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The last couple of decades have seen the outburst of a number of satellite-based\udprecipitation products with Tropical Rainfall Measuring Mission (TRMM) as the\udmost widely used for hydrologic applications. Transition of TRMM into the Global\udPrecipitation Measurement (GPM) promises enhanced spatio-temporal resolution\udalong with upgrades to sensors and rainfall estimation techniques. The dependence\udof systematic error components in rainfall estimates of the Integrated\udMulti-satellitE Retrievals for GPM (IMERG), and their variation with\udclimatology and topography, was evaluated over 86 basins in India for year\ud2014 and compared with the corresponding (2014) and retrospective (1998–2013)\udTRMM estimates. IMERG outperformed TRMM for all rainfall intensities across a\udmajority of Indian basins, with significant improvement in low rainfall\udestimates showing smaller negative biases in 75 out of 86 basins. Low\udrainfall estimates in TRMM showed a systematic dependence on basin\udclimatology, with significant overprediction in semi-arid basins, which\udgradually improved in the higher rainfall basins. Medium and high rainfall\udestimates of TRMM exhibited a strong dependence on basin topography, with\uddeclining skill in higher elevation basins. The systematic dependence of error\udcomponents on basin climatology and topography was reduced in IMERG,\udespecially in terms of topography. Rainfall-runoff modeling using the Variable\udInfiltration Capacity (VIC) model over two flood-prone basins (Mahanadi and\udWainganga) revealed that improvement in rainfall estimates in IMERG did not\udtranslate into improvement in runoff simulations. More studies are required\udover basins in different hydroclimatic zones to evaluate the hydrologic\udsignificance of IMERG

Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale