This dataset provides high-resolution (60 m) global irrigation maps to support water resource and agricultural management. It identifies the likely irrigation status (rainfed or irrigated) and water source (groundwater or surface water) of croplands for 2000, 2005, 2010, and 2015. We downscaled a 10-km irrigation dataset derived from national and subnational statistics (GMIA) using (i) spatial patterns between high-resolution (30 m) cropland and nearby surface water, and (ii) irrigation water requirements from a global crop model. Validation used household agriculture surveys in India (N = 8,355) and a U.S. well database (N = 1,505,371). In the U.S., our method achieved 85% accuracy in distinguishing groundwater use within 2 km of wells – substantially higher than GMIA (25%). In India’s groundwater-dominated regions, our estimates performed comparably to GMIA (73% vs. 72%). These results suggest our dataset offers a more accurate and spatially detailed representation of irrigation water sources, enabling improved analysis of agricultural water use.

City residents face increasing heat risks due to the combined effects of rising temperature, extreme heats and urban heat island (UHI) effects. However, assessing these risks remains a major research challenge. Traditional heat risk assessments integrate physical hazards with social vulnerability and population exposure, while others analyze heat phenomena (e.g., heatwaves) in isolation. This study focuses specifically on quantifying heat hazard risks, setting aside the complexities of vulnerability and exposure, to enable improved understanding of its drivers and supporting more focused mitigation strategies. We present a Heat Hazard Index (HHI) framework that utilizes remote sensing and global datasets to systematically integrate three fundamental heat components: temperatures (static risk), UHI effects (spatial anomalies), and heatwaves (temporal anomalies). This approach enables consistent cross-city comparison by focusing exclusively on measurable physical parameters while integrating spatial and temporal heat risks. As demonstrated in a pilot project of twelve global cities, the HHI reveals Jakarta, Beijing, and Amman as heat-hazard hotspots, with their relative risk rankings sensitive to three key methodological considerations: (1) metric selection, (2) temporal and spatial aggregation, and (3) normalization approaches for metric integration. Rather than ranking cities, our goal is to provide a transparent, adaptable tool for dialogue, learning, and comparative assessment. The modular HHI framework supports urban planners in diagnosing dominant heat drivers, prioritizing mitigation strategies, and tracking intervention effectiveness across diverse geographic contexts.

Power companies need to adapt their generation expansion planning in response to changing market, climate and regulatory conditions as global warming, electrification, and technology breakthroughs continue. To fortify energy system resilience, it is critical to understand the collective effects of their autonomous decisions on power systems operations and reliability. To this end, we developed an integrated framework, an agent-based model (ABM) coupled with a power dispatch model (PDM) (referred to as ABM-PDM), tested on the Texas 123-bus transmission system in the Electric Reliability Council of Texas (ERCOT) region. Agents (power generation companies) can invest in natural gas, solar, and wind technologies to maximize profits from 2021 to 2050, using market information from the PDM based on their capital budget and perceived costs, financial incentives for renewable energy, and climate risks. We applied ABM-PDM to assess how power companies respond to future technological advancements and climate change. After demonstrating model credibility, we explored 25 combinations of cost and capacity factors reflecting a variety of technological evolution trajectories. Results indicated that to replace wind over solar for replacing existing fossil-fuel power plants due to lower costs and higher capacity factors. Additionally, as more agents invest, the energy market becomes more competitive, and systemwide electricity prices drop. We also analyzed the impacts of temperature increases on investments using seven projections, from 0 to 6 degrees C, during the modeling period. The results showed that as temperatures rise, agents invest more to accommodate the increasing loads. ABM-PDM incorporates risk attitude and learning into companies' decision-making, providing additional information on generation expansion for the non-optimal future of power systems.

Managing water resources to meet increasing energy and food demands while maintaining environmental sustainability under climate change is a major challenge, especially when this nexus occurred in a coupled natural–human system (CNHS), where heterogeneous human activities affect the natural hydrologic cycle and vice versa. The relevant research has been limited by the lack of models that can effectively integrate human dynamics and hydrologic conditions with spatial details to examine co-evolutionary systems. To address this challenge, this paper develops a modeling framework that integrates an agent-based model (ABM; human behavior model) into a large-scale, process-based distributed hydrologic model to simulate human decisions endogenously in the hydrologic cycle. We then apply the Decision Scaling approach, an ex-post scenario analysis method, with our integrated model to study the bidirectional feedback of the CNHS under future changing climate conditions. With the Columbia River Basin (CRB) selected as the case study area, the calibration results show that the integrated model can simultaneously capture the historical irrigated water consumption and streamflow dynamics. Modeling results show that the trade-off between irrigated water consumption, hydropower generation, and streamflow will become more pronounced under hotter and wetter climate conditions at both the entire basin and regional (states and provinces) levels. Special attention should be given to “temperature thresholds” of different regions when the trade-off pattern started. The trade-off results can potentially inform the Columbia River Treaty renegotiation and provide insights for long-term water management policies.

Financial incentives, such as carbon credits and feed-in tariffs, are effective policy tools to mobilize renewable energy investment for combating climate change. However, climate and policy uncertainties also induce substantial financial risks to power companies’ investments. A company may view renewable energy as an opportunity or a risky business depending on its perception of how renewable technologies and energy policies evolve. To explore how the diverse response from individual companies affects the power system's adoption of renewables, this study develops an agent-based modeling framework that includes renewable technology advancement, market conditions, and changes in incentive programs in the agents’ decision-making. Power companies (i.e., agents) are assumed profit-driven and have different risk attitudes toward climate and energy policy uncertainty. For illustration, we applied the method to the Texas power system as a case study where a group of agents are randomly generated to represent the power companies’ aggregated behaviors. Agents’ risk attitudes are inferred based on a survey, historical data, and model diagnosis. Results of future scenarios highlight renewable adoption prediction uncertainties and the need to develop holistic modeling approaches to facilitate energy policy and power system planning. This modeling framework creates a flexible representation of the power industry and serves as a building block of our vision toward holistic power system modeling and planning. We discuss future research directions that extend the framework through model coupling for system reliability assessment and improve agent representation regarding risk perception and market dynamics.

The common‐pool nature of groundwater resources creates incentives to over pump that contribute to their rapid global depletion. In transboundary aquifers, users are separated by a territorial border and might face substantially different economic and hydrogeologic conditions that can alternatively dampen or amplify incentives to over pump. We develop a theoretical model that couples principles of game theory and groundwater flow to capture the combined effect of well locations and user asymmetries on pumping incentives. We find that heterogeneities across users (here referred to as asymmetries) in terms of either energy cost, groundwater profitability or aquifer response tend to dampen incentives to over pump. However, combinations of two or more types of asymmetry can substantially amplify common‐pool overdraft, particularly when the same user simultaneously faces comparatively higher costs (or aquifer response) and profitability. We use this theoretical insight to interpret the emergence of the Disi agreement between Saudi Arabia and Jordan in association with the Disi‐Amman water pipeline. By using bounded non‐dimensional parameters to encode user asymmetries and groundwater connectivity, the theory provides a tractable generalized framework to understand the premature depletion of shared aquifers, whether transboundary or not.

The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer's water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019-2060 simulation period, depending on the paths of farmers' adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision making models require further investigation and the parameters with the higher uncertainty reduction potentials. By conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers' adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling.

To cope with the uncertainty of green infrastructure planning, many cities take an adaptive approach and use learning‐by‐doing to improve estimates of the cost and efficacy of stormwater management practices (SMPs) and use that information to improve stormwater plans. However, deciding whether that learning is worth its expense has been a challenge for practitioners. We propose a modeling framework to assess the economic value of learning. Methodologically, we present a generalized adaptive planning method that includes learning from direct and indirect investments and multiple degrees of learning. The formulation enables users to specify possible knowledge gains from near‐term actions and quantify its value by assessing its impacts on subsequent decisions and their performance. Further, we quantify the values of both learning and adaptability by calculating differences in expected system performance between three types of decision making: non‐adaptive (no learning between decisions), passive adaptive (adaptive planning that passively accepts incidental learning), and active adaptive (adaptive planning that considers potential learning opportunities when choosing investments). For illustration, we apply the framework to an example inspired by a real stormwater management setting in Philadelphia, PA. In the example, the ability of SMPs to reduce runoff is evaluated by hydrological simulation. The literature and expert opinions inform estimates of costs, SMP performance deterioration over time, and predictions of possible knowledge gains. The results show that active adaptive planning supported by stochastic optimization can achieve substantial cost savings.

One major challenge in water resource management is to balance the uncertain and nonstationary water demands and supplies caused by the changing anthropogenic and hydroclimate conditions. To address this issue, we developed a reinforcement learning agent-based modeling (RL-ABM) framework where agents (agriculture water users) are able to learn and adjust water demands based on their interactions with the water systems. The intelligent agents are created by a reinforcement learning algorithm adapted from the Q-learning algorithm. We illustrated this framework in a case study where the RL-ABM is two-way coupled with the Colorado River Simulation System (CRSS), a long-term planning model used for the administration of the Colorado River Basin, for assessing agriculture water uses impacts on water scarcity. Seventy-eight intelligent agents are simulated, which can be grouped into three categories based on their parameter values: the “aggressive” (swift actions; low regrets), the “forward-looking conservative” (mild actions; high regrets; fast learning), and the “myopic conservative” (mild actions; median regrets; slow learning). The ABM-CRSS results showed that the major reservoirs in the Upper Colorado Basin might experience more frequent water shortages due to the increasing water uses compared to the original CRSS results. If the drought continues, the case study also demonstrates that agents can learn and adjust their demands.

Reoccurring drought through the early 2000s has caused a serious water scarcity issue in the Colorado River Basin. Previous modeling studies have focused on the impact of climate change without considering the adaptive behaviors of farmers and under-utilized Indian water rights. In this paper, we use a coupled agent-based water resource model (ABM) to investigate how the adaptive decisions of farmers can affect water resource management under both climate change impacts and fully utilized Indian water right conditions. We used five General Circulation Model projections with RCP8.5 scenarios for the study. The results of farm-level decision-making showed different responses in irrigated areas that were changing due to climate change impact. While winter precipitation changes might partially explain the behavior changes, no specific pattern could be concluded based on their location. Also, farmers' responses about annual water diversion showed more significant inter-year variation compared to irrigated areas. Basin-level metrics showed that climate change impacts will generally worsen water scarcity issues as measured in Navajo Reservoir storage, flow to Lake Powell, and instream flow requirement. But these basin-level water scarcity metrics cannot reflect individual farm-level impacts under climate change, which is why modeling the bottom-up management actions is necessary. When the under-utilized Indian water rights are fully used, it is more likely to trigger the shortage sharing agreement due to the higher tribal water depletion. Evaluation of model uncertainty and a more realistic setup for adaptive actions under drought contingency plans are suggested for future research.

To cope with the uncertainty of green infrastructure planning at city scale, many cities take an adaptive approach and use learning-by-doing to improve understanding of the urban systems. However, whether that learning is worth it has been a challenge to adaptive management practitioners. In this paper, we propose an evaluation and planning framework for green infrastructure (GI) to address this issue and demonstrate its use by an application to the Wingohocking water-shed, Philadelphia, PA, USA. The framework allows users to specify possible knowledge gains from near-term actions and assess the impacts of this learning on subsequent decisions, which enables evaluation of the net benefits of alternative investment plans. In the case study, we consider two types of learning: learning to reduce uncertainty and learning to improve performance. This learning can happen through investments or knowledge transfer from experience at other locations. Estimates of cost, performance, and deterioration over time of GI and the prediction of possible knowledge gains are based on the literature and expert opinions. The results propose optimal investment strategies over a 25-year planning horizon and describe tradeoffs between the risk of poor performance and reductions in expected annual stormwater runoff. Finally, by calculating differences in expected total costs between non-adaptive, passive adaptive, and active adaptive decision-making, we quantify the economic value of learning and adaptability.

We investigate how the effectiveness of green infrastructure (GI) to mitigate the frequency and magnitude of significant discharge events and combined sewer overflows (CSOs) depend on both climate and sewershed characteristics and propose a theoretical framework for a holistic assessment of GI's efficacy. The framework is based on the comparison of three characteristic timescales that control the production of peak discharge: rainfall duration ( t r ) , travel time in the sewer network ( t n ) , and the duration of rain that would be required to fill the GI's storage ( t GI ) . Storm events can then be characterized by two ratios of these timescales: T n = t n / t GI and T r = t r / t GI . A third dimensionless number characterizes critical storms during which adverse events (such as CSOs) occur and allows us to identify the combinations of T n and T r for which GI may substantially mitigate those events. The results of numerical experiments with the model demonstrate that the storms for which GI can substantially reduce peak discharge and CSO volume typically occur in a narrow band of T n and T r . Within that band, the efficacy of GI may depend on the location of GI within the sewershed if network routing substantially affects the timing and magnitude of flood peaks. The proposed framework is applied to examine the efficacy of GI using historical precipitation data from two major U.S. cities: Philadelphia, PA, and Seattle, WA, and the results of this comparative analysis suggest that GI location is an important control on catchment‐scale GI efficacy in Philadelphia, but less so in Seattle.

Urban stormwater management is shifting its attention from traditional centralized engineering solutions to a distributed and greener approach, namely Green Infrastructure (GI). However, uncertainties concerning GI's efficacy for reducing runoff and pollutants are a barrier to the adoption of GI. One strategy to deal with the uncertainty is to implement GI adaptively, in which stormwater managers can learn and adjust their plans over time to avoid undesired outcomes. We propose a new class of GI planning methods based on two-stage stochastic programming and Bayesian learning, which accounts for projected information gains and decision makers' objectives and willingness to accept risk. In the hypothetical example, the model identifies four categories of investment strategies and quantifies their benefits and costs: all-in, greedy investment plus deferral, mixed investments plus deferral, and learn-and-adjust. Which strategy is optimal depends on the user's risk attitudes, and the alternatives' costs and risks.

Focusing on the neighborhood where the Village of Arts and Humanities is, we have developed, evaluated, and tested a method for incorporating community input regarding ancillary green infrastructure (GI) benefits into a multi-objective GI optimization model. The model adapts the Stormwater Investment Strategy Evaluation (StormWISE) model for use in urban settings where the community interests are expressed using additive utility functions with weights elicited from community workshops. The ancillary benefits quantified in the case study are the highest priority benefits identified by the GreenPhilly Community Advisory Research Board (GCARB) though exercises created for the workshops. Four weighting methods are compared to assess consistency and sensitivity of the results.

Philadelphia's Green City Clean Waters program is an innovative plan to reduce the frequency and environmental impact of combined sewer overflows through massive, citywide implementation of green infrastructure practices. The program has been approved by regulatory agencies based on the assumption that a variety of direct benefits and co-benefits will be realized over a 25-year period. We report research on methodology for developing green stormwater infrastructure (GSI) benefit functions that express direct runoff reduction benefits and ancillary co-benefits of GSI as functions of investment levels. Functions for direct benefits are generated by coupling a multiobjective evolutionary optimization algorithm (MOEA) to a hydrologic simulation model. Functions for co-benefits are developed through community-based participatory research enabling prioritization of benefits and GSI implementation strategies that reflect the values of the communities that are served. Benefit functions are used in the StormWISE multiobjective decision support framework to optimize GSI investments at the subwatershed level.