Prairie Pothole Management Support Tool: A web application for evaluating prairie pothole flood risk

Abstract

The Prairie Pothole Management Support Tool (PPMST) is an open‐source, accessible web application that provides educational and informational evaluations of the flood risk of individual farmed prairie potholes within the Des Moines Lobe (DML) of Iowa. The PPMST provides a comparative numeric assessment of pothole flood risk, predicted from a random forest machine learning model, based on simple field characteristics. The PPMST allows landowners or extension specialists and consultants to assess a range of tillage and artificial drainage options and can compare traditional corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotations with perennial planting. The PPMST will be useful for conservation planning and precision agriculture by helping identify and prioritize these marginal land areas for continued farming or conservation.

A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication

“…Some web-based decision support systems can be considered educational and/or informational tools, including risk scenarios or allowing their users to change the drivers and provide risk evaluation for diverse purposes (Jones et al, 2014;Knight et al, 2015). For instance, MiDAS (Mitigation and Damage Assessment System) evaluates the community's flood risk at the property level in Iowa (Alabbad et al, 2022), and the Prairie Pothole Management Support Tool (PPMST) estimates the flood risk of individual farmed prairie potholes in Iowa (Nahkala et al, 2022). Some of these web tools creates flood plain maps for the Mississippi River Basin (Rajib et al, 2021), flood hazard and risk maps for Armenia (Dobrinkova and Stefanov, 2020), Bangladesh using LiDAR technology (Tupas et al, 2016), for families, buildings, population, industries, and cultural heritages in Italy (Iadanza et al, 2021), and evaluates the vulnerability of an urban system to the effects of disasters for City of Toronto and London in Canada (Irwin et al, 2016).…”