Long Short-Term Memory networks (LSTMs) have been around since the early 90’s but only in the last few years have LSTMs gained significant popularity in the hydrological sciences. Related publication counts have grown exponentially, and LSTMs power some of the largest-scale operational flood forecasting systems.

In this presentation, I'll look back at my relatively short career as a student and researcher at the intersection of hydrology and machine learning. I don't claim to have introduced LSTMs to hydrology, but I'll share my own experience helping to develop this modeling approach into what it is today. We will look at what I saw in this neural network architecture, and why I thought it was well suited for hydrologic applications.

The tale goes as follows: Once upon a time, in a land (not so) far far away, a (not so young) master student of environmental engineering was teaching himself the dark arts of machine learning (ML). While studying ML for automated fish detection, he stumbled upon the LSTM architecture. Having just concluded a course on the design of conceptual hydrological models, he noticed the underlying similarity between the LSTM and these established approaches — and more generally, the conceptual approach for modeling the water cycle. With one of his dearest colleagues and friends, he started to work night and day (actually more nights than days) to see if the LSTM is indeed suitable for hydrology. From initial attempts at emulating the ABC and HBV models, to first real-world experiments in individual catchments, the LSTM was showing great potential. But it was not until he discovered the CAMELS dataset and started experimenting with large-sample hydrology that he fully understood the potential of LSTMs for applications in hydrology. Equipped with nothing more than his first GPU, he embarked on a quest to explore the wondrous lands of academia. Countless nights were spent on the computer, forging transatlantic friendships, conducting experiments and writing publications. Eventually, he ascended to the ranks of PhDs by defending his research against Reviewer #2 and the high council of the PhD committee. Fast forward in time, today, LSTMs are widely used and among others, power Google’s current operational, global-scale flood forecasting model. And thus, the now (not so) old research scientist lived happily ever after with his wife and his children, and continues, to this day, to do much the same as he had in those earlier years.

If there is one thing that I would like for you to take away from this talk, it is that I hope my presentation will motivate young scientists to stay curious, to follow their own ideas, to not get demotivated by initial pushback and to not be afraid of reaching out to more senior researchers. I want to advocate strongly the importance of open science, of reproducibility, of collaborations, of benchmarking and of open data sharing to advance science.

How to cite: Kratzert, F.: Long Short-Term Memory networks in hydrology: From free-time project to Google’s operational flood forecasting model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5863, https://doi.org/10.5194/egusphere-egu25-5863, 2025.

Darcy’s experiments for the public fountains in Dijon in the 1850s aimed at estimating a single parameter value, namely the saturated hydraulic conductivity (Ksat). Lumped hydrological models that are nowadays used to simulate streamflow at the catchment scale use at least a handful of model parameters. These parameters can not be measured in the field and are typically poorly defined. Therefore, despite all our efforts, catchment hydrological modelling still faces equifinality issues that can not be solved by the dramatically increased computational opportunities since Darcy’s work. In addition to the increased computational power, data availability for hydrological modelling has dramatically improved as well. Especially in the last decade, the emergence of large-sample datasets for various regions around the globe has enable modelling studies using data from hundreds of catchments. This has helped to ensure more generally applicable findings. These new data sets also allow us to study the value of data in more detail. This is interesting, for instance, when we want to evaluate the potential value of different datasets, including those of public observations in citizen science projects, such as CrowdWater. In this lecture, I will present findings of recent modeling studies based on large samples of catchments with a focus on the value of different types of data and the question how to best simulate (almost) ungauged catchments.

How to cite: Seibert, J.: Developments in hydrological modelling: from Darcy’s work on public fountains to observations by the public  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10709, https://doi.org/10.5194/egusphere-egu25-10709, 2025.

The increasing water demand by human societies raises concerns on the extent to which it is possible to feed the world with the limited freshwater resources of the planet. The growing competition for water between human uses and environmental needs limits the development of suitable water security scenarios for a sustainable future.  Human appropriation of water resources is for large part instrumental to the enjoyment of human rights to food. To what extent can such rights be reconciled with other human needs as well as the needs of Nature? This seminar will show how humanity is placing unprecedented pressure on the global agricultural system and the water resources it relies on. Through a suite of ecohydrological and socio-environmental analyses, we evaluate the biophysical and social justice limits to the sustainable use of water resources through a variety of perspectives accounting for hydrologic constraints, human needs, environmental flows, and globalization.

How to cite: D'Odorico, P.: The ‘safe operating space’ for global and local water use under climate and societal change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13721, https://doi.org/10.5194/egusphere-egu25-13721, 2025.

Groundwater is a vital resource for domestic, agricultural, and industrial purposes in many regions. However, the increasing demand and unsustainable extraction practices have raised concerns about the long-term viability and sustainability of groundwater storage (GWS), especially in areas where groundwater is the primary source of meeting various demands. Here, we focus on GWS changes in India’s Northwestern states, including Gujarat, Rajasthan, Punjab, Haryana, Uttara Pradesh, and Delhi over two decades (2002-2023). These states encompass 875,249 km² area within the Indus and Ganges river basins, constitute approximately 59% cultivated land, and sustain 525.52 million people. Leveraging GRACE-based TWS data and GLDAS model data, our analysis reveals significant (P<0.05) declining GWS trends with a slope of −20.88 ± 0.53 mm/year, which is more acute than previously reported estimates. Some trend change points in February 2008 and June 2016 are detected that lead to segmented trends with slopes of −18.97 ± 2.45 mm/year (Jan-2002 to Feb-2008), −9.16 ± 1.96 mm/year (Feb-2008 to Jun-2016), −11.80 ± 2.51 mm/year (Jun-2016 to Dec-2023). Spatially divergent trends are found with high decreasing trends of more than 40 mm/year in Punjab, Haryana, Delhi, and some parts of Rajasthan and Uttara Pradesh. This is primarily due to anthropogenic activities like groundwater extraction for domestic and agricultural purposes. In contrast, Gujrat shows subtle positive trends, less than 10 mm/year, due to improved water management, irrigation practices, artificial recharge efforts, monsoonal rainfall, and efficient water extraction management​. Multi-decadal variability and the recent depletion across these six states may foster discussions on policy actions and enhanced multilateral cooperation for a sustainable future, especially in the face of escalating groundwater extraction and a warming climate. This highlights the critical need for immediate attention to water resource challenges in the Northwestern states of India.

Keywords: Groundwater storage (GWS); GRACE; GLDAS; Anthropogenic activities; Policy interventions.

How to cite: Anjaneyulu, R. and Abhishek, A.: A more acute and continuous decline in Groundwater in Northwest India , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-543, https://doi.org/10.5194/egusphere-egu25-543, 2025.

Urbanization has exacerbated challenges faced by urban watersheds, including increased impervious surfaces, deteriorating water quality, and heightened flood risks. Previous research has extensively employed the Genetic Algorithm (GA)  to optimize urban grey-green infrastructure (GGI), primarily focusing on preventing system-wide overflow during design storm events. However, the high costs associated with these solutions have often hindered their implementation. This study proposes a practical approach to enhance urban stormwater management by prioritizing interventions at critical locations within watersheds. A multi-index fuzzy comprehensive evaluation (MFCE) model was developed to identify critical nodes in the drainage network based on hazard (overflow volume and duration), topological characteristics (degree and Katz centrality), and vulnerability (peak hour traffic flow). Problematic segments within the drainage network, including those with adverse slopes, mismatched pipe diameters, and ground depressions, were identified using a combination of SWMM simulations and graph-based analyses. Subsequently, the Genetic Algorithm (GA) was employed to optimize the design and placement of grey-green infrastructure solutions, subject to the constraint of preventing overflow at these critical nodes during design storm events. A case study in Guangzhou, China, demonstrated the efficacy of this approach. The optimized grey-green infrastructure system significantly reduced budgetary costs and peak flow compared to traditional grey infrastructure systems, while enhancing flood control and improving the overall resilience of the urban watershed.

How to cite: Yang, G., Huang, G., and Zeng, B.: Developing Practical Green-Grey Solutions Based on Critical Node Identification for Stormwater Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2656, https://doi.org/10.5194/egusphere-egu25-2656, 2025.

The sustainable management of soil moisture and salinity is a critical challenge for semi-arid regions like the Mahabad Plain in northwestern Iran. This study applies the HYDRUS-1D model, calibrated using sensor-based data, to simulate water and salt dynamics in a 4 HA sugar beet field. The Mahabad Plain, covering 249 km², experiences annual precipitation of 402 mm and evaporation rates of 1,560 mm. Despite its fertile soils, the region faces persistent challenges such as waterlogging, salinity, and unsustainable irrigation practices, exacerbated by agricultural expansion and climate variability. Sensor data were collected every other day from four soil depths (0–25 cm, 25–50 cm, 50–75 cm, and 75–100 cm) in a single sugar beet field between late June 2024 and late July 2024. These measurements were used to calibrate the HYDRUS-1D model, optimizing parameters such as residual and saturated water content, hydraulic conductivity, and dispersion coefficients. Calibration metrics, including RMSE and Nash-Sutcliffe efficiency, confirmed the reliability of the simulations in replicating observed conditions. The results revealed critical inefficiencies in irrigation practices. Over-irrigation was observed, particularly in deeper soil layers, where moisture levels exceeded the optimal range of 18–25% for sugar beet cultivation. Surface layers (0–25 cm) also exhibited frequent waterlogging after irrigation events, with moisture levels surpassing 25%. Electrical conductivity (EC) levels, however, remained within the safe range of 0.6–1.3 dS/m, indicating effective salt leaching and no immediate risk to crop health. Simulations demonstrated that increasing irrigation intervals by 1–2 days could reduce water consumption by 15–30%, prevent excessive soil saturation, and promote healthier root growth. This approach ensures that soil moisture remains within the optimal range while maintaining crop yield and quality. This study is the first of its kind for the Mahabad Plain, offering a novel application of sensor-calibrated HYDRUS-1D modeling. It provides actionable recommendations for addressing water scarcity and improving agricultural sustainability. By integrating field observations with advanced modeling, the research bridges gaps in water resource management and offers replicable solutions for semi-arid agricultural systems worldwide. The findings are especially relevant as the region faces increasing agricultural demands and environmental challenges, including efforts to restore Lake Urmia. By improving irrigation efficiency and reducing agricultural water consumption, more water can be directed toward Lake Urmia, contributing to its restoration and the broader ecological balance of the region.

How to cite: Hossaini Baheri, M. and Tajrishy, M.: Simulation of Salt and Moisture Dynamics in Agricultural Fields Using HYDRUS: Insights from a Sensor-Based Calibration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2901, https://doi.org/10.5194/egusphere-egu25-2901, 2025.

Fractured aquifer parameters are expected to have complex non-Gaussian spatial distributions. Gaussian Mixture Models, known for their effectiveness in representing non-Gaussian distributions, present a promising alternative for capturing the complex heterogeneity of fractured geologic settings however their usage in the fractured geologic settings is unexplored. In this study we extended the application of Gaussian mixtures to transient hydraulic tomography on laboratory-based fractured geologic settings using sequential Gaussian Mixture Model (GMM). We further examined the impact of the number of Gaussian components, sampling strategies and the amount of pumping data on the performance of the sequential GMM. Results demonstrate that GMM with an optimal number of Gaussian components effectively identifies high and low conductivity regions, fracture connectivity, and reasonably predicts drawdowns (R² = 0.61) pumping from validation ports. Stratified sampling of GMM parameters (R² = 0.74, average RMSE_median= 9.89 mm) outperforms other sampling strategies like random (R² = 0.61, average RMSE_median= 20.64 mm ), uniform (R² = 0.64, average RMSE_median= 11.70 mm) and quasi-random sampling (R² = 0.67, average RMSE_median= 11.40 mm) techniques in mapping the fracture connectivity and parameter distribution. Stratified sampling with reduced and information-based pumping data maintains commensurable accuracy (R² = 0.75, average RMSE_median= 11.34 mm). Overall, our findings suggest that the sequential GMM combined with stratified sampling technique effectively captures the spatial variability of aquifer parameters in fractured media.

How to cite: Muraharirao, P. C. and Kbvn, P.: Sequential Gaussian Mixtures for Transient Hydraulic Tomography Inversion in Fractured Aquifers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4107, https://doi.org/10.5194/egusphere-egu25-4107, 2025.

Recent water demands have created immense stress on groundwater, especially in the region facing water scarcity. Hence, optimizing groundwater pumping and developing sustainable water management strategies becomes important for such areas. The traditional mesh-based methods, such as finite difference (FDM) and finite element methods (FEM) for groundwater modelling requires high-quality mesh generation. In these methods, generating a high-quality mesh for complex aquifers is a time-consuming task. Therefore, meshless methods that work with scattered field nodes and avoid mesh generation are more suitable for complex groundwater problems. The present study uses the meshless generalized finite difference method (GFDM) for modelling the groundwater flow and integrating it with particle swarm optimization (PSO) to determine the optimal pumping rates for a hypothetical aquifer system. In this work, optimal pumping rates for different groundwater withdrawal scenarios are obtained through the proposed meshless simulation-based optimization model (GFDM-PSO), indicating its application to real-world problems.

How to cite: Singh, K. G. and Pathania, T.: Optimization of groundwater pumping rates using meshless simulation-based optimization model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4251, https://doi.org/10.5194/egusphere-egu25-4251, 2025.

Title: Hydrogeochemical Dynamics of Middle Andaman: Unraveling the Impact of Seawater Intrusion and Limestone Caves on Groundwater Chemistry

Pardeep Kumar^1,2#, Saumitra Mukherjee^1*

^*Corresponding author- saumitramukherjee3@gmail.com

^#Presenting Author: Pardeepranga001@gmail.com

¹School of Environmental Sciences, Jawaharlal Nehru University, New Delhi

²Quality Council of India, New Delhi

Abstract: Groundwater resources in coastal and island aquifers are increasingly threatened by seawater intrusion, exacerbated by climate change, sea level rise, erratic rainfall patterns, and over-extraction of groundwater. These challenges are particularly pronounced in Middle Andaman, where the interaction of groundwater, surface water, and seawater occurs within a complex hydrogeological framework. To assess the groundwater chemistry and its suitability for drinking and irrigation, a comprehensive study was conducted using geochemical, geospatial, and statistical methods.

Groundwater samples (n=24) and a reference seawater sample were analyzed for major ionic compositions using ICP, spectrophotometry, and flame photometry. Hydrogeochemical indices, including Chloro-Alkaline Indices (CAI), Water Quality Index (WQI), and agricultural suitability indices such as total hardness (TH), residual sodium carbonate (RSC), and magnesium adsorption ratio (MAR), were evaluated. A combination of ionic ratios—Cl/HCO₃, Ca/(HCO₃ + SO₄), (Ca + Mg)/Cl, Ca/Mg, and others—was used to characterize the influence of seawater intrusion and the dissolution of limestone minerals in the aquifers.

The results revealed that 24% of groundwater samples were unsuitable for drinking based on WQI, while 80% and 12% of samples were unsuitable for irrigation based on TH and MAR, respectively. The Durov plot and Schoeller's diagram indicated a dominance of Ca-HCO₃ and Na-HCO₃ water types in 48% and 24% of the samples, respectively, with enrichment of alkali and alkaline earth metal salts due to seawater intrusion. Chloride ion relationships suggested a reverse ion exchange process in 64% of samples, while X-ray diffraction analysis confirmed the presence of limestone minerals such as aragonite, calcite, dolomite, and magnetite.

Geospatial integration of hydrochemical data showed that 44% of the region was moderately affected, and 54% was slightly affected by salinity. Active tectonic lineaments and interconnected faults were found to facilitate seawater intrusion into the deep aquifer, highlighting the role of structural geology in the region's hydrogeochemical dynamics. This study underscores the urgent need for sustainable water resource management strategies to mitigate the adverse impacts of seawater intrusion on groundwater quality in Middle Andaman.

Keywords: Middle Andaman; Groundwater; Seawater intrusion; Water quality Index; Limestone caves

How to cite: Kumar, P. and Mukherjee, S.: Hydrogeochemical Dynamics of Middle Andaman: Unraveling the Impact of Seawater Intrusion and Limestone Caves on Groundwater Chemistry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4752, https://doi.org/10.5194/egusphere-egu25-4752, 2025.

The relationship between groundwater and seismicity has been documented in various regions worldwide. Mexico is no exception to this phenomenon. On September 19, 2017, a magnitude 7.1 earthquake struck between the states of Puebla and Morelos, as reported by the National Seismological Service.

Approximately 50 km from the epicenter, the Agua Hedionda spring exhibited significant physical and chemical changes as a result of the earthquake. These changes highlight the dynamic interactions within the critical zone—the near-surface environment where rock, soil, water, air, and living organisms interact to shape the Earth's surface. The spring's discharge showed notable alterations, including a decrease in flow rate, reductions in major ion concentrations, and shifts in its isotopic composition, providing clear evidence of the connection between regional seismicity and the quality and availability of groundwater.

The analysis of changes in the spring's groundwater over time revealed its vulnerability to losing essential properties, either temporarily or permanently. Hydrochemical and volumetric flow rate data indicated that the spring underwent noticeable changes even before the earthquake. While the water chemistry showed gradual recovery by 2022, the flow rate only returned to approximately 25% of its pre-earthquake level.

In a country like Mexico, where groundwater is essential for numerous activities and where the interaction of five tectonic plates creates a dynamic seismic environment, studying the interplay between seismicity, groundwater, and processes within the critical zone is crucial for understanding and managing water resources sustainably.

How to cite: Sierra Garcia, B. A., Escolero, O., Olea Olea, S., and Medina Ortega, P.: Seismicity and Groundwater Dynamics: Impacts on the Critical Zone in spring of center Mexico, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5314, https://doi.org/10.5194/egusphere-egu25-5314, 2025.

Hydropower development in river basins has significantly promoted economic growth while greatly changing the river ecosystems. Effective reservoir management is crucial to maintaining economic benefits while minimizing impacts on fish species. This study focuses on Reservoir X, which has annual regulation capacity, and proposes an ecological scheduling model for the fish spawning period using the NSGA-II algorithm combined with water temperature and TDG (Total Dissolved Gas) predictions. The model framework is as follows: first, hydrological analysis is conducted based on natural flow data at the dam site to determine the flow requirements for target fish species during their spawning period, providing constraints for optimization. Second, multiple regression methods are used to predict the discharge water temperature and TDG saturation of Station X. Finally, multi-objective optimization is performed considering hydropower generation, fish spawning period water temperature requirements, and TDG risks as objectives, with flow requirements during the spawning period, flood control, and water balance as constraints. The proposed model provides practical parameters for reservoir operation and guidance for different optimization objectives across various reservoirs.

How to cite: He, C. and Liang, R.: Study on Reservoir Ecological Scheduling Based on Multi-Objective Optimization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9851, https://doi.org/10.5194/egusphere-egu25-9851, 2025.

Anthropogenic impact on aquifers leads to variations of groundwaters chemical content. This study is determined to describe current geochemical characteristics of springs in Shelkovo district in order to assess the quality of the water that is used for drinking purposes by residents.

The geological structure of the territory includes Devonian, Upper Carboniferous, Upper Jurassic and Lower Cretaceous terrigenous-carbonate rocks, overlapped by thin Quaternary sandy deposits. Surface sediments are permeable to polluted runoff waters, which can increase the vulnerability of groundwater and reduce its quality.

This research presents the obtained results of water parameters (COD, pH, electrical conductivity), the content of major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, NH₄⁺, HCO₃^-, Cl^-, SO₄^2-, NO₃^-)  for 12 springs. The spring waters are slightly mineralized (M=0.1-0.5 g/l), pH values vary from 5.5 to 7.5.  The total hardness is 0.63-5.7 mg-eq/l. The composition of the water is variable. Springs could be divided by the content of major anions: the dominance of HCO₃^- which is due to natural causes. In some cases the presence of Cl^- and SO₄^2- because of the use of fertilizers and deicing reagents in urban territories. 

The concentration of major ions was compared to maximum permissible concentrations in drinking water (by WHO standards). It was noted to slightly exceed the limit for nitrate ion as well as for chemical oxygen demand.  Some waters had a pH indicator lower than the standard range.

Comparison of the ratios Cl^-/(Cl^-+Na⁺) and Na⁺/(Na⁺+Cl^-) to total dissolved salt was applied in order to figure out the mechanism of spring waters forming (Gibbs, 1970). The results showed that chemical composition is primarily controlled by rock weathering. The ratio relationships between equivalent content Cl^-/Na⁺, HCO₃^-/Na⁺, Ca²⁺/Na⁺ indicate the type of rocks as a silicate (Gaillardet, 1999). The effect of human impact on groundwaters used to be assessed by comparing the equivalent ratios Cl^-/Na⁺ and NO₃^-/Na⁺ (Zhang et al, 2024). The calculations performed summarised anthropogenic impact, including agronomic activities. Significantly connections between various major ions were pointed out due to correlation analysis: as well as fertilizer components and pesticides, anti-icing reagents for roads in winter season and household chemicals from sewers were detected. 

The studied waters were formed by dissolving silicate rocks by atmospheric precipitation. As it was figured out by a significant role of chloride and sulfur ions, and presence of nitrogen-ions, the area of springs' feeding is located in permeable contaminated quaternary sediments. But despite anthropogenic impact, the chemical composition of springs correspond to WHO standards for drinking waters.

References

Gaillardet J., Dupre B., Louvat P., Allegre C.J. Global silicate weathering and CO₂ consumption rates deduced from the chemistry of large rivers // Chemical geology – 1999. – Т. 159. – №. 1-4. – С. 3-30.

Gibbs R. J. Mechanisms controlling world water chemistry //Science. – 1970. – Т. 170. – №. 3962. – С. 1088-1090. 

Zhang, H., Wang, Z., Wang, X. et al. Hydrochemical characterization and health risk assessment of different types of water bodies in Fenghuang Mountain Area, Northeast China. Environ Geochem Health 46, 292 (2024)

How to cite: Gusarova, D. and Yablonskaya, D.: Ecohydrogeological characteristics of spring waters in rural areas (northeast of Moscow region), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11817, https://doi.org/10.5194/egusphere-egu25-11817, 2025.

Accurate estimation of the soil hydraulic conductivity function (SHCF), which describes the relationship between hydraulic conductivity and matric suction in soil, is essential for modeling flow and transport processes in the vadose zone. Traditional steady-state methods for directly determining SHCF are often laborious, time-consuming, and sometimes inadequate for capturing transient-state flow conditions. This study aims to propose a simple, quick, and accurate method for estimating SHCF that facilitates transient-state flow analysis during vadose zone modeling. The proposed method involves inverse numerical modeling using cumulative infiltration and final moisture content data from surface infiltration tests conducted with a handy mini disc infiltrometer (MDI). To validate this approach, the MDI-inverse modeling results were compared with SHCF results from another transient-state method, the instantaneous profile method (IPM), under similar initial soil conditions. The MDI infiltration tests were performed in homogeneously packed soil columns for two soils (identified as loam and silty clay loam textures) collected from nearby field sites. For each soil, separate IPM tests were conducted in soil columns equipped with soil moisture and matric suction sensors at various depths to facilitate calculation of reference SHCF. A comparison between the MDI and reference IPM results revealed a good agreement, with a low normalized RMSE (under 15%) for the estimated SHCFs and a low relative error (under 35%) for the optimized van Genuchten parameters α and n. The findings indicate that MDI-based cumulative infiltration measurements can reliably estimate SHCF via inverse simulation, providing a practical solution for field applications where traditional sensor deployment is challenging. Moreover, the results also establish MDI as a rapid, convenient, and non-invasive tool for determining SHCF for transient-state flow scenarios.

How to cite: Naik, A. P. and Pekkat, S.: Evaluating a rapid approach for estimating soil hydraulic conductivity function from near-surface infiltration measurements , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15178, https://doi.org/10.5194/egusphere-egu25-15178, 2025.

Sociohydrology, an interdisciplinary field exploring the dynamic interactions between human and water systems, has emerged as a critical area of study to address the growing complexity of water management challenges in the Anthropocene. Transdisciplinary practices in sociohydrology extend beyond traditional academic boundaries, integrating diverse knowledge systems, stakeholder perspectives, and real-world practices. These approaches bridge the gap between science and society, enabling the co-creation of solutions that are socially equitable, environmentally sustainable, and contextually relevant. This study explores the transformative potential of transdisciplinary approaches in sociohydrology, emphasizing collaborative governance, stakeholder engagement, and sustainable water management. Drawing on an extensive review of literature and following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), the research highlights diverse applications of transdisciplinary methodologies in water management, ranging from integrating citizen science frameworks to fostering adaptive strategies for climate resilience. Case studies spanning the Katari River Basin in Bolivia to community-led monitoring in Australia's Great Barrier Reef illustrate how integrating ecological, social, and economic dimensions can address complex hydrological challenges. These practices underscore the importance of co-producing knowledge among researchers, policymakers, and communities, thus bridging gaps between scientific inquiry and real-world implementation. By synthesizing insights from multi-scalar analyses, the paper offers a framework for designing adaptive, equitable, and sustainable water management strategies. The findings advocate for institutional reforms and capacity-building initiatives to strengthen collaborative governance and propose a roadmap for applying transdisciplinary methodologies to global water crises. This research contributes to the evolving discourse on sociohydrology, emphasizing the need for integrated systems thinking and participatory processes to achieve long-term water security.

How to cite: Kabir, Md. H.: Advancing Collaborative Governance and Sustainable Water Management: Transdisciplinary Practices in Sociohydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15437, https://doi.org/10.5194/egusphere-egu25-15437, 2025.

Currently, climate change and increasing water demand pose a growing threat to the future availability of water for human societies and ecosystems that depend on it. At the same time, growing evidence suggests that groundwater is playing an increasingly active role in the global water cycle, particularly in sustaining river flows worldwide (Xie et al., 2024). In this context, quantifying the water exchange between these two components of the hydrological cycle becomes essential for an integrated assessment of water availability. For this purpose, baseflow separation methods are valuable tools, though their limitations remain a subject of debate.

Several authors have suggested that commonly used baseflow separation methods should be applied with caution, since these methods often produce large estimation errors, when they are compared with results obtained using three-dimensional flow numerical models (hereafter referred to as 3D models), thereby limiting their applicability. Nevertheless, these methods remain a widely used alternative due to their lower data and resource requirements compared to 3D models. To address these limitations, we proposed a novel methodology based on baseflow separation methods for analysing the interactions between a shallow alluvial aquifer system and the overlying river network. Subsequently, we tested its performance against a 3D model.

The study area is the alluvial aquifer system located at the confluence of the Tarn, Aveyron and Garonne rivers. A 3D model was developed using the BRGM’s MARTHE software. The study area was divided into sub-zones that meet the same isolation conditions for the river network delimited for the analysis of the results to ensure a more robust validation. Time series of flow and cumulative volume for components of the water balance in the river network, as well as flow at gauging points, were analysed. Additionally, different integration periods (quarterly, half-yearly, annual, and biannual) were examined. Several baseflow separation methods were tested, including both digital filtering and graphical methods.

The results showed that the methods proposed by Chapman (1991) and Chapman and Maxwell (1996) consistently outperformed all others across the entire study area and for all integration periods. R² coefficients of determination greater than 0.8 were obtained in both cases for integration periods exceeding six months. Notably, shorter integration periods better captured the temporal variation of water exchange between the aquifer and the river network. However, longer integration periods produced more accurate overall results, likely because the filters struggled to capture flow reversals between the aquifer and river network during flood events.

Acknowledgments: Authors acknowledge the funding provided by project WaMA-WaDiT (PCI2024-153483) funded by MICIU /AEI /10.13039/501100011033/ UE

References

Chapman, T. G. (1991). Evaluation of automated techniques for base flow and recession analyses. Water Resources Research, 27(7), 1783–1784. https://doi.org/10.1029/91WR01007

Chapman, T. G., & Maxwell, A. I. (1996). Baseflow separation: Comparison of numerical methods with tracer experiments. Paper presented at the Hydrology and Water Resources Symposium: Water and the Environment, Institution of Engineers, Australia.

Xie, J., Liu, X., Jasechko, S., et al. (2024). Majority of global river flow sustained by groundwater. Nature Geoscience, 17, 770–777. https://doi.org/10.1038/s41561-024-01483-5

How to cite: García Montealegre, J. P., Caballero, Y., and Del Jesus Peñil, M.: Are baseflow separation methods suitable for assessing shallow alluvial aquifers’ contribution to streamflow?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15495, https://doi.org/10.5194/egusphere-egu25-15495, 2025.

The Indo-Gangetic plain, well-known for its Alluvial landscape for human settlement, is currently facing unprecedented industrialization, and urbanization population, leading to high stress on its aquifer. On the other hand, co-contamination of arsenic (As) and chromium (Cr) in shallow aquifers has been showing an alarming global presence that varies with redox conditions, geochemical signatures, and human activities. We aim to address the influence of the suburban and urban land use on the co-contamination of As and Cr, using various geostatistical tools, models, and indices. Among twenty-six (n=26) groundwater samples, the majority of water types were found to be Mg²⁺-HCO₃^- and Na⁺-K⁺ exhibiting carbonate weathering and evaporation enrichments with saturation indices depicting the supersaturation of calcite and dolomite. The aquifer conditions in both suburban and urban settings were identified as reducing, facilitating the desorption of arsenic. Probability exceedance implied inverse correlation between contaminant concentrations and the probability of their likelihood of surpassing regulatory thresholds. Factor analysis indicates that the natural alignment of contaminants, particularly As and Cr, is maintained under suburban land use but significantly altered in urban settings. The influences of oxidation-reduction potential (ORP), dissolved oxygen (DO), pH, and iron (Fe) concentration on As and Cr co-contamination are effective in suburban environments, while urban aquifers face additional confounding factors, including artificial sources from industries and subsurface leaching. An integrated cluster heatmap has identified a trifecta of As, Cr, and lead (Pb), closely linked to pH, DO, and K⁺, highlighting the effects of increased anthropogenic activities in alluvial floodplains. Finally, a conceptual model was developed to clarify the common processes in these environments, facilitating the creation of universal management strategies for aquifers impacted by As and Cr co-contamination.

Keywords: arsenic; chromium; redox; mid-Gangetic plains; co-contamination

How to cite: Saxena, A., Kumar, M., and Bahukhandi, K. D.: Land use alters the alignment of Arsenic and Chromium co-contamination in the unconsolidated aquifer under reducing environments of the Mid-Gangetic Plains, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16583, https://doi.org/10.5194/egusphere-egu25-16583, 2025.

Groundwater assessment in the Deccan basalt region of India is challenging due to its heterogeneous geology and complex aquifer dynamics. This study integrates hydrogeophysical methods, including DC resistivity and time domain Induced Polarization (DCIP) and slug tests, to evaluate aquifer potential near Bhopal, Madhya Pradesh. The research focuses on both shallow unconfined and deeper semi-confined aquifers within weathered and fractured basalt formations.

Electrical resistivity surveys included more than 25 DCIP profiles targeting weathered and fractured zones. Resistivity values ranged from 15–70 Ωm in weathered/fractured basalts and varied based on the degree of water saturation and fracturing, reflecting lithological heterogeneity. ERT profiles revealed low-resistivity and moderate-to-high chargeability zones, indicative of fracture porosity and groundwater retention. Fracture anisotropy and resistivity contrasts provided critical insights into aquifer connectivity and dynamics.

Slug tests conducted at a borehole with a drilled depth of 61 m validated geophysical findings. Hydraulic parameters, including hydraulic conductivity (3.9E-7 m/s), transmissivity (1.9E-5 m²/s), storativity (0.001), and specific storage (2.1E-5 m^-1), were estimated using Bouwer-Rice and Cooper-Bredehoeft-Papadopulos solutions. These localized parameters complement the spatially extensive data from geophysical surveys. Seasonal water-level fluctuations emphasize the significance of monsoonal recharge in sustaining aquifers.

This integrated approach highlights the role of fractures, weathered zones, and advanced geophysical techniques in delineating groundwater zones and assessing recharge potential. The findings contribute to effective groundwater exploration and sustainable management strategies, addressing water scarcity challenges in basaltic terrains of the Deccan Traps.

Keywords: Aquifer potential, Bouwer-Rice and Cooper-Bredehoeft-Papadopulos solution, ERT, slug test, weathered/fractured basalts, hydrogeology.

How to cite: Khalique, A., Singh, A., and Gaurav, K.: Integrating Geophysical and Hydrogeological Methods for Groundwater Assessment in the Deccan Basalt Region of India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17885, https://doi.org/10.5194/egusphere-egu25-17885, 2025.

This abstract investigates the evolution of urban stormwater management, contrasting traditional methods with emerging approaches, emphasizing the integration of Low Impact Development (LID) strategies and Building Information Modeling (BIM). A comprehensive review of Scopus and Web of Science articles synthesizes existing research to identify trends, challenges, and opportunities in this interdisciplinary domain. Key insights include the effectiveness of LID practices such as permeable pavements, rain barrels, and the application of simulation tools like SWMM and HEC-RAS in reducing runoff and enhancing urban hydraulic modeling. The findings highlight the critical role of green infrastructure in mitigating rainfall impacts and the importance of cost-benefit analyses for evaluating LID implementation. Despite proven benefits, gaps persist in integrating LID into land-use planning, particularly in addressing future climate risks and accommodating urban growth. The study underscores the potential of 3D digital technologies to enhance stormwater management strategies, especially under extreme rainfall conditions. Additionally, the review identifies the lack of high-resolution data as a barrier to informed decision-making. It advocates for stronger collaboration between researchers and policymakers to foster sustainable urban development, improve water conservation, and minimize flooding risks. LID practices, integrated with Building Information Modeling offer a cost-effective solution to urban stormwater challenges, paving the way for resilient and sustainable cities.

How to cite: Fahimi, F. and Ostad Mirza Tehrani, M. J.: Enhancing Urban Stormwater Management: Traditional Measures versus Future Perspectives, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18797, https://doi.org/10.5194/egusphere-egu25-18797, 2025.

The resilience of a large-scale water infrastructure system to cascading effects is
fundamentally dependent on the interdependencies of its components within the
infrastructure network. These interdependencies—which means that the states of
two or more infrastructure components are tightly interrelated through mechanisms
such as physical connection, geographical proximity, and information relay—can
cause a localized event to spread into a system-wide event. Of these, logical
interdependencies remain poorly understood. Little is known about how two
infrastructures affect the state of each other through human decisions and how such
logical connections can be detected and measured. In this study, we tackled this
gap by conducting an applied case study on the Lake Mendocino Reservoir in
California, USA. Crucially, our approach focuses on reservoir institutions (rules)
that structure human decisions around reservoir systems. Reservoir management
relies heavily on operational rules and regulations, but climate change demands
more adaptive and discretionary decision-making by operators. This may further
introduce logical interdependencies in a reservoir system. We develop a novel
framework that integrates Institutional Analysis using Large Language Models to
advance Natural Language Processing (NLP) techniques and Bayesian Network
Modeling to systematically analyze and quantify risk associated with logical
interdependencies. We aim to improve decision-making and risk management in
reservoir operations. This research provides essential insights into enhancing the
resilience of water management infrastructures, particularly in the face of climate
change.

How to cite: Gautam, S., Yu, D. J., and Hoon Cheol, S.: Enhancing Resilience in Human-Reservoir Systems with NLP and AI Frameworks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20066, https://doi.org/10.5194/egusphere-egu25-20066, 2025.

Moisture content available in soil, is a critical parameter for understanding the health of ecosystems, agricultural productivity, and the management of water resources. Soil moisture is an essential component in the growth of vegetation, climate regulation, and the hydrological cycle. The correct estimation of soil moisture is very crucial for optimizing irrigation, enhancing crop yields, and managing water resources. Spatial coverage limits traditional in-situ measurements, while remote sensing-based approaches, especially using SAR imagery, provide scalability to large-scale spatial coverage for soil moisture estimation. This study compares five machine learning-based models- Long Short-Term Memory (LSTM), Random Forest (RF), Multiple Linear Regression (MLR), Multi-layer Perceptron (MLP), and Support Vector Machines (SVM)-for deriving estimates of soil moisture using features based on VV and VH polarizations and incidence angle from SAR imagery. Model performance was also evaluated using in-situ measurements from Vaira Ranch in California's Central Valley, which comprises grasslands and wetlands. Meteorological data, which include precipitation and antecedent rainfall from the ERA5, were used to improve prediction. Each model was hyperparameter tuned, with LSTM adjusting layers, units, and learning rate; RF optimizing tree number, depth, and feature selection; MLR modifying regularization strength; MLP refining layers, neurons, and activation function; and SVM fine-tuning kernel type, C, and gamma. Performance metrics used for evaluation included R² and Root Mean Square Error (RMSE). The results indicated that LSTM outperformed other models with a R² of 0.89, followed by SVM at a value of 0.81 and RF at a value of 0.78. MLP and MLR values were lower at 0.67. This research focuses on the advantages of the integration of remote sensing data and meteorological information for better soil moisture estimation using machine learning and show that the advanced models such as LSTM and RF can effectively predict soil moisture, with important implications for improving agricultural management and resource planning.

How to cite: Sharma, V., Barbhuiya, S., and Gupta, V.: Enhancing Soil Moisture Estimation through Machine Learning Models and Remote Sensing Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20070, https://doi.org/10.5194/egusphere-egu25-20070, 2025.

New global challenges, such as the intense desire for development and climate change, can exacerbate water conflicts among stakeholders (at local, regional, national, and international levels). Material interests are often regarded as effective tools for resolving conflicts and fostering a spirit of cooperation among riparian countries in transboundary river basins. However, discourses and narratives produced by the media, alongside material factors, play a decisive role in shaping water interactions, a topic that has received less attention. The primary objective of this study is to examine the impact of media on transboundary water interactions. To achieve this goal, a systematic review of existing research across various databases was conducted, alongside an analysis of library resources on the influence of media on water interactions. The findings indicate that conflict is an inherent and natural feature of water systems, particularly in shared river basins. However, most media articles and reports tend to intensify water conflicts in shared basins, with limited coverage dedicated to pathways for cooperation that could bring riparian stakeholders together. The use of media to advance the interests of states within a basin often strengthens the potential for water conflicts. Therefore, constructive changes in water diplomacy and conflict transformation require an understanding of the media's role in water cooperation and disputes. This understanding is essential for shaping the water policies of riparian countries.

How to cite: farzaneh, F., Mianabadi, H., Andik, B., and Ghadimi, S.: The role of media in shaping the hydropolitical interactions in the transboundary basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20284, https://doi.org/10.5194/egusphere-egu25-20284, 2025.

Contaminant co-occurrence in water resources poses significant threats to public health and ecosystem stability, necessitating comprehensive monitoring and analysis. This study investigates the spatiotemporal distribution of arsenic, fluoride, and perfluorooctane sulfonate (PFOS) contamination in groundwater and surface water across Yorkshire from 2000 to 2023. Data for this assessment were obtained from the Environment Agency, ensuring reliable and standardised measurements across the study period. The results reveal a concerning trend of increasing arsenic and fluoride concentrations, particularly in the eastern and southern regions, with arsenic levels exceeding 10 µg/L and fluoride concentrations surpassing 1.5 mg/L in several areas by 2023. The PFOS contamination, assessed in both groundwater and surface water for 2023, highlights significant contamination in the southern regions, with concentrations exceeding 0.001 µg/L in some hotspots. The co-contamination maps indicate overlapping regions of high contaminant concentrations, suggesting potential sources of industrial pollution and agricultural runoff. This study emphasises the need for targeted mitigation strategies and continuous monitoring to protect public health and ensure water quality standards across the region.

How to cite: Agarwal, V., Kumar, M., and Saxena, A.: Spatiotemporal Assessment of Arsenic, Fluoride, and PFOS Co-Contamination in Yorkshire's Water Resources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20494, https://doi.org/10.5194/egusphere-egu25-20494, 2025.

The prediction of soil moisture movement remains challenging due to the complexity of underground flow processes and the availability of accurate soil parameters. There have been attempts to overcome this issue with parametric models and inverse modeling, but it remains challenging because it requires knowledge of initial and boundary conditions. While deep learning offers a solution, the one significant constraint remains not to violate the physical constraints. I present a novel physics-informed neural network (PINN) framework that integrates the soil moisture movement governing equation constraints with deep learning to predict soil moisture dynamics. The new approach follows mass conservation principles and soil hydraulic properties into the neural network's loss function. The model ensures physically consistent predictions. The framework simultaneously learns soil hydraulic parameters and water content distributions, adapting to heterogeneous soil conditions through a hybrid optimization strategy. The model incorporates the Van Genuchten parameterization within the physics-informed architecture to ensure consistency and accuracy. This methodology bridges the gap between computationally intensive traditional numerical solutions and pure data-driven approaches, offering a new paradigm for modeling soil water dynamics.

How to cite: Pathak, V. S.: Physics-Informed Deep Learning for Soil Water Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20587, https://doi.org/10.5194/egusphere-egu25-20587, 2025.

The adoption of community-based water purification technology in rural communities is strongly influenced by psychological factors, yet these factors often suffer from endogeneity, leading to biased estimations of their true impact. Our study investigates this critical issue, revealing that traditional estimation methods significantly underestimate the effects of key psychological determinants. Specifically, we found that perceived ease of access and descriptive norms, when treated as exogenous, were underestimated by 175% and 76%, respectively. This oversight highlights the importance of addressing endogeneity to accurately capture the relationship between psychological factors and adoption behavior. The endogenous nature of perceived benefits and descriptive norms highlights a crucial bidirectional relationship: as adoption increases, so do positive social norms and perceived benefits, creating a reinforcing cycle that further drives adoption within the community. Interventions that fail to consider this mutual reinforcement risk undervaluing key psychological factors, potentially undermining their effectiveness. We propose that cultural factors serve as instrumental variables (IVs) to mitigate endogeneity and offer a clearer pathway through which psychological factors influence behavior. For instance, cultural traits such as "work-luck" dynamics shape individuals' proactive or passive approaches to overcoming barriers to technology access. Similarly, generalized morality, which prioritizes communal welfare over individual gain, strengthens descriptive norms that promote widespread adoption. In collectivist societies, these norms hold significant influence, compelling individuals to adopt technologies to maintain social cohesion and uphold communal values.

Our study introduces a robust theoretical framework that integrates cultural factors into the analysis of technology adoption. By leveraging cultural traits, interventions can align more closely with community values, enhancing the likelihood of sustainable adoption. This approach not only provides deeper insights into the dynamics of technology adoption but also offers practical strategies for designing culturally sensitive interventions.

In conclusion, addressing the endogeneity of psychological factors through the lens of cultural influences provides a more accurate and comprehensive understanding of the adoption process. This study advocates for the incorporation of cultural contexts in intervention strategies, ensuring they resonate with the community’s intrinsic values and beliefs. Future research could expand on this dynamic by employing system dynamic models to further explore the bidirectional feedback between psychological factors and behavior, ultimately contributing to more effective and sustainable adoption of community-based water purification technologies.

How to cite: Raj, M., Pande, S., and Ramesh, M.: Exploring Endogeneity in Psychological Determinants of Community-Based Water Purification Technology Adoption, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20733, https://doi.org/10.5194/egusphere-egu25-20733, 2025.

The Coastal Unconfined Shallow Sandy Aquifer of the Keta Basin, made up of Quaternary gravel, sand and clay and Neogenic Limonic deposits is the most economically accessible aquifer in Southern Volta, Ghana, West Africa. Water from the aquifer supports domestic supply and vegetable farming, which is the main stay of the area’s economy (Nerquaye-Tetteh 1993; Helstrup et al. 2007; Yidana et al. 2007). Despite the importance of this shallow aquifer, it is vulnerable to contamination from saline intrusion and agricultural activity in the area (Gill 1969; Nerquaye-Tetteh 1993; Kortatsi 1994; Kortatsi et al. 2005; Helstrup 2006). Protecting and managing this aquifer effectively will require the appreciation of the flow regime and dynamics via the collection of hydrogeological information such as geochemical properties and their variation over the years, hydraulic gradient, flow direction, well density, and their abstraction rates. However, this data is non-existent. This work set out to collect these basic hydrogeological information. This work was done via: geochemical sampling and analyses of thirty-five (35) wells for facies discrimination; the hydraulic head mapping of forty-five (45) wells for flow direction mapping and hydraulic gradient distribution, and particle size distribution testing of sampled aquifer material for the hydraulic conductivity distribution. The geochemical datasets showed: neutral and well buffered water groundwater; nitrates occurring in all the samples, with [NO₃^-] ranging between 0.35 – 25.3 mg/L, indicative of possible human influence on groundwater in the area; four (4)  main water types from the analyses as: Na-Cl, Ca-(HCO₃)₂, Na-HCO₃,  and Ca-Cl₂ with percentage dominances of 47, 41, 9 and 3% respectively. Na-Cl and Na-HCO₃ waters characterised by very high SECs and  found in farm wells located near the coast and lagoons suggest saline intrusion (due to heavy pumping on farms) from the sea and lagoon. The central part of the area, has fresh water which with the Ca-(HCO₃)₂ water type, indicative of natural rock weathering processes and flow dynamics. Analysing the irrigation water use parameters from the geochemical showed that the water in the area was suitable with respect to residual sodium carbonate (RSC) and magnesium absorption ratio (MAR), whereas waters mostly affected by saline intrusion did not meet the  sodium percentage (Na%), sodium absorption ratio (SAR) and Kelly’s ratio (KR). Heavy  groundwater abstraction without regulation is fingered for causing saline intrusion in the area because of reduction of groundwater levels. The hydraulic gradient in the area mimics that of the natural ground level, with relatively gentle slope of 0.002, with the dominant groundwater flow direction of north to south. This work is novel as it sets the tone for the first-ever initial hydrogeological characterisation of the aquifer, whose state can be continuously monitored for advising the Government, and the Water, Agricultural and Health Directorates of the Municipal Assembly for the regulation of agriculture and abstraction in the area, so as to protect the aquifer and human health.

How to cite: Agbotui, P. Y., Amissah, M. B., Ewusi, A., and Woode, A.: A step towards the protection and management of the Shallow Aquifer of the Keta Basin, in Ghana West Africa: an initial physico-chemical characterisation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21834, https://doi.org/10.5194/egusphere-egu25-21834, 2025.

We investigate the potential of using Long Short-Term Memory (LSTM) neural networks for estimating streamflow in (sub)tropical catchments under contrasting hydroclimatic regimes (semi-arid and humid). We have used 176 Brazilian catchments with at least 30 years of streamflow data and LSTM models with 16 static catchment attributes as input features. We tested different LSTM model configurations to assess their sensitivity to varying input sequence lengths (lookbacks). The primary objective was to explore the hydrological insights offered by LSTM-based streamflow models and compare their performance with the traditional GR4J hydrological model. With this design, we aim to address two research questions: (i) Does the performance of LSTM models depend on catchments' hydroclimatic characteristics? (ii) How effective are LSTM-based models for streamflow simulation in tropical and subtropical catchments under semi-arid and humid conditions? We adopt two modeling approaches: (1) regionalized models trained on catchments within the same hydroclimatic regime and (2) a composite model trained on a heterogeneous sample combining both arid and humid catchments. The findings reveal distinct sensitivities of LSTM models to hydroclimatic conditions. LSTM models exhibit higher sensitivity to the length of input sequences (lookbacks) in humid catchments, with longer sequences yielding better performance. This is attributed to the dominant hydrological processes in humid regions, which are influenced by long-term memory effects such as soil moisture and groundwater storage. Conversely, this sensitivity is not observed in semi-arid catchments, where streamflow dynamics are primarily driven by short-term precipitation events and exhibit less dependence on long-term hydrological processes. Furthermore, the composite model, which combines semi-arid and humid catchments, demonstrates a decrease in performance for semi-arid catchments. This suggests that adding catchments with contrasting hydroclimatic characteristics introduces heterogeneity in the dataset, potentially reducing the model's ability to capture the specific dynamics of semi-arid catchments. Overall, the regionalized LSTM models outperformed the GR4J model in both semi-arid and humid regimes, particularly in humid catchments. Approximately 87% of humid catchments and 50% of semi-arid catchments achieved Kling-Gupta Efficiency (KGE) values above 0.60 during the testing phase of the regionalized LSTM models. These results highlight the potential of LSTM networks for streamflow regionalization, especially in humid regions where long-term hydrological memory plays a critical role. The study underscores the strengths and limitations of LSTM models in tropical and subtropical catchments with contrasting hydroclimatic regimes. The findings suggest that LSTM models could serve as valuable tools for regional hydrological applications, aiding local and regional decision-making processes. Additionally, the results emphasize the importance of tailoring LSTM model configurations to the specific hydrological characteristics of catchments, particularly the choice of input sequence length, to maximize model performance. 

How to cite: Maria de Andrade, J., Nóbrega, R., Ribeiro Neto, A., Rico-Ramirez, M., Coxon, G., and Montenegro, S.: Streamflow simulations using regionalized Long Short-Term Memory (LSTM) neural network models in contrasting climatic conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-580, https://doi.org/10.5194/egusphere-egu25-580, 2025.

Spatio-temporal variability of the terrestrial hydrological processes (land heat and water storage anomalies) has important implications in the climate predictability through their effects on surface energy and water fluxes. The changes in seasonal precipitation patterns associated with the Indian Summer Monsoon can alter the hydrological processes; for a given catchment, which in turn can influence the exchange of water and energy at the land surface-atmosphere interface. Hence the reliable prediction of the basin-scale water cycle components in a physically based high-resolution hydrological model equipped with sophisticated Land Surface Models (LSMs) is of prime requirement. The modern LSMs can provide detailed representations of important biophysical, biogeochemical and hydrological processes of varying spatial and temporal scales by incorporating the necessary feedbacks between the land and the atmosphere. When coupled to a physically based fully distributed hydrological model, it can affect the soil moisture patterns means of recycling the surface and sub-surface runoff (lateral terrestrial flow). However, despite the role of lateral terrestrial hydrological processes for the improved simulation of soil moistures, the sensitivity studies involving the land surface and sub-surface feedbacks are less pronounced especially for a tropical humid region with complex physiographic settings (presence of complex topography) under monsoon regimes (strong synoptic forcings). Therefore, in the present study, we examined a process based diagnosis regarding the role of the lateral flow on the terrestrial hydrological processes (Evapotranspiration, surface and sub-surface runoff, stream flow) and surface energy fluxes (latent heat, sensible heat) by using a multi-configured modeling framework of offline WRF-Hydro with Noah-Multi parameterizations (MP) LSM to enable systematic evaluation of the multiple physical parameterizations of hydrologic process representation; the validation has been done with the reanalysis dataset, a remotely sensed product and ground based observations.

How to cite: Sarkar, S. and Lakshmikanthan, P.: Modeling the impact of lateral flow on terrestrial water balance components and surface energy fluxes using WRF-Hydro with multi-configuration ensembles: a study over Krishna River Catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1783, https://doi.org/10.5194/egusphere-egu25-1783, 2025.

Flash floods are one of the most devastating natural disasters, posing significant risks to both human life and infrastructure. The classification of their underlying drivers—such as high precipitation, dam breaches, landslides, and melting snow—remains a critical yet challenging task, especially in regions like China, where diverse geographical and climatic factors exacerbate disaster complexity. In this study, we propose a Transformer-based Graph Network (TGN) designed to tackle these challenges by leveraging a dataset of over 53,000 flash flood events, each characterized by non-uniform geographical attributes and varying levels of data completeness. Unlike traditional graph neural networks (GNNs) that depend on predefined graph structures, TGN dynamically learns and refines edge weights during training, enabling it to uncover asymmetric dependencies. This adaptability is particularly valuable when explicit relationships between nodes are unavailable or incomplete.

Integrating multi-head self-attention mechanisms from Transformer architectures, TGN captures complex interdependencies across watershed features while maintaining interpretability through sparsity and diversity constraints. A distinguishing feature of this framework is its ability to identify meaningful graph structures without prior knowledge, offering insights into critical connections and interactions within disaster-prone regions. For instance, our experiments demonstrate how TGN emphasizes high-risk upstream-downstream relationships, providing actionable knowledge for localized flood management. The model significantly outperforms traditional GNNs and machine learning methods in accuracy and robustness, achieving superior classification performance across all four disaster categories. Furthermore, the TGN framework is supported by rigorous evaluation metrics, including Precision, Recall, F1-score, and Overall Accuracy, ensuring its reliability in real-world applications.

By combining innovative graph-based modeling with interpretable mechanisms, this study bridges the gap between theoretical advancements and practical disaster management. The proposed approach not only enhances prediction capabilities but also provides an analytical lens for understanding the intricate relationships among flash flood drivers, paving the way for more effective mitigation strategies and informed decision-making. This work underscores the transformative potential of adaptive graph neural networks in addressing complex environmental challenges and advancing the state of flood risk assessment.

How to cite: Wang, H., Xuan, Y., Zhang, Z., Antunes Meira, M., Li, Q., and Liu, C.: A Transformer-based Graph Network for Flash Flood Disaster Classification, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2646, https://doi.org/10.5194/egusphere-egu25-2646, 2025.

This study proposes a novel approach for analyzing block minima data within the Generalized Extreme Value Distribution (GEVD) framework by incorporating the Negative Power Transformation (NPT). The NPT method, which includes a hyper-parameter to adjust data bounds (effectively reducing to the Reciprocal Transformation (RT) when the hyper-parameter is 1), aims to improve the accuracy and robustness of long-term return level (RL) estimations. Traditional transformation methods often exhibit limitations in accurately predicting RLs for extended return periods. Through extensive Monte Carlo simulations, we demonstrate that the NPT-GEVD method outperforms conventional approaches in terms of bias, standard error (SE), and root mean square error (RMSE) for return periods of 25, 50, and 100 years. Notably, the NPT-GEVD consistently provides reliable RL estimates across various parameterizations and sample sizes, particularly when using L-moments for estimation with smaller datasets. The efficacy of the NPT-GEVD method is further validated through its application to inter-arrival time (IAT) rainfall data from South Korea. The analysis revealed that RLs for detecting the time to exceed hourly cumulative rainfall thresholds of 60 mm, 90 mm, and 110 mm varied significantly across locations, ranging from 30 minutes to over 4 hours. This research underscores the significance of advanced transformation techniques in enhancing the accuracy and reliability of environmental risk assessments. The NPT-GEVD method offers valuable insights for improving flood prediction and mitigation strategies in the face of climate change.

How to cite: Yoon, S. and Prahadchai, T.:  Transformed Technique for Applying the Generalized Extreme Value Distribution to Block Minima, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2660, https://doi.org/10.5194/egusphere-egu25-2660, 2025.

Climate change and urbanization intensify urban pluvial flooding, posing significant threats to human lives and infrastructure. This situation underscores the critical need for efficient and accurate predictive systems for disaster prevention and mitigation. Traditional flood simulation models, while precise, are often limited by their data-intensive requirements and substantial computational complexity. In contrast, deep learning (DL) models show their advantages by high efficiency and powerful capability in processing large-scale non-linear data, making them highly appropriate for modeling complex flood dynamics. Consequently, integrating DL with conventional urban flood models has emerged as a promising strategy to enhance the accuracy and efficiency of flood prediction systems. However, existing research predominantly focuses on inland flooding, with limited attention to the role of tidal levels in coastal cities, which can significantly impact the accuracy of urban flood simulations.
To bridge the GAP, this study proposes an innovative hybrid DL approach that explores spatial and temporal data to improve the accuracy and efficiency of urban flood simulations, particularly in coastal areas. Simulation results from physics-based urban flood models are utilized to construct a comprehensive database for the DL model. Afterwards, patch-size and random sampling methods are employed to construct the sample dataset for training DL models. The convolutional neural network (CNN)-based data-driven urban pluvial flood model can simulate floods using topographic, rainfall, and tidal data, enabling the simulation of large urban areas within seconds. Incorporating diverse input data and advanced network architectures enhances model robustness and generalization across various scales and rainfall events. Fusion models that combine the strengths of DL and traditional hydrological models demonstrate improved prediction accuracy and computational efficiency by integrating tidal data and other environmental factors. Consequently, these hybrid models hold significant potential for integration into early warning systems and supporting decision-making processes in urban flood risk management.

How to cite: Zeng, B., Huang, G., and Yang, G.:  A Diversity Driven Deep Convolutional Network for Enhanced Coastal Urban Flood Risk Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2662, https://doi.org/10.5194/egusphere-egu25-2662, 2025.

South Korea experiences significant regional variation in precipitation due to its unique topographical features. Over the years, the intensification of summer rainfall concentration has led to recurring damage from floods and torrential downpours. To mitigate such impacts, the Korea Meteorological Administration monitors precipitation using observed data from weather stations and estimated values for non-observed locations. The Generalized Extreme Value (GEV) distribution is commonly employed to model annual maximum precipitation, enabling the calculation of return levels that serve as foundational data for flood prevention. This study aims to estimate the spatially generalized additive GEV distribution of daily maximum precipitation using data from 54 Automatic Synoptic Observation System (ASOS) stations between 1972 and 2024. Spatial elements (latitude, longitude, altitude) and temporal elements (year) were incorporated into the model. The location, scale, and shape parameters of the GEV distribution were estimated using the maximum likelihood method, with smoothing functions accounting for spatial and temporal factors. The results indicate that the location and scale parameters, influenced by latitude and longitude, are relatively lower in central regions, while the shape parameter, influenced by altitude, shows similar trends. Furthermore, return levels for 50-year and 100-year return periods are notably higher in mountainous regions. Goodness-of-fit tests, such as the Anderson-Darling test, were performed on the GEV distributions of 53 ASOS stations, excluding one. However, 12 stations located in island regions, high-altitude areas, or regions affected by typhoons exhibited distributions that were difficult to explain spatially. These findings are expected to aid in the development of efficient water resource management strategies and regional flood prevention measures based on the distribution characteristics of precipitation.

How to cite: Lee, B., Hwang, Y., and Yoon, S.: Spatial and Temporal Extreme Modeling of Daily Maximum Precipitation Based on a Generalized Additive Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2663, https://doi.org/10.5194/egusphere-egu25-2663, 2025.

Asset-based Dynamic Flood Risk Assessment: Case Study of London Downtown

Flooding poses significant risks to urban centres, with particular challenges faced by business hubs where disruptions can have devastating consequences on national and global economies [1]. Business hubs are the lifeblood of national and global economies. During flood events, businesspeople encounter disruptions that not only obstruct daily operations but also ripple through supply chains and financial systems [2-3]. This study emphasises the importance of protecting critical assets in Downtown London, a vital business hub, to mitigate economic and social impacts during floods. Through a watershed-based approach, Downtown London, a vibrant business hub with numerous critical assets, has been selected as the case study area. The district contains key commercial buildings and infrastructure that are vital to economic and social continuity. Using Digimap and Verisk, essential commercial buildings and critical assets are pinpointed based on their usage and significance. These tools facilitate generating an accurate map of assets requiring priority attention during flood events.

The proposed decision support system (DSS) is developed to aid risk management authorities, including policy-makers, decision-makers, and technical staff. The system operates on two key bases. Real-time population density data for critical assets is obtained using Google API. This data helps evaluate the human vulnerability component during flood scenarios. A flood forecasting system is integrated to predict water levels at 15-minute intervals for the coming hours. This system provides granular and actionable insights into evolving flood conditions. For each critical asset, two risk values are computed: one based on population density and another on forecasted water levels. These values are combined to derive a dynamic risk level for each time step, enabling authorities to respond effectively. The integration of real-time data and predictive modeling in the DSS offers a comprehensive framework for flood risk assessment. By prioritising critical assets based on dynamic risk levels, authorities can implement targeted preparedness and response measures such as early warnings and evacuation plans. This approach ensures both human safety and economic resilience. The findings have demonstrated the feasibility of applying real-time data and cutting-edge modeling to enhance urban flood resilience. By combining flood risk maps, real-time population density, and a comprehensive prioritisation framework, this approach provides a promising tool for urban planners and emergency responders to protect critical business assets and ensure economic continuity during flood events.

References

[1] Bakhtiari, V., Piadeh, F., Behzadian, K. and Kapelan, Z. (2023). A critical review for the application of cutting-edge digital visualisation technologies for effective urban flood risk management. Sustainable Cities and Society, p.104958.

[2] Bakhtiari, V., Piadeh, F., Chen, A.S. and Behzadian, K. (2024). Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review. Expert Systems with Applications, 236, p.121426.

[3] Piadeh, F., Behzadian, K. and Alani, A.M. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.

How to cite: Bakhtiari, V. and Piadeh, F.: Asset-based Dynamic Flood Risk Assessment: Case Study of London Downtown, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3504, https://doi.org/10.5194/egusphere-egu25-3504, 2025.

Social media applications have emerged as reliable communication channels, especially when traditional methods falter [1]. Their integration into emergency management presents significant advantages, including enhanced situational awareness during unfolding events, rapid dissemination of news and alerts to broader audiences, and improved coordination among decision-makers and stakeholders [2]. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring [3]. To better understand these advantages and challenges, a comprehensive review and analysis of the literature on the application of social media in this field was conducted.  Social media facilitates participatory and collaborative structures, enabling collective knowledge-building in public information and warning systems. To realise this vision, the authors examined, 73 studies conducted from 2014 to 2024 to systematically evaluate the current literature surrounding communication on social media and the latest research in social media informatics related to disasters. This review identified key challenges within existing studies. The articles included 23 related to pluvial floods, 12 related to fluvial floods, 17 related to storm floods and 21 paper that were unspecified The majority of the studies were conducted in China, followed by the United States. Various software platforms, including Twitter, YouTube, and other social media networks, were analysed. Data extraction from these platforms was performed using Python programming. The study periods ranged from 1 to 3,650 days. These findings serve as guidance for researchers examining the relationship between social media and disaster management. They aim to develop the use of social networks during disasters, analyse patterns, and create programming to identify best practices for utilising social media in times of crisis. In the future, a mapping framework and tool can be developed to automatically extract information from social media through text and image analysis. By integrating this data with other available information sources, it will be possible to generate more accurate inundation maps in real-time. It is essential to recognise that information about floods obtained from social media may be incomplete during communication interruptions. To address this issue, future research should prioritise integrating big data from urban Internet of Things networks and improving communication infrastructure repairs. By adopting this strategy, we can collect more comprehensive disaster information to enhance flood emergency response effectiveness.

References

[1] Piadeh, F., Behzadian, K. and Alani, A.M. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.

[2] Piadeh, F., Ahmadi, M., Behzadian, K. (2020). A Novel Planning Policy Framework for the Recognition of Responsible Stakeholders in the of Industrial Wastewater Reuse Projects. Journal of Water Policy, 24 (9), pp. 1541–1558.

[3] Bakhtiari, V., Piadeh, F., Chen, A., Behzadian, K. (2024). Stakeholder Analysis in the Application of Cutting-Edge Digital Visualisation Technologies for Urban Flood Risk Management: A Critical Review. Expert Systems with Applications, p.121426.

How to cite: Panahi, A., Karkhaneh, N., and Piadeh, F.: Community-based flood early warning system: Current practice and Future directions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3526, https://doi.org/10.5194/egusphere-egu25-3526, 2025.

Flooding has posed a significant challenge to urban infrastructure, necessitating effective and real-time risk management strategies [1]. One of the most devastating impacts is on urban transportation, where disruption can lead to significant economic losses or even human casualties [2-3]. This study has focused on the key financial and commercial areas in downtown London, where an innovative system has been developed to integrate real-time flood risk forecasting with traffic data visualisation and dynamic decision support for emergency response and resource allocation. First, with access to the Google Maps API, real-time and forecast traffic data have been collected for local streets. Then, these datasets can facilitate a 15-minute resolution forecast for the next 8 hours, enabling an in-depth understanding of traffic flow patterns during flood events. Furthermore, by employing flood forecasting measures on these real-time datasets, streets at risk of inundation can be identified faster, with their traffic conditions assessed accordingly.

A key aspect of this study is to consider different factors dynamically for weighting and prioritising streets. On one hand, pre-existing factors such as road hierarchy, connectivity, access to critical facilities, land use, infrastructure vulnerability, and proximity to evacuation zones are converted into dynamic factors by attaching a temporal variable to these pre-existing factors. On the other hand, real-time dynamic ones include flood depth, traffic congestion, accessibility for emergency services, and community needs reported. The integration of all these factors leads to the development of a transportation-based decision support system (TBDSS) tailored to urban flood management. The TBDSS has facilitated the allocation of emergency resources, prioritisation of street reopening, and planning for evacuation or relief operations. For instance, streets connecting to hospitals or shelters have been given higher priority, while those serving industrial or low-density areas have been weighted lower. As such, our proposed system can dynamically adjust priorities based on evolving flood and traffic conditions, ensuring optimal response strategies.

The findings have demonstrated the feasibility of leveraging real-time data and advanced modeling to enhance urban flood resilience. By combining flood risk maps, traffic forecasts, and a comprehensive prioritisation framework, this approach has provided a promising tool for urban planners and emergency responders.

[1] Piadeh, F., Behzadian, K., Alani, A. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.

[2] Gao, G., Ye, X., Li, S., Huang, X., Ning, H., Retchless, D., Li, Z. (2024). Exploring flood mitigation governance by estimating first-floor elevation via deep learning and google street view in coastal Texas. Environment and Planning B: Urban Analytics and City Science, 51(2), 296-313.

[3] Naghedi, S. N., Piadeh, F., Behzadian, K., and Hemmati, M.: Unveiling the Interplay: Flood Impacts on Transportation, Vulnerable Communities, and Early Warning Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13189, https://doi.org/10.5194/egusphere-egu24-13189, 2024.

How to cite: Naghedi, R., Piadeh, F., Huang, X., and Wu, M.: Real-time Transportation-Based Flood Warning System: A Case Study in Downtown London, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3543, https://doi.org/10.5194/egusphere-egu25-3543, 2025.

Flash floods are one of the most severe natural hazards worldwide; they can occur within a few minutes or hours, and can move at high flow velocities, striking with violence and little warning. Early warning of flash floods is extremely important for vital risk mitigation and requires the knowledge of the critical rainfall producing flooding that is typically considered as “warning index”. The small spatial and temporal scales at which flash floods occur make the prediction of critical rainfall challenging, particularly in data-poor environments, where high-resolution weather models and advanced monitoring networks may not be available.

In this research, we present a methodology to estimate the critical rainfall for flash flooding based on an integrated hydrologic-hydrodynamic model. The model is applied in the Lilantas River catchment in Evia, Greece, considering a relatively large number of rainfall and soil moisture conditions scenario combinations in order to (1) determine inflow hydrographs used as boundary conditions for the hydrodynamic model and (2) calculate the distribution of “critical hazard” across the cells of the two-dimensional (2D) computational domain. In the present work, we define critical hazard combining the main hydrodynamic characteristics that are water depth and flow velocity, and we import all calculated “critical hazard” values into a GIS-based database.

Key findings include maximum peak discharges from all simulated scenarios, allowing a sensitivity analysis of varying Curve Number and soil moisture conditions, as well as the effects of rainfall duration and intensity combinations on flood responses. Furthermore, based on the calculated critical hazard, estimates of critical rainfall values for the selected study area are provided, along with an example of the flood warning system’s operation.

How to cite: Papoulakos, K., Mitsopoulos, G., Baltas, E., and Stamou, A. I.: Estimating critical rainfall for flash flood warning systems using integrated hydrologic-hydrodynamic modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3970, https://doi.org/10.5194/egusphere-egu25-3970, 2025.

Landslides are a widespread geohazard with significant impacts on lives and economies worldwide. While past research has primarily emphasized creating inventories, and analysing spatial and temporal patterns, the objective of this study is to explore the relationship between landslides events taken place in different catchments using only topographical and physical attributes from the disasters’ areas. The aim is to improve the understanding of the occurrence and susceptibility of such events, as well as the possible similarities between the events and the catchments. To this end, multicollinearity and mutual information analysis were performed to identify both linear and nonlinear relationships between the variables, assisting on the identification of the most relevant driving factors to historical landslides in the study area. Furthermore, the events were grouped using 5 different unsupervised clustering techniques, KMeans, Mean Shift, DBSCAN, Hierarchical and Spectral Custering, to analyse the relationship between landslides taken place in different catchments and their underlying driving forces. Clustering evaluation metrics, i.e. Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used assess the performance of these algorithms. The results show that, for a preliminary study and providing insights on the relevance of driving factors and similarities between events, unsupervised learning proves to be an important tool. Nevertheless, to find more applicable and in-depth associations between extreme disasters and its driving factors, more robust machine learning techniques can and should be used.

How to cite: Antunes Meira, M., Xuan, Y., and Wang, H.: Using Unsupervised Learning to Explore Landslides Driving Factors from Topographic and Hydrological Catchment Features, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6708, https://doi.org/10.5194/egusphere-egu25-6708, 2025.

Drought is one of the most severe climate-induced phenomena; with significant impacts on agriculture, water resources, and ecosystems. Drought monitoring under climate change scenarios becomes crucial, particularly in regions vulnerable to water scarcity, such as semi-arid areas in Iran. Although Global Climate Models (GCMs) contain coarse spatial resolutions, they provide valuable insights in better assessing the variability of drought characteristics—such as duration, severity, and intensity in the future. To achieve this aim, downscaling of climate variables as triggers of droughts is required to monitor drought in local scale. Latyan region in Iran, as an important area to supply water, is a critical place based on its climate, drought event occurrences, and water demand and supply stress. This study tried to accurately downscale and bias-correct the climate variables utilizing the latest CMIP6 models (ACCESS-CM2, BCC-ESM1, CanESM5, HadGEM3-GC31-LL, and MIROC6) and AI techniques in the case study. This research employs a predictor selection technique in conjunction with a stack generalization model to improve the accuracy of the downscaling process. After careful examination of predictors, surface temperature, precipitation, and surface air pressure have been used along with annual cycles for training four machine learning models including Multilayer Perceptron (MLP), Support Vector Regression (SVR), Random Forest and Stack Generalization (SG) models for the sake of downscaling. Results showed that MIROC6 model is the best model according to all downscaling methods. In addition, among MLs, stacked generalization model improved the statistical metrics considerably with a Nash-Sutcliffe Efficiency (NSE) of 0.64, Mean Squared Error (MSE) of 1051.3, and Kling-Gupta Efficiency (KGE) of 0.68 for MIROC6 model. Selection of the proper GCM and downscaling method can help decision-makers take proper measures against drought to reduce drought impacts.

How to cite: Mirdarsoltany, A., Rahimi, L., Anderson, C., and Graf, T.: Fusion of Stacked Generalization and Predictor Selection Technique for Downscaling in Drought Monitoring: A Case Study in a Semi-Arid Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7150, https://doi.org/10.5194/egusphere-egu25-7150, 2025.

Soil moisture dynamics play a crucial role in hydrological processes, influencing runoff generation, drought stress, and water management. To better understand the complex drivers of soil moisture dynamics, we present a novel hybrid architecture integrating Vision Transformers (ViT), spatial attention mechanisms, and Long Short-Term Memory (LSTM) networks. This architecture enables investigation of controlling factors across diverse landscapes in the Continental United States (CONUS) by incorporating spatial awareness at two levels: through ViT's ability to capture spatial patterns and through explicit spatial attention between neighboring stations. We leverage a comprehensive set of environmental data sources, including in-situ measurements from the International Soil Moisture Network (ISMN), ERA5 climate reanalysis, USGS elevation products, MODIS land cover, and SoilGrids soil characteristics. Initial results from a one-year training period and three-month testing period (R² = 0.73, 0.72, 0.73 for 24h, 48h, and 72h predictions) reveal important insights about the hierarchical importance of different drivers across prediction windows. Our preliminary analysis shows that static physical properties (particularly slope and soil structure) and hydraulic characteristics maintain high importance across temporal scales, while the influence of dynamic weather features varies with prediction horizon. The model's dual spatial attention mechanisms and temporal components enable discovery of both local and regional controls on soil moisture dynamics. The identified feature importance hierarchies provide initial insights into the spatiotemporal controls on soil moisture dynamics across CONUS. Ongoing work extends the training to the full temporal extent of available data to develop a more comprehensive understanding of these driving factors. This approach advances our fundamental understanding of soil moisture processes at continental scales, with implications for a future tool for land characterization and ecological site classification.

How to cite: Rahman, M., Meles, M., Bradford, S., and Nearing, G.: Drivers of Soil Moisture Dynamics over Continental United States, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7192, https://doi.org/10.5194/egusphere-egu25-7192, 2025.

Accurately predicting Snow Water Equivalent (SWE) has become increasingly crucial. It holds particular significance for managing water resources in regions heavily reliant on snowmelt. The present study introduces an integrated Long Short-Term Memory (LSTM) model that incorporates extreme heat events and diverse climate change projections to generate detailed SWE distribution maps and long-term trend analyses. By including lagged SWE observations and climate indicators, the model captures the intricate temporal dynamics of snowfall accumulation and melt processes, thereby improving forecast accuracy and stability.

Previous studies indicate that areas dependent on seasonal snowpack face accelerated snowmelt timing and reduced water availability under rising temperatures. These shifts can exert critical impacts on agricultural irrigation, ecosystem habitats, and water allocation strategies, highlighting the importance of robust forecasting tools for proactive resource management. Furthermore, the development of comprehensive risk maps pinpoints high-risk hotspots where anticipated temperature increases coincide with substantial changes in SWE and snowmelt patterns. These zones are prime candidates for early adaptation measures, including infrastructure upgrades and policy interventions aimed at mitigating potential water shortages.

As global warming persists, this modeling framework provides stakeholders, policymakers, and local communities with valuable insights into emerging water resource risks. The integration of climate change scenarios into the LSTM model underscores the necessity of forward-looking research that can inform both short-term operations and long-term planning. Ultimately, this approach lays the groundwork for crafting sustainable adaptation strategies, preserving agricultural output, protecting ecosystems, and ensuring water security in regions where snowmelt is pivotal to resource availability.

How to cite: Tsang, P. J. and Tsai, W. P.: Risk Mapping and Adaptation Strategies: Enhancing SWE Predictions with an LSTM Model for Snowmelt-Dependent Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7201, https://doi.org/10.5194/egusphere-egu25-7201, 2025.

South America holds vast freshwater reserves, contributing to its global prominence across various sectors. Understanding streamflows at different levels—minimum flows for ecosystem maintenance, mean flows for hydropower and navigation, and high flows associated with floods—is critical for ensuring societal and ecological resilience. These streamflows are influenced by changes in catchment characteristics and climate change, yet the relationship between climate and catchment drivers with streamflows, particularly in tropical regions, remains poorly understood. Recent advances in explainable artificial intelligence (XAI) offer promising avenues for addressing these gaps by linking observational data to potential causal inference. Here, we investigated the climatic and catchment drivers influencing five streamflow types (Q₁, Q₅, Q_mean, Q₉₅ and Q₉₉) across 735 Brazilian watersheds using XAI approaches. Random Forest models were trained with 16 most important attributes for each streamflow type. SHapley Additive exPlanations were applied to explain the directionality and magnitude of each driver's impact, while inflection points were delineated to capture critical thresholds for streamflow changes. Results showed the aridity index (potential evapotranspiration/precipitation) as the most impactful predictor globally, likely due to its role in long-term water balance. However, for Q₉₉, soil sand content emerges as the dominant factor, showing that catchment characteristics rival climatic factors in importance for rare streamflow events. The analysis highlighted critical thresholds, such as reductions in streamflow when the aridity index exceeds 1.30 and potential declines in streamflow for soil carbon content below 30%, likely due to reduced water infiltration and storage capacity. Similarly, forest cover below 40% potentially increases streamflows, possibly due to reduced evapotranspiration and water retention in soils. Regional differences were also observed: in central Brazil, land cover and land use, and topography potential response for decreased the low streamflows, while in the south and northeast, climatic factors such as aridity and precipitation seasonality control the potential decreases. Rare high events (Q₉₉) in the south this watershed scale attributes height above the nearest, permeability and porosity potential increases the magnitude of events. These findings highlight that, while climatic attributes dominate streamflow relationships at a national scale, regional variations underscore the importance of catchment characteristics. This study demonstrates how data-driven models have the potential to capture the complex interplay between climatic and catchment attributes, linking these factors to streamflow dynamics.

How to cite: Brandão, A., Husic, A., Almagro, A., Schwamback, D., and Oliveira, P.: Climate and catchment influences on streamflows in Brazilian watersheds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10531, https://doi.org/10.5194/egusphere-egu25-10531, 2025.

The accurate estimation of crop evapotranspiration (ETc), root zone soil moisture depletion, and irrigation demands is critical for optimizing water resource management and enhancing sustainability in precision agriculture. The FAO-56 model serves as a foundational tool for these predictions; however, its conventional workflow necessitates the manual acquisition of essential inputs such as climatic data and soil moisture from disparate external sources. This process can be time-intensive, cost-prohibitive, and susceptible to human error. Furthermore, the deterministic nature of FAO56 can lead to inaccuracies if reference evapotranspiration and crop coefficients are not meticulously estimated. This study introduces NeuralFAO56, a Python package that integrates advanced machine learning models and real-time data acquisition with the FAO-56 framework to automate and improve the estimation of ETc and irrigation demands. By leveraging application programming interfaces (APIs) to automatically collect real-time climatic data from meteorological stations and NASA’s Soil Moisture Active Passive (SMAP), NeuralFAO56 dynamically updates model inputs. The package incorporates a range of machine learning models, including Long Short-Term Memory (LSTM) and transformer architectures, to generate data-driven ETc estimations, thereby enhancing the accuracy and adaptability of irrigation predictions. NeuralFAO56 is designed with a modular architecture, enabling users to customize its functionalities for diverse agro-hydrological contexts. This tool provides a robust, user-friendly platform for researchers, water resource managers, and agricultural professionals, facilitating intelligent irrigation decision-making, improving water-use efficiency, and contributing to sustainable agricultural practices.

How to cite: Neupane, A. and Samadi, V.: A NeuralFAO56 Python Package for data-driven Irrigation Demand Calculation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13200, https://doi.org/10.5194/egusphere-egu25-13200, 2025.

The São Francisco River Basin is crucial for Brazil’s agriculture, hydropower, and water security. However, climate change has intensified challenges like reduced water flow and frequent extreme events, threatening its socio-economic sustainability. This study aims to forecast flow in the São Francisco River Basin, enabling proactive decision-making to mitigate risks associated with both droughts and floods. To address these challenges, this study propose a novel methodology based on Artificial Intelligence (AI), combining Recurrent Neural Networks (RNN) and complex network techniques. The method creates new features and assigns importance weights to enhance the algorithm’s ability to generate probabilistic flow forecast. The results are promising, demonstrating the method’s ability to deliver accurate probabilistic forecasts. This research can support risk mitigation strategies and improve water resource management in the São Francisco Basin. Additionally, the proposed framework is scalable, offering potential applications to other critical watersheds facing similar challenges

How to cite: Caseri, A., Aparecido Rodrigues, F., and Victal Cerqueira, M.: Ensemble Approach for Hydrological Forecasting Based on Recurrent Neural Networks and Complex Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13991, https://doi.org/10.5194/egusphere-egu25-13991, 2025.

Flood forecasting is essential for hydrological assessment and catastrophe mitigation, particularly in flood-prone areas such as Bangladesh. Nonetheless, the direct measurement of water levels (WL) and discharge frequently encounters obstacles related to time, technological limits, and economical constraints. This study posits that flood levels can be accurately predicted utilising accessible data during flood events, employing a trained Artificial Neural Network (ANN) model. The complexity of hydrological systems, exacerbated by transboundary contributions from significant rivers like the Brahmaputra-Jamuna, hinders accurate forecasting. To tackle these problems, the study employed Artificial Neural Networks (ANN), a flexible and data-driven methodology adept at modelling non-linear relationships, to predict flood water levels with a lead time of up to seven days in Sirajganj, a district particularly susceptible to river flooding and bank erosion. Daily Data on water levels and rainfall were collected from the Bangladesh Water Development Board (2002–2015) for the monsoon season (May–October) were analysed, utilising information from four rainfall stations and six water level stations located 62–237 km upstream. The ANN model, employing a Sigmoid activation function with one to three hidden layers, indicated that augmenting the number of hidden layers provided only negligible enhancements in performance. Performance metrics, such as the goodness-of-fit (R²: 0.985–0.554), Root Mean Square Error (RMSE: 0.024–0.617), and Mean Absolute Error (MAE: 0.087–0.604), demonstrated a marginal improvement when rainfall and water level data were combined. This study highlights the efficacy of Artificial Neural Networks (ANN) in tackling hydrological prediction issues, confirming its ability to utilise readily accessible datasets to provide reliable and effective flood forecasts, thus aiding disaster preparedness and mitigation efforts in resource-limited areas such as Bangladesh.

How to cite: Das, P. and Mohib, K. M.: Data-Driven Flood Forecasting Using ANN: A Resource-Efficient Approach for High-Risk Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14431, https://doi.org/10.5194/egusphere-egu25-14431, 2025.

Predicting streamflow in ungauged basins remains a significant challenge in hydrological studies. In recent years, data-driven models have been shown to outperform traditional physics-based models in streamflow prediction for ungauged catchments. However, few studies have examined the potential of such models for predicting streamflow in ungauged basins within India. This study aims to evaluate the performance of two machine learning models, namely Support Vector Regression (SVR) and Random Forest (RF), alongside two deep learning models, Long Short-Term Memory (LSTM) and Bi-LSTM, in the context of streamflow regionalization within the Krishna River Basin in India. Each prediction model is trained using meteorological variables as input features, with streamflow as the output variable. K-means clustering is employed to group selected catchments (based on data availability) into an optimum number of clusters based on spatial proximity and physical similarity. It is assumed that catchments within a cluster share homogeneous characteristics. Regionalization is achieved by sharing model parameters across catchments within the same cluster. For each cluster, one gauged catchment is designated as the donor catchment, while the others are treated as pseudo-ungauged. Each proposed model is trained and tested using the meteorological inputs and streamflow data available at the gauged donor catchment. The trained model for each cluster is then transferred to the remaining receptor catchments within the cluster, where the meteorological variables corresponding to each ungauged catchment are used as inputs. The performance of the models in ungauged catchments is rigorously evaluated by comparing the simulated streamflow against observed streamflow using metrics such as Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Coefficient of Determination (R²), and Percentage Bias (PBIAS). This study highlights the advantages of utilizing data-driven methods for streamflow prediction in both gauged and ungauged basins, particularly due to their ability to capture complex, non-linear relationships between meteorological inputs and streamflow generation. The findings of this study are expected to be instrumental in water resources planning and management, flood assessment, and the design of hydraulic structures in the Krishna River Basin.

How to cite: Kaur, S. and Chavan, S.: Data-driven models for streamflow regionalization in Krishna River Basin, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15731, https://doi.org/10.5194/egusphere-egu25-15731, 2025.

Runoff forecasting remains a critical challenge in many basins worldwide, particularly those featuring a complex topography, where the scarcity of hydrometeorological data is a prevalent challenge. Data fusion offers a promising alternative to conventional single-source data modelling, which often fails to capture the full spatial and temporal variability of precipitation. By integrating multiple sources, data fusion seeks to generate enhanced satellite precipitation datasets, essential for data-driven runoff forecasting models. This study aims to evaluate the effectiveness of the Three-Cornered Hat (TCH) method for fusing satellite precipitation products (SPPs) and its influence on the performance of a Random Forest-based runoff forecasting model.

Three scenarios were evaluated: (i) a TCH-fused dataset combining three SPPs: Integrated Multi-satellitE Retrievals for GPM – Early Run (IMERG-ER), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Cloud Classification System (PERSIANN-CCS) and the Global Satellite Mapping of Precipitation – Near Real Time (GSMaP-NRT); (ii) an individual SPP (IMERG-ER); and (iii) an already fused benchmark product, the Multi-Source Weighted-Ensemble Precipitation (MSWEP). All scenarios performed comparably for lead times of 3, 6, 12, and 24 hours, with MSWEP slightly outperforming across Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency, and Root Mean Square Error metrics. However, TCH demonstrated better bias reduction as reflected by the Percent Bias metric.

A key limitation of the fusion method was identified at hourly scales, where statistical dependence arises during periods with no precipitation over the basin, hindering the effectiveness of TCH. The introduction of a matrix regularization step addressed this issue. This study provides valuable insights for enhancing SPP fusion methods and offers a replicable framework for improving runoff forecasting, particularly in data-scarce regions and other hydrological contexts.

How to cite: Luna-Abril, P., Muñoz, P., Samaniego, E., Muñoz, D. F., Merizalde, M. J., and Célleri, R.: Evaluating the Three-Cornered Hat Method for Satellite Precipitation Data Fusion and its Influence on Runoff Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16242, https://doi.org/10.5194/egusphere-egu25-16242, 2025.

The release of low-temperature water from a reservoir can have negative impacts on downstream fish spawning and crop growth in irrigation areas. Therefore, predicting the discharged water temperature accurately and swiftly is crucial. This study focused on the Pubugou Hydropower Station, a major project situated on the Dadu River in the upper reaches of the Yangtze River, and evaluated the impacts of meteorological factors and reservoir operational parameters on the released water temperature using Spearman correlation coefficients (R). To predict the discharged water temperature of Pubugou Reservoir, five models were optimized by genetic algorithms including random forests, support vector regression, convolutional neural network, long short-term memory network, and the lightweight gradient boosting machine respectively. The results showed that: (1)The dew point temperature exhibited the highest correlation with discharged water temperature (R = 0.89), However, the correlation coefficient between wind speed, cloud cover, solar radiation, dam front water level, and discharge water temperature was not found to be 0.4. (2) All the five models optimized by genetic algorithms performed well on the training set, especially the random forest model (R² = 0.997). The worst performing model is the long short-term memory network model (R² = 0.985). (3) In the prediction of discharge water temperature, all models have good fitting effects, with r² greater than 0.93, average absolute error not greater than 0.662 ℃, and mean square error not greater than 0.852 ℃. Random forest models and lightweight gradient boosting machine models have shown good performance on the most of sample data, with a small residual range, while support vector regression models and convolutional neural network models have smaller maximum residuals. This research indicated that machine learning methods can effectively predict water temperature released from reservoirs, providing more reliable decision support for formulating relevant measures to alleviate the impact of reservoir discharge water temperature.

How to cite: Junguang, C.: Application of Machine Learning in Predicting the Water Temperature Released from Reservoirs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18342, https://doi.org/10.5194/egusphere-egu25-18342, 2025.

The prior literature on hydrologic model performance is dispersed, encompassing a small number of catchments, different methodology, and rarely linking the results to specific catchment characteristics. This study addresses these constraints by systematically attributing model performance to catchment variables in 671 US catchments, providing a formal framework for determining the best models for specific conditions. Daily streamflow estimation was performed using eight process-based (PB) models and three deep learning (DL) models, with performance measured using the Nash-Sutcliffe Efficiency (NSE). The PB models were tested with a variety of optimization techniques, and the most effective approach for each model was chosen based on the number of catchments that exceeded a predetermined performance threshold. Four models were selected as the top performers based on three performance metrics. Further analyses, such as Classification and Regression Tree (CART) and SHAPley, were used to correlate model performance with catchment variables across all models.
The results showed that PB models (GR4J, HBV, and SACSMA) performed well in catchments with low to medium aridity and a high Q/P ratio, indicating quick hydrologic responses. In contrast, the LSTM-based DL model performed well in medium to high aridity regions but had limits in catchments with rapid precipitation responses and low sand percentages. These findings provide a thorough understanding of the links between model performance and catchment descriptors.

Keywords: Process-based models, Deep learning model, CART analysis, SHAPley analysis, catchment characteristics.

How to cite: sourya, D. A., manikanta, V., and rathinasamy, M.: Can the catchment features influence the performance of the conceptual hydrological and deep learning models? A study using large sample hydrologic data , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18458, https://doi.org/10.5194/egusphere-egu25-18458, 2025.

Knowing evapotranspiration (ET) accurately at fine spatial scales is very important. This would improve understanding hydrological processes and contribute to the advancement of water resource management. In this study, we set a framework based on deep learning to downscale Terra Net Evapotranspiration Gap-Filled 8-Day Global 500m dataset, developed and managed by NASA's Earth Observing System. This approach resulted in a scale enhancement of 20 times. The U-Net architecture was used for this purpose. It incorporated MODIS Land Cover Type 1 (LC Type 1) as an auxiliary variable. This was done to account for land cover changes. The study covered a diverse region that encompasses latitudes 28° to 32°N and longitudes 74° to 78°E. A synthetic design of experiments was utilized to systematically generate and evaluate training data, this ensures robust model performance and reliable downscaling outcomes across the heterogeneous terrain of the study area. Model training, validation, and testing were conducted using the 2001–2014 dataset, 2015–2018, and 2019–2023 dataset, respectively. The model showed excellent performance on the testing dataset. The average PSNR was 34.35 dB and the mean SSIM was 0.8517. The U-Net module effectively downscale and enhance the spatial resolution of ET data. The results show ET's spatial and structural features are well preserved. This study shows how deep learning improves climate data spatial resolution. It provides reliable local hydrological and agricultural resources.

How to cite: Jha, S. K. and Gupta, V.: Downscaling MODIS ET using deep learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18599, https://doi.org/10.5194/egusphere-egu25-18599, 2025.

In a mountainous watershed, there are many confluences at which two or more streams join. Due to inaccessible terrain and associated costs, river discharge data is collected only at a few confluences. It is, therefore, important to assess which confluence is critical. By critical, we mean the junction which will create maximum fragmentation in a river network. In this study, we analysed river networks with uneven topography in the Alaknanda River basin, which is vulnerable and prone to geo-hydro hazards. We applied Unsupervised Machine Learning (UML) algorithms such as Isolation Forest, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Linear Integer Programming (LIP) to identify the critical confluence locations. We compare our results with the well-established graph-based centrality metrics (Degree centrality, Betweenness centrality, Closeness centrality, and Eigen Vector Centrality). Our results suggest that DBSCAN outperformed other approaches in terms of detecting crucial nodes. We obtained better results using LIP than other techniques except DBSCAN. The outcome of this study will help the Central Water Commission, in deciding which confluence to focus on, and in assessing the locations of new gauges.

Keywords: Critical nodes; Alaknanda Basin; Machine Learning; Hazards

How to cite: Rajouria, N., Parajapati, P., and Jha, S. K.: Application of Unsupervised Machine Learning Algorithms for identifying critical river confluence in a mountainous watershed., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19050, https://doi.org/10.5194/egusphere-egu25-19050, 2025.

Accurate and timely representation of spatiotemporal precipitation patterns is critical for monitoring and predicting hydrological extremes, particularly in operational hydrology and early warning systems. In regions with limited in-situ precipitation data, satellite precipitation products (SPPs) offer an accessible solution. However, the latency of these datasets—the delay between data collection and availability—remains a key challenge for real-time applications. This study developed a machine learning model based on the Random Forest (RF) algorithm to predict precipitation using low-latency data from GOES-16 Advanced Baseline Imager (ABI) bands. The model was applied to the Jubones River basin (3,391 km²) in southern Ecuador, a region characterized by complex terrain and hosting a key hydropower project. Leveraging hourly data over a five-year period, the RF model addressed the five-hour latency of traditional SPPs by generating near-real-time precipitation maps with a latency of only 10 minutes. The model’s performance was evaluated using quantitative and qualitative metrics across temporal scales, demonstrating progressive accuracy improvements with larger temporal aggregations. Root Mean Square Error (RMSE) values decreased from 0.48 to 0.05 mm/h, while Pearson’s Cross-Correlation (PCC) improved from 0.59 to 0.87 for scales ranging from hourly to monthly. Qualitative metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI), further validated the approach. These findings highlight the potential of integrating advanced hydroinformatics techniques with remote sensing for managing hydrological extremes in diverse basins. The study underscores the importance of leveraging low-latency satellite data and machine learning to enhance real-time forecasting and operational hydrology. Future work will focus on refining the model for improved detection of extreme precipitation events and exploring its integration into stakeholder-driven decision-making frameworks.

How to cite: Muñoz, J., Muñoz, P., Muñoz, D. F., and Célleri, R.: Leveraging machine learning and satellite precipitation data to overcome latency challenges in operational hydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19086, https://doi.org/10.5194/egusphere-egu25-19086, 2025.

About 90% of extreme precipitation in the midlatitudes can be assoicated with front boundaries. Therefore, it is important to identifiy frontal locations for short term weather forecasting or long-term prediction of precipitation in climatology. Deep learning (DL) refers to machine learning alogrithms that use multiple layers of neural networks to derive features from input data. It is generaly useful to process 2-dimensional image data. One of the potential advantages of employing DL to detect surface weather fronts is that the developed DL based functions can be applied to automatic detection of surface weather fronts for climate models. However, justifications of its applicability on climate models are needed.

In this study, we developed deep learning based methodology to detect surface weather fronts. Specifically, a U-shape convolutional network (U-net) based deep learning model is developed to predict surface weather fronts over Japan and surrounding sea in summer (June, July, and August). We justify the applicability of the deep learning model in predicting surface fronts in summer on outputs from large-scale Global Climate Models (i.e. GCMs) from two aspects. First, the coarse resolution of GCMs (e.g., 1.25 degrees) can capture the general morphological features of surface fronts. Second, models trained in a colder climate are applied to predict fronts in a warmer climate with some decrease in predicted peak frequency of fronts, but the general features of the spatial distribution of fronts can be represented by the deep-learning model predictions. We also see that the locations of peak frequency tend to move slightly more southwesterly in a slant zone within the belt region between 25N to 40N as climate warms in the future.

How to cite: Mao, Y. and Yamada, T.: Applicability of deep learning based detection of surface weather fronts on large scale climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19530, https://doi.org/10.5194/egusphere-egu25-19530, 2025.

Understanding and modeling surface and groundwater resources are critical due to the effects of droughts and climate change, especially in semi-arid, arid, or hyper-arid regions. GeoLinkage, developed by Troncoso (2021), facilitates the creation of linkage files for integrated models. These linkage shapefiles act as a communication interface between a surface hydrological domain (D₁) and an aquifer domain (D₂). The surface domain (D₁) comprises nodes and arcs that represent hydrological elements and their relationships, while the aquifer domain (D₂) contains geometric elements such as grids or Quadtree diagrams. D₁ defines a surface topology (τ₁), D₂ defines a groundwater topology (τ₂), and the linkage file establishes a surface-groundwater topology (τ_1-2). This new topology, τ_1-2 ,imposes constraints that influence the relationship between τ₁ and τ₂. For instance, the superposition of elements in τ_1-2 should be considered a spatial relationship. Depending on the type of superposed elements, this relationship must be reflected in τ₁  or τ₂. To enforce these τ_1-2 specific restrictions, GeoLinkage has been enhanced with a post-processing module called GeoChecker. This module evaluates the quality of the resulting linkage files. GeoChecker currently performs a superposition check to ensure that overlaps between cells in the linkage file—whether between groundwater and catchments or groundwater and demand sites—are accurately represented as connections in the surface model (WEAP). The aquifer is represented by a MODFLOW model fully linked to the WEAP model. GeoLinkage2.0 and GeoChecker were developed using the tutorial WEAP-MODFLOW model, considered a small model, and were tested in large integrated models, such as the Azapa Valley (3,000 km²) and the Limarí River Basin (12,000 km²), Chile.

How to cite: Sanzana, P., Torga, A., Hitschfeld, N., and Lobos, C.: GeoLinkage2.0 and GeoChecker: Hydroinformatics tools for large and complex hydrological-hydrogeological models using WEAP-MODFLOW. Case Study: Severe drought in the Limarí River Basin, Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20678, https://doi.org/10.5194/egusphere-egu25-20678, 2025.

Digital twins, virtual representations of physical systems, integrate sensor data and predictive models to enable real-time simulation and analysis. They are instrumental in monitoring weather, infrastructure health, and water levels, particularly in flood management. By modeling mitigation techniques, forecasting risks, and enhancing emergency responses, digital twins improve decision-making, reduce economic losses, and enhance public safety in flood-prone areas [1][2]. This study developed a digital twin system to monitor and forecast flood disasters in western Iran. The system combined multidimensional sensor data on temperature, flood flow, vegetation cover, and water levels using an offline databank. Time-series analysis tracked trends, while a linear regression-based predictive model estimated future flood conditions. Threshold values for flood warnings and high-risk alerts were defined using hydrological principles and environmental data [3]. Game theory concepts were employed to optimize flood management strategies by modeling interactions among stakeholders, including authorities, responders, and communities. A non-cooperative game theory approach simulated conflicting objectives, such as minimizing economic losses and optimizing resource allocation. Stable solutions were identified through the Nash equilibrium, ensuring no stakeholder could unilaterally improve outcomes. Visualization dashboards presented time-series data, risk levels, and stakeholder strategies, facilitating informed decision-making. Simulation results demonstrated the system's effectiveness in flood risk assessment. Water levels remained below the 2.5-meter warning threshold but rose significantly during simulated abnormal conditions. In later stages, some areas approached the 3.0-meter high-risk threshold, indicating zones vulnerable to flooding. Flood flow rates frequently exceeded the 40 m³/s threshold, with peaks above 60 m³/s, highlighting the need for continuous flow monitoring. Temperature fluctuations were minimal, consistently below the 25°C threshold, suggesting limited influence on flood risks during the study. However, vegetation cover often fell below the 30% threshold, correlating with increased flood risks and reinforcing its importance in mitigation. The system effectively categorized risk levels, with most instances classified as "Normal" or "Warning." High-risk alerts were concentrated during elevated water levels and flows. This research highlights the potential of digital twins for real-time flood monitoring and collaborative decision-making, providing a robust framework to enhance disaster resilience.

Keywords: Digital Twin; Flood Risk Assessment; Game Theory; Predictive Modeling; Multidimensional Data Analysis.

References

[1] Ghaith, M., Yosri, A., & El-Dakhakhni, W. (2021, May). Digital twin: a city-scale flood imitation framework. Canadian Society of Civil Engineering Annual Conference (pp. 577–588). Singapore: Springer Nature Singapore.

[2] Gheibi, M., & Moezzi, R. (2023). A Social-Based Decision Support System for Flood Damage Risk Reduction in European Smart Cities. Quanta Research, 1(2), 27–33.

[3] Kreps, D. M. (1989). Nash equilibrium. In Game Theory (pp. 167–177). London: Palgrave Macmillan UK.

How to cite: MohammadZadeh, F., Eghbalian, H., Gheibi, M. G., Yeganeh-Khaksar, R., Ghazikhani, A., and Behzadian, K.: A Digital Twin Framework for Real-Time Flood Monitoring and Multidimensional Prediction: A case study in Iran, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20713, https://doi.org/10.5194/egusphere-egu25-20713, 2025.

The increasing frequency and intensity of climate hazards, as emphasized by the IPCC’s Sixth Assessment Report, underscore the urgent need for effective disaster risk reduction strategies. Using the devastating floods of September 2024 in Nepal’s Kathmandu Valley, and the April 2024 floods in Kenya’s Nairobi, this study examines the persisting gaps in flood resilience despite early warnings using disaster forensics techniques. The Kathmandu floods, which were triggered by an extreme rainfall event resulting from the convergence of a low-pressure system from the Bay of Bengal and a cyclonic circulation from the Arabian Sea, caused extensive loss of life, property damage, and economic disruption in the Nakhu Khola watershed, despite timely early warnings issued by the government. In Kenya, a notable gap exists in the warning systems, whether in their issuance, dissemination, or uptake, despite the presence of advanced operational forecasting systems. Encroachment on floodplains, unplanned urbanization, and land-use changes have exacerbated vulnerability, while weak governance and poor enforcement of disaster risk management legislation has left populations and assets exposed. Additionally, risk assessment efforts are scarcely integrated into government plans or those of other stakeholders, highlighting a critical area for improvement in disaster preparedness and management.

Using the UNDRR’s forensic disaster analysis framework, this research investigates the underlying causes, risk drivers, and lessons from these events. The populations most affected are identified, including those living in floodplains, including marginalized communities, and critical infrastructure. Local investments in disaster preparedness are also critically examined for efficacy. Results highlight that while early warnings were disseminated through various channels, gaps in risk communication and community-level preparedness persisted. The findings emphasize the need for education and awareness and integrated approaches to disaster risk management that address root causes such as unplanned urban growth and environmental degradation. Empowering youth and fostering leadership in disaster risk reduction are critical to ensure climate resilient societies of tomorrow. This research contributes actionable insights to reduce vulnerability, enhance preparedness, and minimize losses in future climate hazard events in the Kathmandu Valley and Kenya, as well as similar rapidly urbanizing regions.

How to cite: Kettner, A., Gupta, A., Singh Shrestha, M., Trigg, M., Cohen, S., Hawker, L., Prades, L., Rudari, R., Salamon, P., Tellman, B., Sperna Weiland, F., and Wu, H.: Devastating Flooding Despite Early Warning: Lessons Learned from the Nepal and Kenya Floods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21199, https://doi.org/10.5194/egusphere-egu25-21199, 2025.

Analyzing deep soil water use (DSWU) response to precipitation change and its impact on tree physiology is necessary to disentangle tree mortality mechanisms, especially in drylands. In this study, a process-based model parameterized with in-situ measured fine root distribution data for 0-2000 cm and a root-cutting (below 200 cm) numerical experiment were used to explore DSWU strategies across different precipitation years and its contribution to total water consumption, as well as its relationship to tree gas exchange traits in mature apple (Malus pumila Mill) and black locust (Robinia pseudoacacia L.) plantations in both a wetter (Changwu, 583 mm) and a drier (Yan’an, 534 mm) sites on China’s Loess Plateau. Results showed that DSWU at 200-2000 cm depth in different precipitation years of both species mainly occurred during the early growing seasons. On average, DSWU contributed 22.9% and 25.1% to the total water consumption of apple trees and black locust, respectively, and its contribution increased to 26.0% and 36.7% in extremely dry years. Moreover, the lack of DSWU significantly decreased (p<0.05) stomatal conductance (by 16.9%, 16.9%, 47.4% and 11.4%, respectively) and photosynthetic rates (by 37.1%, 20.1%, 28.5% and 16.4%, respectively) of Changwu apple trees, Yan’an apple trees, Changwu black locust and Yan’an black locust in extremely dry years. Similar reductions occurred only in Yan’an for both tree species in normal years. In contrast, no significant differences were found in gas exchange traits in extremely wet years. Our results highlight that DSWU is an important strategy for plantations in deep vadose zone region to resist extreme drought.

How to cite: Zhao, X., Shao, X., and Gao, X.: Deep soil water use can compensate drought effect on gas exchange in dry years than in wet years for dryland tree plantations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1681, https://doi.org/10.5194/egusphere-egu25-1681, 2025.

Per- and polyfluoroalkyl substances (PFAS) represent a significant and persistent threat to water quality worldwide, posing major challenges due to their chemical stability, resistance to conventional treatment methods, and documented health risks. These contaminants, once released, persist in the environment for extended periods and have been detected in drinking water, surface water, groundwater, and even human blood. Conventional remediation techniques, such as granular activated carbon (GAC) adsorption, ion-exchange resins, and reverse osmosis, often struggle with shorter-chain PFAS compounds and merely shift contamination from one medium to another. As climate change intensifies rainfall and extreme weather, PFAS transport through runoff becomes increasingly likely, heightening the need for advanced treatment solutions.

In response to this pressing need, our work investigates an innovative remediation approach employing far-UVC radiation (222 nm) delivered by krypton chloride (KrCl*) excimer lamps. Unlike conventional low-pressure UV (LPUV) systems, which typically emit at 254 nm, or vacuum UV (VUV) systems at 185 nm, far-UVC at 222 nm offers a unique balance of high photon energy and minimal absorption by water. This balance enables deeper penetration into the water matrix and provides the potential for enhanced PFAS photolysis and subsequent defluorination. Preliminary findings indicate that certain PFAS, previously resistant to direct UV photolysis, may be more susceptible under far-UVC irradiation, thereby opening a promising new pathway for their degradation.

While direct photolysis at 222 nm shows considerable promise, integrating far-UVC treatment with electrochemical oxidation (EOP) and UV-advanced reduction processes (UV-ARP) can further enhance PFAS degradation. EOP effectively removes dissolved organic matter (DOM), which often competes with PFAS for reactive species, thus reducing the overall efficiency of PFAS degradation. Meanwhile, UV-ARP generates highly reactive hydrated electrons (e_aq⁻) capable of breaking down PFAS. Although adding sulfide ions is one way to produce e_aq⁻, applying a sufficiently negative potential at a GAC cathode can also generate e_aq⁻ without introducing sulfur species. This approach requires careful consideration of competing hydrogen evolution reactions (HER), which may be thermodynamically unfavorable at a GAC cathode. By combining EOP with in situ e_aq⁻ generation in UV-ARP, PFAS can be more effectively targeted and degraded without adding extra chemicals. This integrated treatment aims to meet or surpass stringent U.S. Environmental Protection Agency (EPA) standards, ultimately facilitating the development of a portable, cost-effective, chemical-free, point-of-use water treatment system. Such a system would be especially valuable for communities experiencing environmental vulnerabilities, such as those in Puerto Rico studied by the PROTECT Center at Northeastern University, where limited infrastructure, contaminated water sources, and heightened susceptibility to adverse health outcomes underscore the urgency for sustainable PFAS remediation solutions.

By advancing the understanding of PFAS photolysis under far-UVC radiation and harnessing the combined power of EOP and UV-ARP, this work endeavors to provide an innovative solution. In doing so, it seeks not only to bridge the gap between laboratory research and practical application but also to enhance the resilience of water treatment systems against emerging contaminants and the challenges posed by a changing climate.

How to cite: Arekhi, M., Ehsan, M. F., and Alshawabkeh, A.: Enhancing PFAS Degradation through Far-UVC Photolysis Coupled with Electrochemical Oxidation and UV-Advanced Reduction Processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2175, https://doi.org/10.5194/egusphere-egu25-2175, 2025.

Accurately capturing rainfall patterns is crucial for hydrometeorological applications, particularly in regions like Burkina Faso, West Africa, where rainfall variability significantly impacts water resources and agricultural productivity. However, challenges remain in identifying the most reliable rainfall products for such purposes. This research evaluates the effectiveness of satellite precipitation products (SPPs) and soil moisture-derived rainfall products (SM2RPPs) in representing rainfall patterns in Burkina Faso. Results show that SPPs generally perform better than SM2RPPs across daily to annual timescales. An analysis of total bias components highlights that hit biases dominate but are more pronounced in SM2RPPs. Systematic errors contribute significantly to these hit biases, indicating the potential for improvement through bias correction. Wavelet analysis reveals that both SPPs and SM2RPPs capture seasonal and annual rainfall variability effectively. However, all products exhibit limitations in accurately representing extreme rainfall indices, although SPPs demonstrate superior performance compared to SM2RPPs. While SM2RPPs currently underperform relative to SPPs in Burkina Faso, they show promise for hydrometeorological applications and could achieve comparable or improved results with enhanced bias correction techniques.

How to cite: Yonaba, R., Belemtougri, A., Fowé, T., Mounirou, L. A., Nkiaka, E., Dembele, M., Akpoti, K., Coly, S. M., Koïta, M., and Karambiri, H.: Rainfall Estimation in West Africa: A Performance Comparison of Satellite and Soil Moisture-Derived Products, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2576, https://doi.org/10.5194/egusphere-egu25-2576, 2025.

Concerning water supply in mountainous regions where surface water plays an important role, the understanding of lake stratification or even hypolimnia can be important for water treatment actions.

The historical dam reservoirs were used for the continuous water supply to the ore mines in the Upper Harz Mountains. The first reservoirs were built in the 16th century. The dam heights reach up to 15 m and the stored water volumes are between 10,000 and 600,000 m³. There are about 70 of such lakes around Clausthal-Zellerfeld. Today only few of them are directly used for drinking water supply in the surrounding communities.

Hydrogeochemical data of the lakes have been investigated for about ten years. The specific electrical conductivity (SEC) of the lakes’ surface water ranges between 30 and 280 µS/cm (Bozau et al., 2015, Schäfer et al. 2024). Three lakes (Kiefhölzer, Langer and Oberer Grumbacher Teich) differing in chemical composition and morphometry (area, mean depth and maximum depth) were selected for the investigation of seasonal changes in the water columns. Samples were taken by boat with a Ruttner sampler. SEC and pH were measured on the boat. The titration for HCO₃ was done directly after sampling. The main ions were analyzed by ion chromatography and the trace elements by ICP-MS.

Stratification during summer could be clearly observed in all of the three lakes. The degradation of organic material and accompanying redox reactions are seen in the measured pH, SEC, HCO₃^-, Fe(II), NO₃^-, NH₄⁺ and SO₄^2- concentrations. Each lake showed a characteristic temporal and chemical behaviour. The development of an anoxic hypolimnion above the lake sediments was obvious in the two shallower lakes Langer Teich (max. depth ~ 5 m) and Kiefhölzer Teich (max. depth ~ 7 m) as being accompanied by H₂S-odor in the water column starting ~ 1 m above sediment.  This feature was absent in the deepest lake Oberer Grumbacher Teich (max. depth ~ 9 m), which also showed weaker increase of SEC and HCO₃^- in the profile. The aeration of the hypolimnion started in autumn leading to a well mixed, chemically uniform water column. 

Bozau, E., Licha, T., Stärk, H.-J., Strauch, G., Voss, I., Wiegand, B. (2015): Hydrogeochemische Studien im Harzer Einzugsgebiet der Innerste. Clausthaler Geowissenschaften, 10, 35-46.

Schäfer, T., Bozau, E., and Hutwalker, A.: Reservoir lakes in the Upper Harz Mountains (Germany): GIS Implementation and hydrochemical development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5085, https://doi.org/10.5194/egusphere-egu24-5085, 2024.

How to cite: Schäfer, T., Bozau, E., and Hutwalker, A.: Seasonal Changes in Water Columns of Historical Reservoir Lakes in the Upper Harz Mountains (Germany), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2730, https://doi.org/10.5194/egusphere-egu25-2730, 2025.

Probable Maximum Precipitation (PMP) is a key input in the design and risk assessment of critical infrastructures such as large dams and nuclear power plants. Traditionally, PMP is computed as a fixed upper bound of the precipitation assuming a stationary climate. However, due to climate change, the stationarity assumption may not remain valid in the future. Limited attempts have been made in the past to develop methods for estimating PMP by accounting for non-stationarity in the related hydroclimatic variables. In view of shortcomings associated with those methods, three new nonstationary models are proposed and their potential in determining PMP in a changing climate is illustrated through application to three major flood-prone river basins in India. In this analysis, historical records of precipitation, surface temperature and relative humidity, and their future projections corresponding to eleven CMIP-6 SSPs (Coupled Model Intercomparison Project-6 Shared Socio-economic Pathways) were utilized. The results indicate that PMP estimates obtained using the proposed nonstationary models are significantly higher than those obtained from their underlying conventional stationary model, especially for high-emission scenarios in the near future. The results obtained from this study could be utilized to update historical PMP values and to determine the increase in risk associated with the corresponding probable maximum flood.

How to cite: Bhatt, J. and Srinivas, V. V.: Revising probable maximum precipitation (PMP) estimates under changing climate , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4786, https://doi.org/10.5194/egusphere-egu25-4786, 2025.

The growing need for environmentally friendly wastewater treatment technology has prompted researchers to look into natural alternatives. Among these, algal-bacterial systems have received attention for their capacity to combine biological treatment and biomass production. This study focuses on the use of algal-bacterial flocculent biomass for wastewater treatment in a 400 L pilot-scale raceway pond, with a focus on its potential as a sustainable option for lowering environmental impacts. The synergistic interactions between algae and bacteria in the consortia improve nutrient removal from wastewater, while also providing biomass for future use. The aim was to develop a high-concentration flocculent algal-bacteria biomass. The raceway system was placed in a greenhouse with water temperature 32±8^oC for about 230 days. The pilot-scale experiment evaluates treatment efficiency of domestic wastewater in a batch mode procedure.  The removal of chemical oxygen demand, ammonia, nitrate, and total phosphorus was over 95%.  The biomass concentration stabilized at about 4 g/L after 70 days of operation. The implementation of algal-bacteria flocculent processes for the treatment of domestic or source-separated domestic wastewater shows great promise as a low-cost, sustainable, and efficient solution.

How to cite: Kakavas, D., Biliani, S., and Manariotis, I.: Long-term behavior of syntrophic algal-bacterial biomass in a pilot-scale raceway pond treating domestic wastewater , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6521, https://doi.org/10.5194/egusphere-egu25-6521, 2025.

Cyanotoxins in agricultural waters pose a human and animal health risk. These chemical compounds can be transported to nearby crops and soil during irrigation practices; they can remain in the soils for extended periods and be adsorbed by root systems. Additionally, in livestock watering ponds cyanotoxins pose a direct ingestion risk. This work evaluated the performance of the randomForest algorithm in estimating microcystin concentrations from eight in situ water quality measurements at one active livestock water pond (Pond 1) and two working irrigation ponds (Pond 2 and 3) in Georgia, USA. Sampling was performed monthly from June of 2022 to October of 2023. Measurements of microcystin along with eight in situ sensed water quality parameters were used to train and test the machine learning model. The model performed better at Pond 1 (R² = 0.601, RMSE =3.854) and Pond 2 (R² = 0.710, RMSE = 2.310) compared to Pond 3 (R² = 0.436, RMSE = 0.336). Important variables for microcystin prediction differed among the three ponds, temperature and chlorophyll, phycocyanin and turbidity, and temperature and phycocyanin in Ponds 1, 2 and 3, respectively. Separating nearshore and interior samples in Ponds 1 and 2 lead to better predictive capacity of the model in nearshore samples compared with the interior samples. Overall, the random forest algorithm explained 50% to 70% of the microcystin concentration variation in three Georgia agricultural ponds with data from in situ sensing. In situ sensing showed a potential to aid in the water sampling design for microcystin to characterize the spatial variation of concentrations in studied ponds using readily available in situ sensing data.

How to cite: Smith, J., Stocker, M., Hill, R., and Pachepsky, Y.: Microcystin concentrations and water quality in three agricultural ponds: A machine learning application, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6696, https://doi.org/10.5194/egusphere-egu25-6696, 2025.

North China Plain is one of the agricultural region in the world with severe water shortage. Flood irrigation is still the most popular irrigation method in NCP, and have caused very low water use efficiency. Groundwater depletion becomes the most concerned issue for sustainable development. To determine the water & nitrate fluxes is important for better water resouces management. We built up a 48-m in depth of cassion and a 36 lysimeter group for this purpose to study the water budget and water/nitrate movement in the deep vadose zone. In this study, we will present the observation facts using these two facilities to reveal the differences between water transport velocity and celerity in the deep vadose zone of nearly 50 meters. This is the first time to observe the variations or responses of soil potential, moisture, temperature, and electricity conductivity to water inputs from land surface, such as extreme rainfall, directly in the deep vadose zone of 48 meters. We  will also present the fresh observation results from the 36 lysimeters about ET and drainage fluxes of different cropping patterns, with different watering and fertilizing treatments. The latter experiment could provide useful information for improving the water/nutrients management for different cropping systems in NCP, and will be beneficial to sustainable groundwater management at the aspects of quantity and quality. The results of the observatoins using these new facilities is presented at international conference at the first time. We hope it could be interested by the colleagues worldwide. 

How to cite: Shen, Y., Zhang, Y., Min, L., Wu, L., Li, H., and Li, H.: Water/nitrate fluxes and tranport in deep vadose zone of typical irrigated cropland in North China Plain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6894, https://doi.org/10.5194/egusphere-egu25-6894, 2025.

Climate change significantly impacts water quality and quantity, intensifying extreme weather events, such as floods, droughts, and heat waves. Rising temperatures can increase humidity and dryness, disrupt the water cycle, cause saltwater intrusion into upstream lakes due to sea-level rise, and reduce dissolved oxygen in rivers, thereby deteriorating freshwater quality. Thus, accurate prediction of key climate variables, such as precipitation and temperature, is essential for mitigating detrimental impacts. This study evaluates three modeling approaches, including Process-Based (PB) models, Deep Learning (DL) models, and Process-Based Deep Learning (PBDL) models, to highlight their strengths and limitations.

Our assessment shows that PB models, which are based on physical laws and account for complex interactions between the atmosphere, land, and water bodies, require high parameterization and computational simplifications, which can lead to inaccurate results. DL models can uncover complex relationships from large datasets. They are effective in co-predicting variables, simulating General Circulation Model (GCM) outputs, optimizing PB models, and filling spatiotemporal data gaps. However, their performance depends on the availability of extensive temporal-spatial data, particularly for extreme events. The other group, PBDL models, known as physics-informed or hybrid models, can integrate the strengths of PB and DL approaches. Indeed, these models consider physical laws, such as mass balance and energy conservation, while leveraging DL's pattern recognition capabilities. Even with limited data, these models achieve superior predictions by combining pre-trained PB model outputs, which reduces computational demands.

Although these methods are used to evaluate (actual) evapotranspiration, snowmelt rate, soil permeability, hydraulic conductivity, and the effect of a warming climate on water temperature and streamflow, the interconnected influences on water systems, especially water quality indicators such as dissolved oxygen, heavy metals, nutrients, and water clarity, remain underexplored, presenting a critical research gap. Findings confirm that incorporating simultaneous predictions from DL models with proper variable selection and hyperparameter tuning can further enhance model robustness. Advancing PBDL models through integrating well-calibrated hydrological models, expanding spatiotemporal data coverage, and improving measurement accuracy yields more reliable climate change predictions and bolsters sustainable water resource management strategies.

To identify promising solutions, researchers are encouraged to address the non-stationary behavior of natural systems, considering not only meteorological factors (e.g., wind speed and solar radiation) but also the compound impacts of anthropogenic climate change on water resources. Additionally, selecting appropriate models and coupling them can improve an overall understanding of climate and water system interactions.

How to cite: Karimnejad, A., khorashadi zadeh, F., and Moghim, S.: Assessment of climate change-water resources interaction by different models , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7025, https://doi.org/10.5194/egusphere-egu25-7025, 2025.

Escherichia coli (E. coli) is a key marker for monitoring microbial water quality, with significant consequences for both public health and agricultural practices. To address the challenges of traditional water quality assessments, remote sensing offers a promising alternative. In this research, we implemented the random forest (RF) algorithm to forecast E. coli levels in irrigation ponds using three distinct data sources: (1) conventional water quality measurements, (2) multispectral reflectance values from drones, and (3) remote sensing indices derived from these reflectance values. To enhance the model’s accuracy, a linear transformation was applied during postprocessing. The RF model achieved strong performance (R² = 0.74) with conventional water quality variables, while moderate results were obtained using multispectral reflectance values alone (R² = 0.56). The best outcomes were observed when remote sensing indices were used as inputs, yielding an R² of 0.76. Shapley additive explanations (SHAP) were employed to evaluate the importance of individual variables. Dissolved oxygen, pH, and Chlorophyll-a emerged as critical predictors among water quality parameters. Meanwhile, the visible atmospherically resistant index (VARI) and normalized difference turbidity index (NDTI) were the most significant remote sensing indices. Furthermore, location-based comparisons highlighted differences in the impact of VARI and NDTI between interior and nearshore sampling sites. These findings suggest that remote sensing indices effectively capture water quality features crucial for E. coli persistence. This study underscores the potential of using drone-derived multispectral data to enhance predictions of E. coli concentrations in irrigation ponds.

How to cite: Hong, S., Morgan, B., Stocker, M., Smith, J., Kim, M., Cho, K. H., and Pachepsky, Y.: Assessing the effectiveness of remote sensing indices for predicting E. coli concentrations in an irrigation pond, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7313, https://doi.org/10.5194/egusphere-egu25-7313, 2025.

Antimicrobial resistance (AMR) in irrigation waters is a major worldwide health issue. Crops irrigated with waters containing antibiotic resistant bacteria (ARB) or related genes (ARG) can serve as a vector for AMR throughout food supply systems. The current extent of AMR in irrigation waters is poorly understood and even less so for small lentic waters such as farm ponds. The objectives of this work were to characterize the variability of ARG concentrations in an actively used irrigation pond and to determine if stable spatial patterns in the concentration data exist which can be used to inform monitoring designs. Water sampling was conducted on 9 dates between June and September 2023 at 20 locations within an actively used irrigation pond in Maryland, USA. The ARG tetracycline gene tetA was enumerated using dQPCR in all collected samples. Due to the presence of non-detects, the robust regression on ordered statistics (ROS) method was applied to the dataset to impute non-detectable concentrations on each date. Spatial variation of tetA concentrations was date-dependent with coefficients of variation ranging from 97 % to 377 % with an average of 181 %. Concentrations steadily declined throughout the observation period which significantly correlated with increases in water temperature (r_s = - 0.738; p = 0.023). Rainfall events throughout the observation period did not result in higher concentrations of tetA in the pond. On a majority of dates, significant outliers in the data were identified according to the extreme studentized deviate test.  The mean relative difference analysis revealed that samples collected at the pond banks contained higher tetA concentrations than those collected in the pond interior. Elevated concentrations of the ARG at bank sites were attributed to on-land activities as well as hydrological conditions within the waterbody. Sampling sites were identified that best represented the spatiotemporal average of the concentration data which is useful if large sample sets cannot be collected. This work is the first to evaluate fine-scale spatial variation of ARG in lentic waters used for irrigation and the results show that the choice of where to sample for ARG enumeration in ponds or lakes should not be made arbitrarily.

How to cite: Stocker, M., Smith, J., Pachepsky, Y., Gabriel, E., Sharma, M., and Gutierrez, A.: Fine-scale spatial patterns of antibiotic resistance gene concentrations in irrigation pond water, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7320, https://doi.org/10.5194/egusphere-egu25-7320, 2025.

Modeling is an efficient approach for predicting microbial water quality and suggesting related management practices. Escherichia coli or enterococci concentrations are commonly used to indicate microbial contamination and characterize microbial water quality. Small watersheds provide drainage into first- or second-order creeks, exhibit significant variation in land use, management, and conservation practices. Modeling microbial water quality in the small watersheds can help account for and mitigate the heterogeneity within larger hydrologic response units. A model for microbial water quality should incorporate key hydrologic components such as runoff, in-stream water fluxes, and meteorological inputs such as precipitation, air temperature, and solar radiation. Additionally, animal waste management, including the quantity and application schedule, are also important for microbial water quality simulations. The Agricultural Policy Environmental eXtender is a useful tool for hydrological, meteorological, and management drivers of microbial water quality, as it has been developed for small watersheds. Major microbial fate and transport processes include animal waste deposition, degradation, erosion, survival on soil, release from waste and transport by rainfall or irrigation, and microbial survival and resuspension in water or sediment. These processes can be simplified, for instance, by modeling proportional release of the indicators and animal waste during erosion. We can also use a two-phase survival model for manure and temperature-dependent rate of microbial survival in surface waters. Animal waste aging should also be considered in the microbial model, as daily bacterial survival and erodibility are influenced by it. The microbial module in APEX was used to the headwater watershed of Conococheague Creek in Pennsylvania, USA. The total watershed area is 34321.6 ha, with 15 subareas and the dominant land use is deciduous forest. Three years of hourly stage observations with rating curves and weekly E. coli concentrations at the outlet were available. The primary source of E. coli was animal waste from white-tailed deer, with an average density of 19 deer per square kilometers. Deer population dynamics reflect seasonal changes including fawn births, predation, pre-hunting, and post-hunting population phases. E. coli concentrations at the watershed outlet varied seasonally, ranging from 5 to 500 CFU (100 mL)^-1. The model reasonably captured the temporal fluctuations in E. coli concentrations at the outlet. Ongoing improvements to the model include incorporating deer behavior patterns, animal waste preservation in snow, and runoff during snowmelt.

How to cite: Lee, J., Harriger, D., Hong, S., Jeong, J., Guber, A., Hill, R., and Pachepsky, Y.: Modeling fate and transport of indicator microorganisms in small rural watersheds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7321, https://doi.org/10.5194/egusphere-egu25-7321, 2025.

Coffee-banana intercropping, widely practiced by smallholder farmers in South America and East Africa, is recognized for its potential to combine sustainability with resilience to climate change. This practice promotes crop diversification, but may also enhance water-use efficiency. However, its effectiveness may vary depending on the local conditions and agricultural practices. The lack of quantitative data on drought stress and the complexity of interactions within coffee-banana intercropping systems pose significant challenges in modelling and optimizing water use efficiency. This study aims to develop and refine innovative methods to assess drought stress in coffee-banana intercropping systems, with a focus on stable carbon isotope values (δ¹³C), leaf temperature, and mid-infrared spectroscopy (MIRS). While stable carbon isotope analysis is a promising tool, its application may face challenges due to factors such as crop size, canopy heterogeneity, banana-coffee canopy overlapping, leaf age, orientation, or position (leaf morphological aspects), leading to variable competition for water and light. These factors affect the way sampling for stable carbon isotope and leaf temperature analysis should be conducted, in addition to physiological differences between coffee genotypes, agronomic practices, and complexities in data interpretation. Sampling and analytical protocols must be adapted to address these factors and their effects, while accounting for leaf morphology and microenvironmental parameters. Initially, we evaluated the influence of these factors on δ¹³C variability in coffee leaf samples, in addition to their correlation with leaf temperature. Samples were collected from a 0.15 hectares experimental farm managed by the Agricultural Research Company of Minas Gerais (EPAMIG) in Brazil, an intercrop of Arabica coffee and Cavendish banana plants at 3.6 a distance apart. Coffee leaves were sampled using a metal puncher and leaf temperature was measured using an infrared thermometer, considering varying levels of sunlight exposure. Ten plants of the Catuaí Vermelho IAC 44 coffee cultivar were randomly selected: five under conventional management (chemical fertilizers) and five under organic management (cattle manure). For each plant, samples were taken at three different heights (Top, Middle and Bottom), three orientations (South, East and West), and two branch sides, including young and mature leaves, resulting in 36 leaves per plant. The poster presents key findings on the variability of δ¹³C isotopes in coffee leaves within a banana-coffee intercropping system and their relationship with leaf temperature under different management practices (organic and conventional). This presentation highlights the observed effects of leaf sampling parameters, such as age, position, and sunlight exposure, on δ¹³C values, as well as the implications for improving drought stress screening methodologies.

How to cite: Bernardo, T., Vezzone, M., Felizardo, J. P., Rodrigues, C., Moura, W., Gomes Soares, L., Sebastião Sant' Anna Andrade, H., Victor Vieira Queiroz, C., Nakamya, J., Vantyghem, M., Dercon, G., and Meigikos dos Anjos, R.: Unravelling Sampling Bias in δ¹³C Isotope Variability in Coffee-Banana Intercropping for Drought Stress Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9556, https://doi.org/10.5194/egusphere-egu25-9556, 2025.

Reducing the carbon footprint of residential buildings has become increasingly crucial for decarbonizing the construction sector globally. Implementing various sustainable practices is essential for attaining carbon neutrality and addressing climate change. Therefore, integrating Green Concrete Materials (GCMs) and Nature-Based Carbon Dioxide Removal (Nb-CDR) strategies represents sustainable solutions for reducing CO₂ emissions and achieving a circular economy (CE) in residential buildings. In this regard, the study aims to investigate the potential synergies of sustainable building materials and eco-friendly building systems by utilizing Recycled Aggregate Concrete (RAC), Fly Ash (FA), Green Roof system (GR), and a Green Façade system (GF) as an attempt for reducing CO₂ emissions for residential building sector significantly. The Design for Integration (DFI) approach is used to develop novel sustainable solutions for future residential buildings and investigate how integrating different strategies can substantially enhance the overall benefits of reducing the sector’s carbon footprint. The system dynamics are used to create a simulation model that can estimate the synergies between GCMs and Nb-CDR to reduce CO₂ emissions and clarify the inner variables’ relations using Vensim software. Thereby, a comparative analysis between the traditional and optimized building designs is applied to the new Egyptian residential buildings. The results indicated potential integration could significantly lower a building’s CO₂ emissions during the building life cycle compared to conventional solutions. Additionally, it promotes circularity performance and decarbonization for the construction sector. The study demonstrated that incorporating eco-friendly materials and green building systems requires more attention in the early design stage of residential buildings. Public awareness should be considered, and new policies should be implemented to promote incentives and influence the effectiveness of Nb-CDR with GCMs in the future.

 Keywords:  Residential Buildings; Green concrete; Nb-CDR; System Dynamics; Design and simulation; CO₂ emissions.

How to cite: Marey, H., Kozma, G., and Szabó, G.: The Potential Synergies Between the Integration of Green Concrete Materials and Natural-Based Carbon Dioxide Removal Strategies in Residential Buildings Sector, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9762, https://doi.org/10.5194/egusphere-egu25-9762, 2025.

Taiwan plays a crucial role in the global supply chain as a major semiconductor manufacturer. Semiconductor production depends heavily on water resources, making the stable supply of industrial water from upstream reservoirs essential to maintaining the global supply chain. However, international water risk assessments often fail to capture Taiwan’s regional hydrological variations due to their large spatial scale, obscuring the real physical and financial risks related to water resources under climate change. Given Taiwan's distinct climate with pronounced wet and dry seasons, short and fast-flowing rivers, and limited surface water retention, reservoirs are critical for regulating water supply. This study employs hydrological models and reservoir operational models to develop a reservoir risk assessment framework, which is the foundation of water resource management. The assessment procedure aids in understanding regional climate-related water risks. Utilizing this assessment tool to adjust reservoir operations will offer strategies for rational water resource management and enhanced climate resilience.

How to cite: Lai, Y.-P. and Lee, T.-Y.: A Framework for Assessing Water Availability and Risk of Reservoirs in Taiwan under Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12004, https://doi.org/10.5194/egusphere-egu25-12004, 2025.

This work introduces a new webGIS tool to estimate the Crop Water Requirement (CWR), using time series of satellite images and meteorological data, at high spatial resolution and a global scale. This CWRweb tool provides users with information on the temporal evolution of the CWR, as a first approach of the crop evapotranspiration, as well as other parameters of interest. This process is implemented via web and requires no proficiency in remote sensing.

The implemented calculation of the evapotranspiration under standard conditions (ETc) stands on the robust FAO-56 methodology, based on the relationship between the Crop Coefficient and the Reference Evapotranspiration (Kc-ETo). The CWRweb tool adopts the single crop coefficient approach, combining the effects of both, crop transpiration and soil evaporation into a single coefficient (Kc). These Kc values derive from the NDVI time series of Sentinel-2 multispectral satellite images, for a broad range of crops (horticulture, woody crops, and other crops) and natural vegetation, assuming a general component for the soil evaporation.

The CWRweb tool benefits from the potential of the Sentinel-2A & B satellite constellation to provide users with free time series of images with a spatial resolution of 10m × 10m and a revisit frequency of 2-3 days. The high frequency of Sentinel-2 imagery allows to obtain daily Kc values through interpolation of NDVI data from cloud-free images at high spatial resolution. Online access to massive databases of satellite images, such as those of the Copernicus Data Space Ecosystem program (https://dataspace.copernicus.eu/), together with recent advances on meteorological numerical models to provide global ETo layers at different gridding size, are boosting the operational use of the CWRweb tool.

The CWRweb tool runs and graphically displays daily ETc, as well as NDVI, Kc, and ETo values used in its calculation, for a selected time interval. Results can be provided at both, field and pixel scales. An assessment of the CWRtool was conducted by comparison against the OpenET tool on a selection of crops-sites in California, USA. An average uncertainty of RMSE=0.9 mm·d^-1, with a negligible bias, was obtained in a performance analysis using the OpenET ensemble outputs as a reference, using 15 different locations, and data for the period 2016-2024. These results are promising and reinforce the potential of the CWRweb tool for the operational estimation of global evapotranspiration at a high spatial resolution.

How to cite: Campoy, J., Sánchez, J. M., Beltrán, A., Pérez, Y., Molina, A., and Calera, A.: Remote-Sensing based Global Evapotranspiration estimates at high spatial resolution through Sentinel-2 satellite imagery and meteorological data. The CWRweb tool., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12878, https://doi.org/10.5194/egusphere-egu25-12878, 2025.

Transpiration significantly depletes terrestrial subsurface water stores and plays a crucial role in 
the hydrological cycle. While extensive research has been conducted in the Maimai M8 catchment 
(New Zealand) and across many catchments on streamflow generation processes and streamflow 
sources, we still know little about the sources of transpiration and when transpiration and 
streamflow sources are hydrologically connected. Here we leverage M8, a long-term studied 
catchment with well-described streamflow generation mechanisms, to investigate the transpiration 
source water of Pinus radiata and its connectivity to streamflow sources. We combined monthly 
observations of isotopic signatures (δ¹⁸O and δ²H) of xylem, bulk soil water, mobile water, 
subsurface flow, and stream water with continuous monitoring of tree water stress across a 
hillslope to answer: (1) What is the seasonal source of transpiration at Maimai? And (2) how does 
transpiration source water interact with streamflow sources? Our data showed that transpiration 
sources across the hillslope were not distinct but changed seasonally. During summer, when trees 
showed greater periods of water stress, trees relied on shallow soil water. In contrast, during the 
winter, trees’ isotopic signatures plotted along the local meteoric water line (LMWL), overlapping 
with mobile soil and stream water. Xylem isotopic signatures were not statistically distinct from 
stream signatures in the winter, contrasting with distinct isotopic signatures during the summer. 
Our results showed that transpiration source water in the Maimai M8 catchment changes 
seasonally, influenced by tree water stress and wetness conditions. Overall, our findings suggest 
an ecohydrological connectivity between transpiration and streamflow sources during winter 
months in this wet temperate climate.

How to cite: Simmons, C., Dudley, B., McDonnell, J., and Nehemy, M.: Seasonal transpiration source water and ecohydrological connectivity with streamflow sources in the Maimai M8 Catchment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14282, https://doi.org/10.5194/egusphere-egu25-14282, 2025.

The Peruvian coast is undergoing significant landscape transformations driven by environmental and climatic factors, with extreme precipitation events exerting a pivotal influence on the morphology of river channels and floodplains. This study leverages advanced technologies, including unmanned aerial vehicles (UAVs) and post-processing kinematic (PPK) techniques, to address these dynamic changes. The methodology involves co-registering point clouds using ground control points (GCPs) to produce high-resolution and temporally stable digital elevation models (DEMs).The research focuses on a 0.5 km² area within a coastal basin in Peru, with data collection scheduled across two distinct timeframes. The primary objective is to identify areas exhibiting minimal elevation changes and quantify rates of erosion and sediment deposition over a defined period. Specifically, the study measures erosion in gullies and riverbanks, as well as sediment deposition, enabling the estimation of volumetric changes in cubic meters (m³). These findings are critical for advancing the understanding of regional geomorphological processes and informing the development of effective management and mitigation strategies. By employing UAVs and PPK techniques, this research delivers actionable insights into sediment dynamics, supporting sustainable water resource management and land use planning in Peru’s coastal basins. Ultimately, the study contributes to mitigating the adverse impacts of extreme precipitation on the region’s landscapes.

How to cite: Cubas-Arteaga, E. and Cárdenas-Gaudry, M.: Monitoring Geomorphological Changes in the Peruvian Coast Using UAVs and PPK Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15513, https://doi.org/10.5194/egusphere-egu25-15513, 2025.

At a local level, river sections maintenance represents a reduction condition of hydrological risk where soil defense work have been carried out.

In this context, this paper describes how the hydrological-hydraulic monitoring of a soil protection intervention can represent the first step for an integrated management strategy of the river ecosystem aimed at maintaining hydraulic safety at inter-municipal level and at the economic-financial sustainability of the interventions.

The case study concerns the soil defense work of - Celone valley - within the framework of agreement memorandum between the municipalities of Castelluccio Valmaggiore, Celle Di San Vito, Faeto and Troia.

The intervention received funding from the Environment Italian Ministry as part of the Puglia Development Pact. The Implementing Body was the Government Commissioner for hydrogeological risk in Puglia.

The study area is located in northern Puglia as part of Celone basin, the portion closed by Torrebianca Dam. The area is surrounded in Daunia Apennines and is characterised by provincial roads that connect the municipalities affected by flooding phenomena. Specifically, we would like to recall the flood event of 12.13.2015 in which two Danish technicians died near the SP124, overwhelmed by a flood wave.

During the above-mentioned work, solid material transport was identified as a trigger for the landslide and its controlled removal could become a sustainable management strategy.

Therefore, starting from the post-operam monitoring, a solid transport indirect monitoring was planned in order to design the controlled extraction of material and its reuse, allowing the river sections upgrading and its hydraulic safety.

Preliminary and qualitative obtained results show the feasibility and economic sustainability of project. This strategy, codesigned and shared with all stakeholders, aims to become a long-term best practice for sustainable territorial management of the river ecosystem.

How to cite: Mita, L., Doria, A., and Godano, F.: Exploring a sustainable solid transport management strategy at local level, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16590, https://doi.org/10.5194/egusphere-egu25-16590, 2025.

Temperature plays a critical role in the functioning of river ecosystems. Hence, understanding the processes that control water temperature in river networks across daily to multi-year scales is important when trying to manage river thermal regimes. This is particularly urgent in alpine semi-arid basins with substantial human impact, and, especially within the context of global change, where river ecosystem integrity is at risk. A process-based model has been developed to simulate water temperature in lakes and rivers at a regional (watershed) scale. The physically based and fully distributed hydrological model provides comprehensive hydrological and hydraulic simulations of river flow, including contributions from snowmelt, groundwater, and direct runoff at each node of the network. Additionally, the model accounts for the discharge of urban wastewater at its respective nodes. To overcome the computational cost and numerical problems associated with Eulerian methods in long-term simulations, the model uses a semi-Lagrangian approach to discretize the one-dimensional heat conservation equations in river reaches. Reservoir stratification and withdrawal temperatures are simulated with a 1D Lagrangian model (General Lake Model). This methodology ensures the accurate and detailed simulation of water temperature dynamics in rivers by integrating meteorological, hydrological, and hydraulic data, along with the impact of urban wastewater discharges and reservoir outflows. The model is applied to simulate water temperature in a small semi-alpine watershed upstream of the city of Granada that includes two water-supply reservoirs (Canales and Quéntar). Autonomous temperature sensors deployed at different sites are used for model validation. The model is forced with climate databases (reanalysis, regional climate simulation conducted with WRF, and measured data bases) and used in hindcast/forecast exercises to assess the impact of climate change on the thermal regime of inland waters.

Acknowledgments: This research has been supported by the project: STAGES-IPCC (TED2021-130744B-C22) from the Spanish Ministry of Science, Innovation and Universities

How to cite: Gulliver, Z., López-Padilla, S., Herrero, J., Huertas-Fernández, F., Collados-Lara, A. J., García-Valdecasas Ojeda, M., Ramón, C. L., Esteban-Parra, M. J., Pulido-Velázquez, D., and Rueda, F. J.: Long-term water temperature modeling in semi-arid alpine basins , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18985, https://doi.org/10.5194/egusphere-egu25-18985, 2025.

The land system dries up quickly and intensely during rapid/flash droughts under climate warming are of widespread concern owing to their adverse impact on nation’s economy. During these periods, reduction in precipitation deficits is frequently followed by abrupt increases in evaporative demand, which causes significant drops in soil moisture and discernible effects on agricultural production and the environment. The need for a better knowledge on rapid drought conditions to effectively manage its effects has been highlighted in several recent publications; Nevertheless, the lack of consistent definitions have limited progress toward its assessment. There are several factors and climatic forces that are typically connected to the development of flash droughts, thus it's conceivable that no one definition will fully encapsulate the phenomenon. But it's imperative to ensure that flash droughts (lasts for short duration) can be recognized and differentiated from more traditional drought occurrences (longer duration) due to their quick onset, quick intensification, and severe character. With the increasing use of rapid /flash drought term within the research community, this study explores the extent to which pentad-scale precipitation series across India can be used to represent historical flash droughts, providing a simple framework for the phenomenon. The result shows the categorization of rapid/flash drought at various hotspot location in India and explain it’s causing and triggering factor linked with acute precipitation deficits, one of meteorological variable. The findings of this study can be further utilized in the accurate prediction of flash/rapid drought with the robust evidence from precipitation series in identifying flash drought episodes across the nation. Consequently, our findings indicate that constant monitoring of rapid drought conditions and drivers is crucial for effective preparedness.

How to cite: Kumari, P. and Vinnarasi, R.: Enhanced Modulation of Rapid/Flash Drought in India: An Elegant Framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-705, https://doi.org/10.5194/egusphere-egu25-705, 2025.

Accurate error characterization is essential for validating satellite-based geophysical products. Triple Collocation (TC) estimates random error variances of three mutually independent datasets but assumes a common spatial scale—a condition rarely met in practice. Spatial heterogeneity in the ground truth and mismatches in spatial resolution introduces "spatial representativeness errors", whose influence on error variance estimates remains unexamined. In this study, we have analyzed the sensitivity of the triple collocation estimates using the synthetically generated soil moisture dataset under varying sample sizes and spatial heterogeneity. Our results indicate that sample size (N) affects the TC estimates, with % bias decreasing from ±15% to ±2% for N ranging from 100 to 1000. The study finds that % bias also varies with the degree of spatial heterogeneity across the area under consideration. Additionally, the TC framework exhibits an equal likelihood of overestimation and underestimation. These findings underscore the critical importance of addressing spatial heterogeneity to enhance the reliability and robustness of error characterization in geophysical measurement systems. The study provides valuable insights for improving the applicability of TC in satellite product validation and underscores the need for more advanced approaches to handling spatially diverse datasets.

How to cite: Gupta, D. and Dhanya, C. T.: Influence of Spatial Heterogeneity in Error Characterization Using Triple Collocation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3139, https://doi.org/10.5194/egusphere-egu25-3139, 2025.

Flooding is one of the most devastating natural disasters, significantly impacting human lives, infrastructure, and ecosystems. Severe rains when combined with a lack of proper infrastructure in urban areas can lead to floods. Thereby accurate flood predictions and modelling are essential for efficient flood control in such environments. A critical component of this process is obtaining reliable hydrological outputs over watersheds, which forms the foundation of precise flood forecasting. Flood inundated areas can be generated by hydrological and hydraulic modelling to provide valuable insights into high-risk zones. Modelling helps in interpreting timely and reliable flood information from the generated flood maps to reduce damages in flood areas. In this study Hydrological Response in the form of runoff is computed for a region of the Upper Ganga basin, India by using HEC Series and thus flood inundation maps were generated for different return periods. Data sets required for the study included satellite images, digital elevation model, daily precipitation and soil map. To model flood inundated areas for a return period of 2,5,10,25,50,100 years, HEC-HMS and HCE-RAS were employed. Flood inundation maps were generated and flood risk areas were identified for different return periods. Results showcased that 2-years return period flood inundates approximately 0.29 sq. km, accounting for nearly 2% of the total study area and 100-years return period flood inundates approximately 4.42 sq. km covering nearly 31% of the study area. This study provides a framework for similar research in other flood prone areas and suggest implementation of low-impact development strategies for regions prone to frequent flooding in the study area. The findings underscore the importance of integrating advanced flood modelling techniques with historical data to enhance disaster preparedness and resilience.

Keywords: Climate Change, Hydrological Modelling, Flood Inundated Areas, Return Period.

How to cite: Thakur, K. and Pathak, S.: Flood Frequency Analysis on Ganga Basin Catchment using Geospatial Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3986, https://doi.org/10.5194/egusphere-egu25-3986, 2025.

Data driven algorithms have been largely proven to be accurate tools for modelling many hydrological variables including aggregated river flows. Many studies have tested the suitability of a wide range of data-driven algorithms for predicting the recorded flows with times-steps ranging from a few minutes to monthly or even seasonal observations fed on a wide variety of inputs. They existing works often achieve brilliant performance indicators. In this work we pay our attention to a well-known hydrological process which is the flow hydrograph generation from rainfall hyetographs based on the mass conservation law within the catchment. Our assumption is that given many different physically based theories can provide accurate estimates of the expected flow hydrograph just providing the recorded hyetograph and a set of physical parameters of the catchment, data-driven approaches should also be able to successfully estimate the flow recorded hydrographs. For testing that hypothesis, we have selected two small mountain catchments (rivers Aragón in Canfranc and Valira Oriente in Andorra catchments in the Pirineos mountains in Spain and Andorra) easily parametrizable with no water depletion. We have checked the performance of different data-driven algorithms for predicting the 15-minutes recorded hydrographs fed on 15-minutes rainfall records and the set of physical variables involved in the Green-Ampt infiltration model. Over this process we have faced several issues and observed the data-driven algorithms are unable to provide the performance indicators commonly achieved in the published works.

How to cite: Zubelzu, S., Patricio, M. Á., Berlanga, A., and Molina, J. M.: Performance of data-driven approaches for estimating flow hydrographs from rainfall hyetographs in small mountain catchments. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4321, https://doi.org/10.5194/egusphere-egu25-4321, 2025.

This study employs a composite baseflow model to estimate baseflow, effective recharge, and hydraulic conductivity. Baseflow recession analysis is a hydrological method used to analyze the gradual decline of streamflow during dry periods when groundwater serves as the primary source of water for rivers and streams. Previous approaches often rely on either linear or nonlinear Boussinesq equations, both of which have limitations. The linear Boussinesq equation fails to capture the nonlinear behavior of baseflow, while the nonlinear equation struggles to represent low discharge values, where baseflow recession is most occurred. Furthermore, the nonlinear model introduces assumptions and overlooks baseflow contributions from below the stream’s water level. To address these issues, this study applies the composite model for baseflow estimation. The composite model effectively separates the baseflow component of stream discharge. Following this, effective recharge and hydraulic conductivity are estimated using a high-resolution MODFLOW model, providing more accurate and comprehensive insights into groundwater-surface water interactions.

How to cite: Alattar, M.: Application of a Composite Model to Estimate Baseflow, Effective Recharge, and Hydraulic Conductivity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4662, https://doi.org/10.5194/egusphere-egu25-4662, 2025.

Soil Moisture (SM), acknowledged by the Global Climate Observing System (GCOS) and the European Space Agency’s Climate Change Initiative (ESA CCI) as an Essential Climate Variable (ECV), is a fundamental hydrological parameter that plays a pivotal role in bridging Earth's surface and atmospheric interactions. Understanding SM status and dynamics is critical for various meteorological, hydrological, and climatological applications. Furthermore, it provides insights into the water, energy, and carbon cycles while aiding in the forecasting of extreme climatic events, such as droughts and floods. In consequence, accurate global monitoring of SM with suitable temporal and spatial resolutions is imperative.

This study focuses on the validation of multiple satellite-derived near-surface SM products against field measurements to evaluate their accuracy and reliability. The research was conducted over the northeastern Spain and southern France, covering a 7-year span from January 2015 to December 2021. Ground truth data were obtained from the International Soil Moisture Network (ISMN) database, which included observations from 30 stations across four networks (COSMOS, FR-Aqui, IPE, and SMOSMANIA). The analysis assessed four microwave-based sensors, encompassing both active and passive systems: ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity), SMAP (Soil Moisture Active Passive), and CCI.

Following data acquisition and processing for both satellite images and ground observations, a comprehensive validation was performed using statistical metrics, scatter plots, and linear regression analysis of the respective time series. Results highlighted that the SMAP mission delivered the most reliable outcomes, achieving a near-unity slope, an intercept close to zero, a correlation coefficient of R = 0.72, and a Root Mean Square Error of RMSE = 0.07 m³/m³. The CCI product followed, while ASCAT and SMOS showed larger uncertainties and weaker correlations, respectively. In addition, an analysis of the in situ depth effect using SMAP indicated that measurements at 0–6 cm (integrated) and 5 cm (point-specific) depths yielded optimal results. Nevertheless, despite remarkable advances in SM monitoring, this work underscores the need for further research to align satellite-derived data more closely with field-level precision.

Acknowledgements: This study was carried out in the framework of the PID2020-118797RBI00 (Tool4Extreme) project, funded by MCIN/AEI/10.13039/501100011033, and also the PROMETEO/2021/016 project, funded by Conselleria d’Educació, Universitats i Ocupació de la Generalitat Valenciana.

How to cite: Tomás-Portalés, G., Valor, E., Niclòs, R., and Puchades, J.: Validation of Satellite-Derived Soil Moisture Products Using Ground Observations in Southern Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5173, https://doi.org/10.5194/egusphere-egu25-5173, 2025.

The efficiency of an advanced oxidation process (AOP) using direct and indirect ozonation for the removal of pharmaceutical residues from hospital wastewater was examined. Both direct and indirect ozonation demonstrated 34% to 100% removal of the parent compounds. However, based on the products’ chemical structure and toxicity, we suggest that despite using accepted and affordable ozone and radical concentrations, the six parent compounds were not fully degraded, but merely transformed into 25 new intermediate products. The transformation products (TPs) differed slightly in structure, but were mostly similar to their parent compounds in their persistence, stability and toxicity; a few of the TPs were found to be even more toxic than their parent compounds. Therefore, an additional treatment is required to improve and upgrade the traditional AOP toward degradation and removal of both parent compounds and their TPs for safer release high qaulity effluent into the environment. 

How to cite: Avisar, D.: Pharmaceutical transformation products formed by ozonation – does degradation occur? , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8477, https://doi.org/10.5194/egusphere-egu25-8477, 2025.

Ensuring the safety and sustainability of drinking water sources is a critical component of modern water resource management. The recast Drinking Water Directive (EU 2020/2184) emphasizes the delivery of safe drinking water by strengthening protections along the entire supply chain, from source to tap, and adopting a risk-based approach to water safety as recommended by the World Health Organisation. Assessments of water treatment costs tend to focus on the current level of treatment, and not the potential additional costs associated with treatment of new emerging contaminants, many of which are of low molecular weight requiring specialist treatment technologies with expensive CAPEX and OPEX costs. The impacts of climate change on the raw water quality source water abstractions are also likely to result in increasing costs of water treatment systems. In Ireland, the inclusion of emerging substances on the 2023 Drinking Water Regulations and on the first European Commission’s Watch List reflects the evolving nature of water safety management in response to pollutants of emerging concern and environmental pressures. This study presents a robust methodology with a view to inform future funding and targeting of water quality measures and source protection work. Applied across six case studies, the four-stage process (pre-screening, coarse screening, fine screening, and final comparative analysis) guides decision-making. The framework incorporates open-source data from the Environmental Protection Agency (EPA) of Ireland, including land-use maps, Water Framework Directive (WFD) waterbody status and significant pressures such as agriculture, forestry, industry, and hydro-morphology, alongside local pressures on water sources. Source protection measures and treatment technologies were derived from extensive literature review of national and international projects and were tailored to specific goals for each case study, with independent evaluations for both strategies. The process concludes with a comparative analysis to identify optimal solutions for each scenario. The study provides recommendations, based on economic assessments and the evaluation of environmental and technological gaps to support the stakeholders in decision making and policy development. The selected strategy for each case is dependent on a suite of site-specific features, including the raw water source type, the catchment size, the mapped WFD pressures exerted into the water source and the latest WFD status and the Water Treatment Plant capacity. The findings highlight the importance of adopting integrated approaches to ensure the resilience of drinking water systems in the face of future uncertainties.

How to cite: Sousa, D., Ali Khan, U., Bradshaw, S., and Grace, M.: Integrated catchment and treatment strategies for safeguarding drinking water quality: an adaptive decision-making tool, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10589, https://doi.org/10.5194/egusphere-egu25-10589, 2025.

Cotton is a one of the major crops in the southeastern United States. It significantly impacts regional water resources since it consumes a large amount of freshwater for irrigation. Current irrigation practices fail to optimize water use accurately since they are largely dependent on soil moisture sensors and grower experience. They do not consider dynamic factors such as soil texture, prevailing weather conditions, and the crop's phenological stage. In this paper we propose an innovative approach to enhance the irrigation efficiency through the use of Deep Reinforcement Learning (DRL) model. It takes into consideration the dynamic variables and optimizes irrigation. We utilize a crop growth simulation model as a learning environment to devise an optimal irrigation strategy. By continuously learning from crop feedback and environmental inputs, the DRL system dynamically modifies irrigation amount to optimize production while consuming the least amount of water. Our approach presents a viable alternative for sustainable irrigation decisions in water-intensive crops, since preliminary findings indicate that it can greatly conserve water without sacrificing crop health or productivity. The goal of this research is to aid in the advancement of precision irrigation technologies that guarantee cotton production's sustainability and resource efficiency. 

How to cite: Panthi, K., Samadi, V., and Toxtli, C.: Optimizing Irrigation for Cotton Crops using Deep Reinforcement Learning Algorithms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14673, https://doi.org/10.5194/egusphere-egu25-14673, 2025.

This study explores an IoT soil moisture monitoring network designed to improve agricultural efficiency and sustainability. The system integrates LoRaWAN-enabled soil moisture and temperature sensors, strategically deployed across agricultural fields, with a Raspberry Pi 4 gateway that processes and transmits data to the cloud. The combination of low-power, long-range communication and dual connectivity options—Wi-Fi and LTE 4G—ensures reliable operation even in remote areas, making the system ideal for large-scale agricultural monitoring.

The core of the network is a robust edge processing framework that enhances data accuracy, security, and efficiency. The framework begins with noise filtering, using techniques such as median filtering to remove anomalies from raw data. Once filtered, the data is aggregated over specific time periods to reduce transmission bandwidth and provide actionable summaries of soil conditions. Adaptive data rate adjustments further optimize resource use by increasing data collection frequency during significant environmental changes and reducing it during periods of stability.

Data security is ensured through encryption at the edge, protecting sensitive environmental information from unauthorized access. Local processing also supports predictive analytics, using models like decision trees or linear regression to forecast future soil moisture and temperature conditions based on historical trends. These forecasts enable proactive decision-making, such as adjusting irrigation schedules to maintain optimal soil moisture levels, improving resource efficiency and crop health.

Anomaly detection is another critical component of the system, identifying unusual patterns in sensor readings that could indicate malfunctions or unexpected environmental changes. This ensures data integrity by flagging or excluding erroneous data. In addition, real-time event-driven alerts notify users of critical thresholds, such as dangerously low soil moisture or rapid temperature changes, allowing for immediate interventions. Alerts are delivered through SMS, email, or cloud dashboards for maximum accessibility and responsiveness.

The system's scalability supports the seamless addition of sensors, accommodating expanding agricultural operations without significant modifications. Local data logging provides redundancy, preserving raw and processed data even during network outages. This ensures uninterrupted monitoring and allows for post-event analysis, enhancing reliability and resilience.

The network’s design offers substantial benefits for agriculture. Adaptive resource management conserves bandwidth, power, and computational resources, reducing operational costs while extending system lifespan. By combining edge processing with cloud analytics, the system provides timely and actionable insights, empowering farmers to make data-driven decisions. Enhanced security through encryption protects sensitive data, while predictive analytics and anomaly detection ensure proactive and accurate responses to changing field conditions.

Overall, the IoT soil moisture monitoring network is a robust and efficient solution for modern agriculture. It enhances real-time monitoring, decision-making, and resource management, enabling farmers to optimize irrigation, improve crop health, and boost productivity. The system's scalability and adaptability make it a practical choice for addressing the growing demands of precision agriculture, contributing to sustainable farming practices and improved food security.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under ScaleAgData project (Grant Agreement No. 101086355).

How to cite: Vlachos, M., Mitro, N., and Amditis, A.: Enhancing Agricultural Efficiency through an IoT-Based Soil Moisture Monitoring Network Utilizing LoRaWAN and Edge Computing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15376, https://doi.org/10.5194/egusphere-egu25-15376, 2025.

The Himalayan belt contains over 12,000 glaciers that have witnessed accelerated glacial melt due to concomitant climate change, leading to the formation of numerous unstable glacial lakes. These lakes, dammed by glacial deposits, pose significant mountain hazards due to their potential for sudden discharge of water and debris, causing devastating floods in the downstream reaches. To address the precipitous Glacial Lake Outburst Flood (GLOF) risks, there is a dire need to account for the impacts at near-real time, given their lesser warning times. The study proposes to develop a pre-simulated GLOF inundation library through a set of scenarios based on breach depths, breach widths, and moraine failure times to model extreme GLOF events over Safed Lake, a sensitive glacial lake in the Uttarakhand, India. At the first place, a geospatial analysis is carried out with a set of Landsat 9 images to ascertain the spatio-temporal dynamics. Using a set of scenarios within the 1D-2D coupled MIKE+ model, we perform flood inundation simulations to create a GLOF inundation library. This library will facilitate the selection of the closest inundation map based on near-real-time data; Thus, enhancing effective flood risk communication and preparedness. This innovative approach to GLOF modeling and flood risk communication is crucial for managing unstable glacial lakes with high flooding probabilities and short warning times. The findings underscore the importance of advanced modeling and timely communication in mitigating the impacts of glacial lake outburst floods and improving resilience in the Himalayan region.

Keywords: Climate change; Flood Risk Management; Glacial Lake Outburst Flood; Inundation library; Landsat 9; MIKE+;

How to cite: Saha, S., Pandey, A., Rao, B. S., and Prakash Mohanty, M.: Development of a GLOF forecasting system through a novel concept of pre-simulated library over the Hindu Kush Himalaya region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15646, https://doi.org/10.5194/egusphere-egu25-15646, 2025.

Soil moisture is a crucial parameter that influences various environmental and socioeconomic processes, including flood and drought mitigation, sustainable agricultural productivity, and industrial applications. This study analyses soil moisture dynamics using data from 25 sensing stations distributed across various regions of Karnataka State. These sensing stations were installed under the REWARD (Rejuvenating Watersheds for Agricultural Resilience through Innovative Development Programme) project funded by World Bank. These stations encompass diverse topographic, soil, rainfall, and crop characteristics. High-frequency data collected from these stations at 15-minute intervals is aggregated into daily averages to analyse soil moisture responses to rainfall, recovery times, and depth-wise correlations between 5 cm and 50 cm. This study also validates soil moisture products from SMAP and EOS-04 satellites using ground-based measurements at these 25 locations. The validation was performed for both raw satellite data and data filtered using the Soil Wetness Index (SWI). The Soil Wetness Index (SWI) filter is applied as a background layer to effectively capture soil moisture dynamics across different spatial scales. The accuracy of soil moisture retrievals is evaluated for SMAP products at spatial resolutions of 9 km, 1 km, and 400 m, as well as for EOS-04 data at a 500 m resolution. When the SWI filter is applied, the remotely sensed retrievals show the strongest agreement with in-situ measurements across cultivated crop areas throughout the year. The findings from this study enhance the understanding of soil moisture dynamics and offer actionable recommendations for selecting the best satellite soil moisture products and optimizing soil moisture modelling. These insights are valuable for agricultural planning, water resource management, and disaster mitigation strategies in regions with diverse environmental conditions.

How to cite: Parekattuvalappil Shaju, A., Gupta, V., and Muddu, S.: Validation of SMAP and EOS-04 Soil Moisture Products Over Karnataka’s Heterogeneous Agricultural Landscapes Using Ground Measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15812, https://doi.org/10.5194/egusphere-egu25-15812, 2025.

The Piedmont Plain (NW Italy) is characterized by a shallow phreatic aquifer hosted in fluvial complex (gravel and sand),  overlying a fluvial-lacustrine and marine complex (gravel and sand with silty clayey levels) containing deep confined/semiconfined aquifers.

Deep aquifers are essential for the supply of drinking water in the Piedmont Plain. However, detailed information on deep aquifers is lacking, such as a regional piezometric map, a continuous monitoring of the water table variations over time and a regional characterization of GW quality. Moreover, the deep groundwater chemical values in the Piedmont Po Plain show significant temporal variability and need to be characterized.

The aim of this study was to analyze the trends (period 2000–2021) in the main physicochemical parameters (electrolytic conductivity (EC), pH) and main ions (Ca, Mg, HCO₃, Na, Cl, NO₃ and SO₄) in 70 wells in the deep aquifers in order to identify the main ongoing processes. Furthermore, to gain a deeper understanding of specific processes, the temporal distribution of threshold exceedances ​​for the sum of pesticides (period 2009-2021) was evaluated. The potential interaction with shallow aquifers was evaluated making a comparison of the average concentrations for the main ions and parameters between shallow and deep aquifers. In general, shallow aquifers are exploited for agricultural purposes and show higher concentrations compared than  deep aquifers.

Additionally, the temporal trends of ion exchange (Ca+Mg/Na index) were evaluated to highlight the contribution from silty-clayey layers, which represent the less permeable portions of the deep aquifers.

Results highlight relevant increasing trends for EC, Ca, Mg and Cl in more than 60% of the monitored wells, and increasing trends for HCO₃ and Na in more than 40% of the monitored points. For these parameters, decreasing trends exist for less than 10% of the monitored points. SO₄, NO₃ and pH show heterogeneous trends. In particular, several monitored wells show significant variation over time, with concentrations doubling from the beginning of the time series. The sum of pesticides shows greater exceedances of the threshold values in the most recent period (2016-2021) compared to the previous one (2009-2015).

The temporal trends of ion exchanges reveal the presence of trends in 61% of the monitored wells, with a prevalence of increasing trends, corresponding to direct ion exchange. For the main ions, the comparison between the average concentrations in the shallow and deep aquifers shows higher values in the shallow aquifers.

These results suggest an increase in the recharge of the deep aquifers by the shallow aquifers and an increased contribution from silty-clayey layers of the deep aquifers. These processes are consistent with excessive withdrawal from deep aquifers. Furthermore, the increasing concentrations represent a significant issue, leading to the progressive deterioration of deep groundwater quality. In conclusion, the main processes responsible for the variation in groundwater chemistry in the deep aquifers were identified, defining the existence of impacting and worrying processes at a regional scale.

How to cite: Cocca, D., Lasagna, M., and De Luca, D. A.: Groundwater chemical trends analyses in the deep aquifers of the Piedmont Po Plain (NW Italy): preliminary evaluation of ongoing processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15983, https://doi.org/10.5194/egusphere-egu25-15983, 2025.

Agro-hydrological modeling is crucial for designing climate change adaptations such as irrigation management. However, the accuracy of the simulation results greatly relies on the availability and accessibility of reliable ground data. Many countries extremely vulnerable to climate change have limited ground data as input for agro-hydrological modeling that restricts the validity of model results. A ‘model inversion’ technique can potentially tackle this data-scarce situation. Here, we combine alternative data sources, such as remote sensing for the estimation of crop development, with intense simulations to find missing input data such as irrigation.

The present study aims to assess the performance of the model inversion technique using the AquaCrop model under different synthetic scenarios. The main research question is, ‘Is an inverted AquaCrop model able to identify the irrigation pattern of the crop growing period?’ The different synthetic scenarios for testing the performance include variations in the rainfall amount, irrigation amount and interval, soil texture, and initial soil moisture conditions. Preliminary results for synthetic scenarios show that inverse modeling is feasible for the estimation of irrigation patterns. The results indicate that under conditions of zero rainfall and dry initial soil moisture state, best inversion results were produced in both scenarios where continuous and non-continuous irrigation was applied. The scenarios near real-world conditions yielded the best results when continuously using uniform irrigation. Further research will investigate whether integrating remote sensing-based crop growth indicators like LAI or NDVI into the inverse modeling approach can improve scenarios' simulation with non-continuous irrigation.

How to cite: Saravanan, A., Karthe, D., and Schütze, N.: Assessing the Performance of Crop Model Inversion Technique in the AquaCrop Model under Different Synthetic Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18154, https://doi.org/10.5194/egusphere-egu25-18154, 2025.

The Luni River Basin situated in the arid and semi-arid regions of Rajasthan, faces growing challenges related to flooding, despite receiving low annual rainfall, with some areas recording less than 250 mm. The Luni being an ephemeral river, is primarily influenced by monsoonal precipitation which drives the majority of surface runoff within the basin. However, the increasing frequency and intensity of extreme rainfall events have significantly altered its hydrological dynamics. These sudden and intense downpours increasingly trigger flash floods, which disrupt the already fragile water dynamics of the region. Flood events in the Luni Basin are particularly severe due to the interplay of geomorphological and anthropogenic factors. The basin predominantly has sandy soil, coupled with high salinity levels result in limited infiltration capacity. This, combined with enhanced surface runoff exacerbates the frequency and impact of floods. Moreover, extensive groundwater extraction, rapid land-use changes, urbanization, and the expansion of irrigation systems reliant on canal-fed networks have heightened the basin’s susceptibility to flooding. These floods not only damage critical infrastructure and agricultural lands but also complicate water storage and long-term resource management strategies. This study focuses on modeling the flash flood events in the Luni River Basin over the period from 1979 to 2024 to better understand their impacts on the arid and semi-arid regions of Rajasthan. Advanced hydrodynamic models, such as HEC-RAS and ANUGA, have been utilized to simulate these flood events, providing a detailed representation of flood behavior and extent. The accuracy of these models has been enhanced through validation against satellite-derived data for recent events. This ensures reliable flood extent mapping, offering valuable insights into the basin's hydrological responses and supporting the development of effective flood mitigation and management strategies.

How to cite: Dahiwale, A. V., Nema, S., Jatav, M. S., Barman, D., Choudhary, S. S., Rao, M. S., and Sharma, A.: Modeling Flash Flood Events in The Arid And Semi Arid Regions of The Luni River Basin India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18401, https://doi.org/10.5194/egusphere-egu25-18401, 2025.

The vast network of glaciers in the Himalayas serves as a vital source of freshwater for the main river systems. These are essential in determining a region's climate and hydrology. Ecological balance, agricultural output, and hydrological systems all depend on these glaciers. However, the stability of hydrological systems and long-term water availability have become major concerns in recent decades due to the acceleration of glacier melting brought on by climate change. In this study, globally available gridded satellite and reanalysis datasets, including ERA5, IMDAA, IMD, APHRODITE, and others, were evaluated to identify the most accurate dataset for the Bhilangana Basin. A thorough performance evaluation was conducted to assess the suitability of these datasets for the region. Furthermore, a hybrid rainfall dataset was developed using a bias correction approach to improve accuracy and reliability, ensuring a more robust representation of precipitation dynamics. The Spatial Processes in Hydrology (SPHY) model was utilized to examine the dynamics of snow-glacier melt during the years 2020–2023. The performance matrix revealed that the ERA5 dataset performed better than other datasets except the hybrid precipitation dataset. The average variation during 2000-2023 in snow q was found in the range of 15 to 26 percent, rain q from 12 to 58 percent, glacier q from 56 to 18 percent and base q from 8 to 18 percent. The analysis further revealed that 11 parameters were found to be critical in influencing the model's output e.g. Degree day factor for snow(DDFS), Glacier debris degree day factor(DDFG), Tcritical, Glacier melt frac runoff. The SPHY model's applicability for studying snow-glacier melt runoff dynamics and the significance of combining various climate datasets to precisely forecast the water resource scenarios in glaciated basins are further highlighted by this study.

How to cite: Joshi, B., Singh, V., Chandola, V. K., and Singh, A.: Modelling Snow-Glacier Melt Runoff Dynamics In Bhilangana Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19490, https://doi.org/10.5194/egusphere-egu25-19490, 2025.

The catchments of the Quéntar and Canales reservoirs are two adjoining valleys on the north-western side of the Sierra Nevada in Spain. Canales drains the northern slope of the river Genil, with 15 linear km of peaks above 3000 meters, culminating in the highest in the Iberian Peninsula, Mulhacen, at 3479 meters. With 83 km2 above 2000 m, this river exhibits a clear nival hydrological regime. In contrast, the Quéntar basin, which collects water from the Padules and Aguas Blancas rivers, drains a smaller area with a maximum altitude of 2336 m and only 7 km2 above 2000 m. Its regime is pluvio-nival, with a much more marginal influence of snow.

To understand and predict the hydrological behaviour of these catchments under climate change scenarios, we have calibrated two different hydrological models. These models will provide the predictive tools needed to calculate river temperature and substances, particularly nitrogen (N) and phosphorus (P). The first model, SWAT (Soil and Water Assessment Tool), is a well-known conceptual semi-distributed parametric model based on linear reservoir equations that simulates snow using a modified degree-day model. The second model, NIVAL, is a distributed model based on physical processes, featuring a specific snow module that relies on mass and energy balance, specifically designed for use in the Sierra Nevada.

The two models differ significantly in terms of preparation, calibration and performance. SWAT's advantages are those of any distributed model: fast computation, easy calibration (facilitated by automatic algorithms) and a reduced need for input data. These features make SWAT a practical choice for many applications. On the other hand, NIVAL offers a more detailed representation of the hydrological processes and greater robustness to changes in scenarios outside the calibration range. This makes NIVAL particularly valuable for studying individual processes and hypothetical future scenarios.

It was expected that the flow adjustment in SWAT would be less accurate than in NIVAL, especially in the Canales basin due to the significant snow influence. However, the calibration and validation of both models on daily flows for both basins yielded very similar results in the most common statistics. For instance, the Nash-Sutcliffe Efficiency (NSE) values were around 0.63/0.70, the Kling-Gupta Efficiency (KGE) was 0.70/0.74, and the Percent Bias (PBIAS) was 2.49/19.08 for the Canales and Quéntar cases. These results demonstrate that SWAT is a reliable option for calculating total flows in historical scenarios.

Nevertheless, NIVAL's detailed process representation makes it more reliable for studying individual processes or hypothetical future scenarios. The next step in this research is to compare these models against various climate change scenarios to assess the differences in their predictions. This will help us understand the strengths and limitations of each model and improve our ability to predict and manage water resources in snow-covered Mediterranean catchments under changing climate conditions.

Aknowledments: This research has been supported by Grant TED2021-130744B-C22 funded by MICIU/AEI /10.13039/501100011033 and by the European Union Next GenerationEU/ PRTR

How to cite: Herrero, J., Galván, L., Fernández de Villarán, R., Gulliver, Z., López-Padilla, S., Pulido-Velázquez, D., and Rueda, F.: Comparison of a Semi-Distributed Empirical Model and a Distributed Physical Model in a Snow-Covered Mediterranean Catchment Under Climate Change Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19528, https://doi.org/10.5194/egusphere-egu25-19528, 2025.

Studying of surface water and groundwater interaction is crucial in understanding the changes in the ecosystems, thus affecting the quality as well as the quantity of hydrology of the catchment. Non -perennial rivers account around 50% of the world’s rivers and such interaction plays a prominent role in determination of seasonal availability and quality of such catchments. The present study aims to identify the river water and groundwater interaction using hydrogeochemistry and stable isotopes in Cauvery, a major non-perennial river of southern India. The river water as well as groundwater was collected once in four months from 2013 to 2021. The samples were analysed for major ions from 2013-2021 whereas stable isotopes δ¹⁸O and δ²H were analysed during 2018 and 2021. Inverse modelling was carried out to understand the hydrogeochemical reactions during surface water and groundwater interaction. Both river water and groundwater was  dominanted by Ca-Mg-HCO₃ and Na-Cl type. Seasonal variation of major ions in river water and groundwater shows similar variation. The inverse modelling indicates the weathering of hornblende, plagioclase, biotite, K-Feldspar into stable clay minerals along with the leaching of major ions into the water. The stable isotopes indicates that both river water falls between precipitation and the evaporation during wet seasons, whereas few samples have been isotopically enriched during the dry season as a result of evaporation, suggesting that groundwater contributes to the river water. Also, the interaction between river water and surface water is more evident during wet seasons, whereas during dry periods the interaction persists in headwater regions. falls between precipitation and the evaporation during wet seasons, whereas few samples have been isotopically enriched during the dry season as a result of evaporation, suggesting that groundwater contributes to the river water. The present study on river water and groundwater interactions acts a baseline framework in developing sustainable water management in non-perennial rivers. The temporal variation of major ions between groundwater and river water shows similar pattern, indicating their interrelationships. The isotope results shows that groundwater and river water falls between precipitation and the evaporation during wet seasons, whereas few samples have been isotopically enriched during the dry season as a result of evaporation, suggesting that groundwater contributes to the river water. The weathering of hornblende, plagioclase, biotite, K-feldspar occurs during groundwater -river water interaction which then transforms to stable clay minerals. It was evident that at the lower part of the basin, the river water discharges into groundwater during the wet periods and vice versa during dry seasons. Thus, this current study on river water- groundwater interactions act as a baseline knowledge in developing sustainable water management plan in the river basins.

How to cite: Ramesh, R., Lingaiah, K., and Lakshmanan, E.: Identification of surface water and groundwater interaction in a non perennial river using hydrogeochemistry and stable isotopes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20562, https://doi.org/10.5194/egusphere-egu25-20562, 2025.

Atmospheric deposition is an important pathway for PFAS to enter soil, surface water and groundwater, but their persistence and mobility complicate source identification. Understanding these pathways is crucial for safeguarding drinking water production, as PFAS poses risks to human health. This study investigated atmospheric PFAS deposition in two drinking water production locations located 135 km apart, highlighting its potential impact in the contaminating soil, surface water, and groundwater. Aerosol and deposition samples were collected along with meteorological data at both locations. To link the deposition of PFAS fluxes to surface water and groundwater contamination, water samples were collected from hydrologically isolated heathland pools. PFAS concentrations were analysed in all samples, and tracer ions (Na⁺, Mg²⁺) were measured in aerosols to explore associations with sea-spray aerosols (SSA). PFAS concentrations in aerosols were consistent between the two sites, with 12 of 14 PFAS detected at both locations. Trifluoroacetic acid (TFA) and trifluoromethanesulfonic acid (TFMS) were most abundant PFAS compounds, followed by PFBA, PFOA, PFOS, and 6:2 FTS. The PFAS composition of deposition fluxes were similar to aerosol concentrations, suggesting relatively unbiassed atmospheric removal of PFAS by deposition. A unique PFAS fingerprint was identified for future source tracing, while the absence of 6:2 FTS in deposition samples highlighted its distinct atmospheric behaviour. PFAS patterns in heathland pools matched those in aerosol and deposition samples, confirming atmospheric deposition as a the main contamination source. PFPeS, PFHxS, PFHpS, and branched PFOA were present in water samples, but lacking in aerosol and deposition samples. This absence is likely due to historical deposition and accumulation processes, highlighting the potential impact of legacy PFAS inputs. Soils and surface waters may act as both sinks and secondary sources of PFAS, releasing contaminants into groundwater and perpetuating risks. Wind data indicated a potential HFPO-DA source northwest of one location, while PFAS levels were not linked to SSA tracer ions at either location. Consistent results between the two locations indicate that the bulk of PFAS contamination is linked to diffuse, rather than local, sources. The findings of this study highlight the important role of atmospheric deposition as a source of diffuse PFAS contamination to soils, surface waters and groundwater and emphasize that historical PFAS input and accumulation processes should be taken into account when assessing risks and mitigation strategies to protect drinking water supplies and public health.

How to cite: Hockin, A., Amato, E., van Leeuwen, J., and Hartog, N.: Atmospheric deposition as a diffuse source of PFAS contamination of soils, ground and surface water resources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1703, https://doi.org/10.5194/egusphere-egu25-1703, 2025.

Locating geographical sources of per- and polyfluoroalkyl substances [PFAS] to rivers, and quantifying the loading attributable to those sources, requires temporal and spatial analysis of PFAS loads across river basins. However, as most studies focus on the measurement of PFAS concentrations, there is a distinct lack of mass loading data for rivers worldwide. As a result, we do not have scientifically robust estimates of how much PFAS enter our rivers from different sources within a river basin or how much PFAS flows from rivers into the oceans.

Here, we present a temporal and spatial analysis of PFAS loads in the River Mersey Basin, England, a heavily industrialised and urbanised river basin. Our primary aim was to locate and quantify sources of PFAS to the river and to elucidate the spatial and temporal dynamics of PFAS transport.

Using a combined field sampling [n = 112] and modelling approach applied at the tributary to river basin-scale, our three-year study provides the first temporally robust estimates of PFAS export for a European river system and identifies the location and magnitude of PFAS river inputs across a major urban river basin.

Analysis of gadolinium anomalies at the river basin-scale reveals approximately 50% of PFAS are associated with effluents from wastewater treatment works [WwTWs]. This is confirmed by a mass balance analysis of river and WwTW PFAS loads. High spatial resolution studies of PFAS loads at the tributary-scale demonstrate large contributions [up to 70% of total loads] from industry and airports. For example, inputs from a major international airport are responsible for 66% of the total perfluorooctane sulfonic acid [PFOS] load in the River Bollin.    

Monitoring and analysis of PFAS river loads, rather than concentrations alone, allows the geographical location and magnitude of PFAS entry to rivers to be established. Temporally robust and catchment-scale PFAS river loading data are essential to help prioritize catchment management and remediation interventions and to reliably estimate the flux of PFAS from river basins to the oceans.

How to cite: Byrne, P., Mayes, W., James, A., Comber, S., Biles, E., Riley, A., Runkel, R., and Verplanck, P.: A Spatial and Temporal Analysis of PFAS Sources and Loads in the First Industrial River Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3793, https://doi.org/10.5194/egusphere-egu25-3793, 2025.

Trifluoroacetate (TFA) is an emerging contaminant that originates from various human sources. The degradation of fluorinated gases in the atmosphere leads to an ubiquitous input through precipitation. Degradation of agricultural pesticides and pharmaceuticals in waste water add to the amount of TFA pollution. Once released into the environment, the TFA molecule is nearly conservative due to its negative charge, high solubility in water, and absence of degradation pathways. Consequently, TFA concentrations in the environment are constantly increasing, following the industrial production of fluorinated precursor substances. Previous studies suggested accumulation of TFA in plants or retention in organic soil. This knowledge, however, is based on a small number of samples or laboratory labelling experiments. Catchment-scale studies are so far missing. In particular, hydrological processes controlling adsorption and desorption are poorly understood. We therefore analyzed a two-year dataset of weekly major ion and isotope tracers together with TFA in the mountainous Dreisam catchment (Black Forest, Germany). We sampled precipitation, the discharge of three nested catchments and a hillslope spring. A balancing approach suggested that TFA was not permanently retained in forested headwaters. In agricultural parts, we found a surplus of TFA which added up to an annual input of 11 kg km^-2 on arable land. Major ions suggested that previously retained TFA was flushed from soils under wet conditions following large precipitation events. This was true both for agricultural and non-agricultural areas. These findings indicate that TFA concentrations in soils may be higher than average concentrations found in rain or streamflow. Therefore, future research should focus on the unsaturated zone.

How to cite: Frenzel, I., Nöltge, D., Müller, M., and Lange, J.: Assessing Trifluoroacetate Accumulation and Transport in Agricultural and Forested Areas  in a Mountainous Catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5921, https://doi.org/10.5194/egusphere-egu25-5921, 2025.

Per- and polyfluoroalkyl substances (PFAS), particularly perfluorooctanoic acid (PFOA, C₇F₁₅COOH), have been widely used in industry due to their high stability and heat resistance. Their release during manufacturing and treatment processes has led to contamination of aquatic systems and groundwater. While PFOA is generally resistant to degradation under typical aqueous environments, it can be degraded in the presence of catalysts or strong oxidizing / reducing agents. Previous studies have reported that PFOSA derivatives could be transformed into PFOA and shorter-chain PFCAs in the presence of montmorillonite under sunlight irradiation. The Fe³⁺-containing material mentioned above are widely distributed in natural environments, indicating that the potential for PFOA to undergo photocatalytic degradation facilitated by natural media. This study aims to investigate the potential role for photocatalytic transformation of PFOA using various forms of Fe³⁺ found in natural environments (structural iron in clay minerals, magnetite, goethite, etc.) under both 254 nm UV light and natural sunlight conditions (including UV radiation of 290-400 nm). When 50 μM PFOA and 500 μM Fe³⁺-containing montmorillonite were exposed to 254 nm UV light for 3 days at pH 7, a defluorination ratio of 18.2 % was achieved. Future studies will aim to investigate the photocatalytic behavior of structural iron containing clay minerals under natural sunlight irradiation. The photocatalytic reaction between PFOA and nontronite (22.3 wt%), which contains approximately ten times higher structural iron (Fe³⁺) content than montmorillonite (2.3 wt%) will be investigated. To further understand the potential of PFOA phototransformation under natural conditions, reaction mixtures will be prepared with various forms of naturally occurring Fe³⁺ media, such as iron oxides to simulate environmental conditions.

How to cite: Ko, J. and Nam, K.: Study on photocatalytic transformation characteristics of PFOA in the presence of structural iron containing clay minerals and iron oxides, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7586, https://doi.org/10.5194/egusphere-egu25-7586, 2025.

Environmental and health concerns surrounding per- and polyfluoroalkyl substances (PFAS) have garnered increasing attention in recent years. The persistence and high mobility of PFAS present significant challenges in understanding their fate and transport in the environment. To address these challenges and gain insights into the contamination status at large catchment scale, as part of the EU Horizon 2020-project, we further developed the regionalized emission model system “MoRE”, to make it capable of quantifying PFAS emissions via multiple pathways across the Upper Danube Basin(Germany, Austria, Czech Republic, Slovakia, Hungary).

The model operates on an annual temporal scale from 2015 to 2021 and with a spatial resolution of 526 sub-catchments in the size of 354 ± 352 km². General input data were sourced from a combination of open-access databases and local ministry records. Hydrological information was obtained using the Wflow model developed by Deltares, while PFAS concentrations were derived from a comprehensive database integrating data from a 1.5-year monitoring campaign conducted across various environmental compartments within the investigated catchment, as well as additional information from previous studies.

The model accounts for multiple emission pathways, including point sources such as urban wastewater treatment plants and industrial dischargers, and diffuse pathways, such as atmospheric deposition, groundwater flow, surface runoff, and soil erosion. Validation of the model against observational data from multiple river monitoring stations demonstrated pleasing performance, particularly for perfluoroalkyl carboxylic acids (PFCAs). These results underscore the model’s effectiveness in predicting in-stream PFAS loads and concentrations. However, the underestimation of certain substances suggests the presence of unaccounted emission sources.

Key findings reveal that diffuse pathways, especially those associated with inhabitants and legacy contaminated spots (e.g.former firefighting foam applications and municipal landfills), contribute substantially to overall PFAS inputs. Furthermore, point-source emissions from industrial facilities, especially a PFAS production site, significantly influence PFAS concentrations, particularly for "replacement compounds" like ADONA and GenX.

By identifying key contamination hotspots and evaluating potential risks in the context of proposed regulatory thresholds and scenario evaluations, this study provides helpful insights for the water management sector. The model can guide targeted monitoring, inform decision-making for remediation efforts, and support the development of more effective regulatory frameworks to mitigate PFAS pollution at regional and catchment scales.

How to cite: Liu, M., Kittlaus, S., ten Velden, C., Meijers, E., Boisgontier, H., Hartgring, S., and Zessner, M.: Modelling PFAS Emission and Transport at Large-Catchment Scale with a Regionalised Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8778, https://doi.org/10.5194/egusphere-egu25-8778, 2025.

Rapid urbanization rates in combination with climate change may lead to increasing urban runoff volumes and pollutant loads. Many organic pollutants are transported sorbed to particles. Therefore, high discharge events present a major pathway for pollutant transport in rivers. In recent years, per- and polyfluoroalkyl substances (PFAS) have gained growing attention due to their persistent nature, ubiquitous occurrence, and toxicity. Many studies have focused on the transport of PFAS in rivers in the aqueous phase, often overlooking particle-facilitated transport, which is particularly relevant for PFAS precursors (i.e. polyfluoroalkyl substances that can degrade into perfluoroalkyl end-products) due to their generally strong sorption affinity to solids.

In this study, particle-facilitated PFAS transport, including precursor compounds of perfluorocarboxylic acids (PFCA), is investigated during high discharge events in contrasting river catchments in southwest Germany. Additionally, polycyclic aromatic hydrocarbons (PAH) are analyzed. 29 high discharge events were sampled at eight different rivers over 1.5 years. Concentrations of PFAS precursors (∑_PFCA,ox) on the suspended river sediments measured by a chemical oxidation assay (dTOP assay) were between 33.9 ± 0.4 and 100.9 ± 10.6 µg kg^-1, while PAH (∑_PAH16) concentrations ranged from 0.07 – 3.92 mg kg^-1. No apparent correlation was found between ∑_PFCA,ox and ∑_PAH16. While PAH have been shown to correlate with urban pressure strongly, PFAS precursors appear to exhibit an elevated ubiquitous signal in the environment, as they were detected in a remote river catchment at concentrations comparable to those in more urbanized areas. Further source apportionment included the sampling of stormwater overflows from residential and highway areas. PFCA precursor concentrations were more variable and generally higher than those observed in river samples, suggesting that, similar to PAH, one potential source is urban particles and street debris being washed into the river during heavy rainfall events.

PFAS precursor concentrations on suspended sediments in the rivers were more or less independent of the event, likely since rivers act as integrators of numerous small streams and inflows. This was particularly true for the Neckar River, the largest stream investigated, and also holds for PAH. By combining sediment yield data or online turbidity measurements with information on suspended sediment loading, it is possible to estimate the contaminant flux of PFAS precursors. The Neckar River alone transports approximately 1.7 kg year^-1 of PFAS precursors through the city of Tübingen, ultimately carrying them towards the North Sea, where they may degrade into stable PFCA over time.

How to cite: Renner, D., Fabregat-Palau, J., Rügner, H., Ebner, M., and Grathwohl, P.: Particle-Facilitated Transport of PFAS and their Precursors in Contrasting River Catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8881, https://doi.org/10.5194/egusphere-egu25-8881, 2025.

The Red River is one of the most important watercourses in northern Vietnam, providing water for agriculture, industries and domestic uses. Given the proximity of metropolitan areas, agricultural and industrial zones, the Red River is under strong anthropogenic influence and susceptible to contamination by persistent substances, such as Poly- and Perfluoroalkyl Substances (PFAS). Being highly resistant to extreme heat and repellent to both water and oil, PFAS have been widely used in aqueous firefighting foam and in various consumer goods, e.g. cooking wares, food packaging, textile[1], [2]. However, epidemiological studies have suggested that PFAS can induce cancers, toxic effects, and other health problems [3], [4]. Hence, PFAS have been listed as priority substances by several regulatory agencies and added as Persistent Organic Pollutants (POPs) by the Stockholm Convention.

Being a member state of the Stockholm Convention, Vietnam has restricted the usage of Perfluorooctanoic acid (PFOA) and Perfluorohexane sulfonic acid (PFHxS) in the industrial sector (Decree 82/2022/ND-CP). However, PFAS are until now not included in the national environmental quality standards and a deficit of PFAS research effort makes their occurrence in the Red River unknown. Therefore, the aim of this research is to apply a targeted approach (54 PFAS) along with the Total Oxidizable Precursor Assay (TOPA) in water samples collected in the Red River in June and September 2023 and estimate their flux towards the ocean. After the solid phase extraction on mixed mode Weak Anion Exchange cartridges, samples were analyzed by Ultra-High Performance Liquid Chromatography coupled with Mass Spectrometry Orbitrap Exploris^TM 120.

While twenty-one PFAS were detected in samples from June (from 3.0 to 109 ng.L^-1), it was only twelve for September samples (from < limit of quantification to 9.2 ng.L^-1). Perfluorobutanoic acid (PFBA) was the most predominant PFAS in samples from both sampling campaigns. An exception was reported in one sample in June where the 6:2-Fluorotelomersulfonic acid (6:2-FTS) concentration reached up to 99.9 ng.L^-1. The concentrations of PFOA and Perfluorooctane sulfonic acid (PFOS), the two most extensively targeted PFAS, were well below the European and American standard limits, contributing to less than 10% of the PFAS burden. Besides the legacy perfluoroalkyl acids (PFAAs), emerging PFAS analogs could also be quantified in water samples namely the fluorotelomer sulfonic acids and the ether sulfonic acids. The occurrence of emerging PFAS suggests they are being used as substitutes for the regulated PFOA and PFOS in industrial and commercial applications. TOPA consistently demonstrated significantly higher PFAS concentrations, up to one order of magnitude, implying the presence of non-targeted or unknown PFAS besides the 54 selected ones. Positive correlations (p < 0.05) between certain PFAAs and dissolved organic carbon (DOC) suggest either common sources for both DOC and PFAAs or the preferential binding of PFAAs to DOC. The estimated average riverine flux of PFAS varied from several kg to ton.yr^-1, depending on the flow variability and estimation approach (targeted or TOPA). 

References 

[1] Schmidt et al. (2019), doi: 10.1016/J.MARPOLBUL.2019.110491

[2] Evich et al. (2022), doi: 10.1126/science.abg9065

[3] Fenton et al. (2021), doi: 10.1002/ETC.4890

[4] Kim et al. (2021), doi: 10.1016/J.ENVPOL.2021.116929

How to cite: Vu, T. K., Riboul, D., Martinot, P., Guigue, C., Bui, V. H., Malleret, L., and Fauvelle, V.: Poly- and Perfluoroalkyl Substances: A first glimpse of the “forever chemicals” in water samples from Red River, Vietnam, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11000, https://doi.org/10.5194/egusphere-egu25-11000, 2025.

The continuous release of perfluoroalkyl acids (PFAAs) from the transformation of per- and polyfluoroalkyl substances (PFAS) precursors presents a significant and often overlooked challenge in contaminated soils. In south-western Germany a large-scale agricultural topsoil contamination PFAS was discovered, which is known as the Rastatt case, and was traced back to the past application of paper sludge as soil amendment. In this study, 40 PFAS were monitored in eight topsoil samples from Rastatt according to the EPA 1633 method. Additionally, non-target screening was performed to identify PFAS precursors. FTMAPs, diPAPs, and diSAmPAP were identified and accounted for > 80% of the total PFAS burden, which ranged from ~ 280 to 9,700 ng PFAS g^-1. These levels were confirmed by both, non-target screening (semi)quantifications and chemical oxidation of precursors (TOP assay) in order to close the fluorine mass balance against extractable organic fluorine (EOF). Notably, in some organic carbon rich samples, repeated oxidation was needed to achieve a complete fluorine mass balance, highlighting the need to include EOF as quality assurance of TOP assays and (semi)quantifications derived from non-target screening approaches.

Batch microcosm incubations were additionally set up to assess short-chain PFAS production over time. The linear increase of short-chain PFAS concentrations in solution, in combination with TOP estimates, allows to derive respective production rate constants and, therefore, estimate contamination time scales. This methodology may potentially apply to other precursor-driven contaminant sources such as those present in aqueous film-forming foam (AFFF) sites. Contamination time scales in the assessed locations indicate that leaching of short-chain PFAS to groundwater resulting from ongoing precursor transformation will continue for decades. The variability in time scale estimates across the eight examined soils encouraged the examination of specific soil properties affecting PFAS production rates, particularly assessing the role of certain phosphatase enzymatic activities and microbial biomass carbon. FTMAPs, diPAPs, and diSAmPAP all contain a phosphate moiety which is hydrolyzed during biotransformation processes. A principal component analysis (PCA) indicated the positive role of both acid phosphomonoesterase activities and, in lesser extent, microbial biomass carbon on the production of short-chain PFAS in soils. Nonetheless, further research on isolated bacteria strains is needed to elucidate the role of phosphatases as well as other enzymatic activities in the decay of P-containing PFAS precursors.

How to cite: Fabregat-Palau, J., Zweigle, J., Renner, D., Zwiener, C., and Grathwohl, P.: Assessment of PFAS contamination in soils: non-target identification of precursors, fluorine mass balance and microcosm studies   , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11049, https://doi.org/10.5194/egusphere-egu25-11049, 2025.

PFAS compounds have emerged as a major concern, due to their effects on human health, widespread occurrence, complex physico-chemical behaviour, very low biodegradability and lack of removal/degradation technologies that could be effective for the very diverse members of this huge family of molecules. Unlike other organic compounds released in environment by anthropogenic activities, such as petroleum hydrocarbons, polychlorinated biphenyls or pesticides, PFAS compounds concentration in the environment is at the parts-per-billion (ppb) and parts-per-trillion (ppt) levels. As a consequence, the microbial communities of most environmental compartments were not exposed to high doses of PFAS, their adaptation strategies largely remain to be explored. They could give useful clues for a better description of the impacts of these molecules and for the development of models of their fate in environment including the biological compartment. Here, 4 different soils presenting contrasting physico chemical properties were artificially contaminated by a mixture of 4 PFAS molecules (PFOS, PFOA, PFHxS and PFBS), in concentrations fixed at 10 mg.kg-1 each and incubated. The impact of PFAS addition was monitored on carbon mineralization activity, enzymatic activities, and evolution of the structure and composition of bacterial, archaeal and fungal communities during 70 days of incubation. The PFAS concentrations were not significantly modified during the incubation, but the mixture of PFAS molecules affected the structure and activity of soil microbial communities differently depending on the type of soil.

How to cite: Battaglia-Brunet, F., Crampon, M., Norini, M.-P., Thouin, H., Charron, M., Tris, H., and Beskoski, V.: Impact of a mixture of PFAS molecules on the activity and structure of soil microbial communities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11533, https://doi.org/10.5194/egusphere-egu25-11533, 2025.

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are a class of anthropogenic organic chemicals that have emerged as persistent environmental contaminants. In 2013, widespread contamination of surface water, groundwater, and drinking water was identified in three provinces of the Veneto Region, northern Italy, significantly impacting nearly 30 municipalities. The contamination was mainly caused by industrial discharges of fluorinated chemicals into local water bodies from a chemical manufacturing facility and other industrial activities. While the production and use of PFAS chemicals have since been regulated in the region, concerns remain regarding the long-term persistence of PFAS in groundwater due to their environmental stability. This study analyzes groundwater monitoring data collected over a 10-year period (2013–2023) to evaluate temporal trends and spatial variability in PFAS concentrations across the region. The dataset contains concentrations of key PFAS compounds, including perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and other prevalent species, collected from multiple groundwater monitoring wells. We used statistical methods to analyze temporal patterns and geostatistical methods for spatial mapping of contamination hotspots. Preliminary findings indicate heterogeneous spatio-temporal trends among various PFAS compounds, with some exhibiting significant variations over time and location, while others remained relatively stable. These findings provide valuable insights into the behavior of PFAS in groundwater, aiding in developing future monitoring strategies and mitigation efforts.

How to cite: Younas, A., Aruffo, E., Lanuti, P., Tariq, M., and Di Carlo, P.: Spatio-Temporal Variability of PFAS Compounds in Groundwater in the Veneto Region, Italy (2013–2023), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12081, https://doi.org/10.5194/egusphere-egu25-12081, 2025.

In France, two-thirds of the water withdrawn for drinking water supply comes from groundwater (OFB, 2017), hence monitoring PFAS is essential to document spatial distribution, dynamics and anticipate potential impacts on water quality.

A good monitoring resolution in term of spatial extend, frequency, analytic performance is crucial to better understand sources, pathways and potential impacts. We propose a focus on the regulatory groundwater monitoring for France, where an increasing number of PFAS compounds have been regulated for monitoring, going from 6 in 2015 to 20 in 2022, in compliance with the European Drinking Water Directive (Directive 2020/2184). However, as PFAS represent a family of more than 10,000 compounds, it is necessary to assess the total PFAS contamination, beyond the list of regulated parameters.

The concept of the “total PFAS” is not yet clearly defined and is open to question. Measurement by combustion ion chromatography (CIC) provides access to adsorbable organic fluorine (AOF), i.e. the total measurement of fluorinated organic compounds, without the need to identify each individual compound. It is a fast, inexpensive method that gives an indication of the overall level of contamination. However, it can also include substances other than PFAS (e.g. fluorinated pharmaceuticals). Methodological and analytical developments under the framework of the H2020 PROMISCES project (GA No 101036449) will contribute to the deployment of this approach in water monitoring.

At the French level, we analysed the available data on PFAS concentrations in groundwater (from the ADES[1] database) from different perspectives.

In terms of spatial contamination, PFAS occurrences were mapped in relation to different hydrogeological contexts and pressures (emission sources, aquifer types, density of use,…). These groundwater occurrence maps are of interest for the implementation of health monitoring of water intended for drinking water supply, both from the point of view of geographical sectors and water ressources, but also for the analysis of the contexts to be targeted. This will support decision making for drinking water supply where health risk is of primary interest.

In terms of time, the first PFAS analyses are reported for the period 2009-2011. In the whole dataset, the more densely monitored parameters (> 30,000 analyses) are PFOA, PFOS, PFHpA, PFHxA, PFDS, and PFHxS.

Quantification rates vary considerably between the different molecules analysed. PFOS is the compound with the highest quantification frequency (17.8%). It is also the most frequently researched compound (about 38,000 analyses). Other compounds researched with the same intensity (about 35,000 analyses) have lower quantification frequencies: PFHxA (12.3%), PFOA (11.2%), PFHxS (10.5%), PFBS (7.8%), PFHpA (7.1%), PFBA (5.4%). For the dataset considered, 12 of the 20 compounds were found, on average, less than 3 times out of 100.

Our work highlights that PFAS are widely observed in French groundwaters. Quantification rates are among the highest reported for micropollutants at the national level. Given the potential time-delay effect, due to stock effect, in the soil and unsaturated zone is suspected, and the documented adverse effects on human health, careful monitoring of these compounds is essential in the near future to support decision-making.

How to cite: Lions, J., Togola, A., Henriot, A., and Lopez, B.: PFAS Monitoring in groundwater: Current status and challenges in France, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15524, https://doi.org/10.5194/egusphere-egu25-15524, 2025.

Elevated ammonium (NH₄⁺) concentrations in groundwater (GW) pose significant challenges to existing GW treatment systems, particularly in simplified systems such as household sand filters (HSFs), which are widely used in developing countries. We previously conducted a series of column experiments (sand filter materials collected in Hanoi, Vietnam) mimicking HSFs. These experiments revealed limited and fluctuating NH₄⁺ removal, highlighting the need for a comprehensive process-based model to elucidate the complex interplay of physical and biochemical processes that influence NH₄⁺ concentration dynamics in these systems. Here, we established a one-dimensional advective-dispersive-reactive model conditioned on data from column experiments under laboratory (artificial GW inflow with sand materials from local HSFs) and field conditions (natural GW inflow with sand materials from local supplier), accounting for temporal variations in reaction kinetics, transport processes, and a previously unconsidered inter-phase transfer process for nitrate (NO₃^-). The modeled breakthrough curves capture the complex dynamics of NH₄⁺, nitrite (NO₂^-), and NO₃^- concentrations under both laboratory and field conditions. The reaction rates of the nitrogen species show strong hysteresis in response to substrate (NH₄⁺ and NO₂^-) concentrations, suggesting that potential lags in the biochemical reactions caused by inhibitions and low retention time lead to the incomplete NH₄⁺ removal. Our scenario analysis indicates that, without inhibition effects, the current bio-reactive environment could reduce NH₄⁺ concentrations to the legal target level (within up to eight hours retention time under field conditions). This study represents one of the few process-based modeling efforts mimicking HSFs. Future modeling research should parameterize various inhibition effects into the existing reactive transport models in order to gain quantitative insight into enhancement methods for HSFs.

How to cite: Wei, R., Le, A. V., Liu, B., Azari, M., Nowak, W., Kappler, A., and Oladyshkin, S.: Modeling the Ammonium Removal Processes in Household Sand Filters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-205, https://doi.org/10.5194/egusphere-egu25-205, 2025.

Abstract

Arsenic (As) contamination in groundwater is a serious environmental and public health issue, particularly in regions where groundwater is a primary source of drinking water. Many Asian countries, particularly Bangladesh, India and parts of Southeast Asia are adversely affected with As contaminated aquifers. The present study thus aims to explore the source, distribution and release mechanism of As into the groundwater in the parts of southern bank of the Upper Brahmaputra floodplains in Assam, India.  Groundwater samples (n=100) were collected from the shallow aquifers (< 30 m) from two the districts Jorhat and Golaghat, and were analysed for major ions (Ca²⁺, Na⁺, K⁺, Mg^2+, Cl^-,HCO₃^-, NO₃^-,SO₄^2-) and trace elements (As, Fe, Mn, Pb, Co, Cu, Zn). Concentration of As, Fe and Mn in the aquifers has exceeded the permissible limits set by WHO (World Health Organisation) posing serious threat to human health. 54% of groundwater samples have exceeded WHO permissible limit of 10 µg/L for As (range: bdl (below detection limit) to 480 µg/L, mean: 31µg/L). While 94% (range: 0.076 mg/L to 41.37 mg/L, mean: 10.92) and 77% (range: 0.002 mg/L to 9.06 mg/L, mean: 0.6 mg/L) of groundwater samples have exceeded the WHO permissible limit of 0.3 mg/L and 0.05 mg/L for Fe and Mn respectively. Aquifers enriched with As was found adjacent to Naga foothills while low As was found near the Brahmaputra river. Aquifer lithology reveals the presence of thick clay layer near the Naga hills (also indicated by higher Al₂O₃ in the sediments) and subsequently minerals like illite and kaolinite was found in these clay layers (confirmed by the XRD peaks). The clay minerals might have acted as the active site for adsorption of As, thus acting as the host for As in the studied region. Moreover, the average value (mean: 80) of Chemical Index of alteration (CIA) indicates intense chemical weathering at the source area in warm and humid condition leading to formation of copious amount of clay minerals like kaolinite. No strong co relation was seen between As and the redox sensitive elements viz; Fe and Mn, nor with HCO₃, NO₃ or SO₄ indicating multitudinous processes or reactions viz competitive exchange with anion, reductive dissolution and pH dependent sorption might have control the release of As in the studied region. Higher LREE compared to HREE indicates the source of these clastic sediments could be from felsic or intermediate composition of rocks. Principle Component Analysis (PCA) and cluster analysis indicated the dominance of geogenic factors as the main contribution of these contaminants in the groundwater of the study area. Regular monitoring and intervention of groundwater in the region is crucial for its prolong use. The present study will assist stakeholders and policy maker in taking evidence-based decision and providing As safe drinking water to the affected communities

Key words: Arsenic; Groundwater; Brahmaputra floodplains; Sediment Geochemistry; Hydrochemistry

How to cite: Medhi, S. and Choudhury, R.: Arsenic toxicity in Groundwater of Brahmaputra Floodplains of Assam, India: Concerns for drinking water quality, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1106, https://doi.org/10.5194/egusphere-egu25-1106, 2025.

Cryptosporidium parvum is a pathogen causing gastrointestinal infections, occasionally leading to death in immunocompromised individuals. It can contaminate surface water and groundwater, and consequently drinking water supplies, through agricultural activities such as cattle and dairy farming or the spreading of manure as fertilizer. The importance of removing C. parvum by filtration is of great interest because of its long-term persistence in water as oocysts and its resistance to chemical disinfection owing to its thick cell wall. This is relevant for both subsurface filtration and engineered filtration processes. Therefore, it is necessary to evaluate its removal efficiency in subsurface media and during the filtration stage of drinking water treatment. This study aimed to select an appropriate surrogate for C. parvum oocysts that exhibits similar attenuation and transport behaviour through porous media, is cost-effective, and poses no harm to humans or the environment, enabling its application in engineered installations and field studies.

Bacillus subtilis is commonly used as a conservative surrogate for C. parvum for subsurface transport studies, and aerobic spores have been included by the U.S. Environmental Protection Agency as an indicator for C. parvum in groundwater under the direct influence (GWUDI) of surface water. While B. subtilis may be a cost-effective option, its smaller size (nearly 6 times smaller in diameter), different shape (rod-shaped vs. spherical), and distinct surface characteristics present limitations. This study evaluated the attenuation and transport of B. subtilis spores, oocyst-sized unmodified (yellow-green and yellow-orange) and glycoprotein-coated microspheres, along with UV inactivated C. parvum in columns packed with silica sand. The objective was to determine the significance of size, surface charge, and macromolecules on the cell wall surface, on the reduction of the oocysts. Glycoprotein-coated microspheres, exhibiting similar physicochemical properties (including macromolecules) to oocysts, were found to be the most effective surrogate. The study results highlight the importance of selecting appropriate surrogates for accurate evaluation of the transport of C. parvum in the subsurface and its removal in water treatment through sand filtration.

How to cite: Stevenson, M. E., Pang, L., Farnleitner, A. H., Lindner, G., Kirschner, A. K. T., Blaschke, A. P., and Sommer, R.: Selecting an Appropriate Surrogate for Assessing Filtration Removal of Cryptosporidium parvum for Water Treatment Applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3798, https://doi.org/10.5194/egusphere-egu25-3798, 2025.

The transport of pathogens through rocks is often regarded as negligible unless there are fractures in the medium. However, sedimentary rocks may have a porosity that allows the migration of pathogens even when they are unfractured.
In this work, we investigated the transport behavior of several pathogens (namely Escherichia coli and Enterococci faecalis) through a sedimentary porous rock made of 98.5 wt.% of calcite (CaCO3), hydraulic conductivity 6·10−6 m/s, and porosity 0.43. Core flooding experiments were performed under variable head conditions, ensuring full saturation of the samples. During the experiments, the flow and pathogen concentration were monitored. After an initial stabilization of the core, a suspension containing a known concentration of pathogens was superimposed onto the sample and allowed to drain through. Upon complete suspension drainage, several cycles of sterile saline solution (0.9 vol.%) were performed until the pathogen concentration at the outlet became negligible. A reactive transport model through saturated porous media was developed and implemented to describe the tests. The model couples conservation laws for flow and transport under variable head conditions with constitutive equations of straining and attachment/detachment. The data show significant retention of pathogens within the core during suspension drainage and rapid mobilization during distilled water infiltration. This behavior is well captured by the model and shows that rocks can act as bioreactors for pathogens that favor accumulation and growth during loading and mobilization during flooding with low-salinity water. This may suggest that porous rock deposits may exacerbate contamination of the underlying aquifers under intermittent conditions of accumulation/growth and release rather than protecting underground water resources, as generally assumed.
This topic is the objective of DY.MI.CR.ON. project “Predictive dynamics of microbiological contamination of groundwater in the earth critical zone and impact on human health (DY.MI.CR.ON Project)” funded by the European Union – Next Generation EU, mission 4 component 1, CUP I53D23000500001.

How to cite: Ghirotto, A., Prigiobbe, V., Caputo, M. C., De Giglio, O., Cassiani, G., Ekiz, M. Ç., Turturro, A. C., Savino, A. F., Colella, D., Barboza, G., Milani, M., and Verani, M.: Transport of pathogens in saturated porous rocks under variable flow and salinity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4185, https://doi.org/10.5194/egusphere-egu25-4185, 2025.

Streams and ponds used for local irrigation tend to demonstrate high spatiotemporal variability of water quality. Microbial water quality monitoring becomes overly resource-demanding if the water quality metrics are treated as purely random values. Research on several irrigation ponds and streams showed relatively stable spatial patterns of microbial and other water quality metrics. Detection of those patterns was achieved by setting 20 to 30 monitoring locations, visiting each location seven to ten times during the irrigation season, measuring the water quality metrics at each location with in situ sampling in water samples, computing relative differences between the measurements in each sampling location, computing the average value of those measurements across the water source for each visit, and finally computing the mean relative differences (MRD) for each location over all the visits. Positive MRDs indicated the preponderance of elevated values of water quality variables, and negative MRDs indicated the prevalence of low values. The nearshore locations typically had the largest MRD in ponds, and the locations with more populated stream reaches. 

Unmanned aerial vehicles were used for multispectral imaging of some ponds on each visit to several ponds before the water sampling. Both reflectance and remote sensing indices were determined at the same locations where water quality metrics were measured. The stable temporal patterns were detected for reflectance and remote sensing indices. Strong significant Spearman correlations were found between stable patterns of some water quality variables and remote sensing indices. Those correlations indicate the opportunities to use UAV-based remote sensing of irrigation water sources to inform the design of sampling water across ponds. Correlations between stable patterns of water quality variable patterns may help in developing monitoring design schemas when the more readily available water quality variable patterns are known.

Establishing temporally stable spatial patterns via the mean relative differences points to locations where monitoring locations could be placed to represent the average across the pond or stream. Also, locations with low MRDs of the microbial pollution metrics appeared to be more suitable for establishing the irrigation water intake. Overall, stable water quality patterns, when detected, can provide useful guidance for establishing and monitoring water quality for those water sources.

How to cite: Pachepsky, Y., Stocker, M., Smith, J., Widmer, J., Harriger, D., Jeon, D., Morgan, B., Hong, S., van Tassel, A., Baek, I., Dunn, L., Coffin, A., Pisani, O., and Kim, M.: Temporal stability of microbial water quality in small  irrigation water sources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7415, https://doi.org/10.5194/egusphere-egu25-7415, 2025.

Transboundary rivers are crucial resources for drinking water, recreation, and irrigation. However, wastewater emissions and global environmental and demographic changes can impair raw water quality and pose risks to public health. This study aims to assess the impact of emissions from wastewater treatment plants, combined sewer overflows (CSOs), and inland waterway transport on microbiological water safety along the upper Danube River Basin (DRB).

To achieve this, we developed a stochastic mathematical model to trace pathogen load emissions throughout the river network. The model predicts concentrations of reference pathogens (Cryptosporidium, Giardia, Campylobacter, norovirus, enterovirus) in the river for current conditions and a future climate scenario represented by a selected CORDEX RCP 8.5 high emission scenario. The study estimates bathing water infection risks and determines the required pathogen logarithmic reduction in raw river water to ensure safe drinking water. The model accounts for exponential pathogen inactivation rates influenced by water temperature, solar ultraviolet radiation, and lake sedimentation (for protozoan cysts/oocysts). High-resolution navigational information based on automated identification system (AIS) data, detailing the number and location of ships, were used to model the impact of inland waterway transport. The data were aggregated into monthly average daily ship volumes across three ship types (cruise, passenger, and freight) along a section of the Danube River. Model validation was conducted using monthly data sets spanning 2–4 years, including cultivation-based standard fecal indicators, human-associated genetic fecal microbial source tracking markers (HF183/BacR287, BacHum), and reference pathogens (Cryptosporidium, Giardia). Additionally, the study investigates whether the crAssphage marker (CPQ_056) serves as a suitable proxy for human viral fecal contamination in water quality modeling, compared to standardized viral indicators such as somatic coliphages. To understand the effects of future changes on water safety, various scenarios and combinations up to the year 2100 are analyzed, including population growth (affecting wastewater emissions), climate change (impacting river discharge and microbial inactivation), advanced wastewater treatment, reduction of CSOs (in line with the recast of the European Urban Wastewater Treatment Directive), and changes in inland navigation and ship wastewater handling.

The findings indicate that tertiary treated municipal wastewater currently has the greatest impact on river water safety. However, if additional disinfection (quaternary treatment) is implemented, other pollution sources, such as ship navigation and CSOs, as well as climate change effects, will play an increasingly significant role in determining microbiological water safety.

How to cite: Derx, J., Valent, P., Steinbacher, S., Ameen, A., König, A.-M., Demeter, K., Linke, R., Sommer, R., Lindner, G., Schmalwieser, A. W., Walochnik, J., Kirschner, A., Mach, R. L., Cervero-Aragó, S., Zessner, M., Kittlaus, S., Blöschl, G., Stevenson, M., Blaschke, A. P., and Farnleitner, A. H.: Resilience to Future Changes: Assessing the Impact of Human Wastewater Emissions on Microbiological Water Safety Along the Danube , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8167, https://doi.org/10.5194/egusphere-egu25-8167, 2025.

To ensure the supply of clean water, we need tools to accurately predict where microorganisms of fecal origin come from, how they move in the environment and where they go to. To date, however, there have been few studies that have looked at bacterial overland transport (BOT). The current state of knowledge is mainly based on data from point sources (sewage treatment plants), whereas little is known about diffuse fecal sources from wildlife and livestock. The aim of this study is therefore to investigate the influence of the type of fecal matter (cow and red deer) as well as storage time and conditions (temperature and moisture) on resuspension and re-mobilization of (genetic) fecal indicators and/or pathogens. For this purpose standardized fecal samples from cow and deer were prepared in the laboratory and stored for different lengths of time (0 to 120 days) under diverse climatic conditions reflecting seasons. Fecal samples were then used in shaking experiments in which the samples were covered with water in Erlenmeyer flasks and placed in a shaking incubator. Different rainfall intensities were simulated by different shaking speeds (60 rpm and 85 rpm) and the effect of the rainfall duration was simulated by the duration of shaking (10 min and 60 min). Cultivation-based methods were used to determine fecal indicator organisms (FIB) such as E. coli, enterococci and Clostridium perfringens spores as well as somatic coliphages in the water. A panel of different qPCR-based DNA and/or RNA markers will then be used to determine host-associated genetic markers (qPCR). This multifactorial experimental approach provides the first quantitative estimates of the persistence and mobility of microbial target organisms in standardized fecal pellets from cattle and deer. The chosen multi-parametric and multi-method approach allows 1) comparison of culture-based with qPCR-based results and 2) comparison of RNA vs. DNA targets. NGS (next generation sequencing) data allows to draw conclusions on intestinal microbial persistence and to evaluate whether they reflect the mobilized load, an important information for the subsequent modelling approach. To summarize, the present study represents the first holistic quantitative approach to determine bacterial overland transport. The state-of-the-art combination of quantitative, microbiological and molecular methods and parameters will provide the scientific basis for accurate prediction of BOT.

How to cite: Linke, R., Zhou, Y., Lindner, G., Hochenegger, N., Borovec, T., Reischer, G., Priselac, K., Hykollari, A., Stalder, G., Sommer, R., Derx, J., and Farnleitner, A.: From the pasture to the water: multiparametric laboratory experiments to determine microbial release from feces, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8224, https://doi.org/10.5194/egusphere-egu25-8224, 2025.

The risk of infection by enteric pathogens in bathing waters is generally indicated by monitoring fecal indicator bacteria (FIB) concentrations. Mechanistic models are efficient tools for predicting FIB concentrations and corresponding contributions from various impact factors based on historical records and different climatic scenarios. However, most existing FIB physicobiochemical models are limited by the availability of FIB observations and knowledge of the physicobiochemical processes. Modeling studies that performed advanced sensitivity analyses or model comparisons to disentangle the contributions from different processes and impact factors, are rare.

To enhance the understanding of the relative importance of the various processes that affect FIB concentrations in different aquatic systems, we developed a comprehensive and generic FIB physicobiochemical model, including an improved die-off module and sediment interaction module. The new die-off module includes a cumulative endogenous photo-inactivation. By developing the relationship between dissolved organic carbon (DOC) concentrations and Ultraviolet diffuse attenuation coefficients, the module calculates the Ultraviolet-A (UVA) and Ultraviolet-B (UVB) extinction by waters. The penetrated UVA + UVB light under different wavelengths is used for endogenous photoinactivation rate calculation via the biological weighting function.  Distinct from using a constant partition rate in previous sediment interaction modules, the new sediment interaction module calculates the dynamic partition rate based on not only suspended sediment (SS) concentrations but also its composition via two different classes of SS: sand and clay.

Separate validation of the two sub-modules demonstrated the reliability of our modeling approach. Contrary to previous die-off modules, our new die-off module implied an improvement after adding UV endogenous photo-inactivation. According to sediment interaction module validation, the dynamic partitioning coefficient can reasonably allocate E. coli between water and sediment through sedimentation and resuspension, which is an essential precondition for incorporating sediment into the model as a reservoir for E. coli.

The sensitivity analysis result showed that 1) photo-inactivation is important in low DOC waters, but not in high DOC waters since the UV penetration is limited; 2) The impact of sediment interaction is insignificant under steady E. coli input conditions, but vital during and after a peak event. Interactions with sediments can extend the half-life of E. coli in water columns up to four times after a peak event.

This work demonstrated the significance of sediment interactions and DOC concentrations for predicting the duration of episodes of insufficient bathing water quality. The new generic module enables better simulation of bathing water quality across different types of aquatic environments and conditions. Future applications can choose processes selectively from the new FIB physicobiochemical model and couple it with hydrological or hydrodynamic models to address specific environmental conditions and research purposes.

How to cite: Wang, H., Blauw, A., van Gils, J., Boelee, E., and Medema, G.: Innovative modeling of the physicobiochemical determinants of fecal indicator bacteria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10607, https://doi.org/10.5194/egusphere-egu25-10607, 2025.

Arsenic contamination in drinking water is a pressing global issue, with over 250 million individuals lacking access to water that meets the World Health Organization's recommended limit of 10 µg/L. Arsenic, a confirmed carcinogen, poses significant health risks, necessitating efficient and cost-effective removal strategies. Adsorption remains one of the most prevalent methods for arsenic removal, employing materials such as metal oxides, graphene-based metal oxides, nanocomposites, and carbonaceous materials and organic-metallic frameworks. One of the most researched materials for the removal of arsenic from drinking water is Zero-Valent Iron (ZVI).  However, ZVI, while widely utilized, exhibits limitations including reduced efficacy for As(III), extended reaction times, sensitivity to competing ions, narrow operational pH and DO range, and iron leaching into the water. This study explores the potential of magnetite/reduced graphene oxide (MrGO)-coated sand as an advanced alternative. MrGO's structural synergy, combining highly adsorptive magnetite nanoparticles with the enhanced stability and properties of reduced graphene oxide, addresses many of ZVI’s shortcomings. However, its application in column studies as a fixed nanoparticle remains underexplored, limited to theoretical and batch studies and pelletized or layered column studies. A novel approach to arsenic removal by integrating MrGO-coated sand and ZVI in column systems is presented in this work. The study evaluates their performance independently and in combination, focusing on removal efficiency, operational range, and cost-effectiveness. This includes the development of MrGO-coated sand for enhanced applicability in column systems and the optimization of MrGO-to-ZVI ratios to achieve maximum removal efficiency under conditions representative of real-world groundwaters. Preliminary findings suggest that MrGO-coated sand demonstrates the ability of the material to adequately remove arsenic while maintaining a broader operational conditions compared to ZVI. By investigating optimal ratios and conditions, this study aims to balance performance with economic feasibility, providing a scalable solution for arsenic-contaminated water treatment, contributing to the advancement of arsenic removal technologies and highlighting the potential of reduced-graphene-oxide-based materials in addressing critical water quality challenges.

How to cite: Şenol, A., Çelebi, S., Elkawefi, O. A. I. M., Şengör, S. S., Ertaş, G., and Ünlü, K.: A Comparative Study of Arsenic Removal from Drinking Water Using Zero-Valent Iron (ZVI) and Magnetite/Reduced Graphene Oxide (MrGO) Coated Sand in Column Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10696, https://doi.org/10.5194/egusphere-egu25-10696, 2025.

In 2021, diarrheal diseases were responsible for around 1.17 million deaths worldwide. Sub-Saharan Africa is one of the most impacted regions, where 440,000 deaths were recorded in 2024. This high mortality rate can be explained by 1) significant bacteriological pollution of surface waters by pathogenic micro-organisms responsible for diarrheal diseases, 2) widespread use of untreated water by the population and3) lack of sanitation and community health infrastructures. In addition, ongoing climate change is likely to have a negative impact on water quality, and potentially increase the presence and transmission of pathogens.

Tele-epidemiology, the combination of satellite observations and epidemiology, is a powerful tool for studying climate-environment-health relationships and for understanding and predicting the spatio-temporal distribution of pathogens and diseases through the use of satellite and in-situ data. We aim at using this method to indirectly monitor water quality and reveal environmental factors conducive to the emergence of critical health situations by modeling the dynamics of E. coli in West Africa. E. coli is considered the best indicator of faecal contamination (IFC) in temperate zones, and is recommended as a proxy for assessment of water contamination. In Burkina Faso, Robert et al (2021) demonstrated a significant correlation between E. coli, intestinal enterococci and cases of diarrhea. E. coli therefore appears to be a good IFC in West Africa, and would be relevant for predicting diarrheal diseases.

The first objective is to study the links between E. coli concentration in water and environmental parameters 1) measured in-situ in West African surface waters (Bagre reservoir in Burkina Faso and Kongou - Bangou Kirey in Niger) from 2018 to 2024 (concentration of suspended particulate matter, particulate organic carbon, etc.), 2) measurable by satellite (surface water reflectances mesured by Sentinel-2) or 3) estimable by satellite algorithm (precipitation, hydrometeorological parameters, NDVI, etc.). We then use key environmental parameters to model the concentration of E. coli in these sites over several years, firstly using all parameters, and then only using satellite data to study their robustness. Various machine learning models (Random Forest, SVM, KNN, etc.) were tested and compared with each other (calculation of R², RMSE, MSE and MAPE). 

For the Bagre site, the best model of E. coli concentration had showed a R² of 0.76 (RMSE 0.49 log10 MPN/100mL) using in-situ and satellite data, and R² of 0.69 with only satellite data (RMSE 0.56 log10 MPN/100mL). For Kongou, the best model had showed a R² of 0.7 using in-situ and satellite data, and R² of 0.65 with only satellite data.

This work will allow to create health hazard indices that can be used by public health players, firstly in West Africa without the need to collect data in the field, and then more generally for other sites facing similar public health problems.

How to cite: Mant, M.-A., Robert, E., Grippa, M., Kergoat, L., Boubacar moussa, M., Funatsu, B., Perez Saez, J., Emma, R. N., and Robin, M.: Temporal modeling of surface water bacteriological quality in West Africa using remote sensing and machine learning methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11562, https://doi.org/10.5194/egusphere-egu25-11562, 2025.

Water contamination caused by enteric microbial pathogens from humans and animals poses serious risks to public health. Rainfall events can induce the release of microorganisms from feces, and the health risks posed by these pathogens to water bodies are highly dependent on their transport and survival characteristics. Novel molecular tools and diagnostic capabilities have rapidly advanced in recent years, offering significant potential to revolutionize the study of microbial contamination and transport in water bodies and to enhance the modeling of overland transport of microorganisms through the application of these advanced diagnostic methods. This study employs rainfall-release experiments and pathogen enumeration in runoff and infiltrated water to investigate bacterial release and overland transport mechanisms from fresh cow feces, aiming to address gaps in advanced molecular techniques and to assess the impacts of fecal shape and aging on the precise quantification of bacterial overland transport (BOT).

Artificial rainfall experiments are conducted on fresh cow pat samples which are placed onto bare surfaces to study bacterial release and onto small scale undisturbed soil plots to study bacterial overland transport. The experimental setup includes three rainfall intensities (40 mm/h, 60 mm/h and 80 mm/h) and two slopes (5% and 25%). In addition, the effects of different fecal shapes are investigated (large and small surface area-to-volume ratios). The quantitative analyses are done for different microbial parameters (FIB, bacterial MST markers) using both culture-based and qPCR-based methods and the effects of experimental setups, microbial parameters, and enumeration methods will be compared and evaluated. The release will be modelled using the Bradford-Schijven model formulations, and Kineros2/STWIR will be used for modelling the BOT.

This study will improve the understanding of the release and transport of manure-borne pathogens from fresh cow pats and provide a more precise quantitative approach to measuring BOT using advanced diagnostic methods.

How to cite: Zhou, Y., Linke, R., Sommer, R., Lindner, G., Strauss, P., Ramler, D., Hykollari, A., Stalder, G., Anton Schatz, R., Priselac, K., Leifels, M., Stevenson, M., Demeter, K., Paul Blaschke, A., Schijven, J., Farnleitner, A., and Derx, J.: Multiparametric Modeling of Bacterial Release and Overland Transport from Feces: Insights from Rainfall Experiments and Molecular Diagnostic Tools , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12690, https://doi.org/10.5194/egusphere-egu25-12690, 2025.

 Waterwater-based epidemiology (WBE) may be able to monitor public health emergencies by analyzing human urinary biomarkers in wastewater. This work proposes a novel approach utilizing WBE for the spatial and temporal evaluation of PAHs exposure using hydroxyl derivatives of PAHs. These are 1-hydroxynaphthalene, 2-hydroxynaphthalene, 2-hydroxyfluorene, 9-hydroxyfluorene, 9-hydroxyphenanthrene, 1-hydroxypyrene and 3-hydroxybenzo(a)pyrene. Most target markers were found at quantifiable concentrations in raw and treated wastewater. The total loads identified in raw sewage ranged from 88.33 g/day  to 154.77 g/day during the summer period and from 137.66 g/day to 283.78 2 g/day during the winter period. The obtained results for the removal efficiencies of OH-PAHs indicate a seasonal dependency in their degradation. Removal efficiencies were higher in January compared to August.

The results of the back calculations allowed to estimate that during the summer, on average, a resident of Krakow could absorb approximately 2.1 µg of the assessed OH-PAHs per day, while in winter, this value increased to 4.1 µg. This is close to the reported in the literature value that the total daily exposure to OH-PAHs is estimated at 3 µg/day.

Moreover, the risk quotation (RQ) values on the base of acute and chronic data base for compounds present in effluents were calculated. The RQ values in January were relatively low, but in August the RQ values were higher, indicating a high concentration of effluent and nitrogen in summer as these compounds were removed in winter and summer.

To the authors’ knowledge, this is the first time wastewater profiling of OH-PAHs in wastewater for the evaluation of exposure to PAHs have been used, also their removal as well emission with effluent were determined. 

How to cite: Styszko, K., Pamuła, J., Sochacka-Tatara, E., Pac, A., and Kasprzyk-Hordern, B.:  Estimate public exposure to PAHs and environmental risks through wastewater-based epidemiology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12827, https://doi.org/10.5194/egusphere-egu25-12827, 2025.

Nitrification (NI) and urease inhibitors (UI) are added as organic trace substances to agricultural land during the application of nitrogen and urea fertilizers. They inhibit the nitrification processes and urease activity in soil and thus ensure a reduced emission of gaseous ammonia and nitrous oxide. The application of UI has been mandatory since 2020 following the amendment to the German Fertilizer Ordinance, which means that increased use is to be expected. The substances can enter surface waters through translocation and leaching processes in soils. So far, the stability of NI and UI in the aqueous phase has only been investigated in ultrapure water and tap water. Therefore, the aim of this study was to investigate the stability of five NI, i. e. 1H-1,2,4-triazoles (triazole), dicyandiamides (DCD), 3,4-dimethylpyrazoles (3,4-DMP), 3-methylpyrazoles (3-MP), N-((3(5)-Methyl-1H-pyrazol-1-yl)methyl)acetamid (MPA) and one UI, i. e. N-(2-nitrophenyl)phosphoric acid triamide (2-NPT), in two natural surface waters at 20 °C and at different pH values (i. e. pH 5, 7 and 9) using batch experiments. We distinguished between the processes of hydrolysis, sorption and microbial degradation. Hence, three differently treated triplicate batch samples were set up after the removal of suspended matter (> 0.63 mm). To investigate hydrolysis, the test water was filtered through a 0.22 μm polyamide membrane. For the investigation of sorption on suspended solids, a sodium azide solution was added to the water to inactivate microorganisms (final concentration in batch samples 100 mg/L). To investigate microbial degradation, the test water was used in its natural composition. pH values were adjusted using dilute HNO₃ and NaOH, respectively. The six inhibitors were added as a mixture to each batch sample with a target concentration of 5 μg/L each. Batch samples were taken, subsequently filtered (0.45 µm, regenerated cellulose) and measured by HPLC-MS/MS over a period of 8 days for the sorption tests and 83 days for the hydrolysis and microbiological degradation experiments.

None of the investigated inhibitors showed any sorption on suspended solids. With regard to hydrolysis and microbial degradation, the results differed depending on inhibitor and pH. For MPA no decomposition by hydrolysis could be detected at all three pH values. However, MPA was microbially degraded at pH 7 and pH 9 and was no longer detectable after about 55 days. 2-NPT was hydrolytically degraded at pH 5 and 9 over the entire test period, but no hydrolysis was observed at pH 7. At pH 7, no microbial degradation could be detected for 70 days. Thus, 2-NPT is persistent at pH 7. At pH 9, our results did not show any microbial degradation for 2-NPT. The stability of inhibitors in surface waters is driven by hydrolysis and microbial degradation and may vary greatly with pH.

How to cite: Michael, L., Flores, E., Pabst, S., and Klitzke, S.: Stability of selected nitrification and urease inhibitors in surface water, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13044, https://doi.org/10.5194/egusphere-egu25-13044, 2025.

Nitrification and urease inhibitors (NI,UI) added to fertilisers can increase the availability of nitrogen to plants. By inhibiting certain processes, they could contribute to a reduction in the emission of N₂O and NH₃ compounds and a reduction in nitrate leaching. Some of these substances have already been detected in surface water and groundwater and are considered to be harmful to human health. The inhibitors are therefore an area of conflict between climate change mitigation and increased fertiliser efficiency on the one hand, and soil and groundwater protection on the other. However, knowledge of their abiotic and biotic degradation in water and saturated sediment systems is currently very limited.

The aim of this study is to determine the fate of the 6 most commonly used inhibitors 1H-1,2,4-Triazol (Triazole), Dicyandiamide (DCD), 3,4-Dimethylpyrazol (3,4-DMP), N-[(3 or 5-methyl-1H-pyrazol-1-)methyl]acetamid (MPA), 3-Methylpyrazol (3-MP) and N-(2-Nitrophenyl) phosphoric triamide (2-NPT) in surface water and saturated sandy sediments.

Triplicate batch samples containing either saturated water-sediment mixtures (solid-solution-ratio 1:3) or surface water only were spiked with six inhibitors (target concentration 1.5 µg/L each). Both sets were maintained under biotic or abiotic (autoclaved water and sediment) conditions at room temperature. The supernatant was sampled periodically for 90 days and analysed for inhibitors, pH, dissolved organic carbon and electrical conductivity.

None of the inhibitors were sorbed to the sediment except Triazole, which showed only minimal sorption of less than 10%. The urease inhibitor 2-NPT was partly decomposed by hydrolysis alone under the studied pH between 7-8.6. Degradation in general was most pronounced in the biotic water-sediment mixture where DCD and MPA were completely degraded and 3,4-DMP, 2-NPT and 3-MP were partially degraded. The Inhibitor MPA was very susceptible to biodegradation even in surface water, however, its forming metabolite 3-MP is not. Triazol was not degraded under any conditions in this study.

With the exception of Triazole, saturated sediments (for instance in a bank filtration scenario) could probably reduce most of the inhibitors’ concentrations if the microbial community is intact. However, four out of six inhibitors were not completely degraded even under biotic conditions within 90 days, making them susceptible to breakthroughs into groundwater. Therefore, their fate in the environment should be studied further.

How to cite: Zieger, A., Flores, E., Pabst, S., and Klitzke, S.: Fate of urease and nitrification inhibitors in surface water and saturated sediment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13064, https://doi.org/10.5194/egusphere-egu25-13064, 2025.

Cigarette smoking and its negative health effects are well documented; however, the environmental impacts of cigarettes are poorly understood. Moreover, considering cigarette butts (CBs) as one of the most littered items globally, exploring the relative environmental effects of different end-of-life (EoL) pathways is essential to design effective mitigation strategies. So, the objective of this study is to identify the environmental hotspots across the cigarette lifecycle—both upstream and downstream of consumption—and to compare the environmental impacts of three potential EoL pathways of the CBs using a comprehensive cradle-to-grave life cycle assessment (LCA). The results depicted that cigarette manufacturing accounted for the highest environmental impact (98.36%) among the upstream processes of the cigarette life cycle. Especially human carcinogenic toxicity emerged as the highest potent impact category (0.168 kg 1,4-DCB), followed by freshwater eutrophication (0.0014 kg P eq.) and freshwater ecotoxicity (0.0525 kg 1,4-DCB) for a single cigarette production. Additionally, the cigarette consumption phase also depicted the highest environmental impacts contributing to human carcinogenic toxicity (96.3%), primarily due to the release of hazardous air pollutants during smoking. Further, the relative environmental impacts of three EoL scenarios were analysed for a CB—incineration, littering and landfilling disposal. Among the EoL scenarios analysed, littering of CBs caused the highest environmental impacts, mainly due to the leaching of toxic contaminants into water bodies. Despite evidence that CBs contain over 150 highly toxic chemicals with carcinogenic and mutagenic properties, the lack of detailed, standardized databases for these substances limits the precision of environmental impact analyses. Future research should address this gap by developing comprehensive databases and standardized methodologies to incorporate the specific contaminants in CBs. Such advancements are essential for a more accurate and holistic evaluation of the environmental pollution associated with CB disposal and to guide effective mitigation strategies.

How to cite: Lourembam, N. and Vanapalli, K. R.: Life cycle assessment of Cigarette from cradle to grave: Identifying environmental hotspots and end-of-life scenario analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15223, https://doi.org/10.5194/egusphere-egu25-15223, 2025.

Pesticides have been found in surface water and groundwater sources for decades. They are not only widely considered as a major threat for these waters, but also for drinking water. Not only pesticides that are still authorized by the European Commission can be found in the sources for drinking water, also banned compounds, such as atrazine (banned in 2007), still cause exceedances of environmental threshold values (as measured in 2020).

A comprehensive study was conducted in Dutch drinking water sources analyzing pesticides and their metabolites in both surface water and groundwater. Observations in surface water sources were divided in intake water and pre-treated water. For groundwater sources, observations were divided in observation wells, individual abstraction wells and all abstraction wells per particular drinking water abstraction station. Dutch drinking water standards were used as a measure of the implications for drinking water production processes.

This study shows that 156 different pesticides or metabolites were found at the nine surface water intake points considered. The standard for this substance group was exceeded on several occasions at all intake points. Pesticide residues have also been found in 40% of groundwater abstractions for drinking water production, particularly in phreatic ones (77%). This also involves a diversity of different substances. Pesticide residues exceeded the standard in 20% of groundwater abstractions. The pressure on surface water quality due to pesticides as indicated by the Removal Requirement Index seems to decrease slightly in recent years, whereas the pressure on groundwater intended for drinking water production seems to increase. Due to the diversity of pesticides found in ground- and surface water, trends are not obvious. Which and how many pesticides were observed differs between observation wells, abstraction wells and the combined abstraction wells, as well as aquifer properties.

How to cite: van Loon, A., van Driezum, I., Pronk, T., and Clevers, S.: Pesticide contamination across drinking water sources in the Netherlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16997, https://doi.org/10.5194/egusphere-egu25-16997, 2025.

Disinfection of water in distribution systems is essential for ensuring the microbiological safety of drinking water. However, disinfection byproducts (DBPs) are chemical compounds formed when disinfectants, such as chlorine, react with natural organic matter (NOM) and other constituents in water. The formation of DBPs in drinking water distribution systems can pose health risks, including cancer and reproductive issues, necessitating robust strategies to predict and mitigate their presence.

As part of the EU-funded IntoDBP project, a comprehensive real-world case study was conducted in Limassol, Cyprus, to investigate DBP formation within a water distribution system. The study included monitoring the hydraulic and water quality states of the system, from the Drinking Water Treatment Plant (DWTP) to end-users. This complete system perspective allowed for the evaluation of key factors affecting DBP formation, such as water source characteristics, residence time, and chlorination practices.

This work presents the process of creating a DBP modeling tool, detailing the methods used to address challenges related to the integration of heterogeneous data sources. Data from the DWTP, re-chlorination points, and distribution nodes were harmonized to ensure accurate representation of both hydraulic conditions and chemical reactions. Models capable of predicting DBP formation were developed as part of the study, with a specific focus on trihalomethanes (THMs) and haloacetic acids (HAAs). These models were validated using real-time data from sensors and manual sampling.

Alongside these models, a GIS-based software tool was developed to explore strategies for minimizing DBP levels within distribution networks. This tool visualizes data from multiple sources, including SCADA systems, water quality sensors, and GIS data, enabling dynamic simulations and scenario testing. Advanced simulation techniques using EPANET-MSX facilitated the simulation of multi-species reactions and the incorporation of uncertainties, such as variations in source water composition and operational conditions. The tool provides researchers and practitioners with the capability to evaluate and optimize chlorination practices, water mixing strategies, and operational configurations to mitigate DBP risks effectively.

Results from the case study highlighted the critical role of water residence time and source water composition in DBP formation. Nodes farther from chlorination points and those receiving water with higher NOM levels exhibited elevated DBP concentrations, emphasizing the importance of optimizing hydraulic and chemical parameters throughout the distribution system. The developed software tool demonstrated the potential of integrating GIS and hydraulic data with chemical analyses. It also showed promise in evaluating various mitigation strategies, including dynamic chlorination schedules and adjustments in flow management, within a realistic setup. This tool offers a valuable resource for researchers and water utility operators, providing a benchmark platform for developing and validating innovative DBP management strategies.

How to cite: Eliades, D. G., Vrachimis, S. G., Pavlou, P., and Kyriakou, M.: Development and Validation of a GIS-Based Tool for Disinfection Byproduct Formation Prediction in Water Distribution Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19192, https://doi.org/10.5194/egusphere-egu25-19192, 2025.

Antimicrobial resistance (AMR) is a silent pandemic, which is transmitted and spread through the environment. Throughout the global south, large urban areas interact with, and often exert considerable control on, both the hydrological and pollution dynamics on the rivers they are built around. Despite this, little is known about the prevalence, sources, and transport of AMR through these common, yet complex environments. Here, we quantified taxonomic and resistance genes (ARGs), sensitive and resistant bacteria (ARBs), and environmental conditions in both river water and sediment along the Musi River in Hyderabad, a city renowned for antimicrobial manufacturing and urban dominance of the river environment. We also developed estimates of urban wastewater inputs and a hydraulic model to understand the rapid changes in river flow and pollution concentrations occurring along the river length through the city. This reveals increasing, though variable, concentrations in ARGs along the river through the dry season, and stronger discrete point source and flow dilution dynamics in the wet season. The riverbed sediment stores far higher concentrations than the water column, especially in the dry season, and has more dynamic interaction with the river during the wet season. This study reveals the importance of both flow and removal dynamics in controlling AMR prevalence in the environment, in a context that is both common and expanding throughout the global south.

How to cite: Larsen, J., Sonkar, V., Kashyap, A., Pallarés-Vega, R., Modi, A., Uluseker, C., Mohapatra, P., Graham, D., and Kreft, J.-U.: AMR pollution dynamics determined by the untreated wastewater domination of both the hydrology and point source loads to the Musi River, Hyderabad, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19916, https://doi.org/10.5194/egusphere-egu25-19916, 2025.

Aquatic pollution from emerging contaminants, including antibiotics and antimicrobial resistance (AMR) genes, is an important environmental concern particularly pertinent in megacities such as Bangkok, Thailand, impacted by rapid urbanization and massive water demand.  Using a suite of environmental and hydrogeochemical tracers including inorganics and organics, nutrients, metal(loids), select antibiotics and AMR genes [1, 2], we characterize the distribution and spatial patterns of a range of contaminants in a ~ 150 km transect of the Chao Phraya River Basin in Thailand capturing areas both upstream and downstream of Bangkok.  A range of antibiotics and AMR genes were identified in parts of the transect and downstream trends are investigated.  Co-occurrence between selected antibiotics and AMR genes was not statistically significant, although other significant hydrogeochemical relationships (e.g. between pH and selected AMR genes) were observed, suggesting complex controls and selection pressures.  Comparisons are made with the types and concentrations of similar compounds detected in other major river and groundwater systems near other rapidly developing cities in South Asia (e.g. Patna, India) [3-5].  This work highlights the added interpretive value of a comprehensive range of analytes and provides insight on the potential co-occurrence of antibiotics, antimicrobial resistance genes and wastewater indicators that may be observed in surface waters in such settings.

Acknowledgements: We acknowledge support from a UKRI ODA allocation (via UoM to DP et al), a UoM-KTH strategic partnership seedcorn award (to LAR & ZC), a UKRI Future Leaders Fellowship (MR/Y016327/1 to LAR et al), a DKOF (to LAR), Cookson Studentship (to AR), and the Resistomap team.

References: [1] Larsson & Flach, Nature Reviews Microbiology, 2022, https://doi.org/10.1038/s41579-021-00649-x; [2] Hutinel et al., Science of the Total Environment, 2022, https://doi.org/10.1016/j.scitotenv.2021.151433; [3] Wilson et al., Environmental Pollution, 2024, https://doi.org/10.1016/j.envpol.2024.124205; [4] Richards et al., Environmental Pollution, 2023, https://doi.org/10.1016/j.envpol.2023.121626; [5] Richards et al., Environmental Pollution, 2021, https://doi.org/10.1016/j.envpol.2020.115765.

How to cite: Richards, L., Wilson, G. J. L., Roshan, A., Ahmed, F. T., Perez-Zabaleta, M., Otaiza-González, S. N., Rodríguez-Mozaz, S., Cetecioglu, Z., and Polya, D. A.: Co-occurrence of antibiotics, antimicrobial resistance genes and wastewater indicators in surface waters near Bangkok, Thailand: Characterization, Distribution & Controls, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21206, https://doi.org/10.5194/egusphere-egu25-21206, 2025.

Microplastic (MP) is ubiquitously found in aquatic environments and poses a significant environmental challenge. However, what controls MP deposition and burial in river networks is unclear, especially when sediments are in motion. This study addresses this gap by examining the impact of streambed motion and particle size on microplastic deposition in sandy streambeds. Experiments were conducted in a stainless-steel flume (650 cm x 20 cm) filled with 25 cm of silica sand (D₅₀ = 0.6 mm) and water (depth = 12 cm). A centrifugal pump circulated the water and maintained a constant stream water velocity. During the first experiment, the velocity of the water was 0.53 m/s, and the streambed celerity was 4 m/hr. The second experiment was conducted under stationary bedforms, with a water velocity of 0.15 m/s. Polypropylene (PP) fibers at lengths of 25 μm, 100 μm, 200 μm, and 2000 μm, and carboxylated Polystyrene (PS) microspheres (diameter of 0.5 μm, 1 μm, and 5 μm) were added to the stream water and their concentration in the water was measured over three days. The deposition of the MP was inferred from the decline of MP in the streamwater. A control experiment was conducted by repeating the same experiments but without sediments. After the relatively fast initial decline in MP, further reduction in MP concentrations in the water occurred due to deposition. Different deposition dynamics were observed for fibers and microspheres. Buried MP particles were partly resuspended during the scouring of the ripples during their movement. It was found that PP fibers 25 μm and 0.5 μm spheres were more mobile in the sediment than longer fibers and larger spheres, respectively. We explain their higher deposition than larger particles by a potential advective movement through the porous media, leading to their transport below the scour zone. PP fibers ≥ 100 μm were immobile within the sediment, and thus, their deposition was only due to burial by the ripple motion. In my presentation I will be comparing the results from the moving bed experiment to the stationary bed experiment and highlight the effect of bed motion. Our results hint at the significant influence of moving sediments on MP and the importance of considering MP size for catchment-scale modeling to predict MP fluxes to oceans. Deposition locations are also likely to be affected by bed motions and thus should be considered when developing effective sampling strategies.

How to cite: Levy Sturm, V. and Arnon, S.: Microplastic deposition in streams under moving bedforms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-904, https://doi.org/10.5194/egusphere-egu25-904, 2025.

The phenomenon of littering along roadways has been extensively studied [1][2][3][4], with research indicating that a significant portion of roadside litter now consists of plastic materials [1][3]. This issue has detrimental effects on the aesthetic value of landscapes, terrestrial and aquatic ecosystems, and human health [5]. These risks are particularly pronounced in mountainous regions, which are especially vulnerable due to the necessity of constructing road infrastructure in valley bottoms adjacent to river channels. The transfer of plastic from roads to rivers is influenced by its intrinsic properties (e.g., mass-to-surface-area ratio) and various extrinsic factors (e.g., terrain slope, wind, precipitation, land cover, and vegetation types).

Here, we present initial findings from a year-long field experiment conducted at 16 locations within the valley bottom of the Kamienica Gorczańska River in the Polish Carpathians. This study tracked the movement of 288 litter objects, including various types of macroplastics. The results demonstrated that plastic debris can be remobilized over distances exceeding 8,5 meters within a single season, with this displacement influenced by slope (R²= 0,46) and type of plastic debris. These findings highlight the critical need to understand the interactions between roadways and river ecosystems to better evaluate the contribution of roadside littering to plastic pollution in rivers, particularly in mountainous environments.

Keywords: plastic pollution, road system, mountain rivers.

References:

[1] Cowger, W., Gray, A., Hapich, H., Osei-Enin, J., Olguin, S., Huynh, B., Nogi, H., Singh, S., Brownlee, S., Fong, F., Lok, T., Singer, G., Ajami, H., 2022, Litter origins, accumulation rates, and hierarchical composition on urban roadsides of the Inland Empire, California, (17), 015007

[2] Gray, N., Gray, R., 2004, Litter deposition on minor rural roads in Ireland, Municipal Engineer, (157), 185-192.

[3] Ledieu, L., Tramoy, R., Ricordel, S., Astrie, D., Tassin, B., & Gasperi, J. (2022). Amount, composition and sources of macrolitter from a highly frequented roadway. Environmental Pollution, 303. https://doi.org/10.1016/j.envpol.2022.119145

[4] Pietz, O., Augenstein, M., Georgakakos, C. B., Singh, K., McDonald, M., & Walter, M. T. (2021). Macroplastic accumulation in roadside ditches of New York State’s Finger Lakes region (USA) across land uses and the COVID-19 pandemic. Journal of Environmental Management, 298. https://doi.org/10.1016/j.jenvman.2021.113524

[5] MacLeod M., Arp, HPH., Tekman, MB., Jahnke, A. (2021). The global threat from plastic pollution. Science. 373(6550):61-65. doi: 10.1126/science.abg5433

How to cite: Haska, W., Liro, M., Gorczyca, E., and Mikuś, P.: From roads to rivers: Field experiments on road-related macroplastic input to mountain river system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-952, https://doi.org/10.5194/egusphere-egu25-952, 2025.

River networks are the major pathways for microplastic (MP) transport from terrestrial environments to oceans. However, the ability to quantify the water–sediment exchange of MPs, locations of deposition, and the time scales over which burial occurs is limited and thus often our estimation of where MP deposit is biased. To fill this gap, previous work on processes that control MP deposition will be briefly reviewed, with the aim of enhancing our understanding of the dynamic interplay between flow, sediment transport, and MP deposition. Detailed studies on MP deposition onto surficial sediment show that MP transport can be explained by the shear stress theory, hyporheic exchange, and bioturbation. Nevertheless, these processes cannot fully explain the observed distribution of MPs in deeper river sediments. It is proposed that bedform movement, channel reworking, bar formation, and aggradation/degradation at the river network scale should be included when estimating MP deposition. Results from flume experiments and a numerical model will be shown to explain potential processes that can lead to the burial of MP beneath the moving streambeds, which provides a mechanism for long-term accumulation and may explain resuspension events characterized by high MP loads during floods. It is argued that incorporating data on MP distribution in riverbeds with fluvial geomorphological and particle transport models will improve the current evaluation of MP transport in river networks.

How to cite: Arnon, S., Sturm, V., Peleg, E., and Teitelbaum, Y.: Leveraging Sedimentary Process Insights to Enhance Understanding of Microplastic Deposition in Rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2055, https://doi.org/10.5194/egusphere-egu25-2055, 2025.

Nanoplastics (NPs) are defined as nanoparticles that are intentionally manufactured in this size range or that originate from the unintentional degradation of larger plastic fragments. Nanoplastics (NPs) are defined as nanoparticles that originate from the unintentional degradation of plastic that
breaks down into nanoscale particles. Because of their size, buoyancy, and surface properties, NPs are very mobile. In fact, they can be found in aquatic environments, soils, the atmosphere, and even in the human body. During travel across long distances, NPs undergo several processes, including advection, dispersion, and aggregation. If the first two are considered in conventional transport models, the latter is generally neglected. Aggregation consists of the formation of closely attached NPs that can reach the size of colloids, favoring settling and retention within the soil, thereby reducing the NP migration distance. Developing a model that accounts for aggregation is, therefore, a paramount for accurate transport prediction of NPs in the environment. 
Here we present a mechanistic model of NP aggregation over a broad range of conditions that resemble natural aquatic environments. The model combines the mass conservation equation of the population balance equation (PBE) with the constitutive equations based on the extended DLVO theory. The model was verified with data of the evolution of the hydrodynamic diameter of polyester NPs from the literature and used to predict the behavior of a variety of plastic materials such as polypropylene (PP), polyethylene (PE), and polyvinyl chloride (PVC). The model agrees very well
with the data, and no parameter fitting is required as it is based on the physical-chemical properties of the system, e.g., the zeta potential of the suspension. The results in general show that as pH or salinity increase NPs aggregation becomes more important; whereas, organic material inhibits aggregation. The change of the polymer type may affect the magnitude of the aggregation phenomenon but in all cases the effect of bio-geochemical properties change of the solution stays the same.

How to cite: Kozhaya, M., Prigiobbe, V., and Zhang, D.: Modeling the evolution of nanoplastic particle aggregation in aquatic systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3035, https://doi.org/10.5194/egusphere-egu25-3035, 2025.

The transport mechanisms of plastic pollution in rivers are currently poorly understood, hindering our ability to accurately monitor plastics, predict their fluxes, and ultimately intercept them. However, it has recently been shown that turbulent transport models designed for natural sediments, such as the Rouse model, can be used to quantify the vertical distribution of suspended plastic pollution in rivers with an uncertainty of within ±10%, despite differences in bed load or surfaced transport. These models are based on the ratio of the river’s shear velocity to the plastic’s settling (or rising) velocity, which has been shown to be represented by mono- or multi-model probability functions, due to variation in plastic shape, size, density and biofilm colonisation. In order for a Rouse-based model to be applicable as a method of monitoring plastics, and quantifying their concentration and transport in real rivers, a database of the settling velocities of the most commonly occurring macroplastic pollution in rivers, and their probability functions, is needed.

This study aims to explain the settling/rising velocities of the most commonly observed riverine plastics and to describe their full statistical functions. This includes calculating each plastic’s mono- or multi-model settling/rising velocity probability distribution. To achieve this, a state-of-the-art 2 × 2 × 2 m³ settling tank and a high-speed, synchronous multi-camera set up, with an automated plastic detection routine, will be used to describe the dynamics and calculate the settling/rising velocities and full probabilistic functions of the most prominent macroplastic items found in rivers. The plastic samples used in experiments will represent categories within the River-OSPAR litter index, which is an index used to classify plastics pollution by their type, size and material. This includes categories such as plastic cups, food wrappers, and cigarette filters. The data generated from these experiments can be used to shape Rouse-like transport models that can predict the vertical positioning and the concentration profiles of River-OPSAR plastics in the suspended layers of rivers, thus supporting the development of more accurate monitoring strategies for plastics in rivers and improving plastic quantification methods.

How to cite: Lofty, J., Valero, D., and Franca, M.: Determining the settling and rising velocities of the top polluting macroplastics in rivers , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3136, https://doi.org/10.5194/egusphere-egu25-3136, 2025.

Plastic pollution is a significant environmental problem of high magnitude, with far-reaching impacts on terrestrial and marine ecosystems. Plastic has various pathways to reach the ocean, with the land-to-ocean route being a critical one. Across both terrestrial and marine environments, plastic pollution threatens biodiversity, disrupts food chains, and accumulates in remote regions. Despite its importance, the mechanisms governing this transition remain understudied, particularly in overland systems. In this study, experiments in the laboratory are used in conjunction with numerical modeling tools to study the hydrodynamics of plastic transport overland under ideal conditions. Laboratory experiments utilized a controlled flume setup that simulates overland flow and analyzes the movement of plastic bottles, considering variations in size, shape, orientation, and weight. Then, numerical simulations were conducted to recreate the laboratory experiments as a simplified finite-element flume domain using a loosely coupled hydrodynamic and particle tracking framework. The framework was then validated against experimental results that proved the model's capability to reproduce the observed transport patterns. After validation, simulations were extended to a real-life-scale idealized domain to investigate the sensitivity of plastic transport to various parameters through a sensitivity analysis. The main results show the dependency of plastic mobility on hydrodynamic forces and particle characteristics. This integrated approach gives insight into overland plastic transport and informs mitigation strategies for plastic pollution in terrestrial environments. Future work will extend these findings to real-world scenarios, evaluating the interplay of rainfall and coastal flooding in plastic mobilization, such as during compound flood events. Thus, this research lays the foundation for developing comprehensive models that enhance our understanding of plastic pollution dynamics and support efforts to protect terrestrial and aquatic ecosystems from the escalating impacts of plastic waste.

How to cite: Oruc Baci, N., Santiago Collazo, F. L., and Jambeck, J.: Tracing Plastic Pathways: Laboratory Validation and Sensitivity Insights in Overland Transport Numerical Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3846, https://doi.org/10.5194/egusphere-egu25-3846, 2025.

Benthic biofilms are known for their ability to stabilise and trap fine estuarine sediments and contaminants. Due to their presence on the sediment surface, benthic biofilms interact with emerging contaminants such as microplastics (MPs) that settle to the bed, potentially influencing MP transport dynamics from land to sea. This study explores the influence of benthic biofilm development on the capture and retention of MPs under flow and how other chemical stressors adsorbed to the MPs, such as heavy metals, may influence biofilm retention of MPs. We hypothesised that i) higher biofilm development would increase MP capture and retention under flow and ii) that heavy metals associated with MPs would negatively affect the biofilm community and its ability to retain MPs.

Sieved sediment (500µm to remove large fauna) was added to small 250mL chambers and inoculated with 30mL of biofilm-rich surface sediment (control-absent, low and high biofilm biomass). Tidal simulation in an outdoor greenhouse promoted biofilm development in chambers over 21 days. High-density MPs (polyamide) MPs were added to the chambers at ‘high tide’ on day 14. These MPs were mechanically- and UV-aged then exposed to heavy metals (control (no metal), copper (Cu) and lead (Pb)) prior to their use. MPs were also fluorescently stained with a non-toxic dye to aid in MP erosion measurements. At the end of the incubation period, intact inner cores were gently removed, flushed and placed level with the bed of a small benchtop recirculating flume and exposed to incremental increases in flow velocity to erode the MPs. All experimental runs were filmed under UV light to fluoresce MPs and image analysis was used to determine the critical erosion threshold for MP motion from the video footage based on the loss of coverage across the core area. The remaining sediment from the chambers was extracted for biochemical analysis. 

Significantly higher critical shear stresses were required to remove MPs from the bed when biofilm was present, while Cu and Pb contamination had minimal effects on MP resuspension. This suggests that benthic biofilms have the potential to mediate MP resuspension dynamics and therefore MP transport from land-to-sea. Comparing our MP erosion thresholds (for PA particles only) against the bed shear stresses from a 2-D hydrodynamic model of the macrotidal Humber estuary, UK, it was found that MP erosion thresholds would be exceed in ~76% of the modelled cells during a 10-day simulation period covering a late-neap, spring, early-neap tidal cycle in the absence of biofilms. However, the presence of biofilm reduced the area of critical shear stress exceedance to ~36%. Without biofilms, MP erosion thresholds would be exceeded in 80% of the permanently inundated cells and 11.3% of the intertidal cells. Again, with biofilms present, MP erosion thresholds would be exceeded in 38% of the permanently inundated cells and just 3.8% of the intertidal cells. These findings can help improve our understanding of MP fluxes across the sediment-water interface in estuaries and provides evidence that the role of benthic biofilms should be included when parameterising MP transport models. 

How to cite: Hope, J., Chocholek, M., Rimmer, J., Baar, A., Thomas, R., Paterson, D., Harrison, L., Fernández, R., and Parsons, D.: The role of benthic biofilms in trapping estuarine microplastics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4210, https://doi.org/10.5194/egusphere-egu25-4210, 2025.

Microplastic (MP) particles in the environment are covered by a so-called eco-corona. The eco-corona is made up of natural organic matter (NOM) like biomolecules, humic substances and other natural molecules. NOM substantially changes the surface properties of MP particles and therefore the interaction with other surfaces in the aqueous environment influencing their heteroaggregation behaviour.

Using Colloidal Probe-AFM we studied the interactions of eco-corona covered MP particles with model sand particles on the nanoscale. Measurements were performed in different ionic concentrations to mimic changing environmental conditions. We found that the eco-corona is able to *pull* at the model sand colloidal probe by macromolecular bridging. Simulations verified the stability of these heteroaggregates under flow. With heteroaggregation experiments and following Raman-Imaging we verified the presence and stability of these aggregates on the microscale.

In conclusion, we present macromolecular bridging as an eco-corona mediated heteroaggregation mechanism. It is present at monovalent salt concentrations > 1 mM and dependent on the eco-corona surface coverage. This mechanism is able to contribute substantially to MP particle heteroaggregation in the aqueous environment and explains how MPs cross the buoyancy barrier.

How to cite: Witzmann, T., Ramsperger, A. F. R. M., Liu, H., Schmalz, H., Greiner, A., Laforsch, C., Gekle, S., Kress, H., Fery, A., and Auernhammer, G. K.: How Microplastics Cross the Buoyancy Barrier, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5849, https://doi.org/10.5194/egusphere-egu25-5849, 2025.

The fragmentation of plastic in the environment directly influences particle size, buoyancy, transport, and sedimentation dynamics, shaping their fate and interactions within aquatic systems. Notably fragmentation of plastic particles in coastal environments has been observed to be faster than in the open ocean due to high temperature (heat-build up in the sand), high oxygen levels, high exposure to solar radiation (due to little vegetation coverage) and mechanical forces from waves and sediment movements which accelerates the cracking and fragmentation of plastic debris. However, understanding the drivers of fragmentation based on intrinsic properties of plastic—such as brittleness, a key precursor to fragmentation—remains challenging due to the irregular shapes, weathered conditions, and small sizes of environmental samples, which often do not meet the criteria for standardized mechanical testing. Here we present our study where we investigated the fragmentation and brittleness of field-collected plastic particles using a simple laboratory fragmentation test. Our fragmentation test assessed whether beach-sampled plastic particles would break (brittle) or remain intact (ductile) under fixed pressure, enabling the collection of a large dataset (16,322 plastic particles) for statistical analysis of the number of brittle particles on the beach. To further investigate what drives the brittleness of the sampled polypropylene (PP) and polyethylene (PE) particles, we investigated a subsample of PE and PP particles, including both brittle and ductile particles, focusing on their physicochemical traits that might explain their extent of weathering and links to their observed brittleness. We found that the brittleness of sampled plastics strongly correlates with advanced degradation, characterized by very low average molecular weights (as low as 7 kg mol⁻¹) and the presence of oxidation products. Furthermore, we observed that brittle particles were significantly smaller than their ductile counterparts, underscoring the role of fragmentation in shaping the size distribution of plastics on beaches. Additionally, through this fragmentation test, we established, to our knowledge, the first embrittlement criterion for plastics collected from the environment. To further explore fragmentation of plastics in coastal environments, we use a beach swash-zone laboratory simulator to investigate the fragmentation of polymers aged under controlled laboratory conditions. This allows us to correlate the extent of degradation with fragmentation behavior, providing additional insights into the fragmentation processes of brittle plastics in dynamic coastal environments. By highlighting the brittleness of plastic samples on beaches, our study underscores the significant risks of secondary microplastic generation from degraded, brittle plastics in dynamic beach environments. This study also aims to contribute to the overall understanding of the chemical and physical processes that drive fragmentation in aquatic systems, in our case coastal environments, which can aid to inform targeted intervention strategies to prevent microplastics from entering the environment.

How to cite: Delorme, A., Lebreton, L., Royer, S.-J., Arhant, M., Le Gall, M., and Le Gac, P.-Y.: Towards Understanding Drivers of Plastic Embrittlement and Fragmentation in Coastal Environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7830, https://doi.org/10.5194/egusphere-egu25-7830, 2025.

The environmental pollution from plastics is steadily increasing, reaching 390.7 million tons in 2021 (Plastics Europe, 2022). Between 2% and 5 % of MPs produced worldwide, may ultimately find their way into the ocean, where they accumulate on the deep seafloor (Phuong et al., 2021), infiltrate into hyporheic zones (Mancini et al., 2023) or may remain in suspension in the water column (Zobkov et al. 2019). MPs have been reported not only in marine and coastal areas (Jung et al., 2021) but also in Marine Protected Areas (Zachello Nunes et al., 2023). Consequently, plastic pollution is recognized one of the most serious anthropogenic generated pollutants affecting aquatic ecosystems.

MPs can be transported and deposited by turbidity currents from shallow waters to the deep ocean (Pohl et al., 2020). This study contributes to further knowledge about the transport and the depositional patterns of MPs by turbidity currents related to different factors:  the MP shape, the MP density and the sediments’ characteristics. To mimic turbidity currents transporting MPs, lock-exchange flume experiments were performed with sediment contaminated with three types of microplastics: PET fibers, PVC fragments, and melamine fragments. These MPs were selected to represent a range of densities and shapes. The study revealed distinct sedimentation patterns: higher sediment concentrations enhance MP transport, and turbidity currents with finer sediments transported MPs over greater distances, highlighting the important role of sediment in transporting MPs in the propagation of turbidity currents. Further, MP sedimentation patterns varied with MP-particle shape, size, and density, highlighting the crucial role of MP particle properties in determining MP distribution in turbidites. These findings enhance our understanding of the mechanisms controlling the spatial distribution of MPs in marine sedimentary-environments and underscores the importance of considering both hydrodynamic and particle-specific factors when addressing the complex behaviour of MPs.

References

Plastics Europe, 2022. Plastics- the Facts 2022. An analysis of European plastics production, demand and waste data.

Phuong, N.N., Fauvelle, V., Grenz, C., Ourgaud, M., Schmidt, N., Strady, E., Sempéré, R., 2021. Highlights from a review of microplastics in marine sediments, STOTEN, 777.

Mancini M., Francalanci S., Innocenti L., Solari L., 2023a. Investigations on microplastic infiltration within natural riverbed sediments. STOTEN, 904, 167256.

Jung, J.W., Park, J.W., Eo, S., Choi, J., Song, Y.K., Choi, Y., Hong, S.H. and Shim, W.J. 2021. Ecological risk assessment of microplastics in coastal, shelf and deep sea waters with a consideration of environmentally relevant size and shape. Environmental Pollution. 270, 116217.

Pohl, F., Eggenhuisen, J. T., Kane, I. A., Clare, M. A., 2020. Transport and Burial of Microplastics in Deep-Marine Sediments by Turbidity Currents. Environmental Science & Technology, 54(7), 4180–4189.

Zachello Nunes, B., de Oliveira Soares, M., Zanardi-Lamardo, E., Braga Castro, I. 2023. Marine Protected Areas Affected by the most extensiveOil Spill on the Southwestern Atlantic coast. Ocean and Coastal Research, 71(2), e23028,

Zobkov, M.B. , Esiukova, E.E. , Zyubin, A.Y. , Samusev, I.G., 2019. Microplastic content variation in water column: The observations employing a novel sampling tool in stratified Baltic Sea, Marine Pollution Bulletin, 138, 193-205. 

How to cite: colomer, J., soler, M., pohl, F., and serra, T.: Microplastics in turbidity currents: transport and sedimentation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9865, https://doi.org/10.5194/egusphere-egu25-9865, 2025.

Microplastics and antibiotics are prevalent and emerging pollutants in aquatic ecosystems, but their interaction in aquatic food chains remains largely unexplored. This study investigated the impact of polypropylene microplastics (PP-MPs) on oxytetracycline (OTC) trophic transfer from the shrimp (Neocaridina denticulate) to crucian carp (Carassius auratus), and determined the responses of gut microbiota and antibiotic resistance genes (ARGs) by macrogenomic sequencing. The carrier effects of PP-MPs promoted OTC bioaccumulation and trophic transfer, which exacerbated enteroclysis, vacuolization and eosinophilic necrosis of fish hepatocytes. The presence of PP-MPs significantly enhanced the inhibitory effect of OTC on the intestinal lysozyme activity and complement C3 level in shrimp and fish, as well as the hepatic immunoglobulin M level in fish (p < 0.05). The single exposure of OTC induced the abundance of Actinobacteria and Firmicutes in shrimp, and Bacteroidetes in fish, while the combination with MPs obviously increased the abundance of Actinobacteria in shrimp and Firmicutes in fish, which caused disturbances in carbohydrate, amino acid and energy metabolism. Moreover, OTC exacerbated the enrichment of antibiotic resistance genes (ARGs) in aquatic animals, and the carrier effects of PP-MPs obviously increased the diversity and abundance of ARGs and facilitated the trophic transfer of teta and tetm in the co-exposure group. Tetracycline (tetm, tetb, tet36, tetc) and streptomycin (aac6ib) resistance genes were significantly positively correlated with the potential hosts Clostridium, unclassified_f_Clostridiaceae and Bacteroides. Our findings disclosed the impacts of PP-MPs on the mechanism of antibiotic toxicity in the aquatic food chain and further emphasized the importance of the trophic transfer of ARGs by the gut microbiota, which contributed to a deeper understanding of the potential risks posed by combined pollution of MPs and antibiotics on aquatic ecosystems.

Granphical Abstract

How to cite: Zhang, P. and Lu, G.: Effect of microplastics on oxytetracycline trophic transfer: Immune, gut microbiota and antibiotic resistance gene responses, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10343, https://doi.org/10.5194/egusphere-egu25-10343, 2025.

At the scale of a watershed, the microplastic (MP) fluxes within and between compartments contribute to the contamination of the air-soil-water continuum. The sources and fluxes of microplastics vary greatly depending on land use and human activities within the watershed. The quantifying of MP – sampling and analysis – is a time-consuming task which limits the monitoring of the fine temporal dynamics of MP. The objective of this work is to simulate the MP dynamics in the outlet of a peri-urban watershed and to gain insight into the sources. The study site is the Avenelles sub-catchment, 50 km east of Paris (France), and surface of ~50 km², and it is an experimental site highly instrumented for physico-chemical parameters of the hydrographic network and meteorology. The subcatchment is dominated by agricultural activities, which account for 81% of its surface area, while 18% is forested, and 1% is urbanized. The arable land in the catchment is drained and its influence on the flow rate regime is characterized by flash floods. Samples of MP were collected at the watershed outlet during 2023 using a Universal Filtration Object and analysed using the micro Fourrier Transform Infra-Red. The main sources contributing to the MP dynamics are: loss of MP stocks in the soil via drainage system; remobilization of MP stocks in the sediments; the effluent of the water treatment plant (WWTP) and the stormwater overflows.

The MP modelling approach is based on a multilinear model using hydrological variables. The hydrological variables used were (i) the baseflow and the quickflow, estimated from a conceptual automated process, and, (ii) the filling rate of routing reservoir and the production reservoir simulated by the hydrological model GR4H. Simulations evaluated at the MP dynamics at hourly timestep at annual and rainy season time scales. Besides the streamflow characteristics and storages, the precipitation, via the index of previous precipitation, the water conductivity and the total suspended solids are input variables as well. On an annual scale, the most significant variables in the regression appear to be the TSS, the filling rate of the routing reservoir and the water conductivity (R²=0.89). Isolating the hydrological variables, the baseflow and the total flow presented significancy (R²=0.64). For the rainy season, fast flow and total flow are the variables contributing to the MP dynamics (R²=0.86). These results can indicate the MP sources : at annual simulation, denoting TSS and base flow as significant variables, the WWTP discharge might be the main source as it is constant throughout the year; During the rainy season, presents quick flow as major contribution highlighting the drainage system and thus the MP stocks in the soil as major source. In conclusion, this simple model provides a better understanding of the sources of MP at a catchment and a better estimate of the dynamics and contamination of MP.

How to cite: Calabro Souza, G., Bessaire, C., de Lavenne, A., J. Lemaire, B., Friceau, L., Tassin, B., and Dris, R.: Simulating microplastics temporal dynamics, driving mechanisms and giving insights on sources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10584, https://doi.org/10.5194/egusphere-egu25-10584, 2025.

Microplastics (MPs) may be an important component of suspended particulate matter (SPM) in aquatic environments. These particles can be transported independently or as part of larger aggregates (flocs). Recent studies have highlighted that small microplastics (<160 µm) are predominantly transported within flocs across various aquatic systems. Flocculation notably affects the transport dynamics of MPs, particularly by modifying their settling velocities. This process is especially pronounced in estuarine environments, where salinity gradients, turbulence, high suspended sediment concentrations, and organic matter, creates unique conditions for floc formation and movement. This study investigates the settling behavior of small MPs (50-125 µm) and their mixtures with fine cohesive sediment under laboratory conditions. An optical settling column (System of Characterisation of Agregates and Flocs SCAF) was used to measure the settling velocities of MPs with varying characteristics (shape, size and density), both in clear water and when mixed with fine suspended sediments at concentrations representative of turbid estuaries, under previously agitated and no-agitated conditions. The results reveal that regular-shaped MPs exhibit higher settling velocities compared to irregular ones among larger particles (90–125 µm) with similar  density, while no such difference was observed for smaller particles (50–90 µm), highlighting the varying influence of particle shape with size. As expected, high-density particles settle faster, while larger particles also exhibit increased settling velocities due to reduced drag relative to their mass. The presence of fine sediments further enhances the settling velocities of smaller (50-90 µm) regular-shaped MPs and both smaller and larger (50-125 µm) fragmented MPs, particularly under previously agitated conditions, suggesting the occurrence of aggregation. A preliminary evaluation of several settling velocity models based on the calculation of drag coefficients, suggests that, unlike large microplastics, models conceived for natural particles align closely with observed data.

How to cite: Ruiz-Gonzalez, V., Jalon-Rojas, I., and Defontaine, S.: Evaluating settling velocities of microplastics-sediment mixtures under laboratory conditions , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10844, https://doi.org/10.5194/egusphere-egu25-10844, 2025.

Settling velocity is a fundamental property of plastic particles that helps in understanding transport processes by evaluating hydraulic threshold conditions for different transport modes, such as incipient motion, bed load, and suspended load. It is essential for predicting the pathways and accumulation zones of plastic particles in aquatic environments. In this study, settling column experiments were conducted to investigate the influence of suspended sediment concentration (SSC) on the settling behavior of irregularly shaped, negatively buoyant microplastic particles (MPs). Polyethylene terephthalate (PET) particles of cylindrical and flaky shapes, with a density ranging from 1290 to 1470 kg m^-3, were selected for the experiments. The terminal settling velocity (W_s) of these 13 irregularly shaped MPs was measured under three distinct treatments: clear water (without sediments), with an SSC of 50mg/L, and 100mg/L. Each experiment performed three times and a total of 117 tests were conducted. The results showed that the terminal settling velocity (W_s) of cylindrical particles is higher than that of flaky-shaped particles, highlighting shape as a main influencing parameter. As SSC increases from 50mg/L to 100mg/L, W_s decreases for both the particles. Compared to clear water results, at 50mg/l and 100 mg/l SSC, the W_s decreased by 3.56% and 6.28% for cylindrical and 1.84% and 3.60% for flakes particles, respectively. The drag coefficient of microplastic particles rises with increasing SSC due to the presence of suspended sediment particles, which provide additional resistance to the settling process. Furthermore, the larger surface area relative to their volume leads to slower settling velocities. An equation was developed to predict the terminal settling velocity as a function of particle density, shape, size, and sediment concentration. This study provides valuable insights into the settling behavior of irregularly shaped microplastic particles in sediment-rich environments.

Keywords: Terminal settling velocity, suspended sediment concentration, irregularly shaped microplastics, settling column experiments.

How to cite: Owk, P., Chandra, V., and Schuttrumpf, H.: Influence of suspended sediment concentration on the settling velocity of irregularly shaped microplastic particles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14846, https://doi.org/10.5194/egusphere-egu25-14846, 2025.

Rivers serve as primary pathways for transporting microplastics from land to oceans, but they also can retain these small particles within their sedimentary deposits. A critical factor in this process is the role of fine cohesive sediments, which can adhere to microplastic particles and form aggregates. These aggregates can be transported as part of the bedload, resulting in enhanced accumulation of both microplastics and sediments. This study explores the mechanisms of microplastic-sediment aggregation and their influence on the transport and deposition of microplastics in rivers through laboratory experiments. A rotating wheel (34 cm diameter, 7 cm depth) was used to keep the mud in suspension and promote aggregation, while a settling column equipped with a high-resolution camera was utilised to analyse floc properties and their settling velocities. Five experiments were conducted, analysing approximately 4,000 flocs per experiment. Four experiments involved mixtures of water, sediment (1 g/L), and 500 µm-long microplastic fibres (MPs-sediment ratio, in weight, of 1:25), using four different plastic polymers: Polypropylene (PP, ρ=0.9 g/cm³), Polyamide (PA, ρ=1.14 g/cm³), Polyester (PES, ρ=1.38 g/cm³), and Aramid (AR, ρ=1.44 g/cm³). A fifth experiment, serving as a control, was conducted to observe floc formation without microplastics. Mud with a median grain size of 40 µm sampled from a real river (Arno River, Tuscany, central Italy) was used as the sediment. The mixtures were subjected to a shear rate of 10 s⁻¹ in the rotating wheel for two hours. After complete settling, flocs were collected and moved into the settling column. The recorded videos were analysed using an image analysis tool to determine floc size, shape, and settling velocity. Results showed that including microplastic fibres within flocs formed larger flocs that settled faster than flocs containing only sediment. Furthermore, while flocs formed from plastic-free mud generally exhibit a rounded shape, flocs containing microfibres display greater variability in shape, with some maintaining a rounded morphology but others exhibiting more elongated forms.

How to cite: Uguagliati, F., Ali, W., Chassagne, C., Waldschläger, K., Zattin, M., and Ghinassi, M.: Flocculation and its impact on microplastic transport mechanisms in rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15327, https://doi.org/10.5194/egusphere-egu25-15327, 2025.

The presence of microplastics in rivers and estuaries poses environmental challenges. To effectively address these challenges, it is important to identify microplastic sinks within the aquatic environment. This can be achieved through modeling the fate of microplastics with numerical transport models. A key process influencing their fate is microplastic-sediment aggregation. In this process, microplastics and sediments form flocs that are larger and denser compared to individual microplastics, leading to enhanced settling of microplastics in floc form.

This research aims to parameterize microplastic sediment aggregation by conducting flocculation experiments in the lab. These experiments will involve microplastics in the form of fibers, fragments, and spheres with varying sizes and densities. Flocs will be generated by adding microplastics and sediment to a continuously stirred jar under organic-based, salt-induced, or combined flocculation conditions. Floc formation and growth will be continuously monitored using the Malvern Mastersizer to measure particle size distributions. The settling velocities of microplastic-sediment flocs will be determined using a settling column, coupled with a floc camera for detailed analysis.

The results obtained from the experiments will be used to parameterize microplastic-sediment aggregation for different types of microplastics and conditions. This is achieved by using a novel method for parameterizing microplastic-sediment aggregation using the logistic growth model to describe floc formation and floc growth. By incorporating the parameterization of microplastic-sediment aggregation into numerical transport models, we aim to improve the accuracy of predicting the fate and transport of microplastics in aquatic environments.

How to cite: Oosterhoff, N., Melsen, L., and Waldschläger, K.: Experimental study on parameterizing microplastic-sediment aggregation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15485, https://doi.org/10.5194/egusphere-egu25-15485, 2025.

Microplastics are ubiquitous in aquatic ecosystems, posing significant environmental concerns due to their impacts on organisms and potential risks to human health. Recent studies have shown that microplastics can interact with coexisting contaminants, such as microcystins–hepatotoxins produced by Microcystis aeruginosa during cyanobacterial blooms. These toxins persist in freshwater environments and adsorb onto microplastic surfaces, facilitating their transport across ecosystems. Despite extensive research on toxicological effects of microplastics and microcystins individually or in co-exposure scenarios, the toxicity of microcystins adsorbed onto microplastics remains poorly understood. This study investigated the role of microplastics as carriers of microcystins and evaluated their ecotoxicological effects using Daphnia magna (D. magna) as a model organism. Microcystin-LR (MC-LR), a potent hepatotoxin, and 220 nm polystyrene (PS) microplastics were selected as test materials. Experimental groups included PS microplastics and MC-LR individually as controls, and MC-LR adsorbed onto PS microplastics (with an adsorption capacity of approximately 307.30 µg per gram of PS) to assess combined toxicity. Toxicity assessments were conducted by analyzing behavioral (e.g., swimming patterns), physiological (e.g., heart rate and reproduction), biochemical (e.g., enzyme activity), and molecular (e.g., gene expression) parameters in D. magna. This study enhances our understanding of microplastics’ role as environmental contaminants and carriers of MC-LR, emphasizing their ecological risks and broader implications for aquatic ecosystems.

How to cite: Kim, N. and Lee, E.-H.: Toxicity of microcystin-LR adsorbed onto microplastics: Impacts on Daphnia magna , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16117, https://doi.org/10.5194/egusphere-egu25-16117, 2025.

The transport and deposition of floating particles in flowing water is a key mechanism that drives the fate of contaminant, organic materials, and debris along river networks. The ability of human-made and naturally buyout particles to sit on the water surface makes them able to travel long distances. The mechanisms that allow these particles to deposit are closely linked to the hydraulics of the channel and the morphology of the river. Research has shown that particles such as plastics and debris tend to accumulate behind obstacles and recirculation areas, creating accumulation hotspots. The location of such hotspot also depends on the interplay between particle shape and size and flow conditions.  Although the influence of river morphology and flow regime is well acknowledged, the precise interaction between these components remains unclear.

To investigate this relationship, we used a new Eulerian-Lagrangian method based on a 2D depth-averaged flow solver simulating transport and dispersion of floating particles in a typical alpine river floodplain. This method offers a computationally efficient way to track the trajectory of single particles moving onto a flow field. Our approach integrates model simulations with data derived from outdoor and laboratory experiments, where we recorded deposition location of particles of different size, shape and material under different discharge conditions.

The results show that the number of floating particles decreases exponentially with the distance from the release point, with decay rates primarily correlated with the water discharge. We find that the deposition of particles depends on the hydraulics of the channel and the roughness elements in the channel, with particle sizes playing a secondary role. Unsteady flow conditions, namely receding water levels, promote particle deposition on shallow areas and channel shorelines. The use of the particle tracking model allowed us to extend the parameter space investigated experimentally, allowing for an in-depth analysis of the spatial and temporal dynamics of particles transport and deposition during floods.

These results deepen our understanding of transport processes of floating material at the reach scale, providing quantitative evidence on the central role played by channel hydromorphology. Although the effect of particle shape and size is not fully understood, the study can offer valuable insights into the dispersion mechanisms of different floating particles, from plastics to organic materials.

How to cite: Caponi, F., Fink, S., Conde, D. A. S., Hatstatt, A., Guiducci, I. R., Demuth, P., and Vetsch, D. F.: A Eulerian-Lagrangian approach to study the effect of river hydro-morphology on (small) floating particle transport and deposition, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16125, https://doi.org/10.5194/egusphere-egu25-16125, 2025.

Microplastics, defined as plastic particles smaller than 5 mm, are pervasive contaminants in aquatic environments. Their high surface area to volume ratio facilitate the adsorption of coexisting pollutants, enabling them to act as carriers for various contaminants, including antibiotics. Antibiotics, widely detected in aquatic environments due to extensive use in medicine and agriculture, may interact with microplastics, thereby altering their distribution and environmental impact. This study investigated the role of polystyrene (PS) microplastics as carriers for tetracycline (TC), a representative antibiotic, and evaluated their toxic effects on Escherichia coli. PS particles with a diameter of 1060 nm were used as model microplastics, and TC was adsorbed onto their surfaces to prepare TC-carrying PS particles. The effects of TC-carrying PS were assessed by examining bacterial growth and viability, with TC and PS alone serving as controls. Exposure to TC-carrying PS resulted in significant decreases in bacterial growth and cell viability in E. coli. Further investigations into the toxicological mechanisms included reactive oxygen species (ROS) generation, lactate dehydrogenase (LDH) release, and malondialdehyde (MDA) levels. Gene expression analyses investigated alterations in pathways related to membrane integrity, oxidative stress response, and DNA repair mechanisms. These findings enhance our understanding of the interplay between microplastics and antibiotics, highlighting the potential ecological risks posed by TC-adsorbed PS microplastics and their implications for environmental health.

How to cite: Kim, S. and Lee, E.-H.: Toxic effects of tetracycline-adsorbed polystyrene microplastics on E. coli, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16212, https://doi.org/10.5194/egusphere-egu25-16212, 2025.

The widespread presence of microplastics (MPs) in freshwater environments underscores the need to better understand their temporal and spatial dynamics. Investigating the settling velocity (W) of MPs in the water column is crucial for comprehending their transport mechanisms within river systems. Several models have been proposed to estimate the W of this type of pollutant. However, to date, none of them account for the simultaneous presence of suspended sediments. This study aims to address this knowledge gap by conducting laboratory experiments to analyze the W of 12 different types of MPs with various shapes, under both clear and turbid water conditions in a still water tank. For each experimental run trajectories are captured by using high resolution camera and UV lighting to enhance the visibility of MPs. Both vertical and horizontal W components, tilt angles, oscillation frequencies and trajectory angles have been calculated. Appropriate non-dimensional parameter (i.e. Reynolds number (Re), Galileo Number (Ga), Stability Number (I*), Strouhal number (St)) have been used to better describe the MPs hydrodynamics. Results have shown, for the first time, that suspended sediments influence the MPs falling behavior by inducing secondary motions that increase MPs settling velocity. Particularly, the more elongated the MPs the greater the increasing rate of W. Findings have also shown a Gaussian probability distribution of the particle’s lateral position along the water column (with respect to the vertical axis of the tank) suggesting a Fickian-type diffusion of MPs throughout vertical water profile with several implications for their accumulation in calm water environment.

The above findings highlight the importance of including suspended sediment as a key factor in developing MP transport models, due to its significant impact on the mass balance of MPs in aquatic ecosystems.

How to cite: Mancini, M., Serra, T., Colomer, J., Francalanci, S., and Solari, L.: Sedimentation of microplastics interacting with sediment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16991, https://doi.org/10.5194/egusphere-egu25-16991, 2025.

Microplastics (MPs) are pervasive contaminants, yet understanding their pathways and fate in the marine environment remains unclear. A key challenge is the lack of in-situ, complementary measurements linking MP quantification with oceanographic parameters, particularly concerning submesoscale processes and density fronts. Submesoscale dynamics, including filaments, eddies, and fronts, significantly influence the transport and accumulation of MPs by creating convergence zones and sharp density gradients. Density fronts serve as critical hotspots for MP aggregation, concentrating particles through upwelling and downwelling processes. Despite their importance, these interactions remain poorly studied, emphasizing the need for integrated approaches to directly measure the interplay between MPs and the physical processes that drive their distribution.

This study addresses this gap by utilizing in-situ measurements collected with an autonomous surface vehicle (ASV) in the southern North Sea, simultaneously collecting water samples for MP analysis and key oceanographic data. The ASV simultaneously sampled air, sea surface microlayer, and underlying water for MP analysis. A weather station and conductivity, temperature, and depth (CTD) sensors were deployed on the ASV to further contextualize the distribution of MPs. Additionally, CTD profiles were obtained by an accompanying research vessel to investigate the influence of stratification and temporal dynamics on MP distribution. An acoustic Doppler current profiler measured water current velocities and flow direction.

The measurements underscore the pivotal role of submesoscale fronts and filaments in shaping the accumulation and distribution of MP. Upwelling and downwelling processes at these fronts and filaments concentrated MP up to 30.48 µg MP L⁻¹, and distributed MPs vertically across depth profiles and horizontally across fronts. Wind direction was found to influence the presence of MP in the atmosphere, while wind speeds appeared to enhance heterogeneity in MP composition and concentration within the water.

Submesoscale fronts and filaments are highlighted as key zones for MP accumulation, driven by the interplay of horizontal and vertical water flow linked to ageostrophic circulation. The data provide novel insights into their transport mechanisms in the marine environment.

How to cite: Meyerjürgens, J., Goßmann, I., Albinus, M., Achtner, C., Robinson, B.-T., Held, A., Lehners, C., Gassen-Bertzbach, L., Ayim, S. M., Badewien, T. H., Scholz-Böttcher, B. M., and Wurl, O.: What Influences Microplastic Distribution in the Marine Environment? A Study Highlighting the Role of Fronts and Submesoscale Processes in the North Sea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17177, https://doi.org/10.5194/egusphere-egu25-17177, 2025.

Plastic pollution is a very active research topic for a wide variety of scientific disciplines. While existing reviews of plastic pollution in the ocean cover the topic from different disciplinary and interdisciplinary viewpoints, this review addresses the contributions from laboratory experiments towards the geophysical processes important in marine plastic pollution research. We review the laboratory research on the transport, transformations, and origin and fate of marine plastic pollution with recommendations for future research [1].

[1] Marie Poulain-Zarcos; Nimish Pujara; Gautier Verhille; Matthieu J. Mercier. Laboratory experiments related to marine plastic pollution: a review of past work and future directions. Comptes Rendus. Physique (2024), pp. 1-32. doi : 10.5802/crphys.217

How to cite: Mercier, M., Poulain-Zarcos, M., Pujara, N., and Verhille, G.: Laboratory experiments related to marine plastic pollution: a review of past work and future directions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17744, https://doi.org/10.5194/egusphere-egu25-17744, 2025.

Rivers serve as a big pathway for microplastics (MPs) to reach the oceans. This transport exhibits seasonality, while microplastics tend to deposit on sediment beds and riverbanks during dry seasons, heavy rainfall directly increases the resuspension rate of microplastic particles. The turbulence intensity during such events plays a significant role in particle resuspension. The detachment of MPs embedded in a sediment bed containing other granular phases with a contrasting density ratio remains a complex process that deserves further research. This study aims to investigate the micromechanical effects of microplastic resuspension through a combined experimental and numerical campaign. An experimental closed-loop channel facility is being constructed to analyze the mobilization of millimeter-sized MPs particles from a sediment bed composed of glass beads and polymer pellets of the same diameter. An ultrafast X-ray computed tomography system will be used to scan three-dimensional opaque sediment bed, allowing the mapping of the entrapped polymer MPs particles in space and time for different flow conditions. Additionally, the individual particles crossing two measurement planes will be recorded with this system using a temporal resolution of 1000 samples per second. Thus, the velocity distribution of the MPs particles will be measured in both the sediment bed and the core flow. The experimental study is supplemented with grain-resolving Direct Numerical Simulations (DNS) to replicate the idealized conditions of the experimental setup. This approach allows for a detailed exploration of the hydrodynamic forces acting on particles and permits an investigation of details beyond the experimental capabilities. The findings of this combined experimental and numerical study will contribute to a better understanding of the mechanisms of microplastic resuspension in environmental flows and guide mitigation strategies to limit plastic pollution in aquatic environments.

How to cite: Höhn, R., Vowinckel, B., and Lecrivain, G.: Investigating Microplastic Resuspension in Environmental flows: Experimental and Numerical Approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18648, https://doi.org/10.5194/egusphere-egu25-18648, 2025.

There is a significant mismatch between the estimated amounts of ocean plastic and the expected plastic ingress by rivers (De Fockert et al., 2024; OECD, 2022). Most plastic transport monitoring data used for global modelling of plastic flux estimations is based on the number of items transported at the water surface (González-Fernández, 2023). However, the amount of the submerged plastic items transport in rivers is not accounted for or is based on approximations (Blondel & Buschman, 2022; Hurley et al., 2023). Notably, submerged plastic transport can exceed surface transport by 4-5 orders of magnitude (Vriend et al., 2023).

Numerical models often oversimplify the macro plastic transport by focusing solely on rising velocity, neglecting physical processes like particle characteristics and free surface interaction (Wickramarachchi et al., 2024). This study addresses these aspects through a detailed experimental investigation on the vertical distribution of near-neutrally buoyant macro-plastics in a controlled laboratory environment to provide validation data to improve the current parametrisation in the particle tracking model Delft3D-PART as part of the Delft3D open source software suite (Stupary et al., 2015).

Experiments were conducted in a  45m long, 1.2m high, and 1m wide flume at Deltares. Large quantities of different types of plastic items were released close to the flume bed 30m upstream of the measurement location, where the position of each item was measured and counted to obtain a vertical distribution. The plastic items used in the campaign were similar to commonly found riverine litter such as bags, foils, cups and spheres, with varying size and plastic type (PP & HDPE). Additionally, the hydrodynamic conditions were varied allowing testing of different turbulent flow conditions.

Acoustic Doppler Velocimeters (ADV) measurements were performed to characterize the flow field and turbulence. Computer vision AI algorithms were used to track the plastic particle positions within the water column, enabling the construction of plastic vertical distribution profiles.

Similar to Valero et al. (2022), the experiment confirmed distinct surfaced and suspended transport layers for near-neutrally buoyant plastics for low turbulent flows. Under low turbulent flow conditions, plastic items concentrated at the free surface, confirming dominance of buoyancy and surface tension effects over turbulent mixing. Within the suspended transport layer, the plastic particles exhibited an inverse Rouse profile. At higher turbulence, the vertical distribution of observed plastics became more uniform for plastic bags, while smaller sized plastics remained well-represented by the inverse Rouse profile. This suggests that classical inverse Rouse theories, which neglect particle size, may not adequately describe the plastic observation profiles of larger sized plastic transport in rivers.

Based on these findings, a Delft3D-FLOW hydrodynamic model was developed and validated against the ADV measurements, in which the particle tracking model Delft3D-PART was adapted to incorporate surface interaction effects based on particle dimensions. This parameterization enables more accurate simulation of riverine plastic concentrations by considering hydraulic dynamics, surface interaction, and plastic dimensions. The improved model parameterization will enhance the accuracy of predicting plastic transport and contribute to the development of effective mitigation strategies.

How to cite: van Batenburg, T., Moreno-Rodenas, A., Bakker, W., Valero, D., Kleissen, F., Buschman, F., Vriend, P., Franca, M. J., and de Fockert, A.: Experimental and numerical investigation of the vertical distribution of macro plastic transport in rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18928, https://doi.org/10.5194/egusphere-egu25-18928, 2025.

Microplastic (MP) particles pose a significant threat to the health of aquatic ecosystems, particularly in inland standing waters such as lakes, ponds, and drinking water reservoirs. During their settlement, MP particles are affected not only by seasonal variations in hydrodynamic forces but also by interactions with other MP particles settling simultaneously, which impact their settling velocity and, in turn, their distribution in the water column. Seasonal variations determine the nature of hydrodynamics in the water column, dictating whether it remains mixed or becomes thermally stratified. In addition to the influence of these seasonal hydrodynamic variations on MP particles, the interactions between MP particles settling in close proximity also play a significant role in shaping their settling behavior. These combined hydrodynamics-MP and MP-MP interactions result in changing the settling behavior of MP particles under different seasonal conditions.

To investigate how these interactions affect the residence time of MP particles in a water column, we utilized a numerical simulation framework in OpenFOAM, an open-source computational tool for solving partial differential equations in the field of computational fluid dynamics (CFD). The model, incorporating mass, momentum, and energy conservation equations along with two-way (particle-flow) and four-way (particle-particle) coupling, is calibrated and validated using data from an experimental mesocosm under open-air conditions influenced by real-time meteorological fluctuations. This numerical framework is then utilized to explore (1) how seasonal hydrodynamic variations impact interactions between MP particles, water, and other particles during settling and (2) how changes in MP particles' properties, such as size and density, alter their settling behavior under different coupling scenarios.

These potential findings aim to shed light on how seasonal variations in hydrodynamics and particle interactions in standing waters influence the settling velocity and residence time of MP particles in a water column. The outcomes of this study are expected to provide valuable insights for developing targeted strategies to mitigate the risks of MP particles to freshwater ecosystems and human health.

How to cite: Ahmadi, P., Elagami, H., Dichgans, F., Gilfedder, B. S., and Fleckenstein, J. H.: Influence of seasonal hydrodynamic variations and particle interactions on microplastic particle settling in water columns, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19700, https://doi.org/10.5194/egusphere-egu25-19700, 2025.

Due to their persistent presence in the environment and the largely unknown long-term impacts on biota, plastic waste has increasingly become the focus of scientific research in recent years. Particularly in the field of microplastic monitoring in rivers, there remains a significant need for research. As methodologies are not yet standardized, and often not representative and comparable, efficient sampling poses a major challenge.

Within the framework of the "Alplast" project, intensive efforts were made to develop a methodology capable of accurately capturing microplastic transport in rivers. Various carrier systems tailored to different river sizes were developed for net sampling, which targets the coarser fractions of microplastics (> 250 µm). In addition, a novel isokinetic pump was designed to analyze finer microplastic fractions from 50 to 250 µm

Given the spatial and temporal variability of plastic transport, multipoint sampling under varying hydrological conditions is strongly recommended. Larger microplastic particles occur less frequently, making it essential to sample large volumes of water to representatively capture this size range. Using net sampling, up to 1,000 m³ of water can be filtered at a single sampling point. However, finer particles, which cannot be captured by the limited mesh size of nets due to clogging especially under turbid boundary conditions, must be analyzed using pump samples. The number of sampling points across the river profile is often limited related to the high costs of analysis.

The newly developed isokinetic pump addresses this gap by measuring flow velocity at the intake area of the sampling device. A control unit regulates the pump speed so that the pumped water volume matches the natural flow in the sampling cross-section. This isokinetic sampling approach offers two major advantages. First, the plastic flow is not disproportionately altered during sampling, and second, a direct weighting of the spatial distribution in flow and concentration is automatically accounted for. This significantly reduces the number of required samples, which is particularly beneficial given the high costs associated with sample analysis.

The new methodology was successfully combined with net sampling and applied at multiple measurement sites. Results demonstrate that this approach enables efficient and representative monitoring of microplastic transport in rivers.

How to cite: Liedermann, M., Pessenlehner, S., Mayerhofer, E., and Gmeiner, P.: Isokinectic pump sampling – a methodoligy addressing the small size ranges of microplastic in rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20227, https://doi.org/10.5194/egusphere-egu25-20227, 2025.

Plastic nanoparticles, widely used in various consumer products, have become a significant contributor to soil pollution, making it essential to understand their transport in soils, where organic substances are prevalent. This study aimed to investigate the influence of low-molecular-weight organic acids (LMWOAs) on the transport of polystyrene nanoparticles (PS-NPs) through saturated quartz sand. The focus was on seven specific organic acids: three dibasic acids—malonic acid (MA1), malic acid (MA2), and tartaric acid (TA)—and four monobasic acids—formic acid (FA), acetic acid (AA), propanoic acid (PA), and glycolic acid (GA). The effects were evaluated across a range of pH levels (4.0, 5.5, and 7.0) and in the presence of two cations, Na⁺ and Ca²⁺. The results showed that, in the presence of Na⁺, dibasic acids significantly enhanced the transport of PS-NPs, with TA being the most effective, followed by MA2 and MA1. This enhancement was attributed to the adsorption of LMWOAs onto the PS-NPs and quartz sand, leading to a more negative ζ-potential. This negative shift increased electrostatic repulsion between the particles, reducing their deposition and facilitating transport. The Derjaguin-Landau-Verwey-Overbeek (DLVO) theory further explained that higher pH levels increased the energy barrier, which reduced PS-NPs deposition by stabilizing them in the suspension. In contrast, the monobasic acids—apart from GA—exhibited minimal impact on PS-NP transport. These acids slightly diminished the hydrophilicity of the PS-NPs, as evidenced by a minor increase in the water contact angle, which in turn reduced their mobility. However, GA, with its additional hydroxyl group, acted similarly to dibasic acids, promoting both enhanced hydrophilicity and increased transport of PS-NPs. When Ca²⁺ was present, the transport enhancement was similar to that observed with Na⁺. The complexation and bridging effects of Ca²⁺ with the organic acids and PS-NPs contributed to this effect. Overall, these findings offer valuable insights into the factors influencing the mobility of PS-NPs in soils, which is crucial for understanding their environmental behavior and potential ecological impacts.

How to cite: Lu, T., He, J., and Yang, M.: Impact of Low-Molecular-Weight Organic Acids on the Transport of Polystyrene Nanoplastics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-512, https://doi.org/10.5194/egusphere-egu25-512, 2025.

Biodegradable film mulching has attracted considerable attention as an alternative to conventional plastic film mulching. However, how much of microplastics is being formed during the film degradation and their impact on soil health during long-term use of biodegradable plastic film are not known. We quantified the amounts of microplastics (0.1-5 mm in size) in the topsoil (0-20 cm) of two cotton fields with different mulching cultivations: (1) continuous use of conventional (polyethylene, PE) film for 23 years (Plot 1), and (2) 15 years use of conventional film followed by 8 years of biodegradable (polybutylene adipate-co-terephthalate, PBAT) film (Plot 2). We further assessed the impacts of the microplastics on soil carbon contents and flows. The total amount of microplastics was larger in Plot 2 (8507 particles kg^{− 1}) than in Plot 1 (6767 particles kg^{− 1}). The microplastics (0.1-1 mm) were identified as derived from PBAT and PE in Plot 2; while in Plot 1, the microplastics were identified as PE. Microplastics > 1 mm were exclusively identified as PE in both plots. Soil organic carbon was higher (27 vs. 30 g C kg^-1 soil) but dissolved organic carbon (120 vs. 74 mg C kg^{− 1} soil) and microbial biomass carbon were lower (413 vs. 246 mg C kg^{− 1} soil) in Plot 2 compared to the Plot 1. Based on ¹³C natural abundance, we found that in Plot 2, carbon flow was dominated from micro- (<0.25 mm) to macroaggregates (0.25–2 and >2 mm), whereas in Plot 1, carbon flow occurred between large and small macroaggregates, and from micro-to macroaggregates. Thus, long-term application of biodegradable film changed the abundance of microplastics, and organic carbon accumulation compared to conventional polyethylene film mulching.

How to cite: Jiang, R. and Wang, K.: Impact of long-term conventional and biodegradable film mulching on microplastic abundance and soil organic carbon in a cotton field, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1849, https://doi.org/10.5194/egusphere-egu25-1849, 2025.

The widespread use of plastics has undeniably brought numerous advantages to society, facilitating countless advancements in technology, industry, and daily life. However, the proliferation of plastic debris in various environmental systems has become an escalating concern. Recognizing this pressing issue, our research team at the Civil Engineering Department of the Indian Institute of Technology (IIT) Roorkee has undertaken an experimental investigation to study the transport behavior of microplastics within soil matrices.

Specifically, we focus on coumarin 6 dyed microplastic particles, sized between 35 to 40 microns, as tracers to understand their migration patterns. This study employs vertical soil columns as experimental setups, designed to mimic natural subsurface conditions. Each soil column has a depth of 30.5 cm and is packed with carefully prepared soil types to replicate varying real-world scenarios. By using artificial drip irrigation systems to simulate rainfall or water infiltration, we aim to elucidate the mechanisms by which microplastics are transported through soil systems, potentially leaching into underlying groundwater reservoirs.

The primary objective of the study is to systematically investigate the factors influencing the downward movement of microplastics in different soil types. For this purpose, two types of soil have been used: fluvial sand and gravel soil. The influence of several variables on microplastic transport was examined, including variations in soil pH, organic matter content, drip irrigation intensity. These parameters were chosen because of their potential to alter the physicochemical properties of the soil environment, thereby affecting the mobility of microplastics.

To begin the experiments, vertical soil columns were packed with either fluvial sand or gravel soil. The soil was pre-conditioned to achieve specific pH levels and organic matter contents, ensuring controlled and reproducible conditions across trials. Microplastic particles stained with coumarin 6 dye were introduced at the top of the soil column along with water droplets, mimicking natural infiltration processes under varying drip irrigation intensities.

The effluent from the outlet at the bottom of the soil column was collected at regular intervals to quantify the number of microplastic particles that had traversed the column. These collected samples were then subjected to analysis under a fluorescent microscope, which enabled accurate detection and quantification of microplastic particles.

The study also highlighted the impact of drip irrigation intensity on microplastic migration. Higher flow rates were found to promote greater transport of microplastics, as the increased water velocity reduced the residence time of particles within the soil and minimized opportunities for retention or adsorption. Conversely, lower flow rates allowed for more pronounced interactions between the microplastics and the soil matrix, leading to increased retention.

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How to cite: Khan, M. F. and Ojha, C. P.: Drip Irrigation Promoted Migration of Microplastic Particles Across Vertical Soil Columns., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2391, https://doi.org/10.5194/egusphere-egu25-2391, 2025.

Ubiquitous microplastics (MP) have emerged as a global environmental concern, recently also in soils. However, limited attention has been given to the behaviour of small-sized MP (< 10 μm) due to the challenges associated with separating and quantifying MP from an exceedingly complex matrix. Here, we show that magnetic labelling of MP greatly increases the efficiency of MP extraction from soil using a magnetic field. Magnetic labelling was achieved by exploiting the glass transition of polystyrene MP sphere. By heating MP (4 µm polystyrene spheres), to induce surface melting in a suspension containing Fe₃O₄ magnetic nanoparticles (MNS), the MNS were adsorbed onto the MP surface. Subsequent cooling to room temperature, led to fixation of the MNS into the MP surface layer enabling MP extraction using a magnet. Incubating MP and MNS at 90°C for 2.5 h gave the highest MP recovery rate of 92 ± 7% in water. The same MP were then added to a sandy soil suspension to assess and optimize labelling and extraction efficiency of the MP from the soil. The following parameters were optimized: dispersant type, organic matter digestion, and MNS size, concentration, and storage time. Compared to conventional MP detection methods, the MP recovery using magnetic extraction improved from 26% to 94 ± 12%. To the best of our knowledge, this research represents the first successful quantitative extraction of MP < 10 μm from soil and opens new possibilities for fate assessing of small MP and cleaning the environment.

How to cite: Liu, Y., Hu, J., Huang, Y., Krekelbergh, N., Novita Kusumawardani, P., Sleutel, S., Parakhonskiy, B. V., Kollannur Biju, M. S., Hoogenboom, R., De Neve, S., and Skirtach, A.: Magnetic labelling and extraction of micrometer-sized microplastics from soil, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5376, https://doi.org/10.5194/egusphere-egu25-5376, 2025.

Soil aggregates play a pivotal role in soil organic carbon dynamics and microbial activity. However, their influence on the pressing issue of microplastic (MP) contamination in soils remains poorly understood. This lack of attention may be attributed to the inherent complexity and heterogeneity of soil, which renders plastic isolation and identification in soil is particularly challenging. This study aims to investigate MPs redistribution among soil aggregate fractions during the process of soil aggregation. Two soil textures (silt loam and sandy loam) were amended with organic matter (OM) to promote aggregation during a two-month incubation period,  with 0.1 % microplastics powder added to the soils. A self-made pristine and aged LDPE and PET microplastics (<50 µm) were used in this experiment. Subsequently, physical fractionation were implemented to separate the soils into aggregate fraction (macro-aggregate, micro-aggregates and within associated fractions and silt+clay fractions). Organic matter was removed via oxidation to prevent interference with MP analysis. MPs were subsequently extracted through density separation, filtration, and examined using a Keyence VH-Z500 digital microscope. Unexpectedly, even small amounts of MPs significantly influenced soil aggregation, with effects varying by polymer type, weathering state, and soil texture. LDPE was predominantly retained in the micro-aggregate fractions in both soil textures, except for aged LDPE in loam soil, where over 60% accumulated in the silt+clay fraction. Conversely, PET was primarily retained in the macro-aggregates of silt loam soils and the micro-aggregates of sandy loam soils. Furthermore, the redistribution of MPs during soil aggregation exhibited notable differences, with silt loam soils demonstrating the highest degree of MP redistribution. These findings are relevant as soil aggregates provide different levels of physical protection against degradation and mobility, influencing the bioavailability of microplastics and their potential transfer to other environmental compartments.

How to cite: Kusumawardani, P. N., Amaya, D. P. T., Krekelbergh, N., Liu, Y., Sleutel, S., Skirtach, A., and De Neve, S.: The Re-distribution of Pristine and Aged Microplastics (<50 µm) in Soil Aggregate Fractions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5584, https://doi.org/10.5194/egusphere-egu25-5584, 2025.

Nanoplastic (NP) exposure to the terrestrial water cycle poses an emerging threat to subsurface ecosystems, while the continuous release of NP increases the risk of drinking water contamination.
Fungal communities are a crucial component of terrestrial ecosystems. Traditionally, their presence and functions have been studied in shallow soils as part of the soil microbiome or above ground as decomposers or pathogens. Recent mycobiome screening studies of groundwater wells have revealed the presence of fungal species in deeper aquifers. This confirms the presence of fungi across all compartments of the terrestrial water cycle, highlighting the need to investigate their role in contaminant transport processes.
Fungi have demonstrated the ability to immobilize dissolved organic contaminants, heavy metals, and pharmaceuticals from polluted waters. However, studies examining their effect on NP removal remain limited. Existing research generally lacks the integration of liquid flow dynamics, which is crucial for understanding fungal interactions in natural water systems.
We present a dataset, which shows dynamics of NP-fungi interaction across multiple laboratory scales. Our study compares batch adsorption experiments with transport experiments conducted in inoculated microfluidic chips and transport columns. Carboxylated polystyrene nanoparticles of 100 nm and 250 nm serve as model NPs. Following fungal inoculation in growth media, the experiments are conducted under various ion concentrations of CaCl and flow velocities ranging from 1 m/d and 30 m/d.
Our results indicate scale-dependent modes of NP-fungal interactions. In batch-scale experiments, higher ion concentrations significantly enhance the adsorption efficiency of NPs to fungal hyphae. In contrast, experiments conducted in microfluidic chips and transport columns reveal altered behavior, with notably lower adsorption efficiencies observed.
This suggests that in natural environments, factors such as the spatial distribution of hyphae, ion concentration, flow rates, and consequently reaction times, collectively influence the efficiency of NP removal by fungal communities.

How to cite: Müller, S., Zou, H., Lundqvist, M., Cedervall, T., Mafla Endara, M., and Hammer, E.: Nanoplastic- Fungi interaction – insights from various laboratory scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5999, https://doi.org/10.5194/egusphere-egu25-5999, 2025.

Microplastics (MP) are present in the soil mainly due to the use of agricultural mulch films and sewage sludge as fertilisers. They are small particles of less than 5mm in size, whose presence impacts agricultural productivity by modifying the soil pore structure, affecting its water and nutrient retention properties, and altering the soil water characteristic curve (SWCC). The objective of this study was to investigate the effects of the size and concentration of polypropylene (PP) microplastics on the SWCC of silty sand. The soil comprises of 72.2% sand, 27.7% silt and 0.1% clay. Polypropylene particles of size (0–50 µm and 50–100 µm) were added to the soil at three concentrations (0.1%, 0.2%, and 0.4% w/w) in the range reported in the literature. The SWCC was measured using a pressure-plate assembly designed such that the mass balance of water imbibition and exudation can be verified at all stages. A genetic algorithm from python library (pygad) was employed to fit the observed soil moisture data and estimate the parameters of the Van Genuchten (VG) model. As the pressure increased, the MP of size 50-100 µm showed a significant decrease in the soil water holding capacity up to a pressure of 0.7 bar, beyond which there were no significant differences with the SWCC for the control soil without MP. Increasing MP content decreased the soil water retention capacity.  The field capacity decreased by 5.8% at a concentration of 0.4% for larger-sized microplastics. The smaller size MP (0–50 µm) at low concentration of 0.1% did not significantly affect the soil water content, while the accumulation of PP-MP at higher concentrations (0.2% and 0.4%) resulted in a significant decrease in the soil water holding capacity. The differences in microplastic sizes and concentrations led to variations in the SWCC, which was reflected in the variations in the fitted parameters of the VG model. The presence of larger MP may have disrupted the original capillary pore structure and weakened the soil capillarity, contributing to a decline in soil water retention. The small amount of finer MP may have been transported through the soil pores without significantly affecting the water retention properties of the soil. These findings highlight MP potential risk to soil hydraulic properties and its negative impact on plant growth.

How to cite: Gupta, P. and Guha, S.: Effect of Polypropylene Microplastic on Soil Water Characteristic Curve, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6106, https://doi.org/10.5194/egusphere-egu25-6106, 2025.

Microplastics are ubiquitous and have been detected across all environments. While the focus has been on pollution threats posed by plastic particles e.g. derived from fragmented plastic packaging or tyres – the dominant form of microplastic particles identified in environmental samples tends to be microfibres, shed from textiles. Microfibres are believed to enter the environment mainly via laundering of garments, with soil environments forming an important sink for microfibres due to sewage sludge applications from wastewater treatment plants. There is growing awareness that these microfibres are not only synthetic (plastic) but also originate from natural textiles, such as cotton and wool, which have been largely overlooked from an environmental science perspective. With 100 billion new garments made every year, we know little about the environmental impact during ‘wear-and-use’ and at the ‘end-of-life’ of textile microfibres. Therefore, we need to understand the release of microfibres from natural and synthetic fibres from across the garment life-cycle (from manufacture to end-of-life).  We set up an incubation study burying 5 x 5 cm sections of different fabrics in soil, along a gradient of cotton-polyester blends to determine textile biodegradation, microfibre fragmentation and impacts to soil properties. We selected fabrics with contrasting plain dyes (light vs dark colours) to test whether dye quality affected biodegradation rates. Over the course of the short-term incubation, fabric and soil samples were retrieved and analysed for various properties to track changes in fabric samples, microfibres and soils. Here we present some data from the experiment to begin to understand how natural and synthetic fibres biodegrade in soil and their impact on soil properties and soil health.

How to cite: Prendergast-Miller, M., Rogers, A., Mutambo, N., Sheridan, K., and James, A.: Biodegradation of cotton-polyester textiles to understand fate of natural and synthetic microfibres in soil, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7447, https://doi.org/10.5194/egusphere-egu25-7447, 2025.

Plastics have become an integral part of our lives due to their versatility and durability. Conventional plastics are made from non-renewable resources that are harmful to humans and the environment. They release toxic substances and break down into microparticles. The use of biodegradable plastics, such as polylactic acid (PLA), is a viable alternative to reduce the above-mentioned problematics. This study investigates the long-term (9 months) biodegradability of PLA in soil under laboratory conditions in a closed chamber system. The experiment was set up according to the European standard ISO 17556:2012. The biodegradability percentage of the plastic was calculated by measuring the production of CO₂ by microorganisms in the soil on PLA. The percentage biodegradability of the PLA was calculated using soil CO₂ emission rates (measured by titration method). PLA was used in net and film forms under two experimental conditions: untreated soil and soil modified with two natural soil amendments (natural zeolites and biochar) to evaluate their potential impacts on PLA decomposition rates and CO₂ emissions. Cellulose (100% biodegradable) was used as a positive control.  For comparison, PLA degradation was also studied under temperature-controlled composting conditions (58°C) with the same experimental setup.  The morphological changes of PLA were analysed using a scanning electron microscope. The results showed different trends over time and significant differences between the treatments, especially concerning the presence of soil amendments, highlighting the complexity of the interactions between PLA and the soil microbial community and physico-chemistry of the substrate.

How to cite: Pignoni, E., Calosi, M., Ferretti, G., Botezatu, C., Alberghini, M., Bertoldo, M., and Coltorti, M.: Long-term biodegradability of Poly-Lactic Acid (PLA) in soil by measuring carbon dioxide evolution in a closed system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9909, https://doi.org/10.5194/egusphere-egu25-9909, 2025.

Qualifying and quantifying microplastic (MP) pollution in sediments represent a methodological challenge. Most used methods often require sample pretreatments to separate the MP particles of interest from the rest of the sediment. It has been demonstrated that the sampling preparation can influence the analytical results. To overcome these difficulties, Romero-Sarmiento et al. (2022) proposed the use of the pyrolysis and oxidation based-thermal method (a Rock-Eval® device), originally developed to characterize sedimentary organic matter. According to these authors, several parameters calculated from Rock-Eval® analyses, such as Total HC (quantity of hydrocarbons released during pyrolysis) and Tpeak (cracking temperature of plastic polymers), could be used to qualify and quantify microplastics, without sediment pretreatments. However, unpublished preliminary studies have shown that the Tpeak parameter, can vary with catalytic and thermal desorption effects, depending on the nature of the associated mineral and organic matrices. To understand the origin of these effects, we studied matrix effects during the thermal analysis of composite samples. In this study, synthetic mixtures of various mineral matrices, organic materials and pure polymers at different concentrations were analyzed using a Rock-Eval®. As expected, the results show that Total HC varies with the amount of polymer present in the samples. However, Total HC also varies according to the type of the mineral matrix. Indeed, some samples, such as clays and particularly goethite, show retention effects when the mineral matrices are characterized by a high adsorption capacity. Furthermore, for a given polymer, the Tpeak parameter can vary according to the mineral matrix. For example, it seems that some mixtures of polymers and mineral matrices enhance the catalytic cracking of hydrocarbons. While highest expected Tpeak values were obtained for some synthetic blends indicating a delayed release of hydrocarbons. The same mineral matrix can also induce different effects depending on the organic compounds present in the sample. In addition, analyses using natural versus artificial matrices were performed to compare these effects. Obtained results were complemented by scanning electron microscope observations and X-ray diffraction measurements at different temperatures during pyrolysis, to visualize the possible organo-mineral interactions and analyze morphological changes respectively. Understanding these effects of the mineral and organic matrices on the determination of MP concentrations will enable us to refine a more accurate method to quantify the impact of plastic pollution in sediments.

Romero-Sarmiento, M.-F., Ravelojaona, H., Pillot, D., Rohais, S., 2022. Polymer quantification using the Rock-Eval® device for identification of plastics in sediments. Science of The Total Environment 807, 151068. https://doi.org/10.1016/j.scitotenv.2021.151068

How to cite: Ricard, C., Baudin, F., Lieunard, V., Friceau, L., Rohais, S., Copard, Y., Wolfgang, L., and Romero-Sarmiento, M.-F.: Influence of mineral and organic matrices on the thermal characterization of microplastics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10207, https://doi.org/10.5194/egusphere-egu25-10207, 2025.

The presence of NPs in drinking water has raised wide concerns due to their potential impacts on human health. Riverbank filtration (RBF), a natural water treatment process that involves the passage of water through soil, is employed by some Dutch drinking water companies that source water from rivers. Given the emergence of NPs as pollutants, it is essential to understand their transport and removal behavior during riverbank filtration to ensure the safety and quality of drinking water.

The deposition of NPs in porous media is strongly influenced by the physicochemical properties of aquifer grain surfaces. Natural biofilms, consisting of complex communities of bacteria and other microorganisms, typically form on these surfaces and alter their properties. During RBF, biofilm-associated microbial activity leads to rapid oxygen consumption due to the degradation of organic matter, resulting in the formation of localized anoxic and anaerobic zones. However, the impact of this spatial heterogeneity of biofilms on the removal of NPs remains unclear. In this study, we aim to investigate how biofilm spatial heterogeneity influences NP deposition during RBF.

We constructed several long columns (90 cm in length), each composed of ten short columns (9 cm in length, 2 cm in diameter), packed with technical sand (particle size: 0.4–0.6 mm). River water sourced from an RBF site was pumped through the columns at a flow rate of 0.1 mL/min (0.054 m/h) to facilitate biofilm growth over periods of 1, 3, and 6 months. After biofilm formation, columns were segmented into ten short columns to assess NP transport behavior by analyzing breakthrough and retention curves at different biofilm depths. Europium-doped polystyrene NPs (30 mg/L) suspended in synthetic river water with a similar ionic composition to natural river water were used as tracers to evaluate NP transport and retention at an increased flow rate of 0.75 mL/min.

So far, we have obtained breakthrough curves for NP transport in columns with 1- and 3-month biofilms. Preliminary results indicate that site blocking contributed to the concentration- and time-dependent deposition of NPs. The inherent surface roughness of the technical sand created heterogeneous sites that contributed to multi-site NP deposition. Compared to the original sand grains, biofilms exhibited a less negative surface charge, facilitating stronger interaction with NPs. It suggest that biofilms created more favorable heterogeneous sites, enhancing both irreversible and reversible deposition of NPs. The maximum retention capacity of sand grains decreased with depth, with the shallow biofilm layers, which had the highest biomass, greatly enhancing NP retention. Additionally, biofilms grown for 3 months demonstrated a stronger capacity to retain NPs compared to those grown for 1 month.

The remaining experiments are expected to be completed by May next year. This study will enhance the understanding of NP deposition mechanisms during RBF, with a particular focus on the critical role of the spatial heterogeneity of natural biofilms. The findings will provide valuable insights into the potential removal efficiency of NPs in RBF systems, as well as the associated risks of NP exposure in downstream environments and drinking water sources.

How to cite: Xu, Y., van der Hoek, J. P., Liu, G., and Maren Lompe, K.: Biofilm heterogeneity affects the mobility of nanoplastics during riverbank filtration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10477, https://doi.org/10.5194/egusphere-egu25-10477, 2025.

How to cite: Schneidewind, U., Krause, S., Kelleher, L., Reiss, J., Perkins, D., Barrett, N., and Robertson, A.: Microplastic particles in groundwater systems worldwide, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11153, https://doi.org/10.5194/egusphere-egu25-11153, 2025.

Microplastics (MPs) have emerged as a significant environmental concern, particularly due to their pervasive presence in various ecosystems, including soil and groundwater. These small plastic particles, often resulting from the degradation of larger plastic items, pose serious risks to ecological health and human safety. Their infiltration into groundwater systems is alarming, especially in agricultural practices utilizing mulching techniques, where microplastics can permeate porous media and potentially disrupt soil health and crop productivity. Despite the growing body of research on microplastics, most studies have focused on uniform soil matrices or sediments, neglecting the complexities of layered and heterogeneous aquifer systems. This study investigates the dynamics of polyethylene microplastics within soil column tests, specifically examining their transport behavior through stratified layers of coarse sand and small gravel. We utilized medium-sized microplastics (25 microns) embedded within a layered column consisting of coarse sand (500-1000 microns) and small gravel (4-8 mm), packed uniformly to simulate real-world conditions. Groundwater was injected into the columns at a flow rate of 12 mm per minute for 360 minutes. Water samples were collected at intervals of 5, 10, and 20 minutes for microplastic quantification using fluorescence microscopy after filtration. Post-experiment, sediment layers were sequentially removed every 6 cm to isolate and count microplastics using density separation methods. Results indicated a significantly faster movement of microplastics through gravel compared to sand, with the highest concentrations detected in the outflow from gravel columns. In mixed columns where gravel was positioned below sand, a greater number of microplastic particles were observed compared to when sand was below. This suggests that while gravel facilitates rapid transport, the arrangement of layers plays a critical role in determining the concentration of microplastics in the outflow. Additionally, entrapment of microplastics was most pronounced in the sand layers, while minimal retention occurred in gravel. Notable variations in microplastic counts were observed at the interface between gravel and sand in mixed sediment columns, highlighting the influence of layer interactions on transport dynamics. In conclusion, this study underscores the critical need to consider soil layering when assessing microplastic transport in agricultural settings. The findings reveal that microplastic dynamics are significantly affected by substrate composition and layering, which could have profound implications for groundwater quality and ecosystem health in agricultural landscapes. Further research is essential to explore the long-term effects of microplastic contamination on soil biota and crop systems, as well as to develop effective management strategies to mitigate their impact on environmental health.

Keywords: Microplastics, Soil layering, Transport dynamics, Groundwater contamination

How to cite: Dehbandi, R., Alqrinawi, F., Singh, J., Schneidewind, U., and Krause, S.: Impact of Layering and Heterogeneity on the Transport Dynamics of Microplastics in Soil Columns: Implications for Groundwater Contamination, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15142, https://doi.org/10.5194/egusphere-egu25-15142, 2025.

Riverbed sediments have been identified as temporary and long-term accumulation sites for microplastic particles (MPs), but the transport and retention mechanisms still need to be better understood. Here we assess the occurrence and spatial distribution of MPs in surface water, riverbed sediments, and groundwater of two German lowland rivers (Teltow Canal and Havel), under prevailing infiltrating conditions. Surface water and groundwater samples were collected at each site on a monthly and three monthly basis over the course of one year, respectively. At each site, three sediment freeze cores up to a depth of 100 cm were taken, and, together with the water samples, analysed by near-infrared spectroscopy (NIR) to infer the number of MPs (Ø > 100 µm), polymer types, and particle sizes.

The number of MPs detected varies considerably between the compartments (with concentrations in the groundwater being approximately one order of magnitude lower than in the river), the sampling sites, and also, but to a much lower extent, over the seasons. MPs were also detected throughout the entire depth of the sandy riverbed sediment, thereby highlighting the retention capacity of the riverbed sediments, but also the partial mobility of MPs from the river through the subsurface into the groundwater. These observations were supported by saturated column experiments with fluorescent polystyrene particles (fPS), which demonstrated that the vast majority of fPS were retained in the upper 20 cm or 15 cm of gravelly or sandy sediments for common filtration rates. However, it was also observed that approximately 0.3% of the introduced MPs, with sizes ranging from 100 µm to 500 µm, were transported throughout the entire column for a high filtration rate.

These results demonstrate that riverbed sediments have the capacity to retain MPs originating from surface water. Furthermore, they indicate that these sediments can also act as potential vectors for the infiltration of small MPs into local groundwater aquifers, especially under prevailing infiltrating conditions.

How to cite: Munz, M., Loui, C., Pittroff, M., and Oswald, S. E.: Occurrence and transport of microplastics across the streambed interface during bank filtration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15405, https://doi.org/10.5194/egusphere-egu25-15405, 2025.

Research on microplastics (MP) in soils is much complicated due to the lack of dedicated (extraction) methodologies and strong matrix interferences for MP detection, and there is almost no research on the dynamics of the smallest MP in soil. In our research we first compared the possible detection of the smallest MP fraction (1-2 µm) by µ-Raman spectroscopy and fluorescence microscopy in matrices of highly varying complexity. Subsequently, we have demonstrated that it is possible to use fluorescent MP to monitor and measure the rate of the leaching process of small MP in soils under field conditions.

In a first experiment, samples of pure quartz sand, soils with variable texture (sandy loam, silt loam, clay loam) and removal of native soil organic matter (SOM), and a sandy loam soil with native SOM still present were amended with fluorescent polystyrene (PS) microparticles (diameter 1.7 µm) in different concentrations ranging from 0.1 to 0.001%. After mixing and compaction both the Raman spectra and fluorescence microscopy images were obtained. Characteristic PS Raman fingerprint peaks (main peak at 1001 cm^-1) were visible in quartz sand (all concentrations) as well as in sandy and silt loam soils without SOM (some concentrations), but not in the other situations, whereas fluorescence microscopy clearly visualized the MPs at all concentrations in all matrices.

In a second experiment, fluorescent PS microparticles were amended under field conditions to a sandy loam soil on a small surface area (circles of ± 0.2 m diameter), to a depth of 3 cm and at a rate of 70 mg kg^-1 soil. At regular time intervals, samples were taken up to a depth of maximally 100 cm. The results of the experiment demonstrate the fast process of downward transport of the MP, reaching a depth of 30 cm after only 40 days and subsequently moving further through the vadose zone to get into range of the fluctuating groundwater table. Unambiguous fluorescent MP detection in real soil thus opens up new avenues for monitoring the vertical redistribution of the smallest MP fractions in the soil profile.

How to cite: Krekelbergh, N., Li, J., Kusuwardania, P. N., Liu, Y., Sleutel, S., Parakhonskiy, B., Hoogenboom, R., Skirtach, A., and De Neve, S.: First experimental evidence of fast leaching of small (1.7 µm) microplastics added to soil in field conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16631, https://doi.org/10.5194/egusphere-egu25-16631, 2025.

Microplastics (MPs) are ubiquitous environmental contaminants that not only accumulate in sediments and biota but also act as vectors for toxic substances, facilitating the transport and dispersion of hazardous chemicals within ecosystems. Their environmental impact is exacerbated by their propensity to adsorb pollutants from their surroundings, a process influenced by the physicochemical properties of both the polymers and the contaminants. While MPs typically exhibit limited surface porosity, exposure to natural environmental factors, such as weathering, abrasion, and photo-oxidation, significantly alters their surface characteristics. These transformations result in structural modifications, including the incorporation of oxygen-containing functional groups, sulfhydryl groups, and persistent free radicals. Such alterations lead to the generation of negatively charged surfaces, which enhance the adsorption of metal cations and other pollutants, thereby amplifying their environmental persistence and toxicity.

This study aimed to investigate the distribution of MPs and their interactions between surface water (SW) and groundwater (GW) systems, with a particular focus on understanding how redox potential (ORP), and surface modifications influence their structural transformations, pollutant adsorption mechanisms, and role as carriers of hazardous substances within natural ecosystems. SW and GW samples were collected from various locations across Uttarakhand, India. In-situ parameters, including pH, conductivity, TDS, DO, temperature, salinity, pressure, ORP, turbidity, and alkalinity, were measured using a portable multiparameter probe (HANNA-HI9829-01201). The average concentration of MPs in the GW and SW samples was found to be 34 MPs/L and 29 MPs/L, respectively. In SW, the relationship between MPs and ORP appears less direct but is still influenced by ionic parameters such as conductivity and TDS, reflecting the potential for pollutant adsorption in regions with high redox activity. In GW, MPs exhibit a moderate correlation with ORP and alkalinity, suggesting that redox conditions may play a significant role in their behavior or interaction with other pollutants. Notably, pH and ORP were clustered together in both GW and SW, suggesting a link between acidity/alkalinity and redox conditions driven by shared environmental or geochemical processes. This research provides key insights into MPs' behavior, aiding strategies to combat water pollution and guide policies to protect ecosystems and public health.

Keywords: microplastics; redox; emerging contaminants; transport; interactions.

How to cite: Dogra, K.: Microplastic-Facilitated Transport of Emerging Contaminants in Redox-Active Environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17448, https://doi.org/10.5194/egusphere-egu25-17448, 2025.

Microplastics in rivers originate from various sources and can be transported by river water. During their time in the river, the properties of microplastics may change leading to a temporary deposition and accumulation in the riverbed. In particular, during floodings, stronger flow velocities occur and can remobilize microplastics and sediments, transporting them further downstream and to adjacent floodplains. Floodplains represent dynamic and vegetation-rich environments, where the vegetation increases the floodplains’ roughness, resulting in slower flow velocities during floods and potentially enhance deposition of sediments and microplastics. While previous research has shown that factors such as local topography and flood frequency influence microplastic distribution in floodplains, however, the role of vegetation in microplastic filtering during floods has not been studied. 
This study investigates the retention of microplastics and natural sediments by floodplain grassland vegetation during a major river flood. Directly after the flood event in July 2021, we sampled vegetation from a formerly flooded Rhine floodplain north of Cologne, Germany. For comparison we sampled vegetation from an adjacent non-flooded grassland, which was only affected by atmospheric microplastic deposition. After rinsing the deposits from the vegetation, we used ZnCl₂ density separation to extract microplastics, followed by enzymatic-oxidative purification to remove organic material and µ-FPA-FTIR imaging for microplastic analysis.  
Our findings show that microplastics from fluvial and atmospheric origin differ in terms of their numbers, shapes, sizes, and polymer types. Concerning the samples from flooded vegetation, higher vegetation biomass was associated with increased deposition of both natural sediments and small microplastics. However, we observed distinct deposition patterns for natural sediments and microplastics. Our results provide valuable insights into the role of floodplain vegetation in the retention, accumulation, and distribution of microplastics at the interface between aquatic and terrestrial ecosystems.

How to cite: Rolf, M., Laermanns, H., Laforsch, C., Löder, M. G. J., and Bogner, C.: The role of floodplain vegetation in filtering microplastics during a major Rhine flood event, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17588, https://doi.org/10.5194/egusphere-egu25-17588, 2025.

Microplastics (MPs) have emerged as contaminants of global concern due to their ubiquity and potential ecological risks. Understanding MP transport, behavior, and fate in soils is crucial for assessing their interactions with soil organisms and for conducting environmental risk assessments. Most studies on MP transport are conducted in laboratory settings, often using soil column experiments. These experiments typically examine MP mobility by assessing the retention of MPs in different soil types under varying flow conditions. To ensure consistent MP application over time or volume, dispersants are frequently added to MP suspensions, particularly when targeting floating or highly hydrophobic MPs. However, dispersants may alter both the behavior of MPs and their interactions with soil particles, potentially introducing biases when attempting to understand natural MP transport—a critical aspect that remains underexplored.

Therefore, this study developed an improved soil column experiment protocol that excludes dispersants while maintaining consistent MP application through a low-liquid-level, continuously stirred suspension. The Coefficient of Variation (< 5%) for this improved experimental design is found to be statistically acceptable.

Based on this improved method, MP transport was investigated in soil column experiments with quartz and natural sandy soil as matrices. Rhodamine B-stained polystyrene (RhB-PS) particles (D90 < 10 µm) were intermittently pumped upward into the columns, with and without the dispersant (0.25% v/v Tween 20). Drainage samples were collected after each RhB-PS application and during intermittent flushing with artificial rainwater. Fluorescence microscopy was used to quantify RhB-PS concentrations in the drainage samples on haematocrit plates.

The analysis of drainage samples revealed that dispersants significantly enhanced MP mobility, allowing more MPs to bypass soil retention. The clay and organic matter in natural sandy soil, through their fine particles and surface charges, may potentially enhance the interaction between microplastics and soil, thereby reducing their movement within the natural sandy soil, regardless of whether dispersants were used. These results suggest that existing transport studies and related models, which are based on dispersant-assisted experiments, may not accurately reflect the natural behavior of MPs in soils.

How to cite: Lu, Y., Rolf, M., Brehm, J., Liu, H., Wagenhofer, J., Khaleel, R., Laermanns, H., Laforsch, C., Nitsche, F., G.J. Löder, M., Gekle, S., and Bogner, C.: Avoiding Bias in Microplastic Transport: Development of an Improved Dispersant-Free Soil Column Experiment Protocol, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18093, https://doi.org/10.5194/egusphere-egu25-18093, 2025.

In recent years, microplastics (MPs), have infiltrated diverse environments, including oceans, rivers, lakes, wetlands, groundwater, soils, sediments, air, human tissues, food systems, and even the atmosphere. Among these, groundwater, a critical freshwater source for industrial, agricultural, and domestic applications faces increasing threats from MPs pollution. There is still a knowledge gap in understanding the mechanisms and the pathways governing the transport of MPs in complex groundwater systems, which remains a significant research challenge. Hence, to understand the transport of MPs through the porous media, we have conducted a series of one-dimensional (1D) column transport experiments.  To quantify the transport of MPs through the porous media, two types of MPs with different functional groups, polypropylene (PP, C­­₃H₆) and polyethylene terephthalate (PET- C­­₁₀H₈O₄) of size 100-200 µm are considered for the present study. The experiments are conducted using porous media of IS Grade I (2mm-1mm, d₅₀= 1.5mm) and Grade II (1mm- 0.5mm, d₅₀= 0.75mm) experimental sand. Various flow velocities were used to determine the most vulnerable pore-water velocity on the transport of these contaminants through the porous media.  For each case, the experiment is conducted for 10 pore volume and after 10 pore volumes, the sand samples from different depths of the column are taken to determine the number of particles attached to the sand grains. We have observed that, as pore volume increases, the MPs count rises steadily for most samples, indicating enhanced transport through the porous media. This suggests that MPs are progressively mobilized through the porous media as the pore volume expands, with certain volumes contributing more significantly to the overall transport dynamics. Coarser sands (Grade I) with more prominent pores facilitate higher MPs movement, while finer sands (Grade II) reduce transport due to greater retention. Additionally, higher pore-water velocity enhances MP mobility, suggesting that environmental conditions with coarser soil and increased water flow can lead to greater MPs dispersion, impacting its distribution in natural soil-water systems. The findings of this study can play a crucial role in applying indirect site interventions to avoid the spreading of MPs through porous media at polluted sites.

How to cite: Yadav, S., Yadav, P. B. K., and Krause, P. S.: Transport of Microplastics Through Porous Media: Influence of Porosity and Pore-Water Velocity , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18937, https://doi.org/10.5194/egusphere-egu25-18937, 2025.

Microplastics (MPs) in urban areas threaten ecosystems, causing water and soil contamination, as well as health risks. This study examined MPs in road sediments from five functional areas in Daxing District, Beijing, using characterization and risk assessment methods. The abundance of MPs ranged from 2060 to 8680 items/kg, with business areas showing the highest levels, followed by traffic, residential, leisure, and cultural/educational areas. These variations are likely influenced by human activities, urbanization, and traffic volume. MPs were primarily in fragmented forms, with polypropylene (PP) (43%-87%) and polyethylene (PE) (5%-33%) being the most common polymers. Fragmentation characteristics varied, with cultural/educational areas showing the highest α values despite fewer large MPs. Lower λ values (2.80-5.00) suggest a higher potential for MPs to break down, possibly contributing to stormwater pollution. Multiple risk assessments indicate that the presence of polymers like PP and PE contributes to elevated MP risks in both traffic and residential areas. These areas have been identified as "hotspots" with moderate to high pollution risks. Despite frequent street cleaning in traffic areas, contamination persists. In contrast, leisure areas, with lower human activity, have a reduced risk of MP contamination. These findings can inform effective control measures for MP pollution in urban road sediments.

How to cite: Gu, Y., Zhang, X., Liu, J., Deng, J., Zhang, Z., Tan, C., Li, H., and Hu, Y.: Microplastics in Road Sediment of Typical Urban Districts of Beijing: Characteristics and Risk Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21474, https://doi.org/10.5194/egusphere-egu25-21474, 2025.

While there have been advances in understanding the above ground plastic cycle, there is still a substantial lack of understanding the sources and activation mechanisms of plastic pollution affecting the entry, fate, transport, transformation and impact of microplastics into soils, riverbeds, sediment and groundwater aquifers.

We here present the initial outcomes of integrated field and laboratory analytical experimental approaches and mathematical modelling studies to provide mechanistic understanding of the overall magnitude as well as hot spots (and hot moments) of microplastic entry into subsurface ecosystems and their transport and transformation pathways. Our model results highlight that a large proportion (>95%) of all mismanaged plastic waste emitted since the 1950s is temporarily stored in river basins and able to enter subsurface ecosystems in the long-term. Using multi-scale modelling studies in combination with artificial river simulators (flumes) and laboratory column experiments we evidence that hyporheic exchange represents a preferential input mechanism for smaller and lighter microplastics into streambed sediments and underlying groundwater ecosystems. This finding maps directly onto field experimental findings from our global monitoring programmes which identified distinct hotspots of microplastic accumulation. Soil and streambed sediment columns were deployed to explore the controls on microplastic transport once they have entered the subsurface, highlighting that in particular intermittent pulsed hydraulic forcing increases the potential for fast particle transport.

How to cite: Krause, S., Schneidewind, U., Alqrinawi, F., Chen, Z., Fraga, B., Dehbandi, R., Gomez Velez, J., Mecaj, P., Cardoza Pedroza, L., Simon, L., Mermillod Blondin, F., Mourier, B., Volatier, L., Lassabatiere, L., Kelleher, L., Comer-Warner, S., Quinones, Z., Lynch, I., Singh, J., and Yadav, B.: The plastic underground – Exploring the mechanisms controlling the fate and transport of microplastics in the subsurface, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21555, https://doi.org/10.5194/egusphere-egu25-21555, 2025.

TEMBO Africa is a Horizon Europe project that seeks to improve in situ sensing of weather and water in sub-Saharan Africa. To ensure beyond-the-project sustainability, we are using innovative sensors to measure variables such as rainfall, bathymetry, river flow, and large-scale soil moisture. TEMBO also develops services for hydropower, agriculture, and disaster management. These services will produce societal and economical value, for which governments and companies are willing to pay. These payments, in turn, serve to maintain the observation networks. One guiding principle is that the new data gathering method should cost less than 10% of existing methods in term of total costs of ownership. This principle implicitly pays special attention to the local availability of human resources. Many monitoring projects in Africa consist of installation by experts from the Global North, followed by a short training of local technicians. This works nicely until something breaks down. In TEMBO, African universities and spin-off companies are co-developing the technologies such that any operational problems can be solved without flying in expensive foreign experts.  

In this presentation, we will go through the sensor innovations and how these feed into different products and services.

How to cite: van de Giesen, N., Annor, F., Ayambila, S. N., Duah, K., Fico, T., Gatti, A., Hoes, O., Kranjac-Berisavljevic, G., Peña-Haro, S., Realini, E., Samboko, H., and Winsemius, H.: From innovative sensors to steady data streams: The TEMBO Africa project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-400, https://doi.org/10.5194/egusphere-egu25-400, 2025.

Precise estimation of hydraulic conductivity (K) in porous media is vital for advancing hydrological and subsurface flow investigations. Groundwater experts have increasingly adopted neural computing approaches to indirectly determine K in porous media, offering a more efficient alternative to conventional methods. The research focuses on developing the Feed-Forward neural network (FFNN) and Kohonen Self-organizing maps (KSOM) models to compute the K using easily measurable porous media parameters i.e., grain-size, uniformity coefficient, and porosity. The observed data were split into 70% and 30% for the development and validation phase, respectively. The developed model's performance was examined via statistical indicators, including root mean square error (RMSE), determination coefficient (R²), and mean bias error (MBE). The findings suggest that the FFNN model significantly outperforms the KSOM model in estimating the K value, with the KSOM model achieving only moderate accuracy. During the validation phase, the FFNN model shows a stronger correlation with the measured values, yielding RMSE, R², and MBE values of 0.016, 0.94, and 0.006, while the KSOM model returns values of 0.024, 0.91, and -0.004 respectively. The FFNN model's superior predictive ability makes it a reliable tool for accurate K estimation in aquifers.

How to cite: Chandel, A. and Shankar, V.: Hydraulic conductivity estimation in Porous Media: Insights from Neural computing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1828, https://doi.org/10.5194/egusphere-egu25-1828, 2025.

Soil moisture is a critical factor for understanding the interactions and feedback between the atmosphere and Earth's surface, particularly through energy and water cycles. It also plays a key role in land climate and hydrological processes. Recent advancements in autonomous robotic applications for precision agriculture have introduced significant solutions, particularly in remote sensing. Currently, these platforms enable autonomous soil parameter measurement and on-site data collection, which is essential for resource optimization and data-driven agricultural decision-making. However, challenges persist, especially in real-time soil moisture monitoring—a key focus for improving irrigation efficiency, water use, and crop yields. Soil moisture measurement in-situ techniques include the accurate oven-drying method and soil moisture sensors, while satellite remote sensing uses optical, thermal, and microwave imaging to estimate surface soil moisture from a broader perspective. However, fully autonomous robotised sampling procedures for optimising the process, increasing repeatability and overall accuracy, as well as increasing the reachability of the sampling of remote areas, are still not utilized.

Soil moisture measurement in-situ techniques include the accurate oven-drying method and soil moisture sensors, while satellite remote sensing uses optical, thermal, and microwave imaging to estimate surface soil moisture from a broader perspective. However, fully autonomous robotised sampling procedures for optimising the process, increasing repeatability and overall accuracy, as well as increasing the reachability of the sampling of remote areas, are still not utilized.

Measuring soil moisture presents a significant challenge due to its reliance on human labour, which is required to cover extensive areas for sensor measurements manually. Additionally, soil moisture measurements at a specific point vary with time and environmental conditions, making these values unstable. While satellites offer a potential solution to some of these issues, their accuracy is affected by environmental factors such as cloud cover and dense vegetation, while they only describe the upper soil layer. Moreover, ground measurements of surface soil moisture are still necessary for calibrating and training the satellite systems. To address these challenges, we propose an adaptable in situ method for automating soil moisture measurements.

Our approach introduces AgriOne, an autonomous soil moisture measurement robot leveraging a surface-aware data collection framework to achieve precise and efficient soil moisture assessments, thereby minimizing reliance on permanent sensors and reducing associated costs and labour. The hardware of AgriOne consists of a UGV Husky A200 from Clearpath Robotics loaded with the soil moisture sensor TEROS12 from Meter Group. The sensor is mounted on a linear actuator probe, as shown in the figure.  

To evaluate the proposed approach, we conducted two field experiments in different locations under different weather and soil conditions. The experiments were successful in both cases, and we collected 70 and 66 measurements, respectively, of surface soil moisture. For the first experiment, we covered an area of 380m² in 57 minutes, and for the second experiment, we covered an area of 800m² in 2,5 hours. The results showed proof of concept because of the workability of the AgriOne robot and the reliability of the data collection framework. 

How to cite: Tsimpidi, I., Soulis, K., and Nikolakopoulos, G.: Large-scale Soil Moisture Monitoring: A New Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1910, https://doi.org/10.5194/egusphere-egu25-1910, 2025.

Bathymetric surveys and underwater structure inspections are critical for ensuring the safe operation of hydraulic engineering projects. Accurate data on topographical changes and structural conditions help mitigate operational risks caused by erosion, scouring, or structural deficiencies. However, traditional manned vessels face significant limitations in shallow and complex areas, such as downstream spillways, due to accessibility and maneuverability challenges.

The development of unmanned surface vehicles (USVs) offers an efficient and precise alternative for surveying and inspection in shallow water environments. This study utilized the Huawei-3 USV to conduct a bathymetric survey and underwater structure inspection in the shallow downstream area of the spillway at the Wangfuzhou Hydropower Station, Hubei Province, China.

The survey employed the Huawei-3 USV, equipped with high-precision echo sounders and RTK systems, to collect bathymetric and structural data. Water surface elevation data were acquired using RTK measurements, with water levels observed five times before and after the survey to establish a reference elevation. In areas less than 2 meters deep, RTK was also used to directly measure the bottom elevation. The USV combined its draft depth and transducer depth with RTK-derived water surface elevations to calculate the bottom elevation. Satellite imagery was used for pre-planning survey lines, which were aligned parallel to the downstream protective apron, spaced 5 meters apart, ensuring a point spacing of approximately 2 meters. In complex or nearshore areas, manual control was applied to densify survey lines. Data processing involved converting depth to elevation, noise filtering, and generating CAD and 3D models.

The results revealed significant scouring near the downstream protective apron, forming a scour pit with an area of 2,897.2 m², a minimum elevation of 70.26 m, and a proximity of 6.87 m to the reinforced apron edge. The overall underwater topography of the reinforced apron section closely matched the design, with a minimum measured elevation of 70.937 m, differing by only 6.3 cm from the designed elevation of 71 m, indicating stability. However, a portion of the 73 m design elevation zone showed scouring depths up to 25 cm, with an average depth of 12.5 cm. No significant deepening of scour was observed between 2022 and 2024.

The findings demonstrate that USV-based bathymetric systems are highly applicable in shallow water environments, achieving data accuracy that meets regulatory standards. These systems effectively identify scour pits and structural changes, providing reliable data support for ensuring the safe operation of hydraulic engineering projects. Moreover, the method shows significant potential for application in other shallow, complex water environments in the future.

How to cite: Gan, X., Wan, P., Li, J., and Ding, B.: Bathymetric Survey and Underwater Structure Inspection for Hydraulic Engineering in Shallow Waters Using Unmanned Surface Vehicles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2786, https://doi.org/10.5194/egusphere-egu25-2786, 2025.

Evaporation from the water surface is among the main water losses from natural and artificial lakes and ponds. Air temperature (T_a), wind speed (v_a), relative humidity (RH), atmospheric pressure (p_a), surface water temperature (T_w) and radiation (R) are among the physical controls of this process. In recent years, water temperature data have increasingly become available so that the question arises, if the measurement of radiation (which, in turn, affects water temperature) may still be required.

The method employed in this study is modelling of daily evaporation by means of artificial neural networks (ANNs) of the multilayer perceptron type (backpropagation, one hidden layer), using varying sets of input variables. Evaporation data from a white Class A pan (Qiu et al., 2022) served as target (50 patterns of daily averages). A logistic activation function was used. Data records were divided 2:1 into training and testing sets, resp.

Data were scaled to the interval between 0.1 and 0.9, and for each run (10⁵ epochs) the root mean square error (RMSE) of the scaled output was computed.

Learning rate (η), momentum (α) and number of hidden nodes were subject to optimization for three different sets of input variables. ANN runs of series S1 comprised T_a, v_a, RH, p_a, T_w and incoming solar radiation (R) as inputs (6 in total). Series S2 and S3 were subsets of S1, with S2 using T_a, v_a, RH, p_a and T_w as inputs. For the input data of Series S3, water temperature T_w  was replaced by radiation R.

The neural networks achieved a fair representation of the evaporation data. Optimization yielded a minimum RMSE for Series S1 of 0.0514 and 0.0669 for training and testing, resp. (6 hidden nodes, η=0.009 and α=0.0). 

Using the same input variables with the exception of the incoming radiation (in total, therefore, 5 inputs) S2 reached a minimum training RMSE of 0.0557 and a minimum testing RMSE of 0.0887 (5 hidden nodes, η=0.012 and α=0.0).

Series S3 with the 5 inputs T_a, v_a, RH, p_a and R (with water temperature left out), finally achieved an RMSE of 0.0545 for training and 0.0775 for testing, resp. (6 hidden nodes, η=0.006 and α=0.2).

Comparison of Series S2 and S3 shows that, in the case of the data set studied here, the ANNs including incoming radiation among their input variables (but excluding water temperature) outperformed those explicitly accounting for water temperature in lieu of radiation. Using both radiation and water temperature as inputs (S1) resulted in a notable improvement of the ANN output as compared to the runs with either of these variables not accounted for explicitly.

References

Qiu, G. Y., Gao, H., Yan, C., Wang, B., Luo, J., & Chen, Z. (2022): An improved approach for estimating pan evaporation using a new aerodynamic mechanism model. Water Resources Research, 58, e2020WR027870. https://doi.org/10.1029/2020WR027870.

How to cite: Schmid, B.: On the relative importance of water temperature versus radiation for ANN-based pan evaporation modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2944, https://doi.org/10.5194/egusphere-egu25-2944, 2025.

The primary objective of groundwater analysis is to determine the direction and velocity of water flow, which are essential for effective groundwater resource management and contaminant investigation. Conventional methods of evaluating groundwater flow direction, such as using solute or thermal tracers, require the installation of multiple observation wells and are typically laborious, expensive, and time-consuming. Furthermore, the uneven distribution of observation wells and the heterogeneity of aquifers often lead to inaccurate estimations of groundwater flow velocity and direction. Accordingly, this study proposes a novel approach: the thermal vector distributed temperature sensor (TV-DTS) method, combined with a heated line source, to overcome these challenges. The TV-DTS apparatus consists of a single heated fiber and four sensing fibers. The heated fiber functions as the heat source, while the sensing fibers are used to measure temperature changes. These measurements are then used to determine the direction and velocity of water flow by the analytical solution derived from the heat transfer with a heated line source. This method employs only a single-well heating test to estimate both the direction and velocity of groundwater flow, eliminating the need for multiple wells and significantly reducing the time and financial resources. Besides, the TV-DTS has several advantages, such as the ability to provide continuous spatial-temporal temperature data, ensuring reliable and high-resolution monitoring. Two groundwater contamination sites in northern and southern Taiwan have be selected to demonstrate the effectiveness of TV-DTS. The preliminary results showed that at the northern site, the flow direction was predominantly northeast to southwest, with velocities ranging from 0.25 - 0.34 m/day at different depths. In contrast, at the southern site, the flow direction was mainly toward west with higher velocities of 0.1 – 8.0 m/day. The estimated directions and velocities from both sites aligned with previous studies; however, uncertainties were higher at the southern site due to greater velocities observed. This method provides a high-resolution, cost-effective approach for hydrogeological investigations and contaminated sites assessment, serving as a valuable reference for the future investigation and evaluation of hydrogeological characterization.

Keywords: groundwater flow direction, groundwater flow velocity, heat transfer, distributed temperature sensors, borehole, uncertainty, contamination

How to cite: Liu, C. H. and Chiu, Y. C.: Utilizing distributed temperature sensors in a single well with a heating line source to simultaneously estimate the direction and velocity of groundwater flow, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3004, https://doi.org/10.5194/egusphere-egu25-3004, 2025.

Herein we present an analysis of the performance of the Image Wave Velocimetry Estimation (IWaVE), a python library for image-based river discharge calculations.  IWaVE simultaneously performs a 2D velocimetry analysis and calculates the stream depth through 2D Fourier transform, exploiting the sensitivity of water wave dynamics to flow conditions. Unlike existing velocimetry approaches such as Particle Image Velocimetry (PIV), Particle Tracking Velocimetry (PTV) or Space-Time Image Velocimetry (STIV), the uniqueness of this approach lies in: 1) velocities that are advective of nature can be distinguished from other wave forms such as wind waves. This makes the approach particularly useful in estuaries or river stretches affected strongly by wind, or in shallow streams in the presence of standing waves. 2) The velocity is estimated based on the physical behavior of the water surface, accounting for the speed of propagation of waves and ripples relative to the main flow. This makes the approach more robust than traditional methods when there are no visible tracers. 3)  If the depth is not known, it can be estimated along with the optimization of x and y-directional velocity. Depth estimations are reliable only in fast and shallow flows, where wave dynamics are significantly affected by the finite depth.

We analyzed 2 videos recorded from a drone on a site in the Netherlands over a tidal channel in Zeeland at Waterdunen - Breskens. One of the videos has strong winds, which creates waves moving upstream. ADCP measurements for both videos are available. The videos were taken at different moments during different tidal conditions, they were processed using IWaVE, a LSPIV and a STIV methods. The results show that the LSPIV, STIV and IWaVE are in good agreement with the ADCP measurements for the case where there is no wind. However when there is wind the LSPIV and STIV methods fail to obtain the correct surface velocity, while the velocity calculated with IWaVE is in good accordance with the ADCP.

How to cite: Peña-Haro, S., Dolcetti, G., and Winsemius, H.: Performance of the Image Wave Velocimetry Estimation for physics-based non-contact discharge measurement in rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3849, https://doi.org/10.5194/egusphere-egu25-3849, 2025.

With the increase in urbanisation and climate change around the globe, there is an increased risk of surface water flooding. Although extreme flood events are commonly discussed, smaller, more frequent flood events also cause significant disruptions that impact human life and put financial stress on authorities. The majority of urban flooding is due to drainage failure. For effective surface water management, it is important to assess the effectiveness of existing surface drainage assets and accordingly plan asset maintenance or retrofitting of new drainage assets (both traditional and nature-based solutions). The surface drains usually fail because they are either not positioned where the surface water accumulates or are blocked and not maintained to meet the standards. Microtopography significantly influences the surface water flow movement, flow path, flow direction, and velocity, and consequently, dictates the areas of water accumulation.  Thefore, this study explores a novel approach to evaluate storm drain inlet positions using high-resolution topographic indices maps derived from Unmanned Aerial System (UAS) imagery. The Topographic Wetness Index (TWI) and Topographic Control Index (TCI) were employed to identify drains misaligned with surface water pathways and pinpoint critical drains in the sink points of the topography, respectively. 

Storm drain inlets were classified as functional or non-functional based on their intersection with the flow path defined by the optimal TWI threshold. The optimal threshold was determined to be the 90th percentile at a value of 6.19 based on the spatial similarity of the delineated runoff-contributing flow path with the 1 in 100 year surface water flood map produced by the Environment Agency. The validation of the classification of storm drains effectiveness based on TWI using field data yielded an overall accuracy of 53 %, 75% precision, and an F1 score of 62%, indicating a moderate success of TWI in identifying functional drains. Although validation with LIDAR data showed a slight improvement in accuracy and precision, the results generally demonstrated that TWI has a strong capability to correctly identify functional drains; however, it is slightly more challenging to identify nonfunctional drains. 

A comparison of the UAS-derived TCI map with the LIDAR-derived TCI map demonstrated a 90% match in the identified sink areas and a high accuracy of 93% in identifying critical drains in the sink areas. The results suggest that the combined use of TWI and TCI offers a promising approach for assessing storm drain effectiveness, based on its position and guiding authorities in identifying areas with drainage deficits and preparing targeted drainage maintenance strategies. The findings of this research provide valuable insights for urban planners and decision-makers to not only optimise the placement and maintenance of storm drain inlets but also highlight the potential for alternative nature-based low-impact development (LID) solutions in locations where traditional drainage is found to be inefficient. This would ultimately enhance the resilience of urban areas to surface-water flooding.

How to cite: Ramachandran, R., Rivas Casado, M., Bajon Fernandez, Y., and Truckell, I.: Enhancing Urban Resilience to Surface Water Flooding: A Novel Approach Using UAS-Derived Topographic Indices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4525, https://doi.org/10.5194/egusphere-egu25-4525, 2025.

The ability to record high-resolution data for extended periods using affordable systems can improve the study of hydrological and environmental processes. Unlike commercial alternatives, publicly available open-source sensors can be implemented at a significantly lower cost, allowing higher spatiotemporal resolution and continuous, real-time monitoring. In this presentation, I will outline the fundamental principles, advantages, and challenges of using open-source, self-assembly hardware for hydrological related monitoring using two novel systems. The first system is an incubation chamber system composed of O₂, CO₂, CH₄ low-cost sensors for monitoring gas fluxes from sludge samples, specifically tested on wetland samples under different temperature, oxygen, and light conditions. The second system consists of a portable photoreactor/spectrophotometer driven by Raspberry Pi and Arduino UNO microcontrollers. Validation tests of the photoreactor system were performed in one preliminary design for Rhodamine B dye photodegradation, in which the spectral module was constituted by seven arrays of high-power LED of different wavelengths (UVC and VIS), bismuth ferrite (BiFeO₃) catalyst, and hydrogen peroxide. Results showed significant dye degradation (39.7%) at high chamber temperature (45 °C). The performance of this system is improved in a new design, which includes an exchangeable light module, sampling system, and a spectrophotometer for real-time monitoring of the photocatalytic process in water. Complete technical guides on design, assembly, and installation are provided for both systems, aiming to promote their reproducibility and application for new microbial activity studies and laboratory water treatment applications.

How to cite: Orozco, D.: New open-source, self-assembly tools to study microbial activity and water treatment applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5229, https://doi.org/10.5194/egusphere-egu25-5229, 2025.

Soil Aquifer Treatment (SAT) is a widely adopted managed aquifer recharge technique that employs natural soil filtration processes to improve the quality of secondary treated wastewater. As treated wastewater percolates through the unsaturated zone, complex interactions occur between dissolved organic matter (DOM) and the soil matrix, leading to the transformation or retention of organic contaminants. Understanding the fate of DOM within SAT systems is essential for optimizing water quality outcomes and ensuring the sustainability of water reuse practices.

Fluorescent dissolved organic matter (fDOM) has emerged as an effective tracer for characterizing DOM dynamics in water systems. By utilizing excitation-emission matrices (EEMs) in conjunction with parallel factor analysis (PARAFAC), fDOM allows for the identification of distinct molecular fractions, their origins (such as microbial or terrestrial), and their reactivity within SAT environments. However, the mechanisms that govern the retention and transformation of specific fDOM fractions during soil passage remain inadequately understood.

In this study, we employed advanced fluorescence spectroscopy to monitor the behaviour of fDOM molecules in a full-scale SAT basin recharging treated wastewater. By integrating EEM-PARAFAC analysis with in-situ water sampling along the vertical profile of the soil, we uncovered complex and varied transformations in DOM as treated wastewater permeated through the soil. Shifts in fluorescence signals indicated a dynamic interplay of processes affecting DOM fractions, including changes in composition and reactivity throughout the infiltration pathway. These patterns illuminate the evolving interactions between organic matter and the soil environment, influenced by biotic and abiotic factors.

This research underscores the potential of fluorescence-based monitoring tools to provide high-resolution, molecular-level insights into DOM dynamics in SAT systems. Such advancements can enhance the design and operation of SAT basins for improved water quality management and resource sustainability.

How to cite: Adler‬‏, O., Nakonechny, F., and Arye, G.: The fate of fluorescent dissolved organic matter molecules in recharged secondary treated wastewater within soil aquifer treatment (SAT) basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5916, https://doi.org/10.5194/egusphere-egu25-5916, 2025.

The growing demand for high-resolution hydrological data necessitates innovative, scalable, and cost-effective monitoring solutions. This study presents the development of a low-cost soil moisture sensor station designed to address these challenges by leveraging advancements in open-source hardware and software.

The sensor station employs modified versions of commercially available capacitive soil moisture sensors, selected after a thorough review of existing technologies and preliminary evaluations to balance affordability and robustness. Built around the Raspberry Pi Pico microcontroller, the station features modular MicroPython programming, combined with a real-time clock (RTC) and an SD card module for robust data logging. Reconfiguration is streamlined through a JSON-based setup, avoiding the need for firmware modifications.

A custom-designed power supply unit, powered by a Li-Poly battery recharged using a 5W solar panel, ensures long-term operation. The station employs power-saving sleep modes during dormant periods, enabling continuous logging at intervals as low as 15 minutes even under suboptimal sunlight conditions in continental Europe, as per conservative estimates. Housed in a 3D-printed enclosure, the main control unit integrates ports for connecting up to three capacitive soil moisture sensors (3.3/5 V Analogue Out) at various depths, a (DHT 11) temperature and relative humidity sensor, and a UART interface for real-time access to runtime logs.

The affordability of the proposed design potentially allows for the deployment of multiple stations for the cost of a single commercially available system. This scalability is particularly critical for applications requiring dense sensor networks, such as watershed-scale studies, hydrological forecasting, or localized climate impact assessments. While acknowledging that the precision and robustness of such systems may not fully match commercial counterparts, this trade-off is expected to be offset by their adaptability and wide applicability in aforementioned cases.

Advancements in monitoring and communication technologies brought about by the "Industry 4.0" phenomena have been instrumental in enabling the design and development of this sensor station. By harnessing these innovations, the study demonstrates how innovative, cost-efficient technologies can be adapted for hydrological monitoring applications. This work wishes to not only showcase the potential of such advancements to bridge the technological and economic barriers in environmental monitoring but also wishes to highlight their role in addressing the growing gap between the demand for hydrological data and its availability.

This study aspires to facilitate and encourage further translation of advancements in monitoring and communication technologies from the "Industry 4.0" era into hydrological monitoring systems in the hope that such advancements could help democratize access to hydrological monitoring technologies, potentially addressing critical data gaps, and in-turn enabling better-informed water management and research practices.

How to cite: Kootanoor Sheshadrivasan, V. and Langhammer, J.: Development of a Low-Cost Soil Moisture Sensor Station for Hydrological Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6748, https://doi.org/10.5194/egusphere-egu25-6748, 2025.

The combination of in-situ measuring systems and non-intrusive optical technologies can highly improve the monitoring of water quantity and water quality in rivers and reservoirs. This paper presents two applications about innovative camera-based and satellite-based approaches to estimate flow velocity, water level, discharge, turbidity and chlorophyll concentration. The river site of the first case study presented is equipped with a DischargeKeeper, a camera-based discharge measuring system for a continuous measurement of water level, velocity and discharge in real time, and with a Multi-Parameter System MPS for water quality measurement. The MPS measures water temperature, turbidity, oxygen concentration, oxygen saturation, electric conductivity and total suspended solids TSS. The MPS probe is connected to a data logger with data transmission module to deliver measured data in real time. The DK offers. The DK consists of a video camera, an infrared beamer for illumination, a central unit for data processing, a modem for data transfer and a power supply. In operational use the camera takes video sequences of around 5s in predefined intervals, usually ranging from a few minutes to several hours. To determine the surface flow velocity of the river a processing technique called Surface Structure Image Velocimetry (SSIV) is applied. The transmitted proof images with time stamp are very helpful for the optical verification of the measurement especially during flood events.  Furthermore, the camera used can be installed at almost any position with respect to the flow, regardless of the presence of a bridge, as far as the flow is in the view of the camera with a good resolution.

Optical satellite sensors, which is the second case study of this paper, provide the opportunity to determine water constituents for whole water bodies. It is possible to derive optically active substances, which leads to good assessment of chlorophyll concentration as a proxy for algal blooms, of the water turbidity, coloured dissolved organic matter and suspended sediment. If the concentration of algae is high enough (appr. > 10 µg/l), also the occurrence of cyanobacteria can be detected. For deriving these parameters, atmospheric correction and in-water retrieval are most important processing steps. The products derived from satellite data can be aligned with the in-situ measurements acquired within DIWA which provides a complementary view on a water body. In our case we aim in combining high temporal, but punctual in-situ data with the spatial information derived from satellite data. They both contribute to the warning system for exceptional high algal blooms or occurrence of cyanobacteria. In case of river systems, the detection of a bloom that occurs upstream can already help to prepare for measures further downstream. Besides the added value that satellite data provide, limits come with reduced data availability due to cloud coverage and limits in spatial resolution for very small water bodies or very narrow river systems.

Both case studies presented showed a very good applicability of image processing technologies for measuring various hydrological and water quality parameters.

How to cite: Hansen, I., Peña-Haro, S., Luethi, B., Stelzer, K., and König, M.: Hydrological Monitoring of Rivers and Reservoirs Using Innovative Image Processing and Satellite-based Approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6948, https://doi.org/10.5194/egusphere-egu25-6948, 2025.

Water level information is essential for monitoring and modelling river systems. Traditional, water level monitoring is done using intrusive gauging methods, such as pressure gauges; however, these sensors might be lost during an intense flood. Furthermore, in extreme flood or droughts events, measurements may become insufficient. Camera gauges have gained attention over recent years. These techniques emerge to be a low-cost and remote sensing approach for river monitoring. Camera gauges provide a more flexible and convenient setup, with cameras installed in a safe location. Moreover, they can efficiently monitor a wide range of water level values. Additionally, image sequences can be used to estimate water surface velocity and to eventually measure river discharge. Common camera gauge setups use one camera requiring additional information, e.g., a 3D model of river reach and ground control points (GCPs). On this setup, the water area is extracted from the images, and the water surface contour is reprojected into the 3D model, with the reprojection process being supported by GCPs. However, capturing 3D models can be challenging and is sometimes not possible. Further, due to cross-section change over time, there is a need to update the 3D model to ensure precise measurement. Here, we propose to change the camera gauge paradigm by using two cameras and applying stereo-photogrammetry. Using a traditional stereo-photogrammetry approach, points from two images can be projected into a 3D space, without the need for a 3D model. In this setup, the only required additional information besides the interior camera geometry is the distance between the two cameras, e.g., the baseline. After retrieving the relative camera positions, images can be densely matched to produce high resolution point clouds of the river cross-section.

For stereo-reconstruction, one of the first steps is the matching of key points between the images. Matched points are used to retrieve the relative camera poses (position and orientation). The matching can be done using standard matching algorithms (e.g., SIFT, and SURF). However, these can fail in cases where images have low texture or when they are captured in challenging light conditions. Deep learning has gained attention as an alternative to improving stereo processing. Neural networks for the feature matching achieved state-of-the-art results, being more robust in challenging conditions. Attempts to fully replace the traditional stereo reconstruction have been made (e.g., DUSt3R and MASt3R). These approaches can be used in stereo reconstruction without any prior information; however, they were not yet evaluated for camera gauge applications.

The overall goal of our research is to produce an easy and robust stereo camera gauge setup that to flexibly estimate a 3D model of river cross-sections. Thereby, we can deliver a more robust long-term camera gauge, lowering system deployment costs and maintenance efforts, allowing for a flexible densification of the hydrological monitoring network.

How to cite: Zamboni, P. A. P., Krüger, R., and Eltner, A.: Stereo photogrammetry for river water level and cross-section update: classical and deep learning approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9375, https://doi.org/10.5194/egusphere-egu25-9375, 2025.

Dissolved oxygen (DO) levels are crucial for aquatic life, especially under climate change, making continuous monitoring essential for effective lake management. However, local measurements are often costly and time-intensive, whether collected through field campaigns or permanent gauges. This study investigates the feasibility of using remote sensing techniques, coupled with machine learning; to track and estimate DO in a shallow eutrophic lake. Because DO cannot be directly measured with optical sensors, we first identify optically sensitive parameters—chlorophyll-a (Chl-a), temperature, and water depth—that correlate statistically with ground-measured DO. A two-step pipeline is then introduced: (1) estimating water level changes, Chl-a, and surface temperature from satellite data, and (2) predicting DO based on these derived parameters.

Model development starts with developing three separate models to estimate Chl-a (Sentinel-2), water level changes (Sentinel-1), and lake surface temperature (MODIS), using the Google Earth Engine Python API for data processing and analysis. Subsequently, both remotely sensed parameters and local measurements are used to train a DO prediction model. The training procedure explores 16 machine learning frameworks with hyperparameter tuning, using a 70%–15%–15% time-series split for training, validation, and testing, implemented in scikit-learn and Optuna. Search stopped with the model with R² values of 0.89 and 0.64 and mean absolute errors of 0.81 mg/L and 1.29 mg/L for locally measured and predicted test datasets, respectively. These results highlight the potential of combining remote sensing-derived parameters with machine learning to estimate DO, an otherwise non-optically measurable parameter.

This approach offers a cost-effective alternative for modeling continuous temporal variations in DO and supports comprehensive temporal assessments of DO concentrations in shallow eutrophic lakes. Ultimately, this framework shows promise for broader applications and generalizations, thereby contributing to the effective monitoring of non-optical water quality parameters and advancing sustainable aquatic ecosystem management.

How to cite: Ünalan, U. B., Yüzügüllü, O., and Aksoy, A.: Prediction of Temporal Dissolved Oxygen Concentrations in a Lake Using Remote Sensing and Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9460, https://doi.org/10.5194/egusphere-egu25-9460, 2025.

Flash floods in Mediterranean regions pose significant threats to lives, infrastructures, and economies. Recent episodes of extreme rainfall in one such region led to devastating flash floods, resulting in loss of life, destruction of homes, and widespread disruption of transportation networks. Therefore, there is a critical need for advanced methods to monitor and analyze the flood dynamics, especially in urban areas. This study investigates the use of two advanced image-based techniques, Fudaa-LSPIV (Coz et al., 2014) and SSISM-Flow (Ljubičić et al., 2024) for surface velocity and discharge estimation of urban flash floods. The research used videos or images of historical urban flood events and estimated the surface velocity. To analyze the urban floods, Matera, a city of southern Italy, was selected as case study. Matera was chosen because its historical city center, the “Sassi”, was affected by extreme rainfall events in the last few years, e.g. 2014, 2018, 2019, and 2023. Five extreme past flood events occurred on 3 Aug 2018, 12 Nov 2019, 2 Jun 2023, and 2 & 21 July 2024 were recorded for estimation of surface velocity. Fudaa-LSPIV works according to the Particle Image Velocimetry (PIV) principles, while SSISM-Flow is a user-friendly and Python-based innovative tool with OpenCV integration for precise surface velocity filed extraction. These methods involve steps such as image stabilization, camera calibration, orthorectifications, and velocity calculation. Both techniques were evaluated based on their accuracy, performance, and application to overcome the limitations of analyzing the surface flow of urban floods. This study is innovative in comparing methods to estimate surface velocity of real-time flash floods in urban areas. Using these techniques, the surface velocities were estimated along key transects, and results were cross-validated using the Float Time method as benchmark. The outcomes of both approaches turned out to be consistent with benchmark data, confirming their reliability in monitoring urban floods. This comprehensive flow analysis provided insights for calibrating flood models and enhanced risk management. This study introduced a novel application of these techniques in real-time urban flood monitoring. Furthermore, it contributes to the development of an early warning system, enhances management strategies, and mitigates flood risks in vulnerable areas.

Reference

Ljubičić, R., et al., 2024.  SSIMS-flow: image velocimetry workbench for open-channel flow rate estimation. Environ. Model. Softw. 173, 105938.

Coz, Jérôme Le, wt al., 2014. Image-Based Velocity and Discharge Measurements in Field and Laboratory River Engineering Studies Using the Free Fudaa-LSPIV Software. In Proc.of the Inter.  Conf. on Fluvial Hydraulics, River Flow, 1961–67.

Acknowledgments

This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of 'Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Albano, R., Asif, M., Dal Sasso, S., and Sole, A.: Using citizens recorded videos to estimate water surface velocity and dischargefor urban flash flood monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9477, https://doi.org/10.5194/egusphere-egu25-9477, 2025.

The growing prevalence of urban floods necessitates the development of cost-effective and scalable monitoring solutions. Traditional water-level sensors are often prohibitively expensive for widespread deployment. Moreover, existing image-based methods frequently encounter limitations in generalizability, particularly the difficulty of harmonizing selected reference features in large-scale quantitative measurements. To address this research gap, we present a novel method that utilizes traffic camera imagery to provide a lightweight solution for quantitatively monitoring urban flood inundation depths with high spatial and temporal resolution. Specifically, the waterline in flood images is recognized by a neural network and localized using world coordinates calibrated by common road markings, allowing for accurate inundation depth measurement based solely on the imagery. This method eliminates the need for costly point cloud data collection or pre-calibrated measurement objectives in urban settings. Additionally, this method enables the simultaneous collection of waterlogging depths from multiple reference objectives within the same image, yielding more robust measurements. This innovative approach paves the way for cost-effective, high-resolution, and reliable quantitative monitoring of urban flood inundation depths, ultimately providing crucial data support for emergency responses and long-term flood mitigation strategies.

How to cite: Qin, J. and Ping, S.: A Scalable and Lightweight Urban Flood Monitoring Solution Utilizing Only Traffic Camera Images, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10710, https://doi.org/10.5194/egusphere-egu25-10710, 2025.

Urban flooding, intensified by climate change, presents significant risks to public safety and infrastructure, necessitating effective early warning systems. These events are categorized into fluvial and pluvial flooding, with the latter becoming increasingly challenging to predict due to its localized nature and short lead times.

In this work we develop a novel low-cost device based on Internet of Things (IoT) useful for urban flooding monitoring. The proposed sensor leverages advances in open-source technology, using ESP32 development boards, to create an accessible and cost-effective solution based on ultrasonic and reed-switch mechanisms. The system features innovative design principles, including a 3D-printed structure, low power consumption, and reliable connectivity through LoRaWAN and MQTT protocols used as potential early warning system.

The system’s primary objective is to detect storm drain overflow caused by intense rainfall, triggering timely alerts to mitigate flood impacts. Functional requirements emphasize ease of installation, durability, and cost-effectiveness, enabling widespread adoption in diverse urban contexts. The sensor design incorporates a float mechanism, reed switch, and microcontroller housed in a compact, water-resistant case.

Preliminary testing demonstrated the system's ability to detect water level changes and transmit alerts efficiently. Further work includes refining the design to minimize false positives and enhance system reliability under various environmental conditions. This development represents a significant step toward scalable, low-cost flood monitoring systems, contributing to global efforts in urban flood risk management.

How to cite: Cancelliere, A., Buonacera, G., Palazzolo, N., Campisano, A., Gullotta, A., and Peres, D. J.: Development of a low-cost IoT system for monitoring storm drain overflow during urban flooding , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11952, https://doi.org/10.5194/egusphere-egu25-11952, 2025.

Eutrophication is a significant environmental concern, which is often monitored through Chlorophyll-a (Chla) concentrations in inland and coastal waters. While traditional in-situ measurement methods are accurate, these are time-intensive, labor-demanding, and limited in spatial and temporal resolution. In recent years, remote sensing and machine learning approaches have emerged as promising alternatives for environmental monitoring, although their effectiveness is limited by challenges such as constrained in-situ data availability, the variability of water characteristics, and difficulties in transferring models across regions. Existing global models prioritize data quantity over quality, often lacking in comprehensive analysis of relationships between water quality parameters and remote sensing bands and indices. This study aimed to enhance global Chla prediction accuracy by improving data quality and identifying key predictive features using Earth Observation (EO) data. Two feature groups were examined: Group 1 (reflectance values from single bands and band ratio indices) and Group 2 (reflectance values from single bands combined with mathematical transformations of multiple bands). Machine learning models, including Random Forest (RF), Least Squares Boosting (LSBoost), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), were assessed for overall performance, cross-validation accuracy, and transferability to external datasets. Among tested models with their own dataset, GPR achieved the highest overall accuracy (R² = 0.95, RMSE = 2.82 µg/L with Group 2 features), while SVR exhibited the weakest performance. For transfer validation using data from external lakes, RF (R² = 0.73, RMSE = 12.39 µg/L) and LSBoost demonstrated the greatest transferability. Spatial-temporal predictions of Chla over 2023–2024 successfully captured seasonal trends by revealing reliable and consistent patterns of Chla distribution. The present study highlights the potential of the proposed framework for global Chla monitoring in inland waters, also, emphasizing the potential in areas outside the training dataset.

Keywords: global chla monitoring, transferability, remote sensing, machine learning

How to cite: Moe, A. C., Saddi, K. C., Zhuang, R., Miglino, D., Saavedra Navarro, J., and Manfreda, S.: Global Framework for Chlorophyll-a Monitoring in Inland Lakes: Integrating Remote Sensing, Machine Learning, and Databases - Achievements and Challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12047, https://doi.org/10.5194/egusphere-egu25-12047, 2025.

Handling unstructured and missing data (UMD) remains a significant challenge in environmental monitoring and precision agriculture. This study focuses on the imputation of UMD in soil matric potential (SMP) datasets, a critical parameter in assessing soil water availability and managing irrigation systems. Missing data can distort trends, complicate analysis, and hinder decision-making in critical areas such as water management and precision irrigation. Using Extreme Learning Machine (ELM) and Time Series Models with Exogenous Inputs (TSMX), the research reconstructs missing SMP records by integrating adjacent sensor datasets and explanatory environmental variables. This approach demonstrates the potential of advanced data-driven techniques to enhance the reliability of agricultural and hydrological datasets. The dataset encompasses hourly SMP measurements and explanatory variables, including meteorological inputs such as relative humidity, air temperature, and soil properties, collected across multiple sensors in a precision agriculture setup. Exploratory analysis revealed variations in data structure, including non-stationary trends and significant statistical differences between training and testing datasets. These insights guided the selection of inputs and model configurations, emphasizing the importance of autocorrelation analysis in determining the most significant predictors. The ELM model exhibited superior performance in imputing missing SMP values, achieving an R-value of 0.992, RMSE of 0.164 cm, and NSE of 0.983 using five key inputs. This robustness highlights ELM's capability to generalize across diverse input combinations effectively. Additionally, TSMX has also been explored for its potential to leverage temporal dependencies and explanatory variables for consistent imputation. The incorporation of adjacent sensor data in modeling efforts underscores the importance of spatial and temporal relationships in enhancing accuracy, particularly in heterogeneous environmental conditions. This research underscores the critical role of input selection and model tuning in addressing UMD in SMP datasets. The findings demonstrate the complementary strengths of ELM and TSMX, offering practical insights for improving data reliability in precision irrigation and environmental monitoring. Future studies could explore integrating additional explanatory variables and employing advanced machine learning architectures to optimize imputation performance under varying environmental conditions further.

Keywords: Missing Data Imputation; Soil Matric Potential; Extreme Learning Machine; Time Series Models; Exogenous Inputs; Precision Agriculture; Environmental Monitoring.

How to cite: Zeynoddin, M., Gumiere, S. J., and Bonakdari, H.: Bridging Data Gaps in Soil Matric Potential for Enhanced Water Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14585, https://doi.org/10.5194/egusphere-egu25-14585, 2025.

The monitoring of small headwater catchment represents a major issue in hydrology, especially in remote areas, where gathering real-time hydrological data is often prohibitive due to the limited availability of power and connectivity. However, recent advances in non-contact computer vision and informatic technology offer an opportunity to fill such technical gap. In this regard, we designed and developed the MagicHydroBox prototype (MHB), and all-in-one camera and processing unit system aimed at monitoring the water level in small headwater rivers. The tasks performed by MHB include image collection, image processing, storage and transmission of the processed data. Since the MHB is equipped with NIR (NearInfrared) leds and camera, the image collection can be carried out both during the day and the night period. The image processing takes place directly in the MHB, to guarantee the onsite analysis, it is based on the Otsu’s segmentation method to identify a properly placed target within the images, and results in the direct estimation of water depth. Finally, we built in the MHB the possibility to transmit the processed data both through Gprs (mobile data) and LoRaWan (a long-range, low-power system). The MHB is also equipped with a GUI that allows the user to set and calibrate the instrument. We carried out preliminary field tests to evaluate the effectiveness of the MHB in providing an accurate measure of the target and transmitting the processed data. The preliminary results highlight the potential of the MHB to estimate the water level, especially in NIR images, and to provide a real-time hydrological monitoring where Internet signal is available. The main innovation of the MHB is represented by the fact that it automated a series of tasks that were instead manually performed in previous works. The concentration of all the necessary tasks within the MHB simplify the data acquisition, the processing and the management providing an useful tool where frequent maintenance or monitoring surveys are not possible. Moreover, the MHB is promising for future implementation of algorithms to measure surface velocimetry and discharge.

How to cite: Noto, S., Durighetto, N., Tauro, F., Apollonio, C., Petroselli, A., and Grimaldi, S.: Hydrological monitoring in small catchments: the MagicHydroBox, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16334, https://doi.org/10.5194/egusphere-egu25-16334, 2025.

Flow velocity and water surface elevation (WSE) are fundamental for understanding hydraulic phenomena in river engineering. Although underwater flow properties are not directly observable, these two parameters encapsulate the hydraulic properties governing river flow, as described by the conservation of mass and momentum equations. This information enables the understanding of actual hydraulics and facilitates the creation of digital twins, even during large scale flood events.

To measure flow velocity from UAV imagery, we developed a novel, reference-free image analysis method based on image conversion. This method eliminates the need for physical reference points, addressing practical challenges in field deployments. It leverages readily available camera information, including position (x, y, z) and orientation (pitch, roll, yaw). Complementary WSE data, obtainable from various sources, completes the required input. This allows accurate conversion of video pixel data to surface coordinates, enabling velocity measurements at any point within the river flow. Particle image velocimetry (PIV) is then applied to the converted images to derive the velocity field.

For WSE determination, we explored three approaches: Light Detection and Ranging (LiDAR), Structure from Motion (SfM), and edge-based downscaling of SfM. LiDAR data, while valuable and easy to observing, exhibits lower point density on the water surface compared to the surrounding non-water areas, depending on water surface conditions. However, even sparse LiDAR data in the mid-channel provides crucial hydraulic information. For SfM, we employed multiple UAVs capturing images at appropriate timing to resolve temporal WSE changes. As a downscaling approach using a single UAV, WSE data extracted solely from the riverbank can also be utilized.

We have begun accumulating observations of large-scale flow phenomena. Our results reveal cellular secondary currents and flow patterns over bedforms. Observations of cellular secondary currents show boiling-type phenomena occurring on the order of seconds, and more persistent cellular structures when averaging the flow field over one minute. From an engineering perspective, although these events are infrequent, they can significantly impact float-based discharge measurements when they occur. Observations of flow over bedforms show spatial variations in velocity and WSE along the flow direction, exhibiting wave-like patterns. The out-of-phase relationship between these wave patterns suggests they are associated with micro-bedforms, indicating active sediment transport. Furthermore, this understanding of sediment hydraulics can be used to estimate water depth.

How to cite: yorozuya, A. and kudo, S.: Flow structures in actual rivers obtained by areal measurement of flow velocity and water surface elevation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16904, https://doi.org/10.5194/egusphere-egu25-16904, 2025.

Digital twin, as a virtual representation of physical systems, is increasingly recognised as a core component of timely and accurate water management, particularly for interconnected and rapidly changing systems. Digital twin supports the simultaneous monitoring, simulation, and optimisation of real-world operations by integrating multiple data sources, including in-situ measurements, remote sensing and modelling data. By enabling a detailed characterisation of catchment functioning and its ecological boundary conditions, a digital twin facilitates equitable water allocation across sectors and supports timely and evidence-based decision-making.

Developing a digital twin requires extensive datasets, robust scientific evidence, and a clear grasp of ecological boundaries, reflecting the interconnected nature of multi-sectoral decision-making. The Bode River Basin, one of the best-monitored catchments in central Europe, serves as a showcase for designing and implementing a digital twin system for multi-sectoral and sustainable water management at catchment scale. The recent prolonged droughts (2017–2021) and their impacts on various water bodies offer a real-world “experiment” of extreme climate scenarios, highlighting the vulnerabilities and risks within the catchment and illustrating the complex trade-offs inherent in water resource management.

This study integrates long-term, high-resolution monitoring strategies with coupled surface water, groundwater, and water quality models into a unified framework that addresses both quantitative and qualitative aspects of water systems. Such a comprehensive approach enables forecasting climate change impacts and optimising water resource allocation across sectors. Overall, this work demonstrates the potential of digital twins to advance sustainable water resource management under changing climatic conditions.

Acknowledgment

This work was supported by the OurMED PRIMA Program project funded by the European Union’s Horizon 2020 research and innovation under grant agreement No. 2222.

How to cite: Rouhani, A., Mate Marin, A., Moya Diez, A., Gómez-Hernández, J. J., Rode, M., and Jomaa, S.: Transforming water resources management at river basin scale with digital twin technology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17076, https://doi.org/10.5194/egusphere-egu25-17076, 2025.

The use of digital cameras in river monitoring activities can increase our knowledge of water quality status, solving the cost and spatial and temporal data resolution limitations of the existing techniques. The challenge of image-based procedures using camera systems is the proper red, green, and blue (RGB) bands signal interpretation and processing. The actual water upwelling light that reaches the camera lens is the sum of various reflectance components of the suspended particles, the riverbed background and the water itself. One component could prevail over the others, depending on the variability of hydrological (water level, flow velocity, etc.) and environmental (suspended solids concentration, floating pollutants, etc.) characteristics of the river. The effect of water level and turbidity concentration on the riverbed component of the total water upwelling light can be substantial, especially for shallow water. As a result, the riverbed reflectance component, if neglected, can significantly affect the evaluation of the water reflectance, and hence, water turbidity.

In our field campaign, a synthetic turbidity event was recreated by adding a natural clay tracer into the river, and we monitored it using a camera system. Two turbidimeters were installed within the river section to validate the results. Moreover, a submerged panel was fixed directly on the riverbed. This choice was prompted by the shallow water conditions during the experiment, where the riverbed reflectance significantly contributed to the total upwelling light captured by the camera, particularly under low turbidity levels. We defined a clear water condition in which the panel was fully visible, where turbidity level was considered equal to zero. As turbidity increased and the panel visibility decreased, we applied an image-based procedure to assess the actual river turbidity level. In addition, we applied a pixel-by-pixel mean of the camera frames every 2 minutes, for minimizing the signal distortions due to the effect of ripples, sun glare and shadows within the analyzed region of interest of the river surface.  These methodological steps allowed us to properly decompose the image into different reflectance components, and to enhance long-term monitoring practices that are subject to a wide range of environmental and hydrological variability.

This study focuses on implementing camera systems in real-world settings, supporting existing river monitoring techniques with early warning networks, and developing innovative solutions for water resource management.

Keywords: camera system, river monitoring, turbidity, image processing, remote sensing, water quality

How to cite: Miglino, D., Jomaa, S., Saddi, K. C., Moe, A. C., Rode, M., and Manfreda, S.: The Role of Riverbed Background Reflectance in Long Term Turbidity Monitoring Using Camera Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17492, https://doi.org/10.5194/egusphere-egu25-17492, 2025.

Image-based techniques have gained significant attention for monitoring natural and artificial rivers, thanks to their many advantages over traditional methods. These non-intrusive and highly-versatile optical approaches provide accurate flow discharge measurements, even in challenging conditions like flood events, while ensuring the safety of both operators and equipment. However, the accuracy of optical measurements is affected by several factors, including environmental conditions, river flow characteristics, field acquisition protocols, and the parameterization of the processing software.

Image-based techniques follow a three-phase workflow: (i) seeding, (ii) recording, and (iii) processing. Seeding introduces natural or artificial tracers onto the water surface to detect motion. Recording captures video sequences from stationary or mobile platforms (e.g., UASs – Unmanned Aerial Systems). Processing extracts the surface velocity field and flow metrics. The latter phase is divided into three sub-steps: pre-processing, surface velocity evaluation, and post-processing. Pre-processing includes stabilization, orthorectification, and graphical enhancement; surface velocity evaluation uses correlation-based or similar algorithms to track tracers across frames; finally, post-processing refines velocity data by filtering noise, interpolating missing data, and extracting relevant metrics.

Among the steps of optical techniques, graphical enhancement is particularly critical. By increasing the contrast between tracers and the background, it enhances the ability of software algorithms to accurately track motion, thereby reducing errors. However, an inadequate parametric setup of the processing software can also result in the estimation of biased velocities. To investigate these interdependencies, this study conducted a comprehensive sensitivity analysis, evaluating the combined effects of graphical enhancement techniques and processing parameters on the performance of image-based analyses. The analysis compares traditional algorithms with more innovative approaches, including colorspace transformation, and assesses the impact of varying processing parameters under different operational conditions. A dataset of videos acquired from UAS platforms and fixed stations during discharge measurement campaigns on Sicilian rivers, in Italy, was used. The videos were analyzed using PIVlab and SSIMS-Flow software, and the results were benchmarked against ADCP measurements.

The findings reveal that both the choice of graphical enhancement methods and the optimization of key software parameters significantly affect the accuracy of velocity and discharge estimates. The study also provides valuable insights into selecting the most appropriate enhancement techniques and configuring processing parameters, tailored to specific field conditions and operational requirements, further demonstrating the potential of image-based methods for hydraulic monitoring.

How to cite: Alongi, F., Robert Ljubičić, R., Pumo, D., Dal Sasso, S. F., and Noto, L. V.: Optimizing Image-Based Techniques for River Monitoring: Insights into Graphical Enhancement and Parameter Sensitivity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18462, https://doi.org/10.5194/egusphere-egu25-18462, 2025.

How to cite: Kovačević, R., Todorović, A., De Michele, C., Nebuloni, R., and Ceppi, A.: Evaluating the Potential of Personal Weather Stations (PWS) for Semi-distributed Hydrological Modelling , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18603, https://doi.org/10.5194/egusphere-egu25-18603, 2025.

Extreme weather events, including sudden and intense rainfall, have become increasingly frequent due to the growing impact of climate change. This rapid influx of water often carries a variety of pollutants, including nutrients, heavy metals, and microbial contaminants, significantly modifying the physicochemical and microbiological characteristics of urban streams. This study aims to evaluate the effects of rainfall events on the physicochemical and microbiological properties of the Tócó Stream, focusing on changes in key water quality parameters and microbial dynamics. Two sampling points were selected to represent different environmental areas: one site was located in a near-natural area, and the other was situated in an industrial zone, surrounded by facilities and a highway connecting road. Measurements were conducted both before and after the rainfall event. On-site measurements were performed included precipitation (mm), water level, dissolved oxygen content, and water temperature, while water samples were collected for laboratory analysis. The collected samples were tested for pH and electrical conductivity (EC) as well as for nutrient-concentrations of NH₄⁺, NO₂⁻, NO₃⁻, PO₄³⁻, K⁺, SO₄²⁻, chemical oxygen demand (COD) and biological oxygen demand (BOD₅) were also determined from the samples. In case of microbiological parameters, total coliforms, yeasts, and total plate count were determined. Our results revealed differences between the two sampling sites and the pre- and post-rainfall conditions. At the industrial site the nutrient contents have decreased due to the rainfall, while at the near natural site we did not determine such change in connection with these elements. The same trend were detected in the case of EC as well. The microbiological analysis of the water samples clearly showed that while both total bacterial count and total coliform count showed an increasing trend after the rainfall at the first site, this trend was much less pronounced at the site reflecting the natural state. Our objecitve was to study the influence of sudden rainfall events, for the reason that these effects remain understudied, particularly in terms of their short- and long-term impacts on water quality and microbial properties.

The research presented in the article was carried out within the framework of the Széchenyi Plan Plus program with the support of the RRF 2.3.1 21 2022 00008 project.

How to cite: Kun, S., Boczonádi, I., Nagy, P. T., Szabó, A., Tóth, F. A., Fehér, Z. Z., Magyar, T., Madar, L. A., Szűcs, I., Tamás, J., and Nagy, A.: Impact of intense rainfall event on the physicochemical and microbiological characteristics of an urban stream, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19738, https://doi.org/10.5194/egusphere-egu25-19738, 2025.

Discharge estimation at a river site depends on local hydraulic conditions identified by recording water levels. In fact, stage monitoring is straightforward and relatively inexpensive compared with the cost necessary to carry out flow velocity measurements which are, however, limited to low flows and constrained by the accessibility of the site. In this context, the mean flow velocity is hard to estimate for high flow, affecting de-facto the reliability of discharge assessment for extreme events. On the other hand, the surface flow velocity can be easily monitored by using radar sensors allowing to achieve a good estimate of discharge by exploiting the entropy theory applied to rivers hydraulic (Chiu,1987). The growing interest towards the use of no-contact methods to estimate discharge (Tauro et al., 2018) in field applications has shown that the cross-track velocity distribution can be inferred with sufficient accuracy using the surface velocities, u_surf, sampled using Surface Velocity Radars (SVR) (Fulton and Ostrowski, 2008; Moramarco et al., 2017, Alimenti et al. 2020), the quantitative imaging techniques as LSPIV (Fujita et al., 1998) or PTV (Tauro et al., 2019). In this context, overall the velocity-area method is applied to estimate the mean flow velocity starting from the depth-averaged velocity, u_vert, which is inferred through the velocity index, k=u_vert/u_surf.. For many river gage sites configurations, k has been set to 0.85. However, considering k refers to a monotonous velocity profile, not taking account of dip phenomena, the application may fail in estimating the depth-averaged velocity (Moramarco et al., 2017; Koussis et al., 2022, Pumo et al., 2025). Based on that, this work proposes a new entropy-based approach to estimate the depth-averaged velocity starting from the measured surface velocity retrieved by conventional and/or no-contact measurements. The approach exploits the dependence of the entropy parameter M with the hydraulic and geometric characteristics of channel (Moramarco and Dingmann, 2017), allowing to derive formulations on Manning’s roughness, shear velocity and water surface slope. Based on these features, the entropy-based method by using the measured surface velocity and the geometry of the river site is able to turn u_surf  into u_vert considering for each  u_surf  an index which depends on the local water surface slope. The application to river sites along the Tiber River, Po River and Amazon River has shown the effectiveness of the approach in estimating the depth-averaged velocities with a fair accuracy along all verticals. Therefore, the method well lends itself to be integrated in the field of no-contact streamflow measurements.

How to cite: Moramarco, T.: Entropy-based depth-averaged velocity assessment from surface flow velocity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19742, https://doi.org/10.5194/egusphere-egu25-19742, 2025.

Hydrological observations are essential for understanding the complex interactions between the land surface and the atmosphere, improving water resource management, strengthening flood defense, and advancing hydrological modeling. However, the long-term maintenance of experimental basins like Fiumarella di Corleto presents significant challenges, requiring continuous updates to address environmental changes and technological advancements. This study reviews over 20 years of observations at the Fiumarella basin in Southern Italy, focusing on its evolution, challenges, and future directions. The Fiumarella basin, covering an area of 32.5 km², includes a sub-basin of 0.65 km². Since 2002, a hydrometeorological network has been monitoring key variables such as rainfall, temperature, wind, and streamflow, capturing hydrological variability across spatial and temporal scales. In 2006, 22 soil moisture probes were installed along a 60-meter transect at depths of 30 and 60 cm. Additionally, high-resolution LiDAR data and pedological studies have enhanced the understanding of the basin’s morphology and soil characteristics. The maintenance of this experimental basin has posed substantial challenges. Frequent extreme flood events have resulted in significant damage to hydrometric stations, requiring reconstruction and recalibration. Moreover, the sediment and debris accumulation in the retention basin of the sub-basin necessitated periodic clearing to maintain functionality and ensure continuous data collection. These challenges underscore the effort and adaptability required to sustain long-term monitoring in dynamic environments. Data collected from the basin have significantly contributed to hydrological science. Analyses of peak flow events and antecedent soil moisture conditions have provided insights into flood response mechanisms. Spatial and temporal variability in hydrological processes has informed the calibration and validation of semi-distributed hydrological models, enhancing their accuracy and reliability. These findings highlight the importance of integrating diverse datasets such as soil moisture, precipitation, topography, and land use—for comprehensive hydrological research. Looking ahead, planned upgrades aim to further enhance the basin’s capabilities. The installation of a meteorological radar would improve rainfall measurement precision and expand spatial coverage, thereby addressing existing data gaps. Additional hydrometric sensors and automated systems would increase the granularity and reliability of observations, supporting high-resolution analyses. These advancements will ensure that the Fiumarella basin remains a state-of-the-art research facility capable of addressing emerging challenges in hydrology and climate science.

This abstract is part of the project NODES which has received funding from the MUR-M4C2 1.5 of PNRR funded by the European Union - NextGenerationEU (Grant agreement no. ECS00000036).

The present research has been carried out within the RETURN Extended Partnership and received funding from the European Union Next-Generation EU (National Recovery and Resilience Plan -NRRP, Mission 4, Component 2, Investment 1.3 - D.D. 1243 2/8/2022, PE0000005).

How to cite: Aung, H. H., Onorati, B., Fiorentino, M., Dal Sasso, S. F., Sileo, B., Pizzolla, T., Manfreda, S., and Margiotta, M. R.: Long-Term Evolution and Challenges of Hydrological Observations at the Fiumarella Basin in Southern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20109, https://doi.org/10.5194/egusphere-egu25-20109, 2025.

We present a novel method to track fluctuations in the atmospheric boundary layer (ABL) using a cluster of mini-radiosondes. Each radiosonde is lightweight, expendable and carried by biodegradable balloons. This system collects statistics of turbulence fluctuations in the ABL and warm clouds within it. The first operational prototype of the radiosonde cluster developed at POLITO was tested in several field campaigns from November 2022 to September 2024. These campaigns, which included six cluster launch experiments, were conducted in collaboration with CNR-INRIM, MET-OFFICE, NCAS, ARPA Piemonte, ARPA-FVG, and OAvdA (see Fig. 1). The system provides critical insights for modeling ABL dynamics and dispersion, a major source of uncertainty in climate and meteorological simulations [1].

Figure 1: In-field experiments with the radiosonde cluster network. LoRa P2P radio transmission, 12 km range, 868 MHz, 0.2 Hz data acquisition frequency. Left: radiosonde trajectories. Middle: vertical profiles of temperature and mean Brunt-Vaisala frequency from 3 radiosondes. The purple color indicates positive temperature gradients, while green indicates negative ones. Right: wind speed, magnetic field, and acceleration fluctuation spectra. A) Alpine environment, St. Barthelemy, Aosta, Italy, November 2023. B) Rural near-maritime Atlantic coast, Chilbolton, UK, July 2023, WESCON campaign. C) Subalpine region, Udine, Italy, June 2024. D) Rural near-maritime Atlantic coast, Chilbolton, UK, September 2024. Ground-level wind speeds: A: 1 m/s, B: 17 m/s, C: 0.5 m/s, D: 10 m/s.

Each radiosonde is a radioprobe board [1, 2] mounted on a biodegradable balloon [3] filled with a helium-air mixture, allowing a float time of several hours. It measures air temperature, pressure, humidity, and four vector quantities (position, velocity, acceleration, and Earth's magnetic field) along each trajectory (Fig. 1). Passive tracking of multiphase cloud parcels provides a multi-point view of flow structures. Recent experiments have explored turbulent dispersion analysis using a distance neighbor graph algorithm [4], addressing aspects of atmospheric turbulence not previously measured in field observations. The system can advance models for cloud microphysics and turbulence schemes for atmospheric tracer dispersion [5]. Our methodology uses high-frequency atmospheric data and improves understanding of turbulence. This enables advances in cloud modeling, weather prediction, and climate simulation. The biodegradable balloon has a volume of ~40 liters and weighs ~18 grams. Optimizing the size and weight of the circuit board (halving both dimensions) will reduce the balloon volume by 30%, allowing for simultaneous deployment of swarms of ~100 mini-radiosondes. The future radioprobe will have sensors for VOCs, greenhouse gases, and UV radiation integrated into the PCB to expand its use cases. An energy harvesting module will extend the lifetime of the probe.

1. Abdunabiev S. et al., Measurement 224, 113879 (2024)

2. Paredes et al., Sensors 21, 1351 (2021)

3. Basso et al., Mat. Chem. Phys. 253, 123411 (2020)

4. Richardson, Proc. R. Soc. Lond. A 110, 709 (1926)

5. Mirza et al., Q. J. R. Meteorol. Soc. 150, 761 (2024)

How to cite: Abdunabiev, S., Gallino, N., and Tordella, D.: Innovative Lagrangian Radiosonde Clusters for ABL Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3546, https://doi.org/10.5194/egusphere-egu25-3546, 2025.

Spores are the survival and dissemination units of fungi. Many are designed for optimal airborne dispersal while maintaining long-term survival. Depending on the chemical and structural nature of their walls, they are highly resistant to extreme temperatures and UV radiation. For example, Botrytis cinerea conidia stored dry at -80°C are still able to germinate after more than 20 years in storage. Given their anemochorous nature and resistance to abiotic factors, it would therefore be possible for spores of pathogenic fungi to be aeroported through the stratosphere. However, little is known about the spread of pathogenic fungi in high-altitude airspace.

In order to investigate the presence of fungal spores in the stratosphere and explore the diversity of viable and non-cultivable fungi, we designed a low-cost sampling device capable of sampling particles in the stratosphere. It consists in a sealed polystyrene box with two ports on the top and bottom sides, allowing air circulation. A rotating arm sampler spins in the resulting airflow, with four sticks coated with petroleum jelly. The opening of the ports is controlled by mobile covers driven by servomotors, managed by an Arduino Uno microcontroller connected to a high-pressure pressure sensor. Moreover, an on-board radiosonde continuously transmits GPS position, relative humidity, and temperature data. An internal camera captures the opening, closing, and sampling processes during the desired altitude segment. Additionally, a control box, that never opens during flight,  monitors potential contamination below the stratosphere.

Both the measurement and control boxes are sterilized under UV-C, sealed and attached to a meteorologic radiosonding balloon. Upon reaching an altitude of 12,000 meters, the covers open, and airborne particles are collected. Once the balloon bursts (at around 35,000 m; -63°C), a parachute deploys during the descent, and the cover closes at 12,000 meters.  The prompt recovery of the sample at landing is assisted by a specifically dedicated mobile app, that extrapolates the descent trajectory and guides the crew to the expected landing location.

Five test flights between October 2023 and June 2024 up to 35,000 meters altitude, allowed us to optimize and validate the device, the sampling conditions, and the sample recovery procedures and analysis. The collected samples were both cultured on fungal medium and prepared for deep DNA sequencing. The control box remained sterile, confirming the absence of contamination. Furthermore, several species of cultivable fungi were identified in the sample, demonstrating the viability of spores despite low pressure and temperature, while the DNA sequencing revealed the presence of many species, including exotic ones.

This setup opens the way to routine monitoring of stratospheric airborne fungi spores and other biological aerosols, in view of a better understanding of their dispersal and survival.

How to cite: Kasparian, J., Leoni, S., Devisme, O., Hervo, M., Romanens, G., and Gindro, K.: Sampling spores and microorganisms in the stratosphere, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3756, https://doi.org/10.5194/egusphere-egu25-3756, 2025.

Observations of conditions within and beneath the ice of glaciers and ice sheets are required to better constrain models of glacier dynamics and provide more reliable forecasts of how ice responds to a changing climate. We have developed and deployed two wireless instruments intended to provide long-term observations of englacial and subglacial environments.  A third instrument has been developed for use in streams and rivers – this may be used in either glacial or temperate environments.

Cryoegg is a spherical instrument deployed in subglacial channels via boreholes, or in moulins. It measures temperature, water pressure and electrical conductivity and provides data live by radio link through the ice to a receiver on the surface. The spherical shape allows it to travel within water channels and report on conditions within the hydrological system. We demonstrate how it has provided 5 months of data from within a glacier moulin in west Greenland, and that the radio link can operate through 2,500m of ice in north-east Greenland.

Cryowurst is a cylindrical instrument deployed in a borehole and measures both subglacial hydrological parameters (water pressure, temperature and electrical conductivity) but also its tilt and orientation change as the ice moves. It also reports wirelessly to a datalogger on the glacier surface. It has provided 5 months of data during a deployment in Yukon, Canada.

Hydrobean is an instrument intended for citizen scientists studying streams and small rivers in temporate regions. It shares some common technology with the two cryospheric instruments. Hydrobean consists of a hemispherical unit deployed on the river bed, which sends data by radio link to a data logger on the bank. It measures water pressure, water temperature and electrical conductivity and is intended to help identify pollution events (which may raise both the temperature and electrical conductivity of the water). Hydrobean has been tested in the River Usk in Wales and the river Dart in south-west England. We also intend to deploy Hydrobean in supraglacial streams during future glaciological fieldwork.

The data loggers which receive the data from all three wireless instruments store the data locally but can also forward data to a web portal using cellular or satellite links. This has allowed us to closely monitor and retrieve data in close to real time and reduces the risk of data loss from equipment damage in a harsh environment.

How to cite: Prior-Jones, M., Jonathan, H., Craw, L., Bagshaw, E. A., Dow, C. F., Mason-Jones, A., Alnader, H., von Benzon, E., Copland, L., Dahl-Jensen, D., Main, B., James, J., Livingstone, S., Mann, S., Peacey, M., Perkins, R., and Rahn, S. M.: Cryoegg, Cryowurst and Hydrobean: wireless instruments for glaciology and hydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12080, https://doi.org/10.5194/egusphere-egu25-12080, 2025.

Rodents pose a significant threat to agriculture, causing extensive damage to crops, infrastructure, and ecosystem health. This pressing issue necessitates innovative, sustainable management solutions. SPYCE, a rodent-monitoring device (RMD), is designed to provide a flexible, adaptable solution for rodent detection, monitoring, and behavior-analysis. Developed as part of the MED4PEST project, which focuses on advancing ecologically based rodent management by reducing reliance on synthetic pest-control methods and promoting sustainable, eco-friendly farming systems tailored to the Mediterranean-region. SPYCE’s modular, customizable configuration allows users to select sensors based on operational requirements and budget constraints, emphasizing open accessibility, tailored functionality, and cost-effective deployment.

SPYCE is a T-shaped device designed for flexible deployment at greenhouse entry points and fenced agricultural fields. Its design allows rodents to enter and exit freely, facilitating precise monitoring. The T-joint structure includes a horizontal base pipe equipped with PIR sensors at each entrance to detect movement. A housing at the top of the vertical pipe contains critical sensors such as an ultrasonic sensor, ultrasonic microphone, and infrared camera oriented downward toward the T-joint, all integrated with a Raspberry-Pi. A mmWave-radar sensor monitors external movement signatures. A temperature-humidity sensor collects environmental data, while a protective top cover shields the electronics from dust and water.

The system firmware, developed in Python, supports three operational modes for various monitoring needs. In Mode-1, PIR sensors at the entrances activate the system, which waits for ultrasonic-sensor confirmation to initiate data collection. In Mode-2, the ultrasonic sensor detects motion at the central joint, directly triggering data acquisition. In Mode-3, the infrared camera operates continuously, detecting motion through background changes and activating other sensors when a rodent is detected. Across all modes, temperature-humidity data are recorded at regular intervals. Additionally, separate code records movement signatures using the mmWave radar. SPYCE’s modular design adapts to diverse operational requirements while maintaining accuracy and reliability in data collection. Furthermore, SPYCE is open-source, with hardware designs, scripts, and implementation details available on GitHub (https://github.com/superworld-cyens/MED4PEST), enabling researchers and practitioners to replicate and customize SPYCE for rodent monitoring.

SPYCE is currently deployed at pilot sites in Greece and Turkey, actively collecting rodent-activity data. This data will serve as the foundation for developing a multi-modal deep-learning model capable of detecting, counting, and analyzing rodent behavior with high precision. Additionally, multi-modal anomaly-detection techniques will investigate behavioral changes in rodents under EBRM and non-EBRM conditions, providing valuable insights. These pilot deployments will validate SPYCE’s potential as an effective tool for assessing EBRM strategies. This work can also extend to broader rodent-management applications, including population estimation, behavioral analysis, and ecological monitoring.

Funding: This work is part of MED4PEST, funded under the PRIMA Programme, an Art.185 initiative co-funded by Horizon-2020, the EU’s Research and Innovation Programme. Additional funding was provided by the General Secretariat for Research and Innovation, Greece; the Scientific and Technological Research Council of Turkey; the EU Horizon-2020 Research and Innovation Programme (grant No. 739578); and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination, and Development.

How to cite: Padubidri, C., Louloudakis, I., Daliakopoulos, I., Esin, S., and Kamilaris, A.: SPYCE: A Multi-Modal Rodent Monitoring Device for Enhanced Detection, Monitoring, and Behavior Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13702, https://doi.org/10.5194/egusphere-egu25-13702, 2025.

The development of new affordable sensors, and the ability to log high-resolution data for long periods of time can potentially revolutionize environmental sciences. Collecting high-resolution spatiotemporal data requires sensor grids that are often costly and not necessarily modular enough to fit a specific experimental objective. These are limiting factors that can be solved using open-source hardware. In our Open Digital Environmental Lab, we develop and integrate complex sensor arrays into our research that simultaneously measure multiple parameters, such as water content and CO₂  and O₂  concentrations. We rely on integrating IoT (Internet of Things) concepts and aim to meet not only our current research goals, but also to enable new capabilities at a fraction of traditional sensor costs but with similar accuracy. Our vision is that open-source sensors will: (1) “Democratize science” by reducing cost limitations, and (2) Be game-changers for measuring environmental parameters with the ability to capture process-related heterogeneity. At the conference, we will present various projects, ranging from lab-oriented devices to field networks for real-time monitoring of soil and river parameters that allow new modeling and mechanistic understandings.

How to cite: Levintal, E., Freiman, E., Nguyen, T. T., Orozco, D., Norman, T., Kahsu, R., and Altman, A.: The Open Digital Environmental Lab, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14559, https://doi.org/10.5194/egusphere-egu25-14559, 2025.

The UK Floods and Droughts Research Infrastructure (FDRI) is a £38 million investment from the UK Government to support transformative research and applications on flood and drought resilience. The infrastructure will consist of a combination of in-situ monitoring infrastructure, an overarching digital infrastructure to support telemetry, analytics, and data integration, and an extensive portfolio of capacity development, training and community building activities.

FDRI aims to be a state-of-the-art infrastructure that supports transformative research. This means that innovation sits at the heart of the infrastructure – both technological innovation using novel and emerging technologies, but also social innovation to explore novel arrangements for data collection, analysis, and knowledge co-production.

Open hardware provides unprecedented opportunities to support such innovation, not only as a source of new sensing and data processing technologies and setups, but also as a catalyst for engaging makers, inventors, entrepeneurs, citizen scientist and other innovation communities in FDRI.

Here we give an overview of the vision and implementation strategy of FDRI, as well as the specific opportunities for engagement, from early experimentation and prototyping to contributing to designing for cost-effectiveness, accuracy, robustness, longevity and long-term sustainability.

How to cite: Buytaert, W., Dussaillant, A., and Veness, W.: Open Hardware in the UK Floods and Droughts Research Infrastructure (FDRI), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18431, https://doi.org/10.5194/egusphere-egu25-18431, 2025.

Measuring evaporation through Bowen ratios requires measuring a wet and a dry air temperature, something that is challenging to reliably accomplish in outdoor field conditions. In the context of forest evaporation, the desire to estimate Bowen ratios as a function of height (e.g., to partition evaporation above and below the canopy) adds another dimension of complexity to this measurement challenge.

As part of the Ruijsdael Observatory in the Loobos, Netherlands, we aim to continuously measure evaporation throughout a forest profile, using a dry and a wetted fiber-optic table along a 40 m tower to measure temperature profiles using Distributed Temperature Sensing (DTS). Previous studies have used continuous pumping with relatively large flow rates to ensure wetness, but this is not feasible for long term installations because of large water volume requirements.

In this contribution a smart and open-source solution for keeping a wet temperature wet and a dry temperature dry over a 40 m profile is presented. Two peristaltic pumps are regulated using two microcontrollers that modulate the pumping rate along different environmental conditions. For instance, no pumping could be needed at nighttime since there is negligible evaporation and pumping is stopped at low temperatures to prevent frost damage. A capacitive method is presented to attempt to quantify wetness, tank levels are monitored, and solutions for recycling water to limit the water volume requirements are introduced. Microcontrollers are connected to WiFi to enable convenient monitoring from the office.

With this contribution we hope to contribute to generalized solutions to measure evaporation or, in general, to inspire on methods about how to keep hydrological sensors wet or dry.

How to cite: Vis, G. and Coenders-Gerrits, M.: Automated wetting of a fiber-optic cable for forest evaporation partitioning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18763, https://doi.org/10.5194/egusphere-egu25-18763, 2025.

We use surface drifters to sample individual traces of floating macro plastic. These in-situ measurements provide input for the development of Lagrangian simulations to analyze both the dispersion patterns, and the physical transport processes of the plastic. An important component of drifters is the transmission of data directly from the water surface. This is challenging both due to the remoteness of the locations where the transmissions take place, and due to the dynamical movement of the water, which impedes signal transmission. For this reason, expensive satellite modems are often used, with careful design considerations that make the communication possible. The aims of our current research project are twofold: We want to test established and alternative terrestrial communication technologies at tens of kilometers from shore, and extend the knowledge about these data transmissions in challenging environments. This is done with a custom waterproof instrument that can be deployed and kept next to a research vessel. The instrument contains transition modems for satellite, cellular and LoRa communication. We present the construction of the instrument and results of a recent measurement campaign in the North Sea, off the Dutch coast.

How to cite: Schneiter, M., Hut, R., and Van Sebille, E.: Signal Transmission from the Water Surface for Plastic Pollution Tracking, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19559, https://doi.org/10.5194/egusphere-egu25-19559, 2025.

The need for continuous local and long-term observations in the vadose zone has been growing for many years, as they are essential for improving our understanding of the processes occurring in the vadose zone of the soil and enhancing seasonal forecasts from numerical models.

Lysimeters and Ecotrons are the main tools to directly access water and nutrient transport over long periods of time. In France, with the impulsion of the ONEWATER project, a French lysimeter network is in development since April 2024, taking benefice of the existing structure.

A workshop was organised to identify all the sites in France and to collect expectations. We  considered about the major scientific questions that could be supported by such a network, and identifying the measurement systems and instruments that are compatible with our ambitions, as well as considering the management and diffusion of the data.

In 2024, 32 lysimeter sites have been identified in France, with a total of 650 lysimeters. These sites are very heterogeneous : i) different type and size of devices : (columns, boxes, plates, mini-lysimeters, porous cells, Ecotrons, etc.); ii) different filling methods (undisturbed or reconstituted), iii) different measurements (probes, frequency…), iv) different atmospheric condition (natural or controlled)… Despite each site is unique and has specific scientific objectives, they all measure drainage.

The site managers expect this network will help sharing experience in terms of device management, data valorisation and probe development, and to enable the data collected in the sites to be more used.

A main issue with this heterogeneous network is to be able to compare and interprete each site. To do so  several methods will be used, from in situ temporary experiment to numerical simulations. Additional, the individual sites would benefit from some upgrade, with the use of  similar low-cost probes  and  effort will be done to share and valorize the lysimetric data.

How to cite: Sobaga, A., Faure-Catteloin, P., Abiven, S., Habets, F., Enjelvin, N., and lysimetric network community, F.: The First year of the French lysimetric network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6050, https://doi.org/10.5194/egusphere-egu25-6050, 2025.

How to cite: Vercauteren, C., Claessens, V., and Soudzilovskaia, N.: Investigating the Effects of Future Climate Scenario on Arbuscular Mycorrhizal Fungal Spore Dynamics in a Belgian Pear Orchard Ecosystem, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7456, https://doi.org/10.5194/egusphere-egu25-7456, 2025.

Soil solution sampling is a critical approach to understand the dynamics of water and nutrient transport in terrestrial ecosystems, however little information is available for high-elevation environments. During the summer 2024, soil solution was sampled at 10 cm depth in the Long Term Ecological Research-LTER site Istituto Mosso (2650 – 2900 m a.s.l., NW Italian Alps), using 30 soil disc lysimeters among 3 distinct vegetation communities belonging to alpine tundra ecosystem: snowbed communities, Carex curvula grasslands, and mixed conditions. This work presents new insights in the application of soil suction lysimeters at high-elevated, logistically-complex environments. By collecting and analyzing the soil solution, we aimed to contribute to the comprehension of the functioning of alpine tundra ecosystems, particularly under the pressure of climate change, focusing on the possible shift in vegetation cover from snowbed communities toward Carex curvula grasslands due to higher air/soil temperature and earlier spring snowmelt. These measurements were complemented by continuous monitoring of soil temperature and moisture, providing a comprehensive understanding of soil dynamics in these ecosystems. Special attention was paid to the transport processes of water and nutrients (namely carbon and nitrogen), which are fundamental to understand biogeochemical cycling in alpine areas. Notably, the content of Dissolved Organic Carbon (DOC) was the highest in Carex curvula grasslands, while nitrate concentrations exceeded those of ammonium across all sites. The outcomes of this study are expected to contribute to advancing methodologies in soil solution sampling and provide critical information for evaluating alpine ecosystem responses to changing climatic conditions. These findings will also help refining our understanding of water and nutrient dynamics, offering implications for both ecological research and management strategies in vulnerable high-elevation environments.

Research supported by NBFC - University of Turin/DISAFA, funded by the Italian Ministry of University and Research, PNRR, Mission 4 Component 2, “Dalla ricerca all’impresa”, Investment 1.4, Project CN00000033

How to cite: Benech, A., Pintaldi, E., Colombo, N., and Freppaz, M.: Soil solution chemistry along a land cover transect in the alpine tundra (NW Italian Alps), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8343, https://doi.org/10.5194/egusphere-egu25-8343, 2025.

For studying the effects of future climate conditions on plant physiological, biogeochemical, hydrological and atmospheric processes in agroecosystems, we developed a large-scale research infrastructure, called AgraSim. AgraSim is an experimental simulator consisting of six mesocosms, each of them consisting of an integrated climate chamber, plant chamber and lysimeter system. The system makes it possible to simulate the environmental conditions in the mesocosms in a fully controlled manner under different weather and climate conditions ranging from tropical to boreal climate. Moreover, it provides a unique way of imposing future climate conditions which presently cannot be implemented under real-world conditions. It allows monitoring and controlling states and fluxes of a broad range of processes in the soil-plant-atmosphere system. This information can then be used to give input to process models, to improve process descriptions and to serve as a platform for the development of a digital twin of the soil-plant-atmosphere system. In detail, each mesocosm consists of a high-precision lysimeter (weighable, control of temperature and lower boundary) with a monolithic soil core (1 m2 surface area and 1.5 m depth) and a transparent, fully controllable plant chamber (7 m3 volume) with an LED light source very similar to the natural solar spectrum with a maximum intensity of 2,500 μmol of photosynthetically active photons per square meter and second. With an in-house developed, fully automated process control system, defined climatic and weather conditions as well as air compositions can be set and varied on the basis of a predefined weather data profile. The inner surfaces of the plant chambers have the purest and most inert properties possible, with the aim of minimizing interactions between the ambient air of the plants and the chamber wall. Strong LED-based plant lighting provides light conditions similar to daylight, which prevents too large heat input into the chamber. A new concept was developed and implemented to dissipate this heat by avoiding condensation at all times, as condensation dissolves gas molecules from the air in the condensate, changing the isotope composition and thus impeding the atmospheric measurements. The process technology includes the precise control of the supply air volume flow, pressure, humidity, carbon dioxide content, air temperature, light intensity within the plant chamber, soil temperature and irrigation.

How to cite: Neumann, J., Brüggemann, N., Chaumet, P., Hermes, N., Huwer, J., Kirchner, P., Lesmeister, W., Mertens, W. A., Pütz, T., Wolters, J., Vereecken, H., and Natour, G.: Development of the "Agricultural Simulator" AgraSim for comprehensive experimental simulation and analysis of environmental impacts on processes in the soil-plant-atmosphere system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8359, https://doi.org/10.5194/egusphere-egu25-8359, 2025.

Projected climatic conditions, such as more frequent and prolonged droughts, are expected to become more common in many regions of the world according to the IPCC 2023 report, particularly in the Mediterranean. These conditions can reduce plant CO₂ uptake, gross primary productivity, and decomposition rates, potentially disrupting the carbon cycle. While higher soil biodiversity might mitigate these adverse drought effects by enhancing productivity and decomposition stability, the net effect on ecosystem CO₂ exchange remains largely uncertain, making future carbon cycle predictions challenging.

Using a reconstructed Mediterranean understory model ecosystem, we conducted a three-year experiment in 16 lysimeters (1m³ soil volume, 1m² surface area) at the Montpellier European Ecotron (www.ecotron.cnrs.fr). We tested two levels of decomposer functional diversity (low and high) under ambient summer drought and more intense drought conditions (-30% precipitation and longer drought spells). Our results show that higher decomposer functional diversity maintained up to 25% higher gross primary productivity (GPP) during the early stages of drought. This response was partly due to better water uptake from the deeper soil layers, as indicated by volumetric water content sensors.

How to cite: Milcu, A., Barantal, S., Gritti, E. S., Laoue, J., Nahmani, J., and Hattenschwiller, S.: Higher decomposer functional diversity bolsters ecosystem gross primary productivity resistance under drought: a three-year ecotron study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11225, https://doi.org/10.5194/egusphere-egu25-11225, 2025.

The dynamic changes of soil water and salt are crucial for crop growth and agricultural productivity. Understanding soil water and salt movement mechanisms, influenced by natural and human factors like climate change, groundwater, and brackish water irrigation, remains challenging. This study focused on the Yellow River Irrigation District, a critical grain-producing area with limited freshwater resources and saline soils. Using Yucheng Station as a case study, field experiments (2004–2020) and model simulations (2023–2053) were conducted to investigate the dynamics and influencing factors of soil water and salt under winter wheat-summer maize rotation.

Field experiments revealed that crop yields decreased with groundwater depth, significantly impacting soil water and salt dynamics. HYDRUS-1D simulations, calibrated with monitoring data (2020–2023), effectively captured these dynamics, achieving high accuracy in soil moisture and salt concentration predictions. Climate change scenarios showed soil water and salt fluctuations aligned with crop growth cycles, with rainfall intensity and crop evapotranspiration being key factors. Higher rainfall in SSP585 scenarios enhanced salt leaching compared to SSP245, while salt accumulation in the cultivation layer was prominent during dry years.

Groundwater depth significantly influenced farmland-water interactions. At shallower depths (2 m), groundwater contributed substantially to crop water use but posed risks of soil salt stress. Conversely, deeper depths (4 m) reduced these contributions, highlighting the balance needed for optimal groundwater management. Long-term brackish water irrigation showed increasing soil salt trends, with salt migration influenced by rainfall and groundwater depth. To mitigate risks and enhance brackish water use, irrigation with ≤3 g/L salt concentration and groundwater depth control at 3 m is recommended for sustainable soil water and salt management, ensuring crop productivity and food security under future climate conditions.

How to cite: Li, F., Qiao, Y., and Li, Z.: Dynamics of Soil Water and Salt in Saline Farmlands: Implications for Brackish Water Irrigation and Climate Resilience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16521, https://doi.org/10.5194/egusphere-egu25-16521, 2025.

Controlled Environment Facilities (CEFs) – including phytotron, ecotron, and lysimeter systems – are essential tools in experimental plant research. Studies conducted in CEFs have substantially advanced our understanding of ecological, physiological, and molecular responses to environmental factors, and have played an important role in the development and parameterization of mechanistic models.

Until recently, climate change research in controlled environments primarily focused on the static manipulation of a single (or few) parameters, notably temperature. However, modern CEFs now enable the highly precise, simultaneous control of multiple environmental variables, such as temperature, VPD, light, soil temperature, and soil moisture, as well as the accurate manipulation of atmospheric gases (e.g., CO₂ and ozone).

The ability to maintain these factors at high temporal resolution effectively turns CEFs into “time machines,” allowing researchers to investigate plant and model-ecosystem responses under realistic climate change scenarios. Although the technical implementation of complex climate series has become more feasible, the core challenge lies in generating climate series that capture potential future conditions while avoiding oversimplification and meeting the scientific requirements for standardization and reproducibility.

In this contribution, we present examples from various experiments conducted at the TUM Model EcoSystem Analyser (TUMmesa). These range from incremental manipulation of individual environmental variables, through the replication of historically recorded climate series, to the dynamic downscaling of global climate models driven by representative concentration pathway (RCP) scenarios.

These recent advancements highlight the potential of modern CEFs to deepen our understanding of plant-environment interactions and support robust investigations of climate change impacts on terrestrial ecosystems.

How to cite: Jákli, B., Meier, R., and Baumgarten, M.: The Ecotron Time Machine – Simulating Climate Change in Controlled Environment Facilities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18588, https://doi.org/10.5194/egusphere-egu25-18588, 2025.

Droughts in the Netherlands have been exacerbated by climate change, urging better scientific understanding of the hydrological cycle. Moreover, reliable predictions and management rely on accurate water observations at the surface. To date, the Royal Netherlands Meteorological Institute (KNMI) primarily estimates evaporation based on the meteorological conditions such as precipitation and temperature. Meanwhile, the Cabauw Experimental Site for Atmospheric Research has maintained decades of in-situ evaporation observations, exploring a range of indirect in-situ methods. Nonetheless, to better understand how moisture leaves the surface, more direct methods are required. A new smart lysimeter has been deployed which measures the water inflow and outflow of a representative soil and vegetation column. We evaluate this direct method for measuring evapotranspiration and 
compare the performance to other established methods, such as the eddy covariance method. Lysimeter measurements, although precise, are spatially limited, sensitive to small-scale variations, and require rigorous validation. Therefore, we present the initial results of the validation and explore the lysimeter’s potential as a reference standard for more accessible instruments that could broaden the scope of the evaporation observations network. Furthermore, by integrating supplementary in-situ measurements, our findings suggest that applying validated lysimeter data may lead to better closure of the surface energy balance. Looking towards the future, these results have the potential to advance hydrological research, 
inform models, as well as environmental decision-making. 

How to cite: de Bruijn, E. I. F. and Strickland, J. M. I.: Measuring Evapotranspiration at Cabauw (The Netherlands) , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21639, https://doi.org/10.5194/egusphere-egu25-21639, 2025.

Lysimeter systems play a crucial role in understanding the complex interactions within the soil-plant-atmosphere  continuum.  In  the  context  of  climate  change,  where  precise  insights  into  water  and nutrient fluxes, energy exchange, and greenhouse gas dynamics are essential, lysimeters equipped with advanced hydraulic and thermal controls are increasingly indispensable. A key innovation in this field is the integration of suction-controlled hydraulic boundary conditions and active temperature regulation, which significantly enhances the capability of lysimeters to mimic natural processes while maintaining  experimental  control.  These  functionalities  are  particularly  critical  in  ecotron experimental platforms, where controlled yet realistic environmental conditions are required for high-resolution and high-quality observations. 
Our  research  focuses  on  the  optimization  of  lysimeter  design  and  functionality  using  advanced computational tools. Specifically, we developed a 2D- and a comprehensive 3D-modeling approaches to  investigate  and  refine  the  technical  design  of  lysimeter  systems  equipped  with underpressure-controlled hydraulic boundary conditions and temperature regulation mechanisms. Two simulation models,  HYDRUS  and  FEFLOW,  were  systematically  tested  and  compared  for  their  suitability  in simulating these complex systems. 
We  present  the  results  of  scenario  analyses  conducted  to  evaluate  and  optimize  critical  design parameters, including (1) the number and spatial arrangement of suction cups required to achieve precise suction-controlled hydraulic boundary conditions, (2) the number, positioning, and dimensions of  heat  exchanger  pipes  for  effective  temperature  regulation  and  (3)  the  influence  of  insulation thickness at the bottom of the lysimeter on thermal efficiency and system stability. Our findings also demonstrate the strengths and limitations of both HYDRUS and FEFLOW in capturing the dynamics of water and energy transport in lysimeters. Our work not only contributes to the technical advancement of lysimeter and ecotron platforms but also supports their broader application in ecosystem research. By  integrating  robust  design  methodologies  with  cutting-edge  simulation  tools,  we  provide  a framework for enhancing the reliability and functionality of these experimental systems.  
In  conclusion,  this  study  highlights  the  potential  of  modeling  and  scenario-based  optimization in improving the design and operational efficiency of lysimeters with advanced hydraulic and thermal controls.  The  insights  gained  from  our  research  are  expected  to  support  future  applications of lysimeter and ecotron systems in addressing critical questions related to climate change impacts on terrestrial ecosystems. 

How to cite: Vrzel, J., Mursaikova, M., Kupfersberger, H., and Klammler, G.: Advancing Design and Functionality of Lysimeter/Ecotron Systems through Modeling , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21644, https://doi.org/10.5194/egusphere-egu25-21644, 2025.

High-resolution hydrometric monitoring of rivers is crucial as climate change significantly impacts the frequency and intensity of extreme events, leading to rapidly evolving flood and drought risk profiles. However, hydrometric data is often limited, with insufficient spatial resolution and coverage, especially in remote or hard-to-access rivers in alpine, arctic, and tropical regions.

Traditional hydrometric monitoring relies on station-based, in-situ measurements. Parameters such as water surface elevation, flow velocity, bed geometry, and river discharge are typically recorded using sensors installed directly in or near the flow.

In this study, we analyzed velocity measurements obtained using range-Doppler radar and compared them to Doppler radar experiments. Our findings demonstrate that range-Doppler radar is more effective for measuring surface velocity from a moving platform. Here, we discuss the methodology and rationale for adopting this innovative approach in future research on water observation systems. By utilizing range-Doppler radar, we aim to achieve high-resolution, extensive spatial coverage for key hydrometric variables like surface velocity.

How to cite: Grubesa, S., Drmic, L., Orlic, N., and Grubeša, T.: Advancing River Monitoring: High-Resolution Surface Velocity Measurement Using Range-Doppler Radar from Moving Platforms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1140, https://doi.org/10.5194/egusphere-egu25-1140, 2025.

With the rapid development of UAV technology, the challenge of selecting appropriate sampling areas and flight altitudes to ensure that collected data is both representative and accurate has gained increasing attention in the field of sedimentology. This study specifically investigates UAV-based sampling methods for riverbed grain size analysis, focusing on the critical task of determining optimal flight altitudes and sampling areas that can accurately capture the distribution of riverbed sediments.

By comparing systematic sampling with other traditional methods, this research aims to validate the precision and reliability of data collected through UAV imaging. The results show that under carefully selected conditions, such as a 5m×5m area with a flight altitude of 20 meters, it is possible to balance the need for detailed resolution with the goal of reducing errors caused by abnormal grain size distributions. This study further contributes to the optimization of UAV sampling methods, providing guidelines for practitioners seeking to enhance the accuracy and representativeness of their sediment research.

The findings of this research not only offer practical recommendations for UAV-based sediment analysis but also introduce strategies to improve sampling methodologies in river systems. These strategies aim to reduce biases and improve the reliability of UAV-generated data in riverbed sediment studies, ultimately contributing to more robust environmental monitoring and management practices.

How to cite: te wei, W.: UAV image analysis of particle size distribution in rivers　　, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3383, https://doi.org/10.5194/egusphere-egu25-3383, 2025.

River monitoring is of particular importance in river engineering due to decision-making related to protecting life and property from water-related hazards, such as floods and water resources management.

In this direction, three cross-sections (XSs) were surveyed along a 10 km stretch of the Rönne River in Sweden. Ground-truth surface velocity measurements were obtained using an electromagnetic velocity sensor (OTT MF Pro). Additionally, videos captured by a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques (Zhou et al., 2024). The bathymetry data for all cross-sections were recorded by the water penetrating radar.

The Entropy model was applied to the three different selected sites to estimate the two-dimensional cross-sectional velocity distribution by exploiting the available data, with the aim to estimate river discharge. Specifically, the surface velocities and bathymetry data for each section were considered as input for the Entropy model (Bahmanpouri et al., 2022a, b). The phenomenon of the velocity dip induced by the secondary current was also implemented in the estimation of the vertical velocity distribution where, for aspect ratios (river width/flow depth) lower than 5, the maximum velocity was observed below the water surface. Secondary currents result in a vertical shift in momentum, enhancing the turbulence and shear stress near the bed. Finally, the discharge rate was calculated for each cross-section using the mean velocity of the section and the observed flow area. The results highlighted the potential of the combination of the UAS‐Borne Doppler Radar and the theoretical Entropy model to estimate the velocity distribution and flow discharge with high accuracy. The suggested methodology would be of particular benefit in estimating the velocity distribution and flow discharge for inaccessible locations especially during high flow conditions where there are in-situ dangers for operators to measure flow characteristics. The work is funded by the European Union's Horizon Europe research and innovation programme as part of the UAWOS project (Unoccupied Airborne Water Observing System).

Keywords: Entropy, Velocity distribution, Velocity dip, Flow discharge, UAS‐Borne Doppler Radar, Rönne River

Bahmanpouri, F., Barbetta, S., Gualtieri, C., Ianniruberto, M., Filizola, N., Termini, D., & Moramarco, T. (2022). Prediction of river discharges at confluences based on Entropy theory and surface-velocity measurements. Journal of Hydrology, 606, 127404.

Bahmanpouri, F., Eltner, A., Barbetta, S., Bertalan, L., & Moramarco, T. (2022). Estimating the average river cross‐section velocity by observing only one surface velocity value and calibrating the entropic parameter. Water Resources Research, 58(10), e2021WR031821.

Zhou, Z., Riis‐Klinkvort, L., Jørgensen, E. A., Lindenhoff, C., Frías, M. C., Vesterhauge, A. R., ... & Bauer‐Gottwein, P. (2024). Measuring river surface velocity using UAS‐borne Doppler radar. Water Resources Research, 60(11), e2024WR037375.

How to cite: Bahmanpouri, F., Barbetta, S., Hu, X., Zhou, Z., Wennerberg, D., Tarpanelli, A., and Bauer‐Gottwein, P.: Applying the Entropy theory to estimate river flow using the surface velocity by UAS‐Borne Doppler Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4136, https://doi.org/10.5194/egusphere-egu25-4136, 2025.

With the increasing frequency of extreme weather events, such as river flooding, there is a growing need for more cost-effective and efficient methods for hydrometric river monitoring. Moreover, traditional in-situ hydrometric surveys often face challenges when applied to remote or hard-to-access river locations. Therefore, we investigated the potential of using Unoccupied Aerial Systems (UAS) hydrometry surveys to develop a hydraulic model for extracting rating curves, which can then be used to derive discharge from satellite altimetry-based Water Surface Elevation (WSE) measurements.

This study employed UAS-borne Water Penetrating Radar (WPR) to map river bathymetry, while Digital Elevation Models (DEM) were used to extract the non-submerged portions along the WPR cross section. A Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) receiver provided ground truth WSE measurements. Additionally, the pixel cloud data product of the Surface Water and Ocean Topography (SWOT) satellite mission was used to extract WSE along the river. Furthermore, based on the cross sectional geometry information, we constructed two one-dimensional hydraulic models, one using a steady gradually-varied flow solver and the other using the MIKE+ hydrodynamic solver.

The study site is located along the Torne River in northern Scandinavia, which forms part of the national border between Sweden and Finland. From September 3rd to 9th, 2024, surveys were conducted at 23 field sites distributed across two areas of interest along the river. Manning's numbers for the river reaches were calibrated against WSE observations derived from the SWOT pixel cloud dataset using the steady gradually-varied flow solver. Hydraulic models were employed to construct rating curves at chainage locations where observations from the Sentinel-3 satellite mission were available at two defined virtual stations: Övertorneå and Pello. These rating curves were subsequently used to convert WSE observations by the SWOT pixel cloud and Sentinel-3 to discharge, enabling the construction of a river discharge time series.

How to cite: Zhou, Z., Damgaard Christensen, F., Flendsted Jensen, V., Andreas Pedersen, M., Nielsen, S., Wennerberg, D., Fagerström, V., Gustafsson, D., Cendagorta, D., Jose Escorihuela, M., and Bauer-Gottwein, P.: Virtual station rating curves derived from hydraulic models informed with UAS hydrometry and SWOT WSE , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5749, https://doi.org/10.5194/egusphere-egu25-5749, 2025.

Forecasting the magnitude and frequency of floods has become progressively important under climate change. Flood inundation, determined by flow depth and velocity, results from the interplay between gravitational and frictional forces. Flow resistance, a critical factor in determining velocity, is often parameterized using the roughness coefficient Manning’s n. Despite its importance in geomorphology and hydrology, constraining n in natural rivers remains challenging, as synoptic data on riverbed geometry and roughness are sparse. Topo-Bathymetric LiDAR (TBL) data open up new opportunities to constrain the spatial variation of n in rivers by providing detailed and accurate measurements of riverbed and water surface geometry. This study presents a novel, iterative approach to estimate spatially variable n values across a Digital Elevation Model (DEM), given that the channel bed, water surface, and total discharge are independently constrained. The method adjusts n iteratively until the best agreement between predicted and measured water depth is achieved. The model is first validated on artificial, homogeneous reference surfaces to establish statistical criteria for convergence to an optimal solution. We demonstrate the model's ability to resolve complex n distributions by introducing different n patches with varying patch sizes and testing how backwater effects influence model accuracy across varying channel slopes, n patch sizes, and river discharges. Finally, we apply our approach to a 1 m resolution DEM created from a high-resolution TBL dataset covering the 25 km-long Ardèche Gorge, France. This application highlights the method's effectiveness in natural environments, emphasizing its potential to enhance flow resistance parameterization linked to morphological characteristics when channel bed, water surface, and discharge data are available.