• investigate the combined impacts on water quality and quantity from climate change and/or extremes across local to global scales, including climate impact attribution studies;
• investigate the impacts of present and future socio-economic developments on surface and/or groundwater quality;
• quantify and couple supply and demand in support of water quality management including vulnerability assessment, scenario analysis, indicators, and the water footprint;
• project future water scarcity or water security (combining water quality & quantity) supply and demand in the context of a changing climate and other global change drivers;
• quantify the uncertainty of water quality model under drivers of global change;
• interpret and characterize uncertainties in machine-learning, AI and data-mining approaches that are trained on large, possibly high-resolution data sets;
• address the problem of temporal and spatial scaling (e.g. disparity of scales between processes, observations, model resolution and predictions) in water quality modelling;
• test transferability and generalizability of water quality findings;
• involve stakeholders in water quality model development to inform risk analysis and decision support;
• application of remote sensing in water quality estimates at multiple scales.
Orals: Tue, 29 Apr | Room 2.31
A MACRO-SCALE FRAMEWORK TO ANALYZE INTEGRATED NITROGEN DYNAMICS IN LARGE DRAINAGE SYSTEMS: APPLICATION TO THE MISSISSIPPI RIVER BASIN
Charles J. Vörösmarty1,2
1 Environmental Sciences Initiative of the CUNY Advanced Science Research Center at the Graduate Center, New York, NY (USA)
2 Department of Geography and Environmental Science at CUNY Hunter College, New York, NY (USA)
Global disturbance of the nitrogen cycle is driven by anthropogenic alteration to ecosystem function through increased fertilizer use for agriculture, urbanization/sewage, and destruction of natural habitat. The Mississippi River Basin/Gulf of Mexico (MRB/GoM) is among the clearest examples of such disruption, leading to deterioration of water quality and hypoxic bottom water developing each summer at the coast. Increasing flux of reactive nitrogen (N) to rivers represents significant vulnerabilities to human health, economic productivity, and ecosystem function. Climate is a major component in determining the system’s N metabolism, and extremes such as droughts and floods have been known to result in N from fertilizer lost to the atmosphere or surface/ground water, rather than incorporation into crops. This talk describes an environmental surveillance system to monitor and understand dynamics of the near contemporary N cycle across the MRB/GoM land-to-ocean continuum. The multi-institutional effort focuses on near real-time N cycle responses to 5 categories of climate events: short-term wetting/drying, rapid freeze/thaw, heatwaves, extreme precipitation/flooding, and drought. It tests the hypothesis is that the fluxes of reactive N from the Mississippi River drainage basin to the Gulf of Mexico over the recent past are determined by the conjunction of nature-based and human-engineered infrastructures associated with a relatively small fraction of the total land mass drained by the river. We address this hypothesis via six technical objectives: (1) coalesce and integrate remotely sensed and modeled geospatial data for estimation of terrestrial loading of N for ingestion by biogeochemical models, (2) apply estimation techniques (modeling, remote sensing, and in-situ data integration) for land-to-atmosphere gaseous losses and analyze the impact of climate variability, (3) create aquatic transport and processing model estimates of N flux, representing the behavior of both engineered and natural systems, (4) carry out and validate remotely sensed inland and coastal plume analysis, (5) reconfigure existing technical integration frameworks to create C-FrAMES, uniting results and workflows described under objectives 1-4, (6) engage stakeholders including through NASA mission early adopters. The discussion explains how these activities poise us to move from contemporary monitoring into forecast mode.
How to cite: Vorosmarty, C.: A Macro-Scale Framework to Analyze Integrated Nitrogen Dynamics in large Drainage Systems: Application to the Mississippi River Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13459, https://doi.org/10.5194/egusphere-egu25-13459, 2025.
Excessive levels of nutrients, particularly nitrogen and phosphorus, can trigger eutrophication leading to harmful algal blooms and oxygen depletion thus endangering freshwater fish species. Despite the widespread awareness of these risks, efforts to protect freshwater fish species remain largely ineffective, especially under climate change and other anthropogenic pressures. This study therefore identifies potential future hotspots for nutrient pollution in African river systems, specifically assessing the risk that elevated total nitrogen (TN), and total phosphorus (TP) levels pose to freshwater fish species. We identified areas where nutrient concentrations are likely to exceed critical thresholds, that could increase the occurrence of eutrophication and thus threatening freshwater fish biodiversity. Using two large-scale water quality models; SWAT+ and IMAGE-GNM, we analysed annual concentrations of TN and TP for 2010 and 2050 under the combined shared socio-economic pathways (SSPs) and representative concentration pathways (RCPs) combined scenario, SSP5-RCP8.5. Basing on the United Nations Environment Programme (UNEP) target thresholds used for the assessment of SDG indicator 6.3.2 that designates a waterbody as having “good ambient water quality”, both models predicted that from 2010 to 2050, the percentage of African rivers exceeding the critical thresholds of 0.7 mg/L for TN and 0.02 mg/L for TP will increase by 15% under the SSP5-RCP8.5 scenario. High nutrient levels in river basins such as the Niger, Nile, and Limpopo overlap with areas of high fish species richness, posing a significant threat of exposure to nutrient pollution. At continental scale, from 2010 to 2050, the proportion of freshwater fish species at high risk from TN pollution significantly increased by 23%. In contrast, >90 % of fish species remained highly vulnerable to TP pollution throughout the 2010 and 2050 periods. Our findings highlighted regions where proactive management and policy interventions should be prioritised to mitigate the potential adverse effects of nutrient pollution on freshwater fish biodiversity.
How to cite: Nakkazi, M. T., La Fuente, S., Lekarkar, K. L., Suresh, K., Peyton, J., H.W Beusen, A., and van Griensven, A.: Future nutrient pollution increases risk to Africa’s freshwater fish biodiversity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15563, https://doi.org/10.5194/egusphere-egu25-15563, 2025.
In Europe, despite advanced environmental legislation, excessive nutrient pollution from intense agriculture and high population density compromises water quality, affecting both human and ecosystem needs. Climate change, extreme weather events, and socio-economic developments will continue to impact the delivery of nutrients to freshwaters and marine waters. To anticipate risks and identify effective strategies for increasing water resilience, it is essential to understand the combined impacts of climate and socio-economic changes on water quality and quantity.
Using scenario modeling, we project changes in nitrogen and phosphorus water quality out to 2050, accounting for the expected effects of EU environmental policies and climate variability. We employ a source-to-sea approach, examining the impacts on both freshwater and coastal/marine waters. We conduct regional-specific analyses, examining the relationships between freshwater quality and ecological conditions, as well as potential risks for eutrophication in coastal waters. By linking sources to impacts, our scenario analysis identifies the nutrient reductions needed to achieve water quality objectives for both freshwater and marine waters, ensuring future water security by considering both water quality and quantity.
This study provides insights into the nutrient reductions required to meet environmental policy objectives in European fresh and coastal waters under future climate, informing strategies for sustainable water management.
How to cite: Grizzetti, B., Udias, A., Vigiak, O., Pistocchi, A., Bouraoui, F., Galimberti, F., Aloe, A., Zanni, M., Zampieri, M., Piroddi, C., and Macias, D.: Nutrients in European waters in 2050, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19277, https://doi.org/10.5194/egusphere-egu25-19277, 2025.
Good water quality is essential for society and ecosystems, but it has been a pressing issue in many rivers and coastal waters. Harmful algal blooms resulting from eutrophication are examples of such matters. Eutrophication is often linked to excessive nutrient loadings and climate change (e.g. temperature and precipitation changes). Global water quality models can be used to understand better how nutrients respond to changes in climate and socio-economic developments. Changing climates result in changing hydrological cycles including river discharges. Nutrient flows from land to seas are, in turn, dependent on these hydrological cycles. Increased runoff, resulting from increases in long-term precipitation or precipitation intensity, may transport more nutrients from land to rivers. As a result, climate change may further exacerbate nutrient problems in the future. However, our understanding of how nutrients in coastal waters respond to uncertainties in climate-driven hydrological changes on land is limited at large scales. This especially holds for global water quality models projecting nutrient loadings to coastal waters worldwide. Water quality models rely on hydrological projections using global hydrological models (GHMs), which are further driven by Global Climate Models (GCMs). Numerous GCMs exist, each simplifying complex systems, and adding uncertainty to their projections. Uncertainties may then propagate through the modelling chain, potentially affecting the robustness of global water quality model results.
Here, we aim to better understand how future nutrient exports by rivers respond to hydrological changes driven by different GCMs and how this affects model reliability. For this, we use a soft-coupled model system accounting for water quantity (VIC model1) and water quality (MARINA-Multi model2) under a rapid urbanisation and high global warming scenario. Then, we introduce an approach to compare projected trends of nutrient loadings to coastal waters for 2050 across five selected GCMs, which diverge in their climate forcings. This study contributes to the first global-scale water quality model intercomparison effort as initiated by the Water Quality sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Preliminary results reveal that climate-driven hydrological changes will mainly add uncertainties to projections in arid regions. Nevertheless, a vast majority of the global surface areas agree on trends in nutrient exports by rivers for at least three out of five GCMs. Yet, agreements may differ across sea regions. Our approach can be used to identify robust trends in future nutrient loadings to seas under climate-driven hydrological changes, enhancing the reliability of global water quality models. This aids in identifying effective solutions for coastal water pollution worldwide under climate change.
1Van Vliet, M. T. H., Van Beek, L. P. H., Eisner, S., Flörke, M., Wada, Y., & Bierkens, M. F. P. (2016). Multi-model assessment of global hydropower and cooling water discharge potential under climate change. Global Environmental Change, 40, 156-170. https://doi.org/10.1016/j.gloenvcha.2016.07.007
2Micella, I., Kroeze, C., Bak, M. P., Tang, T., Wada, Y., & Strokal, M. (2024). Future scenarios for river exports of multiple pollutants by sources and sub-basins worldwide: Rising pollution for the Indian Ocean. Earth's Future, 12, e2024EF004712. https://doi.org/10.1029/2024EF004712
How to cite: Bak, M., Micella, I., Tang, T., and Strokal, M.: Responses of nutrient loadings entering coastal waters to climate-driven hydrological changes worldwide, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9569, https://doi.org/10.5194/egusphere-egu25-9569, 2025.
Water temperature is a key abiotic factor for determining the health, functioning and services provided by aquatic ecosystems. While analysis of existing observational data indicates that freshwaters are warming across the globe, the availability of long-term water temperature monitoring data remains limited in several regions of the world (e.g. Africa, South America, parts of Asia).
Models offer unique possibilities to explore the spatial and temporal dynamics of surface water temperature beyond what is possible through monitoring efforts alone. Water temperature models have been developed and applied for past and future conditions across various spatial scales, from individual lakes and streams to global applications. With a few notable exceptions, comparisons of water temperature simulations across different models are scarce. Aligning with ongoing activities within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), here we compare simulations of surface freshwater temperature from 11 surface water quality models (7 river and 4 lake models) that consistently used bias-corrected climate forcing from either CMIP5 (ISIMIP2) or CMIP6 (ISIMIP3).
Our multi-model ensemble suggests that surface water temperatures have risen substantially over the last 40 years, with global average annual water temperatures already 0.5 – 0.8 ºC warmer than at the turn of the century, and that warming will extend and intensify with future climate change throughout the 21st century. For example, the multi-model ensemble suggests that global average annual water temperatures will rise by approximately +1 ºC under RCP2.6, +2 ºC under RCP4.5, +2.5 ºC under RCP6.0, +3 ºC under RCP7.0 and +4 ºC under RCP8.5 by the end of the century, compared to a historical reference period (1981-2000). Despite the consistent projections of warming, inter-model differences can be substantial. Furthermore, water temperature simulations are demonstrated to be highly sensitive to the meteorological forcing from different global climate models. To further unpack these aspects, in addition to evaluate model performance and better elucidate spatio-temporal patterns in water temperature projections, in this presentation we will display additional analysis on modelled output from three river water temperature models (CWatM-WQ, DynQual and WaterGap2) run using the state-of-the-art ISIMIP3 climatological forcing. To illustrate a potential societal impact of these projected water temperature rises, we quantified the associated reduction in usable capacity of existing thermoelectric powerplants globally.
How to cite: Jones, E. R. and van Vliet, M. T. H.: A multi-model assessment of global freshwater temperature under climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2718, https://doi.org/10.5194/egusphere-egu25-2718, 2025.
Total Organic Carbon (TOC) refers to the total amount of carbon contained in all organic matter present in water and is used as a key indicator of water pollution. Elevated TOC concentrations in water can lead to decreased dissolved oxygen levels and accelerated eutrophication, causing severe impacts on river and aquatic ecosystems. Moreover, the increase in toxic substances and pathogenic microorganisms may compromise the safety of drinking water sources.
Recent changes in rainfall patterns, rising water temperatures, and ecosystem shifts driven by climate change have further increased uncertainties in water quality monitoring and TOC prediction. To mitigate potential socio-economic damages caused by delays in greenhouse gas reduction and carbon neutrality policy implementation, this study aims to predict the TOC concentrations of Korea’s four major rivers—the Geum, Nakdong, Yeongsan, and Han Rivers—using various machine learning algorithms and climate change scenarios based on the IPCC Sixth Assessment Report’s RCP and SSP frameworks.
Water quality data from 2008 to 2022, including water temperature, DO, BOD, COD, chlorophyll-a, TN, TP, pH, conductivity, dissolved total phosphorus, dissolved total nitrogen, NH3-N, NO3-N, SS, and TOC, were combined with daily average temperature, background CO2 concentration, and precipitation data. Various machine learning algorithms, including CNN, ANN, Random Forest, and XGBoost, were employed to compare TOC prediction performance and identify the optimal model. Using the machine learning models trained on historical data, future water TOC concentrations were predicted by inputting scenario-based temperature and precipitation data. Climate change scenario data, specifically the SSP5-8.5 detailed daily data for South Korea, were utilized to predict and compare future TOC concentrations in water from 2023 to 2100 across different time periods.
Through this study, we aim to forecast the changing trends of TOC in Korea’s four major rivers and analyze the significance of TOC in achieving carbon neutrality. This research will contribute to the development of water quality management strategies aligned with climate change mitigation efforts.
How to cite: Lee, S., Moon, J., Jung, S., Suh, S., Baek, J., Ok, C., and Pyo, J.: Scenario Analysis of Total Organic Carbon Changes in South Korea's Four Major Rivers under Climate Change Using Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5442, https://doi.org/10.5194/egusphere-egu25-5442, 2025.
Arsenic concentrations in the groundwater in the Hungarian Great Plain region are naturally high with background levels up to 225 μg/L. Being largely dependent on this arsenic-rich water, Hungary has suffered compromised drinking water quality for decades. In 2016, the European Commission issued an infringement notice calling for compliance with the European Union Drinking Water Directive arsenic regulations for 66 non-compliant zones out of the 365 water supply zones in Hungary. As of 2022, the number of zones in non-compliance was reduced to 13 through the Environment and Energy Operative Program. Despite reforms, drinking water systems in Hungary are still susceptible to arsenic contamination due to accumulation and infiltration in the water distribution system. Trace amounts of arsenic not removed in water treatment processes can accumulate in biofilms, mineral deposits, and pipe scale within the distribution system. These arsenic-laden masses may release concentrated arsenic deposits when disturbed by pressure alterations triggered by repair and maintenance activities, power interruptions, and valve operations in the system. Arsenic-rich groundwater also enters the potable water supply during negative pressure events through pipe breakage and leaks in the pipe network. These pathways for arsenic exposure have not been thoroughly investigated and subsequent impacts to drinking water, especially in aging distribution systems and under increasing climate stressors, is uncertain.
Arsenic exposure was investigated in a small water distribution system in the Hungarian Great Plain because small systems are susceptible to high water age and low flows which can exacerbate contaminant buildup in the system. Weekly water samples from public faucets and source water were analyzed for temporal and spatial fluctuations in arsenic, manganese, iron, pH, conductivity, alkalinity, and oxidation/reduction potential from September 2024 to May 2025, with a 3-month break for winter. Total samples collected will be around 300. Nine fire hydrant-mounted pressure sensors were used with a hydraulic model to investigate hydraulic influences on water quality, while rainfall and water temperature were recorded to account for climatic factors. Water system maintenance activities were noted to account for external interference in normal system operations. Localized spikes in arsenic up to 14 μg/L were detected. The highest arsenic concentration was concurrent with maintenance activity, atypically high redox potential, and pressure drops in the system. Trend analysis and predictive modeling results will be presented to describe the relationships between hydraulic, climatic, and water quality parameters in the system.
How to cite: Clayton, J., Terry, L., and Mihucz, V.: Arsenic Speciation and Pressure Monitoring in a Hungarian Water Distribution Network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6274, https://doi.org/10.5194/egusphere-egu25-6274, 2025.
Urban swimming has re-emerged as a popular activity, especially in France as the Paris 2024 Olympic and Paralympic Games races in the Seine River marked a significant milestone in the revival of open-water swimming. Yet, maintaining water quality in urban areas poses important challenges, especially with the increasing frequency of extreme weather events linked to climate change. Indeed, heavy rainfall leads to sewer system overflow.
This study presents a framework to estimate health risks at urban bathing sites by linking rain intensity to microbial contamination. High-frequency bacteriological timeseries monitoring based on a new monitoring system were collected from the Seine River, between 2021 and 2023. In parallel, meteorological and hydrological data were collected in the upstream urban watershed.
The timeseries dataset was split into discrete events. An event is defined as a rainfall period, possibly extended to a bacterial peak. Each event was characterised by indicators such as total rainfall, mean flow rate, maximum bacterial concentration, etc. The dimension reduction was first based on a Principal Component Analysis (PCA) and then on a Manifold Isomap technique.
PCA confirmed correlations between rain parameters and bacterial concentrations. Then, Manifold Isomap synthesised selected rain characteristics into a single dimensionless indicator (Global Rain Parameter, GRP). A threshold effect appeared in the relationship between GRP values and bacterial peaks. Below this threshold (GRP = 1.5), no bacterial contamination is observed. Above this threshold contamination increases linearly with GRP. The method was then tested to predict water quality during the Olympic and Paralympic Games. It successfully forecasted future rain events as problematic or not and estimated periods for safe swimming conditions.
The proposed framework opens up new perspectives for the future management of the public bathing sites that will open as a legacy of the Olympic and Paralympic Games in summer 2025. Furthermore, this methodology could be adapted to a wide range of applications when it comes to forecasting surface water quality in urban areas.
How to cite: Guillot - Le Goff, A., Cartier, Y., Vinçon-Leite, B., Boyaval, S., Kennouche, P., and Carmigniani, R.: From rain to health risk: forecasting microbiological contamination in urban rivers using dimension reduction techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11429, https://doi.org/10.5194/egusphere-egu25-11429, 2025.
In the context of Water-Energy-Food (WEF) nexus security, it is imperative to place greater emphasis on the water quality dimension to ensure sustainable and resilient systems. While traditionally much focus has been placed on the availability of water, recently the quality of water emerged as a critical factor that limits its supply across various sectors, including agriculture, energy production, human and ecological needs.
The impacts of global change—including climate change and land use intensification to meet socio-economic development needs—are reshaping water availability and quality in complex ways, influencing both the quantity of usable water and its suitability for specific purposes. Understanding these interconnections is vital for assessing the broader implications of clean water availability, as poor water quality can constrain sectoral efficiency and undermine ecosystem health. A spatial Bayesian Network (BN) model has been developed to predict the conjoined impacts of future climate change and land use trajectories on water chemistry in the Upper Adige River basin in Northern Italy. It allows to predict different water quality indicators (e.g. nutrient concentration, Dissolved Oxygen, temperature, pH, Total Suspended Solids) at the sub-catchment and seasonal scale and to classify their status (i.e. LIMeco Index) according to the Water Framework Directive 2000/60/EC. The model has been implemented using ARIES (Artificial Intelligence for Environment and Sustainability), a Machine Reasoning platform for data and model integration. The model has been trained with historical water quality data from 2013-2022, considering as predictors specific indicators that serve as proxies for the different nexus sectors as well as external drivers (i.e. climate and land use). The strength of this work lies in enabling a spatial understanding of the drivers influencing water quality, allowing the identification of critical sources of pressures on water quality related to different economic sectors, and the spatial mapping of priority areas most affected by these pressures, as well as the prediction of the conjoined impacts of different scenarios (i.e. climate change, land use change, anthropic stressors). The findings highlighted that diffuse sources attributable to agricultural activities, forest management, and the presence of highly urbanised areas play a greater role in influencing nutrient concentration than point sources and that while expected land use changes are quite significant in some basins, their impacts are moderated by hydroclimatic variables such as flow conditions and temperature, which vary considerably between seasons. By identifying hotspots of nutrient pollution and the key variables influencing water quality, the findings provide valuable tools for local authorities to implement measures and plans aimed at mitigating water quality deterioration. In the broader context of WEF nexus management, the results of this research underscore the importance of proactive water management strategies that account for the complex interactions between land use, climate, and water quality.
How to cite: Vogt, M., Sperotto, A., Márquez Torres, A., Balbi, S., and Critto, A.: Bayesian Network application to assess Water Quality from a Water-Energy-Food Nexus perspective: A case study in the Upper Adige River Basin (Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9694, https://doi.org/10.5194/egusphere-egu25-9694, 2025.
The increase in anthropogenic activities and rise in population in the Yamuna River basin have a significant impact on water quality, necessitating comprehensive assessment techniques for sustainable management. The objective of the present study is to develop a Water Quality Index (WQI) for the Upper Yamuna River, India by combining geospatial methods with a multicriteria decision-analysis technique. The data of different physio-chemical parameters for the upper Yamuna River was collected from the Central Water Commission for a period of 25 years (1997-2022). The spatial variability of water quality was highlighted in Geographic Information Systems (GIS) and the hotspots were identified. The methodology comprises of four steps: a) parameter selection, b) development of raring curves, c) use of principal component analysis to extract the principal components, d) use of hybrid aggregation technique to develop the WQI. At Poanta, WQI lies in the range of 44.04 to 87.09 with an average value of 65.02, while at Kalanaur, WQI varies in between 19.93 to 81.11 with an average value of 60.11. at Mawi, WQI ranges from 47.91 to 88.48 with an average value of 63.24 whereas, at Palla, WQI varies from 42.88 to 74.08 with an average value of 61.19. The average WQI value of all the locations characterizes the water quality as good. Although the water quality at the Kalanaur location varies significantly from very poor quality to good quality due to the improper disposal of industrial waste into the Yamuna River. The study underscores the sections of the river that are having poor water quality at urban and industrial locations due to high pollution levels. The study emphasizes the importance of integrating spatial analysis with water quality modeling to effectively handle challenging environmental issues. Further, the experts may implement the WQI to deploy mitigation measures and develop strategic planning for the sustainable water quality management of Upper Yamuna River.
Keywords: GIS, Multicriteria Decision-Making, Spatial Variability, Sustainable Management, Yamuna River.
How to cite: Gani, A., Pathak, S., and Hussain, A.: Development of Water Quality Index for Upper Yamuna River, India by using Multicriteria Decision Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1191, https://doi.org/10.5194/egusphere-egu25-1191, 2025.
This study simulated the terrestrial losses of total phosphorus (TP) likely to be delivered to 6,836 standing waters in Scotland via surface and sub-surface pathways to explore the potential of measures to mitigate the impact of future climatic and land cover change on TP loads. TP losses from land were simulated in kg-1 ha-1 yr-1 at 100 × 100 m raster resolution, using a spatial Bayesian Belief Network (BBN)1. Diffuse sources through drains and by soil erosion; incidental losses from farmyards; sewage treatment works (STWs) and septic tanks (STs) were included.
To understand the effects of future climate change, two Representative Concentration Pathways RCP2.6 (~1.5oC warming by 2080) and RCP6 (~3 oC warming by 2080) were coupled with future land cover change until 2040, 2060 and 2080 from CRAFTY-GB2 (based on stakeholder-elaborated Shared Socioeconomic Pathways SSP1 – Sustainability and SSP3 – Regional rivalry). In addition, land management mitigation measures were simulated to examine their potential to reduce TP losses from land during baseline period. Modelled scenarios included fertiliser application rates ‘at’ and ‘below’ agronomic optimum; increase in extent of buffer strips to 8m and a combination of measures.
Expansion of arable land and intensification of agriculture under the higher emissions scenario RCP6 linked to unfavourable land use changes in SSP3, could more than double TP inputs to standing waters, while sustainable land use reconfiguration in SSP1 associated with lower emissions scenarios RCP2.6 was found to reduce TP losses by up to 20% by 2080.
Land-based mitigation measures focused on maintaining soil nutrient status at, or below, the agronomic optimum reduced TP inputs to standing waters, in some cases by more than 40% during the baseline period. This shows that holistic management of soils to maximise soil organic matter content and nutrient use efficiency, supported by soil testing and optimisation of fertiliser applications, would reduce terrestrial TP losses. Conversely, smaller-scale interventions, such as buffer strips, did not affect TP losses to water significantly at a catchment scale.
- May, L., Glendell, M., Adams, K., Gagkas, Z., Gouldsbrough, L., Gunn, I., Hannah, M., Roberts, M., Spears, B., Taylor, P., Thackeray, S., Troldborg, M., Zaja, E. (2024) Mitigating Climate Change Impacts on the Water Quality of Scottish Standing Waters. Centre of Expertise for Waters https://www.crew.ac.uk/publication/mitigating-climate-change-phase-2
- Brown, C. et al. (2022) Agent-Based Modeling of Alternative Futures in the British Land Use System. Earth’s Futur. 10. 10.1029/2022EF002905
How to cite: Glendell, M., Gagkas, Z., Adams, K., May, L., and Taylor, P.: Understanding the effects of future climate, land cover and land management change on total phosphorus losses to lakes in Scotland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2851, https://doi.org/10.5194/egusphere-egu25-2851, 2025.
Excessive nutrient loading into rivers, lakes, and estuaries has been a primary driver of aquatic ecosystem degradation worldwide, and European lakes are no exception. Nutrient inputs from agriculture, compounded by climate change, threaten the ecological integrity of these water bodies. Sustainable management strategies must prioritize reducing external nutrient and sediment inputs from catchments, focusing on source control measures such as maintaining nitrogen (N) and phosphorus (P) levels in agricultural soils at or below optimal agronomic conditions, while enhancing natural attenuation processes along water and solute transport pathways. This study evaluates the impacts of different land use management options on European lake ecosystems under current and future climate change and socio-economic drivers. Using the Soil and Water Assessment Tool (SWAT+), we simulate nutrient and sediment load transport from catchments to receiving lakes, while the GPLake-M model assesses lake ecological regime shifts to identify optimal management and restoration strategies. The methodology is applied to case studies of Lakes Vico, Dümmer, and Bisret, serving as demonstration sites to inform broader applications across Europe. Our findings highlight pathways for reducing nutrient loading and achieving sustainable lake management, aligning with freshwater and climate policy objectives in the context of a changing climate, degrading aquatic ecosystems, and rising demands on land and food systems.
How to cite: Nkwasa, A., Pelorosso, R., Nicolina Ripa, M., Nilca, M., Puiu, I., and Kahil, T.: Nutrient mitigation pathways for sustainable lake ecosystems in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5193, https://doi.org/10.5194/egusphere-egu25-5193, 2025.
Nitrogen pollution in European landscapes poses persistent challenges to aquatic ecosystems, human health, and water quality. The European Union has set a goal to achieve zero pollution by 2050, including the reduction of air, water, and soil pollution to levels that no longer harm health or natural ecosystems. However, the feasibility of achieving this goal for legacy contaminants like nitrogen (N) under changing climate and land-use management is not well understood. This study employs a multimodel approach to provide a comprehensive assessment of nitrogen pollution across European river systems under varying climate emission and land-use management scenarios. We used a suite of hydrological and biogeochemical models (mHM-mQM, SWAT, and IMAGE-GNM) driven by an ensemble of climate projection datasets (CMIP) operating under diverse emission scenarios (RCPs; 2.6, 4.5, and 8.5) and shared socioeconomic pathways (SSPs; 1-5). These climate-driven runs were complemented with nitrogen input scenarios adhering to different SSPs, accounting for strategies managing agricultural land and technological innovations while considering future factors such as food production, economic growth, and environmental requirements. Ensemble hydrologic and nitrogen export simulations are constructed for the period spanning 1971 to 2070. Our analysis highlights notable progress in reducing nitrogen loads across European river systems by the 2050s compared to the 2010s. Regionally, our ensemble simulations identify Central Europe as a persistent area of concern, with relatively higher nitrogen exports projected under both conservative (SSP1-RCP2.6) and conventional development (SSP5-RCP8.5) scenarios. Despite overall improvements, many European river systems are projected to exceed critical nitrogen concentration thresholds (e.g., 2–3 mg N/L) by the 2050s. The majority of ensemble simulations consistently reveal similar hotspot regions in countries like Germany, France, Poland, Italy, and Spain. This may be linked to ongoing nitrogen exports that gradually deplete legacy reservoirs (e.g., soil and groundwater). By integrating multimodel insights, our study aims to provide a robust framework and assessment for anticipating and addressing the challenges of nitrogen pollution in pursuit of realizing EU zero-pollution goals.
How to cite: Kumar, R., Nguyen, T. V., Beusen, A., Nkwasa, A., Ebeling, P., and Musolff, A.: Multimodel Assessment of Nitrogen Pollution in European River Systems under Changing Climate and Shared Socioeconomic Pathways, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15833, https://doi.org/10.5194/egusphere-egu25-15833, 2025.
Climate change poses significant challenges to water quality in river catchments globally, particularly in regions heavily dependent on natural water systems for agriculture, domestic use, and biodiversity conservation. The Yala River Catchment in Western Kenya, a critical water resource in the region, is increasingly threatened by climate variability and its associated impacts. Rising temperatures and changing rainfall patterns exacerbate sediment transport, nutrient runoff, and overall water quality degradation. This study evaluates the long-term impacts of climate change on water quality in the Yala Catchment, providing insights to support adaptive management strategies. The study employed the Soil and Water Assessment Tool Plus (SWAT+), a process-based hydrological model, to simulate climate change impacts on the Yala River Catchment. Climate projections from ISIMIP3b (Inter-Sectoral Impact Model Intercomparison Project) were used to drive the model, capturing changes in temperature and precipitation for the period 2030 to 2100 under various shared socioeconomic pathways (SSPs). The SWAT+ model was calibrated and validated using historical climate and hydrological data. Simulations were run to assess baseline water quality conditions and future scenarios, focusing on key indicators such as sediment yield, nutrient runoff, and surface water quality under varying climate conditions.
Preliminary findings reveal significant climate-driven changes in the Yala River Catchment: Increased temperatures and rainfall: Projections indicate an average temperature rise of 2–3°C and an increase in extreme rainfall events, particularly during the wet season. Enhanced sediment and nutrient runoff: Higher rainfall intensity and frequency contribute to elevated soil erosion and nutrient transport, particularly in agricultural areas and steep terrains. Decline in water quality: Increased sediment and nutrient loads lead to reduced water clarity and heightened concentrations of nitrogen and phosphorus, posing risks to aquatic ecosystems and water usability.
The results demonstrate that climate change will exacerbate water quality challenges in the Yala River Catchment, driven by increased sediment and nutrient fluxes from intensified rainfall and rising temperatures. These impacts highlight the need for urgent, adaptive management strategies to mitigate the adverse effects of climate variability and ensure the sustainability of water resources.
To address the projected impacts of climate change on water quality in the Yala River Catchment, the following actions are recommended: Implement sustainable land and water management practices to reduce sediment and nutrient runoff, such as riparian buffer zones and conservation agriculture. Develop and enforce climate-adaptive policies for catchment management that incorporate long-term climate projections. Enhance monitoring systems to provide real-time data on water quality and climate trends for proactive decision-making. Foster community engagement and capacity-building programs to encourage adoption of climate-resilient practices.
How to cite: Olala, S., Kiplangat Ronoh, E., Benedict Mukama, E., Lesaalon Lekarkar, K., Nkwasa, A., Nyolei, D., Maina Nyongesa, J., Nyadawa, M., and van Griensven, A.: Evaluating the Impacts of Climate Change on Catchment Water Quality in Yala River Catchment, Western Kenya., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19580, https://doi.org/10.5194/egusphere-egu25-19580, 2025.
Freshwater salinization poses a threat to river ecosystems, with anthropogenic influences playing a crucial role. A low discharge rate can further increase river salinity. The destructive impact of this issue became evident in Germany and Poland in August 2022, when elevated chloride levels in the Oder River facilitated the bloom of Prymnesium parvum, a toxic brackish-water alga. The release of its toxin led to an ecological disaster with a massive fish kill, highlighting the urgent need for analysis and preventive measures to avoid similar incidents in the future.
Using data from 1,628 stream water monitoring stations in Germany, this study examines which rivers are particularly affected by chloride concentrations above critical thresholds. By quantifying the concentration-discharge (C-Q) relationship for chloride at 250 stations, we assessed whether chloride concentrations can be reliably predicted from discharge data. Correlation analyses with catchment characteristics allowed the discussion of chloride input pathways and their influence on the C-Q model parameters. Finally, station-specific discharge values were determined, at which critical chloride thresholds are exceeded, thereby impeding the achievement of a good ecological status and promoting the spread of the alga P. parvum.
We found that more than 70% of all stations reach or exceed 50 mg/l chloride at least once. For a threshold value of 200 mg/l it was 16%, with 8% of these showing near-permanent exceedances. Around 9% of the monitoring stations surpass a critical value of 300 mg/l. Distinct spatial patterns of elevated chloride levels are particularly noticeable in the Weser River network, the Saale, the Oder River near the Polish border, and the Ems, as well as in or in proximity to major cities such as Berlin and Frankfurt. Our results further indicate that the C-Q relationship varies significantly across river systems. While more than half the stations (64.8%) exhibit a dilution pattern between discharge and chloride concentration, stations showing chemostatic behavior suggest more complex input pathways. The correlation analysis revealed that chloride concentrations are controlled by hydroclimatic characteristics, land use and the input of wastewater. Surprisingly, lithological and hydrogeological factors have a comparatively minor impact on surface water chloride levels.
These results illustrate the complex, region-specific dynamics of chloride pollution in rivers, and underscore the need for targeted management strategies that account for hydrological variability. Refined predictive models that consider both temporal and spatial variability of chloride sources and the dilution potential of rivers are essential for developing such management strategies.
How to cite: Teltsch, L., Musolff, A., Volk, M., and Wachholz, A.: Chloride Concentrations in German Rivers and Their Impact on the Potential Distribution of the Golden Algae Prymnesium parvum , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9079, https://doi.org/10.5194/egusphere-egu25-9079, 2025.
This study examines the trophic state of Lake Lanalhue, in the south-central region of Chile, and its relation with the anthropogenic pressure. To assess the impacts on water quality, Landsat-8 OLI satellite imagery was integrated with in-situ data collected between 2014 and 2022. The analysis focused on estimating Chlorophyll-a (Chl-a) and Total Suspended Solids (TSS), by developing retrieval models based on spectral relationships and direct measurements obtained from the lake. For Chl-a estimation, an exponential relationship derived from green band showed strong correlation with in-situ data. Estimation of TSS was based on the spectral red/blue ratio, optimized to capture the optical scattering characteristics associated with suspended sediments in the water column. These models enabled the generation of spatial distribution maps highlighting differences in water quality, with the southern sector of the lake being the most impacted by eutrophication process. This area consistently recorded the highest concentrations of Chl-a and TSS, confirming an advanced trophic state associated with significant nutrient and sediment inputs from agricultural, livestock, and urban activities in the watershed. A comparison of 2014 and 2022 data revealed an intensification of the eutrophication process. The study underscores the utility of remote sensors as an efficient tool for long-term environmental monitoring of water bodies. This information is essential for guiding environmental management and informing the implementation of effective management strategies.
Keywords: Water Quality; Eutrophication; Landsat-8 OLI; Retrospective Analysis; Chilean Lakes
How to cite: Yépez, S., Ballesteros, F., Velásquez, G., Cristóbal, J., and Rodriguez-López, L.: Monitoring Eutrophic Conditions in Lanalhue Lake (Chile): Insights into Pollution Sources with Landsat-8 OLI Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9219, https://doi.org/10.5194/egusphere-egu25-9219, 2025.
Future coastal water quality is expected to be at risk due to growing socioeconomic developments including economy, population, urbanization, and agriculture. While challenges to water quantity are widely acknowledged and water quality studies for single pollution types are available, inequality aspects are hardly addressed between coastal water pollution and socioeconomic drivers worldwide in a spatially explicit way. Economic inequalities play a key role in shaping the impacts of coastal water pollution, making low-income communities more vulnerable to its effects on health and livelihoods while limiting their ability to respond to pollution reduction. Research on economic inequalities in coastal water pollution has predominantly focused on single pollutants at regional levels, particularly in the Global North (e.g., the United States and Europe). In contrast, the Global South (e.g., Africa and Asia), which is expected to face severe multi-pollution challenges, has been, less studied. Moreover, most studies rely on empirical approaches and do not often incorporate socioeconomic distributions into pollution modelling frameworks. Furthermore, comprehensive global models that address multiple pollutants, their sources, and the distribution of pollution hotspots across different income groups remain scarce.
This study models the distribution of future multi-pollutant hotspots for coastal waters and analyses them in relation to income classes and future socioeconomic developments. Using the MARINA-Multi model1 (developed in previous studies), we project river exports of nutrients, plastics and chemicals under an economic-driven scenario with reactive environmental management, and we identify coastal water pollution hotspots and their drivers, and analyze them concerning income levels. Our preliminary results reveal pronounced regional and income-based disparities in future pollution hotspots, with stark contrasts between Africa and Asia. By 2050, Asia is projected to face severe pollution driven by rising fertilizer use, related to agricultural intensification, and plastic waste despite ongoing efforts to reduce fertilizer and manure use. In contrast, Africa’s pollution challenges will primarily originate from rapid population growth and inadequate sanitation systems, reflecting a lack of advancement in wastewater and solid waste management services that cannot keep pace with its fast-growing population. These differing drivers highlight the distinct socio-economic and infrastructural challenges faced by each region. By linking socio-economic factors to pollution, this research supports strategies for improving water quality and advancing Sustainable Development Goals 6 and 14, offering critical insights for better global water resource management.
References
1 Micella, I., Kroeze, C., Bak, M. P., Tang, T., Wada, Y., & Strokal, M. (2024). Future scenarios for river exports of multiple pollutants by sources and sub-basins worldwide: Rising pollution for the Indian Ocean. Earth's Future, 12, e2024EF004712. https://doi.org/10.1029/2024EF004712
How to cite: Micella, I., Bak, M., Tang, T., and Strokal, M.: Inequalities in future hotspots of coastal water pollution and their socioeconomic drivers in Africa and Asia: a multi-pollutant modelling approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9551, https://doi.org/10.5194/egusphere-egu25-9551, 2025.
The Huasco river basin, located in the arid north of Chile (28-30° S), is particularly important due to the ecosystem services that provides in the Huasco Valley. To contribute to the conservation of their aquatic ecosystems and associated ecosystem services, a Secondary Environmental Quality Standard (Norma Secundaria de Calidad Ambiental, NSCA) was recently established to protect the surface waters of the basin.
In addition to intense and incremental demand for water resources, primarily by agriculture, due to climate change a significant decrease in precipitation and increase in maximum and minimum temperature are projected in the basin.
In order to evaluate the future compliance of the NSCA in the Huasco river basin, we implemented a hydrological model (SWAT+) and water quality model (WASP) considering the corresponding surveillance areas of the Huasco river and its tributaries, and selected water quality parameters included in the NSCA. Due to the start of the operation of the Santa Juana reservoir in 1997, which regulates the volume assigned annually for water rights in the basin, the period from 1999 to 2020 is used for the calibration and validation of the models. Under different climate change scenarios, flow series are being generated for the near and mid future (2030 – 2060), which will allow us to evaluate the susceptibility of the water quality parameters to change in flows, generate their projections, evaluate their expected behavior and ultimately the future compliance with the established water quality standards.
How to cite: Fuentes, I., Peredo, M., Vargas, X., Lizama, K., and Pérez, A.: Future evolution of surface water quality in the Huasco river basin, Chile under climate change scenarios: compliance with environmental standards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10112, https://doi.org/10.5194/egusphere-egu25-10112, 2025.
Groundwater acts as a long-term water quality buffer due to its wide range of residence times, spanning days to thousands of years (Maxwell et al., 2016). This makes groundwater a critical freshwater resource, especially where surface water quality or quantity is limited. However, contamination from emerging pollutants, influences of climate change, and changing water management, land use, and agricultural practices, likely increasingly lead to undesirable groundwater quality worldwide (Lapworth et al., 2023). Despite identification of this trend, the extent of these changes remains poorly understood, except for specific local and regional groundwater systems.
Mechanistic, physically-based models for predicting groundwater quality at global scales are not yet available, and field data are sparse. In this study, we review groundwater quality models for catchment scales and above and assess whether these approaches are scalable for global applications. Current models are constrained by computational demands, and insufficient subsurface data and knowledge of kinetic processes. Using numerical experiments for nitrogen cycling, we highlight the associated geochemical uncertainties to these subsurface conditions.
With the upscaling of 2D transport models to the kilometer grid scale we demonstrate what limitations follow from global hydrological models when used for groundwater quality modelling. At coarse kilometer-scale grids, sinks and sources tied to landscape features are poorly represented, causing inaccuracies in flow paths and therefore groundwater composition and fluxes. Additionally, when using kilometer scale grids, low flow velocities compared to grid dimensions and subgrid source heterogeneity prevent meaningful groundwater quality representation over the century timescales used in climate change and socio-economic development scenarios.
Future hyperresolution hydrological models may enable direct numerical simulations of groundwater quality, though subsurface transport property and reactivity data could remain limiting to a model’s coverage. For short-term global groundwater quality assessments, we recommend using data-driven approaches combined with conceptual groundwater cycling models as alternatives to mechanistic methods. This work highlights the need for improved modeling frameworks to enhance global understanding of groundwater quality dynamics, critical for informed water management and sustainable use of the groundwater resource under climate and land-use change.
Lapworth, D., Boving, T., Brauns, B., Dottridge, J., Hynds, P., Kebede, S., Kreamer, D., Misstear, B., Mukherjee, A., Re, V., Sorensen, J., & Vargas, C. R. (2023). Groundwater quality: global challenges, emerging threats and novel approaches. Hydrogeology Journal, 31(1). https://doi.org/10.1007/s10040-022-02542-0
Maxwell, R. M., Condon, L. E., Kollet, S. J., Maher, K., Haggerty, R., & Forrester, M. M. (2016). The imprint of climate and geology on the residence times of groundwater. Geophysical Research Letters, 43(2). https://doi.org/10.1002/2015GL066916
How to cite: Teuling, F. S. R., Hofstra, N., and de Graaf, I. E. M.: Upscaling groundwater quality models to the global domain scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10209, https://doi.org/10.5194/egusphere-egu25-10209, 2025.
Climate and land-use changes are posing increasing threats to freshwater-related ecosystem services, acting both on the supply and demand sides. These changes disrupt critical processes such as nutrient cycling, sediment transport, and water flow regulation, leading to declining water quality and reduced ecosystem resilience. There is an urgent need for a deeper understanding of the dynamics of these threats, which can help enhance water management, environmental protection, and human well-being. To effectively tackle these risks, it is essential to quantitatively combine physical hazards and vulnerabilities by pinpointing hotspots where multiple stressors greatly increase the risk of water quality degradation. In response to this challenge, Bayesian network models offer a promising decision-support tool for evaluating adaptation options for water resource management, as they can integrate both quantitative and qualitative data. Building upon this approach, we developed a Spatial Bayesian Network (SBN) model to predict the probability of potential risks to water quality at the river basin scale in Italy and support the goal of achieving good chemical and ecological status according to the Water Framework Directive. This integrated model incorporates the complex relationships between land use change, climate change indicators (e.g., flood and drought intensity), and their combined impacts on water degradation. First, the baseline model uses historical patterns of climate change metrics from the CMCC DSS dataset and land use indicators from the Corine Land Cover as inputs to generate probabilistic predictions of potential risks to water quality. Then, the relationships between these variables are captured in their conditional probabilities, allowing for quantifying interactions and identifying key stressors, paving the way for scenario analysis. Finally, different future scenarios will be developed to predict the changes in water quality, considering projected climate data and socio-economic conditions. The outcome of this analysis contributes to developing an integrated management strategy that will help water managers make decisions and ultimately improve the resilience of freshwater ecosystems while supporting the implementation of adaptation strategies to address such problems.
Keywords: Water Framework Directive, Water quality, Climate change, Land-use/ land-cover change Bayesian Network (BN) model
How to cite: Rufo, O. J. M., Casagrande, S., Pham, V., and Critto, A.: Spatial Bayesian Network model for assessing the impact of land use and climate change on water quality in Italian watersheds., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16246, https://doi.org/10.5194/egusphere-egu25-16246, 2025.
Nature-based solutions (NbS) have emerged as innovative and sustainable approaches to address environmental challenges, including water quality degradation in river catchments. These solutions leverage natural processes and ecosystems to enhance water quality while providing co-benefits such as biodiversity conservation and climate resilience. The Yala River Basin in Kenya faces significant water quality challenges due to increased sediment yields and nutrient loading, primarily driven by agricultural activities, deforestation, and land-use changes. This study explores the application of NbS, such as riparian buffer strips, agroforestry, and wetland restoration, in mitigating water quality issues in the Yala Basin. The Soil and Water Assessment Tool Plus (SWAT+) was employed to model the impacts of NbS on water quality in the Yala Basin. SWAT+ is a robust, process-based hydrological model that simulates land use, climate, and management interventions at the catchment scale. Climate data from ISIMIP3b (Inter-Sectoral Impact Model Intercomparison Project) were used to provide high-resolution projections of climate variability and change. Baseline land-use data were derived from remote sensing and validated using ground surveys. Hydrological response units (HRUs) were defined to capture spatial heterogeneity in land cover, soil, and topography. Scenarios with and without NbS interventions were simulated to assess their impact on sediment and nutrient yields.
Preliminary results indicate that the implementation of NbS significantly reduces sediment yields and nutrient concentrations in the Yala Basin. Specifically, hydrological response units with NbS interventions demonstrated: Reduced sediment yields: Areas with riparian buffer strips and restored wetlands showed up to a 15% reduction in sediment transport compared to baseline scenarios. Decreased nutrient loads: Agroforestry practices and vegetation buffers reduced nitrogen and phosphorus runoff by approximately 4% and 7%, respectively. These reductions were particularly pronounced during peak rainfall events, demonstrating the effectiveness of NbS in mitigating runoff-related pollution.
The application of NbS in the Yala Basin demonstrates their potential to significantly improve catchment water quality while delivering ancillary ecosystem services. SWAT+ modeling highlights the ability of NbS to address sediment and nutrient-related challenges effectively, even under varying climatic conditions. These solutions not only provide immediate water quality benefits but also contribute to long-term catchment resilience to climate change and anthropogenic pressures.
Based on the findings, it is recommended that policymakers and stakeholders prioritize the integration of NbS in catchment management plans. Key recommendations include: Scaling up riparian buffer zones and agroforestry systems in critical hydrological response units. Establishing incentive programs for local communities to adopt NbS practices. Enhancing monitoring and evaluation frameworks to measure the long-term impacts of NbS on water quality. Strengthening partnerships between government agencies, research institutions, and community organizations to promote the co-design and implementation of NbS.
How to cite: Ronoh, E., Annika Schlemm, A., Benedict Mkama, E., Nyolei, D., Nyongesa, J., Nyadawa, M., and van Griensven, A.: Application of Nature-Based Solutions (NbS) for Catchment Water Quality Management in Yala River Basin, Western Kenya., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18259, https://doi.org/10.5194/egusphere-egu25-18259, 2025.
Despite decades of efforts to reduce nutrient pollution, Europe is still confronted with elevated nutrient concentrations in ground- and surface waters. This is a result of nutrient inputs from intensive agriculture, wastewater collection and treatment and atmospheric deposition. As a consequence, inland and marine waters suffer from persistent eutrophication problems manifested as algal blooms, changes in species composition, oxygen depletion, and no full recovery of seagrass. Water management must address this problem by finding additional nutrient reduction measures and assess their effectiveness to ensure good water quality also under a changing climate. To plan nutrient reduction measures and assess their effectiveness, predictive modelling tools are essential but challenging to apply at large spatial scales and under changing boundary conditions. Here, we present results of the EU-funded project NAPSEA addressing eutrophication and nutrient management in the Elbe and Rhine basins and the receiving Wadden Sea (the intertidal zone of the south-eastern North Sea). More specifically, we explore future trajectories of reactive nitrogen (N) concentrations and loads exported from the Elbe and Rhine basins under different nutrient input scenarios. We use the transit-time based catchment water quality model mQM calibrated to long-term observations (>10 yrs) in more than 140 sub-catchments. The model takes nitrogen surplus as a diffuse source that is routed through different soil compartments and the subsurface, with flowpaths to the streams represented by dynamic transit time distributions. In the river network, inputs from wastewater point source and instream removal are considered. The modelled scenarios address the impact of climate change on the hydrological cycle (RCP4.5) and the planned measures for the different nitrogen pathways between 2022 and 2050. More specifically, we quantify the effects of the new urban wastewater treatment directive, the revised German fertilizer ordinance, the expected reduction in atmospheric deposition and nature-based solutions such as reactivated floodplains. We found that the projected changes in discharge and the joint nutrient reduction measures will have a similar magnitude of effect on nutrient exports. The effectiveness of nutrient reduction measures is spatially heterogeneous,depending on the land use composition and the natural attenuation potential of the different basins. The reduction of agricultural N surplus and atmospheric deposition has a higher impact on the Rhine basin, unlike the Elbe basin where the benefits of regulations on urban wastewater prevail. Overall, our results reveal a 15-27% reduction in nutrient exports to the Wadden Sea (average 2045-2050) compared to the average export in the years 2010-2020. While these results are encouraging, a significant gap remains to the estimated reduction needs to sufficiently reduce nitrate pollution of inland waters and to reach safe ecological boundaries of the Wadden Sea. More ambitious nutrient reduction measures are needed to ensure future a good status of inland and coastal waters.
How to cite: Musolff, A., Gericke, A., Nguyen, T. V., Ebeling, P., van Beusekom, J. E. E., and Kumar, R.: Projecting future riverine nitrogen exports to the Wadden Sea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4332, https://doi.org/10.5194/egusphere-egu25-4332, 2025.
The sustainability of freshwater availability and quality is seriously threatened by climate change (CC) and land-use/land-cover change (LULC). On the other hand, extreme weather and climate-related events depend strongly on LULC. Multiple evidence suggests that rapid global development is the main driving factor altering all the fundamental processes that control the hydrologic cycle and temporal and spatial variations of river basins. Moreover, the interaction between the upstream and downstream of a basin significantly impacts the overall status of the basin. Understanding the “source-to-sink” effect is crucial for managing water quality in river basins. This study aims to understand the impacts of LULC on water quality at the river basin scale in Italy, providing a baseline model for predicting the probability of achieving good ecological status for each watershed under different Shared Socioeconomic Pathways (i.e., SSP2 and SSP5) and Representative Concentration Pathways (i.e., RCP4.5 and RCP8.5) for mid- and long-term timeframe (i.e., 2050 and 2100). To fulfill this bold objective, this study integrates Principal Component Analysis (PCA) and several regression models to explore the influence of various landscape metrics on the ecological status of each watershed, taking into account the effect of changes in land use from upstream watersheds to downstream ones. The outcomes reveal that conserving natural areas is essential for improving water quality across the territory. However, conservation efforts alone are insufficient without restoring places that were natural previously but are now used for agriculture and urban development. Implementing agricultural practices that promote harmony and links between natural regions and farmed areas may effectively reduce the harm from unsustainable farming practices. Future work will focus on integrating climate change variables and spatio-temporal occurrence of extreme events, such as flood and drought hotspots. Moreover, advanced probabilistic models (e.g., Bayesian Network) and Machine Learning will be employed to assess the possible interactions between LULC and CC and their impacts on water quality. The outcomes of this analysis contribute to developing adaptive strategies that safeguard water resources and ensure the long-term sustainability of freshwater ecosystems.
How to cite: Pham, H. V., Casagrande, S., Rufo, O., and Critto, A.: Exploring the interplay between land use and surface water quality across Italy's watersheds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16139, https://doi.org/10.5194/egusphere-egu25-16139, 2025.
River water quality is critical in maintaining ecosystem health, as it can directly influence biodiversity and access to clean water. However, the interaction between extreme climate events and human activities can lead to compounded effects that significantly alter water quality dynamics. The impacts of these combined factors are often complex, non-linear, and poorly understood, posing significant challenges for water resource management. Supervised machine learning and explainable artificial intelligence offer innovative tools to address these complexities. This study applied an integrated framework combining Random Forest (RF) Classifiers with SHapley Additive exPlanations (SHAP), to reveal the intricate relationships between land use, climate extremes, and their compounded effects on water quality at a high spatial resolution (867 elemental river basin), testing it in Veneto Region (northeastern Italy). The framework was applied to provide annual predictions of impacts on water quality elements to support the evaluation of ecological status according to the Water Framework Directive 2000/60/EC. The models have been applied on water quality data from 2010-2022, considering as predictors seasonal hot, dry and wet extreme climate hazard indicators, together with land use/cover metrics and territorial characteristics to represent specific river basins’ vulnerabilities. Three RF models were developed for physicochemical elements, specific pollutants, and biological alterations water quality indicators, and resulted in overall accuracies of 0.87, 0.81, and 0.85, respectively. The findings highlighted that temperature extremes acted as critical drivers, particularly when combined with droughts. Specific natural features (i.e. % of basin vegetated area, natural river typology, soil permeability) were identified as buffers against adverse impacts on water quality following extreme climate conditions. Conversely, anthropogenic land use intensified negative effects, especially when exceeding specific thresholds. The results confirm that the applied approach has the potential to aid decision-making by providing insights into multi-hazard-risks on water quality and highlighting the importance of holistic river basin management plans that prioritize nature-based solutions, ecosystem restoration, and strategic land use policies to strengthen climate resilience.
How to cite: Ngoc Nguyen, D., Furlanetto, J., Torresan, S., and Critto, A.: Interplay Between Climate Extreme Events, Land Use, And Water Quality: An Artificial Intelligence Multi-Risk Assessment Approach , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5585, https://doi.org/10.5194/egusphere-egu25-5585, 2025.
Riverine nitrogen (N) loading is increasing rapidly due to both climate change and human activities, posing severe threats to global water quality. However, the contributions of precipitation, temperature and their interactions in driving these increases remain insufficiently understood at global scale. Here, we establish a Global River Nitrogen and Discharge (GRIND) observations database and develop a machine learning model to generate high-resolution (5-arcminute) spatially explicit estimates of global riverine N loading, using 14 explanatory predictors to elucidate the complex interactions between climate change and anthropogenic N inputs. Our findings show that the top 20% of high-loading river basins contribute 61% of global riverine N loading, among which 89% of these basins locate in regions experiencing high precipitation or intensive anthropogenic N inputs. Notably, rising precipitation and temperature amplify N loading in high-input regions, with the most significant effects occurring when precipitation ranges from 500 to 1500 mm yr⁻¹, and temperature and soil nitrogen content exceed 6°C and 450 cg/kg, respectively. Under future climate change scenarios, global riverine N loading is projected to increase by 0.5-3.6 Tg yr⁻¹ by the late century, even if current “business-as-usual” N input levels persist. Precipitation-driven increases are most pronounced in tropical and temperate regions between 40°S and 40°N, where an estimated 16.7±19.6% rise in N loading is expected, escalating water quality risks in these densely populated areas. In contrast, temperature-driven increases dominate in the Arctic region North of 60°N, exceeding 10%. To address the growing complexity of global water quality deterioration, we propose the Climate-Sensitive Nitrogen Reduction (CLEAN) framework, which identifies high-risk regions for future N loading and recommends strategies to mitigate the combined risks posed by climate change and human activities.
How to cite: Li, J. and Kahil, T.: Climate change amplifies the impacts of anthropogenic inputs on nitrogen loading in global rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9508, https://doi.org/10.5194/egusphere-egu25-9508, 2025.
