Abstract:
For rainfall-runoff modeling, precise rainfall measurements are required to examine the
geographical and temporal patterns of rainfall at various scales. In many impoverished
nations, such as Ethiopia, accurate and reliable rainfall monitoring is scarce. The Climate
Hazards Group Infrared Precipitation with Stations (CHIRPS) and Precipitation
Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate
Data Record (PRESIANN_CDR) satellite rainfall products for stream flow simulation are
evaluated in this study at daily temporal and 0.05°×0.05° and 0.04°×0.04° spatial
resolutions, respectively and simulated by HEC-HMS model. The Soil Conservation
Service_Curve number, SCS_Unit Hydrograph, and Muskingum methods were utilized for
loss, transformation, and routing computations, respectively, at the stated period and one day time step. The model was calibrated over fifteen years and the daily rainfall readings
were verified during five years. The performance of HEC-HMS model and satellite rainfall
was assessed using a coefficient of determination (R2
), Net-Sutcliffe Efficiency (NSE), Root
Mean Square Error (RMSE), and Percent of BIAS. The model Calibration and Validation
results were described here (R2 =0.81, NSE =0.84, RMSE = 0.5, PBIAS = +30.10) and
(R
2=0.79, NSE = 0.82, RMSE = 0.4, PBIAS = +28.72) throughout the periods,
respectively. Satellite rainfall products could be useful inputs for modeling in areas where
field data is lacking for a variety of hydrological applications. The results revealed that
the observed and simulated hydrographs were almost identical. The HEC-HMS model
accurately predicted catchment runoff for both satellite rainfall products according to the
findings. As a consequence, Neshi discharge was successfully simulated for the time period
studied and the results suggested that the utilization of satellite rainfall data was
appropriate or solve the problem of data scarcity and the model was adequate for
hydrological simulations in the Neshi Watershed.
