Models can help to optimize monitoring schemes and provide assessments of future changes and management options. However, insufficient temporal and/or spatial resolution, a short duration of observations and the widespread use of different analytical methods limit the potential for model application. Moreover, model-based water quality calculations are affected by errors in input data, model errors, inappropriate model complexity and insufficient process knowledge or implementation. In addition, models should be capable of representing changing land use and climate conditions to meet the needs of decision makers under uncertain future conditions. Given these challenges, there remains a strong need for advances in water quality modeling.
This session aims to bring together scientists working on both experimental and modelling studies to improve the prediction and management of water quality constituents (e.g. nutrients, organic matter, algae, sediment) at the catchment scale. Contributions addressing the following topics are welcome:
- Experimental and modelling studies on the identification of sources, hot spots, pathways and interactions of nutrients and other, related pollutants at the catchment scale
- New approaches to develop effective water quality monitoring schemes
- Innovative monitoring strategies that support both process investigation and improved model performance
- Advanced modelling tools for integrating catchments and/or simulating in-stream processes
- Observational and modelling studies at the catchment scale that relate and quantify water quality changes to changes in land use and climate
- Measurements and modelling of abiotic and biotic interaction and feedback involved in the transport and fate of nutrients and other pollutants at the catchment scale
- Catchment management: pollution reduction measures, stakeholder involvement, scenario analysis for catchment management
Orals: Tue, 5 May, 14:00–18:00 | Room 3.29/30
Severe and prolonged summer-droughts and subsequent bark beetle outbreaks have increased forest vulnerability and mortality, altering the export of carbon (C), nitrogen (N), and phosphorus (P) from forested catchments. Currently, comparative field studies of how these three substances respond to forest dieback, as well as evaluation of process-based model simulations, remain limited. This study aims to analyze and compare the impacts of drought-induced forest dieback on catchment C, N, and P export and their underlying drivers, and the capability of a process-based hydrological water quality model to simulate those matter fluxes under rapid forest change. We applied a modified dynamic HYPE model to three headwater catchments in the Harz Mountains (Germany) with different land-use compositions. The conifer-dominated catchments Warme Bode and Rappbode experienced severe forest dieback of approximately 57% and 75%, respectively, between the 2018 drought until 2024, whereas the agricultural catchment Hassel showed a lower forest loss of about 15%. The model was calibrated by simultaneously optimizing hydrological and C–N–P process parameters using long-term discharge and water-quality observations. Model performance was overall acceptable, with good performance for hydrology and N simulations (mean NSE = 0.80 for discharge and 0.71 for nitrogen), moderate performance for C (mean NSE = 0.68), and the weakest performance for P (mean NSE = 0.53). Results showed clear increases in C, N, and P exports in the forest-dominated catchments after forest dieback, whereas changes in the agricultural catchment were minor. Among the three substances, N showed the strongest increase after forest dieback, driven by increased nitrogen availability associated with reduced plant uptake and enhanced soil mineralization. The increase in C export resulted from elevated organic carbon availability in surface soils, and was also controlled by changes in hydrological processes. P showed a relatively weaker response to forest dieback, with changes primarily driven by increased runoff magnitude and intensity, as well as enhanced flushing due to the loss of vegetation cover. Differences in simulation capabilities of these three substances further indicate distinctions in their generation and transport mechanisms. N dynamics are mainly governed by subsurface flow paths and biogeochemical availability, whereas C export depends more on surface runoff and flow-path connectivity, which are relatively well represented in the model. P export relies more on high-flow events, which are roughly generalized and simplified in the model parameterization. Therefore, the simultaneous optimization of C, N, and P points towards a more realistic representation of the various runoff components and biogeochemical processes in the model. Overall, this study advances the understanding of forest dieback impacts on catchment nutrient and carbon exports, reveals the limitations of multi-substance modeling, and provides suggestions for model development.
How to cite: Rode, M., Chen, M., Sauke, F., Rinke, K., Futter, M., and Jomaa, S.: Responses of Carbon, Nitrogen, and Phosphorus Export to Drought-Induced Forest Dieback Based on Multi-Substance Hydrological Modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8345, https://doi.org/10.5194/egusphere-egu26-8345, 2026.
Coastal eutrophication has been linked to excessive nitrogen inputs from intensive agricultural practices on contributing watersheds and is associated with multiple ecological alterations such as green tides and toxic algal blooms, as observed in the Bay of Brest (Brittany, France). Mechanistic modelling is a powerful tool for improving our understanding of nitrogen transfers from the land to the sea through river networks. However, its effectiveness strongly depends on the accurate estimation of territory-specific boundary conditions, which remains challenging due to the scarcity of existing observational measurements, especially in small watersheds.
The sensitivity of simulated riverine nitrate concentrations to different boundary condition datasets was assessed using the pyNuts-Riverstrahler modelling platform applied to the two main watersheds draining into the Bay of Brest (~320 and 1700 km2). This model explicitly represents water fluxes (including discharges from wastewater treatment plants, water withdrawals, and dams) and associated concentrations of carbon and nutrients (N, P, and Si) across the entire river network at a kilometre-scale spatial resolution. Simulated riverine nitrate concentrations were compared to observational data to evaluate the performance of different input datasets for each watershed. First, two alternative baseflow estimation approaches were tested, namely a statistical recursive digital filter (BFLOW) and the conductivity mass balance (CMB) method. Second, diffuse agricultural nitrogen inputs through surface runoff were estimated from the GRAFS methodology at two spatial scales: regional and municipal.
Results indicate that estimates derived from the CMB method predict lower baseflow contribution to total streamflow and show greater spatial variability across the watersheds than those obtained with BFLOW. Nitrate simulations driven by municipality-scale GRAFS inputs better reproduce observed nitrate concentrations and their spatial heterogeneities along the river network, despite data gaps due to the partial confidentiality of agricultural statistics at this scale. The simulation combining CMB methodology with municipality-scale GRAFS inputs appears to be the most representative, both in terms of nitrate concentration levels and seasonal dynamics simulation, particularly in areas with complex hydrogeological functioning.
Overall, this work highlights the critical role of boundary condition estimation in mechanistic hydro-biogeochemical modelling, with direct implications for understanding and managing coastal eutrophication in agricultural watersheds.
How to cite: Roussel, E., Raimonet, M., Thieu, V., and Silvestre, M.: Sensitivity of riverine nitrate modelling to territorial estimations of diffuse sources in agricultural catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4062, https://doi.org/10.5194/egusphere-egu26-4062, 2026.
Water quality signature of agricultural catchments is shaped by non-point and point sources contributing a wide range of solutes (e.g., nutrients, carbon, pesticides, emerging contaminants) and sediments to the drainage and stream network. In this presentation we evaluate storm event contribution of non-point sources in a small agricultural headwater (7.2 km2, 58° 35' N and 16° 11' E ) dominated by clay soils, crop production and tile drainage. We used a combination of experimental and modelling data for points along the drainage and stream network, from individual drainage wells to the catchment outlet. For the 6 nested sampling locations, we used concentration-discharge (C-Q) metrics from both high-frequency in situ data and process-based model to aswer the following research questions: 1) does C-Q slope for solutes and sediments change signficantly along the drainage and stream network or is it a constant catchment-feature, 2) do the shape and direction of the C-Q relationships change significantly along the drainage and stream network in response to different contribution of flow pathways. Information about the contribution of different flow pathways was derived from a calibrated/validated HYPE model which differentiated surface, subsurface and tile drainage flow pathways. Our results showed that the contribution of different flow pathways changes from a tile drainage dominance for the drainage wells to a more balanced contribution of different flow pathways at the outlet . This led to more stable and chemostatic C-Q slopes at the outlet compared to steeper and more variable C-Q slopes at the drainage wells. We also showed that the changing contribution and timing of different flow pathways during storm events control the shape and the direction of the C-Q relationships for both solutes and sediments. Overall, our results showed that there was a surprisingly large variation in the C-Q relationships between different locations in the catchment despite its small size and fairly uniform land use.
How to cite: Bieroza, M.: From a drainage well to the catchment outlet - propagation of water quality signatures through a headwater agricultural catchment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1389, https://doi.org/10.5194/egusphere-egu26-1389, 2026.
Nutrient pollution in freshwater systems remains a major environmental challenge, driving eutrophication, and ecological degradation, and requiring robust modelling tools to support effective water management. However, catchment-scale water quality modelling is often constrained by sparse and uneven monitoring networks, scale mismatches between processes and observations, and high parameter uncertainty associated with complex biogeochemical dynamics. These limitations hinder reliable load estimation, pollution sources identification, and scenario analysis, particularly in large and data-scarce catchments.
This study presents an integrated modelling framework combining the existing MOBIDIC hydrological model with a newly developed BIO–ALGAE reactive component, to simulate nutrient dynamics at the catchment scale. The model simulates eight key water quality constituents, including dissolved oxygen, carbonaceous biochemical oxygen demand, organic and inorganic nitrogen and phosphorus species, and algal biomass. To address parameter non-identifiability and spatial heterogeneity, the framework employs a spatially regularized ensemble calibration strategy using the PEST++ iterative ensemble smoother. This ensemble-based framework enables efficient estimation of spatially distributed diffuse loads while providing a robust quantification of predictive uncertainty. Tikhonov regularization is employed to enforce spatial smoothness of the parameters, while a combination of localization matrices and singular value decomposition is used to stabilize the inversion in high-dimensional parameter spaces.
The model was applied to the Arno River catchment (7990 km2) in central Italy, simulating water quality dynamics over a ten-year period (2011–2020) across a network of more than 3600 river reaches. Calibration relied on 8151 spot observations from 70 monitoring stations. Despite the sparse and discontinuous nature of the dataset, the model demonstrated good predictive capability across multiple constituents and successfully reproduced observed spatial and temporal patterns. The results revealed pronounced pollution hotspots, particularly associated with urban and peri-urban areas, characterized by elevated ammonium and organic loads, while phosphorus exhibited a more heterogeneous distribution indicative of multiple source contributions.
Despite limitations under low dissolved oxygen conditions, the approach captured first-order reactive processes and provided spatially explicit load estimates with uncertainty bounds. This framework offers a practical decision-support tool for targeted water quality management in data-scarce catchments.
Acknowledgements
This work was carried out within RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005). The authors also wish to acknowledge Fondazione Cassa di Risparmio Firenze for co-funding this work within the project ECO-C – “ECOidrologia dei corsi d’acqua urbani nel contesto dei Cambiamenti climatici e socioeconomici”.
How to cite: Masi, M., Castelli, F., Barati Moghaddam, M., and Arrighi, C.: A water quality model for distributed nutrient load estimation in sparsely monitored catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19118, https://doi.org/10.5194/egusphere-egu26-19118, 2026.
Intensification of agricultural production has increased access to food, but puts significant stresses on water quality. Mathematical models of watershed processes are used to guide the management of nutrients, though their use is subject to significant uncertainties. This talk presents a project that uses a data-driven approach to estimate the effect of conservation practices on water quality. While the project focuses on cover crops, it is generalizable to other measures for which spatial data is available. We show that by combining spatial assessments of cover crop extents and stream event sampling with Generalized Additive Modelling, we can estimate the effect of cover crops on different species of nutrient loss. We show that cover crops result in less loss of particulate phosphorus but more loss of dissolved phosphorus with respect to bare fields. We also conduct scenario analyses to evaluate the effects of increased adoption of cover crops on water quality. We conclude with an assessment of data requirements and identify a number of opportunities for applying this approach elsewhere.
How to cite: Wellen, C., Ahmadi, L., and Parsons, C.: Checking the hype: A data driven approach to assess the effect of conservation measures on water quality in agricultural catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16970, https://doi.org/10.5194/egusphere-egu26-16970, 2026.
India is predominantly an agrarian country, with ~46% of its population dependent on agriculture. Meeting rising food demand often relies on intensive synthetic fertiliser use, which boosts crop yields but causes environmental impacts. Nutrient runoff from fields is a significant source of riverine nitrate pollution, contributing to the gradual degradation of water quality. To explore the link between agricultural intensification and riverine nitrate, this study applies multiple machine learning models, including Linear, Polynomial, Decision Tree, Random Forest, Support Vector, XGBoost, Neural Network, and Multi-layer Perceptron, to predict nitrate concentrations using hydrological, socioeconomic, and agricultural nitrogen input variables across major river basins and identify key controlling factors. The best-performing model achieved an R² of 0.57. Results show significant spatial and temporal variation of riverine nitrate flux across major river basins between 1966 and 2017. At the national scale, the average nitrate flux declined from 535.6 kg/km²/year to 443.8 kg/km²/year, reflecting ~17.1% an overall reduction. The decline in nitrate is primarily attributed to reduced precipitation and an increase in consecutive dry days, as shown by the overall trend analysis. Analysis suggests that lower rainfall reduces surface runoff, thereby limiting the transport of nutrients to rivers. Despite an overall decline in nitrate, larger basins such as the Brahmaputra and the Ganga maintained high concentrations due to their high discharge, greater catchment area, and intensive agriculture. Basin-wise correlation analysis further shows a positive correlation between precipitation, discharge, and nitrate export, confirming that these hydrological variables are the dominant controls, as they enhance runoff. This increased runoff strengthens hydrological connectivity between agricultural fields and river channels, thereby mobilising nitrogen from soils and fertilisers into surface water. Furthermore, our Pearson correlation analysis indicates that net anthropogenic nitrogen inputs contribute more strongly to soil nitrogen build-up, groundwater contamination, and atmospheric emissions, rather than directly influencing riverine nitrate through runoff pathways. Overall, the major drivers of riverine nitrate dynamics across Indian basins are precipitation and discharge, with agricultural practices and basin hydrology acting as secondary influences.
Keywords: Machine Learning, Riverine Nitrate, Agricultural Landscapes, Environmental Impact
How to cite: Kumar, A., Ilampooranan, I., and Narayanan, M.: Machine Learning Based Prediction of Riverine Nitrate Flux for Indian Rivers from Agricultural Landscapes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1187, https://doi.org/10.5194/egusphere-egu26-1187, 2026.
Assessing and managing water quality in data-scarce tropical basins remains challenging due to rapid land-use change, intensifying human pressures, and increasing climate variability. In the transboundary Mara River Basin (MRB), these factors strongly influence sediment and nutrient dynamics, yet traditional monitoring networks lack the temporal and spatial resolution needed to characterize pollution sources, transport pathways, and event-driven responses. To address these gaps, this study integrates citizen-science observations with satellite-derived hydro-climatic and land-use variables to model turbidity (NTU), nitrate (NO₃), and phosphate (PO₄) across 40 sub-basins. Two machine-learning approaches: Random Forests (RF) and Artificial Neural Networks (ANN) were employed to evaluate water-quality variability and identify dominant drivers under heterogeneous environmental conditions. RF outperformed ANN across all indicators, providing more robust predictions under noisy and nonlinear data constraints. SHAP analyses revealed that precipitation and river flow velocity dominate short-term, event-based fluctuations of turbidity, while population density represents persistent drivers of NO₃ concentration. These findings highlight the basin’s sensitivity to climate-driven changes in rainfall intensity and seasonality and demonstrate how hybrid monitoring–modelling frameworks can enhance the identification of nutrient hotspots, improve source attribution, and support adaptive water-quality management under land-use and climate-change scenarios.
How to cite: Liu, X., Chisute, H., Nyongesa, F., Mkole, R., Warner, S., Nader, G. E., Boldrini, A., Polvani, A., Cirrone, R. G., Galgani, L., and Loiselle, S. A.: Machine Learning and Citizen Science for Catchment-Scale Water-Quality Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5814, https://doi.org/10.5194/egusphere-egu26-5814, 2026.
Predicting riverine algal blooms remains a major challenge due to the high stochasticity of aquatic systems and the complex, non-linear interactions among environmental drivers. To address this, the present study establishes a robust probabilistic forecasting framework for the middle and lower Han River, China, by integrating multi-source datasets into a Bayesian Neural Network (BNN), complemented by a hybrid interpretability approach merging Bayesian posterior inference with Generalized Additive Model (GAM). By fusing heterogeneous long-term hydrological, meteorological, water quality, and ecological data, the model effectively captures dynamic environmental interactions and therefore provides reliable probabilistic forecasts of chlorophyll-a (Chl-a) concentrations for 1-to-7-day horizons. The BNN architecture explicitly performs uncertainty quantification to mitigate inherent data noise and model uncertainty by delivering exceedance probability of bloom predictions, which help decision-makers minimize false negatives near critical alert thresholds. Key ecological findings elucidate a dual driving mechanism, whereby short-term forecasts are predominantly governed by algal biological inertia, whereas medium-to-long-term trends are constrained by environmental carrying capacity. Specifically, bloom outbreaks hinge on a multi-factor environmental window featuring water temperature exceeding 23°C, optimal light intensity, and stable hydrological conditions. GAM analysis reveals a nonlinear relationship between total phosphorus (TP) and Chl-a, indicating the limited efficacy of nutrient reduction in high-phosphorus regimes. Methodologically, this study underscores the necessity of combining multi-source data fusion with uncertainty quantification and non-linear attribution to advance deep learning applications in complex ecological systems.
Keywords: Bayesian Neural Network; Uncertainty quantification; Algal Bloom Prediction; Middle and Lower Han River; Multi-source datasets; Generalized Additive Model
How to cite: Li, Y. and Zhang, X.: Probabilistic prediction of chlorophyll-a in a highly regulated river using a multi-source Bayesian Neural Network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6508, https://doi.org/10.5194/egusphere-egu26-6508, 2026.
As a typical complex aquatic ecosystem, the Taihu Basin relies heavily on the material exchange between the lake and its connected rivers, a critical factor driving eutrophication and algal blooms. However, existing water quality monitoring data suffer from spatiotemporal sparsity and insufficient sample sizes, limiting the accuracy of deep learning models in simulating long-term nitrogen and phosphorus (N/P) migration and mining algal bloom mechanisms. To address these challenges, this study proposes a comprehensive framework integrating data augmentation with graph deep learning. Specifically, a Conditional Spatio-Temporal Generative Adversarial Network (CST-GAN) was first constructed to learn inherent distribution patterns and generate high-quality augmented data, significantly expanding the sample scale. Subsequently, the Attention-based Spatio-Temporal Graph Convolutional Network (A-STGCN) was employed to model the river-lake system as a topological graph. Crucially, leveraging the interpretability of the attention mechanism embedded within A-STGCN, this study moved beyond black-box prediction to successfully identify primary N/P input channels, quantify the response mechanisms of algal blooms to N/P migration fluxes, and pinpoint the key driving factors triggering outbreaks. This research demonstrates a closed-loop approach from data augmentation to system simulation, providing a scientific basis for precise pollution control and early warning of algal blooms in the Taihu Basin.
How to cite: Yao, J., Ruan, X., and Saavedra Melendez, F.: Coupled CST-GAN Data Augmentation and A-STGCN for Predicting Nitrogen-Phosphorus Migration and Unraveling Algal Bloom Mechanisms in the Taihu River-Lake System , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2344, https://doi.org/10.5194/egusphere-egu26-2344, 2026.
Elevated nitrate concentrations remain a persistent problem for European inland water bodies. Recent unprecedented multi-year droughts have challenged our understanding of nitrate exports from catchments. While both increasing and decreasing concentrations have been observed in response to droughts (Saavedra et al. 2024), summer droughts and subsequent re-wetting were found to decrease the nitrate retention capacity of catchments and increase exported loads. Furthermore, drought-induced forest dieback has created additional nitrate sources, further elevating instream concentrations and fluxes (Musolff et al. 2024). To understand and robustly predict future concentration trajectories in response to climate extremes, we need to first understand the reactive transport processes shaping these observed responses. To this end, we analyze long-term (>40 years) time series of nitrate concentrations in two catchments in Central Germany with diverse land uses and nitrate sources, spanning the recent drought years. Despite relatively constant nutrient inputs over the last 20 years, we found diverging trajectories for annual maximum and minimum concentrations. Drought years amplified intra-annual concentration ranges by increasing high-flow and decreasing low-flow concentrations. Annual maximum concentrations were sensitive to temporal changes in hydroclimatic conditions, with exceptionally high winter concentrations, following low summer drought concentrations. We attribute these high winter concentrations to the rapid mobilization of strong nutrient sources in shallow, hydrologically well-connected agricultural and riparian forest soils. Conversely, annual minimum concentrations responded to slowly reacting groundwater heads of deeper aquifers; lower groundwater levels corresponded to lower summer concentrations. These changes in low-flow concentrations are therefore a function of hydraulic heads controlling the influx of nitrate-rich deeper groundwater to the stream. Thus, we observe a strong effect of hydrological states in shallow and deep storages on the flow paths connecting nutrient sources to streams making export dynamics highly sensitive to hydroclimatic extremes. This data-driven indication raises questions about whether travel-time-based water quality models adequately capture the complexity of flow-paths and connected water ages and nitrate concentration dynamics providing a basis for future model development.
References:
Musolff, A., Tarasova, L., Rinke, K., & Ledesma, J. L. J. (2024). Forest Dieback Alters Nutrient Pathways in a Temperate Headwater Catchment. Hydrological Processes, 38(10). https://doi.org/10.1002/hyp.15308
Saavedra, F., Musolff, A., Von Freyberg, J., Merz, R., Knöller, K., Müller, C., Brunner, M., & Tarasova, L. (2024). Winter post-droughts amplify extreme nitrate concentrations in German rivers. Environmental Research Letters, 19(2). https://doi.org/10.1088/1748-9326/ad19ed
Winter, C., Nguyen, T. V., Musolff, A., Lutz, S. R., Rode, M., Kumar, R., & Fleckenstein, J. H. (2023). Droughts can reduce the nitrogen retention capacity of catchments. Hydrology and Earth System Sciences, 27(1), 303-318. https://doi.org/10.5194/hess-27-303-2023
How to cite: Musolff, A., Nguyen, T., Kumar, R., Wu, S., and Ebeling, P.: Hydroclimatic extremes reveal shifting nitrogen export behavior from catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11697, https://doi.org/10.5194/egusphere-egu26-11697, 2026.
Understanding how seasonal hydroclimatic variability and vegetation conditions influence the nutrient dynamics in agricultural catchments is crucial for sustainable management of surface water quality. In this study, we assessed the seasonal impact of hydroclimatic variability and shifts in vegetation conditions on >30 years of total nitrogen (TN) and total phosphorous (TP) in the Aurajoki catchment draining to the Baltic Sea, Southwest Finland. The datasets included long-term monitoring records of nutrient concentrations, discharge, temperature, precipitation, and the Landsat imagery series. Prior to the analyses, we characterized hydroclimatic variability using the Standardized Precipitation Index (SPI), Accumulated Winter Season Severity Index (AWSSI), and vegetation conditions were represented using Normalized Difference Vegetation Index (NDVI). We used Mann – Kendall statistics to assess seasonal trends in both TN and TP concentrations and loads, and generalized additive models (GAMs) to quantify the influence of NDVI and hydroclimatic conditions. Seasonal trend analysis revealed significant increases in both TN and TP during autumn and decreases during spring. Modeling results indicated that NDVI influenced nutrient concentrations but had no significant effect on nutrient loads. In contrast, the influence of hydroclimatic class was confined to nutrient loads and varied depending on the combination of hydroclimatic class and NDVI. These results highlight the relevance of accounting for seasonal hydroclimatic variability and NDVI-derived vegetation conditions when assessing nutrient dynamics in the agricultural catchments. The findings further support understanding of nutrient‑driven eutrophication in systems such as the Baltic Sea.
How to cite: Plutova, B., Gonzales Inca, C., Kakaei Lafdani, E., Seppä, I., Kasvi, E., Kämäri, M., Alho, P., and Kankare, V.: The impact of seasonal hydroclimatic variability and NDVI on long-term nutrient dynamics in a Finnish agricultural catchment draining to the Baltic Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13943, https://doi.org/10.5194/egusphere-egu26-13943, 2026.
Rapid-response, mixed land-use, groundwater-dominated catchments represent a unique yet highly complex class of river systems, where the identification of multiple pollution sources is hindered by the coexistence of several competing hydrological and biogeochemical processes. Traditional regulatory water-quality monitoring typically captures baseline conditions but often fails to resolve short-lived, rainfall-driven pollution dynamics. In this context, concentration–discharge (C–Q) analysis applied to high-frequency datasets provides a powerful framework for disentangling event-scale processes and pollutant sources.
Here, we analyse high-frequency water-quality data from a series of high-flow events in the Pix Brook, a groundwater-dominated chalk stream in Hertfordshire and Central Bedfordshire (England) characterised by flashy hydrological behaviour and heterogeneous land use (approximately 40% agricultural and 60% urban). The C–Q relationships of four parameters (ammonium, turbidity, conductivity, and dissolved oxygen) were examined across 18 rainfall-generated events. Event dynamics were quantified using established hysteresis metrics (Hysteresis Index, HI; Flush Index, FI) alongside a newly developed Complexity Index (CI) to characterise source proximity and process interactions.
Results reveal consistent buffering of hydrological responses by groundwater contributions, while ammonium, conductivity, and dissolved oxygen frequently exhibit dilution during high flows, suggesting limited in-stream sources or rapid dilution by surface runoff. In contrast, turbidity consistently shows accretion, indicating systematic sediment mobilisation during events. Notably, a temporal shift from distal to proximal sediment sources is observed midway through the monitoring period, pointing to the potential emergence of new, faster sediment delivery pathways. Overall, this study demonstrates how high-frequency C–Q hysteresis analysis can effectively resolve event-based water-quality processes and disentangle multiple pollution sources in complex mixed-use catchments, supporting targeted monitoring and pollution mitigation strategies.
How to cite: Panici, D., Ashe, J., Russell-Verma, S., and Melville-Shreeve, P.: High-frequency data reveal complex water quality processes in a mixed land use, groundwater dominated catchment in England, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15325, https://doi.org/10.5194/egusphere-egu26-15325, 2026.
Understanding the responses of riverine nutrient concentrations to combined land-use and climate pressures is essential for effective catchment management and water quality protection. However, water quality monitoring data are frequently sparse and irregularly sampled, particularly in regions with limited resources, presenting a widespread challenge for the robust analysis of nutrient dynamics. Generalized Additive Models (GAMs) provide a flexible statistical framework capable of capturing non-linear relationships, accounting for seasonal and interannual variability, and handling uneven temporal observations, making them well suited for analysing limited water quality datasets.
This study investigated the historical changes in nitrate and phosphate concentrations across three Irish river catchments representing contrasting land-use patterns: the predominantly rural Midleton catchment, the semi-urban Lee catchment, and the highly urbanised Liffey catchment. Observations collected between eight and fifteen times per year over an eight-year period were analysed using GAMs to quantify associations with climatic drivers, evolving land-cover characteristics, and temporal trends. The relationships between nitrate and phosphate concentrations were examined to identify how the two nutrients respond together under different environmental conditions.
To explore potential future trajectories, land-use and land-cover changes were projected using an Artificial Neural Network–Cellular Automata (ANN–CA) modelling framework. Spatially explicit land-cover scenarios were generated under two Socioeconomic Pathways: SSP4.5 and SSP8.5, representing moderate and high climate forcing and socio-economic development. These land-cover projections were integrated with corresponding climate scenario data to examine expected changes in both nitrate and phosphate concentrations, assessing how catchment characteristics modulate nutrient responses under alternative climate and land-use futures.
By applying a consistent analytical framework across rural, semi-urban, and urban catchments, the study enables a comparative assessment of how land-use intensity, hydrological context, and climate variability may influence the nutrient dynamics. Combining GAM-based statistical analysis with ANN–CA land-cover projections provide a reliable and adaptable approach for studying nutrient interactions in catchments with limited data. This framework can support evidence-based catchment management, nutrient control strategies, and the evaluation of possible future changes in water quality under different land-use and climate scenarios.
How to cite: Basu, B., Sarkar Basu, A., and O'Loughlin, F.: Assessing Climate- and Land-Use-Driven Water Quality Change in Irish Catchments Using Statistical and Spatial Modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16028, https://doi.org/10.5194/egusphere-egu26-16028, 2026.
Białka Tatrzańska is a tourist-oriented locality and one of the largest ski resorts in southern Poland, situated in the Carpathian Mountains. In recent years, dynamic population growth and intensified tourism development have resulted in increasing anthropogenic pressure on the natural environment, including the waters of the Czerwonka Stream, which flows through the central part of the village. The objective of this study was to assess the impact of anthropogenic pressure on changes in water quality along the course of the Czerwonka Stream.
In 2023–2025, systematic monitoring of physicochemical and microbiological parameters of surface waters was conducted within the Czerwonka catchment. Hydrochemical analyses were performed using ion chromatography, determining concentrations of 14 major ions and biogenic compounds (H, Ca, Mg, Na, K, NH₄, Li, HCO₃, SO₄, Cl, NO₃, NO₂, PO₄, F, Br). Microbiological analyses included the determination of fecal indicator bacteria (Escherichia coli and Enterococcus faecalis) using culture-based methods on selective media.
Along the course of the Czerwonka Stream, abrupt increases in electrical conductivity were observed, associated with the influence of numerous point pollution sources. These include effluents from thermal facilities, tourist accommodation infrastructure, household and hotel wastewater treatment plants, as well as surface runoff from ski slopes, including waters derived from technical snowmaking. An additional factor deteriorating water quality is the poorly developed water and wastewater management system, largely based on septic tanks, which are often leaky and prone to overflow.
Identified local pollution hot spots caused increases in electrical conductivity of stream water of up to 560%, while maximum conductivity values recorded in inflowing wastewater reached nearly 9750 µS/cm. The natural hydrochemical type of the Czerwonka Stream, dominated by calcium–bicarbonate waters, undergoes substantial transformation along the stream course, leading to the dominance of chloride and sodium ions. Concurrently, a marked increase in fecal indicator bacteria was observed, with Escherichia coli predominating and a significant contribution of Enterococcus faecalis, clearly indicating anthropogenic sources of contamination.
This research was partially funded by BANIA SP. Z O O (eng. Ltd.) (project number - K/KDU/000942).
How to cite: Kaflińska, O., Bojarczuk, A., Jelonkiewicz, Ł., Lenart-Boroń, A., Suwalska, W., and Żelazny, M.: Anthropogenic Impact in a Small Mountain Catchment: A Case Study of the Czerwonka Stream (Białka Tatrzańska, Carpathians, Southern Poland), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2197, https://doi.org/10.5194/egusphere-egu26-2197, 2026.
Assessing anthropogenic impacts on surface water quality is essential for developing water quality management strategies. Such assessment relies on sufficient data including anthropogenic effluent and observed concentrations along the river network. These data records are often incomplete or unavailable in many areas. A water quality model provides means for extrapolation and integration of the available data to the full catchment extent, allowing catchment-wide quantification of pollution patterns and simulation of potential interventions. This study implements a model-based water quality assessment under data scarce conditions applied to the Hindon River Basin in India where surface water is highly polluted due to alleged contributions from industrial, domestic, and agricultural activities leading to emissions into the basin. A catchment modelling framework was implemented by linking a distributed hydrological model (wflow_sbm), which includes anthropogenic demand and allocation, with a substance-based emission model (D-Emissions) and in-stream water quality model (D-Water Quality), set up with mostly open-source datasets. The modelling tool was used to determine the sources, hot spots, and pathways of nutrients and other pollutants across the catchment and river network and assess the seasonal (pre-monsoon, monsoon, and post-monsoon) variations. To quantify the influence of data scarcity on the model output, a sensitivity analysis was conducted on the principal inputs (industrial effluent, domestic wastewater, and fertilizer use) and the resulting variabilities of simulated concentrations were compared against (limited) observations. Finally, management scenarios were simulated including changes in treatment of industrial and domestic wastewater and fertilizer application rates. The results reveal the relative contribution of each of the principal anthropogenic sectors (industries, domestic, and agriculture) on the surface water pollution and implications for catchment water quality management including pollution reduction measures and monitoring requirements for improved model predictions.
How to cite: Mendoza, R., Loos, S., Sperna Weiland, F., and Weerts, A.: Revealing anthropogenic influences on catchment surface water quality under data scarcity: case of Hindon River Basin, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20054, https://doi.org/10.5194/egusphere-egu26-20054, 2026.
Eutrophication remains one of the major drivers of change in freshwater ecosystems worldwide. Phosphorus (P) is of particular concern since it is considered the limiting nutrient for phytoplankton growth in lakes. An excess of P can lead to increased phytoplankton biomass, reduced water clarity, and undesirable ecological changes. Consequently, reducing P loading from anthropogenic sources in the catchment has historically been the main restoration approach for eutrophic lakes. Many lakes have shown improvements in water quality following reductions in external P loading. However, others have not responded as expected, and their recovery has been delayed by years or even decades, often attributed to internal cycling of P. Lake Vombsjön in southern Sweden exemplifies these issues, as it continues to experience high P concentrations despite catchment mitigation efforts. Previous studies have indicated that internal P loading sustains high P concentrations, but this has not been quantified.
In this study, we applied a weekly ecosystem-scale mass-balance approach to quantify the internal P loading of Lake Vombsjön during the summer period and to identify the key physical drivers. We combined weekly monitoring of inflowing and outflowing streams with in-lake measurements of total phosphorus and other water quality parameters. We found that internal P loading fluctuated throughout the summer and, in total, dominated the summer P budget. A substantial proportion of the mobilised P remained within the lake, indicating continued internal retention that may delay recovery. Variations in internal loading were primarily associated with physical processes related to wind conditions and thermal structure. These findings demonstrate that internal P loading is a key factor controlling summer P dynamics in Lake Vombsjön and highlight the importance of accounting for internal P loading when designing management strategies for eutrophic lakes.
How to cite: Söderman, A., Sandell, V., Klante, C., and Alsterberg, C.: Estimating Summer Internal Phosphorus Loads and Their Drivers in an Eutrophicated Lake in Southern Sweden, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10186, https://doi.org/10.5194/egusphere-egu26-10186, 2026.
The population growth and the resulting increased food demand resulted in more intense agricultural practices. This resulted in significant changes in land use and a substantial rise in phosphorus inputs to aquatic systems over the past few decades, primarily through fertilizer application and runoff. Lakes, particularly those situated within agricultural landscapes, now face an increased risk of eutrophication due to elevated nutrient inputs. This poses serious threats to water quality, ecosystem health, and the services these freshwater systems provide. Degraded water quality, algae blooms, which deplete oxygen and threaten aquatic life, are serious ecological consequences. Beyond these impacts, decreased recreational value and complications for drinking water production are effects to the human usage of such water sources. Even though the main drivers of eutrophication are fairly well understood, the dynamics within the lake, especially the mobilization and the release of phosphorus, are poorly known.
Lake Vomb, located in southern Sweden, is an important drinking water resource for 5% of the Swedish population. Mitigation measures have been implemented within the lake's catchment, with limited effect on the lake's P concentration. The lake regularly experiences harmful algal blooms affecting the lake’s ecosystem as well as drinking water production, which increases the necessity for further management measures. Previous studies have indicated that internal P loading continues to sustain the lake's high P concentration, particularly during the summer. Furthermore, this appears to be coupled to physical forcing, such as wind direction. To implement successful measures is only possible when the internal dynamics of the phosphorus in the lake are fully understood.
To analyze the pathways, patterns, and future development of phosphorus levels in the lake together with the effluent water quality, a multidimensional model of Lake Vomb has been developed. The model combines long-term monitoring data of hydrology, in-situ measurements of total phosphorous, determining water quality parameters, and publicly available meteorological data. Modeling was performed using a combination of the open TELEMAC-MASCARET model with the WAQTEL module. Possibilities to extend the model through the application of the AED2 library are explored and analyzed.
The results of the model analysis will be used to optimize lake management strategies, including the placement of nature-based mitigation measures. In addition, current drinking water treatment methods and strategies will benefit from increased understanding of phosphorous transport. Finally, the concept of this specific model and results may be applied to other regions facing similar problems.
How to cite: Klante, C., Söderman, A., Bjerkén, A., and Alsterberg, C.: Modeling Phosphorous Dynamics in an important Swedish Drinking Water Source, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14600, https://doi.org/10.5194/egusphere-egu26-14600, 2026.
UK waterways face significant pollution from both treated and untreated sewage discharges and agricultural runoff. This can lead to serious health issues for water users, environmental damage to ecosystems and losses for the economy via closed bathing waters. Management, however, is hampered by a lack of information on the relative contribution and severity of point and diffuse sources of pollution and their spatial and temporal dynamics. Conventional assessment of water quality, currently undertaken via the EU Water Framework Directive and Bathing Waters Directive, relies on infrequent sampling at limited locations, often failing to capture the dynamic nature of river pollution. In addition, analysis methodologies often require lengthy wait times, limiting the ability to respond to pollution events. To manage water quality sustainably at a catchment scale, and implement effective measures for targeted pollution reduction, there is a clear need for a monitoring framework that allows for more agile, accurate and context-sensitive assessments of water quality and pollution risk.
This study presents a ‘living laboratory’ approach implemented in a small agricultural and sewage-impacted catchment in Southwest England. The work focusses on the application of a novel water quality monitoring technology within a multi-parameter, catchment-scale monitoring framework centred around clear rural (agricultural) and urban (sewage) pollution hotspots for near real-time assessment of bacteriological quality in partnership with community and citizen science groups active within the catchment. This was integrated with high-resolution monitoring undertaken continuously for 12 months alongside frequent in-field sampling for physicochemical, hydrological, nutrient and bacterial measurements to capture baseflow and storm conditions, validated with real-time sewage discharge data.
Elevated nutrient concentrations (>1 mg/L P) showed clear spatial signatures associated with diffuse pollution from small tributaries draining agricultural land, as well as the influence of a rural wastewater treatment works. Discrete pollution signals downstream of an urban centre further reflected the impact of untreated sewage inputs. Additional key findings highlight the ability to generate rapid (13-minute) bacterial measurements comparable to standard reference methods, with wet-weather events exerting the strongest control on bacterial pollution, particularly from small agricultural tributaries where E. coli (CFU/100 mL) concentrations exceeded the Bathing Water Regulation (2013) threshold by up to 30 times. Together, these spatial and temporal patterns provided detailed insight into the source dynamics of both nutrient and bacterial pollution across the catchment. Data fusion across chemical, hydrological, and microbial datasets underpinned the development of a predictive modelling framework, enabling novel rapid bacterial measurements to be evaluated against “gold standard” methods and linked to routine water quality monitoring data.
This work develops a transferable framework for catchment-scale water quality assessment that overcomes delays associated with conventional sample analyses while encouraging stakeholder participation in data collection. The multiple dimensions of this dataset support diagnostic evaluation of the relative importance of agriculture vs sewage pollution sources through space and time, allowing for targeted pollution reduction management and regulatory decision making.
How to cite: Perrin, E., White, A., Good, C., Brandão, J., Jackson, S., and Blake, W.: A validation study of a high-resolution catchment-scale water quality monitoring framework utilizing a novel bacterial screening device., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19679, https://doi.org/10.5194/egusphere-egu26-19679, 2026.
Globalization-driven food demand and rapid urbanization have intensified agricultural intensification and land-use changes, leading to widespread deforestation, increased human settlements, and a heavy reliance on chemical fertilisers, pesticides, and animal manure. These practices have significantly degraded surface and groundwater quality through nutrient-laden agricultural runoff, particularly nitrate and ammoniacal nitrogen, across many watersheds. In light of the deteriorating water quality for irrigation, indexing techniques have been regarded as the most advanced methods for assessing water quality since the late 20th century. This study evaluates the suitability of conventional irrigation water quality indices for assessing surface water used for irrigation in the heavily impacted Hindon River Basin, India. Monthly water samples were collected from 16 strategically selected sites following standard protocols. Key irrigation suitability parameters and indices were computed, including sodium adsorption ratio (SAR), percentage sodium (%Na), permeability index (PI), Kelly ratio (KR), residual sodium carbonate (RSC), and magnesium adsorption ratio (MAR). Results revealed that Irrigation Water Quality Index (IWQI) values at most sites ranged between 25 and 50, classifying the water as marginally suitable with minor treatment required. In contrast, a few downstream sites exceeded 75, indicating severe unsuitability. A marked deterioration in water quality was observed during the pre-monsoon and monsoon periods compared to the post-monsoon period, largely attributed to runoff and leaching processes. However, despite mathematically acceptable index values at several locations, the water remains unsuitable for sustained irrigation due to elevated concentrations of toxic and emerging contaminants that are not incorporated into existing irrigation indices. The study highlights a critical limitation of conventional irrigation water quality indices: their inability to account for trace elements and other non-classical pollutants that pose long-term risks to soil health and crop safety. Findings underscore the urgent need to develop modified or composite indices that account for the trace elements and tailored to agro-industrial basins and to safeguard irrigation water quality, ensure agricultural productivity, and promote environmental sustainability in rapidly urbanizing catchments.
How to cite: Reddy, M. S. B., Maturi, K. C., and Rajendran, V.: Are Existing Water Quality Indices (WQIs) Fit for Purpose? Evaluating Their Applicability to Irrigation Surface Water Quality, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-966, https://doi.org/10.5194/egusphere-egu26-966, 2026.
Agricultural intensification over recent decades has led to a growing reliance on irrigation schemes and nitrogen-based fertilizers to sustain crop productivity (FAO, 2020; Lassaletta et al., 2021, 2014). However, these practices enhanced nitrogen leaching processes, increasing the risk of groundwater pollution that can persist for more than a decade (Bijay-Singh and Craswell, 2021; FAO and IWMI, 2018; Lassaletta et al., 2014; Martin et al., 2021; Sebilo et al., 2013). Identifying zones prone to nitrogen leaching is therefore essential for sustainable agricultural management and long-term groundwater protection.
This study aims to identify and map potential high nitrogen leaching zones in the agricultural area near Valencia, Spain, covering the lowlands of the Júcar River basin, before its discharge into the Mediterranean Sea. The approach combines hydrological simulation with spatial analysis of agricultural practices. The distributed TETIS hydrological model (Frances et al., 2007, 2021) with its nitrogen module (Puertes et al., 2020) was applied for the period 1966 to 2015, to simulate water balance components and nitrogen transport under different irrigation and fertilizer practices. We evaluated five distinct management scenarios based on Pool et al (2022), comparing flood versus drip irrigation with nitrogen application rates ranging from 133 to 182 kg N ha⁻¹ year⁻¹. All simulations employed a calibrated set of 12 water balance parameters (Pool et al., 2021b, 2021a), together with the nitrogen cycle parameters estimated by Puertes et al (2021).
Based on the outputs, monthly, annual, and maximum daily recharge and leaching amounts were derived for each irrigation–fertilizer practice and parameterization. These values were analysed individually and in combination to delineate potential nitrogen leaching zones across the study area. The resulting spatial patterns provide valuable information that can support the Acequia Real del Júcar irrigation authority in identifying strategic locations for monitoring and controlling nitrogen leaching within the irrigated system.
How to cite: Güiza-Villa, N., Pool, S., Garcia-Prats, A., Francés, F., and Jiménez-Martínez, J.: Mapping potential high nitrogen leaching zones using TETIS hydrological model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13592, https://doi.org/10.5194/egusphere-egu26-13592, 2026.
Microplastic (MP) research in drinking water faces a persistent challenge: methods, analytical capacities, and legislative frameworks differ widely across countries, making comparison and coordinated action difficult. Within the Interreg Danube Region project MicroDrink, we developed the MicroDrink Knowledge Base to address this issue. The Knowledge Base is an open-access, multilingual online platform that compiles structured information on MP sampling methods, analytical methods, laboratory capabilities, instrumentation, legislation, and projects across the Danube Basin.
The platform is organized into six interconnected sections—Sampling Methods, Analytical Methods, Instruments, Projects, Legislation & Guidelines, and Laboratories. Each section contains curated content that has been reviewed and standardized to support comparison between countries and institutions. Sampling and analytical methods include preparation steps and performance characteristics used by partner laboratories. Instrument entries document specifications, supplier details, and typical detection limits. Legislative and guideline summaries highlight national frameworks relevant to MP monitoring, providing essential context for interpreting results. A directory of laboratories presenting their MP analysis capacity enables practitioners to identify regional expertise.
To support accessibility, the Knowledge Base includes multilingual data sheets available in six languages, making technical content more usable for water suppliers and national institutions. Embedded submission forms allow researchers, laboratories, and agencies to contribute updated methods, new instruments, and legislative changes. All submissions are checked before being uploaded, ensuring the resource remains accurate and up to date.
By consolidating dispersed knowledge into a single platform, the MicroDrink Knowledge Base enables saving of time and resources and strengthens cooperation across scientific, regulatory, and operational sectors. Its integration into the main MicroDrink website ensures long-term visibility and supports ongoing harmonization efforts in the Danube Region. This contribution presents the structure, content, and practical applications of the Knowledge Base, demonstrating its value as a shared reference point for MP monitoring in drinking water resources.
How to cite: Alqadi, M. and the Microdrink project team: The MicroDrink Knowledge Base: A Multilingual Platform for Harmonizing Microplastic Monitoring in Drinking Water Across the Danube Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5824, https://doi.org/10.5194/egusphere-egu26-5824, 2026.
Suspended Sediment Concentration (SSC) is an indicator of a river system's quality and has several ecological impacts on aquatic life. Increased levels of SSC typically reduce the transparency of the water, thereby reducing photosynthetic activity. Moreover, it has an adverse effect on aquatic organisms due to impediments to respiration and altered habitat conditions. Additionally, contaminants, including heavy metals and organic pollutants, can be carried by suspended sediments as they attach to them. Thus, understanding and prediction of SSC became an active research topic in hydrologic science for better analyzing the sources and variations of SSC . The current research aims to estimate and predict SSC levels using measured data from the flow station 07373420 located on the Mississippi River in West Feliciana Parish in the Gulf of Mexico operated by the US Geological Survey. Over 29 years (1992 to 2021) of monthly flow and SSC data were obtained and used in this research. The SSC values were estimated using three machine learning-based modeling frameworks: Multilayer Perceptron (MLP), hybrid Quantum-Inspired Algorithms - Multilayer Perceptron (QIS-MLP), and Bayesian Optimization-Multilayer Perceptron (BO-MLP). The MLP models the complex, nonlinear relationship between independent and dependent variables using an artificial neural network. Each neuron receives the weighted inputs from the previous layer and processes them through the activation function, allowing the MLP to detect nonlinear relationships among the various input variables. With BO, the optimal hyperparameters of the MLP model (number of hidden layers, number of neurons, and learning rate) were tuned automatically. BO-MLP selects promising configurations through iterative evaluations of the acquisition function, optimally exploring the hyperparameter search space for the MLP model. The QIS-MLP utilizes an improved global search strategy combined with enhanced ability to conduct local searches by virtue of its advanced quantum optimization algorithm. To evaluate the performance of each model, the standard statistical measures of model performance were utilized, including Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Correlation Coefficient (CC). Among all tested input combinations, the QIS-MLP with three lagged SSC values and three lagged discharge values represents the best performance with the highest degree of accuracy and greatest potential for generalization of SSC values (RMSE = 37.34, NSE = 0.418, KGE = 0.537, and CC = 0.7). As a result, the QIS-MLP achieved superior output through optimization of functions and reduction in error rates. Based on the results, QIS-MLP were found to demonstrate superior accuracy and reliability of monthly SSC estimates and, therefore, considered an excellent candidate for intelligent modeling of complex hydrological systems.
Keywords: Hydrological Modeling, Quantum-Inspired Algorithms, Suspended Sediment Concentration
How to cite: Beirami, N., Samadianfard, S., and Gündüz, O.: Quantum Computing and Bayesian Optimization-Inspired Multilayer Perceptron Approach for Suspended Sediment Concentration Estimates at Rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9219, https://doi.org/10.5194/egusphere-egu26-9219, 2026.
Significant changes in long-term hydrological values have been observed, resulting in an increase in the proportion of wastewater discharged from wastewater treatment plants (WWTPs) in watercourses. During periods of reduced flow, this proportion can exceed 50% of the flow in the receiving watercourse. For most parameters, concentrations increase in the watercourse downstream of the WWTP, thereby deteriorating water quality (nutrients, chlorides, conductivity, coliform bacteria, dissolved substances). However, the assessment is less clear-cut for some parameters. With decreasing flow, the differences in concentrations upstream and downstream of the WWTP discharge increase for most parameters. A notable increase in water temperature and pH downstream of the WWTP is observed during the warmer months, accompanied by a more rapid decomposition of organic matter. This results in smaller differences for ammonium nitrogen, and conversely, larger differences in its concentrations during the colder months.
In addition to the well-known problems with nutrients, the situation with increasing salinity of watercourses may also worsen with more frequent occurrences of low flow periods. Unlike nutrient parameters, salinity parameters do not have targets for good ecological status set in the Czech Republic, so they are not included in the assessment of the status of water bodies and no measures to improve their ecological status need to be proposed. Phosphorus and chloride concentrations exhibit a marked increase downstream of the WWTP throughout the whole year.
The paper will present the impacts of discharges from WWTPs that have been selected as significant for the Czech Republic in the Plan for Managing Drought and Water Scarcity in the Czech Republic. These examples are used to find flow rates that would eliminate the combined impact of drought and wastewater treatment plant discharges on the ecological status of the affected surface water bodies. The results of an assessment of the impact of the first stage of reconstruction of the largest WWTP in the Czech Republic, which treats wastewater from the capital city of Prague, on improving water quality in the Vltava River will also be presented.
How to cite: Barešová, L., Nesládková, M., Svoboda, V., and Kodeš, V.: The behaviour of physicochemical parameters in watercourses downstream of WWTPs under reduced flows, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11125, https://doi.org/10.5194/egusphere-egu26-11125, 2026.
Wastewater effluent can introduce nutrients that drive eutrophication and oxygen depletion in river systems, impacting their chemical and ecological status under the Water Framework Directive. Microbial degradation of effluent can also generate indirect greenhouse gases, which are increasingly relevant for meeting Net Zero goals. Understanding effluent behaviour in tidal rivers, where transport and mixing are complex, is therefore critical for effective catchment management. This study investigates the spatial and temporal dynamics of effluent discharge in the lower River Tyne, which receives continuous treated wastewater from Howdon Sewage Treatment Works, by applying a multi-phased approach that combines geospatial analysis, hydrodynamic modelling, and field measurements. Historical datasets have been analysed to characterise patterns and variability in effluent discharge, highlighting the need for targeted monitoring to capture biogeochemical hotspots. Two- and three-dimensional hydrodynamic simulations will be used to explore effluent transport and mixing under varying tidal and flow conditions and will be validated against in situ observations. Ongoing field campaigns monitor several water quality parameters, alongside Acoustic Doppler Current Profiler surveys to resolve flow structure and stratification. Model outputs will be integrated with observed data to identify zones of minimal and maximal effluent impact and to estimate indirect greenhouse gas emissions. Overall, this research will enhance our understanding of effluent dynamics in aquatic systems, providing insights for monitoring strategies and catchment management under current and future environmental conditions.
How to cite: Fairless, O.: Spatial–temporal dynamics of wastewater effluent in a tidal river system: the lower River Tyne, UK, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14703, https://doi.org/10.5194/egusphere-egu26-14703, 2026.
Short-term prediction of chlorophyll-a (Chl-a) is essential for eutrophication management and early warning of algal blooms. Existing Chl-a prediction studies commonly rely on remotely sensed observations, process-based models, or data-driven models. However, remotely sensed observations are often discontinuous, data-driven models alone may struggle with nonlinear and network-dependent dynamics, and process-based models are limited in capturing observation-consistent, short-term Chl-a variability.
To address these limitations, we develop a multimodal prediction framework based on a graph neural network (GNN) that explicitly represents the river network as a directed graph. The framework integrates (i) remotely sensed Chl-a observations and meteorological data with (ii) process-based hydrological and water-quality states that provide continuous, physically consistent information on streamflow and constituent transport along the river network. These process-based variables are generated using the Soil and Water Assessment Tool (SWAT), and provide physics-guided information that complements observation gaps and supports learning of upstream–downstream dynamics along the river network.
The proposed framework is applied to Geumho River Watershed (2,092 km2), and predictive performance is evaluated using the coefficient of determination and the root mean square error. Comparative analyses are conducted with an RNN model and conventional machine learning models, including Random Forest and XGBoost, to assess the validity of the GNN-based approach in learning structural connectivity within river networks. This study demonstrates the applicability of a graph-based multimodal prediction framework integrating satellite observations and physics-guided hydrological information and provides a foundation for the development of intelligent early warning systems.
How to cite: Jung, S., Yoon, Y., Park, J. H., Kim, D., Park, S., Lee, B., and Lee, S.: Physics-guided graph-based multimodal prediction of chlorophyll-a in river networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16241, https://doi.org/10.5194/egusphere-egu26-16241, 2026.
Understanding eutrophication at the river network scale is essential for sustainable and effective river water-quality management, yet most assessments have focused on local drivers and neglected the inevitable interactions between river network structure and spatial nutrient loading across stream orders. To address the knowledge gap, this study investigates how catchment form, land-cover distribution, and nutrient loads discharged from wastewater treatment plants (WWTPs) affect the coupled dynamics of nutrient and algal communities across river networks. As the proof-of-concept activity, we employ artificial river networks with contrasting geometries (elongated, rectangular, and square) but identical total area, generated on the basis of the Optimal Channel Network theory and constrained to realistic morphometric properties. These networks are coupled with archetypical land-cover configurations that impose equal total phosphorus (P) loads but differ in spatial organization, including homogeneous, random, and upstream- or downstream-clustered anthropogenic inputs from urban and agricultural areas as well as point source discharge from WWTPs. The coupled dynamics of nutrient P and algae are elaborated through each configuration-based simulations of the CnANDY model, which is a parsimonious mechanistic river-network-scale model for pelagic and benthic algae competing for light and phosphorus under steady hydrologic conditions. By comparing the different loading configurations, the study examines the implications of focusing management measures on headwaters versus downstream reaches, as well as the potential role of WWTP relocation, and end-of-pipe solutions. Overall, our findings are expected to support a more network-aware perspective on eutrophication assessment and management at the entire catchment scale.
How to cite: Heinemann, N., Yang, S., Borchardt, D., and Carraro, L.: Network structure, wastewater treatment plants and land cover control algal and nutrient dynamics in rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20165, https://doi.org/10.5194/egusphere-egu26-20165, 2026.
The hydrological network in agricultural areas in Latvia consist of subsurface drainage systems and agricultural ditches. The excess water along with nutrients mainly in the soluble forms on nitrogen and phosphorus is collected from agricultural areas by subsurface drainage systems and discharged into agricultural ditches. It is known that the amount and quality of water leaving agricultural areas depends of multiple factors, e.g., meteorology (precipitation and air temperature) and catchment specific (catchment area, land use distribution, topography, soils, crops). This study aims to assess the concentrations of nitrogen and phosphorus in relation to the catchment area and length of the selected agricultural ditches. The outcomes of this study will support decision makers to evaluate potential criteria for implementation of buffer strips of different width along agricultural ditches in Latvia.
For the purpose of this study 112 agricultural ditches, which are evenly distributed in the territory of Latvia and among all four geomorphological regions of minimum runoff, were selected for detailed investigation. The analysis of geospatial data showed that the ditches are in the length from 325 m to 1524 m, while the catchment area ranges from 0.37 to 10.98 km2. In 107 of 112 ditches water samples were collected and analyzed for total nitrogen (TN) and total phosphorus (TP) concentrations in the accredited laboratory according to the national standards. It was not possible to collected water samples in 5 ditches due to lack of water.
The results of this study showed that 108 of the catchment areas of the selected agricultural ditches were smaller than 2 km2, while the length of the same ditches varied from 325 m to 1475 m. The linear regression analysis indicated that there is a weak and positive relationship between the catchment area and length. The observed concentrations of TN varied in the range from 0.49 to 13.95 mg l-1, while TP concentrations were in the range from 0.003 to 0.911 mg l-1, where neither catchment area nor length were detected as the factors affecting TN or TP concentrations. The results of this study indicate that the catchment area and length of the agricultural ditches cannot be directly applied as the parameters to define different widths for buffer strips as these parameters does not directly affect water quality.
This study was funded by the Ministry of Agriculture of the Republic of Latvia within the scope of the research project “The assessment of hydrological conditions and water quality during the vegetation period in agricultural ditches in Latvia”, the decision No. 10.9.1-11/25/1545-e.
How to cite: Straume, A.: The relationship between concentrations of nitrogen and phosphorus and catchment area and length of agricultural ditches in Latvia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21283, https://doi.org/10.5194/egusphere-egu26-21283, 2026.
Groundwater contamination in intensively cultivated catchments frequently threatens drinking‑water supplies. However, continuous, low‑cost monitoring of recharge fluxes and the related water quality remains elusive. We present WICKIE (WICK‑sampler In‑situ Estimations), an on-line, passive system that quantifies percolating water directly in the field and allows to take water samples or in-situ water quality monitoring systems. The device combines 2 m‑long fiberglass wicks, fixed in a collector to operate at field‑capacity suction, with a 3D‑printed PETG tipping‑bucket that records each drainage event as an absolute volume. A stainless‑steel collector (1 m × 0.12 m) is inserted horizontally into a pre‑excavated pit wall, preserving the surrounding pore structure and providing an active sampling area of 900 cm².
In addition, we introduce an in-situ, low-cost optical sensor for real-time, in-situ monitoring of nitrate (NO3-) and dissolved organic carbon (DOC) concentrations in natural water. Utilizing absorbance and fluorescence at specific wavelengths with LEDs and photodiodes, this sensor system offers a practical alternative to expensive and complex laboratory or in situ spectrometer methods. Although the sensor's accuracy does not yet fully match that of much more expensive commercial sensors, it maintains strong predictive capabilities with comparative accuracies and correlations. Challenges such as the interference of DOC and turbidity with the nitrate absorbance signal, intense calibration procedures and site-specific variability remain, necessitating further refinement.
A half-year field trial at a cropping–grassland interface in southwestern Germany demonstrated that WICKIE effectively captures the temporal lag between precipitation and percolation, yielding high‑resolution flux data with low external power supply. WICKIE’s key advantages are reduced soil disturbance compared to lysimeter, enhanced lateral representativeness compared to alternative methods, direct volumetric flux measurement facilitating water balance calculations, inexpensive material cost, and modular adaptation to diverse soils and land uses. WICKIE facilitates precise groundwater recharge assessments and promotes sustainable agricultural and aquifer‑management through the provision of continuous, real‑time, cost-effective recharge and water quality data.
How to cite: Weiler, M., Paulsen, H., König, F., and Henstritt, B.: WICKIE - an in-situ monitoring system to measure groundwater recharge flux and water quality, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21307, https://doi.org/10.5194/egusphere-egu26-21307, 2026.
The climatic conditions in Latvia can be characterized as humid, where the annual
precipitation exceeds evapotranspiration. Therefore, sustainable and economically
sound agricultural production is not possible without drainage systems including
subsurface tiles and open ditches. Open ditches not only receive and convey excess
moisture from agricultural fields, but also serve as a pathway to transport nutrients to
downstream waterbodies. The aim of this study was to assess phosphorus
concentrations in the selected agricultural ditches during the vegetation period. The
monitoring results obtained within this study will characterize water quality in the
selected agricultural ditches, which are located in all four geomorphological regions
of minimum runoff as designated by the Regulations on Latvian Building Code LBN
224-15 “Land reclamation systems and hydrotechnical structures”, thus making it
possible to identify the need and importance of establishment of buffer strips along
agricultural ditches to improve water quality.
Overall, 112 agricultural ditches were selected for this study, whose locations evenly
cover the territory of Latvia. The water samples were collected in 107 ditches using a
manual grab sampling approach during one sampling campaign carried out from
September 20 to October 30, 2025, the water flow in 5 ditches had dried out. Water
samples were collected in 0.5 l polyethylene bottles, which were stored in a
refrigerator at 2º – 4º C before transportation to the accredited laboratory. Total
phosphorus (TP) and orthophosphate (PO 4 -P) concentrations were determined
according to the national standards. The geospatial dataset of Corine Land Cover
2018 was applied to extract the information on land use patterns in the catchment
areas of the selected agricultural ditches.
In the case of 70 water samples collected in the agricultural ditches, the estimated
ratio between PO 4 -P and TP was greater than 50% indicating for phosphorus losses
from agricultural fields, where the excess moisture and soluble form of PO4-P are
collected by the subsurface drainage systems and transported to the ditches. By
comparing TP concentrations detected in the selected ditches with the threshold value
of the good water quality relevant for small and slow flowing rivers (0.09 mg l -1 ) as
specified in the River Basin Management Plans in Latvia, it can be concluded that in
the case of 18 ditches the concentrations of TP exceed the threshold value. In these
ditches buffer strips would be a relevant measure to reduce losses of phosphorus from
agricultural fields. No distinct patterns in TP concentrations were observed relative to
spatial representation of the agricultural ditches within the geomorphological regions
of minimum runoff. The analysis of the share of land use and water quality
monitoring results showed that there is a positive, but weak relationship between the
share of agricultural land and TP concentrations in the selected agricultural ditches.
This study was funded by the Ministry of Agriculture of the Republic of Latvia within
the scope of the research project “The assessment of hydrological conditions and
water quality during the vegetation period in agricultural ditches in Latvia”, the
decision No. 10.9.1-11/25/1545-e.
How to cite: Paeglite, R.: The assessment of phosphorus concentrations in agricultural ditches during thevegetation period in Latvia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21359, https://doi.org/10.5194/egusphere-egu26-21359, 2026.
Agricultural ditches are essential components of the hydrological network and agricultural landscape in Latvia, yet they serve as primary pathways for nutrient transport from agricultural fields to downstream water bodies. The water quality monitoring during the vegetation period is needed to understand the levels and patterns of nitrogen losses and identify measures to reduce negative impact of agricultural activities on the environment.
Within this study 112 agricultural ditches representing all four geomorphological regions of minimum runoff in Latvia were selected for water sampling. The water samples were collected in 107 agricultural diches using a manual grab sampling method during the time period from September 20 to October 30, 2025. In 5 ditches the water flow was not observed. In the accredited laboratory the water samples collected were tested for concentrations of total nitrogen (TN), nitrate nitrogen (NO3-N) and ammonium nitrogen (NH4-N) according to the national standard methods. The Corine Land Cover 2018 datasets have been utilized to study the effects of land use distribution on nitrogen concentrations relevant for the catchment areas of the selected ditches.
The monitoring results showed a wide range of concentrations across the study sites. TN concentrations varied from 0.49 to 13.95 mg l-1, while NO3-N between 0.001 and 13.62 mg l-1. The wide range of TN and NO3-N concentrations indicate for a heterogeneous intensity of agricultural activities carried out within the catchment areas of interest, where the intensity agricultural activities differs from very low to very high. The water samples collected in 37 agricultural ditches indicated for the increased ratio (over 75%) between NO3-N and TN concentrations. NO3-N, as a highly soluble and mobile form of nitrogen, is lost from agricultural fields mainly via subsurface drainage systems when the nitrogen surplus is present and excess water percolates through the soil profile until reaches the depth of subsurface drainage systems. The increased ratio between NO3-N and TN concentrations showcase the negative impact of tile drained arable land on nitrogen losses. Contrary, 17 monitoring sites showed a low NO3-N and TN ratio (below 25%) thus indicating for the minor impact of arable land on water quality. NH4-N concentrations in general terms were low with the mean concentration of 0.066 mg l-1, minimum of 0.002 mg l-1 and maximum of 1.609 mg l-1 showing that nitrogen losses from sources of organic origin are not present in the catchments, except of one case.
TN concentrations have a visually inexpressive and statistically insignificant relationship with the share of agricultural lands in the catchment areas of interest. A closer relationship between the two parameters involved in this analysis is limited by the specificity of the data sets applied, when in the cases of increased share of agricultural lands both high and low TN concentrations have been observed.
This study was funded by the Ministry of Agriculture of the Republic of Latvia within the scope of the research project “The assessment of hydrological conditions and water quality during the vegetation period in agricultural ditches in Latvia”, the decision No. 10.9.1-11/25/1545-e.
How to cite: Melbardis, E.: The assessment of nitrogen concentrations in agricultural ditches during the vegetation period in Latvia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21360, https://doi.org/10.5194/egusphere-egu26-21360, 2026.
Climate change is driving rapid glacier retreat, facilitating the expansion of vegetation into previously unvegetated terrain, and is altering the nutrient dynamics of aquatic ecosystems downstream of glaciers. Here, we measured nutrient concentrations in catchments with varying degrees of glacier coverage in western Norway to determine the likely impacts of glacial retreat on nutrient cycling. Field investigations were conducted during 2024–2025 in five catchments (25–110 km²) draining the largest ice cap in mainland Europe, Jostedalsbreen, and alpine valley glaciers in the Jotunheimen mountain range. These glaciers have undergone sustained thinning and recession since the end of the Little Ice Age (c. 1750 CE). Water chemistry, including concentrations of nutrients, dissolved organic carbon, and suspended sediment, was measured along the longitudinal profiles of the studied streams, and incorporated subglacial, supraglacial, and proglacial waters. Regression analyses revealed that the concentrations of major ions (e.g., Ca²⁺, Na⁺, K⁺, HCO₃⁻, SO₄²⁻) increase as glacier cover within the catchment decreases. This pattern may suggest enhanced chemical denudation and intensified leaching of soil material in non-glaciated areas. In three of the investigated catchments, declining glacier coverage was associated with reduced concentrations of nitrogen compounds (NO₃⁻, NH₄⁺, and total N), indicating that glaciers might play a critical role in the delivery of nitrogen to downstream aquatic systems. Conversely, vegetation likely reduces nitrogen fluxes through biological uptake from soils. Positive Matrix Factorization (PMF) modelling corroborated these findings, identifying distinct sources affecting water chemistry. An atmospheric factor (dominated by Na⁺, Cl⁻, and total N), a biogenic factor (associated primarily with nitrogen compounds), and geogenic factors (linked to mineral weathering and chemical denudation) were distinguished. Biogenic and atmospheric contributions predominated in catchments with ≥60% glacier coverage, whereas geogenic contributions were more pronounced in catchments with <40% glacier cover. Overall, continued glacier retreat and vegetation encroachment are expected to increase the concentration of major ions in streamwaters while diminishing the export of nitrogen compounds, potentially reshaping the biogeochemical functioning of downstream aquatic ecosystems.
How to cite: Płaczkowska, E., Stachnik, Ł., Yde, J. C., Jelonkiewicz, Ł., Hawkings, J., Łopuch, M., Raczyk, H., Raczyk, J., and Szczypińska, M.: Glacier retreat reshapes nutrient dynamics in mountain streams of Norway, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21705, https://doi.org/10.5194/egusphere-egu26-21705, 2026.
Despite agri-environmental measures implemented to protect water quality, there are still ongoing water quality issues and eutrophic impacts at different scales. Phosphorus (P) sources are diverse but evidence on the relative contribution of diffuse- and point-sources to P loading in rivers is limited, estimated from models or based on low frequency datasets that don’t capture brief but high magnitude P discharges. Using hourly measurements of P concentrations, the aim of this study was to investigate the relative contribution of diffuse- and point-sources to P loading in a large, complex catchment river noted as major contributor to downstream lake hypertrophication. Stream water total P (TP) concentrations were measured every hour at the outlet of two nested catchments: an ‘upstream’ catchment dominated by diffuse agricultural P sources and a ‘downstream’ catchment which included additional point urban P sources. Hourly TP loads were computed for each catchment to quantify the diffuse vs point P source percentage contributions to TP loading. Results showed that over February-June 2025, median TP concentrations were 0.092 mg L-1 and 0.137 mg L-1 at the outlet of the ‘upstream’ and ‘downstream’ catchment, respectively. Total diffuse- and point-sources TP loads were 5.8 tons (22.7 kg km-2) and 2.4 tons (6.2 kg km-2) and contributed on average 70% and 30% of the hourly TP load, respectively. When comparing these relative contributions for different flow conditions, the P pressure from diffuse sources increased at high flows (median of 77.7% for the 15% highest flows) while pressure from point sources increased at low flows (median of 42.2% for the 15% lowest flows). The study emphasizes the imperative to reduce both diffuse- and point‑source P losses to safeguard river ecosystems during low‑flow periods, when ecological vulnerability is greatest, and to prevent lakes from receiving excessive P inputs during high‑flow events. This need becomes increasingly critical as climate change amplifies the frequency and severity of hydrological extremes.
How to cite: Fresne, M., Jordan, P., and Cassidy, R.: Quantifying diffuse- vs point-source contributions to phosphorus loading using high-frequency monitoring in a large-scale nested catchment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5614, https://doi.org/10.5194/egusphere-egu26-5614, 2026.
The Agricultural Runoff Monitoring Programme has been implemented in Latvia since 1995 until present. Water quality monitoring activities along with hydrological measurements are carried out at subsequent spatial scales including groundwater (20 wells), experimental plots (1 site with 16 plots), subsurface drainage fields (6 sites), small catchments (10 sites), small and medium size rivers (23 sites). The main objective of the programme is to document and assess the current status and long-term changes in nitrogen concentrations and losses as affected by natural and anthropogenic factors.
Water samples are collected on a monthly basis using a grab sampling approach or composite flow proportional sampling where discharge measurement structures and data loggers are installed. Water samples are analyzed for nitrogen and phosphorus compounds according to the national standards.
The study results show a large variation in NO3-N concentrations among the spatial scales of monitoring with the lowest mean annual concentrations in groundwater (below 1.0 mg l-1) and the highest in the discharge from subsurface drainage fields and experimental plots (over 7.0 mg l-1). Overall, NO3-N concentrations follow the patterns of discharge having the highest concentrations during high flow conditions in winter and spring, while the lowest concentrations during low or no flow conditions in summer and autumn. These patterns highlights the great importance of subsurface and surface drainage systems, which act as pathways for transport of excess water and soluble forms of nitrogen from agricultural fields to surface waters.
It is essential to continue activities within the Agricultural Runoff Monitoring Programme also in the future, especially in the light of need to quantify changes in water quality as related to implementation of the Farm to Fork strategy aiming to reduce the use of fertilisers by at least 20% and nutrient losses by at least 50% by 2030.
How to cite: Lagzdins, A., Veinbergs, A., and Siksnane, I.: The long-term results of the Agricultural Runoff Monitoring programme in Latvia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21413, https://doi.org/10.5194/egusphere-egu26-21413, 2026.
