As climate change intensifies, regions worldwide face growing impacts from extreme weather events (EWEs), including compound and sequential extreme events. These events pose significant risks to agriculture, where weather variability directly affects crop yields and revenue. Quantifying the share of damages attributable to human-induced climate change is essential for targeted mitigation and adaptation planning. Here, we estimate yield and revenue losses from EWEs attributable to human-induced climate change in Germany during 2018-2020. Using crop-specific statistical models, we compare simulated yields under factual (observed) and counterfactual (without climate change) conditions to assess climate change-attributable agricultural damages. We find that EWE-driven yield losses were statistically significantly affected by human-induced climate change across Germany, with the strongest negative impacts averaged over 2018-2020 for silage maize (-3.21%; [-4.73%, -1.68%], 95% range across 23 climate models). Winter crops showed smaller losses (e.g. winter wheat, -0.39% [-0.72%, -0.07%]) or even gains (e.g. winter barley, 1.04% [0.67%, 1.42%]). A pronounced north-south gradient emerged, with greater losses in northern and central Germany. Nationally, we estimate an average annual revenue loss of & euro;287 [& euro;212, & euro;363] million, equivalent to 2.8% [2.2%, 3.3%] of the total counterfactual revenue. This loss accounts for roughly one-third of the estimated direct EWE-driven damages for German agriculture. Decomposition analysis reveals that these impacts are predominantly driven by temperature increases. Our results highlight the growing economic burden of EWEs on agriculture under climate change and offer actionable insights for climate-resilient agricultural policy, adaptation planning, and economic evaluation of mitigation strategies.

Climate change intensifies water stress globally, necessitating expensive infrastructure interventions to maintain reliable supply. To fund infrastructure, utilities often raise rates, increasing water bills for low-income households. The resulting affordability impacts depend on utility costs and interactions between rate design, financing, climate and household demands. Here we develop a city-scale modelling framework to estimate climate change impacts on water affordability, integrating climate, utility adaptation decisions and demand. In Santa Cruz, California, we find that climate change alone could double water bills by mid-century, leaving an additional 7-16% of Santa Cruz households with unaffordable water. Our results suggest that climate change may lead to greater water affordability challenges than previously estimated in hotspots where supply is vulnerable to climate change. This highlights the need for policy intervention and financing to ensure climate adaptation does not compromise affordability. The magnitude of climate-related affordability challenges depends on local context, requiring city-scale assessments.

Hydroclimatic extremes, particularly increasingly frequent and severe droughts and heat waves, are intensifying pressures on agricultural systems even in historically overall water-abundant regions such as Germany. Irrigation is often promoted as an effective adaptation strategy to climate variability and extremes. However, irrigation expansion can create path-dependent lock-ins into high and potentially unsustainable water use, amplifying systemic risks across the food–water–energy (FWE) nexus.This study examines how food, water and energy sector policies shape farmers’ adaptive land use and irrigation decisions under future hydroclimatic and socioeconomic change, and how these decisions propagate trade-offs across sectors. Using an innovative hybrid modeling framework that links hydrological and machine-learning models with a hydro-economic multi-agent system capturing adaptive farmer behavior, we assess the ex-ante impacts of six sectoral policies on land use, irrigation demand, and FWE nexus indicators for eight major field crops in Germany.Our results reveal strongly divergent adaptation pathways. Water sector policies such as abstraction limits and pricing can substantially curb irrigation expansion under intensifying climatic extremes and socioeconomic change, while maintaining farm profitability if implemented early. In contrast, bioenergy subsidies further increase irrigation demand and energy use, while irrigation efficiency subsidies fail to deliver net water savings due to rebound effects, and drought compensation payments reinforce maladaptive land use choices.Overall, uncoordinated policy responses risk triggering an “irrigation trap” that deepens cross-sector trade-offs and constrains future transformation pathways. We show that timely, coordinated governance across the FWE nexus is critical to avoid maladaptation and to steer agricultural systems toward more resilient and sustainable trajectories. By considering heterogeneous and adaptive farmer behavior, the study provides a starting point to assess how far agricultural land use adaptation can mitigate on-farm losses and systemic risks under intensifying hydroclimatic extremes.

Droughts produce cascading biophysical and economic impacts, where yield losses trigger market price adjustments and farmer autonomous adaptation responses. Many existing studies quantify drought losses by estimating agronomic yield reductions and valuing them at constant prices and land use, ignoring market dynamics and adaptive behavior in shaping losses. We use an integrated modelling framework that attributes impact of the 2018–2019 droughts in Germany to yields, prices and autonomous land-use adaptation. Yield losses would have caused €1.85 and €0.57 billion in damages at constant prices and land use in 2018 and 2019 respectively. Market-driven price increases reduced these by €0.45 and €0.18 billion, with additional mitigation from land-use adaptation (€0.35–0.49 billion). These loss reductions reflect significant economic redistribution patterns, where higher prices shield producers by transferring drought losses towards consumers. We conclude that conventional assessments ignoring market dynamics and autonomous adaptation not only overestimate agricultural losses but also ignore their impact on consumers.

Anticipating future water security is essential for sustainable development, food security, and economic stability across regions and sectors. Water scarcity and abundance are increasingly shaped by the combined effects of socio-economic development and climate change, yet their spatial patterns under deep uncertainty have not yet been systematically explored, particularly for regions such as Thuringia in Germany, which has historically been considered water abundant. Previous studies have typically assessed water scarcity using single-sector models or limited scenario frameworks, neglecting sectoral and regionally specific patterns of change. This limits our ability to identify regions that may remain water abundant despite profound change.This study examines where and to what extent water scarcity and abundance emerge under combined socio-economic and climatic change, and how robust these outcomes are under deep uncertainty. We show that explicitly representing interacting water users within a multi-agent hydro-economic framework fundamentally alters the projected spatial distribution of future water stress and surplus.Using a high-resolution multi-agent system (MAS) that integrates industrial, household, and agricultural water demands within a large-scale model of Thuringia, we construct an ensemble of water-use trajectories coupled with soil moisture projections from mHM as well as groundwater and surface water projections. These trajectories combine policy adjustments with regionalized SSP–RCP scenarios of population development, GDP, agricultural prices, and climate impacts. We employ econometric water demand functions for industrial water use across 19 districts and household demand across 800 water supply areas. Agricultural water use is represented through the coupled hydro-economic model DroughtMAS, comprising more than 1000 representative agricultural agents calibrated via Econometric Mathematical Programming (EMP) and driven by projected yield anomalies under droughts and hydro-climatic extremes, which are derived from a LASSO-regression–parametrized yield model.Our results reveal that despite overall declining water use, localized changes—such as urban growth, increasing irrigation demand, and regional declines in water availability —can intensify potential water scarcity. In a broader context, these findings demonstrate that future water risk is not solely climate-driven but driven by a combination of socio-economic development pathways and policy choices. Accounting for these dynamics is therefore critical for identifying resilient regions and developing robust water governance under uncertainty, and provides a framework to explicitly quantify water abundance alongside scarcity within a coupled socio-economic and hydro-climatic system.

Climate change affects agriculture directly through crop yield losses and indirectly through changes in crop prices. Farmers need to adapt their land-use decisions to these changing conditions. These decisions are made in advance and under considerable uncertainty. The extent to which farmers adapt their crop area allocations to changing yields and prices remains unclear, particularly at the national scale. We present an approach for measuring the adaptedness of observed crop area allocations to changing yields and prices in terms of the number of hectares differing from an economically optimal cropping pattern. We demonstrate the approach in a case study in Germany (1999–2020). To identify economically optimal crop area allocations under changing yields and prices, we employ an econometric mathematical programming (EMP) approach within a multi-agent system modeling framework. Over the 20 years, we find persistent changes in crop area allocations averaging 36% ±4% of each district’s agricultural area. Of these changes, 43% improved the adaptedness to today’s yields and prices. We observe a trend of increasing adaptedness over time, though significantly lower in districts facing drought conditions. Adaptation, however, prevails both under drought and non-drought conditions. Our results suggest that the economic impacts of changing yields and prices play a significant role in shaping observed land-use patterns, with important implications for land-use dynamics under climate change. Our approach opens promising avenues for further research seeking to disentangle the drivers of land-use adaptation over time and to inform future policies aimed at supporting agricultural adaptation to increasing weather extremes and changing climatic conditions.

Abstract Climate change is expected to intensify water stress even in historically overall water‐abundant countries like Germany, where expanding irrigation depicts an effective adaptation strategy. Such path‐dependent irrigation growth—here referred to as an irrigation trap—can lock farmers into high and potentially unsustainable water use, heightening trade‐offs between food, water, and energy (FWE) sector policy objectives. However, no comprehensive assessment of future policy impacts and cross‐sector trade‐offs within the FWE nexus exists for Germany. This study evaluates the impacts of six sectoral policies on the future irrigation demand of eight major field crops and related FWE indicators, using a hybrid modeling framework that links hydrological and machine learning models with a hydro‐economic multi‐agent system. The results show divergent effects of sectoral policies on the FWE nexus. Water sector policies—such as abstraction limits and pricing—substantially curb future irrigation expansion under climatic and socioeconomic change, with only marginal profit losses if implemented early. In contrast, bioenergy policies further raise irrigation demand, reaching up to 13.7‐fold the historic levels by the far future (2069–2098). Drought compensation schemes weaken incentives for farmers to adapt to climate change, and irrigation efficiency subsidies do not deliver net water savings. By using a hybrid modeling framework capturing irrigation expansion and adaptive farmer behavior, our ex ante policy assessment shows that unregulated irrigation expansion triggers increasingly steep trade‐offs between FWE objectives. Avoiding this irrigation trap requires timely and coordinated cross‐sector policies to balance competing demands under growing climatic and socioeconomic pressure.

Climate change intensifies water stress globally, necessitating expensive infrastructure interventions to maintain reliable supply. To fund infrastructure, utilities often raise rates, increasing water bills for low-income households. Resulting affordability impacts depend on utility costs and interactions between rate design, financing, climate, and household demands. We develop a city-scale modeling framework to estimate climate change impacts on water affordability, integrating climate, utility adaptation decisions, and demand. In Santa Cruz, California, we find that climate change alone could double water bills by mid-century, leaving an additional 7-16% of Santa Cruz households with unaffordable water. Our results suggest that climate change may lead to greater water affordability challenges than previously estimated in hotspots where supply is vulnerable to climate change. This highlights the need for policy intervention and financing to ensure climate adaptation does not compromise affordability. The magnitude of climate-related affordability challenges depends on local context, requiring city-scale assessments.

This article presents household-level socioeconomic data on food-water-energy nexus consumption collected through a survey conducted during the first quarter of 2020 in the urban areas of the Pune Metropolitan Region, India. The dataset includes 1872 observations from households residing in both formal and informal settlements. Data were collected via door-to-door interviews in the local language using a comprehensive, structured questionnaire administered through a computer-assisted web interviewing mobile application developed by the World Bank. Quality control was ensured through digital data capture, daily monitoring during fieldwork, and post-collection data validation. The dataset comprises 606 variables, including consumption data for water, energy, and food, alongside socioeconomic factors such as household composition, income, housing conditions, migration history, and household-level strategies to cope with intermittent water supply. The dataset can be used for econometric modeling of household demand, parameterization of multi-agent models, comparative analyses across regions, and empirical studies examining household challenges related to water, energy, and food security.

Unequal water access will be a major driver of increasing water insecurity in the 21st century, exacerbating the impacts of climate change. An estimated one billion people in cities in low- and middle-income countries currently face varying degrees of public water supply interruptions, subjecting them to unequal water access. This number is expected to grow, as water scarcity intensifies and supply infrastructure deteriorates. Yet, insights into the quantitative effect of water supply intermittency on urban water access inequality are so far limited. Here, we assess insights from hydro-economic multi-agent modeling case studies in South Asia and in the Middle East to analyze the effects of intermittent public water supply on the water security of heterogeneous urban household populations. We find that public water supply interruptions lead to severe disparities in household water consumption across cases. This also leads to household reliance on costly alternative water sources, jeopardizing water affordability. By 2050, climate change and population growth exacerbate the effects of water supply intermittency, causing severe deterioration in water security and increasing water access inequality. The results indicate that improved monitoring of water access inequality and reducing supply intermittency are key to mitigating urban water insecurity in the coming decades.

Jordan is a relatively small country with limited natural resources, but it faces a burgeoning demand for water, energy, and food to accommodate a growing population, refugee migration, and the challenges of climate change that will persist through the rest of this century. Jordan’s Main Water Conveyance System is the backbone of distributing scarce water resources to meet domestic and agricultural demands. Therefore, understanding how the future energy requirements of this system may change is critical for managing the country’s water, energy, and food resources. This paper applied a water balance model to calculate the energy consumption of Jordan’s Main Water Conveyance System between 2015 and 2050, and the results point to high energy requirements for the future of distributing Jordan’s water. In the base year of 2015, the unmet water demand was 134.55 MCM, and the supplied water volume delivered was 438.75 MCM, while the energy consumption was 1496.7 GWh. The energy intensities for water conveyance and water treatment were 7.11 kWh/m3 and 0.5 kWh/m3, respectively. We examined five scenarios of future water and energy demand within Jordan: a reference scenario, a continuation of current behavior, two scenarios incorporating improved water management strategies, and a pessimistic scenario with no interventions. According to all scenarios, the energy consumption is expected to be doubled by the year 2050, reaching approximately 3172 GWh. It is recommended that Jordan prioritizes solar-powered conveyance and pumping to reduce the projected doubling of energy demand by 2050. Across all scenarios, the demand for nonrenewable energy associated with water conveyance is projected to rise significantly, particularly in the absence of renewable integration or efficiency interventions. Total water demand is expected to increase by up to 35% by 2050, with urban and agricultural sectors being the primary contributors.

As climate changes globally and locally, the risk of temperature anomalies, heat waves and droughts have significantly increased. Studies have demonstrated that droughts exert adverse biophysical effects on crop production, posing an unprecedented threat to harvests and resulting in substantial economic losses in Europe. Assessing these biophysical drought impacts on agriculture is crucial for developing effective strategies for drought preparedness, mitigation, and adaptation. This paper contributes to this effort by presenting a framework to estimate economic costs associated with droughts that specifically captures the biophysical impact of climate change on crop output. Existing analyses for drought damages in agriculture are developed for a specific drought event and primarily focus on the reduction in farmer’s income or crop yields in drought events. In these assessments, the biophysical impacts of droughts are not isolated and evaluated from their effects on other economic variables such as output prices, resulting in inaccurate damages. Additionally, lack of single universal definition of drought adds complexity to estimating the costs of droughts. This paper is aimed to contribute by focusing on agricultural droughts, which occurs when variability in soil moisture affects plant growth and development. We simulate this biophysical effect of drought on crop yields by applying a statistical crop yield model to data on soil moisture, temperature and perception. This approach helps isolate the direct impact of drought on agriculture from other changes in aggregate economic production (e.g. business conditions, commodity prices) and farmer management decisions (e.g. intermediate input use). The simulated biophysical yield effects are then quantified into monetary terms to estimate economic damages of droughts. We further look into the relationship of the economic damages and the intensity of droughts to determine drought thresholds that lead to increased economic losses. The results provide bottom-up estimates of the economic damages of drought induced water deficiency in agriculture across Germany for the years 2016-2020. The spatio-temporal patterns of drought impacts can be useful for drought policy planning at local and national level. The economic costs estimation framework could be valuable in estimating farmer compensations and loss and damage of droughts. The results of the study can provide reliable estimates of the costs of climate-change-related extreme weather events, which may help inform macroeconomic and integrated impact assessment models of economic losses (and gains).

Urban water utilities in rapidly developing regions face growing challenges in ensuring continuous supply. Intermittent public water supply leads to unreliable and inequitable access, compelling households to adopt energy-intensive coping strategies. This creates a nexus between water and energy demand at the household level. Few econometric analyses of household water demand have explicitly addressed this demand-side nexus in developing regions. Using survey data from the city of Pimpri-Chinchwad, India, where intermittent water supply is prevalent, we analyze household expenditures related to water access and estimate a piped water demand function with a Discrete-Continuous Choice model. We find that electricity expenditures for accessing water exceed water bills for approximately one-third of households. Including these costs in affordability calculations reveals hidden financial burdens, particularly for middle-income households. Water and electricity prices, income, and household size significantly influence water demand, with an income elasticity of 0.177 and water price elasticities ranging from 0 to −0.876. The cross-price elasticity of −0.097 indicates weak complementarity between electricity and piped water, suggesting electricity price changes do affect water use but are insufficient to drive substantial behavioral shifts. Targeted price increases in high-consumption blocks are more effective at curbing overuse, while simultaneous increases in water and electricity prices may heighten household vulnerability. These findings highlight the need for integrated, nexus-aware demand management strategies, particularly in regions with intermittent supply.

Understanding water use conflicts and anticipating their possible future trajectories requires knowledge of the drivers of household and commercial water use. Water demand is primarily shaped by long-term demographic and socio-economic trends, alongside seasonal fluctuations in weather, which are susceptible to the impacts of climate change. Because the availability of water resources and their use by the various economic sectors are spatially very heterogeneous, it is necessary to use spatially explicit models to investigate water conflicts, which differentiate in particular between rural and urban regions. Here we simulate the regional water use of households and commercial enterprises for the Free State of Thuringia at a high spatial resolution. The model is based on household water demand functions for representative household types and regions. The household and communal water use is then simulated based on local characteristics of each supply area. The model distinguishes between base water demand, which is explained by socio-demographic factors, and seasonal water demand, which is explained by weather factors. To parametrize the model, we use various regression techniques with public data, and daily water discharge of representative suppliers. Our results show different trajectories of water consumption quantities. Thereby we combine socio-demographic scenarios using statistically downscaled Shared Socio-Economic Pathways and climate scenarios using Regionalized Concentration Pathways. We provide a range of different distributions of water use patterns in Thuringia, informing decision makers about integrated water management options and the effect of demand-side policy measures such as tariffs.

Unreliable and unequal public water supply already affects around one billion urban residents around the world. In many cities, informal water markets have emerged to fill public supply gaps by delivering water via tanker trucks, depleting scarce rural groundwater sources. A quintessential example of this can be found in the highly water-scarce country of Jordan. In Jordan, intermittent public water supply and rapid urban growth have led to a surge of uncontrolled groundwater abstractions by pervasive illegal tanker water markets. Here, we use a rigorous coupled human-natural systems model to assess a range of policy options for mitigating the groundwater impacts of informal water markets in Jordan with regards to their effectiveness and impacts on household water access. The model represents spatially distributed feedbacks between Jordan’s water sector and groundwater resources in country-wide scenario simulations until 2050. We find that investments in supply augmentation have limited impact on tanker water demand, unless they are combined with a more equitable and efficient distribution of public water supply. Jordan’s current policy of closing illegal tanker wells is found to impede the access of water-stressed households to tanker deliveries. Approaches for the legalization of tanker water markets provide more efficient policy options. Policy design is shown to be decisive for safeguarding household water access. Our findings show that understanding the role of informal water markets in urban water supply can be critical for reconciling sustainable groundwater management and household water security.

Agricultural systems in regions with previously low scale irrigation such as Thuringia, Germany, face an increase of droughts and weather extremes through climate change. Farmers are expected to adapt by increasing irrigation as a means of securing incomes. For Thuringia this entails water security implications due to limited groundwater resources and strong reliability on surface water highlighting the need to understand the feedbacks between human and natural systems in order to ensure efficient allocation and protection of water resources. So far, few studies have simultaneously combined hydro-economic models of the agricultural sector with hydrological models on a high spatial disaggregation to inform the future resilience of human-natural systems in historically water abundant regions such as Central Europe. We apply the DroughtMAS model, which simulates agricultural agents representing the production conditions of the local area, to Thuringia on a 4x4 km grid. We calibrate the model with plot-level remote sensing data using Econometric Mathematical Programming (EMP) to simulate cropping and irrigation decisions. Potential yields under climate change are calculated using machine-learning models. Socio-economic and climatic futures are simulated based on a plausible set of downscaled scenarios for Thuringia expanding upon the Shared Socio-Economic Pathways (SSPs) and Regionalized Concentration Pathways (RCPs). Regional water demand is linked to models of available groundwater to assess water security. We find a locally differentiated increase in irrigation demand and water insecurity under scenarios of drought and socio-economic change, implying the need for demand-side interventions or a provision of sufficient reservoir capacities. A spatially explicit coupled hydro-economic multi-agent approach enables an economic valuation of demand and supply side management options to inform the adaptation options towards climate resilient agricultural and hydrological systems.

The shifting precipitation patterns and rising temperatures in Central Europe and Germany present an existential challenge for farmers. Recent severe summer droughts, such as those in 2003 and 2018, underscore the imperative for farmers to adapt to evolving climatic conditions, for instance through the application of irrigation in areas where it was previously unnecessary or economically unfeasible. However, expanding the currently only 3% irrigated agricultural area in Germany has the potential to significantly impact freshwater resources and hydrological processes. Here, we model the adaptive behavior of farmers regarding irrigation, by employing an empirically validated multi-agent system (MAS) model. This model simultaneously simulates decisions about annual crop choices, acreages, and irrigation water application. Spatially disaggregated, the MAS model is calibrated using an Econometric Mathematical Programming (EMP) approach, based on historical land use data for eight major field crops. To account for the implications of future climate change, we couple the MAS model with a statistical crop yield model driven by meteorological indicators and soil moisture anomalies derived from the mesoscale Hydrologic Model (mHM) for a EURO-CORDEX scenario ensemble (RCP2.6, RCP4.5, RCP8.5). Socioeconomic variables that influence farmers' decisions, including changes in crop prices, costs, and subsidies, are projected based on Shared Socioeconomic Pathway (SSP) scenarios. Across various combinations of SSP and RCP scenarios, we find a notable surge in irrigation water demand. This development is particularly pronounced in SSP3-RCP8.5, where the MAS model projects several irrigation hotspots with a high irrigation water demand. Shifts in cropping patterns thereby significantly affect the resulting irrigation water demand. To dissect the effects of hydrometeorological, socioeconomic, and policy changes on irrigation water demand, we conduct sensitivity analyses on individual parameters. The MAS model emerges as a robust tool for analyzing farmers' adaptive behavior and assessing the impact of diverse policies on future irrigation water demand. This research contributes valuable insights into agricultural adaption under changing environmental and socioeconomic conditions.

Germany's predominantly rainfed agricultural sector faces growing challenges from climate change-induced drought and heat stress. To adapt, farmers may expand irrigation, exacerbating competition for water and depleting groundwater resources. Here, we project the future irrigation water demand for field crops under four integrated socioeconomic and climatic scenarios using a hybrid modeling approach. This combines a hydro-economic multi-agent system (MAS) model, scenario-based price projections from the Shared Socioeconomic Pathways (SSPs), and a machine learning crop yield model. The crop yield model is driven by meteorological data and soil moisture outputs from the mesoscale Hydrologic Model (mHM) along three Representative Concentration Pathways (RCPs). The MAS model, calibrated using Positive Mathematical Programming, simulates land use and irrigation decisions at the district level and is validated with land use data from 2000-2020. Our results underscore the critical role of socioeconomic factors in expanding irrigation. In the far future (2069-98), mean irrigation intensity increases by + 7 % and + 22 % across all scenarios, but total irrigation demand varies significantly: in SSP1-RCP2.6, it decreases by-38 %, while in SSP2-RCP4.5, it doubles. Under SSP5-RCP8.5, demand increases by + 6 %, whereas in SSP3-RCP8.5, it rises 8.1-fold. Nevertheless, high uncertainty from crop price projections significantly influences these results. Spatial heterogeneity strongly shapes adaptation, with farmers adjusting their land use in response to declining rainfed crop yields. This study underscores the importance of integrating multi-agent, process-based, and machine learning models to enhance irrigation demand projections and support proactive water resource management under climate and socioeconomic change.

Assessing the economic implications of droughts has become increasingly important due to their substantial impacts on agriculture. Existing empirical analyses for drought damages are often conducted on a national scale without spatially distributed data, which might bias estimates. Furthermore, the cumulative effects of multiple weather extremes, such as heat or preceded frost co-occurring with drought, are often overlooked. Measuring the direct biophysical impacts of such extremes on agriculture is essential for more precise risk assessment. This study presents a comprehensive approach to measure the cumulative economic damages of droughts and other hydrometeorological extremes on agriculture, focusing on eight major field crops in Germany. By utilizing a statistical yield model, we isolate the effects of multiple extremes on crop yields from other influencing factors (such as pests and diseases or farm management) and analyse their contribution to revenue losses during droughts at the district level from 2016-2022. Our findings indicate that the average annual direct biophysical damage caused by extremes under drought conditions during this period amounts to EUR 781 million (sensitivity range: EUR 766 million-EUR 812 million) across Germany. The study also reveals that biophysical impacts of extremes alone account for 60 % of reported revenue damages during widespread drought years. For maize, direct biophysical damage explains up to 97 % (2018) of revenue losses. Additionally, comparison of national level damage estimates using aggregated and spatially disaggregated data shows that the aggregated data matches overall results, but diverges for maize and wheat, highlighting the importance of spatially distributed damage assessment. In this paper, we provide detailed estimates of extremes-driven direct biophysical damages at the district level, offering a high-resolution understanding of the spatial and temporal variability of these impacts. Assessing the extent of revenue losses resulting from these extremes alone can provide valuable insights for the development of effective drought mitigation programmes and guide policy planning at local and national levels to enhance the resilience of the agricultural sector against future climate extremes. Future integration of routine drought damage estimation into operational monitoring and forecasting systems would enhance early warning capabilities, improve economic preparedness against increasing weather extremes, and support more proactive adaptation strategies.

Pune, near Mumbai, is India’s is 9th most populated city. As an emerging megacity, Pune is projected to grow from 7.4 to 11.4 million residents by 2050. At that time, a two-year drought under moderate climate change would lead to extraordinary water supply challenges, especially for the urban poor. Without policy interventions by mid-century, the low-income urban population will be unduly affected by water shortages as indicated by a water supply Gini coefficient exceeding 0.4. This inequity occurs as low-income households experience unaffordable water costs (10%-15% of income), and most receive 6 continuous months. Using a coupled human-natural systems model, we explored various measures aimed at alleviating this catastrophe. While many actions are shown to be ineffective, a comprehensive suite of supply-side and demand-side interventions can reduce inequity, cutting the future Gini coefficient in half, and reducing water expenditures from 15% to 5% of income. The single most effective action comes from a water-market structure that enables surrounding agricultural groundwater to be pumped and provided to the city during drought periods. However, further measures are needed to secure this expensive water for the urban poor, as it can be readily captured by wealthy urban households.