Abstract
Satellite-based precipitation estimates (SPEs) available at a high spatio-temporal resolution with near-global coverage are substitutes for ground-based datasets. In this study, the quality of popular SPEs was evaluated at daily time scale over agro-climatic (ACL) zones of India. The SPEs were analyzed in two different groups, i.e., (i) satellite-only precipitation estimates (SOPEs) including 5 products namely, CMORPH, PERSIANN, SM2RAIN-ASCAT, SM2RAIN-CCI, and SM2RAIN-GPM, and (ii) gauge-corrected satellite precipitation estimates (GCSPEs) including 4 products namely, CHIRPS, GSMaP, IMERG and MSWEP. The analysis was carried out by quantifying the qualitative and quantitative performance metrics as well as by decomposing the errors into random and systematic components. In general, SPEs underestimated the high rainfall events and overestimated the low rainfall events. The performance of SPEs was observed to be poor over the western Himalayan region. The efficiency and agreement of Global Precipitation Measurement (GPM) based products (including SM2RAIN-GPM, GSMaP and IMERG) with observed precipitation were better. The superiority of SM2RAIN-GPM, GSMap and IMERG products has also been supported by the lower magnitude of errors and biases. The error decomposition reveals high random errors in most of the SPEs; however, SM2RAIN-based products have low random error. It was also observed that the total bias is highly underestimated due to opposite-natured bias components. Further, the association of vegetation and topographic complexity with the performance of precipitation products was investigated, and a negative/positive association of performance metrics of SPEs with topographic/vegetation characteristics was observed. A multi-criteria decision-making (MCDM) approach was then employed to rank the datasets based on multiple performance indicators. MCDM approach reveals the superiority of SM2RAIN-GPM product amongst SOPEs and GSMaP (followed by IMERG) amongst GCSPEs over a majority of the regions of India.
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