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Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

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Fresh water availability was an important variable that influenced prehistoric human settlement on California's northern Channel Islands. Previous attempts to understand settlement on the islands use watershed size as a proxy for water at canyon mouths. In semi‐arid regions, this approach has limitations because streams may lose much or all of their flow to groundwater. We developed a distributed hydrological model for Santa Rosa Island that incorporates geospatial and temporal data for climate (precipitation, solar radiation, wind speed, relative humidity, temperature), soils, vegetation, and topography to simulate the complex land‐surface‐groundwater behavior of island hydrology for hypothetical wet, dry, and median centuries. Our simulations show that water flow is greatest in drainages on the northwest and east coasts of the island. This correlates with some of the earliest and most persistent settlement on the island. During the most extreme droughts of the last 2000 years during the Medieval Climatic Anomaly (1150–600 cal BP), island populations contracted to a small number of large coastal villages. We argue that this was related in part to the greater availability of surface water at these locations. This study expands the theoretical and methodological scope of past studies that have applied hydrological simulation to archaeological investigations.