 360 120 crop type in FAO (1992), for which cropspecific data are used banana beans cotton citrus grass maize rice sugar cane tomato Irrigated areas in 1996/98 Information about the size of irrigated areas at state level in Ceará and Piauí diverges widely. The Agricultural Census of Ceará and Piauí (IBGE 1998a, 1998b) provides information for all municipalities in both states, but Brazilian experts say that the total irrigated areas published by IBGE are strongly overestimated. For Ceará, we used the most reliable information from COGERH (1995, 1998a) and SRH (1998), instead of data of the Agricultural Census. COGERH keeps a register of total irrigated farm land in some municipalities of the Curu and the Jaguaribe valley. SRH registers all irrigation licenses given to farmers in Ceará. These licenses include also the irrigated area per crop. The total irrigated area per municipality is taken from the COGERH and the SRH-register. The lacking data in 23 municipalities are filled with downscaled IBGE data considering expert knowledge. Because in the municipalities Ico and Morada Nova the registered data seem to be much to high, they are replaced with data from the Agricultural census. Here licenses are already given for public irrigation projects which will be implemented in the future. In Piauí, data of irrigated areas are taken from the Agricultural Census, because more reliable data are lacking. There is no reasonable correlation between the total irrigated area per municipality and the sum of the harvested areas of significant irrigated temporary crops per municipality, both given in the Agricultural Census of Ceará and Piauí (IBGE 1998a, 1998b). In many municipalities the total irrigated area is considerably less than the sum of the areas of the temporary crops. Additionally the permanent crops should be integrated. That calls the given total irrigated area in question. Hence, the harvested areas of irrigated temporary crops per municipality are considered 35 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis as their irrigated areas in the year 1996. Furthermore, the harvested area of banana and all fruit trees are set as their irrigated areas assuming that these crops are always irrigated. The total irrigated area per municipality is calculated as the sum of the harvested area of the irrigated temporary crops, the harvested area of banana and the harvested area of fruit trees. The total irrigated area is scaled down to 13170 ha, because Brazilian experts do not regard 18148 ha of irrigated land in Piauí in 1996 to be realistic. For this purpose, the size of irrigated areas in the municipalities close to markets (Floriano, Miguel Alves, Oeiras, Parnaiba, Picos, Piripiri, São Raimundo Nonato, Teresina and União, all together 8625 ha) are retained. In all other municipalities the irrigated area is reduced by factor 3. 2.3.2 Irrigated areas in 2025 The increase of irrigated areas up to the year 2025 is designed for the two reference scenarios of WAVES, the Coastal Boom and Cash crops scenario (RSA) and the Decentralization: Integrated Rural Development scenario (RSB) (Döll et al., 2000). The total irrigated area is the sum of large public irrigation projects and private irrigation. The increase of both parts is considered differently in both reference scenarios. While the Coastal Boom and Cash Crops scenario continues the business as usual, the Decentralization scenario shows an alternative development. The present governments of Ceará and Piauí favor a strong increase of large public irrigation projects. These projects are already planned for years at locations best suited for irrigation and provide opportunities for the agro-industry, and middle-sized and small farmers. RSA reflects this policy. On the other hand, an increase of irrigated land of private farmers distributed upon the whole territory matches best the story line of RSB. In RSA, the total irrigated area of 1996/98 is increased until 2025 by a factor of 4.5, and in RSB by a factor of 2. Coastal Boom and Cash Crops scenario (RSA) For the year 2025 it is assumed that all large public irrigation projects planned in 1998 will be implemented in the specified municipalities given in Lopes Neto (1998). Additionally, an increase of the private irrigated area is assumed per scenario region such that by 2025 irrigated areas increased as listed in table 2. The additional total private irrigated area per scenario region is equally distributed to all municipalities of the region. Decentralization scenario (RSB) For the year 2025 is assumed that only a quarter of the large public irrigation projects planned in 1998 will be implemented in the specified municipalities. The reasoning is that these projects were begun in the end of the 90s, but not concluded under the political conditions of decentralization. The increase in private irrigation is also assumed to be smaller in RSB than in RSA. The additional private irrigation area is distributed in each scenario region in the following way: a) 50% of this area is distributed in proportion to the present irrigated area (assuming that existing irrigation occurs in best suited places), and b) 50% of this area is equally distributed in all municipalities of the scenario region (assuming the promotion of a decentralized economic activity). 36 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Table 2 summarizes the change in total irrigated area per scenario region for both reference scenarios. Table 2: Scenarios of the increase of the irrigated areas irrigated area in ha Scenario region Teresina Metropolitan area of Fortaleza and Pecem Coastal region South of Piauí Region with high potential water resources (Piauí) Region with high potential water resources (Ceará) Region with low potential water resources (Piauí) Region with low potential water resources (Ceará) Total (Piauí and Ceará) change of irrigated area in % 1996/98 2025 RSA 2025 RSB 1996/98- 1996/982025A 2025B 1,018 4,072 1,527 + 300 + 50 695 4,170 2,085 + 500 + 200 10,476 1,470 10,402 41,904 11,760 62,412 20,952 8,820 26,005 + 300 + 700 + 500 + 100 + 500 + 150 28,138 112,552 42,207 + 300 + 50 103 309 309 + 200 + 200 3,964 15,856 11,892 + 300 + 200 56,266 252,035 113,797 + 348 + 102 Not only the total irrigated area, but also the crop mix is changed in the scenarios. 50% of the new irrigated area per municipality gets the same crop distribution as in 1996/98. The other part is planted according to the standard crop mix of 2025. "Standard crop mix" means the average crop mix of a state (Piauí or Ceará). Brazilian experts expect that the percentage of banana, fruit trees, vegetables and cotton will rise, the percentage of beans, maize and grass will remain at the same size, and the percentage of rice and sugar cane will decrease (Table 3). Table 3: Standard crop mixes 1996/98 and 2025 in Ceará and Piauí [in % per crop class] crop class banana beans cotton fruit trees grass maize rice sugar cane vegetables CE 1996/98 5.4 18.0 3.9 21.2 12.4 5.2 24.4 3.9 5.6 CE 2025 6 18 7 25 12 5 18 0 9 37 PI 1996/98 7.4 6.2 0.2 8.8 5.8 6.1 31.4 26.2 4.3 PI 2025 10 9 5 25 6 7 28 0 10 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 2.4 Calculation of irrigation water use In NoWUM, the net irrigation requirement irr_req of a planted area is calculated as irr _ req[i ][ l ][ k ] = petp[i ][ l ]∗ Kc [l ][ k ] − raineff [i ][l ] i: l: k: irr_req: petp: Kc: raineff: (F 2.2) municipality ten-days-time-step of the year crop class irrigation requirement [mm] potential evapotranspiration [mm] specific crop coefficient [-] effective precipitation [mm] The irrigation requirement is calculated for each municipality i, for every ten-day time step of the year l and for all crops k, which are planted in the municipality. The crop coefficient Kc varies within the growing season. It is 0 before the crop's planting and after its harvest. Altogether four development steps are distinguished with fixed crop coefficients during the first and the third step. The coefficient during the second and the fourth step augments or decreases linearly. During the second step it starts with the value of the first step and finishes with the value of the third step. During the fourth step it starts with the value of the third step and finishes with the given final value of the fourth step. Table 4 shows an example: Table 4: Planting date, duration and crop coefficients of growing phases of crop 1: crop 1 phase 1 planting day [day of the year] 10 duration of growing periods 30 [number of days] crop coefficient Kc 0.5 0.5 + phase 2 phase 3 phase 4 40 50 30 11 . − 0.5 *n 1 + 40 10 n: number of time steps 1.1 11 . + 1.0 − 11 . *n 1 + 30 10 n: number of time steps If the irrigation requirement for a crop of each municipality [mm = l/m²] is multiplied with the municipal irrigated area of the respective crop, the water amount for irrigation water results. wat _ irr[i ][ l ][ k ] = irr _ req[i ][ l ][ k ]∗ area[i ][ k ] (F 2.3) i: municipality l: time step k: crop type wat_irr: water amount for irrigation [m³/time step] irr_req: irrigation requirement [m³/m²] area: planted area with crop k [m²] Summing over all crops and over each month, the monthly consumptive use of irrigation within one municipality con_irr[i][j] is obtained. The withdrawal water use is computed as 38 Hauschild and Döll wd _ irr[i ][m] = Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis con_ irr[i ][m] eff _ irr[i ] (F 2.4) i: municipality m: month wd_irr: total irrigation withdrawal water use [m³/month] per municipality con_irr: total irrigation consumptive water use [m³/month] per municipality eff_irr: irrigation water use efficiency of municipality [-] NoWUM works with a constant water use efficiency of 0.6 for all municipalities in 1996/98 according to Brazilian expert knowledge. In the future Brazilian experts expect that the irrigation water use efficiency will increase to 0.7. The higher efficiency considers the technological change; since NoWUM does not include a price control of irrigation. Moreover, this version does not consider multiple cropping (planting crops with short growing periods two or three times a year) yet, consequently the irrigation water use is underestimated. 3 Livestock water use Based on the Agricultural Census (IBGE 1998a, 1998b), eleven types of livestock are distinguished. For 1996 the number of animals per species are taken from the Agricultural Census. In the scenarios of the future animal populations grow with the same annual population growth rates as used for the human population per municipality. The withdrawal water use for livestock is computed by summing up the products of the livestock-specific water use (Table 5) and the number of that livestock in each municipality. The consumptive water use is computed by multiplying the withdrawal water use with a typical livestock water use efficiency of 1.0 (USGS, 1998). Only yearly values are computed, which are - in order to obtain monthly values - just divided by twelve. Table 5: Mean daily water volume consumed by livestock species [l/d/cap]. Values calculated from given minimum and maximum values in EMPRAPA-CPATSA/SUDENE (1984). For donkey and mule the value of horse is assumed; for rabbit, poultry and quails the value of chicken. species daily consumed water volume [l/d/cap] 68.0 11.0 54.5 8.5 0.3 cow pig horse, donkey, mule sheep, goat chicken, poultry, quail, rabbit 39 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 4 Domestic water use 4.1 Method Domestic water use is the product of population and the water use per person. People connected to the public water supply (piped water supply) are differentiated from those that are not connected. While the water use of people connected to the public water supply is determined from municipality-specific monthly data on connected households and supplied volume, the water use of people not connected to the public water supply is assumed to be a constant in the present version of NoWUM. For the scenarios of the future the water use per capita of a person connected to the public water supply is modeled as a function of the water price and the average income of the person. 4.2 Input data of population and public water supply volumes 4.2.1 Present state NoWUM requires data of the total population and the population connected to public water supply. The latest population data (total, urban and rural) are published in the Demographic Census of IBGE (1997), reference day 01-08-1996. With monthly growing rates of population per municipality, which we took from internal reports of the public water supply companies AGESPISA (1997) and CAGECE (1997), we computed monthly total and urban population for the year 1997. The supplied population is calculated as follows for every municipality: hhconn * hhsizeurban popsupp = popurban popsupp: popurban: hhconn: hhsizeurban: if hhconn * hhsizeurban < popurban if hhconn * hhsizeurban ≥ popurban (F 4.1) supplied population of municipality urban population of municipality amount of households connected to public supply urban household size per municipality The data of amount of households connected to public supply were derived from internal reports of AGESPISA (1997), CAGECE (1997), FNS (1997), SISAR (1998), FUNASA (1998) and SOHIDRA (1999) as well as the data of urban household size from the Demographic Census on IBGE (1997). In the present model version, the supplied population is calculated outside of NoWUM. For computing the specific water use of people connected to the public water supply, the produced water volume of the supply companies for each municipality are necessary. In the accessible internal reports of 1997 from AGESPISA, CAGECE and FNS, these data are only given by CAGECE for all months and by AGESPISA from January up to March 1997. Instead of this, we take the billed water volumes, provided by all companies for all months, as input data, although the billed water volume in general is smaller than the produced (and supplied) volume, in some municipalities up to factor 2. If households have no water meter, they pay for a fixed volume of 10 m³, although they probably consume more water. This could explain the large 40 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis difference between the two water volumes. Finally the water use efficiencies per municipality are read from a separate input file, although we have no local data yet. 4.2.2 Scenarios The WAVES working group Scenarios designed two reference and one intervention scenario up to the year 2025 for the eight scenario regions (Döll et al., 2000). The two reference scenarios are (A) the "Coastal Boom and Cash Crops scenario and (B) the Decentralization: Integrated Rural Development scenario. The report of working group Scenarios presents the story lines of the scenarios and detailed information on how the working group came up with the quantification of the driving forces in detail. The growth of the total population is the same in both scenarios (Table 6) but it is different in the scenario regions in scenario A and B (Table 7). In the field of water management scenarios, assumptions of the percentage of persons connected to public water supply were made for the reference and intervention scenarios (Table 8). The intervention scenario assumes an even larger expansion of the public water supply as already assumed in the reference scenarios. The explicit increase of the connections to public water supply expresses the structural change in the future. Table 6: Population growth in Ceará and Piauí from 1996 to 2025 for the reference scenarios year 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 total population (million) 9.46 9.57 9.68 9.79 9.90 10.00 10.10 10.20 10.29 10.39 10.48 10.57 10.66 10.75 10.83 year 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 41 total population (million) 10.91 10.99 11.07 11.14 11.22 11.29 11.37 11.44 11.51 11.58 11.65 11.72 11.79 11.86 11.93 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Table 7: Fraction of total population in each scenario region [%] Scenario region Teresina Metropolitan area of Fortaleza and Pecem Coastal region South of Piauí Regions with high potential water resources Regions with low potential water resources 1991 6.7 26.1 12.7 3.1 33.9 17.6 1996 RSA 2025* RSB 2025* 6.9 8.1 7.3 27.6 35.4 30.6 12.8 17.4 13.3 2.9 2.4 2.9 32.9 26.9 31.9 16.9 9.8 14.0 Table 8: Population connected to public water supply scenario region Teresina Metropolitan area of Fortaleza and Pecem Coastal region South of Piauí Regions with high potential water resources (PI) Regions with high potential water resources (CE) Regions with low potential water resources (PI) Regions with low potential water resources (CE) Fraction of population connected to public water supply (in % of the urban population) 1997 2025 2025 2025 2025 RSA* RSB* ISA* ISB* 93.6 (100) 95 (98) 95 (98) 97 97 68.3 (70) 80 (82) 80 (82) 98 98 30.5 (57) 60 (80) 45 (64) 79 75 41.6 (97) 50 (100) 50 (82) 58 66 41.5 (87) 55 (92) 55 (79) 66 75 37.1 (68) 50 (77) 50 (67) 70 79 22.1 (76) 30 (81) 30 (71) 46 51 33.7 (69) 40 (73) 40 (67) 62 66 * RS: reference scenario / IS: intervention scenario For the future NoWUM calculates the water use per capita per person connected to public water supply as a function of prices and income (see below). The scenarios include rates of change in gross domestic product per capita per scenario region (Table 9) as well as an annual increase of the water price. In order to come up with a reasonable price increase, a ten year time series (19891998) of water tariffs from CAGECE was analyzed. In this period occurred several monetary reforms. The base tariff for the first 15 m³ increased at 11% per year from 1989 to 1998. Nowadays, CAGECE's water price covers operation and maintenance costs (O&M costs), and it is expected that the O&M costs will rise in future. For the scenarios a constant water price increase of 6% per year is assumed for all scenarios in all scenario regions. 42 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Table 9: Scenarios of the growth rate of per capita gross domestic product (GDP) in each scenario region. Constant annual rates are assumed between 1996 and 2025. annual growth of GDP per capita between 1996 and 2025 [%] RS A RS B 2.5 2.7 2.7 2.7 2.7 2.5 Scenario region Teresina Metropolitan area of Fortaleza and Pecem Coastal region South of Piauí Regions with high potential water resources Regions with low potential water resources 2.2 2.2 2.2 2.2 2.4 2.2 4.3 Computation of domestic water use In NoWUM, the specific water use of people connected to the public water supply and those not connected are differentiated. We have no information on the water use of people not connected to the public water supply. As a first estimate, we assume a value of 50 l/d/p, a value defined by Gleick (1996) as the basic human water requirement. This might underestimate water use particularly when people have their own private wells. Thus, the domestic withdrawal water use is computed as follows: wd _ domestic[i ][m] = popsupp[i ][m]∗ con _ supp[i ][m] + ( poptotal[i ][m] − popsupp[i ][m])∗ con _ nsupp[i ][ m] i: m: wd_domestic: popsupp: p_supp: poptotal: p_nsupp: (F 4.2) municipality month total withdrawal use of municipal population [m³/month] supplied population [-] withdrawal use per supplied person [m³/month/p] total population of a municipality [-] withdrawal use per non-supplied person [m³/month/p] Consumptive water use is computed by multiplying the withdrawal water use with a typical domestic water use efficiency of 0.2, which is the average value for the USA (USGS, 1998) Economists use the concept of elasticities to consider the responsiveness of consumers' purchases to varying price. A measure of this responsiveness, the elasticity of demand with respect to a demand determining variable, is defined as the percentage by which the demand changes in response to a one percent change in the variable. The most frequently used elasticity concept is the price elasticity, which is defined as the percentage change in water use if price is changed by one percent. The income elasticity of demand is a related concept: it measures the percentage change in quantity demanded associated with a one percent change in consumer income. Almost all estimates of long run price elasticity of residential water demand in the U.S. seem to fall between -0.3 and -0.7, meaning that other factors held constant, a ten percent increase in price would lead to a 3-7% decrease in the amount of water purchased (Young, 1998). Income 43 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis elasticity is found to be small but positive; an increase in income brings about a less than proportional increase in quantity consumed. The future water use per capita per person connected to water supply is calculated as a function of the water price and the average income (GDP per capita). It is calculated for each municipalities as follows: [( ) ] V j +1 = ε p , j +1 ∗ rp, j +1 + εi , j +1 ∗ ri , j +1 + 1 ∗ V j j: Vj+1: εp: rp: εi: ri: (F 4.3) year volume consumed per person connected to public water supply in year j+1 price elasticity in year j+1 price rate in year j+1 income elasticity in year j+1 change rate of gross domestic product per capita in year j+1 NoWUM uses a linear decreasing price elasticity from 1996 to 2025; it starts with -0.55 (value for Northeast of Brazil, (BNB/PBLM, 1997)) in 1996 and ends with -0.3 (average value for Europe and U.S.). According to Gomez (1987), NoWUM uses a constant income elasticity of 0.7. The Brazilian value of 0.78 was reduced, because the scenarios consider explicitly new connections. 5 Industrial use 5.1 Methods and calculation Two methods were developed to calculate the range of the industrial withdrawal water use. With method 1, industrial water use is computed based on municipality-specific information of the waterworks (IWUWW) and from COGERH (IWURW) and a statewide estimate of the amount of water that is self-supplied. With method 2, the industrial output of 19 industry branches in each municipality is taken into account as well as industry-specific water use intensities (water use per industrial output) typical for Germany. For most municipalities, method 1 leads to a smaller value than method 2. The real values might be somewhere in between. The consumptive water use is calculated from withdrawal water use using an industrial water use efficiency of 0.2 (USGS, 1998). 5.1.1 Method 1 IWUWW + IWUSS + IWURW if data available wd _ industry_1 = if no data available wd _ industry_2 ∗ c (F 5.1) i: municipality m: month IWUWW: industrial water supplied by waterworks [m³/month] IWUSS: self-supplied industrial water use [m³/month] IWURW: industrial raw water supplied by COGERH [m³/month] wd_industry_1: total industrial withdrawal water use [m³/month] per municipality calculated with method 1 wd_industry_2: total industrial withdrawal water use [m³/month] per municipality calculated with method 2 c: correction factor [-] (diminishing wd_industry_2) 44 Hauschild and Döll 5.1.2 Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Method 2 19 wd _ industry _ 2[i ][m] = ∑ IGDPb [i ][m] ∗ WUI b (F 5.2) b =1 i: municipality m: month b: industrial branch (19 are distinguished) wd_industry_2: total industrial withdrawal water use [m³/month] per municipality calculated with method 2 IGDP: industrial gross domestic product per industrial branch [US$ 1995] WUI: water use intensity per industrial branch [m³/1000 US$ 1995] (adapted from German industrial data of 1995) In the scenarios of the future the industrial water use is also a function of prices in both options as showed in the following. [( ) ] V j +1 = ε p , j +1 ∗ rp , j +1 + 1 ∗ V j j: Vj+1: εp: rp: (F 5.3) year industrial water volume in year j+1 price elasticity in year j+1 price rate in year j+1 Prices stand indirectly for technological change. Due to the lacking of industrial specific data, NoWUM uses also a price rate of 6% per year for the industrial water use scenarios as derived from CAGECE's data for the domestic water use. The price elasticity decreases linear between 1997 and 2025 from -0.74 (value for Northeast of Brazil, BNB/PBLM (1997)) to -0.4 (average value for Europe). 5.2 Input data 5.2.1 Input of industrial water use data Industry gets water from different sources. Mostly the small industry is supplied by the public waterworks (IWUWW). Often industry additionally supplies itself from own wells or reservoirs (IWUSS). In Ceará water intensive industry like breweries and industrial districts buy raw water (IWURW) directly from COGERH, the state company of water management who manages the big reservoirs. The IWUWW for municipalities of Piauí were averaged over data of AGESPISA for April 1998 and January 1999. For Ceará only data of CAGECE supplied municipalities were available; the monthly averages of 1998 were used. Marwell Filho (1995) published the self-supplied industrial water use for the watersheds of Piauí. The author remarked that these data are probably too low, because making this investigation, firms were interviewed by telephone and not all firms answered. To get an IWUSS per municipality, the water was equally divided to all municipalities within a basin having firms in 1991. Piauí's capital Teresina has the biggest industrial concentration of Piauí. It's special position was taken into account assigning half of the self-supplied water in the "Poti" basin to 45 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Teresina and the rest equally portioned to all other municipalities in the watershed. The IWUSS of Piauí is 1.57 times higher than the IWUWW of Piauí (state averages). This unique scale factor was used to compute the IWUSS for the municipalities of Ceará already having an IWUWW. The IWURWs for the four municipalities of Ceará were taken from COGERH's bill 1998. 5.2.2 Correction factor In some municipalities the situation is contradictory: according to the information from the waterworks, they do not have an industrial water use (IWU) but they do have an IGDP. Their IWU was computed by method 2 described above. Therefore, a correction factor c was necessary. It reduces the IWU to a size corresponding with the measured data of IWU. The cCeará is 0.554 and cPiauí is 0.215. The c for one state each was calculated as n c= ∑ IWUWW + IWUSS i i =1 + IWURWi (F 5.4) n ∑ wd _ industry _2 i =1 5.2.3 i i Input of industrial water use intensity The application of method 2 needs an industrial water use intensity per industrial branch. Since these data are not yet available for Brazil, we used data from German industry. To link the Brazilian with German data, a common classification of industry branches was necessary. Even Ceará and Piauí have some differences in their classifications. Therefore, the German system was taken as basis and all data were transformed into this classification considering 19 industrial branches. Table 10: Average water use intensity (WUI) per industrial branch for Germany 1995 Industrial branch Average WUI [m³/ 1000 US$ ('95)] Mining Extraction and transformation of minerals (stones and clay) Food products Beverages Tobacco Textiles and clothing industry Leather products Wood products, except furniture Paper and products, printing and publishing Petroleum refineries and coal products Chemical products Rubber products Plastic products Glass and pottery products Production and transformation of metals, metal products 46 110.58 29.10 9.35 10.10 0.16 15.32 1.97 0.89 27.52 8.35 57.97 4.15 3.47 3.50 13.25 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Machinery, except electrical Machinery electrical Transport equipment Manufacture of furniture, jewelry, musical instruments, others 0.83 1.10 1.99 1.86 A second unification was necessary to compare economic data of several times and places. For the calculations within the industrial sector, all data were transformed into US$ of 1995. Table 10 lists the industrial water use intensity (WUI) per industrial branch for Germany 1995. The branch specific WUIs were computed with annual data of the industrial gross domestic product (IGDP) per branch and the water used in firms per branch in Germany (Statistisches Bundesamt, 1998, 1999). 5.2.4 Input of industrial gross domestic product (IGDP) The availability of data of IGDP in Ceará and Piauí is very different. For all municipalities of Ceará branch specific data from 1993 to 1996 exist in the internal database of IPLANCE. Whereas for Piauí, SEPLAN (1997) published the total gross domestic product (GDP) of Piauí 1996 and SUDENE (1996a, 1996b) published preliminary data of sectoral GDPs for the three main sectors: agriculture, industry and services for 1995. Neither data of the total GDP per municipality nor the IGDP per municipality do not exist for Piauí. Branch-specific IGDPs for the all municipalities of Piauí were assigned in three steps: 1) Assuming that Piauí and Ceará have the same firm-specific IGDP per industrial branch, for each municipality of Piauí an IGDP was assigned via the number of firms per branch and an IGDP/firm per branch: 19 IGDP IGDPi = ∑ firmsb , i ∗ (F 5.5) firm b b =1 i: b: IGDP: firms: IGDP/firm: municipality of Piauí industrial branch industrial gross domestic product [US$1995] number of firms industrial gross domestic product per firm per branch [US$1995/firm], mean value of Ceará The IGDPs/firm per branch are average values for the state of Ceará 1995. They were calculated from the number of firms per branch and municipality (IPLANCE, 1997) and the IGDP per branch and municipality (internal data of IPLANCE). This procedure was used, because the number of firms per branch and municipality were the only industrial data available for both states (IPLANCE, 1997; FIEPI, 1991). 2) For those municipalities of Piauí which only have an industrial water use IWUWW but no firms, the total IGDP resulted from the quotient of their IWUWW and an average industrial water use intensity of Piauí (IWUIPiauí). The IWUIPiauí was calculated from all municipalities of Piauí having both, IGDP and IWUWW; it's value is 5.442 m³/1000 US$1995. For comparison: the average IWUI of Ceará is 4.475 m³/1000US$ and of Germany 15.87 m³/1000US$. 47 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 3) Municipalities of Piauí which neither had firms in 1991 nor industrial water from AGESPISA 1998/99 got the IGDP zero. Finally, for the computation of industrial water use the following IGDP data per branch per municipality were used: for Ceará the given data by IPLANCE, for Piauí the self-produced IGDP data as described above. 5.2.5 Scenarios The important driving force of the industrial water use scenarios is the industrial gross domestic product (IGDP) while all other factors like industrial mix, water use intensity and the fraction of IGDP on total GDP are held constant for the scenarios. The annual IGDP per municipality is calculated from the total GDP per year and municipality and it's percentage of GDP in 1997. The annual changing population multiplied by the annual increasing GDP per capita (see Table 9) gives the total GDP per municipality. Driving forces set for one scenario region are used for all municipalities within this region. With NoWUM, structural change of industry could be shown if the industrial mix will be varied. To do this in a sophisticated way, macroeconomic knowledge has to be consulted. 6 Tourism water use 6.1 Method and calculation The tourism withdrawal water use per municipality is calculated as wd _ tourism[i ][m] = i: m: wd_tourism: tourists: stay: tourist_use: tourists[i ][m]∗ stay[i ][m]∗ tourist _ use[i ][m] (F 6.1) municipality month total tourism withdrawal water use [m³/month] per municipality number of tourists [p/month] per municipality medium stay of tourists [d] per municipality withdrawal use per tourist [m³/month/p] A water use efficiency of 0.2 (USGS, 1998) is used to calculate the tourism consumptive water use out of the tourism withdrawal water use. 6.2 Input data 6.2.1 Present state The present number of tourists per "touristic" municipality in Piauí was personally given by the co-director of the tourism agency of Piauí PIEMTUR. Touristic municipalities here means touristic municipalities and municipalities with tourism potential classified by EMBRATUR (1997). For the municipalities of Ceará the number of tourists was derived from several regional studies on tourism in Ceará (Table 11). 48 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis As medium stay of tourists per municipality the given values of 9 days for the Ibiapaba region (SETUR/CE 1998 c), 8 days for the coastal region (SETUR/CE 1997 a) and 5 days for the Cariri region (SETUR/CE 1998 d) and the rest of Ceará (SETUR/CE 1997 c) are used in the simulations. For Piauí the medium values of Ceará are taken over: 8 days for the coastal municipalities and 5 days for the municipalities of the interior. The water requirement in hotels varies between 300 and 500 l/d/p (Stephenson, 1998). In NoWUM is used a high touristic water requirement of 500 l/d/p in the coastal municipalities reflecting the better hotel infrastructure (swimming pools, irrigation of gardens) and relating activities of tourists (several baths per day). In all other municipalities a water requirement of 300 l/d/tourist is used. 6.2.2 Scenarios For the two reference scenarios we made different assumptions for the number of tourists and the touristic water use, while the medium stay of tourists is used as for the present state. For the "coastal boom scenario" (A) the number of tourists increases with factor 5 towards the present state in the municipalities of the coastal region and the metropolitan area of Fortaleza. In the municipalities of the interior it increases only with factor 3. In the "decentralization scenario" (B) the number of tourists is assumed to increase with factor 3 towards the present state in all municipalities. While for the scenario B the touristic water use is used as in the present state, it differs in scenario A. In the coastal boom scenario A the touristic water use for the municipalities of the regions Teresina, metropolitan area of Fortaleza and the coastal region is set to 500 l/p/d; in all other municipalities to 300 l/p/d. For pilgrims NoWUM uses only 100 l/p/d. 49 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Table 11: Assessment of number of tourists in municipalities of Ceará 1997/98 Region I Ceará total Tourists 5.660.483 Source Distribution in the municipalities 7, 8 It is obvious that in 7 not all pilgrims were counted, because in the estimation of 720.157 tourists/a only 168.517 pilgrims are included and 1.831.483 pilgrims lacking. Consequently, the same number has to be added to the total number of tourists. It rises from 3.829.000 to 5.660.483 tourists per year. Persons arriving at Fortaleza with any kind of transport (plane, bus, car, etc.) are assumed to stay 5 d in Fortaleza. Following they visit other municipalities (the percentile quota to single municipalities in: 1). Source 2 includes the monthly division. The number of tourists is equally divided into all of the seven municipalities of the Ibiapaba region. The monthly division of tourists is taken over from the situation in Fortaleza (2). 23,4% of the 720.157 tourists in the Cariri region have a religious motivation for their visit. The tourists, except pilgrims, are monthly divided as in Fortaleza (2). Single municipalities get the number of tourists according to their number of beds (from: 3). Estimation of pilgrims per year in newspapers. Pilgrims appear only in the month of February, July, September and November. They are distributed to the three municipalities Juazeiro do Norte, Crato and Barbalha according to their number of beds (from: 3). This amount of pilgrims appears during the 10 days procession in October. II Fortaleza and from there to other municipalities of Ceará 1997 970.000 2, 4, 5 III Ibiapaba 1998 117.196 6 IV Cariri without pilgrims 1998 551.640 7 V Cariri-pilgrims 1998 2.000.000 VI Canindé average of 97/98 VII Sum (II up to VI) VIII Rest (VII - I) 700.000 12, 13 11 4.338.836 1.321.726 The rest of tourists is distributed to the touristic municipalities classified by EMBRATUR except Canindé, the seven municipalities of the Ibiapaba and the six municipalities of the Cariri region. For this, the 21 coastal municipalities were weighted with factor 3 and the interior municipalities with factor 1. In case a municipality still got tourists under point II, these tourists are added. PI: 767.200 tourists per year without double appearance in several municipalities like at point II. 1 SETUR/CE,1997 a 4 SETUR/CE,1998 a 7 SETUR/CE,1998 d 10 SEBRAE/CE,1998 13 Diário do Nordeste,1998 b 2 SETUR/CE,1997 b 5 SETUR/CE,1998 b 8 SETUR/CE,1998 e 11 O Povo,1998 14 EMBRATUR,1997 3 SETUR/CE,1997 c 6 SETUR/CE,1998 c 9 SEBRAE/CE, 12 Diário do 15 Stephenson, D.,1998 SETUR,1998 50 Nordeste,1998 a Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 7 Model input Most of the parameters are read from one input file each. The data are read into matrixes of size number of municipalities and number of months or number of days. NoWUM needs several input data files: 1. daily values of precipitation per municipality 2. daily values of evapotranspiration per municipality 3. planting data, growing periods and crop coefficients of irrigated crops 4. irrigated areas per crop of one year per municipality 5. numbers of livestock species per municipality 6. total population of one year per month per municipality 7. supplied population of one year per month per municipality 8. monthly water volume supplied by public water supply system per municipality 9. water use efficiency of public water supply system per municipality 10. best guess of industrial water use per municipality 11. industrial gross domestic product per industrial branch per municipality 12. water use intensity per industrial branch (constant for all municipalities) 13. number of tourists per municipality 14. medium stay of tourists per municipality 15. tourist water use per municipality Some driving forces of the scenarios are given only per scenario region such as population growth, price rates and growth of gross domestic product per capita. They are read from customized input files. Appendix 1 gives examples for the input data files. Data of input files 1 to 4 are used to calculate the irrigation water use, data of file 5 for livestock water use, data of files 6 to 9 for domestic water use, data of files 10 to 12 for industrial water use and data of files 13 to 15 for tourism water use calculation. For executing NoWUM, all data file names (input and output) are either read from the desktop or from file input. 8 Model output NoWUM computes monthly sectoral and total withdrawal and consumption water use [m³/month] for all municipalities of Ceará and Piauí. Examples of some of the output files are given in Appendix 2: 1. Total withdrawal water use of all sectors per month per municipality 2. Total consumptive water use of all sectors per month per municipality 3. Withdrawal water use of irrigation per month per municipality 4. Consumptive water use of irrigation per month per municipality 5. Withdrawal water use of livestock per month per municipality Consumptive water use of livestock per month per municipality 6. Withdrawal water use of domestic sector per month per municipality 7. Consumptive water use of domestic sector per month per municipality 51 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 8. Withdrawal water use of industrial sector option 1 per month per municipality 9. Consumptive water use of industrial sector option 1 per month per municipality 10.Withdrawal water use of industrial sector option 2 per month per municipality 11.Consumptive water use of industrial sector option 2 per month per municipality 12.Withdrawal water use of tourism sector per month per municipality 13.Consumptive water use of tourism sector per month per municipality Each sectoral procedure has a query for missing input data. If one parameter is missing, the output file gets in the same position the missing value -9999, too. Besides, a list of missing values is written to a protocol file. 9 References AGESPISA (1997): Boletim estatistico mensal 01/97 - 12/97, unpublished manuscript. AGESPISA (1998): Relatorio 49, unpublished manuscript BNB/PBLM (1997): Execução de servicios técnicos sobre a demanda de água no Nordeste do Brasil, Relatorio Final, Agosto 1997. CAGECE (1997): CAGECE Data Base, Fortaleza, unpublished. CAGECE (1998): CAGECE Data Base, Fortaleza, unpublished. CAGECE (1999): Relatorio 12: Dados operacionais - consolidado 1989 - 1998. unpublished manuscript. COGERH (1995): Cadastramento dos usarios de água bruta da bacia do rio Curu. VBA, Fortaleza. COGERH (1998a): Estudo preliminar da operação conjunta do sistema Jaguaribe e sistema Metropolitano. Fortaleza. COGERH (1998b): Projeção da demanda industrial de água bruta. unpublished manuscript. de Wit, M., Schmoll, O. (1999): Emission estimates: rubbish in - rubbish out? In: Fohrer, N., Döll, P. (ed): Modellierung des Wasser- und Stofftransports in großen Einzugsgebieten. Kassel, Kassel University Press. Diário do Nordeste (1998 a): Juazeiro lembra morte de Padre Cícero, Diário do Nordeste, 20.07.1998, Fortaleza. Diário do Nordeste (1998 b): Juazeiro, 87 anos de emancipação, Diário do Nordeste, 22.07.1998, Fortaleza. DNOCS (1994a): Planejamento Agrícola - 1994, Calendário Geral de Ocupação da Área Irrigada. DNOCS DIRGA-GAP (Diretoria de Irrigação - Grupo de Apoio à Produção), Fortaleza. DNOCS (1994b): Relatórios Agropecuários 2° Semestre/94. DNOCS DIRGA-GAP (Diretoria de Irrigação - Grupo de Apoio à Produção), Fortaleza. DNOCS (1995a): Planejamento Agrícola - 1995, Calendário Geral de Ocupação da Área Irrigada. DNOCS DIRGA-GAP (Diretoria de Irrigação - Grupo de Apoio à Produção), Fortaleza. DNOCS (1995b): Relatórios Agropecuários 2° Semestre/95. DNOCS DIRGA-GAP (Diretoria de Irrigação - Grupo de Apoio à Produção), Fortaleza. DNOCS (1996a): Planejamento Agrícola - 1996, Calendário Geral de Ocupação da Área Irrigada. DNOCS DIRGA-GAP (Diretoria de Irrigação - Grupo de Apoio à Produção), Fortaleza. DNOCS (1996b: Relatórios Agropecuários 2° Semestre/96. DNOCS DIRGA-GAP (Diretoria de Irrigação - Grupo de Apoio à Produção), Fortaleza. EMBRAPA-CPATSA/SUDENE (1984): Projeto Sertanejo. Captação e Conservação de Água de Chuva para Consumo Humano - Cisternas rurais - Dimensionamento, Construção e Manejo. Circular Tecnica, Numero 12. EMBRATUR (1997): Deliberação normativa No 385 de 28 novembro de 1997. Ministério da Indústria, do Comércio e do Turismo. 52 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis FAO (1992): CROPWAT - A Computer Program for Irrigation Planning and Management. FAO Irrigation and Drainage Paper 46, Rome. FIEPI (1991): Cadastro industrial do Piauí 1990/91. Federação das indústrias do estado do Piauí, Teresina. FNS (1997): Dados técnicos mês a mês do SAAE 1997. Fortaleza. unpublished manuscript. FUNASA (1998): Quadro demonstativo das obras trabalhadas. FUNASA - Coordenação regional do Piauí. Teresina. unpublished manuscript. Gleick, P.H. (1996): Basic water requirements for human activities: Meeting basic needs. Water International 21, 83-92. Gomez, C. (1987): Experience in predicting willingness to pay on water projects in Latin America. In: Monatanari, F. W. e. a. (ed): Resource Mobilization for Drinking Water and Sanitation in Developing Nations. New York, American Society of Civil Engineers, 242-254. IBGE (1997): Contagem da População 1996. Instituto Brasileiro de Geografia e Estatística (IBGE), Rio de Janeiro. IBGE (ed) (1998a): Censos Economicos de 1995 - 1996. Censo Agropecuário Ceará. IBGE. No. 11. Rio de Janeiro. IBGE (ed) (1998b): Censos Economicos de 1995 - 1996. Censo Agropecuário Piauí. IBGE. No. 10. Rio de Janeiro. IPLANCE (1999): PIB municipal detalhado 1993-1996. Fortaleza, unpublished manuscript. Lopes Neto, A. (1998): Possibilidades de Modernização Rural do Ceará através da Agricultura Irrigada e da Fruticultura. CNPq, SECITECE, Fortaleza. Marwell Filho, P. (1995): Análise de Sustentabilidade do Estado do Piauí quanto aos Recursos Hídricos. Projeto ÁRIDAS: Uma Estratégia de Desenvolvimento Sustentável para o Nordeste, Tema 7, Governo do Estado do Piauí - Secretaria de Planejamento - Grupo Recursos Hídricos. O Povo (1998): Festa de São Francisco em Canindé reúne 600 mil romeiros em 10 dias, O Povo, 25.10.1998, Fortaleza. PIEMTUR (1998): Personal communication. SEBRAE/CE, SETUR (1998): Perfil do Turista Nacional de Fortaleza. SEBRAE/CE (1998): Relatório da Pesquisa de Demanda Hoteleira. SETUR/CE (1997 a): Demanda Turística via Fortaleza Julho de 1997. SETUR/CE (1997 b): Demanda turística mensal via Fortaleza 1996/1997 SETUR/CE (1997 c): Manual de Informações turísticas SETUR/CE (1998 a): O Aeroporto e o Turismo no Ceará SETUR/CE (1998 b): Síntese do Desempenho Recente do Turismo no Ceará. SETUR/CE (1998 c): Demanda Turística a Ibiapaba, in: Agregados Turísticos do pólo Araripe. SETUR/CE (1998 d): Pólo de Ecoturismo Araripe/Cariri, Agregados e Indicadores Turísticos. SETUR/CE (1998 e): Cenários dos Agregados Turísticos: 1998-2020 SISAR (1998): Resumo gerencial mensal 01/98 - 12/98. Unpublished manuscript. SOHIDRA (1998): Subprojetos liberados/não liberados por município e comunidade encaminhados à unidade técnica de coordenção do Projeto São José. Fortaleza. unpublished manuscript. Statistisches Bundesamt (1998): Wasserversorgung im Bergbau und Verarbeitenden Gewerbe 1995. Wiesbaden. Statistisches Bundesamt (1999): Bruttowertschöpfung nach Wirtschaftsbereichen 1995. Wiesbaden. Stephenson, D. (1998): Water Supply Management, Kluwer Academic Publishers. USGS (1998): National Water Use Data 1995. U.S. Geological Survey. Young, R. A. (1998): Water Management Options for Ceará and Piauí, Brazil, in the Prospect of Global Changes. Colorado, unpublished manuscript. 53 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Appendix B 55 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Municipalities of Ceará 1996 Jijoca de Jericoacoara Cruz Camocim Barroquinha Bela Cruz Chaval Luís Correia Marco Martinópole Granja Uruoca Cocal Moraújo Itapipoca Morrinhos Senador Sá Viçosa do Ceará Acaraú Itarema Trairi Paraipaba Paracuru Tururu Uruburetama São Gonçalo do Amarante Amontada Santana do Acaraú Massapê Miraíma Meruoca Coreaú Frecheirinha Itapagé Tianguá Ubajara Piracuruca Forquilha Croatá Ipu Caucaia Sobral Ibiapina Cariré Pacujá Groaíras São BeneditoGraça Reriutaba Brasileira Carnaubal Varjota Guaraciaba do Norte Domingos Mourão Pires Ferreira Umirim Irauçuba Paramoti Santa Quitéria Hidrolândia Ipueiras Itaitinga Pacatuba Aquiraz Maranguape Pindoretama Palmácia Guaiúba Horizonte Caridade Pacoti Pacajus Cascavel Mulungu Redenção Baturité Barreira Beberibe Chorozinho Aratuba Aracoiaba Ocara Tejuçuoca Apuiarés General Sampaio Canindé Pedro II Fortaleza Pentecoste Aracati Palhano Itaiçaba Itapiúna Itatira Catunda Nova Russas Poranga Ararendá Tamboril Ipaporanga Monsenhor tabosa Ibaretama Choró Russas Jaguaruana Madalena Quixadá Ibicuitinga Morada Nova Boa Viagem Buriti dos Montes Quixeré Limoeiro do Norte Quixeramobim astelo do Piauí Crateús Banabuiú Pedra Branca Independência Jaguaribara Solonópole Piquet Carneiro Deputado Irapuan Pinheiro Tauá Alto Santo Jaguaretama Mombaça São Miguel do Tapuio Quiterianópolis São João do Jaguaribe Tabuleiro do Norte Senador Pompeu Milhã Novo Oriente Iracema Potiretama Jaguaribe Ererê Pereiro Acopiara Arneiroz Catarina Quixelô Orós Parambu Pimenteiras ença do Piauí Icó Iguatu Jucás Saboeiro Cedro Aiuaba Inhuma Santo Antônio de Lisboa São João da Canabrava Monsenhor Hipólito Pio IX antana do PiauíBocaina Alagoinha do Piauí Francisco Santos São Julião Fronteiras Picos Alegrete do Piauí Jaicós Itainópolis Patos do Piauí Umari Lavras da Mangabeira Várzea Alegre Ipaumirim Farias Brito Granjeiro Assaré Altaneira Aurora Caririaçu Potengi Nova Olinda Barro Juazeiro do Norte Santana do Cariri Missão Velha Crato Araripe Milagres Barbalha Abaiara Mauriti Campos Sales Salitre Padre Marcos Caldeirão Grande do Piauí Marcolândia aías Coelho Cariús Tarrafas Antonina do Norte Porteiras Brejo Santo Jardim Simões Jati Penaforte Jacobina do Piauí 57 Fortim Icapuí Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Municipalities of Piauí 1996 Parnaíba Jijoca de Jericoacoara Cruz Camocim Barroquinha Bela C ruz Chaval Luís Correia Bom Princípio do Piauí Granja Uruoca Buriti dos Lopes Cocal Moraújo Luzilândia Matias Olímpio Porto Santana d Massapê Meruoca Coreaú Frecheirinha Tianguá Ubajara São José do Divino Esperantina Piracuruca M Senador Sá Viçosa do Ceará Joaquim Pires Ma Martinópole Forquil Ibiapina Nossa Senhora dos Remédios Batalha Barras Miguel Alves Piripiri Cariré Pacujá Groaíras São BeneditoGraça Reriutaba Brasileira Carnaubal Varjota Guaraciaba do Norte Domingos Mourão Pires Ferreira Croatá Cabeceiras do Piauí Lagoa Alegre Capitão de Campos União Pedro II Ipu Hidrolândia Ipueiras Catun Nova Russas Poranga Ararendá Tamboril Ipaporanga Mon José de Freitas Campo Maior Sigefredo Pacheco Teresina Altos Coivaras Buriti dos Montes Castelo do Piauí Crateús Alto Longá Demerval Lobão Beneditinos Independência São João da Serra Novo Oriente Monsenhor Gil Prata do Piauí Passagem Franca do Piauí São Miguel do Tapuio Barro Duro Água Branca São Félix do Piauí Santa Cruz dos Milagres São Gonçalo do Piauí Hugo Napoleão Jardim do Mulato Aroazes Elesbão Veloso Palmeirais Quiterianópolis Tauá Arn Amarante Parambu Pimenteiras Regeneração Valença do Piauí Novo Oriente do Piauí Várzea Grande Arraial Francisco Ayres Santa Rosa do Piauí Ipiranga do Piauí Guadalupe Marcos Parente Jerumenha Antônio Almeida Floriano Nazaré do Piauí Oeiras São Francisco do Piauí Monsenhor Hipólito Pio IX Santana do PiauíBocaina Alagoinha do Piauí Francisco Santos São Julião Fronteiras Picos Alegrete do Piauí Santa Cruz do Piauí Colônia do Piauí Landri Sales Santo Inácio do Piauí São José do Peixe Itaueira Canavieira Bertolínia Jaicós Itainópolis Isaías Coelho Campinas do Piauí Paes Landim Simplício Mendes Flores do Piauí Uruçuí Rio Grande do Piauí Ribeiro Gonçalves Aiuaba Inhuma Santo Antônio de Lisboa São João da Canabrava Patos do Piauí Jacobina do Piauí Conceição do Canindé Paulistana Colônia do Gurguéia Manoel Emídio São João do Piauí Canto do Buriti Baixa Grande do Ribeiro Lagoa do Barro do Piauí Queimada Nova Palmeira do Piauí Cristino Castro Coronel José Dias São Braz do Piauí São Raimundo Nonato Anísio de Abreu Bom Jesus Santa Filomena Santa Luz Caracol Bonfim do Piauí São Lourenço do Piauí Várzea Branca Dirceu Arcoverde Fartura do Piauí Redenção do Gurguéia Monte Alegre do Piauí Gilbués Avelino Lopes Barreiras do Piauí Parnaguá Curimatá Corrente Cristalândia do Piauí 59 Dom Inocêncio Salitre Padre Marcos Caldeirão Grande do Piauí Marcolândia Socorro do Piauí Eliseu Martins Campos Simões Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 1) NoWUM output: Annual withdrawal water uses [m3] of present state 1996/98 Muni_nr 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 Municipality Abaiara Acarape Acarau Acopiara Aiuaba Alcantaras Altaneira Alto Santo Amontada Antonina do Norte Apuiares Aquiraz Aracati Aracoiaba Ararenda Araripe Aratuba Arneiroz Assare Aurora Baixio Banabuiu Barbalha Barreira Barro Barroquinha Baturite Beberibe Bela Cruz Boa Viagem Brejo Santo Camocim Campos Sales Caninde Capistrano Caridade Carire Caririacu Carius Carnaubal Cascavel Catarina Catunda Caucaia Cedro Chaval Choro Chorozinho Coreau Crateus Crato Croata Cruz Deputado Irapuan Pinheiro Erere Eusebio Farias Brito Forquilha Fortaleza Fortim Frecheirinha General Sampaio Graca Granja Granjeiro Groairas Guaiuba Guaraciaba do Norte Guaramiranga Hidrolandia Horizonte Ibaretama Ibiapina Ibicuitinga Icapui Ico Iguatu Independencia Ipaporanga Ipaumirim Ipu Ipueiras Iracema Iraucuba irrigation 0 2,168,710 37,938 0 219,605 0 0 469,498 89,385 0 3,512,936 35,073 1,345,779 1,720,107 51,994 0 40,168 0 934,274 879,076 0 1,278,062 193,063 4,509 0 61,737 153,408 7,498,886 0 340,308 1,485,066 2,872,824 0 206,799 265,909 50,872 405,693 56,816 53,891 555,686 4,270,690 0 569,994 365,712 0 0 103,728 399,902 0 2,631,202 3,466,352 334,405 382,224 307,214 128,940 76,694 0 13,099 17,033 310,127 0 1,336,621 76,186 173,874 4,106 659,499 54,269 2,047,331 140,551 54,296 0 40,894 1,303,410 44,429 5,688,722 15,105,705 2,219,132 219,300 0 0 377,869 57,676 55,218 28,081 livestock 143,857 57,822 376,608 1,276,789 600,919 84,735 56,448 624,640 393,420 138,665 248,042 482,547 265,104 330,329 226,614 328,824 106,948 509,458 513,222 658,134 240,012 680,148 249,319 185,621 434,765 129,961 230,751 441,546 535,431 1,559,740 686,297 335,728 377,390 1,325,671 154,330 261,590 542,409 378,262 375,926 127,172 379,091 400,917 320,933 638,297 574,550 99,205 417,960 178,052 323,709 1,507,009 415,845 133,373 192,449 314,121 265,445 64,310 345,935 278,019 133,075 49,565 96,042 124,463 155,092 801,551 84,680 129,347 98,728 233,522 27,987 472,880 218,851 418,396 171,659 269,486 177,711 1,149,947 1,015,845 1,641,552 312,433 289,881 368,528 553,573 438,117 724,065 domestic 170,802 283,082 1,009,700 1,298,276 247,734 164,988 97,830 318,723 768,436 207,488 289,462 1,036,430 1,741,239 533,632 184,032 390,844 236,714 161,814 343,008 592,192 151,961 504,735 1,374,160 305,172 500,380 234,126 919,930 753,768 591,259 1,812,524 627,084 1,798,885 774,684 2,383,098 359,418 307,442 429,518 421,254 314,622 398,456 1,200,779 272,845 213,588 4,676,272 419,436 271,172 206,010 312,778 555,567 2,202,374 1,719,378 382,683 361,158 142,776 154,444 489,708 474,268 494,691 94,489,352 247,766 264,854 128,590 258,642 1,133,252 104,507 277,676 437,336 759,598 118,288 442,094 471,254 195,426 506,742 159,768 709,036 2,028,464 4,278,522 623,823 198,108 285,578 1,521,876 836,444 462,596 452,876 61 industry_1 25,538 31,383 458 1,282 160 0 540 458 70 2,564 611 1,091,418 14,593 501 0 3,083 458 1,062 519 1,007 120 76,190 13,524 39 15,690 2,957 6,869 5,953 1,343 8,660 30,529 1,464,935 2,351 65,041 458 885 1,496 180 308 946 2,259 811 0 117,931 17,505 611 18 135 1,191 3,022 330,311 1 1,429 0 458 126,598 44 7,237 13,895,401 4,152 416 0 0 4,549 0 218 458 1,130 107 519 730,853 222 3,721 0 2,281 8,249 251,285 2,351 0 2,778 3,376 608 11,112 185 industry_2 46,121 244,298 44,381 16,282 290 0 974 18,425 127 39 2,218 691,761 693,068 904 0 226 2,898 1,918 937 26,174 217 137,597 559,323 70 28,336 5,340 77,935 76,180 1,754 15,639 55,134 2,645,625 37,365 117,461 145 73,259 4,607 326 556 33 224,089 1,465 0 2,298,910 31,614 721,470 32 244 245 49,022 596,532 2 2,581 0 1,663 228,631 79 13,069 22,657,712 8,597 751 0 0 8,216 0 394 24,121 1,520 194 32,585 326,028 401 6,721 0 4,120 14,897 453,812 2,295 0 27,034 6,098 1,097 5,119 335 tourism 0 0 157,028 2,924 19,629 0 0 0 157,028 0 0 1,018,816 640,568 1,464 0 19,629 19,629 0 0 19,629 0 19,629 239,476 1,464 0 157,028 24,015 574,272 0 1,464 1,464 164,824 0 1,060,238 0 0 0 19,629 0 45,203 227,220 0 0 1,244,992 19,629 19,629 0 0 0 22,552 342,400 19,629 157,028 0 0 1,464 0 0 4,037,028 184,320 0 0 0 0 0 0 19,629 45,203 19,629 0 7,312 0 45,203 0 184,320 19,629 26,940 0 0 0 19,629 0 0 0 total_1 340,197 2,540,997 1,581,732 2,579,271 1,088,047 249,723 154,818 1,413,319 1,408,339 348,717 4,051,051 3,664,284 4,007,283 2,586,033 462,640 742,380 403,917 672,334 1,791,023 2,150,038 392,093 2,558,764 2,069,542 496,805 950,835 585,809 1,334,973 9,274,425 1,128,033 3,722,696 2,830,440 6,637,196 1,154,425 5,040,847 780,115 620,789 1,379,116 876,141 744,747 1,127,463 6,080,039 674,573 1,104,515 7,043,204 1,031,120 390,617 727,716 890,867 880,467 6,366,159 6,274,286 870,091 1,094,288 764,111 549,287 758,774 820,247 793,046 112,571,889 795,930 361,312 1,589,674 489,920 2,113,226 193,293 1,066,740 610,420 3,086,784 306,562 969,789 1,428,270 654,938 2,030,735 473,683 6,762,070 18,311,994 7,791,724 2,487,026 510,541 578,237 2,291,278 1,448,301 967,043 1,205,207 total_2 360,780 2,753,912 1,625,655 2,594,271 1,088,177 249,723 155,252 1,431,286 1,408,396 346,192 4,052,658 3,264,627 4,685,758 2,586,436 462,640 739,523 406,357 673,190 1,791,441 2,175,205 392,190 2,620,171 2,615,341 496,836 963,481 588,192 1,406,039 9,344,652 1,128,444 3,729,675 2,855,045 7,817,886 1,189,439 5,093,267 779,802 693,163 1,382,227 876,287 744,995 1,126,550 6,301,869 675,227 1,104,515 9,224,183 1,045,229 1,111,476 727,730 890,976 879,521 6,412,159 6,540,507 870,092 1,095,440 764,111 550,492 860,807 820,282 798,878 121,334,200 800,375 361,647 1,589,674 489,920 2,116,893 193,293 1,066,916 634,083 3,087,174 306,649 1,001,855 1,023,445 655,117 2,033,735 473,683 6,763,909 18,318,642 7,994,251 2,486,970 510,541 602,493 2,294,000 1,448,790 961,050 1,205,357 Hauschild and Döll Muni_nr 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 Municipality Itaicaba Itaitinga Itapage Itapipoca Itapiuna Itarema Itatira Jaguaretama Jaguaribara Jaguaribe Jaguaruana Jardim Jati Jijoca de Jericoacoara Juazeiro do Norte Jucas Lavras da Mangabeira Limoeiro do Norte Madalena Maracanau Maranguape Marco Martinopole Massape Mauriti Meruoca Milagres Milha Miraima Missao Velha Mombaca Monsenhor Tabosa Morada Nova Moraujo Morrinhos Mucambo Mulungu Nova Olinda Nova Russas Novo Oriente Ocara Oros Pacajus Pacatuba Pacoti Pacuja Palhano Palmacia Paracuru Paraipaba Parambu Paramoti Pedra Branca Penaforte Pentecoste Pereiro Pindoretama Piquet Carneiro Pires Ferreira Poranga Porteiras Potengi Potiretama Quiterianopolis Quixada Quixelo Quixeramobim Quixere Redencao Reriutaba Russas Saboeiro Salitre Santana do Acarau Santana do Cariri Santa Quiteria Sao Benedito Sao Goncalo do Amarante Sao Joao do Jaguaribe Sao Luis do Curu Senador Pompeu Senador Sa Sobral Solonopole Tabuleiro do Norte Tamboril Tarrafas Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis irrigation 10,179 524,258 195,117 367,504 1,145,904 0 307,532 482,174 3,733,986 28,434,242 15,914,661 21,043 1,339,471 0 4,218,896 0 295,280 9,303,074 496,863 0 0 0 0 207,220 764,386 818,414 0 137,709 186,986 65,860 117,450 0 7,706,456 0 0 0 25,842 0 109,179 64,393 38,935 2,869,096 1,549,696 239,711 285,526 137,798 49,624 0 10,208,556 48,290,300 170,974 454,078 777,030 0 8,946,162 83,867 0 31,808 362,986 319,709 0 0 24,786 202,604 1,411,174 652,950 7,144,398 2,400,351 311,760 0 17,248,616 0 29,774 25,812,002 875,859 0 2,566,932 4,497,328 88,325 2,265,292 476,322 92,357 716,754 51,274 5,132,521 0 59,124 livestock 120,768 130,118 317,595 752,483 319,577 346,459 367,769 1,616,494 732,129 1,672,416 524,587 448,887 164,213 65,208 214,677 461,534 801,289 473,580 563,018 70,749 583,608 303,872 83,523 303,232 891,087 50,117 410,937 500,138 467,643 470,791 1,327,391 472,712 1,610,977 179,501 203,135 115,228 37,358 146,322 429,005 669,496 297,093 425,969 307,680 113,579 95,218 81,625 127,800 86,071 169,564 163,486 1,215,762 251,124 859,792 109,698 508,772 216,599 133,033 346,303 189,033 285,003 309,432 188,474 294,917 606,564 1,520,924 636,818 1,879,442 221,672 74,959 243,268 620,757 629,930 330,391 830,204 381,064 1,753,764 239,422 331,917 222,398 91,773 606,821 122,800 1,289,586 855,504 480,548 1,207,719 162,639 domestic 172,428 601,714 1,048,730 3,285,300 314,793 538,046 259,740 444,715 215,770 1,625,646 670,024 450,108 186,296 207,251 8,237,949 796,568 897,534 1,676,956 361,464 3,546,790 2,462,652 522,948 170,084 761,449 872,531 212,157 625,588 338,707 190,422 741,263 836,328 381,592 2,549,716 192,277 396,268 351,950 147,546 352,423 1,092,161 492,974 365,904 677,490 925,622 1,063,102 223,257 159,230 187,284 222,296 504,830 496,992 702,772 272,985 1,106,182 170,358 856,890 358,148 244,386 229,770 196,056 204,858 345,024 226,436 140,381 396,803 2,117,684 371,772 2,558,184 397,777 512,034 484,155 1,286,380 383,394 223,956 520,787 767,810 800,514 1,032,196 772,468 355,887 323,286 750,208 159,965 656,014 552,220 897,538 710,402 141,840 62 industry_1 458 1,465 247,768 245,652 474 2,073 104 211 1,801 30,942 5,404 1,410 1,404 0 108,712 191,806 3,083 753,547 183 3,117,147 298,385 5,587 1,313 5,587 153 4,829 1,374 10 0 1,771 988 37 98,018 1,496 672 0 2 1,343 8,036 20 0 1,496 1,203,709 560,531 2,765 1,723 1,038 78 488 488 30 916 8,817 824 5,861 1,068 232 20 0 80 733 259 0 0 61,850 646 38,221 3,572 1,557 733 12,822 0 0 1,343 5,709 28,514 9,342 977 1,198 40 2,717 702 5,495 617 13,585 580 0 industry_2 380 3,790 447,460 443,640 856 3,744 188 381 362 55,881 220,381 2,547 1,405 0 536,828 346,395 6,183 1,360,881 330 4,206,348 744,657 82 4,768 4,868 13,566 8,722 17,946 18 0 11,684 1,785 66 177,018 5,434 1,549 0 4 179,096 14,513 36 0 14,379 369,007 1,009,726 4,993 3,111 90,210 141 66,073 176 54 59,103 15,923 2,175 8,574 1,561 419 36 0 145 18 469 0 0 32,408 1,167 69,026 222 89,729 565 363,291 0 0 9,253 265,679 54,669 4,677 91,956 2,163 72 6,545 2,551 1,204,564 1,115 55,647 9,515 0 tourism total_1 total_2 0 2,342 22,552 164,824 0 168,728 19,629 0 0 2,924 0 3,312 0 387,092 3,222,494 0 0 26,940 0 0 36,084 0 1,464 0 0 19,629 0 0 0 17,544 2,924 0 19,629 0 0 0 19,629 19,629 2,924 0 0 21,092 22,552 31,406 19,629 0 0 19,629 254,512 231,116 0 0 0 0 19,629 19,629 98,142 0 0 19,629 0 0 0 0 28,402 0 22,552 0 21,092 0 0 0 0 0 2,234 0 45,203 293,512 0 0 4,386 0 34,251 0 0 0 0 303,833 1,259,897 1,831,762 4,815,763 1,780,748 1,055,306 954,774 2,543,594 4,683,686 31,766,170 17,114,676 924,760 1,691,384 659,551 16,002,728 1,449,908 1,997,186 12,234,097 1,421,528 6,734,686 3,380,729 832,407 256,384 1,277,488 2,528,157 1,105,146 1,037,899 976,564 845,051 1,297,229 2,285,081 854,341 11,984,796 373,274 600,075 467,178 230,377 519,717 1,641,305 1,226,883 701,932 3,995,143 4,009,259 2,008,329 626,395 380,376 365,746 328,074 11,137,950 49,182,382 2,089,538 979,103 2,751,821 280,880 10,337,314 679,311 475,793 607,901 748,075 829,279 655,189 415,169 460,084 1,205,971 5,140,034 1,662,186 11,642,797 3,023,372 921,402 728,156 19,168,575 1,013,324 584,121 27,164,336 2,032,676 2,582,792 3,893,095 5,896,202 667,808 2,680,391 1,840,454 375,824 2,702,100 1,459,615 6,524,192 1,918,701 363,603 303,755 1,262,222 2,031,454 5,013,751 1,781,130 1,056,977 954,858 2,543,764 4,682,247 31,791,109 17,329,653 925,897 1,691,385 659,551 16,430,844 1,604,497 2,000,286 12,841,431 1,421,675 7,823,887 3,827,001 826,902 259,839 1,276,769 2,541,570 1,109,039 1,054,471 976,572 845,051 1,307,142 2,285,878 854,370 12,063,796 377,212 600,952 467,178 230,379 697,470 1,647,782 1,226,899 701,932 4,008,026 3,174,557 2,457,524 628,623 381,764 454,918 328,137 11,203,535 49,182,070 2,089,562 1,037,290 2,758,927 282,231 10,340,027 679,804 475,980 607,917 748,075 829,344 654,474 415,379 460,084 1,205,971 5,110,592 1,662,707 11,673,602 3,020,022 1,009,574 727,988 19,519,044 1,013,324 584,121 27,172,246 2,292,646 2,608,947 3,888,430 5,987,181 668,773 2,680,423 1,844,282 377,673 3,901,169 1,460,113 6,566,254 1,927,636 363,603 Hauschild and Döll Muni_nr 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 Municipality Taua Tejucuoca Tiangua Trairi Tururu Ubajara Umari Umirim Uruburetama Uruoca Varjota Varzea Alegre Vicosa do Ceara Agricolandia Agua Branca Alagoinha do Piaui Alegrete do Piaui Alto Longa Altos Amarante Angical do Piaui Anisio de Abreu Antonio Almeida Aroazes Arraial Avelino Lopes Baixa Grande do Ribeiro Barras Barreiras do Piaui Barro Duro Batalha Beneditinos Bertolinia Bocaina Bom Jesus Bom Principio do Piaui Bonfim do Piaui Brasileira Buriti dos Lopes Buriti dos Montes Cabeceiras do Piaui Caldeirao Grande do Piaui Campinas do Piaui Campo Maior Canavieira Canto do Buriti Capitao de Campos Caracol Castelo do Piaui Cocal Coivaras Colonia do Gurgueia Colonia do Piaui Conceicao do Caninde Coronel Jose Dias Corrente Cristalandia do Piaui Cristino Castro Curimata Demerval Lobao Dirceu Arcoverde Dom Expedito Lopes Domingos Mourao Dom Inocencio Elesbao Veloso Eliseu Martins Esperantina Fartura do Piaui Flores do Piaui Floriano Francinopolis Francisco Ayres Francisco Santos Fronteiras Gilbues Guadalupe Hugo Napoleao Inhuma Ipiranga do Piaui Isaias Coelho Itainopolis Itaueira Jacobina do Piaui Jaicos Jardim do Mulato Jerumenha Joaquim Pires Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis irrigation 0 20,858 3,175,054 398,576 109,828 20,417,018 143,202 1,649,713 73,693 0 4,034,080 50,414 0 0 124,673 6,685 0 327,866 1,047,442 345,072 72,382 38,607 571,619 413,031 15,445 39,927 2,643,184 1,090,592 88,205 634,628 135,860 17,215 126,061 168,602 1,030,113 13,357 10,027 73,223 1,520,448 9,459 115,579 98,462 31,383 1,365,240 14,591 693,871 389,875 79,901 24,798 380,353 94,350 17,568 62,903 13,572 28,644 602,110 413,970 1,619,632 130,294 176,437 0 145,573 0 13,875 30,011 21,627 254,824 0 55,417 2,107,775 344,125 21,974 30,671 0 330,668 117,962 19,059 389,674 205,088 0 178,034 259,143 24,063 45,712 42,568 368,934 394,146 livestock 2,497,134 290,251 416,510 322,450 104,251 258,113 326,122 280,063 61,854 249,575 108,801 534,784 541,630 27,640 115,571 219,063 150,931 918,083 787,261 323,557 150,734 331,135 219,320 292,013 149,047 497,140 339,778 951,466 247,815 88,637 1,067,270 472,239 705,382 178,803 846,178 190,006 185,557 325,817 824,903 378,554 410,776 234,583 459,392 1,992,082 284,195 1,050,418 189,551 371,615 860,730 801,564 161,645 100,138 327,973 509,682 237,417 1,523,254 524,310 333,635 839,459 145,003 360,688 76,796 239,774 911,994 723,528 229,916 711,990 280,460 182,955 734,839 92,767 161,367 164,926 350,834 501,450 185,942 47,849 257,497 134,066 400,901 571,339 484,576 560,521 680,534 103,848 264,754 564,237 domestic 1,381,618 203,148 1,499,794 778,216 239,106 690,886 175,320 382,670 481,959 233,166 487,105 682,412 1,042,096 243,303 552,604 197,090 101,754 387,786 892,300 929,338 244,174 216,084 120,714 222,220 195,970 330,256 216,501 1,356,715 130,204 182,120 555,167 279,744 407,646 144,996 922,992 169,920 87,318 190,688 718,926 109,836 175,293 98,424 120,703 1,067,400 149,272 950,861 212,779 417,095 778,454 567,442 141,696 171,503 162,388 272,056 69,768 845,062 170,606 571,788 323,506 616,854 151,734 390,564 175,271 162,036 704,572 172,204 1,371,927 131,886 105,552 1,860,040 149,792 150,846 158,729 259,073 387,118 798,720 152,571 431,939 302,041 197,925 334,763 330,243 90,216 668,062 110,986 158,498 322,195 63 industry_1 794 0 8,945 916 0 4,549 7 733 2,229 1,587 672 26,573 672 38,356 2,358 0 0 972 3,390 1,116 708 180 228 0 0 1,044 0 5,575 2,300 1,134 312 1,458 132 0 1,374 0 0 432 4,777 0 642 0 0 4,093 72 858 66 0 1,398 9,125 0 0 0 0 0 2,544 120 10,528 1,920 1,218 372 678 792 0 2,154 108 27,632 0 540 52,024 0 0 0 1,452 2,582 3,830 972 2,322 60 0 0 38,596 0 624 324 444 0 industry_2 1,560 0 11,996 153 0 4,118 12 1,778 28,890 579 2,440 47,990 1,719 9,925 34,056 0 0 7,774 98,455 11,769 3,995 376 476 0 0 2,181 0 28,960 5,357 51,274 652 9,925 276 0 21,553 0 0 903 11,985 0 1,341 0 0 90,950 150 45,134 138 0 10,840 6,775 0 0 0 0 0 63,254 3,060 49,032 13,368 11,794 777 1,417 1,655 0 29,263 226 45,042 0 1,128 189,519 0 0 0 10,844 10,651 3,308 4,035 4,682 125 0 0 14,607 0 15,631 677 12,751 0 tourism 0 0 45,203 168,728 0 45,203 0 0 19,629 1,464 0 19,629 45,203 0 0 0 0 0 0 9,000 0 0 0 0 0 0 0 7,200 0 0 5,400 0 0 0 0 1,800 0 0 0 0 0 0 0 25,200 0 5,400 0 1,800 0 0 0 0 0 0 14,400 5,400 0 5,400 0 0 0 0 0 0 0 0 9,000 0 0 45,000 0 0 0 0 0 3,600 0 0 0 0 0 0 0 0 0 0 0 total_1 total_2 3,879,546 514,257 5,145,506 1,668,886 453,185 21,415,769 644,651 2,313,179 639,364 485,792 4,630,658 1,313,812 1,629,601 309,299 795,206 422,838 252,685 1,634,707 2,730,393 1,608,083 467,998 586,006 911,881 927,264 360,462 868,367 3,199,463 3,411,548 468,524 906,519 1,764,009 770,656 1,239,221 492,401 2,800,657 375,083 282,902 590,160 3,069,054 497,849 702,290 431,469 611,478 4,454,015 448,130 2,701,408 792,271 870,411 1,665,380 1,758,484 397,691 289,209 553,264 795,310 350,229 2,978,370 1,109,006 2,540,983 1,295,179 939,512 512,794 613,611 415,837 1,087,905 1,460,265 423,855 2,375,373 412,346 344,464 4,799,678 586,684 334,187 354,326 611,359 1,221,818 1,110,054 220,451 1,081,432 641,255 598,826 1,084,136 1,112,558 674,800 1,394,932 257,726 792,630 1,280,578 3,880,312 514,257 5,148,557 1,668,123 453,185 21,415,338 644,656 2,314,224 666,025 484,784 4,632,426 1,335,229 1,630,648 280,868 826,904 422,838 252,685 1,641,509 2,825,458 1,618,736 471,285 586,202 912,129 927,264 360,462 869,504 3,199,463 3,434,933 471,581 956,659 1,764,349 779,123 1,239,365 492,401 2,820,836 375,083 282,902 590,631 3,076,262 497,849 702,989 431,469 611,478 4,540,872 448,208 2,745,684 792,343 870,411 1,674,822 1,756,134 397,691 289,209 553,264 795,310 350,229 3,039,080 1,111,946 2,579,487 1,306,627 950,088 513,199 614,350 416,700 1,087,905 1,487,374 423,973 2,392,783 412,346 345,052 4,937,173 586,684 334,187 354,326 620,751 1,229,887 1,109,532 223,514 1,083,792 641,320 598,826 1,084,136 1,088,569 674,800 1,409,939 258,079 804,937 1,280,578 Hauschild and Döll Muni_nr 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Municipality Jose de Freitas Lagoa Alegre Lagoa do Barro Piaui Landri Sales Luis Correia Luzilandia Manoel Emidio Marcolandia Marcos Parente Matias Olimpio Miguel Alves Miguel Leao Monsenhor Gil Monsenhor Hipolito Monte Alegre do Piaui Nazare do Piaui Nossa Senhora dos Remedios Novo Oriente do Piaui Oeiras Padre Marcos Paes Landim Palmeira do Piaui Palmeirais Parnagua Parnaiba Passagem Franca do Piaui Patos do Piaui Paulistana Pedro II Picos Pimenteiras Pio IX Piracuruca Piripiri Porto Prata do Piaui Queimada Nova Redencao do Gurgueia Regeneracao Ribeiro Goncalves Rio Grande do Piaui Santa Cruz do Piaui Santa Cruz dos Milagres Santa Filomena Santa Luz Santana do Piaui Santa Rosa do Piaui Santo Antonio de Lisboa Santo Inacio do Piaui Sao Braz do Piaui Sao Felix do Piaui Sao Francisco do Piaui Sao Goncalo do Piaui Sao Joao da Canabrava Sao Joao da Serra Sao Joao do Piaui Sao Jose do Divino Sao Jose do Peixe Sao Jose do Piaui Sao Juliao Sao Lourenco do Piaui Sao Miguel do Tapuio Sao Pedro do Piaui Sao Raimundo Nonato Sigefredo Pacheco Simoes Simplicio Mendes Socorro do Piaui Teresina Uniao Urucui Valenca do Piaui Varzea Branca Varzea Grande irrigation 1,275,315 75,523 18,537 559,269 39,198 272,560 43,433 4,644 37,140 115,095 5,534,678 2,351,283 233,014 0 69,275 55,604 10,490 106,484 1,892,731 12,755 74,245 231,058 1,083,652 376,326 1,525,958 202,117 32,145 147,181 648,110 5,602,346 334,622 5,565 280,662 1,831,169 484,820 32,139 12,729 345,986 55,980 87,024 32,460 31,963 1,434,080 65,808 25,372 43,023 48,474 21,357 69,476 13,764 56,347 40,727 42,118 7,614 8,261 398,959 463,902 1,181 113,975 0 0 421,576 306,166 213,375 41,993 57,508 459,626 12,775 11,838,604 61,960,764 212,454 564,019 0 25,875 livestock 782,052 217,771 427,217 235,702 567,806 754,222 308,603 48,065 182,329 163,827 567,518 56,393 236,957 190,769 369,918 341,266 129,828 234,008 1,069,493 459,857 292,491 248,826 392,390 1,286,405 472,851 197,925 327,660 1,367,381 977,039 959,697 467,654 589,631 1,240,237 693,587 279,402 87,306 508,980 335,115 212,494 271,018 420,046 425,040 548,212 311,020 103,184 52,496 283,796 145,320 329,468 91,242 302,398 365,774 54,295 191,905 510,487 1,942,223 336,896 495,582 103,771 172,805 295,615 1,020,507 173,799 556,875 468,103 858,388 810,676 193,978 829,887 656,588 395,835 468,099 169,671 169,933 domestic 1,351,317 137,312 96,048 393,760 623,616 893,828 260,732 104,886 230,605 238,343 512,252 58,724 273,124 204,791 318,419 269,758 146,206 261,778 2,058,868 289,394 129,404 129,613 293,622 296,010 8,966,711 137,700 139,536 725,024 1,244,404 4,358,408 337,124 341,634 1,018,424 2,911,652 486,431 134,214 140,094 242,973 778,596 301,478 410,600 344,380 178,398 131,972 165,676 65,808 272,964 203,534 142,136 72,252 307,701 247,126 236,207 170,681 197,326 1,260,369 133,176 241,266 177,632 205,994 76,896 609,683 646,112 868,818 350,982 444,078 580,634 134,578 52,060,440 1,071,309 561,168 639,587 79,596 230,238 industry_1 4,309 0 0 0 9,341 26,984 684 0 36 0 26,402 38,428 1,212 366 492 0 0 0 1,686 1,044 0 0 38,404 588 22,835 0 0 2,106 4,513 26,592 264 720 5,965 7,693 0 0 0 0 2,568 2,300 38,356 744 0 126 1,936 0 588 36 0 0 0 456 636 0 0 1,674 144 780 624 0 0 78 732 5,772 0 906 390 0 334,465 26,552 3,008 4,626 0 0 industry_2 25,635 0 0 0 26,467 23,013 1,374 0 75 0 25,322 9,014 13,233 765 1,028 0 0 0 38,510 3,308 0 0 5,369 3,308 320,903 0 0 19,470 29,434 184,047 552 9,014 62,782 106,236 0 0 0 0 49,499 27,880 9,925 26,600 0 263 9,014 0 1,229 75 0 0 0 953 13,273 0 0 38,203 301 2,669 9,054 0 0 163 21,837 80,721 0 1,830 815 0 1,858,892 38,600 60,345 28,996 0 0 tourism total_1 total_2 14,400 0 0 0 0 0 0 0 0 0 0 0 1,800 0 0 0 0 0 28,200 0 0 0 0 0 1,056,000 0 0 5,400 13,800 36,000 0 0 36,000 37,500 0 0 0 0 0 0 0 0 9,450 0 0 0 0 0 0 0 0 0 0 0 0 7,200 0 0 0 0 0 0 0 29,250 0 0 0 0 390,000 0 0 7,200 0 0 3,427,393 430,606 541,802 1,188,731 1,239,961 1,947,594 613,452 157,595 450,110 517,265 6,640,850 2,504,828 746,107 395,926 758,104 666,628 286,524 602,270 5,050,978 763,050 496,140 609,497 1,808,068 1,959,329 12,044,355 537,742 499,341 2,247,092 2,887,866 10,983,043 1,139,664 937,550 2,581,288 5,481,601 1,250,653 253,659 661,803 924,074 1,049,638 661,820 901,462 802,127 2,170,140 508,926 296,168 161,327 605,822 370,247 541,080 177,258 666,446 654,083 333,256 370,200 716,074 3,610,425 934,118 738,809 396,002 378,799 372,511 2,051,844 1,126,809 1,674,090 861,078 1,360,880 1,851,326 341,331 65,453,396 63,715,213 1,172,465 1,683,531 249,267 426,046 3,448,719 430,606 541,802 1,188,731 1,257,087 1,943,623 614,142 157,595 450,149 517,265 6,639,770 2,475,414 758,128 396,325 758,640 666,628 286,524 602,270 5,087,802 765,314 496,140 609,497 1,775,033 1,962,049 12,342,423 537,742 499,341 2,264,456 2,912,787 11,140,498 1,139,952 945,844 2,638,105 5,580,144 1,250,653 253,659 661,803 924,074 1,096,569 687,400 873,031 827,983 2,170,140 509,063 303,246 161,327 606,463 370,286 541,080 177,258 666,446 654,580 345,893 370,200 716,074 3,646,954 934,275 740,698 404,432 378,799 372,511 2,051,929 1,147,914 1,749,039 861,078 1,361,804 1,851,751 341,331 66,977,823 63,727,261 1,229,802 1,707,901 249,267 426,046 NoWUM output: Annual withdrawal water uses of present state 1996/98 CE [m³] PI [m³] CE [km³] PI [km³] total [km³] irrigation livestock 323,995,431 127,452,637 324 127 451 81,209,462 225,541,178 65,127,274 123,574,949 81 226 65 124 146 349 domestic 64 industry_1 25,580,029 882,097 26 1 26 industry_2 46,215,772 4,132,062 46 4 50 tourism 16,742,384 1,810,800 17 2 19 total_1 673,068,484 318,847,757 673 319 992 total_2 693,704,227 322,097,722 694 322 1,016 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 2) NoWUM output: Annual consumptive water uses [m3] of present state 1996/98 Muni_nr 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 Municipality Abaiara Acarape Acarau Acopiara Aiuaba Alcantaras Altaneira Alto Santo Amontada Antonina do Norte Apuiares Aquiraz Aracati Aracoiaba Ararenda Araripe Aratuba Arneiroz Assare Aurora Baixio Banabuiu Barbalha Barreira Barro Barroquinha Baturite Beberibe Bela Cruz Boa Viagem Brejo Santo Camocim Campos Sales Caninde Capistrano Caridade Carire Caririacu Carius Carnaubal Cascavel Catarina Catunda Caucaia Cedro Chaval Choro Chorozinho Coreau Crateus Crato Croata Cruz Deputado Irapuan Pinheiro Erere Eusebio Farias Brito Forquilha Fortaleza Fortim Frecheirinha General Sampaio Graca Granja Granjeiro Groairas Guaiuba Guaraciaba do Norte Guaramiranga Hidrolandia Horizonte Ibaretama Ibiapina Ibicuitinga Icapui Ico Iguatu Independencia Ipaporanga Ipaumirim Ipu Ipueiras Iracema Iraucuba irrigation 0 1,373,881 23,369 0 118,177 0 0 209,541 54,348 0 2,125,879 23,767 986,238 1,281,826 30,818 0 23,610 0 438,676 967,810 0 875,673 82,355 4,923 0 41,040 93,145 5,056,910 0 198,024 632,592 2,083,078 0 132,785 176,051 31,368 306,321 30,340 30,447 470,623 2,677,790 0 371,266 215,076 0 0 74,455 293,647 0 1,753,309 1,815,162 327,907 193,948 166,947 64,770 53,246 0 20,284 11,484 207,092 0 913,123 56,998 136,544 1,904 479,727 34,409 1,440,273 74,621 34,978 0 37,650 1,093,814 37,976 4,074,186 7,605,972 1,260,042 113,083 0 0 308,040 37,168 28,847 17,455 livestock 143,857 57,822 376,608 1,276,789 600,919 84,735 56,448 624,640 393,420 138,665 248,042 482,547 265,104 330,329 226,614 328,824 106,948 509,458 513,222 658,134 240,012 680,148 249,319 185,621 434,765 129,961 230,751 441,546 535,431 1,559,740 686,297 335,728 377,390 1,325,671 154,330 261,590 542,409 378,262 375,926 127,172 379,091 400,917 320,933 638,297 574,550 99,205 417,960 178,052 323,709 1,507,009 415,845 133,373 192,449 314,121 265,445 64,310 345,935 278,019 133,075 49,565 96,042 124,463 155,092 801,551 84,680 129,347 98,728 233,522 27,987 472,880 218,851 418,396 171,659 269,486 177,711 1,149,947 1,015,845 1,641,552 312,433 289,881 368,528 553,573 438,117 724,065 domestic 34,160 56,616 201,940 259,655 49,547 32,998 19,566 63,745 153,687 41,498 57,892 207,286 348,248 106,726 36,806 78,169 47,343 32,363 68,602 118,438 30,392 100,947 274,832 61,034 100,076 46,825 183,986 150,754 118,252 362,505 125,417 359,777 154,937 476,620 71,884 61,488 85,904 84,251 62,924 79,691 240,156 54,569 42,718 935,254 83,887 54,234 41,202 62,556 111,113 440,475 343,876 76,537 72,232 28,555 30,889 97,942 94,854 98,938 18,897,868 49,553 52,971 25,718 51,728 226,650 20,901 55,535 87,467 151,920 23,658 88,419 94,251 39,085 101,348 31,954 141,807 405,693 855,704 124,765 39,622 57,115 304,375 167,289 92,519 90,575 65 industry_1 5,108 6,277 92 256 32 0 108 92 14 513 122 218,284 2,919 100 0 617 92 212 104 201 24 15,238 2,705 8 3,138 591 1,374 1,191 269 1,732 6,106 292,987 470 13,008 92 177 299 36 62 189 452 162 0 23,586 3,501 122 4 27 238 604 66,062 0 286 0 92 25,320 9 1,447 2,779,080 830 83 0 0 910 0 44 92 226 21 104 146,171 44 744 0 456 1,650 50,257 470 0 556 675 122 2,222 37 industry_2 9,224 48,860 8,876 3,256 58 0 195 3,685 25 8 444 138,352 138,614 181 0 45 580 384 187 5,235 43 27,519 111,865 14 5,667 1,068 15,587 15,236 351 3,128 11,027 529,125 7,473 23,492 29 14,652 921 65 111 7 44,818 293 0 459,782 6,323 144,294 6 49 49 9,804 119,306 0 516 0 333 45,726 16 2,614 4,531,542 1,719 150 0 0 1,643 0 79 4,824 304 39 6,517 65,206 80 1,344 0 824 2,979 90,762 459 0 5,407 1,220 219 1,024 67 tourism 0 0 31,406 585 3,926 0 0 0 31,406 0 0 203,763 128,114 293 0 3,926 3,926 0 0 3,926 0 3,926 47,895 293 0 31,406 4,803 114,854 0 293 293 32,965 0 71,942 0 0 0 3,926 0 9,041 45,444 0 0 248,998 3,926 3,926 0 0 0 4,510 68,480 3,926 31,406 0 0 293 0 0 807,406 36,864 0 0 0 0 0 0 3,926 9,041 3,926 0 1,462 0 9,041 0 36,864 3,926 5,388 0 0 0 3,926 0 0 0 total_1 total_2 183,125 1,494,596 633,414 1,537,285 772,601 117,733 76,122 898,017 632,875 180,675 2,431,936 1,135,647 1,730,622 1,719,274 294,239 411,536 181,918 542,033 1,020,603 1,748,510 270,428 1,675,932 657,106 251,879 537,979 249,823 514,059 5,765,254 653,952 2,122,294 1,450,704 3,104,535 532,797 2,020,025 402,356 354,623 934,933 496,815 469,359 686,716 3,342,932 455,649 734,916 2,061,212 665,864 157,487 533,621 534,282 435,060 3,705,908 2,709,426 541,743 490,319 509,623 361,195 241,109 440,798 398,688 22,628,912 343,904 149,096 1,063,304 263,818 1,165,655 107,485 664,653 224,621 1,834,981 130,213 596,381 460,735 495,176 1,376,605 339,416 4,431,024 9,167,188 3,187,236 1,879,869 352,055 347,552 985,544 758,151 561,706 832,133 187,242 1,537,179 642,199 1,540,285 772,627 117,733 76,209 901,610 632,887 180,170 2,432,257 1,055,716 1,866,317 1,719,355 294,239 410,964 182,406 542,204 1,020,687 1,753,544 270,448 1,688,213 766,266 251,885 540,508 250,300 528,272 5,779,300 654,034 2,123,690 1,455,625 3,340,673 539,800 2,030,509 402,294 369,097 935,555 496,844 469,408 686,534 3,387,298 455,779 734,916 2,497,408 668,686 301,659 533,624 534,304 434,871 3,715,108 2,762,670 541,743 490,550 509,623 361,436 261,516 440,805 399,854 24,381,376 344,793 149,163 1,063,304 263,818 1,166,389 107,485 664,688 229,354 1,835,059 130,230 602,794 379,770 495,212 1,377,205 339,416 4,431,392 9,168,518 3,227,742 1,879,858 352,055 352,403 986,088 758,249 560,507 832,163 Hauschild and Döll Muni_nr 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 Municipality Itaicaba Itaitinga Itapage Itapipoca Itapiuna Itarema Itatira Jaguaretama Jaguaribara Jaguaribe Jaguaruana Jardim Jati Jijoca de Jericoacoara Juazeiro do Norte Jucas Lavras da Mangabeira Limoeiro do Norte Madalena Maracanau Maranguape Marco Martinopole Massape Mauriti Meruoca Milagres Milha Miraima Missao Velha Mombaca Monsenhor Tabosa Morada Nova Moraujo Morrinhos Mucambo Mulungu Nova Olinda Nova Russas Novo Oriente Ocara Oros Pacajus Pacatuba Pacoti Pacuja Palhano Palmacia Paracuru Paraipaba Parambu Paramoti Pedra Branca Penaforte Pentecoste Pereiro Pindoretama Piquet Carneiro Pires Ferreira Poranga Porteiras Potengi Potiretama Quiterianopolis Quixada Quixelo Quixeramobim Quixere Redencao Reriutaba Russas Saboeiro Salitre Santana do Acarau Santana do Cariri Santa Quiteria Sao Benedito Sao Goncalo do Amarante Sao Joao do Jaguaribe Sao Luis do Curu Senador Pompeu Senador Sa Sobral Solonopole Tabuleiro do Norte Tamboril Tarrafas Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis irrigation 7,536 322,258 126,678 234,730 1,005,099 0 212,528 317,504 2,488,604 19,273,282 13,024,714 8,685 686,218 0 2,212,204 0 207,350 7,630,443 351,107 0 0 0 0 235,128 326,891 489,614 0 90,324 115,879 32,024 84,395 0 6,300,660 0 0 0 14,924 0 79,874 43,794 28,791 1,650,656 1,113,602 156,214 157,896 93,063 40,402 0 7,186,258 34,071,844 104,924 298,570 442,631 0 4,952,106 43,958 0 22,021 280,513 204,219 0 0 12,542 142,238 1,128,938 318,505 5,273,000 2,050,175 151,480 0 13,736,007 0 14,753 13,796,053 456,470 0 2,296,863 3,227,489 51,188 1,797,728 275,775 51,732 507,103 35,156 2,974,664 0 29,599 livestock 120,768 130,118 317,595 752,483 319,577 346,459 367,769 1,616,494 732,129 1,672,416 524,587 448,887 164,213 65,208 214,677 461,534 801,289 473,580 563,018 70,749 583,608 303,872 83,523 303,232 891,087 50,117 410,937 500,138 467,643 470,791 1,327,391 472,712 1,610,977 179,501 203,135 115,228 37,358 146,322 429,005 669,496 297,093 425,969 307,680 113,579 95,218 81,625 127,800 86,071 169,564 163,486 1,215,762 251,124 859,792 109,698 508,772 216,599 133,033 346,303 189,033 285,003 309,432 188,474 294,917 606,564 1,520,924 636,818 1,879,442 221,672 74,959 243,268 620,757 629,930 330,391 830,204 381,064 1,753,764 239,422 331,917 222,398 91,773 606,821 122,800 1,289,586 855,504 480,548 1,207,719 162,639 domestic 34,486 120,343 209,746 657,060 62,959 107,609 51,948 88,943 43,154 325,129 134,005 90,022 37,259 41,450 1,647,590 159,314 179,507 335,391 72,293 709,358 492,530 104,590 34,017 152,290 174,506 42,431 125,118 67,741 38,084 148,253 167,266 76,318 509,943 38,455 79,254 70,390 29,509 70,485 218,432 98,595 73,181 135,498 185,124 212,620 44,651 31,846 37,457 44,459 100,966 99,399 140,554 54,597 221,236 34,072 171,378 71,630 48,877 45,954 39,211 40,972 69,005 45,287 28,076 79,361 423,537 74,354 511,637 79,555 102,407 96,831 257,276 76,679 44,791 104,157 153,562 160,103 206,439 154,494 71,177 64,657 150,042 31,993 131,203 110,444 179,508 142,080 28,368 66 industry_1 92 293 49,554 49,130 95 415 21 42 360 6,188 1,081 282 281 0 21,742 38,361 617 150,709 37 623,429 59,677 1,117 263 1,117 31 966 275 2 0 354 198 7 19,604 299 134 0 0 269 1,607 4 0 299 240,742 112,106 553 345 208 16 98 98 6 183 1,763 165 1,172 214 46 4 0 16 147 52 0 0 12,370 129 7,644 714 311 147 2,564 0 0 269 1,142 5,703 1,868 195 240 8 543 140 1,099 123 2,717 116 0 industry_2 76 758 89,492 88,728 171 749 38 76 72 11,176 44,076 509 281 0 107,366 69,279 1,237 272,176 66 841,270 148,931 16 954 974 2,713 1,744 3,589 4 0 2,337 357 13 35,404 1,087 310 0 1 35,819 2,903 7 0 2,876 73,801 201,945 999 622 18,042 28 13,215 35 11 11,821 3,185 435 1,715 312 84 7 0 29 4 94 0 0 6,482 233 13,805 44 17,946 113 72,658 0 0 1,851 53,136 10,934 935 18,391 433 14 1,309 510 240,913 223 11,129 1,903 0 tourism 0 468 4,510 32,965 0 33,746 3,926 0 0 585 0 662 0 77,418 292,512 0 0 5,388 0 0 7,217 0 293 0 0 3,926 0 0 0 3,509 585 0 3,926 0 0 0 3,926 3,926 585 0 0 4,218 4,510 6,281 3,926 0 0 3,926 50,902 46,223 0 0 0 0 3,926 3,926 19,628 0 0 3,926 0 0 0 0 5,680 0 4,510 0 4,218 0 0 0 0 0 447 0 9,041 58,702 0 0 877 0 6,850 0 0 0 0 total_1 total_2 162,881 573,480 708,084 1,726,369 1,387,729 488,229 636,192 2,022,983 3,264,248 21,277,600 13,684,387 548,538 887,971 184,076 4,388,726 659,208 1,188,762 8,595,512 986,455 1,403,537 1,143,032 409,579 118,095 691,767 1,392,515 587,054 536,329 658,206 621,606 654,931 1,579,834 549,038 8,445,110 218,256 282,523 185,618 85,717 221,001 729,504 811,889 399,065 2,216,640 1,851,659 600,800 302,244 206,879 205,866 134,472 7,507,788 34,381,048 1,461,247 604,474 1,525,423 143,934 5,637,355 336,326 201,585 414,282 508,757 534,136 378,583 233,813 335,535 828,162 3,091,449 1,029,807 7,676,233 2,352,116 333,376 340,246 14,616,604 706,609 389,936 14,730,683 992,684 1,919,569 2,753,633 3,772,797 345,003 1,954,167 1,034,058 206,666 1,935,841 1,001,228 3,637,436 1,349,916 220,606 162,866 573,945 748,022 1,765,966 1,387,805 488,563 636,209 2,023,017 3,263,960 21,282,588 13,727,382 548,766 887,971 184,076 4,474,348 690,126 1,189,382 8,716,979 986,484 1,621,377 1,232,287 408,478 118,786 691,623 1,395,197 587,833 539,644 658,207 621,606 656,914 1,579,993 549,043 8,460,910 219,043 282,698 185,618 85,718 256,551 730,799 811,892 399,065 2,219,217 1,684,719 690,639 302,689 207,157 223,701 134,484 7,520,906 34,380,988 1,461,252 616,111 1,526,845 144,205 5,637,898 336,425 201,623 414,285 508,757 534,148 378,440 233,854 335,535 828,162 3,085,561 1,029,911 7,682,394 2,351,447 351,010 340,212 14,686,698 706,609 389,936 14,732,265 1,044,678 1,924,800 2,752,700 3,790,993 345,196 1,954,173 1,034,824 207,035 2,175,654 1,001,328 3,645,849 1,351,703 220,606 Hauschild and Döll Muni_nr 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 Municipality Taua Tejucuoca Tiangua Trairi Tururu Ubajara Umari Umirim Uruburetama Uruoca Varjota Varzea Alegre Vicosa do Ceara Agricolandia Agua Branca Alagoinha do Piaui Alegrete do Piaui Alto Longa Altos Amarante Angical do Piaui Anisio de Abreu Antonio Almeida Aroazes Arraial Avelino Lopes Baixa Grande do Ribeiro Barras Barreiras do Piaui Barro Duro Batalha Beneditinos Bertolinia Bocaina Bom Jesus Bom Principio do Piaui Bonfim do Piaui Brasileira Buriti dos Lopes Buriti dos Montes Cabeceiras do Piaui Caldeirao Grande do Piaui Campinas do Piaui Campo Maior Canavieira Canto do Buriti Capitao de Campos Caracol Castelo do Piaui Cocal Coivaras Colonia do Gurgueia Colonia do Piaui Conceicao do Caninde Coronel Jose Dias Corrente Cristalandia do Piaui Cristino Castro Curimata Demerval Lobao Dirceu Arcoverde Dom Expedito Lopes Domingos Mourao Dom Inocencio Elesbao Veloso Eliseu Martins Esperantina Fartura do Piaui Flores do Piaui Floriano Francinopolis Francisco Ayres Francisco Santos Fronteiras Gilbues Guadalupe Hugo Napoleao Inhuma Ipiranga do Piaui Isaias Coelho Itainopolis Itaueira Jacobina do Piaui Jaicos Jardim do Mulato Jerumenha Joaquim Pires Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis irrigation 0 12,252 2,397,235 269,154 70,532 17,755,048 91,934 1,134,531 45,581 0 2,444,680 30,447 0 0 62,983 5,113 0 178,496 675,821 142,295 35,557 15,551 224,181 264,805 6,837 17,600 1,322,764 647,959 50,132 301,086 93,940 10,966 75,565 96,857 472,169 6,278 5,874 46,346 827,022 10,213 73,254 51,964 16,344 952,206 7,780 292,368 213,554 32,606 14,019 146,303 52,626 10,464 30,096 8,423 12,557 294,852 182,341 789,692 60,294 97,872 0 86,978 0 6,483 17,143 10,211 155,740 0 27,262 952,549 171,192 9,792 18,422 0 142,718 56,423 9,878 221,893 128,717 0 109,592 234,891 20,991 30,928 18,036 285,080 263,454 livestock 2,497,134 290,251 416,510 322,450 104,251 258,113 326,122 280,063 61,854 249,575 108,801 534,784 541,630 27,640 115,571 219,063 150,931 918,083 787,261 323,557 150,734 331,135 219,320 292,013 149,047 497,140 339,778 951,466 247,815 88,637 1,067,270 472,239 705,382 178,803 846,178 190,006 185,557 325,817 824,903 378,554 410,776 234,583 459,392 1,992,082 284,195 1,050,418 189,551 371,615 860,730 801,564 161,645 100,138 327,973 509,682 237,417 1,523,254 524,310 333,635 839,459 145,003 360,688 76,796 239,774 911,994 723,528 229,916 711,990 280,460 182,955 734,839 92,767 161,367 164,926 350,834 501,450 185,942 47,849 257,497 134,066 400,901 571,339 484,576 560,521 680,534 103,848 264,754 564,237 domestic 276,324 40,630 299,959 155,643 47,821 138,177 35,064 76,534 96,392 46,633 97,421 136,482 208,419 48,661 110,521 39,418 20,351 77,557 178,460 185,867 48,835 43,217 24,143 44,444 39,194 66,051 43,300 271,343 26,041 36,424 111,033 55,949 81,529 28,999 184,598 33,984 17,464 38,138 143,785 21,967 35,059 19,685 24,141 213,480 29,854 190,172 42,556 83,419 155,691 113,488 28,339 34,301 32,478 54,411 13,954 169,012 34,121 114,358 64,701 123,371 30,347 78,113 35,054 32,407 140,914 34,441 274,385 26,377 21,110 372,008 29,958 30,169 31,746 51,815 77,424 159,744 30,514 86,388 60,408 39,585 66,953 66,049 18,043 133,612 22,197 31,699 64,439 67 industry_1 159 0 1,789 183 0 910 1 147 446 317 134 5,315 134 7,671 472 0 0 194 678 223 142 36 46 0 0 209 0 1,115 460 227 62 292 26 0 275 0 0 86 955 0 128 0 0 819 14 172 13 0 280 1,825 0 0 0 0 0 509 24 2,106 384 244 74 136 158 0 431 22 5,526 0 108 10,405 0 0 0 290 516 766 194 464 12 0 0 7,719 0 125 65 89 0 industry_2 312 0 2,399 31 0 824 2 356 5,778 116 488 9,598 344 1,985 6,811 0 0 1,555 19,691 2,354 799 75 95 0 0 436 0 5,792 1,071 10,255 130 1,985 55 0 4,311 0 0 181 2,397 0 268 0 0 18,190 30 9,027 28 0 2,168 1,355 0 0 0 0 0 12,651 612 9,806 2,674 2,359 155 283 331 0 5,853 45 9,008 0 226 37,904 0 0 0 2,169 2,130 662 807 936 25 0 0 2,921 0 3,126 135 2,550 0 tourism 0 0 9,041 33,746 0 9,041 0 0 3,926 293 0 3,926 9,041 0 0 0 0 0 0 1,800 0 0 0 0 0 0 0 1,440 0 0 1,080 0 0 0 0 360 0 0 0 0 0 0 0 5,040 0 1,080 0 360 0 0 0 0 0 0 2,880 1,080 0 1,080 0 0 0 0 0 0 0 0 1,800 0 0 9,000 0 0 0 0 0 720 0 0 0 0 0 0 0 0 0 0 0 total_1 total_2 2,773,616 343,133 3,124,533 781,176 222,604 18,161,288 453,122 1,491,275 208,198 296,818 2,651,036 710,953 759,225 83,971 289,547 263,594 171,282 1,174,331 1,642,220 653,743 235,267 389,939 467,689 601,262 195,078 581,000 1,705,842 1,873,322 324,448 426,374 1,273,386 539,445 862,502 304,659 1,503,220 230,628 208,895 410,388 1,796,666 410,734 519,217 306,232 499,876 3,163,626 321,844 1,534,210 445,675 487,999 1,030,719 1,063,180 242,610 144,903 390,546 572,516 266,807 1,988,708 740,796 1,240,870 964,838 366,490 391,109 242,023 274,987 950,884 882,015 274,589 1,149,442 306,837 231,435 2,078,801 293,917 201,329 215,095 402,939 722,107 403,595 88,435 566,242 323,204 440,486 747,884 793,235 599,555 845,199 144,146 581,623 892,130 2,773,770 343,133 3,125,143 781,023 222,604 18,161,202 453,123 1,491,484 213,531 296,617 2,651,390 715,237 759,434 78,285 295,886 263,594 171,282 1,175,691 1,661,232 655,874 235,925 389,978 467,739 601,262 195,078 581,227 1,705,842 1,877,999 325,059 436,402 1,273,454 541,139 862,531 304,659 1,507,256 230,628 208,895 410,482 1,798,107 410,734 519,357 306,232 499,876 3,180,997 321,860 1,543,065 445,689 487,999 1,032,607 1,062,710 242,610 144,903 390,546 572,516 266,807 2,000,850 741,384 1,248,571 967,128 368,605 391,190 242,171 275,159 950,884 887,437 274,613 1,152,924 306,837 231,553 2,106,300 293,917 201,329 215,095 404,818 723,721 403,490 89,048 566,714 323,217 440,486 747,884 788,437 599,555 848,200 144,217 584,084 892,130 Hauschild and Döll Muni_nr 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Municipality Jose de Freitas Lagoa Alegre Lagoa do Barro Piaui Landri Sales Luis Correia Luzilandia Manoel Emidio Marcolandia Marcos Parente Matias Olimpio Miguel Alves Miguel Leao Monsenhor Gil Monsenhor Hipolito Monte Alegre do Piaui Nazare do Piaui Nossa Senhora dos Remedios Novo Oriente do Piaui Oeiras Padre Marcos Paes Landim Palmeira do Piaui Palmeirais Parnagua Parnaiba Passagem Franca do Piaui Patos do Piaui Paulistana Pedro II Picos Pimenteiras Pio IX Piracuruca Piripiri Porto Prata do Piaui Queimada Nova Redencao do Gurgueia Regeneracao Ribeiro Goncalves Rio Grande do Piaui Santa Cruz do Piaui Santa Cruz dos Milagres Santa Filomena Santa Luz Santana do Piaui Santa Rosa do Piaui Santo Antonio de Lisboa Santo Inacio do Piaui Sao Braz do Piaui Sao Felix do Piaui Sao Francisco do Piaui Sao Goncalo do Piaui Sao Joao da Canabrava Sao Joao da Serra Sao Joao do Piaui Sao Jose do Divino Sao Jose do Peixe Sao Jose do Piaui Sao Juliao Sao Lourenco do Piaui Sao Miguel do Tapuio Sao Pedro do Piaui Sao Raimundo Nonato Sigefredo Pacheco Simoes Simplicio Mendes Socorro do Piaui Teresina Uniao Urucui Valenca do Piaui Varzea Branca Varzea Grande irrigation 633,010 40,986 8,683 226,441 21,735 141,236 19,317 2,482 14,005 47,052 2,413,390 1,330,323 109,274 0 30,488 25,792 4,288 89,275 1,143,074 8,814 37,909 115,673 446,351 168,934 879,555 107,306 15,917 96,003 489,528 3,559,270 222,669 2,845 164,968 1,135,592 201,309 17,351 9,570 128,169 34,510 48,040 12,950 16,731 833,945 35,366 11,997 19,017 25,997 11,815 35,718 5,057 28,736 18,814 24,266 7,020 6,567 199,201 251,172 1,296 75,113 0 0 270,444 144,746 75,712 30,008 37,998 253,039 6,149 6,960,376 35,955,340 82,204 380,631 0 11,517 livestock 782,052 217,771 427,217 235,702 567,806 754,222 308,603 48,065 182,329 163,827 567,518 56,393 236,957 190,769 369,918 341,266 129,828 234,008 1,069,493 459,857 292,491 248,826 392,390 1,286,405 472,851 197,925 327,660 1,367,381 977,039 959,697 467,654 589,631 1,240,237 693,587 279,402 87,306 508,980 335,115 212,494 271,018 420,046 425,040 548,212 311,020 103,184 52,496 283,796 145,320 329,468 91,242 302,398 365,774 54,295 191,905 510,487 1,942,223 336,896 495,582 103,771 172,805 295,615 1,020,507 173,799 556,875 468,103 858,388 810,676 193,978 829,887 656,588 395,835 468,099 169,671 169,933 domestic 270,263 27,462 19,210 78,752 124,723 178,766 52,146 20,977 46,121 47,669 102,450 11,745 54,625 40,958 63,684 53,952 29,241 52,356 411,774 57,879 25,881 25,923 58,724 59,202 1,793,342 27,540 27,907 145,005 248,881 871,682 67,425 68,327 203,685 582,330 97,286 26,843 28,019 48,595 155,719 60,296 82,120 68,876 35,680 26,395 33,135 13,162 54,593 40,707 28,427 14,450 61,540 49,425 47,241 34,136 39,465 252,074 26,635 48,253 35,526 41,199 15,379 121,937 129,222 173,764 70,196 88,816 116,127 26,916 10,412,088 214,262 112,234 127,917 15,919 46,048 industry_1 862 0 0 0 1,868 5,397 137 0 7 0 5,280 7,686 242 73 98 0 0 0 337 209 0 0 7,681 118 4,567 0 0 421 903 5,318 53 144 1,193 1,539 0 0 0 0 514 460 7,671 149 0 25 387 0 118 7 0 0 0 91 127 0 0 335 29 156 125 0 0 16 146 1,154 0 181 78 0 66,893 5,310 602 925 0 0 industry_2 5,127 0 0 0 5,293 4,603 275 0 15 0 5,064 1,803 2,647 153 206 0 0 0 7,702 662 0 0 1,074 662 64,181 0 0 3,894 5,887 36,809 110 1,803 12,556 21,247 0 0 0 0 9,900 5,576 1,985 5,320 0 53 1,803 0 246 15 0 0 0 191 2,655 0 0 7,641 60 534 1,811 0 0 33 4,367 16,144 0 366 163 0 371,778 7,720 12,069 5,799 0 0 tourism 2,880 0 0 0 105,600 0 0 0 0 0 0 0 360 0 0 0 0 0 5,640 0 0 0 0 0 105,600 0 0 1,080 2,760 7,200 0 0 7,200 7,500 0 0 0 0 0 0 0 0 1,890 0 0 0 0 0 0 0 0 0 0 0 0 1,440 0 0 0 0 0 0 0 5,850 0 0 0 0 130,000 0 0 1,440 0 0 total_1 total_2 1,689,067 286,219 455,109 540,895 821,732 1,079,620 380,203 71,524 242,462 258,548 3,088,640 1,406,147 401,458 231,800 464,187 421,010 163,357 375,639 2,630,318 526,759 356,281 390,422 905,146 1,514,659 3,255,915 332,771 371,484 1,609,890 1,719,111 5,403,166 757,801 660,947 1,617,284 2,420,548 577,997 131,500 546,569 511,879 403,237 379,814 522,787 510,795 1,419,727 372,805 148,703 84,675 364,504 197,849 393,613 110,749 392,674 434,105 125,929 233,062 556,520 2,395,273 614,732 545,287 214,535 214,004 310,994 1,412,903 447,913 813,355 568,307 985,383 1,179,920 227,042 18,399,244 36,831,500 590,874 979,012 185,590 227,498 1,693,332 286,219 455,109 540,895 825,158 1,078,826 380,340 71,524 242,470 258,548 3,088,424 1,400,264 403,862 231,880 464,295 421,010 163,357 375,639 2,637,683 527,212 356,281 390,422 898,539 1,515,203 3,315,529 332,771 371,484 1,613,363 1,724,095 5,434,658 757,859 662,606 1,628,647 2,440,257 577,997 131,500 546,569 511,879 412,623 384,930 517,101 515,967 1,419,727 372,833 150,119 84,675 364,632 197,857 393,613 110,749 392,674 434,204 128,457 233,062 556,520 2,402,578 614,764 545,665 216,221 214,004 310,994 1,412,920 452,134 828,345 568,307 985,568 1,180,005 227,042 18,704,130 36,833,908 602,341 983,886 185,590 227,498 NoWUM output: Annual consumptive water uses of present state 1996/98 CE [m³] PI [m³] CE [km³] PI [km³] total [km³] irrigation livestock domestic 223,285,775 71,934,474 223 72 295 81,209,462 65,127,274 81 65 146 45,108,235 24,714,992 45 25 70 68 industry_1 5,116,008 176,419 5 0 5 industry_2 9,243,155 826,414 9 1 10 tourism 2,856,391 414,160 3 0 3 total_1 357,575,859 162,367,315 358 162 520 total_2 361,703,016 163,017,311 362 163 525 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 3) NoWUM output: Annual withdrawal water uses [m3] of 2025 Coastal Boom and Cash Crops scenario (RSA) Muni_nr 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 Municipality Abaiara Acarape Acarau Acopiara Aiuaba Alcantaras Altaneira Alto Santo Amontada Antonina do Norte Apuiares Aquiraz Aracati Aracoiaba Ararenda Araripe Aratuba Arneiroz Assare Aurora Baixio Banabuiu Barbalha Barreira Barro Barroquinha Baturite Beberibe Bela Cruz Boa Viagem Brejo Santo Camocim Campos Sales Caninde Capistrano Caridade Carire Caririacu Carius Carnaubal Cascavel Catarina Catunda Caucaia Cedro Chaval Choro Chorozinho Coreau Crateus Crato Croata Cruz Deputado Irapuan Pinheiro Erere Eusebio Farias Brito Forquilha Fortaleza Fortim Frecheirinha General Sampaio Graca Granja Granjeiro Groairas Guaiuba Guaraciaba do Norte Guaramiranga Hidrolandia Horizonte Ibaretama Ibiapina Ibicuitinga Icapui Ico Iguatu Independencia Ipaporanga Ipaumirim Ipu Ipueiras Iracema Iraucuba irrigation 6,048,178 4,365,486 16,837,336 500,146 967,055 556,791 4,896,798 9,794,656 2,507,999 5,677,410 2,933,236 3,603,397 3,104,782 3,145,463 728,716 5,840,426 2,598,107 596,285 8,325,732 5,054,858 5,532,582 7,677,790 10,102,176 957,864 5,037,913 2,727,785 2,332,527 7,368,386 32,070,318 889,478 21,635,310 4,661,630 696,924 837,365 2,674,268 656,407 862,730 5,890,579 8,243,970 6,853,362 5,428,551 560,812 1,143,845 2,970,173 5,031,075 1,975,592 974,915 2,298,262 508,670 34,648,568 8,832,847 5,385,258 2,885,522 742,848 8,339,296 2,570,775 4,949,062 335,879 3,450,178 1,851,124 572,690 1,526,491 879,305 3,309,964 3,156,500 1,117,200 2,129,528 8,462,590 2,864,752 888,459 1,700,615 3,966,675 7,095,694 4,339,064 6,730,544 21,194,198 31,060,434 901,961 478,243 4,783,804 6,539,738 652,221 6,525,523 903,008 livestock domestic 147,074 200,226 98,529 541,572 641,784 2,843,334 929,412 907,848 437,437 266,856 61,695 177,708 57,705 164,118 638,585 379,818 670,423 1,558,776 141,742 164,448 180,559 201,486 767,414 2,749,620 451,778 2,944,260 562,916 1,297,812 164,948 198,228 336,174 470,634 182,240 492,228 370,848 125,478 524,664 575,466 672,818 706,500 245,366 154,842 695,339 525,510 254,873 1,304,568 316,324 921,312 444,480 588,078 221,431 706,818 393,216 1,442,472 752,448 2,027,472 912,413 1,360,554 1,135,419 1,189,506 701,600 1,052,082 572,116 2,853,612 274,715 484,452 965,004 1,303,332 262,976 732,492 190,405 231,324 394,852 370,650 386,743 706,752 384,316 527,838 130,024 375,102 646,001 4,183,374 291,850 198,264 233,638 153,888 1,015,106 11,935,740 587,403 703,704 169,037 551,874 304,227 221,898 303,443 674,244 235,638 424,236 1,097,021 1,213,680 425,144 2,884,674 136,345 447,360 327,976 1,999,548 228,664 153,804 271,381 167,736 102,277 1,581,600 353,661 576,954 202,381 288,228 211,635 120,216,280 84,481 479,142 69,926 199,404 90,592 86,844 112,882 278,604 1,365,936 2,246,562 86,581 125,118 94,152 148,764 157,010 788,586 238,719 888,348 47,686 236,694 344,244 353,208 372,956 1,421,712 427,721 327,870 175,501 523,758 275,497 268,044 302,817 1,567,470 1,175,620 2,024,352 1,038,536 3,139,764 1,194,958 445,584 227,441 213,408 296,345 301,536 376,748 1,436,202 402,986 603,036 447,890 405,330 527,104 339,708 69 industry_1 55,189 112,771 1,652 1,885 230 0 1,153 984 249 5,491 879 3,691,880 52,646 1,787 0 6,614 1,627 1,536 1,107 2,149 256 163,528 29,258 139 33,656 10,526 24,618 21,275 4,816 12,447 65,558 5,246,760 3,388 93,808 1,625 1,272 2,139 383 657 2,033 8,143 1,176 0 401,741 37,335 2,206 26 482 1,711 4,359 710,856 2 5,087 0 980 424,278 94 10,401 47,145,436 14,979 600 0 0 16,087 0 315 1,544 2,434 383 747 2,659,606 475 8,019 0 8,174 17,601 538,923 3,379 0 5,943 7,193 871 23,742 266 industry_2 99,669 877,855 160,081 23,947 416 0 2,081 39,569 451 84 3,189 2,339,982 2,500,326 3,227 0 484 10,295 2,774 1,998 55,857 463 295,327 1,210,047 247 60,783 19,008 279,312 272,254 6,292 22,478 118,395 9,475,479 53,844 169,413 515 105,289 6,589 693 1,186 71 807,763 2,124 0 7,831,422 67,428 2,604,285 46 872 351 70,705 1,283,784 3 9,190 0 3,558 766,228 169 18,782 76,874,896 31,014 1,083 0 0 29,055 0 569 81,311 3,274 694 46,869 1,186,429 857 14,484 0 14,763 31,785 973,277 3,298 0 57,831 12,992 1,572 10,937 481 tourism 0 0 785,140 8,770 58,887 0 0 0 785,140 0 0 5,094,080 3,202,840 12,200 0 58,887 163,575 0 0 58,887 0 58,887 718,430 12,200 0 785,140 200,125 2,871,360 0 4,392 4,392 824,120 0 729,136 0 0 0 58,887 0 135,610 1,136,100 0 0 6,224,960 58,887 163,575 0 0 0 67,658 1,027,202 58,887 785,140 0 0 12,200 0 0 20,185,140 921,600 0 0 0 0 0 0 163,575 135,610 163,575 0 60,938 0 135,610 0 921,600 58,887 80,820 0 0 0 58,887 0 0 0 total_1 6,450,667 5,118,358 21,109,246 2,348,061 1,730,465 796,194 5,119,774 10,814,043 5,522,587 5,989,091 3,316,160 15,906,391 9,756,306 5,020,178 1,091,892 6,712,735 3,437,777 1,094,147 9,426,969 6,495,212 5,933,046 9,121,054 12,409,305 2,207,839 6,104,127 4,451,700 4,392,958 13,040,941 34,348,101 3,231,242 23,458,942 14,158,238 1,459,479 3,928,645 3,671,361 1,079,408 1,630,371 7,043,344 9,156,781 7,496,131 11,402,169 1,052,102 1,531,371 22,547,720 6,418,404 2,862,284 1,501,066 3,276,431 1,170,255 37,031,286 13,880,723 6,027,852 6,003,273 1,125,316 8,779,393 4,691,130 5,879,771 836,889 191,208,669 3,351,326 842,620 1,703,927 1,270,791 6,938,549 3,368,199 1,360,431 3,240,243 9,727,701 3,313,090 1,586,658 6,215,827 4,722,741 7,938,582 4,882,605 9,530,605 24,470,658 35,858,477 2,545,882 919,092 5,387,628 8,418,768 1,659,114 7,402,485 1,770,086 total_2 6,495,147 5,883,442 21,267,675 2,370,123 1,730,651 796,194 5,120,702 10,852,628 5,522,789 5,983,684 3,318,470 14,554,493 12,203,986 5,021,618 1,091,892 6,706,605 3,446,445 1,095,385 9,427,860 6,548,920 5,933,253 9,252,853 13,590,094 2,207,947 6,131,254 4,460,182 4,647,652 13,291,920 34,349,577 3,241,273 23,511,779 18,386,957 1,509,935 4,004,250 3,670,251 1,183,425 1,634,821 7,043,654 9,157,310 7,494,169 12,201,789 1,053,050 1,531,371 29,977,401 6,448,497 5,464,363 1,501,086 3,276,821 1,168,895 37,097,632 14,453,651 6,027,853 6,007,376 1,125,316 8,781,971 5,033,080 5,879,846 845,270 220,938,129 3,367,361 843,103 1,703,927 1,270,791 6,951,517 3,368,199 1,360,685 3,320,010 9,728,541 3,313,401 1,632,780 4,742,650 4,723,123 7,945,047 4,882,605 9,537,194 24,484,842 36,292,831 2,545,801 919,092 5,439,516 8,424,567 1,659,815 7,389,680 1,770,301 Hauschild and Döll Muni_nr 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 Municipality Itaicaba Itaitinga Itapage Itapipoca Itapiuna Itarema Itatira Jaguaretama Jaguaribara Jaguaribe Jaguaruana Jardim Jati Jijoca de Jericoacoara Juazeiro do Norte Jucas Lavras da Mangabeira Limoeiro do Norte Madalena Maracanau Maranguape Marco Martinopole Massape Mauriti Meruoca Milagres Milha Miraima Missao Velha Mombaca Monsenhor Tabosa Morada Nova Moraujo Morrinhos Mucambo Mulungu Nova Olinda Nova Russas Novo Oriente Ocara Oros Pacajus Pacatuba Pacoti Pacuja Palhano Palmacia Paracuru Paraipaba Parambu Paramoti Pedra Branca Penaforte Pentecoste Pereiro Pindoretama Piquet Carneiro Pires Ferreira Poranga Porteiras Potengi Potiretama Quiterianopolis Quixada Quixelo Quixeramobim Quixere Redencao Reriutaba Russas Saboeiro Salitre Santana do Acarau Santana do Cariri Santa Quiteria Sao Benedito Sao Goncalo do Amarante Sao Joao do Jaguaribe Sao Luis do Curu Senador Pompeu Senador Sa Sobral Solonopole Tabuleiro do Norte Tamboril Tarrafas Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis irrigation 3,246,036 3,246,126 2,615,313 2,322,397 1,309,069 1,757,798 1,017,108 64,316,648 8,494,518 26,006,464 18,322,720 9,798,901 6,384,894 1,958,960 9,970,486 462,357 7,444,454 60,968,328 959,248 2,033,642 1,991,494 31,208,042 2,018,842 680,074 15,431,585 1,277,890 5,602,704 556,299 560,403 3,722,370 671,144 563,002 34,059,640 541,454 1,836,395 474,949 2,488,193 4,722,944 817,598 762,508 2,774,343 7,720,451 3,735,922 3,585,204 2,684,981 809,350 2,107,139 1,812,092 9,183,146 68,502,112 1,021,510 919,807 1,379,949 5,293,952 7,328,868 6,520,352 1,792,423 874,960 838,502 848,544 5,587,712 5,679,128 6,735,966 1,070,089 7,692,524 4,599,089 11,163,872 6,637,889 3,305,015 487,694 44,797,020 497,548 790,369 20,413,984 7,462,623 540,340 7,495,658 5,453,694 4,442,746 4,129,773 1,081,520 929,702 1,204,643 8,728,827 11,055,034 579,395 4,781,754 livestock 123,475 206,926 541,230 1,282,291 232,627 590,377 267,712 1,652,609 748,480 1,709,778 536,329 458,914 167,885 111,102 219,478 335,961 819,177 484,145 409,821 112,528 928,140 517,821 142,326 220,729 910,979 36,471 420,106 364,071 340,425 481,305 966,239 344,096 1,646,980 130,669 346,165 83,881 63,651 149,601 312,278 487,332 506,253 435,468 524,312 180,615 162,253 59,424 217,785 146,664 288,982 278,606 885,017 182,790 625,853 112,137 370,383 221,426 226,708 252,104 137,594 207,471 316,360 192,676 301,521 441,553 1,554,891 651,036 1,921,409 226,631 127,722 177,093 634,613 458,550 240,501 1,414,758 389,554 1,276,618 244,770 527,832 227,367 156,396 441,723 89,408 938,748 874,593 491,284 879,147 166,276 domestic 175,470 1,239,102 1,670,586 5,835,792 248,310 1,292,424 279,780 526,170 208,422 1,322,946 832,944 755,154 180,618 725,868 5,463,576 536,532 993,228 1,554,114 257,910 7,943,094 4,780,848 926,172 349,530 473,700 1,083,768 317,442 694,146 228,408 205,116 824,154 753,150 301,404 2,503,686 124,104 787,884 243,288 445,440 392,838 563,370 484,254 1,104,660 680,970 1,991,628 2,347,932 488,946 104,688 363,402 432,234 1,234,062 936,024 560,814 195,168 805,494 160,572 519,042 368,538 737,778 247,488 390,750 220,668 395,742 224,898 149,478 328,704 1,809,300 454,392 2,551,548 445,098 1,195,674 388,818 1,206,000 282,222 241,242 898,374 1,143,630 552,858 1,050,156 2,369,766 416,760 539,256 473,418 118,470 1,815,612 589,026 834,372 644,634 237,972 70 industry_1 983 4,968 887,341 878,869 682 7,450 149 452 3,872 66,409 11,593 3,002 3,009 0 236,173 276,403 6,579 1,619,932 263 11,058,594 1,007,582 20,168 4,647 8,028 327 6,953 2,957 14 0 3,782 1,422 53 210,014 2,153 2,398 0 7 2,903 11,583 29 0 3,212 4,390,300 1,924,231 9,861 2,491 3,730 279 1,766 1,772 43 1,317 12,673 1,774 8,412 2,285 827 29 0 115 1,571 557 0 0 132,739 1,375 81,833 7,664 5,583 1,054 27,684 0 0 7,492 4,974 63,532 20,082 3,269 2,560 144 3,902 1,008 7,946 1,322 29,220 834 0 industry_2 815 12,852 1,602,507 1,587,211 1,231 13,457 270 815 779 119,933 472,784 5,424 3,013 0 1,166,238 499,175 13,196 2,925,543 473 14,922,712 2,514,546 297 16,877 6,995 28,993 12,558 38,620 26 0 24,950 2,570 95 379,279 7,821 5,527 0 15 387,152 20,918 52 0 30,873 1,345,881 3,466,260 17,806 4,498 324,181 506 239,159 638 78 84,962 22,888 4,682 12,306 3,340 1,495 52 0 208 39 1,007 0 0 69,551 2,483 147,788 477 321,733 813 784,371 0 0 51,620 231,490 121,808 10,053 307,646 4,622 260 9,401 3,663 1,741,758 2,389 119,690 13,682 0 tourism total_1 total_2 0 19,520 187,938 824,120 0 843,640 58,887 0 0 8,770 0 9,936 0 1,935,460 3,450,109 0 0 80,820 0 0 300,700 0 12,200 0 0 58,887 0 0 0 52,632 8,770 0 58,887 0 0 0 163,575 58,887 8,770 0 0 63,274 187,938 261,720 163,575 0 0 163,575 1,272,560 1,155,580 0 0 0 0 58,887 58,887 490,712 0 0 58,887 0 0 0 0 85,208 0 67,658 0 175,762 0 0 0 0 0 6,700 0 135,610 1,467,560 0 0 13,158 0 102,753 0 0 0 0 3,545,964 4,716,642 5,902,408 11,143,469 1,790,688 4,491,689 1,623,636 66,495,879 9,455,292 29,114,367 19,703,586 11,025,907 6,736,406 4,731,390 19,339,822 1,611,253 9,263,438 64,707,339 1,627,242 21,147,858 9,008,764 32,672,203 2,527,545 1,382,531 17,426,659 1,697,643 6,719,913 1,148,792 1,105,944 5,084,243 2,400,725 1,208,555 38,479,207 798,380 2,972,842 802,118 3,160,866 5,327,173 1,713,599 1,734,123 4,385,256 8,903,375 10,830,100 8,299,702 3,509,616 975,953 2,692,056 2,554,844 11,980,516 70,874,094 2,467,384 1,299,082 2,823,969 5,568,435 8,285,592 7,171,488 3,248,448 1,374,581 1,366,846 1,335,685 6,301,385 6,097,259 7,186,965 1,840,346 11,274,662 5,705,892 15,786,320 7,317,282 4,809,756 1,054,659 46,665,317 1,238,320 1,272,112 22,734,608 9,007,481 2,433,348 8,946,276 9,822,121 5,089,433 4,825,569 2,013,721 1,138,588 4,069,702 10,193,768 12,409,910 2,104,010 5,186,002 3,545,796 4,724,526 6,617,574 11,851,811 1,791,237 4,497,696 1,623,757 66,496,242 9,452,199 29,167,891 20,164,777 11,028,329 6,736,410 4,731,390 20,269,887 1,834,025 9,270,055 66,012,950 1,627,452 25,011,976 10,515,728 32,652,332 2,539,775 1,381,498 17,455,325 1,703,248 6,755,576 1,148,804 1,105,944 5,105,411 2,401,873 1,208,597 38,648,472 804,048 2,975,971 802,118 3,160,874 5,711,422 1,722,934 1,734,146 4,385,256 8,931,036 7,785,681 9,841,731 3,517,561 977,960 3,012,507 2,555,071 12,217,909 70,872,960 2,467,419 1,382,727 2,834,184 5,571,343 8,289,486 7,172,543 3,249,116 1,374,604 1,366,846 1,335,778 6,299,853 6,097,709 7,186,965 1,840,346 11,211,474 5,707,000 15,852,275 7,310,095 5,125,906 1,054,418 47,422,004 1,238,320 1,272,112 22,778,736 9,233,997 2,491,624 8,936,247 10,126,498 5,091,495 4,825,685 2,019,220 1,141,243 5,803,514 10,194,835 12,500,380 2,116,858 5,186,002 Hauschild and Döll Muni_nr 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 Municipality Taua Tejucuoca Tiangua Trairi Tururu Ubajara Umari Umirim Uruburetama Uruoca Varjota Varzea Alegre Vicosa do Ceará Agricolandia Agua Branca Alagoinha do Piauí Alegrete do Piauí Alto Longa Altos Amarante Angical do Piauí Anisio de Abreu Antonio Almeida Aroazes Arraial Avelino Lopes Baixa Grande do Ribeiro Barras Barreiras do Piauí Barro Duro Batalha Beneditinos Bertolinia Bocaina Bom Jesus Bom Principio do Piauí Bonfim do Piauí Brasileira Buriti dos Lopes Buriti dos Montes Cabeceiras do Piauí Caldeirao Grande do Piauí Campinas do Piauí Campo Maior Canavieira Canto do Buriti Capitao de Campos Caracol Castelo do Piauí Cocal Coivaras Colonia do Gurgueia Colonia do Piauí Conceicao do Caninde Coronel Jose Dias Corrente Cristalandia do Piauí Cristino Castro Curimata Demerval Lobao Dirceu Arcoverde Dom Expedito Lopes Domingos Mourao Dom Inocencio Elesbao Veloso Eliseu Martins Esperantina Fartura do Piauí Flores do Piauí Floriano Francinopolis Francisco Ayres Francisco Santos Fronteiras Gilbues Guadalupe Hugo Napoleao Inhuma Ipiranga do Piauí Isaias Coelho Itainopolis Itaueira Jacobina do Piauí Jaicos Jardim do Mulato Jerumenha Joaquim Pires Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis irrigation 626,523 487,584 9,160,705 2,493,524 2,620,039 19,931,596 7,818,830 3,289,007 2,404,304 503,278 13,898,396 8,054,520 5,107,638 2,181,005 2,472,017 51,142 65,282 2,954,304 3,072,506 2,826,393 2,693,014 103,299 4,003,067 3,760,232 2,820,440 2,102,105 5,019,870 3,699,246 1,661,506 3,223,742 2,418,711 2,178,298 1,549,739 2,814,330 2,804,236 2,266,486 61,133 2,594,880 3,879,192 1,250,129 2,466,511 141,157 3,482,433 3,509,490 1,702,393 3,233,843 3,223,488 137,041 2,329,183 3,353,319 2,686,904 1,326,858 2,775,076 89,208 115,117 2,634,356 2,737,779 34,241,836 2,346,926 2,017,454 72,360 3,373,564 1,693,772 106,947 2,195,718 2,294,329 2,620,040 65,487 2,089,756 4,305,362 3,212,662 3,008,796 3,470,851 67,723 2,507,255 72,973,904 2,419,282 3,134,366 3,075,548 2,552,516 2,429,872 1,406,267 82,308 109,505 3,078,624 2,252,149 2,266,928 livestock 1,817,744 211,277 425,838 549,500 177,646 263,864 333,433 477,245 105,391 181,677 79,217 546,717 553,743 28,246 118,140 159,459 109,876 938,570 804,870 330,807 154,097 241,063 224,202 298,531 152,369 505,812 345,693 972,704 252,142 90,625 1,091,092 482,765 717,708 182,794 860,951 194,236 135,093 333,090 843,313 387,025 419,954 170,793 469,657 2,036,550 289,144 1,068,766 193,768 270,515 879,953 819,460 165,260 101,875 335,296 371,030 172,834 1,549,851 533,465 339,462 854,109 148,258 262,570 78,520 245,114 663,862 739,672 233,948 727,902 204,153 186,136 751,256 94,828 164,969 168,598 255,395 510,198 190,084 48,940 263,251 137,090 409,854 584,085 493,046 408,012 495,382 106,163 270,638 576,822 domestic 929,682 218,820 1,298,982 1,994,118 430,710 695,370 188,112 685,386 794,058 179,694 250,374 864,324 1,243,434 195,684 453,822 136,218 61,458 419,916 859,182 769,404 189,630 151,524 132,546 248,046 234,162 339,186 222,900 1,417,638 135,102 154,008 561,564 273,372 398,046 165,006 794,526 154,410 88,608 201,420 848,400 199,134 208,974 99,882 138,246 1,935,210 171,672 1,422,126 204,222 303,570 776,310 559,878 106,734 126,696 168,084 182,790 70,800 778,314 175,878 496,056 336,666 479,130 100,026 247,530 216,924 164,454 567,588 134,712 1,210,620 73,794 102,072 1,380,624 136,272 148,692 146,118 146,466 400,746 486,906 110,442 410,568 251,316 272,196 432,774 308,130 91,548 446,538 131,244 139,824 349,350 71 industry_1 1,145 0 19,365 3,282 0 9,781 15 2,609 7,969 2,279 974 56,989 1,435 82,484 5,085 0 0 2,121 7,327 2,411 1,541 261 492 0 0 2,262 0 12,076 4,983 2,463 676 3,147 281 0 2,965 0 0 930 10,360 0 1,391 0 0 8,804 153 1,832 143 0 3,039 19,853 0 0 0 0 0 5,522 259 22,732 4,132 2,647 547 1,478 832 0 4,643 230 59,991 0 1,154 112,249 0 0 0 2,106 5,756 8,294 2,087 5,049 131 0 0 83,112 0 914 699 974 0 industry_2 2,251 0 25,970 547 0 8,854 25 6,331 103,293 831 3,537 102,922 3,671 21,344 73,448 0 0 16,961 212,811 25,426 8,693 546 1,028 0 0 4,727 0 62,732 11,607 111,357 1,412 21,421 587 0 46,511 0 0 1,943 25,993 0 2,906 0 0 195,631 321 96,349 299 0 23,566 14,740 0 0 0 0 0 137,301 6,609 105,870 28,772 25,636 1,144 3,087 1,738 0 63,081 481 97,789 0 2,412 408,913 0 0 0 15,730 23,747 7,164 8,662 10,181 273 0 0 31,454 0 22,902 1,462 27,959 0 tourism 0 0 135,610 843,640 0 135,610 0 0 163,575 4,392 0 58,887 135,610 0 0 0 0 0 0 27,000 0 0 0 0 0 0 0 21,600 0 0 16,200 0 0 0 0 5,400 0 0 0 0 0 0 0 75,600 0 16,200 0 5,400 0 0 0 0 0 0 43,200 16,200 0 16,200 0 0 0 0 0 0 0 0 27,000 0 0 135,000 0 0 0 0 0 10,800 0 0 0 0 0 0 0 0 0 0 0 total_1 total_2 3,375,094 917,681 11,040,500 5,884,064 3,228,395 21,036,221 8,340,390 4,454,247 3,475,297 871,320 14,228,961 9,581,437 7,041,860 2,487,419 3,049,064 346,819 236,616 4,314,911 4,743,885 3,956,015 3,038,282 496,147 4,360,307 4,306,809 3,206,971 2,949,365 5,588,463 6,123,264 2,053,733 3,470,838 4,088,243 2,937,582 2,665,774 3,162,130 4,462,678 2,620,532 284,834 3,130,320 5,581,265 1,836,288 3,096,830 411,832 4,090,336 7,565,654 2,163,362 5,742,767 3,621,621 716,526 3,988,485 4,752,510 2,958,898 1,555,429 3,278,456 643,028 401,951 4,984,243 3,447,381 35,116,286 3,541,833 2,647,489 435,503 3,701,092 2,156,642 935,263 3,507,621 2,663,219 4,645,553 343,434 2,379,118 6,684,491 3,443,762 3,322,457 3,785,567 471,690 3,423,955 73,669,988 2,580,751 3,813,234 3,464,085 3,234,566 3,446,731 2,290,555 581,868 1,052,339 3,316,730 2,663,585 3,193,100 3,376,200 917,681 11,047,105 5,881,329 3,228,395 21,035,294 8,340,400 4,457,969 3,570,621 869,872 14,231,524 9,627,370 7,044,096 2,426,279 3,117,427 346,819 236,616 4,329,751 4,949,369 3,979,030 3,045,434 496,432 4,360,843 4,306,809 3,206,971 2,951,830 5,588,463 6,173,920 2,060,357 3,579,732 4,088,979 2,955,856 2,666,080 3,162,130 4,506,224 2,620,532 284,834 3,131,333 5,596,898 1,836,288 3,098,345 411,832 4,090,336 7,752,481 2,163,530 5,837,284 3,621,777 716,526 4,009,012 4,747,397 2,958,898 1,555,429 3,278,456 643,028 401,951 5,116,022 3,453,731 35,199,424 3,566,473 2,670,478 436,100 3,702,701 2,157,548 935,263 3,566,059 2,663,470 4,683,351 343,434 2,380,376 6,981,155 3,443,762 3,322,457 3,785,567 485,314 3,441,946 73,668,858 2,587,326 3,818,366 3,464,227 3,234,566 3,446,731 2,238,897 581,868 1,074,327 3,317,493 2,690,570 3,193,100 Hauschild and Döll Muni_nr 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Municipality Jose de Freitas Lagoa Alegre Lagoa do Barro Piauí Landri Sales Luis Correia Luzilandia Manoel Emidio Marcolandia Marcos Parente Matias Olimpio Miguel Alves Miguel Leao Monsenhor Gil Monsenhor Hipolito Monte Alegre do Piauí Nazare do Piauí Nossa Senhora dos Remedios Novo Oriente do Piauí Oeiras Padre Marcos Paes Landim Palmeira do Piauí Palmeirais Parnagua Parnaiba Passagem Franca do Piauí Patos do Piauí Paulistana Pedro II Picos Pimenteiras Pio IX Piracuruca Piripiri Porto Prata do Piauí Queimada Nova Redencao do Gurgueia Regeneracao Ribeiro Goncalves Rio Grande do Piauí Santa Cruz do Piauí Santa Cruz dos Milagres Santa Filomena Santa Luz Santana do Piauí Santa Rosa do Piauí Santo Antonio de Lisboa Santo Inacio do Piauí Sao Braz do Piauí Sao Felix do Piauí Sao Francisco do Piauí Sao Goncalo do Piauí Sao Joao da Canabrava Sao Joao da Serra Sao Joao do Piauí Sao Jose do Divino Sao Jose do Peixe Sao Jose do Piauí Sao Juliao Sao Lourenco do Piauí Sao Miguel do Tapuio Sao Pedro do Piauí Sao Raimundo Nonato Sigefredo Pacheco Simoes Simplicio Mendes Socorro do Piauí Teresina Uniao Urucui Valenca do Piauí Varzea Branca Varzea Grande irrigation 3,350,389 2,064,030 106,469 2,002,142 1,698,278 39,630,244 2,486,301 82,759 2,193,793 2,909,613 9,993,124 4,125,396 2,679,698 62,895 2,251,730 1,978,368 1,824,240 2,061,977 3,913,979 64,496 3,679,424 2,263,168 3,801,119 2,592,137 3,434,960 2,345,180 3,165,910 226,230 3,040,053 7,217,974 2,678,937 96,748 2,441,970 4,789,702 2,125,045 2,391,232 67,644 2,907,507 1,719,518 1,773,105 2,613,095 4,258,691 4,936,666 1,845,492 1,911,254 3,114,275 3,421,947 3,199,663 3,702,348 95,836 3,056,165 2,745,906 1,963,756 1,836,485 1,703,417 3,297,648 3,148,878 903,753 3,255,838 63,877 72,211 3,407,580 2,799,904 241,298 2,228,065 113,388 3,710,967 3,783,133 30,712,226 38,924,148 2,216,574 3,190,496 64,683 3,428,146 livestock 799,510 222,629 310,986 239,816 967,581 771,053 313,996 35,005 185,530 167,500 580,200 57,635 242,256 138,841 376,382 347,227 132,711 239,243 1,093,362 334,737 299,029 253,168 401,134 1,308,841 805,770 202,330 334,973 995,363 998,835 981,137 478,116 429,217 1,267,932 709,064 285,659 89,248 370,506 340,965 217,254 275,753 427,390 434,538 560,450 316,456 104,991 53,666 290,155 148,580 336,810 66,451 309,166 373,930 55,516 196,178 521,878 1,985,597 344,410 504,235 106,090 125,795 215,191 1,043,294 177,682 405,374 478,563 624,846 828,784 198,328 1,212,922 671,238 402,732 478,534 123,493 173,708 domestic 1,210,152 160,980 97,476 241,458 1,056,090 911,850 251,676 106,446 173,232 232,866 519,954 51,336 270,744 116,856 402,168 274,746 138,330 264,690 1,792,230 206,586 126,096 156,240 303,234 306,516 10,163,160 138,036 150,144 477,546 1,352,550 3,489,174 372,378 225,354 895,644 2,179,794 475,356 100,812 142,194 199,890 579,942 254,868 441,060 361,518 110,448 121,374 145,968 119,304 228,186 166,686 152,484 73,338 182,682 256,416 215,778 229,050 212,904 1,502,496 134,172 212,940 207,744 96,396 78,054 611,424 522,942 432,672 443,574 322,158 553,626 166,458 61,820,124 1,099,842 453,162 553,452 80,778 258,750 industry_1 9,301 0 0 0 33,538 58,187 1,454 0 77 0 57,055 83,973 2,650 539 1,053 0 0 0 3,655 1,523 0 0 83,639 1,278 82,327 0 0 3,075 9,794 57,491 575 1,059 12,959 16,595 0 0 0 0 5,557 4,957 83,001 1,615 0 276 4,165 0 2,820 78 0 0 0 988 1,368 0 0 3,621 313 1,683 1,338 0 0 169 1,583 8,425 0 1,322 846 0 981,958 57,601 6,481 10,106 0 0 industry_2 55,333 0 0 0 95,027 49,625 2,921 0 161 0 54,721 19,697 28,933 1,126 2,200 0 0 0 83,474 4,828 0 0 11,692 7,191 1,156,948 0 0 28,424 63,876 397,902 1,202 13,256 136,395 229,170 0 0 0 0 107,107 60,084 21,477 57,759 0 577 19,392 0 5,893 163 0 0 0 2,064 28,550 0 0 82,627 654 5,761 19,415 0 0 353 47,227 117,819 0 2,670 1,768 0 5,457,534 83,738 130,025 63,346 0 0 tourism total_1 43,200 0 0 0 2,640,000 0 0 0 0 0 0 0 5,400 0 0 0 0 0 84,600 0 0 0 0 0 2,640,000 0 0 16,200 41,400 108,000 0 0 108,000 112,500 0 0 0 0 0 0 0 0 28,350 0 0 0 0 0 0 0 0 0 0 0 0 21,600 0 0 0 0 0 0 0 87,750 0 0 0 0 1,950,000 0 0 21,600 0 0 5,412,552 2,447,639 514,931 2,483,416 6,395,487 41,371,334 3,053,427 224,210 2,552,632 3,309,979 11,150,333 4,318,340 3,200,748 319,131 3,031,333 2,600,341 2,095,281 2,565,910 6,887,826 607,342 4,104,549 2,672,576 4,589,126 4,208,772 17,126,217 2,685,546 3,651,027 1,718,414 5,442,632 11,853,776 3,530,006 752,378 4,726,505 7,807,655 2,886,060 2,581,292 580,344 3,448,362 2,522,271 2,308,683 3,564,546 5,056,362 5,635,914 2,283,598 2,166,378 3,287,245 3,943,108 3,515,007 4,191,642 235,625 3,548,013 3,377,240 2,236,418 2,261,713 2,438,199 6,810,962 3,627,773 1,622,611 3,571,010 286,068 365,456 5,062,467 3,502,111 1,175,519 3,150,202 1,061,714 5,094,223 4,147,919 96,677,230 40,752,829 3,078,949 4,254,188 268,954 3,860,604 total_2 5,458,584 2,447,639 514,931 2,483,416 6,456,976 41,362,772 3,054,894 224,210 2,552,716 3,309,979 11,147,999 4,254,064 3,227,031 319,718 3,032,480 2,600,341 2,095,281 2,565,910 6,967,645 610,647 4,104,549 2,672,576 4,517,179 4,214,685 18,200,838 2,685,546 3,651,027 1,743,763 5,496,714 12,194,187 3,530,633 764,575 4,849,941 8,020,230 2,886,060 2,581,292 580,344 3,448,362 2,623,821 2,363,810 3,503,022 5,112,506 5,635,914 2,283,899 2,181,605 3,287,245 3,946,181 3,515,092 4,191,642 235,625 3,548,013 3,378,316 2,263,600 2,261,713 2,438,199 6,889,968 3,628,114 1,626,689 3,589,087 286,068 365,456 5,062,651 3,547,755 1,284,913 3,150,202 1,063,062 5,095,145 4,147,919 101,152,806 40,778,966 3,202,493 4,307,428 268,954 3,860,604 NoWUM output: Annual withdrawal water uses of 2025 Coastal Boom and Cash Crops scenario (RSA) irrigation CE [m³] PI [m³] CE [km³] PI [km³] total [km³] 1,158,550,830 550,147,860 1,159 550 1,709 livestock domestic 81,762,239 296,797,402 64,313,226 130,290,378 82 297 64 130 146 427 72 industry_1 industry_2 84,952,613 147,624,259 2,204,686 10,774,831 85 148 2 11 87 158 tourism total_1 total_2 64,661,772 1,686,724,856 1,749,396,502 8,324,400 755,280,550 763,850,695 65 1,687 1,749 8 755 764 73 2,442 2,513 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 4) NoWUM output: Annual withdrawal water uses [m3] of 2025 Decentralization scenario (RSB) Muni_nr 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 Municipality Abaiara Acarape Acarau Acopiara Aiuaba Alcantaras Altaneira Alto Santo Amontada Antonina do Norte Apuiares Aquiraz Aracati Aracoiaba Ararenda Araripe Aratuba Arneiroz Assare Aurora Baixio Banabuiu Barbalha Barreira Barro Barroquinha Baturite Beberibe Bela Cruz Boa Viagem Brejo Santo Camocim Campos Sales Caninde Capistrano Caridade Carire Caririacu Carius Carnaubal Cascavel Catarina Catunda Caucaia Cedro Chaval Choro Chorozinho Coreau Crateus Crato Croata Cruz Deputado Irapuan Pinheiro Erere Eusebio Farias Brito Forquilha Fortaleza Fortim Frecheirinha General Sampaio Graca Granja Granjeiro Groairas Guaiuba Guaraciaba do Norte Guaramiranga Hidrolandia Horizonte Ibaretama Ibiapina Ibicuitinga Icapui Ico Iguatu Independencia Ipaporanga Ipaumirim Ipu Ipueiras Iracema Iraucuba irrigation 342,021 2,286,926 4,315,970 318,078 791,148 354,148 277,061 1,211,189 606,959 321,134 4,490,169 758,448 1,681,916 2,189,195 502,036 330,242 594,050 379,255 1,122,776 1,484,342 312,908 1,393,466 694,900 224,917 284,862 640,693 619,616 7,241,779 7,947,948 870,407 4,262,632 3,186,119 443,244 684,563 760,204 450,677 904,653 382,671 500,943 785,784 4,557,127 356,688 1,137,176 1,019,034 284,446 430,328 697,199 789,315 323,541 14,718,196 2,922,964 557,210 908,975 827,222 563,910 606,527 280,012 255,452 705,948 676,640 364,243 2,110,282 617,405 834,167 188,428 1,229,789 501,171 2,013,465 722,090 601,673 370,438 267,164 1,324,851 287,376 5,486,773 19,173,628 9,088,479 747,105 304,191 270,517 636,453 458,596 415,661 598,951 livestock 174,419 75,333 490,557 1,327,748 624,893 88,132 68,438 757,271 512,463 168,094 257,938 663,359 345,306 430,265 235,683 398,645 139,313 529,787 622,221 797,900 290,981 824,576 302,251 241,796 527,098 169,291 300,561 575,138 697,427 1,622,008 832,023 437,325 392,449 1,378,575 201,039 272,028 564,051 458,574 455,771 154,177 493,787 416,938 333,732 877,457 696,556 129,224 434,637 231,939 336,619 1,567,160 504,147 161,693 250,667 326,657 321,808 88,415 419,389 289,107 182,936 64,559 99,858 129,407 161,284 1,044,073 102,641 134,526 135,695 283,116 36,456 491,771 285,091 507,222 208,139 326,711 231,483 1,394,121 1,231,546 1,707,064 324,911 351,429 446,805 575,665 531,158 752,957 domestic 228,588 354,540 1,787,256 1,253,760 368,196 245,214 186,882 433,050 998,280 187,488 278,550 2,178,348 1,912,326 834,246 273,510 537,204 331,818 173,574 655,278 805,152 176,874 596,940 1,485,738 592,134 669,654 454,272 946,212 1,308,582 893,004 1,632,426 1,197,960 1,827,876 667,818 1,797,402 483,546 320,424 510,846 804,756 601,050 428,664 2,521,776 275,136 213,018 9,448,170 801,276 362,256 306,180 456,306 583,506 1,676,316 3,284,634 510,114 1,165,440 212,208 191,352 1,251,444 657,186 398,814 95,083,880 322,026 275,550 120,078 384,402 1,461,066 142,950 205,716 625,722 1,013,274 159,282 486,492 911,136 373,338 598,728 305,220 924,756 2,299,500 3,558,762 615,090 294,438 343,944 1,627,572 835,050 461,562 468,390 73 industry_1 60,133 74,776 1,096 2,472 301 0 1,254 1,070 165 5,966 1,151 2,772,463 34,913 1,186 0 7,206 1,078 2,011 1,204 2,339 278 177,936 31,879 92 36,549 6,982 16,315 14,101 3,190 16,331 71,300 3,487,355 4,449 123,075 1,078 1,668 2,798 418 716 2,213 5,394 1,541 0 301,691 40,734 1,462 34 320 2,247 5,718 774,093 2 3,375 0 1,068 318,262 102 13,663 35,350,820 9,925 785 0 0 10,667 0 413 1,159 2,642 253 978 1,764,635 516 8,736 0 5,413 19,205 587,329 4,444 0 6,487 7,826 1,144 25,932 349 industry_2 108,599 582,087 106,172 31,400 545 0 2,263 43,054 299 92 4,176 1,757,239 1,658,122 2,140 0 527 6,818 3,631 2,174 60,794 503 321,347 1,318,452 164 66,006 12,609 185,103 180,450 4,167 29,491 128,765 6,298,052 70,713 222,268 342 138,099 8,618 756 1,292 77 535,105 2,784 0 5,881,076 73,565 1,725,793 60 578 461 92,764 1,397,988 4 6,096 0 3,878 574,768 184 24,673 57,642,704 20,551 1,418 0 0 19,266 0 746 61,018 3,553 459 61,417 787,189 932 15,778 0 9,777 34,682 1,060,696 4,338 0 63,129 14,136 2,064 11,945 631 tourism 0 0 471,084 8,770 58,887 0 0 0 471,084 0 0 3,056,448 1,921,704 4,392 0 58,887 58,887 0 0 58,887 0 58,887 718,430 4,392 0 471,084 72,045 1,722,816 0 4,392 4,392 494,472 0 729,136 0 0 0 58,887 0 135,610 681,660 0 0 3,734,976 58,887 58,887 0 0 0 67,658 1,027,202 58,887 471,084 0 0 4,392 0 0 12,111,084 552,960 0 0 0 0 0 0 58,887 135,610 58,887 0 21,938 0 135,610 0 552,960 58,887 80,820 0 0 0 58,887 0 0 0 total_1 805,161 2,791,575 7,065,963 2,910,828 1,843,425 687,494 533,635 2,402,580 2,588,951 682,682 5,027,808 9,429,066 5,896,165 3,459,284 1,011,229 1,332,184 1,125,146 1,084,627 2,401,479 3,148,620 781,041 3,051,805 3,233,198 1,063,331 1,518,163 1,742,322 1,954,749 10,862,416 9,541,569 4,145,564 6,368,307 9,433,147 1,507,960 4,712,751 1,445,867 1,044,797 1,982,348 1,705,306 1,558,480 1,506,448 8,259,744 1,050,303 1,683,926 15,381,328 1,881,899 982,157 1,438,050 1,477,880 1,245,913 18,035,048 8,513,040 1,287,906 2,799,541 1,366,087 1,078,138 2,269,040 1,356,689 957,036 143,434,668 1,626,110 740,436 2,359,767 1,163,091 3,349,973 434,019 1,570,444 1,322,634 3,448,107 976,968 1,580,914 3,353,238 1,148,240 2,276,064 919,307 7,201,385 22,945,341 14,546,936 3,073,703 923,540 972,377 2,777,543 1,870,455 1,434,313 1,820,647 total_2 853,627 3,298,886 7,171,039 2,939,756 1,843,669 687,494 534,644 2,444,564 2,589,085 676,808 5,030,833 8,413,842 7,519,374 3,460,238 1,011,229 1,325,505 1,130,886 1,086,247 2,402,449 3,207,075 781,266 3,195,216 4,519,771 1,063,403 1,547,620 1,747,949 2,123,537 11,028,765 9,542,546 4,158,724 6,425,772 12,243,844 1,574,224 4,811,944 1,445,131 1,181,228 1,988,168 1,705,644 1,559,056 1,504,312 8,789,455 1,051,546 1,683,926 20,960,713 1,914,730 2,706,488 1,438,076 1,478,138 1,244,127 18,122,094 9,136,935 1,287,908 2,802,262 1,366,087 1,080,948 2,525,546 1,356,771 968,046 165,726,552 1,636,736 741,069 2,359,767 1,163,091 3,358,572 434,019 1,570,777 1,382,493 3,449,018 977,174 1,641,353 2,375,792 1,148,656 2,283,106 919,307 7,205,749 22,960,818 15,020,303 3,073,597 923,540 1,029,019 2,783,853 1,871,375 1,420,326 1,820,929 Hauschild and Döll Muni_nr 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 Municipality Itaicaba Itaitinga Itapage Itapipoca Itapiuna Itarema Itatira Jaguaretama Jaguaribara Jaguaribe Jaguaruana Jardim Jati Jijoca de Jericoacoara Juazeiro do Norte Jucas Lavras da Mangabeira Limoeiro do Norte Madalena Maracanau Maranguape Marco Martinopole Massape Mauriti Meruoca Milagres Milha Miraima Missao Velha Mombaca Monsenhor Tabosa Morada Nova Moraujo Morrinhos Mucambo Mulungu Nova Olinda Nova Russas Novo Oriente Ocara Oros Pacajus Pacatuba Pacoti Pacuja Palhano Palmacia Paracuru Paraipaba Parambu Paramoti Pedra Branca Penaforte Pentecoste Pereiro Pindoretama Piquet Carneiro Pires Ferreira Poranga Porteiras Potengi Potiretama Quiterianopolis Quixada Quixelo Quixeramobim Quixere Redencao Reriutaba Russas Saboeiro Salitre Santana do Acarau Santana do Cariri Santa Quiteria Sao Benedito Sao Goncalo do Amarante Sao Joao do Jaguaribe Sao Luis do Curu Senador Pompeu Senador Sa Sobral Solonopole Tabuleiro do Norte Tamboril Tarrafas Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis irrigation 201,679 1,237,766 701,606 779,309 1,731,217 382,890 870,769 14,737,168 3,741,538 22,036,580 16,371,284 569,179 1,618,443 426,703 3,308,162 294,043 630,915 23,641,240 1,096,385 406,900 398,463 7,734,268 439,744 823,162 2,930,902 1,433,008 316,834 510,987 607,671 324,791 520,509 358,081 18,407,442 344,393 400,010 302,098 559,235 267,209 595,645 534,486 633,432 4,565,829 2,312,266 966,952 796,317 618,788 495,374 394,724 9,632,847 47,894,948 777,462 960,582 1,442,091 299,312 12,432,753 430,889 390,436 580,185 838,502 785,179 315,976 321,224 401,847 813,914 1,404,154 1,034,512 6,604,287 2,197,909 939,563 310,200 28,652,328 316,420 523,014 23,481,182 1,041,681 343,671 2,339,131 7,287,636 558,638 3,943,254 1,522,062 653,132 1,306,044 784,079 8,301,097 368,509 323,366 livestock 146,411 178,871 413,682 980,150 332,334 451,277 382,455 1,959,759 887,612 2,027,566 635,990 544,192 199,089 84,946 260,257 479,962 971,455 574,140 585,502 97,262 802,287 395,825 108,796 315,310 1,080,301 52,101 498,223 520,101 486,303 570,762 1,380,392 491,578 1,953,088 186,654 264,587 119,818 48,671 177,412 446,137 696,233 386,957 516,424 400,788 156,150 124,026 84,890 166,468 112,093 220,873 212,954 1,264,266 261,164 894,099 132,984 529,057 262,626 173,282 360,131 196,564 296,376 375,144 228,487 357,556 630,768 1,843,882 772,021 2,278,563 268,745 97,656 252,985 752,596 655,070 343,559 1,081,370 461,994 1,823,770 290,267 456,289 269,642 119,539 631,035 127,709 1,341,027 1,037,175 582,604 1,255,933 197,184 domestic 200,106 982,962 1,116,870 3,580,428 343,476 844,656 386,040 599,076 237,972 1,499,394 948,966 859,866 206,142 444,030 6,227,628 735,780 1,129,710 1,765,560 356,280 6,300,630 3,783,216 605,928 230,172 654,360 1,235,652 434,370 791,196 315,366 283,020 939,858 1,039,554 415,824 2,836,596 171,426 514,398 335,652 286,278 446,268 777,564 668,502 709,950 774,708 1,286,880 1,859,946 326,640 144,444 240,306 289,824 796,656 626,136 775,128 270,084 1,108,980 183,444 717,408 420,696 474,192 341,484 532,830 304,476 451,962 256,680 170,754 454,032 2,063,040 517,434 2,887,710 507,210 768,642 536,538 1,381,056 390,576 332,862 633,702 1,288,518 769,956 1,197,918 1,871,358 471,084 354,024 653,676 163,098 2,593,710 668,586 949,500 885,576 270,960 74 industry_1 1,072 3,717 589,196 582,062 893 4,941 195 492 4,209 72,430 12,657 3,266 3,276 0 257,236 361,785 7,178 1,764,813 345 8,299,898 754,974 13,413 3,087 10,495 357 9,129 3,215 19 0 4,124 1,862 70 229,229 2,822 1,589 0 5 3,160 15,192 38 0 3,504 2,913,596 1,442,559 6,530 3,263 2,471 185 1,171 1,177 57 1,729 16,609 1,934 11,023 2,489 549 38 0 151 1,711 605 0 0 144,580 1,501 88,911 8,353 3,691 1,378 30,159 0 0 4,969 5,407 83,422 21,926 2,450 2,788 95 5,121 1,322 10,403 1,440 31,767 1,092 0 industry_2 890 9,615 1,064,068 1,051,186 1,612 8,924 354 887 847 130,808 516,171 5,899 3,279 0 1,270,252 653,373 14,396 3,187,194 621 11,200,069 1,884,131 197 11,211 9,144 31,617 16,487 41,995 34 0 27,204 3,364 125 413,983 10,249 3,663 0 10 421,430 27,437 68 0 33,675 893,186 2,598,589 11,792 5,892 214,781 336 158,609 424 102 111,568 29,996 5,106 16,125 3,638 992 69 0 273 42 1,095 0 0 75,756 2,711 160,570 520 212,739 1,063 854,497 0 0 34,234 251,612 159,943 10,976 230,636 5,033 173 12,337 4,804 2,280,378 2,601 130,125 17,915 0 tourism total_1 total_2 0 7,027 67,658 494,472 0 506,184 58,887 0 0 8,770 0 9,936 0 1,161,276 3,450,109 0 0 80,820 0 0 108,252 0 4,392 0 0 58,887 0 0 0 52,632 8,770 0 58,887 0 0 0 58,887 58,887 8,770 0 0 63,274 67,658 94,219 58,887 0 0 58,887 763,536 693,348 0 0 0 0 58,887 58,887 294,428 0 0 58,887 0 0 0 0 85,208 0 67,658 0 63,274 0 0 0 0 0 6,700 0 135,610 880,536 0 0 13,158 0 102,753 0 0 0 0 549,268 2,410,343 2,889,012 6,416,421 2,407,920 2,189,948 1,698,346 17,296,495 4,871,331 25,644,740 17,968,897 1,986,439 2,026,950 2,116,955 13,503,392 1,871,570 2,739,258 27,826,573 2,038,512 15,104,690 5,847,192 8,749,434 786,191 1,803,327 5,247,212 1,987,495 1,609,468 1,346,473 1,376,994 1,892,167 2,951,087 1,265,553 23,485,242 705,295 1,180,584 757,568 953,076 952,936 1,843,308 1,899,259 1,730,339 5,923,739 6,981,188 4,519,826 1,312,400 851,385 904,619 855,713 11,415,083 49,428,563 2,816,913 1,493,559 3,461,779 617,674 13,749,128 1,175,587 1,332,887 1,281,838 1,567,896 1,445,069 1,144,793 806,996 930,157 1,898,714 5,540,864 2,325,468 11,927,129 2,982,217 1,872,826 1,101,101 30,816,139 1,362,066 1,199,435 25,201,223 2,804,300 3,020,819 3,984,852 10,498,269 1,302,152 4,416,912 2,825,052 945,261 5,353,937 2,491,280 9,864,968 2,511,110 791,510 549,086 2,416,241 3,363,884 6,885,545 2,408,639 2,193,931 1,698,505 17,296,890 4,867,969 25,703,118 18,472,411 1,989,072 2,026,953 2,116,955 14,516,408 2,163,158 2,746,476 29,248,954 2,038,788 18,004,861 6,976,349 8,736,218 794,315 1,801,976 5,278,472 1,994,853 1,648,248 1,346,488 1,376,994 1,915,247 2,952,589 1,265,608 23,669,996 712,722 1,182,658 757,568 953,081 1,371,206 1,855,553 1,899,289 1,730,339 5,953,910 4,960,778 5,675,856 1,317,662 854,014 1,116,929 855,864 11,572,521 49,427,810 2,816,958 1,603,398 3,475,166 620,846 13,754,230 1,176,736 1,333,330 1,281,869 1,567,896 1,445,191 1,143,124 807,486 930,157 1,898,714 5,472,040 2,326,678 11,998,788 2,974,384 2,081,874 1,100,786 31,640,477 1,362,066 1,199,435 25,230,488 3,050,505 3,097,340 3,973,902 10,726,455 1,304,397 4,416,990 2,832,268 948,743 7,623,912 2,492,441 9,963,326 2,527,933 791,510 Hauschild and Döll Muni_nr 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 Municipality Taua Tejucuoca Tiangua Trairi Tururu Ubajara Umari Umirim Uruburetama Uruoca Varjota Varzea Alegre Vicosa do Ceara Agricolandia Agua Branca Alagoinha do Piaui Alegrete do Piaui Alto Longa Altos Amarante Angical do Piaui Anisio de Abreu Antonio Almeida Aroazes Arraial Avelino Lopes Baixa Grande do Ribeiro Barras Barreiras do Piaui Barro Duro Batalha Beneditinos Bertolinia Bocaina Bom Jesus Bom Principio do Piaui Bonfim do Piaui Brasileira Buriti dos Lopes Buriti dos Montes Cabeceiras do Piaui Caldeirao Grande do Piaui Campinas do Piaui Campo Maior Canavieira Canto do Buriti Capitao de Campos Caracol Castelo do Piaui Cocal Coivaras Colonia do Gurgueia Colonia do Piaui Conceicao do Caninde Coronel Jose Dias Corrente Cristalandia do Piaui Cristino Castro Curimata Demerval Lobao Dirceu Arcoverde Dom Expedito Lopes Domingos Mourao Dom Inocencio Elesbao Veloso Eliseu Martins Esperantina Fartura do Piaui Flores do Piaui Floriano Francinopolis Francisco Ayres Francisco Santos Fronteiras Gilbues Guadalupe Hugo Napoleao Inhuma Ipiranga do Piaui Isaias Coelho Itainopolis Itaueira Jacobina do Piaui Jaicos Jardim do Mulato Jerumenha Joaquim Pires Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis irrigation 398,480 331,214 2,726,196 821,172 643,138 15,326,086 537,686 1,922,680 573,810 320,111 9,673,615 489,451 288,770 370,308 549,170 51,142 32,604 787,941 1,426,515 839,548 556,726 98,381 1,479,354 1,003,094 493,582 1,138,004 12,155,918 2,034,114 1,050,162 1,119,865 574,777 388,052 1,128,875 669,758 4,058,639 399,329 60,179 512,231 3,861,386 238,876 538,526 178,695 620,675 1,803,594 886,535 2,963,901 897,801 171,380 423,526 907,307 561,250 734,994 536,859 63,719 90,918 2,472,933 2,126,810 17,602,956 1,419,755 538,704 36,307 699,221 287,568 69,905 406,432 1,194,574 700,411 32,814 1,148,584 2,739,926 853,057 531,433 618,555 33,845 1,860,151 18,359,250 431,100 904,999 705,071 433,417 647,621 1,658,134 98,500 146,999 558,808 1,255,021 906,520 livestock 2,596,796 301,845 504,949 420,012 135,787 312,914 395,363 364,794 80,576 259,535 113,164 648,346 656,664 33,497 140,125 227,797 156,949 1,113,042 954,426 392,254 182,759 344,330 265,875 354,036 180,690 0 0 1,153,520 0 107,468 1,293,899 572,526 0 216,766 0 230,342 192,967 394,993 1,000,072 458,937 498,010 243,953 556,943 2,415,083 0 0 229,801 386,431 1,043,511 971,777 195,972 0 397,635 530,009 246,896 0 0 0 0 175,785 375,073 93,129 290,690 948,397 877,172 0 863,178 291,652 0 890,891 112,452 195,632 199,956 364,837 0 225,448 58,030 312,175 162,533 486,028 692,683 0 582,913 707,694 125,895 320,966 684,051 domestic 1,284,366 301,920 1,483,338 1,300,398 294,210 793,866 215,058 457,392 515,292 248,466 346,356 986,910 1,417,170 222,024 517,032 188,946 86,052 478,422 982,200 869,658 215,844 211,662 150,426 280,716 264,654 384,768 251,886 1,609,842 152,352 176,070 640,800 311,766 444,258 186,720 889,194 175,284 122,982 229,782 965,076 226,038 238,380 138,618 157,410 2,196,684 192,108 1,584,282 234,102 418,260 880,860 642,072 121,224 143,460 191,898 252,450 98,262 878,082 199,026 554,838 382,326 543,228 139,566 279,972 244,968 228,204 642,978 152,118 1,374,498 102,630 116,376 1,572,876 155,214 169,086 166,986 204,456 451,056 550,164 125,508 466,776 285,582 308,460 491,202 347,934 127,056 619,926 149,100 158,814 399,264 75 industry_1 1,504 0 21,026 2,171 0 10,662 16 1,727 5,285 2,988 1,276 61,901 1,562 89,744 5,537 0 0 2,312 7,981 2,622 1,680 342 534 0 0 2,372 0 13,154 5,228 2,684 738 3,424 295 0 3,112 0 0 1,013 11,254 0 1,515 0 0 9,578 161 1,922 156 0 3,310 21,591 0 0 0 0 0 5,780 271 23,803 4,330 2,881 720 1,608 907 0 5,055 242 65,339 0 1,208 122,316 0 0 0 2,766 6,031 9,033 2,277 5,500 142 0 0 86,982 0 1,199 761 1,061 0 industry_2 2,956 0 28,197 362 0 9,652 28 4,191 68,499 1,090 4,631 111,792 3,995 23,222 79,970 0 0 18,490 231,798 27,648 9,478 715 1,116 0 0 4,956 0 68,329 12,176 121,354 1,541 23,307 616 0 48,821 0 0 2,116 28,237 0 3,165 0 0 212,836 337 101,080 326 0 25,665 16,030 0 0 0 0 0 143,707 6,919 110,856 30,150 27,895 1,504 3,361 1,895 0 68,669 505 106,506 0 2,525 445,586 0 0 0 20,655 24,879 7,803 9,450 11,090 296 0 0 32,919 0 30,033 1,589 30,460 0 tourism 0 0 135,610 506,184 0 135,610 0 0 58,887 4,392 0 58,887 135,610 0 0 0 0 0 0 27,000 0 0 0 0 0 0 0 21,600 0 0 16,200 0 0 0 0 5,400 0 0 0 0 0 0 0 75,600 0 16,200 0 5,400 0 0 0 0 0 0 43,200 16,200 0 16,200 0 0 0 0 0 0 0 0 27,000 0 0 135,000 0 0 0 0 0 10,800 0 0 0 0 0 0 0 0 0 0 0 total_1 total_2 4,281,146 934,979 4,871,119 3,049,937 1,073,135 16,579,138 1,148,123 2,746,593 1,233,850 835,492 10,134,411 2,245,495 2,499,776 715,573 1,211,864 467,885 275,605 2,381,717 3,371,122 2,131,082 957,009 654,715 1,896,189 1,637,846 938,926 1,525,144 12,407,804 4,832,230 1,207,742 1,406,087 2,526,414 1,275,768 1,573,428 1,073,244 4,950,945 810,355 376,128 1,138,019 5,837,788 923,851 1,276,431 561,266 1,335,028 6,500,539 1,078,804 4,566,305 1,361,860 981,471 2,351,207 2,542,747 878,446 878,454 1,126,392 846,178 479,276 3,372,995 2,326,107 18,197,797 1,806,411 1,260,598 551,666 1,073,930 824,133 1,246,506 1,931,637 1,346,934 3,030,426 427,096 1,266,168 5,461,009 1,120,723 896,151 985,497 605,904 2,317,238 19,154,695 616,915 1,689,450 1,153,328 1,227,905 1,831,506 2,093,050 808,469 1,475,818 834,564 1,735,862 1,989,835 4,282,598 934,979 4,878,290 3,048,128 1,073,135 16,578,128 1,148,135 2,749,057 1,297,064 833,594 10,137,766 2,295,386 2,502,209 649,051 1,286,297 467,885 275,605 2,397,895 3,594,939 2,156,108 964,807 655,088 1,896,771 1,637,846 938,926 1,527,728 12,407,804 4,887,405 1,214,690 1,524,757 2,527,217 1,295,651 1,573,749 1,073,244 4,996,654 810,355 376,128 1,139,122 5,854,771 923,851 1,278,081 561,266 1,335,028 6,703,797 1,078,980 4,665,463 1,362,030 981,471 2,373,562 2,537,186 878,446 878,454 1,126,392 846,178 479,276 3,510,922 2,332,755 18,284,850 1,832,231 1,285,612 552,450 1,075,683 825,121 1,246,506 1,995,251 1,347,197 3,071,593 427,096 1,267,485 5,784,279 1,120,723 896,151 985,497 623,793 2,336,086 19,153,465 624,088 1,695,040 1,153,482 1,227,905 1,831,506 2,038,987 808,469 1,504,652 835,392 1,765,261 1,989,835 Hauschild and Döll Muni_nr 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Municipality Jose de Freitas Lagoa Alegre Lagoa do Barro Piaui Landri Sales Luis Correia Luzilandia Manoel Emidio Marcolandia Marcos Parente Matias Olimpio Miguel Alves Miguel Leao Monsenhor Gil Monsenhor Hipolito Monte Alegre do Piaui Nazare do Piaui Nossa Senhora dos Remedios Novo Oriente do Piaui Oeiras Padre Marcos Paes Landim Palmeira do Piaui Palmeirais Parnagua Parnaiba Passagem Franca do Piaui Patos do Piaui Paulistana Pedro II Picos Pimenteiras Pio IX Piracuruca Piripiri Porto Prata do Piaui Queimada Nova Redencao do Gurgueia Regeneracao Ribeiro Goncalves Rio Grande do Piaui Santa Cruz do Piaui Santa Cruz dos Milagres Santa Filomena Santa Luz Santana do Piaui Santa Rosa do Piaui Santo Antonio de Lisboa Santo Inacio do Piaui Sao Braz do Piaui Sao Felix do Piaui Sao Francisco do Piaui Sao Goncalo do Piaui Sao Joao da Canabrava Sao Joao da Serra Sao Joao do Piaui Sao Jose do Divino Sao Jose do Peixe Sao Jose do Piaui Sao Juliao Sao Lourenco do Piaui Sao Miguel do Tapuio Sao Pedro do Piaui Sao Raimundo Nonato Sigefredo Pacheco Simoes Simplicio Mendes Socorro do Piaui Teresina Uniao Urucui Valenca do Piaui Varzea Branca Varzea Grande irrigation 1,798,481 439,015 75,303 2,220,004 418,403 10,308,240 1,337,401 46,988 1,170,180 596,894 13,131,122 2,668,609 693,957 31,397 1,265,655 1,095,312 324,689 485,004 2,490,254 77,540 688,954 1,539,334 1,744,812 1,975,445 2,075,313 646,746 571,533 376,953 1,093,980 7,348,964 830,054 56,661 697,217 4,124,962 1,335,318 437,317 65,387 2,074,930 385,632 1,037,774 1,370,035 752,414 1,953,579 1,050,041 1,042,309 569,575 622,148 563,604 685,996 67,069 566,829 509,679 390,072 326,508 301,232 983,000 951,117 462,657 653,853 31,887 36,220 957,459 823,482 343,758 425,717 152,036 1,039,769 653,703 11,518,259 53,150,800 1,493,021 1,098,666 32,412 605,181 livestock 948,142 264,042 444,268 0 739,588 914,375 0 49,984 0 198,613 688,027 68,381 287,287 198,369 0 0 157,385 283,710 1,296,596 478,226 354,609 0 475,732 0 615,910 239,968 397,243 1,421,939 1,184,516 1,163,484 566,976 613,157 1,503,597 840,875 338,727 105,865 529,289 0 257,599 0 0 515,314 664,647 0 0 63,646 344,065 176,177 399,422 94,887 366,596 443,458 65,836 232,677 618,901 2,354,647 408,440 0 125,802 179,704 307,414 1,237,219 210,725 579,085 567,487 892,657 982,838 235,174 1,093,144 796,049 0 567,500 176,434 206,015 domestic 1,369,476 183,570 135,282 270,120 739,284 1,039,860 281,766 147,744 194,628 273,300 616,176 58,284 309,336 162,378 450,084 309,660 162,030 299,778 2,024,706 288,798 143,742 176,208 345,924 350,016 6,191,118 156,726 170,964 663,732 1,537,644 3,943,332 422,034 315,486 1,017,438 2,470,602 540,048 114,414 197,310 226,560 657,720 285,240 493,218 409,824 125,388 138,972 164,442 135,426 259,662 189,234 173,628 101,760 207,012 290,244 244,188 259,962 242,082 1,698,432 152,790 239,118 235,680 133,956 108,300 697,944 591,354 601,380 500,322 449,184 626,514 188,784 52,542,032 1,250,556 513,018 630,876 112,098 293,724 industry_1 10,151 0 0 0 22,301 63,422 1,526 0 80 0 62,098 91,342 2,886 707 1,106 0 0 0 3,975 1,994 0 0 90,878 1,341 54,679 0 0 4,031 10,669 62,578 627 1,387 14,150 18,094 0 0 0 0 6,042 5,199 87,045 1,763 0 290 4,372 0 3,070 85 0 0 0 1,075 1,490 0 0 3,937 341 1,767 1,459 0 0 183 1,725 11,036 0 1,737 921 0 812,901 62,754 6,797 11,001 0 0 industry_2 60,391 0 0 0 63,189 54,089 3,064 0 168 0 59,559 21,426 31,514 1,478 2,310 0 0 0 90,797 6,319 0 0 12,704 7,546 768,408 0 0 37,269 69,581 433,114 1,309 17,369 148,929 249,866 0 0 0 0 116,462 63,022 22,524 63,045 0 605 20,356 0 6,414 178 0 0 0 2,246 31,091 0 0 89,844 713 6,049 21,168 0 0 383 51,449 154,342 0 3,508 1,925 0 4,517,947 91,229 136,353 68,954 0 0 tourism total_1 total_2 43,200 0 0 0 1,584,000 0 0 0 0 0 0 0 5,400 0 0 0 0 0 84,600 0 0 0 0 0 1,584,000 0 0 16,200 41,400 108,000 0 0 108,000 112,500 0 0 0 0 0 0 0 0 28,350 0 0 0 0 0 0 0 0 0 0 0 0 21,600 0 0 0 0 0 0 0 87,750 0 0 0 0 1,950,000 0 0 21,600 0 0 4,169,450 886,627 654,853 2,490,124 3,503,576 12,325,897 1,620,693 244,716 1,364,888 1,068,807 14,497,423 2,886,616 1,298,866 392,851 1,716,845 1,404,972 644,104 1,068,492 5,900,131 846,558 1,187,305 1,715,542 2,657,346 2,326,802 10,521,020 1,043,440 1,139,740 2,482,855 3,868,209 12,626,358 1,819,691 986,691 3,340,402 7,567,033 2,214,093 657,596 791,986 2,301,490 1,306,993 1,328,213 1,950,298 1,679,315 2,771,964 1,189,303 1,211,123 768,647 1,228,945 929,100 1,259,046 263,716 1,140,437 1,244,456 701,586 819,147 1,162,215 5,061,616 1,512,688 703,542 1,016,794 345,547 451,934 2,892,805 1,627,286 1,623,009 1,493,526 1,495,614 2,650,042 1,077,661 67,916,336 55,260,159 2,012,836 2,329,643 320,944 1,104,920 4,219,690 886,627 654,853 2,490,124 3,544,464 12,316,564 1,622,231 244,716 1,364,976 1,068,807 14,494,884 2,816,700 1,327,494 393,622 1,718,049 1,404,972 644,104 1,068,492 5,986,953 850,883 1,187,305 1,715,542 2,579,172 2,333,007 11,234,749 1,043,440 1,139,740 2,516,093 3,927,121 12,996,894 1,820,373 1,002,673 3,475,181 7,798,805 2,214,093 657,596 791,986 2,301,490 1,417,413 1,386,036 1,885,777 1,740,597 2,771,964 1,189,618 1,227,107 768,647 1,232,289 929,193 1,259,046 263,716 1,140,437 1,245,627 731,187 819,147 1,162,215 5,147,523 1,513,060 707,824 1,036,503 345,547 451,934 2,893,005 1,677,010 1,766,315 1,493,526 1,497,385 2,651,046 1,077,661 71,621,382 55,288,634 2,142,392 2,387,596 320,944 1,104,920 NoWUM output: Annual withdrawal water uses of 2025 Decentralization scenario (RSB) CE [m³] PI [m³] CE [km³] PI [km³] total [km³] irrigation livestock 488,163,676 265,903,412 488 266 754 93,888,438 260,482,382 60,252,990 125,556,416 94 260 60 126 154 386 domestic 76 industry_1 industry_2 64,243,556 114,286,712 2,089,483 9,792,787 64 114 2 10 66 124 tourism 41,558,214 6,212,400 42 6 48 total_1 948,336,266 460,014,701 948 460 1,408 total_2 998,379,422 467,718,005 998 468 1,466 Hauschild and Döll Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis 5) NoWUM output: Annual withdrawal water uses [m3] of 2025 Coastal Boom and Cash Crops intervention scenario (ISA) Muni_nr 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 Municipality Abaiara Acarape Acarau Acopiara Aiuaba Alcantaras Altaneira Alto Santo Amontada Antonina do Norte Apuiares Aquiraz Aracati Aracoiaba Ararenda Araripe Aratuba Arneiroz Assare Aurora Baixio Banabuiu Barbalha Barreira Barro Barroquinha Baturite Beberibe Bela Cruz Boa Viagem Brejo Santo Camocim Campos Sales Caninde Capistrano Caridade Carire Caririacu Carius Carnaubal Cascavel Catarina Catunda Caucaia Cedro Chaval Choro Chorozinho Coreau Crateus Crato Croata Cruz Deputado Irapuan Pinheiro Erere Eusebio Farias Brito Forquilha Fortaleza Fortim Frecheirinha General Sampaio Graca Granja Granjeiro Groairas Guaiuba Guaraciaba do Norte Guaramiranga Hidrolandia Horizonte Ibaretama Ibiapina Ibicuitinga Icapui Ico Iguatu Independencia Ipaporanga Ipaumirim Ipu Ipueiras Iracema Iraucuba irrigation 6,048,178 4,365,486 16,837,336 500,146 967,055 556,791 4,896,798 9,794,656 2,507,999 5,677,410 2,933,236 3,603,397 3,104,782 3,145,463 728,716 5,840,426 2,598,107 596,285 8,325,732 5,054,858 5,532,582 7,677,790 10,102,176 957,864 5,037,913 2,727,785 2,332,527 7,368,386 32,070,318 889,478 21,635,310 4,661,630 696,924 837,365 2,674,268 656,407 862,730 5,890,579 8,243,970 6,853,362 5,428,551 560,812 1,143,845 2,970,173 5,031,075 1,975,592 974,915 2,298,262 508,670 34,648,568 8,832,847 5,385,258 2,885,522 742,848 8,339,296 2,570,775 4,949,062 335,879 3,450,178 1,851,124 572,690 1,526,491 879,305 3,309,964 3,156,500 1,117,200 2,129,528 8,462,590 2,864,752 888,459 1,700,615 3,966,675 7,095,694 4,339,064 6,730,544 21,194,198 31,060,434 901,961 478,243 4,783,804 6,539,738 652,221 6,525,523 903,008 livestock domestic 147,074 226,152 98,529 609,054 641,784 3,317,706 929,412 1,057,176 437,437 314,490 61,695 209,430 57,705 191,190 638,585 435,606 670,423 1,788,978 141,742 188,976 180,559 231,306 767,414 3,031,452 451,778 3,335,820 562,916 1,484,454 164,948 233,604 336,174 531,828 182,240 538,104 370,848 143,028 524,664 670,344 672,818 814,218 245,366 173,418 695,339 629,940 254,873 1,516,098 316,324 1,053,978 444,480 684,606 221,431 808,602 393,216 1,619,208 752,448 2,310,510 912,413 1,526,442 1,135,419 1,499,904 701,600 1,225,530 572,116 3,274,464 274,715 577,908 965,004 1,546,062 262,976 817,272 190,405 258,996 394,852 443,274 386,743 823,272 384,316 614,856 130,024 417,678 646,001 5,055,606 291,850 216,210 233,638 173,850 1,015,106 13,265,596 587,403 819,714 169,037 619,098 304,227 261,510 303,443 734,208 235,638 520,572 1,097,021 1,411,122 425,144 3,360,240 136,345 512,202 327,976 2,481,000 228,664 181,242 271,381 190,896 102,277 1,764,990 353,661 669,306 202,381 326,880 211,635 134,680,016 84,481 525,342 69,926 230,550 90,592 99,528 112,882 328,344 1,365,936 2,542,242 86,581 139,950 94,152 170,142 157,010 856,062 238,719 1,013,208 47,686 259,212 344,244 425,640 372,956 1,630,668 427,721 381,924 175,501 581,382 275,497 312,246 302,817 1,926,828 1,175,620 2,425,776 1,038,536 3,857,322 1,194,958 521,754 227,441 251,478 296,345 343,806 376,748 1,768,236 402,986 677,466 447,890 471,624 527,104 403,980 77 industry_1 55,189 112,771 1,652 1,885 230 0 1,153 984 249 5,491 879 3,691,880 52,646 1,787 0 6,614 1,627 1,536 1,107 2,149 256 163,528 29,258 139 33,656 10,526 24,618 21,275 4,816 12,447 65,558 5,246,760 3,388 93,808 1,625 1,272 2,139 383 657 2,033 8,143 1,176 0 401,741 37,335 2,206 26 482 1,711 4,359 710,856 2 5,087 0 980 424,278 94 10,401 47,145,436 14,979 600 0 0 16,087 0 315 1,544 2,434 383 747 2,659,606 475 8,019 0 8,174 17,601 538,923 3,379 0 5,943 7,193 871 23,742 266 industry_2 99,669 877,855 160,081 23,947 416 0 2,081 39,569 451 84 3,189 2,339,982 2,500,326 3,227 0 484 10,295 2,774 1,998 55,857 463 295,327 1,210,047 247 60,783 19,008 279,312 272,254 6,292 22,478 118,395 9,475,479 53,844 169,413 515 105,289 6,589 693 1,186 71 807,763 2,124 0 7,831,422 67,428 2,604,285 46 872 351 70,705 1,283,784 3 9,190 0 3,558 766,228 169 18,782 76,874,896 31,014 1,083 0 0 29,055 0 569 81,311 3,274 694 46,869 1,186,429 857 14,484 0 14,763 31,785 973,277 3,298 0 57,831 12,992 1,572 10,937 481 tourism 0 0 785,140 8,770 58,887 0 0 0 785,140 0 0 5,094,080 3,202,840 12,200 0 58,887 163,575 0 0 58,887 0 58,887 718,430 12,200 0 785,140 200,125 2,871,360 0 4,392 4,392 824,120 0 729,136 0 0 0 58,887 0 135,610 1,136,100 0 0 6,224,960 58,887 163,575 0 0 0 67,658 1,027,202 58,887 785,140 0 0 12,200 0 0 20,185,140 921,600 0 0 0 0 0 0 163,575 135,610 163,575 0 60,938 0 135,610 0 921,600 58,887 80,820 0 0 0 58,887 0 0 0 total_1 6,476,593 5,185,840 21,583,618 2,497,389 1,778,099 827,916 5,146,846 10,869,831 5,752,789 6,013,619 3,345,980 16,188,223 10,147,866 5,206,820 1,127,268 6,773,929 3,483,653 1,111,697 9,521,847 6,602,930 5,951,622 9,225,484 12,620,835 2,340,505 6,200,655 4,553,484 4,569,694 13,323,979 34,513,989 3,541,640 23,632,390 14,579,090 1,552,935 4,171,375 3,756,141 1,107,080 1,702,995 7,159,864 9,243,799 7,538,707 12,274,401 1,070,048 1,551,333 23,877,576 6,534,414 2,929,508 1,540,678 3,336,395 1,266,591 37,228,728 14,356,289 6,092,694 6,484,725 1,152,754 8,802,553 4,874,520 5,972,123 875,541 205,672,405 3,397,526 873,766 1,716,611 1,320,531 7,234,229 3,383,031 1,381,809 3,307,719 9,852,561 3,335,608 1,659,090 6,424,783 4,776,795 7,996,206 4,926,807 9,889,963 24,872,082 36,576,035 2,622,052 957,162 5,429,898 8,750,802 1,733,544 7,468,779 1,834,358 total_2 6,521,073 5,950,924 21,742,047 2,519,451 1,778,285 827,916 5,147,774 10,908,416 5,752,991 6,008,212 3,348,290 14,836,325 12,595,546 5,208,260 1,127,268 6,767,799 3,492,321 1,112,935 9,522,738 6,656,638 5,951,829 9,357,283 13,801,624 2,340,613 6,227,782 4,561,966 4,824,388 13,574,958 34,515,465 3,551,671 23,685,227 18,807,809 1,603,391 4,246,980 3,755,031 1,211,097 1,707,445 7,160,174 9,244,328 7,536,745 13,074,021 1,070,996 1,551,333 31,307,257 6,564,507 5,531,587 1,540,698 3,336,785 1,265,231 37,295,074 14,929,217 6,092,695 6,488,828 1,152,754 8,805,131 5,216,470 5,972,198 883,922 235,401,865 3,413,561 874,249 1,716,611 1,320,531 7,247,197 3,383,031 1,382,063 3,387,486 9,853,401 3,335,919 1,705,212 4,951,606 4,777,177 8,002,671 4,926,807 9,896,552 24,886,266 37,010,389 2,621,971 957,162 5,481,786 8,756,601 1,734,245 7,455,974 1,834,573 Hauschild and Döll Muni_nr 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 Municipality Itaicaba Itaitinga Itapage Itapipoca Itapiuna Itarema Itatira Jaguaretama Jaguaribara Jaguaribe Jaguaruana Jardim Jati Jijoca de Jericoacoara Juazeiro do Norte Jucas Lavras da Mangabeira Limoeiro do Norte Madalena Maracanau Maranguape Marco Martinopole Massape Mauriti Meruoca Milagres Milha Miraima Missao Velha Mombaca Monsenhor Tabosa Morada Nova Moraujo Morrinhos Mucambo Mulungu Nova Olinda Nova Russas Novo Oriente Ocara Oros Pacajus Pacatuba Pacoti Pacuja Palhano Palmacia Paracuru Paraipaba Parambu Paramoti Pedra Branca Penaforte Pentecoste Pereiro Pindoretama Piquet Carneiro Pires Ferreira Poranga Porteiras Potengi Potiretama Quiterianopolis Quixada Quixelo Quixeramobim Quixere Redencao Reriutaba Russas Saboeiro Salitre Santana do Acarau Santana do Cariri Santa Quiteria Sao Benedito Sao Goncalo do Amarante Sao Joao do Jaguaribe Sao Luis do Curu Senador Pompeu Senador Sa Sobral Solonopole Tabuleiro do Norte Tamboril Tarrafas Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis irrigation 3,246,036 3,246,126 2,615,313 2,322,397 1,309,069 1,757,798 1,017,108 64,316,648 8,494,518 26,006,464 18,322,720 9,798,901 6,384,894 1,958,960 9,970,486 462,357 7,444,454 60,968,328 959,248 2,033,642 1,991,494 31,208,042 2,018,842 680,074 15,431,585 1,277,890 5,602,704 556,299 560,403 3,722,370 671,144 563,002 34,059,640 541,454 1,836,395 474,949 2,488,193 4,722,944 817,598 762,508 2,774,343 7,720,451 3,735,922 3,585,204 2,684,981 809,350 2,107,139 1,812,092 9,183,146 68,502,112 1,021,510 919,807 1,379,949 5,293,952 7,328,868 6,520,352 1,792,423 874,960 838,502 848,544 5,587,712 5,679,128 6,735,966 1,070,089 7,692,524 4,599,089 11,163,872 6,637,889 3,305,015 487,694 44,797,020 497,548 790,369 20,413,984 7,462,623 540,340 7,495,658 5,453,694 4,442,746 4,129,773 1,081,520 929,702 1,204,643 8,728,827 11,055,034 579,395 4,781,754 livestock 123,475 206,926 541,230 1,282,291 232,627 590,377 267,712 1,652,609 748,480 1,709,778 536,329 458,914 167,885 111,102 219,478 335,961 819,177 484,145 409,821 112,528 928,140 517,821 142,326 220,729 910,979 36,471 420,106 364,071 340,425 481,305 966,239 344,096 1,646,980 130,669 346,165 83,881 63,651 149,601 312,278 487,332 506,253 435,468 524,312 180,615 162,253 59,424 217,785 146,664 288,982 278,606 885,017 182,790 625,853 112,137 370,383 221,426 226,708 252,104 137,594 207,471 316,360 192,676 301,521 441,553 1,554,891 651,036 1,921,409 226,631 127,722 177,093 634,613 458,550 240,501 1,414,758 389,554 1,276,618 244,770 527,832 227,367 156,396 441,723 89,408 938,748 874,593 491,284 879,147 166,276 domestic 200,616 1,348,044 1,841,304 6,951,402 283,002 1,455,894 329,712 613,716 234,834 1,626,678 963,834 879,654 204,390 866,730 6,284,496 682,410 1,172,070 1,859,820 299,280 8,649,312 5,330,388 1,042,284 390,924 550,008 1,242,690 414,534 798,540 266,808 241,728 942,438 883,554 355,878 3,091,008 144,180 888,342 287,106 509,568 470,334 661,320 566,784 1,263,738 801,798 2,267,316 2,590,836 539,328 123,492 404,844 475,002 1,406,076 1,031,082 646,170 221,148 976,080 179,634 597,912 416,358 844,032 291,684 530,424 260,064 444,672 254,964 167,394 381,906 2,072,166 529,140 3,188,028 513,882 1,367,538 457,782 1,308,912 319,704 284,304 936,666 1,499,664 572,412 1,196,904 2,695,980 527,688 604,878 552,516 143,778 1,815,618 711,786 978,594 802,746 277,194 78 industry_1 983 4,968 887,341 878,869 682 7,450 149 452 3,872 66,409 11,593 3,002 3,009 0 236,173 276,403 6,579 1,619,932 263 11,058,594 1,007,582 20,168 4,647 8,028 327 6,953 2,957 14 0 3,782 1,422 53 210,014 2,153 2,398 0 7 2,903 11,583 29 0 3,212 4,390,300 1,924,231 9,861 2,491 3,730 279 1,766 1,772 43 1,317 12,673 1,774 8,412 2,285 827 29 0 115 1,571 557 0 0 132,739 1,375 81,833 7,664 5,583 1,054 27,684 0 0 7,492 4,974 63,532 20,082 3,269 2,560 144 3,902 1,008 7,946 1,322 29,220 834 0 industry_2 815 12,852 1,602,507 1,587,211 1,231 13,457 270 815 779 119,933 472,784 5,424 3,013 0 1,166,238 499,175 13,196 2,925,543 473 14,922,712 2,514,546 297 16,877 6,995 28,993 12,558 38,620 26 0 24,950 2,570 95 379,279 7,821 5,527 0 15 387,152 20,918 52 0 30,873 1,345,881 3,466,260 17,806 4,498 324,181 506 239,159 638 78 84,962 22,888 4,682 12,306 3,340 1,495 52 0 208 39 1,007 0 0 69,551 2,483 147,788 477 321,733 813 784,371 0 0 51,620 231,490 121,808 10,053 307,646 4,622 260 9,401 3,663 1,741,758 2,389 119,690 13,682 0 tourism total_1 total_2 0 19,520 187,938 824,120 0 843,640 58,887 0 0 8,770 0 9,936 0 1,935,460 3,450,109 0 0 80,820 0 0 300,700 0 12,200 0 0 58,887 0 0 0 52,632 8,770 0 58,887 0 0 0 163,575 58,887 8,770 0 0 63,274 187,938 261,720 163,575 0 0 163,575 1,272,560 1,155,580 0 0 0 0 58,887 58,887 490,712 0 0 58,887 0 0 0 0 85,208 0 67,658 0 175,762 0 0 0 0 0 6,700 0 135,610 1,467,560 0 0 13,158 0 102,753 0 0 0 0 3,571,110 4,825,584 6,073,126 12,259,079 1,825,380 4,655,159 1,673,568 66,583,425 9,481,704 29,418,099 19,834,476 11,150,407 6,760,178 4,872,252 20,160,742 1,757,131 9,442,280 65,013,045 1,668,612 21,854,076 9,558,304 32,788,315 2,568,939 1,458,839 17,585,581 1,794,735 6,824,307 1,187,192 1,142,556 5,202,527 2,531,129 1,263,029 39,066,529 818,456 3,073,300 845,936 3,224,994 5,404,669 1,811,549 1,816,653 4,544,334 9,024,203 11,105,788 8,542,606 3,559,998 994,757 2,733,498 2,597,612 12,152,530 70,969,152 2,552,740 1,325,062 2,994,555 5,587,497 8,364,462 7,219,308 3,354,702 1,418,777 1,506,520 1,375,081 6,350,315 6,127,325 7,204,881 1,893,548 11,537,528 5,780,640 16,422,800 7,386,066 4,981,620 1,123,623 46,768,229 1,275,802 1,315,174 22,772,900 9,363,515 2,452,902 9,093,024 10,148,335 5,200,361 4,891,191 2,092,819 1,163,896 4,069,708 10,316,528 12,554,132 2,262,122 5,225,224 3,570,942 4,833,468 6,788,292 12,967,421 1,825,929 4,661,166 1,673,689 66,583,788 9,478,611 29,471,623 20,295,667 11,152,829 6,760,182 4,872,252 21,090,807 1,979,903 9,448,897 66,318,656 1,668,822 25,718,194 11,065,268 32,768,444 2,581,169 1,457,806 17,614,247 1,800,340 6,859,970 1,187,204 1,142,556 5,223,695 2,532,277 1,263,071 39,235,794 824,124 3,076,429 845,936 3,225,002 5,788,918 1,820,884 1,816,676 4,544,334 9,051,864 8,061,369 10,084,635 3,567,943 996,764 3,053,949 2,597,839 12,389,923 70,968,018 2,552,775 1,408,707 3,004,770 5,590,405 8,368,356 7,220,363 3,355,370 1,418,800 1,506,520 1,375,174 6,348,783 6,127,775 7,204,881 1,893,548 11,474,340 5,781,748 16,488,755 7,378,879 5,297,770 1,123,382 47,524,916 1,275,802 1,315,174 22,817,028 9,590,031 2,511,178 9,082,995 10,452,712 5,202,423 4,891,307 2,098,318 1,166,551 5,803,520 10,317,595 12,644,602 2,274,970 5,225,224 Hauschild and Döll Muni_nr 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 Municipality Taua Tejucuoca Tiangua Trairi Tururu Ubajara Umari Umirim Uruburetama Uruoca Varjota Varzea Alegre Vicosa do Ceara Agricolandia Agua Branca Alagoinha do Piaui Alegrete do Piaui Alto Longa Altos Amarante Angical do Piaui Anisio de Abreu Antonio Almeida Aroazes Arraial Avelino Lopes Baixa Grande do Ribeiro Barras Barreiras do Piaui Barro Duro Batalha Beneditinos Bertolinia Bocaina Bom Jesus Bom Principio do Piaui Bonfim do Piaui Brasileira Buriti dos Lopes Buriti dos Montes Cabeceiras do Piaui Caldeirao Grande do Piaui Campinas do Piaui Campo Maior Canavieira Canto do Buriti Capitao de Campos Caracol Castelo do Piaui Cocal Coivaras Colonia do Gurgueia Colonia do Piaui Conceicao do Caninde Coronel Jose Dias Corrente Cristalandia do Piaui Cristino Castro Curimata Demerval Lobao Dirceu Arcoverde Dom Expedito Lopes Domingos Mourao Dom Inocencio Elesbao Veloso Eliseu Martins Esperantina Fartura do Piaui Flores do Piaui Floriano Francinopolis Francisco Ayres Francisco Santos Fronteiras Gilbues Guadalupe Hugo Napoleao Inhuma Ipiranga do Piaui Isaias Coelho Itainopolis Itaueira Jacobina do Piaui Jaicos Jardim do Mulato Jerumenha Joaquim Pires Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis irrigation 626,523 487,584 9,160,705 2,493,524 2,620,039 19,931,596 7,818,830 3,289,007 2,404,304 503,278 13,898,396 8,054,520 5,107,638 2,181,005 2,472,017 51,142 65,282 2,954,304 3,072,506 2,826,393 2,693,014 103,299 4,003,067 3,760,232 2,820,440 2,102,105 5,019,870 3,699,246 1,661,506 3,223,742 2,418,711 2,178,298 1,549,739 2,814,330 2,804,236 2,266,486 61,133 2,594,880 3,879,192 1,250,129 2,466,511 141,157 3,482,433 3,509,490 1,702,393 3,233,843 3,223,488 137,041 2,329,183 3,353,319 2,686,904 1,326,858 2,775,076 89,208 115,117 2,634,356 2,737,779 34,241,836 2,346,926 2,017,454 72,360 3,373,564 1,693,772 106,947 2,195,718 2,294,329 2,620,040 65,487 2,089,756 4,305,362 3,212,662 3,008,796 3,470,851 67,723 2,507,255 72,973,904 2,419,282 3,134,366 3,075,548 2,552,516 2,429,872 1,406,267 82,308 109,505 3,078,624 2,252,149 2,266,928 livestock 1,817,744 211,277 425,838 549,500 177,646 263,864 333,433 477,245 105,391 181,677 79,217 546,717 553,743 28,246 118,140 159,459 109,876 938,570 804,870 330,807 154,097 241,063 224,202 298,531 152,369 505,812 345,693 972,704 252,142 90,625 1,091,092 482,765 717,708 182,794 860,951 194,236 135,093 333,090 843,313 387,025 419,954 170,793 469,657 2,036,550 289,144 1,068,766 193,768 270,515 879,953 819,460 165,260 101,875 335,296 371,030 172,834 1,549,851 533,465 339,462 854,109 148,258 262,570 78,520 245,114 663,862 739,672 233,948 727,902 204,153 186,136 751,256 94,828 164,969 168,598 255,395 510,198 190,084 48,940 263,251 137,090 409,854 584,085 493,046 408,012 495,382 106,163 270,638 576,822 domestic 1,077,672 257,874 1,461,138 2,250,942 464,628 784,602 208,554 756,762 900,390 205,632 284,850 973,080 1,431,924 214,416 482,586 158,004 63,558 446,394 890,868 864,876 202,836 161,772 145,002 276,552 263,466 357,168 237,606 1,543,074 145,032 159,606 590,220 288,516 441,072 183,996 873,252 168,516 102,210 212,136 912,192 217,350 220,176 115,224 147,528 2,112,384 188,730 1,585,026 207,396 378,516 849,732 566,976 116,274 134,172 176,118 222,240 81,654 834,012 186,636 546,108 353,196 527,340 108,414 276,942 245,358 189,678 628,374 144,000 1,319,412 83,058 105,720 1,468,326 145,266 160,212 151,746 157,656 432,618 548,790 119,730 443,334 272,316 300,702 472,734 329,196 105,600 512,658 142,494 151,776 363,000 79 industry_1 1,145 0 19,365 3,282 0 9,781 15 2,609 7,969 2,279 974 56,989 1,435 82,484 5,085 0 0 2,121 7,327 2,411 1,541 261 492 0 0 2,262 0 12,076 4,983 2,463 676 3,147 281 0 2,965 0 0 930 10,360 0 1,391 0 0 8,804 153 1,832 143 0 3,039 19,853 0 0 0 0 0 5,522 259 22,732 4,132 2,647 547 1,478 832 0 4,643 230 59,991 0 1,154 112,249 0 0 0 2,106 5,756 8,294 2,087 5,049 131 0 0 83,112 0 914 699 974 0 industry_2 2,251 0 25,970 547 0 8,854 25 6,331 103,293 831 3,537 102,922 3,671 21,344 73,448 0 0 16,961 212,811 25,426 8,693 546 1,028 0 0 4,727 0 62,732 11,607 111,357 1,412 21,421 587 0 46,511 0 0 1,943 25,993 0 2,906 0 0 195,631 321 96,349 299 0 23,566 14,740 0 0 0 0 0 137,301 6,609 105,870 28,772 25,636 1,144 3,087 1,738 0 63,081 481 97,789 0 2,412 408,913 0 0 0 15,730 23,747 7,164 8,662 10,181 273 0 0 31,454 0 22,902 1,462 27,959 0 tourism 0 0 135,610 843,640 0 135,610 0 0 163,575 4,392 0 58,887 135,610 0 0 0 0 0 0 27,000 0 0 0 0 0 0 0 21,600 0 0 16,200 0 0 0 0 5,400 0 0 0 0 0 0 0 75,600 0 16,200 0 5,400 0 0 0 0 0 0 43,200 16,200 0 16,200 0 0 0 0 0 0 0 0 27,000 0 0 135,000 0 0 0 0 0 10,800 0 0 0 0 0 0 0 0 0 0 0 total_1 total_2 3,523,084 956,735 11,202,656 6,140,888 3,262,313 21,125,453 8,360,832 4,525,623 3,581,629 897,258 14,263,437 9,690,193 7,230,350 2,506,151 3,077,828 368,605 238,716 4,341,389 4,775,571 4,051,487 3,051,488 506,395 4,372,763 4,335,315 3,236,275 2,967,347 5,603,169 6,248,700 2,063,663 3,476,436 4,116,899 2,952,726 2,708,800 3,181,120 4,541,404 2,634,638 298,436 3,141,036 5,645,057 1,854,504 3,108,032 427,174 4,099,618 7,742,828 2,180,420 5,905,667 3,624,795 791,472 4,061,907 4,759,608 2,968,438 1,562,905 3,286,490 682,478 412,805 5,039,941 3,458,139 35,166,338 3,558,363 2,695,699 443,891 3,730,504 2,185,076 960,487 3,568,407 2,672,507 4,754,345 352,698 2,382,766 6,772,193 3,452,756 3,333,977 3,791,195 482,880 3,455,827 73,731,872 2,590,039 3,846,000 3,485,085 3,263,072 3,486,691 2,311,621 595,920 1,118,459 3,327,980 2,675,537 3,206,750 3,524,190 956,735 11,209,261 6,138,153 3,262,313 21,124,526 8,360,842 4,529,345 3,676,953 895,810 14,266,000 9,736,126 7,232,586 2,445,011 3,146,191 368,605 238,716 4,356,229 4,981,055 4,074,502 3,058,640 506,680 4,373,299 4,335,315 3,236,275 2,969,812 5,603,169 6,299,356 2,070,287 3,585,330 4,117,635 2,971,000 2,709,106 3,181,120 4,584,950 2,634,638 298,436 3,142,049 5,660,690 1,854,504 3,109,547 427,174 4,099,618 7,929,655 2,180,588 6,000,184 3,624,951 791,472 4,082,434 4,754,495 2,968,438 1,562,905 3,286,490 682,478 412,805 5,171,720 3,464,489 35,249,476 3,583,003 2,718,688 444,488 3,732,113 2,185,982 960,487 3,626,845 2,672,758 4,792,143 352,698 2,384,024 7,068,857 3,452,756 3,333,977 3,791,195 496,504 3,473,818 73,730,742 2,596,614 3,851,132 3,485,227 3,263,072 3,486,691 2,259,963 595,920 1,140,447 3,328,743 2,702,522 3,206,750 Hauschild and Döll Muni_nr 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 Water Use in Semi-arid Northeastern Brazil – Modeling and Scenario Analysis Municipality Jose de Freitas Lagoa Alegre Lagoa do Barro Piaui Landri Sales Luis Correia Luzilandia Manoel Emidio Marcolandia Marcos Parente Matias Olimpio Miguel Alves Miguel Leao Monsenhor Gil Monsenhor Hipolito Monte Alegre do Piaui Nazare do Piaui Nossa Senhora dos Remedios Novo Oriente do Piaui Oeiras Padre Marcos Paes Landim Palmeira do Piaui Palmeirais Parnagua Parnaiba Passagem Franca do Piaui Patos do Piaui Paulistana Pedro II Picos Pimenteiras Pio IX Piracuruca Piripiri Porto Prata do Piaui Queimada Nova Redencao do Gurgueia Regeneracao Ribeiro Goncalves Rio Grande do Piaui Santa Cruz do Piaui Santa Cruz dos Milagres Santa Filomena Santa Luz Santana do Piaui Santa Rosa do Piaui Santo Antonio de Lisboa Santo Inacio do Piaui Sao Braz do Piaui Sao Felix do Piaui Sao Francisco do Piaui Sao Goncalo do Piaui Sao Joao da Canabrava Sao Joao da Serra Sao Joao do Piaui Sao Jose do Divino Sao Jose do Peixe Sao Jose do Piaui Sao Juliao Sao Lourenco do Piaui Sao Miguel do Tapuio Sao Pedro do Piaui Sao Raimundo Nonato Sigefredo Pacheco Simoes Simplicio Mendes Socorro do Piaui Teresina Uniao Urucui Valenca do Piaui Varzea Branca Varzea Grande irrigation 3,350,389 2,064,030 106,469 2,002,142 1,698,278 39,630,244 2,486,301 82,759 2,193,793 2,909,613 9,993,124 4,125,396 2,679,698 62,895 2,251,730 1,978,368 1,824,240 2,061,977 3,913,979 64,496 3,679,424 2,263,168 3,801,119 2,592,137 3,434,960 2,345,180 3,165,910 226,230 3,040,053 7,217,974 2,678,937 96,748 2,441,970 4,789,702 2,125,045 2,391,232 67,644 2,907,507 1,719,518 1,773,105 2,613,095 4,258,691 4,936,666 1,845,492 1,911,254 3,114,275 3,421,947 3,199,663 3,702,348 95,836 3,056,165 2,745,906 1,963,756 1,836,485 1,703,417 3,297,648 3,148,878 903,753 3,255,838 63,877 72,211 3,407,580 2,799,904 241,298 2,228,065 113,388 3,710,967 3,783,133 30,712,226 38,924,148 2,216,574 3,190,496 64,683 3,428,146 livestock 799,510 222,629 310,986 239,816 967,581 771,053 313,996 35,005 185,530 167,500 580,200 57,635 242,256 138,841 376,382 347,227 132,711 239,243 1,093,362 334,737 299,029 253,168 401,134 1,308,841 805,770 202,330 334,973 995,363 998,835 981,137 478,116 429,217 1,267,932 709,064 285,659 89,248 370,506 340,965 217,254 275,753 427,390 434,538 560,450 316,456 104,991 53,666 290,155 148,580 336,810 66,451 309,166 373,930 55,516 196,178 521,878 1,985,597 344,410 504,235 106,090 125,795 215,191 1,043,294 177,682 405,374 478,563 624,846 828,784 198,328 1,212,922 671,238 402,732 478,534 123,493 173,708 domestic 1,349,448 169,962 112,440 265,710 1,110,258 962,742 276,240 122,778 188,136 233,340 520,026 56,022 281,874 132,606 441,918 295,380 138,672 293,574 2,022,066 218,334 133,392 167,562 319,212 315,786 12,177,552 150,378 160,206 541,266 1,460,478 3,925,890 410,916 234,234 972,204 2,404,620 515,760 110,316 164,022 211,032 636,954 280,098 485,988 398,178 120,456 123,810 157,092 130,236 244,590 181,932 162,570 84,576 201,714 285,396 240,612 250,260 229,860 1,688,094 142,968 231,570 227,718 109,488 90,024 640,950 585,888 490,164 505,800 351,822 617,352 182,916 62,266,768 1,186,254 480,210 586,116 93,186 282,282 industry_1 9,301 0 0 0 33,538 58,187 1,454 0 77 0 57,055 83,973 2,650 539 1,053 0 0 0 3,655 1,523 0 0 83,639 1,278 82,327 0 0 3,075 9,794 57,491 575 1,059 12,959 16,595 0 0 0 0 5,557 4,957 83,001 1,615 0 276 4,165 0 2,820 78 0 0 0 988 1,368 0 0 3,621 313 1,683 1,338 0 0 169 1,583 8,425 0 1,322 846 0 981,958 57,601 6,481 10,106 0 0 industry_2 55,333 0 0 0 95,027 49,625 2,921 0 161 0 54,721 19,697 28,933 1,126 2,200 0 0 0 83,474 4,828 0 0 11,692 7,191 1,156,948 0 0 28,424 63,876 397,902 1,202 13,256 136,395 229,170 0 0 0 0 107,107 60,084 21,477 57,759 0 577 19,392 0 5,893 163 0 0 0 2,064 28,550 0 0 82,627 654 5,761 19,415 0 0 353 47,227 117,819 0 2,670 1,768 0 5,457,534 83,738 130,025 63,346 0 0 tourism total_1 43,200 0 0 0 2,640,000 0 0 0 0 0 0 0 5,400 0 0 0 0 0 84,600 0 0 0 0 0 2,640,000 0 0 16,200 41,400 108,000 0 0 108,000 112,500 0 0 0 0 0 0 0 0 28,350 0 0 0 0 0 0 0 0 0 0 0 0 21,600 0 0 0 0 0 0 0 87,750 0 0 0 0 1,950,000 0 0 21,600 0 0 5,551,848 2,456,621 529,895 2,507,668 6,449,655 41,422,226 3,077,991 240,542 2,567,536 3,310,453 11,150,405 4,323,026 3,211,878 334,881 3,071,083 2,620,975 2,095,623 2,594,794 7,117,662 619,090 4,111,845 2,683,898 4,605,104 4,218,042 19,140,609 2,697,888 3,661,089 1,782,134 5,550,560 12,290,492 3,568,544 761,258 4,803,065 8,032,481 2,926,464 2,590,796 602,172 3,459,504 2,579,283 2,333,913 3,609,474 5,093,022 5,645,922 2,286,034 2,177,502 3,298,177 3,959,512 3,530,253 4,201,728 246,863 3,567,045 3,406,220 2,261,252 2,282,923 2,455,155 6,996,560 3,636,569 1,641,241 3,590,984 299,160 377,426 5,091,993 3,565,057 1,233,011 3,212,428 1,091,378 5,157,949 4,164,377 97,123,874 40,839,241 3,105,997 4,286,852 281,362 3,884,136 total_2 5,597,880 2,456,621 529,895 2,507,668 6,511,144 41,413,664 3,079,458 240,542 2,567,620 3,310,453 11,148,071 4,258,750 3,238,161 335,468 3,072,230 2,620,975 2,095,623 2,594,794 7,197,481 622,395 4,111,845 2,683,898 4,533,157 4,223,955 20,215,230 2,697,888 3,661,089 1,807,483 5,604,642 12,630,903 3,569,171 773,455 4,926,501 8,245,056 2,926,464 2,590,796 602,172 3,459,504 2,680,833 2,389,040 3,547,950 5,149,166 5,645,922 2,286,335 2,192,729 3,298,177 3,962,585 3,530,338 4,201,728 246,863 3,567,045 3,407,296 2,288,434 2,282,923 2,455,155 7,075,566 3,636,910 1,645,319 3,609,061 299,160 377,426 5,092,177 3,610,701 1,342,405 3,212,428 1,092,726 5,158,871 4,164,377 101,599,450 40,865,378 3,229,541 4,340,092 281,362 3,884,136 NoWUM output: Annual withdrawal water uses of 2025 Coastal Boom and Cash Crops intervention scenario (ISA) irrigation CE [m³] PI [m³] CE [km³] PI [km³] total [km³] 1,158,550,830 550,147,860 1,159 550 1,709 livestock domestic 81,762,239 337,921,398 64,313,226 138,071,068 82 338 64 138 146 476 80 industry_1 industry_2 84,952,613 147,624,259 2,204,686 10,774,831 85 148 2 11 87 158 tourism total_1 total_2 64,661,772 1,727,848,852 1,790,520,498 8,324,400 763,061,240 771,631,385 65 1,728 1,791 8 763 772 73 2,491 2,562