Assessment of the impact of rainfall uncertainties on the groundwater recharge estimations of the Tikur-Wuha watershed, rift valley lakes basin, Ethiopia

Spatial recharge estimation uncertainty is directly proportional to uncertainty in input precipitation data Thus, the main objective of this study was to investigate the recharge uncertainty by using improved spatial rainfall observations. The physically based fully distributed hydrological model WetSpa was used to simulate 20,000 possible combinations of parameters for two model setup. The M1 model setup was developed based on the rainfall measurements obtained from rain gauge stations scattered in and around the Tikur-Wuha watershed in Ethiopia, while M2 model setup was developed using bias-corrected satellite rainfall estimates (SREs) based on Climate Hazards Group InfraRed Precipitation (CHIRP) merged with relevant ground station records. The required parameter combinations were generated using Monte Carlo simulation stratified by applying Latin Hypercube Sampling (LHS). One hundred best performing parameter combinations were selected for each model to generate spatial recharge statistics and assess the resulting uncertainty in the recharge estimates. The results revealed that enhanced spatial recharge estimates can be produced through improved CHIRP-based SREs. The long-term mean annual recharge (218.29 mm) in the Tikur-Wuha watershed was estimated. Model parameter calibration performed using discharge measurements obtained from the Wosha rain gauge station located in the subcatchment area of the Tikur-Wuha watershed had a Nash-Sutcliffe efficiency of 0.56. Seventy percent of the watershed showed a coefficient of variation (Cv) < 0.15 for M2, while 90 % of the area exhibited a Cv < 0.15 for M1. Furthermore, the study findings highlighted the importance of improving evapotranspiration data accuracy to reduce the uncertainty of recharge estimates. However, the uncontrolled irrigation water uses and the total recharge coming from the irrigation fields scattered across the Tikur-Wuha watershed were not considered in the study, which is a limitation of the study. Future studies should consider the contribution made by irrigation water to the total recharge of the watershed

Evaluation of a multi-staged bias correction approach on CHIRP and CHIRPS rainfall product: a case study of the Lake Hawassa watershed

A promising future development area to improve the accuracy of satellite rainfall estimates (SREs) is accessing merits from different sources of data through combining algorithms. The main objective of this study is to assess the accuracy and importance of the fused multistage approach of bias correction. Accordingly, two versions of resampled and spatially bias-corrected Climate Hazards Group Infrared Precipitation (CHIRP) estimates were merged with ground measurements using a conditional merging procedure. Results of applied performance measures (i.e. seven) on corrected and merged CHIRP SREs show that the Percent of Detection (POD) and Percent Volume Error (PVE) have improved. Depending on the combination of coupled stations for validation, up to 70 and 50% PVE improvement was achieved at some stations for wet and dry periods, respectively. Moreover, the bias-corrected and conditionally merged CHIRP SREs have outperformed the estimates by resampling CHIRP with station dataset (CHIRPS) over the sparsely populated western part of the watershed. However, the devised method was limited in considering dry-day events during bias correction, which in turn has affected the performance of the bias correction of the CHIRPS product. Finally, future research should concentrate on such methods of fusing to understand the benefits of various approaches and produce more precise rainfall records. HIGHLIGHTS The research provides a fused multi-staged approach for reducing errors in CHIRP and CHIRPS satellite rainfall estimates.; Application of parametric QM for spatial bias correction followed by conditional merging improves the quality of CHIRP SREs.; Bias-corrected and conditionally merged CHIRP estimate outperforms the estimates by CHIRPS.; Incorporating additional ground station records improves the estimates of SREs.