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1. SWAT ungauged: Water quality modeling in the Upper Mississippi River Basin

   https://doi.org/10.1016/j.jhydrol.2020.124601  

   Qi, Junyu ; Zhang, Xuesong ; Yang, Qichuan ; Srinivasan, R. ; Arnold, Jeffrey G. ; Li, Jia ; Waldholf, Stephanie T. ; Cole, Jefferson ( May 2020 , Journal of Hydrology)
2. Effect of Watershed Delineation and Climate Datasets Density on Runoff Predictions for the Upper Mississippi River Basin Using SWAT within HAWQS

   https://doi.org/10.3390/w13040422  

   Chen, Manyu ; Cui, Yuanlai ; Gassman, Philip ; Srinivasan, Raghavan ( February 2021 , Water)

   The quality of input data and the process of watershed delineation can affect the accuracy of runoff predictions in watershed modeling. The Upper Mississippi River Basin was selected to evaluate the effects of subbasin and/or hydrologic response unit (HRU) delineations and the density of climate dataset on the simulated streamflow and water balance components using the Hydrologic and Water Quality System (HAWQS) platform. Five scenarios were examined with the same parameter set, including 8- and 12-digit hydrologic unit codes, two levels of HRU thresholds and two climate data densities. Results showed that statistic evaluations of monthly streamflow from 1983 to 2005 were satisfactory at some gauge sites but were relatively worse at others when shifting from 8-digit to 12-digit subbasins, revealing that the hydrologic response to delineation schemes can vary across a large basin. Average channel slope and drainage density increased significantly from 8-digit to 12-digit subbasins. This resulted in higher lateral flow and groundwater flow estimates, especially for the lateral flow. Moreover, a finer HRU delineation tends to generate more runoff because it captures a refined level of watershed spatial variability. The analysis of climate datasets revealed that denser climate data produced higher predicted runoff, especially for summer months.
3. Detecting Signals of Large‐Scale Climate Phenomena in Discharge and Nutrient Loads in the Mississippi‐Atchafalaya River Basin

   https://doi.org/10.1029/2018GL081166  

   Smits, A. P. ; Ruffing, C. M. ; Royer, T. V. ; Appling, A. P. ; Griffiths, N. A. ; Bellmore, R. ; Scheuerell, M. D. ; Harms, T. K. ; Jones, J. B. ( April 2019 , Geophysical Research Letters)
4. Analysis of alternative climate datasets and evapotranspiration methods for the Upper Mississippi River Basin using SWAT within HAWQS

   https://doi.org/10.1016/j.scitotenv.2020.137562  

   Chen, Manyu ; Gassman, Philip W. ; Srinivasan, Raghavan ; Cui, Yuanlai ; Arritt, Raymond ( June 2020 , Science of The Total Environment)
5. On the Use of NLDAS2 Weather Data for Hydrologic Modeling in the Upper Mississippi River Basin

   https://doi.org/10.3390/w11050960  

   Qi, Junyu ; Wang, Qianfeng ; Zhang, Xuesong ( May 2019 , Water)

   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-scalemore »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.« less