Modeling Surface Water Resources for Effective Water

Jaleta & al./ Appl. J. Envir. Eng. Sci. 5 N°4(2019) 402-419

402

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

mailto:[email protected]


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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


408

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.


417

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.

References

1. D. K. Weragala, Neelanga. Water Allocation Challenges in Rural River Basins: A Case Study from the Walawe

River Basin, Sri Lanka. Ph.D. thesis, Civil and Environmental Engineering, Utah State University Logan, Utah.

(2010).

2. A.D.M. Thou, Community and Social Responses to Land Use Transformations in the Nairobi Rural-Urban

Fringe, Kenya. (accessed on 11 July 2014).

3. M. Williams, Forest and tree cover, in Meyer WB, turner BL II, eds. Changes in Land use and Land Cover: A

global perspective. Cambridge University press, (2010) 97-124.

4. K. Arms, Environmental science. Austin, Texas: Holt, Rinehart and Winston. (2008).

5. D. Myronidis, D. Stathis, K. Ioannou, D. Fotakis, Water Resour. Manag. 26 (2012) 4587–4605.

6. M. Falkenmark, D. Molden, Int. J. Water Resour. Dev. 24 (2008)201-215.

7. B. K. Mishra, S. Herath, D. S. Sampath, K. Fukushi, and K. Fukushi, NEAJ Newsletter, 1(1)(2015)1-10.

8. M.C. Roa-García, Equity, Water Alternatives, 7(2) (2014) 298-319.

9. Anisfeld, S. Water resources (Foundations of Contemporary Environmental Studies Series) (1 ed.). (J. Speth, Ed.)

, Washington: Island Press, (2010).

10. P.H. Gleick, and M. Heberger, Water Conflict Chronology. In: The World‘s Water: The Biennial Report on

Freshwater Resources. Island Press/Center for Resource Economics, Washington, D.C., 8 (2014)173-219.

11. R. Speed, Y. Li, T. L., Quesne, G., Pegram, and Z. Zhiwei, Basin Water Allocation Planning: Principles,

procedures and approaches for basin allocation planning. Paris: UNESCO, (2013).

12. SEI (Stockholm Environment Institute). WEAP (Water Evaluation and Planning System): Guideline and Tutorial

of WEAP model, (2012).

13. Stockholm Environmental Institute SEI. WEAP User Guide, Studies, (2015).

14. Stockholm Environment Institute. WEAP Water Evaluation and Planning System User Guide. Stockholm

Environment Institute, U.S. Center Somerville, MA 02144 USA, (2011).

15. FAO. Water Resources, Development and Management Service Rome, Italy, (1990).

16. K.C. Patra, Hydrology and water resources engineering, India, (2002).

17. R. Leander, and T. A. Buishand, Resampling of regional climate model output for the simulation of extreme river

flows. Journal of Hydrology 332(2007) 487–496.


419

18. SEI (Stockholm Environment Institute) WEAP (Water Evaluation and Planning System): Guideline and Tutorial

of WEAP model. (2012).

19. D. Yates, J.Sieber, D. Purkey, A. Huberlee, Water International, 30(4) (2005) 487–500.

20. M. M. Osoro George, A. M. Khaldoon, Lars Ribbe. Water Demand Simulation Using WEAP 21: A Case Study

of the Mara River Basin, Kenya. Inter. J. of Natural Resource Ecology and Management, 3(1)(2018)9-18.

21. N.Reuben, Application of the WEAP Model in Integrated Water Resources Management of the Nyando River

Basin, Kenya. Jomo Kenyatta University, (2007).

22. P. Moriasi, J. G. Arnold, D. N. W. Gassman, K. C. Abbaspour, M. J. White, R. Srinivasan, C. Santhi, R. D.

Harmel, A. van Griensven, M. W. Van Liew, N. Kannan, M. K. Jha., Swat: Model Use, Calibration, and

Validation. Transactions of the ASABE, 55(4) (2012)1491-1508, American Soc. of Agr. and Bio.l Eng.ISSN

2151-0032

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