• investigate the combined impacts on water quality and quantity from climate change and/or extremes across local to global scales, including climate impact attribution studies;
• investigate the impacts of present and future socio-economic developments on surface and/or groundwater quality;
• investigate the implications of compound and cascading extreme climate events (e.g., wildfire and floods, drought and heatwaves) on water quality;
• quantify and couple supply and demand in support of water quality management including vulnerability assessment, scenario analysis, indicators, and the water footprint;
• project future water scarcity (combining water quality & quantity) supply and demand in the context of a changing climate;
• quantify the uncertainty of water quality models under drivers of global change;
• interpret and characterize uncertainties in machine-learning, AI and data-mining approaches that are trained on large data sets;
• address the problem of temporal and spatial scaling in water quality modelling;
• test transferability and generalizability of water quality predictions;
• involve stakeholders in water quality model development to inform risk analysis and decision support;
• application of remote sensing and/or citizen science in water quality estimates at multiple scales.
Orals: Wed, 6 May, 16:15–18:00 | Room 2.31
Surface and groundwater quality is influenced by contaminant losses from the farm, and variation caused by climate and time lags caused by the pathways that contaminants take from farms to waterbodies. Using data from 50 years of national and international studies, we find clear evidence that after controlling for variation, we can attribute a degree of confidence in water quality improvements across New Zealand to actions taken on farm designed to mitigate contaminant losses from farms. The degree of confidence varies, depending on how, when and where data have been collected and is subsequently analysed. As a result, the evidence base spans a wide spectrum, from robust, well-funded farm- and catchment-scale studies that monitor water quality and actions across time and space, to well-intentioned but poorly designed studies that collect limited data at few sites over short periods, making it difficult to attribute observed changes to specific farm actions.
To maximise the likelihood of implementing the right actions to improve water quality a five-step framework has been implemented. The framework is designed for farmers, industry bodies, regulators and the community to use as part of a collaborative catchment process focused on action. The process begins with the establishment of a water quality target (Step 1), which requires a plan to reduce contaminant losses—typically by a specified percentage of current levels. The second step sees the land manager identifying mitigation actions for their farm plan. These actions are selected to mitigate the target contaminants, based on the suitability of actions for their farm. The third step ensures actions are implemented in the right place at the right time. This is determined by understanding the current catchment context and risk of contaminant loss, identifying hotspots of risk (e.g., critical source areas) within the farm, and applying suitable and cost-effective actions to maximise outcomes. The fourth step sees appropriate monitoring put in place to connect what's being done on farms with changes in the receiving water body. The final step assesses the level of confidence that improvements in water quality can be attributed to the implemented actions, after accounting for potential confounding factors such as climate variability and changing production. Where well implemented, this framework is able to manage stakeholder expectations about where and when water quality will improve under both voluntary and regulatory regimes.
How to cite: McDowell, R.: Closing the Loop: Attributing Water Quality Improvement to On-Farm Action, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5990, https://doi.org/10.5194/egusphere-egu26-5990, 2026.
The global aquatic threat imposed by agriculturally-sourced pollution is further exacerbated due to the shifts in the weather patterns, resulting in changes in catchment hydrology, water cycle, and soil processes. Understanding the timing, extent, and impact of the extreme-weather-events on nutrient losses is hence essential to develop efficient climate-smart adaptation measures to avoid further increases in nutrient pollution in receiving water bodies.
Our research has applied an empirical modelling (EM) approach on +14 years of very high-temporal resolution weather and water quality data from six hydrologically-diverse agriculturally-dominated catchments in Ireland in order to i-detect climate-induced increases in Nitrogen (N) and Phosphorous (P) losses during 2010-2024, and ii- estimate likelihood occurrence of similar loss events until turn of the 21st century using climate change projections. We considered the following criteria to model the historical prevalence of loss-events, and to project the possibility of occurrence in the future under two representative concertation pathways of RCP 4.5 (moderate) and 8.5 (extreme) for three different time periods of 30-years each: effective rainfall (ER)>five mm in one day and/or the day before, ER>10mm over one day, and average air temperature>15℃ over 5 consecutive days.
Although the sensitivity of each catchment to prolonged warm period and intensive short-term precipitation depended highly on the catchment-characteristics, the monthly trend analysis of projected temperature and precipitation indicated very significant increase in the extent of warm and dry periods, as well as the intensity of wet/very wet days, toward the end of the century. The EM captured over 60% of events triggering N losses and up to 80% of P-loss triggering events.
Considering the potential underestimation of projected temperature and precipitation probability, and assuming no changes in N and P inputs in the future scenarios, the average annual number of temperature-related and precipitation-related N-triggering events would reach 120 and 79, respectively, in the far-future RCP 8.5. EM also projected >60% increase in the number of P loss events under both representative pathways while the projections indicated average discharge of over 8 mm per a single event which would directly contribute to increases in mass loads leaving the catchments.
As the water quality is threatened further by the changing weather patterns, it is critical to incorporate the influence of climate change on nutrient losses and develop climate-resilient measures that are tailored to different catchment typologies.
How to cite: Ezzati, G., Murphy, C., and Mellander, P.: Impacts of extreme weather events on water quality and nutrient losses in agricultural catchments: Past, Present, and Future, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1028, https://doi.org/10.5194/egusphere-egu26-1028, 2026.
Urban water systems worldwide face escalating scarcity challenges driven by the combined pressures of climate change and socioeconomic development. Population growth and urbanization are concentrating water demand in cities, while climate variability and extremes increasingly threaten reliable supply. Simultaneously, competing demands for human activities and ecosystems functioning intensify pressure on finite freshwater resources, making conventional supply strategies insufficient in many regions.
Potable water reuse (PR), the process of treating and recycling wastewater to produce an alternative source of drinking water, is one promising solution to mitigating urban water scarcity. Here, we quantify the global potential of municipal PR as a water scarcity mitigation strategy and explore how its effects vary across development levels and governance contexts. We use the Global Change Analysis Model (GCAM), an integrated multi-sectoral model capturing long-term interactions between economy, climate, water, energy, and land systems, to assess PR potential within coupled socioeconomic and resource systems. We systematically generate a comprehensive scenario ensemble using combinatorial experimental design, simultaneously examining six key drivers related to water supply, demand, and allocation rules. This exploratory modeling framework enables comprehensive assessment of deep uncertainty while identifying critical factor combinations and threshold conditions where PR delivers improved water security outcomes across 235 global water basins.
Preliminary results highlight that PR can significantly reduce freshwater withdrawals, buffer urban demand during shortages, and indirectly relieve pressure on agricultural systems. However, our analysis reveals substantial spatial heterogeneity in PR effectiveness. Development level and governance capacity strongly mediate implementation potential, creating clear patterns of implementation inequality i.e. regions with greatest water stress often face the steepest barriers to adoption. Critically, our findings underscore that PR is best viewed as a complementary tool within a broader portfolio alongside conservation, desalination, and improved allocation mechanisms rather than a standalone solution.
How to cite: Sarfraz, A., Niazi, H., Graham, N., Wild, T., Wanders, N., Bierkens, M. F. P., and Gold, D.: Global Potential of Potable Reuse as a Water Scarcity Solution Across Coupled Climate and Socioeconomic Future, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1923, https://doi.org/10.5194/egusphere-egu26-1923, 2026.
Dissolved oxygen is expected to decline in many of the world’s rivers due to increasing water temperatures under climate change. This may cause significant adverse effects for freshwater ecosystems, such as mass mortality events and fish kills. However, previous studies related to the effects of climate change on dissolved oxygen are mostly carried out at local or regional scales. In our study, we perform the first global-scale analysis of dissolved oxygen concentrations under climate change for both historic (1980-2019) and future periods (2020-2100) (Graham et al., 2025). This involves the development of a hybrid process-based and machine learning model framework of dissolved oxygen concentration at the global-scale. The model framework includes the process-based DynQual surface water quality model and a random forest machine learning model for error correction, trained on roughly 2.6 million observations of dissolved oxygen concentrations.
The hybrid approach shows a significantly improved performance in simulating dissolved oxygen concentrations compared to the process-based model alone. For instance, there is on average a 43% reduction in the normalised Root-Mean-Squared-Error (nRMSE) when applying residual error correction with machine learning. Additionally, the hybrid DynQual_Random Forest model was able to better capture the impacts of extremes compared to the standalone process-based model. We applied the hybrid model globally at 5arcmin (approximately 10km) spatial and daily resolution for the periods 1980-2019 and 2020-2100. Our results show significant decreasing trends in dissolved oxygen concentration for the majority of rivers worldwide, which leads to on average 8.8 ± 2.3 more hypoxia days (with DO < 3 mg l-1) per decade globally over the period 2020-2100. This study highlights the strengths of a hybrid process-based and machine learning modelling framework to capture water quality responses at high spatial and temporal resolution as well as during hydro-climatic extremes. It shows that increasing water temperatures and increasing biochemical oxygen demand (BOD) are likely the key drivers of decreasing dissolved oxygen concentrations under climate change. Furthermore, our study emphasises the importance of dissolved oxygen as a key driver of freshwater ecosystem health in the coming decades.
References
Graham, D.J., Bierkens, M.F.P., Jones, E.R. et al. Climate change drives low dissolved oxygen and increased hypoxia rates in rivers worldwide. Nat. Clim. Chang. 15, 1348–1354 (2025). https://doi.org/10.1038/s41558-025-02483-y
How to cite: Graham, D., Bierkens, M., Jones, E., Sutanudjaja, E., and van Vliet, M.: Declining dissolved oxygen levels in the world’s rivers due to climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12540, https://doi.org/10.5194/egusphere-egu26-12540, 2026.
Algal blooms are a major cause of declining lake water quality and are expected to intensify under climate change. Machine-learning approaches have increasingly been used to predict algal blooms; however, most studies emphasise short-term predictive accuracy rather than longer-term bloom risk. In addition, thermal stratification is often treated as a secondary driver, despite its potential importance in a warming climate. Ensemble models are also frequently applied with limited transparency, restricting interpretability and confidence for decision-making.
To address these gaps, we implemented an interpretable ensemble modelling framework to simulate dynamic chlorophyll-a variability using long-term monitoring data from the southern basin of Lake Windermere, UK. The framework integrates multiple commonly used machine-learning models within a transparent stacking structure, calibrated using Bayesian optimisation, and incorporates post-hoc explanation methods to support interpretation of model behaviour and driver importance.
Results indicate that, alongside meteorological and hydrological drivers, temperature-related variables (particularly indicators of thermal stratification) play an important role in controlling chlorophyll-a variability. Scenario simulations were conducted to explore climate sensitivity, including warming experiments, perturbations to thermal stability, and long-term climate projection scenarios. These experiments suggest that warming and increased water-column stability are generally associated with higher chlorophyll-a concentrations and prolonged periods of elevated bloom risk.
Overall, this study presents a transparent and transferable modelling framework for exploring long-term algal bloom risk under climate change, with relevance for lake management and climate adaptation planning.
How to cite: Ding, D. and Erfani, T.: Interpretable ensemble machine learning for assessing algal bloom risk under climate warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3493, https://doi.org/10.5194/egusphere-egu26-3493, 2026.
Efficient monitoring and reporting of Sustainable Development Goal (SDG) indicator 6.3.2 is essential for assessing progress toward good ambient water quality. However, data limitations, particularly in developing regions such as Africa, hinder accurate assessment of water quality in rivers. This study addresses this challenge by employing modelling to map progress of the SDG indicator 6.3.2 in Africa for the reporting years 2020, 2030, 2040 and 2050 under three socio-economic scenarios; SSP1-RCP2.6, SSP3-RCP7.0 and SSP5-RCP8.5. This indicator will provide an overview of the state and trends of future water quality in different African regions and identify hotspots of water pollution. We utilize model simulations from two global water quality models; DynQual and SWAT+ to generate water quality indexes (WQIs) using four core parameter groups at level I reporting namely salinity (Total Dissolved solids), nitrogen (Total Nitrogen), phosphorus (Total Phosphorus) and oxygen (Biological Oxygen Demand). Model simulations are compared to target values to derive country and river basin level WQIs. Additionally, we assess the impact of level II parameters on the overall indicator by adding fecal coliform (FC) to the calculation. Lastly, we compute the percentage of population exposed to the deteriorating water quality across these periods and scenarios. This study's robust methodology for SDG mapping significantly enhances our understanding of future water quality dynamics. The findings will inform targeted interventions, policy formulation, and sustainable water resource management, contributing to the achievement of SDG 6 and broader environmental sustainability objectives across the continent.
How to cite: Nakkazi, M. T., Nkwasa, A., Tera Orsini, J., and van Griensven, A.: Mapping hotspots for future water pollution in Africa using SDG indicator 6.3.2 , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22312, https://doi.org/10.5194/egusphere-egu26-22312, 2026.
In Europe, nutrient pollution from intensive farming and high population density degrades water quality, undermining the water needs of humans and natural ecosystems. Water quality outcomes result from the interaction of management actions and a changing climate, making it difficult to separate the influence of each driver. To devise effective mitigation strategies, a clear understanding of river basin dynamics, nutrient source distributions, and projected climate evolution is required, together with region specific load targets that respect the source to sea continuum—including surface water, groundwater, and marine discharge. Water quality models offer tools for exploring future trajectories that consider changes in climate and nutrient source management.
We (i) compiled existing nutrient load targets for European river basins, highlighting their spatial variability; (ii) developed a set of coupled climate change and EU policy scenarios extending to 2050, representing plausible trajectories of precipitation and policy stringency; (iii) applied a scenario modelling framework that links these drivers to basin scale nutrient fluxes, allowing us to quantify the reductions needed to meet the defined targets; and (iv) evaluated a suite of basic nutrient reduction measures (e.g., optimized fertilizer application, enhanced riparian buffers, upgraded wastewater treatment) for their effectiveness in delivering the required load cuts.
This research highlights the nutrient loading reductions needed to achieve European water policy targets for both inland and coastal ecosystems in a changing climate, offering guidance for sustainable water resource strategies.
How to cite: Grizzetti, B., Udias, A., Bouraoui, F., Vigiak, O., Galimberti, F., Pistocchi, A., Aloe, A., Zanni, M., Zampieri, M., Piroddi, C., and Macias, D.: Reducing nutrient pollution in European river basins under climate change and evolving EU policy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22282, https://doi.org/10.5194/egusphere-egu26-22282, 2026.
Freshwater quality in the future will be determined by the combined effect of climate change, land-use changes, and socioeconomic developments, with important consequences for ecosystem health and clean water availability. Robust comprehensive modelling frameworks are therefore needed to quantify the impact of these pressures across space and time, while accounting for the uncertainty in projected changes. However, simulating multiple pollutants across complex hydrological systems over multi-decadal periods at national scale presents substantial methodological and practical challenges. Here, we present an integrated modelling framework for UK surface and groundwater quality and discuss challenges and successes in producing reliable national-scale projections.
We present the modelling framework developed within the Long-Term Large-Scale Freshwater Ecosystems (LTLS-FE) project, building on an existing long-term integrated model (LTLS-IM). It dynamically couples process-based representations of surface and subsurface hydrology, agricultural and seminatural soils, sewage and septic tank emissions, and in-stream transport and fate. Using observational datasets, we evaluate modelled freshwater concentrations for a wide range of substances, including fine sediment, macronutrients, metals, and diverse micropollutants such as pesticides, antibiotics, pharmaceuticals, personal care products, industrial chemicals, and polycyclic aromatic hydrocarbons.
The framework is applied to project water quality across the UK from 1981 to 2080 under six future scenarios combining UKCP18 climate projections (RCP2.6–RCP8.5) with different Shared Socioeconomic Pathways (SSP1–SSP5). Simulations are conducted at a two-hourly timestep on a 5 km grid, producing monthly outputs of river flow, temperature, pH, biochemical oxygen demand, pollutant concentrations, and fluxes to the sea.
Challenges include limited pollutant input and validation datasets, particularly for Northern Ireland, as well as slow groundwater equilibration periods. Integrating multiple process-based sub-models while maintaining computational efficiency also required careful model design and optimisation. Despite these challenges, comparisons with national monitoring data show that the framework captures observed spatial patterns, seasonal dynamics, and long-term trends for major pollutant groups. Here we present the project outputs compared against observations and interpret future trends. These results demonstrate the feasibility and value of national-scale, multi-pollutant modelling to support future assessments of water quality risks under future change.
How to cite: Missault, N., Rhodes-Smith, M., Bell, V., Davies, H., Rameshwaran, P., Lofts, S., Chen, H., Milne, A., Jackson, T., Whitmore, A., Lapworth, D., Bianchi, M., Palumbo-Roe, B., Merchant, B., Perry, W., Vaughan, I., and Kelly, M.: National scale water quality modelling: Challenges and successes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12143, https://doi.org/10.5194/egusphere-egu26-12143, 2026.
Multiple pressures, both past and present, influence the chemical and biological quality of UK freshwaters. While some of these pressures (e.g. metals, acidification, oxygen-consuming substances) appear to have eased in recent decades, others (e.g. industrial and personal organic micropollutants, nitrogen and phosphorus) remain and may be increasing. Reductions in levels of freshwater pollution following the introduction of regulations (e.g. European Urban Waste Water Treatment Directive) have driven a degree of biological recovery across the UK. Whether these improvements in UK freshwater quality and biodiversity will be maintained long term is of great interest to the public who rely on freshwaters for recreation, to water companies for drinking water supply, to industry and to statutory regulators.
Our Long Term Large Scale Freshwater Ecosystems (LTLS-FE) project aims to understand the effects of multiple pressures and drivers on freshwater quality at a national scale using a multidisciplinary modelling approach. We are working to develop and analyse future scenarios of water quality and biodiversity in UK freshwaters which take account of both climate and socioeconomic change (RCP/SSP combinations).
Here, we present the multidisciplinary modelling approach used in LTLS-FE, which links models of soil processes, agriculture and point source releases of pollutants to surface waters with a hydrological model of transport and transformation in the freshwater environment. These will then be used to drive a national-scale ecological model to predict impacts on freshwater biota. We will present the innovations that have been required to achieve this goal, including a national-scale model of sewage treatment, national datasets of metal fluxes from mine waters and anthropogenic abstractions, and scenarios of pollutant releases from domestic and industrial sources to 2080. We are now in the final year of our four-year project and will demonstrate how well the LTLS-FE freshwater model performs on historical periods before sharing early results of future scenarios.
How to cite: Bell, V., Lofts, S., Lapworth, D., Kelly, M., Vaughan, I., Whitmore, A., Bianchi, M., Chen, H., Davies, H., Jackson, T., Marchant, B., Milne, A., Missault, N., Palumpo-Roe, B., Perry, W., Rameshwaran, P., and Rhodes-Smith, M.: Developing Freshwater Quality Futures for the UK, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10476, https://doi.org/10.5194/egusphere-egu26-10476, 2026.
Posters on site: Wed, 6 May, 14:00–15:45 | Hall A
Freshwater ecosystems host a disproportionate share of global biodiversity yet are increasingly exposed to declining water quality driven by nutrient enrichment, chemical contamination, thermal stress, salinisation, and emerging pollutants. While water quality standards and regulatory limits exist for many constituents, their relevance for safeguarding freshwater biodiversity remains fragmented, taxon-specific, and unevenly documented across regions and ecosystem types. This review synthesises current knowledge on critical water quality limits associated with adverse responses of freshwater biodiversity, with a focus on identifying lethal and sublethal thresholds across major aquatic taxa and key water quality constituents.
We systematically assess reported biodiversity responses covering fish, amphibians, macroinvertebrates, reptiles, freshwater-adapted mammals, and where evidence exists, groundwater-associated biota to changes in nutrients (nitrogen and phosphorus), temperature, dissolved oxygen, biochemical oxygen demand, salinity, suspended sediments, metals, plastics, pharmaceuticals, and contaminants of emerging concern, including PFAS and microplastics. Reported thresholds are evaluated across lentic and lotic systems to account for ecosystem-specific sensitivities. Where possible, we distinguish between acute (lethal) and chronic (sublethal) response levels and document observed exceedance events.
Beyond synthesising established thresholds, the review explicitly highlights constituents for which biodiversity-relevant limits are poorly defined or absent, as well as geographic regions where water quality degradation is likely occurring but biodiversity impacts remain under-reported. By consolidating dispersed evidence and identifying critical gaps, this review aims to support biodiversity-relevant water quality assessments, inform monitoring and modelling efforts, and provide a foundation for integrating ecological thresholds into freshwater management and policy frameworks under accelerating global change.
How to cite: Nkwasa, A., Theresa Nakkazi, M., J. Brumm, K., van Griensven, A., and Kahil, T.: Critical water quality limits for aquatic freshwater biodiversity , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14401, https://doi.org/10.5194/egusphere-egu26-14401, 2026.
Nitrogen pollution of surface water bodies remains one of the most persistent environmental challenges across European landscapes, largely resulting from widespread agricultural intensification since the early 20th century. In this study, we present a seamless national-scale framework for the comprehensive assessment of nitrogen dynamics in German river systems by combining long-term nitrogen input sources (e.g., mineral fertilizers, manure applications, and wastewater effluents) with a process-oriented modeling framework to estimate both current and legacy sources of nitrogen pollution. We built a harmonized dataset covering both diffuse and point nitrogen sources from the 1950s [1,2] to the present and tracked the terrestrial transfer of nitrogen through soils, groundwater, and river networks. Our analysis is based on the multiscale water quality model mQM [3], which explicitly accounts for nitrogen legacy storage and delayed release and transport processes across terrestrial compartments. Model parameterization follows a stepwise approach: catchment-scale parameters are first constrained using riverine N concentration data from more than 100 gauging stations, compiled within the QUADICA database [4,5]. This allows for a robust basin-scale model configuration covering diverse German landscapes: natural forested regions, intensive croplands, and livestock-based systems; and capturing a range of varying hydroclimatic conditions, subsurface soil and groundwater characteristics, and socioeconomic factors.
Subsequently, to enable seamless model application across the entire German river network, we apply a transferable model parameterization that utilizes spatial proximity, physiographic similarity, and landscape characteristics using statistical methods and machine-learning techniques (e.g., regression relationships and Random Forests). Our near-century-long data and model-based analysis show pronounced spatial and temporal heterogeneity in N input sources and riverine N concentrations across German landscapes. Regions with excessive nitrogen surplus are associated with livestock-intensive systems in northern Germany as well as mineral fertilizer-dominated cropland regions in central Germany, while differences in catchment functioning and hydroclimatic conditions modulated how excess N signals propagate to riverine N concentration levels. Despite national-scale reductions in nitrogen surplus and substantial improvements in wastewater treatment, there are regions of Germany (e.g., in the Central Elbe River) that continue to exceed critical nitrogen thresholds (>2.5 mg/L). Our analysis provides a seamless framework for assessing nationwide nitrogen pollution and supports the development of intervention strategies for sustainable nitrogen management.
[1] https://doi.org/10.1038/s41597-022-01693-9
[2] https://doi.org/10.5194/essd-16-4673-2024
[3] https://doi.org/1029/2022GL100278
[4] https://doi.org/10.5194/essd-14-3715-2022
[5] https://doi.org/10.5194/essd-2025-450
How to cite: Kumar, R., Nguyen, T., Ebeling, P., Attinger, S., and Musolff, A.: Seamless National-Scale Assessment of Legacy Nitrogen Pollution in German Rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14310, https://doi.org/10.5194/egusphere-egu26-14310, 2026.
Decades of river water quality monitoring in Taiwan have revealed a clear trend of deterioration, with ammonia-nitrogen (NH₃–N) concentrations at several monitoring stations frequently exceeding the regulatory thresholds established by the Ministry of Environment. This degradation arises from the combined influence of natural biogeochemical processes and diverse anthropogenic pressures. Accurately modeling the spatial variability of river water quality is therefore both challenging and essential for protecting riverine ecosystems and public health. In recent years, data-driven machine learning (ML) approaches have demonstrated strong capability in capturing complex, nonlinear relationships in both surface and subsurface water systems. In this study, we develop a predictive model for riverine NH₃–N concentrations using an artificial neural network (ANN) trained on an extensive suite of multivariate datasets compiled across multiple government ministries. Model performance is rigorously evaluated through three-fold cross-validation, confirming that the ANN effectively captures the primary spatiotemporal variability of NH₃–N and provides reliable predictive accuracy. To further interpret the model, SHAP analysis is conducted to identify key predictors. The results show that average precipitation in November, the extent of land undergoing human modification, the density of food-product and animal-feed manufacturing activities, and the forest land-use group are among the most influential drivers of NH₃–N concentrations. Identifying such dominant variables is crucial for guiding evidence-based river water quality management and for formulating targeted pollution mitigation strategies.
How to cite: Liang, C.-P., Chang, J.-Y., and Chen, J.-S.: Nationwide Modeling of River Ammonia-Nitrogen Concentrations in Taiwan Using Machine Learning , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14851, https://doi.org/10.5194/egusphere-egu26-14851, 2026.
Groundwater nitrate contamination remains a persistent challenge across Europe, despite decades of regulation and monitoring. Although nitrate pollution is commonly framed as a diffuse agricultural pressure, exceedance patterns are spatially structured and strongly associated with management intensity. This supports a management-relevant modelling pathway: risk mapping can be extended into scenario-based evaluation of mitigation strategies, enabling spatial prioritization and transparent comparison of intervention options. In this study we present a management-focused scenario framework to quantify future changes in groundwater nitrate hotspot risk across Europe on a harmonized grid. Groundwater nitrate monitoring data are linked to land-use composition predictors (fractions of cropland, grassland, forest, wetlands, and impervious surfaces), alongside regional landscape descriptors and climate–hydrological covariates. Model outputs are provided as (i) a continuous probability of hotspot occurrence and (ii) a binary hotspot classification defined by exceedance of the drinking-water nitrate threshold. To improve continental transferability and reduce over-optimistic performance from spatial autocorrelation, model development and evaluation rely on spatial cross-validation and region-based holdouts. The predictive core is an AI-based ensemble designed to capture nonlinear interactions between land systems, hydroclimate, and nitrate outcomes. Management scenarios include: (1) cropland reallocation to grassland/forest to represent extensification and protection-zone land-use transitions; (2) wetland restoration to increase landscape retention and reduce leaching susceptibility; (3) fertilizer-pressure reduction implemented as proportional decreases in agricultural nitrogen intensity. For each strategy, we compare uniform implementation against risk-based targeting applied only in high-probability hotspot cells. Finally, all scenarios are evaluated under 2050 climate conditions (multi-scenario climate projections), allowing assessment of mitigation robustness under altered recharge regimes and climate-driven changes in leaching potential. The framework provides an operational route from EU-scale monitoring to management-ready, climate-aware decision support for prioritizing groundwater nitrate mitigation across Europe.
How to cite: Ahmadi, K., Berndtsson, R., and Naghibi, A.: Assessing groundwater nitrate hotspot mitigation through management scenarios and AI-based risk prediction across Europe under climate change , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19546, https://doi.org/10.5194/egusphere-egu26-19546, 2026.
While surface freshwater (i.e., water in lakes, reservoirs, and rivers) plays an essential role in sustaining human life, both its quantity and quality are increasingly threatened by human activities and climate change. Combining field measurements with a mechanistic model T&C-BG, we investigate nutrient export (dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP)) from catchments to Singapore’s reservoirs considering six land covers (i.e., forest, grassland, golf courses, agricultural land, bare soil, and impervious surfaces). Results show that the T&C-BG model reproduces well measurements of soil nutrients, soil respiration, and nutrient leakage for different land covers. At the plot scale, DOC export tends to increase in the future for all vegetated surfaces because of stimulated plant photosynthesis by CO2 fertilization effects. In contrast, TN and TP exports can either increase or decrease depending on land cover. Increases in TN and TP export occur when net primary production is reduced and hence nutrient uptake decreases; the opposite occurs when net primary production increases. While upscaling to the catchment scale, DOC export increases for all reservoirs in the future but TN and TP export trends vary regionally depending on the distribution of land cover types in upstream catchments. Moreover, regardless of the spatial scale, climate internal variability plays an important role in regulating nutrient exports in all experiments. Our findings provide insights for the sustainable management of surface freshwater resources in a changing climate.
How to cite: Luo, Z., Ren, J., Chen, M., Senaratna, K. Y. K., Te, S. H., Han, H., Gin, K. Y.-H., and Fatichi, S.: Nutrient export from catchments to Singapore’s reservoirs in a changing climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8622, https://doi.org/10.5194/egusphere-egu26-8622, 2026.
Intensive agricultural production in the Indo-Gangetic Plain has led to widespread overapplication of fertilizers, particularly in sugarcane-based systems, resulting in elevated nitrate concentrations in groundwater. In the Hindon River Basin, the Central Ground Water Board (CGWB) has reported significant nitrate contamination, which poses a threat to both agricultural sustainability and drinking water security. Addressing this degradation while maintaining productivity requires quantitative tools to evaluate management interventions. This study employs the SWAT+ model to evaluate the impact of reduced fertilizer application and alternative irrigation practices on groundwater nitrate leaching and sugarcane yield in the sugarcane-dominated Hindon Basin. The model's ability to represent basin hydrology and crop growth was evaluated through calibration and validation using observed streamflow at two gauging locations, and sugarcane yield data from three districts. The model demonstrated satisfactory performance, with streamflow calibration and validation producing average KGE values of 0.74 and 0.73, respectively, and an average percent bias (PBIAS) of 12% and +9%. Crop yield simulations yielded average KGE values of 0.76 and 0.83 during calibration and validation, respectively, with PBIAS values of -4% and -6%. These results confirm the model's reliability for management-oriented assessments. Four management scenarios were simulated against a baseline that reflected current farmers' practices, as identified through field surveys. Scenarios included fertilizer reductions of 15% and 30%, implemented under both furrow and drip irrigation systems. Groundwater quality responses were evaluated using annual average nitrate percolation below the root zone for leaching, while sugarcane yield was used to assess productivity trade-offs. Across all alternative scenarios, nitrate percolation decreased by 46% to 68% relative to the baseline. Changes in sugarcane yield were minimal, remaining within 1-2% of current practices. Drip irrigation demonstrated greater nitrate reduction compared to furrow irrigation at the same fertilizer levels, highlighting the importance of irrigation efficiency in mitigating nutrient loss. These findings suggest that moderate decreases in fertilizer use, combined with drip irrigation, can significantly reduce groundwater nitrate contamination in the Indo-Gangetic Plain without compromising yields.
Keywords: Nitrate, Agriculture, Fertilizer, Irrigation, Groundwater, SWAT+
How to cite: Tiwari, V., Ilampooranan, I., Vinnarasi, R., and Jain, S. K.: Can Agricultural Nitrate Leaching to Groundwater Be Reduced Without Compromising Crop Yields?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19661, https://doi.org/10.5194/egusphere-egu26-19661, 2026.
ABSTRACT: Rural depopulation and urban expansion have driven widespread natural revegetation in mountainous areas, resulting in substantial land-use changes. Combined with recent climate variability, these processes affect hydrological behavior and sediment dynamics by altering runoff generation, infiltration, and sediment transport. However, their integrated impacts remain poorly quantified at large spatial scales in mountain regions.
This study assesses changes in water and sediment fluxes in the northern sector of the Ebro Basin using the SWAT+ model, a semi-distributed hydrological model operating at a daily time step. The study area was divided into twelve sub-basins, each represented by an individual model. Model calibration and validation were conducted sequentially. Streamflow was first calibrated using observations from more than 30 gauging stations, and performance was evaluated using the Nash–Sutcliffe Efficiency (NSE). Sediment calibration was subsequently performed using reservoir bathymetry data and published sediment yield estimates. The final models achieved NSE values between 0.53 and 0.95, with a basin-wide mean of 0.75, indicating good model performance.
Future hydrological and sediment responses were simulated using climate projections from the NEX-GDDP-CMIP6 dataset. Seventeen climate models were processed at a daily scale and statistically downscaled. Bias correction was applied using Empirical Quantile Mapping (EQM), calibrated over the 2015–2020 period and applied from 2021 onwards. Extreme values were further corrected using a Generalized Pareto Distribution with a Peak-Over-Threshold (GPD/POT) approach, followed by a delta-change adjustment to better match observed conditions.
The overall objective is to compare the impacts of the 17 climate models across the 12 sub-basins. Preliminary results from one sub-basin indicate a consistent decrease in future streamflow, highlighting potential implications for water availability in mountainous Mediterranean basins.
ACKNOWLEDGMENTS: This work is funded by the European Research Council (ERC) through the Horizon Europe 2021 Starting Grant program under REA grant agreement number 101039181 - SEDAHEAD.
How to cite: Ortiz-Elorza, A. and Juez, C.: Multi-Model Climate Projections of Hydrological and Sediment Change in the Ebro River Basin Using SWAT+, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7160, https://doi.org/10.5194/egusphere-egu26-7160, 2026.
River water quality status is increasingly challenged by the combined effects of climate extremes and human activities. Furthermore, monitoring water quality parameters remains sparse and irregular across many river networks to conduct timely and effective assessments. To address these challenges, a new framework was developed to support river network-wide estimation of ecological water quality status. The framework combines a multi-risk approach with deep learning to investigate the relationship between climate hazards, anthropogenic pressures, exposure, and vulnerabilities under hot-dry and wet-dry conditions. It was implemented for the Veneto Region (Italy) to predict annual LIMeco, a nutrient-oxygen physico-chemical index used in regional assessments, for 865 river segments over 2010-2023, with data missingness reaching 66.8%. Hazard conditions were represented by annual hydroclimatic indicators capturing hot and wet/dry conditions and extremes, while anthropogenic pressures were described through land use composition and nutrient load proxies. Exposure and vulnerability were represented through basin characteristics (e.g., soil properties and topography), together with the presence of riparian and wetland areas as proxies of buffering capacity and management levels. To translate these drivers into spatially coherent predictions while acknowledging missing observations due to incomplete datasets, a hybrid spatio-temporal Graph Neural Network (GNN) was implemented in which (i) recent hydroclimatic variability was summarized over a short input window using the Gated Recurrent Units, (ii) information was propagated along upstream-downstream connectivity using GNN, and (iii) eco-hydrological clusters of basins were represented through a data-driven regime label with an unsupervised Machine Learning (ML), derived from the multi-risk indicators, enabling the information transfer between well-monitored and poorly-monitored river segments that have similar climate and land-based regimes. The hybrid spatio-temporal GNN was tested over multiple configurations and against other ML approaches (i.e., Multilayer perceptron and eXtreme Gradient Boosting). The comparison demonstrates that the hybrid GNN achieved the best performances with the highest test accuracies (RMSE = 0.028; NSE = 0.98) when using the embedded river basin clusters, providing the most stable performance across basin types and missingness levels. This highlighted how the inclusion of physically-based river network dynamics and auxiliary information can help to address missing data compared to other tested methodologies. The proposed framework can support scenario-oriented analysis for decision making and planning, given the representation of management-relevant indicators (e.g., riparian condition, wetland presence, land-use pressure), allowing for exploring future scenarios and responses under climate and socio-economic changes to support adaptation strategies.
How to cite: Ngoc Nguyen, D., Furlanetto, J., Niazkar, M., Torresan, S., and Critto, A.: Linking hydroclimatic hazards and catchment vulnerability to river ecological status under a data-sparse condition using hybrid graph neural networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6405, https://doi.org/10.5194/egusphere-egu26-6405, 2026.
Diffuse nutrient pollution during rainfall–runoff events is a major pressure on lake water quality, particularly in agricultural catchments. Short-lived runoff events can deliver disproportionally large nutrient loads, yet event-scale runoff observations are rare due to monitoring limitations. This restricts the design and evaluation of nature-based solutions (NBS) and catchment restoration measures.
Within the Horizon Europe EUROLakes project, Lake Vico (Central Italy) is used as a pilot site to test innovative approaches for monitoring diffuse pollution. Lake Vico is a volcanic lake affected by agricultural nutrient inputs. To better capture event-driven nutrient transport, an experimental surface runoff monitoring setup was established in the Cerreto sub-catchment.
A representative sub-basin was identified based on land use and topography, and an automatic runoff sampling system was installed to trigger autonomously during surface flow events. The system is integrated with a low-cost time-lapse camera acquiring images every 30 minutes, providing continuous visual information on soil moisture conditions and the occurrence of overland flow. Runoff samples are analyzed for key water-quality parameters, including nitrite, ammonium, reactive phosphorus, total nitrogen, and total phosphorus, allowing nutrient dynamics to be quantified at the event scale.
To detect runoff directly from the image time series, a Python-based automated classification workflow is being developed. The method uses two regions of interest per image and simple grayscale features to distinguish dry soil from active runoff, accounting for day-night conditions. The workflow processes the full image archive and produces runoff flags and diagnostic indicators for further analysis.
We present preliminary results from the first monitoring period and assess the performance of the image-based runoff detection. Key uncertainties related to illumination changes, vegetation dynamics, and night-time conditions are discussed. Ongoing work focuses on linking runoff occurrence to rainfall intensity and duration. Beyond site-specific insights, the resulting event-scale runoff and water-quality dataset provides a critical empirical basis for calibrating process-based hydrological models (e.g. SWAT) and supports the evaluation of NBS in data-limited lake catchments.
How to cite: Mancini, G., Iavarone, C., Pelorosso, R., Nkwasa, A., Patriarca, A., Recanatesi, F., and Ripa, M. N.: Event-scale nutrient transport revealed by integrated runoff monitoring and time-lapse imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7683, https://doi.org/10.5194/egusphere-egu26-7683, 2026.
