Performance of various gridded temperature and precipitation datasets over Northwest Himalayan Region

• Published in: Environmental Research Communications 2020-08, Vol.2 (8), p.85002
• Main Authors: Kanda, Neha, Negi, H S, Rishi, Madhuri S, Kumar, Anant
• Format: Article
• Language: English
• Subjects: Bias; Climatic zones; Climatology; CRU-TS; Data integration; Datasets; ERA-Interim; gridded datasets; Hydrology; Observatories; Performance evaluation; Precipitation; Rainfall; Rainfall measurement; Seasonal variations; TRMM satellite; Water resources; western disturbances (WDs)
• ISSN: 2515-7620; 2515-7620
• DOI: 10.1088/2515-7620/ab9991

This study evaluated the performance of 07 gridded datasets viz. Asian Precipitation Highly-resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE), Climate Research Unit Time-Series (CRU-TS), University of Delaware (UDEL), Tropical rainfall Measurement Mission (TRMM)/ TMPA (TRMM Multi-Satellite Precipitation Analysis), Global Precipitation Climatology Centre (GPCC), Princeton Global Forcings Dataset (PGF), and European Reanalysis Interim (ERA-I) in capturing the amount, seasonality and trend of precipitation over different climatic zones of Northwestern Himalaya (NWH) i.e. Lower Himalaya (LH), Greater Himalaya (GH) and Karakoram Himalaya (KH). A similar comparison was also done for the temperature data but only with 05 datasets, viz. APHRODITE, CRU-TS, PGF, UDEL and ERA-I since TMPA and GPCC are precipitation datasets only. This study is a maiden attempt where in situ observation includes the data from elevations above 5000 m amsl (07 observatories) in NWH (Indian sub-region). Results reveal that for precipitation over NWH; ERA-I, GPCC, and TMPA/TRMM were found to be quite reliable datasets. For temperature, all datasets performed quite well but CRU-TS and ERA-I provided more reliable estimates. The mean absolute error ranged from 13.5 mm/month to 150.7 mm/month for precipitation and 0.75°C/month to 9.9°C/month for temperature. High values of the errors underpin the need for bias correction. On the basis of this analysis, monthly correction factors for wintertime temperature and precipitation have also been suggested for each dataset which when multiplied with corresponding datasets would result in closely approximated values for the area of interest. These results can serve as a guide for bias correction and selection of appropriate gridded datasets for use in studies pertaining to hydrological modeling over NWH.