Evaluating the present annual water budget of a Himalayan headwater river basin using a high‐resolution atmosphere‐hydrology model. Issue 9 (3rd May 2017)

• Record Type: Journal Article
• Authors: Li, Lu; Gochis, David J.; Sobolowski, Stefan; Mesquita, Michel D. S.
• Is Part Of: Journal of geophysical research. Volume 122:Issue 9(2017)
• Page Start: 4786
• Page End: 4807
• Publication Date: 2017-05-03
• Subjects: WRF‐Hydro -- microphysics -- hydroclimate -- high‐altitude precipitation -- water budget -- glacier wastage; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2016JD026279
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Understanding the present water budget in Himalayan Basins is a challenge due to poor in situ coverage, incomplete or unreliable records, and the limitations of coarse resolution gridded data set. In the study, a two‐way coupled implementation of the Weather Research and Forecasting (WRF) Model and the WRF‐Hydro hydrological modeling extension package (WRF/WRF‐Hydro) was employed in its offline configuration, over a 10 year simulation period for a mountainous river basin in North India. A triple nest is employed, in which the innermost domain had 3 km for atmospheric model grids and 300 m for hydrological components. Two microphysical parameterization (MP) schemes are quantitatively evaluated to reveal how differently MP influences orographic‐related precipitation and how it impacts hydrological responses. The WRF‐Hydro modeling system shows reasonable skill in capturing the spatial and temporal structure of high‐resolution precipitation, and the resulting stream flow hydrographs exhibit a good correspondence with observation at monthly timescales, although the model tends to generally underestimate streamflow amounts. The Thompson Scheme fits better to the observations in the study. More importantly, WRF shows that for high‐altitude precipitation, a high "bias" is exhibited in winter precipitation from WRF, which is about double to triple that as estimated from valley‐sited rain gauges and remotely sensed precipitation estimates from Tropical Rainfall Measuring Mission and Asian Precipitation ‐ Highly‐Resolved Observational Data Integration Towards Evaluation. Given the full annual cycle pattern and amount in high‐altitude precipitation and the statistical correspondence in discharge, it is concluded that the WRF‐Hydro modeling system shows potential for explicitly predicting potential changes in the atmospheric‐hydrology cycle of ungauged or poorly gauged basins. Plain Language Summary: Understanding the present water budget in Himalayan Basins is a challenge due to poor in situ coverage, incomplete or unreliable records, and the limitations of coarse resolution gridded data set. In a Himalayan headwater river basin, the Weather Research and Forecasting (WRF)‐Hydro modeling system shows reasonable skill in capturing the precipitation and the resulting stream flow hydrographs exhibit a good correspondence with observation at monthly timescales. More importantly, WRF shows that for high‐altitude precipitation, a high "bias" is exhibited in winter precipitation from WRF, which is about double to triple that as estimated from valley‐sited rain gauges and remotely sensed precipitation estimates from both Tropical Rainfall Measuring Mission and Asian Precipitation ‐ Highly‐Resolved Observational Data Integration Towards Evaluation. Given the full annual cycle pattern and amount in high‐altitude precipitation and the statistical correspondence in discharge, it is concluded that the WRF‐Hydro modeling system shows potential for explicitly predicting potential changes in the atmospheric‐hydrology cycle of ungauged or poorly gauged basins. Key Points: A significant disagreement in high‐altitude precipitation estimates is found between gauge observations, TRMM, APHRODITE, and the WRF A large amount of precipitation in high mountainous areas in Beas Basin is occurring and is not properly accounted for in TRMM or APHRODITE WRF‐Hydro modeling system shows skill in capturing monthly discharge variability and the daily discharge distribution