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Hydrology 2018, 5(4), 57; https://doi.org/10.3390/hydrology5040057

Influence of Precipitation Forcing Uncertainty on Hydrological Simulations with the NASA South Asia Land Data Assimilation System

1
Department of Earth and Planetary Sciences, Johns Hopkins University, 301 Olin Hall, 3400 N. Charles Street, Baltimore, MD 21218, USA
2
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
3
International Centre for Integrated Mountain Development (ICIMOD), GPO Box 3226 Kathmandu, Nepal
4
NASA Marshall Space Flight Center, Earth Science Branch, 320 Sparkman Drive, Huntsville, AL 35805, USA
5
Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, 10300 Baltimore Avenue, Beltsville, MD 20705, USA
*
Author to whom correspondence should be addressed.
Received: 23 August 2018 / Revised: 25 September 2018 / Accepted: 3 October 2018 / Published: 10 October 2018
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Abstract

Accurate meteorological estimates are critical for process-based hydrological simulation and prediction. This presents a significant challenge in mountainous Asia where in situ meteorological stations are limited and major river basins cross international borders. In this context, remotely sensed and model-derived meteorological estimates are often necessary inputs for distributed hydrological analysis. However, these datasets are difficult to evaluate on account of limited access to ground data. In this case, the implications of uncertainty associated with precipitation forcing for hydrological simulations is explored by driving the South Asia Land Data Assimilation System (South Asia LDAS) using a range of meteorological forcing products. MERRA2, GDAS, and CHIRPS produce a wide range of estimates for rainfall, which causes a widespread simulated streamflow and evapotranspiration. A combination of satellite-derived and limited in situ data are applied to evaluate model simulations and, by extension, to constrain the estimates of precipitation. The results show that available gridded precipitation estimates based on in situ data may systematically underestimate precipitation in mountainous regions and that performance of gridded satellite-derived or modeled precipitation estimates varies systematically across the region. Since no station-based data or product including station data is satisfactory everywhere, our results suggest that the evaluation of the hydrological simulation of streamflow and ET can be used as an indirect evaluation of precipitation forcing based on ground-based products or in-situ data. South Asia LDAS produces reasonable evapotranspiration and streamflow when forced with appropriate meteorological forcing and the choice of meteorological forcing should be made based on the geographical location as well as on the purpose of the simulations. View Full-Text
Keywords: South Asia land data assimilation system (South Asia LDAS); precipitation; ET; streamflow; APHRODITE; MERRA2; GDAS; CHIRPS; ALEXI; Indus; Kosi; Hindu Kush-Himalayan region South Asia land data assimilation system (South Asia LDAS); precipitation; ET; streamflow; APHRODITE; MERRA2; GDAS; CHIRPS; ALEXI; Indus; Kosi; Hindu Kush-Himalayan region
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ghatak, D.; Zaitchik, B.; Kumar, S.; Matin, M.A.; Bajracharya, B.; Hain, C.; Anderson, M. Influence of Precipitation Forcing Uncertainty on Hydrological Simulations with the NASA South Asia Land Data Assimilation System. Hydrology 2018, 5, 57.

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