Abstract
Excess nutrients (nitrogen and phosphorus) in lakes can lead to eutrophication, hypoxia, and algal blooms that may harm aquatic life and people. Some U.S. states have established numeric water quality criteria for nutrients to protect surface waters. However, monitoring to determine if criteria are being met is limited by resources and time. Using R code and the publicly available lake data, we introduce a downloadable interactive user interface for modeling relationships between watershed land use, climate, and other variables and surface water nutrient concentrations. Random Forest modeling identified watershed agricultural and forest land coverage, fertilizer inputs, and lake depth as the most important predictors of total phosphorus. The analytical framework implemented in this application can be applied to different locations and other surface water types to be leveraged by decision makers to identify the most influential drivers of excess nutrient concentrations and to prioritize watersheds for restoration.