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Showing 1 to 5 of 5 for “"Noah-MP"”.
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Snow Hydrology and Streamflow Response to Snowmelt: A Case Study on SWE Dataset Comparisons, Regional Water Balance, and Snowmelt-Induced Runoff within the Smith River Watershed, Montana
… datasets (UA 4km, UA 800m, WUS-SR, SNODAS, and Noah-MP via WLDAS) and SnowModel were validated against observations from five Snow Telemetry (SNOTEL) stations using correlation (r), mean absolute error (MAE), and root mean square error (RMSE). Models assimilating SNOTEL data (SNODAS, UA 800m, …
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From groundwater to the atmosphere: using RELAMPAGO observations and modeling to understand changes in the hydrologic cycle of southeastern South America
Land-atmosphere interactions play an important role in modulating the hydroclimate of Southeastern South America (SESA). While climate variability and extreme events impact surface hydrology, the land surface of the region influences evapotranspiration, groundwater table depth, near-surface soil …
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Multi-scale features of atmospheric rivers and the linkages with local-scale hydrological impacts on the U.S. West Coast
… water vapor transport in the atmosphere, are an important component of the hydrologic cycle and are characterized by a multi-scale nature. On the one hand, ARs are embedded in the planetary-scale Rossby waves and account for the majority of poleward moisture transport in the midlatitudes. On the …
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Evaluating Changes in Terrestrial Hydrological Components Due to Climate Change in the Chesapeake Bay Watershed
… evaluation is performed to determine the impacts of climate change on terrestrial hydrological components and the Net Irrigation Water Requirement (NIWR) throughout the Chesapeake Bay watershed in the mid-Atlantic region of the United States. The Noah-MP land surface model is calibrated and …
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Evaluation of Snow and Streamflow in the National Water Model with Analysis using Machine Learning
… and recognize the areas where it could be improved for future model developments. The goal was also to evaluate if the recent advancements in machine learning techniques is useful for predicting snow in mountainous terrain where numerical prediction models such as the NWM have been known to …