Irrigated agriculture has led to aquifer depletion in many places, necessitating effective groundwater conservation policies to slow unsustainable water use. However, the success of conservation policies is contingent on complex interactions between agronomic production, aquifer response, and risk management tools, all of which shape agricultural water diversions. This study uses an agent-based model to evaluate how uniform pumping restrictions and crop insurance, a popular risk management tool, jointly influence farmer decisions in the Sheridan-6 Local Enhanced Management Area (SD-6 LEMA) collaboratively regulated region of western Kansas. Calibrated against observed data under widespread crop insurance coverage and LEMA regulation, the model captures farmers' adaptive decision-making shaped by prior-year satisfaction. Scenario-based analysis reveals that pumping restrictions reduce groundwater withdrawals by up to 19.9% during drought years and modestly promote crop diversification. Crop insurance indirectly supports diversification by stabilizing behavior following the 2012 drought and 2013 policy change, though the direct impact of crop insurance on groundwater diversions is limited (1.4% reduction). Behavioral differences—not direct financial incentives—drive divergence between insured and uninsured farmers: insured farmers maintain stable strategies, while uninsured farmers shift in response to environmental variability and peer influence. Stricter irrigation restrictions sustain aquifer levels but have limited additional effects on cropping patterns when crop insurance is present. During droughts, crop insurance buffers profit losses under strict water limits by nearly half. Overall, integrating groundwater conservation with crop insurance enhances aquifer sustainability and farm-level economic resilience. Findings offer insights into designing coordinated water governance and agricultural policies amid growing climate uncertainty.

Groundwater conservation policies are typically evaluated using historical observations. However, in coupled human-water systems, outcomes can vary substantially even under unchanged mean conditions of the climatic, economic, and land-use inputs due to nonlinear dynamics and spatiotemporal interactions. This phenomenon is referred to as internal variability. Here, we quantify how internal variability shapes inference about policy performance using the Sheridan-6 Local Enhanced Management Area (SD-6 LEMA) in western Kansas, a well-documented irrigator-led conservation program. We employ an agent-based human-hydrologic model to generate large ensembles through alternative temporal sequences of precipitation and crop prices and alternative spatial configurations of initial crop fields, while preserving fixed mean system states. These ensembles are explicitly contrasted with scenarios representing shifts in mean climate, economic, and land-use conditions. A three-way factorial analysis of variance shows that internal variability accounts for more than half of total variance in both policy-relevant indicators (groundwater withdrawal and saturated thickness) and behavioral outcomes (crop choice and farmer behavioral state transitions). The influence of internal variability is further amplified under higher irrigation intensity, where tighter constraints increase system sensitivity to spatiotemporal fluctuations. These findings do not challenge the effectiveness of the SD-6 LEMA policy itself, but instead demonstrate that policy performance inferred from observational data or a single historical realization can be misleading over short to medium timescales. We conclude that robust groundwater policy evaluation requires ensemble-based approaches that characterize outcome distributions under fixed institutional rules, with important implications for policy robustness and transferability in complex adaptive groundwater systems.

Growing societal water demands and decreasing water supplies are straining the water available for communities in many basins. Once water supplies have been fully allocated and developing new water supplies is infeasible, the best option to meet growing water demands is often to reallocate water from rural agricultural water uses. Yet, the dynamics and implications of these rural-to-urban water transfers are poorly understood. Here, we integrate an agent-based model with an input-output model to capture the behavior of individual irrigators and examine how their water transfer decisions propagate through the broader rural economy and shape social dynamics. As a demonstration of our model, the rural community represents Alamosa County while the city represents the city of Denver, both located in Colorado, United States. We find that the greatest long-term decline in crop water use corresponds with higher city growth rates while the greatest short-term decline corresponds with larger farmer discount rates. As farmers sell their water rights to the city, economic activity from the crop production sector declines, causing unemployment in the crop production sector to increase and demand from the service sectors to decrease, which results in output declining in these economic sectors as well. Thus, a negative impact on the agricultural sector will cause some negative impact on other economic sectors, such as professional, health care, and recreational services. This research brings new insights that can be used to evaluate the socioeconomic impacts of water transfers and shape policy to minimize potential negative externalities associated with water transfers.

Aquifer depletion due to intensive irrigation threatens global economies, food security, and ecosystems. This Perspective examines the hydrological, social, and economic complexities of managing groundwater resources, focusing on the Sheridan 6 Local Enhanced Management Area in the US High Plains aquifer. Here, irrigator-led conservation efforts reduced groundwater use by 25% and slowed aquifer depletion by 65% while maintaining farmers’ incomes. This success resulted from a hybrid integration of bottom-up rule development with top-down enforcement, providing flexible multi-year water allocations and aligning management with local conditions. From this, we identify transferable governance tenets for sustainable groundwater management in similar regions.

The Crop-Hydrological-Agent Modeling Platform (PyCHAMP) is a Python-based open-source package designed for modeling agro-hydrological systems. The modular design, incorporating aquifer, crop field, groundwater well, finance, and behavior components, enables users to simulate and analyze interactions between human and natural systems while considering both environmental and socioeconomic factors. This study demonstrates PyCHAMP's capabilities by simulating dynamics in the Sheridan 6 Local Enhanced Management Area, a groundwater-conservation program in the High Plains Aquifer in Kansas. We highlight how a model empowered by PyCHAMP accurately captures human-water dynamics, including groundwater level, water withdrawal, and the fraction of cropland dedicated to each crop. We also show how farmer behavior and its representation drive system outcomes more strongly than environmental conditions. The results indicate PyCHAMP's potential as a useful tool for human-water research and sustainable groundwater management, offering prospects for future integration with detailed sub-models and systematic evaluation of model structural uncertainty.

Groundwater wells are essential for monitoring and using groundwater, yet the United States has lacked a unified, standardized inventory of their characteristics and locations. The United States Groundwater Well Database addresses this gap by compiling more than 14.2 million state and federal well records spanning 1763–2023. Available as tables and vector points, the database includes attributes such as purpose, location, depth, and capacity and applies cross-verification procedures to improve reliability. It provides a national resource for studying how groundwater is accessed and managed across regions and sectors.

This study integrates an agent-based model of human decisions with a large-scale, process-based distributed hydrological model to examine food–energy–water trade-offs under climate change. Applied to the Columbia River Basin with a Decision Scaling analysis, the framework reproduces historical irrigation-water consumption and streamflow while representing human decisions within the hydrologic cycle. Results indicate that trade-offs among irrigation, hydropower, and streamflow intensify under hotter and wetter conditions, with region-specific temperature thresholds. The findings can inform long-term basin management and Columbia River Treaty discussions.

The rise in smart water technologies has introduced new cybersecurity vulnerabilities for water infrastructures. However, the implications of cyber-physical attacks on systems like urban drainage systems remain underexplored. This research addresses this gap by introducing a method to quantify flood risks in the face of cyber-physical threats. We apply this approach to a smart stormwater system—a real-time controlled network of pond-conduit configurations fitted with water-level detectors and gate regulators. Our focus is on false data injection attacks, in which adversaries introduce deceptive data that mimics legitimate system noise and evades detection. Our risk assessment incorporates factors such as sensor noise and weather-prediction uncertainty. Findings reveal that false data injection can amplify flood risks by feeding the control system false data, leading to erroneous outflow directives. Notably, these attacks can reshape flood-risk dynamics across different storm intensities, accentuating risks during less severe but more frequent storms. This study offers insights for investments in smart stormwater systems and cyber-defense planning, and the risk-quantification method can be extended to irrigation channels, multi-reservoir systems, and other water-system networks.

This study develops a coupled agent-based tool for comparing agricultural sediment-management incentives across the Susquehanna River Basin. The model represents farmers' adoption of best management practices, including responses to social norms and performance bonuses, and compares conventional pay-for-practice support with individual- and group-based pay-for-performance programs. Simulations indicate that performance-based policies can reduce more suspended sediment at lower cost, while the group-based design delivers the greatest basin-wide reduction with the least uncertainty and lower sensitivity to bonus levels.

Studies have recently focused on using coupled natural-human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings: one static, two adaptive, and two learning adaptive. Learning adaptive models have both a learning component capturing long-term trends and an adaptive component capturing short-term variations, while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, model output uncertainty is decomposed into climate change scenario uncertainty, climate internal variability, and different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.

HydroCNHS is an open-source, semidistributed hydrological model for coupled natural–human systems. Its four application programming interfaces allow users to integrate agent-based representations of reservoirs, river diversions, transbasin conveyance, drainage, and other human decisions while preserving routing logic and water balance. A unified configuration file organizes hydrological and human-system inputs, and parallel computing supports model calibration. A case study in the northwestern United States demonstrates the package and its potential for water-resources planning, uncertainty analysis, and more complex agent designs.

This study examines bidirectional feedbacks in coupled natural–human systems by linking RiverWare with agent-based models of irrigation decisions in Washington's Yakima River Basin. The coupled framework better reproduces historical irrigation diversions and streamflow than the baseline river-system model. The analysis also explores how farmers' social norms influence decisions and how a water-reallocation policy changes diversion behavior. The results illustrate how coupled models can support environmental policymaking while highlighting the need to evaluate model structure and parameter uncertainty.

This study evaluates the spatial information most useful for physics-informed, machine-learning forecasts of typhoon inundation. Hydrodynamic simulations supplied training and testing data for neural-network, neuro-fuzzy, support-vector-machine, and hybrid support-vector-machine/multi-objective-genetic-algorithm models. Using rainfall gauges, radar rainfall, typhoon characteristics, and conventional areal rainfall estimates for Yilan County, Taiwan, the analysis identifies influential features and suitable time lags. The hybrid model produced the strongest forecasts when supplied with the most consequential inputs and their appropriate lag lengths.

This study develops a climate-adaptation algorithm that combines the six-step climate-change adaptation process with a structured climate-risk template. The framework links hazard, exposure, and vulnerability while guiding users through problem and objective definition, current and future risk analysis, and evaluation of adaptation options. An application shows how the approach can clarify governance concerns and stakeholder objectives, trace relationships among risk components, and identify strategies for reducing impacts. The resulting workflow is intended as a standardized decision-support procedure for climate-risk assessment.

Under the impact of global warming and climate change, the frequency of extreme precipitation and drought events have been increasing. In Taiwan, the government usually guides agricultural fallows to meet the needs of domestic, public, and industrial water during droughts. However, this situation causes that the cultivation rights of farmers are not guaranteed even though the priority of agricultural water is higher than that of industrial water. To this end, a framework of virtual water trading market is proposed in this study to protect the cultivation right. The water supply system and field water balance models that use the historical drought event during 2003 to 2004 as the hydrological scenario have been completed to support the gaming water-trading auction. Through the gaming simulation process, agricultural or industrial stakeholders are invited to carry on the round-designed water trading. Based on the results of player's decisions, we discuss the feasibility of water market in this region and investigate the possibility of adopting the water market as a solution of the water reallocation during times of drought. An application with the principle of user charge in the Taoyuan region of Taiwan is successfully implemented. The results show that industrial stakeholders are willing to pay more costs of water purchase to prevent severe loss of production line shutdown caused by water shortage. After several times of the gaming auction, stakeholders may learn the trading information in terms of evaluating the price of water and estimating the possible amount of water. This study can further provide a potential substitute solution to the government and policy makers when the water reallocation is considered during drought periods.

The gap between governmental policy and actions taken by local officials often exists and this gap could be even more severe when it is climate adaptation policy. On the other hand, most of existing studies tend to pay more attention to the structure and the performance of the model rather than the practical operation procedure. To minimize the gap and to evaluate the effectiveness of the adaptation policy, a cross-scale and cross-sector adaptation assessment model, Agriculture and Hydrology assessment model (AgriHydro), is proposed in this study. AgriHydro is established physically to embed top down policies and bottom up local actions and combine with the standard assessment framework, Climate Adaptation 6-Steps and Climate Risk Template, to support decision-making. For sub-models of the AgriHydro, the Aquacrop crop model and the system dynamic water distribution model were calibrated and integrated to form the feedback mechanism. The weather generator (WthGen) modular including climate change scenarios is also established and coupled into the AgriHydro to form a fully functional adaptation assessment tool. An actual application for analyzing the adaptation policy for water supply and crop yield in Taoyuan City of Taiwan is implemented due to the diversity and the complexity of Taoyuan City in land use and water system. The results reflect the possible trade-off of future risks between water resource and agriculture fields under different climate and social scenarios. Moreover, this study shows that the proposed model can provide the linkage to the standard assessment framework, which allows a consist understanding among users on risk and model output. This enables AgriHydro to become a practical and operational tool for decision making and ready for further application.