Comparing flow predictions of five multi-scale hydrologic models with edge-of-field data in western Lake Erie basin, USA

Abstract:

This study evaluated the ability of National Oceanic and Atmospheric Administration (NOAA) hydrology models to predict flows at smaller scales in upstream landscapes that can be used to schedule weather-based farm operations to limit nutrient runoff. SAC-HT, SAC-HTET and NWM v2.1 models; NOAA’s hydrologic models are used to generate risk of runoff for a real-time decision support tool (RRAF: Runoff Risk Advisory Forecast) to manage fertilizer applications in Great Lakes Region. This study compared flow predictions simulated by SAC-HT, SAC-HTET, NWM and SWAT, models using observed surface and subsurface flow data from edge-of-field (EOF) monitoring sites in the Maumee watershed (Ohio, USA). This is the largest Great Lakes watershed, and its land use is dominated by row-crop agriculture that is one the leading sources of nutrient loadings contributing to Lake Erie’s harmful algal blooms. A field-scale SWAT model evaluated the potential impact of the RRAF tool to reduce nutrient runoff based on changes in fertilizer timing. The models differed in spatial scale as well as simulating subsurface flow. All models were able to simulate the timing of peak flows, but differed their ability to simulate flow magnitudes. Among four hydrologic models, SACHT performed better than SAC-HTET, NWM and SWAT. The study also identified systematic biases in the models, and identified likely factors causing these biases in hydrologic processes. These results will improve the use of models as management tools to reduce nutrient runoff from agricultural fields in the watershed.