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© 2022 The Authors Journal of Water and Climate Change Vol 13 No 8, 3170 doi: 10.2166/wcc.2022.193

Flood inundation mapping under climate change scenarios in the Boyo watershed of
Southern Ethiopia

Muluneh Legesse Edamo a, Tigistu Yisihak Ukumo b, Tarun Kumar Lohani a, *, Kinfe Bereda Miranic
and Mesfin Amaru Ayele a
a
Faculty of Hydraulic and Water Resources Engineering, Water Technology Institute, Arba Minch University, Arba Minch, Ethiopia
b
Faculty of Water Resources and Irrigation Engineering, Water Technology Institute, Arba Minch University, Arba Minch, Ethiopia
c
Faculty of Water Supply and Environmental Engineering, Water Technology Institute, Arba Minch University, Arba Minch, Ethiopia
*Corresponding author. E-mail: tklohani@gmail.com

MLE, 0000-0001-6099-2599; TYU, 0000-0003-2897-2015; TKL, 0000-0003-4804-9711; MAA, 0000-0002-7123-722X

ABSTRACT

This research aims to map flood inundated areas under changing climate in the Boyo watershed of Southern Ethiopia. A semi-distributed
physically based Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) and Hydrologic Engineering Center-River Analysis
System (HEC-RAS) were used to simulate the flood events and maps, respectively, for climate scenarios. The bias-corrected data of four cli-
mate models were used for the baseline (1976–2005), mid-term (2041–2070) and long-term (2071–2100) cycles under RCP4.5 and RCP8.5
scenarios. The 50- and 100-year return period flood events were generated from the baseline and future period streamflow data. The
HEC-RAS model was used to simulate the inundation areas and depths from the flood events. The result exhibited that the average
annual rainfall and maximum and minimum temperatures of the catchment will increase in the future with an increase in annual runoff.
The severity of annual floods would increase in the future under RCP4.5 and RCP8.5 scenarios. Approximately, 193 ha of the study may
be flooded with flood events having a return period of 100 years under the RCP8.5 scenario in the long-term period, which is an extreme
case. The result is a benchmark to reduce the flood risk and management of floodplains in this watershed.

Key words: flash flood, modeling, natural flood management, river

HIGHLIGHTS

• The result is a benchmark to reduce the flood risk and management of floodplains in this watershed.
• This study will be helpful for cities, farming communities nearby the river bank, local community and government bodies concerned with
risk management.

INTRODUCTION
The most common natural danger in many parts of the world is floods (Netzel et al. 2021). Flood is causing loss of lives and
damaging properties throughout the world. It leads to more than 2.5-billion USD per year financial losses and over 1000
deaths per year in the world (Prastica et al. 2018). Floods can happen at any time of the year and are typically driven by
hydro-meteorological factors, such as intense rainfall, quick melting of a substantial snowpack, ice jams and the failure of
a natural or artificial dam. Globally, their frequency, size and expense are increasing, which could lead to increased flood
damage in the future. Climate change is ongoing and has significant effects on the hydrological cycle at the watershed
scale. The frequency and intensity of extreme occurrences, particularly floods, may change significantly as a result of the
altered climatic variables (Dobler et al. 2012).
The effects of climate change on floods have received less attention and are subjected to greater uncertainty (Shimada
2022). Additionally, the dominant focus of all studies has been on the watershed of industrialized countries (Shrestha &
Lohpaisankrit 2017). This suggests that the watersheds of developing nations located in sub-Saharan regions have got little
focus because they are obviously more vulnerable to flooding in areas with high levels of precipitation (Zhang et al.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and
redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
 Journal of Water and Climate Change Vol 13 No 8, 3171

2019). Under climate change scenarios, many regions of the world, including Ethiopia, are predicted to see a change in the
intra-annular variability in the amount of runoff that exists today (Robi et al. 2018).
Ethiopia is experiencing an increase in the frequency and severity of extreme weather occurrences due to climate change.
The Boyo watershed of Rift Valley Lake Basin is too prone to flooding and has destroyed 874 households submerging 432-ha
land of the cash crop in the recent past (Gebreselassie et al. 2018). The severity of impacts may further increase in the future
due to climate change (Kaito et al. 2000). Mapping the flood-prone areas plays a vital role in flood-risk management to miti-
gate any disaster that is expected to occur (Singh et al. 2021), manage and decrease the risk to property, environment and
people. Maps showing flood inundation are an excellent tool for disaster preparedness, management, communication,
response and mitigation. It has a great impact on flood-risk reduction (Tiwari et al. 2020).
Although many studies on water availability have been conducted in the Bilate catchment of Ethiopia, no studies were
attempted on the impact of climate change on extreme events in the Boyo watershed. Despite several floods in the Boyo
watershed, most research works have concentrated on how climate changes have affected water availability (Tekle 2015).
Wodaje et al. (2016) investigated the temporal and spatial variabilities of rainfall in the highlands of Bilate River. The out-
come indicated that rainfall and evapotranspiration are the major climatic variables that need due attention to conduct
successful rain-fed agriculture. The authors did not use any climate model to forecast rainfall variability in the catchment.
Tekle (2015) used the global circulation method (GCM) and regional climate model (RCM) with A2a and B2a as input to
the soil and water assessment tool (SWAT) model. The authors used a single GCM that makes the results questionable.
The use of GCMs’ and RCMs’ datasets for future climate prediction without bias correction is another significant element
that has significantly influenced the climate change impact research (Crochemore et al. 2016). Despite the fact that RCMs
execute nested dynamic downscaling to the outputs of the GCMs, the geographical resolution renders the data untrustworthy
for investigations of the impacts on watersheds, necessitating the bias correction (Shrestha & Lohpaisankrit 2017). Leander
& Buishand (2007) successfully used the power law transformation approach to correct bias from RCM outputs, and the
method has become appropriate for bias correction (Gunavathi 2021).
Hydrological reactions to climate change have been extensively studied using the scenarios published in the Special Report
on Emission Scenarios (SRES) in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change
(IPCC) (Kundzewicz et al. 2014). New scenarios built on a variety of technological advancements are included in the IPCC’s
Fifth Assessment Report (AR5), which was published in 2014. In order to promote the study on the effects and potential
policy responses to climate change, these new scenarios, known as representative concentration pathways (RCPs), are a
set of greenhouse gas concentrations and emission pathways were developed (Tenfie et al. 2022). Therefore, it is crucial to
conduct watershed-scale research on the impact of climate change on flood inundation using data from multiple climate
models and new emission scenarios.
This study focused on mapping of the current and future period flood in flood-prone areas in the Boyo catchment with four
climate models. The methods and outcomes of this research work are vibrant to planners, policymakers, river basin manage-
ment authority and the scientific community in relation to flood-risk mitigation.

MATERIALS AND METHODS

Description of the study area
The Boyo watershed is one of the sub-catchments of the Abaya-Chamo Lake basin, Ethiopia, which is situated in the south-
western part of the major catchments in the Ethiopia Rift Valley Lakes basin located between 37,0470 600 –80200 1400 E and
60330 1800 –8060 5700 N. It spreads through an area of 845.4 km2, where a wide floodplain dominates the downstream part of
the watershed. The altitude of the catchment ranges from 1300 m at Lake Abaya to 3050 m above the mean sea level at
Ambaricho Mountain (Sulamo et al. 2021), consisting of a diversified landscape (Figure 1).

Data collection
The flood mapping under the changing climatic conditions in the Boyo watershed using semi-distributed conceptual hydro-
logical and hydraulic models was incorporated using Hydrologic Engineering Center-Hydrologic Modeling System (HEC-
HMS) and Hydrologic Engineering Center-River Analysis System (HEC-RAS). The secondary data such as rainfall, tempera-
ture, relative humidity, wind speed and sunshine hours were collected from the National Meteorological Agency (NMA) of
Ethiopia. Streamflow data were obtained from the Ministry of Water, Irrigation and Energy (MoWIE) of Ethiopia. The
 Journal of Water and Climate Change Vol 13 No 8, 3172

Figure 1 | Description of the study area: (a) basins of Ethiopia, (b) Rift valley basin and (c) Boyo watershed.

primary data such as latitude, longitude and elevation were collected by field surveying at river cross-sections. A field survey
was conducted to obtain actual locations of the flood-prone areas in the river reach.
HEC-RAS requires geometric data and flow data to calculate the water surface profile. River system schematic is required to
define the schematic of the river system. The model also required the contraction and expansion loss coefficient. The contrac-
tion coefficient is in the range of 0.0–0.6, and the expansion coefficient is in the range of 0.0–0.8. The default values used were
0.1 for contraction and 0.3 for expansion. Cross-sections’ data along the river flow were collected using total station and its
accessories like reflector, tripod with rod and hand-held GPS on 3 December 2022. According to the field survey, about
4.8 km along the river reach of the study area was a flood-risk zone. By using the collected field data, 10.510.5 m resolution
of the terrain model was prepared. The terrain development procedures were explained well in Merwade et al. (2005), which
are also applied in this study. The photo of bed materials was captured during the field survey to determine the Manning’s
roughness coefficient.

Climate models and bias correction

The climate data were downloaded from the Earth System Grid Federation (ESGF) site of Coordinated Regional Climate
Downscaling Experiment (CORDEX) program archives (accessed from https://esgf-node.llnl.gov/projects/esgf-llnl/). Two
RCP scenarios such as RCP4.5 and RCP8.5 projections were selected. The regional CORDEX Africa data having spatial
grid resolutions of 0.440.44° (∼5050 km) were used. In this study, outputs from four climate models, namely UQAM-
CRCM5, CCCma-CanRCM4, ICHEC-RACMO22T and CNRM-RCA4, were used. Often, the downscaled data cannot be
directly used for impact assessment as the computed variables may differ systematically from the observed ones (Biniyam
& Kemal 2017; Karypidou et al. 2021). Errors in GCM simulations relative to historical observations are large in local
 Journal of Water and Climate Change Vol 13 No 8, 3173

settings. Therefore, bias correction was applied to compensate for any tendency of overestimation or underestimation of the
mean of downscaled climatic variables.
The climate models were selected based on the previous studies of the catchment and other watersheds in Ethiopia, which
perform better than the other climate models (Ukumo et al. 2022). To select the appropriate climate models from various
available models, multiple GCM-RCM are considered as a better approach for proper assessment (Endris et al. 2013;
Dibaba et al. 2019; Tenfie et al. 2022).

Precipitation bias correction

The bias corrections were carried out for the climate data. The power transformation method was used to reduce the systema-
tic errors in RCMs (Park et al. 2021). When applied to all temporal scales, linear scaling is followed by a parametric power
transformation method and the method produced excellent hydrological results based on the monthly approach according to
Ghimire et al. (2019) and also this method is easy to apply. It needs information on monthly observed statistics such as mean
and coefficient of variation (CV) (Equation (1)):

P ¼ aPb (1)

where P* is the corrected rainfall and P is the uncorrected rainfall. The parameters a and b were found for every month of the
year. The b parameter was determined iteratively with the help of the Excel solver command. Solver in Microsoft excel was
loaded using the following steps. 1. Click on the File tab, click Options, and then click the Add-ins category. 2. In the Manage
box, click Excel Add-ins, and then click Go. 3. In the Add-ins available box, select the Solver Add-in check box. 3. Now on the
Data tab, in the Analysis group, you should see the Solver command. The solver calculates the optimum parameter value
using the objective function (Equations (2) and (3)):

CVobs
Objective  function ¼ (2)
CVbHist

Obs
a¼ b
(3)
Hist

where CV is the coefficient of variation, obs stands for observed data, Hist denotes the historical data from climate models,
and the over bar symbol represents average. The value of b was optimized using the Solver in Microsoft excel 2016. The CV is
only a function of parameter b according to: CV(P)¼function (b); then, the parameter ‘a’ is determined such that the mean of
the transformed daily values matches with the mean of observed rainfall data. The resulting parameter ‘a’ depends on the
value of ‘b’, whereas the parameter b depends only on the CV and is independent of the value of parameter ‘a’. Finally, in
order to compare the performance of the satellite and the observed rainfall data, both should be the same in terms of spatial
resolution. To do this, the point data of gauge rainfall products were changed into areal rainfall data by using the Thiessen
polygon method.

Temperature bias correction

The linear scaling bias correction approach was employed for temperature data correction. The correction of temperature
involves only shifting and scaling to adjust the mean and variance. This method is capable of perfectly adjusting climatic influ-
ences (Luo et al. 2018) when monthly mean values are included. For each sub-basin, the corrected daily temperature T ∗ was
obtained from the following equation (Leander & Buishand 2007):

s(T0 )
T ¼ T0 þ [TR  T0 ] þ (T0  TR ) (4)
s(TR )

where T* is the corrected temperature, TR is the uncorrected daily temperature from the RCM, T0 is the observed daily average
temperature, the bar denotes the average over the considered period and σ refers to the standard deviation.
The accuracy of climate data from the climate model was checked using statistical measures. These are the bias in %, root
mean squared error (RMSE) in mm/year, correlation coefficient (R) () and CV in % (Ukumo et al. 2022). The ensemble
mean values from multiple RCMs were used as these perform better than others (Dosio et al. 2019).
 Journal of Water and Climate Change Vol 13 No 8, 3174

Hydrological modeling
The HEC-HMS was used in this study (USACE 2016). It was used to simulate a single catchment or a system of multiple
hydrologically connected catchments (Halwatura & Najim 2013).

HEC-HMS model calibration and validation

Each model that is included in the HEC-HMS program has parameters. The value of each parameter must be specified to use
the model for estimating runoff or routing hydrographs.
The appropriate values for the parameters were selected from the available rainfall and streamflow observations through
calibration. Calibration uses observed hydro-meteorological data in a systematic search for parameters that yield the best fit of
the computed results to the observed runoff. This search is often referred to as optimization. The calibration procedure begins
with data collection. For rainfall–runoff models, the required data are rainfall and flow time series. The next step is to select
initial estimates of the parameters. As with any search, the better these initial estimates (the starting point of the search), the
quicker the search will yield a solution (USACE 2016).
From the initial estimates of the parameters, the models included in the program were used with the observed boundary
conditions (rainfall or upstream flow) to compute the output, either the watershed runoff hydrograph or a channel outflow
hydrograph. The program is compared with the computed hydrograph to the observed hydrograph. For example, it simulated
the runoff in the watershed and compared it to the observed hydrograph. The goal of this comparison is to judge how well the
model ‘fits’ the real hydrologic system. If the fit is not satisfactory, the program systematically adjusts the parameters and
reiterates. When the fit is satisfactory, the program will report the optimal parameter values. The presumption is that these
parameter values then can be used for runoff or routing computations that are the goal of the flood runoff analyses.
To compare a computed hydrograph with an observed hydrograph, the HEC-HMS program computes an index of the good-
ness of fit. Algorithms included in the program search for the model parameters that yield the best value of an index are also
known as objective function.
The four objective functions are the sum of absolute error, the sum of squared residuals, percent error in peak and peak-
weighted mean square error. Only one of the four objective functions included in the program can be used, depending upon
the needs of the analysis. The goal of all four calibration schemes is to find reasonable parameters that yield the minimum
value of the objective function.
Percent error in peak objective function is selected depending on the objective of the study. It measures only the goodness
of fit of the computed-hydrograph peak with the observed peak. It also quantifies the fit as the absolute value of the difference,
expressed as a percentage, thus treating overestimates and underestimates as equally undesirable. It does not reflect errors in
volume or peak timing. This objective function is a logical choice if the information needed for designing or planning is lim-
ited to peak flow or peak stages. This might be the case for a floodplain management study that seeks to limit development in
areas subject to inundation, with flow and stage uniquely related (USACE 2016).
Sensitivity analysis was undertaken in most modeling studies to identify key parameters and precision required for cali-
bration (Azam et al. 2017). The most fundamental sensitivity analysis technique utilizes partial differentiation, whereas the
simplest method involves perturbing parameter values one at a time (Hamby 1994). In this study, model sensitive parameters
were evaluated manually by changing the value of one parameter at a time while keeping the value of the remaining par-
ameters constant. It has been observed that simulated streamflow volume for the study area is more sensitive to the
changes in the constant rate (CR) and less sensitive to the base-flow (BF) parameter.
Model calibration and validation were carried out before using the hydrological model to estimate the future streamflow
(Meenu et al. 2013). The model calibration was conducted by adjusting model parameter values until the model computed
and observed streamflow values were matched well. Calibration was done by first considering the least sensitive model par-
ameters and subsequently the most sensitive parameters by systematic adjustment of the parameter values which provide the
best fit between the observed and simulated flows (Bhuiyan et al. 2017).
Model validation was conducted using an independent set of streamflow data and the model parameters determined during
the model calibration period. Two-thirds of the observed flow data were used for model calibration and one-third of the data
were used for model validation (Arnold et al. 2012). Some of the data were used for model initiation or spin-up. One-year
(2000), eight-year (2001–2009) and four-year (2010–2013) daily flow data were used for initialization, model calibration
and validation, respectively.
 Journal of Water and Climate Change Vol 13 No 8, 3175

Performance of the HEC-HMS model

Model performance was evaluated using statistical indices such as the Nash–Sutcliffe efficiency (NSE) coefficient, coefficient
of determination (corr), percentage error in total runoff volume (RVE) and percentage error of peak flow (PEPF). These are
measures of model performance by comparing the model simulated to the corresponding observed values. The NSE indicates
how well the plot of observed versus simulated streamflow values fits the 1:1 line (Mills 2001; Almeida et al. 2018). The NSE
ranges between ∞ and 1.0. Values between 0.75 and 1.0, 0.60 and 0.75, and 0.36 and 0.60 generally indicate a very good,
good and satisfactory model performance, respectively. The NSE value of 0.00 indicates that the mean observed value is a
better predictor than the simulated value, which indicates unacceptable model performance (Farokhzadeh et al. 2018). If
the NSE is negative, model predictions are very poor, and the average value of an output is a better estimate than the
model prediction. The NSE is given by the following equation (Zeybek 2018):

P
n
(Qo,i  Qs,i )2
NSE ¼ 1  i¼1 (5)
Pn 2
(Qo,i  Qo,i )
i¼1

where Qo,i is the observed streamflow at the time step i, Qs,i is the simulated flow at the time step i, n is the number of obser-
vations and the bar symbol denotes the mean of the data.
The percentage error in total runoff volume (RVE), on the other hand, ranges between ∞ and þ∞. The model perform-
ance is very good, good and satisfactory if RVE values are between 5 and 5%, 5 and 10%, and 10 and 5%, respectively.
The RVE is given by the following equation (Ayele et al. 2022):

P
n
(Qo,i  Qs,i )
i¼1
RVE ¼ 1   100 (6)
P
n
Qo,i
i¼1

PEPF is used to evaluate the model performance for the peak flow (Ukumo et al. 2022) (Equation (7)):

 
Qo,i(peak)  Qs,i(peak) 

PEPF ¼    100 (7)
Qo,i(peak) 

where Qo,i (peak) is the peak observed discharge and Qs,i(peak) is the peak simulated discharge.
The coefficient of determination (corr) is the employed statistical index to indicate the strength of the relationship between
the observed and modeled values. A value of 0 means no correlation at all, whereas 1 means the prediction is equal to that of
the observation (Ukumo et al. 2022). Bias, on the other hand, measures whether the average tendency of the simulated data is
larger or smaller than the observed values. Bias is expressed in percentage; the lower the absolute value of the bias, the better
will be the model performance. RMSE has the unit of observed variable, which makes its interpretation relatively easy. The
RMSE value close to 0 indicates the better performance of a model. Correlation coefficient (corr) is used to evaluate the linear
relationship between the observed and modeled rainfall amounts. The value of 1.0 suggests the perfect linear relationship
between the model output and observed data (Ayele et al. 2022) (Equations (8)–(11)):

P
n
(RRCM  RRCM )(Robs  Robs )
i¼1
corr ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (8)
P n 2P n 2
(RRCM  RRCM ) (Robs  Robs )
i¼1 i¼1

(RRCM  Robs )
Bias ¼ x100 (9)
RRCM
 Journal of Water and Climate Change Vol 13 No 8, 3176

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
uN
uP
u (RRCM  Robs )2
ti¼1
RMSE ¼ (10)
N
dR
CV ¼ x100 (11)
R

where R is the average rainfall over the watershed; RCM and obs subscripts represent rainfall amount over the watershed
from RCM simulation or observed datasets, respectively. δ indicates the standard deviation of either the RCM or observed
rainfall data. R represents estimated statistics individually either for RCM or observed rainfall amount.

Hydraulic modeling
The HEC-RAS v.5.0.7 Hydraulic model was used to define the water surface profile of a flood event and used to complete the
river geometry analysis on the River Analysis System (RAS) mapper (Aryal et al. 2020). The river geometry, Manning’s rough-
ness coefficient, discharge, normal depth and synthetic unit hydrograph were used as input data to HEC-RAS model.
The computational procedure in HEC-RAS is based on an iterative solution of the Saint-Venant equation given by the fol-
lowing equation Vashist (2021):

 
@Q @QU @Z
þ þ gA þ Sf ¼ 0 (12)
@t @x @x

where Q is the discharge (m3/s), A is the cross-sectional area, ∂Z/∂x is the water surface slope, g is acceleration due to gravity
(m/s2), U is the longitudinal velocity of the flow (m/s), Sf is the friction slope, t refers to time and x refers to the direction of
flow.
Hydrologic Engineering Center-Geographic RAS (HEC-GeoRAS) is a set of procedures, tools and utilities for processing
geospatial data in ArcGIS for export into HEC-RAS (Maskong 2019). The geometric data necessary for hydraulic modeling
in HEC-RAS are extracted from the digital elevation model (DEM) of the channel and surrounding land surface.
The first step in the preRAS methodology consists of the creation of a series of two-dimensional line themes that represent
particular topographic elements of the stream network. The centerline of the streams, main channel banks, flow-paths of the
stream and overbanks, and cross-section cut lines are created using ArcGIS tools.
The river network was represented by the stream centerline. It was created starting from the upstream end and working
downstream following the channel path. The stream centerline theme was used for assigning river stationing for the cross-
sections and to display the network as a schematic in the HEC-RAS Geometric editor. The River Name (Stream ID) and
Reach Name (Reach ID) were provided for river reach. The created stream centerline menu item was used to create a
new editable shape file (with a default name of stream.shp). After creating the river network, the stream centerline theme
was completed by adding river identifier using the River ID tool.
The Banks menu item created was selected next, which separates the main channel from the over bank areas. Create Banks
produces a new shapefile named Banks.shp, where it is editable by the user. Bank station lines were created on the right side
of the channel to identify the main conveyance channel from the overbank areas. Two bank lines that crossed each cross-sec-
tion were represented by a cut line.
The create flow-paths menu item was used to identify the hydraulic flow-path in the left over bank, main channel and right
over bank. Create Flow-paths creates a new shape file named Flow-path.shp in the ArcGIS that was editable by the user. The
Flow-path ID tool was used to specify the designation of each flow-path according to the geometry of the stream network.
Flow-paths were created in the direction of flow. Downstream reach lengths are calculated between cross-sections’ cut
lines along the flow-path centerlines. The Create cross-section (XS) cut lines menu item was selected last, to identify the
location, position and expanse of each cross-section. It created an editable theme called XScutlines.shp.
Cross-section cut lines were drawn from the left over bank to the right over bank (looking downstream) and crossed the
flow-path lines and two bank station lines exactly once. Cross-sectional cut lines were drawn perpendicular to the direction
of flow and were not intersected; otherwise, it causes error in the floodplain mapping.
 Journal of Water and Climate Change Vol 13 No 8, 3177

The data extraction began after creating center flow line, left flow line and right flow line. The first step is the selection of
the theme setup menu, where the appropriate themes are specified for input data and the user specifies the RAS-GIS Import
file. There are three processes that take place once the appropriate themes have been identified, namely Centerline Com-
pletion, Cross-section Attributing and Cross-section Elevations. Each of the items can be accomplished in one step, but
they are comprised of several algorithms that can be activated individually if desired by the user.
The Centerline Completion menu computes the river reach lengths (Lengths/Stations menu item), establishes the connec-
tivity and orientation of the river network (Centerline Topology menu item) and creates a 3D-shape file from the Stream
Centerline theme (Centerline Z Extract menu item). Cross-section attributes are added to the Cross-Section Cut Line
theme using the XS Attributing menu item. The XS Attributing adds stream and reach names to the Cross-Section Cut
Line theme, adds the cross-sectional stating data based on the intersection of the cross-sectional cut lines and the stream cen-
terline, extracts Manning’s n-values from the Land Use theme, computes bank station positions for each cross-section from
the intersection of the cross-sectional cut lines and bank station lines (calculated as the percent distance along the cut line
from its start in the left over bank), and adds downstream reach lengths to each cross-section cut line based on the intersec-
tion of the flow-path centerlines and the cut lines.
The XS Elevation function created a 3D-shape file from the cross-section cut line theme, where station-elevation data were
extracted from the Triangular Irregular Network (TIN) at the edge of each triangle along a cut line. The final step in the
preRAS menu was the Generate RAS-GIS Import function, where the header information was written (in ASCII format)
to a text file that contains general information based on the 3D Stream Centerline, Cross-Section Surface Line and Terrain
TIN data. The stream network data were also written specifying each river reach endpoint, the stream centerline coordinates
and the distance to the downstream endpoint. Finally, the geometric data for each cross-section were written to the import
file, including river and reach identifiers, cross-section stationing, bank station locations, downstream reach lengths, cross-sec-
tion cut line coordinates and cross-section surface line coordinates (x, y, z) to develop flood inundation maps.

HEC-RAS model calibration

The Manning’s roughness coefficient, n, which is used to represent the resistance to flow from the river bed and flood plain at
each cross-section, is an important parameter used in HEC-RAS model calibration (Kadhim Hameed & Tawfeek Ali 2013).
The trial-and-error process of model calibration was conducted where various sets of model options and parameters were
used until a match between the observed and model computed flood depth of the flooded area was achieved. The simulation
with the smallest error was found by running several simulations, each using the different values of Manning’s n. The Man-
ning’s roughness coefficient depends primarily on the length, density, distribution and also the material and size of the
riverbed. D’ippolito et al. (2021) developed a method that takes two factors into account which influence the estimation
of the Manning’s roughness coefficient: (1) type and size of the channel bed and the channel material and (2) the shape
of the channel. The roughness coefficient was determined by using the photo of bed materials captured from the field.
The method employed by Zhu et al. (2020) for estimating roughness coefficient for debris flows was employed in this study.

RESULTS AND DISCUSSIONS

Evaluation of climate model performance
The average observed rainfall of the Boyo watershed is 1318.7 mm/year. Accuracies of the climate models are not the same in
representing the observed rainfall as the models differ significantly in predicting the average rainfall. In order to reduce the
differences between the simulated and observed rainfall, the bias correction was conducted. The smallest bias in rainfall
(0.68%) was obtained for the CNRM-RCA4 model, which indicates the better performance of all. On the other hand, the
ICHEC-RACMO22T has the largest bias (2.3%). The performance of the CCCma-CanRCM4 model was good
(corr¼ 0.73). The CV value of ICHEC-RACMO22T (11.9%) is close to the observed value (11.0%). All models underestimated
the observed rainfall. Averaging the models’ outputs (ensemble mean) reduces the bias by at least 0.54% and CV by 10.1%,
indicating that the ensemble mean performed well (corr¼0.83). The ensemble mean estimated the observed mean annual
rainfall amount by 98.2% compared with the other four models. The climate model simulations reasonably reproduced
the magnitude and pattern of the observed rainfall (Figure 2). The ensemble mean based on its performance evaluation cri-
teria, including its ability to capture the annual observed rainfall amount relative to the four climate models, was selected
(Zhang et al. 2021).
 Journal of Water and Climate Change Vol 13 No 8, 3178

Figure 2 | Bias-corrected annual rainfall cycle (1990–2005).

There is a mixed-signal from the climate models in simulating the monthly rainfall in the mid-term (2050s) and long-term
(2080s) periods (Table 3). Previous studies indicated that for the A2a scenario, there may be an increase of about 29% in the
2050s. In the case of the B2a scenario, the precipitation will change by 32 to 33% in 2050s. However, climate change studies
in the Boyo watershed were neglected (Tekle 2015; Demissie et al. 2016). This study will serve as a good information source
for water resource management in the Boyo watershed.
Table 1 shows climate model performance for capturing the maximum temperature. It shows that the ensemble mean has
performed better than the other models flowed by the ICHEC-RACMO22T model (corr¼0.41). However, the ICHEC-
RACMO22T model performed poorly in terms of bias (bias¼8.31%) and CV (CV¼0.98%). Negative bias indicates under-
estimation, whereas positive bias shows overestimation (Politi et al. 2021). In general, the climate models underestimated
the observed maximum temperature.
The bias-corrected maximum temperature of the four models captured trends in the observed maximum temperature
(Figure 3).

Table 1 | Climate model performance for capturing the maximum temperature (yearly statistics) for the period 1990–2005

Observed and climate models Average annual Tmax (°C) Bias (%) CV (%) RMSE (°C) corr (–)

Observed 26.5 – 1.18 – –

UQAM-CRCM5 25.4 4.84 1.34 1.48 0.04
CCCma-CanRCM4 25.5 4.36 0.73 1.34 0.28
ICHEC-RACMO22T 24.7 8.31 0.98 2.27 0.41
CNRM-RCA4 25.9 3.04 0.83 1.02 0.27
Ensemble mean 26.4 1.95 0.5 0.71 0.52

Figure 3 | Bias-corrected annual maximum temperature cycle (1990–2005).

 Journal of Water and Climate Change Vol 13 No 8, 3179

The simulated monthly maximum temperature has shown varying signals in the mid-term (2050s) and long-term (2080s)
periods. These results share the truth with many previous studies (Elshamy et al. 2009; Keller 2009; Tekle 2015).
The performance of simulated average minimum temperature is presented in Table 2. CCCma-CanRCM4 and ICHEC-
RACMO22T models performed well (corr¼0.55). The UQAM-CRCM5 model showed the least bias (bias¼36.6%) compared
with the other models. The CCCma-CanRCM4 model performed worst in terms of RMSE (RMSE¼12.73 °C). All the climate
models overestimated the observed minimum temperature.
The bias-corrected minimum temperature of all models captured trends in the observed minimum temperature (Figure 4). The
minimum temperature was checked by performances’ evaluation criteria and, finally, ensemble mean gratified the criteria.
In the mid- and long term, the minimum temperature of the Boyo watershed will increase in the future (Table 3). The rise of
minimum temperature in Ethiopia was also indicated in another research work earlier (Keller 2009), despite it not being
specified for the Boyo watershed.

HEC-HMS and HEC-RAS model performances

Figure 5 shows the observed and simulated flows (left y-axis) along with the rainfall (right x-axis). Daily flow was used for
model calibration and validation. It can be depicted that the model underestimated the peak streamflow values in particular.
This is due to the fact that hydrological models are designed to capture typical averages rather than exceptional events
(Hamdi et al. 2021).
Table 4 shows statistical indices for testing the model performance (Bhuiyan et al. 2017). The realization of the model was
good when evaluated using objective functions. The performance of the model for calibration and validation periods showed
good performance (NSE¼0.79 and 0.72). In general, the performance of the HEC-HMS model is good, as can be evaluated by
using the statistical indices. Therefore, the model can be used to predict streamflow under climate change conditions for
future periods with reasonable accuracy.

Table 2 | Accuracy of dynamically downscaled yearly minimum temperature from GCM–RCM (1990–2005)

Observed and climate models Average annual Tmin (°C) Bias (%) CV (%) RMSE (°C) corr (–)

Observed 12.3 – 14.04 – –

UQAM-CRCM5 19.4 36.60 14.29 10.53 0.13
CCCma-CanRCM4 21.6 42.94 15.12 12.73 0.55
ICHEC-RACMO22T 21.5 42.67 9.72 12.39 0.55
CNRM-RCA4 20.7 40.49 17.74 12.00 0.52
Ensemble mean 11.72 15.25 12.62 8.61 0.84

Figure 4 | Bias-corrected annual minimum temperature cycle (1990–2005).

 Journal of Water and Climate Change Vol 13 No 8, 3180

Table 3 | Monthly and annual climate change analysis with respect to baseline

Model Period RCP Variables Monthly Annual

UQAM-CRCM5 2050s 4.5 Tmax (°C) 1.2 1.55

Tmin (°C) 0.75 0.91
Rainfall (%) 9.7 4.2
8.5 Tmax (°C) 1.3 0.6
Tmin (°C) 0.83 1.1
Rainfall (%) 22.0 17.3
2080s 4.5 Tmax (°C) 1.1 1.42
Tmin (°C) 0.86 1.3
Rainfall (%) 1.8 2.3
8.5 Tmax (°C) 1.5 0.3
Tmin (°C) 1.63 0.5
Rainfall (%) 28.9 15.5
CCCma-CanRCM4 2050s 4.5 Tmax (°C) 1.0 0.4
Tmin (°C) 0.8 0.31
Rainfall (%) 17.9 3.7
8.5 Tmax (°C) 0.6 0.25
Tmin (°C) 1.1 2.6
Rainfall (%) 19.8 8.3
2080s 4.5 Tmax (°C) 1.1 0.7
Tmin (°C) 1.21 0.6
Rainfall (%) 10.6 3.2
8.5 Tmax (°C) 1.5 0.1
Tmin (°C) 0.9 2.7
Rainfall (%) 16.2 5.2
ICHEC-RACMO22T 2050s 4.5 Tmax (°C) 1.15 0.4
Tmin (°C) 1.32 0.6
Rainfall (%) 25.7 21.4
8.5 Tmax (°C) 1.74 1.6
Tmin (°C) 1.1 0.82
Rainfall (%) 27.1 22.2
2080s 4.5 Tmax (°C) 1.51 0.2
Tmin (°C) 0.95 0.5
Rainfall (%) 13.2 2.3
8.5 Tmax (°C) 1.1 0.2
Tmin (°C) 1.23 0.4
Rainfall (%) 13.3 6.2
CNRM-RCA4 2050s 4.5 Tmax (°C) 1.6 0.1
Tmin (°C) 1.4 0.3
Rainfall (%) 22.2 18.4
8.5 Tmax (°C) 1.5 0.3
Tmin (°C) 0.1 0.4
Rainfall (%) 28.2 9.2
2080s 4.5 Tmax (°C) 1.12 0.53
Tmin (°C) 1.5 0.3
Rainfall (%) 29.73 23.2
8.5 Tmax (°C) 0.8 0.1
Tmin (°C) 1.3 0.3
Rainfall (%) 8.3 3.1

The performance of the HEC-RAS model was also evaluated by using the Manning coefficient. Based on trial-and-error
model runs, the roughness coefficients varied between 0.022 and 0.048 m1/3/s depending on the characteristics of natural
channel and floodplain. The roughness values were fixed based on the channel and floodplain that were collected during
the field survey.
 Journal of Water and Climate Change Vol 13 No 8, 3181

Figure 5 | Observed and simulated streamflow for model initialization, calibration and validation periods at the Weira gauging station.

Table 4 | Model performance statistics for evaluating model performance

Objective function Calibration Performance Validation Performance

NSE (–) 0.79 Very good 0.72 Very good

corr (–) 0.8 Very good 0.78 Very good
RVE (%) 5.6 Good 6.1 Good
PEPF (%) 18 Good 16 Good

Projected changes in floods

Flood inundated area and depth for the baseline period (1976–2005)
Simulations were carried out for the present and future scenarios of rainfall events. The extent of flood inundation area under
the current condition was analyzed using the historical records of streamflow in the Boyo watershed. The peak discharge was
estimated using log Pearson type-III distribution after a thorough comparison with the other distributions (Samantaray &
Sahoo 2020). Model analysis showed that the extent of variation between 50- and 100-year return period flood depths differs
significantly (Figure 6). For the historical period, the 50-year return period flood inundated nearly 64 ha in the area, whereas
flood with a return period of 100 years could be 78 ha.
The average flood depth in the baseline period was 2.0 m for a 50-year return period. According to the HEC-RAS model, the
maximum flood depth reaching approximately 2.2 m has been estimated for 100-year return floods. Residential areas near to
the bank of Boyo wetland were severely affected by flood according the field observations and narratives form elderly people
in the study area. The 50 and 100-year inundation map indicated that 31.2 and 42.8% of the cultivated land will be affected by
flood events, respectively.

Changes in 2050s (2041–2070) flood inundation area and depth

The future flood inundation was simulated for 2050s under RCP4.5 and RCP8.5 scenarios. It can be observed that under the
RCP4.5 scenario, the spatial coverage of flooded area increased for 50- and 100-year return periods relative to the baseline
period. The total flooded areas under RCP4.5 were 81.4 and 92.7 ha for the 50- and 100-year return periods, respectively.
The flood inundation map for the 2050s under the RCP4.5 scenario is shown in Figure 7. The average flood inundation
depth will be 2.5 m for a 50-year return period. In addition, for the 100-year return period, the simulated flood inundation
depth is 2.63 m.
Figure 8 shows the mid-term flood inundation map under RCP8.5 for 50- and 100-year return periods. The flooded areas
under RCP8.5 were 97.6 and 121.3 ha for 50- and 100-year return periods, respectively, in 2050s. Under RCP4.5, the 50-year
inundation map indicated that 39.4% of the cultivated land will be affected by flood and the 100-year flood map displayed that
43.2% of the forest will be damaged in the 2050s.
 Journal of Water and Climate Change Vol 13 No 8, 3182

Figure 6 | Map of inundated area and depth for the baseline period: (a) flood inundation map for the 50-year return period, (b) depth map for
the 50-year return period, (c) flood inundation map for the 100-year return period and (d) depth map for the 100-year return period.

Figure 7 | Map of inundated area and flood depth for RCP4.5 in the 2050s: (a) flood inundation map for 50-year return period, (b) depth map
for the 50-year return period, (c) flood inundation map for the 100-year return period and (d) depth map for the 100-year return period.
 Journal of Water and Climate Change Vol 13 No 8, 3183

Figure 8 | Map of inundated area and depth for RCP8.5 in the 2050s period: (a) flood inundation map for the 50-year return period,
(b) depth map for the 50-year return period, (c) flood inundation map for the 100-year return period and (d) depth map for the 100-year
return period.

Changes in the 2080s (2071–2100) flood inundation area and depth

The future flood inundation was also simulated for the 2080s period for the RCP4.5 and RCP8.5 scenarios. The total flooded
areas under RCP4.5 were 103.58 and 114.6 ha for the 50- and 100-year return periods, respectively. The flooded areas under
RCP8.5 were 150.2 and 167.8 ha for 50- and 100-year return periods, respectively, in the 2080s.
The probable flood inundation map for the 2080s (long term) under the RCP4.5 scenario is shown in Figure 9. The pro-
jected flood inundation depth will be 2.78 m for a 50-year return period. On the other hand, for a 100-year return period,
the likely flood inundation depth will be 2.93 m (Dinh et al. 2012).
The future flood inundation map for the period of 2080s under the RCP8.5 scenario is shown in Figure 10. A 50-
and 100-year inundation maps indicated that approximately 115 and 193 ha will be damaged under RCP8.5, respect-
ively, in 2080s. The expected average flood inundation depth projected for the long-term period under RCP8.5 is
2.83 m for a 50-year return period. It is also estimated that the flood inundation depth for a 100-year return period
is 3.1 m.
The 50- and 100-year inundation maps specified that 48 and 51% of the cultivated land will be affected, respectively,
under RCP4.5 in the 2080s. Under RCP8.5, the 50-year inundation map indicated that 59% of the urban area will be
affected by flooding phenomena and the 100-year flood map showed that 76.2% of the forest could be distracted in the
2080s. This clearly displays that the concerned stakeholders should take cautionary measures for tackling flood risk in
the coming 2080s.
The flooding conditions in the future periods will be increased and more areas will get inundated compared with the base-
line period. Therefore, any development and future settlements in flood plains of downstream reaches of the Boyo watershed
need due attention so that flood damages shall be minimized. The research also explained the fear and damage from flooding
for people living near the bank of the rivers (Baan & Klijn 2004; Ukumo et al. 2022). According to the community, flood
protection structures built on the river to reduce the risk of flooding such as land terracing, planting trees and harvesting
immature crops were not effective to prevent flood. These traditional flood-risk reduction measures were insufficient to
 Journal of Water and Climate Change Vol 13 No 8, 3184

Figure 9 | Map of inundated area and depth for RCP4.5 in the 2080s period: (a) flood inundation map for the 50-year return period, (b) depth
map for the 50-year return period, (c) flood inundation map for the 100-year return period and (d) depth map for the 100-year return period.

Figure 10 | Map of inundated area and depth for RCP8.5 in the 2080s period: (a) flood inundation map for the 50-year return period,
(b) depth map for the 50-year return period, (c) flood inundation map for the 100-year return period and (d) depth map for the 100-year return
period.
 Journal of Water and Climate Change Vol 13 No 8, 3185

reduce the negative consequences such as damage to crops, disease outbreaks and livestock losses (Baan & Klijn 2004).
Therefore, alternative flood prevention measures such as avoiding settlement in flood plains and watershed management
are recommended. Expanding modern irrigation systems and creating flood-diversion infrastructure such as ditches may
also improve people’s adaptive capabilities to flooding and flood damage.
The inhabitants near to the bank of river were severely affected by flood in 2013. During the survey period, the farm lands
were clearly observed. The climate change analysis also indicated that the risk from flooding will increase in the future. How-
ever, previous research ignored the impact of climate on river flooding in the watershed (Kuma et al. 2021; Nannawo et al.
2021; Orke & Li 2021; Ayele et al. 2022). The residents living adjacent to the river banks and flood plains shall take stringent
cautionary measures to avoid flood damage in future periods under the changing climate situations.
The prepared flood inundation maps are important for protecting peoples’ lives in flood-prone areas and for allowing con-
cerned bodies to provide early warning mechanisms before a flood occurs. Farmers should also plan and choose strategically
which crops to cultivate in flood-prone areas in order to reduce flood-related field crop losses. As a result, while addressing
vulnerabilities to potential flood damages in the watershed as a result of climate change, adequate land use planning, water
resource management and risk-based design of hydraulic infrastructure must be integrated as part of a mitigation strategy.

CONCLUSION
The future variation of precipitation and temperature can be caused by climate change and ultimately lead to hydrologic
extremes such as flood. Four climate models, namely UQAM-CRCM5, CCCma-CanRCM4, ICHEC-RACMO22T and
CNRM-RCA4, were used. The climate change analysis was performed for baseline (1976–2005), mid-term (2041–2070)
and long-term (2071–2100) periods. All the climate models underestimated observed rainfall and maximum temperature
and overestimated minimum observed temperature. The area inundated by flood under climate change shows that the
100-year return period is riskier than the 50-year return period. The expected average maximum flood inundation will be
3.1 m under RCP8.5 in the 2080s. It showed that 193 ha area of land will be inundated in the long-term period under the
RCP8.5 scenario. The findings indicated that the challenge from flooding will increase in 2050s and 2080s because of climate
change as one of the driving forces. Therefore, it is recommended that precautionary measures need to be taken to avoid
flooding and flood damage in the study area in the future periods. This study will be helpful for cities, farming communities
nearby the river bank, government bodies concerned with risk management and local community in the Boyo flood-prone
areas.

FUNDING SOURCES
No funding has been received from any source for this research work.

DATA AVAILABILITY STATEMENT

Data cannot be made publicly available; readers should contact the corresponding author for details.

CONFLICT OF INTEREST
The authors declare there is no conflict.

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First received 30 May 2022; accepted in revised form 24 July 2022. Available online 2 August 2022