A Diagnostic Framework and Data Inventory to Analyze Human Intervention on Streamflow

Abstract Growing recognition of human impacts on streamflow regimes has driven efforts to integrate water‐management modules into hydrological models to improve simulation accuracy. Yet data constraints often force simplifying assumptions, which may introduce unintended biases and obscure true human influences. To address this, we compile a data inventory of human interventions in hydrological systems for the Contiguous United States. , which encompasses reservoir operations, inter‐basin transfers, and water supplies for irrigation, municipal use, industry, and thermoelectric cooling, aims to replace oversimplifications with realistic, computationally efficient representations in large‐scale hydrological models. Next, we develop a modeling framework that leverages and the Budyko hypothesis to diagnose which management activities most strongly modify streamflow regimes and where those impacts occur. Applied to the Mississippi River Basin, our framework reveals that reservoir operation and irrigation together substantially alter flows in the Missouri and Arkansas‐White‐Red regions. Furthermore, the analysis identifies critical data and modeling gaps that must be addressed to obtain accurate streamflow simulation in different hydrologic regions, such as missing canal‐diversion records on the Platte River (Missouri region), insufficient tile‐drain representations in the Ohio region, and surface‐groundwater interactions in the Arkansas‐White‐Red region.