On the Use of NLDAS2 Weather Data for Hydrologic Modeling in the Upper Mississippi River Basin

Abstract

Weather data are the key forces that drive hydrological processes so that their accuracy in watershed modeling is fundamentally important. For large-scale watershed modeling, weather data are either generated by using interpolation methods or derived from assimilated datasets. In the present study, we compared model performances of the Soil and Water Assessment Tool (SWAT), as driven by interpolation weather data, and NASA North American Land Data Assimilation System Phase Two (NLDAS2) weather dataset in the Upper Mississippi River Basin (UMRB). The SWAT model fed with different weather datasets were used to simulate monthly stream flow at 11 United States Geological Survey (USGS) monitoring stations in the UMRB. Model performances were evaluated based on three metrics: coefficient of determination (R2), Nash–Sutcliffe coefficient (NS), and percent bias (Pbias). The results show that, after calibration, the SWAT model compared well at all monitoring stations for monthly stream flow using different weather datasets indicating that the SWAT model can adequately produce long-term water yield in UMRB. The results also show that using NLDAS2 weather dataset can improve SWAT prediction of monthly stream flow with less prediction uncertainty in the UMRB. We concluded that NLDAS2 dataset could be used by the SWAT model for large-scale watersheds like UMRB as a surrogate of the interpolation weather data. Further analyses results show that NLDAS2 daily solar radiation data was about 2.5 MJ m−2 higher than the interpolation data. As such, the SWAT model driven by NLDAS2 dataset tended to underestimate stream flow in the UMRB due to the overestimation in evapotranspiration in uncalibrated conditions. Thus, the implication of overestimated solar radiation by NLDAS2 dataset should be considered before using NLDAS2 dataset to drive the hydrological model.

Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network

“…Others have observed the changing water quantity of the Mississippi. One study using NLDAS data focuses on a subsection of the Mississippi with a higher quantity of streamflow target sites [46]. Another group considers a different data system altogether for watershed modeling on the upper Mississippi basin [47].…”

Enhanced Watershed Modeling by Incorporating Remotely Sensed Evapotranspiration and Leaf Area Index

“…Localized variations in precipitation have frequently been observed in this study area, which may have further contributed to the underestimation of the peak streamflow (Lee et al, 2016;Yeo et al, 2014). Spatially continuous climatic data, including the North American Land Data Assimilation System (NLDAS) and the Next-Generation Radar (NEXRAD), have been shown to reduce prediction uncertainty from climatic data taken from stations (Qi et al, 2019;Sexton et al, 2010). The use of these data may better mimic the peak streamflow.…”

Modeling sediment diagenesis processes on riverbed to better quantify aquatic carbon fluxes and stocks in a small watershed of the Mid-Atlantic region

“…At the same time, the scheduling of crop rotations was generated using 2008–2012 data from the USDA-National Agriculture Statistics Service (NASS) Cropland Data Layer (CDL). High resolution (~ 1/8°) National Astronautics and Space Administration (NASA) North-American Land Data Assimilation System 2 (NLDAS2) climate forcing data [ 65 ] were used to provide daily weather inputs including precipitation, temperature, solar radiation, relative humidity, and wind speed [ 66 ]. For more information regarding SWAT model setup in the TW, please refer to Lee, Yeo [ 61 ].…”