Abstract
In Lake Ziway watershed in Ethiopia, the contribution of river inflow to the water level has not been quantified due to scarce data for rainfall-runoff modeling. However, satellite rainfall estimates may serve as an alternative data source for model inputs. In this study, we evaluated the performance and the bias correction of Climate Hazards Group InfraRed Precipitation (CHIRP) satellite estimate for rainfall-runoff simulation at Meki and Katar catchments using the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model. A non-linear power bias correction method was applied to correct CHIRP bias using rain gauge data as a reference. Results show that CHIRP has biases at various spatial and temporal scales over the study area. The CHIRP bias with percentage relative bias (PBIAS) ranging from −16 to 20% translated into streamflow simulation through the HBV model. However, bias-corrected CHIRP rainfall estimate effectively reduced the bias and resulted in improved streamflow simulations. Results indicated that the use of different rainfall inputs impacts both the calibrated parameters and its performance in simulating daily streamflow of the two catchments. The calibrated model parameter values obtained using gauge and bias-corrected CHIRP rainfall inputs were comparable for both catchments. We obtained a change of up to 63% on the parameters controlling the water balance when uncorrected CHIRP satellite rainfall served as model inputs. The results of this study indicate that the potential of bias-corrected CHIRP rainfall estimate for water balance studies.
Highlights
Rainfall-runoff modeling requires accurate rainfall input data
We obtained a change of up to 63% on the parameters controlling the water balance when uncorrected Climate Hazards Group InfraRed Precipitation (CHIRP) satellite rainfall served as model inputs
The CHIRP satellite rainfall was quantitatively evaluated against rain gauge observations using five performances of statistical measures at point and catchment scales on a daily and a monthly basis
Summary
Rainfall-runoff modeling requires accurate rainfall input data. The accuracy of the rainfall input significantly influences the performance of hydrological models. Accurate and consistent rainfall observations are limited in many regions, in developing countries, due to limited rain gauge networks and density of deployment [1,2]. Satellite rainfall estimates (SREs) may serve as an alternative data source for model inputs, as they provide rainfall datasets at various temporal and spatial coverage, including ungauged basins [3,4]. The results of previous studies indicate that SREs can be subjected to substantial biases [5,6,7]. It is necessary to either minimize or remove the bias before the SREs can be used in any subsequent applications
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