Assessment of GPM Satellite Precipitation Performance after Bias Correction, for Hydrological Modeling in a Semi-Arid Watershed (High Atlas Mountain, Morocco)

Abstract

Due to its important spatiotemporal variability, accurate rainfall monitoring is one of the most difficult issues in semi-arid mountainous environments. Moreover, due to the inconsistent distribution of gauge measurement, the availability of precipitation data is not always secured and totally reliable at the instantaneous time step. As a result, earth observation of precipitation estimations could be an alternative for overcoming this restriction. The current study presents a framework for either the hydro-statistical evaluation and bias correction of the Global Precipitation Measurement (GPM) Integrated Multi-SatellitE Retrievals version 06 Early (IMERG-E), Late (IMERG-L), and Final (IMERG-F) products. On a sub-daily duration, from the Taferiat rain gauge-based station, which was used as a benchmark from 1 September 2014 to 31 August 2018. Statistical analysis was performed to examine each precipitation product’s performance. The results showed that all Post_Real_Time and Real_Time IMERG had a high level of awareness accuracy. The IMERG-L results were statistically similar to the gauge data, succeeded by the IMERG-F and IMERG-E. The Cumulative Distribution Function (CDF) has been employed to adjust the precipitation values of the three IMERG products in order to decrease bias estimation. The three products were then integrated into the “HEC-HMS” hydrological model to assess their dependability in flow modeling. Six flood occurrences were calibrated and validated for each product at 30-minute time steps. With a mean Nash-Sutcliffe coefficient of NSE 0.82, the calibration findings demonstrate that IMERG-F provides satisfactory hydrological performance. With an NSE = 0.80, IMERG-L displayed good hydrological utility, slightly better than IMERG-E with an NSE = 0.77. However, when the flood events were validated using the initial soil conditions, IMERG F and IMERG E overestimated the discharge by 13% and 10%, respectively. While IMERG L passed the validation phase with an average score of NSE = 0.69.

Evaluation of GPM IMERG Satellite Precipitation Products in Event-Based Flood Modeling over the Sunshui River Basin in Southwestern China

“…For example, Ren (2019) evaluated the accuracy of the IMERG V3, V4, and V5 hourly products in mainland China using ground observation data as a benchmark. The authors revealed that the accuracy of the V5 product generally surpasses that of V3 and V4 [43].…”

Comparative Application of Rain Gauge, Ground- and Space-Borne Radar Precipitation Products for Flood Simulations in a Dam Watershed in South Korea

“…Yuan et al [30] and Jiang et al [31] presented the comprehensive studies on the feasibility and hydrologic utility of GPM IMERG for flood simulations over the Chindwin River and Mishui basins in China using XAJ, respectively. Gilewski and Nawalany [32], Saouable et al [33], Benkirane et al [34], Min et al [35], Tang et al [36], Soo et al [37], and Patel et al [38] applied the HEC-HMS model to the river basins in Poland (Upper Skawa River), Morocco (Ghdat River and Tensift River), China (Xin'an River and Shouxi River), Malaysia (Langat River), and India (Upper Ganga River) for flood modeling, respectively. Nonetheless, these IMERG-used flood analyses lack sufficient evaluation of the modeling accuracy compared to traditional methods that rely on gauge observations or precipitation estimates from weather radars, such as those conducted by Li et al [10] and Gilewski and Nawalany [32].…”