Identification of wet-prone regions over Northwest Himalaya using high-resolution satellite seasonal estimates

Pravat Jena and Sarita Azad ()
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Pravat Jena: University of Petroleum and Energy Studies
Sarita Azad: Indian Institute of Technology Mandi

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 112, issue 2, No 30, 1727-1748

Abstract: Abstract The present study aims to evaluate four satellite estimates, namely CMORPH v0.x, PERSIANN-CDR, TMPA-V7 and TMPA-V7-RT, over four seasons (during 2003–2017) against ground observations (0.25° × 0.25° lat./long). For this purpose, precipitation statistics such as MEAN (mean overall days), AWET (mean over wet days), NDRY (no. of dry days), NWET (no. of wet days (rain rates ≥ 1 mm)) and rainfall extremes ranging from 90 to 99.99th percentile, and contingency statistics such as the improved probability of detection (IPOD) are employed. The results reveal that TMPA-V7-RT captures NWET and AWET both in June–July–August (JJA) and September–October–November (SON), whereas TMPA-V7 and PERSIANN-CDR adequately represent them in December–January–February (DJF) and March–April–May (MAM). The satellite CMORPH v0.x performs poorly in representing precipitation statistics over all seasons. Based on the contingency statistics of IPOD, TMPA-V7 detects rainfall associated with the 99th percentile with the highest probabilities of 37.7%, 13.4%, 53.8% and 41.7%, in DJF, MAM, JJA and SON, respectively, followed by TMPA-V7-RT and PERSIANN-CDR. A conceptual model is proposed for delineating vulnerable regions that identify catastrophic events such as heavy, extreme rainfall and cloudburst events. Given the need for real-time monitoring of wet-prone areas across the NWH, the presented work is very much need of the hour. Also, it may be useful for landslide modeling, policymakers and various hydrological applications. The analysis concludes that the TMPA-V7-RT can detect rainfall extremes with a high probability followed by TMPA-V7. Based on the agreement of satellite estimates, some parts of Uttarakhand (UK) and Jammu and Kashmir (J&K) are identified to be the most wet-prone locations over the NWH.

Keywords: Satellite estimates; Seasonal variation; Extremes; IMD gridded data; Wet-prone regions; NWH (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05246-6

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