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Groundwater wells are critical infrastructure that enable the monitoring, extraction, and use of groundwater, which has important implications for the environment, water security, and economic development. Despite the importance of wells, a unified database collecting and standardizing information on the characteristics and locations of these wells across the United States has been lacking. To bridge this gap, we have created a comprehensive database of groundwater well records collected from state and federal agencies, which we call the United States Groundwater Well Database (USGWD). Presented in both tabular form and as vector points, USGWD comprises over 14.2 million well records with attributes, such as well purpose, location, depth, and capacity, for wells constructed as far back as 1763 to 2023. Rigorous cross-verification steps have been applied to ensure the accuracy of the data. The USGWD stands as a valuable tool for improving our understanding of how groundwater is accessed and managed across various regions and sectors within the United States. [ABSTRACT FROM AUTHOR]

Modeling coupled natural–human systems (CNHS) to inform comprehensive water resources management policies or describe hydrological cycles in the Anthropocene has become popular in recent years. To fulfill this need, we developed a semidistributed hydrological model for coupled natural–human systems, HydroCNHS. HydroCNHS is an open-source Python package supporting four application programming interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling concept, into HydroCNHS. Specifically, we designed Dam API, RiverDiv API, Conveying API, and InSitu API to integrate, respectively, customized man-made infrastructures such as reservoirs, off-stream diversions, transbasin aqueducts, and drainage systems that abstract human behaviors (e.g., operator and farmer water use decisions). Each of the HydroCNHS APIs has a unique plug-in structure that respects within-subbasin and inter-subbasin (i.e., river) routing logic for maintaining the water balance. In addition, HydroCNHS uses a single model configuration file to organize input features for the hydrological model and case-specific human systems models. Also, HydroCNHS enables model calibration using parallel computing power. We demonstrate the functionalities of the HydroCNHS package through a case study in the Northwest United States. Given the integrity of the modeling framework, HydroCNHS can benefit water resources planning and management in various aspects, including uncertainty analysis in CNHS modeling and more complex agent design. [ABSTRACT FROM AUTHOR]