Supporting data and model outputs for hydrologic alteration modeling in the Pearl and Pascagoula river basins

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. Statistically significant alteration was determined by hypothesis testing. The pre- and post-alteration flow duration curves were used to calculate the net change across 60 years. Cubist models were developed for both basins to predict hydrologic alteration and to identify important basin characteristics. This data release includes 3 input files [...]