Spatio-temporal evaluation of gridded precipitation products for the high-altitude Indus basin

© 2021 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.The high-altitude Indus basin is one of the most complex and inadequately explored mountain terrains in the World, where reliable observations of precipitation are highly lacking. Therefore, spatially distributed precipitation products developed at global/regional scale are often used in several scientific disciplines. However, large uncertainties in precipitation estimates of such precipitation data sets often lead to suboptimal outcomes. In this study, performance of 27 widely used gridded precipitation products belonging to three different categories of gauge-based, reanalysis and merged products is evaluated with respect to high-quality reference climatologies of mean monthly precipitation. Widely used statistical measures and quantitative analysis techniques are used to analyse the spatial patterns and quantitative distribution of mean monthly, seasonal and annual precipitation at sub-regional scale. Mean annual precipitation estimates of the gridded data sets are cross validated with the corresponding adjusted streamflows using Turc-Budyko non-dimensional analysis. Results reveal poor to moderately good performance of the gridded data sets. Marked differences in spatiotemporal and quantitative distribution of precipitation are found among the data sets. All data sets are consistent in their patterns showing negative or dry bias in wet areas and positive or wet bias in dry areas, although considerable differences in the magnitudes of the biases are noticed at sub-regional scale. None of the data sets is equally good for all sub-regions due to very high spatiotemporal variability in their performance at sub-regional scale. Gauge-based and merged products performed better in dry regions and during monsoon season, while reanalysis products provided better estimates in wet areas and during winter months. GPCC V8, ERA5 and MSWEP2.2 are found better than their counter-grouped data sets. Overall, ERA5 is found most acceptable for all sub-regions, particularly at higher-altitudes, in wet areas and during winter months.