Bias Correction of High‐Resolution Regional Climate Model Precipitation Output Gives the Best Estimates of Precipitation in Himalayan Catchments

Abstract

The need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognized, as is identifying precipitation extremes for assessing hydro‐meteorological hazards. Here, we investigate the ability of bias‐corrected Weather Research and Forecasting model output at 5‐km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low‐intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early‐morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a nonlinear bias‐correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modeling in Himalayan catchments, suggesting that bias‐corrected high‐resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably underrepresented in the gridded datasets, suggesting that bias‐corrected regional climate model output is also necessary for hydro‐meteorological risk assessments in Himalayan catchments.

Enhancing production and flow of freshwater ecosystem services in a managed Himalayan river system under uncertain future climate

“…For the Himalayas and their Foothills (Fig. 1) where topography strongly influences local hydro-meteorological processes, baseline meteorological data were generated through high-resolution 5-km grid dynamical downscaling of ERA-Interim reanalysis data using the Weather Research and Forecast (WRF) model by Bannister et al (2019), including bias correction and validation; for the Plains, ERA-Interim data downscaled to~0.5-degree resolution with RegCM4 was downloaded from CORDEX. Both datasets were spatially aggregated by sub-catchment and elevation band in their respective regions.…”

Adaptation by Himalayan Water Resource System under a Sustainable Socioeconomic Pathway in a High-Emission Context

“…Most precipitation falls as rainfall during the monsoon season between July and late September; however, there is a significant precipitation input as snowfall in winter brought by the westerly disturbances (Bannister et al 2019) In India, the standard norm for domestic water consumption is 40 lpcd (liters per capita per day) for rural areas and 135 lpcd in urban areas (WaterAid 2005), except at Delhi for which the per capita water consumption is 225 lpcd (according to BBMB); New Delhi receives a part of its drinking water supply from the two reservoirs. The per capita demand figures were used to arrive at the water demand shown in Table 1.…”

“…The meteorological data used for the historical period (i.e. baseline: 1990-2007) was generated using the Weather Research Forecasting (WRF) model at a 5 km x 5 km resolution (Bannister et al 2019) and the future projections in 10-year intervals from 2010 to 2100 were obtained from the Climate and Global Dynamics Laboratory for the SSP1 scenario (CGD 2020). The data are in 1-km grid cells as re-downscaled by Gao (2017).…”

Measuring Changes in Snowpack SWE Continuously on a Landscape Scale Using Lake Water Pressure

“…Measurements of falling and accumulated snow are used to develop, test, and drive weather, climate, and hydrology models; hence, the lack of observations constitutes a critical observational gap in the terrestrial water budget (McCrary et al 2017;Yao et al 2018;Xu et al 2019;Yoon et al 2019). This gap is the most important unsolved problem in snow hydrology (Dozier et al 2016), and the ultimate cause of large water resource uncertainties and biases, particularly in the headwaters of High Mountain Asia's major river basins (Yatagai et al 2012;Smith and Bookhagen 2018;Wortmann et al 2018;Bannister et al 2019;Lievens et al 2019;Momblanch et al 2019;Orsolini et al 2019;Yoon et al 2019).…”