Performance evaluation of satellite-based rainfall estimates for hydrological modeling over Bilate river basin, Ethiopia

Abstract

Purpose The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models. Design/methodology/approach Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively. Findings The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R2 = 0.72) is better as compared to MSWEP (NSE = 0.69; R2 = 0.7), TRMM (NSE = 0.67, R2 = 0.68) and CHIRPS (NSE = 0.58 and R2 = 0.62). Practical implications Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area. Originality/value This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

Global scale evaluation of precipitation datasets for hydrological modelling

“…In addition to the uncertainty in the precipitation datasets, the poorer performance in some regions presented in this and previous studies (Beck et al, 2017a;Lin et al, 2019;Harrigan et al, 2020) observations from field-based meteorological stations, in addition to a large set of satellite and reanalysis datasets (Beck et al, 2017a(Beck et al, , 2019a. Other studies have also shown the good performance of MSWEP for hydrological modelling in different parts of the world (Beck et al, 2017a;Lakew, 2020;Li et al, 2022a;Reis et al, 2022;Gu et al, 2023;López López et al, 2017;Satgé et al, 2019;Ibrahim et al, 2022). For example, Satgé et al (2019) evaluated 12 satellite-based precipitation estimates such as MSWEP, CHIRPS and PERSIANN-CDR in South America (Lake Titicaca region) and found MSWEP was the best precipitation dataset for realistic simulation of river discharge.…”

Merging Satellite Products and Rain-Gauge Observations to Improve Hydrological Simulation: A Review

“…For this purpose, continuous and categorical statistical measures can be applied [59,60]. Here, continuous statistical measures are important to quantify the overall performance of the merged precipitation, whereas categorical measures are other valuation methods which can be calculated using a contingency table [61]. For the purpose of simplicity, the most widely used performance measurement metrics are summarized in Table 2.…”

“…Others also applied both statistical and hydrological model methods. For instance, most recently, [61] evaluated the performance of CHIRPS, multi-source weightedensemble precipitation (MSWEP), and TRMM for Bilate basin, Ethiopia. In their work, the researchers applied point-to-pixel-wise comparison and a hydrological method by comparing observed and simulated flow rates derived by individual satellite precipitation products.…”