The Blue Nile Basin is confronted by land degradation problems,
insufficient agricultural production, and a limited number of developed energy
sources. Hydrological models provide useful tools to better understand such
complex systems and improve water resources and land management practices. In
this study, SWAT was used to model the hydrological processes in the upper
Blue Nile Basin. Comparisons between a Climate Forecast System Reanalysis (CFSR)
and a conventional ground weather dataset were done under two
sub-basin discretization levels (30 and 87 sub-basins) to create an
integrated dataset to improve the spatial and temporal limitations of both
datasets. A SWAT error index (SEI) was also proposed to compare the
reliability of the models under different discretization levels and weather
datasets. This index offers an assessment of the model quality based on
precipitation and evapotranspiration. SEI demonstrates to be a reliable
additional and useful method to measure the level of error of SWAT. The
results showed the discrepancies of using different weather datasets with
different sub-basin discretization levels. Datasets under 30 sub-basins
achieved Nash–Sutcliffe coefficient (NS) values of
Water resources in the upper Blue Nile Basin are not being managed adequately; land use changes, fast population growth, land erosion, and deforestation are some of the causes currently affecting the watershed. Therefore, in order to improve and provide better land use management practices and mitigate the alarming erosion problems researchers need to understand the hydrological conditions of the basin. Physically based, distributed models have provided a very efficient alternative for watershed researchers for analyzing the impact of land management practices on soil degradation, agriculture, water allocation, and chemical yields (Setegn et al., 2008). Due to its versatility and applicability to complex watersheds, researchers have identified the Soil and Water Assessment Tool (SWAT) as one of the most intricate, consistent, and computationally efficient models (Neitsch et al., 2009; Gassman et al., 2007). Recent studies prove that SWAT has received international and interdisciplinary acceptance for modeling large and small watersheds (Malunjkar et al., 2015; Me et al., 2015; Rafiei Emam et al., 2016; Wang et al., 2017). SWAT provides a wide range of parameters to work with, allowing users to analyze several hydrological processes. It also has the advantage to have been developed to analyze the interaction of several hydrological parameters and the impact of land management practices specifically for large and complex basins; thus, it is a good model to be applied in the upper Blue Nile Basin. However, due to the lack of a unifying theory to accurately model the interaction of the hydrological processes, complex hydrological models suffer from overparameterization and high predictive uncertainty (Sivapalan, 2006). Therefore, it is difficult to simulate the complex interactions of hydrological processes and weather conditions of watersheds without uncertainties.
Among all the input parameters, the meteorological data have the most significant impact on the water balance of a watershed. However, a common problem to set up hydrological models of the upper Blue Nile Basin is related to data limitations. In developing countries, the distribution of meteorological stations is irregular and dispersed (Worqlul et al., 2014). Other weather data problems are related to measuring gauges; many weather data parameters contain missing data periods, and in several cases erroneous measurements are also possible. Thus, many models are often set up based on limited and incomplete data, which may lead to less reliable models. This lack of hydrological and climatic data has impeded in-depth studies of the hydrology of the upper Blue Nile Basin (Tekleab et al., 2011). Several previous studies have modeled the entirety and also small catchments of the Nile Basin, providing good and meaningful results (Tibebe and Bewket, 2011; Setegn et al., 2008, 2010; Swallow et al., 2009; Mulungu and Munishi, 2007). However, most of the hydrological models are built for the Lake Tana basin and its sub-basins: Gumara, Ribb, Gilgel Abay, and Koga (Chebud and Melesse, 2009; Setegn et al., 2008, 2010a, b; Wale, 2008). Dessie et al. (2015) and Kebede et al. (2006) performed a very detailed daily water balance analysis and annual water budget for the Lake Tana basin where the runoff and outflows of ungauged catchments were estimated. Uhlenbrook et al. (2010) performed an analysis of the hydrological processes and responses of Gilgel Abay and Koga catchments by applying the HBV model. Other studies have modeled the entire upper Blue Nile Basin; for instance, Abera et al. (2017) performed a water budget analysis in the upper Blue Nile Basin where precipitation, outflow, and evapotranspiration analyses were done. Betrie et al. (2011) and Easton et al. (2010) also modeled and calibrated the upper Blue Nile Basin using discharge data to estimate sediment yield and erodible areas of the basin; values of the calibrated parameters for flow and sediment were also shown. Dessie et al. (2014) also performed a runoff and sediment yield analysis in the upper Blue Nile Basin, although the main analysis was done at the Lake Tana region. Tekleab et al. (2011) also modeled the upper Blue Nile Basin, where an interesting water balance analysis was done and monthly streamflow for several subcatchments was modeled. However, most of the studies at large scale in the upper Blue Nile Basin do not provide detailed values for the each of the water balance components of the basin. Another important issue when setting up SWAT models concerns the right number of sub-basins, because the number of meteorological stations to be used by SWAT will depend on the number of sub-basins. For instance, if two stations are located within one sub-basin, SWAT will choose the station nearest to the center of the sub-basin; the other station will be disregarded. However, if more sub-basins are created in a model, and these two stations lie in different sub-basins, then both stations will be considered by SWAT, which provides different water balance results.
Therefore, the first objective of this study has been the comparison of
different weather datasets at large scale and under different sub-basin
discretization levels. Two models were created using different subcatchment
discretization (30 and 87 sub-basins), hereafter named SWAT30 and SWAT87,
respectively (Fig. 3). The time frame of the models was from 1990 to 2004,
using a 4-year warm-up period (1990–1993), a 6-year calibration
period (1994–1999), and a 5-year validation period (2000–2004). This comparison
provided a better understanding of the effects of different sub-basin
discretization levels on the total water balance of a watershed. It also
helped to identify the temporal and spatial constraints of both datasets.
Roth and Lemann (2016) performed a comparison between CFSR and conventional
data in small catchments in the Ethiopian highlands, where they showed that
the CFSR data provided unreliable results. However, Roth and Lemann (2016)
made it clear that the CFSR data were tested only in very small catchments
ranging from 112 to 477 ha and not at large scale, also suggesting
that CFSR data should be carefully checked and compared with conventionally
measured data of similar climatic stations. Furthermore, this study proposes
an integration of CFSR and conventional weather data to be used at large
scale in the upper Blue Nile Basin with an area of approximately 199 812 km
Official sub-basin distribution of the upper Blue Nile Basin.
After a hydrological model has been set up, a critical point to determine its quality is the water balance. Therefore, in addition to graphical assessments, other statistical indicators as the Nash–Sutcliffe coefficient (NS), percent bias (PBIAS), and ratio of the root mean square error (RMSE) to the standard deviation of measured data were proposed by Moriasi et al. (2007). Based on these commonly used statistical indicators, most of the SWAT models provide very good results for discharge values at the outlet of a basin (van Griensven et al., 2012). However, the evaluation of the models based on both evapotranspiration and water balance is not discussed in detail, and the evapotranspiration behavior of a catchment is usually not presented. Several published documents could even report unrealistic parameter values (van Griensven et al., 2012). Therefore, the second objective of this study has been to propose an index, the SWAT error index (SEI), to quantify the level of error of a hydrological model. The SEI uses flexible weighting values for the relative root mean square error (rRMSE) obtained from measured flow discharge data and satellite evapotranspiration data. SEI proved to be a useful additional method to develop models that can provide a better representation of the water balance of a watershed.
The upper Blue Nile Basin, also known as Abay Basin, is located in the
northwestern highlands of Ethiopia, approximately between latitude 7
A Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) from the Consultative Group on International Agricultural Research – Consortium for Spatial Information (CGIAR-CSI) was used to set up the model. This DEM has a resolution of 90 m and was used to perform an automatic watershed delineation of the upper Blue Nile Basin, where the flow direction, flow accumulation, and stream network were automatically determined by SWAT.
The second input dataset was a land use map, which was obtained from the GIS
portal of the International Livestock Research Institute (ILRI) and
corresponds to the year 2004 (
FAO/UNESCO soil map of the upper Blue Nile Basin.
Weather and hydrometric gauging stations in the upper Blue Nile Basin under two discretization levels of 30 and 87 sub-basins (SWAT30 and SWAT87).
Spatial annual rainfall variation in the upper Blue Nile Basin using
two different data sources: the CFSR dataset
The soil map used for these models was developed by the Food and Agriculture
Organization of the United Nations (FAO-UNESCO). This world soil map was
prepared by FAO and UNESCO at 1 : 5 000 000 scale (
The last input dataset was the meteorological information. Two weather
datasets from different sources were used to set up the models. The first
weather dataset was collected from the National Meteorology Agency of
Ethiopia (NMA). The data used for these models correspond to 42 stations
distributed in the upper Blue Nile Basin (Fig. 3). However, only 15 of
these stations are capable of measuring all five parameters needed to set up
SWAT: rainfall, temperature, relative humidity, solar radiation, and wind
speed. Moreover, few of these 15 station have complete and
continuous data available for the entire period under study (1990–2004). For instance,
the collected data for solar radiation were limited to 2 stations only, wind
speed was available for 4 stations, only maximum temperature was available
for 4 stations, relative humidity was available for 3 stations, and
precipitation was available for all 42 stations. Additionally, the quality
of these observed data is somehow questionable. Many meteorological stations
are more than 10 years old, and their constant technical failure due to the
lack of continuous expert maintenance also questions the quality of the
data. A large part of the available ground data has been collected from old
stations that could have in many cases malfunctioning, defected, and outdated
devices. The second weather dataset was the Climate Forecast System
Reanalysis (Fig. 3), a dataset that has been produced by the National
Centers for Environmental Prediction (NCEP) (
For the analysis of the quality of the SWAT models, monthly flow discharge
data and evapotranspiration data were used. The flow discharge data were
obtained from the Ministry of Water, Irrigation and Electricity of Ethiopia
and correspond to the gauging stations at Kessie and El Diem at the main
stream of the upper Blue Nile Basin (Fig. 3). For the evapotranspiration
analysis, data from the MOD16 Global Terrestrial Evapotranspiration Project
(
Water balance in watersheds is one of the most important factors used to
determine if a model is good enough for any particular application. Hence,
analyses of the processes involved in the estimation of the water balance of
a watershed (evapotranspiration, runoff, and groundwater) can provide more
details about the hydrological behavior of a watershed and can be used to
understand the interaction of main hydrological processes (Zhang et al.,
1999). For the input data processing and hydrological estimation, SWAT
uses two levels of discretization: sub-basins and hydrologic response units (HRUs).
HRUs are contained in the sub-basins and are defined based on the
land use map, soil map, and slope classes. HRUs allow the model to reflect
differences in evapotranspiration and other hydrologic conditions for each
crop and soil type. The water balance in SWAT is calculated for each HRU
using the following formula (Neitsch et al., 2009):
SWAT can estimate the evapotranspiration using several methods, from which
the Hargreaves and Penman–Monteith methods were compared in this study (Figs. 11
and 12). The Hargreaves method calculates the potential
evapotranspiration using minimum and maximum daily temperatures as input data
(Hargreaves and Samani, 1982). This method was chosen as a better option for
the upper Blue Nile Basin due to the data scarcity of the meteorological
stations in the basin. The Hargreaves equation can be used with the sole input
of temperature data, while the Penman–Monteith requires more data, for instance,
wind speed, solar radiation, and relative humidity. The Hargreaves method has
been recommended for computing potential evaporation in cases when only the
maximum and minimum temperatures are available (Allen et al., 1998). A study
from Tekleab et al. (2011) was also able to successfully use the Hargreaves
equation to calculate the potential evaporation in the upper Blue Nile
Basin. Several improvements were made to the original equation since 1975
(Hargreaves and Samani, 1982). The final form of the Hargreaves equation
used in SWAT and published in 1985 (Hargreaves and Samani, 1985) is as follows
(Neitsch et al., 2009):
Following the potential evapotranspiration, the actual evapotranspiration
must be calculated. Initially, SWAT calculates the evaporated water
intercepted by the canopy; then, maximum transpiration and soil evaporation
are calculated. Evaporation from canopy is very significant in forested
areas and in several cases can be higher than transpiration. Transpiration
for the Hargreaves equation is calculated as (Neitsch et al., 2009)
Evaporation from the soil on a given day is calculated with following
equation (Neitsch et al., 2009):
If input data are used without the respective analyses, models will provide less reliable results. Also, even small errors in temperature or precipitation can result in considerable inaccuracies and impacts on the model results (Maraun et al., 2010). Tekleab et al. (2011) and Uhlenbrook et al. (2010) checked the data quality of streamflow data in the upper Blue Nile Basin based on comparison graphs and additionally a double mass analysis. In this study, the data quality and consistency of the time series on monthly basis in terms of magnitude and spatial distribution of the five input variables required by SWAT were also analyzed through comparison graphs (Figs. 4–6) to determine the deficiencies of the two datasets (CFSR and ground datasets) and to form an integrated dataset.
In the first case, the ground dataset was used without alterations to create the SWAT models. This ground dataset obtained from the NMA corresponds to 42 stations in the upper Blue Nile Basin, where most of the meteorological stations were located in the eastern part of the watershed (Fig. 3). Additionally, the data obtained from these stations had several months of missing data, leading to temporal uncertainties.
For the second case, the SWAT models were set up using the CFSR dataset, also without alterations. This dataset is evenly distributed at 38 km resolution, with over 100 stations available for the upper Blue Nile Basin, and is temporally continuous.
Comparisons between the ground and CFSR weather datasets. Panels
Significance of matching between the ground and CFSR weather datasets.
Panels
However, after performing a quality check through a comparison of maps and
graphs between the ground and CFSR datasets (Figs. 4–6), it was noticed
that not all the weather variables from CFSR are
reliable. The precipitation distribution appeared to be underestimated in
the eastern region of the upper Blue Nile Basin and overestimated in the
western region (Fig. 4). The map created from the ground stations (Fig. 4b)
showed a precipitation distribution in the western region that was
the result of SWAT using the precipitation values from the nearest stations.
Two stations in the eastern part, Alem Ketema and Adet (Figs. 5a, b and 6a, b),
showed the underestimation of the CFSR rainfall at the
eastern region, and Ayehu (Figs. 5c and 6c) showed the
overestimation of the CFSR rainfall in the western region. For this reason,
additional CFSR rainfall stations were not used in the integrated dataset.
However, the graphical and statistical comparisons of the few available
stations for relative humidity, temperature, and solar radiation showed an
acceptable level of agreement between the ground and CFSR datasets. The
seasonal behavior and magnitudes of the values for these variables are
similar; additionally, the
Therefore, these two datasets were integrated to form a third input dataset for SWAT with the objective of overcoming their spatial and temporal limitations. Tekleab et al. (2011) and Uhlenbrook et al. (2010) filled in missing streamflow data of the upper Blue Nile Basin using regression analysis, which is also a good approach to fill in missing meteorological values. However, in this study, the missing values of the ground dataset refer to complete time series of a specific station and variable. Thus, to create the integrated dataset, the 42 rainfall stations of the ground dataset were taken as the basis; this means that the locations of the weather stations of the final integrated dataset correspond to the locations of the 42 rainfall stations of the ground dataset. From there, the missing variables (relative humidity, temperature, and solar radiation values) of those 42 rainfall stations were completed by using the variables of their nearest CFSR stations. The integrated dataset has 42 stations where the data for each variable were combined as follows: the precipitation is formed by 42 rainfall stations taken entirely from the ground dataset; the relative humidity is formed by 3 stations from the ground dataset and 39 stations from the CFSR dataset; the maximum temperature is formed by 4 stations from the ground dataset and 38 stations from the CFSR dataset; the values for the minimum temperature were taken totally from the CFSR dataset; the solar radiation was formed by 2 stations from the ground dataset and 40 stations from the CFSR dataset; no wind speed data were used in the models. However, missing daily values within a variable were completed by the built-in SWAT weather generator. This integrated dataset contained more data than the ground dataset and also provided more reliable precipitation values and distribution than those provided by the CFSR dataset.
One of the major constrains of hydrological modeling is the difficulty of
the parameterization of different variables (Hauhs and Lange, 2008). The
correct combination of the values of the parameters influencing the ground
water, runoff, and evapotranspiration processes is a key point in a model
calibration. The characterization of watersheds considering their most
influential variables is a good approach to determine the predictive
capabilities of a model (McDonnell et al., 2007). Initially, it is
recommended to perform calibrations for annual discharge values once
acceptable results are acquired; a calibration based on monthly values can
be performed to achieve more detailed results (Neitsch et al., 2009). During
a model calibration, a potential value can be assigned for each parameter
and for each HRU, which would generate a large number of parameters.
However, these values can also be applied as a global modification to
estimate parameters by multiplying or adding values. Table 2 shows the
parameterization applied to the respective regions in the watershed to
calibrate streamflow at Kessie and El Diem, where
In the case of hydrological modeling, the limitation of the data quality and
capabilities of the model to represent the complexity of the hydrological
process often constitutes obstacles. Therefore, models must be calibrated,
and a statistical analysis is also required to determine how reliable the
results of the model are prior to their applications (Bastidas et al.,
2002). Since sediment data for the upper Blue Nile Basin are very limited,
the calibration and validation of the models were done using flow discharge
data only. The calibrated stations were Kessie and El Diem at the mainstream
of the Blue Nile River (Fig. 3). For the automatic calibration, the
Sequential Uncertainty Fitting version 2 (SUFI-2) was used to efficiently
calculate the coefficient of determination (
SUFI-2 is a sequential parameter estimation method that operates within
parameter uncertainty domains (Tanveer et al., 2016). SUFI-2 performs several
iterations, where each iteration provides better results than the previous
iteration and reduces the parameters ranges. In SUFI-2, the objective is to
capture most of the observed values within the 95PPU (95 % prediction
uncertainty) range at the same time that thinner 95PPU range is preferable.
The 95PPU represents the uncertainty in the model outputs. Therefore, the
simulation starts assuming large and physically meaningful parameter ranges,
so that the measured data fall within the 95PPU and continuously decrease
the ranges of the 95PPU and produce better results. The final 95PPU is the
95 % of the observed data captured within the final 95PPU band, which are
defined by the final parameter intervals. Therefore, the best simulation is
the best iteration within the 95PPU, and considering that is difficult to
claim a specific parameter range for a certain watershed, any solution
within the 95PPU should be an acceptable solution. The fit of simulated
results within the 95PPU is quantified through the
The coefficient of determination (
NS is widely used as goodness-of-fit indicator
that expresses the potential predictive ability of a hydrological model
(Nash and Sutcliffe, 1970). The Nash–Sutcliffe objective function provided
in SWAT-CUP is as follows:
In addition to the calibration and validation of the SWAT models with flow
discharge, comparisons with evapotranspiration data could also provide more
details to quantify the reliability of hydrological models. Therefore,
actual evapotranspiration data for the upper Blue Nile Basin were obtained
from the MODIS Global Terrestrial Evapotranspiration Project (MOD16). These
are global estimated data from land surface by using satellite remote
sensing data. These data are intended to be used to calculate regional water
balances; hence, they are a very important source of data for watershed management and
hydrological models analyses. The original MOD16 evapotranspiration (ET) algorithm (Mu et al.,
2007) was based on the Penman–Monteith equation (Monteith, 1965), while the
current MOD16 ET has used the improved evapotranspiration algorithm (Mu et
al., 2011). In this improved algorithm, the sum of the evaporation from the
wet canopy surface, transpiration from the dry canopy surface, and
evapotranspiration from the soil surface constitute the total daily ET (Mu et
al., 2011). The formulae for the total daily ET(
Previous studies have already shown that the annual ET values derived from the MOD16 algorithm are lower than those provided by hydrological models, principally when using the Hargreaves method. For instance, Ruhoff et al. (2013) detected an underestimation of 21 % in the evapotranspiration provided by MOD16 in the Rio Grande Basin, Brazil, where the underestimation was mainly caused by the misclassification of the land use. Sun et al. (2007) also identified certain disadvantages in the MOD16 evapotranspiration. Nevertheless, in this study, the evapotranspiration estimations from SWAT were compared with satellite evapotranspiration data. This was done only as comparison and not with the objective of calibrating the models, and also as a test to understand the performance of the proposed SEI.
Evapotranspiration estimations shown as percentage of the average annual precipitation are frequently given for the upper Blue Nile Basin. However, these percentages would yield totally different amounts depending on the average annual precipitation provided by different weather data sources and under different sub-basin discretization. Therefore, a comparison of the actual evapotranspiration data provided by MOD16 with the values calculated by SWAT under the Hargreaves and Penman–Monteith equations was done to show the level of discrepancy between datasets (Figs. 11, 12, and 14). MOD16 ET data are available only for the period 2000–2010; hence, the comparison was done only for 5 years (2000–2004).
A common problem of hydrological models is the wrong combination of the
values of the calibrated parameters, which can also lead to good graphical
results, and consequently good statistical values, but wrong water balance
values. Consequently, good
Average annual water balance components in the upper Blue Nile Basin based on different literature.
Several reliable measured flow discharge datasets are available for rivers,
but that is not the case for evapotranspiration data. However, satellite
evapotranspiration data are available for most watersheds in the world.
Furthermore, the measured discharge dataset and the satellite estimated
evapotranspiration dataset do not have the same level of reliability.
Therefore, SEI uses different weighting values (
SEI ranges from 0 to
Calibration and validation of SWAT30 at El Diem. Calibration results
achieved
Calibration and validation of SWAT87 at El Diem. Calibration results
achieved
After analyzing the different datasets under different discretization levels, it was detected that the input data and the parameterization have a critical impact not only on the water balance but also on the sub-basins' distribution. The water balance analysis was done for two calibrated stations, three datasets, and two different sub-basin distributions. Water balance results for the upper Blue Nile Basin and also the values for the different hydrological processes and models are given in Table 3; values for these hydrological processes from the literature are also given in Table 1 (Cherie, 2013; Mengistu and Sorteberg, 2012). The average annual precipitation in the upper Blue Nile Basin differs between the literature (Table 1) and also between dataset sources (Table 3). The uncertainty of the rainfall in the upper Blue Nile Basin is also noticeable when models with different sub-basin delineations are compared and show different values (Table 3, Figs. 7 and 8 for El Diem; Figs. 9 and 10 for Kessie; with SWAT30 and SWAT87, respectively). With the values provided in Table 2, it was possible to obtain good statistical values for the calibrated models (Table 4).
Calibration and validation of SWAT30 at Kessie. Calibration results
achieved
Calibration and validation of SWAT87 at Kessie. Calibration results
achieved
Average monthly evapotranspiration analysis using SWAT87 and the
Hargreaves method, with
Average monthly evapotranspiration analysis using SWAT87 and the
Penman–Monteith method, with
Flow discharge in the Ribb subcatchment. Calibration with outflow data
achieved
Average monthly evapotranspiration in the Ribb subcatchment. Statistical
results achieved
Figures 7 and 8 show the magnitude and dynamics of the measured and
estimated monthly discharge flow at El Diem. The integrated dataset provided
good statistical values for
SWAT30 under the CFSR dataset provides an average annual precipitation of
1253 mm (Table 3), while SWAT87 average annual precipitation
increases to 1481 mm. This rainfall increase provided by the CFSR dataset is
caused by the number of sub-basins, SWAT87 considered more stations than
SWAT30. However, both average annual precipitation values compared to the
other two datasets and to the literature (Table 1) are still within
acceptable ranges for the upper Blue Nile Basin, and it is not the main factor
affecting the water balance, but its distribution in the watershed
(Fig. 4). Figures 9 and 10 showed how CFSR data underestimate the
precipitation in the eastern part of the basin (at Kessie) compared to those
provided by the ground and integrated datasets. Figures 9 and 10 also
showed the effect of the number of sub-basins on the simulated discharge
flow. The flow discharge provided by the CFSR data is slightly higher in
SWAT87 compare to SWAT30, although in both cases this dataset continues to
underestimate the flow discharge at Kessie. As the precipitation in the
watershed changes in magnitude and distribution, the parameterization for
the calibration of the models will be different. Therefore, in order to meet
good
The evapotranspiration has been another critical factor subject to analysis
in this study. Depending on the weather dataset, the evapotranspiration
values in the upper Blue Nile Basin varied from 729 mm yr
Parameterization of the SWAT models using the SUFI-2 algorithm for the period 1990–2004. BSN means applied to the entire basin.
Water balance analysis in the upper Blue Nile Basin (1990–2004).
Statistical results for the calibrations and validations with outflow data at the El Diem and Kessie gauging stations. Bold values mean “models with low performance”.
SEI results for the upper Blue Nile Basin.
Statistical results for the Ribb subcatchment in the Lake Tana region of the upper Blue Nile Basin.
In the first case, SEI results for the El Diem station (Table 5) showed that
the behavior and capability of SEI to quantify the level of error of a model
through an evaluation of both flow discharge and evapotranspiration
estimations is good. For instance, values in Table 5 showed that the lower
the value of the discharge data is, the more the value for evapotranspiration tends
to increase. This is because the flow discharge data are being matched;
however, the evapotranspiration increases and tends to overestimate those
values provided by MOD16 ET. If MOD16 ET had a good representation of the
evapotranspiration data of a watershed, then the rRMSE values for both
discharge and evapotranspiration values should be closer to 0, which could
provide better SEI values (in the second test done at Ribb subcatchment). However,
SEI showed that the models using the integrated datasets are more reliable
than the other two datasets, achieving SEI values of 0.29 and 0.27 for
SWAT30 and SWAT87, respectively. It also demonstrated that the CFSR dataset
is less accurate, with an SEI value of 0.4 for both SWAT30 and SWAT87. In the
second test done at the Ribb subcatchment, the calibration with flow
discharge data provided good statistical results, where the CFSR dataset
achieved
The CFSR dataset and a conventional observed ground dataset were analyzed in terms of statistical results, water balance, and precipitation distribution in the upper Blue Nile Basin. After detecting their limitations and disadvantages, an integration of both datasets was proposed with the purpose of overcoming their uncertainties and limitations. This data integration method was effectively used in the upper Blue Nile Basin to create a better SWAT model and can also be applied in other watersheds where observed data are limited and incomplete. However, data analyses and tests should always be performed before performing an integration for other watersheds. Despite its limitations, the CFSR dataset continues to be an important source that can be very useful in regions where conventional measured data are not available.
A comparison of the three datasets under different discretization levels was also performed. This comparison was important for obtaining a better understanding of how crucial the sub-basin discretization process is during a SWAT model setup. The comparisons showed that the three input datasets, under models with a different number of sub-basins, yield different results. The number of sub-basins in a SWAT model will affect the magnitude of the flow discharge and hence the total water balance of a watershed.
The comparison of the results of SWAT30 demonstrates that the values for the total annual average precipitation at El Diem are similar for the three datasets. Nevertheless, only the model using the CFSR dataset was not able to achieve good water balance results under similar parameterization. The quality of the CFSR rainfall data is not reliable for the upper Blue Nile Basin, although this case cannot be generalized for other watersheds in the world. However, this dataset needs to be equally verified in other watersheds before it is used. For the second case, the three datasets were analyzed in more detail using SWAT87, and although an exact number of the correct precipitation amounts in the upper Blue Nile Basin cannot be given, CFSR data showed an overestimation of the rainfall and also a wrong precipitation distribution compared to the other datasets. Additionally, the model under 87 sub-basins was the model that provided more details in terms of the number of HRUs and also achieved better statistical values. Therefore, this study proposes that 87 is a suitable number of sub-basins for the upper Blue Nile Basin. SWAT87 is more suitable to perform several types of hydrological analyses and propose watershed management practices in the Blue Nile Basin.
Furthermore, the SEI has proved to be a useful
additional tool to express the level of error of SWAT models. This index
used the weighted rRMSE of the discharge
and evapotranspiration data. SEI was tested in two locations, being the
second case done at the Ribb subcatchment more accurate. Nevertheless,
further tests and improvements should be done to this index. SEI also showed
that the integrated dataset successfully achieved better and more reliable
results than the ground and CFSR datasets. The integrated dataset improved
the results of the model, obtaining better
Although further improvements must be made to the methods proposed in this study, the integration of datasets, the sub-basin delineation, and the application of the SEI are important approaches that can be applied in other watersheds and can significantly help to develop better hydrological models.
All the data used in this study are available at the following
websites. Land use map:
The authors declare that they have no conflict of interest.
The authors are very grateful to the Deutscher Akademischer Austauschdienst (DAAD) and the International Graduate School of Science and Engineering (IGSSE) of the Technische Universität München (TUM) for their financial support. The authors also express their gratitude to the National Meteorology Agency (NMA) and the Ministry of Water, Irrigation and Electricity of Ethiopia for providing the necessary weather and flow discharge data, respectively. Edited by: Stefan Uhlenbrook Reviewed by: two anonymous referees