Generalization of Runoff Risk Prediction at Field Scales to a Continental‐Scale Region Using Cluster Analysis and Hybrid Modeling

Abstract

Abstract As surface water resources in the U.S. continue to be pressured by excess nutrients carried by agricultural runoff, the need to assess runoff risk at the field scale continues to grow in importance. Most landscape hydrologic models developed at regional scales have limited applicability at finer spatial scales. Hybrid models can be used to address the scale mismatch between model simulation and applicability, but could be limited by their ability to generalize over a large domain with heterogeneous hydrologic characteristics. To assist the generalization, we develop a regionalization approach based on the principal component analysis and K‐means clustering to identify the clusters with similar runoff potential over the Great Lakes region. For each cluster, hybrid models are developed by combining National Oceanic and Atmospheric Administration's National Water Model and a data‐driven model, eXtreme gradient boosting with field‐scale measurements, enabling prediction of daily runoff risk level at the field scale over the entire region.

Keywords

• runoff potential
• clustering
• XGBoost
• National Water Model
• hybrid modeling
• generalization