pdf Kassel World Water Series 3

Transcrição

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
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
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.
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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 ______________________________________________________________________
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40
40
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44
44
45
48
48
48
51
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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
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65
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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
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Hauschild and Döll
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
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Hauschild and Döll
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 efficiency
climate
irrigated area
crops
number of livestock
water price
income
population
supplied water
households connected
to public water supply
total water use
per municipality
(withdrawal and consumptive use)
industrial output
industrial mix
water price
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.
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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.
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Hauschild and Döll
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.
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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
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Hauschild and Döll
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
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Hauschild and Döll
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
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Hauschild and Döll
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.
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Hauschild and Döll
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).
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Hauschild and Döll
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
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
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
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
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
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
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
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
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
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
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
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
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.
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Appendix A
31
Hauschild and Döll
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.
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Hauschild and Döll
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
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
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
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
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] =
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
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
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
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
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
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
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
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
c:
correction factor [-] (diminishing wd_industry_2)
44
Hauschild and Döll
5.1.2
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)
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
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
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
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
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
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
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
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 (1995b): Relatórios Agropecuários 2° Semestre/95. 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
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
Appendix B
55
Hauschild and Döll
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
Municipalities of Piauí 1996
Parnaíba
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
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
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
Juazeiro do Norte
Jucas
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
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
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
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
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
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
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
Juazeiro do Norte
Jucas
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 Luis do Curu
Senador Pompeu
Senador Sa
Sobral
Solonopole
Tabuleiro do Norte
Tamboril
Tarrafas
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
Barras
Barreiras do Piaui
Barro Duro
Batalha
Beneditinos
Bertolinia
Bocaina
Bom Jesus
Bonfim do Piaui
Brasileira
Buriti dos Lopes
Buriti dos Montes
Cabeceiras 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
Coronel Jose Dias
Corrente
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
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
Municipality
Jose de Freitas
Lagoa Alegre
Landri Sales
Luis Correia
Luzilandia
Manoel Emidio
Marcolandia
Marcos Parente
Matias Olimpio
Miguel Alves
Miguel Leao
Monsenhor Gil
Monsenhor Hipolito
Nazare do Piaui
Oeiras
Padre Marcos
Paes Landim
Palmeira do Piaui
Palmeirais
Parnagua
Parnaiba
Patos do Piaui
Paulistana
Pedro II
Picos
Pimenteiras
Pio IX
Piracuruca
Piripiri
Porto
Prata do Piaui
Queimada Nova
Regeneracao
Ribeiro Goncalves
Rio Grande do Piaui
Santa Cruz do Piaui
Santa Filomena
Santa Luz
Santana do Piaui
Santa Rosa do Piaui
Sao Braz do Piaui
Sao Felix do Piaui
Sao Joao da Serra
Sao Joao do Piaui
Sao Jose do Divino
Sao Jose do Peixe
Sao Jose do Piaui
Sao Juliao
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
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
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
Juazeiro do Norte
Jucas
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 Luis do Curu
Senador Pompeu
Senador Sa
Sobral
Solonopole
Tabuleiro do Norte
Tamboril
Tarrafas
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
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
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
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
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
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
Regeneracao
Ribeiro Goncalves
Rio Grande do Piauí
Santa Cruz do Piauí
Santa Filomena
Santa Luz
Santana do Piauí
Santa Rosa do Piauí
Santo Inacio do Piauí
Sao Braz do Piauí
Sao Felix do Piauí
Sao Francisco do Piauí
Sao Goncalo do Piauí
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 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
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
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
Juazeiro do Norte
Jucas
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 Luis do Curu
Senador Pompeu
Senador Sa
Sobral
Solonopole
Tabuleiro do Norte
Tamboril
Tarrafas
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
Barras
Barreiras do Piaui
Barro Duro
Batalha
Beneditinos
Bertolinia
Bocaina
Bom Jesus
Bonfim do Piaui
Brasileira
Buriti dos Lopes
Buriti dos Montes
Cabeceiras 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
Coronel Jose Dias
Corrente
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
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
Municipality
Jose de Freitas
Lagoa Alegre
Landri Sales
Luis Correia
Luzilandia
Manoel Emidio
Marcolandia
Marcos Parente
Matias Olimpio
Miguel Alves
Miguel Leao
Monsenhor Gil
Monsenhor Hipolito
Nazare do Piaui
Oeiras
Padre Marcos
Paes Landim
Palmeira do Piaui
Palmeirais
Parnagua
Parnaiba
Patos do Piaui
Paulistana
Pedro II
Picos
Pimenteiras
Pio IX
Piracuruca
Piripiri
Porto
Prata do Piaui
Queimada Nova
Regeneracao
Ribeiro Goncalves
Rio Grande do Piaui
Santa Cruz do Piaui
Santa Filomena
Santa Luz
Santana do Piaui
Santa Rosa do Piaui
Sao Braz do Piaui
Sao Felix do Piaui
Sao Joao da Serra
Sao Joao do Piaui
Sao Jose do Divino
Sao Jose do Peixe
Sao Jose do Piaui
Sao Juliao
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
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
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
Juazeiro do Norte
Jucas
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 Luis do Curu
Senador Pompeu
Senador Sa
Sobral
Solonopole
Tabuleiro do Norte
Tamboril
Tarrafas
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
Barras
Barreiras do Piaui
Barro Duro
Batalha
Beneditinos
Bertolinia
Bocaina
Bom Jesus
Bonfim do Piaui
Brasileira
Buriti dos Lopes
Buriti dos Montes
Cabeceiras 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
Coronel Jose Dias
Corrente
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
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
Municipality
Jose de Freitas
Lagoa Alegre
Landri Sales
Luis Correia
Luzilandia
Manoel Emidio
Marcolandia
Marcos Parente
Matias Olimpio
Miguel Alves
Miguel Leao
Monsenhor Gil
Monsenhor Hipolito
Nazare do Piaui
Oeiras
Padre Marcos
Paes Landim
Palmeira do Piaui
Palmeirais
Parnagua
Parnaiba
Patos do Piaui
Paulistana
Pedro II
Picos
Pimenteiras
Pio IX
Piracuruca
Piripiri
Porto
Prata do Piaui
Queimada Nova
Regeneracao
Ribeiro Goncalves
Rio Grande do Piaui
Santa Cruz do Piaui
Santa Filomena
Santa Luz
Santana do Piaui
Santa Rosa do Piaui
Sao Braz do Piaui
Sao Felix do Piaui
Sao Joao da Serra
Sao Joao do Piaui
Sao Jose do Divino
Sao Jose do Peixe
Sao Jose do Piaui
Sao Juliao
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

pdf Kassel World Water Series 3

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