Abstract
AbstractThe 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.
Highlights
The Hindu Kush, Karakoram, and Himalaya (HKKH) mountain ranges are the source of many of the major rivers of the Indian subcontinent, including the Indus, the Ganga, and the Brahmaputra, which support the lives of over a billion people by providing water resources for domestic consumption, industry, irrigation, hydro‐electric power, etc (Eriksson et al, 2009)
While dynamically downscaled reanalysis or global climate model data are being increasingly used to provide precipitation estimates over specific regions in the HKKH (e.g., Akhtar et al, 2008; Ali et al, 2015; Li et al, 2016; Narula & Gosain, 2013; Nepal, 2016; ul‐Hasson, 2016), their accuracy has yet to be conclusively proven as studies comparing model output and measurements, and the impact of bias correction on the model output, are still limited in both number and scope
In this study, we focused on comprehensively assessing the ability of bias‐correcting regional climate model to reproduce the observed detailed patterns of precipitation in the HKKH region by examining the performance of a high‐resolution Weather Research and Forecasting (WRF) model hindcast simulation generated for a long period of time, over a large region, and assessed against a large number of in situ precipitation measurements
Summary
The Hindu Kush, Karakoram, and Himalaya (HKKH) mountain ranges are the source of many of the major rivers of the Indian subcontinent, including the Indus, the Ganga, and the Brahmaputra, which support the lives of over a billion people by providing water resources for domestic consumption, industry, irrigation, hydro‐electric power, etc (Eriksson et al, 2009). The reliability of the gridded precipitation dataset based on TRMM (Tropical Rainfall Measuring Mission; Huffman et al, 2007) satellite estimates over the HKKH region is compromised, as TRMM has deficiencies in detecting local orographically induced precipitation ( over glaciated areas), which requires adjustment using in situ measurements (Andermann et al, 2011; Yin et al, 2008) Given their rather coarse spatial resolution and dependence on the assimilation of meteorological observations, precipitation estimates over the HKKH region from global atmospheric reanalysis datasets such as ERA‐Interim (Dee et al, 2011) are unsuitable for forcing hydrological models (Ma et al, 2009). Understanding the performance of the TRMM, APHRODITE, and ERA‐Interim in the HKKH region is important as they have been used for water budget studies, validation of regional climate model output, and extreme precipitation analysis without any attempt to assess the quality of their data
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