Impacts and Biases of Storm Regime and Sampling Networks on Extreme Precipitation Measurements across the Western Himalayas

Abstract

Precipitation data are critical for developing a fundamental understanding of hydrologic processes and reducing flood losses, but the acquisition of reliable and representative precipitation data is extremely challenging in high mountain environments, such as the Himalayas. Remotely sensed and ground-based observations are the two primary sources of precipitation data, but can have important biases, particularly in areas of complex and steep topography. Remotely sensed [Tropical Rainfall Measuring Mission (TRMM)] and ground-based precipitation measurements in the Sutlej River basin, India, were compared to better understand the biases and uncertainties associated with variable storm processes and precipitation sampling programs in high mountain environments. Results highlight how extreme rainfall values were the highest across the Lower Himalayas where synoptic weather systems are more prevalent, whereas extreme convective intensities were the highest across the Inner Himalayas, where orography triggers convective precipitation events. In addition, deviations between extreme precipitation values measured by TRMM and ground-based observations reversed with increasing elevations, with TRMM measurements underestimating precipitation relative to ground-based observations for lower elevation sites and overestimating precipitation at the higher elevation sites. Finally, large subgrid differences in the variance of the extreme rainfall distributions were found within a single TRMM pixel, which appear to be driven in large part by local topographic relief, underscoring the complexities of bias correction of gridded rainfall measurements using ground-based stations. Recommendations are made for advancements needed to apply remotely sensed precipitation data for improving understanding of physical processes and forecasting of floods in mountain areas.