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Modeling Surface Water Resources for Effective Water Allocation Using
Water Evaluation and Planning (WEAP) Model, A Case Study on
Finchaa Sub basin, Ethiopia
Tesfaye Negasa Jaleta1, Mamuye Busier Yesuf
2, Deme Betele Hirko
2
1College of Engineering, Assosa University, Assosa, Ethiopia
2 Faculty of Civil and Environmental Engineering, Jimma Institute of Technology, JIT, Jimma University, Jimma,
Oromia, Ethiopia
Corresponding author, E-mail: [email protected] ,
Received 09 Sep 2019, Revised 13 Nov 2019, Accepted 23 Dec 2019
Abstract
The objective of the study is to model the surface water resources of the sub basin for effective water
allocation which is a key to sustainable water management. For this study Water Evaluation and Planning
(WEAP) model was used to model the current situation of water supply and demands and also to create
scenarios for future water demands and supply. All the required data by the model was collected from
different sources and the model was set up for a current account year and last year of scenarios based on
the available data. The result from the current situation of water demands among water users were
indicated that all demands were satisfied fully and the unmet demand under the base year was zero. Four
scenarios for future water demand were created to forecast the trends of future water demands. The results
of these scenario showed that the increment of water demands and unmet water demands from year to
year. In addition, one scenario was created for future water availability and the result showed that the
decrement of future water availability due to the impact of climate change. Finally, different options were
proposed to get balance of supply and demand.
Key words: Demand; Sub basin; Scenario; Water allocation; WEAP
1. Introduction
The processes of population increase, urbanization and industrialization has resulted in a rapid demand
increase for water resources in the developing world. Due to this reason, water managers in the river
basins of the developing world face the increasingly difficult task of allocating the limited water resources
among competing users. As a result, the difference between available water resources and water demands
is ever increasing [1-4]. In addition, insufficient knowledge of available water resources, lack of
coordination in water resources allocation and management, and drought episodes in the river basin often
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result in water deficits and the overexploitation of limited water resources which have hampered the
harmonious development and destroyed the ecological balance in the basin [5-6]. Moreover, many
emerging and developing countries lack adequate supply of water for different uses due to inefficient
infrastructural and allocation arrangements [7-8]. Above on, the climate change, higher living standards
and the agricultural sector have also resulted in increased demand of water causing supply variation that
increases the uncertainty of water allocations [9-11].
The above discussed problem leads to water resources scarcity which is one of the determinants which
restricts social and economic sustainable development in the sub-basin. It is therefore timely and crucial to
understand the balance between water demand and availability to formulate a tool for planning and
decision making in prioritization of water development projects and allocation options in the sub-basin so
that both socioeconomic and ecological objectives are sustainably attained. The objective of this study is
to model the surface water resources of the sub basin for effective water allocation by using Water
Evaluation and Planning (WEAP) model in order to attain sustainable social, economic and environmental
benefits.
2. Materials and methods
2.1. Materials Used
The materials and software used in this study were:
i) ArcGIS: for delineation of the study area, mapping and geo-referencing of the collected information and
to refine the area boundaries,
ii) WEAP: for the assessment of water resources, estimation of water demands and creation of scenarios,
iii) CROPWAT: to determine crop water requirement of the crops,
iv) PEST: for calibration and validation,
v) MS-Excel: for data processing
vi) GPS: to collect location of existing demand site.
2.2. Calculation Algorithm in WEAP
2.2.1. Demand Calculations
A demand site's (DS) demand for water is calculated as the sum of the demands for all the demand site's
bottom-level branches (Br). Annual water demand was then calculated as follows:
Annual Demand DS = (Total Activity Level Br x Water Use Rate Br)…………..…….. (2.1)
The total activity level for a bottom-level branch is the product of the activity levels in all branches from
the bottom branch back up to the demand site branch (where Br is the bottom-level branch, Br' is the
parent of Br, Br'' is the grandparent of Br, etc.). The total activity level was given as:
Total Activity Level Br = (Activity Level Br x Activity Level Br' x Activity Level Br'' x......) (2.2)
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Monthly demands were calculated based on each month‘s fraction specified as data under
demand\monthly variation of the adjusted annual demand as follows:
Monthly Demand DS,m = Monthly Variation Fraction DS,m * Adjusted Annual Demand DS
(2.3)[12].
2.2.2. Hydropower Calculations
Hydropower generation is computed from the flow passing through the turbine, based on the reservoir
release or run-of-river stream flow, and constrained by the turbine's maximum flow capacity. The amount
of water that flows through the turbine was calculated as: Release H = Downstream Out flow H The
volume of water that passes through the turbines is bounded by the maximum turbine.
Volume through turbine H = Min(Release H , Max turbine flow H)…………………………(2.4)
The gigajoules (GJ) of energy produced in a month, Energy full month:
GJ H = Volume through turbine H x Hydro generation factor H .................................................... (2.5)
Hydro generation factor H = 1000 (kg / m^3) * Drop elevation H x Plant factor H x Plant efficiency H *
9.806 / (1,000,000,000 J / GJ) ………………………………….……………. (2.6)
For reservoirs, the height that the water falls in the turbines is equal to the elevation at the beginning of the
month minus the tail water elevation.
Drop elevation H = Begin month elevation H – Tail water elevation H ……………… (2.7)[13].
2.2.3. Calculation Algorithm for Soil Moisture Method
The soil moisture method was used for this work for the assessment of surface water potential of the sub-
basin and the water balance of the sub-catchment was given as,
Rdj
= Pe(t) – PET(t)Kc, j(t)(
) – Pe(t)
– fjks,jz1
2,j – (1-fj)ks,jz1
2,j…………...(2.8)
Where,
z1, j = the relative storage given as a fraction of the total effective storage of the root zone,
Rdj (mm) = water balance components for land cover fraction, j,
Pe = the effective precipitation,
PET= the Penman-Montieth reference crop potential evapotranspiration,
kc,j = the crop/plant coefficient for each fractional land cover.
Pe(t)
= surface runoff,
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RRFj = the runoff resistance factor of the land cover,
fjks ,jz12,j = the interflow,
(1-fj)ks,jz12,j = the deep percolation,
ks,j = the root zone saturated conductivity,
fj = a partitioning coefficient related to soil, land cover type, and topography.
The total surface and interflow runoff, RT, from each sub-catchment at time t is,
RT(t) = ∑ )
+ fjks,j z12
,j )………….……………………………………….…(2.9)
Where,
RT(t) = the total surface and interflow runoff
Aj = the watershed unit's contributing area
Base flow emanating from the second bucket is computed as:
Smax
= (∑ ) ks,j z1
2,j) –ks2z2
2 …………………..……………………..…………(2.10)
Where,
Smax = the deep percolation from the upper storage,
Ks2= the saturated conductivity of the lower storage,
z2 = the relative storage given as a fraction of the total effective storage of the bottom bucket [13].
2.3. Methods of Data Collection and Collected Data
To acquire the required information needed to meet the objectives of the study, both primary and
secondary data collection techniques were employed for this study. Primary data collection technique such
as field observation of the study area and collection of UTM locations by using GPS were undertaken.
Secondary data collection technique such as document review of previous studies and other related books;
journals, articles, newspapers and magazines and from internet were undertaken. For this study data such
as hydrological data (stream flow data), meteorological data (rainfall, temperature, relative humidity,
sunshine hour, wind speed), DEM data, land use data, water supply data (population number, growth rate,
percapita water consumption), irrigation data (agricultural land area, agricultural monthly variation
demands, water requirements per hectare of the crops), hydropower data (storage capacity, initial storage,
volume elevation curve, net evaporation from the reservoir, reservoir zoning, maximum turbine flow, tail
water elevation, plant factor and generating efficiency) and instream flow requirement data were required.
This all data was collected from different places or agencies and is shown in the table 1 below.
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Table 1: Data type and their respective sources
Data Source
Hydrological Data Ministry of Water, Irrigation and Electricity of Ethiopia
(MWIE)
Meteorological Data National Metrological Service Agency (NMSA) of Ethiopia
and design document
Spatial Data Ministry of Water, Irrigation and Electricity of Ethiopia
Water
Demand Data
Domestic Central Statistical Agency of Ethiopia, Ministry of Water,
Irrigation and Electricity of Ethiopia
Irrigation Ministry of Water, Irrigation and Electricity of Ethiopia,
Oromia Irrigation Development Authority
Hydropower Ethiopian Electric Power Corporation, MWIE of Ethiopia
Environmental Ethiopian Electric Power Corporation (EEPCO)
Data for Scenario Creation MWIE of Ethiopia, RCM outputs of CORDEX-Africa
Data for Catchment
Simulation
Land Use MWIE of Ethiopia, FAO and design document
Climate NMSA of Ethiopia and design document
Data for Calculation of Crop Water
Requirement (CWR)
NMSA of Ethiopia, design document, FAO
2.4. Methods of Data Analysis
After the data was collected, an analysis of all the collected data was made. The acquired data were
checked for any outliers and missing values by using the following methods.
Filling missed stream flow data (Interpolation method) [14]
Filling missed rainfall and temperature data (linear regression method) [15]
Checking consistency of recording stations (Double mass curve)
Determination of aerial rainfall (Thiessen polygon method) [16]
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Bias correction (Power transformation and scaling approach) [17]
2.5. Modeling Process of WEAP
After all the required data were prepared as per input to WEAP model the following modeling process
/stepwise approach were followed to achieve the objective of the study.
2.5.1. Modeling Process for Water Demand
To model the water demands of domestic, agriculture, hydropower and environmental flow requirement
by using WEAP model the following steps/process were followed.
o Creation of geographic representation of the area
o Setting of general parameters
o Entering elements into the schematics
o Entering of data for demand sites/nodes
o Connecting demand with supply
o Creating return flow links
o Running of the model and getting the results
2.5.2. Modeling Process for Catchment Simulation
To enable assessment of the availability of surface water resources within a sub-basin using WEAP
model, simulation of natural hydrological processes such as precipitation, evapotranspiration, runoff and
infiltration is essential. These parameters can be simulated by using the WEAP catchment simulation
methods. The soil moisture method was used for this work to assess the surface water resources of the
sub-basin and the modeling process are described below.
o Creating of a new catchment node
o Selection of catchment method
o Entering of the data
o Running of the model and getting the results
2.5.3. Modeling Process for Scenario Creation
In WEAP, the typical scenario modeling effort consists of the following steps:-
a) Choosing current account year
The current account (base year) is the year for which good demand data are available and from which
future forecasts could be made. It is also the year with most current water use information is reliable and
complete data are available and acts as the start year for period of analysis [18]. Accordingly, the year
2015 was set as current accounts year and the year 2050 was set as the last year of scenarios for this study.
b) Establishing of the reference scenario and creating of what-if scenario
Scenarios are alternative sets of assumptions such as different operating policies, costs, and factors that
affect demand such as demand management strategies, alternative supply sources and hydrologic
assumptions, with changes in these data able to grow or decline at varying rates over the planning horizon
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of the study [19]. Accordingly, the created scenarios in this study depends on the assumptions that, what
will happen in the sub- basin on the future trends of water demands if the proposed master plan of Abay
basin of the Finchaa sub-basin will be implemented. The following scenarios were developed based on the
current situation and previously planned projects in the sub- basin which was along with the line of the
Abay basin integrated master plan.
1. Reference Scenario: Reference scenario (2016-2050) represents the changes that are likely to occur in
the future without intervention of new policy measures.
2. Scenario one: Population growth in medium (2016-2030) and long term (2031-2050) plan
This scenario considers the effect of population growth on the future water demands. Under this scenario,
both under long term and medium term plan only population growth is considered. All the other factors
which affect either water demand or water supply are assumed to be constant. Accordingly, two different
population growth rates were considered depending on the growth rate planned by the Abay basin
integrated development master plan. These are: 2.19% growth rate which will be implemented in the
medium term plan (2016-2030) and 1.63% growth rate which will be implemented in the long term plan
(2031-2050).
3. Scenario two: Water demands in medium term plan (2016-2030)
This scenario answer what if questions on sectorial water demands if some of the parameters which affect
water demand changes. Water demand for irrigation and domestic depends on parameters such as annual
activity level, annual water use rate, monthly variation and consumption. From these parameters under this
scenario change in annual activity level and water use rate were considered. Thus, this scenario will bring
answers to what happen to irrigation and domestic water demands in 2016-2030 as compared to the
reference scenario if some of the irrigation project will be increased and some of the irrigation project will
be implemented and if the consumption rate will be increased.
4. Scenario three: Water demands in long term plan (2031-2050)
This scenario answers what if questions on water demands if some of the parameters which affect water
demands change in the long term plan. Under this scenario change in annual activity level and water use
rate were considered. Thus, this scenario will bring answers to what happen to irrigation and domestic
water demands between 2031 and 2050 as compared to the base year and previous scenarios if some of the
irrigation project is increased and if the consumption rate will be increased.
5. Scenario four: Impact of climate change on the future water availability
Under this scenario the effects of climate change such as temperature and rainfall on the available water
resources in the medium and long term plan was considered. Among, the four Representative
Concentration Pathways (RCP) which were (RCP 2.6, RCP 4.5, RCP 6 and RCP 8.5) the RCPs 2.6, 4.5
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and 8.5 were selected to check the impacts of climate change on the future availability of water resources
under lower, medium and higher emission scenarios. Thus the considered scenario was: What happen to
the surface water resources of the sub-basin in the long and medium term plans if the impacts of climate
change will be considered?
c) Running of the model and getting of the results
When the result view of the WEAP was clicked the computation for the scenarios has been done and the
results of the model were read in the form of graph, table and schematic from the results view of the
WEAP.
2.6. Model Calibration, Validation and Performance Evaluation
The calibration procedure was undertaken using the PEST routine within the WEAP system.
PEST utilizes a nonlinear estimation technique; Gauss- Marquart Levenberg method, which saves time by
doing fewer model run [20]. Validation was done by applying the calibrated model using a different data
set out of the range of calibration without changing the parameter values. Observed and simulated stream
flow values were compared as in the calibration procedure. If the resultant fit is acceptable then the
model‘s prediction as valid. Finally the model performance was evaluated for both calibration and
validation using efficiency criteria. For the performance evaluation criteria‘s the Nash-Sutcliffe efficiency
(NSE) and Coefficient of determination (R2) were commonly used by different authors [21-22] and also
selected for this study.
3. Results and Discussion
3.1. Calibration and Validation
Before applying PEST for calibration the most sensitive parameters were selected by running the PEST
using the initial values of the parameters. This was followed by varying the value of sensitive parameters
within prescribed range and running the model. Changing the value of the sensitive parameters and re-
running of the PEST was continued until the observed stream flow was approached with the model
simulated stream flow and the value of the objective functions become within the acceptable range. After
a number of optimization trial the observed and simulated stream flow shows a good agreement as shown
in the figure below.
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Figure 1: Calibration and validation results of monthly observed and simulated flows
Finally the results of the model performance evaluation for both calibration and validation using R2 and
ENS were shown in the table 2 below.
Table 2: observed and simulated flow during calibration and validation
Evaluation criteria R2 ENS
calibration 0.828 0.6
validation 0.901 0.66
Observed Vs Simulated Stream flow
Jan
1996
Oct
1996
Aug
1997
Jun
1998
Apr
1999
Feb
2000
Dec
2000
Oct
2001
Aug
2002
Jun
2003
Apr
2004
Feb
2005
Dec
2005
Oct
2006
Aug
2007
Jun
2008
Apr
2009
Millio
n C
ub
ic M
ete
r140
130
120
110
100
90
80
70
60
50
40
30
20
10
0
Observed stream flow
Simulated stream flow
Observed Vs Simulated Streamflow
Jan
2010
Jun
2010
Nov
2010
Apr
2011
Sep
2011
Feb
2012
Jul
2012
Dec
2012
May
2013
Oct
2013
Mar
2014
Aug
2014
Jan
2015
Jun
2015
Nov
2015
Millio
n C
ub
ic M
ete
r
130
120
110
100
90
80
70
60
50
40
30
20
10
0
Observed stream flow
Simulated stream flow
Calibration
Validation
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3.2. Assessment of Surface Water Resources and Modeling of Water Demands for the Base Year (2015)
After running the WEAP model to find the surface water potential of the Finchaa sub-basin results such as
precipitation, Actual evapotranspiration, surface runoff, interflow and base flow were obtained. The
results of the assessed surface water potential of the sub-basin are shown in table 3 below.
Table 3: Surface water potential of the sub-basin for the base year (2015)
Branches Precipitation
Evapotran
spiration Interflow
Base
Flow
Surface
Runoff
Average monthly sum in
billion cubic meter (BCM)
45.36032 21.1867 2.75368 10.525
2
7.41489
Mean annual value(mm) 1656.8 777.209 101 386.1 272.0065
Current situation of water demand for the base year (2015) of the selected demand sites was modeled
before any scenario was developed in order to know the situation of these water demands in the base year
(2015) in the sub-basin. Accordingly the result of the situation of water demands in the base year for
domestic, irrigation, hydropower and environmental uses are shown in the Table 4 below.
Table 4: Total water demands in the base year (2015)
Demand types Domestic
Water
demand
Irrigation
Water demand
Hydropower
Water Demand
Instream Flow
Requirement
Sum
(MCM)
Total water
needed in
million cubic
meter (MCM)
0.30545 1.55852 21.40294 55.27017 78.53908
As shown in the above tables, the sub basin has mean annual surface runoff of 7.41BCM (272 mm) (table
3) and needs a total of 78.54MCM (table 4) of water for a selected demand sites. This indicates that when
the available surface water resources are compared to the water requirements, available potential is much
greater than the demanded water. Therefore, the surface water resource of the Finchaa sub- basin has more
than enough potential to meet the demanded water in the base year.
3.3. Scenario Analysis
3.3.1. Reference scenario
Reference scenario represents the changes that are likely to occur in the future without intervention or new
policy measures. The result of this scenario is portrayed in table 5 below for the selected demand sites.
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Table 5: Annual water demands under reference scenario
Water demands under reference
scenario (2016-2050)
Unmet water demands under reference
scenario (2016-2050)
Demand type Demand type
Do
mes
tic
(MC
M)
Irri
gat
ion
(MC
M)
Hy
dro
po
wer
(MC
M)
IFR
(M
CM
)
To
tal
(MC
M)
Do
mes
tic
(MC
M)
Irri
gat
ion
(MC
M)
Hy
dro
po
wer
(MC
M)
IFR
(M
CM
)
To
tal
(MC
M)
Su
m 10.69
1
54.5
48
754.7
2
1935.
651
2755.542 Su
m
3.6450
4
6.7145
05
304.920
8
389.74
69
705.02
72
As indicated in the table 5 above, a total annual water demand of 2755.61MCM is required by the demand
sites between 2016 and 2050. In addition, as indicated in the Table 5 above, a total unmet water demand
of 705.0272MCM will be occurred between 2016 and 2050 if no policy change is considered. This means
if all the factors which affect irrigation, domestic, hydropower and instream water demand is assumed to
be constant and no policy change will be occur between the years 2016 and 2050, from the total of water
required for irrigation, domestic, hydropower and instream flow 74.41% is fully met.
3.3.2. Scenario One: Consideration of Population Growth
a. Scenario One in the Medium Term Plan (2016-2030)
If the population in the sub-basin will grow with a growth rate of 2.19% in the years between 2016 and
2030, the results of the changes that will observed on the domestic water demand and unmet water
demand are shown below in the form of graph. The result of annual water demand and unmet water
demand for domestic sector in which the population will grow with a growth rate of 2.19% in the medium
term plan (2016-2030) is portrayed in the Figure 2 and 3 below.
As shown from the Figures 2 and 3 above, both water demand and unmet demand for domestic is
increased from year to year. This is because domestic water demand is directly proportional to the
controlling factor under this scenario which is the population. The simulation result of the Figure 3 above
shows that, with population growth rate of 2.19%, Finchaa sub- basin will face water deficits in 2016,
which is only one year later than the base year. This means the sub-basin is currently under water deficit.
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Figure 2: Annual domestic water demand in the medium term plan of scenario one
Figure 3: Annual unmet domestic water demand in the medium term of scenario one
Finally, a total of 1181.81774MCM of water will required for domestic, irrigation, instream and
hydropower purpose between the years 2016 and 2030 if the population of the sub-basin will grow by
2.19% growth rate. More over a total of 304.250205MCM unmet water demand will be occurred between
the years 2016 and 2030 if the population of the sub-basin will grow by 2.19% growth rate. This means
from the total water needed for the considered demand sites, 25.77% is not met with in 15 years (2016-
2030) if the population will grow with a growth rate of 2.19%.
b. Scenario One in the Long Term Plan (2031-2050)
If the population in the sub-basin will grow with a growth rate of 1.63% the result of the change that will
be observed on a domestic water demand and unmet water demand is shown in Figures 4and 5 below.
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Figure 4: Annual domestic water demand in the long term plan of scenario one
Figure 5: Annual domestic unmet water demand in the long term plan of scenario one
As can be portrayed from the result of the Figures 4 and 5, water demand and unmet water demand for
domestic is increased from year to year due to the increment of population. This implies that population
growth rate has significant impact on the demand of water in a long-term perspective and reflects the need
to develop new technologies, new cooperation, or better water management plans to offset this anticipated
shortfall.
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3.3.3. Scenario Two: Increment of Irrigated Area and Consumption Rate in Medium-Term Plan (2016-
2030)
Figure 6 below indicates the result of annual water demands for irrigation and domestic in scenario two.
Figure 6: Annual water demand for domestic and irrigation in scenario two
As depicted from the result of the figure 6 above, increase in the water demands are observed as compared
to the other scenarios and the base year. Water demand for irrigation and domestic in the years (2016-
2030) is 33.39MCM which was increased by 31.53MCM from the base year (1.86MCM) and increased by
4.54MCM from medium term plan of scenario one (28.85MCM). This increment is caused by addition of
new project, expansion of the existing project and increment in water use rate. Finally, a total of
1186.36MCM water is required by the selected demand sites under this scenario if the scenario is
implemented according to the plan. Similarly, in relation to the base year the annual total unmet water
demand for irrigation and domestic sector is increased from 0MCM in base year to 22.13MCM in scenario
two (2016-2030).
3.3.4. Scenario Three: Expansion of Irrigated Area and Increment of Consumption Rate in Long-
Term Plan (2031-2050)
Figure 7 and 8 below portrays the results of the annual water demand and unmet water demand for
irrigation and domestic for the sub-basin in scenario three.
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Figure 7: Annual water demand for irrigation and domestic in scenario three
Figure 8: Annual unmet water demand for domestic and irrigation in scenario three
As indicated in the figure 7 above, increment in water demands are occur from 2031 to 2050. Water
demands are increased by, 26.88MCM from scenario two (33.39MCM) to scenario three (60.27MCM)
and increased by 58.41MCM from the base year (1.86MCM) to scenario three (60.27MCM). Similarly, as
shown in the Figure 8 above, starting from 2032 unmet water demand for irrigation and domestic sectors
are observed. This is due to the expansion of the existing one and increasing of the consumption rate
which cause the competition for water among sectors.
3.3.5. Scenario Four: Impact of Climate Change on the Future Water Availability
The result of the sum of average monthly water balance components under the three Representative
Concentration Pathways (RCPs) (RCP2.6, RCP4.5 and RCP8.5) is shown in the table 6 below.
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Table 6: Total average monthly water balance components under the three RCPs
RCPs
Branches in billion cubic meter (BCM)
Precipitation Evapotranspiration Base Flow Interflow Surface Runoff
RCP2.6 46.86023 27.0526 9.937741 2.604413 7.157481827
RCP4.5 46.67761 28.1154 9.34871 2.444267 6.74
RCP8.5 46.61448 28.5845 9.311186 2.436061 6.55963
As indicated in the table 6 above, the mean annual surface runoff that leaves from the sub-basin is
7.16BCM, 6.74BCM and 6.56BCM under the RCP2.6, RCP4.5 and RCP8.5 emission scenarios,
respectively. The decline in runoff will be relatively higher for RCP8.5 as compared to the other RCPs and
the base period because this is a representative concentration with more emissions and hence a higher
value of radiative energy. The results of the above table also shows that the decrement of the available
surface water resources under all the RCPs as compared to the base period. Accordingly, surface water
resources are reduced from 7.41BCM to 7.16BCM (3.37%) under RCP2.6, to 6.74BCM (9%) under
RCP4.5 and to 6.56BCM (11.41%) under RCP8.5 emission scenarios.
3.4. Proposing Options to Resolve Supply and Demand Imbalances
The results of this study shows that, the total water resource has enough potential to fulfill current and
future water demands among multiple water users and no unmet demands were encountered for a current
account year and for future water demands if the available water resource is used properly. The result also
indicates that there is scarcity of supply in all scenarios and unmet water demand were observed. This
shows that the existence of either supply delivery problem to a particular demand site or lack of enough
storage structures rather than the water availability in the sub-basin. Thus, in order to solve the problem of
supply and demand imbalance and to improve the water security system by balancing demand and supply
water allocation options such as building of new hydraulic structure and rehabilitation of the existing
structure, use of ponds and tanks, increasing the number of small reservoirs, use of on-farm options and
enhancing of water transmission networks were proposed.
4. Conclusion
In this research surface water resources of the Finchaa sub-basin was modeled in order to balance the
available supply with the demand in a sustainable manner for social, economic and environmental
benefits. The Water evaluation and planning (WEAP) model was successfully used to model the surface
water resources of the sub-basin for optimum water allocation. In order to model the surface water
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resources of the sub-basin, assessment of the available surface water potential is essential and this was
carried out by using the soil moisture method of WEAP. Accordingly, the results showed that the sub-
basin has a surface water potential of 7.41BCM which is much greater than the water needed
(78.54MCM) for the base year. In addition, water demand and supply scenarios were created to forecast
the future trends of water demand and availability. For future water demands, the result indicated that
there is an increment of water demands and unmet water demands from year to year. But for future water
availability, the result indicated that the decrement of surface water resources from year to year. Finally,
this study proposed different water allocation options in order to resolve the supply and demand
imbalances in the sub-basin.
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