Agricultural green water scarcity (GWS)—restricted crop growth due to insufficient rainfall—is one of the key challenges in rainfed systems. It limits crop production, which can impact not only farmers' incomes but also lead to more major issues such as food insecurity, conflicts over water resources, and supply chain disruptions.
This study presents a new dataset on monthly GWS of the world’s major crops over the 1990–2019 period. The simulations are performed with a process-based global gridded crop model ACEA utilising best-to-date input datasets on climate, soil, and crop parameters. The results are compared to other relevant datasets across different spatial and temporal scales ensuring the robustness of the GWS estimates. The final files are provided as NetCDF rasters at a 5-arcminute spatial resolution (~8.3 km around the equator) per crop per month of each considered year. Such detailed segregation allows for detecting the effects of changing climate (including droughts and heat waves) on the availability of green water resources and, hence, on the GWS severity across different crops and regions.
This work provides a necessary foundation for further studies on the environmental and socio-economic implications of GWS, paving the way for solutions for more water-sustainable agrifood systems.
How to cite: Mialyk, O.: Global dataset on agricultural green water scarcity in 1990–2019, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1698, https://doi.org/10.5194/egusphere-egu25-1698, 2025.
Quantifying historical extreme drought is crucial to better understand and contextualize historical extreme droughts and prepare for extreme drought events that may occur in the future. However, the potential impacts of extreme droughts such as those in historical records considering modern day drought resistance and mitigation capacities remain unclear. In order to present the methods of reconstructing historical drought recurrence and conduct a historical drought recurrence scenario analysis under the current defense conditions, a modern day recurrence of the Guangxu drought during the Qing Dynasty from 1875 to 1879 was proposed using the Qing Palace Archives. In which, the historical annual precipitation in core drought areas was quantitatively reconstructed based on snow-rain records derived from the Qing Dynasty archives. And the extreme Guangxu drought was analyzed by establishing the corresponding relationship between precipitation anomaly percentage and the historical drought catalog.. This allowed for the characterization of possible impacts of severe drought on water resources, water supply, food production, and economy under current defense conditions. The results showed that if the Guangxu drought occurred today under the current natural geographical conditions, core drought areas like Beijing, Tianjin, Hebei, Shanxi, Shaanxi, Henan, and Shandong would experience water shortages greater than 50% of their multi-year average water resources. In addition, we found that water transfer projects and large-medium-sized reservoirs will play central roles in drought mitigation in the event of an historical extreme drought.
How to cite: Qu, Y.: Recurrence analysis of extreme historical drought under the current defense conditions in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2996, https://doi.org/10.5194/egusphere-egu25-2996, 2025.
Understanding crop yield responses to rainfall is essential for food systems adaptation under climate change. While there are ample evidences of crop yield responses to seasonal rainfall variation, the geographic sensitivities and driving mechanisms of sub-seasonal rainfall events remain elusive. We used long-term nationwide observations to explore the sensitivity of maize and soybean yields in response to event-based rainfall across Chinese agroecological regions. While maize and soybean yield showed concave downward responses to event-based rainfall depth at the national scale, these responses were differed considerably among regions. These differences can be primarily explained by soil moisture preceding rainfall events, soil erosion and sunshine hour reduction during rainfall. Our projections reveal that focusing on seasonal rainfall or national-level sensitivity analysis suggests a 0.3-5.9% increase in maize yields due to future rainfall, yet considering spatial variations unveils a contrasting reality, with maize yields declining by 9.1±0.3% under a medium-range emission scenario (SSP2-4.5) by the end of century (2085–2100). The future rainfall effect on soybean yield is the opposite, leading to a 20.6±3.9% reduction nationally without spatial consideration, but an increase (by 7.0±1.0%) when spatial variations are factored in. These findings underscore the critical necessity of incorporating regional variation in yield responses to sub-seasonal rainfall events, which could otherwise lead to vastly different impact estimates, even reversing the expected crop yield response to future rainfall change.
How to cite: Fu, J., Jian, Y., Wang, C., and Zhou, F.: Regionally Variable Responses of Crop Yield to Rainfall Events in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5346, https://doi.org/10.5194/egusphere-egu25-5346, 2025.
Rapid dietary change to more plant-based diets and reduced animal products consumption is a powerful leverage for plummeting the environmental and climate impacts of food habits, key to achieve international agreements’ targets on climate and biodiversity. Current eating patterns are shifting towards affluent diets high in sugar, fats, animal-source foods, highly processed products and empty calories. Nutritionally inadequate diets and reduced physical activity rates drive the incidence of overweight and non-communicable diseases, while increasing anthropogenic pressures on the environment.
While the optimal composition of more sustainable and healthy diets has been extensively studied, the current stage of food systems from which their transformation should begin remains underexplored. In this study, we present a statistical analysis of dietary patterns from 1970 to 2021 of 189 countries and 17 essential foods. We examine the evolution of dietary energy intake along with gross domestic product, both at global and country scale, to identify transitions in countries' food demand and highlight heterogeneities from the global pattern.
Our analysis extends the concept of the nutrition transition from a country process to a globally emerging one, characterized by increasing animal-products caloric intake and declining dietary energy supplied by cereals and plant-based foods. Consistently across high-income countries, the prevalence of sugars in diets declines of 27% towards healthier intakes. Among these countries, we identify transitions in dietary energy supply from animal products to cereals and, less frequently, plant-based foods, providing novel evidence for a reconfiguration of diets towards a reduced reliance on animal-foods, potentially suggesting the onset of a new phase in the nutrition transition.
How to cite: Giordano, V., Tuninetti, M., and Laio, F.: Shifting away from animal-source calories in High-Income countries contrasts global Nutrition Transition patterns, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10698, https://doi.org/10.5194/egusphere-egu25-10698, 2025.
Irrigation plays a crucial role in mitigating the impacts of climate variability and extreme climate events on agricultural productivity, helping to limit drought risks and reduce yield volatility. Given projected increases in the frequency and magnitude drought and water risks to crop production, expansion and intensification of irrigation will be an important component of agricultural adaptation to climate extremes. At the same time, there are significant concerns about the impacts that increased dependence of agriculture on irrigation could have for water resource sustainability and freshwater-dependent ecosystems.
Understanding how climate extremes will alter the value of irrigation is critical for managing these trade-offs, and for supporting development of policies to deliver sustainable and efficient use of water in agriculture. Here, we develop a global gridded modeling framework based on the AquaCrop-OSPy model to estimate the impacts of climate change on the agronomic and economic return on investment from irrigation under multiple potential climate futures. Our initial analyses are focused on quantifying changes in the value added from irrigation for four major crop types – Maize, Wheat, Rice, and Soybean – across four shared socio-economic pathways— SSP245 (moderate), SSP370 (regional rivalry), and SSP5-8.5 (extreme) scenarios – drawing on downscaled climate projections from a range of CMIP6 models out to 2100.
Our analysis highlights hotspots of change in variability of irrigation across major agricultural production systems and hydrologic basins. Our results show that the value of irrigation is likely to increase not only in regions that are currently water stressed but also in those where irrigation has historically been more limited. Furthermore, we demonstrate a significant increase in the inter-annual volatility of irrigation value across many production regions globally, in particular driven by greater variability in precipitation and drought-related production risks. Our findings provide insights to guide the development of irrigation expansion strategies, while also highlighting potential areas where future increases in irrigation could increase potential for conflict over limited water resources.
How to cite: Tyagi, S., Bowden, C., and Foster, T.: Global changes in the value of irrigation as a buffer against climate extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11980, https://doi.org/10.5194/egusphere-egu25-11980, 2025.
It is becoming more and more crucial to comprehend and manage the effects of extreme temperatures in order to preserve agricultural productivity and food security as climate change continues to intensify weather variability. Due to the increasing demand for olive products such as olive oil and olives in the world in recent years, olive cultivation has started to be cultivated economically in countries with a Mediterranean climate. This study investigates the future changes in growing degree days (GDD) for olive in southwestern Türkiye during the 21st century, using convection-permitting simulations under the SSP3-7.0 scenario. Future climate projections based on the SSP3-7.0 scenario suggest pronounced warming trend, particularly in the summer season. In comparison to the reference period of 1995-2014, temperatures in the 2040-49 period are expected to rise by 2.5°C, and by 3.5°C in the 2070-79 period. The second largest warming trends occur in spring and autumn, approaching those of summer in the second projection decade. After the significant decrease in the winter season, the autumn season is expected to experience the second largest reduction in precipitation, with a similar drying trend extending into the spring months in 2070-79, exacerbating the already reduced winter precipitation. Considering the ten-year average of olive production in Türkiye, our study area contains most of the cities with the highest production. Therefore, GDD is calculated for olives over the period between 1 April and 15 October, using a base temperature of 12oC. The results show a clear increase in GDD for olives in the future climate projections, especially in the second period. This increase indicates that olives will grow faster and mature earlier due to the accumulation of more heat. If the olives ripen too early, this can affect the yield and quality of the oil. Nevertheless, the relative effects of an increase in GDD across phenological stages may expose the plant to the risk of damage due to higher frequency of extreme weather events (e.g. heatwaves and late spring frosts). Insufficient water can lead to slower growth and smaller fruit sizes, and also lower oil content in olives that reduces the production of high-quality extra virgin olive oil. Olive growers in southwest Türkiye can mitigate some of the negative consequences of climate change while preserving their productivity and quality by focusing on climate-resilient types, water conservation, and sustainable farming methods.
This work is funded by the project titled 'ACLIFS - Assessment of Climate Change Impacts on Food Safety and Enhancing the Resilience of Rural Communities' which is implemented under the “Climate Change Adaptation Grant Program (CCAGP)”.
Key words: Growing degree days, convection-permitting model, COSMO-CLM, future projections, SSP3-7.0
How to cite: Sonuç, C. Y., Yaylacı, N., Keske, B., Kaya, F., and Ünal, Y.: Predicting the Impact of Climate Change on Olive Cultivation in Southwestern Türkiye under SSP3-7.0 Scenario, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16073, https://doi.org/10.5194/egusphere-egu25-16073, 2025.
The agriculture industry is increasingly challenged by the rising severity and frequency of extreme weather events. To assess the impact of climate extremes on croplands, various weather stress indices have been employed in studies. However, a region-specific index that effectively captures local variations remains lacking. The primary objective of this study is to identify climate-related stressors that impact agricultural systems at a regional scale. To achieve this, we leverage multiple high-resolution, gridded observational datasets of temperature and precipitation, alongside a large ensemble of statistically downscaled global climate models. These datasets allow us to examine the prevailing and future effects of climate change on major cropping regions across the U.S. over the coming decades. Crop-specific resilience and vulnerabilities across the CONUS are analyzed using a weighted averaging technique, which minimizes redundancy and helps create a tailored, region-specific weather stress index. This research will provide valuable insights into the resilience and stability of crops under varying climate conditions across the CONUS. The resulting weather stress indices will be made publicly available through a Climate Atlas, equipping stakeholders with the information needed to make informed decisions.
How to cite: Vishwakarma, S. and Ashfaq, M.: Assessing agricultural weather stress indices for optimizing crop resilience across CONUS, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11672, https://doi.org/10.5194/egusphere-egu25-11672, 2025.
Water is the main source of sustenance for millions of people living within the Great Ruaha River Basin (GRRB). However, water scarcity resulting from dwindling river discharges has emerged as a major challenge, affecting livelihoods and threatening the survival of dependent ecosystems. With the ongoing global climate change, it is anticipated that water stress in the basin will intensify as a result of disrupted hydrological cycle. This study assessed the potential changes in discharge resulting from future climate change in the GRRB during the (i) the mid-future (2036–2065) and (ii) the far future (2071–2099) periods. Five Global Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), applied under two Shared Socioeconomic Pathways (SSP) scenarios (SSP3-7.0 and SSP5-8.5), were utilized. The calibrated Soil and Water Assessment Tool (SWAT+) was used to evaluate the impact of climate change on discharge patterns. Climate projections indicated that, temperatures are expected to rise by 2–4°C by the end of the century under both scenarios, with evapotranspiration rates increasing by 0–2%. Annual average precipitation is projected to vary by -1% to 3% compared to the historical baseline (1981–2010). Interannual variability showed a projected decrease in precipitation during the mid-future and an increase in the far future. Similarly, long-term annual discharge trends revealed declines in the mid-future under both scenarios, with increases toward the far future. Mean monthly discharge indicated minor changes (-1% to 11%). Low flows are projected to remain relatively stable while high flows will exhibit mixed patterns, ranging from -8% to 7%. These findings highlight increased water stress in the mid-future, with potential recovery in the far future, underscoring the need for sustainable, climate-resilient water management to protect livelihoods and GRRB’s ecosystems in the face of changing climate.
Keywords: Water scarcity, Climate projections, SWAT+
How to cite: Mukama, E. B., Ronoh, E., Addisu Yimer, E., Baptist Mbungu, W., Dondeyne, S., and van Griensven, A.: Future Changes in River Discharge: Insights from CMIP6 Model Simulations for the Great Ruaha River Basin, Tanzania, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21941, https://doi.org/10.5194/egusphere-egu25-21941, 2025.
Groundwater acts as a vital buffer for human activities, offering resilience against environmental, economic, and social challenges. However, the depletion of groundwater resources intensifies these challenges, particularly in Mediterranean regions where water resources are already under significant stress.
This study investigates groundwater drought dynamics in the Bruna River catchment in Tuscany, Italy, with a focus on the temporal and spatial patterns of drought and its attribution to climate change. The analysis employs various hydroinformatics approaches, including the Standardized Precipitation Index (SPI), Standardized Groundwater Index (SGI), a threshold-based method, and the Combined Drought Index (CDI), which integrates precipitation, soil moisture, and vegetation data to monitor agricultural drought, offering early warnings and identifying areas that are either affected or recovering. Furthermore, a SWAT+ gwflow model is being developed to explore drought attribution using both factual and counterfactual climate datasets.
Preliminary findings highlight obvious temporal and spatial drought patterns, even when utilizing short time series. The SGI exhibits strong temporal correlations with SPI12, SPI24, and the monthly threshold Q20. Moreover, SGI demonstrates good temporal and spatial alignment with CDI, underscoring its utility in groundwater drought assessments.
Future efforts will focus on finalizing and validating the SWAT+ gwflow model by calibrating it against observed data and performing sensitivity analyses. The validated model will facilitate an in-depth exploration of groundwater drought attribution by comparing outcomes under factual and counterfactual climate scenarios. These analyses aim to enhance the understanding of groundwater drought, which can inform water resource management and policy decisions.
How to cite: Elsaidy, A., Addisu Yimer, E., Villani, L., and van Griensven, A.: Temporal and Spatial Patterns of Groundwater Drought in the Bruna River Catchment, Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2746, https://doi.org/10.5194/egusphere-egu25-2746, 2025.
The flow duration curve (FDC) serves as an essential tool for analyzing streamflow variability and supporting effective river management. However, constructing FDCs in ungauged basins presents a significant challenge due to the lack of sufficient data. This study leverages data-driven approach to predict FDCs in ungauged basins, thus offering practical solutions for improving hydrological forecasting and enhancing water resource management. The research aims to identify the key hydrologic, meteorological, and topographic factors influencing FDCs, and by evaluating different combinations of predictor variables, it assesses the influence of various precipitation metrics on flow predictions while comparing the performance of data-driven models. The study predicted low (Q80%, Q90%, Q95%), medium (Q30%, Q40%, Q50%, Q60%, Q70%), and high flows (Q5%, Q10%, Q20%), including extreme low flows (Q95%) and extreme high flows (Q5%). Feature importance analysis highlighted the watershed area and precipitation as critical for high flow predictions, and land use and basin characteristics influenced medium and low flows. Scenario testing confirmed that including all variables resulted in the most accurate predictions. Interestingly, variations in precipitation metrics had minimal impact on model performance, suggesting the prominence of other predictors. These results emphasize the potential of data-driven approaches in improving FDC predictions, particularly in diverse hydrological contexts where conventional methods fall short. This study highlights the potential of advanced hydroinformatics techniques to predict FDCs in ungauged basins, improving the accuracy of hydrological forecasting and water resource management through innovative, data-driven methodologies.
Funding: This work was supported by Korea Environment Industry & Technology Institute(KEITI) through the Water Management Project for Drought, funded by Korea Ministry of Environment(MOE) (2022003610004).
How to cite: Yoon, J., Yi, S., Lee, C., Lee, S., Ji, J., Lee, E., and Yi, J.: Predicting Flow Duration Curves in Ungauged Basins Using Data-Driven Approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7508, https://doi.org/10.5194/egusphere-egu25-7508, 2025.
Predicting streamflow in ungauged catchments using regionalization methods has been extensively studied. While numerous free and open-source software (FOSS) tools exist for predicting regional flow-duration curves (FDCs) at ungauged sites, a general FOSS tool specifically designed to generate continuous streamflow series from these FDCs is lacking. This study introduces FDC2Qt, an R-package developed within a collaboration between the University of Bologna, the Po River Basin Authority, and the Emilia-Romagna Regional Authority. FDC2Qt generates long synthetic hourly streamflow series in ungauged catchments, utilizing a regional dataset of daily streamflow observations from neighboring sites and basin morphoclimatic descriptors. The methodology comprises three key steps: (1) FDC Prediction: a regional period-of-record FDC is predicted for the ungauged site by combining an index-flow approach (Castellarin et al., WRR, 2004) with a region of influence approach (Burn, WRR, 1990); (2) Daily Streamflow Synthesis: a synthetic daily streamflow series is generated at the ungauged site using a non-linear spatial interpolation method based on FDCs (Smakhtin et al., HSJ, 1997; Smakhtin, J. Hydrol., 2001), referencing one or more observed daily streamflow series from neighboring catchments; (3) Hourly Streamflow Downscaling: the synthetic daily series is downscaled to an hourly time step using a scaling law that primarily considers the morphological features of the ungauged catchment. Validation experiments demonstrate that the synthetic hourly streamflows accurately capture the primary hydrological characteristics of observed streamflow series and exhibit reliability comparable to that of hourly streamflow series simulated by regionalized lumped rainfall-runoff models.
How to cite: Spadoni, A., Foraci, R., Di Lorenzo, M., Simonelli, T., and Castellarin, A.: An Open-Source Tool for Generating Hourly Synthetic Streamflow Series in Ungauged Basins Using Regional Flow-Duration Curves, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18192, https://doi.org/10.5194/egusphere-egu25-18192, 2025.
Effective water resource management in hydrologically diverse regions requires the integration of advanced decision-support systems with collaborative approaches. This study presents the development and application of the ERA TOOL, a hydroinformatics platform designed to evaluate water resources across multiple jurisdictions in Colombia. Initially developed through collaboration between the Stockholm Environment Institute (SEI) and regional environmental authorities (CARs), the tool has been implemented for four CARs and is currently being adapted for a fifth, highlighting its scalability and adaptability to diverse hydrological and institutional contexts.
The ERA TOOL builds on the framework of the Regional Water Assessment (ERA, by its acronym in Spanish), a process mandated by Colombian policy to assess water availability, quality, and vulnerability while addressing anthropogenic pressures. By integrating localized hydrological modeling using the Water Evaluation and Planning (WEAP) system and geospatial analyses, the tool provides a comprehensive Decision Support System (DSS) for evaluating current conditions and projecting future scenarios under climate variability and increasing demands.
Key features of the ERA TOOL include dynamic visualizations of regional indicators such as flow duration curves, groundwater recharge, and surface water availability and quality indicators, all presented through an intuitive, interactive interface. The platform also facilitates the co-design of solutions by incorporating feedback from stakeholders during its development and deployment. This collaborative process ensures that the tool meets the specific needs of each CAR, enhancing institutional capacity and fostering a shared understanding of water resource dynamics.
The implementation across multiple CARs has demonstrated the versatility of the ERA TOOL in addressing diverse regional climates and challenges, from hydrological extremes to water allocation and quality management. By linking data-driven insights with participatory processes, the tool has empowered decision-makers to implement evidence-based strategies that promote equitable and sustainable water governance. All platforms developed are freely accessible, with the latest version available for consultation at the following link: https://latinoamericasei.shinyapps.io/ERA_CARDER/.
This ongoing initiative underscores the potential for replicating and adapting hydroinformatics solutions across regions with varying hydrological and institutional conditions. It provides a model for leveraging innovative tools and participatory research to bridge gaps between technical expertise and practical application in water resource management.
How to cite: Forero-Hernández, A. T., González-Ayala, C. A., Aedo-Quililongo, S., and Santos-Santos, T. F.: Leveraging Hydroinformatics and Stakeholder Engagement for Regional Water Resource Management in Colombia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14432, https://doi.org/10.5194/egusphere-egu25-14432, 2025.
Seasonal water flow patterns in Central Valley rivers within California's Sacramento-San Joaquin Delta watershed have been profoundly disrupted by dams, conveyance systems, and land use changes. These alterations have led to habitat degradation, declines in fish populations, and reduced ecosystem services. Environmental flows—quantities and qualities of instream water essential for ecosystem health—are critical for sustainable water management. However, implementing environmental flows in Central Valley rivers necessitates significant changes to current water management practices, with uncertain implications for other water uses. The COllaboratory for EQuity in Water Allocations (COEQWAL), a publicly funded initiative, seeks to improve understanding of California’s water future through participatory scenario planning. As part of COEQWAL, we investigate the impacts of water operation alternatives and climate scenarios on maintaining environmental flows based on Functional Flow targets, which represent flow regime components that support key ecosystem functions. We demonstrate that allocating specific monthly water volumes as an environmental water budget—tailored to river basin and water year type—can achieve these targets. Furthermore, we evaluate how climate warming influences the feasibility of achieving environmental flows under various water management scenarios. Our results highlight both the opportunities and challenges associated with managing environmental flows in California’s Central Valley rivers. They also provide valuable insights into the interplay between water management strategies and ecological outcomes, helping guide sustainable management practices for the future.
How to cite: Yi, S., Stanford, B., Yarnell, S., Murdoch, L., and Grantham, T.: Assessing Environmental Flows in the Central Valley Across Different Management Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5361, https://doi.org/10.5194/egusphere-egu25-5361, 2025.
With the ongoing global vegetation greening and the increasing frequency of drought events, the likelihood of drought disturbances to vegetation is rising. The time it takes for vegetation to return to their normal state after a drought is known as recovery time. Investigating how vegetation greening influences drought recovery time is vital for the sustainable development of terrestrial ecosystems. In this study, we assessed global vegetation changes and recovery time over the past forty years using vegetation data. We employed a machine learning model to analyze the relationships between influencing factors and recovery time, while also determining the spatial distribution of the main influencing factors. The results indicate that approximately 40% of global regions exhibit a significant greening trend, with over 90% of drought recovery events occurring between January and April. We found vegetation greening enhances recovery resilience and reduces recovery time. Soil moisture, vapor pressure deficit (VPD), and temperature during recovery can hinder vegetation regrowth when they are at extreme values. In some areas, prolonged greening may lead to longer recovery time following droughts. These findings suggest that while greening typically decreases recovery time, sustained greening may extend recovery duration once a certain regional threshold is reached. Therefore, it is crucial to consider the trade-offs between greening and vegetation resilience in the context of ongoing global greening.
How to cite: Li, Z. and Liu, W.: The reducing effect of global greening on vegetation recovery time is disappearing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2325, https://doi.org/10.5194/egusphere-egu25-2325, 2025.
Climate change significantly alters agricultural processes, impacting the movement and loss of essential nutrients like nitrogen (N) and phosphorus (P) in farming systems. This study investigates nonpoint source pollution associated with three major crops—rice, maize, and wheat—across global watersheds, focusing on the effects of extreme climate conditions through model-based analysis. The results show that nutrient losses exhibit nonlinear responses to precipitation changes. Under dry conditions, nutrient losses decreased steadily with reduced precipitation, without abrupt drops under extreme dry conditions. In contrast, wet conditions led to progressively higher nutrient losses, with N and P losses surging significantly under extreme wet conditions (P: 63.8–115.6%; N: 32.7–106.7%). Extreme wet years occurred more frequently than extreme drought years, exerting a greater impact on agricultural systems. Despite varying climate conditions affecting total nutrient loss, the proportional contributions of pathways like runoff and erosion remained relatively consistent. Further analysis revealed significant differences in nutrient loss patterns under extreme wet conditions across watersheds. Regions with high absolute nutrient losses tended to show smaller relative increases, while regions with smaller absolute losses often experienced larger relative increases. This variation highlights the need for tailored mitigation strategies. In areas with high absolute nutrient losses, the focus should be on controlling total loss through measures like precision fertilization, optimized nutrient management, and conservation tillage to reduce runoff and erosion. Meanwhile, regions with high relative increases, due to their sensitivity to extreme wet conditions, require dynamic nutrient management strategies aligned with precipitation patterns. Utilizing residual soil nutrients effectively and avoiding fertilization during wet periods can minimize additional losses. Enhancing system resilience by improving soil organic matter content is also critical, as it strengthens water retention and erosion resistance. By addressing the distinct needs of different regions, these strategies provide a solid foundation for reducing nutrient loss under extreme climate conditions, supporting sustainable agriculture and enhancing the resilience of farming systems to climate variability.
How to cite: Liu, W., Li, M., Huang, Y., Makowski, D., Su, Y., and Ciais, P.: Nonpoint pollution under extreme climate conditions and possible mitigation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2409, https://doi.org/10.5194/egusphere-egu25-2409, 2025.
ABSTRACT: Central Asia (CA), as a representative arid region globally, faces severe water scarcity, which has posed significant threats to its sustainable development over the past decades. A systematic understanding of the dynamic changes in surface water area (SWA) and terrestrial water storage (TWS) is crucial for ensuring human survival and maintaining the regional ecosystem balance. While previous studies have documented water resource depletion in Central Asia, they often lacked comprehensive analyses of the primary drivers of surface water area decline. To address this gap, we analyzed interannual variability and trends in SWA and TWS across Central Asia from 1990 to 2023. This analysis used Landsat-5/7/8/9 surface reflectance data, Gravity Recovery and Climate Experiment (GRACE) mascon data, an improved robust water mapping algorithm, and the Google Earth Engine (GEE) cloud computing platform. Results indicate a continuous and substantial decline in SWA across the CA, primarily driven by irrigation withdrawals and surface water evaporation. Additionally, population growth and extreme climate change have posed a new potential threat to regional water security. This study highlights the critical importance of ecological water resource management in promoting the coordinated development of regional food, water, and ecological security, thereby supporting long-term sustainable development.
Key words： Surface water body；Terrestrial water storage；Landsat；Google Earth Engine
How to cite: Liu, B., liu, W., and yang, S.: Impacts of Population Dynamics and Climate Extremes on Water Resource Security in Central Asia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5855, https://doi.org/10.5194/egusphere-egu25-5855, 2025.
In this study we applied a large-scale hydrological model, with an irrigation routine based on the FAO guidelines for computing crop water requirements. The aim was to quantify the changes in irrigation-water demand under three different climate scenarios until the end of the century for nine different types of crops in almost 40,000 sub-basins covering Sweden (450 000 km2) located on the Scandinavian Peninsula in northern Europe. Our results showed that, on average, irrigation-water demand is projected to increase by the end of the century. However, we found that the driest years are not significantly drier but rather more frequent. There is a large discrepancy between the climate scenarios; under RCP2.6 there will be little or no significant change in irrigation-water demand while under RCP 8.5 an average year might become as dry as the driest year under the 1981-2010 reference period. A warmer climate will lead to an earlier growth start, ranging from a few days earlier under RCP2.6 to 2–4 weeks earlier on average, depending on crop, under RCP8.5. The RCP4.5 results are in between those extremes but will reach similar conditions by the end of the century as RCP8.5 shows by mid-century. The main conclusion from our study is that in a warmer climate the agricultural water demand in Sweden will increase and more water allocated for irrigation can be anticipated for climate adaptation.
How to cite: Rudebeck, H., Arheimer, B., Persson, M., and Elenius, M.: Modeling crop irrigation needs in Sweden under climate change , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5984, https://doi.org/10.5194/egusphere-egu25-5984, 2025.
Network resilience refers to a system's ability to adapt its functions and maintain the continuity of essential operations in the face of external environmental changes or internal disruptions. The resilience of the global food trade network is increasing challenged by structural disturbances such as dynamic shifts in internal and external environments, making it a topic of significant interest. Extreme climate events-such as floods, droughts, is a key concern for food production and food trade network. However, little in-depth theoretical and empirical research has been conducted in relation to the link between exposure to extreme climate and the underlying mechanisms that explicate this relationship. This study introduced the static and dynamic food trade network resilience assessment method and applied linear mixed effect model to estimate the effect of extreme climate impact on the food trade network resilience. The results shown that the extreme climate events reduced the maize, rice and soybeans export value and then decreased the network resilience. It also demonstrated that intensity of export competition plays a critical role in shaping the resilience of the network. Specifically, the findings shown that the network's resilience declines more sharply when nodes with higher weighted degrees are removed sequentially, compared to the removal of nodes with lower weighted degrees. In link disruption scenarios, the removal of links with higher competition intensity causes a steeper decline in resilience than the removal of weaker links. Additionally, in weight modification experiments, networks with a higher proportion of strong competition links exhibit greater stability compared to networks with fewer such links. These results highlight the importance of maintaining a strong level of export competition to sustain the stability of the global trade competition network when facing the disturbance of extreme climate events.
How to cite: Liu, X. and Ren, X.: Extreme climate impact on the food trade network resilience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18646, https://doi.org/10.5194/egusphere-egu25-18646, 2025.
The project aims to develop effective adaptation strategies to address floods and droughts in Marlborough, a town traversed by the River Kennet—a rare and valuable chalk stream sustained by groundwater from chalk aquifers. These aquifers act as natural sponges, absorbing excess water during heavy rainfall and gradually releasing it during dry periods. However, climate change is compromising their ability to regulate water levels, making them less reliable buffers against flooding and drought (Seneviratne et al., 2021). This initiative is motivated by Marlborough's history of recurring floods, most recently the severe flood on 5th January 2024, which significantly impacted the community (Wiltshire Council, 2014; Dalton, 2024). In response, the project seeks to bridge the gap between science and the community, fostering collaboration, knowledge exchange, and stakeholder-driven decision-making to build resilience.
The project integrates two interconnected components, forming a strong foundation for adaptive strategies that balance scientific precision with community engagement.
Component 1: Localising Hydrological Models for Improved Predictions
This component focuses on enhancing prediction accuracy using SWAT+ software. The model will downscale future climate predictions while incorporating the unique spatial and temporal characteristics of the catchment area. SWAT+ enables the creation of multiple scenarios, allowing exploration of various future possibilities. The model is designed to simulate the impacts of land management, climate variability, and human activities on water resources, sediment transport, and agricultural productivity across complex watersheds (Wang et al., 2019).
Component 2: Stakeholder Engagement
To foster community engagement, a participatory and collaborative modelling framework (Basco-Carera et al. 2017) will be implemented alongside a community modelling method (Landstrom et al. 2019). This approach actively involves community members in scenario development and decision-making, ensuring their knowledge and lived experiences shape the model’s outcomes, which, in turn, reflect the community's needs. The iterative process empowers residents to make informed decisions, co-creating adaptation strategies with diverse stakeholders to ensure they are both effective and equitable. This approach transforms Marlborough’s residents into active contributors. By integrating local insights—such as historical flood knowledge and land use practices—with scientific data, the project enhances the model’s accuracy, relevance, and acceptance (Iwaniec et al., 2020).
Furthermore, recognising the growing need to make science more engaging and accessible, the project takes an innovative approach by incorporating the story-line method (Shepherd et al., 2019) and art-based techniques (Leavy, 2020) into its community engagement sessions.Local representatives will not only contribute to decision-making but also actively participate in the modeling process using SWAT+, highlighting its practical value. Additionally, art-based activities will encourage creativity and interaction, making science more approachable and meaningful to the community.
How to cite: Peklanska, E.: Socio-scientific synergies: transdisciplinary approaches to shaping hydro-futures with catchment communities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-955, https://doi.org/10.5194/egusphere-egu25-955, 2025.
Forecasting stream flow accurately is essential for managing water resources, preventing flooding, and designing the environment for intricate watersheds. This study conducts a comprehensive assessment of streamflow simulation models—Multilayer Perceptron (MLP), Extreme Learning Machine (ELM), Support Vector Machine (SVM), and the Soil and Water Assessment Tool (SWAT) hydrological model—covering the period from 1985 to 2018 in the Astore Basin, Pakistan. The GMRC-WAPDA and SWHP-WAPDA provided daily streamflow data from the Doyian gauging station in the Astor River Basin, as well as meteorological data collected from two automatic weather stations (AWS) located at Rama, Rattu and Astor. A total number of four soil classes (lithosols, calcaric, gleysols and fluvisol) was observed in the basin. The primary objective was to comprehensively assess the predictive performances of these models across distinct time segments and gauge their reliability in simulating streamflow dynamics. The study commenced with examining the SWAT model's performance, utilizing the NSE, PBIAS, R2, and RMSE metrics during calibration (1985–2000) and validation (2001–2009) periods. While the SWAT model effectively estimated streamflow, it exhibited limitations in accurately predicting peak and low-flow conditions. Subsequently, the machine learning models (MLP, ELM, and SVM) were scrutinized concerning their performance metrics—R2, NSE, PBIAS, and RMSE—across training (1985–1995), validation (1996–2005), and testing (2005–2009) datasets. ELM displayed superior performance during the training phase, boasting a remarkable R2 of 0.94, followed by SVM and MLP. MLP showcased consistent strength in validation, maintaining an R2 of 0.73, while SVM followed with an R2 of 0.71. Despite their merits, none of the models precisely replicated observed streamflow patterns, as evidenced by the discrepancies between the observed flow and the SWAT model's simulations. This emphasizes the necessity for ongoing refinement and validation to enhance predictive accuracy and ensure closer alignment with real-world hydrological dynamics. This extensive comparative analysis offers critical insights into the nuances of MLP, ELM, SVM, and SWAT model performances, highlighting their varied strengths and limitations across distinct temporal segments. It underscores the importance of continual refinement and validation to improve predictive capabilities, which is essential for accurate streamflow simulations and effective water resource management in the Astor Basin and similar hydrological contexts.
How to cite: Rashid, M. and Parise, M.: Assessing the Robustness of SWAT Model and Machine Learning Techniques in Predicting Extreme Streamflow Events. A Case Study of Astor Basin, Pakistan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6405, https://doi.org/10.5194/egusphere-egu25-6405, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot A
EGU25-19086 | ECS | Posters virtual | VPS9
Leveraging machine learning and satellite precipitation data to overcome latency challenges in operational hydrologyTue, 29 Apr, 14:00–15:45 (CEST) | vPA.7
Accurate and timely representation of spatiotemporal precipitation patterns is critical for monitoring and predicting hydrological extremes, particularly in operational hydrology and early warning systems. In regions with limited in-situ precipitation data, satellite precipitation products (SPPs) offer an accessible solution. However, the latency of these datasets—the delay between data collection and availability—remains a key challenge for real-time applications. This study developed a machine learning model based on the Random Forest (RF) algorithm to predict precipitation using low-latency data from GOES-16 Advanced Baseline Imager (ABI) bands. The model was applied to the Jubones River basin (3,391 km²) in southern Ecuador, a region characterized by complex terrain and hosting a key hydropower project. Leveraging hourly data over a five-year period, the RF model addressed the five-hour latency of traditional SPPs by generating near-real-time precipitation maps with a latency of only 10 minutes. The model’s performance was evaluated using quantitative and qualitative metrics across temporal scales, demonstrating progressive accuracy improvements with larger temporal aggregations. Root Mean Square Error (RMSE) values decreased from 0.48 to 0.05 mm/h, while Pearson’s Cross-Correlation (PCC) improved from 0.59 to 0.87 for scales ranging from hourly to monthly. Qualitative metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI), further validated the approach. These findings highlight the potential of integrating advanced hydroinformatics techniques with remote sensing for managing hydrological extremes in diverse basins. The study underscores the importance of leveraging low-latency satellite data and machine learning to enhance real-time forecasting and operational hydrology. Future work will focus on refining the model for improved detection of extreme precipitation events and exploring its integration into stakeholder-driven decision-making frameworks.
How to cite: Muñoz, J., Muñoz, P., Muñoz, D. F., and Célleri, R.: Leveraging machine learning and satellite precipitation data to overcome latency challenges in operational hydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19086, https://doi.org/10.5194/egusphere-egu25-19086, 2025.
EGU25-6708 | ECS | Posters virtual | VPS9
Using Unsupervised Learning to Explore Landslides Driving Factors from Topographic and Hydrological Catchment FeaturesTue, 29 Apr, 14:00–15:45 (CEST) | vPA.8
Landslides are a widespread geohazard with significant impacts on lives and economies worldwide. While past research has primarily emphasized creating inventories, and analysing spatial and temporal patterns, the objective of this study is to explore the relationship between landslides events taken place in different catchments using only topographical and physical attributes from the disasters’ areas. The aim is to improve the understanding of the occurrence and susceptibility of such events, as well as the possible similarities between the events and the catchments. To this end, multicollinearity and mutual information analysis were performed to identify both linear and nonlinear relationships between the variables, assisting on the identification of the most relevant driving factors to historical landslides in the study area. Furthermore, the events were grouped using 5 different unsupervised clustering techniques, KMeans, Mean Shift, DBSCAN, Hierarchical and Spectral Custering, to analyse the relationship between landslides taken place in different catchments and their underlying driving forces. Clustering evaluation metrics, i.e. Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used assess the performance of these algorithms. The results show that, for a preliminary study and providing insights on the relevance of driving factors and similarities between events, unsupervised learning proves to be an important tool. Nevertheless, to find more applicable and in-depth associations between extreme disasters and its driving factors, more robust machine learning techniques can and should be used.
How to cite: Antunes Meira, M., Xuan, Y., and Wang, H.: Using Unsupervised Learning to Explore Landslides Driving Factors from Topographic and Hydrological Catchment Features, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6708, https://doi.org/10.5194/egusphere-egu25-6708, 2025.
EGU25-16242 | ECS | Posters virtual | VPS9
Evaluating the Three-Cornered Hat Method for Satellite Precipitation Data Fusion and its Influence on Runoff ForecastingTue, 29 Apr, 14:00–15:45 (CEST) | vPA.9
Runoff forecasting remains a critical challenge in many basins worldwide, particularly those featuring a complex topography, where the scarcity of hydrometeorological data is a prevalent challenge. Data fusion offers a promising alternative to conventional single-source data modelling, which often fails to capture the full spatial and temporal variability of precipitation. By integrating multiple sources, data fusion seeks to generate enhanced satellite precipitation datasets, essential for data-driven runoff forecasting models. This study aims to evaluate the effectiveness of the Three-Cornered Hat (TCH) method for fusing satellite precipitation products (SPPs) and its influence on the performance of a Random Forest-based runoff forecasting model.
Three scenarios were evaluated: (i) a TCH-fused dataset combining three SPPs: Integrated Multi-satellitE Retrievals for GPM – Early Run (IMERG-ER), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Cloud Classification System (PERSIANN-CCS) and the Global Satellite Mapping of Precipitation – Near Real Time (GSMaP-NRT); (ii) an individual SPP (IMERG-ER); and (iii) an already fused benchmark product, the Multi-Source Weighted-Ensemble Precipitation (MSWEP). All scenarios performed comparably for lead times of 3, 6, 12, and 24 hours, with MSWEP slightly outperforming across Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency, and Root Mean Square Error metrics. However, TCH demonstrated better bias reduction as reflected by the Percent Bias metric.
A key limitation of the fusion method was identified at hourly scales, where statistical dependence arises during periods with no precipitation over the basin, hindering the effectiveness of TCH. The introduction of a matrix regularization step addressed this issue. This study provides valuable insights for enhancing SPP fusion methods and offers a replicable framework for improving runoff forecasting, particularly in data-scarce regions and other hydrological contexts.
How to cite: Luna-Abril, P., Muñoz, P., Samaniego, E., Muñoz, D. F., Merizalde, M. J., and Célleri, R.: Evaluating the Three-Cornered Hat Method for Satellite Precipitation Data Fusion and its Influence on Runoff Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16242, https://doi.org/10.5194/egusphere-egu25-16242, 2025.
EGU25-20678 | Posters virtual | VPS9
GeoLinkage2.0 and GeoChecker: Hydroinformatics tools for large and complex hydrological-hydrogeological models using WEAP-MODFLOW. Case Study: Severe drought in the Limarí River Basin, ChileTue, 29 Apr, 14:00–15:45 (CEST) | vPA.10
Understanding and modeling surface and groundwater resources are critical due to the effects of droughts and climate change, especially in semi-arid, arid, or hyper-arid regions. GeoLinkage, developed by Troncoso (2021), facilitates the creation of linkage files for integrated models. These linkage shapefiles act as a communication interface between a surface hydrological domain (D1) and an aquifer domain (D2). The surface domain (D1) comprises nodes and arcs that represent hydrological elements and their relationships, while the aquifer domain (D2) contains geometric elements such as grids or Quadtree diagrams. D1 defines a surface topology (τ1), D2 defines a groundwater topology (τ2), and the linkage file establishes a surface-groundwater topology (τ1-2). This new topology, τ1-2 ,imposes constraints that influence the relationship between τ1 and τ2. For instance, the superposition of elements in τ1-2 should be considered a spatial relationship. Depending on the type of superposed elements, this relationship must be reflected in τ1 or τ2. To enforce these τ1-2 specific restrictions, GeoLinkage has been enhanced with a post-processing module called GeoChecker. This module evaluates the quality of the resulting linkage files. GeoChecker currently performs a superposition check to ensure that overlaps between cells in the linkage file—whether between groundwater and catchments or groundwater and demand sites—are accurately represented as connections in the surface model (WEAP). The aquifer is represented by a MODFLOW model fully linked to the WEAP model. GeoLinkage2.0 and GeoChecker were developed using the tutorial WEAP-MODFLOW model, considered a small model, and were tested in large integrated models, such as the Azapa Valley (3,000 km²) and the Limarí River Basin (12,000 km²), Chile.
How to cite: Sanzana, P., Torga, A., Hitschfeld, N., and Lobos, C.: GeoLinkage2.0 and GeoChecker: Hydroinformatics tools for large and complex hydrological-hydrogeological models using WEAP-MODFLOW. Case Study: Severe drought in the Limarí River Basin, Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20678, https://doi.org/10.5194/egusphere-egu25-20678, 2025.
EGU25-2646 | ECS | Posters virtual | VPS9
A Transformer-based Graph Network for Flash Flood Disaster ClassificationTue, 29 Apr, 14:00–15:45 (CEST) | vPA.24
Flash floods are one of the most devastating natural disasters, posing significant risks to both human life and infrastructure. The classification of their underlying drivers—such as high precipitation, dam breaches, landslides, and melting snow—remains a critical yet challenging task, especially in regions like China, where diverse geographical and climatic factors exacerbate disaster complexity. In this study, we propose a Transformer-based Graph Network (TGN) designed to tackle these challenges by leveraging a dataset of over 53,000 flash flood events, each characterized by non-uniform geographical attributes and varying levels of data completeness. Unlike traditional graph neural networks (GNNs) that depend on predefined graph structures, TGN dynamically learns and refines edge weights during training, enabling it to uncover asymmetric dependencies. This adaptability is particularly valuable when explicit relationships between nodes are unavailable or incomplete.
Integrating multi-head self-attention mechanisms from Transformer architectures, TGN captures complex interdependencies across watershed features while maintaining interpretability through sparsity and diversity constraints. A distinguishing feature of this framework is its ability to identify meaningful graph structures without prior knowledge, offering insights into critical connections and interactions within disaster-prone regions. For instance, our experiments demonstrate how TGN emphasizes high-risk upstream-downstream relationships, providing actionable knowledge for localized flood management. The model significantly outperforms traditional GNNs and machine learning methods in accuracy and robustness, achieving superior classification performance across all four disaster categories. Furthermore, the TGN framework is supported by rigorous evaluation metrics, including Precision, Recall, F1-score, and Overall Accuracy, ensuring its reliability in real-world applications.
By combining innovative graph-based modeling with interpretable mechanisms, this study bridges the gap between theoretical advancements and practical disaster management. The proposed approach not only enhances prediction capabilities but also provides an analytical lens for understanding the intricate relationships among flash flood drivers, paving the way for more effective mitigation strategies and informed decision-making. This work underscores the transformative potential of adaptive graph neural networks in addressing complex environmental challenges and advancing the state of flood risk assessment.
How to cite: Wang, H., Xuan, Y., Zhang, Z., Antunes Meira, M., Li, Q., and Liu, C.: A Transformer-based Graph Network for Flash Flood Disaster Classification, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2646, https://doi.org/10.5194/egusphere-egu25-2646, 2025.
