Victor L Roland, Ph.D (Former Employee)

The polygon datasets were created to assist in visualizing the results of salinity modeling in Gulf of Mexico estuaries and bays. Statistical algorithms (Asquith and others, 2023) were developed to predict daily salinities for 91 salinity monitoring sites (Rodgers and Swarzenski, 2019) operated by 7 agencies in near coastal United States waters of the Gulf of Mexico. These monitoring sites are ass

The U.S. Geological Survey (USGS) developed models to estimate the amount of water that is withdrawn and consumed by thermoelectric power plants (Diehl and others, 2013; Diehl and Harris, 2014; Harris and Diehl, 2019). The thermoelectric water use models are based on linked heat-and-water budgets that are constrained by power plant generation and cooling system technologies, the amount of fuels co

This data release contains input and output data from hydrologic simulations of streamflow conditions in the Red River Basin (RRB) using the Precipitation-Runoff Modeling System (PRMS). The RRB PRMS model predicts components of the water balance at 3065 hydrologic response units (HRU) and streamflow for 1614 stream segments within the model domain for the simulation period 1981 to 2016. The data r

Two methods of calculating hydrologic alteration were applied to modeled daily streamflow data for 9,201 12-digit hydrologic unit code (HUC12) pour points draining to the Gulf of Mexico (Robinson and others, 2020). The first method is a new modified method of calculating ecosurplus and ecodeficit called hydro change. For this project, ecosurplus and ecodeficit have been combined to assess overall

Anthropogenic hydrologic alteration threatens the health of riverine ecosystems. This study assesses hydrologic alteration in the Pearl and Pascagoula river basins using modeled daily streamflow. Machine learning was used to identify locations that have undergone statistically significant streamflow alteration, quantify the volume of the alteration, and predict alteration using cubist models. Stat

The U.S. Geological Survey's SPAtially Referenced Regression On Watershed attributes (SPARROW) for the Southeastern United States was used to simulate changes in total nitrogen, total phosphorus and suspended sediment load in streams under two scenarios: (1) where all forests are urbanized and (2) where all forests are urbanized and runoff is adjusted based on a non-forested landscape. This data r

Nitrogen and phosphorus losses from agricultural areas have impacted the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown but assessments of their effectiveness at large spatial scales have been sparse. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW

The U.S. Geological Survey's (USGS) SPAtially Referenced Regression On Watershed attributes (SPARROW) model was used to aid in the interpretation of monitoring data and simulate streamflow and water-quality conditions in streams across the Southeast Region of the United States. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environ

This data release consists of the data used to develop SPAtially Referenced Regression On Watershed(SPARROW) attributes models for estimating loads of total phosphorus and total nitrogen in Tennessee streams. These data support the publication containing the Tennessee SPARROW models results (Hoos and others, 2019) and include model input used in the South Atlantic-Gulf Drainages and Tennessee Rive

Daily streamflow discharge data from 139 streamgages located on tributaries and streams flowing to the Gulf of Mexico were used to calculate mean monthly, mean seasonal, and decile values. Streamgages used to calculate trends required a minimum of 65 years of continuous daily streamflow data. These values were used to analyze trends in streamflow using the Mann-Kendall trend test in the R package