This session invites scientists focused on the compilation and exploration of large water quality datasets, aiming to unravel natural and anthropogenic drivers shaping water quality patterns. We welcome studies on any solutes, such as major ions, nutrients, metals, and pollutants, in inland waters ecosystems (streams, lakes, groundwater) and soils.
Topics of interest include but are not limited to:
1.Development and improvement of large datasets:
•Dataset compilation, harmonization across regions and data platforms development
•QA&QC, addressing data gaps and uncertainty
•Emerging opportunities and challenges in large-scale research
2.Innovative approaches (e.g., machine learning) to handle big datasets:
•Application of diverse statistics and machine learning methods
•Empirical and mechanistic analyses of concentration–discharge (C–Q) relationships
3.Spatial patterns, temporal trends and controls:
•Regional contrasts and patterns, multi-scale temporal trend analysis
•Driver-response relationships including lagged responses to changes in management and pressures
•Influence of physical processes, land cover, climate variability, hydrological regimes, soil/geology and human activity
4.Hot spots and hot moments:
•Event-based or threshold-triggered changes during extremes (floods and droughts)
•Spatial clustering of high-impact areas or sources
•Seasonal timing of pollution loading or retention
The growing availability of long-term, open observational datasets has expanded the scope of comparative and multi-site water quality research. Many contemporary water quality questions, including changing organic matter dynamics, nonstationary concentration–discharge relationships, and the role of extremes, were not anticipated when most monitoring programs were originally designed. These questions can be addressed because long-term observation systems provide the temporal context and continuity needed to understand change across sites and regions. This presentation highlights how existing long-term and open datasets from national and international collaborative monitoring networks are being used in novel ways to move from monitoring toward insight. The focus is on data reuse and synthesis to interpret long-term water quality trends as environmental drivers change, to inform and evaluate model structures, and to reveal emergent spatial and temporal patterns across regions. Key challenges are also discussed, including data harmonization, evolving analytical methods, and sustaining scientific value over multi-decadal timescales. These examples underscore both the scientific value of long-term observation systems and the risks associated with losing continuity in long-term observational records.
How to cite: Boyer, E.: From monitoring to insight: how long-term, open data enable the next generation of water quality science, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14639, https://doi.org/10.5194/egusphere-egu26-14639, 2026.
The growing availability of water quality datasets presents new opportunities to understand the dynamics of river health across multiple spatial and temporal scales. Citizen scientist data on pollution events, particularly from combined sewage overflows, have successfully increased scrutiny of polluted inland waterways. At the same time, there is a need to avoid undermining the importance of continued high-quality, long-term monitoring. These debates raise a critical question: has increasing data availability translated into improved understanding of, and outcomes for, river health?
Based on insights from a scoping review of published academic and grey literature, qualitative case studies from the perspectives of regulators, water companies and wild swimmers, and a systems mapping workshop with interdisciplinary scientists, data providers and users, we examine how existing UK river water quality data are collected and integrated. This combined approach allows us to explore how data are used to support scientific understanding and decision-making by river users and managers across multiple scales.
One of the key points made during the workshop was that perceived data gaps may be smaller than initially envisioned if all the data were brought together in one place. As a first step towards improved integration, we will present a systems map and accompanying database of English river datasets and data platforms spanning governmental organisations, private companies, researchers and citizen scientists. These data include CSO spills, faecal indicator organisms, physicochemical variables, macroinvertebrates, major nutrients and other chemical and ecological variables such as microplastics, PFAS and trace elements.
Our analysis highlights persistent challenges related to trust, consistency and bias, mismatches between spatial and temporal data resolution and decision needs, limited analytical capacity, and wider political-economic constraints. We argue that while data availability is necessary, it is not sufficient to improve river health. Progress depends not only on continued investment in monitoring, but also on shifting emphasis from identifying data gaps towards improving the integration, interpretation and decision relevance of existing data, enabling more effective understanding of water quality patterns and drivers across scales.
How to cite: Speight, L. and Nowicki, S.: Necessary but not sufficient: Exploring the role of diverse water quality datasets in UK river health, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16618, https://doi.org/10.5194/egusphere-egu26-16618, 2026.
Trophic status serves as a fundamental water quality indicator, directly affecting biodiversity and ecosystem functioning in aquatic environments. While riverine nutrient pollution has remained a critical global challenge, recent evidence suggests a widespread transition from eutrophic towards oligotrophic states. However, the recovery is spatially heterogenous driven by contrasting anthropogenic pressures and management efficacies. Consequently, the true spatial extent and ecological implications of oligotrophication remained insufficiently explored, a deficit further compounded by inconsistent definitions and indicator parameters.
To address these gaps, we systematically reviewed 1,034 scientific publications (Scopus: 955; Web of Science: 526) and conducted a full-text analysis of 102 studies to address the following questions: (1) How are eutrophication and oligotrophication defined, and what are the most commonly associated variables? (2) What is the global distribution of oligotrophication and eutrophication in river systems? We identified key indicator variables used to analyse long-term trends in trophic status (≥ 10 years), and reporting either eutrophication, no change, or oligotrophication.
The global distribution of long-term nutrient trends exposes a stark regional divergence between mature and emerging economies. Rivers of European countries predominantly demonstrate a declining trajectory (oligotrophication), attributed to successful legislative intervention. France and Germany exhibit significant nitrate reductions in 74% and 91% of long-term observations, respectively, alongside a "resounding success" in phosphorus decrease driven by tertiary wastewater treatment and detergent bans. Conversely, Asian basins display a pronounced upward trajectory (eutrophication); in Japan, 55% of rivers show increasing nitrate levels, meanwhile the Yangtze River (China) exhibits continuous increases in nitrate flux, driven by intensive agriculture and forest loss. Degradation extends to the African continent, where nearly 60% of South African catchments exhibit significant phosphate increases linked predominantly to mining and dysfunctional sewage infrastructure. North America presents a complex, transitional profile characterised by "stalled recovery" in urbanised systems (e.g. the Charles River) and hydrological decoupling of concentration and load (e.g. the Maumee River). Superimposed on anthropogenic impact, climate factors are increasingly modulating long-term trends, with intensified drought driving "chemodynamic" behaviour (e.g. the Spree River, Germany) and warming temperatures amplifying natural denitrification (e.g. the Po River, Italy).
Riverine ecosystems divide along with two distinct trajectories globally: widespread oligotrophication in developed regions (e.g. in Europe), contrasted against intensifying eutrophication in developing regions (e.g. in Asia and Africa). While climate change increasingly alters long-term nutrient baselines, a critical research asymmetry persists. Although oligotrophication trends become more prevalent in riverine datasets, the scientific literature remains heavily skewed towards eutrophication assessments. Future research need to urgently redress the imbalance and investigate the ecological implications of trophic status recovery. As an example, greater attention is required to understand how river communities respond and may restructure both in terms of composition and functionally.
How to cite: Duc Thinh, N. V., Tóth, F., Benito, X., Minaudo, C., and Abonyi, A.: Global trends in river eutrophication and oligotrophication: A systematic review, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21138, https://doi.org/10.5194/egusphere-egu26-21138, 2026.
Water-quality forecasting and attribution at large spatial scales remain challenging because observations are sparse and heterogeneous, monitoring networks are nonstationary, and river systems impose strong directional and time-delayed connectivity. These constraints are further complicated by abrupt pollution shocks that cannot be explained by routine upstream–downstream propagation, yet are critical for risk-aware water management. Consequently, existing approaches often face a trade-off between predictive accuracy and interpretability: data-driven models capture complex spatiotemporal patterns but provide limited insight into underlying drivers, while causal analyses offer mechanistic understanding but are difficult to operationalize for real-time, multi-step forecasting across large monitoring networks.
Here we develop a physically constrained, topology-aware causal forecasting framework that unifies large-scale water-quality prediction with driver attribution in river networks. The framework explicitly represents three defining characteristics of fluvial systems: unidirectional upstream-to-downstream transport, travel-time-dependent propagation delays, and dynamic monitoring configurations in which stations appear or disappear over time. By embedding physical flow constraints into a data-driven causal representation, the framework jointly learns evolving spatiotemporal dependencies while remaining robust to extreme data sparsity and uneven sampling typical of water-quality observations.
We apply the framework to China’s national surface-water monitoring network, comprising more than 1,900 stations and multiple water-quality indicators, together with hydro-meteorological covariates. The framework achieves strong multi-step predictive skill across the full network under realistic data gaps, while providing an interpretable decomposition of dynamics into local persistence, upstream propagation, and externally driven disturbances. This decomposition enables real-time identification of dominant drivers of water-quality change at individual stations, distinguishes systemic trends from short-lived pollution shocks, and localizes influential upstream contributors consistent with river-network connectivity. Beyond forecasting, the framework supports event-oriented causal diagnostics and prioritization of high-information monitoring locations, helping to optimize sampling strategies and enhance early-warning capability under limited monitoring resources.
Our results demonstrate that physically constrained causal forecasting can bridge the long-standing divide between prediction and explanation in water-quality modelling at scale. Crucially, the framework remains operational under nonstationary station availability, enabling consistent forecasting and attribution as monitoring configurations evolve. By integrating topology-aware learning with interpretable attribution, the proposed framework establishes a coherent pathway from forecasting to diagnosis and source localization, supporting proactive, data-informed water-quality management and rapid response to pollution events in complex river networks.
How to cite: Yu, K., Li, Z., and Qu, S.: Bridging prediction and causal attribution in large-scale river water-quality networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21949, https://doi.org/10.5194/egusphere-egu26-21949, 2026.
High-frequency water-quality monitoring is essential to understand nutrient dynamics, contaminant “hot moments,” and ecohydrological feedback in rapidly changing river catchments. However, the cost and maintenance requirements of commercial sensors constrains their deployment, limiting use particularly in remote or logistically challenging environments. This study evaluates the feasibility of open source and lower-cost surrogate-based monitoring for nitrate and dissolved organic matter (DOM), UK. The scalability of open source sensors for broader applications, such as dense and smart sensor networks are also explored.
Commercial field-deployed sondes continuously recorded optical and physicochemical variables—temperature (Tw), turbidity, dissolved oxygen (DO), electrical conductivity (EC), nitrate, and fluorescence (DOM fractions) —over seasonal cycles (sub-hourly data, i.e. 15 min frequency). Complementary low-cost sensors (e.g., EC, Tw and turbidity) captured in-situ hydrodynamic and water-quality variations. Empirical proxy models were developed to test whether low-cost parameters can represent DO, NO₃⁻ and fluorescent DOM (fDOM) dynamics, and whether turbidity, Tw, and EC enhance predictive power. Comparison with reference-grade instruments showed strong consistency, with coefficients of determination (R²) between 0.50 and 0.90 across flow regimes. Deviations during high-turbidity and runoff events highlight the need for adaptive calibration and uncertainty quantification.
The results demonstrate that open source and low-cost sensor networks, when properly calibrated, can capture fine-scale variability in nutrient and DOM fluxes, offering a scalable and affordable alternative to commercial systems. With onboard telemetry the sensors allow for integrating real-time data assimilation and harmonised workflows that supports data-driven catchment management and strengthens environmental monitoring in resource-limited regions. The findings align with innovative and classical monitoring frameworks, promoting uncertainty reduction and advancing transferable methodologies for next-generation smart river monitoring.
Keywords: Monitoring; Sensors; Dissolved Oxygen; Surrogates; Calibration; Water Quality
How to cite: Singh, J., Kelleher, L., Khamis, K., Hannah, D., and Krause, S.: Demonstrating Open Source and Low-Cost Sensors as Surrogates for River Water Quality Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1030, https://doi.org/10.5194/egusphere-egu26-1030, 2026.
The summer of 2025 in Europe provided another striking example of the severe consequences that riverine heatwaves, i.e. periods of extremely high river temperatures, can have on natural ecosystems and human societies. They led to a general decrease of water quality, massive fish die-offs, and in France and Switzerland, to the shutdown of nuclear power plants because of a lack of cooling capacity. Under continued climate change, river temperatures are expected to further increase, potentially leading to even more frequent and severe riverine heatwaves. Although the drivers of river temperatures have been widely studied, the factors causing riverine heatwaves remain largely unknown, in particular across large spatial scales. To address this research gap, we compiled the first large-sample dataset of river temperatures at the European scale and used it to assess the dominant hydro-climatic generation processes leading to riverine heatwaves over the period 1985-2020. For this assessment, we developed a systematic typology of riverine heatwaves, which classifies these events according to their associated antecedent hydro-climatic conditions. We used this typology to quantify the relative importance of each generation process for riverine heatwave development across 957 catchments, and describe the spatial and seasonal distribution of the different riverine heatwave types. We show that riverine heatwaves are mainly occurring during periods of anomalously warm air temperatures and that many severe summer events are occurring because of the cumulative effect of warm air temperatures and low discharge. Our results demonstrate that the importance of each generation process can significantly vary in space and time. They highlight the complex processes leading to riverine heatwaves, pointing towards the need to develop flexible and location-specific mitigation and adaptation measures
How to cite: Chartier-Rescan, C., Bruno, G., Grundmann, M., Frank, C., and Brunner, M. I.: Riverine heatwaves and their dominant generation processes: a systematic typology., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6656, https://doi.org/10.5194/egusphere-egu26-6656, 2026.
Record-breaking heatwaves have become a hallmark of the warming climate, disrupting water, energy and food systems and straining public health. While a nascent body of research has begun to document riverine heatwaves, their co-occurrence with atmospheric events and the emerging threat remain largely unexplored. Here we analyze 796 river basins in the US and Europe to characterize atmospheric-riverine compound heatwaves (ARCH) events, where atmospheric and riverine heatwaves co-occur. Using observational data and deep learning model, and we find ARCH frequency has tripled over the past four decades, at an increasing rate of +0.4 events per decade (p< 0.001). This trend is driven by the rapid intensification of riverine heatwaves (RHWs), with increases in frequency (114%), duration (148%), and intensity (95%) between the 1981–1990 and 2010–2019 decades, far outpacing changes in their atmospheric counterparts (AHWs). The occurrence of ARCH events is primarily controlled by climatic (59.2%), topographic (22.4%), and hydrological (18.3%) factors, with amplified trends in high-elevation (>3000 m) mountain rivers (+128% per decade), highlighting the vulnerability of these critical water sources to global change. These compound events exert greater thermal and oxygen stress on aquatic ecosystems than isolated heatwaves. Projections under a high-emissions scenario (SSP5-85) indicate that by 2100, nearly all riverine heatwaves will coincide with atmospheric heatwaves. These findings highlight the escalating threat of compound heatwaves to freshwater ecosystems and the importance of incorporating ARCH dynamics into future water risk assessments.
How to cite: zhou, Y. and zhi, W.: Climate change triples the frequency of atmospheric-riverine compound heatwaves in US and Europe rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1591, https://doi.org/10.5194/egusphere-egu26-1591, 2026.
Riverine and lacustrine hypoxia is an escalating environmental concern, increasing in both frequency and spatial extent. However, hypoxic events occur more frequently at night, yet existing research lacks quantitative analyses of these occurrences and cannot capture sub-daily fluctuations or the true occurrence and intensity of nighttime hypoxia due to insufficient high-frequency monitoring. Using a global compilation of high-frequency dissolved oxygen (DO) and water temperature (WT) records, we show that low-frequency observations substantially underestimate hypoxia, with more than 90% of sites affected and many underestimating hypoxia days by over 50%. On average, daytime DO concentrations were ~4% higher than nighttime values, and sites with larger diel amplitudes exhibited disproportionately high underestimation. Across global rivers, 64% of sites were classified as night-dominated hypoxia, 24% as day-dominated, and 12% as balanced diel hypoxia, highlighting the dominant role of nocturnal oxygen depletion in driving systematic underestimation. Diel DO changes were positively correlated with diel WT changes and varied strongly across major habitat types, with the largest daytime enrichment in Oceanic islands (1.16 ± 2.18 mg L⁻¹) and temperate upland rivers (0.64 ± 0.91 mg L⁻¹), but slightly negative values in large lakes (–0.039 ± 0.67 mg L⁻¹) and temperate coastal rivers (–0.045 ± 0.27 mg L⁻¹). These results emphasize the need for incorporating high-resolution DO monitoring to accurately assess ecosystem stress, guide water quality management, and reveal the true dynamics of oxygen fluctuations in riverine systems.
How to cite: Shen, Y. and Zhi, W.: Widespread nighttime hypoxia revealed in global high-frequency dissolved oxygen data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1567, https://doi.org/10.5194/egusphere-egu26-1567, 2026.
Rivers are the link among terrestrial, oceanic, and atmospheric carbon pools, with longitudinal transport and vertical emission processes serving as critical pathways for riverine carbon export from river ecosystems. However, the spatiotemporal dynamics and determinants of riverine carbon export processes in large river systems remain poorly understood due to limited quantification of riverine carbon fluxes. In this study, the spatiotemporal patterns and determinants of longitudinal transport and vertical emissions of riverine carbon (i.e., dissolved organic carbon concentration (CDOC) and partial pressure of carbon dioxide (pCO2), respectively) were revealed using machine learning methods in the world’s third-largest river (the Yangtze River). Long-term, monthly, and river-reach scale estimation of riverine CDOC derived from Random Forest and Recursive Feature Elimination methods identified upstream hotspots of annual variation (approximately 23.6% of all basin reaches) and a spatial pattern of higher tributary concentrations, which corresponded to an annual DOC export of approximately 0.80 to 1.55 Tg to the ocean. Riverine pCO2 was higher than the atmospheric level, exhibited an increasing trend from upstream to downstream, and showed monthly fluctuations that, as identified by the k-Shape clustering algorithm, gradually evolved from smooth (upstream) to bimodal mode (downstream). Both riverine CDOC and pCO2 exhibited a seasonal pattern with high values in summer and autumn, whereas a distinct springtime peak in pCO2 was observed in the downstream. Climate and vegetation served as major determinants of the spatiotemporal patterns of riverine CDOC and pCO2. Precipitation, air temperature, and cumulative gross primary productivity exhibited significant and nonlinear increasing effects on riverine CDOC, with their importance second only to elevation. Air temperature was the most important determinant for riverine pCO2, with a relative contribution ranging from 17.8±1.3% to 40.0±0.9%. Vegetation factors exerted stronger influences on riverine pCO2 with strong fluctuation than on pCO2 with a smooth mode. This suggested that both longitudinal transport and vertical emission processes in the Yangtze River system would strongly respond to global warming, wetting and greening trends. Consequently, the DOC-enriched Yangtze River (with approximately 45.9% of river reaches being significantly transport-limited and only 0.6% being significantly source-limited) might export more DOC to the ocean, and the peak time of riverine CO2 emissions might vary in the future. In summary, this study revealed the spatiotemporal dynamics and determinants of riverine carbon longitudinal transport and vertical emission processes in the Yangtze River, emphasizing that riverine carbon export processes need to be further concerned under the global change.
How to cite: Chen, M., Cheng, L., Wu, Y., Lu, M., Chang, L., Wu, S., Zhang, L., and Liu, P.: Identifying the spatiotemporal dynamics and determinants of riverine DOC and pCO2 in the Yangtze River using machine learning methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9142, https://doi.org/10.5194/egusphere-egu26-9142, 2026.
Here, we introduce Caravan-Qual, a new water quality dataset which represents the first global integration with large-sample hydrology. The dataset contains >70 million river water quality observations covering 100 water quality constituents, compiled from a range of national-to-global datasets covering the period of 1980-2025. By leveraging the Caravan dataset and open-source software, we have matched water water quality monitoring stations to streamflow gauges and have derived meterological variables which together provide contextual environmental data which is envisaged to aid our understanding of the water quality data and facilitate research into topics including:
- Spatio-temporal analysis of river water quality dynamics at local to global scales.
- Investigation of the relationships between (constituent-specific) river water quality responses and hydrological, meteorological and catchment characteristics.
- The development and evaluation of process-based, hybrid and data-driven water quality models across diverse hydrological and climatic conditions.
How to cite: Jones, E. R., Kratzert, F., and T. H. van Vliet, M.: Introducing Caravan-Qual: A comprehensive global river water quality dataset, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21504, https://doi.org/10.5194/egusphere-egu26-21504, 2026.
Increases in nitrogen (N) fertilizer application, livestock densities, and human population over the last century have led to widespread nitrate contamination. While increases in riverine N loads are well documented, the total magnitude of N accumulation in groundwater remains poorly constrained. Here we provide a first data-driven estimate of groundwater N mass accumulation in the Upper Mississippi River Basin (UMRB), a region of intensive row-crop agriculture and the primary contributor to Gulf of Mexico hypoxia.
Using approximately 49,000 groundwater nitrate well concentration measurements spanning a range of depths, along with a suite of hydrogeologic and land-use predictors, we developed a Random Forest model to generate gridded predictions of depth-varying nitrate concentrations. Our results indicate that approximately 15 Tg of N (328 ± 167 kg-N ha⁻¹) is currently stored in UMRB groundwater recharged over the past 50 years.
For context, we compare these estimates to those from a lumped statistical model, which predicts accumulation of 387 ± 133 kg-N ha⁻¹, and to a simple basin-scale N mass balance, which places an upper bound of approximately 1000 kg-N ha⁻¹ for the period 1967–2017. These findings underscore the importance of legacy N when forecasting future water quality, as nitrogen stored in the subsurface will continue to degrade drinking water quality and elevate surface water N loads for decades.
How to cite: Van Meter, K.: Data-driven approaches demonstrate legacy N accumulation in Upper Mississippi River Basin groundwater, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15985, https://doi.org/10.5194/egusphere-egu26-15985, 2026.
Carbon (C), Nitrogen (N) and Phosphorus (P) are key macronutrients controlling ecosystem functioning; however, human activities have caused severe disturbances both in concentration levels as well as in their C-N-P ratio with potential consequences for ecosystem health. Large-scale assessment of C, N and P concentrations based on in situ data at high temporal resolution remains challenging due to discontinuous and spatially limited data availability.
To address this gap we leveraged the recently published low-frequency (biweekly to monthly) German water-quality dataset QUADICA v2 (Ebeling et al., 2025) to develop three regional deep learning models to predict daily concentrations of dissolved organic carbon (DOC), nitrate (NO3) and phosphate (PO4). These species are commonly used as proxies for the reactive and bioavailable fractions of C, N, and P, with NO3 representing the dominant form of dissolved inorganic nitrogen in the study catchments. We selected catchments with at least 20 years of concentration data and 200 samples for each compound as well as discharge observations, resulting in a total of 155 catchments. For each compound, we trained a single Long Short-Term Memory (LSTM) model across all catchments. Model performance is satisfactory in most of the catchments with median Kling–Gupta efficiencies of 0.55, 0.62 and 0.45 for DOC, NO3 and PO4 respectively (average across cross-validation folds).
We used SHAP to explain spatial and temporal variabilities in predicted concentrations. Results for spatial variability indicate that DOC is mainly controlled by topographic and climatic factors, while NO3 is controlled by land use and soil properties, and PO4 variability is governed by geology, climate and point sources. For temporal variability, we further cluster catchments into groups with similar dominant drivers based on temporal SHAP values. For DOC and nitrate, the clusters are mainly explained by precipitation and temperature variability. In contrast, phosphate exhibits three distinct clusters characterized by either precipitation and temperature, discharge or seasonality. Our results demonstrate that low-frequency water-quality data combined with deep learning and explainable AI can provide new insights into daily C, N, P dynamics at the large scale. This basis allows us to further characterize C, N, P archetypes, nutrient interactions and their dominant drivers.
How to cite: Saavedra, F., Ebeling, P., Remeta, L., Kumar, R., V. Nguyen, T., Siebert, C., and Merz, R.: Leveraging machine learning and large-scale datasets to elucidate the spatial and temporal dynamics of C, N and P, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15497, https://doi.org/10.5194/egusphere-egu26-15497, 2026.
Characterizing large-scale spatiotemporal variability in river water qaulity and its drivers is challenging because monitoring data are irregular in time and space, and the underlying hydrological, biogeochemical, and human processes are complex and interconnected. These difficulties are acute for nitrate, whose behaviour reflects interacting natural and human drivers that vary across catchments and over time. In contrast to large-sample streamflow prediction, where measurements are frequent and relatively stable, large-sample water quality prediction usually needs to cope with sparse, uneven sampling and human-driven changes in both pressures and responses.
To meet these challenges, we designed a domain-guided, four-step workflow that emphasizes realistic handling of irregular monitoring data and trains a regional LSTM so that sites can share information and learn common patterns from many catchments. First, we assign one monitoring station to each catchment outlet using distance along the river network and apply quality checks to identify comparatively reliable sites. Second, we select and process input variables around nitrate-relevant processes and human activities (e.g., meteorology, land use, agricultural and urban pressures). Third, we train a single, England-wide long short-term memory (LSTM) model on historical records and evaluate performance using time-based tests within catchments and space-based tests across catchments (regions) to assess temporal and spatial generalisation. Finally, we apply attribution analysis to separate the roles of meteorological variability and static catchment characteristics and to examine how dominant drivers vary spatially for national upscaling.
Using nitrate measurements from the Environment Agency Water Quality Archive, the LSTM ingests diverse input categories to generate daily nitrate predictions at more than 2000 Water Framework Directive (WFD) catchment outlets. Results show that predictive skill varies across catchments; station screening improves generalization relative to models trained on all stations; and attribution reveals differing roles of meteorological drivers versus static properties across contrasting catchment settings. Overall, the framework produces daily predictions from irregular and limited observations, provides interpretable and water quality-focused insights into drivers at scale, and offers a large-scale view of how nitrate controls vary in space. It also supports future work on transfer learning and local fine-tuning to enable scalable assessment and management.
How to cite: Tang, J., Chun, K., and Mijic, A.: Large-Sample Nitrate Forecasting with a Regional LSTM: Multi-Source Inputs, Station Screening, and Attribution Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13835, https://doi.org/10.5194/egusphere-egu26-13835, 2026.
Rivers are major pathways transferring terrestrial nutrients to coastal waters, where both the magnitude and stoichiometry of exported nutrients play a critical role in regulating primary production and coastal ecosystem functioning. Nutrient export is strongly controlled by hydrological processes, yet large-scale understanding of how seasonal hydrological variability shapes multi-nutrient export and stoichiometry remains limited, particularly when hydrological circulation and biological activity re-initiate during spring. Here we investigated springtime (March–May) export dynamics of total organic carbon (TOC), total nitrogen (TN), and total phosphorus (TP) across 19 major estuaries discharging into the West (Yellow Sea), South, and East coastal regions of South Korea over the period 2012–2023. We compiled a paired concentration–discharge–precipitation (C–Q–P) dataset by spatially matching river water quality, streamflow, and precipitation monitoring stations. Nutrient loads, long-term trends, and responses to different hydrological conditions (e.g., dry, normal, and wet years) were analyzed with spatial patterns in C:N:P stoichiometry. Springtime nutrient export was highly uneven across coastal regions, with 73–80% of total loads delivered to West Sea, reflecting geomorphological and hydrological controls. Over the study period, springtime TOC, TN, and TP loads exhibited declining trends, with mean decreases of 511, 668, and 53 ton/yr, respectively. However, load dynamics differed markedly among nutrient species. While TOC and TN exports were predominantly discharge-driven, showing symmetric responses to hydrological variability, with 34–36% reductions in dry years and 26–39% increases in wet years, TP export displayed a pronounced asymmetric response, decreasing by 57% in dry years but increasing by only 4% in wet years. This result suggested strong regulation by concentration dynamics, soil retention processes, and phosphorus management regulations. Stoichiometric analysis revealed widespread nitrogen-enriched conditions along the Korean Peninsula coastal lines except for the southern estuary, a distinct hotspot with near-Redfield C:N:P ratios. Along the southern coastal lines, the probability to balanced stoichiometry increased during dry years with co-occurrence of higher chlorophyll-a concentrations, indicating coupled hydrological and biological controls. Our results demonstrated that spring hydrological variability induces nutrient-specific and asymmetric export responses, highlighting the need to account for both magnitude and stoichiometry in management for not only in-land catchments but also coastal ecosystems.
How to cite: Kim, Y. and Kam, J.: Asymmetric springtime responses of carbon, nitrogen, and phosphorus export to hydrological variability across South Korean estuaries, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8922, https://doi.org/10.5194/egusphere-egu26-8922, 2026.
Rivers control the land-ocean phosphorus flux that affect ecosystem health and food security. Yet, systematic trend analysis of the global phosphorus flux is lacking, primarily due to sparse and inconsistent observations. Here, we develop a machine learning framework integrating multimodal data and 280,000 TP measurements to reconstruct TP flux patterns over 1980–2019 across 420 major rivers. Results reveal a deceptive global equilibrium. While TP flux declines in the Northern Hemisphere, driven by dam trapping, it rises in the Southern Hemisphere due to increased fertilizer use and deforestation. Notably, the number of small rivers with rising TP flux is nearly double that of large rivers. Our findings highlight a shifting global phosphorus landscape and underscore the need for more targeted, sustainable phosphorus management strategies.
How to cite: Liu, G. and Li, D.: Human impact on land–ocean total phosphorus flux in the world's rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2574, https://doi.org/10.5194/egusphere-egu26-2574, 2026.
Although the hydrological impacts of land use changes are well studied, few datasets comprehensively capture the influence of land management on hydrochemical processes and solute fluxes. The long-term Wüstebach catchment experiment within the TERENO network (TERrestrial Environmental Observatories) provides a unique infrastructure for monitoring key water balance components, numerous anions and cations, as well as spatiotemporal soil moisture—both before and after partial deforestation and subsequent forest management measures such as thinning and underplanting.
We present long-term hydrochemical observations, including macro- and micronutrients, dissolved aluminum, and dissolved organic carbon, collected three years before and thirteen years after deforestation. Hourly concentrations and fluxes were estimated using the R package LOADFLEX. Predicted nitrate concentrations were compared with high-resolution reference data to select the optimal modeling approach. Comparable flux data were determined for a neighboring reference catchment with similar characteristics but without clear-cutting, enabling the isolation of deforestation and reforestation effects on nutrient cycling and transport.
Using flux data from both catchments, we applied a Before–After–Control–Impact (BACI) framework to quantify hydrochemical responses and feedbacks to forest management. Three phases were distinguished: pre-deforestation, the first two years after deforestation, and a later post-deforestation phase (three years after). The BACI analysis revealed distinct short- and long-term responses in solute fluxes, with the strongest effects observed for NO₃⁻, dissolved organic carbon (DOC), and Fe. Notably, fluxes during the two-year period immediately following deforestation differed significantly from both the pre-deforestation phase and the later post-deforestation phase, indicating a pronounced but transient disturbance effect. This dataset offers valuable opportunities to investigate the long-term impacts of deforestation and reforestation on hydrochemical fluxes under varying climatic conditions.
Bogena, H.R., F. Herrmann, A. Lücke, T. Pütz and H. Vereecken (2025): Long-term hourly stream-water flux data to study the effects of forest management on solute transport processes at the catchment scale. Earth Syst. Sci. Data. 17: 6965–6992. DOI: 10.5194/essd-17-6965-2025
How to cite: Bogena, H., Herrmann, F., Lücke, A., and Vereecken, H.: Long-term Effects of Forest Management on Hydrochemical Fluxes: Insights from the TERENO Wüstebach Experiment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12422, https://doi.org/10.5194/egusphere-egu26-12422, 2026.
Algal blooms pose increasing threats to lakes and reservoirs worldwide, with harmful species such as cyanobacteria and dinoflagellates releasing toxin that endanger ecosystem and human health. However, the dominant bloom type varies across systems due to differences in climatic conditions and morphometric characteristics. This study aims to identify the key drivers influencing algal bloom types in freshwater systems. We compiled a global dataset of 160 lakes and reservoirs that have experienced either cyanobacterial or dinoflagellate blooms, incorporating climate variables, morphometric features, and physico-chemical water quality parameters. Using XGBoost and Logistic Regression models, we found that lake morphology, particularly depth and surface area, as well as wind speed are critical determinants of bloom type. Notably, a simple depth-area function (H=7.8×A0.3) effectively differentiate between the two bloom categories, underscoring the strong influence of lake morphology on bloom dynamics. In addition, the dimensionless morphometric index CS=H√(π/A), combined with wind speed, further improves classification performance. Given that lake morphology reflects underlying climatic, hydrodynamic, and biogeochemical conditions, these findings offer practical guidance for assessing bloom risk and developing targeted management strategies.
How to cite: Shi, T., Chen, Y., Zhang, H., Wang, H., and Liu, Z.: Morphometry of reservoirs and lakes reveals differences in algae blooms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13581, https://doi.org/10.5194/egusphere-egu26-13581, 2026.
Accurate assessment of irrigation water quality is essential for sustainable water-resource management in arid regions, where both water scarcity and quality degradation constrain agricultural production. This study evaluates the robustness and sensitivity of the Water Quality Index (WQI) for irrigation assessment using long-term monitoring data from a major reservoir in Jordan. Monthly records spanning 2015–2021 were analyzed for 13 physicochemical parameters, including EC, SAR, HCO₃⁻, Na⁺, Cl⁻, Ca²⁺, Mg²⁺, SO₄²⁻, K⁺, pH, B, NO₃–N, and PO₄–P.
The WQI was calculated using a weighted arithmetic method, in which individual parameter concentrations were converted into unitless sub-indices based on irrigation guideline limits and aggregated using rank-based weights derived from the Rank Order Centroid (ROC) method. Sensitivity analysis was conducted by progressively reducing the number of parameters from thirteen to one to evaluate WQI stability under both Average Permissible Limit (APL) and Maximum Permissible Limit (MPL) threshold frameworks.
Results indicate that WQI scores are highly sensitive to both parameter selection and threshold definition. MPL-based WQI consistently produced lower scores, classifying irrigation water quality as excellent to good, whereas APL-based WQI yielded higher and more conservative classifications. Across both frameworks, WQI values declined systematically as the number of parameters decreased from thirteen to five, indicating reduced redundancy. A five-parameter configuration (WQI-5) yielded the lowest and most stable water-quality scores, closely matching results obtained using the full parameter set, while further parameter reduction (WQI-4 to WQI-1) increased variability and reduced diagnostic reliability.
Principal Component Analysis (PCA) identified EC, SAR, Na⁺, Cl⁻, and HCO₃⁻ as the dominant contributors to irrigation water-quality variability, supporting the optimal performance of WQI-5. The combined sensitivity and PCA framework offers a robust and efficient approach for irrigation water quality assessment in data limited arid environments.
How to cite: Gharaibeh, M., Albalasmeh, A., and Obeidat, M.: Sensitivity Based Optimization of the Water Quality Index for Irrigation Assessment in Arid Regions: Insights from a Major Irrigation Dam, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2379, https://doi.org/10.5194/egusphere-egu26-2379, 2026.
The desert steppe on the northern foothills of the Yinshan Mountains serves as a critical ecological barrier and an agro-pastoral ecotone in northern China. Sustainable utilization of groundwater resources is essential for safeguarding regional ecological security and supporting socio-economic development. In the region, groundwater not only sustains fragile ecosystems but also constitutes the sole source of drinking water for local. However, the groundwater quality exhibits spatial heterogeneity due to the complex geological settings and anthropogenic activities. The study focused on the Tabu River Basin, a representative area of the desert steppe, where 107 groundwater samples were collected. By integrating conventional hydrochemical analysis, self-organizing map (SOM), explainable artificial intelligence (XAI) methods, and health risk assessment coupled with Monte Carlo simulation, we systematically characterized groundwater chemistry, evaluated its suitability for drinking purposes, and identified the dominant factors controlling water quality variations. The results showed that the self-organizing map classified three groundwater clusters, and hydrochemical facies were primarily identified as HCO₃⁻–Ca²⁺, Cl⁻–Na⁺, and mixed HCO₃⁻–Ca²⁺·Na⁺ types, primarily governed by cation exchange and human activities. The entropy-weighted water quality index (EWQI) showed that 23.3% of the samples were classified as excellent, 36.5% as moderate, while 15.9% and 24.3% fell into the poor and very poor categories, respectively. Further analysis employing the XGBoost model combined with SHAP (Shapley Additive Explanations) interpretability techniques identified nitrate (NO₃⁻) and total dissolved solids (TDS) as the key drivers of water quality deterioration. Health risk assessment results indicated that 98.9%, 92.0%, and 80.5% of groundwater samples exceeded the acceptable threshold for total non-carcinogenic health risks for children, adult females, and adult males, respectively. By synergistically combining traditional hydrochemical approaches with unsupervised machine learning (SOM) and interpretable machine learning (XGBoost+SHAP), the study establishes a multidimensional and highly interpretable analytical framework, which not only advances the understanding of groundwater evolution mechanisms in arid and semi-arid inland basins but also provides robust scientific support for the sustainable management and utilization of regional groundwater resources.
How to cite: Wang, Z., Jia, Y., Liao, Z., Jin, J., Zhang, J., and Deng, T.: Unraveling Hydrochemical Drivers of Groundwater Quality and Assessing Associated Health Risks Using Self-Organizing Map, Explainable Artificial Intelligence, and Monte Carlo Simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12984, https://doi.org/10.5194/egusphere-egu26-12984, 2026.
The growing availability of large-scale environmental datasets offers a foundational opportunity to decode continental water quality dynamics; however, disentangling the scale-dependent interplay between transient meteorological forcings and persistent catchment attributes remains a challenge. Focusing on the pronounced hydro-climatic gradients of mainland China, this study presents a comprehensive synthesis of heterogeneous environmental data to bridge the gap between data-driven predictability and mechanistic understanding. We curated an extensive dataset anchored by 4,077 monitoring sections across nine major river basins, incorporating continuous daily observations from April 2014 to February 2025. This dataset covers 10 critical water quality parameters, encompassing a broad spectrum of physicochemical, nutrient, and biological indices.To systematically characterize driving mechanisms, we structured a multi-source explanatory framework that explicitly partitions predictors into two categories: 12 dynamic time-varying forcings (capturing transient fluctuations via meteorological variables and hydrological fluxes) and over 30 static attributes (representing physiographic contexts and anthropogenic footprints, such as land use intensity and reservoir regulation). To decode the nonlinear dynamics of this high-dimensional system, we propose a Physics-Data Coupled Framework employing an Encoder-Decoder architecture integrated with Multivariate Singular Spectrum Analysis. A key innovation is the embedding of explicit physical mechanism gates within the network, designed to ensure hydrological and biogeochemical consistency in deep learning predictions. Beyond enabling robust long-term forecasting, this framework facilitates multi-scale interpretability, allowing for the assessment of how dominant drivers shift across weekly, monthly, and annual resolutions. The analysis elucidates the differential roles of meteorological events in modulating high-frequency variability versus static landscape features in defining long-term baselines, offering a consistent methodological paradigm to support decision-making under changing environmental conditions.
How to cite: Hu, J., Zhi, W., Qin, Y., and Jiang, D.: Spatiotemporal Patterns and Multiscale Drivers of Riverine Water Quality in China: A Continental-Scale Analysis via Physics-Informed Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4161, https://doi.org/10.5194/egusphere-egu26-4161, 2026.
Background. Moldova has a legacy of intensive pesticide use associated with historical agricultural practices and obsolete pesticide storage, with documented contamination affecting river-associated water systems in major transboundary basins such as the Dniester and Prut (Sapozhnikova et al., 2005; Ivanova et al., 2021). Widespread pesticide application combined with inadequate waste management during the Soviet and post-Soviet periods resulted in long-term contamination structured along river networks and basin-scale hydrological organization. This study examines whether spatial and temporal patterns of cancer incidence and prevalence align with hydrologically structured downstream exposure rather than with conventional population-level epidemiological risk proxies, with emphasis on long-latency environmental signals.
Methods. A district-level ecological analysis was conducted using national cancer incidence and prevalence rates per 100,000 population aggregated across three periods, 2008 to 2012, 2013 to 2017, and 2018 to 2022. District-level mean rates were calculated for each period to avoid pseudoreplication. Hydrological vulnerability was defined by integrating official contamination site inventories with peer-reviewed river pollution data and downstream river basin organization. QGIS was used to link river catchments with administrative district boundaries. Seventeen vulnerable districts were compared with eighteen non-vulnerable districts. Group differences were assessed using Welch's t-tests and Mann-Whitney U tests, with effect sizes quantified using Cohen’s d. Correlations with alcoholism, liver hepatitis and cirrhosis, respiratory disease as a proxy for smoking, and population age structure were examined. Analyses were performed using R.
Results. From 2008 to 2017, vulnerable downstream districts showed lower cancer incidence and prevalence, with no statistically significant differences. In contrast, from 2018 to 2022, vulnerable districts exhibited higher cancer incidence by 14.2 percent and higher cancer prevalence by 27.6 percent. Both outcomes were statistically significant with p < 0.05 and showed the largest effect sizes, with incidence Cohen’s d equal to 0.93 and prevalence Cohen’s d equal to 1.59. Temporal trends were highly parallel between groups, with r equal to 0.969 for incidence and 0.999 for prevalence, indicating divergence driven by level rather than trend shape. Risk proxies showed weak and non-significant correlations.
Conclusions. The post-2018 emergence of excess cancer burden in downstream districts represents a delayed spatial pattern consistent with long-latency environmentally mediated exposure structured by river basin hydrology rather than by short-term population-level risk factors. This exploratory study demonstrates the value of hydrology-informed public health analysis for detecting environmentally structured disease patterns and identifying priority downstream corridors for targeted monitoring of river-associated water systems in transboundary basins.
How to cite: Borodulin, N.: Datasets and drivers of catchment-scale river pollution: spatial correlations with district-level cancer incidence in Moldova , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4998, https://doi.org/10.5194/egusphere-egu26-4998, 2026.
Chemical weathering of continental rocks plays a key role in regulating river water chemistry and global biogeochemical cycles. This study investigates the spatial and seasonal variability in major and trace element geochemistry of rivers in the Ganga basin to constrain dominant weathering processes and their downstream evolution. A total of 200 water samples and sediment samples were collected across different hydrological regimes and physiographic settings. The results indicate that dissolved loads are primarily controlled by the weathering of silicate and carbonate lithologies. In the Himalayan headwaters, river chemistry is dominated by carbonate and Ca²⁺–Mg²⁺-rich silicate weathering, with Ca²⁺ + Mg²⁺ and HCO₃⁻ contributing approximately 85% of total cations and anions. In contrast, downstream reaches exhibit a systematic decrease in Ca²⁺ + Mg²⁺ contributions and an increase in Na⁺ + K⁺ proportions (up to ~50%), suggesting enhanced influence of silicate weathering and/or alkaline soil inputs. Trace elements such as Pb, Hg, Th, Sr, Rb, Mo, U, Ba, and V reveal spatially variable source contributions across different catchments. Sodium-normalized trace metal ratios and Ca/Na* relationships indicate additional contributions from carbonate or Ca²⁺–Mg²⁺-rich silicates, particularly during high-discharge periods. Strontium isotope ratios (87Sr/86Sr) of the Ganga River reflect chemical weathering and sediment sources in the Himalayan region. The river drains diverse lithologies of the Himalaya, causing spatial and seasonal isotopic variations. Higher 87Sr/86Sr values indicate silicate weathering of radiogenic continental crust, especially during monsoon periods. Lower ratios reflect inputs from carbonate rocks and recycled sediments. Sr isotopes highlight the role of Himalayan weathering in controlling riverine Sr flux.These observations highlight the combined influence of lithology, hydrology, and seasonal discharge on riverine geochemistry and provide new constraints on chemical weathering processes and trace element fluxes from the Ganga basin to the ocean.
How to cite: Parida, R. K. and Rai, S. K.: Chemical Weathering Dynamics and Riverine Geochemistry of the Ganga River Basin: Spatial and Seasonal Controls on Elemental Fluxes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16801, https://doi.org/10.5194/egusphere-egu26-16801, 2026.
Dry periods can negatively impact river ecosystems through deficits in streamflow (hydrological droughts) and deteriorations in water quality. Yet, literature on water quality extremes is in its infancy and the co-occurrence of hydrological droughts and water quality extremes remains poorly understood, especially for different hydro-climatic regimes. With hydrological models often struggling to capture extreme conditions, observation-based studies from multiple catchments are crucial to gain insights on regional-scale patterns. High-frequency observations - at least daily - are needed to properly characterize often short-lived extreme events, but such observations are rarely available in current large-sample water quality datasets, which mostly have a relatively low temporal resolution (e.g., monthly). Therefore, data fragmentation, both in space and time, has hampered our understanding of the co-occurrence of hydrological droughts and water quality extremes to date. Specifically, here we ask the questions: Where and when do ecologically-relevant water quality extremes occur during hydrological droughts? and What are the hydro-climatic factors influencing these events? To address these questions, we rely on a newly-assembled dataset of water quantity and quality observations (i.e., streamflow, water temperature, electrical conductivity, and dissolved oxygen) at a daily resolution for 43 catchments across Europe and the USA and the period 2005−2024. For this case study, we derive hydrological droughts and water quality extremes (namely, sustained periods of abnormally low flows, abnormally high water temperature and electrical conductivity, and abnormally low dissolved oxygen) using a percentile-based approach with seasonally and catchment varying thresholds. We then focus specifically on concurrent hydrological droughts and water quality extremes, and in particular on those events exceeding thresholds that can lead to stress for aquatic animals. We finally characterize these events using metrics describing their magnitude and timing, and perform regression analyses to shed light on our research questions. Preliminary results indicate that hydrological droughts co-occurring with water quality extremes exhibit notable spatio-temporal variability. They further suggest that ecologically-relevant thresholds are exceeded in terms of water temperature in particular, and especially in catchments with human influence and minimal snow contribution. This study will enhance our mechanistic understanding on the potential impacts of hydrological droughts and water quality extremes on river ecosystems, by providing relevant information for river management under drying and warming.
How to cite: Bruno, G., Li, L., Twining, C. W., and Brunner, M. I.: Spatio-temporal patterns of ecologically-relevant river water quality extremes during hydrological droughts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3887, https://doi.org/10.5194/egusphere-egu26-3887, 2026.
Inland waters (lakes, reservoirs, and rivers) serve as important regulators of global climate change and carbon (C) cycling. China's inland water systems significantly regulate regional C budgets. However, our understanding of the long-term spatiotemporal patterns and underlying mechanisms of dissolved carbon (DC) storages and fluxes in inland waters remains limited. This study examined lake and reservoir DC storage and river DC flux, quantifying their changes in China over the past three decades. We found that inland water DC stocks in China increased from 96 Tg C in the 1990s to 142 Tg C in the 2010s while DC river flux did not significantly change (13.2 ± 0.4 Tg C/yr). Findings also showed that a combination of climate change, anthropogenic disturbance, and water chemistry collectively drove inland water DC dynamics. River DC was more directly driven by climate and anthropogenic factors (>50%) while lakes and reservoirs were more directly influenced by water chemistry (>70%). Additionally, climate factors can explain changes in dissolved inorganic carbon (DIC) concentrations via water chemistry factors (i.e., electrical conductivity (EC) and pH), while, collectively, climate and the nutrient status can typically explain changes in DOC concentrations. This study emphasizes the important role that inland water plays in the global C balance and underscores the necessity of considering it in future C budgets.
How to cite: Wang, S., Benoit, G., Raymond, P., Yu, G., Zhou, F., Liu, S., Miao, C., Sun, K., Li, Z., Jia, J., and Gao, Y.: Dissolved carbon storage and flux dynamics in China’s inland waters over the past 30 years, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17653, https://doi.org/10.5194/egusphere-egu26-17653, 2026.
The dynamics of rainfall-runoff processes, driven by climate change, impact the timing and magnitude of nutrients mobilization from land to river. However, understanding of concentration-discharge (C-Q) relationships of nutrients in event scale from high-frequency observations remains insufficient. This study analyzes event-scale concentration-discharge (C-Q) relationships for total phosphorus (TP) and total nitrogen (TN) using two-year high-frequency monitoring data from seven nested sub-catchments in the Wei River Basin. Based on 92 identified rainfall-runoff events, an advanced power-law C-Q model was applied to derive concentration intercepts, slopes, and hysteresis indices, with controlling factors examined via K-means clustering and Support Vector Machine classification. Results indicate that daily direct flow positively correlates with TP concentration but negatively with TN. Notably, 88.6% of TP C-Q slopes were positive, demonstrating that runoff processes substantially enhance TP export, whereas only 31.1% of TN slopes were positive, primarily in northern catchments. Cluster analysis revealed that TP response patterns (dilution, facilitation, and lagged-facilitation) are largely governed by rainfall/runoff duration and antecedent flow, whereas TN patterns (dilution, weak-function, and lagged-facilitation) are predominantly controlled by rainfall characteristics such as peak intensity, peak ratio, and antecedent rainfall. In conclusion, this research highlights distinct export mechanisms and drivers for TP and TN during hydrological events, providing a process-based framework for predicting nutrient responses under changing climate conditions in semi-arid river basins.
How to cite: Chen, X.: Event-Scale Nutrient Export Dynamics Revealed by High-Frequency Monitoring in a Semi-Arid River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3728, https://doi.org/10.5194/egusphere-egu26-3728, 2026.
Large-sample hydrology aims to identify general spatial and temporal patterns and their exceptions across diverse catchments and to infer underlying processes by linking observed responses to hydroclimatic, biogeochemical, and anthropogenic drivers. While large-sample datasets for water quantity are now well established, similarly comprehensive resources for water quality have remained limited.
QUADICA (water QUAlity, DIscharge and Catchment Attributes) addresses this gap for Germany. Here, we present QUADICA v2, an extended large-sample water quality dataset covering 1386 catchments. The update expands temporal coverage to 2020, adds ecologically relevant water quality variables (including water temperature, oxygen, and chlorophyll a), and introduces long-term time series of nitrogen and phosphorus inputs from both diffuse and point sources. By linking QUADICA with CAMELS-DE, the number of stations with concurrent water quality and discharge data is effectively doubled (now 637 stations).
Beyond extending data availability, QUADICA v2 enables new analyses of driver–response relationships, network-topological patterns, and ecological impact studies across gradients of climate, land use, and pollution pressure. The dataset supports comparative large-sample studies, data-driven and machine-learning approaches, and the calibration and evaluation of process-based water quality models, providing a ground for understanding and management of freshwater systems.
How to cite: Ebeling, P., Hubig, A., Wachholz, A., Scharfenberger, U., Haug, S., Nguyen, T., Sarrazin, F., Batool, M., Musolff, A., and Kumar, R.: QUADICA v2: Expanded large-sample water quality data for Germany offer new opportunities for cross-catchment analyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17607, https://doi.org/10.5194/egusphere-egu26-17607, 2026.
Hydrological research increasingly benefits from large-sample datasets for better understanding and modelling hydrological processes. Recent studies have shown how compiling and integrating large-scale water quality datasets can shed new light on water quality baseline conditions and understanding its controls. However, large-sample water quality datasets remain relatively scarce despite rising global river pollution.
In this study, we compiled around 64 million water quality records for Great Britain’s rivers by harmonising different datasets provided by Natural Resources Wales (NRW) and the Environment Agency of England. We matched these water quality records to existing catchments within CAMELS-GB, a large-sample hydrology dataset containing hydro-meteorological timeseries and catchment attributes for 671 catchments across Great Britain. We applied rigorous quality assurance and control procedures to account for detection limits, outliers, and duplicate entries in the water quality time series. We aim to release this harmonized CAMELS-GB-Chem dataset for national-scale analyses of river water quality.
Using the new CAMELS-GB-Chem dataset, we characterize baseline water quality and trends across Great Britain. We then explore whether these baseline conditions can be linked to catchment attributes such as climatic indicators, hydrological signatures, geology, soil properties, land cover and topography. Our preliminary results reveal that climatic variables (e.g., aridity and mean rainfall) and streamflow metrics (e.g., Q95 and mean discharge) are the dominant controls, while land cover, geology, and soils exert varied influence on different water quality indicators. Future work will incorporate anthropogenic influences into our analysis.
In sum, our work not only fills a critical water quality data gap at the national scale but also lays a scientific foundation for monitoring, modelling, and managing the water quality in Great Britain’s rivers under environmental change.
How to cite: Pianosi, F., Zheng, Y., Howden, N., Woods, R., Coxon, G., and Johnes, P.: Toward a National Understanding of River Chemistry: Analyzing Water Quality Baselines and Controls across Great Britain from the new CAMELS-GB-Chem dataset, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12346, https://doi.org/10.5194/egusphere-egu26-12346, 2026.
Salinization and nitrate contamination are major global threats to groundwater sustainability, particularly in semi-arid regions. In southern Tunisia, these issues are intensified by the combine effect of surface-water scarcity, low average annual precipitation, and excessive groundwater abstraction, emphasizing the need for continuous integrated hydrochemical monitoring and further exploration labors. The Bou Omrane–Sabkhet Ennaouel is ~1668 Km2 country extending between southeastern Gafsa and southern Sidi Bouzid regions in south Central Tunisia and relies exclusively on its own groundwater resources for both domestic supply and agricultural irrigation. Groundwater abstraction therein targets both shallow (phreatic) and deep aquifers, both constitute together the primary freshwater sources of the region. In this work, a physicochemical characterization including analyses/ measures of pH, electrical conductivity (EC), Total dissolved Solids (TDS), major ions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, CO₃²⁻), and nutrient-related pollution indicators (NO₃⁻, NO₂⁻, NH₄⁺, PO₄³⁻) was done at the Laboratory for Applied Geology and Hydrogeology, Department of Geology, Ghent University (Belgium), for 19 and 20 samples from shallow and deep aquifers, respectively. In addition, hydrochemical relationships were examined using correlation analyses, while water quality was evaluated through a multi-index framework integrating the Water Quality Index (WQI) for drinking purposes and the irrigation criteria index.
It was found that the groundwater chemistry is dominated by Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ among cations and SO₄²⁻ > Cl⁻ > HCO₃⁻ > NO₃⁻ among anions. Strong correlations between Na⁺–Cl⁻ and Ca²⁺–SO₄²⁻ suggest common geochemical controls associated with evaporite dissolution and salinization processes. The WQI results indicates the absence of excellent-quality water in both aquifers. In the phreatic aquifer, 12% of samples are classified as good, 21% as fair, 12% as poor, and 55% as extremely poor, whereas the deep aquifer exhibits more severe degradation, with 67% of samples falling into extremely poor class. For irrigation use, salinity constitutes the primary limiting factor. Electrical conductivity, as it is common measure of salinity, classifies 68% of samples as unsuitable for irrigation, while sodicity-related indicators are generally favorable, with most samples presenting SAR values below 10 and acceptable magnesium ratios. The permeability index (PI) and Kelley ratio (Kr) indicate suitable to moderate irrigation water quality, although some samples exhibit PI values below 25. Wilcox and USSL diagrams confirm the predominance of doubtful to unsuitable classes (C3–S1 to C4–S2), indicating significant agronomic risks related to soil structure degradation and crop productivity. Overall, the phreatic aquifer appears more vulnerable due to the limited thickness of the vadose zone, which facilitates contaminant infiltration. These findings highlight the urgent need to strengthen groundwater governance in the Bou Omrane–Sabkhet Ennaouel region through systematic monitoring of groundwater quality and abstraction, integration of field data into administrative databases, and the implementation of adaptive management strategies such as controlled drainage, water blending, and selection of salt-tolerant crops to ensure long-term groundwater sustainability.
How to cite: Ghaib, M., Tanfous, D., Troudi, N., Hermans, T., Dhahri, F., and Walraevens, K.: Integrated hydrochemical and multi-index assessment of groundwater quality in phreatic and deep aquifers of the Bou Omrane–Sabkhet Ennaouel area (Southern Tunisia), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7644, https://doi.org/10.5194/egusphere-egu26-7644, 2026.
This study examines groundwater quality evolution and nitrate transport across a heterogeneous aquifer system in Barcelona Province (NE Spain), assessing compound effects of the 2021–2023 drought (the most severe on record) and anthropogenic pressures over 2016–2024. A total of 3,543 samples from 578 natural springs acting as aquifer discharge points were analyzed for physicochemical and hydrochemical parameters.
Results reveal contrasting trends in nitrate contamination. Mean concentrations decreased 25% (from 39.82 to 29.84 mg/L), attributable to action programs in vulnerable zones under EU Directive 91/676/EEC. However, springs exceeding the 50 mg/L drinking water threshold remained stable at ~24%, indicating persistent structural contamination unresponsive to conventional management. Spatially, elevated nitrates cluster in central and southern sectors coinciding with intensive agricultural and livestock activities.
Hydrochemical characterization confirms carbonate aquifer dominance, with 61.2% of springs exhibiting calcium-bicarbonate facies. Facies evolution between 2023–2024 reveals diagnostic trajectories: transitions toward sulfate facies indicate evaporitic formations contact, while shifts toward chloride facies signal increasing anthropogenic pressure in coastal areas and alluvial plains, reflecting synergistic degradation from drought-induced concentration and diffuse contamination.
The system shows clear climate signals: 0.5°C temperature increase during 2016–2024 and rising electrical conductivity consistent with severe drought. Reduced recharge diminishes natural dilution while increasing contribution of deeper, mineralized flows. This synergy between water stress and pre-existing contamination amplifies degradation beyond what either stressor would produce independently.
These findings demonstrate spring monitoring networks' value as complements to official surveillance, providing higher spatial resolution for early detection of localized deterioration. Differential aquifer vulnerability, with porous alluvial systems showing highest sensitivity to drought and contamination, has direct implications for prioritizing protection in recharge areas.
How to cite: Sigoña León, P., Vázquez-Suñé, E., and Valdivielso, S.: Groundwater Quality Degradation and Nitrate Transport in a Mediterranean Aquifer System: Synergistic Effects of Extreme Drought and Anthropogenic Pressures (Barcelona Province, Spain), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18982, https://doi.org/10.5194/egusphere-egu26-18982, 2026.
In the context of climate change, characterized by increasingly frequent droughts, stronger regional hydrological contrasts, and growing pressure on water resources, understanding and anticipating the evolution of river thermal regimes is essential. River temperature is a key parameter influencing water quality and ecological balance. However, at large scales, thermal responses of hydrosystems remain insufficiently characterized due to limited observational data.
To address this challenge, physical process-based thermal models are commonly used. These models generally require discharge time series as input and are therefore usually coupled with a hydrological model. Nevertheless, the performance of the selected hydrological model can influence thermal simulations—particularly in regions affected by groundwater inputs. In this study, we investigated the sensitivity of a thermal model to different hydrological forcings by applying T-NET thermal model over the Loire basin up to Saumur (81,200 km²) at a spatial resolution of ~1.7 km. This basin exhibits significant hydrological, climatic, and morphological variability, making it a representative case study for testing the sensitivity of thermal models. Specifically, we coupled T-NET with two semi-distributed hydrological models, EROS (developed by BRGM) and MORDOR (developed by EDF), and compared their outputs. Model outputs were validated for the 2008–2016 period using observations from ~400 stations, and the comparison was extended to 1980–2016 at multiple temporal scales (daily, monthly, seasonal, and annual).
Our results show that differences in hydrological model structure and performance influence thermal simulations. This finding is critical for identifying areas with substantial groundwater contributions where mitigation strategies could help limit increasing river temperature trends under climate change. By quantifying the impact of hydrological model choice on thermal simulations, this study provides insights for improving coupled modeling approaches and supports better-informed water management and adaptation strategies.
How to cite: Seyedhashemi, H., Hendrickx, F., Monteil, C., and Lamouroux, R.: Sensitivity of River Thermal Modeling to Hydrological Forcing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10324, https://doi.org/10.5194/egusphere-egu26-10324, 2026.
