HS2.3.3 | Water quality at the catchment scale: measuring and modelling of nutrients, sediment and eutrophication impacts
EDI
Water quality at the catchment scale: measuring and modelling of nutrients, sediment and eutrophication impacts
Convener: Paul Wagner | Co-conveners: Sarah Halliday, Daniel HawtreeECSECS, Nicola Fohrer
Orals
| Wed, 17 Apr, 08:30–12:25 (CEST)
 
Room 1.31/32
Posters on site
| Attendance Wed, 17 Apr, 16:15–18:00 (CEST) | Display Wed, 17 Apr, 14:00–18:00
 
Hall A
Orals |
Wed, 08:30
Posters on site
Wed, 16:15
Land use and climate change as well as legal requirements (e.g. the EU Water Framework Directive) pose challenges for the assessment and sustainable management of surface water quality at the catchment scale. Sources and pathways of nutrients and other pollutants as well as nutrient interactions need to be characterized to understand and manage the impacts in river systems. Additionally, water quality assessment needs to cover the chemical and ecological status to link the hydrological view with aquatic ecology.
Models can help to optimize monitoring schemes and provide assessments of future changes and management options. However, insufficient temporal and/or spatial resolution, a short duration of observations and the widespread use of different analytical methods limit the potential for model application. Moreover, model-based water quality calculations are affected by errors in input data, model errors, inappropriate model complexity and insufficient process knowledge or implementation. In addition, models should be capable of representing changing land use and climate conditions to meet the needs of decision makers under uncertain future conditions Given these challenges, there remains a strong need for advances in water quality modeling.

This session aims to bring together scientists working on both experimental and modelling studies to improve the prediction and management of water quality constituents (e.g. nutrients, organic matter, algae, sediment) at the catchment scale. Contributions addressing the following topics are welcome:

- Experimental and modelling studies on the identification of sources, hot spots, pathways and interactions of nutrients and other, related pollutants at the catchment scale
- New approaches to develop effective water quality monitoring schemes
- Innovative monitoring strategies that support both process investigation and improved model performance
- Advanced modelling tools for integrating catchments and/or simulating in-stream processes
- Observational and modelling studies at the catchment scale that relate and quantify water quality changes to changes in land use and climate
- Measurements and modelling of abiotic and biotic interaction and feedback involved in the transport and fate of nutrients and other pollutants at the catchment scale
- Catchment management: pollution reduction measures, stakeholder involvement, scenario analysis for catchment management

Orals: Wed, 17 Apr | Room 1.31/32

Chairpersons: Paul Wagner, Daniel Hawtree
08:30–08:35
5-minute convener introduction
08:35–08:45
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EGU24-1009
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ECS
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On-site presentation
Assessing the biological quality of freshwater bodies with machine learning technique 
Paola Di Fluri, Giacomo Capitani, Valentina Di Talia, Giacomo Antonioni, and Alessio Domeneghetti

The deterioration of superficial water quality is a relevant issue worldwide and most European rivers do not achieve the qualitative standards required by the Water Framework Directive (WFD). Furthermore, the ecological status is defined referring to surveyed data, which is available only along main watercourses and often appears erratic in time and space. Given the goals of the WFD, a short-cut methodology to perform the assessment of water pressures on rivers starting from easily accessible data is proposed. The methodology relies on machine learning techniques and implements a procedure to: (1) identify river segment exposed to pollution spills with a raster-based numerical model; (2) introduce and estimate the spatial allocation of a Biochemical Quality Index (BQI) for each exposed river segment. The study proposes a predictive tool to assess the water quality status using a machine learning algorithm trained starting from easily available input data, such as climatic and hydrological variables, anthropic pressures, water management techniques. In this prospective, the BQI is used as a reliable proxy variable to represent the anthropogenic pressures that impacts on superficial water bodies. Results show that the BQI is well reflected in the monitoring values of COD, used as proxy variable for the quality status of watercourses. We argue that the methodology can represent a solid tool for decision-making processes and predictive studies in areas with no, or poor, monitoring data.

How to cite: Di Fluri, P., Capitani, G., Di Talia, V., Antonioni, G., and Domeneghetti, A.: Assessing the biological quality of freshwater bodies with machine learning technique, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1009, https://doi.org/10.5194/egusphere-egu24-1009, 2024.

08:45–08:55
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EGU24-10273
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Highlight
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On-site presentation
Riverine nitrogen exports to the Wadden Sea – a travel time-based modelling approach 
Andreas Musolff, José L.J. Ledesma, Tam V. Nguyen, Pia Ebeling, Fanny Sarrazin, Dietrich Borchardt, Sabine Attinger, and Rohini Kumar

Elevated nutrient levels in inland, coastal and marine waters have led to negative eutrophication impacts such as algal blooms and biodiversity loss. In the European Union, measures to reduce nutrient pollution have been implemented as part of the Water Framework Directive, the Nitrates Directive, the Urban Wastewater Directive and the Marine Strategy Framework Directive. However, the water quality targets defined in these frameworks are not always coherent and may be too rigid when considering the future impact of climate change on nutrient cycling. This ambiguity adds to the scientific challenge of assessing current nutrient fluxes and concentrations and their future dynamics under changing boundary conditions.

Within the EU-funded project NAPSEA – N and P from Source to Sea, we address the continuum of nutrient fluxes from terrestrial sources in the Elbe and Rhine basins to the delivery in the Wadden Sea at the Dutch, German and Danish coasts. To model nitrogen (N) concentrations and fluxes, we use the water quality model (mQM, Nguyen et al. 2023) in a setting consisting of more than 500 mesoscale catchments with longer-term riverine N observations in the Elbe and Rhine basins. The model takes into account the storage, removal (denitrification) and release of N in the soil zone as a function of temperature and soil moisture. Importantly, subsurface transport and denitrification are based on a dynamic travel time approach using storage selection functions that explicitly account for N legacy effects. The model runs at an annual time-step, accounting for instream integration and retention of N, and is constrained against observations at the catchment outlets.

In this contribution, we present the model results that allow us to identify the hotspots of N export in the Elbe and Rhine basins. We capture the decadal trajectories of N fluxes and concentrations and quantify the amount of N stored as biogeochemical legacy in soils and as hydrological legacy in groundwater. The model also makes it possible to disentangle the contributions of point vs. diffuse sources to N export in time and space as well as the efficiency of N retention. The calibrated model will allow for future projections of riverine N exports to estuaries and the Wadden Sea with the aim to differentiate the effects of climate change on the one hand, and different nutrient management scenarios on the other.

 

References:

Nguyen, V.T., Sarrazin, F.J., Ebeling, P., Musolff, A., Fleckenstein, J.H., Kumar, R. (2022): Toward understanding of long-term nitrogen transport and retention dynamics across German catchments. Geophys. Res. Lett. 49 (24), e2022GL100278

How to cite: Musolff, A., Ledesma, J. L. J., Nguyen, T. V., Ebeling, P., Sarrazin, F., Borchardt, D., Attinger, S., and Kumar, R.: Riverine nitrogen exports to the Wadden Sea – a travel time-based modelling approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10273, https://doi.org/10.5194/egusphere-egu24-10273, 2024.

08:55–09:05
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EGU24-7448
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ECS
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On-site presentation
Understanding the variable impact of point sources on headwater stream water quality 
Caroline Spill, Lukas Ditzel, and Matthias Gassmann

In rural areas, point sources like wastewater treatment plants or combined sewer overflows are frequently overlooked or only simplistically considered in analyses of whole watersheds. The lack of available data and the difficulty of tracing hydrochemical signatures of (reactive) nutrients measured at the outlets of larger areas back to their origin are common reasons for that. The extent to which these sources influence nutrient dynamics in water bodies and how they interact with nutrients from diffuse sources has been examined in only a few studies.

As part of our study, we installed a comprehensive measurement setup in a selected rural watershed where a local wastewater treatment plant and combined sewer overflows discharge into a small river. To capture the dynamics of these point sources, pressure sensors, water quality probes, and automatic samplers were installed shortly after the treatment plant. The outlet was sampled weekly. Additional samples were taken upstream of the treatment plant and downstream of our monitoring station, to capture in-stream nutrient transformation.

In contrast to the common assumption that the influence of small treatment plants can be considered constant, the water quality of the treated wastewater undergoes significant fluctuations, especially during base flow. Comprehensive statistical analyses show that the treatment plant significantly influences the concentration-discharge relationship in the water body and is responsible for a large portion of nutrient loads. In the water body itself, ammonium constitutes half of the inorganic nitrogen It is detectable only downstream of the wastewater treatment plant outlet, where it undergoes rapid nitrification. During precipitation events, a complex interaction of the treatment plant, combined sewer overflows, and diffuse sources was observed. In particular, the increasing input of ammonium and ortho-phosphate leads to an increase in exported concentrations and loads of these nutrients. At the same time, the system is characterized by frequent activation of combined sewer overflows. Our investigations show that the effects of wastewater treatment plants in rural areas are more differentiated and extensive than commonly assumed. At the same time, there is still a high potential to reduce the discharge of nutrients from point sources and, thus, the discharge of nutrients from low order catchments.

How to cite: Spill, C., Ditzel, L., and Gassmann, M.: Understanding the variable impact of point sources on headwater stream water quality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7448, https://doi.org/10.5194/egusphere-egu24-7448, 2024.

09:05–09:15
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EGU24-9329
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On-site presentation
Enhanced redox mapping at national scale of Denmark through integration of sediment color and groundwater chemistry in a machine learning framework 
Julian Koch, Joel Conde, Birgitte Hansen, Hyojin Kim, Ingelise Møller, Lærke Thorling, Lars Troldborg, Denitza Voutchkova, and Anker Højberg

Redox conditions play a crucial role in determining the fate of geogenic and anthropogenic contaminants in groundwater, impacting ecosystem services vital for both the aquatic environment and human water supply. For example, investigating the reduction of nitrate underscores the importance of data on redox conditions since denitrification takes places in anoxic environments. Specifically, knowledge of the depth to the uppermost reduced layer, i.e., first redox interface, can inform water and land management by identifying agricultural areas vulnerable or robust to nitrate leaching. Assessing redox processes is complicated by geological heterogeneities, resulting in complexities of local to regional groundwater flow paths. Geospatial machine learning techniques have previously successfully mapped redox conditions based on sediment color or water chemistry observations. This study introduces a novel approach that combines both data sources to enhance understanding of subsurface redox conditions in Denmark. In the first step, depth to the first redox interface is mapped using sediment color information from 26,800 boreholes. This depth is derived from sediment color changes, transitioning from oxic to reduced colors of quaternary sediments. The mapping utilizes a regression-based gradient boosting with decision tree algorithm trained against sediment color data and 20 covariates, encompassing information on hydrogeology, lithology, topography, and hydrology. In the second step, the depth of the first redox interface is compared against groundwater chemistry to classify continuous and discontinuous redox conditions. Continuous conditions exhibit the absence of oxic groundwater below the first redox interface, while discontinuous conditions show oxic groundwater below the interface. This classification is performed using a gradient boosting with decision tree algorithm utilizing the same 20 covariate maps and 21,800 classified groundwater samples. Both models undergo comprehensive cross-validation and feature importance analysis. The depth to the first redox interface is modeled with a mean error of 0.001 m and a root-mean-squared error of 8.3 m. The continuous/discontinuous classification attains an accuracy of 69.5 %. Both variables are mapped at a 25 m spatial resolution at the national scale of Denmark. Results indicate a mean depth to the first redox interface of 9.4 m and a standard deviation of 5.7 m, with spatial patterns largely driven by the groundwater table. 66.0% of Denmark is classified as discontinuous, indicating complex redox conditions, predominantly collocated with moraine clay. These maps contribute significantly to understanding subsurface redox processes, supporting national-scale land and water management.

How to cite: Koch, J., Conde, J., Hansen, B., Kim, H., Møller, I., Thorling, L., Troldborg, L., Voutchkova, D., and Højberg, A.: Enhanced redox mapping at national scale of Denmark through integration of sediment color and groundwater chemistry in a machine learning framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9329, https://doi.org/10.5194/egusphere-egu24-9329, 2024.

09:15–09:25
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EGU24-10080
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ECS
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On-site presentation
Elucidate the interaction between catchment and in-stream processes by using high frequency multivariate and multisite data
(withdrawn)
Kenneth Gutiérrez, Gunnar Lischeid, and Michael Rode
09:25–09:35
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EGU24-10230
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ECS
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On-site presentation
Quantifying long-term nutrient sources and pathways in the Ganges River basin 
Hamdy Elsayed, Arthur Beusen, and Lex Bouwman

The Ganges River basin is home to more than 600 million people. Intensive agriculture is widespread owing to the basin’s widespread fertile soils and abundant water availability. Together with urbanization and industrialization which have grown rapidly over the past decades across the basin, this has significantly impacted the river’s water quality with adverse impacts on human health and ecosystem. Elevated nutrient levels in the Ganges River, mainly from intensive agricultural practices and discharge of untreated wastewater, have led to surface and groundwater pollution across the basin. In this study, we employ the spatially explicit Integrated Model to Assess the Global Environment-Dynamic Global Nutrient (IMAGE-DGNM) to investigate nutrient sources and pathways and their fate in the Ganges river system. Basin-wide simulation results over the past five decades (1970-2020) will be presented and analysed along with discussions on the implication of nutrient pollution on the water quality of the Ganges River.

How to cite: Elsayed, H., Beusen, A., and Bouwman, L.: Quantifying long-term nutrient sources and pathways in the Ganges River basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10230, https://doi.org/10.5194/egusphere-egu24-10230, 2024.

09:35–09:45
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EGU24-6269
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ECS
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On-site presentation
Role of urban wetlands in improving catchment river water quality with implications for management 
Fangjun Peng, Leyang Liu, and Ana Mijic

The theme for World Wetlands Day in 2024 is centred on the symbiotic relationship between wetlands and human wellbeing. The urban wetland, as a nature-based solution, notably intertwines with human activities, distinguishing itself among various wetland types. Examining urban wetlands through the aspect of water quality reveals their ability to purify nitrogen and phosphorus from water sources. However, human activities affect river water quality via various sources and processes within an urban environment, including land surface and wastewater discharge, exhibiting significant complexity. Understanding how urban wetlands interact with these processes and their impacts on water quality is needed. To explore the role of wetlands in integrated urban water quality management, our study enhanced the representation of nutrient processes in wetlands and incorporated it into the whole-water cycle simulation tool – the Water Systems Integration Modelling framework (WSIMOD). This is done by quantifying the wetland-urban system interactions within subcatchments, between subcatchments, and at the catchment scale. Our study aims to (1) evaluate urban wetland benefits in water quality improvement via statistical analysis; (2) simulate such impacts through integrated modelling; (3) explore the pipe connections of wetlands and their impacts on systems-level water quality improvement to inform design and management. Analysis of observed water quality data reveals that the nitrogen concentration in a catchment influenced by the urban wetlands network is reduced by approximately 18% to 28%, with the phosphorus concentration showing a reduction of about 4% to 11%. At a local scale, within a single subcatchment, the model is demonstrted to capture the water quality dynamics and the observed impacts well by validating against the sampling data. Furthermore, at a broader scale encompassing the entire catchment, the connectivity of urban wetlands through pipes is expected to achieve better system-level water quality performance. This research emphasises the need to explore how urban wetlands influence and are influenced by various water elements, informing future urban wetland design and management strategies.

How to cite: Peng, F., Liu, L., and Mijic, A.: Role of urban wetlands in improving catchment river water quality with implications for management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6269, https://doi.org/10.5194/egusphere-egu24-6269, 2024.

09:45–09:55
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EGU24-13363
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Highlight
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On-site presentation
Signifying impacts of climate warming on vulnerability to subsurface nitrate contamination in Europe 
Rohini Kumar

Decades of agricultural intensification across Europe have created nitrogen legacy stores that continue to threaten the functioning of aquatic ecosystems and human health. Climate change and climate extremes further aggravate the fate of nitrogen export and retention in the terrestrial system (e.g., soil, groundwater, and rivers) - the extent of which is yet not fully understood. Herein we provide a continental-wide analysis of the projected changes in timings and extent of nitrogen-vulnerable regions across European landscapes. Our assessment relies on a newly devised objective measure based on a Damkoehler number -- that encapsulates the transient nature of hydrologic transport and biogeochemical transformations [1]. We perform a century-long, spatially explicit daily hydrologic simulations forced with a suite of bias-corrected and downscale climate model (CMIP6) runs driven under different emission scenarios (SSP126, SSP370, and SSP585) till the end of the 21st Century. These simulations allow us to derive the transport dynamics of dissolved nitrogen based on a transient aspect of travel-time distributions (TTDs) using temporally resolved water storage and fluxes. On the other hand, biogeochemical processes like denitrification rates are characterized by the first-order decay coefficients that are further modulated by spatially and temporally varying environmental factors imposed by moisture content and temperature constraints.  Contrasting the space-time dynamics of hydrological transport times with reactive timescales of denitrification in soil, our analysis indicates that more than two-thirds of the cultivated areas across Europe are potentially vulnerable to nitrate leaching for at least one-third of the year under the contemporary climate condition (1981-2010). Further, the climate projection-based simulation results indicate that under high emission scenarios (SSP585), arable lands in Central Europe would be more prone to nitrate leaching (times), while drier conditions in Southern Europe favor stronger denitrifications. Limiting climate warming by adhering to a low-emission scenario, such as SSP126, has the potential to decrease the vulnerability of regions to nitrate leaching (extent, duration, and load). By signifying the differentiated impacts of climate warming on nitrate leaching potential, our study contributes towards unraveling the complexity of nitrogen transport dynamics across a diverse range of European landscapes under changing climatic conditions.  

[1]  Kumar, R., Heße, F., Rao, P.S.C., Musolff, A., Jawitz, J.W., Sarrazin, F., Samaniego, L., Fleckenstein, J.H., Rakovec, O., Thober, S. and Attinger, S., 2020. Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe. Nat Commun 11, 6302 (2020). https://doi.org/10.1038/s41467-020-19955-8.

How to cite: Kumar, R.: Signifying impacts of climate warming on vulnerability to subsurface nitrate contamination in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13363, https://doi.org/10.5194/egusphere-egu24-13363, 2024.

09:55–10:05
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EGU24-15228
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Highlight
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On-site presentation
Impacts of the transition to a Nordic bioeconomy on streamflow and nitrogen loads in the Odense Fjord Catchment, Denmark  
Brian Kronvang, Katrin Bieger, and Mette V. Carstensen

Downscaling and extending the global Socioeconomic Shared Pathways (SSPs) into a set of storylines focusing on the Nordic land-based bioeconomy (Nordic Bioeconomy Pathways (NBPs). In short, the NBPs stand for sustainability first (NBP1), conventional first (NBP2), self-sufficiency first (NBP3), city first (NBP4) and economy first (NBP5). Each of the five NBPs includes a set of linked agricultural and forestry attributes that provide a framework for the BIOWATER researchers for generating input data to catchment models by translating qualitative narratives into quantitative values by means of stakeholder workshops.

A societal transformation towards a bioeconomy in the Nordic countries will have extensive implications for the environment and might conflict with the goal of the European WFD to achieve good ecological status of the majority of European water bodies. This study aims to explore the environmental impact of different bioeconomy scenarios combined with climate change on a Danish estuary, the Odense Fjord. We used the Nordic Bioeconomy Pathways (NBPs), which describe five possible future scenarios for a Nordic bioeconomy in 2050, to identify plausible changes in land use in response to the transition. The catchment of the Odense Fjord is intensively farmed, so the attributes selected for this study included changes in farming intensity (chemical fertilizer and manure amount), land cover change (agriculture vs. forest), and nutrient loss mitigation (buffer strips and wetlands).

We used the catchment model SWAT to model the hydrology and nitrogen (N) dynamics in the intensively farmed River Odense catchment. The water monitoring at the river outlet is the most comprehensive in Denmark, and daily data recordings of N concentrations are available for the baseline period 2001-2008. The SWAT model setup for the River Odense catchment includes 23 sub-catchments and 3,882 Hydrological Response Units (HRUs). Scenarios for the baseline and four selected agricultural attributes with and without climate change (RCP4.5 and RCP8.5) were simulated for the period 2041-2070.

The NBP narratives were translated to quantitative values that can be modelled at catchment scale by local stakeholders. The semi-distributed Soil and Water Assessment Tool (SWAT) was used to simulate the land use and climate scenarios. First, extreme values of each attribute were simulated to ensure plausibility of the model response to the changes. Subsequently, the combined effects of all changes were quantified for each NBP with and without climate change. The differences in simulated streamflow between the five NBPs were very small, whereas the impact of the different pathways on the simulated nitrogen loads was more pronounced, especially during the winter months. In both climate change scenarios using the median of an ensemble of climate models conducted for the period 2041-2070, the average annual total N loads from the River Odense catchment decrease slightly in both RCP 4.5 and RCP 8.5. The NBP scenarios showed that the needed reductions in total nitrogen loads to the Odense Fjord for obtaining a good ecological quality could only be reached when following the trajectory of NBP1 being sustainable first scenario and including restoration of all previously drained wetlands in the catchment.

How to cite: Kronvang, B., Bieger, K., and Carstensen, M. V.: Impacts of the transition to a Nordic bioeconomy on streamflow and nitrogen loads in the Odense Fjord Catchment, Denmark , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15228, https://doi.org/10.5194/egusphere-egu24-15228, 2024.

10:05–10:15
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EGU24-16265
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ECS
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On-site presentation
In-stream measurements at low-flow reveals transport and denitrification patterns in the sub-surface 
Camille Vautier, Alexandre Coche, Jean-Raynald de Dreuzy, and Gilles Pinay

Excess nitrogen in surface and groundwater, mainly in the form of nitrates, is a major concern for all stakeholders, because it leads to the degradation of drinking water resources and to the eutrophication of ecosystems. The export of nitrogen from inland to the coast is strongly determined by the transport and denitrification processes occurring in headwater catchments. Yet, most regulatory frameworks, such as the EU Water Framework Directive, impose the monitoring of medium-to-large rivers (> 100 km2) while headwaters, too numerous to be systematically monitored, are poorly understood. Headwater catchments are often characterized by strong connections between surface water and shallow aquifers, leading to a high impact of sub-surface processes on river water quality. Understanding the processes occurring in the sub-surface is thus necessary to predict river water quality, but it remains a major challenge because of the difficult access to groundwater.

Here we propose a new approach to infer sub-surface processes in headwater catchments from in-stream measurements. In an agricultural catchment, we measured nitrate and silica along headwater streams fed by a crystalline shallow aquifer, during low-flow period. Silica was used as a proxy for water residence time. We observed several trends between the nitrate concentrations and the water residence times, interpreted as the result of distinct patterns of transport and denitrification in the sub-surface. Based on this case study, we propose a general framework to infer the processes occurring in the sub-surface from the resulting chemical trends observed in low-flow streams. Regarding the simplicity of the measurement method, this framework appears as a powerful tool for water management practices. Further studies in several areas in the world will allow to validate its broad applicability.

How to cite: Vautier, C., Coche, A., de Dreuzy, J.-R., and Pinay, G.: In-stream measurements at low-flow reveals transport and denitrification patterns in the sub-surface, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16265, https://doi.org/10.5194/egusphere-egu24-16265, 2024.

Coffee break
Chairpersons: Paul Wagner, Daniel Hawtree
10:45–10:55
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EGU24-11225
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ECS
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Highlight
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On-site presentation
Water management is a success story, how water quality changed based on historical development and mid-term hydroclimate. 
Christian Marx, Dörthe Tetzlaff, Reinhard Hinkelmann, and Chris Soulsby

Urban water quality has traditionally been perceived as primarily influenced by point sources such as wastewater treatment plants and (combined) stormwater overflows. However, limited attention has been given to understanding how the urban stream syndrome evolves post the operation of water management facilities, the broader impacts on water quality beyond these measures, and the influence of hydroclimate on urban water quality.

In this study, we present spatially distributed data spanning 66 years of water quality and fertilizer application, 30 years of water quantity, and 20 years of groundwater quality in the urban Panke catchment, Berlin, Germany, aiming to address these questions. Hydroclimatic indicators, specifically the Standard Precipitation Index (SPI), were employed, and the data was analyzed for trend delineation, breakpoint analysis, and concentration-discharge relationships.

Predictably, major water quality changes were attributed to shifts in water management practices, such as the transformation of the former sewage irrigation farm, subsequent replacement with a wastewater treatment plant, and alterations in wastewater redirection. Interestingly, unaffected upstream sites were parallel improving in water quality. While concentration-discharge remained unaffected by hydroclimate, we observed trends towards lower NO3-N and higher NH4-N, oPO3-P, and CL concentrations during droughts. Despite these variations, the upstream sites demonstrated significant overall improvement, reaching the highest water quality classification, demonstrating how effective water management can enhance resilience.

The hydrochemical dynamics in upstream sites suggested altering connectivity during drought, which remains unclear and requires further investigation. Beyond our research findings, we highlight the importance of establishing a structured, long-term monitoring program and promoting knowledge transfer across various institutions. This collaborative approach is deemed crucial for comprehending and contextualizing the gathered data.

 

How to cite: Marx, C., Tetzlaff, D., Hinkelmann, R., and Soulsby, C.: Water management is a success story, how water quality changed based on historical development and mid-term hydroclimate., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11225, https://doi.org/10.5194/egusphere-egu24-11225, 2024.

10:55–11:05
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EGU24-16408
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ECS
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On-site presentation
Empirical large-scale evidence of algae growth control in rivers: Is total phosphorus control (still) a good management strategy? 
Alexander Hubig, Andreas Musolff, Alexander Wachholz, Markus Weitere, Tom Shatwell, Rohini Kumar, and Ulrike Scharfenberger

Since the 1980s, the problem of algae blooms in rivers as a manifestation of eutrophication has been addressed by lowering nutrient inputs with a specific focus on limiting phosphorus. Despite the past achievements in phosphorus control, algae blooms are still frequently occurring, not least during the recent European drought years, with partly severe consequences for river ecology. This implies that additional parameters may have become important in controlling eutrophication. Regarding river management, this raises the question of whether a sole focus on further improvements in phosphorus control is still a good management strategy. Alternatively, an embedding in a multiple-stressor approach from a riverscape perspective might be necessary, particularly with regard to climate-related changes in temperature and precipitation.

Here, we analyze a Germany-wide dataset of chlorophyll a (Chl-a) concentrations over the period from 2000 to 2019 at 358 sites (33489 measurements in total) to address the following questions: (1) Are there geographical regions particularly threatened by algae blooms? (2) How sensitive are different river locations to elevated total phosphorus (TP) levels regarding algae development? (3) Can we explain spatial sensitivity differences by other in-stream parameters or catchment characteristics?

To understand when and where rivers are particularly effective in converting TP into algae biomass and thus prone to algae blooms, we use the measure of the degree of realized eutrophication, which is the ratio between the realized (i.e. the Chl-a measurement) and potential eutrophication (i.e. a theoretical upper Chl-a concentration at a given TP level if all TP is converted to biomass). Spatial differences in this degree of realized eutrophication are then analyzed together with in-stream parameters (e.g. water temperature) and catchment characteristics (e.g. topography, land use) using multivariate statistics.

We find algae blooms (> 30 µg Chl-a/l) across all analyzed German river basins and stream orders, making up 21 % of all measurements from March to November. They most frequently occur in large rivers (stream order 6 to 8) in catchments draining to the Baltic Sea and in the Elbe basin, constituting 58 % and 60 % of the measurements, respectively. For all stations, the median degree of realized eutrophication is only 1.3 %, whereas for single stations, it can go up to 20 %, revealing a large variability between sites. Results from a partial least squares regression analysis suggest that catchment characteristics like network length, seasonality of precipitation, lithology, and soil properties have predictive power, whereas in-stream parameters only play a secondary role.

While phosphorus is a critical prerequisite for algae growth, our results emphasize that its availability alone does not explain the development of algae blooms. For management, this means that a look beyond phosphorus control is necessary for preventing future river eutrophication.

How to cite: Hubig, A., Musolff, A., Wachholz, A., Weitere, M., Shatwell, T., Kumar, R., and Scharfenberger, U.: Empirical large-scale evidence of algae growth control in rivers: Is total phosphorus control (still) a good management strategy?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16408, https://doi.org/10.5194/egusphere-egu24-16408, 2024.

11:05–11:15
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EGU24-18321
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ECS
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On-site presentation
Developing a straightforward precipitation-runoff model for monthly-based nutrient emission modeling in river systems 
Anna Oprei, Victoria Huk, and Markus Venohr

Nutrient emission modeling in river basins includes the estimation of nutrient fluxes and retention in streams and forms a vital part for the management of water resources as well as the exploration of ecological impacts of increased nutrient input in river systems. The nutrient emission model MONERIS (Venohr et al., 2011, Lemm et al., 2021) calculates landuse-specific nutrient fluxes for entire river basins on a monthly basis and a spatial resolution of 1km x 1km and requires an ensemble of input data such as land use, atmospheric deposition, tile drainage cover, connection to sewer systems, and many more. Hydrological flows majorly drive nutrient fluxes and emission pathway composition. Consequently, runoff is one of the key constituents that needs to fit to the emission model requirements in terms of represented pathways and environmental compartments (e.g. land-use types, groundwater-surface water boundaries, spatial-temporal resolution). Using available runoff data derived by third party models can introduce large uncertainties to the resulting nutrient fluxes and water quality. Our aim was to develop a novel runoff model that operates on a monthly basis, provides runoff components for all considered emission pathways and can be applied with commonly available input data. These include, beyond the typical hydrological components (snow storage/melt, surface runoff, natural/artificial interflow, lower interflow and groundwater), flow estimates from urban areas (separate sewers, combined sewer overflows, decentralized/large treatment plants, point sources). The overall goal is to set up a data base for a Europe wide water quantity and quality model. In the presented pilot study, the runoff model was applied to the Odra River Basin (119,000 km²), calibrated against observed runoff data from 11 independent upstream gauges, and validated by runoff data from 36 additional gauges for the years 2010-2020. We compared input data sensitivity and model performance of three available daily gridded precipitation and air temperature datasets (E-OBS, EURADKLIM and CHELSA). Results showed a good model accuracy (NSE: > 0.9, PBIAS: < 7 %) and suggest that, despite its simplicity, the runoff model complements the nutrient emission model MONERIS. The next step will be the modelling of water quantity and quality for Central Europe, and, ultimately, providing an open modelling platform that allows emission modelling of other parameters and substances (e.g. salinity, heavy metals or priority substances) or be extended by additional modules for phytoplankton growth or floodplain retention.

How to cite: Oprei, A., Huk, V., and Venohr, M.: Developing a straightforward precipitation-runoff model for monthly-based nutrient emission modeling in river systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18321, https://doi.org/10.5194/egusphere-egu24-18321, 2024.

11:15–11:25
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EGU24-2155
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ECS
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On-site presentation
Modeling Long-term Dissolved Organic Carbon Patterns Using Environmental Variables      
Kezhen (Jenny) Wang, Rajith Mukundan, Rakesh K. Gelda, and Allan Frei

Organic matter (OM) in rivers is an important food source to sustain aquatic ecosystem health. However, in surface water supply systems where chlorination is often used for disinfection, OM is also a precursor for the carcinogenic and mutagenic disinfection byproducts (DBPs) such as trihalomethanes (THMs) and haloacetic acids (HAAs). Effective management of OM in rivers to maintain both aquatic ecosystem functions and high water-supply quality requires better understanding of the OM transport patterns, where dissolved organic carbon (DOC) can be used as a surrogate measurement of OM. Analysis of DOC data on a watershed scale to estimate fluxes and to determine long-term trends remains challenging, largely due to the spatial and temporal variations in DOC, and low sampling frequency. To help improve the understanding of DOC sources and export processes, we compared long-term temporal patterns in six watersheds in the New York City (NYC) Water Supply System, which supplies drinking water daily to over 8.5 million people in NYC and one million people in the upstate counties. Firstly, we compared six empirical water quality models for DOC prediction. The models include flow-based linear regression (LM), dynamic linear models (DLMs), LOAD ESTimator model (LOADEST), Weighted Regressions on Time, Discharge, and Season (WRTDS), multiple linear regression (MLR), and general additive models (GAMs). Given the differences in dominant land-use and hydrological conditions in the study watersheds, we found that GAMs produced the most robust results. Secondly, we used GAMs with multiple predictor variables to predict long-term daily DOC concentrations in the six study watersheds, which allowed better trend analysis and flux estimates than using the routine grab-sample data with inconsistent sampling frequencies. Lastly, we compared the relationships between temporal patterns in DOC and watershed features to investigate the regional differences, focusing on the watershed mechanistic processes associated with DOC by parsing out the climate signals from the historical trends. The results show that hydrology plays a larger role on DOC temporal patterns in some watersheds whereas nutrient associated production processes are more important in others. The study presents a better performing approach than the solely hydrology driven models and can inform targeted monitoring strategies for DOC management in water-supply source waters.

How to cite: Wang, K. (., Mukundan, R., Gelda, R. K., and Frei, A.: Modeling Long-term Dissolved Organic Carbon Patterns Using Environmental Variables     , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2155, https://doi.org/10.5194/egusphere-egu24-2155, 2024.

11:25–11:35
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EGU24-17237
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On-site presentation
Exploring impacts of rainforest disturbance history and conversion to oil palm on water quality dynamics in eastern Sabah using a novel concentration-discharge pattern approach 
Rory Walsh, Lelavathy Samikan Mazilamani, Kogila Vani Annammala, and Anand Nainar

Assessment of water quality impacts of rainforest disturbance and land-use change on water quality in the wet tropics is hampered by the technical difficulty and prohibitive costs of collection of multi-catchment high-frequency streamflow and water quality datasets for a period of record that covers a sufficiently representative range of storm events and preceding weather and baseflow conditions. This problem is magnified in agricultural plantation areas, where water quality responses in storm events vary also with the nature of management practices, including time since fertilizer application.  There has also been little focus on impacts of land-use history such as multiple phases of logging and mature agricultural plantation.  This paper addresses these issues using a novel classification of  daily C-Q (solute concentration – specific discharge) patterns in a multi-catchment study in eastern Sabah, Malaysian Borneo. The five small (1.7 – 4.6 km2) study catchments  lie in the upper reaches of the Brantian, Kalabakan and Segama river systems within the 10,000 km2 Yayasan Sabah Forest Concession area, where rainforest since the 1970s has been either (1) selectively logged (and left to recover) up to three times, (2) subsequently converted to oil plantations, or (3) protected as primary forest in three large Conservation Areas (Danum Valley, Maliau Basin and Imbak Canyon) or as near-primary forest in  smaller Virgin Jungle Reserves.  Two of the study catchments are under primary  and near-primary rainforest; two are under forest recovering from two and three episodes of selective logging respectively; and the final catchment is covered by mature (>20 year-old) oil palm.  Annual rainfalls for the catchments are 2500-2880 mm.  Water depth, conductivity and turbidity sensors linked to Campbell data loggers have recorded readings at 5-minute intervals in each catchment from 2011. Catchment-specific solute concentration/specific conductance and stage-discharge relationships were used to derive the 5-minute solute concentration (C, mg L-1) and specific discharge (Q, m3 km-2 s-1) data series.  To compare their water quality dynamics, C-Q  relationships for each day over the 22-months period November 2011 to August 2013 were analysed for each catchment.  For each day, the correlation coefficient (r) and slope (b) of the best-fit logC-LogQ regression were calculated and graphs of Log C/Log Q scatter and  C and Q against time were produced.  Days were divided into Storm Days and Recession/Baseflow Days. A typology of C-Q patterns (eight Storm Day and four Recession/Baseflow Day types) was devised using: the r and b values; (for Storm Days) the order and relative dominance of any dilution and flushing response features; and (for Recession/Baseflow Days) the ranges in Q and C values.  Each day of each data series was classified and percentage frequency distributions of C-Q types for each catchment were derived and compared.  The frequency distribution of the oil palm catchment is markedly different (fewer “dilution” and more “flushing” storm days) than for the forested catchments  - which  can be linked to fertilizer sources and lower baseflows of the oil palm catchment. Lesser, but important differences between the forested catchments are also identified and discussed.    

How to cite: Walsh, R., Mazilamani, L. S., Annammala, K. V., and Nainar, A.: Exploring impacts of rainforest disturbance history and conversion to oil palm on water quality dynamics in eastern Sabah using a novel concentration-discharge pattern approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17237, https://doi.org/10.5194/egusphere-egu24-17237, 2024.

11:35–11:45
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EGU24-5496
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ECS
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On-site presentation
Developing Long-term Satellite Based Chlorophyll Estimates via Multiple Machine Learning Methods  
Minyan Zhao and Fiachra O 'Loughlin

Chlorophyll is widely used to assess the level of eutrophication, which is recognized as one of the major causes of deterioration in water quality, especially across Irish inland waters. The use of optical remote sensing for chlorophyll monitoring has already been shown to be able to complement existing in-situ chlorophyll. However, as cloud cover impacts all optical sensors this limits the usefulness in many locations to develop long term records. A potential solution is to combine information from multiple satellites using a machine learning approach. In this study, we develop long-term time series of chlorophyll concentrations for lakes in Ireland using four different remote sensing platforms: Landsat-8, MODIS, Sentinel-2, and Sentinel-3. Several machine learning approaches have been tested, including K-nearest neighbourhood, random forest (RF), XGBoost (Extreme Gradient Boosting), artificial neutral network (ANN), and support vector regression (SVR).

Initial result indicate that a machine learning model utilising all four platforms and in-situ observation is effective in developing long-term chlorophyll concentrations. While the methods are tested and validated for Irish lakes, the methodology has the potential to be applied in a global context.

How to cite: Zhao, M. and O 'Loughlin, F.: Developing Long-term Satellite Based Chlorophyll Estimates via Multiple Machine Learning Methods , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5496, https://doi.org/10.5194/egusphere-egu24-5496, 2024.

11:45–11:55
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EGU24-17232
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ECS
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On-site presentation
Disentangling the water quality dimension of the Water-Energy-Food nexus in the Adige river basin (Italy) 
Anna Sperotto, Mathilda Vogt, Stefano Balbi, Ferdinando Villa, and Andrea Critto

Understanding and effectively managing water quality within the context of global changes necessitates a nexus approach that embraces the interconnected facets of the water-energy-food-environment system. This perspective acknowledges the intricate interplay between diverse processes—physical, chemical, biological, ecological—and human activities that collectively influence water quality. Climate change can impact these components individually and interactively, leading to cascading effects. Only by considering the whole system, including both natural and human factors, we can capture complexity understanding multiple stressors and feedback loops that affect water quality.

One of the major challenges of adopting a nexus approach for water quality assessment is primarily represented by the need to access and combine data and model from many different scientific domains which often remains compartmentalized in silos, to pre-defined scales and fields, into a single, logically consistent integrated framework of analysis. Leveraging integrative technology like Artificial Intelligence emerges as a viable solution to foster this integration permitting to maximize the value of available information. A systemic integrated model for the assessment of the conjoined impacts of climate and land use changes on water quality has been developed and tested at the catchment scale in the Adige river basin in Northern Italy. The model is developed using ARIES (Artificial Intelligence for Environment and Sustainability), an open-source Artificial Intelligence modeler which, using semantics and machine reasoning, allows independently developed models and data to be integrated and automatically assembled into workflows running at the scale most appropriated for the context of analysis. Once trained and validated the model permits to: i) predict the impact of different climate change and land use scenarios on water and ecological quality indicators (e.g. nutrients, suspended solids, water temperature, dissolved oxygen, ecological status); ii) identify sources and hot spots of pollution related with different economic sectors in the catchment; iii) test pollution reduction measures permitting to minimise trade-offs between economic activities and ecosystem health. By presenting the preliminary outcomes of pilot application, this analysis aims to showcase the potential of AI-driven approaches in enhancing data reusability and interoperability, crucial for comprehensively addressing environmental quality challenges and modelling intricate anthropic-environmental interactions at the catchment scale.

How to cite: Sperotto, A., Vogt, M., Balbi, S., Villa, F., and Critto, A.: Disentangling the water quality dimension of the Water-Energy-Food nexus in the Adige river basin (Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17232, https://doi.org/10.5194/egusphere-egu24-17232, 2024.

11:55–12:05
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EGU24-15036
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On-site presentation
Towards a data-effective calibration of a fully distributed catchment water quality model 
Michael Rode, Salman Ghaffar, Xiangqian Zhou, Seifeddine Jomaa, Xiaoqiang Yang, and Günter Meon

Distributed hydrological water quality models are increasingly being used to manage natural resources at the catchment scale but there are no calibration guidelines for selecting the most useful gauging stations. In this study, we investigated the influence of calibration schemes on the spatiotemporal performance of a fully distributed process-based hydrological water quality model (mHM-Nitrate) for discharge and nitrate simulations at Bode catchment in central Germany. We used a single- and two multi-site calibration schemes where the two multi-site schemes varied in number of gauging stations but each subcatchment represented different dominant land uses of the catchment. To extract a set of behavioral parameters for each calibration scheme, we chose a sequential multi-criteria method with 300.000 iterations.

For discharge (Q), model performance was similar among the three schemes (NSE varied from 0.88 to 0.92). However, for nitrate concentration, the multi-site schemes performed better than the single site scheme. This improvement may be attributed to that multi-site schemes incorporated a broader range of data, including low Q and NO3- values, thus provided a better representation of within-catchment diversity. Conversely, adding more gauging stations in the multi-site approaches did not lead to further improvements in catchment representation but showed wider 95% uncertainty boundaries. Thus, adding observations that contained similar information on catchment characteristics did not seem to improve model performance and increased uncertainty. These results highlight the importance of strategically selecting gauging stations that reflect the full range of catchment heterogeneity rather than seeking to maximize station number, to optimize parameter calibration.

How to cite: Rode, M., Ghaffar, S., Zhou, X., Jomaa, S., Yang, X., and Meon, G.: Towards a data-effective calibration of a fully distributed catchment water quality model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15036, https://doi.org/10.5194/egusphere-egu24-15036, 2024.

12:05–12:15
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EGU24-14005
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On-site presentation
Pairing Traditional Approaches with High-resolution In-situ Sensors to Advance the Science of Nutrient Fluxes from Agricultural Catchments  
Gurpal Toor, Charles Burgis, Jesse Radolinski, Bradley Kennedy, Fajun Sun, Emileigh Lucas, and Patricia Steinhilber

Agricultural catchments are hot spots of nutrient (nitrogen, phosphorus) fluxes to downstream watersheds. New tools are needed to disentangle flow pathways, hot spots, and the interplay of nutrient dynamics. Yet, the constraints (cost, labor) have limited our ability to use the new tools to understand nutrient dynamics from land to water. The traditional approaches of water quality monitoring (grab or composite samples collected with autosamplers) remain the gold standard for water quality monitoring, although they yield limited information on the mechanistic controls of nutrient losses. This presentation will discuss how pairing the traditional approaches (such as autosamplers) with in-situ nutrient sensors in agricultural catchments furthered our understanding of hot spots, pathways, and stoichiometric controls on nitrate and orthophosphate losses and advanced the science of water quality monitoring in agricultural catchments.

How to cite: Toor, G., Burgis, C., Radolinski, J., Kennedy, B., Sun, F., Lucas, E., and Steinhilber, P.: Pairing Traditional Approaches with High-resolution In-situ Sensors to Advance the Science of Nutrient Fluxes from Agricultural Catchments , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14005, https://doi.org/10.5194/egusphere-egu24-14005, 2024.

12:15–12:25
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EGU24-18838
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ECS
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Virtual presentation
Integrated assessment of climate change impacts on transitional waters and the role of nature-based solutions in regulating water quality 
Asrat Tekle Asresu, Elisa Furlan, Fabienne Horneman, Ngoc Diep Nguyen, Silvia Torresan, Federica Zennaro, Donata Canu, Leslie Aveytua Alcazar, Celia Laurent, Cosimo Solidoro, Antonio Marcomini, and Andrea Critto

Transitional environments are particularly susceptible to multiple pressures like climate change, land use or pollution that can lead to the deterioration of their water quality (WQ) and ecosystem services. Nature-based solutions (NBS) can be implemented as adaptation strategies essential for maintaining WQ regulation. Numerical models offer valuable support to understand the WQ dynamics of transitional environments and the influence of NBS, together with the evaluation of the effects induced by interacting stressors and different management schemes. The Venice lagoon is a transitional environment of great ecological and socio-economic value where NBS are at play through salt marsh restoration programs. A literature review revealed that current assessments and modelling approaches of the effects of NBS on WQ are characterized by the analysis of short-term observations, lack of integration of multiple ecosystem processes, as well as limited consideration of catchment scale management strategies. Considering these challenges, a new WQ modelling system will be developed for the Venice Lagoon by integrating a vegetation module for saltmarshes into an existing coupled hydrodynamic-biogeochemical model. The vegetation module will represent the effects of NBS, i.e. saltmarshes restoration measures, in order to evaluate their role and effectiveness in regulating WQ through their influence on the hydrodynamics, as well as the nutrient and carbon cycle associated with the distribution, growth, and mortality of saltmarsh vegetation. Onsite monitoring of WQ indicators linked to eutrophication processes in relation to climate-related stressors, hydro-morphodynamic processes, and implementation of restoration activity will be utilized to support and validate the modeling methodology. For this purpose, automatic recording instruments with high temporal resolution have already been placed providing data on different WQ parameters that can be related to hydrodynamic conditions and the ongoing restoration activities. Furthermore, the designed model will support the evaluation of WQ changes in the Lagoon against future climate change scenarios and several ‘what-if’ scenarios representing different NBS, thereby informing management and adaptation decision-making processes. 

Keywords

Climate change impacts, multi-hazards, transitional ecosystem, water quality, eutrophication, integrated modelling, nature-based solutions, salt marshes, biogeochemistry, Venice Lagoon

How to cite: Asresu, A. T., Furlan, E., Horneman, F., Nguyen, N. D., Torresan, S., Zennaro, F., Canu, D., Alcazar, L. A., Laurent, C., Solidoro, C., Marcomini, A., and Critto, A.: Integrated assessment of climate change impacts on transitional waters and the role of nature-based solutions in regulating water quality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18838, https://doi.org/10.5194/egusphere-egu24-18838, 2024.

Posters on site: Wed, 17 Apr, 16:15–18:00 | Hall A

Display time: Wed, 17 Apr, 14:00–Wed, 17 Apr, 18:00
Chairpersons: Paul Wagner, Daniel Hawtree
A.21
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EGU24-14691
Development of a Deep Learning Model for Harmful Algal Blooms Prediction 
Yookyung Jeong and Kyuhyun Byun

The risk of harmful algal blooms (HABs) is exacerbated by extreme climate and hydrologic events, as well as the increased non-point pollutant sources associated with agriculture and industrialization. The resulting deterioration in water quality due to the HABs poses significant threats to water management and aquatic ecosystems. HABs, in particular, emerge from intricate chemical interactions influenced by external conditions and diverse hydrologic and water quality factors.  Existing physical models encounter difficulties in predicting HAB occurrences and concentrations due to their limitations in addressing the intricate interactions of external environments and the characteristics of non-linear, non-stationary systems. In response to this challenge, we aim to develop a deep-learning algorithm based on the wavelet transform, with a focus on key hydrologic and water quality factors specific to the Nakdong River in South Korea. We identify water temperature and Chlorophyll-a as pivotal factors influencing HABs. Leveraging the wavelet transform, we extract denoised HAB data to enhance the robustness of our predictive model. Subsequently, we employ Long Short-Term Memory (LSTM) networks to construct a deep learning model, utilizing the identified key factors and denoised data as input features. Our preliminary results demonstrate a decent level of predictive accuracy showing a high Nash-Sutcliffe Efficiency (NSE) value of 0.88 and a low Root Mean Squared Error (RMSE) of approximately 9800 cells/ml, compared to the average HAB quantity of 14474 cells/ml. These outcomes indicate that the developed deep learning approach allows for accurate simulation of HAB. The implications of our research extend to the precise analysis of HABs, enabling the establishment of pre-emptive responses for effective water resources management.

 

Acknowledgment:

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).

How to cite: Jeong, Y. and Byun, K.: Development of a Deep Learning Model for Harmful Algal Blooms Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14691, https://doi.org/10.5194/egusphere-egu24-14691, 2024.

A.22
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EGU24-3197
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ECS
Event-based high-resolution water quality measurements in rural headwater catchments: DOC quality and DOC export 
Lukas Ditzel, Caroline Spill, and Matthias Gaßmann

In this study, the DOC concentration and DOC quality as well as the discharge of a small spring catchment area in the North Hessian low mountain range were investigated over a period of 1 ½ years. The Nesselbach near Kassel is a small siliceous low mountain stream with an average flow of around 8.5 l/s. The catchment area is primarily characterized by agriculture (75% area share) and is home to a small settlement and forest areas (15% area share). The DOC concentration and the quality indices SUVA254 and SL270 and SL290 were determined using a UV-Vis in-situ probe with a resolution of 5 minutes. These indices can be used to evaluate the strength of the aromatic compounds of the DOC molecules (SUVA254) or the molecular weight (SL275 and SL290). Good DOC quality is generally associated with strong aromatic bonds and a high molecular weight. Spearman correlations were calculated to investigate factors such as water temperature and event characteristics on DOC quality and DOC export. In addition, hysteresis analyses were carried out to interpret the discharge-DOC load relationship with a time lag. The results of the study show an unexpectedly low DOC export from the headwater catchment compared to the relevant literature, as well as a strong correlation between DOC export and DOC quality. The observed fluctuations in DOC quality are mainly determined by the season and the changing land use.

How to cite: Ditzel, L., Spill, C., and Gaßmann, M.: Event-based high-resolution water quality measurements in rural headwater catchments: DOC quality and DOC export, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3197, https://doi.org/10.5194/egusphere-egu24-3197, 2024.

A.23
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EGU24-3673
Water quality and physicochemical conditions drive chlorophyll-a concentrations in two connected subtropical off-stream reservoirs 
Yi-Ming (AiR) Kuo

The assessment of reservoir water quality is vital for preserving ecosystems and ensuring the sustainable use of water resources. Chlorophyll-a (Chl-a) acts as a vital bioindicator, reflecting the dynamics of phytoplankton populations and trophic status of aquatic ecosystems. In this study, we use Generalized Additive Mixed Model (GAMM) models to analyze variations in Chl-a concentration in two connected subtropical off-stream reservoirs (Ren-Yi and Lan-Tan). Water temperature and rainfall are the only two important variables appearing in the optimal GAMM models for both reservoirs. However, Ren-Yi's optimal model additionally includes NH3, total phosphorus (TP), and water level, suggesting these factors may play a larger role in its nutrient levels and fluctuations. This is supported by the significantly higher chemical oxygen demand (COD), TP, and total nitrogen (TN) levels in Ba-Zhang River, which recharges Ren-Yi Reservoir. Lan-Tan's optimal GAMM model incorporates 'sampling depth' variables due to significant differences between shallow and deep sites. Interestingly, in larger datasets (300 points), 'season' emerges as a crucial variable, highlighting intensified seasonal variations in denser data. Therefore, incorporating 'season' as a nominal variable is essential for accurate modeling. Variance structures of Chl-a vary within Lan-Tan by season and within Ren-Yi by sampling site (RY100) and water temperature (RY300). The optimal GAMM captures this inherent variability by incorporating sampling sites or seasons as random effects. The relationship between dissolved oxygen (DO), COD, and Chl-a concentrations is complex and influenced by multiple factors, including nutrient dynamics, algal activity, water circulation patterns, and local conditions. GAMMs are well-suited to capturing the potentially nonlinear and time-varying nature of these relationships, leading to more accurate modeling.

How to cite: Kuo, Y.-M. (.: Water quality and physicochemical conditions drive chlorophyll-a concentrations in two connected subtropical off-stream reservoirs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3673, https://doi.org/10.5194/egusphere-egu24-3673, 2024.

A.24
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EGU24-4691
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ECS
Assessment of the accuracy of salinity simulation using heavy metal and nitrogen cycle in SWAT model in an area exposed to intensive agriculture, Navrood basin, Iran 
Zeynab Kougir Chegini, Negin Sheykhi, Maryam Navabian, Majid Vazifeh Doost, Mohammadreza Ojani, and Szilárd Szabó

The quality of water resources has been considerably changed by human activity in recent decades, resulting in current contamination or posing a risk for the future. Compared to other activities, agriculture contributes more to the depletion of surface water and the degradation of water quality. One of the primary strategies to improve the quality of water resources is agricultural management on watershed-level and water quality simulation models are useful tools for simulating the effects of different activities The catchment basin was Navrood (West Guilan, Iran), and the SWAT model was applied. The salinity load was simulated under two cycles: heavy metals and nitrogen, and the two models were compared. Data series of salinity and discharge from 2006 to 2013 were used to calibrate and validate SWAT. R2 and Nash-Sutcliffe coefficients (NS) were computed to evaluate the model's efficacy. The R2 and NS values were obtained in the river discharge simulation at 0.81 and 0.52, respectively indicating a good model fit. The model makes an acceptable performance of modeling the salinity load in the nitrogen cycle, according to the statistical index that was computed during calibration. Based on the results, the SWAT model can be used to analyze the salinity-induced transfer phenomena in the Navrood basin, considering the values of NS obtained for two stations during the calibration stage, which were equivalent to 0.43 and 0.51. The second method, which simulated the salinity load under the cycle of heavy metals in the basin, did not demonstrate a proper correspondence between the simulated and measured data of the salinity load. The R2 and NS under the cycle of heavy metals were 0.30, -0.71. Therefore, based on the results, simulating the Navrood basin's salinity load using the nitrogen cycle is recommended.

Zeynab Kougir Chegini is funded by the Stipendium Hungarian scholarship under the joint executive program between Hungary and Iran.

The study was elaborated under the research project NKFI K138079.

Keywords: paddy fields, surface water, solute transport

How to cite: Kougir Chegini, Z., Sheykhi, N., Navabian, M., Vazifeh Doost, M., Ojani, M., and Szabó, S.: Assessment of the accuracy of salinity simulation using heavy metal and nitrogen cycle in SWAT model in an area exposed to intensive agriculture, Navrood basin, Iran, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4691, https://doi.org/10.5194/egusphere-egu24-4691, 2024.

A.25
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EGU24-5014
Evaluation of Seasonal Changes in the Eutrophication Index and a Eutrophication Alleviation Technique in Osongji (Osong Pond), Jeonju-si, Korea 
Suyeong Noh, Jiyoung Kang, and Sung-Wook Jeen

Excessive inflow of nitrate and phosphate is a major cause of eutrophication in surface water systems. Eutrophication induces the excessive growth of photosynthetic organisms, leading to the occurrence of algae blooms. Therefore, treating excessive nutrients can be an essential method to alleviate eutrophication. This study aimed to assess the seasonal changes in the eutrophication status in a lake and to evaluate the potential of using Ca-citrate complex for the simultaneous treatment of nitrate and phosphate in the lake environment. Osongji (Osong Pond), a small lake located in Jeonju-si, Korea, was selected as the study site, and the changes in physicochemical parameters and the Korean Eutrophication Index (TSIKO) were used to evaluate the changes in the eutrophication status in the lake. In addition, a eutrophication alleviation technique using ca-citrate complex, was tested under three experimental conditions. The experiments included the application of Ca-citrate complex reagent every two weeks (T1; prevention) or one-time application (T2; one-time application). Closed (T3) and open (T4) controls (without application of the Ca-citrate complex) were also included for comparison. Analysis of seasonal changes in the eutrophication status was conducted every two months from November 2022 to September 2023, and the Ca-citrate complex experiments were conducted from June to September 2023. The results showed that Osongji had the eutrophic condition from November 2022 and the hypereutrophic condition from June to July 2023, and it returned to the eutrophic condition in August 2023. The evaluation of the eutrophication alleviation technique using ca-citrate complex indicated that, in T1 with periodic application, lower TSIKO values maintained compared to the control groups without Ca-citrate complex. In the T2 condition with a one-time application, total phosphorous (T-P) and total nitrogen (T-N) decreased two weeks after application, and TSIKO also decreased. In conclusion, the application of Ca-citrate complex reagent in the lake environment had effectiveness in preventing and alleviating eutrophication. This study can contribute to assessing the degree of eutrophication in a surface water system and developing management strategies for prevention or alleviation of eutrophication.

How to cite: Noh, S., Kang, J., and Jeen, S.-W.: Evaluation of Seasonal Changes in the Eutrophication Index and a Eutrophication Alleviation Technique in Osongji (Osong Pond), Jeonju-si, Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5014, https://doi.org/10.5194/egusphere-egu24-5014, 2024.

A.26
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EGU24-6935
Monitoring spatial and temporal variation of field water quality and sediment organic matter for comparison of satellite image data 
Jun-Ho Lee, Hoi Soo Jung, Huigyeong Ryu, and Han Jun Woo

Sechura Bay (05°12’ to 05°50’S and 80°50’ to 81°12’W) is delimited in the north by Punta Gobernador and Punta Aguja to the south, has an approximate extension of 89 km2, and is within the Piura Region, Peru. It is considered within the transition zone between cold water transported from the south by the Humboldt Current and warm water of the tropical equatorial region. Sechura Bay is an area of high economic importance and an ecosystem with high marine biodiversity due to fan shell (Argopecten purpuratus) production and artisanal fishing. fan shell is an edible marine species of saltwater shellfish, a bivalve mollusk in the family Pectinidae. To use satellite image data in real-time at the survey point, information such as GIS (geographic information system)-based water depth and classification of the characteristics of rock, gravel, sand, silt, and clay content on the surface is required. It usually consists of factors related to the growth of shellfish (sea temperature, salinity, hydrodynamics, chlorophyll-a, etc.) and factors related to the environment surrounding the shellfish (bottom dissolved oxygen, total organic carbon, sediment acid volatile sulfide, benthic diversity, etc.). For example, the suitability score was ranked on a scale from 1.0 points (least suitable) to 8.0 points (most suitable). However, the definition of the score grade must be a decision between artisanal fishing and marine researchers. This GIS-based identify suitable site selection technique, which includes water depth and sedimentary facies information, can be used as the fan shell production management system by supporting spatial variability decision-making in near real-time comparison of satellite image data.

How to cite: Lee, J.-H., Jung, H. S., Ryu, H., and Woo, H. J.: Monitoring spatial and temporal variation of field water quality and sediment organic matter for comparison of satellite image data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6935, https://doi.org/10.5194/egusphere-egu24-6935, 2024.

A.27
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EGU24-8131
Modelling of impacts of combined sewer overflows pollution on urban and rural canal networks 
Matteo Masi, Daniele Masseroni, and Fabio Castelli

The rapid and chaotic expansion of many urban areas, associated with increasing frequency of extreme events, may significantly affect the rainfall-runoff processes and pollutant fate and transport in surface waters. The consequences of such trends affect the combined sewerage systems (CSSs) where wastewater and rainwater mix, leading to a reduction of the efficiency of the existing drainage infrastructures and to the release of pollutants to the water bodies. In this work we developed a coupled hydrologic, hydraulic, and water quality model to simultaneously assess the effects of hydrologic events and CSS discharge on receiving waterbodies (RWBs) on both water quantity and water quality. The modelling framework consists of three modules: (i) the MOBIDIC-U software which is a distributed and raster-based hydrological model to simulate runoff and propagation in canal network in urban and rural areas, (ii) a reactive-transport module able to simulate the advective and dispersive transport and bio-chemical reactions of pollutants in the network, and (iii) an additional software module to assess the mitigation of pollution through the implementation of nature-based solutions (e.g., constructed wetlands). The main quality parameters in the model are: carbon, ammonia nitrogen (NH3 and NH4+), nitrate NO3-, total suspended solids (TSS) and dissolved oxygen. The application of the model to rural areas is particularly critical due to poor availability of data, in particular those related to the morphological characteristics of the network. To overcome this deficiency, we developed an algorithm that automatically extracts the topological/topographical data of the network (e.g., cross sections, elevations) to be provided as input to the model from the digital terrain model. We showcase the application of the model to a case study located in a suburban area of Milan (Italy) to evaluate the effects of sewerage overflows and runoff from polluted urban and agricultural surfaces on the water quality status of the RWBs. The results demonstrate that is possible to define mitigation strategies through the implementation of constructed wetlands with the purpose of obtaining a quality suitable for agricultural and environmental reuses.

How to cite: Masi, M., Masseroni, D., and Castelli, F.: Modelling of impacts of combined sewer overflows pollution on urban and rural canal networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8131, https://doi.org/10.5194/egusphere-egu24-8131, 2024.

A.28
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EGU24-8173
On the improvement of catchment scale simulations of nitrate transport through tile drains 
Anker Lajer Hojberg, Raphael Schneider, David Terpager Christiansen, and Simon Stisen

Nitrate transport from cultivated areas poses a significant risk to water quality of inland and marine water bodies. During subsurface transport nitrate may undergo reduction under anaerobic conditions. However, in temperate climates tile drainage are widely used in agriculture, which provides short-circuits between the root zone and surface water systems, with limited or no nitrate reduction. To identify areas most prone to loss of nitrate to the aquatic environment, it is thus vital to assess and quantify not only the transport out of the root zone, but also the fraction of nitrate being transport by tile drains vs. groundwater transport. The spatio-temporal pattern of tile drainage can be estimated by use of physically-based distributed hydrological models, but their setup and evaluation are generally challenged by limited data on drains with respect to both the tile drain network and in particular with respect to the efficiency of the drains, i.e. the amount of recharging water that is transported via drains. To support water management, the models must cover relevant scales (100 – 1000 km2) posing an additional upscaling modelling challenge.

As part of nitrogen usage regulation in Denmark, a national nitrogen model has been developed, which is currently under revision. An important task is to improve the description of drain transport. This is achieved through detailed hydrological modelling of fields with drain flow observations from which drain fractions, i.e. the fraction of precipitation being drained, are calculated for each model grid. A machine learning algorithm (gradient boosted decision tree) is then used to regionalise the drain fraction to the national scale. Results are used in model calibration to improve the spatial and temporal description of drain flow. While the drainage estimates are needed at a fine scale, preferably at grid scale, data to evaluate model accuracy in terms of nitrate transport is not available at this scale. At catchment scale, the seasonal dynamics of observed nitrogen transport in streams provide valuable information on the amount and temporal variation of the contributions from drains. Analyses of the observed time series are used to further constrain nitrate drainage transport at catchments scale. Uncertainty in model results is assed using a stochastic approach calling for numerous model runs. To limit computational time, model simulations are carried using different spatial resolutions (100 and 500 m grids), where the coarser model is run for an entire 30-year period while the finer resolution is only run for a decade. Results from the overlapping simulation period is used for tuning a downscaling of the drain flow in 500 to a 100 m resolution, thereby providing model results of nitrate transport via drainage at a 100 m resolution for the entire 30 years.

How to cite: Hojberg, A. L., Schneider, R., Christiansen, D. T., and Stisen, S.: On the improvement of catchment scale simulations of nitrate transport through tile drains, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8173, https://doi.org/10.5194/egusphere-egu24-8173, 2024.

A.29
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EGU24-9157
Towards Understanding the Effects of Climate and Land Cover Change on Dissolved Organic Carbon Export in Temperate Forest Catchments 
Tam Nguyen, Rohini Kumar, José L.J. Ledesma, Ebeling Ebeling, Jan H. Fleckenstein, and Andreas Musolff

The amount of dissolved organic carbon (DOC) in surface waters is an important water quality indicator. High levels of DOC in surface waters cause negative impacts on the aquatic ecosystem (e.g., via reducing light penetration and increasing water temperature) and increase the water treatment cost for drinking water supply. DOC mobilization and export from catchments into streams are hydrologically controlled and strongly affected by catchment-specific characteristics (such as topography, soils, and land cover type) and climatic factors. In this study, we developed a simplified process-based model that explicitly includes the hillslope, riparian, and groundwater compartments with the hydro-biogeochemical concept mainly based on the mesoscale Hydrologic Model (mHM) and INCA-Carbon models. The proposed model also allows dynamic carbon input from litterfall and root breakdown. We hypothesize that such a model is needed to understand the role of different catchment compartments and land cover and climate change on instream DOC export. We applied the proposed model for instream DOC simulation in four temperate forest and agriculture catchments located in the Harz Mountains, Germany. Here, we calibrated the model for the period which includes drought years (2018-2019) and the subsequent forest dieback (starting from 2018). The models showed satisfactory results in terms of instream DOC concentrations. Here, we will further evaluate if the model provides the right results for the right reasons by analyzing the physical soundness of the internal carbon export dynamics among different model compartments from our calibrated model. Such evaluation is important when further applying this modeling concept to other areas under similar circumstances.

How to cite: Nguyen, T., Kumar, R., Ledesma, J. L. J., Ebeling, E., Fleckenstein, J. H., and Musolff, A.: Towards Understanding the Effects of Climate and Land Cover Change on Dissolved Organic Carbon Export in Temperate Forest Catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9157, https://doi.org/10.5194/egusphere-egu24-9157, 2024.

A.30
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EGU24-15527
|
ECS
Parameter Estimation of Oxygen Transfer at Hydraulic Structures  
Ashwini Tiwari, Kotnoor Hari Prasad, and Chandra Shekhar Prasad Ojha

Dissolved oxygen is an indicator of water quality, and a minimum of 4 ppm is required for the survival of aquatic life. The oxygen is transferred when water flows over the hydraulic structure by entraining air into the bulk of the flow. The entrained air breaks into small bubbles, increasing the surface area for oxygen transfer. The oxygenation of water removes organic matter, dissolved gases, volatile liquids, offensive taste, and odor, improving the quality of the water flowing in the rivers and streams. The oxygen transfer efficiency depends upon the liquid film coefficient and specific interface area. In this study, parameters, namely liquid film coefficient and specific interface area, are estimated from dissolved oxygen concentration data. The dissolved oxygen concentration data is generated using a liquid film coefficient of 500 m/s, a specific interface area of 0.00035 m2/m3, and dissolved oxygen saturation concentration at 250C. The Parameters are estimated through numerical inversion in which the numerical model representing oxygen transfer over hydraulic structure was optimized using genetic algorithm. The seed value used for optimization is taken as 0.6. The results show that it is not possible to estimate both the liquid film coefficient and specific interface area together from dissolved oxygen concentration data only, and at least one parameter should be known. This finding is supported by the presence of local minima in the liquid film coefficient-specific area parametric space.

How to cite: Tiwari, A., Hari Prasad, K., and Ojha, C. S. P.: Parameter Estimation of Oxygen Transfer at Hydraulic Structures , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15527, https://doi.org/10.5194/egusphere-egu24-15527, 2024.

A.31
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EGU24-16334
|
ECS
Impact of sewage treatment discharges on the water quality of receiving rivers 
Zihan Yang, Fred Worrall, and Julia L.A. Knapp

Most modern societies rely on rivers for both water supply and for disposal of waste. With increasing population there is increasing pressure on receiving rivers, and therefore, the aim of this study was to assess whether discharges from sewage treatments works (STWs) detrimentally impact the water quality of receiving rivers.

 

The approach of this study was to consider any sewage treatment works in England where there was a monitoring point above and below the STW discharge without any other input between these monitoring points. Any determinand could be expected to change downstream with or without the presence of a sewage works discharge, and therefore, the result from river reaches with a sewage discharge were compared to results from control river reaches where there was no sewage discharge present. Downstream changes were assessed relative to date factors; type of reach (control vs. sewage discharge reach); and individual river reach. In addition, for each river reach a series of covariates were also considered – distance between monitoring points; percentile river flow at time of sampling; and upstream altitude. Where significant, results for each sewage treatment works were compared to characteristics of the sewage treatment works to see whether particular treatment processes contributed to, or mitigated, any water quality impact on the receiving rivers. The determinands considered were stream temperature; nitrate; phosphate; biochemical oxygen demand (BOD); chemical oxygen demand (COD); pH; suspended solids.

 

The results show that:

  • Discharge from sewage treatment work significantly altered the temperature, BOD, COD, suspended solids, pH, nitrate, phosphate and specific conductance of the receiving river.
  • Comparing impact on water quality to the nature of the sewage treatment works showed that only stream temperature was significantly altered by the nature of the secondary treatment present at any works.
  • The size of the sewage treatment works, as judged by population equivalence and dry weather flow, had a significant impact on the magnitude of effect for all except nitrate.
  • principal component analysis showed that sewage treatment works grouped together according to their COD or according to their nutrient behaviours.

 

The study shows that the impact of sewage treatment works is widespread, independent of the range of technologies used. At the same time, that the results show that works were rarely a problem for both suspended solids or nutrients.

How to cite: Yang, Z., Worrall, F., and Knapp, J. L. A.: Impact of sewage treatment discharges on the water quality of receiving rivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16334, https://doi.org/10.5194/egusphere-egu24-16334, 2024.

A.32
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EGU24-16580
|
ECS
The effect of land surface characteristics on runoff generation and nitrate fluxes from a Kenyan tea plantation 
Aaron Neill, Suzanne Jacobs, Lutz Breuer, and Sim Reaney

The conversion of tropical montane forests to commercial plantation agriculture affects both the generation of runoff and nutrient water quality, degrading ecosystem service delivery and impacting downstream freshwater environments. Previous empirical studies have inferred that changes to nitrate dynamics at the catchment scale likely reflect the interplay of local topography and climate, crop characteristics (e.g., type and density), fertiliser usage and soil management practices. However, the relative importance of such factors is not well understood. Through the 20th century, the Mau Forest Complex in Kenya experienced dynamic and rapid land use change, including the conversion of pristine forest to commercial tea and tree plantations. Utilising a unique long-term (2015-2021), high-resolution (10-minute) discharge and nitrate dataset collected for such a plantation (33.3 km2), we developed a simple, semi-distributed conceptual model to disentangle the drivers of runoff generation and nitrate fluxes. Insights from weekly stable water isotope data helped to further constrain simulated flow paths. The model represented the main land surfaces of the plantation (tea, eucalyptus, compacted tracks, and impervious and built surfaces) as a set of conceptual stores. Rainfall inputs were weighted by the proportional area of each surface and, where relevant, fertiliser inputs were estimated based on application rates reported in the literature. Lateral water and nitrate fluxes from each conceptual store to the river were delayed and transformed as a function of the mean distance to the river and slope gradient. The available long-term data combined with the structure of the model allowed the relative contribution of each land surface to runoff and nitrate fluxes to be successfully simulated under a range of local hydroclimatic conditions. These insights provide a valuable knowledge base for optimising fertiliser use and implementing mitigation measures to sustain water quality and ecosystem service delivery under conditions of expanding plantation agriculture. 

How to cite: Neill, A., Jacobs, S., Breuer, L., and Reaney, S.: The effect of land surface characteristics on runoff generation and nitrate fluxes from a Kenyan tea plantation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16580, https://doi.org/10.5194/egusphere-egu24-16580, 2024.

A.33
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EGU24-18655
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ECS
Enhancing the HYPE model for simulating dissolved organic carbon in inland surface waters 
Renkui Guo, Martin Berggren, and Zheng Duan

Dissolved Organic Carbon (DOC) in inland surface waters constitutes a significant component of the global carbon cycle, responsible for over half of the carbon transport from terrestrial ecosystems to the ocean. Computational modelling provides an effective method for monitoring spatial and temporal DOC dynamics in inland surface waters, beyond the limited information from in-situ measurements that are often sparse. Numerous process-based models have been developed for simulating DOC in inland surface waters. A common model structure for inland water DOC simulation is to simulate the soil carbon and its subsequent transport to the aquatic environment. Consequently, those models tend to have a complex terrestrial carbon module and comprehensive DOC transport processes from terrestrial to aquatic ecosystems, while their aquatic carbon simulation processes are often simplified. However, such simplification lead to insufficient representation of the interactions, which limits the model capability and undermines our understanding of complete DOC processes and dynamics.

The Hydrological Predictions for the Environment (HYPE) model, a process-based semi-distributed hydrological model developed by the Swedish Meteorological and Hydrological Institute (SMHI), is capable of simulating water quantity (e.g., daily streamflow) and water quality (e.g., nitrogen, phosphorus and carbon concentrations) at various scales. The HYPE model has been validated with reported good performance across the world and it is used by SMHI to provide many operational services in entire Sweden. The HYPE model is among the models that simplify the aquatic organic carbon cycle; it only considers primary production, mineralization, and sedimentation in the DOC simulation. This study aims to enhance the organic carbon module of the HYPE model by improving its presentation of aquatic carbon processes. Specifically, we will develop inclusion of additional key carbon pools and their interactions. For instance, two algae pools (upper and lower) would be added with consideration of algae mortality; the particulate organic carbon would be included in the carbon cycle; the inorganic carbon transport from the soil profile would be considered. As a result, the enhanced HYPE model will be able to represent more detailed aquatic carbon processes. The enhanced HYPE model will be tested in the Krycklan catchment in northern Sweden and several catchments in southern Sweden. Model performance will be evaluated at different timescales with commonly used metrics such as Kling-Gupta efficiency and Nash-Sutcliffe efficiency. We will also perform detailed analyses of parameter sensitivity and model uncertainty. Our study research presents a progressive step in the modelling efforts towards a better DOC simulation and prediction of carbon transport at the catchment scale, which helps us eventually obtain a deeper understanding of DOC dynamics in inland surface waters.

How to cite: Guo, R., Berggren, M., and Duan, Z.: Enhancing the HYPE model for simulating dissolved organic carbon in inland surface waters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18655, https://doi.org/10.5194/egusphere-egu24-18655, 2024.

A.34
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EGU24-18836
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ECS
Proactive management of dissolved organic carbon (DOC) in drinking water catchments  
Ana Lucia Amezaga-Kutija, David Werner, Adam Jarvis, and Stewart Waugh

High levels of organic matter in drinking water catchments are a hazard as there is a known correlation between the presence of organic matter and the formation of trihalomethanes (THMs) during the chlorination stage of water treatment. Organic matter is quantified through the measurement of dissolved organic carbon (DOC) in water.  THMs are potential harmful for human consumption and regulated in drinking water standard, therefore additional chemical loads are required in water treatment to removed THMs which is costly for water companies. Temperature is also known to have a correlation with the formation of THMs during chlorination and the presence of DOC in catchments, meaning this hazard may increase in the future with the predicted changing climate.

This study investigates the potential to proactively manage DOC within a drinking water catchment before drinking water treatment to reduce the risk of THM formation during chlorination. The study focuses on a specific catchment in Northumberland, UK, which includes a reservoir that feeds directly into a drinking water treatment plant. A yearlong monitoring scheme is currently being carried out to discover the dynamics of DOC fluxes throughout the catchment and establishing the pathways and sources of DOC loads. Results so far show that for the main tributary to the reservoir DOC loads vary from 65.01Kg/day in normal conditions to 14402.24kg/day in high rainfall, storm conditions. The data collected is being used to determine relationships between DOC load, land use, land management and climate. These relationships will later be utilized in a model which will be used to simulate various scenarios including some future climate analysis. The final aim of the study is to produce a catchment management plan and business plan considering potential DOC load management methods, stakeholder involvement and scenario analysis.

How to cite: Amezaga-Kutija, A. L., Werner, D., Jarvis, A., and Waugh, S.: Proactive management of dissolved organic carbon (DOC) in drinking water catchments , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18836, https://doi.org/10.5194/egusphere-egu24-18836, 2024.

A.35
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EGU24-20859
Nitrogen sources, retention and exports in the Hunze, Rhine and Elbe river basins 
Xiaochen Liu, Luuk van der Heijden, Joachim Rozemeijer, Tineke Troost, and Anouk Blauw

The study focused on simulating the long-term flux of Nitrogen (N) from source to mouth in the Hunze, Rhine, and Elbe river basins. This was achieved by integrating models that encompass hydrology, nutrient input to surface water, and in-stream retention. The aim was to comprehensively understand the spatial and temporal distribution of N sources, soil budget, delivery to streams, and in-stream retention across these basins. Notably, significant improvements in water quality were observed in these rivers following decades of efforts aimed at reducing nutrient pollution from agricultural and sewage sources. These improvements have brought the water quality close to the EU standard of 2.5 mg/L. However, it was observed that post-2000, the decline in N concentration stagnated. This study elucidates the long-term dynamics of N sources and their contribution to surface water in the three basins. The findings, which offer a spatially explicit nutrient source allocation, are crucial for strategically targeting nutrient reduction policies to foster sustainable water quality management.

How to cite: Liu, X., van der Heijden, L., Rozemeijer, J., Troost, T., and Blauw, A.: Nitrogen sources, retention and exports in the Hunze, Rhine and Elbe river basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20859, https://doi.org/10.5194/egusphere-egu24-20859, 2024.

A.36
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EGU24-4298
 Remote sensing monitoring and trend analysis of Chl-a changes in cultured fish ponds in the Greater Bay Area from 2013 to 2022 
zikang Li

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most economically developed and active regions in China. Fish pond farming is the most important aquaculture model in the GBA. In recent years, climate change and the continuous interference of human activities have led to Chl-a content There are significant differences in time and space, and timely monitoring is crucial to protecting the ecosystems of inland water bodies and adjacent sea areas. Most previous studies have focused on inland lakes or adjacent sea areas, and there have been few studies on fish ponds in the GBA. Based on the BST model, this study selected Landsat image data from 2013 to 2022 to invert the Chl-a concentration of cultured fish ponds in the GBA, and analyzed the temporal and spatial differences in Chl-a contained in cultured fish ponds in the GBA. The results show that: (1) The BST model performs well in retrieving Chl-a concentration in cultured fish ponds in the GBA.(2) In the past ten years, Chl-a has declined year by year from 2013 to 2018, fluctuated slightly from 2018 to 2020, and continued to rise from 2020 to 2022. (3) Aquaculture fish ponds are mainly distributed in the central and southern areas of the GBA. Aquaculture fish ponds near the Pearl River Basin are denser and have higher concentrations. (4) Chl-a has shown an overall upward trend in the past ten years. Among them, the aquaculture fish ponds in Dongguan and Zhongshan have the largest upward trend. Dongguan has the highest increase rate and Guangzhou has the lowest increase rate. (5) Chl-a in cultured fish ponds in the GBA has obvious seasonal variation characteristics, with the highest value in summer and the lowest value in winter. Chl-a concentration in the four seasons is highly correlated with water temperature. Changes in water temperature may be the main factor causing this phenomenon. (6) The concentration of Chl-a in the water bodies of fish farming ponds in the GBA is significantly higher than that of the Pearl River water body and sea water, and the Pearl River water body near the shore is higher than that in the center of the river. This may be due to human overuse of feed and disinfectants Caused. Increasing human activities have caused a significant increase in the degree of eutrophication of water bodies in farmed fish ponds. Control of nutrients such as N and P produced by human activities should be strengthened. The results of this study are important references for water body protection in the GBA and the sustainable development of the aquaculture industry value.

How to cite: Li, Z.:  Remote sensing monitoring and trend analysis of Chl-a changes in cultured fish ponds in the Greater Bay Area from 2013 to 2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4298, https://doi.org/10.5194/egusphere-egu24-4298, 2024.

A.37
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EGU24-19496
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ECS
Cost-effective based Water Quality Monitoring Network design algorithm and  WebGIS Platform 
Jiping Jiang, Wenjing Meng, Qian Liang, and Ashish Sharma

The Water Quality Monitoring Network (WQMN) design is most empirical in practices so far. Design parameters includes water quality indicators, monitoring sites, and sampling frequency, which are all linked to the cost. Among them, location is the most important, which directly affects the accuracy of data and the budget. Previously studies few considered the cost of monitoring stations in the optimization objectives, while means a design in practice need to meet the maximum budgets requirements.  This study main considers this kind of restriction for optimize the response effective of the network as if the pollution events comes.

Therefore, the basic idea is straightforward:(1) minimizing the total cost of water quality monitoring stations and (2) minimizing the average detection time of the contamination events. The candidate sets of monitoring locations are selected by topology at first. The stations are represented by an adjacency matrix L of river network, wherein the element Lij indicates whether the i-th station is adjacent to the j-th station. The velocity of each river section between stations is represented by matrix V.

The average detection time Tr of the network is calculated.

                                           

Where the distance of each station is represented by matrix D, in which the element Dij means the distance. N means the number of river sections.The total cost of stations C is calculated by the formula.

                                                       

The cost required to treat the contaminated water L is calculated by the following formula.

                                       

Where means the cost of sewage treatment per unit mass,  Qvij means river discharge.

The optimization formula F is summarized by the formula.

                                                             

The F value of all potential observation stations is calculated, and the smallest F is the best site. We may also consider the risks distribution among each river reach, descripted by a matrix of R,  according to the local knowledge on pollution sources.

Besides, it integrated a GIS-based module that can automatically identify the necessary of parameters, and calculating the optimal locations and number of monitoring sites. It does not rely on water quality monitoring records, nor on hydraulics. We take Maozhou River in Shenzhen, China, as an example to demonstrate the usability of the WQMN design tool and algorithm. A map of monitoring network is successfully produced with the number of WQMN stations reduced to 38. The platform for global application will be online soon for testing.

 

How to cite: Jiang, J., Meng, W., Liang, Q., and Sharma, A.: Cost-effective based Water Quality Monitoring Network design algorithm and  WebGIS Platform, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19496, https://doi.org/10.5194/egusphere-egu24-19496, 2024.

A.38
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EGU24-20758
Development of a Web-Based Decision Support System for Watershed-Scale Agricultural Conservation Management in the United States 
Yavuz Ozeren, Luc Rébillout, Ahmet Sahin, Nuttita Pophet, Mohammad Al-Hamdan, and Ron Bingner

An automated Web-based decision support tool, Agricultural Integrated Management System (AIMS) is developed to evaluate the impacts of agricultural and channel conservation management practices within any watershed in the United States. AIMS offers a user-friendly Web-GIS framework, enabling convenient interaction with geospatial data layers, automated input data preparation for AnnAGNPS model and visualizing watershed simulation results on any device with internet access. AIMS uses the watershed-scale simulation tool of the USDA Agricultural Research Service (ARS), the Annualized Agricultural Non-Point Source Pollution (AnnAGNPS) model to estimate the runoff, sediment, nutrients, and pesticides that may originate from agricultural areas and impact water quality in rivers, streams, and other water bodies. Running the AnnAGNPS model requires various datasets including topographic, soil, land use and land cover, climate, management data. The topographic data consists of concentrated flows (reaches) and sub-catchments (cells) which are delineated for the entire United States using TopAGNPS, a topographic parameterization program for AnnAGNPS. Soil data is obtained from NRCS Soil Data Access service and processed to produce aggregated data for AnnAGNPS. Historical climate data is derived from the North American Land Data Assimilation System Phase 2 (NLDAS-2) obtained from Hydrology Data Rods. Where NLDAS-2 is unavailable or incomplete, the climate data is supplemented using Daily Surface Weather and Climatological Summaries (DAYMET). The land use data includes the spatial information about the land cover (such as crops) and the management data includes the agricultural operation (such as scheduling of tillage, planting, fertilization, harvesting etc.) performed in the region. The assimilation of the management data to AIMS is currently underway. This presentation summarizes the development of the AIMS framework, dataset preparation for the models, and displays the capabilities of AIMS.

How to cite: Ozeren, Y., Rébillout, L., Sahin, A., Pophet, N., Al-Hamdan, M., and Bingner, R.: Development of a Web-Based Decision Support System for Watershed-Scale Agricultural Conservation Management in the United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20758, https://doi.org/10.5194/egusphere-egu24-20758, 2024.

A.39
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EGU24-21775
Allocation of total load reduction considering the social-economic benefits and water quality impacts of subregions 
Ming Cong, Zhuohang Xin, and Chi Zhang

Reducing nutrient inputs from land-based anthropogenic pollutions is a crucial task to enhance the water quality and maintain the ecological functionality. This study aims to develop a novel method for allocating the total load reduction task to different administrative zones considering the economic and social benefits and water quality effects of each zone, taking the Mainstream of Liao River Watershed (MLRW) in China as an example. The soil and water assessment tool (SWAT) was employed as a water quality model to quantify nutrient load contributions from each pollution source and predict the water quality responses to various allocation schemes. Four load allocation schemes were developed based on environmental efficiencies calculated by Data Envelopment Analysis (DEA) and load contributions of different zones. The impacts on environmental (nutrient load), economic (GDP) and social (crop yield) benefits of the watershed were evaluated. To ensure the equality of allocation results, the environmental Gini coefficient was used to examine the equality level. The results indicated that crop planting was the largest pollution source to total nitrogen (TN), accounting for 48.7%, while animal breeding was the largest pollution source to total phosphorus (TP), accounting for 46.0%. The allocation schemes involving the environmental efficiencies were found to enhance economic and social benefits compared to those solely considered the load contributions of zones. For maximizing economic benefits, the most suitable pollution load reduction scheme involves using economic-environmental efficiency as the adjustment factor for allocation proportion. Likewise, for maximizing social benefits, the preferred scheme is to incorporate the social-environmental efficiency. The pollution load reduction scheme incorporating economic-social-environmental efficiency serves as a balanced compromise, addressing both economic and social benefits. The Gini coefficients of the four schemes were below 0.4, affirming adherence to the equality principle. The analysis framework used in this study provides decision-makers with the flexibility to select allocation schemes tailored to their specific needs when formulating water quality management strategies.

How to cite: Cong, M., Xin, Z., and Zhang, C.: Allocation of total load reduction considering the social-economic benefits and water quality impacts of subregions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21775, https://doi.org/10.5194/egusphere-egu24-21775, 2024.

A.40
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EGU24-21783
Efficiency of Azolla pinnata in Purifying Treated Wastewater in Lebanon via Phytoremediation as a Nature-Based Solution 
Farah Kamaleddine, Rabi Mohtar, Sandra Yanni, Imad Keniar, and Rania Bou Said

Irresponsible wastewater management has caused water pollution to increase to alarming levels in Lebanon. This is compounded by the economic hardships that have forced several wastewater treatment plants (WWTPs) to either shut down their operations or be inconsistent with the treatment level. Given the unsustainable performance of centralized WWTPs and
their vulnerability to economic shocks, integrating nature-based solutions such as phytoremediation has become essential. As such, this study evaluates the potential of growing Azolla pinnata, a floating fern (macrophyte), for the purification of primary,
secondary and tertiary TWW through phytoremediation. Two seasons of experiments were conducted to study the temporal variation in the physicochemical properties of water, nutrient removal efficiency, sediment composition, biomass composition and economic feasibility. All nutrients that were considered in this study were reduced in the presence of A. pinnata in TWW, except for nitrates and sodium. The highest nutrient removal efficiencies were observed in the primary TWW, with an average of 97% for ammonium, 88% for orthophosphates and 90% for potassium. Additionally, chemical oxygen demand (COD)
decreased between 66-86% in the three TWW types. This reduction has been negatively correlated with dissolved oxygen (R= -0.683, p-value=0.000). The results of the phosphorus (P) mass balance have shown that 74% of the P was fixed by Azolla in primary TWW, out of 84% P removal efficiency. In contrast, only an average of 60% and 64% P was absorbed by Azolla in STWW and TTWW out of 100% and 95% P removal efficiency, respectively. Although Azolla has a rich nutritional value, the economic assessment has shown little economic savings from its use in animal feed. Further studies on the expansion of this
technique, microbial and heavy metals contamination in Azolla, palatability of Azolla by different animals, disposal of sediments and the utilization of the Azolla biomass are needed.

How to cite: Kamaleddine, F., Mohtar, R., Yanni, S., Keniar, I., and Said, R. B.: Efficiency of Azolla pinnata in Purifying Treated Wastewater in Lebanon via Phytoremediation as a Nature-Based Solution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21783, https://doi.org/10.5194/egusphere-egu24-21783, 2024.

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EGU24-12278
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ECS
Investigating the water quality of rivers entering the Anzali Wetland 
Mohammadreza Ojani, Eisa Ebrahimi, Zeynab Kougir Chegini, and Szilárd Szabó

The Anzali wetland, a crucial water ecosystem in Iran, has been receiving water input from various rivers over the years and is currently facing a critical condition. We aimed to determine the proportional contribution of biogeochemical loads of anthropogenic origin by the rivers supplying the wetland. Accordingly, we analyzed monthly data from 2013 to 2015, encompassing discharge, total dissolved solids, calcium, magnesium, potassium, sulfate, chloride, bicarbonate, electrical conductivity, and water acidity in the primary river feeding into Anzali wetland. We found that the Pasikhan River exhibited the highest and Chafrood River the lowest average daily water flow, with 48 m3/s and 0.42 m3/s, respectively. The annual average of total soluble solids introduced into Anzali wetland through Pirbazar and Pasikhan rivers was 164,760 and 205,713 tons, respectively. Additionally, the inflow of other substances such as chloride and sulfate into the wetland is substantial. Overall, more than half of the wetland's water originates from the Pasikhan and Pirbazar rivers in the eastern region, where Pasikhan and Pirbazar rivers are dominantly utilized for agricultural and urban purposes, respectively. Based on the multivariate analysis we quantified the contribution of the rivers and the role of land use in the region as main factors of water quality. To rejuvenate the Anzali Wetland, effective catchment area management and governmental support are imperative, with particular emphasis on prioritizing the Pasikhan and Pirbazar rivers.

Mohammadreza Ojani is funded by the Stipendium Hungarian scholarship under the joint executive program between Hungary and Iran.

The study was elaborated under the research project NKFI K138079.

Keywords: Water Pollution, Anzali Wetland, Water Analyze, Rivers, Water chemical parameters

How to cite: Ojani, M., Ebrahimi, E., Kougir Chegini, Z., and Szabó, S.: Investigating the water quality of rivers entering the Anzali Wetland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12278, https://doi.org/10.5194/egusphere-egu24-12278, 2024.

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EGU24-1307
Long-term changes and influencing factors of water quality in aquaculture dominated lakes unveiled by sediment records and time series remote sensing images 
Cheng Chen and Qiuwen Chen

Hydraulic connectivity has great effects on water quality. Enclosure aquaculture can largely alter lake flow regime and thus deteriorate water quality. Understanding the dynamics and influencing factors of water quality in enclosure aquaculture lakes is of great significance to ecosystem restoration of degraded lakes. However, it remains challenging due to the lack of long time series data. In this study, the dynamics of water quality in aquaculture dominated lakes were captured through 210Pb and 137Cs dating based on sediment records. Meanwhile, the effects of landscape pattern within aquaculture lakes on water quality were revealed by long time series of remote sensing images. Results showed the 210Pb and 137Cs sediment dating could provide an effective way to obtain long-term series of lake water quality data in the unmonitored area. The contents of lake sediment nutrients showed an upward trend from 1960 to 2018. The transformation of lakes from agriculture dominated to aquaculture dominated had increased sediment nutrients level. The long-term changes of lake sediment nutrients could be well explained by the landscape pattern metrics within aquaculture lakes, with an average explain power of 87.6%. The configuration metrics at class level had the most contribution (73.6%) to the changes of lake sediment nutrients, followed by the composition metrics (48.8%) and the configuration metrics at landscape level (33.4%). This study can provide a promising method to understand water quality changes in lakes with no historical monitoring data available and is of great benefit to water quality management in aquaculture dominated lakes.

How to cite: Chen, C. and Chen, Q.: Long-term changes and influencing factors of water quality in aquaculture dominated lakes unveiled by sediment records and time series remote sensing images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1307, https://doi.org/10.5194/egusphere-egu24-1307, 2024.