Empirical tool development for prairie pothole management using AnnAGNPS and random forest. (January 2022)

• Record Type: Journal Article
• Authors: Nahkala, Brady A.; Kaleita, Amy L.; Soupir, Michelle L.
• Is Part Of: Environmental modelling & software. Volume 147(2022)
• Publication Date: 2022-01
• Subjects: Prairie potholes -- Random forest -- Machine learning -- Decision support tools; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105241
• Languages: English
• ISSNs: 1364-8152

Abstract

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: R 2 > 0.9, validation: R 2 > 0.7) for individual farmed prairie potholes across a variety of morphologic and management conditions and can be used iteratively to assess alternative actions. Highlights: A flood risk metric was developed using spatial and temporal variables. A random forest regression was calibrated to predict flood risk of prairie potholes. The random forest model enables comparison of agricultural management options.