Empirical tool development for prairie pothole management using AnnAGNPS and random forest

Abstract

Watershed models are robust tools that inform management and policy in a variety of sectors, but these models are often neglected through time due to economic or technical constraints. Additionally, they are not readily accessible tools for key decision makers. Conversely, machine learning models are robust alternatives to common hydrologic modeling frameworks. The random forest algorithm specifically is an interpretable predictive tool. We couple Annualized Agricultural Non-Point Source (AnnAGNPS) model output, an abstract, anthropogenic flood risk metric, and develop a random forest model to provide an empirical tool that benefits decision makers in the Des Moines Lobe of the Prairie Pothole Region in north-central Iowa. The developed model has the capacity to predict our flood risk metric (calibration: R2 > 0.9, validation: R2 > 0.7) for individual farmed prairie potholes across a variety of morphologic and management conditions and can be used iteratively to assess alternative actions.