Comparing outcomes from competing models assessing environmental flows in the Minnesota River Basin

Abstract

The ability to predictively link river hydrology to ecological effects (i.e., flow-ecology relationships) is central to understanding consequences of alternative freshwater management strategies and transparently making trade-offs. While myriad techniques for developing flow-ecology relationships exist, water managers are often concerned about not only model type, but also capacity to reliably predict ecological responses. Here, we develop flow-ecology relationships with three contrasting model development philosophies and examine consistency and robustness of model outcomes as the models are applied to six future land use scenarios in the Minnesota River Basin. All three models are developed from two data sources distributed across the basin: (1) 18 years of modeled streamflow hydrology at 1016 locations and (2) 463 fish richness monitoring events at 314 locations. Model-1 assumes hydrologic change alone is indicative of ecological response, and the relative change in seven synthetic streamflow statistics is applied as a metric of ecological impact. Model-2 develops flow-ecology relationships from a traditional, deductive modeling approach using dimensional analysis and linear regression, which assumes model adoption will increase if the tool is transparent and based on ecological and hydrological principles. Model-3 applies an inductive, machine-learning model (boosted regression trees) to develop flow-ecology relationships, which assumes that lost transparency in modeling is an acceptable trade-off for increased predictive power. All three models were applied to six “alternative futures” (i.e., land use scenarios) in the Minnesota River Basin. Hydrologic model outcomes (Model-1) show large quantitative responses to land use change and provided a useful intermediate outcome for communicating how land use change alters dimensions of streamflow. Although structurally simple, Model-2 estimated fish richness with reasonable predictive accuracy (R2 = 0.38), and dimensional analysis provided a powerful mechanism for scaling predictions across a range of watershed sizes. Although opaque in the ability to visualize outcomes, the machine learning method (Model-3) estimated fish richness with high predictive accuracy (R2 = 0.73), which may be an acceptable trade-off to managers. When applied to six land use scenarios, all three models generally predict qualitatively similar outcomes (e.g., the increased agricultural development scenario has greater ecological impacts than scenarios emphasizing biodiversity or water quality).