Updating and Assessment of the Eastern Nile Model in ...Riverware tools with the ENM, Wheeler's version of the ENM need to be updated. Consequently, this paper aims to update the historical

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 8 (2019) pp. 1772-1781

© Research India Publications. http://www.ripublication.com

1772

Updating and Assessment of the Eastern Nile Model in RiverWare

for Reservoir Management

Asmaa M. Kamel1,2,*, Doaa Amin1 and Mohamed M. Nour Eldin2

1Water Resources Research Institute (WRRI), National Water Research Center (NWRC), Ministry of Water Resources and Irrigation (MWRI), Post Code:13621, Cairo, Egypt.

2Irrigation and Hydraulic Department, Faculty of Engineering, Ain Shams University, Post address:11517, Cairo, Egypt.

*Correspondence Authors

Abstract

Understanding and modelling the complex nature of river

systems are essential for efficient use of the water resources.

The Nile River, a transboundary river shared between 11

countries with high potential conflicts, was modelled to play an

important role in solving conflicts by means of understanding

problems and cooperation options. Many water allocation and

operation models are used to study and produce some scenarios

to support the cooperation among the basin countries.

RiverWare software is one of the most recently used models for

studying the reservoirs operation management. Where the

developed water allocation model called Eastern Nile Model

(ENM) using RiverWare software, has proved its worth

application on the ENM. The effective use of Riverware tools

in ENM requires the updating of hydrological conditions, but

the hydrological data in ENM ceased in 2002, casting doubt on

the possibility of using it for recent periods. In this study, the

hydrological conditions in the model were updated from 2003

to 2014 with a simulated flow data, which were taken from the

output of rainfall-runoff distributed model (Nile Forecast

System (NFS)), the calibrated NFS simulation period. The

methodology of the updating data could be applied again, when

the simulated NFS data will be available. The ENM evaluation

was performed by comparing the simulated with the observed

outflows at the locations of Diem, Khartoum and Dongola

stations using statistical criteria. All metrics were considered

very good or good. As a result, the ENM is ready for

developing reservoirs operational policies with several

alternatives and priorities for filling and the operation stages of

any planned reservoir. Moreover, the ENM is able to quantify

and evaluate the impacts of future development in upstream

countries in the Nile basin.

Keywords: River system modelling; Evaluation; Eastern Nile;

Transboundary Rivers; Riverware; Reservoir simulation

1. INTRODUCTION

Modelling river system is the most important unit for planning,

developing, and managing water resources. In addition,

reservoir system and its operation policy is a primary element

in these models. Seeking optimal operation of reservoir

systems was the target of many researchers. The Eastern Nile

Basin is politically significant, being a transboundary basin

shared by four countries, and covers more than one half of the

Nile Basin [1]. Thus, there are many models that have been

developed in order to investigate the best management for the

Nile system.

River Basin Simulation Model (RIBASIM) river basin

simulation model is one of the most used models in the Nile

river management. RIBASIM was used to quantify the

trade-off between hydropower generation from Tekeze dam,

downstream environmental flow, and new irrigation

development in the western part of Ethiopia [2]. It was used

also to simulate the existing system and management

conditions of the Eastern Nile Basin [1],[3]. Then, Sief ElDin

[4] used RIBASIM to study four scenarios of the Ethiopian

development projects and estimate their impacts on Egypt.

RIBASIM has advanced flexible features in operating goals for

several different types of demand (hydro-power, irrigation,

etc.) and the option to manage the system with priority to

different demands.

Hec-ResSim (Hydrologic Engineering Center - Reservoir

System Simulation) Model is one of such models used to

simulate the water system in the Eastern Nile Basin.

Wondimagegnehu and Tadele [5] used the Hec-ResSim, to

simulate current and future inflows and hydropower generation

on the operation of the Blue Nile cascade of dams, Beko Abo,

Mandaya and Border. The model is used for the entire Eastern

Nile Basin for Abbay–Blue Nile, Tekeze-Setit-Atbara,

Baro-Akobo-Sobat and Main Nile Sub basins [6].

The Nile Basin Initiative (NBI) developed a new decision

support system that improved the regional modeling system.

The Nile Basin Decision Support System (NB-DSS)

encompasses three components: information system, analytical

part containing simulation and optimization tools, and multi

criteria analysis tool [7]. Firstly, Gharib [8] used NB-DSS to

simulate the impacts of alternative development scenarios and

to assess the benefits and trade-offs. Secondly, Hamid [7] used

NB-DSS to assess the impacts of planned Ethiopian dams on

the Sudanese reservoir system up to Khartoum. In addition,

Sileet et al. [9] used NB-DSS to assess the positive and

negative impacts of the planned water projects in the Blue Nile

sub-basin on both national and regional levels, on the Blue Nile

Sub-basin and downstream countries.

Meanwhile, Bahaa [10] used the GERD-HAD Simulation

(GERD-HAD SIM) model, which is a recently developed



1773

Modelling tool to investigate the downstream impacts of the

GERD on HAD and determine the best long-term operating

policy for the GERD that cause minimum impact on HAD.

Apart from the previous river basin models, the Center for

Advanced Decision Support for Water and Environmental

Systems (CADSWES) at the University of Colorado in

Boulder developed a river basin/system modelling tool called

RiverWare, which is an ideal platform for operational

decision-making, responsive forecasting, operational policy

evaluation, system optimization, water accounting, water rights

administration, and long-term resource planning. The flexible

representation of RiverWare allows changing policy objectives

and facilitates clear understanding and communication of

policies to decision makers and stakeholders [11].

RiverWare allows many agencies to improve the water

resources management. For Example, The Tennessee Valley

Authority (TVA) replaced site-specific simulation models by

RiverWare models for daily scheduling of reservoir releases.

Then, TVA began using RiverWare’s optimization solution for

scheduling daily turbine releases. In the meantime, the U.S.

Bureau of Reclamation (USBR) used Riverware models

instead of a suite of models for management of the Colorado

River. The USBR's Riverware models manage 348 reservoirs

and 58 hydroelectric plants in the Western U.S., with total

storage of 245 million acre-feet and 14,300 MW of hydro

generation capacity [12]. An interagency team including the

U.S. Army Corps of Engineers (USACE), USBR, and the U.S.

Geological Survey (USGS) has applied a rule based simulation

and water accounting in RiverWare to a daily time step Upper

Rio Grande Water Operation Model (URGWOM). For the San

Juan River Basin, located in Arizona, Colorado, and New

Mexico, an operation model was developed with efforts from

the USBR and the USGS. The model was used by operating

policies to meet water supply demands, flood control, target

storages, and the filling criteria in its reservoirs. In 2017,

Basheer and Elagib [13] demonstrated the concept of

Water-Energy Productivity (WEP), defined as the amount of

energy produced per unit of water lost in the process, by

developing a water allocation model of the White Nile in

Sudan, including Jebel Aulia Dam (JAD), using RiverWare.

This study was done to illustrate the relationship between

energy generation and water losses by examining the

sensitivity of the Water-Energy Nexus (WEN) to changing

dam operation policy. Afterwards, the impacts of cooperation

in the Blue Nile basin, as a transboundary river basin, on the

Water-Energy-Food nexus (WEF nexus) were quantified and

evaluated using a daily RiverWare model [14]. Recently,

Abudu et al. [15] applied RiverWare modeling approach to

evaluate the management decisions on surface water and

groundwater diversions in the agricultural watershed of the

Urumqi River Basin of Xinjiang in Northwestern China.

Awaad et al. [16] investigated the optimum operation policy

for the GERD by minimizing the differences between the

generated energy and the firm energy and projecting of this

operation policy on the flow at El Diem Gage. This study was

done using RiverWare model with around one thousand

hydro-climatological traces. The variety of physical and policy

situations in these applications demonstrates RiverWare’s

ability to represent site-specific conditions with a variety of

modelling needs [11].

Although several applications were done using ENM on

RiverWare software, (e.g. [13], [14], [17], and [18]), there is a

lake of the hydrological data, which describe the recent period,

where the available data in ENM is until 2002. This shortage of

the available data may question the ability of the model to

simulate the recent period. Therefore, to get benefit of the

Riverware tools with the ENM, Wheeler's version of the ENM

need to be updated. Consequently, this paper aims to update the

historical hydrological conditions in the ENM until 2014, and

then evaluate the model using statistical criteria.

2. STUDY AREA AND DATA USED

2.1. The Nile River

The Nile River is the longest river in the world stretching

nearly 6,700 km, covering more than 35° of latitude [18]. The

Nile River basin is covering an area of approximately

3,762,000 km2 [20]. The climate is mainly tropical in the

upstream parts of the basin and arid and semi-arid in the

downstream parts. The elevation varies from less than 20 to

2,150 m a.m.s.l (meters above the mean sea level). Even though

the Nile is the longest river, the flow volume is not large

compared to major rivers. The total Nile basin water resources

comprise only 84 BCM/year of runoff as measured at Aswan

High Dam [21], [22]. The mean annual rainfall varies from

1,200 mm in the upstream parts to less than 10 mm in the

downstream parts [20].

The Nile basin includes two main regions; the Eastern Nile and

the Equatorial Lakes (Figure 1). The Eastern Nile region stems

from the Ethiopian plateau and contributes with more than 85%

of the total runoff; it includes the sub-basins of the Sobat River,

the Blue Nile, and Atbara River. The Equatorial Lakes region

starts from the Great Lakes of central Africa (Victoria, Albert,

Kyoga, Edward); it is the main source of the White Nile that

expands till the beginning of the huge swamp area (Sudd

region).

2.2. The Blue Nile Basin

The Blue Nile originates from Lake Tana in Ethiopia at an

elevation of 1,780 m a.m.s.l and flows to Sudan through a steep

slope. It has a total catchment area of about 314,000 km2. The

river continues running northward until it joins the White Nile

in the Sudanese capital, Khartoum (Figure 1). The Blue Nile

Basin constitutes only around 10% of the entire Nile Basin

area, however, contributes about 60% of its total mean annual

flow measured at the High Aswan Dam [23], [24] and [25].

Thus, runoff variability in upstream countries is of great

importance to the sustainable development of downstream

countries such as Sudan and Egypt.



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Figure 1. The Nile River basin and main tributaries [26]

2.3. Data Used

The historical flow data was collected from the Nile Basin

Volumes for about five hydrological stations in the Nile Basin.

Table 1 shows the period of the collected data and the

percentage of the filling data. The collected data are monthly

data. These data will used to evaluate the performance of the

ENM after the updating the model by the recent available

simulated data (2003-2014).

Table 1. The collected flow data

Station Start day End day Percentage of

available records

Diem 1/1/2003 31/12/2014 ≈ 100%

Khartoum 1/1/2003 31/12/2014 100%

Atbara 1/1/2003 31/12/2014 100%

Malakal 1/1/2003 30/11/2013 100%

Dongala 1/1/2003 31/12/2014 ≈ 100%

3. METHODOLOGY

3.1. Nile Basin Model Schematic

One of the most important RiverWare applications is the ENM.

This model was developed to contain all the major features in

the Nile Basin that significantly affect water management and

distribution [17]. Then, the recently raised Rosaries Dam, the

newly developed Upper Atbara and Setit Dam complex, and

the GERD were included in simulations of future conditions.

(Figure 2) represent the ENM schematic as developed in the

RiverWare software. A full description of the model Schematic

was illustrated by Wheeler and Setzer [18]. The ENM was

calibrated during the period (1951-1970) and validated during

the period (1971-1990) [17].

Figure 2. Eastern Nile schematic as developed in the

RiverWare software

3.2. Updating the developed Eastern Nile Model in RiverWare Software

The ENM schematic contains 162 control points; each control

point represents the flow from small tributary catchment. The

control point flow is the main input for the model, and it can be

fed by observed, simulated or forecasted information. The

available flow data in the model database is made of monthly

time series from 1900 till the end of 2002. In order to update the

ENM, the control flow points in the model will be updated

from 2003 to 2014 by simulated flow data produced from Nile

Forecast System (NFS).

The NFS is a hydro-meteorological distributed model, which

simulate the Nile flow till the Dongala station (the end point

before flow entering Lake Nasser). The main inputs of NFS

are: the rainfall which estimated from the satellite images and

merged with observed rain gauge data, and the observed mean

monthly evapotranspiration. The main hydrological models in

NFS are; water balance, hill-slope and river routing models,

more details about NFS described in [27] and [28]. The NFS

hydrological models is calibrated and validated several times,

the last calibration was done in 2012 [29]. The calibrated



1775

simulated flow was very close to observed one, especially in

the eastern Nile, where the values of comparison criteria; the

Nash Sutcliffe efficiency (NSE), Root mean square error

(RMSE) and the Mean Absolute Error (MAE) were 0.92, 1.38

BCM and 0.77 BCM respectively [29].

The shortage of the observations in the Nile catchment leads to

use a simulated data in order to update the ENM. The simulated

data were taken from the NFS output. The main control points

on the Nile stream are located in the NFS schematic that run the

hydrological models and introduce simulation flows at each

feeding control point.

The statistical relations between the control points and the main

points or the confluence at the stream, which feed by NFS

output, are used to obtain the control points contributed to the

flow at confluences in the model. This process was done by

computing the contribution percentage for each control point

upstream the confluence as averages from the historical flows

(1900 - 2002). For example, the four control points (1219,

1234, 222 and 228) contributed to the flow at confluence 9 in

the Blue Nile in addition to the flow from upstream. Figure 3

presents an example of the control points contributed to the

flow at confluence 9 and Table 2 shows the contributions of

each control point to the flow at confluence 9.

Figure 3. Example of the control points contributed to the flow at confluence 9

Table 2. The control points contribution

to the flow at confluence 9

Confluence Control point Contribution

percentage (%)

Blue Nile

Confluence 9

1219 0.965

1234 1.278

222 4.481

228 1.092

Then, the control points data in the ENM in RiverWare were

updated for the period (2003-2014). Figure 4 shows example of

some updated control points slots.

Upstream

Inflow



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Figure 4. Example of the updated control points slots

3.3. Evaluation the Eastern Nile Model Schematic

The model control points data was updated with the simulated

flow data for the period (2003-2014), and then the model was

evaluated. The observed flow data for the key locations (Diem,

Khartoum, and Dongala) were used to evaluate the model

performance during the period (2003-2014). The evaluation

was performed by simulating the historical conditions in the

calibrated model including, channel diversions, dam operations

and same pattern of the water user diversion request, as well as

simulating the hydrologic flows. Actual reservoir pool

elevations and historical dam operation policies, built in the

calibrated model, were used to drive the simulation. Three

statistical criteria; Root mean square error (RMSE), Standard

Deviation Ratio of observations (RSR), the Nash Sutcliffe

efficiency (NSE), and the percentage of the BIAS are used for

the comparison.

𝑅𝑆𝑅 =𝑅𝑀𝑆𝐸

𝑆𝑇𝐷𝐹𝑉𝑜𝑏𝑠=

√∑ (𝑍𝑠 − 𝑍𝑜)2𝑛𝑖=1

√∑ (𝑍𝑜 − �̅�𝑜)2𝑛𝑖=1

(1)

NSE = 1 − [∑ (Zo − Zs)2n

i=1

∑ (Zo − Z̅o)2ni=1

] (2)

PBIAS = ∑ (Zs − Zo)n

i=1

∑ (Zo)ni=1

× 100 (3)

Where Zs is the simulated value for the constituent being

evaluated, Zo is the observation for the constituent being

evaluated, Z̅o is the mean of observed data for the constituent

being, and n is the total number of observations.

For RSR, it ranges between 0, which is the optimal value, to a

large positive value. It means that the lower value of RSR

means lower value of RMSE, and the better the model

simulation. NSE varies from 0 to 1 and value of 1 corresponds

to a perfect match of the model simulation outputs to the

observation. The optimal value of PBIAS is 0.0, positive values

indicating overestimation bias, and negative values indicating

underestimation bias. In general, the model simulation can be

judged as ‘‘satisfactory’’ if NSC > 0.50, RSR < 0.70, and

PBIAS < ±25% for the Streamflow [29].

4. RESULTS AND DISCUSSION

The evaluation was performed by comparing the simulated

outflows with the observed outflows at the effective locations

of Diem, Khartoum and Dongola stations respectively, shown

in Figure 5.

Figure 5 (a, b, and c) show the match between the Riverware

simulated flow and the observed data; as it possible to notice, it

does not catch the peak for some flood seasons. The

mismatching in the peaks may be return to the flash floods

which occurs in most of this period in the west of Khartoum

station and affect the main stream directly. These amounts of

water affect the scale of Khartoum station on the Blue Nile and

then the scale of Dongala station. There is a small deviation



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between the simulated flow and the observed at Khartoum

station in the recession period, where the observed data could

have some error in the recession period due to the rating curve

equation. The worst result in Dongala station simulation is the

flood season 2009, the beginning of the Merowe dam

operation. This could be due to the different operation rules in

the first two years of the Merowe dam operation. In general, the

model updating depends on a simulated data (from NFS

simulated flow), which is not error free. however, the error

could be accepted in the absence of observed data. In addition,

the main objective from these kind of models is producing

scenarios to support the decision makers with scientific

information for planning and management, which encouraged

to accept this error.

(a)

(b)

(c)

Figure 5. The comparison of the RiverWare simulation flow and observed flow data at (a) Diem station;

(b) Khartoum station; (c) Dongola station

0

5,000

10,000

15,000

20,000

25,000

30,000

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Mo

nth

ly f

low

(M

CM

/mo

nth

)

Simulated

Observed

0

5,000

10,000

15,000

20,000

25,000

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014M

on

thly

flo

w (

MC

M/m

on

th) Simulated

Observed

0

5,000

10,000

15,000

20,000

25,000

30,000

2003

2004

2005

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2008

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2010

2011

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2014

Simulated

Observed

Mo

nth

ly f

low

(MC

M/m

on

th)



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The evaluation criteria were performed over the period

(2003-2014). Table 3 shows the evaluation results respectively

of the NSE, BIAS % and RSR at three locations; Diem,

Khartoum and Dongala. All metrics were accepted according

to published criteria [29]. The NSE for all stations is > 0.75,

where they are categorized as very good. The RSR values for

all stations < 0.5, where confirmed the category of the accepted

values. The Bias (%) is very small, however the maximum

values (-12.02 %) still within the acceptance range.

Table 3. Evaluation criteria for the simulated flow

compared to the observed

Location

Evaluation period

(2003-2014)

NSE BIAS

(%) RSR

Blue Nile at Diem 0.89 -8.94 0.33

Blue Nile at Khartoum 0.86 -12.02 0.37

Main Nile at Dongola 0.83 -1.51 0.41

Note. NSE = Nash-Sutcliffe efficiency; BIAS (%) = Percent

bias; RSR = RMSE/observations standard deviation ratio.

The evaluation criteria were formed also for the HAD reservoir

to check the calibration for the reservoir routing by comparing

the HAD observed level with simulated level at first day of

each month during the period (1985 – 2014), see Table 4),

which represents the values of the evaluation criteria for the

HAD reservoir. The results of NSE and the RSR is accepted

and categorized as good results and the Bias (%) is very small.

Table 4. Evaluation criteria for the simulated HAD

upstream level compared to the observed

HAD Upstream Level NSE BIAS (%) RSR

0.69 -1.34 0.55

Figure 6 (a, b, and c) show the simulated mean monthly flow

compared with the observed mean monthly flow at the

locations of Diem, Khartoum and Dongola respectively during

the evaluated period (2003-2014). The simulated flow for the

three locations is closed to the observed flow. There is a

slightly overestimation in the flood limb from May till June in

Khartoum, where it is underestimation in Dongola. For the

three locations, the simulated flow doesn't catch the flood peak

values accurately, but the error according to the statistical

criteria could be accepted. It is obvious from the results of the

statistical criteria that the BIAS (%) at Dongola gauge is lower

than its value at Diem gauge, but NSE at Dongola gauge is

lower than NSE at Diem gauge and this is due to the instability

of the operation policies of Merowe dam.

(a)

(b)

Figure 6. Mean monthly simulated and observed flows at (a) Diem station; (b) Khartoum station; (c) Dongola station

0

5,000

10,000

15,000

20,000

Jan

Feb

Mar

Ap

r

May Jun

Jul

Au

g

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Oct

No

v

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c

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an m

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thly

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w

(MC

M/m

on

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SimulatedObserved

0

5,000

10,000

15,000

20,000

Jan

Feb M…

Ap

r

M…

Jun

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(MC

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Jan

Feb

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r

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Oct

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v

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c

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an m

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M/m

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Simulated

Observed



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Figure 7: The simulation of the monthly first level compared with the observed during the period (1985-2014)

Figure 7, shows the simulated of the monthly first day

compared with observed monthly first day during the period

(1985-2014). The simulation is overestimated during the

periods (1985-1998) and (2010-2014), and slight

underestimated from 2005 till 2010, however they are too close

in the period from 1998 till 2005.

5. CONCLUSIONS

This paper succeeded to update the Nile Schematic on

RiverWare software until 2014 after Wheeler et al. [17] fed the

model with data until 2002, while the updating methodology

could be applying at any time the data will be available

The model evaluation was performed by simulating the

historical conditions including, channel diversions, dam

operations and same pattern of the water user diversion request,

as well as simulating the hydrologic flows for the updated data

(2003-2014).

The NSE, BIAS % and RSR at the effective locations of Diem,

Khartoum and Dongola stations were considered as the

statistical criteria for the evaluation process. All metrics were

considered very good or good.

Finally, this study is able to define a well-designed operation-

policy modelling framework that provide accuracy,

transparency, and flexibility to develop and test innovative

solutions and explore several alternatives and solution.

scenarios for the negotiations.

More significant researches are coming up using the output

from this paper to investigate innovative solutions considering

the GERD and other future development projects namely

“Mendaia, Beko Abo and KaraDodi dams”.

Author Contributions: This paper is a part from a PhD

research carried out by A.M.K. under supervision of D.A. and

M.M.N.E. The methodology was proposed by A.M.K and

approved by the supervisors. A.M.K conducted data

preparation, model setup, results analysis and writing of the

first draft of the paper. D.A. contributed in the concept idea of

the paper, the results analysis, the writing and the editing of the

paper. D.A and M.M.N.E. reviewed the paper.

Funding: This research is going under the umbrella of the

Simulation of Eastern Nile Upstream Developments and their

Impacts on the Inflow to Lake Nasser Project, Funded by

Academy of Scientific Research and Technology (ASRT)

Acknowledgments: The author would like to express her

gratitude to the Supervision Committee, who helped with their

advices and assistance. In addition, the successful completion

of the activities of this paper would not have been possible

without the hard work and co-operation of the research team of

the Nile Basin Department at the Water Resources Research

Institute (WRRI), who helped out in achieving the objectives of

this research. Special gratitude goes to the Nile Forecast Center

for supporting with the NFS usage.

Conflicts of Interest: The author declares no conflicts of

interest.

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Documents

Updating and Assessment of the Eastern Nile Model in ...Riverware tools with the ENM, Wheeler's version of the ENM need to be updated. Consequently, this paper aims to update the historical