By approving the Sustainable Development Goals (SDGs) more than 190 countries have made a commitment to “ensure availability ... of water ... to all”. Yet the implications of this high-profile pledge are unclear because of the lack of an internationally accepted definition of “water availability”. Moreover, definitions, where they exist, are usually incomplete because they neglect the important factor of water quality. This is a significant omission because degraded water quality impedes the usage of water resources for drinking water, hygiene, irrigation, habitat for plant and animal communities, and other important uses. It can be argued that contaminated water withdrawals can be treated to make more water available, but this is not an affordable option for much of the Global South except for drinking water. Hence, degraded water quality genuinely reduces water availability.

Until recently water quality could not be included in large-scale (regional to global) assessments of water availability because of the lack of appropriate data and tools. The situation is changing, however, with the development of a new class of large-scale water quality models. These models have broad geographic coverage and a fine enough grid to simulate water quality gradients along river networks.

As an example, as part of a UNEP study, the WorldQual model estimated that pathogen pollution hindered the usage of approximately one-third of the total river network in Latin America, Africa, and Asia for safe bathing and hygienic purposes. Organic pollution was estimated to impede the usage of about one-seventh of this river network for fish production, and salinity pollution about one-tenth of the network for irrigation water supply. These and other preliminary results from the modelling community suggest that water quality should not be overlooked as a potentially important factor in large-scale assessments of water availability.

To improve the performance of large-scale water quality models, and make them more reliable for including in water availability assessments, researchers will have to contend with some difficult challenges including (but not limited to):

The problem of representativeness – A wide range of water quality parameters are relevant to uses of surface water and groundwater, and the challenge is to find a manageable set of representative parameters for analysis that are both measurable and calculable on the large-scale.

The problem of validation – There is a paucity of data available for validating large-scale water quality models, and in some parts of the world the coverage and frequency of data collection is declining rather than increasing. 

The problem of characterising anthropogenic fluxes of contaminants – The data necessary for determining fluxes of contaminants into the water environment (e.g. data on wastewater discharges) are either not accessible or very incomplete in many countries. 

Finally, a particularly high priority for going forward is to establish collaborations between researchers assessing the quantity of water available (for example, under climate change) and those assessing the quality of water. These collaborations are a prerequisite for developing a more comprehensive and realistic concept of water availability.

How to cite: Alcamo, J.: Invited Keynote -- Expanding the global view of water availability to encompass water quality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18007, https://doi.org/10.5194/egusphere-egu24-18007, 2024.

In Europe, intensive agriculture and high population density pose pressures on water resources quality and quantity. Water abstractions, intensive agriculture and wastewaters from urban areas and industries modify natural water availability and quality. The excess of nutrients (nitrogen and phosphorus) in rivers, lakes, groundwater and coastal waters impair water quality for human and ecosystem, and damage the goods and services provided by aquatic ecosystems. Environmental policy have been in place in the EU since the 1990s to reduce nutrient pollution and ensure sustainable water resource management and ecological quality, aiming at restoring and protecting all water bodies (2000/60/EC Water Framework Directive, WFD). Moreover, in recent years the ambitious goal to halve nutrient losses to the environment have been set by the EU Green Deal, Zero Pollution and Biodiversity Strategies. However, achieving this goal might require changes in the current land and water resource management.

Climate change, with shifts in amount, seasonal distribution, and intensity of rainfall, soil moisture regime, and runoff events, affects delivery of nutrients to the fresh and marine waters. In this study, by mean of scenario modelling, we explore the possible combined effects of EU policy measures and climate change on nutrients delivery to European seas at the time horizon of 2050 compared to current condition in Europe. We discuss on the one side the expected impacts of main EU policies (such as the Common Agricultural Policy (CAP), the updated legislation addressing greenhouse gases emissions (Fit For 55 package), and the revision of the Urban Waste Water Treatment Directive UWWTD), and on the other side we look at the concurrent role of climate change (scenario RCP 4.5) on nutrient load delivered to European seas, considering regional variability.

This study helps understanding the future trajectories of nutrient pollution in European fresh and coastal waters, highlighting the respective contribution of policy measures and climate change at the regional scale.

How to cite: Grizzetti, B., Udias, A., Vigiak, O., Pistocchi, A., Aloe, A., Bisselink, B., Bouraoui, F., De Meij, A., Hristov, J., Macias Moy, D., Pisoni, E., Trichakis, I., Weiss, F., Zampieri, M., and Zanni, M.: Effects of EU policy and climate change on future delivery of nutrients to European seas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14412, https://doi.org/10.5194/egusphere-egu24-14412, 2024.

Inadequate availability of clean water presents systemic risks to human health, food production, energy generation and ecosystem functioning. While future alterations to water demands and availability are widely projected to exacerbate water scarcity, the impact of changing water quality is largely unknown. Leveraging a newly-developed global surface water quality model (DynQual¹) which is coupled to a global hydrological model (PCR-GLOBWB2), we make the first projections of future global water scarcity including both water quantity and quality aspects.

We consider three combined RCP-SSP scenarios (SSP1-RCP2.6, SSP3-RCP7.0 and SSP5-RCP8.5), each of which simulated with bias-corrected CMIP6 climate input from five GCMs provided within ISIMIP3b, to encompass a range of possible future conditions and to capture uncertainty inherent in the climatological (GCM) projections. Simulated monthly sectoral water demands (domestic, industrial, livestock and irrigation), water availability (e.g. discharge) and water quality (total dissolved solids, biological oxygen demand and fecal coliform) for 2005-2100² are used as basis for quantifying clean water scarcity, which we express in terms of population exposure.

We find that 57% of the global population (~4 billion people) are currently exposed to clean water scarcity at least one month per year, increasing to 58 – 68% by the end of the century based on different plausible scenarios for climate change and socioeconomic development. Increases in exposure are largest in developing countries – particularly in Sub-Saharan Africa – driven by a combination of water quantity and quality issues. Strong reductions in both human water use and pollution are therefore necessary to minimise the impact of future water scarcity on humans and the environment.

References

¹ Jones, E.R., M.F.P. Bierkens, N. Wanders, E.H. Sutanudjaja, L.P.H. van Beek,  M.T.H. van Vliet (2023), DynQual v1.0: A high-resolution global surface water quality model, Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023

² Jones, E.R., M.F.P. Bierkens, P.J.T.M. van Puijenbroek, L.P.H. van Beek, N. Wanders, E.H. Sutanudjaja, M.T.H. van Vliet (2023) Sub-Saharan Africa will increasingly become the dominant hotspot of surface water pollution, Nature Water, 1, 602–613, https://doi.org/10.1038/s44221-023-00105-5

How to cite: Jones, E. R., Bierkens, M. F. P., and van Vliet, M. T. H.: Future global water scarcity including quality under climate and socioeconomic change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3427, https://doi.org/10.5194/egusphere-egu24-3427, 2024.

Water quality is a critical element of ecosystem health and human well-being. However, it is increasingly challenged by a variety of human induced impacts related to land-use, socio-economic development, and climate change that alter the physical, chemical, and biological components. It is challenging to obtain a comprehensive understanding and effective management of water quality, especially under large uncertainties arising from future climate change and socio-economic developments. An integrated approach is essential for developing adaptive strategies that consider future uncertainties, ensuring the preservation of water quality and the sustainability of aquatic ecosystems in the face of evolving environmental conditions.

This research investigates the intricate dynamics of water quality in transitional environments with a case study in the Venice Lagoon, particularly focusing on the uncertainties arising from future climate change. Utilizing a multi-scenario analysis approach, we explore a range of potential outcomes to understand the complex interactions shaping water quality in these critical ecosystems. The scenarios are designed to simulate future conditions, considering two different climate change scenarios (RCP 4.5 and RCP 8.5), river load (reduced/unvaried river runoff), and the operation of the marine hydraulic interventions for flood prevention (the MOSE system) under medium- and long-term futures. Using data from SHYFEM-BFM - a 3D coupled hydrodynamic and ecological model, key physico-chemical parameters are integrated into a multi-parameter water quality index – the CCMEWQI. This index considers the Scope (number of failed parameters), Frequency, and Amplitude of non-compliant tests to water quality standards for ecological status and aquatic life. By exploring diverse trajectories, we aim to anticipate potential shifts in water quality spatio-temporal dynamics. The multi-scenario analysis unfolds potential future states of water quality in the Venice Lagoon, highlighting critical points of vulnerability and resilience.The outcomes contribute to a more comprehensive understanding of the complexities inherent in transitional water systems, aiding policymakers and water resource managers in making informed decisions to ensure the resilience and sustainability of water quality in the face of an uncertain future.

Additionally, future developments extend the scope of this study to encompass a multi-risk assessment on river networks in the Veneto Regione to understand the multi-risk dynamic for regional water quality management. The multi-risk analysis in the freshwater system incorporates a range of stressors to river water quality, including single/compound extreme climate events and anthropogenic activities. We aim to unravel the multi-risk dynamics through the application of a novel approach employing machine learning techniques that encompass multiple hazards, exposures, and vulnerabilities. Furthermore, future scenarios of climate, land-use, and population changes are integrated into the multi-risk model together with nature-based solutions to create multi-risk scenarios for water quality, providing a holistic view of the potential risks and vulnerabilities in different environmental contexts.

How to cite: Ngoc Nguyen, D., Furlan, E., Torresan, S., Furlanetto, J., Canu, D., Aveytua Alcazar, L., Solidoro, C., and Critto, A.: Advancing water quality assessment under uncertainties: Multi-risk and multi-scenario analyses in the face of future climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9472, https://doi.org/10.5194/egusphere-egu24-9472, 2024.

Many aquatic ecosystems in densely populated delta’s worldwide are under stress from overexploitation and pollution. Global population growth will further increase these pressures in the coming decades, while climate change may amplify the consequences for chemical and ecological water quality. Meteorological variations are a major driver for changes in water quality. Climate change projections foresee higher temperatures and larger extremes in wet and dry periods. Still, the impact of climate change and climate variability on water quality is only poorly understood. 

In this study, we investigated the integrated effects of climatic variability on the chemical and ecological quality of groundwater and surface water in the sandy part of the Netherlands.  We especially exploited the dense monitoring information from Water Board Aa en Maas to evaluate the water quality response on the past 50 years of climate change and climatic variability.

Our results show a direct effect of climate extremes on the leaching of nutrients from agriculture. The 2018-2020 drought for example reduced nutrient concentrations in summer, but the nutrient losses increased in the subsequent wet winter seasons and in the first next wet summer of 2021. In addition, extreme wet conditions give nutrient load pulses and strongly reduce oxygen concentrations which can have both instant and long term effects on downstream ecology. The long-term trends (1990-2022) showed a general improvement in water quality due to reduce inputs, although an accelerated increase in water temperature since 2010 makes the system more vulnerable to eutrophication.

How to cite: Rozemeijer, J. and Gommans, K.: Effects of climate change and climate extremes on water quality from monitoring data in the sandy areas of the Netherlands with highly intensive agricultural land use  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19124, https://doi.org/10.5194/egusphere-egu24-19124, 2024.

Water quality is under threat in many places on Earth. This is associated with impacts of climate change (e.g., droughts, floods) that are integrated with socio-economic developments (e.g., agriculture, urbanization). Computer models have been developed and combine our knowledge and data to quantify water pollution levels, sources of pollution, and impacts of a wide range of pollutants such as salinity, nutrients, pathogens, plastics, and chemicals. These models are diverse in time and space and their modeling approaches. Such diversity offers a great opportunity to compare model results to identify robust pollution hotspots, their sources and explore trends under global change across pollutants, scales, scenarios, and sectors. We take this unique opportunity and develop a protocol for water quality models within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) initiative supported by the Process-based models for Climate Impact Attribution Across Sectors (Proclias COST Action). This protocol serves as a guide for water quality modelers on how to harmonize model inputs and outputs and contributes to cross-scale and cross-sectoral assessments of water quality. Within our community, we identified challenges and opportunities for implementing the protocol. One of the challenges is the diversity of water quality models in their approaches, spatial and temporal level of detail, and water quality constituents that the models consider. The other challenge is inconsistencies in data for model inputs that make the data harmonization more difficult. However, opportunities exist for the large water quality modeling community to creatively identify approaches for model intercomparison purposes. This not only facilitates interactions among the modelers but also contributes to the development of novel model intercomparison approaches for the diverse water quality models. During several workshops throughout 2022-2023, the water quality modeling community (largely focused on large-scale) discussed and identified two promising directions for model intercomparisons. The first direction is qualitatively based. It aims largely at the integration of model outputs (e.g., via indicator-based approaches) from various water quality models to identify robust hotspots, sources and trends across pollutants and scenarios. This direction could fit the recently initiated “Fast Track” with the ISIMIP platform for the water quality sector. The second direction is quantitatively based. It aims largely at the intercomparison of model outputs. An example is the comparison of water pollution levels between two or more models for the same pollutant, scenario, scale, climate model, and sector. This requires at least two model simulations for one water quality constituent. This second direction requires more efforts in harmonizing model inputs across models and could serve as a good basis for the ongoing ISIMIP3 model intercomparison purposes across sectors. The first attempts were made to harmonize model inputs in scenario developments for global water quality assessments by the modeling community of the UN-World Water Quality Alliance. This can be the basis for further model harmonization. In EGU, we will discuss promising examples of the two directions and the ways forward. We will draw lessons on the process to develop such a protocol for model intercomparisons to understand climate change impacts on water quality better. 

How to cite: Strokal, M., Micella, I., P. Bak, M., H. W. Beusen, A., Flörke, M., N. Gosling, S., Van Griensven, A., Grizzetti, B., Hofstra, N., R. Jones, E., Kroeze, C., Nkwasa, A., Troost, T., T.H. van Vliet, M., Wang, M., and Kumar, R.: The Water Quality Protocol for Model Intercomparisons Under Climate Change Impacts , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2180, https://doi.org/10.5194/egusphere-egu24-2180, 2024.

How to cite: Micella, I., Wang, M., Bak, M. P., Hofstra, N., Kroeze, C., Li, Y., Li, S., Strokal, V., Ural-Janssen, A., Zhang, Q., and Strokal, M.: Ten years of MARINA modeling: Multi-pollutant hotspots and their sources under global change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2349, https://doi.org/10.5194/egusphere-egu24-2349, 2024.

A dependable assessment of quality-induced water scarcity (QualWS) is essential for tackling the issue and achieving sustainable development goals. The conventional emission-based grey water footprint (GWF) may over- or under- estimate QualWS, as it solely focuses on local pollutant emissions while disregarding other influential factors. To address this limitation, we propose the State-based GWF to reflect the quality status of local water resources accurately. The indicator is applied in annual and monthly QualWS assessments at the provincial scale in China. In 2021, 19 provinces were identified as QualWS hotspots, comprising seven moderate and 12 slight hotspots for at least one pollutant. Notably, the State-based assessment revealed eight previously overlooked hotspots undetected by conventional methods. Furthermore, Total phosphorus (TP) emerged as the most critical water pollutant, followed by total nitrogen (TN) and chemical oxygen demand (COD). Our assessment presents an innovative perspective for understanding QualWS and establishes a scientific basis for effective aquatic environment management.

How to cite: Liu, S., Liu, J., Zhao, D., and Cao, W.: Revealing neglected hotspots for China’s quality-induced water scarcity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3370, https://doi.org/10.5194/egusphere-egu24-3370, 2024.

Due to the continued increase in land use changes and changing climatic patterns in the Lake Victoria basin, understanding the impacts of these changes on the water quality of Lake Victoria is imperative for safeguarding the integrity of the freshwater ecosystem. Thus, we analyzed spatial and temporal patterns of land cover, precipitation, and water quality changes in the Lake Victoria basin from 2000 to 2022 using processed remote sensing (RS) data. Focusing on chlorophyll-a (Chl-a) and turbidity (TUR) in Lake Victoria, we used statistical metrics (correlation coefficient, trend analysis, change budget, and intensity analysis) to understand the relationship between land use and precipitation changes in the basin with changes in Chl-a and TUR at two major pollution hotspots on the lake i.e. Winam Gulf and Inner Murchison Bay (IMB).

Results show that the Chl-a and TUR concentrations in the Winam gulf increase with increases in precipitation. Through increases in precipitation, the erosion risks are increased and transport of nutrients from land to the lake system, promoting algal growth and turbidity. In the IMB, Chl-a and TUR concentrations decrease with increase in precipitation, possibly due to dilution, but peak during moderate rainfall. Interestingly, LULC changes showed no substantial correlation with water quality changes at selected hotspot areas even though LULC change analysis showed a notable 300% increase in built-up areas across the Lake Victoria basin. These findings underscore the dominant influence of precipitation changes over LULC changes on the water quality of Lake Victoria for the selected hotspot areas.

How to cite: Nakkazi, M. T., Nkwasa, A., Martinez, A. B., and Griensven, A. V.: Unraveling the link between precipitation and land use changes to water quality in Lake Victoria using remote sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17151, https://doi.org/10.5194/egusphere-egu24-17151, 2024.

Excess phosphorus from agricultural intensification has contributed to the eutrophication of rivers and lakes worldwide, including the transboundary Laurentian Great Lakes Basin. Algal blooms have surged in the past decade, threatening ecosystems, drinking water supplies and lake-dependent tourism economies in both large lakes (for example, Lake Erie) and smaller water bodies. Whereas previous research has focused mainly on phosphorus loads to Lake Erie, a comprehensive analysis of phosphorus species across the basin is lacking. Here we analyse changes in soluble reactive phosphorus and total phosphorus concentrations in over 370 watersheds across the Great Lakes Basin from 2003 to 2019. We find widespread increases in soluble phosphorus concentrations (83% of watersheds, with 46% showing significant increase), while total phosphorus concentrations are decreasing or non-significant. Utilizing random forest models, we identify small, forested watersheds at higher latitudes as the areas experiencing the largest relative increases in soluble phosphorus concentrations. Furthermore, we find winter temperatures to be a key driver of winter concentration trends. We propose that the increasing soluble phosphorus concentrations across the basin, along with warming temperatures, might be contributing to the increasing frequency and intensity of algal blooms, emphasizing the need for management strategies to prevent further water-quality degradation.

How to cite: Basu, N., Singh, N., and Van Meter, K.: Dissolved phosphorus concentrations are increasing in streams across the Great Lakes Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13664, https://doi.org/10.5194/egusphere-egu24-13664, 2024.

A Bayesian Network (BN) aimed at calculating stream phosphorus (P) concentrations in agricultural catchments was previously parametrized with high-frequency data in a pilot study. To test model transferability, the BN was applied to three further agriculture-dominated catchments in Ireland with varying land use, hydrology, and P pressures, all monitored through the Agricultural Catchments Programme (ACP) of the Irish Agriculture and Food Development Authority. While the pilot catchment Ballycanew was dominated by poorly drained grassland, the further three catchments were dominated by well-drained grassland (Timoleague), well-drained arable (Castledockrell), and moderately-drained arable (Dunleer), respectively. In all four catchments, the main P source came from agriculture and (minimal) domestic inputs, whilst the well-drained arable catchment also contained Sewage Treatment Works (STWs).

To best fit the characteristics of the catchments, a total of six different BN structures were developed. The models were parametrized using a range of methods, including bootstrapping of high-frequency data to obtain fitted distributions, distribution fitting of literature data, and expert elicitation to quantify in-stream P uptake processes. Model transferability and fit were evaluated using a suit of approaches, including 1) calculating percentage bias between simulated and observed distributions fitted to the observed stream Total Reactive P (TRP) concentration, 2) comparing modelled concentration quantiles and means to the observed, and 3) visually comparing the posterior distributions by plotting them against daily observations.

The original BN structure developed in the pilot study was found to best fit the poorly and moderately drained catchments, irrespective of the dominant land use (78% ≤ PBIAS ≤ 81%), not as well in the groundwater-dominated catchments. This confirms that the initial BN represents the catchment-specific process understanding whereby transport via quick-flow dominates P processes in these catchments. In contrast, the well-drained catchments required more complex BN structures to perform well. The additional processes included groundwater Total Dissolved P (TDP) loads, derived from observed concentrations from piezometer data, STWs loads, and in-stream P uptake calculations. These more complex model implementations yielded good results in Castledockrell and Timoleague (-5% ≤ PBIAS ≤ 14%). In all four catchments, the additional in-stream P removal process improved the model performance, however, it remains a second-order mechanism.

Overall, the unique monitoring programme allowed pilot-testing BN transferability, a research avenue that needs to be further explored across catchment typologies and scales.

How to cite: Negri, C., Schurch, N., Wade, A. J., Mellander, P.-E., and Glendell, M.: Testing Bayesian Network transferability to diverse agricultural catchments with high phosphorus saturation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6414, https://doi.org/10.5194/egusphere-egu24-6414, 2024.

Predictive models, which leverage the relationship between environmental variables and river health, serve as a valuable tool for predicting the river health at unmonitored sites. Such models should be generalizable to unseen data. However, predictions derived from machine learning (ML) models can exhibit large variability even with minor changes in the training dataset. The potentially unstable behaviors of a ML model decrease the model’s generalizability to unseen data, likely limiting its applicability as an assistant tool for decision making. Heterogeneous ensemble models are recognized to achieve greater generalizability compared to single models owing to their structural diversity. In this study, various machine learning (ML) models are employed to understand the relationship between environmental factors and benthic macroinvertebrate health. To obtain a model with better generalizability, the present study compares the generalizability of heterogeneous ensembles with those of homogeneous ensembles and single models by using the bias–variance decomposition. The models classified five grades (very good to very poor) of benthic macroinvertebrate index (BMI). The models incorporated diverse environmental factors, including water quality, hydrology, meteorological conditions, land cover, and stream properties, as input variables. The data were monitored at 2,915 sites in the four major river watersheds in South Korea during the 2016–2021 period. The results indicated better generalizability of the heterogeneous and homogeneous ensembles than single models. Moreover, heterogeneous ensembles tended to show higher generalizability than homogeneous ensembles, although the differences were marginal. Weighted soft voting was the most generalizable of the heterogeneous ensembles, with loss of 0.49. Weighted soft voting also delivered acceptable classification performance on the test set, with accuracy of 0.52. The identified contributions of the environmental factors to BMI predictions and the directions of their effects agreed with established knowledge, confirming the reliability of the predictions. These results demonstrate the usefulness of the heterogeneous ensemble models for increasing the generalizability of ML model predictions. Furthermore, despite the slightly lower generalizability than voting-based ensembles, homogeneous ensembles demonstrated comparable levels of generalizability to heterogeneous ensembles.

How to cite: Park, T., Shin, J., and Cha, Y.: Generalizability evaluation of heterogeneous ensembles models for benthic macroinvertebrate index predictions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3783, https://doi.org/10.5194/egusphere-egu24-3783, 2024.

Water temperature is one of the most important indicators of water quality as it regulates physical, chemical and biological processes in rivers. When water temperatures reach extreme values, this can have potentially severe consequences for the survival of aquatic ecosystems. Extreme water temperatures can be caused by extreme weather phenomena such as heat waves and prolonged droughts. In mountain regions, the complexity of water temperature dynamics is greater than in lowland regions due to changes in the hydrological regime caused by glacier retreat and changes in the contribution of snowmelt to streams. Despite the potential impacts of water temperature extremes, knowledge of the occurrence and driving processes of water temperature extremes in mountain rivers remains limited.

Here, we aim to improve our understanding of the spatial and temporal variability and long-term changes in the occurrence of extremes. In addition we aim to identify the main processes influencing the occurrence of water temperature extremes in mountain rivers in Europe.  First, we compare 30 years of water temperature data in 18 catchments in the Alps to gain insight into the temporal variability of water temperature extremes. We examine the seasonality of these extremes and use trend tests to assess long-term trends. Second, we compare 177 catchments across four different mountain regions in Europe to understand the frequency, severity and variability of water temperature extremes at a regional scale. Finally, we use random forest models to investigate the importance of different processes contributing to water temperature extremes and how the main driving processes vary in both time and space.

The results of the trend analysis in the Alps show that extreme water temperatures, i.e. water temperatures exceeding a locally varying threshold, have increased faster than mean water temperatures during the summer period of 1991-2021. The most severe extreme events are mainly found in low elevation catchments. The number of extreme events has increased over time at all elevations, with the strongest increase for high elevation catchments. Furthermore, the analysis of the driving processes shows that air temperature is the main driver of non-extreme water temperature. However, to predict water temperature extremes, other hydroclimatic variables such as soil moisture, snowmelt, and baseflow should also be considered. This suggests that current water temperature models, which use only air temperature and discharge as input variables, may not be suitable for predicting water temperature extremes at high elevations. These insights into the behaviour of water temperature extremes are valuable for predicting future changes in extremes in mountain rivers. 

How to cite: van Hamel, A. and Brunner, M.: Extreme water temperatures in mountain rivers – changes and driving processes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7691, https://doi.org/10.5194/egusphere-egu24-7691, 2024.

Climate change and other dynamic changes, such as demographic change, pose challenges for public water supply in Germany. This study contributes to the identification of hot spot regions that could experience increased water shortages in the future through nationwide, regionalized forecasts of water demand in domestic, industry and agriculture sectors and their balancing with projections of groundwater recharge. Multi-sectoral water demand forecasts for the periods 2021-2050, 2036-2065 and 2069-2098 were prepared using a top-down approach at NUTS-3 level (cities and districts).

The total water demand in the domestic sector in Germany averages approx. 3.7 billion m³/a in the reference period 1998-2019. In the lower scenario, it decreases to approx. 2.2 billion m³/a by the end of the century. In the upper scenario, total water demand in the domestic sector in Germany increases to around 4.1 billion m³/a. Industrial water demand could fall to around half (10.9 billion m³/a) as early as 2030 compared to the reference period (approx. 21.6 billion m³/a) due to a sharp decline in cooling water demand. From the middle of the 21st century onwards, it is expected to stagnate at around 6.1 billion m³/a. Depending on the scenario, the irrigated agricultural area in Germany will almost double (RCP 2.6) or almost triple (RCP 8.5) by the end of the century, resulting in a near tripling (RCP 8.5) of irrigation volumes. Overall, the total water demand in Germany decreases significantly in both scenarios. In the lower scenario, water demand falls from around 26 billion m³/a to around 9 billion m³/a by the end of the century. In the upper scenario, it is reduced to around 12 billion m³/a by the end of the century. These enormous decreases in total water demand are due to reductions in water demand in the energy sector, which overlay increases in domestic and agricultural water demands.

mHM-simulations of groundwater recharge based on climate projections show constant or increasing groundwater recharge rates in large parts of Germany in the ensemble median for the 2021-2050, 2036-2065 and 2069-2098 time slices, assuming both RCP 2.6 (21 RCMs) and RCP 8.5 (49 RCMs). However, declining groundwater recharge rates may also occur in certain regions, particularly in south-western Germany. In the 25th percentile of the model ensemble, falling groundwater recharge rates occur under RCP 2.6 in southern and western Germany. Towards the end of the century, groundwater recharge rates increase in eastern Germany. Under RCP 8.5, the 25th percentile of the model ensemble shows mostly constant or increasing groundwater recharge rates. However, south-western Germany is characterized by a significant decline in groundwater recharge.

The risk index water balance RIWB was defined as an indicator to evaluate the regional water supply in relation to the balance between water demand and groundwater recharge. The RIWB shows that the ratio of water demand to groundwater recharge can be expected to remain the same or improve in the most regions, while, depending on the scenario, 4% or 11% of districts/cities, particularly in northern Germany, must prepare for a deterioration in this ratio.

How to cite: aus der Beek, T., Zaun, F., Streck, T., Weber, T., Sturm, S., Vollmer, T., Boeing, F., and Marx, A.: Modelling multi-sectoral water demand and water availability to identify future water scarcity regions in Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11771, https://doi.org/10.5194/egusphere-egu24-11771, 2024.

The far-reaching impact of climate change on water resources, particularly its intensification of scarcity, poses a substantial threat to the sustainable management of water in agriculture. To enhance cross-sectoral decision-making at various scales, it is vital to quantify both current and future water consumption, employing methodologies that assess the agricultural water footprint (WF).

This study employs the Site-sPecific Agricultural water Requirement and footprint Estimator (SPARE:WATER) to evaluate the susceptibility of green and blue agricultural WF at various scales across Colombia. The assessment is conducted under two CORDEX (Coordinated Regional Climate Downscaling Experiment)-driven climate scenarios, RCP2.6 and RCP8.5. High-resolution (0.22°) CORDEX climate model projections are used to drive the SPARE:WATER model, while historical weather data from fifteen stations (1977-2005) are employed to bias-correct the model's gridded data using the Equal Quantile Matching (EQM) method. This corrected data was spatialized using IDW interpolation. Ten major crops are selected based on their national production significance, based on the National Agricultural Survey. Crop characteristics such as harvested area, yield, and crop coefficients are obtained from local and FAO sources. The analysis focuses on both green and blue WF for the near future (2060) and far future (2099), compared to the present (2020).

Preliminary findings underscore a national WF of 45 km³/yr, with important variations at the departmental level. The spatial variability of WF is influenced by both wet and dry years.  Cocoa, coffee, and palm oil emerge as crops with the most substantial WF, showcasing respective water requirements of 30 k m³/t, 18 k m³/t, and 8 k m³/t nationally. Regional variations reveal the significance of crops such as plantain and banana in the agricultural WF landscape. Under the RCP2.6 scenario, the green and blue WF projections for 2060 and 2099 exhibit marginal changes relative to 2020. Conversely, under the RCP8.5 scenario, a discernible increase, particularly in blue WF, is evident, with a surge of 96% by 2099. This trajectory underscores the heightened water requirements anticipated for pivotal crops like cocoa and coffee in the future agricultural landscape.

These findings underscore the urgent need for informed water management strategies in the future of Colombian agriculture, particularly in the face of a high-emission scenario. The results of this study can inform policy and decision-making aimed at ensuring sustainable water resources management and food security under the evolving climate landscape.

How to cite: Correa, A., Frank, N., Muzammil, M., Weeser, B., and Breuer, L.: Spatial and Temporal Dynamics of Agricultural Water Footprint in a Changing Climate: A CORDEX-SPARE:WATER Analysis., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19395, https://doi.org/10.5194/egusphere-egu24-19395, 2024.

For communities to adapt, effective water management and governance strategies are necessary due to climate change's pressing challenges. Specifically, the proposed study examines the role of traditional water management systems in supporting land adaptation to harsh climate conditions on Pantelleria, a volcanic island in the Sicilian Channel.

On the island of Pantelleria, there is a rich cultural heritage of water management, exemplifying a complex interaction between desertification, climate change, biodiversity, ecosystem services and land adaptation. As a result of Pantelleria's traditional water management, the landscape mosaic has been able to adapt and remain resilient to climate change impacts. Considering its successful application over time, the ingenious rainwater accumulation, storage, and distribution system demonstrates that this heritage serves not only as a legacy of the past, but as a critical organizing principle for the present. From a social-ecological perspective, preserving cultural heritage shifts the paradigm from innovating traditional knowledge toward reclaiming traditional water management methods that already contribute to the sustainability of local and environmental communities and incorporating them into a perspective of land adaptation to climate change.

This study combines scientific research on desertification and land degradation in Southern Italy with interviews with local stakeholders in an effort to emphasize the importance of cultural heritage knowledge along with bottom-up actions by citizens, and advocates for the systemic vision of rural landscapes by mapping the distribution and abundance of traditional water systems in order to assess their functions in preserving and enhancing ecosystem services in an environment of constant land changing.

Pantelleria serves as a model to demonstrate how similar regions experiencing water-related issues may benefit from its solutions and this study examines the barriers to integrating traditional and modern water management systems based on political, cultural, and institutional factors in order to improve water management and governance in similar harsh environments.

How to cite: De Agostini, C.: Assessing the role of cultural heritage in water management and land adaptation facing climate change: Pantelleria's case study., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9545, https://doi.org/10.5194/egusphere-egu24-9545, 2024.