Conference Agenda

Irrigation accounts for the largest share of global freshwater withdrawals, yet empirical evidence on the responsiveness of irrigation water demand to price signals remains fragmented, heterogeneous and often difficult to interpret for policy design. This paper conducts a comprehensive global meta-analysis of irrigation water price elasticities, synthesising nearly 400 elasticity estimates drawn from around 70 primary studies covering a wide range of crops, irrigation technologies, water sources, pricing regimes and environmental contexts. The analysis applies meta-regression techniques to explain cross-study heterogeneity through structural, institutional, technological and environmental moderators, interpreted within a Water–Energy–Food–Ecosystems (WEFE) framework.

To ensure robust inference, the study adopts an influence-based trimming strategy grounded in regression diagnostics rather than ad hoc exclusion rules. Outliers are identified conditionally, based on their leverage and influence in the regression space, and results are reported for three datasets: a full sample, a trimmed sample excluding highly influential observations and a restricted sample including only elasticity estimates based on observed water prices. This tiered approach allows explicit comparison between behavioural responses to realised pricing regimes and elasticity estimates derived from shadow or simulated prices.

Results confirm that irrigation water demand is generally price inelastic on average, but that responsiveness varies systematically across settings. Elasticities associated with groundwater use, energy-mediated pricing and multi-crop systems are significantly larger, while estimates from single-crop studies and highly water-stressed basins are markedly more inelastic. Water stress emerges as one of the most robust moderators, indicating that binding hydrological and ecosystem constraints substantially limit price responsiveness. Elasticity estimates based on observed prices display lower dispersion and higher explanatory power than those derived from constructed prices, highlighting the importance of distinguishing scarcity valuation from behavioural response.

Overall, the findings suggest that irrigation water pricing can contribute to demand management, but only under specific conditions where water–energy linkages are strong, food production systems allow adjustment and ecosystem constraints are not yet binding. From a WEFE perspective, the results caution against treating water pricing as a standalone policy instrument and underscore the need for integrated approaches that jointly address water allocation, energy pricing, cropping systems and ecosystem sustainability.

Global water systems are increasingly exposed to intensified water scarcity, climate variability, and competing sectoral pressures. These challenges affect not only the availability and quality of freshwater resources but also the sustainability of ecosystems and the resilience of socio-economic systems. Ensuring sustainability requires innovative and integrated governance approaches that foster climate adaptation and move beyond traditional sectoral thinking. This study addresses the gap by developing an integrated hydro-economic model (HEM) that promotes equitable and inclusive cross-sectoral performance. The model links biophysical, hydrologic, economic, and ecological components within a WEFE Nexus framework. It incorporates CMIP6 climate projections, hydrological outputs from TETIS, crop water requirements from AQUACROP, agricultural and energy price projections from CAPRI and PRIMES, and habitat suitability modelling for key fish species. The HEM assesses how uniform and dynamic water pricing policies influence water allocation, cross-sectoral outcomes, and species resilience in the Júcar River Basin under future climate and socio-economic conditions. A marginal resource opportunity cost approach (MROC) is applied to construct a stepwise pricing curve for the dynamic water pricing strategy. The results indicate that both water pricing policies reduce unsustainable water use while preserving economic benefits, improving system efficiency, and alleviating water scarcity. Uniform water pricing considerably reduces water withdrawals by 30%, reaching 759 Mm³ under SSP5-8.5 for the simulation period 2015-2050, compared to the baseline (1078 Mm³), by creating a strong incentive for conservation. However, this approach often does so at the expense of economic efficiency. Its rigid structure disproportionately affects activities with lower economic returns and penalizes crops with lower water productivity, such as cereals, potatoes, or sunflowers. In contrast, dynamic water pricing results in a more moderate reduction in withdrawals, while preserving economic performance by adjusting prices to reflect scarcity conditions. Herbaceous production declines from 562 MT in the baseline to 406 MT under SSP5-8.5, while fruit tree and citrus yields remain mostly stable. Dynamic pricing, therefore, supports better cross-sectoral balance, achieving environmental and energy gains with less economic disruption. Findings also indicate that both pricing policies enhance ecological resilience by reducing the frequency, duration, and severity of habitat stress below ecological thresholds. The analysis demonstrates that water tariffs could optimize cross-sectoral trade-offs, providing operational evidence to support sustainable, inclusive, and nexus-aligned water governance.

Wildfires can sharply increase hillslope erosion and downstream sediment loads, threatening drinking-water treatability and aquatic habitat. Post-fire treatments (e.g., mulching) can reduce these impacts, but limited budgets and tight response windows require cost-effective targeting across the landscape. We develop a decision-support spatial optimization integer programming framework that links spatially explicit post-fire erosion and sediment predictions from the Water Erosion Prediction Project (WEPP) model under “no treatment” and multiple treatment scenarios to a least-cost treatment prioritization problem. Using hillslope-level outputs, the framework computes expected reductions in (i) hillslope sediment yield and (ii) watershed-scale sediment discharge or suspended-sediment indicators, and then solves a binary integer programming model to select the set of hillslope–treatment pairings that meets user-specified biophysical threshold targets at minimum cost. We refer to this integrated decision-support framework as the Post-fire Assessment of Treatments for Hillslopes (PATH) model. The framework incorporates user-defined fixed and variable treatment costs and can include practical eligibility constraints (e.g., slope limits, burn-severity-based targeting), while allowing multiple thresholds to be imposed simultaneously. We demonstrate the approach in an applied watershed setting and summarize results as cost surfaces and tradeoff curves that relate management ambition to budget requirements. Optimal portfolios concentrate treatments on the most influential hillslopes rather than treating uniformly, revealing clear cost–performance tradeoffs as thresholds tighten. When targets are infeasible, PATH provides transparent “second-best” solutions (e.g., maximizing sediment reduction subject to minimum per-hillslope standards), supporting defensible planning under binding biophysical and operational constraints.

Terrestrial water storage (TWS) is the total amount of water in all continental storage compartments and is an essential resource for food and energy production, manufacturing and ecosystem health. Even though absolute TWS is unknown, satellite gravimetry-based methods enable global estimations of spatial and temporal TWS changes, revealing significant alterations over the past two decades due to natural variability, direct human influence, and climate change. Here we show that declines in TWS reduce economic production worldwide. Combining GRACE TWS anomaly measurements from 2004 to 2018 with data on macroeconomic growth from more than 1600 subnational regions worldwide, we isolate the impact of climate-induced TWS changes on growth by means of three complementary empirical strategies. Using a long-difference approach and fixed-effects panel regressions with either a control for human water consumption or an instrumental variable approach, we find consistent evidence for negative economic impacts of TWS declines. Among the regions with the largest TWS-induced growth rate reductions are the Caspian Sea Region and India, whereas others such as in Tanzania or the US Mid-West benefitted, pointing to pronounced regional heterogeneity even within countries. To further investigate this heterogeneity in response to TWS change, we combine a leave-one-out sensitivity analysis with tree-based classification techniques and a feature importance analysis. The results imply that regions with a high share of agricultural area or low income per capita experience higher economic losses under declining TWS, pointing to an exacerbation of global economic inequality. Our findings contribute to the discourse on sustainable water management and its implications for economic prosperity.