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.