Nested hydrological modeling for flood prediction using CMIP6 inputs around Lake Tana, Ethiopia

Abstract

Study region: Tana basin is the origin of the Blue Nile Basin located in Ethiopia. The Lake Tana’s
mean annual precipitation is approximately 1400 mm/yr and its outflow drains an area of about
15,096 km2. There is limited effort to apply nested hydrological models for flood prediction due
to being poorly gauged for validation.
Study focus: The objective of this study is to show how climate simulations can be used to generate
reliable local predictions of flood and runoff for the Blue Nile in the Ethiopian highlands.
In this study, outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were
used to initialize a nested hydrological model to reveal long-term trends of runoff in flood-prone
areas of the Lake Tana basin, Ethiopia. Available satellite and reanalysis datasets were used for
selection of CMIP6 products and the five models with best validation were used to force a nested
hydrological model: Known as Wflow_sbm. A model-independent multi-algorithm optimization
estimation tool was implemented for calibration of Wflow with insitu observations.
New hydrological insights for the region: In terms of simulating runoff and flood events, application
of Wflow_sbm to the Lake Tana basin gave promising results. This study serves as a major step
towards the development and implementation of global model-driven nested hydrological assessments
of flood risk in future projections for Lake Tana basin.

Full text

Journal of Hydrology: Regional Studies 46 (2023) 101343
2214-5818/© 2023 International Water Management Institute (IWMI), 127 Sunil Mawatha, Battaramulla, 10120, Sri Lanka. Published by Elsevier
B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Nested hydrological modeling for ood prediction using CMIP6
inputs around Lake Tana, Ethiopia
Addis A. Alaminie
a
,
b
,
*
, Giriraj Amarnath
b
, Suman Kumar Padhee
b
, Surajit Ghosh
b
,
Seifu A. Tilahun
a
, Muluneh A. Mekonnen
d
, Getachew Assefa
e
, Abdulkarim Seid
b
,
Fasikaw A. Zimale
a
, Mark R. Jury
c
a
Faculty of Civil and Water Resources Engineering, Bahir Dar University, Ethiopia
b
International Water Management Institute, 127 Sunil Mawatha, Battaramulla, Sri Lanka
c
Physics Department, University of Puerto Rico Mayagüez, Mayagüez, Puerto Rico
d
Environmental Sciences Applications Team, Regulatory Applications Branch, Alberta Energy Regulator, Calgary, Canada
e
School of Architecture, Planning and Landscape, University of Calgary, Calgary, Canada
ARTICLE INFO
Keywords:
Climate Projection
GCMs-CMIP6
WFlow_sbm model
Flood prediction
Lake Tana Basin
Ethiopia
ABSTRACT
Study region: Tana basin is the origin of the Blue Nile Basin located in Ethiopia. The Lake Tana’s
mean annual precipitation is approximately 1400 mm/yr and its outow drains an area of about
15,096 km
2
. There is limited effort to apply nested hydrological models for ood prediction due
to being poorly gauged for validation.
Study focus: The objective of this study is to show how climate simulations can be used to generate
reliable local predictions of ood and runoff for the Blue Nile in the Ethiopian highlands.
In this study, outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were
used to initialize a nested hydrological model to reveal long-term trends of runoff in ood-prone
areas of the Lake Tana basin, Ethiopia. Available satellite and reanalysis datasets were used for
selection of CMIP6 products and the ve models with best validation were used to force a nested
hydrological model: Known as Wow_sbm. A model-independent multi-algorithm optimization
estimation tool was implemented for calibration of Wow with insitu observations.
New hydrological insights for the region: In terms of simulating runoff and ood events, application
of Wow_sbm to the Lake Tana basin gave promising results. This study serves as a major step
towards the development and implementation of global model-driven nested hydrological as-
sessments of ood risk in future projections for Lake Tana basin.
1. Introduction
Flood-related hazards are becoming more frequent and globally widespread because of changes in climate and land use an
accelerating hydrological cycle under global warming, and also due to land use wherein vegetation cover is overgrazed and denuded
(Brown et al., 2018).
Future climate is expected to increase the magnitude and frequency of extreme rainfall causing frequent river ooding and ood
hazards (Fleming et al., 2014; Shrestha and Lohpaisankrit, 2017). Previous studies have shown that ood intensity is highly sensitive to
* Correspondence to: Faculty of Civil and Water Resources Engineering, Bahir Dar University, P.O. Box: 1474, Ethiopia.
E-mail address: metaddi@gmail.com (A.A. Alaminie).
Contents lists available at ScienceDirect
Journal of Hydrology: Regional Studies
journal homepage: www.elsevier.com/locate/ejrh
https://doi.org/10.1016/j.ejrh.2023.101343
Received 15 November 2022; Received in revised form 30 January 2023; Accepted 10 February 2023

Journal of Hydrology: Regional Studies 46 (2023) 101343
2
drastic global climate change (Kure and Tebakari, 2012;Habibi et al., 2018; Nile et al., 2010; Prudhomme et al., 2013; Salman et al.,
2020).
Many ood prediction studies have been conducted in Africa, the studies include ood inundation (di Baldassarre et al., 2010), risk,
impact and mitigation (Loudyi and Kantoush, 2020; Ntajal et al., 2017), ood warning, and ood disaster (du Plessis, 2002). In
Ethiopia, identied ood-prone areas include the Baro-Akobo Basin, Awash River Basin, Wabi Shebelle, Ribb and Gumara Areas
(Fogera Plain) (Gashaw and Legesse, 2011). The United Nations Humanitarian Affairs Coordination Ofce (2006) reported that a total
of 357,000 people were affected by ooding in 2006 from which more than 136,000 people were made homeless.
Yet, few studies have been conducted in the gauged watersheds of the Lake Tana basin to assess ood risks using historical datasets,
e.g., (Desalegn et al., 2016; Hagos, 2011; Zelalem et al., 2018). Moreover, most previous studies may not consider the impact of future
climate change on the severity of ooding, or they use one-dimensional hydraulic models, coarse-resolution climate models and
unrealistic emission scenarios (Fekadu et al., 2017; Ukumo et al., 2022), which increase uncertainty. Accurate prediction of ood
inundation areas and dissemination of information on the inundation areas to the disaster risk managers and the general public are
essential to reduce losses in the Lake Tana Basin.
It is crucial to develop appropriate climate change adaptation and mitigation strategies to properly deal with future ood hazards in
ood-prone areas of the Ethiopian highlands. This strategy should make use of recent CMIP6 climate model products, high-resolution
digital elevation models, and compatible distributed hydrological models. In the case of ungauged basins, there are challenges on how
to make the best use of available data and modelling resources for accurate prediction (Pagano et al., 2014), though the reliability of
global ood models is rapidly increasing (Brown et al., 2018). Recent studies that employ global climate models to force nested hy-
drological models have shown promising performances, e.g., (Brown et al., 2018; Yin et al., 2018; Yuan et al., 2019).
Exploring the potential of climate model products to drive hydrological models and investigate their inuence on river ows is
essential for future ood warnings. The Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble models offer new emission
pathways that explore a wider range of outcomes than in CMIP5 (Gidden et al., 2019). CMIP6 has more models, more future scenarios,
and greater number of experiments. Signicant advances in remote-sensing techniques, the coupling of meteorological and hydro-
logical modelling capabilities, and advancements in knowledge and algorithms for analysis - increase the reliability of ood forecasts
(Alaminie et al., 2021; Fekadu et al., 2017).
Several hydrological models have been developed over the past decades to better understand rainfall-runoff processes and have
become important tools for scenario simulations and predictions and decision-making, e.g., (Ajami et al., 2008; Cloke and Pappen-
berger, 2009). The Wow_sbm PCRaster modelling framework is selected as it ts the purpose and requirements of this study. The
Fig. 1. The geographical location of the Lake Tana basin, Gumera and Gilgel Abay sub-watersheds, and streamow and meteorological stations.
A.A. Alaminie et al.

Journal of Hydrology: Regional Studies 46 (2023) 101343
3
distributed Wow_sbm hydrological model (Schellekens, 2022; Werner et al., 2013) has shown good performance in basins across a
large range of elevations and drainage areas e.g., (Hassaballah et al., 2017; Werner and Cannon, 2016). The structure of the Wow_sbm
model released by Deltares is transparent, can be changed by other modellers and the system allows for rapid development. The
PCRaster is a dynamic environmental modelling & programming language within a GIS framework that simplies integration of spatial
environmental models (Imhoff et al., 2020; Karssenberg, 2002; Uhlenbrook et al., 2004) including Wow and other physically based
distributed hydrological models. The Wow PCRaster modelling framework lacks parameter estimation and objective function
optimization capability; so that is supplemented with a model-independent tool to facilitate calibration and validation of Wow.
Fig. 2. The Geophysical (Static) maps which provides information on elevation (a) DEM, (b) land use and (c) soil of the basin.
A.A. Alaminie et al.

Journal of Hydrology: Regional Studies 46 (2023) 101343
4
The overall objective of this study is to investigate the future ood risks in the Lake Tana basin using CMIP6 model products to drive
or force a compatible and exible hydrological modelling framework.
2. Study area and methodology
2.1. Study area
The study considers the Lake Tana basin, which is the source of the Blue Nile (Fig. 1) and drains a catchment of 15,096 km
2
within
10.95◦−12.78◦N latitude and 36.89◦−38.25◦E longitude. The lake itself covers 17% of the catchment, and has a diverse topography
with elevation from 1322 m to 4111 m above m.s.l (Setegn et al., 2008). The major land use classes are cultivated crop area, pasture
grazing, forestry, urban-residential and wetland. Mountainous volcanic terrain has an eastern escarpment; whose orographic mean
annual precipitation is ~1400 mm/yr according to gauge data (Tibebe et al., 2019).
The climate of Lake Tana Basin is tropical highland monsoon with a rainy season locally known as Kiremt (June–September) and a
dry season (October–March). The temperature shows small seasonal and large diurnal variability and mean annual temperature is
20
o
C (Tibebe et al., 2019).
More than 40 small rivers and streams with a total mean annual inow of 158 cumecs reach Lake Tana (Wubneh et al., 2021). The
only surface outow is the Blue Nile (Abbay) River with an annual ow of 3050 cumecs at Bahir Dar gauge station: 37.36◦E and,
11.62◦N. Four major tributaries (Gilgel Abay, Gumara, Ribb, and Megech) contribute more than 93% of the inow to Lake Tana (Fenta
et al., 2018; Setegn et al., 2008). Gumara, Gilgel Abay and Ribb sub-catchments cover an area of 1279 km
2
, 1656 km
2
and 678.15 km
2
respectively. These three sub-catchments are the focus of this study due to data availability.
2.2. Methods
2.2.1. Input datasets and pre-processing
The selected Wow_sbm model (described under section 2.5) requires spatially distributed data (static maps such as digital
elevation model, land use and soil physical parameters maps) and spatially distributed forcing (time series) data such as Precipitation,
Temperature and Potential Evapotranspiration. Historical forcing and streamow data are required to calibrate and validate the
model.
2.2.1.1. Static Maps. The Geophysical (Static) maps provide information about morphological, physical, soil, and land use properties
for each grid of the computational domain. These maps are accessed from the USGS Earth Explorer <https://earthexplorer.usgs.gov/
>: land use (10 m resolution), SoilGrids-SoilGrids250m 2.0 <https://soilgrids.org/>soil (250 m resolution) and Elevation data -
<https://search.asf.alaska.edu/>DEM (12.5 m resolution) as shown in Fig. 2 below.
All inputs were prepared and pre-processed into time series maps, projected into GCS-WGS-84-decimal degrees, resampled to a
resolution of 200 m, and cropped to basin extent using the PCRaster algorithms and GDAL (the Geospatial Data Abstraction Library).
The Wow-Python modelling framework requires special input formats and pre-preparation. Step 1 and Step 2 Python scripts of the
WFlow model were used to create these formats by combining the DEM, land cover, soil and hydrological gauge locations (Schellekens,
2022).
2.2.1.2. Precipitation and temperature. Ethiopian Meteorological Service Agency provided daily rainfall, daily minimum temperature
and daily maximum temperature measurements for 17 stations. Stations data records of the baseline period (1991–2020) underwent a
quality control protocol including the removal of outliers and missing values. Rainfall stations which had signicant number of missing
data (>10%) and extreme outliers were excluded from the analysis leaving 9 stations that were reliable and had a consistent record for
the selected period of analysis.
2.2.1.3. Streamow. Daily river discharge values for Gilgel Abay (1991–2007), Gumera (1991–2020), Ribb (1991–2007) rivers and
the outow river Blue Nile (1991–2000) were collected from the Ethiopian Ministry of Energy and Water Resources, though of poor
quality in terms of missing.
2.2.1.4. Reanalysis, satellite and CMIP6 climate datasets. Major climate variables (precipitation and temperature) were retrieved from
the NASA Earth Exchange (NEX) Earth System Grid Federation Global Daily Downscaled Projections (GDDP) <https://esgf-data.dkrz.
de/projects/esgf-dkrz/>, the Royal Netherlands Meteorological Institute (KNMI-CE) Climate Explorer <https://climexp.knmi.nl/
start.cgi>, the Copernicus Climate Data Store <https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?
tab=form>, the climate engine <https://app.climateengine.com/climateEngine>and climateSERV global <https://climateserv.
servirglobal.net/map>websites.
Retrieved satellite and reanalysis products for precipitation and temperature were Climate Hazards Group Infrared Precipitation
with Stations (CHIRPSv2.0), Evaluation of Global Precipitation Climatology Project (GPCP), Tropical Applications of Meteorology
using SATellite (TAMSAT), National Centers for Environmental Prediction (NCEP- NCAR), the Climatic Research Unit (CRU TS 4.04)
(Harris et al., 2020) and Global Precipitation Climatology Centre (GPCCv2020) (Rudolf et al., 2005), the European Community
Medium-range Weather Forecasts v5 (ECMWF-ERA5) (Hersbach et al., 2020), NOAA-Climate Forecast System Reanalysis v2 (CFSR)
A.A. Alaminie et al.

Journal of Hydrology: Regional Studies 46 (2023) 101343
5
(Hersbach et al., 2020) and NASA-MERRA v2 Modern Reanalysis (Randles et al., 2017).
For the future climate projection and trend analysis of the basin, CMIP6 datasets were extracted from NASA-NEX-ESGF under
medium forcing scenario (SSP2–4.5), medium to high forcing scenario (SSP3–7.0), and a strong forcing scenario (SSP5–8.5). The
detailed descriptions of scenarios are available in (Gidden et al., 2019; O’Neill et al., 2016) and CMIP6 products are summarized in
Table 1 which gives acronyms, resolutions, sources, and references.
2.2.1.5. Potential evapotranspiration (PET). PET were computed from the European Community Medium-range Weather Forecasts v5
(ECMWF-ERA5) and NOAA-Climate Forecast System Reanalysis v2 (CFSR) using the Hargraves method in the R-Evapotranspiration
package. As an alternative input for the Wow_sbm model, MERRA PET computed with Penman-Monteith, and Hargreaves were
directly retrieved from the KNMI-CE and the Climate Engine. Point PET data were then interpolated into a gridded product with a
resolution of 200 m.
2.2.2. CMIP6 products bias correction and evaluation
In this study, the well-known ClimDown ClimDown (A. T. Werner and Cannon, 2016) R-Package with Bias Correction/ Constructed
Analogues with Quantile mapping reordering (BCCAQ) was employed to downscale CMIP6 products. ClimDown is widely applied for
downscaling global climate model (GCM) output to a ne spatial resolution. BCCAQ is a hybrid downscaling method that combines
outputs from Climate Analogues (CA) and quantile mapping at the ne-scale resolution (Gudmundsson et al., 2012; Maurer and Pierce,
2014).
Satellite and reanalysis datasets (CHIRPS, TAMSAT, GPCC, MERRA2, CRU.TS4.05, ECMWF-ERA5, GPCP and NCEP) were validated
with local surface observations using statistical error metrics such as the root mean square error (RMSE), coefcient of determination
(R
2
), percent of bias (PBias) and Nash–Sutcliffe Efciency (NSE). These statistical measures determine different aspects of the data’s
accuracy for the purpose of CMIP6 model validation and selection for climate projections in the Lake Tana basin.
Global climate model (GCMs) simulations commonly exhibit systematic biases in precipitation and temperature. Thus, GCMs needs
to be post-processed to produce reliable estimators of local scale climate. In this study, for precipitation and temperature bias
correction, non-parametric Reboot Quantile Mapping method (RQUNT) was applied. The suitability of RQUNT QM method was
checked using Nash-Sutcliff efciency (NSE) and Mean Absolute Error (MAE) statistical measures of model climatology (1991–2020).
Before evaluating precipitation and temperature for the 24 CMIP6 models, biases were corrected over the basin using the RQUNT
method in the R software package ‘qmap’ developed by the Norwegian Meteorological Institute (Gudmundsson et al., 2012).
The skill of the 24-bias corrected CMIP6 models over the basin against the reanalysis and observations were evaluated through
eight error metrics on R-HydroGOF package. In addition to these error metrics, a Taylor diagram was used to graphically describe how
closely the patterns match ground observations (Taylor, 2001). It considers three error metrics including correlation coefcient,
centered (unbiased) RMSE, and standard deviation in a single diagram to describe the temporal performance of climate models against
local observation and reanalysis (Taylor, 2001). Finally, from these bias corrected CMIP6 models, the best performing models were
selected for future climate projection. The Compromise programming (CP) error minimization method was applied for ranking the
GCMs models (Salman et al., 2020). The CP measures the list distance (Lcp) of each GCM from an ideal value or frontier (Salman et al.,
2020)
Lcp =[∑
z
j=1xj−xj∗
p]1/p
Table 1
List of CMIP6 climate models for the projection of the Lake Tana basin climate.
No. CMIP6 ModelName Res (lon. ×lat.
deg)
References No. CMIP6
ModelName
Res (lon. ×lat.
deg)
References
1. ACCESS-CM2 1 ×1 N/A 13. EC-Earth3 1 ×1 N/A
2. ACCESS-ESM1–5 1 ×1 (Ziehn et al., 2017) 14. EC-Earth3-Veg 1 ×1 N/A
3. BCC-CSM2-MR 1 ×1 (Wu et al., 2019) 15. GFDL-CM4 0.25 ×0.25 (Chen et al., 2021)
4. BCC-ESM1 1 ×1 (Wu et al., 2019) 16. GFDL-ESM4 0.5 ×0.5 N/A
5. CAMS-CSM1–0 1 ×1 (Lurton et al., 2020) 17. GISS-E2–1-H 1 ×1 N/A
6. CESM2 1 ×1 (Danabasoglu et al.,
2020)
18. HadGEM3-GC31-
LL
1×1 N/A
7. CESM2-FV2 1 ×1 (Danabasoglu et al.,
2020)
19. INM-CM5–0 0.5 ×0.5 [47]
8. CESM2-WACCM 1 ×1 (Danabasoglu et al.,
2020)
20. IPSL-CM6A-LR 1 ×1 (Lurton et al., 2020)
9. CESM2- WACCM-
FV2
1×1 N/A 21. MIROC-ES2L 1 ×1 (Hajima et al., 2020)
10. CNRM-CM6–1 1 ×1 (S´
ef´
erian et al., 2019) 22. MIROC6 1 ×1 NA
11. CNRM-ESM2–1 1 ×1 (S´
ef´
erian et al., 2019) 23. MPI-ESM1–2-HR 0.5 ×0.5 (Müller et al., 2018)
12. CanESM5 1 ×1 (Swart et al., 2019) 24. MRI-ESM2–0 1 ×1 (Yukimoto et al.,
2019)
A.A. Alaminie et al.

Journal of Hydrology: Regional Studies 46 (2023) 101343
6
Where:
Z - the number of evaluation metrics used,
Xj- the normalized value of metric j obtained for a certain GCM,
X∗
j- the normalized ideal value of the metric j, and.
p-parameter (1 for linear, and 2 for squared Euclidean distance measures).
2.2.3. Future climate projection and trend analysis
The maximum/minimum temperature and precipitation were analyzed for the baseline period (1991–2020), the near term
(2021–2050), and the long-term (2061–2090) future climate projections. The signicance of temperature and precipitation trends
were then examined for period from 2021 to 2100 using the Mann–Kendall (MK) trend test method (Mann, 1945; Sheng et al., 2002;
Yue et al., 2002). The Z-value was used to detect whether a statistically signicant trend existed. A growing trend in the time series is
shown by a positive Z-value, whereas a falling trend is indicated by a negative Z-value. In this study, signicance (p-value) thresholds
for high (0.01), medium (0.05), and low (0.1) were applied.
2.2.4. Development of the hydrological model
To perform past, current and future hydrological analysis, a distributed hydrological model based on the Wow_PCRaster/Python
framework was developed using the WFLOW model platform (Bouaziz et al., 2018). The WFlow is a state-of-the-art open source
spatially distributed hydrological model developed by the Deltares OpenStreams project (http://www.openstreams.nl). The model is
derived from the CQFLOW model which simulates catchment runoff in both sparse and ample data environments (Schellekens, 2022).
Wow_sbm model is based on TOPOG hydrological tool described in (Vertessy and Elsenbeer, 1999). In this study, Wow_sbm was
chosen for its improved consideration of both inltration and saturation excess runoff generation processes. In the Ethiopian high-
lands, previous research has demonstrated that saturation excess runoff dominates in the sub-humid part of the region during the
monsoonal period – as distinct from saturated areas in the valley bottom (Moges et al., 2017; Tilahun et al., 2015) and degraded areas
could be considered as inltration excess. Fig. 3 shows a schematized representation of Wow_sbm.
The hydrological processes in the Wow_sbm model are represented by three main routines. The Gash model (Gash et al., 1995) is
used to calculate interceptions, which use PET to drive actual evapotranspiration based on soil water content and land cover types. The
Fig. 3. An overview of the different processes and uxes represented by the spatially distributed Wow_sbm model (Schellekens, 2022).
A.A. Alaminie et al.

Journal of Hydrology: Regional Studies 46 (2023) 101343
7
TOPOG sbm calculates the Soil Water Storage (SWS) processes that generate runoff (Vertessy and Elsenbeer, 1999). TOPOG sbm was
specically designed to simulate fast runoff processes; however, Wow_sbm has been signicantly improved to make it more widely
applicable (Schellekens et al., 2011). Kinematic ‘routing’ is used to simulate river drainage and overland ows. To estimate the average
rainfall and transpiration from the wet canopy, rainfall and evaporation in the saturated canopy are calculated for each event. The
remaining water inltrates into the soil and – when the rain falls on partially saturated soil, it directly contributes to surface runoff. At
the same time, evapotranspiration removes some of the soil water. Inltrated water is exchanged between the soil’s unsaturated (U)
and saturated (S) stores (Fig. 3). In Wow_sbm, the soil is considered as a simple bucket model with exponential decay of saturated
hydraulic conductivity (Ksat) with depth (Schellekens et al., 2011).
A topographic wetness index is used to identify and scale the soil depth of the various land cover types in the model (Vertessy and
Elsenbeer, 1999). The runoff is calculated for each grid cell with the whole depth of the cell divided into saturated and unsaturated
zones as the model is fully distributed (Fig. 3). The model uses Darcy’s equation to simulate lateral ow from the saturated zone. The
total runoff from a given catchment is the sum of surface runoff and lateral ow discharged from the river network after routing
according to slopes in the high-resolution DEM.
Lake Tana is considered in Wow_sbm using a mass balance Modied Puls Approach that uses an explicit relationship between
storage and outow to quantify water balance. This makes it possible to calibrate Lake Tana’s outlet, which wasn’t done in earlier
investigations (Setegn et al., 2008). Method description: <https://wow.readthedocs.io/en/latest/wow_funcs.html?
highlight=lake#natural-lakes>.
Model parameters are linked to the Wow model through lookup tables, to generate input parameter maps for land use, soil type,
and sub-catchment. Further details of the Wow model can be found at <https://media.readthedocs.org/pdf/wow/latest/wow.
pdf>. Wow_sbm parameter estimation is based on spatial datasets that provide information on soil properties, soil depth, rooting
depth, and etc. (Imhoff et al., 2020), developed a method for parameterizing the model for any basin in the world using regionalization
methods based on (pedo) transfer functions and upscaling techniques from the literature. Here in this study, the model was run with
Chirps and PET calculated from ERA5 and CFSR reanalysis. Wow_sbm performance was evaluated for the baseline period
(1991–2020) against available daily discharge measurements at the Gumera, Gilgel Abay, Ribb and the basin outlets.
2.2.5. Model calibration and validation
Calibrating a spatially distributed model assigns different parameter values to different grid cells, which poses a risk of articial
parameterization and has computational demands at ne resolutions that inhibits model applications to large basins (Beven, 2006).
Here, the OSTRICH calibration toolkit was automated to calibrate the Wow_sbm simulations. OSTRICH has been created as a
model-independent multi-algorithm optimization and parameter estimation program that allows researchers to automate the pro-
cesses of model calibration and design optimization.
Fig. 4. Flowchart for input data pre-processing, point to grid conversion, model calibration and validation, future climate projection and
streamow analysis.
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Journal of Hydrology: Regional Studies 46 (2023) 101343
8
OSTRICH can be congured to work with any modeling program that uses text-based input and output le formats. Further details
can be found on the Ostrich website <http://www.civil.uwaterloo.ca/lsmatott/Ostrich/OstrichMain.html>. In this study DDS
(Dynamically Dimensioned Search algorithm) (Tolson and Shoemaker, 2007) and statistical error metrics (PBIAS and NSE) were used
to verify whether the predicted and observed streamow agreed.
Daily river discharges at three tributaries and the Lake Tana basin outlet (Gumera, Gilgel Abay, Ribb and Abbay) gauging stations
were used for model calibration and validation. The Wow_sbm model provides an option to consider the Lake Tana in the simulation
period even with abstraction of water from the lake for irrigation and hydropower purposes (after 2003) that is undocumented.
2.2.6. Flood prediction under CMIP6 Model Scenarios
To investigate the possible impacts of climate change on the ood risk, targeted CMIP6 scenarios were chosen from medium-range
to high-forcing scenarios. Model outputs for the SSP245 (medium forcing), SSP370 (medium to high forcing) and SSP585 (strong
forcing) pathway were taken from 5 ensemble means of best-performing models (described in section 2.4) for ood frequency analysis.
The Wow_sbm model for the Lake Tana basin was run for a reference period from 1991–2020 and for the three climate projections
scenarios of the near term (2021–2050) projection.
There is currently no universally acceptable ood frequency analysis method; rather, ranges of models are taken into consideration
(Ukumo et al., 2022).
To understand the effects of climate change on extreme river discharges, an R-script for probability distribution methods (Gumbel-
EVI, Log-normal, Generalized Pareto, and Generalized Logistics) were developed. Based on the L-Moment Ratio diagram, Gumbel and
log-normal distribution methods were chosen to compute the ood magnitudes for 2, 5, 10, 50, 100, 200, 500 and 1000 return periods.
The owchart, which outlines our methodology for ood prediction is presented in Fig. 4 below.
3. Results
3.1. Satellite and reanalysis climate datasets validation (1991–2020)
The robust empirical quantiles (RQUANT) were used to reduce biases of satellite and reanalysis climate variables (precipitation and
temperature). Then, validation of CHIRPSv2.0, GPCP, TAMSATv3, NCEP- NCAR, CRU TS 4.04, GPCCv2020, ECMWF-ERA5, NOAA-
CFSR v2 and NASA-MERRA v2 using area-averaged of nine Lake Tana basin gauging stations observations for the baseline period
(1991–2020) showed that satellite-gauge precipitation data CHIRPSv2 (NSE =0.74, R
2
=0.76, KGE =0.87, NRMSE =0.5) out-
performed the other products. For maximum temperature, ERA5 (NSE =0.92, R
2
=0.76, KGE =0.87, NRMSE =27.7) performed well
while CRU4 (NSE =0.80, R
2
=0.81, KGE =0.90, NRMSE =0.45) was best for minimum temperature.
Eg. Agreeing with local observations as shown in Table 2(a,b) and scatter plots of Fig. 5(a,b,c). The bias of all datasets was corrected
by local observation before evaluation, as a result, the percent of bias (PBIAS) computation shows null values for all temperature
datasets. Table 2(a, b) below summarizes the performance of the satellite/reanalysis precipitation and temperature products for the
study area.
3.2. CMIP6 products bias correction and evaluation
The robust empirical quantiles (RQUANT) quantile mapping bias correction of satellite, reanalysis and 24 CMIP6 (rainfall and
temperature) products were performed (Appendix-A). The Nash– Sutcliff efciency (NSE) and mean absolute error (MAE) values
between the ground observation and the corrected showed that the robust empirical quantiles (RQUANT) was able to remove biases in
Table 2
a. Reanalysis performance with Lake Tana basin area-averaged temperature (
o
C/month) for the baseline period (1991–2020). b. Reanalysis products
performance with Lake Tana basin area-averaged of monthly precipitation(mm/month) for the baseline period (1991–2020).
Reanalysis Temp NRMSE PBIAS RSR rSD NSE md R
2
KGE
Maximum Temperature(
o
C/month)
ERA5 27.70 0.00 0.28 1.00 0.92 0.87 0.92 0.96
CRU 0.35 0.00 0.35 1.00 0.88 0.83 0.88 0.94
MERRA 70.70 0.00 0.71 1.00 0.50 0.67 0.56 0.75
Minimum Temperature(
o
C/month)
CRU 0.45 0.00 0.44 1.00 0.80 0.80 0.81 0.90
ERA5 67.10 0.00 0.67 1.00 0.55 0.69 0.60 0.77
MERRA 130.90 0.00 1.31 1.00 -0.72 0.39 0.02 0.14
Reanalysis Precip. (mm/month) NRMSE PBIAS rSD NSE md R
2
KGE RSR
CHIRPS 0.50 0.02 1.01 0.74 0.78 0.76 0.87 0.50
TAMSAT 54.30 -0.40 1.02 0.70 0.75 0.73 0.85 0.54
GPCC 60.90 -2.60 1.08 0.63 0.73 0.69 0.81 0.61
MERRA2 62.30 -3.10 1.08 0.61 0.73 0.68 0.80 0.62
CRU.TS4.05 64.10 -1.60 1.06 0.59 0.70 0.65 0.80 0.64
ERA5 63.10 0.10 0.99 0.60 0.70 0.64 0.80 0.63
GPCP 70.80 -0.90 1.02 0.49 0.68 0.57 0.75 0.71
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Journal of Hydrology: Regional Studies 46 (2023) 101343
9
CMIP6 models’ precipitation.
The performance of 24 CMIP6 models under different shared socio-economic pathways (SSPs) in simulating future climate vari-
ables (precipitation and temperature) is listed in Appendix-A based on efciency criteria found under ‘hydroGOF’ R package. After
evaluating 24 CMIP6 models against area-average surface observations and applying the compromise programming (CP) error
minimization method, the top ve GCMs models for precipitation: AWI.CM.1.1. MR, EC.Earth3, EC.Earth3. Veg, MPI.ESM1.2. HR and
GFDL.ESM4, for maximum temperature: GFDL.ESM4, CanESM5. CanOE.p2, CanESM5.p2, CanESM5.p1 and GISS.E2.1.G.p3, and for
minimum temperature: CanESM5.p1, CanESM5.p2, CanESM5. CanOE.p2, MIROC.ES2L.f2 and CNRM.CM6.1. HR.f2. These were
selected for future climate projection.
Fig. 6(a,b,c) Taylor diagrams show that most of the CMIP6 evaluated models effectively capture the temporal changes in pre-
cipitation, minimum and maximum temperature over the Lake Tana basin. Fig. 6(a,b,c). Of these, precipitation (AWI.CM.1.1. MR, EC.
Earth3, EC.Earth3. Veg, MPI.ESM1.2. HR and GFDL.ESM4), maximum temperature (GFDL.ESM4, CanESM5. CanOE.p2, CanESM5.p2,
CanESM5.p1 and GISS.E2.1.G.p3), and minimum temperature (CanESM5.p1, CanESM5.p2, CanESM5. CanOE.p2, MIROC.ES2L.f2 and
CNRM.CM6.1. HR.f2 best-performed, as evident by the shorter distances between the red and black overlapped points representing the
observed, CHIRPS, ERA5 and CRU4.
The Taylor diagrams reveal that the selected models have the highest correlation coefcients and lowest standard deviation
compared with station precipitation data, ERA5 maximum temperature and CRU4 minimum temperature. By contrast, some of GCM
models for Precipitation (CNRM.ESM2.1.f2, MRI.ESM2.0, CMCC.CM2. SR5), for Tmax (CNRM.CM6.1. HR.f2, IPSL.CM6A.LR, FGOALS.
g3) and for Tmin (EC.Earth3.1, CNRM.ESM2.1.f2, MPI.ESM1.2. LR, CMCC.CM2. SR5) had lower CC or larger standard deviations,
indicating their poor performances on capturing the temporal changes.
Temperature and precipitation climatology were computed using the ensemble mean of ve GCMs models, ECMWF-ERA5, CRU,
CHIRPS and observed area-averaged of the Tana gauging stations, for the period 1991–2020. Fig. 7 below shows plots of the ensemble
mean of selected 5 top CMIP6 models for maximum temperature, minimum temperature and precipitation with the corresponding
observation data.
Fig. 5. (a,b,c). Scatterplot of observed and reanalysis a) Precipitation(mm/month), b) minimum temperature (
o
C) and c) maximum temperature
(
o
C) products for the period 1991–2020.
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Journal of Hydrology: Regional Studies 46 (2023) 101343
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The non parametric Mann–Kendall trend tests of the selected ve GCMs ensemble mean precipitation projection in the rainy season
JJAS (June, July, August, September) for the period 2031–2100 under SSP2–4.5, SSP3–3.7, and SSP4–8.5 scenarios showed an
increasing trend (Fig. 8a). The annual mean time series of ve GCMs ensemble mean maximum and minimum temperature for
SSP2–4.5, SSP3–3.7, and SSP4–8.5 scenarios also showed an increasing trend with the Z value (3.4–12.25) and p-value of less than
0.0004 which indicates that high signicance levels. During the twenty-rst century, continuous warming was projected as shown in
Fig. 8(b,c). For all scenarios of ve GCMs ensemble mean precipitation and ve GCMs ensemble mean maximum/minimum tem-
perature trend test, it was noted that the rainy season (JJAS) precipitation showed signicantly increasing trend except for SSP3–3.7
whereas, the annual maximum temperature trend was increasing with statistically high signicance except in the low forcing scenario
(SSP2–4.5) as shown in Fig. 8(a,b,c).
Fig. 6. (a,b,c). Taylor diagram comparing the temporal performances of 24 CMIP6 models precipitation(mm/month), maximum temperature (
o
C)
and maximum temperature (
o
C) against ERA5, CRU4 and ground observation for the baseline period (1991–2020).
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Journal of Hydrology: Regional Studies 46 (2023) 101343
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3.3. Wow_sbm model calibration and validation
Using historical reanalysis climate data (described under Section 3.1) and measured streamow data, the Wow_sbm model was
calibrated and validated at the sub-watershed level for Gumera (1991–2018), Gilgel Abay (1991–2000), Ribb (1995–2009), and for the
Tana watershed (1991–2000). The model has 14 land-subsurface and river ow parameters that can be calibrated. Sensitive pa-
rameters were identied in the study areas using ‘Scaling Point-Scale (Pedo) transfer Functions to Seamless Large-Domain Parameter
Estimates for High-Resolution Distributed Hydrologic Modeling’ (Imhoff et al., 2020). In the appendix, Table C2-C5 shows the optimal
parameters and their ranges as obtained through the OSTRICH-DDS algorithm. The most sensitive parameters controlling the outow
were saturated hydraulic conductivity (Ksat), residual water content (thetaR), Canopy Gap Fraction, M parameter (decay of hydraulic
conductivity with depth), and Water content at saturation (thetaS) and Maximum depth of the soil (SoilThickness). The model per-
formance indices for calibration and validation are shown in Table 3 below. Fig. 9(a-d) illustrates the observed and simulated
streamow plots for the calibration and validation period in the Lake Tana watershed and its three sub-watersheds.
The Wow_sbm modelling results demonstrate the modelling approach’s promise and indicate that it can be used to predict the
runoff and seasonal oods in the basin. PBIAS indicates whether the model over- or under-estimates streamow.At the Lake Tana basin
outlet, the streamow was underestimated by 13%, during calibration and 17% during validation.
For all tributaries and the Lake Tana Basin outlet, Peak ow predictions varied above and below gauge, likely due to inherent
uncertainty in the forcing data over complex topography. The model performed well in producing the spatial distribution and
magnitude of different hydrological states such as saturated water depth and river runoff as shown in Fig. 10(a-g) below.
3.4. Flood frequency analysis under projected CMIP6 scenarios
The projected climate, static maps and calibrated parameters were used to simulate the future runoff under three scenarios. Fig. 11
Fig. 7. (a,b,c). Temporal distribution of CMIP6 ve model ensemble mean, (a) ERA5-maximum temperature (
o
C/month), (b) CRU4-minimum
temperature (
o
C/month) and (c) Chirps precipitation (mm/month).
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Journal of Hydrology: Regional Studies 46 (2023) 101343
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shows the simulated discharge at the Lake Tana Basin outlet for three projected scenarios (SSP245, SSP370 and SSP585). As with the
expected precipitation and temperature, there is no discernible rise from the baseline period for the near-term (2021–2050) runoff
simulation.
Fig. 8. (a,b,c). Top ve ensemble mean model projections under SSP245, SSP370, and SSP585 scenarios (a) mean Precipitation (mm), (b) Tmin(
o
C),
(c) Tmax(
o
C) annual cycle.
Table 3
The Wow_sbm model performance indices values for calibration and validation periods of four watersheds.
Watersheds Calibration Validation
NSE R
2
PBIAS KGE RSR rSD NRMSE md NSE R
2
Lake Tana Basin 0.78 0.78 -14 0.86 0.47 0.92 46.6 0.78 0.82 0.85
Gilgel Abay 0.67 0.68 6.8 0.78 0.56 0.87 56.5 0.75 0.67 0.68
Gumera 0.64 0.66 -15.3 0.63 0.60 0.70 59.9 0.72 0.65 0.66
Ribb 0.49 0.50 -18.9 0.54 0.72 0.71 72.4 0.72 0.46 0.50
A.A. Alaminie et al.

Journal of Hydrology: Regional Studies 46 (2023) 101343
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Maximum Annual Discharge series were generated from simulated daily streamow data and tted to four probability distribution
functions to obtain the risk of occurrence of a ood event.
Gumbel and log-normal distributions methods passed the goodness-of-t test based on the L-Moment Ratio diagram, and so were
chosen as the optimal method to compute oods in 2, 5, 10, 50, 100, 200, 500, and 1000 return periods; as shown in Fig. 12. The results
and developed ood frequency curve are presented in Table 4.
While the medium to high scenario SSP3–7–0 resulted approximately similar ood discharge, the medium forcing and strong
forcing resulted higher ood discharge for various return periods. On average, the medium forcing SSP2–4–5 increased the ood by 4%
and the strong forcing increased by 9% from historical ood magnitudes.
Fig. 9. Observed (pink) and simulated (blue) discharge and rainfall (upper) in (a) Gumera, (b) Gilgel Abbay, (c) Ribb and (d) Lake Tana basin outlet
for the calibration (C) and validation (V) period.
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Journal of Hydrology: Regional Studies 46 (2023) 101343
14
Fig. 10. The Wow_sbm model run out puts of saturated water depth (a) Gumera, (c) Gilgel Abay, (e) Ribb, (g) Lake Tana Basin outlet, which
indicates the possible ooding areas and river runoff magnitudes at (a) Gumera, (c) Gilgel Aba, (e). Ribb.
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Journal of Hydrology: Regional Studies 46 (2023) 101343
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4. Discussions
4.1. CMIP6 climate models evaluation and projection
In past hydrological research in the upper Blue Nile catchment (Conway and Hulme, 1993; Getachew et al., 2021; Jury, 2014;
Legesse Gebre, 2015; Roth et al., 2018), the reanalysis and satellite products were used without validation and systemic bias correction
with local observation. In this study, bias correction was done using Robust Quantile mapping and validation via 9 stations forming an
area-averaged observation in a baseline period (1991–2020). Results revealed that CHIRPS precipitation and reanalysis temperature
data from ERA5 and CRU4 were optimal, in line with previous studies in the basin(Dinku et al., 2018; Fenta Mekonnen and Disse,
2018) and were therefore used as forcing of Wow_sbm here. As a result, the Lake Tana basin’s climate projections and model choices
are more condently made.
Despite uncertainties amongst model projections of future climate (Almazroui et al., 2017; Galavi et al., 2019a, 2019b; Galavi et al.,
Fig. 11. Simulated discharge at the Lake Tana Basin outlet for three projected scenarios’ (SSP245, SSP370 and SSP585.
Fig. 12. Flood Frequency Analysis for Lake Tana basin under projected climate change.
Table 4
Probable peak discharge estimated for SSP245, SSP370 and SSP585 of three scenarios at Lake Tana basin outlet.
Return Period (T) Simulated Flow
CMIP6_ SSP245
Simulated Flow
CMIP6_ SSP370
Simulated Flow
CMIP6_ SSP585
Observed Flow Basline Period
2 625.6 559.8 662.3 569.8
5 704.1 655.4 734.9 665.4
10 724.8 683.8 756.2 693.8
50 737.4 703.6 770.8 713.6
100 738.5 705.7 772.3 715.7
200 738.9 706.6 773.0 716.6
500 739.1 707.1 773.3 717.1
1000 739.2 707.3 773.4 717.3
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Journal of Hydrology: Regional Studies 46 (2023) 101343
16
2022; Galavi and Mirzaei, 2020; Knutti and Sedl´
aˇ
cek, 2013). In this study, the top ve GCMs for precipitation, maximum temperature,
and minimum temperature were chosen for future climate projection and trend analysis after evaluating 24 CMIP6 models. Several
previous studies projected the basin’s future climate using different generations of models and produced different results (Alaminie
et al., 2021b).
Most studies predict a 1–7
o
C increase in temperature by the end of the twenty-rst century. The results of this study’s maximum
and minimum temperature change projections using SSP245, SSP370, and SSP585 are similar to those found in (Alaminie et al., 2021;
Legesse, 2016). On the other hand, the direction and intensity of precipitation change in Lake Tana sub-basins is ambiguous, and
dominated by multi-year dry and wet spells that overwhelm the background trend, notwithstanding various downscaling methods,
climate models, and emission scenarios (Fenta et al., 2018; Getachew et al., 2021; Jury, 2014; Legesse Gebre, 2015).
4.2. Wow_sbm model development and ood prediction
The open-source Wow- PCRaster/Python modelling framework is advantageous over other distributed hydrological models,
because of its exibility to enable a dynamic consideration of both inltration and saturation excess runoff generation mechanism
(Hassaballah et al., 2017; M. Werner et al., 2013). In the Ethiopian highlands, previous research showed that the saturation excesses
runoff mechanism generates overland ow during the wet season (Tilahun et al., 2015; Zimale et al., 2018). Saturation occurs when
the water-table rises and incoming rainfall exceeds outow across the landscape, usually in valleys or adjacent degraded hillsides
(Tilahun et al., 2015).
The calibrated Wow_sbm model demonstrated promising outcomes for all tributaries and the basin itself and, shows ability to
simulate rainfall-runoff in data-poor environments.
The overland ow in the valley bottoms close to the rivers (cf. Fig. 10) are similar to (Easton et al., 2010; Zimale et al., 2018), and
consistent with eld observations around Lake Tana (Moges et al., 2017).
The Modied Puls Approach (MPA) method of the lake balance uses an explicit relationship between storage and outow that
makes it possible to Lake Tana’s outlet to the Blue Nile with good performance, improving on earlier investigations (Setegn et al.,
2008) that used SWAT model, which simulates hillside runoff as a function of rainfall – discharge (Getachew and Manjunatha, 2022).
Wow_sbm performed better because of non-inltrating excess water became runoff regardless of rainfall intensity (Dagnew et al.,
2015).
In addition, most research using SWAT (Dile et al., 2013; Easton et al., 2010; Setegn et al., 2008; van Griensven et al., 2012) that
conducted simulations at subbasin level, found model deciencies from poorly measured streamow and unknown abstraction of
water from the lake for irrigation and hydropower purposes.
In terms of simulating runoff and ood events (1991, 1998, 2006, 2017), the Wow_sbm distributed hydrological model in the Blue
Nile basin showed that the locations of overland runoff areas correspond well with eld observations, likely because increased spatial
resolution, better characterization of the drainage network, better delineation of the basin and its sub-basins, and more accurate
parameterization of soil and land cover. The Wow_sbm discharge and ood estimates improved on previous studies using the SWAT
Model (Setegn et al., 2008).
The projected maximum annual discharge under different climate scenarios gave probability distribution functions to obtain the
risk of occurrence of a ood event, which point to climate adaptation and mitigation strategies. The optimal PDF were Gumbel and
Lognormal, comparable to (Fekadu et al., 2017; Ukumo et al., 2022) whose distributions passed the goodness-of-t test for three future
scenarios. Further studies within the Tana basin over longer time periods will be important for a comprehensive evaluation of the
model, to reveal the basin’s hydro-meteorological temporal and spatial variability. However, this research serves as an introduction to
the added value of Wow_sbm hydrological models and climate-forcing products for estimating water resources and ood events
within a poorly gauged river basin.
4.3. Conclusion
In this study, WFlow PCRaster/Python hydrological modelling framework with high-resolution CMIP6 GCM datasets were used to
simulate discharge and maximum annual ood in the Lake Tana basin. The Wow_sbm calibration and validation results demonstrated
that the modelling approach reasonably identied overland runoff areas in Lake Tana Basin. Moreover, it simulated peak ood events
in the three tributaries. The coupling of this model with climate model scenarios of CMIP6 resulted a forecasting maximum annual
discharge in the basin that helps decision makers to plan for adaptation and mitigation. In general, this study showed that Wow_sbm
can be used to compute rainfall-runoff simulation in data-poor environments. This study serves therefore as a major step towards the
development and implementation of a climate model product-driven hydrological model to assess ood risks around Lake Tana basin
under a globally warmed future. However, uncertainty analysis aggregated with proper choice of GCMs, emission scenarios, down-
scaling techniques, and hydrological modeling parameters and techniques was not addressed in this study. As uncertainty contribution
of each source can vary at different temporal and spatial scales, integrated uncertainty analysis and exploring the contribution of
uncertainty sources could be potential future research area in local-scale impact studies.
CRediT authorship contribution statement
Addis A. Alaminie: Conceptualization, Methodology, Data curation, Software, Formal analysis, Investigation, Writing - original
draft, Giriraj Amarnath: Conceptualization, Methodology, Project administration, Supervision, Funding acquisition Suman Kumar
A.A. Alaminie et al.

Journal of Hydrology: Regional Studies 46 (2023) 101343
17
Padhee: Conceptualization, Methodology, Software, Resources, Supervision, Funding acquisition, Surajit Ghosh: Conceptualization,
Project administration, Supervision, Funding acquisition, Seifu A. Tilahun: Conceptualization, Methodology, Software, Writing -
review & editing, Supervision, Muluneh A. Mekonnen: Conceptualization, Methodology, Software, Resources, Writing - review &
editing, Getachew Assefa: Writing - review & editing, Abdulkarim Seid: Writing - review & editing, Fasikaw A. Zimale: Resources,
Software, Mark R. Jury: Conceptualization, Methodology, Investigation, review & editing, Supervision.
Declaration of Competing Interest
The authors declare the following nancial interests/personal relationships which may be considered as potential competing in-
terests: Addis Aschenik Alaminie reports nancial support was provided by International Water Management Institute.
Data availability
Data will be made available on request.
Acknowledgements
The work is funded through the Helmsley Foundation to the International Water Management Institute (IWMI) as part of the Digital
Innovation of Water Secure Africa Initiative (DIWASA). The Ethiopian National Meteorological Agency and the Ethiopian Ministry of
Water Resources provide hydroclimate data. The rst author recognises the support from Dr Birhanu Zemadim Birhanu.
Appendices
Supplementary material related to this article can be found, in the online version.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ejrh.2023.101343.
References
Ajami, N.K., Hornberger, G.M., Sunding, D.L., 2008. Sustainable water resource management under hydrological uncertainty. Water Resour. Res. 44 (11) https://doi.
org/10.1029/2007WR006736.
Alaminie, A.A., Tilahun, S.A., Legesse, S.A., Zimale, F.A., Tarkegn, G.B., Jury, M.R., 2021a. Evaluation of past and future climate trends under CMIP6 scenarios for the
UBNB (Abay), Ethiopia. Water (Switz. ) 13 (15). https://doi.org/10.3390/w13152110.
Almazroui, M., Nazrul Islam, M., Saeed, S., Alkhalaf, A.K., Dambul, R., 2017. Assessment of Uncertainties in Projected Temperature and Precipitation over the Arabian
Peninsula Using Three Categories of Cmip5 Multimodel Ensembles. Earth Syst. Environ. 1 (2) https://doi.org/10.1007/s41748-017-0027-5.
Beven, K., 2006. A manifesto for the equinality thesis. J. Hydrol. 320 (1–2), 18–36. https://doi.org/10.1016/j.jhydrol.2005.07.007.
Bouaziz, L., Weerts, A., Schellekens, J., Sprokkereef, E., Stam, J., Savenije, H., Hrachowitz, M., 2018. Redressing the balance: Quantifying net intercatchment
groundwater ows. Hydrol. Earth Syst. Sci. 22 (12), 6415–6434. https://doi.org/10.5194/hess-22-6415-2018.
Brown, P., Daigneault, A.J., Tjernstr¨
om, E., Zou, W., 2018a. Natural disasters, social protection, and risk perceptions. World Dev. 104, 310–325. https://doi.org/
10.1016/j.worlddev.2017.12.002.
Chen, H.C., Fei-Fei-Jin, Zhao, S., Wittenberg, A.T., Xie, S., 2021. ENSO dynamics in the E3SM-1-0, CESM2, and GFDL-CM4 climate models. Journal of Climate 34 (23),
9365–9384. Dec.
Cloke, H.L., Pappenberger, F., 2009. Ensemble ood forecasting: A review. J. Hydrol. 375 (3–4), 613–626. https://doi.org/10.1016/j.jhydrol.2009.06.005.
Conway, D, Hulme, M, 1993. Recent uctuations in precipitation and runoff over the Nile sub-basins and their impact on main Nile discharge. Climatic change 25 (2),
127–151. Oct.
du Plessis, L.A. (2002). A review of effective ood forecasting, warning and response system for application in South Africa (Vol. 28, Issue 2). 〈http://www.wrc.org.za〉.
Dagnew, D.C., Guzman, C.D., Zegeye, A.D., Tibebu, T.Y., Getaneh, M., Abate, S., Zemale, F.A., Ayana, E.K., Tilahun, S.A., Steenhuis, T.S., 2015. Impact of
conservationractices on runoff and soil loss in the sub-humid Ethiopian Highlands: The Debre Mawi watershed. J. Hydrol. Hydromech. 63 (3), 210–219. https://
doi.org/10.1515/johh-2015-0021.
Danabasoglu, G., Lamarque, J.F., Bacmeister, J., Bailey, D.A., DuVivier, A.K., Edwards, J., Emmons, L.K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C.,
Holland, M.M., Large, W.G., Lauritzen, P.H., Lawrence, D.M., Lenaerts, J.T.M., Lindsay, K., Lipscomb, W.H., Mills, M.J., Strand, W.G., 2020a. The Community
Earth System Model Version 2 (CESM2. J. Adv. Model. Earth Syst. 12 (2) https://doi.org/10.1029/2019MS001916.
Desalegn, A., Demissie, S., Admassu, S., 2016. Extreme Weather and Flood Forecasting and Modelling for Eastern Tana Sub Basin, Upper Blue Nile Basin, Ethiopia.
J. Waste Water Treat. Anal. 7 (3) https://doi.org/10.4172/2157-7587.1000257.
di Baldassarre, G., Montanari, A., Lins, H., Koutsoyiannis, D., Brandimarte, L., Blschl, G., 2010. Flood fatalities in Africa: From diagnosis to mitigation. Geophys. Res.
Lett. 37 (22) https://doi.org/10.1029/2010GL045467.
Dile, Y.T., Berndtsson, R., Setegn, S.G., 2013. Hydrological Response to Climate Change for Gilgel Abay River, in the Lake Tana Basin - Upper Blue Nile Basin of
Ethiopia. PLoS ONE 8 (10). https://doi.org/10.1371/journal.pone.0079296.
Dinku, T., Funk, C., Peterson, P., Maidment, R., Tadesse, T., Gadain, H., Ceccato, P., 2018. Validation of the CHIRPS satellite rainfall estimates over eastern Africa.
Q. J. R. Meteorol. Soc. 144, 292–312. https://doi.org/10.1002/qj.3244.
Easton, Z.M., Fuka, D.R., White, E.D., Collick, A.S., Biruk Ashagre, B., McCartney, M., Awulachew, S.B., Ahmed, A.A., Steenhuis, T.S., 2010a. A multi basin SWAT
model analysis of runoff and sedimentation in the Blue Nile, Ethiopia. Hydrol. Earth Syst. Sci. 14 (10), 1827–1841. https://doi.org/10.5194/hess-14-1827-2010.
Fekadu, A., Teka, D., Teka, K., 2017. Integration of Remote Sensing and Hydraulic Models to Identify Flood Prone Areas in Woybo River Catchment, South Western
Ethiopia. J. Geogr. Nat. Disasters 07 (01). https://doi.org/10.4172/2167-0587.1000190.
A.A. Alaminie et al.

Journal of Hydrology: Regional Studies 46 (2023) 101343
18
Fenta, A.A., Yasuda, H., Shimizu, K., Ibaraki, Y., Haregeweyn, N., Kawai, T., Belay, A.S., Sultan, D., Ebabu, K., 2018. Evaluation of satellite rainfall estimates over the
Lake Tana basin at the source region of the Blue Nile River. Atmos. Res. 212, 43–53. https://doi.org/10.1016/j.atmosres.2018.05.009.
Fenta Mekonnen, D., Disse, M., 2018. Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques. Hydrol. Earth Syst.
Sci. 22 (4), 2391–2408. https://doi.org/10.5194/hess-22-2391-2018.
Fleming, A., Vanclay, F., Hiller, C., Wilson, S., 2014. Challenging dominant discourses of climate change. Clim. Change 127 (3–4), 407–418. https://doi.org/10.1007/
s10584-014-1268-z.
Galavi, H., Mirzaei, M., 2020. Analyzing Uncertainty Drivers of Climate Change Impact Studies in Tropical and Arid Climates. Water Resour. Manag. 34 (6),
2097–2109. https://doi.org/10.1007/s11269-020-02553-0.
Galavi, H., Kamal, M.R., Mirzaei, M., Ebrahimian, M., 2019a. Assessing the contribution of different uncertainty sources in streamow projections. Theor. Appl.
Climatol. 137 (1–2), 1289–1303. https://doi.org/10.1007/s00704-018-2669-0.
Galavi, H., Mirzaei, M., Yu, B., Lee, J., 2022. Bootstrapped ensemble and reliability ensemble averaging approaches for integrated uncertainty analysis of streamow
projections. Stoch. Environ. Res. Risk Assess. https://doi.org/10.1007/s00477-022-02337-5.
Gash, J.H.C., Lloyd, C.R., Lachaudb, G., 1995. Estimating sparse forest rainfall interception with an analytical model. J. Hydrol. Vol. 170.
Gashaw, W., Legesse, D., 2011. Flood Hazard and Risk Assessment Using GIS and Remote Sensing in Fogera Woreda, Northwest Ethiopia. Nile River Basin. Springer,,
Netherlands, pp. 179–206. https://doi.org/10.1007/978-94-007-0689-7_9.
Getachew, B., Manjunatha, B.R., 2022. Impacts of Land-Use Change on the Hydrology of Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia. Glob. Chall. 6 (8),
2200041. https://doi.org/10.1002/gch2.202200041.
Getachew B, Manjunatha BR, Bhat HG. Modeling projected impacts of climate and land use/land cover changes on hydrological responses in the Lake Tana Basin,
upper Blue Nile River Basin, Ethiopia. Journal of Hydrology. 2021 Apr 1;595:125974.
Gidden, M.J., Riahi, K., Smith, S.J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D.P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J.C., Frank, S.,
Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Takahashi, K., 2019. Global emissions pathways under different socioeconomic scenarios
for use in CMIP6: A dataset of harmonized emissions trajectories through the end of the century. Geosci. Model Dev. 12 (4), 1443–1475. https://doi.org/10.5194/
gmd-12-1443-2019.
Gudmundsson, L., Bremnes, J.B., Haugen, J.E., Engen Skaugen, T., 2012. Technical Note: Downscaling RCM precipitation to the station scale using quantile mapping –
a comparison of methods. Hydrol. Earth Syst. Sci. Discuss. 9 (5), 6185–6201. https://doi.org/10.5194/hessd-9-6185-2012.
Habibi, B., Meddi, M., Torfs, P.J.J.F., Remaoun, M., van Lanen, H.A.J., 2018. Characterisation and prediction of meteorological drought using stochastic models in the
semi-arid Ch´
eliff–Zahrez basin (Algeria. J. Hydrol.: Reg. Stud. 16 (March), 15–31. https://doi.org/10.1016/j.ejrh.2018.02.005.
Hagos, B. (2011). Hydraulic Modeling and Flood Mapping of Fogera Flood Plain Hydraulic Modeling and Flood Mapping Of Fogera Flood Plain: A Case Study of Gumera River
School of Graduate Study,Civil Engineering Department,Hydropower Engineering Stream.
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M.A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K.,
Watanabe, S., Kawamiya, M., 2020. Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geosci.
Model Dev. 13 (5), 2197–2244. https://doi.org/10.5194/gmd-13-2197-2020.
Harris, I., Osborn, T.J., Jones, P., Lister, D., 2020. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7 (1), 1–18. https://
doi.org/10.1038/s41597-020-0453-3.
Hassaballah, K., Mohamed, Y., Uhlenbrook, S., Biro, K., 2017a. Analysis of streamow response to land use and land cover changes using satellite data and
hydrological modelling: Case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia-Sudan). Hydrol. Earth Syst. Sci. 21 (10), 5217–5242. https://doi.
org/10.5194/hess-21-5217-2017.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Hor´
anyi, A., Mu˜
noz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S.,
Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Th´
epaut, J.N., 2020. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146 (730),
1999–2049. https://doi.org/10.1002/qj.3803.
Imhoff, R.O., van Verseveld, W.J., van Osnabrugge, B., Weerts, A.H., 2020. Scaling Point-Scale (Pedo)transfer Functions to Seamless Large-Domain Parameter
Estimates for High-Resolution Distributed Hydrologic Modeling: An Example for the Rhine River. Water Resour. Res. 56 (4) https://doi.org/10.1029/
2019WR026807.
Jury, M.R., 2014. Evaluation of coupled model forecasts of ethiopian highlands summer climate. Adv. Meteorol. 2014. https://doi.org/10.1155/2014/894318.
Karssenberg, D., 2002. The value of environmental modelling languages for building distributed hydrological models. Hydrol. Process. 16 (14), 2751–2766. https://
doi.org/10.1002/hyp.1068.
Knutti, R., Sedl´
aˇ
cek, J., 2013. Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Clim. Change 3 (4), 369–373. https://doi.org/10.1038/
nclimate1716.
Kure, S., Tebakari, T., 2012. Hydrological impact of regional climate change in the Chao Phraya River Basin, Thailand. Hydrol. Res. Lett. 6 (0), 53–58. https://doi.
org/10.3178/hrl.6.53.
Legesse, S.A., 2016. The outlook of Ethiopian long rain season from the global circulation model. Environ. Syst. Res. 5 (1), 1–16. https://doi.org/10.1186/s40068-
016-0066-1.
Legesse Gebre, S., 2015. Hydrological Response to Climate Change of the Upper Blue Nile River Basin: Based on IPCC Fifth Assessment Report (AR5). J. Climatol.
Weather Forecast. 03 (01) https://doi.org/10.4172/2332-2594.1000121.
Loudyi, D., Kantoush, S.A., 2020. Flood risk management in the Middle East and North Africa (MENA) region. In: In Urban Water Journal, Vol. 17. Taylor and Francis
Ltd, pp. 379–380. https://doi.org/10.1080/1573062X.2020.1777754.
Lurton, T., Balkanski, Y., Bastrikov, V., Bekki, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Contoux, C., Cozic, A., Cugnet, D., Dufresne, J.L., ´
Eth´
e, C.,
Foujols, M.A., Ghattas, J., Hauglustaine, D., Hu, R.M., Kageyama, M., Khodri, M., Boucher, O., 2020. Implementation of the CMIP6 Forcing Data in the IPSL-
CM6A-LR Model. J. Adv. Model. Earth Syst. 12 (4) https://doi.org/10.1029/2019MS001940.
Mann, H.B. (1945). Nonparametric Tests Against Trend (Vol. 13, Issue 3). 〈https://www.jstor.org/stable/1907187〉.
Maurer, E.P., Pierce, D.W., 2014. Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean. Hydrol.
Earth Syst. Sci. 18 (3), 915–925. https://doi.org/10.5194/hess-18-915-2014.
Moges, M.A., Schmitter, P., Tilahun, S.A., Langan, S., Dagnew, D.C., Akale, A.T., Steenhuis, T.S., 2017a. Suitability of Watershed Models to Predict Distributed
Hydrologic Response in the Awramba Watershed in Lake Tana Basin. Land Degrad. Dev. 28 (4), 1386–1397. https://doi.org/10.1002/ldr.2608.
Müller, W.A., Jungclaus, J.H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine, T., Kornblueh, L., Li, H.,
Modali, K., Notz, D., Pohlmann, H., Roeckner, E., Stemmler, I., Marotzke, J., 2018. A Higher-resolution Version of the Max Planck Institute Earth System Model
(MPI-ESM1.2-HR). J. Adv. Model. Earth Syst. 10 (7), 1383–1413. https://doi.org/10.1029/2017MS001217.
Nile, B., Sensitivity, R., Nawaz, R., Bellerby, T., Sayed, M., & Elshamy, M. (2010). White Rose Research Online URL for this paper: Article: Blue Nile Runoff Sensitivity to
Climate Change.
Ntajal, J., Lamptey, B.L., Mahamadou, I.B., Nyarko, B.K., 2017. Flood disaster risk mapping in the Lower Mono River Basin in Togo, West Africa. Int. J. Disaster Risk
Reduct. 23, 93–103. https://doi.org/10.1016/j.ijdrr.2017.03.015.
O’Neill, B.C., Tebaldi, C., van Vuuren, D.P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.F., Lowe, J., Meehl, G.A., Moss, R., Riahi, K.,
Sanderson, B.M., 2016. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9 (9), 3461–3482. https://doi.org/10.5194/
gmd-9-3461-2016.
Pagano, T.C., Wood, A.W., Ramos, M.-H., Cloke, H.L., Pappenberger, F., Clark, M.P., Cranston, M., Kavetski, D., Mathevet, T., Sorooshian, S., Verkade, J.S., 2014.
Challenges of Operational River Forecasting. J. Hydrometeorol. 15 (4), 1692–1707. https://doi.org/10.1175/jhm-d-13-0188.1.
Prudhomme, C., Haxton, T., Crooks, S., Jackson, C., Barkwith, A., Williamson, J., Kelvin, J., Mackay, J., Wang, L., Young, A., Watts, G., 2013. Future Flows Hydrology:
An ensemble of daily river ow and monthly groundwater levels for use for climate change impact assessment across Great Britain. Earth Syst. Sci. Data 5 (1),
101–107. https://doi.org/10.5194/essd-5-101-2013.
A.A. Alaminie et al.

Journal of Hydrology: Regional Studies 46 (2023) 101343
19
Randles, C.A., da Silva, A.M., Buchard, V., Colarco, P.R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka, Y., Flynn, C.J., 2017.
The MERRA-2 aerosol reanalysis, 1980 onward. Part I: System description and data assimilation evaluation. J. Clim. 30 (17), 6823–6850. https://doi.org/
10.1175/JCLI-D-16-0609.1.
Roth V, Lemann T, Zeleke G, Subhatu AT, Nigussie TK, Hurni H. Effects of climate change on water resources in the upper Blue Nile Basin of Ethiopia. Heliyon. 2018
Sep 1;4(9):e00771.
Rudolf, B., Beck, C., Grieser, J., & Schneider, U. (2005). Global Precipitation Analysis Products of the GPCC.
Salman, S.A., Nashwan, M.S., Ismail, T., Shahid, S., 2020. Selection of CMIP5 general circulation model outputs of precipitation for peninsular Malaysia. Hydrol. Res.
51 (4), 781–798. https://doi.org/10.2166/NH.2020.154.
Schellekens, J. (2022). wow Documentation. 〈https://wow.readthedocs.org/en/stable/〉.
Schellekens, J., Weerts, A.H., Moore, R.J., Pierce, C.E., Hildon, S., 2011. The use of MOGREPS ensemble rainfall forecasts in operational ood forecasting systems
across England and Wales. Adv. Geosci. 29, 77–84. https://doi.org/10.5194/adgeo-29-77-2011.
S´
ef´
erian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., S´
en´
esi, S., Franchisteguy, L., Vial, J.,
Mallet, M., Joetzjer, E., Geoffroy, O., Gu´
er´
emy, J.F., Moine, M.P., Msadek, R., Madec, G., 2019b. Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role
of Earth System Processes in Present-Day and Future Climate. J. Adv. Model. Earth Syst. 11 (12), 4182–4227. https://doi.org/10.1029/2019MS001791.
Setegn, S.G., Srinivasan, R., Dargahi, B., 2008a. Hydrological Modelling in the Lake Tana Basin, Ethiopia Using SWAT Model. In. Open Hydrol. J. Vol. 2.
Sheng, Yue, Paul, Pilon, George, Cavadias, 2002. Power of the Mann±Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series.
J. Hydrol. 259, 254±271. 〈www.elsevier.com/locate/jhydrol〉.
Shrestha, S., Lohpaisankrit, W., 2017. Flood hazard assessment under climate change scenarios in the Yang River Basin, Thailand. Int. J. Sustain. Built Environ. 6 (2),
285–298. https://doi.org/10.1016/j.ijsbe.2016.09.006.
Swart, N.C., Cole, J.N.S., Kharin, V. v, Lazare, M., Scinocca, J.F., Gillett, N.P., Anstey, J., Arora, V., Christian, J.R., Hanna, S., Jiao, Y., Lee, W.G., Majaess, F.,
Saenko, O.A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., Winter, B., 2019. The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci.
Model Dev. 12 (11), 4823–4873. https://doi.org/10.5194/gmd-12-4823-2019.
Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmospheres 106 (D7), 7183–7192. https://doi.org/
10.1029/2000JD900719.
Tibebe, D., Kassa, Y., Melaku, A., Lakew, S., 2019. Investigation of spatio-temporal variations of selected water quality parameters and trophic status of Lake Tana for
sustainable management, Ethiopia. Microchem. J. 148, 374–384. https://doi.org/10.1016/j.microc.2019.04.085.
Tilahun, S.A., Guzman, C.D., Zegeye, A.D., Dagnew, D.C., Collick, A.S., Yitaferu, B., Steenhuis, T.S., 2015. Distributed discharge and sediment concentration
predictions in the sub-humid Ethiopian highlands: The Debre Mawi watershed. Hydrol. Process. 29 (7), 1817–1828. https://doi.org/10.1002/hyp.10298.
Tolson, B.A., Shoemaker, C.A., 2007. Dynamically dimensioned search algorithm for computationally efcient watershed model calibration. Water Resour. Res. 43 (1)
https://doi.org/10.1029/2005WR004723.
Uhlenbrook, S., Roser, S., Tilch, N., 2004. Hydrological process representation at the meso-scale: The potential of a distributed, conceptual catchment model.
J. Hydrol. 291 (3–4), 278–296. https://doi.org/10.1016/j.jhydrol.2003.12.038.
Ukumo, T.Y., Abebe, A., Lohani, T.K., Edamo, M.L., 2022. Flood hazard mapping and analysis under climate change using hydro-dynamic model and RCPs emission
scenario in Woybo River catchment of Ethiopia. World J. Eng. https://doi.org/10.1108/WJE-07-2021-0410.
Vertessy, R.A., Elsenbeer, H., 1999. Distributed modeling of storm ow generation in an Amazonian rain forest catchment: Effects of model parameterization. Water
Resour. Res. 35 (7), 2173–2187. https://doi.org/10.1029/1999WR900051.
van Griensven, A., Ndomba, P., Yalew, S., Kilonzo, F., 2012. Critical review of SWAT applications in the upper Nile basin countries. Hydrol. Earth Syst. Sci. 16 (9),
3371–3381. https://doi.org/10.5194/hess-16-3371-2012.
Werner, A.T., Cannon, A.J., 2016. Hydrologic extremes - An intercomparison of multiple gridded statistical downscaling methods. Hydrol. Earth Syst. Sci. 20 (4),
1483–1508. https://doi.org/10.5194/hess-20-1483-2016.
Werner, M., Schellekens, J., Gijsbers, P., van Dijk, M., van den Akker, O., Heynert, K., 2013. The Delft-FEWS ow forecasting system. Environ. Model. Softw. 40,
65–77. https://doi.org/10.1016/j.envsoft.2012.07.010.
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L., Zhang, F., Zhang, Y., Wu, F., Li, J., Chu, M., Wang, Z., Shi, X., Liu, X., Wei, M.,
Liu, X., 2019. The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev. 12 (4), 1573–1600.
https://doi.org/10.5194/gmd-12-1573-2019.
Wubneh, M.A., Worku, T.A., Fekadie, F.T., Aman, T.F., Shiferaw Kifelew, M., Fekade, F.T., & Kifelew, M.S. (2021). Climate Change Impact On Lake Tana Water Storage,
Upper Blue Nile Basin, Ethiopia. https://doi.org/10.21203/rs.3.rs-927576/v1.
Yin, J., Gentine, P., Zhou, S., Sullivan, S.C., Wang, R., Zhang, Y., Guo, S., 2018. Large increase in global storm runoff extremes driven by climate and anthropogenic
changes. Nat. Commun. 9 (1) https://doi.org/10.1038/s41467-018-06765-2.
Yuan, W., Liu, M., Wan, F., 2019. Calculation of Critical Rainfall for Small-Watershed Flash Floods Based on the HEC-HMS Hydrological Model. Water Resour. Manag.
33 (7), 2555–2575. https://doi.org/10.1007/s11269-019-02257-0.
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E.,
Mizuta, R., Obata, A., Adachi, Y., Ishii, M., 2019. The meteorological research institute Earth system model version 2.0, MRI-ESM2.0: Description and basic
evaluation of the physical component. J. Meteorol. Soc. Jpn. 97 (5), 931–965. https://doi.org/10.2151/jmsj.2019-051.
Zelalem, A., Belay, E., & Markos, A. (2018). Temporal Trajectory Analysis of Lake Surface Area: Case study on Lake Tana, Ethiopia .
Ziehn, T., Lenton, A., Law, R.M., Matear, R.J., Chamberlain, M.A., 2017. The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-
ESM1) - Part 2: Historical simulations. In: Geoscientic Model Development, Vol. 10. Copernicus GmbH, pp. 2591–2614. https://doi.org/10.5194/gmd-10-2591-
2017.
Zimale, F.A., Moges, M.A., Alemu, M.L., Ayana, E.K., Demissie, S.S., Tilahun, S.A., Steenhuis, T.S., 2018a. Budgeting suspended sediment uxes in tropical monsoonal
watersheds with limited data: The Lake Tana basin. J. Hydrol. Hydromech. 66 (1), 65–78. https://doi.org/10.1515/johh-2017-0039.
A.A. Alaminie et al.