Evaluating the Performance of Remotely Sensed Precipitation Estimates against In-Situ Observations during the September 2014 Mega-Flood in the Kashmir Valley

• Published in: Asia-Pacific journal of atmospheric sciences Vol. 55; no. 2; pp. 209 - 219
• Main Authors: Rashid, Irfan, Parray, Ajaz Ahmad, Romshoo, Shakil Ahmad
• Format: Journal Article
• Language: English
• Published: Seoul Korean Meteorological Society 01.05.2019; Springer Nature B.V; 한국기상학회
• Subjects: Atmospheric precipitations; Atmospheric Sciences; Bias; Climatology; Correlation coefficient; Data; Earth and Environmental Science; Earth Sciences; Floods; Geophysics/Geodesy; Hydrologic data; Mountain regions; Original Article; Precipitation; Precipitation data; Precipitation estimation; Rain; Rain gauges; Rainfall measurement; Satellite precipitation estimates; Satellites; Tropical climate; Tropical rainfall; Tropical Rainfall Measuring Mission (TRMM); 대기과학; India; Kashmir India; Satellite-derived rainfall estimates; September 2014 flood; Kashmir Himalaya; Surface rain-gauge observations; Nash-Sutcliffe coefficient
• ISSN: 1976-7633; 1976-7951
• DOI: 10.1007/s13143-018-0071-6

In the present study, 4 gridded satellite precipitation data products for September 2014 flood, IMERG (Integrated Multi-satellitE Retrievals for GPM), GSMaP (Global Satellite Mapping of Precipitation), TRMM-3B42 (Tropical Rainfall Measuring Mission) and INSAT-3D-IMR (INSAT Multispectral Rain), were evaluated against the Indian Meteorological Department rain-gauge data from Sep-1st to Sep-7th 2014. Three evaluation indices; Correlation coefficient (CC), the Relative bias (RB) and the Nash-Sutcliffe coefficient (NSC), were used to evaluate the robustness of satellite precipitation estimates with actual rainfall measurements. IMERG precipitation product has a near perfect positive CC and NSC values of 0.94 and 0.99 respectively; while the CC and NSC values are 0.7 and 0.5 for GSMaP_Gauge; 0.69 and 0.05 for INSAT-3D-IMR; and 0.9 and 0.8 for TRMM-3B42 respectively. The RB estimates indicate that IMERG, with a bias of 2%, is a best-fit dataset when compared to the surface rain-gauge observations. In contrast, TRMM-3B42, GSMaP and INSAT-3D-IMR have underestimation biases of −31%, −58%, and − 86% respectively. Analysis of the indices indicates that IMERG precipitation product performed better than other three satellite precipitation products owing to the closeness of values with surface gauge station data over Kashmir. Owing to scanty observation of rainfall in the region, IMERG has a potential to become a cost effective input data source for designing a flood early warning system (FEWS) for Kashmir. However, it is suggested to evaluate the robustness of different satellite-derived precipitation estimates compared to rain gauge observations by incorporating more extreme events from different mountain regions globally for establishing the best satellite derived precipitation product.