HS2.3.2 | Water quality and availability modeling, risk analysis and decision support under current conditions and future scenarios
EDI
Water quality and availability modeling, risk analysis and decision support under current conditions and future scenarios
Convener: Albert NkwasaECSECS | Co-conveners: Miriam Glendell, Danlu GuoECSECS, Rohini Kumar, Matthew Miller, Olivia MillerECSECS, Michelle van Vliet
Orals
| Wed, 17 Apr, 14:00–18:00 (CEST)
 
Room 2.31
Posters on site
| Attendance Thu, 18 Apr, 16:15–18:00 (CEST) | Display Thu, 18 Apr, 14:00–18:00
 
Hall A
Posters virtual
| Attendance Thu, 18 Apr, 14:00–15:45 (CEST) | Display Thu, 18 Apr, 08:30–18:00
 
vHall A
Orals |
Wed, 14:00
Thu, 16:15
Thu, 14:00
Quantifying and understanding the impacts of environmental change on water quality and availability across space and time is critically important for ensuring that there is enough water of suitable quality to meet human and ecosystem needs now and in the future. Consequently, there is an urgent need for tools such as models, remote sensing, machine-learning and artificial intelligence algorithms that can anticipate these impacts and address the resulting environmental changes. These assessments in turn facilitate more effective water management that safeguards the critical ecosystem goods and services provided by freshwater resources. In addition, some of these tools, within both Bayesian and frequentist paradigms, enable consideration of prediction reliability, relating uncertainties to a decision makers’ attitudes and preferences towards risks, all while accounting for the uncertainty related to our system understanding, data and random processes. We seek contributions that apply modeling and other approaches to:
• investigate climate change impacts on water quality and quantity from local to global scales, including climate impact attribution studies
• quantify and couple supply and demand in support of water management including vulnerability assessment, scenario analysis, indicators, and the water footprint
• project future water supply and demand in the context of a changing climate, land use, population growth, and other potential drivers of change
• quantify the uncertainty of model predictions (due to data, model structure and parameter uncertainty)
• interpret and characterize uncertainties in machine-learning and data mining approaches that learn from large, possibly high-resolution data sets
• address the problem of scaling (e.g. disparity of scales between processes, observations, model resolution and predictions)
• test transferability and generalizability of findings
• assess water quality and quantity in either data-rich or data-sparse environments
• involve stakeholders in model development and maximise the use of expert knowledge to inform risk analysis and decision support, incl. monitoring, reporting and catchment management
• assess robustness in water quality and quantity hotspots

Orals: Wed, 17 Apr | Room 2.31

The oral presentations are given in a hybrid format supported by a Zoom meeting featuring on-site and virtual presentations. The button to access the Zoom meeting appears just before the time block starts.
Chairpersons: Albert Nkwasa, Miriam Glendell, Rohini Kumar
14:00–14:20
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EGU24-18007
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HS2.3.2
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solicited
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Highlight
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On-site presentation
Joseph Alcamo

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.

14:20–14:30
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EGU24-14412
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HS2.3.2
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On-site presentation
Bruna Grizzetti, Angel Udias, Olga Vigiak, Alberto Pistocchi, Alberto Aloe, Berny Bisselink, Faycal Bouraoui, Alexander De Meij, Jordan Hristov, Diego Macias Moy, Enrico Pisoni, Ioannis Trichakis, Franz Weiss, Matteo Zampieri, and Michela Zanni

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.

14:30–14:40
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EGU24-3427
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HS2.3.2
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ECS
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Highlight
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On-site presentation
Edward R. Jones, Marc F.P. Bierkens, and Michelle T.H. van Vliet

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.

14:40–14:50
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EGU24-9472
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HS2.3.2
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ECS
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On-site presentation
Diep Ngoc Nguyen, Elisa Furlan, Silvia Torresan, Jacopo Furlanetto, Donata Canu, Leslie Aveytua Alcazar, Cosimo Solidoro, and Andrea Critto

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.

14:50–15:00
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EGU24-19124
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HS2.3.2
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On-site presentation
Joachim Rozemeijer and Kim Gommans

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.

15:00–15:10
15:10–15:20
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EGU24-2180
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HS2.3.2
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Highlight
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On-site presentation
Maryna Strokal, Ilaria Micella, Mirjam P. Bak, Arthur H. W. Beusen, Martina Flörke, Simon N. Gosling, Ann Van Griensven, Bruna Grizzetti, Nynke Hofstra, Edward R. Jones, Carolien Kroeze, Albert Nkwasa, Tineke Troost, Michelle T.H. van Vliet, Mengru Wang, and Rohini Kumar

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.

15:20–15:30
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EGU24-2349
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HS2.3.2
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ECS
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On-site presentation
Ilaria Micella, Mengru Wang, Mirjam P. Bak, Nynke Hofstra, Carolien Kroeze, Yanan Li, Shiyang Li, Vita Strokal, Aslihan Ural-Janssen, Qi Zhang, and Maryna Strokal

Water quality has been deteriorating in many lakes, rivers and coastal waters. Climate change is one of the drivers that can further deteriorate water quality (e.g., droughts contribute to higher concentrations of pollutants). Meanwhile, human activities add more loadings of pollutants to water, e.g., intensified agriculture, more cities with poor wastewater treatment facilities, and low access to improved sanitation, especially in less developed countries. Examples are nutrients from overfertilized land leading to eutrophication issues in fresh and coastal waters. Pathogens in surface waters from poor sanitation facilities can make people sick. Plastics in surface waters can result from mismanaged solid waste (e.g., macroplastics) and untreated wastewater (e.g., microplastics from laundry, dust, car tires and personal care products). In general, human activities serve as common sources of multiple pollutants. For example, animal manure is often used as fertilizer in agriculture and contains nutrients, pathogens, antibiotics, and heavy metals. Therefore, it is important to better understand common sources of multiple pollutants in water across scales to identify effective solutions. We develop computer models for different scales covering grids, (sub)basins, regions and the globe. Our models are for multiple pollutants, i.e. nutrients, plastics, antibiotics, pathogens (Cryptosporidium) and pesticides. Therefore, in this abstract, we aim to compare our model results for multiple pollutants to identify robust water pollution hotspots and their sources across scales. This will contribute to and support the Fast Track initiative within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) platform. At the EGU conference, we will show examples of multi-pollutant modeling using our MARINA models (Models to Assess River Inputs of pollutaNts to seAs) family and GlowPa (Global Waterborne Pathogens) model developments. We will compare our model results for multiple pollutants by using different global climate models. Accordingly, we will discuss the impact of climate simulations on multi-pollutant hotspots. We will also show examples of identified robust multi-pollutant hotspots globally. We will zoom into regional analyses to better understand the impact of climate change on water pollution. Ultimately, we will highlight the need for such model intercomparisons for multiple pollutants and scales to better understand pollution hotspots and their sources under global change.

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.

15:30–15:40
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EGU24-3370
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HS2.3.2
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ECS
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On-site presentation
Shuman Liu, Junguo Liu, Dandan Zhao, and Wenfang Cao

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.

15:40–15:45
Coffee break
Chairpersons: Matthew Miller, Michelle van Vliet, Olivia Miller
16:15–16:25
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EGU24-17151
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HS2.3.2
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ECS
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On-site presentation
Maria Theresa Nakkazi, Albert Nkwasa, Analy Baltodano Martinez, and Ann van Griensven

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.

16:25–16:35
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EGU24-13664
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HS2.3.2
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On-site presentation
Nandita Basu, Nitin Singh, and Kim Van Meter

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.

16:35–16:45
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EGU24-6414
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HS2.3.2
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ECS
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On-site presentation
Camilla Negri, Nicholas Schurch, Andrew J. Wade, Per-Erik Mellander, and Miriam Glendell

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.

16:45–16:55
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EGU24-3783
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HS2.3.2
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ECS
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On-site presentation
Taeseung Park, Jihoon Shin, and YoonKyung Cha

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.

16:55–17:05
|
EGU24-5163
|
HS2.3.2
|
On-site presentation
The decrease of salinity in lakes on the Tibetan Plateau between 2000 and 2019 based on remote sensing model inversions
(withdrawn)
Liping Zhu, Chong Liu, and Jianting Ju
17:05–17:15
|
EGU24-7691
|
HS2.3.2
|
ECS
|
On-site presentation
Amber van Hamel and Manuela Brunner

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.

17:15–17:25
|
EGU24-11771
|
HS2.3.2
|
On-site presentation
Tim aus der Beek, Florian Zaun, Thilo Streck, Tobias Weber, Sebastian Sturm, Tanja Vollmer, Friedrich Boeing, and Andreas Marx

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.

17:25–17:35
|
EGU24-19395
|
HS2.3.2
|
On-site presentation
Alicia Correa, Natascha Frank, Muhammad Muzammil, Bjoern Weeser, and Lutz Breuer

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.

17:35–17:45
|
EGU24-19357
|
HS2.3.2
|
On-site presentation
Fatemeh Karandish and Markus Berger

Water plays a pivotal role in fostering global sustainable development. Although the agricultural sector has rightfully received considerable attention for its predominant role in global water withdrawal (WW), the industrial sector has often been overlooked. Despite this neglect, the industrial sector significantly influences global WWs, contributing approximately 19% of the total according to FAO estimates. However, the water consumed within the industry necessitates substantial natural and economic investments for the construction of water supply facilities and the required energy. Hence, for the first time, we have developed a straightforward top-down model to quantify time-series high-resolution sub-sectoral industrial water footprints (WFs) globally. The foundational inputs for the model are derived from national statistics reported by FAO spanning the period from 1990 to 2019. Data gaps were addressed through a combination of interpolation and extrapolation methods. Subsequently, the national industrial WWs were downscaled within three distinct industrial sub-sectors: manufacturing, mining and quarrying, and electricity production. National data were further downscaled to grid levels, utilizing a 5x5 arc-minute resolution based on the EDGAR emission datasets. Sub-sectoral industrial WWs were multiplied by conversion ratios to derive the corresponding water footprints (WFs). The model's outcomes demonstrated significantly enhanced reliability in predictions compared to existing sophisticated models. Our findings align closely with AQUASTAT statistics, boasting an accuracy exceeding 90%, while predictions from other available models deviate by 16-71% in industrial water withdrawals (WWs). Furthermore, a robust overall correlation coefficient of 0.96 between our predicted sub-sectoral water footprints (WFs) and available measured statistics strongly indicates the accuracy of our developed top-down approach in accounting for sub-sectoral industrial WFs. As a result, our model can be effectively utilized for impact and scenario assessments, providing valuable insights to formulate effective strategies for addressing challenges in achieving objectives under SDG 6.

How to cite: Karandish, F. and Berger, M.: A Streamlined Model for Assessing Global Industrial Water Footprint with Minimal Input Requirements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19357, https://doi.org/10.5194/egusphere-egu24-19357, 2024.

17:45–17:55
|
EGU24-9545
|
HS2.3.2
|
On-site presentation
|
Carla De Agostini

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.

17:55–18:00

Posters on site: Thu, 18 Apr, 16:15–18:00 | Hall A

The posters scheduled for on-site presentation are only visible in the poster hall in Vienna. If authors uploaded their presentation files, these files are linked from the abstracts below, but only on the day of the poster session.
Display time: Thu, 18 Apr 14:00–Thu, 18 Apr 18:00
Chairperson: Albert Nkwasa
A.33
|
EGU24-19564
|
HS2.3.2
|
ECS
|
Albert Nkwasa, Celray James Chawanda, and Ann van Griensven

Surface water pollution has emerged as one of the predominant environmental challenges of this century, as human activities and climate change considerably alter the natural quality of freshwater ecosystems. However, gauging the true extent of how polluted or impacted freshwaters are remains challenging globally simply due to limited spatial and temporal water quality observations. To address this gap, we present a high-resolution global water quality model utilizing the Soil Water and Assessment Tool (SWAT+). Our objectives are twofold: (1) to offer locally relevant water quality estimates on a global scale and (2) to understand how human activities and climate change are influencing the water quality of rivers on the globally. In this study, we examine future spatial patterns and temporal trends in river nutrients (Total Nitrogen – TN and Total Phosphorus – TP) and sediment load concentrations until 2100, considering changing climate and socioeconomic conditions. Additionally, we attribute the primary contributing drivers to nutrient water pollution, shedding light on the key factors shaping the future of global water quality.

How to cite: Nkwasa, A., Chawanda, C. J., and van Griensven, A.: A high-resolution global SWATplus water quality model: Harmonizing local and global perspectives, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19564, https://doi.org/10.5194/egusphere-egu24-19564, 2024.

A.34
|
EGU24-10198
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HS2.3.2
|
ECS
Carolin Winter, Julia L. A. Knapp, and James W. Kirchner

The severe 2018 drought affected water quantity and quality across Europe. While the consequences for water quantity have received substantial attention, the effects on water quality are intricate and require careful analysis. Nevertheless, understanding these impacts is crucial because the scarcity of water resources elevates the importance of their quality.

Here, we conducted a comprehensive analysis of a high-frequency dataset (hourly) encompassing stream water chemistry across various solutes, including nutrients, heavy metals, and other ions, in the pre-alpine Erlenbach catchment (0.7 km², Switzerland). We used concentration-discharge (C-Q) relationships to detect the drought impacts on solute export patterns at the catchment outlet. The month of July 2018, characterized by the highest annual average temperature, experienced the most pronounced drought conditions. During this period, all solute concentrations exhibited a notable divergence in export patterns compared to the same month in other years with normal conditions (July 2017, 2019, 2020). In August, when conditions returned to normal, some solute concentrations also reverted to typical patterns, while others continued to deviate. These observations suggest a drought-induced alteration in solute mobilization, hydrologic transport, and retention, accompanied by potential solute-source-specific memory effects.

The extensive and unique dataset documenting stream water chemistry in the Erlenbach catchment provides valuable insights into the processes shaping water quality during drought. If this knowledge can be extrapolated to other catchments, it may offer a foundation for safeguarding our precious freshwater resources in the face of an increasing risk for the occurrence of severe and prolonged droughts.

How to cite: Winter, C., Knapp, J. L. A., and Kirchner, J. W.: Drought Affects Export Patterns Across Different Solutes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10198, https://doi.org/10.5194/egusphere-egu24-10198, 2024.

A.35
|
EGU24-22136
|
HS2.3.2
Edward R. Jones, Marc F.P Bierkens, and Michelle T.H. van Vliet

Good surface water quality is essential for safeguarding human water use activities and maintaining ecosystem health. Yet, our quantitative understanding of past developments in surface water quality is mostly predicated upon observations at monitoring stations that are discontinuous in both space and time. Furthermore, very little is known about how both climate and societal change will impact surface water quality in the future.

Process-based models provide unique opportunities to simulate both past and future surface water quality with a consistent spatial and temporal resolution. Representing one of the attempts in the emerging field of large-scale surface water quality modelling, we have developed the dynamical water quality routing model (DynQual) to simulate water temperature (Tw) and total dissolved solids (TDS), biological oxygen demand (BOD) and fecal coliform (FC) concentrations at 5 arc-minute (10km) spatial resolution and with a daily timestep. The model is open-source (https://github.com/UU-Hydro/DYNQUAL) and is coupled to the global hydrological model PCR-GLOBWB2, although hydrological input can alternatively be prescribed as a forcing. The model also incorporates a high-resolution wastewater treatment dataset1 to more realistically account for the impact of these practices on pollutant delivery to surface waters, compared to country-level or regional average rates.

DynQual has been applied and validated against observed in-stream concentrations for the historic period (1980 – 2019) using input from ISIMIP3a2, and used to project future surface water quality (up to 2100) under (uncertain) climate change and socio-economic developments using input from ISIMIP3b3. Based on these modelled results, we assess the spatial patterns, temporal variations and long-term trends in surface water pollutant concentrations to evaluate global water quality dynamics.

Our results show that surface water quality issues exist across all world regions, with current multi-pollutant hotspots especially prevalent in northern India and eastern China. Recent trends towards surface water quality deterioration are most profound in the developing world, particularly Sub-Saharan Africa and southern Asia. Conversely, in highly developed economies, organic (BOD) and pathogen (FC) pollution have decreased over time primarily due to expansions and improvements in wastewater collection and treatment. Simulations of future water quality indicate that pollution will increasingly and disproportionately affect people living in developing countries, with a widening gap in exposure rates between rich and poor countries. In particular, the combination of surface water quality deterioration and demographic changes in Sub-Saharan Africa will establish this region as a new global hotspot of surface water pollution.  

 

References

 

1 Jones, E.R., M.T.H. van Vliet, M. Qadir, M.F.P Bierkens (2021) Country-level and gridded estimates of global wastewater production, collection, treatment and re-use, Earth Syst. Sci. Data, 13, 237–254, https://doi.org/10.5194/essd-13-237-2021

 

2 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

 

3 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.: Past and future global surface water quality modelling using DynQual, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22136, https://doi.org/10.5194/egusphere-egu24-22136, 2024.

A.36
|
EGU24-10292
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HS2.3.2
|
ECS
|
|
Mustafa Onur Onen, Charles Rougé, Isabel Douterelo Soler, and Geoff Darch

Ensuring public access to clean water faces unprecedented challenges. The rising frequency and intensity of hot-dry conditions, coupled with population growth, strain water stocks. Concurrently, the presence of excess phosphorus and nitrogen in freshwater lakes and reservoirs leads to harmful algal blooms (HAB) precisely when hot-dry conditions occur, impacting aquatic life and complicating water treatment for human consumption. Due to uncertainties in future climate, water demands, nutrient discharge, and ecological factors, determination of HAB risk is a complex task. This hard-to-quantify risk impacts water planning since it is unknown whether reservoir water will be usable in the hot, dry summers when it is most needed.

Water planning increasingly involves fast water resource simulators. These tools evaluate the performance of adaptive infrastructure investments within complex water resource systems under changes in supply-demand conditions. Relying on water balance calculations, these fast models prioritise water quantity targets and typically neglect water quality. This overlooks water quality impacts on resource availability. Conversely, advancements in aquatic ecosystem modelling have produced complex water quality simulators, incorporating numerous space and time variant equations for hydrodynamics, biogeochemical and ecological processes, and particularly addressing HABs. These processes are much more complex than water balance dynamics, leading to models with much slower run-times than water resource simulators. In addition, to account for water quality in the design of operating strategies, we need two-way coupling of these models to communicate the simulated water quality and quantity states with each other frequently throughout simulations.

To achieve this two-way coupling, we integrated a high-performing lake model, the General Lake Model (GLM), into a recently developed water resource simulator called Pywr. Thanks to its 1D resolution of physical processes, GLM operates at speeds comparable to Pywr, and this work is, to our knowledge, the first to apply it to water planning. Enhanced communication between the models is facilitated by Pywr's extended parameters, allowing the execution of customised tasks at each timestep. Furthermore, Pywr's advanced scenario handling capability renders the coupled framework ideal for risk assessment. Coupled models are pivotal for designing operating strategies aimed at minimising HAB risk under diverse future conditions (climate, water demands and nutrient transport). Successful implementation will shed light on the feasibility of constructing new reservoirs, evaluating their susceptibility to algal blooms, and informing billion-pound investment decisions.

How to cite: Onen, M. O., Rougé, C., Douterelo Soler, I., and Darch, G.: Coupling water quality and quantity models to integrate climate risk to reservoir water quality into water planning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10292, https://doi.org/10.5194/egusphere-egu24-10292, 2024.

A.37
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EGU24-9619
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HS2.3.2
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ECS
Olinda Jack Mariano Rufo, Samuele Casagrande, Vuong Pham, and Andrea Critto

The degradation of water quality, pivotal for sustainable development, is exacerbated by the interconnected impacts of climate change (CC) and land-use/land-cover change (LULCC). Rapid global development has amplified these effects, disrupting key processes that regulate river flows and runoff. LULCC, particularly in agriculture, plays a central role in nutrient cycle and runoff to the water bodies, thus affecting the water quality and ecosystem resilience. Concurrently, climate change intensifies algal blooms, reduces dissolved oxygen, and alters aquatic ecosystems. Shifts in precipitation and rising temperatures amplify pollutant transport and compromise water quality. Moreover, the complex interactions among LULCC and CC have significant impacts on nutrients, pollution concentrations, and sedimentation rates in freshwater ecosystems. This research focuses on the analysis of the dynamics and impacts of LULCC and climate-induced changes on the state of water quality at the river basin scale in Italy, supporting the achievement of good chemical and ecological status of the Water Framework Directive. First, this study aims to analyse LULCC pattern changes across various spatial scales with the use of geoinformation system (GIS) techniques to identify sensitivity and changes at the pixel level within different land use types. This analysis allows the identification of linkages and dependencies between LULCC indicators and their relationships with water quality parameters in each river basin of Italy. Second, to address the compound effects of climate change, this study examines historical patterns of climate change indicators and their correlation with water quality over time. It also investigates the temporal and spatial occurrence of extreme events, which are linked to changes in nutrient and pollutant levels. The final phase of the methodology involves the development of a machine-learning model aiming at understanding and predicting the complex interplay of multiple risk factors such as the combined effects of land-use change, climate variability, and other anthropogenic influences on water quality. By using machine learning techniques, the study will be able to identify intricate patterns and non-linear relationships between multiple risk factors and their collective influence on water quality dynamics. The results will provide a comprehensive understanding required for adaptive measures and decision-making support in Italy.

How to cite: Rufo, O. J. M., Casagrande, S., Pham, V., and Critto, A.: A comprehensive analysis of land use and climate change impacts on water quality in Italian river basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9619, https://doi.org/10.5194/egusphere-egu24-9619, 2024.

A.38
|
EGU24-4531
|
HS2.3.2
|
ECS
Water quality evaluation and trend analysis during typical operation of large-scale long-distance open water diversion project
(withdrawn)
Benyou Jia, Ziwu Fan, Kun Su, Yipeng Liao, Fan Yang, Wei Wang, Dandan Li, and Chen Xie
A.39
|
EGU24-9238
|
HS2.3.2
|
ECS
Xia Wu, Lucy Marshall, Ashish Sharma, and Qingyun Duan

The presence of errors in water quality and hydrologic variables can significantly impair the calibration of water quality models. To enhance the estimation of model parameters, it is important to accurately identify data errors during the calibration process. However, this task is challenging due to the complex interactions between model parameter uncertainty and data uncertainty. Existing methods for incorporating data uncertainty in model calibration have limitations, such as high-dimensional computation or the inability to handle stochastic errors.

To address these challenges, a novel method called Bayesian Error Analysis with Reordering (BEAR) has been developed. Given that the data uncertainty arises from the data itself and is independent of the model calibration or simulation, the cumulative distribution function (CDF) of the data error can be estimated ahead and regarded as the prior information of Bayesian inference. Then the values of data error series only depend on their ranks in the CDF. BEAR method transforms the values of data error series into their ranks in the CDF. This transformation enables the effective identification of input and/or output data errors in water quality calibration.

The innovation of the BEAR method can be attributed to several key aspects:

1) Modification of the secant method to handle the non-linear transformation from input to output, ensuring the correspondence between the rank of input data error and the residual error of the model.

2) Decomposition of model simulations to calculate the delay between each input and its corresponding output.

3) Utilization of the Autoregressive model to account for the correlation of residual errors.

4) Selection of an appropriate updating logic to minimize the compensation effects among multiple sources of data uncertainty.

Overall, the BEAR method demonstrates flexibility and adaptability to various environmental modelling scenarios, making it a valuable tool for improving model specification under conditions of data uncertainty.

How to cite: Wu, X., Marshall, L., Sharma, A., and Duan, Q.: Identifying input and output data errors in the calibration of a water quality model using Bayesian error analysis with reordering (BEAR) method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9238, https://doi.org/10.5194/egusphere-egu24-9238, 2024.

A.40
|
EGU24-21915
|
HS2.3.2
Seyed Mohammad Mahdi Moezzi, Ali Abousaeidi, Farkhondeh Khorashadi Zadeh, Razi Sheikholeslami, Albert Nkwasa, and Ann van Griensven

This study explores an innovative approach to assess the impact of climate change on water quality and quantity by employing an Artificial Intelligence (AI) model, as an alternative to conventional physically-based models. Physically-based models, like SWAT (Soil and Water Assessment Tool), are widely used but face limitations in climate change scenario analysis due to the extensive input data requirements, complex model parameterization and prolonged simulation times. To overcome these challenges, we investigate the application of AI-based models, specifically the Random Forest (RF) method, for climate change scenario analysis in the Simiyu catchment of the Lake Victoria basin. Using meteorological data as input data, the RF model is trained with SWAT results for river flow and sediment load. Subsequently, the RF model is validated and utilized to predict river flow and sediment load under different climate change scenarios using new meteorological data. The RF model is compared to SWAT, demonstrating advantages, such as reduced input data requirements and computational costs. This research provides a streamlined and efficient approach for projecting climate change impacts on water quality and quantity, emphasizing the potential benefits of AI-based models over traditional physically-based counterparts.

Key words: Climate change scenarios, Artificial intelligence, Random forest, SWAT model

How to cite: Moezzi, S. M. M., Abousaeidi, A., Khorashadi Zadeh, F., Sheikholeslami, R., Nkwasa, A., and van Griensven, A.: Projecting impacts of climate change on water quality and quantity using an AI-based model as an innovative alternative to physically-based models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21915, https://doi.org/10.5194/egusphere-egu24-21915, 2024.

A.41
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EGU24-13488
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HS2.3.2
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ECS
Gayeong Lee, Yuri Kim, Chunggil Jung, Jongyoon Park, and Eunsik Kim

Saltwater intrusion in river estuaries poses significant challenges for water quality management and ecosystem sustainability. In this study, we investigate the characteristics of saltwater intrusion in the Seomjin River by constructing an Environmental Fluid Dynamics Code (EFDC) model for the Seomjin River and Gwangyang Bay, incorporating environmental changes and basic data.  After validating the model through calibration and verification processes, we conduct numerical experiments to explore 28 scenarios for the Songjeong flow rate and Daap intake rate. We begin by constructing and validating the numerical model using historical data of river discharge, tidal levels, and salinity measurements. The main income of this area is collecting a corbicula from the river. However, since the 1970s, many projects such as dam construction, river aggregate collection, and Gwangyang Bay reclamation have been carried out, and now the fishery is suffering salt damage.


To understand the long-term trends and seasonal variability of salt intrusion, we analyze historical datasets spanning multiple years. This analysis helps identify potential shifts in salt intrusion patterns over time, which could be attributed to natural variations or anthropogenic influences. We analyze salinity concentrations at four key points (Dugog, Sinbi, Mogdo, and Hwamog) during the entire period, spring, and neap periods. The spatial plane and stratified distribution of salinity are examined, and a salinity and flux model is developed. The longitudinal distribution of saltwater intrusion from the estuary is analyzed, and a salinity and saltwater intrusion distance model is constructed. Our findings reveal that the changes in salinity concentration range from 4.7 psu for Dugog to 28.2 psu for Hwamog at different Songjeong flow rates. In the spring period, salinity  changes increase, but average concentrations decrease, while in the neap period, salinity changes decrease, but average concentrations increase. Salinity stratification is observed in Sinbi, Mogdo, and Hwamog during the neap period due to significantly increased salt concentrations. Additionally, the effect of the Daap intake rate on salt concentration is found to be small, with a salinity difference of less than 1 psu. Spatially, the maximum salinity concentration decreases as the Songjeong flow rate increases, and the influence of the Songjeong flow rate is more pronounced in the spring period compared to the neap period. Furthermore, we construct quantitative prediction models for salinity reduction scenarios at different points, determining the instream flow required to achieve target salinity concentrations. The study indicates that Hwamog requires a Songjeong flow rate of 100 cms or more to achieve 20 psu during the neap period.
In conclusion, this research sheds light on the complex interactions governing salt intrusion in the Seomjin River, facilitating informed decision-making for water resource management and environmental conservation. The integration of the EFDC model with deep learning techniques offers a comprehensive understanding of saltwater intrusion dynamics and contributes to informed decision-making for water quality enhancement in
estuarine ecosystems

How to cite: Lee, G., Kim, Y., Jung, C., Park, J., and Kim, E.: Investigating Salt Intrusion Characteristics Considering Changesin River Beds, Water Intake from Rivers, and Dam Supply in theSeomjin River using a Numerical Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13488, https://doi.org/10.5194/egusphere-egu24-13488, 2024.

Posters virtual: Thu, 18 Apr, 14:00–15:45 | vHall A

The posters scheduled for virtual presentation are visible in Gather.Town. Attendees are asked to meet the authors during the scheduled attendance time for live video chats. If authors uploaded their presentation files, these files are also linked from the abstracts below, but only on the day of the poster session. The button to access Gather.Town appears just before the time block starts. Onsite attendees can also visit the virtual poster sessions at the PICO spot in the corresponding on-site poster hall (e.g. virtual posters of vHall X4 are visible at PICO spot 4).
Display time: Thu, 18 Apr 08:30–Thu, 18 Apr 18:00
Chairpersons: Danlu Guo, Michelle van Vliet
vA.5
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EGU24-13703
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HS2.3.2
Danlu Guo, Qian Zhang, Camille Minaudo, Shuci Liu, Remi Dupas, Kefeng Zhang, Ulrike Bende-Michl, Clement Duvert, and Anna Lintern

Water quality of rivers and streams can vary over time due to changing hydro-climatic conditions in the interaction with catchment physio-geographic conditions and local human activities. This study is the first exploration of long-term river water quality trends across Australian continent, consolidating 375 catchments with contrasting climate, hydrology, land use and land cover. We focused on five key water quality parameters and estimated their flow-normalized trends over 2000-2019 using the Weighted Regressions on Time, Discharge, and Season method (WRTDS). For each parameter, about half of nation’s catchments have significant trends, which are generally within ±10% per annum relative to the first year (2000). Except for TSS, there is no systematic non-linearity nor abrupt changes over time, while for TSS many catchments had a systematic shift from increasing to decreasing trends since around 2010. A random forest model was developed and found that catchment land characteristics, along with baseline water quality, can explain over a third of the spatial variation of the trends of EC, TN, TP and TSS (32-51% explained), but had limited explanatory power for DO (22% explained). These findings will provide critical information on the waterway health, thereby facilitating natural resources management for Australia.

How to cite: Guo, D., Zhang, Q., Minaudo, C., Liu, S., Dupas, R., Zhang, K., Bende-Michl, U., Duvert, C., and Lintern, A.: Australia’s water quality trends over two decades, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13703, https://doi.org/10.5194/egusphere-egu24-13703, 2024.

vA.6
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EGU24-11824
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HS2.3.2
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ECS
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Shubhangi Umare, Ajay Kumar Thawait, Sumit Dhawane, and Sachin Kumar

The river Ganga, revered as a lifeline of North India, is facing severe water quality degradation due to various man-made activities. The city of Kanpur, Uttar Pradesh, India, located on the west bank of the river Ganga, is known for its leather industrial area. The city produces approximately 450 million litres of municipal sewage and industrial effluent, most of this flowing directly into the holy river Ganga. Due to this, the Ganga River basin pollution is increasing daily, and to overcome this problem scientifically, there is a need to develop different models for water quality, which has been growing in recent years. The key focus of the study is to determine the Water Quality Index (WQI) along with developing a model to assess the changes in water quality over a decade in the Ganga River basin in Kanpur City. The dataset includes monthly measurements (2015-2023) of pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total coliform at seven distinct locations in Kanpur city, encompassing both upstream and downstream areas. Artificial Neural Networks (ANNs) were employed to construct a model to pretend, along with forecasting, the levels of dissolved oxygen (DO). The model has a notable level of accuracy in predicting dissolved oxygen (DO). The scatter plots comparing the actual and anticipated levels of dissolved oxygen (DO) during the training and testing periods exhibited a strong coefficient of determination (R2) with values of 0.90 and 0.92, respectively. Results show that the ANN model can predict water quality effects in advance, and thus, it helps to take protective actions to preserve the river free from pollution.

Keywords: Water Quality, River Ganga, Artificial Neural Network, Dissolved Oxygen.

How to cite: Umare, S., Thawait, A. K., Dhawane, S., and Kumar, S.: Water Quality Assessment for River Ganga in Kanpur City, Uttar Pradesh, India using Machine Learning model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11824, https://doi.org/10.5194/egusphere-egu24-11824, 2024.