Citation:

Pennino, M., M. Fry, R. Sabo, AND Jim Carleton. Nutrient Explorer: An analytical framework to visualize and investigate drivers of surface water quality. ENVIRONMENTAL MODELLING & SOFTWARE. Elsevier Science, New York, NY, 170:105853, (2023). https://doi.org/10.1016/j.envsoft.2023.105853

Impact/Purpose:

This paper was written to show an example use of a downloadable interactive user interface, which we developed using R code, to quantify the relationships between water quality and landscape variables.  For our analysis, we utilized total phosphorus (TP) concentrations measurements from lakes in 17 northern midwestern and northeastern U.S. states (LAGOS-NE), which were combined with watershed-scale landscape metrics, and we created visualizations of the datasets and relationships across space and time.  We found lake TP is correlated with certain anthropogenic, landscape, and climatic metrics.  Also, random forest and multilinear regression models were developed to determine which factors best explain spatial variability in TP and where lake TP is highest across the Upper Midwest and Northeast U.S.      

Description:

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 LAGOS-NE lakes dataset for 17 northern midwestern and northeastern U.S. states, we model relationships between lake morphometric and watershed landscape and land use variables and total phosphorus (TP) concentrations to predict TP in lakes lacking monitoring data.  Random Forest (RF) modeling identified watershed mean tree canopy cover, percent farmland, percent forest, P inputs from farm fertilizer, and maximum lake depth as the most important predictors of TP, while multilinear regression found that watershed percent farmland, maximum lake depth, watershed area, temperature, and P inputs from human waste had the most influence on TP.  This analytical framework and results can be leveraged by decision makers to identify the most influential drivers of excess nutrient concentrations, and be used to prioritize watersheds for restoration.

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