The south west corner of Australia holds a level of plant diversity unmatched outside tropical rainforests; fostered through millions of years of isolation and relative tectonic and climatic stability.  Across deep time, pressures of pollinator scarcity, severe nutrient limitation in an ancient, highly weathered Critical Zone, and fire disturbance have produced what might be the most specialised flora in the world.  Southwest Western Australia (SWWA) is also on the bleeding edge of climatic heating and drying, in a trend that has been apparent since the 1960s.  Water resources management in response to these trends has made the cities of SWWA global leaders in conservation and water technologies – but as the drying continues, groundwater recharge is dropping, phreatophytic plants are dying, and more severe summer heatwaves and droughts are impacting key ecosystems over huge areas.  In this Darcy Oration, I hope to introduce you to the often forgotten, but exceptional set of ecosystems, catchments and Critical Zones of SWWA, and ask how can ecohydrology as a discipline support meaningful adaptation to such climatic changes in this megabiodiverse, hyper-endemic area?  I will present a potential hierarchy of actions and research gaps to consider, and suggest that research in support of making decisions about where and how to adapt is a key challenge for our community.  Finally, I will spend a little time reflecting on my personal experiences as a caregiver to special needs children, and how those caregiving responsibilities impact a career in hydrological science.

How to cite: Thompson, S.: Ecohydrological Adaptation to Climate Change - from Gondwana to the Globe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15399, https://doi.org/10.5194/egusphere-egu26-15399, 2026.

How rapidly advancing climate change will impact the water cycle and its extremes remains poorly understood and is the origin of much uncertainty. This uncertainty limits our ability to build societal and ecosystem resilience – contributing to policy challenges for adaptation to water scarcity and other hydro-climatic risks. In this context, we rely on hydrologic simulation models to provide robust short-term predictions as well as long-term projections of water cycle dynamics across scales. Even though advancements in observational systems and increasingly detailed simulation models enable us to observe and simulate the water cycle at unprecedent resolutions over large domains, intercomparison studies still reveal inconsistent emergent model behavior. These large domain models are difficult to constrain using current observational datasets given that these are often highly imbalanced, while available theory provides only limited guidance regarding which hydrologic processes we can expect to dominate in diverse climates and landscapes. At the same time, the performance of machine learning models improves rapidly, bypassing process knowledge and therefore questioning the basic need for scientific understanding.

In this talk, I argue that process-based evaluation is an important bridge between hydrologic theory, observations and simulation models – even in the presence of high model complexity and significant data imbalances. I will discuss examples of how process-based evaluation can elicit controlling factors on hydrologic processes by utilizing hydrologically relevant gradients in both simulated and observed data. Thus, demonstrating how this approach can provide a pathway towards assessing and ultimately improving the consistency between perceived and simulated hydrologic process controls over large domains.

How to cite: Wagener, T.: Advancing hydrologic science through process-based evaluation of models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14330, https://doi.org/10.5194/egusphere-egu26-14330, 2026.

A wide variety of processes controls characteristics of water extremes: river floods, droughts, episodes of detrimental streamwater quality. Understanding generation processes of these events may assist in uncovering their emergence and support the interpretation of their changes. Here I show how objective event identification and transferable causative classification frameworks are able to overcome the limitations of locally tailored approaches and detect functional changes in extremes over large spatial domains and long temporal scales.

To pave the way towards more efficient adaptation measures for extremes we need to understand intricate links between their hazard and impact components better. Here I demonstrate how different generation processes of extremes are interlinked with the adaptation efficiency, previous societal experiences and awareness uncovering how they might shape socio-economic impacts. The examples show how bridging across domains can help to improve our preparedness and anticipate future impacts.

How to cite: Tarasova, L.: Water extremes under change: from processes to impacts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13436, https://doi.org/10.5194/egusphere-egu26-13436, 2026.

Water resources worldwide are increasingly threatened by growing anthropogenic pressures and inherent hydrogeological constraints, raising concerns about their suitability for domestic use. This study aims to assess the physicochemical quality of certain springs in the Agadir Ida-Ou-Tanane region and evaluate compliance with international thresholds established by the World Health Organization (WHO) for drinking water. A total of twenty-six water samples were collected across the studied region and analyzed for key parameters including Electrical Conductivity (EC), Total Dissolved Solids (TDS) and Total Hardness (TH). The EC values ranged from 275 µS/cm to 4210 µS/cm with an average of 1446.15 µS/cm. For Total dissolved solids, values ranged from 135 ppm to 7140 ppm, with Total hardness presented a maximum value of 3217.02 mg/L and minimum value of 188.9 mg/L. Water Quality Index (WQI) was calculated to provide an integrated evaluation of the overall water quality.Spatial distribution of water quality was further examined through Inverse Distance Weighting (IDW) interpolation. WQI based classification  revealed that 73.1% of the springs were in acceptable quality categories, with 34.6% classified as excellent and 38.5% as good. Despite this generally favorable status, TDS values approach or exceed international thresholds in several locations, indicating the need for region-wide monitoring and treatment strategies. Considering the heavy dependence of rural communities on spring water, these findings underscore the importance of investing in adequate treatment infrastructure and implementing robust protection measures for sustainable water resource management.

How to cite: Raïs, A., Tairi, A., El Mouden, A., Ijlil, S., Ait Moh, H., Hssaisoune, M., and Bouchaou, L.: Geo-statistical and hydrochemical assessment of spring water quality and water sustainability based on WHO standards in the Agadir Ida-Ou-Tanane region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-873, https://doi.org/10.5194/egusphere-egu26-873, 2026.

Sub-daily extreme rainfall (SDER) events frequently lead to natural disasters, including flash floods, urban floods, landslides, and soil erosion. It is essential to make a reliable prediction of its frequency at the local/regional spatial scale for the future period, in order to devise improved disaster mitigation and adaptation strategies. It has been observed that current CMIP6 GCMs have limitations in simulating short-duration (sub-daily) heavy rainfall events, and large biases are often observed in the control run simulations compared to historical observations at various locations worldwide. Hence, there is a lack of confidence in considering the crucial future projections obtained from those GCMs as reliable. In this study, we present a novel statistical methodology for predicting the seasonal frequency of SDER for 99 river sub-basins (RSBs) in India, encompassing tropical, temperate, arid, and polar climates across various topographies. The methodology identifies the scaling relationship between the SDER frequency and the associated potential atmospheric variables/drivers for each RSB. Results indicated that the seasonal frequency of SDER scales with (i) near-surface air temperature (SAT), and (ii) moisture content in the air, which is measured by near-surface dew-point temperature (DPT). The scaling relationship exhibits an increasing (scaling) phase followed by a decreasing (reverse scaling) phase as the (dew point) temperature increases. The range of SAT and DPT in the scaling relationship varies with RSB and climate. The SAT and DPT values at peak frequency are high for mountainous areas and lower for non-mountainous areas. The effectiveness of those scaling relationships in predicting SDER frequency at the seasonal scale was assessed/validated for the recent past (1981-2020). The method performed fairly well for RSBs with non-mountainous topography and moderately well for RSBs with mountainous topography across climate zones, except for years with an abnormally high or low SDER frequency. A finer spatial-resolution scaling relationship is deemed necessary for mountainous topographies where SDER exhibits a rather local nature. In addition, the time trends in simulated and observed frequencies closely matched. The proposed methodology is applied to predict the future seasonal frequency of SDER in the RSBs for different SSP climate scenarios till the end of the twenty-first century. The performance of various GCMs in projecting the seasonal frequency of SDER is also evaluated.

How to cite: Varshney, A. and Srinivas, V. V.: A Statistical Methodology for Regional Scale Future Projection of the Seasonal Frequency of Sub-daily Extreme Rainfall Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1058, https://doi.org/10.5194/egusphere-egu26-1058, 2026.

In various parts of arid and semi-arid region of India such as Rajasthan people are mainly depended on groundwater for fulfil their daily demands like drinking and watering their crops. However, in the Khetri mining region of Jhunjhunu district, extensive mining and smelting of copper and associated sulfide minerals have led to heavy metal contamination and a deterioration in groundwater quality. Therefore, this study evaluates the overall groundwater quality and the related human health risks in the region severely affected by copper mining and metallurgical activities. We have collected total 59 groundwater samples from both pre- and post-monsoon periods and were examined for physicochemical parameters, cations, anions, and heavy metals (Pb, Cd, Cr, Cu, Fe). Multivariate analysis, including PCA and correlation, revealed that geogenic processes, such as carbonate and silicate weathering, dominate natural groundwater chemistry. Whereas anthropogenic inputs from mining, ore processing, agriculture, and industrial waste significantly elevate toxic metal concentrations. The elevated level of Pb, Cd and Cr were detected across many locations, often exceeding permissible limits. Non-carcinogenic risks (HI) for Cr, Pb and Cd surpassed the safe thresholds in many locations, and carcinogenic risks (CR) for Cr, Cd, and Pb exceeds the permitted limit of 1 × 10⁻⁴ at multiple sites, indicating significant long-term health threats. The integrated EWQI–Monte Carlo framework thus combines the objectivity of entropy-based weighting with the statistical power of probabilistic simulation, enabling a more realistic and comprehensive evaluation of both groundwater quality and the related human health risks. In addition of this, the risk assessment for human health (HRA) revealed that children are at more danger than adults due to their greater exposure per body weight. These findings clearly indicate an urgent need for groundwater quality management through the adoption of remediation actions and the exploration of alternative sources to protect community health from contaminated groundwater.

How to cite: Kumar, M., Pathania, T., and Gaurav, K.: Integrated EWQI–Monte Carlo framework for assessing groundwater quality and health risk in the Khetri mining region of Jhunjhunu district, Rajasthan, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1227, https://doi.org/10.5194/egusphere-egu26-1227, 2026.

Riverbank filtration (RBF) is a managed aquifer recharge (MAR) technique applied worldwide, operating at the river–groundwater interface and offering the potential to enhance both groundwater quantity and quality, thereby improving drinking water supply security. However, its sustainable implementation requires a robust understanding of hydraulic interactions between surface water and groundwater as well as hydrochemical processes, supported by targeted local and regional monitoring strategies. Moreover, recharge efficiency and water quality benefits may vary in response to seasonal and event-based fluctuations in river flow, upstream contaminant inputs, and site-specific aquifer heterogeneity. In our study, we investigated river water–groundwater mixing, along with bank filtrate residence times, to improve the understanding of recharge dynamics at the Kępa Bogumiłowicka RBF site, a key regional water supply system located near Tarnów, southern Poland. Environmental tracers, including stable water isotopes, chloride concentration, water temperature and specific electrical conductance, were combined with high-resolution hydrological, meteorological and groundwater abstraction records. The results demonstrate that RBF is the dominant aquifer recharge mechanism, contributing more than 90% of the year-round yield from seven production wells located near the riverbank. Based on this case study, we propose a practical and transferable framework for efficient RBF monitoring and management. The approach integrates multi-tracer observations with ensemble end-member mixing analysis (EEMMA), combining discrete sampling with continuous physicochemical and hydrometeorological monitoring over at least one hydrological year. This cost-effective workflow enables robust recharge-source assessment, supports the evaluation of both quantitative and qualitative groundwater status, and facilitates proactive responses to upstream pollution events and rapid hydrological changes. As such, it provides a valuable template for the long-term, sustainable and resilient management of MAR-based drinking water resources in shallow alluvial aquifers.

How to cite: Janik, K., Rein, A., and Sitek, S.: sMARt riverbank filtration monitoring: how environmental tracers and high-resolution data support resilient drinking water supply, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3097, https://doi.org/10.5194/egusphere-egu26-3097, 2026.

Accurate or exact estimations of hydraulic conductivity (K) and infiltration rate are crucial for understanding soil-water interactions, optimising irrigation practices, evaluating groundwater recharge potential, and designing drainage systems. Conventionally laboratory permeability tests and in-situ infiltrometer tests, provide direct estimates of soil hydraulic behaviour. However, these methods have limitations of their point-specific nature and are unable to capture subsurface heterogeneity across larger spatial scales. In contrast, the electrical resistivity tomography (ERT) technique offers a non-invasive geophysical approach that is capable of detecting subsurface variations in soil electrical resistivity properties. The electrical resistivity estimates can further be interpreted to analyse soil types, soil layer structures, moisture and mineral contents, and pore connectivity. These are ultimately related to soil hydraulic properties, such as hydraulic conductivity and soil-water interaction behaviour, such as vertical infiltration rates. One of the accurate methods of estimating K is a pumping test, which is expensive and time-consuming. Other methods include laboratory permeameter tests, which require the collection of soil samples from the field, which often are disturbed ones and thus may produce K values with considerable uncertainties. The primary goal of this study is to establish the relationship between hydraulic conductivity (K) and electrical resistivity (ER) to replace the tests mentioned above. The second objective of this study is to establish an ER-infiltration rate relationship to convert point-based infiltration measurements into area-wide infiltration maps using resistivity data, minimizing the number of infiltrometer tests needed, saving time, manpower, and resources. Field investigations executed here involve ERT surveys using different electrode configuration arrays, such as the Wenner, Schlumberger, and dipole-dipole, across selected test sites that represent various soil textures and moisture conditions. The resistivity profiles are inverted to generate 2D subsurface sections, enabling identification of moisture zones and shallow saturation patterns. Parallelly, laboratory permeability tests are carried out on undisturbed soil samples to determine hydraulic conductivity, while infiltrometer tests are performed to obtain field-scale infiltration characteristics and steady-state infiltration rates. The combined dataset provided a comparative evaluation of resistivity variations in relation to measured soil-hydraulic parameters. Once these relationships are established, ERT can move beyond the simple imaging and serve as a fast and cost-effective way to estimate how water moves through the soil over a wider area. This will significantly reduce the need for frequent point-based tests and help capture natural variations in soil conditions that are often required in hydrological studies. Site evaluations can thus become faster and efficient, while areas with higher infiltration potential can be identified with greater confidence, and the overall planning of irrigation, drainage, and groundwater recharge strategies becomes more informed and robust.

Keywords: Electrical Resistivity Tomography (ERT); Hydro-geophysical characterization; Hydraulic Conductivity; Infiltration Rate; Groundwater recharge; Soil Heterogeneity.

How to cite: Jaiswal, M., Ganguly, S., and Prashanth, T.: Integration of electrical resistivity tomography, permeability and infiltrometer tests for modelling hydraulic conductivity and infiltration rates in the field, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3766, https://doi.org/10.5194/egusphere-egu26-3766, 2026.

Lake Cuitzeo is the second biggest lake in Mexico. It is placed in a semi-graben
structure, linked to volcanic rocks and fault systems. On the lake shoreline,
hydrothermal bodies emerge. These present arsenic and boron concentrations and
are used in thermal spas. Nevertheless, it is necessary to study the original and
behavior of these hydrothermal bodies, which provides information for the
sustainable management in order to benefit the local users.
The objective of this work is to determine the spatial distribution of the
hydrothermal manifestations, as well as their hydrogeochemical characteristics and
the temperature they reach at depth. The methodology consisted of sampling thermal wells and springs, along with laboratory determination of major ions and
trace elements. Subsequently, hydrogeochemical diagrams, isoline maps, and
geochemical indicators were used to understand their behavior. The results show
that the thermal sites have higher temperatures at depth and are associated with
the presence of faults.
Finally, the information compiled in this study may be useful for defining a safe and
feasible use of the geothermal resource for the communities inhabiting the study
area, whether for energy generation or for direct-use applications.

How to cite: Sierra Garcia, B. A., Olea, S., Israde Alcántara, I., Villanueva Estrada, R. E., Morales Casique, E., Zamora Martínez, O., Tadeo León, J., Gómez Vasconcelos, M. G., Avellán Denis, R., and Ramírez Serrato, N.: “Environmental implications of natural sources of arsenic and boron in hydrothermal bodies in the second biggest lake of México.”, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4336, https://doi.org/10.5194/egusphere-egu26-4336, 2026.

The global demand for groundwater is expected to increase due to changing hydrological and population patterns. Hence, spatio-temporal characterization of groundwater is crucial for its sustainable management options. This study introduces a fully distributed groundwater simulation model, based on the MODFLOW framework, to understand the fluctuations in the groundwater table and basin-scale hydrological dynamics of the Damodar River basin (DRB) in India. Monthly simulations were conducted over a 5-year period (2015–2020) to quantify changes in groundwater head and interactions between the river aquifer and the watershed. Real field abstraction data were used to represent pumping components of water use in the MODFLOW model. Parameter Estimation Test (PEST) based calibration and validation were performed to estimate the unknown hydraulic conductivity and recharge. The model outcomes reasonably align with heads at the observation wells, thereby improving our understanding of the water table in large river basins. The findings highlight the influence of monsoon precipitation and the overall changes observed in DRB. Furthermore, the study combined the tributary stream network and PEST-calibrated recharge, which further enhanced the physical representation and accuracy of the model despite the additional data requirements. Decline in groundwater level was observed, which potentially highlights unsustainable water management practices in the river basin. The results underscore the significance of numerical groundwater models, which are crucial for informed decision-making in robust groundwater planning interventions.

Keywords: Groundwater, MODFLOW, PEST, recharge, river basin

How to cite: Kumari, A. and Pathania, T.: Assessing spatio-temporal variations of groundwater level in the Damodar River basin, India using MODFLOW, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5224, https://doi.org/10.5194/egusphere-egu26-5224, 2026.

Uranium contamination in shallow aquifers is emerging as a concern for groundwater-quality issues across several parts of India. The present study evaluates the spatial distribution, concentration levels in shallow groundwater systems of selected semi-urban regions of India. Secondary data used in this assessment were obtained from the Central Ground Water Board (CGWB), covering semi-urban areas across all Indian states. The results reveal pronounced spatial heterogeneity in uranium concentrations, with numerous locations exceeding the permissible limits prescribed by the World Health Organization (WHO) and the Bureau of Indian Standards (BIS) for drinking water. Analysis of uranium concentration data for the period 2024–2025 indicates that shallow aquifers in parts of Karnataka, Punjab, and Rajasthan exhibit average uranium concentrations of approximately 133 ppb, 48 ppb, and 79 ppb, respectively, while maximum concentrations of 488 ppb, 202 ppb, and 119 ppb respectively, were recorded at select locations. A substantial proportion of groundwater samples were found to exceed WHO guideline values, highlighting widespread contamination concerns. The findings of this study offer critical insights for water-resource managers and policymakers in developing strategies to protect drinking-water security in uranium-affected regions of India.

How to cite: Kumar, D., Khare, S., and Kurre, S.: Spatial Variability of Uranium in Shallow Aquifers of Semi-Urban Indian Landscapes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6358, https://doi.org/10.5194/egusphere-egu26-6358, 2026.

A 2024 climate change study in Taiwan indicated an increase in rainfall of approximately 10-35%, causing flooding in some urban areas where stormwater drainage systems exceeded their original design protection standards. Furthermore, urban stormwater drainage systems improvements in Taiwan often face complex and intertwined spatial issues related to road traffic and underground utility lines, making rapid engineering improvements difficult. Therefore, to address the threats already posed by climate change, the use of big data monitoring of urban areas and surrounding regions, along with rapid AI-powered algorithms for drainage systems, is imperative. The New Taipei City Government, in order to manage urban water information, has developed a series of adaptation strategies for its drainage system. These strategies address environmental factors such as drainage sections affected by tides and storm surges, rainfall characteristics in nearby mountainous areas, and sections with gates and pumping stations that cannot drain by gravity. The aim is to lower urban drainage levels to prevent flooding and shorten flooding duration. This includes practical operational recommendations and early flood warnings. The method is based on historical practical experience and AI-generated water level forecasts to conduct drainage system decision analysis and management value setting. It combines real-time rainfall data from the Internet of Things, road flooding sensors, road CCTV, stormwater sewer water levels, and pumping station water levels. The data used includes actual data from the past 3 hours, forecasted rainfall for the next 6 hours, tidal changes, and real-time water level information at various monitoring locations to formulate adjustment strategies. Synchronous information is released within the drainage system to systematically set stormwater sewer water levels, treating stormwater sewers as flood retention spaces for monitoring and water level control. Based on operational experience gained from the past 3 years of implementation, this method will be used in the future to address the threats posed by increased rainfall due to climate change and to formulate urban flood control strategies to reduce disaster losses.

How to cite: Yang, S., Song, D., Huang, M., Pan, J., Wang, X., Chen, C., and Yeh, K.: Research on the Adaptation Strategies of Urban Stormwater Drainage to Increased Rainfall Due to Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7784, https://doi.org/10.5194/egusphere-egu26-7784, 2026.

The Doongmabulla Springs Complex (DSC) is a cluster of groundwater-dependent wetlands located in central Queensland, Australia. The DSC has over 160 individual springs ranging from over 9 ha, with pools of water, down to vents less than 10 cm across, supporting individual grasses. The springs are home to a variety of plant species (many endemic) that are adapted to the unique physico-chemical parameters of the groundwater discharge on which they rely. Wetlands and scalds of the DSC support a listed Threatened Ecological Community of species dependent on this natural discharge of groundwater from underlying artesian aquifers of the Galilee Basin. The springs are protected under Australia’s State and Commonwealth Environmental legislation. 

Aquifer depressurisation due to mine dewatering occurring to the east of the springs has the potential to threaten spring biodiversity in future by reducing groundwater discharge and consequent wetland persistence. Assessing the likelihood and possible magnitude of these threats requires a multi-disciplinary modelling approach to address complex groundwater – surface water – ecosystem interactions: 1) define groundwater pressure change probabilities; 2) simulate likely wetland response; and 3) evaluate potential ecological impacts. Once such a modelling chain is in place, impact mitigation may be assessed, considering the effect of interventions at each stage of the chain.

To support the numerical modelling, extensive hydrological, ecological and physio-chemical data collection and analysis was undertaken to understand spring wetland area and species microhabitats and distributions over multiple scales and time frames, including seasonal and inter-annual variability. Generation of realistic and defensible conceptualisations for the springs is critical and is described in a companion paper (Cresswell, et al., these proceedings).

Regional numerical groundwater modelling (MODFLOW) generated potential groundwater pressure change responses relevant to the DSC source aquifers. Potential groundwater depressurisation over time at the individual spring locations was utilised in a wetland water balance model (GoldSim) to describe wetland persistence, size and seasonality. Water balance outputs were translated into spatial representations of predicted spring hydrology (TUFLOW), generating area and shape configurations that could be compared to historical wetland persistence data and then utilised to predict potential effects on suitable habitat for key flora species under a range of scenarios including mining-related groundwater drawdown and climate change using maximum entropy species distribution modelling (MaxEnt). This latter process relates known species’ occurrences to measurable variables that describe the environment (such as soil moisture, soil salinity and pH) to predict the presence or absence of a species at unsampled locations and under future groundwater drawdown scenarios. 

The results of the application of this novel chain of models have informed a revised impact assessment for the potential impacts of mine dewatering on the unique vegetation communities at the Doongmabulla Springs Complex and enables development of targeted mitigation approaches for individual wetlands and species.

How to cite: Gibson, A., Cresswell, R., Muller, J., Capon, S., Grieger, R., Lloyd, P., Stanton, D., and Yeates, M.: Using a novel chain of models to mimic source aquifer depressurisation  impacts across the Doongmabulla Springs Complex , Queensland, Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8457, https://doi.org/10.5194/egusphere-egu26-8457, 2026.

Understanding seasonal water uptake depth (WUD) in trees is critical for assessing trees’ physiological responses to seasonal variability in microclimatic conditions and soil water availability. While broadleaf and conifer species in the Northern Hemisphere have been widely studied, studies of seasonal changes in WUD of large and long-lived evergreen conifers in the Southern Hemisphere are rare.

We investigated the effect of season and tree size on the water uptake pattern of Agathis australis (kauri), a large and long-lived endemic conifer, over an 18-month period. Our study site, a remnant kauri-dominated forest, is located in West Auckland, northern New Zealand. We collected stem cores from seven kauri trees (n = 3 < 50 cm diameter, n = 4 > 100 cm diameter) and soil samples underneath each of the seven trees (organic layer (OL), 0-10 cm, 10-20 cm, 20-30 cm, 30-50 cm, 50-70 cm, 70+ cm) across six seasons (austral spring 23,  austral summer 23-24, austral autumn 24, austral winter 24, austral spring 24, and austral summer 24-25). Water from soil and stem cores was extracted using cryogenic vacuum extraction. We measured δ²H and δ¹⁸O in all samples and used a Bayesian mixing model (MixSIAR) to determine WUD.

Our preliminary results show that across season and tree size, kauri obtained a larger proportion of water from the shallow layer (OL to 30 cm depth; ~ 60%) compared to ~ 40% sourced from layers below 30 cm. There was greater reliance on water from the shallow layer (up to 75%) during austral summer 23-24 and 24-25. We did not observe strong differences in WUD between small and large trees across our study seasons. These insights advance ecohydrological research on Southern Hemisphere evergreen conifers and highlight the importance of understanding species-specific response to microclimatic conditions and changing water availability.

How to cite: Boseren, M., Schwendenmann, L., and Boswijk, G.: Seasonal water uptake pattern in Agathis australis (kauri), a large and long-lived Southern Hemisphere conifer, using water stable isotopes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8525, https://doi.org/10.5194/egusphere-egu26-8525, 2026.

In arid and semi-arid regions, the agriculture thrives on the use of irrigation systems such as drip and sprinkler irrigation which ensures the higher irrigation application efficiencies. However, the planning and design of drip irrigation systems continue to rely on generic understanding and manual hydraulic calculations which often leads to sub-optimal performance. The present study addresses this gap by using a numerical model for analysing the drip irrigation for Okra Cultivation in a semi-arid district of Udaipur, Rajasthan, India. Therefore, the objectives of this study are analysing the hydraulic performance of the given network of drip irrigation using a numerical model and evaluate its adequacy and operational efficiency.

The hydraulic adequacy is determined using EPANET 2.2 modelling tool employing pressure driven demand (PDD) approach. The temporal variability in the behaviour of system was captured by running the extended period simulation model. The model incorporates operational control rules to define the variable demands for the different phases of the growth of the plant and scheduling the pump and valve operations thereby enabling the digital twin of the drip irrigation system. The source of water taken as well is explicitly represented in the model while the filtration unit is represented as a non-return valve with high loss coefficient. In addition to the watering, the fertigation of the crops is also simulated in the model according to the fertigation schedule.

The hydraulic performance of the irrigation system is evaluated using standard performance indicators  such as the Coefficient of Uniformity, Coefficient of Variation, and Distribution Uniformity. Furthermore, the reliability of system performance is assessed using network reliability parameter.  Thus, the study will assist farmers and stakeholders in achieving optimal operation of drip irrigation systems by addressing and minimizing the multiple technical and operational challenges associated with this irrigation method.

How to cite: Gupta, K.: Numerical Modelling of Drip Irrigation to Improve Water Use Efficiency in Semi-Arid Agroecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8945, https://doi.org/10.5194/egusphere-egu26-8945, 2026.

Urban and peri-urban regions in semi-arid India are increasingly exposed to contrasting climatic extremes, namely water scarcity and flooding, driven by complex interactions between anthropogenic pressures and biophysical processes. In this context, the present study investigates long-term changes in the annual water yield (AWY) ecosystem service across sub-watersheds of the Godavari River Basin encompassing the semi-arid Warangal district, Telangana, India. The InVEST AWY model is applied for two representative years, 1995 and 2025. The results indicate that AWY ranges from 460-890 mm yr⁻¹ in 1995, with relatively higher values in the north-eastern sub-watersheds, but declines across most sub-watersheds by 2025 to 220-690 mm yr⁻¹. The annual precipitation found to be 1400 to 1230 mm yr⁻¹ over the study period, while potential evapotranspiration increase substantially from 2253 to 2955 mm yr⁻¹, enhancing atmospheric evaporative demand and reducing water availability. Sensitivity analysis (expressed in terms of elasticity, E), shows that AWY is highly sensitive to precipitation variability (E = 1.84) and moderately negatively sensitive to urban-related biophysical parameters (root restricting depth: E = -0.42, crop coefficient (K_c): E = -0.39).  In contrast, sensitivity to potential evapotranspiration is lower (E = -0.36), highlighting the combined influence of climatic forcing and urban expansion. Spatially, urban land use in 1995 is concentrated in the central region, with cropland and forest dominating the western and eastern parts, respectively, yielding a mean AWY of 718.51 mm yr⁻¹. By 2025, relatively higher AWY zones shift toward the north-eastern region, reflecting reduced evapotranspiration associated with urban expansion; however, the overall mean AWY declines to 476.36 mm yr⁻¹, indicating that land-use changes influenced spatial patterns while climatic factors governed the temporal decline. The decline in AWY between 1995 and 2025 corresponds with reduced ecosystem service values (ESV) for water-yield related regulation services, particularly water regulation (ESV₁₉₉₅ = 16.37 to ESV₂₀₂₅ = 12.91 million US$) and water supply (ESV₁₉₉₅ = 84.73 to ESV₂₀₂₅ = 73.69 million US$). Overall, the findings demonstrate the joint role of climate variability and urbanization in shaping sub-watershed water yield and associated ecosystem services, providing insights for climate-responsive urban and landscape management.

How to cite: Dasari, S., Pal, M., and Keesara, V. R.: Ecohydrological Modelling of Annual Water Yield and Water-Related Ecosystem Services in the Semi-Arid Region of Warangal, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9012, https://doi.org/10.5194/egusphere-egu26-9012, 2026.

Scarcity of groundwater level (GWL) data poses a significant challenge to effective groundwater resource modeling, particularly in urban and peri-urban regions where anthropogenic influences further complicate hydrological processes. In this study, a machine learning–based framework is developed to predict groundwater levels for Bhubaneswar city, India, using Long Short-Term Memory (LSTM) neural networks. Given the data-driven nature of machine learning models and the limited availability of long-term observations, a regionalized modeling approach is adopted by coalescing GWL measurements from 31 closely located monitoring wells. To enable the model to capture well-specific variability, each well is characterized using static indicators derived from hydrological and socio-environmental datasets. Multiple combinations of predictor variables are evaluated to identify those most effective in representing groundwater level dynamics. The optimal model, trained on aggregated regional data, demonstrates strong predictive performance during testing, with a correlation coefficient (R) of 0.89 and a Nash–Sutcliffe Efficiency (NSE) of 0.79. The proposed regionalized LSTM framework shows promise for reliable groundwater level prediction at individual wells in data-scarce urban settings, offering a practical tool for groundwater assessment and management.

How to cite: Anshuman, A. and Panigrahi, M.: Groundwater Level Prediction in Urban Areas under Data Scarcity Using a Regionalized LSTM Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10663, https://doi.org/10.5194/egusphere-egu26-10663, 2026.

Extreme precipitation events (EPEs) are expected to increase in frequency and intensity under global warming and can trigger various impacts such as floods and landslides, which can undermine the socio-economic stability by raising the risk of loss of human lives, infrastructure failure and agricultural losses. Consequently, the study of hydroclimatic extremes has grown substantially in the recent decades, supported by high-resolution data and multivariate event-based analytical frameworks that improve understanding and resilience to climate-related risks. The rainfall induced impacts often show a compound nature because the underlying processes are scale-dependent and can overlap and intensify each another. Therefore it is important to consider extremeness across spatio-temporal scales when assessing EPEs. However, the the complex topography and diverse climatic conditions in the Indian Peninsular region pose a key challenge in assessing and characterizing the EPEs. In this context, a comprehensive ranked catalog of EPEs is developed from the 73 year long data set, based on their extremity across spatio-temporal scales. To increase the robustness of the underlying statistical analysis and to make an optimal use of the data, a combination of the peak-over-threshold (POT) method and the cross-scale weather extremity index (xWEI) is introduced to quantify the spatiotemporal extremity. In addition, the study exemplifies the applicability of POT method and compares the resulting extremeness with the conventional annual maxima approach. The catalog identifies EPEs that are jointly extreme across spatial and temporal scales and distinguishes short-lived localized storms from persistent, widespread events, thereby enabling a systematic characterization of EPE typologies. By linking each EPEs xWEI value to the season and meteorological divisions, the catalog offers a consistent basis for comparing historical events, and advances process-based understanding of regional hazard regimes. In summary, the resulting catalog can be a valuable tool in improving the robustness of quantitative risk assessments and enhancing the reliability of climate change attribution analyses.

How to cite: Ganapathiraju, S. A., Voit, P., Marwan, N., and Rathinasamy, M.: Ranked Multiscale Catalog of Precipitation Extremes using Cross Scale Extremity for the Indian Peninsular Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10790, https://doi.org/10.5194/egusphere-egu26-10790, 2026.

Groundwater contamination arising from mining activities represents a persistent and complex environmental challenge, particularly in coal-bearing regions where sulfide-rich mine overburden is extensively exposed to atmospheric conditions. Upon interaction with oxygen and moisture, pyrite oxidation generates acidic by-products and mobilizes dissolved constituents, such as ferrous, ferric iron, sulfate, and hydrogen ions. Understanding and predicting the spatiotemporal evolution of contaminant plumes in such systems remains challenging due to the coupled nature of variably saturated flow, multicomponent geochemical reactions, and microbially mediated processes. This study develops a comprehensive numerical modeling framework for simulating contaminant transport and remediation processes associated with oxidation reactions in unsaturated mine overburden systems. Variably saturated flow is represented through discretization of the governing flow equation in the vertical domain using hydraulic head-based parameters, and the resulting tridiagonal system of linear equations is efficiently solved using the Thomas algorithm. The model couples variably saturated groundwater flow, represented by Richards’ equation, with multicomponent reactive transport equations describing the generation and migration of key oxidation products (Fe²⁺, Fe³⁺, SO₄²⁻, and H⁺). In addition, sulfate reduction mediated by sulfate-reducing bacteria (SRB) is incorporated to capture biologically driven attenuation mechanisms relevant to natural and engineered remediation scenarios. Simulations are performed for a total of 22 years (8030 days). A time step of 0.1 day and a grid size of 0.2 m are identified as the optimal choices for the simulations. The simulation results indicate that the concentrations of oxidation-derived species decrease significantly from 200 to 40 mol/m³ in clay, 300 to 95 mol/m³ in loam, and 1 to 0.2 mol/m³ in sand. Sensitivity analysis shows that peak sulphate sensitivity in clay with a sensitivity index (SI) of 0.65 and in loam with an SI of 0.5 under high saturation condition (water content, w_c = 0.9), while ferrous ions exhibit maximum sensitivity in loam under low saturation condition (w_c = 0.2) with an SI of 750. The findings support the development of predictive frameworks that can inform sustainable groundwater management, optimize remediation strategies, and address key challenges in the practical application of contaminant transport models.

How to cite: Roy, G. and Sivakumar, B.: Hydrogeochemical Forensics of Pyrite Oxidation in Unsaturated Mine Overburden: A Numerical Simulation Framework for Groundwater Contaminant Migration., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12747, https://doi.org/10.5194/egusphere-egu26-12747, 2026.

Quarry lakes, primarily located in alluvial plains, form as a result of the deepening of quarry excavations beyond the water table of the shallow aquifer.

They allow for full exploitation of the deposit without excessively damaging the landscape, limiting land consumption. Quarry lakes play also an important ecological and landscape role, because they provide (i) habitats for aquatic plants, aquatic animal species and birds, and (ii) recreational opportunities. Additionally, they contribute to management of water resources and mitigation of flood risks.

In the Piedmont region (NW Italy), quarry lakes are numerous and of considerable size due to the high market demand for concrete and aggregates. These quarry lakes are mostly located along the Po River, the main river of the region, and its main tributaries.

This study focused on six active quarry lakes and, primarily, a hydrogeological reconstruction of the surrounding areas was carried out. Lake water samples were collected in the summer and autumn of 2025 and analysed for hydrochemical composition. Field parameters, including pH, electrical conductivity, and water temperature, were also recorded.

The hydrochemical results, compared with data from the regional network of groundwater monitoring wells, reveal a strong correlation between lake waters, the surface aquifer, and watercourses. The chemical characterization of these quarry lakes supports the study of their  photochemical activity, and the assessment of their potential use as nature-based basins for quaternary treatment of water, thus allowing to minimize the overexploitation of groundwater resources in a context of more frequent drought events due to climate change.

How to cite: Pigozzi, G., Bianco Prevot, A., Biasio, L., Carena, L., Cocca, D., De Luca, D. A., Egidio, E., Lasagna, M., and Mittaridonna, A.: Hydrogeological and Hydrochemical Characterization of Quarry Lakes in the Piedmont Alluvial Plain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13428, https://doi.org/10.5194/egusphere-egu26-13428, 2026.

The nitrate input in the groundwater and surface water is the main source of contamination in many areas of the world. In Mexico, agricultural activities depending of groundwater and surface water. The groundwater flow system (GFS) is a single system with recharge and discharge zone were the interactions between surface and groundwater are present. In Mexico, the Cuitzeo GFS is a is one of the most agriculturally developed areas in central Mexico and includes the second and third largest lakes, lakes Cuitzeo and Patzcuaro.
The main object of this work is to analyze the nitrate behavior in groundwater and lake waters to understand the spatial changes over two years.
The methodology includes sampling of major ions of 39 sites, including wells, dugwells, springs, and lakes in the dry season for the years 2024 and 2025. Additionally, hydrogeochemical diagrams and spatial analysis were developed. The nitrate concentrations in this country are regulated by Mexican rules.
The results show that 11 sites exceed the permitted limit of concentrations according to these rules. Nitrates predominate in the zone of major population close to Morelia city and close to Lake Cuitzeo. Whereas ammonium is present close to the lake Patzcuaro. These distributions are in groundwater and surface waters, reflecting the same processes in both water bodies. This area presents a rapid expansion and intensification of berry and avocado cultivation, which have displaced local crops and driven unsustainable patterns of agricultural water use.
This study provided valuable information about the source and quantification of nitrate species contaminations, which can help to generate new management strategies.

How to cite: Llanos Solis, A. G., Olea Olea, S., Morales Casique, E., Zamora Martínez, O., Tadeo León, J., Goméz Vasconcelos, M. G., Avellán, D. R., and Ramírez Serrato, N.: Nitrate behavior in a groundwater flow system that discharges into the largest lakes of Mexico, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15246, https://doi.org/10.5194/egusphere-egu26-15246, 2026.

Groundwater is a critical water resource in Morocco, particularly in semi-arid regions where agricultural demand and local uses place increasing pressure on aquifers. In this setting, a clear and spatially explicit assessment of groundwater quality is essential to support monitoring strategies and contribute to more sustainable water management.

This study presents an approach for characterizing groundwater quality in a semi-arid area of Morocco based on physico-chemical analyses of groundwater samples collected from wells and springs. Water quality is identified through the computation of a groundwater quality index derived from multiple measured parameters, providing a synthetic and comparable metric across sampling points. The results are then integrated within a Geographic Information System (GIS) framework to explore spatial patterns and support interpretation at the territorial scale. Spatial interpolation is used to map the distribution of both the individual parameters and the quality index, highlighting local contrasts and potential hotspots within the study area.

Overall, the findings indicate generally satisfactory groundwater quality, while also revealing localized variations that justify targeted follow-up and site-specific attention. The proposed workflow is transferable and can be adapted to other semi-arid settings in Morocco to support diagnosis, prioritization of actions, and long-term, sustainable groundwater resource management.

How to cite: Mouncif, A., Nait-Taleb, O., Karroum, M., Krimissa, S., Namous, M., and Elaloui, A.: Groundwater quality assessment in semi-arid Morocco: spatial analysis and monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15319, https://doi.org/10.5194/egusphere-egu26-15319, 2026.

The subsurface architecture of paleochannel and floodplain deposits, as well as their hydrogeological significance, remains insufficiently characterized in the Ganges upper delta region. The present study evaluates the hydrogeological implications for groundwater resource assessment and aquifer vulnerability in Chakla, North 24 Parganas, West Bengal. Descriptions and discrimination of different subsurface regimes are provided based on electrical resistivity. Vertical Electrical Sounding (VES) surveys are conducted at 51 sites, distributed across paleochannel (n_p = 31) and floodplain (n_f = 20) geomorphic settings. Six-layer resistivity models are developed for each site through inversion analysis. Regime-specific hydrogeological properties are quantified through non-parametric statistical testing (Wilcoxon rank-sum and Kruskal-Wallis) on the modeled VES data. Furthermore, longitudinal conductance and transverse resistance, as obtained from the Dar-Zarrouk parameter analysis, are explored. VES inferences are found to be in well accordance with the borehole lithology from six cores. Paleochannel aquifers and floodplain aquitards exhibit significantly different resistivity distributions due to different grain sizes and saturation. Paleochannel sites reveal higher median resistivity (49.5 Ω·m) and coarser grain sizes, indicating high-capacity aquifers with enhanced investigation depth. On the other hand, floodplain sites are characterized by lower resistivity (19.2 Ω·m), finer grain sizes, and low-permeability confining layers. The findings of the present study support targeted groundwater exploration in paleochannel zones and aquifer protection in floodplain areas, providing key insights for water supply assessment and contaminant vulnerability.

Keywords: Vertical Electrical Sounding (VES), Paleochannel aquifer, Electrical resistivity, Dar-Zarrouk parameters, Hypothesis testing, Non-parametric statistics

How to cite: Dutta, A. D., Yadav, A. K., Mukherjee, A., and Sengupta, P.: Characterization of Paleochannel and Floodplain Aquifers Using Vertical Electrical Sounding: A Case Study from the Western Part of Bengal Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16051, https://doi.org/10.5194/egusphere-egu26-16051, 2026.

The hyporheic zone serves as a critical hotspot for nitrogen attenuation, driven by flow in streambed sediments, biogeochemical reactions, and enhanced microbial activity. It has, however, yet to be determined how the interaction of heterogeneity in sedimentary physical (e.g., permeability) and chemical (e.g., organic matter content) properties influences nitrogen cycling in complex hyporheic environments. Here we developed numerical models coupling porous flow, reactive transport, and microbial dynamics for realistic heterogeneous streambed scenarios. Simulations reveal that small-scale spatial variations in sediments physical and chemical properties exert negligible effects on nitrogen removal, whereas the spatial heterogeneity in functional microbial biomass dominates nitrogen removal dynamics. This is caused by biofilm-induced bioclogging that drastically reduces hyporheic exchange, thereby weakening the role of sedimentary heterogeneity. This study represents the first quantitative assessment of how sedimentary and microbial spatial heterogeneities jointly regulate nitrogen removal in hyporheic systems, offering critical insights for predictive modeling of bedform interfaces.

How to cite: Xian, Y., Xiao, Z., Wen, Z., and Krause, S.: Microbial Heterogeneity Outweighs Sediment Variability in Regulating Hyporheic Nitrogen Removal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16155, https://doi.org/10.5194/egusphere-egu26-16155, 2026.

Rapid groundwater depletion, driven by intensive pumping and growing climate variability, poses a critical threat to water security across India. Concurrently, climate change is intensifying the frequency and magnitude of flood events, generating episodic but potentially significant opportunities for natural aquifer replenishment. However, the contribution of floods to groundwater in India remains poorly quantified. In this study, we systematically quantify flood‐driven groundwater recharge across the major river basins of India. Using the integrated, physically based ParFlow-CLM hydrological model, we evaluate three fundamental attributes of flood recharge: (i) the contribution of flood runoff to total groundwater recharge, (ii) the temporal lag between flood peaks and aquifer response, and (iii) the persistence of flood‐induced recharge signals following an event. These metrics are evaluated across diverse hydrogeological settings to identify where floodwaters are most effectively captured and retained within aquifers. Our results show strong spatial contrasts in flood recharge efficiency. The highly permeable alluvial aquifers of the Indus, Ganga and Brahmaputra basins exhibit the highest flood-to-recharge contribution and the longest persistence, indicating a strong capacity to capture and retain floodwater. In contrast, less permeable and fractured hard-rock aquifers in large parts of central and southern India show weaker and shorter-lived recharge responses to floods. By explicitly linking flood dynamics to subsurface hydrologic response, this study provides a framework for identifying priority regions for flood‐based groundwater management. The results demonstrate how increasing flood extremes under climate change can be strategically harnessed to enhance the resilience of India’s groundwater resources.

How to cite: Roy, R. and Mishra, V.: Flood-Driven Groundwater Recharge for India , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16236, https://doi.org/10.5194/egusphere-egu26-16236, 2026.

Groundwater drought poses a growing threat to water security in India, as groundwater supplies support agriculture, ecosystems, and domestic use. Although meteorological and hydrological droughts and their propagation have been studied, the propagation of drought into groundwater systems in India has not been examined. In this study, we employed Standardized Precipitation Index (SPI), the CGWB-based Standardized Groundwater Index (SGI), and the GRACE-based Groundwater Storage Anomaly (GWSA) to investigate meteorological drought and groundwater drought across the Indian region. We estimated drought propagation duration, recovery duration, mean drought duration, and maximum drought duration. The results show that regions in the north, northwest, northeast, and a few regions in southern India have the longest propagation time from meteorological to groundwater drought, while other zones, such as central India, have relatively shorter propagation times. We also find that regions in northeast and northwest India recover faster from groundwater droughts than other regions. Our results also show that the Dryness Index (DI), Seasonality Index (SI), and Land Surface Controls (NDVI, soil moisture (SM), and Evapotranspiration (ET)) play a significant role in the propagation time of meteorological to groundwater droughts across different zones. Overall, understanding the propagation and recovery plays a vital role in aiding effective management and planning of groundwater resources in India.

How to cite: Aayush, A. and Mishra, V.: Propagation of Meteorological Drought to Groundwater Drought in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16340, https://doi.org/10.5194/egusphere-egu26-16340, 2026.

Hydraulic redistribution (HR), the passive movement of water through plant root systems from wet to dry soil layers, plays a critical role in maintaining plant water status and nutrient uptake in water-limited environments. While HR is well-documented under drought, its dynamics become significantly more complex in saline conditions where total soil water potential is driven by both matric and osmotic components.

In this study, we employed a split-root experimental design using young avocado trees to isolate and quantify HR. The root system of each tree was divided between two pots: a "wet pot" maintained at field capacity and a "drying pot" where irrigation was withheld. We utilized high-precision weighing lysimeters to monitor nocturnal weight changes in the drying pot, alongside soil moisture sensors and isotopic water labelling to track water movement.

Our preliminary results confirm the occurrence of HR in young avocado trees under non-saline control conditions. The phenomenon was clearly identified in two out of three trees monitored during the initial experimental phase, as evidenced by nocturnal increases in soil water content and corresponding weight changes in the drying pots. These findings provide a foundational baseline for the next phase of the research, which aims to evaluate how increasing levels of salt stress (NaCl) in the wet pot influence the osmotic gradients and root hydraulic conductivity that drive HR. By comparing control and saline treatments, we seek to determine whether salinity-induced changes in total water potential suppress or shift the patterns of hydraulic redistribution.

How to cite: Hochman, D. and Schwartz, N.: Understanding the drivers of hydraulic redistribution under salt stress, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17910, https://doi.org/10.5194/egusphere-egu26-17910, 2026.

Soil Aquifer Treatment (SAT) relies on biogeochemical processes occurring within the vadose zone to improve the quality of secondary treated wastewater during infiltration. Dissolved organic matter (DOM) is a key driver of these processes; however, its depth-dependent transformation within the soil profile remains insufficiently resolved at the molecular level under field conditions.

In this study, we investigated the vertical evolution of fluorescent dissolved organic matter (fDOM) within the soil profile of a full-scale SAT infiltration basin. Soil samples were collected from successive depths along the vadose zone, representing progressive stages of soil–water interaction during infiltration. DOM composition was characterized using excitation–emission matrix (EEM) fluorescence spectroscopy with inner-filter correction and Raman normalization. Fluorescence data were analysed using Coble peak integration and Parallel Factor Analysis (PARAFAC) to resolve independent fluorescent components and assess their depth-dependent behaviour.

The results reveal pronounced vertical stratification of DOM composition within the soil profile. Shallow soil layers are dominated by protein-like fluorescence associated with labile, wastewater-derived organic matter. With increasing depth, these protein-like signals show strong attenuation, while humic-like fluorescence becomes increasingly dominant. Coble peak analysis indicates preferential removal of tryptophan- and tyrosine-like peaks (B and T), whereas humic-like peaks (A, C, and M) persist at depth. PARAFAC modelling further identifies distinct fluorescent components exhibiting contrasting depth trends, with protein-like components rapidly decreasing in intensity and humic-like components remaining relatively stable or proportionally enriched.

These findings demonstrate that SAT acts as a selective biogeochemical filter within the soil profile, where biodegradation and sorption processes preferentially remove reactive DOM fractions in the upper vadose zone while more refractory humic material persists at depth. The combined use of EEM–PARAFAC provides mechanistic insight into DOM transformation pathways during soil aquifer treatment and highlights the importance of depth-resolved fluorescence analysis for improving process-based understanding of SAT performance.

How to cite: Adler‬‏, O.: Vertical transformation of fluorescent dissolved organic matter within the soil profile of a soil aquifer treatment basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19818, https://doi.org/10.5194/egusphere-egu26-19818, 2026.

The Brahmaputra River Basin is among the most flood-prone regions globally, experiencing recurrent large floods with severe socio-economic and ecological impacts. Despite extensive flood management interventions, forecasting skill remains limited due to the basin’s complex hydrology, strong monsoon variability, and pronounced land–atmosphere interactions. This study investigates the drivers and dynamics of large floods in the Brahmaputra Basin, with a particular emphasis on coupled land–atmosphere processes. We conduct a composite analysis of major flood events using reanalysis datasets, satellite observations, and hydrological records. Our results show that large floods are consistently associated with anomalously high atmospheric moisture content, extreme and spatially extensive precipitation, and elevated antecedent soil moisture that amplifies runoff generation. The concurrence of saturated catchments with persistent multiday monsoon rainfall leads to rapid escalation of flood magnitude and prolonged flood duration. In addition, enhanced moisture transport into the basin emerges as a critical contributor to the development of large flood events. By integrating these insights into coupled land–atmosphere modeling frameworks, we demonstrate that improved representation of soil moisture dynamics, rainfall persistence, and moisture transport pathways can substantially enhance flood predictability. This work advances the understanding of flood-generating mechanisms in monsoon-dominated river basins and provides actionable insights for improving early warning systems and adaptive flood risk management in the Brahmaputra Basin.

How to cite: Vangala, G. and Mishra, V.: Controls and Predictability of Large Floods in the Brahmaputra River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20263, https://doi.org/10.5194/egusphere-egu26-20263, 2026.

Reliable baseflow estimation plays a crucial role in water resource management across India's monsoon-dominated landscapes, where groundwater contributions sustain river flows through extended dry periods. This study addresses persistent data limitations in Central and Southern India by first compiling daily streamflow records from 4,862 basins sourced from the India Water Resources Information System (IWRIS), followed by rigorous pre-processing and quality control steps that yielded suitable data for analysis across hundreds of representative basins. A comprehensive evaluation of 12 baseflow separation methods was then conducted using Kling-Gupta Efficiency (KGE) against hydrologically verified baseflow benchmarks, revealing digital filter techniques, particularly the Eckhardt filter (median KGE of 0.88) as superior to conventional graphical methods across diverse hydrological regimes. These findings affirm digital filters' reliability for capturing baseflow variability in monsoon recharge areas and arid inland zones, laying a strong foundation for advanced hydrological modeling in data-constrained environments.

Keywords: Baseflow Separation, Digital Methods

How to cite: Samyuktha, P., Dubey, S., and Gautam, S.: Comprehensive Evaluation of Baseflow Separation Methods for Peninsular India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21596, https://doi.org/10.5194/egusphere-egu26-21596, 2026.

Abstract

Reliable hydrological modelling tools that can operate with the type and quality of data commonly available at the basin scale are essential for effective water resources planning. In recent years, the HIDROTURK model has been developed to support national hydrological assessments in Türkiye, especially in modelling tasks undertaken as part of river basin management planning processes. This study presents one of the first comprehensive evaluations of the newly updated HIDROTURK Phase II model and compares its performance with the AQUATOOL + EVALHID hydrological modelling system. The North Marmara River Basin was selected as the test region due to its complex hydrological structure and diverse sub-basin characteristics.

Hydrological simulations were carried out using long-term meteorological inputs derived from precipitation and evapotranspiration records for the period 1989–2014, enabling the examination of the models under a wide range of climatic conditions. Streamflow outputs were compared with observations at 14 calibration points, and model performance was assessed using the Nash–Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) indicators.

The results indicate that the meteorological inputs generated through HIDROTURK’s internal processing tools show a high level of agreement with data prepared using more traditional methods, and that both models produced comparable flow patterns under similar conditions. Overall, the findings demonstrate that HIDROTURK Phase II exhibits stable behavior even at this early stage of development and provides a practical and reliable alternative for hydrological simulations.

Keywords: Hydrological Modelling; Basin-Scale Simulation; Model Comparison; Model Performance Evaluation; Streamflow Calibration

ACKNOWLEDGEMENT: The authors would like to express their gratitude to the projects “Technical Assistance on Preparation of River Basin Management Plans for Six Basins (EuropeAid/140294/IH/SER/TR)” and “Development and Sustainability of the HIDROTURK Model Project” for their support. The authors also thank the Directorate General for Water Management, the State Hydraulic Works, and the General Directorate of Meteorology for providing essential data.

How to cite: Kacikoc, M., Mesta, B., Fistikoglu, O., Ozkaya, H., and Ozdemir-Calli, K.: Comparative Evaluation of the Newly Developed HIDROTURK-Phase II Hydrological Model in the North Marmara River Basin, Türkiye, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1436, https://doi.org/10.5194/egusphere-egu26-1436, 2026.

In the context of the global hydrological cycle, runoff generated from snowmelt plays a key role in water availability, particularly in cold and mountainous regions. In many parts of Canada and the western United States, mountain snowpacks act as natural reservoirs by storing precipitation during cold seasons and releasing it during spring and summer. Accurate estimation of snow water equivalent (SWE) is therefore essential for hydropower reservoir operation, snow-related hazard assessment, and hydrological modeling. However, the coarse spatial resolution of widely available SWE products in northern latitudes, combined with complex mountain topography, introduces substantial uncertainty in their direct application to hydrological models. High-resolution SWE mapping remains a major challenge in these environments. In this study, we propose a multifactor SWE downscaling framework based on a Long Short-Term Memory (LSTM) deep learning approach, applied to the Nechako River watershed in British Columbia, Canada. The framework uses ERA5-Land SWE at 10 km resolution as the target variable, with predictor variables including precipitation, minimum and maximum temperature, solar radiation, and 2-m dewpoint temperature, together with static physiographic information such as elevation and land cover. Daily data from 1981 to 2024 are considered. The model is trained and evaluated at the 10 km resolution before being applied to generate SWE at 5 km resolution, corresponding to the spatial resolution of the CEQUEAU hydrological model. The downscaled SWE fields are designed to retain the large-scale snow patterns provided by ERA5-Land, while adding more spatial detail based on local elevation and land cover. Current work focuses on incorporating these downscaled SWE estimates into the CEQUEAU hydrological model and comparing the resulting runoff simulations with those obtained using CEQUEAU’s internal SWE representation. Rather than aiming to demonstrate clear improvements at this stage, the goal is to better understand how different SWE inputs influence the simulated hydrological response. Preliminary results suggest that LSTM-based downscaling offers a flexible and promising way to generate intermediate-resolution SWE fields in mountainous regions. This approach shows potential as a practical link between coarse-resolution reanalysis products and distributed hydrological models used for water resources and hydropower studies.

How to cite: moghadam, S. H., Arsenault, R., St-Hilaire, A., and Talbot, F.: Evaluating a Long Short-Term Memory (LSTM) approach for Snow Water Equivalent (SWE) downscaling and hydrological modeling in mountainous terrain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2612, https://doi.org/10.5194/egusphere-egu26-2612, 2026.

The complex orography of the Tibetan plateau (TP) and the scarcity and uneven spatial distribution of meteorological stations 
present significant challenges in accurately estimating meteorological variables for hydrological simulations. This study aims 
to enhance the accuracy of daily precipitation and temperature interpolation for hydrological simulations in the Lhasa River 
Basin (LRB), particularly during flood events. We evaluate and compare the performance of deterministic Inverse Distance 
Weighting—IDW and geostatistical (Ordinary Kriging—OK and Kriging with External Drift—KED) interpolation methods for 
estimating precipitation and temperature patterns. Subsequently, we investigate the influence of different interpolation meth
ods on hydrological simulations by using the interpolated meteorological data as input for the Water Balance Simulation Model 
(WaSiM) to simulate daily discharge in the LRB. Our results revealed that geostatistical methods, specifically OK and KED, are 
more effective in capturing the spatial variability and anisotropy inherent in precipitation patterns influenced by the Indian 
summer monsoons. In addition, the KED method effectively captured the daily variation of the temperature lapse rate, indicating 
the inadequacy of using a constant lapse rate for hydrological modelling in high- elevation regions like the TP. The geostatistical 
technique outperformed the Deterministic method, with KED realising the best temperature and precipitation interpolation 
performance based on cross- validation results. However, although KED provides superior results based on cross- validation per
formance, applying its precipitation interpolation as input into WaSiM led to the poorest discharge simulation. The combination 
of OK for precipitation and KED for temperature produced the most accurate discharge simulations in the LRB, highlighting 
the importance of not solely relying on cross- validation results but also considering the practical implications of interpolation 
methods on hydrological model outputs. Our study offers a robust framework for improving flood simulations and water resource 
management in a data- scarce, high- elevation region like the TP.

How to cite: guede, K. G., Yu, Z., and Hofmeister, F.: Geostatistical Interpolation Approach for Improving Flood Simulation Within a Data- Scarce Region in the Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4141, https://doi.org/10.5194/egusphere-egu26-4141, 2026.

A numerical investigation is conducted to study the steady-state concentration field
when a solute is released from multiple continuous line sources in an ice-covered
channel with an absorbing bed under turbulent flow conditions. The governing
equations are solved using the Crank-Nicolson scheme by adopting a two-power law
velocity and a quartic eddy diffusivity profile, which is influenced by the roughness of
the bed layer and the ice cover. Validation against earlier numerical results for a
specific case reveals strong consistency in the concentration profiles. The findings
highlight how the roughness of the boundaries affects the solute concentration. It
further demonstrates the effect of the bed absorption parameter in the early mixing
stages when solute is released near the bed. For zero bed absorption, the solute
concentration asymptotically attains a uniform far-field value of unity, while any non-
zero bed absorption leads to complete depletion of solute downstream.

How to cite: Paul, S. and Ghoshal, K.: Solute dispersion from continuous point sources in ice-covered turbulent flows with bed absorption, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5160, https://doi.org/10.5194/egusphere-egu26-5160, 2026.

Water balance modeling plays a pivotal role in sustainable water management, as it underpins the understanding of hydrological processes that govern resource distribution, ecosystem stability, and long-term environmental planning. Accurate and efficient computational tools are essential to capture the spatial and temporal dynamics of water balance, particularly in complex geological and urban environments. WaterpyBal is an innovative modeling framework specifically designed to construct spatial-temporal water balance models. It effectively integrates multiple stages of hydrological assessment-including data interpolation, evapotranspiration estimation, and infiltration computation-while accounting for soil heterogeneity and components of the urban water cycle. The tool demonstrates robust performance when applied to both synthetic and experimental datasets, providing reliable and scalable results.

In the context of the exascale era, where data-intensive environmental models demand unprecedented computational power, High-Performance Computing (HPC) frameworks are essential to ensure scalability and efficiency. To this end, WaterpyBal has been enhanced through its integration with PyCOMPSs, the Python binding of the COMPSs programming model. PyCOMPSs enables the transparent parallelization of Python applications by identifying task-level parallelism through annotated methods and dynamically constructing a task-dependency graph during runtime. This graph-driven execution model allows efficient scheduling and data management across distributed computing infrastructures such as clusters and cloud platforms.

The integration of WaterpyBal with PyCOMPSs significantly improves its computational performance, enabling the simulation of large-scale, high-resolution water balance models within feasible timeframes. This work demonstrates the potential of combining advanced hydrological modeling with state-of-the-art parallel computing frameworks to address emerging challenges in environmental modeling and resource management at scale.

How to cite: Castillo Reyes, O., Li, J., Hassanzadeh, A., and Vázquez-Suñé, E.: High-performance task-based water balance modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5899, https://doi.org/10.5194/egusphere-egu26-5899, 2026.

The intensification of hydrological cycle driven by global warming leads to an increase in extreme precipitation events, prolonged droughts and higher rates of evaporation. This global change has altered the global hydrological cycle and effect on the long term water availability in many watersheds worldwide. The impact of climate change is usually assessed by using ratio of stream flows and climate variables, which are formally defined as the climate elasticity of water availability This study examines how the hydrological cycle is intensifying over the Kabul Watershed and how this affects water availability using the water balance (P–E) approch.  Here, we used ERA5 land data of annual total precipitation (P) and total surface evaporation (E) from 1976 to 2024 to understand how the land and atmosphere interacted.  The climate elasticity of (P-E) to annual water availability is determined for 1976-2010 and validated for 2011-2024. The results reveal 0.8 °C rise in temperature, 12% decline in annual precipitation, and  7% increase in evaporation in the past 25 years. This caused 15% reduction in the P–E balance, which directly reduced the annual water availability. The climate elasticity factor of 0.55 has been determined to water availability in Kabul for the period of 1976-2010. By using this elastic factor, the average water availability of 19.54 MAF is predicted for the period of 2011-2024 whereas the observed water availability is 20.43 MAF. This finding reflects the sensitivity of a watershed to P-E alteration for water availability and underscore the urgent need of climate resilient water management strategies to mitigate the future impacts of climate change in the Kabul watershed.

How to cite: Kosar, T., Rahim, A., Yaseen, M., Naz, R., Mamoor, M., and Akif, A.: Climate Driven Hydrological Intensification and Its Implications for Water Availability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6269, https://doi.org/10.5194/egusphere-egu26-6269, 2026.

The Critical Zone, extending from the land surface through the vadose zone to groundwater, can store and transfer substantial carbon as CO₂ and dissolved inorganic carbon (DIC). Yet CO₂ behavior below the first meters of soil remains poorly constrained, particularly where water-table fluctuations, gas-water exchange, and water-rock reactions interact. In these settings, deep vadose CO₂ may exhibit atmospheric and soil-respiration signatures with contributions linked to groundwater degassing and carbonate-system reactions, potentially creating transient subsurface CO₂ reservoirs that couple the aquifer and the atmosphere.

We present a repeated sampling design to characterize carbon cycling across the Critical Zone in semi-arid southeastern Spain. We sampled the air columns of 11 boreholes belonging to six groundwater bodies during four campaigns (spring 2022, autumn-winter 2022, spring-summer 2024, and spring-summer 2025). In borehole air, we measured CO₂, H₂O vapor, and its carbon isotopes composition (δ¹³C-CO₂); air was stored in gas-tight bags and analyzed by cavity ring-down spectroscopy (Picarro G2508 and G2201-i). In parallel, groundwater was sampled at each site. In situ, we measured pH, temperature, oxidation–reduction potential (ORP), HCO₃-, and electrical conductivity. In the laboratory we analyzed pH, alkalinity, major ions, total organic carbon and total nitrogen, carbon isotopes of dissolved inorganic carbon (δ¹³C-DIC), and water isotopes (δ²H, δ¹⁸O). Water-table position at the time of sampling was used to interpret gas-water contact.

Critical Zone CO₂ concentrations in borehole air ranged from 614 to 128700 ppm (pCO₂=0.000587-0.102287 atm). Groundwater CO₂ was estimated with the PHREEQC software, yielding values between 2240 and 9550 ppm (pCO₂=0.002240-0.009550 atm), allowing comparison between the air column and the saturated zone, and evaluation of disequilibrium and exchange potential as the water-table varies. Carbon isotopes signatures constrain sources and transformations: δ¹³C-CO₂ ranged from -11.14 to -23.62‰, δ¹³C-DIC from -6.27 to -20.11‰, and host-rock δ¹³C from 2.37 to -7.12‰. All values (δ¹³C‰) are reported relative to VPDB (Vienna Pee Dee Belemnite). Joint interpretation across gas, DIC, and rock enabled discrimination among biogenic CO₂ production, atmospheric mixing, carbonate dissolution/precipitation (based on the saturation indices of the main carbonate mineral phases), and CO₂ transfer from the aquifer to the deep vadose zone. The multi-campaign design provided a basis for quantifying seasonal and interannual shifts in these boreholes and for identifying hydrogeochemical conditions (e.g., pH-alkalinity evolution and redox state) that promote storage/mineralization versus release of CO₂.

Our experimental design characterizes subsurface CO₂ storage and transport at the Critical Zone scale. It identifies when the deep vadose environments act as reservoirs, conduits, or sources linking groundwater and the atmosphere. This information is rarely available but critical for improving carbon budgets and models for the Critical Zone.

This work was supported by the Spanish Ministry of Science and Innovation (projects PID2024-158786NB-C21 and PID2024-158786NB-C22, NATURAL), the University of Granada (project PPJIB2024-53), and the Regional Ministry of University, Research and Innovation, the Spanish Government and the and European Union – NextGenerationEU (projects BIOD22_001 and PCBIO).

How to cite: Echeverría-Martín, E., Fernández-Cortés, Á., Sánchez-Cañete, E. P., Serrano-Ortiz, P., Oyonarte, C., Riba Palou, A., Kowalski, A. S., and Domingo, F.: CO2 Dynamics and Carbon Sources in the Critical Zone: An Isotopic Study in Aquifers of Southeastern Spain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7262, https://doi.org/10.5194/egusphere-egu26-7262, 2026.

Hydroclimatic extremes have extensive social, economic and ecological impacts, thereby making it highly imperative to develop disaster assessment and mitigation strategies. The frequency of extreme events like heatwaves and droughts is  intricately linked with the rising temperature trends and the changing precipitation patterns worldwide. Moreover, lagged responses amongst such extremes can occur across temporal scales due to the existing large-scale climate linkages. However, the association between present-day occurrences of concurrent high temperature and low precipitation days (HTLPs) with the frequency of heatwaves and droughts of a subsequent period is not fully explored. In this global analysis, we estimate the frequency of heatwaves and droughts based on 1-year temporally lagged HTLPs. Our results reveal a significant rising trend in the average number of heatwaves with an increase in the number of HTLPs of the previous year, while no significant trend is observed for droughts. However, a high number of HTLPs (over 100 events) is associated with a slight reduction in the number of heatwaves (5.9 to 5.6) but a pronounced increase in the number of droughts (1.8 to 2.4). During a 10-year validation period, 81% of heatwaves and 85% of droughts globally remain consistent with the HTLP–conditioned behavior inferred from the 34-year training period of the model. Our findings thus demonstrate the applicability and effectiveness of HTLPs in predicting heatwaves and droughts. This study can be used to develop stochastic models to predict heatwaves and droughts with HTLP as a predictor, and hazard-specific probabilistic assessments that can support and improve resource allocation at regional and global scales.

How to cite: Sinha, D. and Rajulapati, C.: The role of High Temperature-Low Precipitation conditions in shaping heatwaves and droughts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8393, https://doi.org/10.5194/egusphere-egu26-8393, 2026.

The intensification of the global hydrological cycle is a well-established consequence of anthropogenic climate change. However, how this intensification manifests across the diurnal cycle remains poorly understood, representing a critical blind spot in climate risk assessments. While daily-aggregated metrics consistently suggest a "wetter and more extreme" climate, they mask fundamentally different responses of daytime and nighttime precipitation to warming.Here we analyse high-resolution observational records from 2,399 stations across China spanning 1972–2024 and identify a distinct nighttime intensification regime that is increasingly dominant under warming. In regions experiencing active wetting, extreme precipitation (R95p) intensifies more rapidly at night than during the day, both in magnitude and spatial extent.This diurnal asymmetry reflects contrasting physical controls. Nighttime wetting is driven almost exclusively by increases in precipitation intensity (p < 0.001, Wilcoxon signed-rank test) and exhibits a tight thermodynamic scaling with background warming. By contrast, daytime precipitation changes arise from a heterogeneous combination of intensity and frequency adjustments, indicating a greater role for dynamical modulation.

These findings reveal a previously underappreciated amplification of nocturnal hydrometeorological hazards, including flash floods and landslides, that is systematically underestimated by daily-mean indicators. As global warming continues, the emerging dominance of nighttime precipitation extremes underscores the urgent need to incorporate diurnally resolved processes into climate risk assessment, infrastructure design and early-warning systems.

How to cite: Meng, J. and Xia, H.: Asymmetric intensification of nighttime versus daytime precipitation extremes under warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9421, https://doi.org/10.5194/egusphere-egu26-9421, 2026.

Surface-groundwater interactions (SGI) plays a crucial role in maintaining stream thermal regime and ecological balance, keeping a check on this is logistically and financially challenging. This study utilises multi-temporal Landsat-8 Thermal Infrared Sensor (TIRS) data to compute Stream Surface Temperature (SST), its anomaly (SSTA), Robust Thermal Deviation Index (R-TDI) and further classifies groundwater influence on the Kharun River, a semi-arid urban catchment in India (approximately 4109 km²).

Due to changes in weather and season, surface water is subject to heating and cooling, but the water system beneath the land surface will be at a constant temperature. The stream reach, influenced by groundwater, will show a relatively stable thermal signature across all seasons. Stream Surface Temperature (SST) derived through radiometric calibration and emissivity-adjusted retrieval across pre-monsoon, monsoon, and post-monsoon periods. To isolate localized hydrological processes from regional climatic forcing, we computed Stream Surface Temperature Anomalies (SSTA) by subtracting reach-wise median SST from pixel-scale values. To account for the non-normal nature of SST, a Robust Thermal Deviation Index (R-TDI) framework was utilised which minimizes atmospheric noise and mixed-pixel interference, allowing for the isolation of persistent thermal signals.

Using statistically defined TDI thresholds, a classification approach was finalised putting stream stretches into high, moderate, and low groundwater influence zones. Results identify spatially consistent cold-water anomalies indicative of groundwater discharge primarily during pre-monsoon and warmer-water anomalies during post-monsoon seasons when thermal contrasts are most pronounced.  These zones coincide with structurally controlled segments and urbanized stretches, suggesting a complex interplay between hydrogeology and anthropogenic modifications. By leveraging open-access satellite data, this research provides a scalable tool for evidence-based river restoration and climate-resilient water management in rapidly urbanizing regions.

Key Words: Thermal remote sensing; Landsat-8 TIRS; Stream surface temperature; Thermal anomaly; Surface–groundwater interaction; Data-scarce catchments

How to cite: Chandel, R. and K. Singh, C.: Thermal Remote Sensing for Qualitative Analysis of Surface Water and Groundwater Interaction: A Case Study of the Kharun River, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9498, https://doi.org/10.5194/egusphere-egu26-9498, 2026.

Understanding catchment behaviour is essential for effective water resources planning and sustainable watershed management. Traditional model evaluation approaches based solely on time-series performance metrics often fail to capture the full spectrum of hydrological functioning. This study employs a hydrological signature–based evaluation framework to assess the capability of the Soil and Water Assessment Tool (SWAT) model in reproducing the dominant hydrological processes in the Bharathapuzha River Basin, a monsoon-dominated river system in southern India. The SWAT model was implemented using spatial datasets of topography, land use, and soil characteristics, together with long-term hydro-meteorological inputs, and calibrated and validated against observed daily streamflow. Beyond conventional performance indices, key hydrological signatures including flow duration curves, runoff ratio, baseflow index, seasonal flow patterns, and characteristics of low- and high-flow events were extracted from both observed and simulated datasets. Comparison of observed and simulated signatures provided a process-oriented evaluation of model behaviour, offering key insights into how well runoff generation, seasonal variability, and hydrological extremes are represented. These perspectives are not readily evident from traditional model performance metrics alone. This study demonstrates the value of hydrological signatures as diagnostic tools for enhancing model realism and improving confidence in hydrological simulations for climate impact assessment and water resources management in monsoon-driven catchments.

How to cite: Krishnan Sreelatha, G., Anupama Rajith, A., Sreekumar, A., Girish, G., and Reghunath, G.: Signature-Based Evaluation of Hydrological Processes Using the SWAT Model in the Bharathapuzha River Basin, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9647, https://doi.org/10.5194/egusphere-egu26-9647, 2026.

Global water security can be achieved by the systematic assessment of available water resources in both large and small river basins. This study investigated the stable isotopic composition of river water in the Yamuna basin, India to fingerprint the major contributing sources of water and their spatial variability along the main river channel. The Yamuna river originates at an altitude of about 6300 m asl in Yamunotri glacier near Bandarpunch, Uttarakhand Himalayas and flows through several states of India like Haryana, Punjab, Madhya Pradesh, Rajasthan and Uttar Pradesh. In this study, the Yamuna river water samples have been collected along main channel from Yamunotri to its confluence with Ganga river during pre-monsoon and post-monsoon seasons of the year 2024. The measured stable isotope ratios of oxygen (δ¹⁸O) and hydrogen (δ²H) in river water are in the range of -2.7 – -11.2 ‰ and -23.4 – -75.2 ‰ respectively for the sampling period. This study reports for the first time that there is a significant spatial variability in the source water of Yamuna river as fingerprinted by the stable isotopic composition. The Yamuna river at upper reaches receives water from sources that are depleted in heavier isotopic content mainly from glacial melt. The higher amount of water diversion to canal networks at different stages as well as water mixing from industrial and urbanized regions have led to relative water degradation of Yamuna river in middle reaches. The downstream isotopic composition reflects possible interaction with groundwater, higher water influx from Peninsular tributaries, and evaporation effect. Seasonality in source water contribution to Yamuna river discharge along the entire stretch has also been traced using stable isotopic composition of water.

How to cite: Tripti, M., Khobragade, S. D., and Rao, S. M.: Stable isotopic fingerprinting of hydrological variability along the Yamuna river, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10986, https://doi.org/10.5194/egusphere-egu26-10986, 2026.

Over the past decade, droughts have drawn increasing attention due to their substantial agricultural and economic consequences, particularly in the U.S. Great Plains area (e.g., the 2012 Central US event and the 2017 Northern Plains event). Although certain large-scale atmospheric and oceanic patterns are necessary for drought development, land- atmosphere interactions can play an important role in the intensification of drought conditions, especially for flash drought. This study aims to predict drought conditions over the U.S. Great Plains at 1-3-week lead times using a convolutional neural network (CNN) model. To forecast drought categories derived from the US Drought Monitor (USDM), the models are trained using multi-source atmospheric and land-surface variables, including 500 hPa geopotential height, precipitation, wind speed, surface radiation, humidity, and temperature from ERA5, soil moisture from Global Land Evaporation Amsterdam Model (GLEAM) and North American Land Data Assimilation System (NLDAS), and Normalized Difference Vegetation Index (NDVI) from satellite products. Model performance is evaluated to unravel the atmospheric and land-surface processes that drive droughts at different lead times and identify their relative contributions to drought development and intensification.

How to cite: Rippeteau, L. and Chen, L.: Drought prediction and understanding the drivers of drought development using a machine learning approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12345, https://doi.org/10.5194/egusphere-egu26-12345, 2026.

Extreme hydrological events have become increasingly frequent in Ukraine in recent decades due to climate change and structural weaknesses in water resources management. According to the Water Strategy of Ukraine up to 2050, inadequate governance practices remain a major source of anthropogenic pressure on water bodies, while climate change creates additional risks through prolonged droughts interrupted by intense rainfall events, leading to flooding. These challenges are particularly critical for southern Ukraine, where limited water resources require extensive hydrotechnical regulation and adaptive management.

Flash floods represent one of the most dangerous manifestations of hydrological extremes. Characterised by rapid water-level rise and high flow velocities, they pose severe risks to settlements, infrastructure, and agriculture due to their sudden onset.

The north-western part of the Black Sea region has experienced several severe flash flood events over the past decade. One of the most significant cases occurred in September 2013 in the Kogilnyk River basin., when anomalously high precipitation totals of 41 - 270 mm were recorded from 10 and 14 September. These extreme rainfall conditions were associated with a stationary cold atmospheric front linked to the Asia Minor depression, resulting in prolonged convective rainfall with thunderstorms, squalls and wind gusts of up to 22 m/s in the southern districts of the Odesa region.

The total volume of storm rainfall during this event is estimated at approximately 250 million cubic meters, which exceeded the mean annual runoff of the Kogilnyk River by a factor of 5.5. Precipitation affected an area of about 1,400 km², corresponding to 35% of the total river basin area. As a result, flash flooding impacted multiple settlements, located in the south-western part of Odesa Oblast as well as extensive agricultural lands in there.

Another notable episode occurred in early August 2019, when unstable atmospheric conditions and active cyclones caused intense rainfall across southern and eastern Ukraine. On 3 - 4 August, precipitation amounts reached 130–220% of the monthly norm in several locations. In the Odesa region, rainfall totals of up to 126 mm - equivalent to nearly three months of precipitation—met the criteria for hazardous meteorological phenomena and triggered debris flows and localized flash flooding, particularly in the village of Moloha (Bilhorod-Dnistrovskyi district).

More recently, in September 2025, an urban flash flood in Odesa highlighted the increasing vulnerability of urban areas to extreme rainfall. Prolonged heavy rains caused widespread flooding, significant damage, and human losses, prompting large-scale rescue operations..

The analysed events indicate a clear increase in flash flood intensity and impacts in the north-western Black Sea region. Under continued climate change, enhanced hydrological monitoring, early warning systems, climate-adaptive urban planning, and integrated water resources management are urgently required in southern Ukraine.

ACKNOWLEDGEMENTS

This contribution builds on the conceptual framework of the applied research project “Sustainable Development of Water Resources Management and Modelling in the North-Western Black Sea Region under Conditions of Increasing Climate Extremes and Anthropogenic Pressure”, approved for funding by the Ministry of Education and Science of Ukraine (Order No. 23, 9 January 2026, see https://surl.li/omqxph).

How to cite: Ovcharuk, V. and Khomenko, I.: Flash Flood Events in the Northwestern Black Sea Region under Climate Change , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14233, https://doi.org/10.5194/egusphere-egu26-14233, 2026.

Although drought indices can be evaluated employing linear and non-linear algorithms, most contributions in the literature have not adequately quantified geospatial-temporal volatility, leading to Type II errors. This study addresses these gaps by comparing ten drought indices across the Colorado and Louisiana regions of the United States over 75 years, examining non-linear and spatio-temporal patterns to ensure a robust assessment of drought. High-resolution European Centre for Medium-Range Weather Forecasts (ECMWF) gridded monthly total precipitation data for 75 years (1950-2024) were used to evaluate the drought indices. The spatial clustering of precipitation patterns was quantified using the second-order semi-parametric eigen-decomposition geospatial autocorrelation to geolocate hot and cold spots of precipitation. We employed the Autoregressive Integrated Moving Average (ARIMA) model, coupled with the Generalized Autoregressive Conditional Heteroscedastic (GARCH) model, and compared five ARIMA-GARCH variants across nine error distributions to address non-asymptotic conditional volatility and temporal persistence in precipitation. Drought indices were examined across five temporal scales and contrasted with simulated parameters derived from the Community Earth System Model (CESM). The temporal lag relationship between meteorological and agricultural droughts was evaluated using the non-parametric Time-Varying Distance Cross-Correlation Function (TV-DCCF). The findings revealed that the ARIMA-eGARCH(1,1) model with a Student’s t distribution precisely detected the non-asymptotic conditional volatility in the precipitation time series. The Standardized Precipitation Index (SPI), China Z Index (CZI), and Z-Score Index (ZSI) were the most applicable indices for drought monitoring in both regions. TV-DCCF revealed that meteorological droughts significantly influenced agricultural droughts, with a lag of up to four months. CESM-derived drought indices were mainly within the ERA5-Land uncertainty range, except for CZI and aSPI, attributable to CESM’s lower spatial resolution and limited sensitivity to localized extreme events.

Keywords: Standardized Precipitation Index (SPI); Global Moran’s Index; Autoregressive Moving Integrated Average (ARIMA); Generalized Autoregressive Conditional Heteroscedastic Model (GARCH); ERA5-Land; Community Earth System Model (CESM).

How to cite: Choudhari, N., Elshorbany, Y., Jacob, B., and Collins, J.: Prognosticative De-Volatility Modeling for Empirically Quantifying CESM and ECMWF Space-Time Heterogeneity of Drought Indices Across Colorado and Louisiana Regions of the USA, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14649, https://doi.org/10.5194/egusphere-egu26-14649, 2026.

The application of isotopic tracers provides a powerful means to unravel complex hydrological systems, including groundwater (GW)-surface water (SW) connectivity. This study investigates the interacting hydrological and geochemical processes within a temperate volcanic lake basin in west-central Mexico, with the objective of assessing hydrogeological connectivity between groundwater and the lacustrine system. Spatially distributed sampling was conducted for major ions, nitrate, strontium, and stable water isotopes (δ¹⁸O and δ²H) across multiple water sources, including precipitation, rivers, lakes, wells, and springs.

Results indicate that direct infiltration of precipitation constitutes the dominant groundwater recharge mechanism in high-elevation, forested zones, where waters exhibit a Ca–Mg–HCO₃⁻ hydrochemical facies. Mixing with deeper groundwater components is also evident, as reflected by elevated temperatures and isotopic compositions indicative of enhanced water-rock interaction. Surface waters, particularly lakes, display pronounced evaporative enrichment, while elevated nitrate concentrations in shallow groundwater point to anthropogenic inputs associated with irrigation return flows and urban activities.

Although sampling was conducted during the dry season and therefore may not capture the full range of annual hydrological variability, the identification of local and regional recharge zones provides a robust framework for future investigations of precipitation-driven recharge and GW-SW interactions. Additionally, strontium concentrations proved effective for tracing subsurface flow paths and fluid exchange along fault-controlled structures, offering valuable insights into hydrogeological processes in tectonically active volcanic settings. The integrated use of hydrochemical and isotopic tracers highlights their critical role in supporting sustainable water-resource management and protecting groundwater quality in complex temperate, semi-arid lake systems increasingly impacted by anthropogenic pressures.

How to cite: Ramírez González, L., Olea Olea, S., Sánchez Murillo, R., Villanueva Estrada, R. E., González Mejía, M. A., González Hita, L., Morales Casique, E., Zamora Martínez, O., Gómez Vasconcelos, M. G., Denis Ramón, A., and Ramírez Serrato, N.: Tracing Groundwater-Surface Water Mixing Using Isotopes in a Semi-Arid Volcanic Lake Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15165, https://doi.org/10.5194/egusphere-egu26-15165, 2026.

Flood hazard assessments are commonly based on rainfall magnitude and frequency; however, flood responses to similar rainfall intensities may change over time due to evolving land use, river morphology, and hydraulic controls. This study investigates temporal changes in flood inundation patterns between 2014 and 2024 over a highly flood prone urban coastal catchment in India under comparable rainfall forcing, with the objective of improving understanding of non-stationary flood behavior. Rainfall events were identified and grouped based on intensity and duration using long-term precipitation records. For each selected event, flood extents were mapped using satellite-based inundation detection implemented on cloud computing platforms, and, where appropriate, complemented by physically based hydraulic modeling. This combined rainfall–flood framework enables consistent inter-annual comparison of flood patterns under equivalent meteorological conditions. The methodological approach focuses on isolating the influence of landscape and hydraulic evolution on flood response by analyzing spatial characteristics of inundation independent of rainfall variability. By integrating remote sensing and hydraulic modeling within a long-term analysis, the study provides a transferable framework for assessing how flood behavior evolves in response to environmental and anthropogenic changes. This work is relevant to flood risk management and climate adaptation, particularly in rapidly changing river basins where traditional stationary assumptions may no longer be valid. The approach supports improved interpretation of historical floods and more robust planning under future uncertainty.

How to cite: Ghosh, M. and Karmakar, S.: Assessing changes in flood inundation patterns using rainfall-controlled event analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15655, https://doi.org/10.5194/egusphere-egu26-15655, 2026.

Understanding hydrological responses to high-intensity rainfall is critical for water resource management in humid tropical regions, where climate change is increasing the frequency and magnitude of extreme storm events. However, runoff generation mechanisms and flow pathway activation in small tropical catchments remain poorly understood. This study investigates hydrological connectivity, flow pathways, and streamflow source contributions in the Acono watershed, Trinidad and Tobago.

A multi-tracer approach combining stable isotopes (δ²H, δ¹⁸O), radioisotopes (³H, ²²²Rn), and major ion geochemistry (SO₄²⁻, Na⁺, Mg²⁺, Ca²⁺, Cl⁻) was applied to characterize water sources and residence times under contrasting hydrological conditions. Periodic sampling was conducted over a 22-month period, complemented by event-based sampling during a minimum of five high-intensity rainfall events. Samples were collected from rainfall, streams, springs, shallow soil water (10–80 cm), and deep groundwater, alongside continuous monitoring of rainfall, soil moisture, and water levels across the catchment. End-member mixing analysis was used to quantify source contributions to streamflow.

Preliminary results indicate that streamflow is predominantly sourced from pre-event (“old”) water under low flow and moderate wet-season conditions, with old water and spring inputs frequently accounting for 60–99% of flow. Direct rainfall contributions are generally limited (average ~7%) and rarely exceed ~30–37%, suggesting strong subsurface buffering and rapid mobilization of stored water rather than dominant overland flow. In contrast, the onset of wetter conditions in early 2025 triggered pronounced, non-linear shifts in source contributions, including sharp increases in deep groundwater and spring contributions (up to ~89% and ~80%, respectively), alongside elevated event water fractions. These patterns suggest threshold-controlled activation of deeper storage and fast-responding subsurface pathways during periods of sustained or intense rainfall.

Data collect is ongoing and additional analyses are expected to improve our understanding of the translation from rainfall to streamflow. This research provides a novel approach to understanding hydrological processes in small island developing states (SIDS).

How to cite: Ramjohn, P. and Farrick, K.: High Intensity Rainfall Event Contributions to Stormflow and Stream Residence Time in the Acono Watershed, Trinidad., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15744, https://doi.org/10.5194/egusphere-egu26-15744, 2026.

Climate change is driving a marked intensification of hydrological extremes, including both droughts and floods. When these opposing conditions occur in close succession, known as hydrological whiplash, they generate compounded impacts on ecosystems, infrastructure, and human livelihoods. We analyze hydrological whiplash across India using observed streamflow data and simulations from the validated H08-CaMa-Flood model. The results indicate that nearly 90% of streamflow stations experienced at least one whiplash event, with drought-to-flood transitions being both more common and more abrupt than flood-to-drought shifts. These events are concentrated primarily during the monsoon season, but their occurrence has increased in the non-monsoon months in recent decades, particularly in high-elevation regions. Moreover, we find that whiplash events are becoming more frequent and more intense, while the interval separating dry and wet extremes is shrinking, signaling an escalation of hydrological volatility across the country. Together, these patterns underscore the need for strengthened monitoring, early warning capabilities, and adaptive water management strategies to reduce the growing risks associated with rapid hydrological transitions under a warming climate.

How to cite: Sharma, P. and Mishra, V.: Hydrological Whiplashes Over India: Patterns, Drivers, and Recurrence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16235, https://doi.org/10.5194/egusphere-egu26-16235, 2026.

Terrestrial water availability in India is increasingly affected by both climate variability and human activities such as groundwater extraction, reservoir operations, and the expansion of irrigation. However, the observed trends in these factors and their future changes at the river basin level are still not well quantified, making it difficult to plan effective water management strategies. In this study, we assess the individual and combined impacts of climate change and human interventions on the water budgets of major Indian river basins using an ensemble framework that includes the Community Water Model (CWatM) hydrological models. We specifically analyze the changes in sectoral water demands, including agricultural, domestic, and industrial, analyzing their historical progression and projected changes from 1951 to 2100. Based on IMD datasets and CMIP6 scenarios, we identify key regions likely to face water stress in the future and estimate uncertainties in water availability. These findings support the development of sustainable water management plans in response to evolving sectoral trends and climate-related challenges across the Indian subcontinent.

How to cite: Kushwaha, A. P. and Mishra, V.: Dynamics of Sectoral Water Demand and Future Water Stress Hotspots in Indian River Basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16329, https://doi.org/10.5194/egusphere-egu26-16329, 2026.

Agriculture poses severe impacts on water quality and health due to diffuse pollution via pesticide use. In this study, the impact of pesticide use on water quality in the Polatlı district—a region of intensive agriculture within the Ankara River Watershed—was assessed using the Grey Water Footprint (GWF) methodology. The study analyzed 34 active ingredients utilized in the 2023 production cycle of wheat, barley, onion, and sugar beet. District-level data on cultivated areas (ha) for each crop were obtained from the Turkish Statistical Institute (TURKSTAT) for 2023. For the GWF calculations, the natural background concentration was assumed to be zero, while the maximum allowable concentrations for each pesticide were retrieved from local regulations. A watershed-scale hydrological model, namely Soil and Water Assessment Tool (SWAT) were constructed for the study area and calibrated against observed streamflow data to ensure reliable simulation of pesticide transport. Pesticide applications were integrated into the model based on actual usage data. The pollutant loads transported from the Polatlı district to the Ankara River were calculated and subsequently utilized in the grey water footprint equation.

Our findings reveal that pesticide impacts vary significantly with respect to crop and active ingredient levels. For example, SWAT model simulation results for deltamethrin reveal a high environmental transport efficiency despite its low application rate (250 ml/ha) compared to other pesticides. This pesticide has an extremely high affinity for soil particles as clear from the organic carbon-water partition co-efficient value (K_oc = 1,000,000 L/kg); it binds strongly to soil rather than dissolve in water. The transport of deltamethrin is entirely driven by soil erosion, leading to its accumulation in riverine sediments. Due to its extreme K_oc value, the pesticide remains associated with suspended solids and bed sediments, posing a significant long-term threat to benthic organisms and aquatic biodiversity. This sediment-related pollution indicates that the GWF of the basin is not only a function of dissolved pollutants, but it can be heavily influenced by sediment quality. No leaching to groundwater or dissolved transport was observed, confirming its strong soil-binding behavior. This substantial variability in GWFs underscores the necessity for region-specific water quality standards to more accurately assess and manage the environmental impact of pesticide use. Our analysis addresses the complexities of mixed cropping systems typical of semi-arid regions, where water scarcity and intensive pesticide use converge to create critical water quality challenges. This study provides a framework for similar assessments in other agricultural regions, aiding in the development of more informed pesticide management strategies to enhance water resource sustainability. Our results highlight specific pesticides requiring priority attention: replacing or limiting high-GWF pesticides is essential for progress toward sustainable water management in the Ankara River basin.

How to cite: Dogan, F. N. and Capar, G.: Assessment of Pesticide-Related Water Pollution in the Ankara River Watershed: A Combined SWAT and Grey Water Footprint Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16687, https://doi.org/10.5194/egusphere-egu26-16687, 2026.

Chemical weathering of continental rocks plays a key role in regulating river water chemistry and global biogeochemical cycles. This study investigates the spatial and seasonal variability in major and trace element geochemistry of rivers in the Ganga basin to constrain dominant weathering processes and their downstream evolution. A total of 200 water samples and sediment samples were collected across different hydrological regimes and physiographic settings. The results indicate that dissolved loads are primarily controlled by the weathering of silicate and carbonate lithologies. In the Himalayan headwaters, river chemistry is dominated by carbonate and Ca²⁺–Mg²⁺-rich silicate weathering, with Ca²⁺ + Mg²⁺ and HCO₃⁻ contributing approximately 85% of total cations and anions. In contrast, downstream reaches exhibit a systematic decrease in Ca²⁺ + Mg²⁺ contributions and an increase in Na⁺ + K⁺ proportions (up to ~50%), suggesting enhanced influence of silicate weathering and/or alkaline soil inputs. Trace elements such as  Pb, Hg, Th, Sr, Rb, Mo, U, Ba, and V reveal spatially variable source contributions across different catchments. Sodium-normalized trace metal ratios and Ca/Na* relationships indicate additional contributions from carbonate or Ca²⁺–Mg²⁺-rich silicates, particularly during high-discharge periods. Strontium isotope ratios (⁸⁷Sr/⁸⁶Sr) of the Ganga River reflect chemical weathering and sediment sources in the Himalayan region. The river drains diverse lithologies of the Himalaya, causing spatial and seasonal isotopic variations. Higher ⁸⁷Sr/⁸⁶Sr values indicate silicate weathering of radiogenic continental crust, especially during monsoon periods. Lower ratios reflect inputs from carbonate rocks and recycled sediments. Sr isotopes highlight the role of Himalayan weathering in controlling riverine Sr flux.These observations highlight the combined influence of lithology, hydrology, and seasonal discharge on riverine geochemistry and provide new constraints on chemical weathering processes and trace element fluxes from the Ganga basin to the ocean.

How to cite: Parida, R. K. and Rai, S. K.: Chemical Weathering Dynamics and Riverine Geochemistry of the Ganga River Basin: Spatial and Seasonal Controls on Elemental Fluxes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16801, https://doi.org/10.5194/egusphere-egu26-16801, 2026.

Hydrodynamic processes strongly influence coastal and estuarine landscapes, especially in low-lying deltaic regions like the Meghna Estuary. Islands in the lower Meghna and at the Padma-Meghna confluence face increased risk of submergence due to sea-level rise and intensified precipitation under future climate scenarios. This study analyzes the long-term hydrodynamic changes in the Meghna Estuary using Delft3D simulations for 2000, 2024, and 2050, focusing on islands such as Nijhum Dwip, Moulovi Char, Domar Char, Char Kukri Mukri, Rajrajeshwar, Hatiya, and Manipura. The model covers the area from Baruriya Transit to the sea, integrating tidal and riverine dynamics. Future discharge for 2050 was generated from MIROC6 climate projections under SSP2-4.5 and SSP5-8.5 scenarios, bias-corrected and simulated via HEC-HMS. HEC-HMS was calibrated using 2022 data and validated with 2023 discharge records from Bhairab Bazar, while Delft3D was calibrated and validated using observed water level data from Daulatkhan over the same period. Results show rising tidal amplitudes and water levels, with high tides near Char Kukri Mukri increasing by 30 to 35 cm by 2050. Tidal inundation is expected to expand during monsoons, increasing flood risk in low-lying areas. Islands like Char Kukri Mukri and Hatiya are losing relative elevation, heightening their vulnerability to flooding and storm surges. Hydrodynamic projections indicate an average increase in water depth of 0.5 to 0.8 m around Rajrajeshwar, Hatiya, and Manipura by 2050, suggesting enhanced tidal energy and flow velocities that are likely to accelerate shoreline erosion and land loss, particularly along their southern and eastern margins. These findings highlight the increasing vulnerability of the Meghna Estuary’s islands to climate change–driven hydrodynamic shifts, emphasizing the urgent need for targeted adaptive management, improved flood risk mitigation, and resilience-building measures to protect the region’s communities and ecosystems from future inundation and erosion risks.

How to cite: Ahmed Dipu, S. U., Gemintang, E., Haque Laskor, Md. A., Bhuiyan, F., Galib Fardin, M. A., Rahman, Md. M., Rabbany, Y., and Islam Fahim, S.: Hydrodynamic Changes of Estuarine Islands in the Meghna River under Future Climate Scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17766, https://doi.org/10.5194/egusphere-egu26-17766, 2026.

Wetlands play a vital role in the hydrologic cycle since they impact stream flow, add to water storage capacity, provide habitat for many species, and provide resiliency in ecosystems. Ondiri Swamp, which is a peatland located in Kenya with approximately an area of 33 hectares and is the headwaters of Nairobi River has very little hydrological understanding, especially regarding subsurface and groundwater contributions since there have not been any continuous in-situ data measurements. This study aims to quantify water storage and investigate potential groundwater presence using an embedded, multi-sensor dataset.
To overcome the limitation of only using traditional optical index methods for surface water detection under dense vegetation, water occurrence data (Global Surface Water), Sentinel-1 SAR and Sentinel-2 multitemporal optical images, DEM images (Copernicus DEM), and NDVI derived vegetation index data will be combined. Measurements of swamp depth and peat thickness will be collected from short-term field campaigns for calibration of volume estimates and provide preliminary data for a preliminary water balance. The precipitation data (CHIRPS) and ET data (FAO WaPOR) will be combined with inflow and outflow estimates to create a preliminary water balance. Surface storage will be estimated, and potential groundwater contributions will be inferred without long-term observatory data sources. The methods used for the quantitative and qualitative assessment of wetland water resources will generate probabilistic wetland water maps using a multi-temporal remote sensing-based classification of existing datasets, as well as using terrestrial calibrations from field data. 
The study will be able to quantify total wetland water storage, determine the degree to which groundwater may influence wetlands, and identify the seasonal dynamics of wetland hydrology. Through a combination of remote sensing, existing datasets, and terrestrial calibrations from field studies, the study provides a strong, scalable framework for conducting wetland hydrology research, managing wetland ecosystems and planning wetland water resources in areas where very few, if any, hydrological observations are available.

How to cite: Ouedraogo, A.: Estimating Surface and Subsurface Water in Ondiri Swamp, Kenya, Using Multi-Sensor Embedded Data and Preliminary Water Balance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19536, https://doi.org/10.5194/egusphere-egu26-19536, 2026.

Snow accumulation and melt dynamics govern water availability and flood timing across high-latitude catchments, yet observational gaps constrain understanding of scale-dependent hydrologic processes. Traditional snow monitoring relies on sparse gauge networks or coarse satellite products, preventing observation of sub-catchment patterns critical for hydrologic connectivity. Recent advances in Sentinel-1 SAR (10m, all-weather) and Sentinel-2 optical (10m, 20m, 60m) constellations offer transformative observational capabilities, yet systematically exploiting their complementary information for continuous fine-resolution snow monitoring across cloud-prone regions remains an operational challenge.

We present an innovative all-weather snow monitoring system that fuses Sentinel-1 SAR backscatter (sensitive to snow wetness and surface properties) with Sentinel-2 optical imagery (discriminating snow from clouds and bare ground) to deliver 10m resolution fractional snow cover estimates across boreal Finland. This fusion approach explicitly addresses the fundamental limitation of optical-only monitoring: persistent cloud contamination prevents observations during critical winter periods in high-latitude regions. Our methodology incorporates quality-aware atmospheric corrections (cloud masks, aerosol optical thickness, water vapor) to extract reliable snow information despite challenging atmospheric conditions.

A data-driven multi-resolution framework bridges the critical scale gap between fine-resolution satellite observations (10m) and operational hydrologic models requiring catchment-aggregated snow states. The system learns scale-dependent aggregation and disaggregation functions directly from observations, preserving fine-scale spatial patterns essential for understanding snow redistribution by wind, sublimation, and terrain-driven processes. This approach captures heterogeneity at forest-canopy scales while remaining compatible with distributed hydrologic model architectures.

Operational validation demonstrates that the system achieves physically realistic snow patterns with spatially coherent uncertainty estimates that appropriately elevate at snow-land boundaries where hydrologic transitions occur. These calibrated uncertainty bounds are critical for risk-informed water management and probabilistic flood forecasting, enabling downstream hydrologic models to appropriately weight observational constraints.

Key scientific innovations: (1) Demonstrated feasibility of all-weather snow monitoring by effectively combining complementary SAR and optical signatures, overcoming the cloud-cover limitation that constrains optical-only approaches during 60-80\% of winter days in boreal regions. (2) Developed a principled multi-scale learning framework that explicitly captures scale-dependent aggregation and disaggregation properties, bridging satellite observations and hydrologic model requirements. (3) Resolved sub-catchment snow heterogeneity previously masked in operational products (MODIS: 500m, VIIRS: 375m), enabling new insights into snow redistribution and hydrologic connectivity across fragmented landscapes. (4) Quantified spatial structure in prediction uncertainty, enabling probabilistic hydrologic forecasting that appropriately reflects observational constraints.

This next-generation observational capability addresses critical scientific and operational data gaps: calibrating distributed snow models at relevant scales, improving melt timing predictions through continuous all-weather depletion monitoring, validating snow-pack simulations in data-sparse headwater regions, and quantifying snow-climate feedbacks across heterogeneous landscapes. The framework's transferability to pan-Arctic and mountain regions demonstrates how integrating complementary space-based observations through data-driven fusion unlocks fine-scale process understanding previously limited by observational constraints, advancing our capacity for water security assessment and climate adaptation planning in snow-dependent regions.

How to cite: Demil, G., Humayun, M. F., Westerlund, T., Heikkonen, J., and Oussalah, M.: Identifying Scale-Dependent Snow patterns from learned fusion of Multi-modal, Multi-resolution satellite observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21040, https://doi.org/10.5194/egusphere-egu26-21040, 2026.

The Syr Darya River Basin is a highly glacierized transboundary system where future water availability is strongly influenced by climate change, reservoir operations, and population growth. This study investigates the role of reservoirs in regulating water supply under future climate scenarios using the Water Evaluation and Planning (WEAP) model. Climate projections from the ISIMIP framework under Shared Socioeconomic Pathways SSP1-2.6, SSP3-7.0, and SSP5-8.5 are used to drive hydrological inputs, including streamflow, precipitation, and temperature, while population growth projections represent evolving water demands.

 Results indicate a strong increase in summer unmet demand by 2050, intensifying further by 2090, with peak deficits occurring in July–August. Reservoir refilling remains seasonal across all scenarios but becomes more variable and less reliable by 2090, with deeper drawdowns and reduced buffering capacity under higher-emission pathways.

How to cite: Abdul Jalil, U., D'Haeyer, B., Prakash, S., and Huang, J.: The Role of Reservoirs in a Glacierized Basin Under Climate Change: An Analysis Using the WEAP Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21606, https://doi.org/10.5194/egusphere-egu26-21606, 2026.

Developing hydrological models, which are process-aware and reliably transferable across diverse environments remains a challenge. We benchmark Shyft – an open-source, fully FAIR (findable, accessible, interoperable and reusable) flexible modeling framework, across 109 catchments in mainland Norway to evaluate how model structure, forcing uncertainty and calibration objective jointly shape streamflow simulation performance. We adopt large sample hydrology perspective to probe five models “stacks”, providing alternative process choices, such as evapotranspiration (Penman-Monteith vs Priestley-Taylor), snowmelt (temperature-index vs semiphysical) and runoff response (Kirchner vs HBV tank and soil) with multiple goal functions drawn from KlingGupta Efficiency (KGE) and Nash-Sutcliffe Efficiency (NSE), with and without catchment specific precipitation correction. We use a suite of evaluation metrics targeting bias, hydrograph dynamics, low flows and interannual variability. We move beyond crude mean-flow benchmarks toward simple climatological benchmarks, providing an objective context for model skill evaluation, given the seasonal nature of Norwegian catchments.

The evaluation revealed that configurations containing temperature-index snow simulation and Kirchner runoff offer the greatest robustness and generality across all hydrological regimes. In terms of objective functions, KGEbased targets outperform NSE-based targets, with metric combining KGE and box-cox transformed KGE (KGE_bcKGE) identified as a promising generalist objective, which performs well across diverse metrics, including low-flow targeted (KGE(1/Q)) and interannual NSE. Furthermore, precipitation correction was found to be essential for improving performance in Mountain and Inland regimes, suggesting snow undercatch as a primary source of precipitation uncertainty. Among simple benchmarks, daily mean was found to be best predictor setting model expectations for future model intercomparisons in the region. Our results demonstrate the need for balance of structural adequacy, forcing uncertainty and equifinality.

This project is supported by Norwegian Research Council NFR project 336621.

How to cite: Silantyeva, O., Huang, S., and Xu, C.-Y.: Benchmarking flexible modelling framework Shyft across mainland Norway, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22432, https://doi.org/10.5194/egusphere-egu26-22432, 2026.

The climate-related risks in South Africa’s Raymond Mhlaba Municipality and similar rural regions include erratic rainfall, recurring droughts, heatwaves, and shifting seasons. These directly threaten agricultural productivity, leading to frequent crop losses and food insecurity. Vulnerability is heightened by reliance on rain-fed small-scale farming, minimal irrigation infrastructure to buffer against climatic shocks, and the use of old farming methods.

The government uses radio, television, newspapers, and flyers to communicate climate change, and universities are trying to produce more extension officers to assist farmers, but the challenge remains unaddressed.  The root causes of this vulnerability are multi-layered: weak data dissemination systems, socio-economic marginalization, land tenure insecurity, infrastructure deficits, and regulatory and governance gaps. Consequently, farmers make key agricultural decisions such as when and what to plant without critical zone science knowledge, leading to frequent crop losses, wasted inputs, and heightened poverty.

As such, having a Climate-Adapt-Farm-Wise-AI (CAFW-AI) which can inform the farmer about the climate change and provide customised suggestions to a farmer to a) use conservation agriculture, drought-tolerant crop varieties, and precision irrigation to enhance productivity and climate resilience b) integrate adaptation and mitigation strategies across the entire food value chain to ensure sustainable food production and reduce greenhouse gas emissions c) employ Sustainable Agricultural Practices (SAPs) such as agroforestry and millet resilience to improve soil health and enhance food security in climate-vulnerable regions, based on their geographical area.

These techniques are crucial for fostering innovation and resilience in agricultural economies, especially in the face of climate change. By integrating these innovations, farmers can enhance productivity, reduce environmental impact, and ensure food security.

The proposed solution to the problem 

The initiative introduces an AI-enabled, open-source mobile platform that delivers localized, real-time agricultural advisories to rural small-scale farmers in climate-vulnerable regions such as the Eastern Cape. Its strategies are threefold:

• Localized Climate-Smart Decision Support:

By integrating real-time weather data from IoT sensors (local weather stations), information, and Indigenous Knowledge Systems (IKS), the AI model generates tailored recommendations on crop selection, planting times, and resource use. This ensures that decisions are data-driven, context-specific, and actionable for farmers with limited resources.

• Accessible Communication Channels: The platform disseminates advisories via SMS/USSD in local languages (e.g., isiXhosa), bridging the digital divide for communities with limited or no smartphone access.
• Feedback-Driven Learning: Farmers contribute local observations (e.g., rainfall, soil moisture, pest outbreaks) into the system. AI processes these inputs alongside satellite and meteorological data, enabling continuous model refinement and ensuring the system evolves with changing conditions.

What sets this initiative apart is the role of real-time weather data from IoT sensors (local weather stations), AI in combining heterogeneous data sources (real-time weather, soil characteristics, and farmer inputs) to generate hyper-local insights that would not be possible through traditional extension methods. Previously, climate advisories were generalized, delayed, and fragmented; now, AI enables predictive analytics and personalized recommendations at scale, even in remote areas.

How to cite: Vambe, W. T.: Climate-Adapt-Farm-Wise-AI (CAFW-AI): Utilizing IoT, AI, and Machine Learning to Enhance Decision-Making and Protect Crops More Effectively Against Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23177, https://doi.org/10.5194/egusphere-egu26-23177, 2026.

A major threat to Falkland Islands (FI) biodiversity and livelihoods is a drying climate. As warming continues, FI water security is a growing concern, but we lack baseline data to inform mitigation and adaptation. This study applies an innovative remote sensing approach to monitor long-term surface water variability across the Falkland Islands, aiming to support climate-resilient water management. Using the Google Earth Engine (GEE) platform, historical dynamics from 1999–2021 were derived from the Global Surface Water (GSW) Explorer dataset, while recent trends (2021–2025) were assessed using Harmonized Sentinel-2 MSI Level-2A imagery. Together, these enable the first continuous, multi-decadal assessment of pond, wetland, and lake dynamics across East Falkland, West Falkland, and Lafonia. Preliminary results show a relative decline in surface water extent across East Falkland, West Falkland, and Lafonia from 1999 to 2021. More recent Sentinel-2 observations reveal regionally distinct trends from 2021 to 2025: East Falkland remains relatively stable, West Falkland shows a modest increase, and Lafonia exhibits a pronounced rise with strong seasonal variability. These results align with limited ground observations from the water level monitoring site, where satellite-derived surface water area strongly correlates with recorded maximum water levels, confirming the hydrological consistency of the satellite data. Despite limited ground validation, this proof-of-concept highlights the capability of cloud-based remote sensing tools to monitor hydrological variability at regional scale. This approach illustrates how open-access Earth observation data and hydroinformatics tools can aid early detection of climate-driven water changes and strengthen water management in data-scarce areas. It also establishes a basis for future studies linking satellite data with peatland hydrology and ecosystem resilience.

Keywords: Climate Change Impacts, Surface Water Variability, Remote Sensing, Sentinel-2, Google Earth Engine

How to cite: Ko, N. T., Baylis, A., Davies, G. M., Barlow, D., and Evans, C.: Surface Water Dynamics under Changing Climate: Integrating Multi-Sensor Satellite Observations (1999–2025) across the Falkland Islands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-245, https://doi.org/10.5194/egusphere-egu26-245, 2026.

Long-Short Term Memory (LSTM) shows exceptional performance for rainfall-runoff modelling, but lacks physical realism. Efforts to integrate mass conserving into the model architecture didn’t translate to significant improvement in predictive accuracy. We build on the Mass-Conserving LSTM (MC-LSTM) by proposing a novel architecture that incorporates two complementary cell states to represent distinct fast and slow hydrologic memory components while maintaining strict mass balance. We introduce a new partition gate for segregating the mass input for long- and short-term memory, and made required architectural changes to incorporate additional cell state. We benchmarked our model against LSTM and MC-LSTM on CAMELS-IND (158 basins) and CAMELS-US (531 basins) using NSE, KGE, Pearson-r, FHV, FLV, and peak timing/magnitude. For the Indian dataset, MC-LSTM-DS surpasses both LSTM and MC-LSTM across all metrics except Pearson-r and FLV, where it exceeds the performance of LSTM but falls short of MC-LSTM. In the low flow regime (FLV), our model decreases the overestimation of LSTM significantly, while MC-LSTM shows severe underestimation. Analysis of the spatial distribution revealed it to be aligned with hydroclimate, where all the models performed better in humid/tropical climates, while performance lacked in arid regions. Investigation of the cell states revealed that the added cell state represents the long-term processes effectively, while the original cell state captures short-term processes. The change in their relative contributions according to the climate characteristic is observed, thus confirming our hypothesis and also providing an interpretable decomposition of the simulated flows. On the CAMELS-US dataset, MC-LSTM-DS demonstrates equal performance as MC-LSTM and LSTM on NSE, and outperforms all the models in KGE, FHV, and Pearson-r. In FLV, it outperforms all the mass-conserving models by a huge margin and is just short of LSTM. This study proposes a novel mass-conserving model that provides an interpretable prediction. We claim MC-LSTM-DS to be the current state-of-the-art for large sample rainfall runoff modelling, as it showed superior performance across two diverse regions. To the best of our knowledge, this study is the first to investigate the effects of strict mass conservation on the diverse Indian region.

How to cite: Toraskar, S., Naik, M. N., Singh, A., and Gaurav, K.: Mass Conserving LSTM with Dual States for Improved Streamflow Prediction through Quickflow and Slow Storage Separation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1031, https://doi.org/10.5194/egusphere-egu26-1031, 2026.

Arid and semi-arid regions are facing more frequent and severe droughts, with annual rainfall often below 200 mm. Large-scale, intensive irrigation further strains these limited water resources. Under these conditions, growers need practical tools to estimate yield and monitor tree health at high spatial detail so they can better manage irrigation and inputs. This work develops and tests an automated, data-driven pipeline for estimating citrus yield at the individual-tree level using UAV imagery and Deep Learning. The pipeline comprises three main components. First, individual trees and orchard rows are segmented using a lightweight Tiny U‑Net model. Second, a CNN-based model predicts tree-level yield from vegetation indices and field measurements. Third, these predictions are validated through detailed fruit sampling.

The study was conducted in a commercial citrus orchard in a semi-arid region under climate and water stress. High‑resolution UAV imagery was processed into orthomosaics and vegetation index maps, and the Tiny U‑Net was optimized for fast, near real‑time semantic segmentation, enabling precise tree crown delineation and accurate tree and row counts. For yield prediction, the CNN model exploited spatial features from vegetation indices combined with in‑situ data. The validation relied on direct comparison between UAV‑based yield estimates and yields obtained from field sampling and laboratory weighing. Both mean and median yields per tree were computed to capture tree‑level variability. The final dataset, consisting of 34 trees and approximately 340 fruit samples, provided a robust basis for assessing model performance. The Tiny U‑Net segmentation model reached high accuracy, with precision and recall of 94.74% and 94.88%, and an inference time of 12.55 ms per image tile. This shows the model is suitable for real‑time or on‑board use and can reliably map orchard structure at large scale. Tree and row counts derived from the segmentation achieved an R² greater than 0.99, confirming the robustness of the approach. For yield estimation, the CNN model outperformed other machine learning methods, achieving an R² of 0.88 at tree level. Field validation confirmed the practical usefulness of the pipeline, UAV‑predicted yields closely matched ground‑truth values, with both indicating an average yield of roughly 50 kg per tree. Most trees fell between 40 and 70 kg, and the model’s output histogram mean 50.9 kg, and median 51.4 kg aligned well with these field observations.

This robust agreement between model outputs and independent field validation data underscores the system's reliability and operational readiness for accurate, tree-level yield mapping. By integrating precise tree segmentation, high-resolution vegetation indices, and rigorously collected ground truth measurements, this study demonstrates that automated yield maps can be produced with sufficient accuracy to support operational decisions in orchards. This offers a cost-effective and scalable tool for precision agriculture, enabling optimized resource allocation, improved harvest planning, and adaptive management under increasing climate stress.

How to cite: Bakas, K., Saddik, A., Dliou, A., Hssaisoune, M., El Hachemy, S., Ait Ichou, H., Hmache, F., El Hafyani, M., Labbaci, A., and Bouchaou, L.: Real-Time UAV-Deep Learning System for Citrus Orchard Structure and Yield Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2054, https://doi.org/10.5194/egusphere-egu26-2054, 2026.

The Souss-Massa region is known as the most important agricultural area in Morocco, and one of the most affected regions by climate change and over-exploitation. This situation has required the intervention of new tools to improve water resource management. In this context, the Unmanned Aerial Vehicles (UAVs) images data were used for weeds detection in a Citrus orchard farm. Two sites were considered, the first one planted with 12-years-old and 1.5 years-old clementine trees. After a panoply of image processing from the data collection, following by the georeferencing, the creation of the digital elevation model, the digital surface model, and the elaboration of the orthomosaic image, the machine learning algorithms (MLA) such as Maximum Likelihood Classification, Minimum Distance Classification, Support Vector Machine, were applied for weeds detection and mapping. For both sites, all MLA showed a Cohen’s kappa coefficient higher than 0.6 and an overall accuracy higher than 60%. This study demonstrates how this emerging technology offers farmers opportunities to enhance production while optimizing water usage.

How to cite: El Hafyani, M., Chaaou, A., Sadik, A., Labbaci, A., Hssaisoune, M., Tairi, A., Abdelfadel, F., Taia, S., Ait-Ichou, H., Elhaid, I., and Bouchaou, L.: A combined approach of UAV data and machine learning algorithms in weeds detection , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2155, https://doi.org/10.5194/egusphere-egu26-2155, 2026.

Coastal wetlands, characterized by their geomorphological sensitivity and tidal dependence, exhibit pronounced vulnerability under global warming. While the persistent threat of sea-level rise to coastal wetlands has been extensively documented at the macroscale, there remains a lack of systematic quantitative frameworks for mapping these trends to the microscale dynamics of wetland evolution. To address this gap, this paper proposes WetFramework, a novel approach for joint modeling of spatial structure and temporal variation in wetlands. (1) In the encoder, Transformer and Mamba modules are integrated to enhance multiscale feature representation through the synergy of global attention and implicit sequence modeling, with a Token-Driven Attention Mechanism (TDAM) designed to facilitate deep interactions between features. (2) In the decoder, a Wavelet-Enhanced Reconstruction Module (WERM) is introduced to improve spatial structure modeling via wavelet transforms, thereby optimizing boundary delineation and fine detail representation for precise mapping of coastal wetland extents. (3) To capture periodic inundation characteristics, a Fourier-Based Inundation Estimation Module (FBIEM) is further proposed, incorporating tidal-height observations to enable unsupervised modeling of pixel-level hydrological responses and quantitative expression of inundation rhythms. Extensive experiments conducted in four representative coastal regions—Yancheng and Dongying (China), Mont-Saint-Michel Bay (France), and San Francisco Bay (USA)—demonstrate that the proposed framework outperforms state-of-the-art models across multiple evaluation metrics and exhibits robust cross-regional generalization and dynamic modeling capabilities. This study provides an effective paradigm for intelligent remote sensing-based wetland identification and long-term hydrological modeling, and offers key hydrological information to support inundation-dynamics monitoring and management decision-making.

How to cite: Liang, J.: WetFramework: A Deep Learning Framework for Coastal Wetland Boundary Extraction and Inundation Frequency Estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2567, https://doi.org/10.5194/egusphere-egu26-2567, 2026.

In 2001, the tamarisk leaf beetle (Diorhabda spp.) was released as a biological control agent for invasive tamarisk (Tamarix spp.), which dominates many floodplains in the western United States (US) and substantially alters riparian ecosystem structure and function. Since its release, the beetle has expanded across thousands of river kilometers, repeatedly defoliating tamarisk far beyond original release sites. Although biological control offers an alternative to mechanical or chemical removal, its ecological benefits and tradeoffs remain uncertain. Here, we synthesize current understanding of one of the most extensive biological control programs implemented in North America, evaluating impacts on riparian evapotranspiration (ET) and riverine hydrology. We assess ongoing challenges and opportunities associated with tamarisk biocontrol and consider how western US riparian forests may evolve under reduced tamarisk dominance.

Early management efforts were driven by the assumption that tamarisk consumed exceptionally large volumes of water, motivating legislative and large-scale removal programs. Subsequent studies, however, demonstrated that tamarisk water use is highly variable and comparable to native riparian vegetation such as cottonwood (Populus spp.) and willow (Salix spp.), as well as mixed shrub communities. Reported tamarisk ET since 2000 ranges widely (109–1456 mm yr⁻¹), with mean values near 850 mm yr⁻¹, depending on stand age, density, health, groundwater depth, soil properties, and salinity.

Defoliation by Diorhabda spp. was expected to enhance streamflow by reducing riparian ET, yet observed hydrologic responses have been inconsistent. In past research ET declines exceed 40% relative to healthy tamarisk at some locations, whereas at other sites, reductions are modest or absent, particularly where baseline ET is low. In this current study, we reassess post-defoliation dynamics by analyzing ET across 27 riparian sites from 2014–2023 using Landsat-derived Nagler ET(EVI2) estimates and gridded climate data. Approximately half of the sites exhibited sustained ET reductions averaging a loss of 18% (−142 mm yr⁻¹), while the remainder showed negligible change or increases in ET of 9% (+54 mm yr⁻¹), likely reflecting tamarisk regrowth or replacement by other vegetation. Across all sites, net water savings were modest, averaging a loss of 7% (−48 mm yr⁻¹), consistent with earlier estimates.

These findings reinforce that hydrologic benefits from tamarisk biocontrol are site-specific, often transient, and frequently offset by vegetation recovery or compositional shifts. Consequently, biological control alone is unlikely to yield substantial or reliable increases in water availability for agricultural or municipal use. Predicting future structure and function of western US riparian forests under tamarisk biocontrol requires explicit consideration of ecosystem complexity, spatial heterogeneity, and interacting drivers that will shape whether alternative states favor native vegetation recovery or secondary invasions.

How to cite: Nagler, P., Palmquist, E., Snyder, K., Jimenez-Hernandez, E., and Hultine, K.: Revisiting a riparian invasive shrub and its biocontrol in the western United States: Measured Changes in Water Use, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3158, https://doi.org/10.5194/egusphere-egu26-3158, 2026.

Farm roadway networks are an important infrastructure in grassland farms providing access between the farmyard and grazing fields. However, during livestock movement, excreta is deposited on the roadways, especially on bends, T-junctions and at corners where their movement is impeded. Nutrient-enriched soiled runoff generated on these roadways can contribute significantly to water quality degradation if connected to waters (including man-made open drainage ditches). Quantifying the risk associated with farm roadway runoff delivery to waters includes mapping the roadway and drainage networks and identifying sections which contain high pollutant loads and have the potential of generating, mobilising and delivering surface runoff to the drainage channels. In this study, a deep learning (DL) approach was employed to automatically identify internal farm roadway networks and open drainage channels in 5 grassland farms. Aerial imagery and LiDAR-derived digital terrain models were used to train the DL models for identifying farm roadways and open drainage ditches, respectively. The flow direction and flow accumulation were determined using digital elevation models to map farm roadway sections that have the potential to generate and deliver runoff to the drainage network.

Across the 5 farms, a total of 16.7 km of roadway and 13.5 km of drainage channels were identified by the DL models, achieving precisions of 79 % and 64 %, and accuracies of 90 % and 96 %, respectively. Flow accumulation maps were established for each farm to assess delivery pathways and the potential of roadway runoff connectivity to waters. Flow pathways through roadway junctions and at corners were considered critical outranking those on straight roadway sections. Breaking the runoff pathway at these locations will help prevent delivery to waters. The findings of this study indicate that mapping of open drainage channels and internal farm roadways in grassland farms can be automated by using deep learning models. Integrating the automated mapping and hydrological modelling enables more precise identification of critical roadway sections, supporting targeted mitigation to reduce soiled runoff from entering waters and thus enhance water quality protection in grassland farming systems.

How to cite: Sifundza, L. S., Murnane, J. G., Daly, K., Adams, R., Tuohy, P., and Fenton, O.: Integrating deep learning and hydrological modelling to assess farm roadway runoff risk to inform targeted mitigation in grassland systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6937, https://doi.org/10.5194/egusphere-egu26-6937, 2026.

Abstract: 

In this work, we develop a hydrological model designed to simulate the water balance and runoff processes at the catchment-scale. Instead of using a rectangular grid discretization, the model represents the catchment using a non-homogeneous two-dimensional triangular mesh framework (similar to a triangular mesh in the Finite Element method). This discretization fits a more flexible representation of complex topography and land boundaries. The model is implemented in the Fortran programming language. It depends on the Digital Elevation Model (DEM) to extract the flow pathways starting from upstream and reaching downstream. That guarantees a physically consistent and explicit flow-routing structure across the triangular mesh.

Evapotranspiration is calculated using the Penman–Monteith equation, as the parameters are considered to suit coastal climate conditions. The model utilizes temperature, solar radiation, wind speed, and vapor pressure as atmospheric inputs. The SCS Curve Number method is used to estimate the surface runoff, considering slope, land cover, and soil properties. Meteorological data measurements, including precipitation, temperature, humidity, as well as inflow and outflow discharges, are integrated into the simulations.

Due to its efficient numerical structure, the model supports simulations with numerous spatial elements and long time series while maintaining the computational cost at its lowest limits. This makes it well-suited for large-scale watershed applications and provides a strong basis for future high-performance computing developments.

Keywords: hydrological modeling, watershed triangulation, flow routing, numerical simulation

How to cite: Dali, N.: Hydrological Modelling Framework for Large-Scale Catchments using triangular nonhomogeneous spatial discretization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8171, https://doi.org/10.5194/egusphere-egu26-8171, 2026.

The hydraulic jump is considered to have one of the largest energy losses in the field of Hydraulics. These losses are caused during transition from super-critical to sub-critical flow conditions in the case of open-surface flows. In this study, we focus on a laboratory-scale hydraulic jump combining both experimental measurements and model simulations using theoretical arguments. The main objective is to identify, quantify, and interpret the uncertainty in both cases through key parameters, such as the (sub/super) critical depths and channel geometry, for various flow conditions, with emphasis on energy dissipation, turbulence, mixing, regime transitions, and flow stability characteristics .

How to cite: Cuny, S., Dimitriadis, P., Koutsoyiannis, D., Sargentis, G.-F., and Iliopoulou, T.: Uncertainty Evaluation of Hydraulic Jumps in Open-Surface Flows, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8394, https://doi.org/10.5194/egusphere-egu26-8394, 2026.

The frequency of disasters induced by heavy precipitation (HP) in the upper reaches of the Yellow River Basin (URYR) has increased notably. This study had further elucidated the structure and interactions of synoptic systems across different pressure levels and quantitatively characterized the anomalous driving factors. Four weather types had been identified: Xinjiang Trough (Type1, constituting 35% of HP), Mongolian Trough (Type2, 14%), Westward–Extension Western Pacific Subtropical High (WPSH) (Type3, 43%), and Cut–Off Cyclone (Type4, 8%). Influenced by the troughs, the moisture anomalies are transported by the southwesterly jet originating from Bay of Bengal low-pressure systems. In Type3, the WPSH and South Asian High demonstrate the greatest zonal expansion and central intensity (reaching 12610 gpm); this type distinguished by maximal moisture and energy, exhibits the most pronounced extreme properties. The most notable characteristic of Type4 is its stability and persistence presented the most favorable dynamic conditions, despite occurring with the lowest frequency. Due to the anomalous evolution of atmospheric circulation, the anomalies in potential vorticity, column-integrated precipitable water, and convective available potential energy increase; negative anomalies in vertical velocity and moisture flux divergence decline dramatically within 12 to 6 hours preceding HP, signaling anomalous moisture convergence coupled with ascending motion. Low-level moisture is impeded and diverted by the TP topography, generating northerly flow along its eastern flank and forming a distinct “moisture corridor”. Orographic uplift introduces pronounced vertical component to the moisture flux vectors and intensifies local circulations, thereby promoting the initiation and organization of mesoscale systems. The vertical moisture advection serves as dominant mechanism driving HP, while zonal or meridional moist enthalpy predominantly contributes to the physical processes driving the ascending motion under different patterns. These findings may offer a scientific basis for the prediction of HP events in the region. 

How to cite: Tan, C., Ma, Y., Chen, X., Li, W., and Zhang, Q.: Analysis of Heavy Precipitation and its Typical Weather Patterns over the Upper Reaches of the Yellow River, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8717, https://doi.org/10.5194/egusphere-egu26-8717, 2026.

Monitoring of inland water bodies is considered crucial for effective water resource management. In this study, a combination of satellite imagery and altimetry products was utilized to monitor changes in water level and surface extent during the different operational filling phases of the Grand Ethiopian Renaissance (GER) Dam. The primary objective was to utilize diverse remote sensing products to provide an accurate estimation of water volume changes over time. Sentinel-1 data were processed using an unsupervised edge Otsu algorithm to map reservoir extents. These output maps were validated against Planet and Sentinel-2 water masks, and a high level of agreement was observed, with overall accuracy values ranging from 0.97 to 0.99. Furthermore, various Surface Water and Ocean Topography (SWOT) satellite products were evaluated for the estimation of reservoir extents. It was found that the SWOT Lake Single Product performed poorly, with an Intersection over Union (IOU) value of approximately 0.33 being recorded. In contrast, moderate agreement with validation sets was demonstrated by the SWOT water mask raster and pixel cloud products, with overall accuracy values ranging from 0.78 to 0.89 being observed. Volume variation across different dam operational phases was estimated through the application of satellite-based observations and a DEM contouring method. Although a high correlation (R² value of 0.98) was exhibited by both methods, significant differences in absolute values were identified (RMSE value of 2736.35 km³). These discrepancies are attributed to a potential scaling error and the inherent water slope present within the GER Dam reservoir.

How to cite: Tazwar, M., Rietbroek, R., Maathuis, B. H. P., and Shakya, A.: Tracking The GER Dam Impoundment Stages Using SWOT and Other Radar Altimetry Products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8732, https://doi.org/10.5194/egusphere-egu26-8732, 2026.

Baseflow is a crucial component of streamflow, essentially driven by changes in groundwater storage, and is vital for sustaining flows during dry periods. Traditional techniques for baseflow quantification using graphical analysis or digital filters require long-term river discharge observations, which are often limited in their spatial extent. However, with the launch of the Surface Water and Ocean Topography (SWOT) mission, global estimates of river discharge are now available over a period of two years, offering a high-resolution dataset with at least one observation every 21 days. Despite its relatively coarse temporal resolution, prior studies have demonstrated SWOT’s ability to accurately estimate average baseflow even at one observation per cycle, based on synthetic SWOT discharge estimates. The high spatial resolution provided by ‘SWOT discharge’ can be utilized to estimate baseflow at a reach-scale and gain new insights into groundwater-surface water interactions in water-stressed river basins.

In this study, we will utilize SWOT’s discharge products over Indian river basins to characterize baseflow dynamics at reach-scale resolution and examine the effects of climate variability and land-use changes on baseflow. By accurately estimating the baseflow recession parameter (k), this study will be able to identify the gaining-to-losing transition in a basin. Furthermore, the research will explore SWOT’s ability to detect temporal shifts in the baseflow recession parameter (k) during the pre-monsoon period and evaluate the effects of anthropogenic extractions on the groundwater table. Finally, these estimates will be integrated into a mass-balance model, baseflow will be converted into upstream groundwater storage (GWS) changes and validated against independent GWS anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) satellites. This study will demonstrate the capability of SWOT to bridge the gap between reach-scale hydraulics and basin-scale storage, providing a vital tool for sustainable water resource management in water-stressed regions.

How to cite: Deshpande, R. S., Vidushi, V., and Syed, T. H.: Characterizing Baseflow in Indian River Basins Using SWOT Discharge Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10090, https://doi.org/10.5194/egusphere-egu26-10090, 2026.

In a context where energy efficiency is a major concern, studying linear and local head-losses in hydraulic networks is essential. These losses are mainly caused by internal fluid friction and network singularities (such as bends, section changes, valves, etc.), have a direct impact on water transport efficiency and management. In this study, we focus on a laboratory-scale hydraulic network combining both experimental measurements and model simulations using theoretical arguments and the EPANET software. The main objective is to identify, quantify, and interpret the uncertainty in both linear and typical local head-losses through key parameters, such as the friction factor and the local-loss coefficient, for various flow conditions.

How to cite: Bourbon, M., Sargentis, G.-F., Iliopoulou, T., Koutsoyiannis, D., and Dimitriadis, P.: Uncertainty Evaluation of Hydraulic Losses in Closed Pipes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10560, https://doi.org/10.5194/egusphere-egu26-10560, 2026.

Climate variability represents one of the most severe threats to lacustrine ecosystems worldwide, leading to water loss in nearly half of the world’s lakes and reservoirs. On the one hand, this variability is associated with drought conditions, manifested by a decline in precipitation. On the other hand, it is linked to rising temperatures, which enhance evaporation rates at lake surfaces.

In Senegal, a Sahelian country, the prolonged drought period of the 1970s led to the desiccation of several water bodies, including Lake Tanma. In this context, the present study contributes to a better understanding of the drying process of Lake Tanma under climate variability conditions, using remote sensing techniques. Lake Tanma is located in Thiès region, approximately 70 km from Dakar.

To achieve this objective, Landsat Earth observation products were used at the beginning and end of each decade between 1984 and 2024. The time series consists of multispectral images acquired in October, corresponding to the end of the rainy season in Senegal. This choice ensures the capture of the lake’s maximum water extent, thereby minimizing seasonal fluctuations. All data were acquired and processed using the Google Earth Engine platform. The Modified Normalized Difference Water Index (MNDWI) was computed for the entire time series to accurately delineate and characterize water-covered surfaces.

The results reveal a highly variable evolution of the inundated surface area of Lake Tanma, with a variation coefficient of 57.8%. The largest flooded area was observed in 1984, covering 969.33 ha, while the smallest extent was recorded in 2024, with only 76.18 ha. Analysis of intra-decadal variations shows a slight decrease (7%) in the flooded surface, between 1984 and 1989. In contrast, subsequent decades exhibit a marked and progressive regression of the lake’s water surface, reaching 21% during the 2000–2009 decade, 61% during 2010–2019, and up to 89% over the 2020–2024 period.

These decrease trends highlight the influence of hydro-climatic parameters, particularly precipitation and evaporation, which constitute the primary drivers of lake recharge and desiccation. Consequently, further investigation, of hydro-climatic factors, namely rainfall and temperature, is required, to better understand the drying process of Lake Tanma and to assess the impacts of hydro-climatic variability on its long-term dynamics.

How to cite: Ndeye, M. D. B., Diene, S. M., Ndao, S., Sarr, S., Guèye, A., and Tamba, S.: Contribution of Remote Sensing to the Analysis of the Drying Process of Lake Tanma under Strong Climate Variability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10679, https://doi.org/10.5194/egusphere-egu26-10679, 2026.

Tunisian agriculture remains a crucial component of the country’s economic development and faces considerable constraints related to increasing water demand and reducing water resources’ availability. Improving the assessment of irrigation water use is a prerequisite for sustainable water management. The present study aims to evaluate the quality of water consumption estimates in the Public Irrigated Area of Lakhmess using open-source data. High-resolution (10 m) Sentinel 2 images, combined with ERA5-land meterological data, were used to assess monthly and seasonal actual evapotranspiration (ET) and water use through the implementation of the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine (GEE) environment. The calculated water uses were combined with the seasonal supplied water to the PIA Lakhmess, collected at plot level.

This study was conducted over eight agricultural campaigns from 2015-2016 to 2022-2023. The method is validated for three sectors Sidi Jaber, Gantra and Gabel, comparing the seasonal water use estimates to water meter observations. Correlation analysis between estimated water use from open-access data and  in-situ measurement yielded correlation coefficients of 0.76, 0.75 and 0.73, with corresponding RMSE values of 0.461, 0.425, and 0,391 mm/day, respectively. In addition, SEBAL-derived evapotranspiration estimates were evaluated through comparison with reference evapotranspiration computed using the FAO-56 Penman-Monteith, resulting in an R²  of 0,68 and an RMSE of 0.315 mm/day. Overall, the methods were deemed satisfactory, as they facilitated the monitoring of excessive water usage by identifying areas where water losses occurred.

Key words: Evapotranspiration, Irrigation, water use, Remote sensing, GEE, SEBAL.

How to cite: Belhaj Kilani, A., Alonso, A., Bousselmi, A., Khlifi, S., and Vanclooster, M.: Estimation of Crops Water Consumption by Remote Sensing: SEBAL Model Calculations Versus Ground Observation In The Irrigated Area of Lakhmess (Siliana, Northern Tunisia), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12323, https://doi.org/10.5194/egusphere-egu26-12323, 2026.

Effectively characterizing uncertainty and error in flood prediction is essential for informed decision-making. This study combines advanced deep neural network architectures, i.e., Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS), and Long Short-Term Memory (LSTM), with multiple uncertainty quantification frameworks to evaluate flood forecasts across several watersheds in the southeastern United States. Bayesian inference, Monte Carlo–based methods, and quantile regression are applied to estimate predictive uncertainty. The comparative analysis examines how different uncertainty approaches perform across a range of flood magnitudes, highlighting their respective advantages and limitations at multiple scales. Results indicate that N-HiTS generally yields narrower and more reliable uncertainty bounds than LSTM. The findings further demonstrate that prior specification in MCMC sampling strongly influences uncertainty estimates and requires careful calibration. While Monte Carlo dropout, which is an approximate Bayesian technique, primarily captures uncertainty near flood peaks, MCMC offers a more complete characterization across the full hydrograph. In addition, this study investigates multi-site training to evaluate model adaptability under diverse hydrological regimes. Collectively, these results advance the integration of deep neural networks and uncertainty quantification to enhance flood modeling capabilities and risk management.

How to cite: Saberian, M., Samadi, V., Wagener, T., and Popescu, I.: Uncertainty-Aware Flood Prediction Using Deep Neural Networks Across Multiple Watersheds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15829, https://doi.org/10.5194/egusphere-egu26-15829, 2026.

Flood is a recurrent natural disaster, causing socio-economic losses and affecting millions of people every year. High-resolution and near-real time monitoring of flood disasters is critical for a densely populated and hydro-climatically diverse country like India. Existing operational frameworks in India largely rely on coarse-resolution hydrological models and hydrodynamic models, biased meteorological forecasts, limited gauge networks, missing observed data for model setup, static land use, and deterministic forecasts, which constrain their ability to capture basin heterogeneity, reservoir regulation, agriculture expansion, urban influences, and short-term extremes. Here, we present a high-resolution, integrated operational flood monitoring framework using hydrological, hydrodynamic, and data-driven models to provide 5-day ahead forecasts of streamflow, water level, and flood inundation at more than 350 stations across India. We first evaluate meteorological forecasts from UKMO, KMA, ECMWF, and GEFS products to quantify their spatio-temporal skill and estimated systematic biases across hydro-climatic regimes. Consequently, we apply a knowledge distillation–based bias correction approach trained on observed rainfall and temperature data from the India Meteorological Department (IMD), enabling the physically consistent correction of meteorological inputs. These corrected forecasts are then integrated with a process-based hydrological model and a sequential long short-term memory network augmented with a multi-headed attention mechanism, which explicitly learns temporal dependencies, upstream connectivity, and the dynamic relevance of predictors. The forecasted streamflow is then fed into the large-scale hydrodynamic model to forecast water level and flood inundation maps. The proposed stochastic framework aims to achieve substantial improvements in short-lead flood prediction skill, enhanced representation of peak flows and water levels, and more realistic flood inundation dynamics compared to existing operational systems. By combining machine learning-based forecast correction, high-resolution modelling, and advanced deep learning, this study provides a scalable pathway for next-generation flood early warning systems in India, offering direct benefits for evacuation and rescue operations, reservoir operation, agriculture management, and disaster risk reduction at both national and sub-basin scales.

How to cite: Solanki, H. and Mishra, V.: High-resolution operational Flood monitoring in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15872, https://doi.org/10.5194/egusphere-egu26-15872, 2026.

Bofedales are high-altitude wetlands whose functioning is closely linked to surface water availability, playing a key role in hydrological regulation and ecosystem resilience in the Andes. Despite their importance, spatially explicit information on their surface water dynamics remains limited, particularly in data-scarce mountain regions. This study investigates the ecohydrological dynamics of bofedales in the Alto Pampas sub-basin (Huancavelica, Peru) over the 2015–2024 period using a multitemporal remote sensing approach combined with climatic information. Seasonal patterns of bofedal extent and surface water presence were mapped from Landsat 8 imagery using vegetation and moisture indices (NDVI and NDII), together with topographic variables. Bofedales were identified through a Random Forest classification framework and subsequently categorized as permanent or seasonal based on the temporal persistence of hydric signals. Changes in surface water extent and bofedal productivity were quantified, and temporal trends were assessed using the Mann–Kendall test. In addition, generalized additive models were applied to examine potentially nonlinear relationships between climatic drivers and key ecohydrological indicators. The results reveal contrasting surface water trajectories among bofedales, reflecting heterogeneous sensitivity to climate variability within the sub-basin. These findings demonstrate the value of satellite-based monitoring for assessing surface water dynamics in high-Andean wetlands and provide relevant insights for water resources management and climate change adaptation in data-poor mountainous regions.

How to cite: Sansoni-Koga, S., Laurente-Torres, J., Ccaico-Atoccsa, M., Flores-Quispe, S., Meza-Fajardo, G., and Cárdenas-Gaudry, M.: Ecohydrological and multitemporal analysis of Andean wetlands under climate variabilitiy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16435, https://doi.org/10.5194/egusphere-egu26-16435, 2026.

Abstract. Predicting flood risk is a complex phenomenon. Several factors influence flood behavior generation and intensity such as intricate interactions between hydrological dynamics, meteorological variability, the overarching influence of climate change and land-use changes. This study explores flood risk within the watershed of Tassaout River located in the central region of Morocco. Three advanced machine learning algorithms were chosen to evaluate flood risk. These algorithms are Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN), Random Forest (RF) and Support Vector Machine (SVM). The models are trained based on 11 different factors derived from remote sensing data. From ALOS digital elevation model, 8 factors are developed: Elevation, Slope, Aspect, Plan Curvature, Profile Curvature, Stream Power Index (SPI), Topographical Wetness Index (TWI), and Surface Roughness. In addition, from Landsat 9 imagery, three flood susceptibility factors are extracted: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Land Surface Temperature (LST). The predictive performance of each model was assessed using standard classification metrics: accuracy, recall, and F1-score. Results indicate that the RF model performed the best with an accuracy of 100%, SVM algorithm achieved good performance, attaining 68% in accuracy and more than 80% in f1-score. However, the ANN model underperformed compared to the other algorithms, with an accuracy of only 59% in accuracy and 70% in f1-score highlighting its limitations in capturing the decision boundaries within the current data configuration. Furthermore, the Shapley Additive exPlanations model (SHAP) was used to enhance the transparency and interpretability of the modelling results.

How to cite: Legsabi, H., Tiai, S., Boussabou, S. M., Najaoui, N., El Mansouri, B., and Erraioui, L.: Modeling Flood Risk in Kalaa Sraghna Region in Morocco Using Explainable Artificial Intelligence Techniques, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19676, https://doi.org/10.5194/egusphere-egu26-19676, 2026.

Reliable validation of satellite altimetry over inland waters relies on long-term, high-quality in-situ water height measurements over different types of waterbodies. The strategy implemented in the St3TART Follow-On (FO) project relies on controlled super sites to produce high quality Fiducial Reference Measurements (FRMs) and on a high number of data provided by public national hydrological networks considered as opportunity sites.

However, these measurements from national hydrological networks remain highly heterogeneous in terms of formats, units, and metadata description, limiting their direct large-scale use for Cal/Val activities. The first step of data uniformization has been performed by vorteX-io team during St3TART-FO project. As an adaptation of the validation strategy for Sentinel-3 is considered for CRISTAL inland waters products, this uniformization work should be extended to cover more virtual stations for other altimetry missions.

This contribution presents the hydrological component of the Hydro-Cryo in-situ platform, INSIGHT, an ESA-funded project, extension of CRISTAL IN-PROVA project, aiming at the centralization and harmonization of publicly available in-situ water surface height data across Europe. This work participates in the preparation for the Cal/Val phase of the future CRISTAL mission and in support of ongoing Sentinel-3 validation activities, with support from the European Environment Agency (EEA) as coordinator of the Copernicus In-Situ component.

In this first phase, the platform will integrate data from twelve national hydrological networks covering France, Switzerland, Belgium (Wallonia), Ireland, Portugal, Norway, Poland, Italy, Slovenia, Croatia, the Netherlands and Germany. The data from fixed in-situ sensors deployed on Cal/Val super sites for Sentinel-3 will also be integrated in the platform. The back-end architecture is designed to easily integrate additional networks in Europe and all over the world. Native temporal resolutions provided by in situ sensors are preserved without aggregation or resampling, and up to ten years of historical observations are considered when available.

The harmonized hydrological datasets will be disseminated on a dedicated Data Hub developed by NOVELTIS together with reference Cryosphere data for satellite altimetry validation. This open-access platform is designed to serve the Cal/Val community by providing a unified entry point for inland water and cryosphere reference measurements relevant to multiple altimetry missions.

The core objective of the hydrological processing chain is the harmonization of in-situ water height measurements by standardizing measurement units and metadata across heterogeneous national public datasets. Attention is given to the consistency of the altimetric reference of the in-situ sensors. This harmonization is essential for the use of in situ stations as FRMs for the validation of both Sentinel-3 and CRISTAL, as well as for others satellite altimetry missions.

Beyond the altimetry community, this platform addresses the broader hydrological community by providing access to a standardized water height dataset from public national networks. By lowering technical barriers to data use, the infrastructure supports cross-border hydrological studies and contributes to the reuse of public hydrological observations.

This project, currently under development, establishes the data infrastructure for the needs of inland water altimetry validation, while simultaneously enabling wider scientific exploitation of harmonized in-situ water level observations at the European scale.

How to cite: Dechamp-Guillaume, M., Fouqueau, V., Hahn, J., Gil, P., Grenier, E., Poisson, J.-C., Le Merle, E., El Hajj, M., Restano, M., and Catapano, F.: Centralizing in-situ Hydrological measurements for satellite altimetry validation: the INSIGHT platform , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20408, https://doi.org/10.5194/egusphere-egu26-20408, 2026.

On September 10, 2023, the city of Derna in northeastern Libya experienced one of the deadliest flood disasters in Mediterranean and African history. Storm Daniel delivered unprecedented rainfall, with Al-Bayda recording 414 mm and Derna receiving over 100 mm within 24 hours, approximately 270 times the region's typical September average of 1.5 mm. This study employs Synthetic Aperture Radar (SAR) from Sentinel and high-resolution Planet imagery to provide a comprehensive analysis of the flood's spatial extent, infrastructure damage, and the interplay between natural and anthropogenic factors that amplified this disaster.

Our flood extent mapping reveals catastrophic impacts on urban infrastructure. The river channel expanded dramatically from 50 meters to approximately 500 meters in width, while the maximum inundated area extended 1.2 km² from the collapsed dams to the Mediterranean Sea over a distance of 2.5 km. The analysis identifies critical damage to infrastructure including the collapse of two upstream dams, destruction of five road flyovers, and significant damage to ports, bridges, and residential areas.

The disaster's severity was substantially amplified by anthropogenic factors. Historical urban development had rerouted the river through artificial canals, with roads and settlements subsequently constructed on the natural riverbed. The two dams, built in the 1970s and unmaintained since 2002, catastrophically failed, releasing an estimated 30 million cubic meters of water. Mann-Kendall trend analysis of 122-year climatic records reveals a statistically significant warming trend (p ≈ 0, Sen's slope = 0.00798) alongside decreasing overall precipitation (p = 0.027, Sen's slope = -0.0389), suggesting a paradoxical pattern where less frequent but more intense rainfall events are becoming more likely.

The socio-economic impacts were devastating, with nearly 4,000 confirmed fatalities in Derna alone, over 10,000 missing, and economic losses estimated at $80 million. Our findings underscore the critical vulnerability created when urban expansion encroaches upon natural floodplains without adequate infrastructure resilience.

This study demonstrates the power of multi-source satellite remote sensing for rapid disaster assessment and highlights the urgent need for integrated flood risk management that considers both climatic extremes and anthropogenic modifications to natural water systems. The lessons from Derna have profound implications for urban planning, dam safety protocols, and climate adaptation strategies in vulnerable Mediterranean regions facing increasingly extreme weather events.

How to cite: Agarwal, V. and Kumar, M.: Man-Made or Natural: Deciphering the Complex Factors Behind the 2023 Derna FloodDisaster, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22246, https://doi.org/10.5194/egusphere-egu26-22246, 2026.

How to cite: Barella-Ortiz, A., Quintana-Seguí, P., Cid-Giménez, J., Clavera-Gispert, R., Altés-Gaspar, V., Villar, J. M., Munier, S., Laluet, P., Olivera-Guerra, L. E., and Merlin, O.: Comparison of Irrigation Scenarios in the Ebro Basin Using the SASER Modelling Chain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22992, https://doi.org/10.5194/egusphere-egu26-22992, 2026.

Anthropogenic activities including industrial and agricultural discharges, sand mining, and water regulation have drastically altered the hydrological regime and water quality of Poyang Lake, China’s largest freshwater lake. These modifications lead to non-stationary inputs of heavy metals (e.g., As, Hg, Cr, Se) and nutrients, driving eutrophication and posing significant risks to aquatic ecosystems.

This study analyses multi-year (2018–2020) water quality data from key inflow sites to quantify human impacts on contaminant regimes. Results reveal strong seasonal patterns: heavy metal concentrations (As, Hg) peak during low-flow periods, whereas nutrient loads and algal blooms intensify following high-flow events linked to agricultural runoff. This dynamic hydrological contamination directly threatens the endangered Yangtze finless porpoise (Neophocaena asiaeorientalis), with tissue analyses showing high bioaccumulation of Hg and Cu in the liver, indicating significant ecological risk.

Building on these findings, we highlight the urgent need for forecasting frameworks tailored to human-influenced catchments. We propose integrating process-based hydrological models with water quality modules and machine learning techniques to simulate contaminant transport under non-stationary climatic and anthropogenic drivers. Furthermore, we demonstrate how remote sensing and continuous sensor data can improve the monitoring of pollutant sources and algal blooms. Finally, we outline a pathway towards ecological risk forecasting by coupling hydrological-water quality predictions with bioaccumulation models for vulnerable species.

This work underscores the critical gap in forecasting tools for heavily modified systems and provides a case for developing coupled human-natural models to support early warning systems and adaptive management strategies for biodiversity conservation.

How to cite: Sabir, A., Hua, W., Hanan, A., Deng, Y., and Wu, X.: From Contamination to Forecast: Linking Anthropogenic Hydrological Change to Ecological Risk in Poyang Lake, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1322, https://doi.org/10.5194/egusphere-egu26-1322, 2026.

• Rainfall-induced shallow landslides pose a persistent hazard in the Eastern Himalaya, particularly along the strategically important Balipara–Charduar–Tawang (BCT) corridor in western Arunachal Pradesh, India. This study develops a region-specific rainfall threshold framework by integrating long-term rainfall trend analysis with empirical landslide-triggering thresholds to enhance early warning capabilities in this data-scarce, high-relief terrain. Daily gridded rainfall data from the India Meteorological Department (2000–2020) and an inventory of 236 landslide events recorded between 2008 and 2015 were analyzed. Trend analysis reveals a statistically significant decline in annual rainfall (–81.05 mm yr⁻¹), accompanied by pronounced inter-annual variability and persistent monsoonal dominance. Empirical analysis indicates that short-term antecedent rainfall plays a critical role in slope failure initiation, with 3-day and 5-day cumulative rainfall showing the strongest correlation with landslide occurrence (R² = 0.508 and 0.480, respectively). Corresponding 80th percentile thresholds of ≥89.24 mm (3-day) and ≥118.80 mm (5-day) are proposed as practical triggering criteria. In addition, an intensity–duration (I–D) threshold derived from 95 rainfall-induced landslides follows a negative power-law relationship (I = 17.26·D⁻⁰·¹⁰), capturing the influence of short-duration, high-intensity rainfall events. The combined use of antecedent rainfall and I–D thresholds effectively represents both progressive soil saturation and rapid-onset rainfall triggers. This integrated threshold framework provides a robust and scalable basis for landslide early warning system development along the BCT corridor and offers broader applicability to similar monsoon-dominated Himalayan regions.

How to cite: kar, S., Chaturvedi, P., and Negi, H. S.: Integrating Intensity–Duration and Antecedent Rainfall Thresholds for Shallow Landslide Prediction in the Eastern Himalaya, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1658, https://doi.org/10.5194/egusphere-egu26-1658, 2026.

Extreme hydro-meteorological events associated with the Coastal El Niño phenomenon represent a critical threat to the socioeconomic stability of the northern coast of Peru. In particular, the Piura River basin, characterized by its complex topography and short concentration times, requires precise monitoring and modeling to address these episodes. Currently, the National Meteorology and Hydrology Service of Peru (SENAMHI) employs a semi-distributed system (ARNO/VIC coupled with RAPID) for the operational assessment of flood risk. However, the increasing intensity and frequency of recent events highlights the need for tools that explicitly represent physical processes at higher resolution. This research proposes the implementation of the fully distributed WRF-Hydro model, focusing the methodology on the reconstruction and analysis of the main extreme flood events within the period covered by the PISCOp_h product, a gridded hourly precipitation observational dataset developed by SENAMHI for 2015–2020. The methodological strategy is based on generating a hybrid meteorological forcing to feed the hydrological model. For this purpose, an atmospheric simulation is carried out with WRF, forced by initial and boundary conditions from the GFS, obtaining high-resolution distributed atmospheric fields. Given the uncertainty of the modeled precipitation, the rainfall field generated by WRF is replaced by the hourly gridded observations from PISCOp_h, ensuring controlled and realistic forcing. With this configuration, model calibration and validation are performed. Calibration prioritizes the highest-magnitude events, highlighting the 2017 Coastal El Niño episode for the adjustment of physical parameters, while validation considers a set of floods recorded between 2015 and 2020, evaluating the robustness of the system. It is expected to demonstrate that this combination of atmospheric dynamics and observational accuracy constitutes a physically consistent and operationally viable tool for predicting intense floods, strengthening flood risk management in Peru.

How to cite: Tufino, J. C., Huerta, A., Lavado-Casimiro, W., De la Cruz, G., Saavedra, D., and Ibañez, A.: Implementation and Evaluation of the WRF-Hydro Model for Hydrometeorological Forecasting in the Piura River Basin, Peru, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3205, https://doi.org/10.5194/egusphere-egu26-3205, 2026.

Accurate runoff estimation is fundamental to improving streamflow forecasting, particularly in large river basins with sparse or uneven rain-gauge coverage. This study investigates the identification of representative rain gauges from a densely but randomly distributed network to support reliable runoff simulation in data-limited regions. The Middle Tapi Basin (MTB), comprising 26 operational rain gauges and extensive ungauged areas, is used as a case study. Four approaches—Hall’s method, K-means clustering, hierarchical clustering (HC), and self-organizing maps (SOM)—are applied to identify key rain gauges that effectively capture the spatial variability of basin-scale rainfall. Hall’s method selected 15 representative stations, whereas the clustering-based approaches identified nine stations each. The performance of the resulting rain-gauge networks is evaluated by simulating basin runoff using a lumped hydrological model. Results indicate that the rain-gauge network derived from Hall’s method consistently produces superior runoff simulations compared to the clustering-based networks, demonstrating improved representation of rainfall inputs at the basin scale. Based on these findings, the use of 15 key rain gauges identified through Hall’s method is recommended for runoff prediction in the Middle Tapi Basin. The proposed framework is transferable and can be applied to other large basins with heterogeneous rainfall patterns and limited monitoring infrastructure, offering a practical approach for optimizing rain-gauge networks to enhance hydrological modelling and flood forecasting.

How to cite: Yadav, S. and Panchal, A.: Designing efficient rain-gauge networks for improved flood forecasting in a large river basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3693, https://doi.org/10.5194/egusphere-egu26-3693, 2026.

Rapid dam construction, rising water demand, climate change, and increasing pollution are exposing critical weaknesses in the governance of freshwater systems worldwide, particularly in shared river basins. Although environmental flows (e-flows) are widely recognised as essential for sustaining riverine ecosystems and long-term water security, their integration in transboundary water governance has remained largely symbolic, weakly enforced, and poorly adapted to climatic uncertainty. Even durable agreements in several regions prioritise volumetric allocation and procedural cooperation, offering limited mechanisms to safeguard e-flow regimes, as illustrated by treaties such as the Indus Water Treaty and the Ganga Water Sharing Treaty. This study argues that the persistent failure to operationalise transboundary e-flows in national and transboundary river basin governance frameworks reflects a deeper and systematic governance implementation gap that has not been adequately addressed in existing literature. Much of the literature examines legal provisions, economic instruments, and monitoring systems as separate domains rather than as interdependent components of operational governance. As a result, many transboundary river agreements pair legal allocation rules with flow monitoring but fail to link these to enforceable e-flow obligations or adaptive responses. To investigate this gap, the study undertook a structured comparative analysis of ten major international treaties and river basin agreements across Asia, Africa, Europe, and North America, covering both bilateral and multilateral transboundary river systems. Existing treaties were assessed to identify why most fail to deliver implementable e-flow solutions, while arrangements where elements of effective implementation exist were examined to extract transferable best practices for future transboundary water agreements. Based on the findings, the study proposes a three-tier governance framework to operationalise transboundary e-flows under climate uncertainty. The framework integrates climate-adaptive legal obligations, economic and financial mechanisms, and monitoring, reporting, and verification systems supported by remote sensing and GIS. By reframing e-flows as an implementable component of cooperative water security, this study makes both a conceptual and practical contribution to transboundary water governance, with implications for ecological resilience, conflict reduction, and long-term regional stability.

Keywords : Water demand, Climate change, River basin treaties, Ecological resilience

How to cite: Malhotra, K. B. and Nema, A. K.: Implementing Environmental Flows in Transboundary Rivers under Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4740, https://doi.org/10.5194/egusphere-egu26-4740, 2026.

Effective management of eutrophication in inland lakes requires spatially continuous information on key water-quality variables at management-relevant scales. However, metre-scale mapping of total phosphorus (reported as “Phosphorus, Total”, PPUT; µg/L) remains difficult to achieve using conventional in-situ sampling, and nearshore gradients and tributary plumes are often poorly resolved by medium-resolution satellite sensors. In this study, we exploit multi-generation PlanetScope imagery (Dove Classic, Dove-R, and SuperDove; 3–5 m, near-daily revisit) to develop a hybrid, physics-informed AI framework for PPUT retrieval in Lake Simcoe, Ontario, Canada. PlanetScope surface reflectance is combined with short-term meteorological descriptors (3–7-day aggregates of air temperature, wind speed, precipitation, and sea-level pressure) and in-situ Secchi depth (SSD) to train five ensemble-learning models (HistGradientBoosting, CatBoost, RandomForest, ExtraTrees, and GradientBoosting) across eight feature-group regimes. Inclusion of SSD yields a substantial performance gain, with mean R² increasing from ~0.67 (SSD-free) to ~0.94 (SSD-aware), confirming that vertically integrated optical clarity is the dominant constraint on phosphorus retrieval and cannot be reconstructed from surface reflectance alone. To enable scalable SSD-free monitoring, we implement a teacher–student knowledge-distillation scheme in which an SSD-aware teacher transfers its representation to a student using only satellite and meteorological inputs. The optimal student, based on a compact subset of 40 predictors, achieves R² = 0.83, RMSE = 9.82 µg/L, and MAE = 5.41 µg/L on unseen monitoring stations, and is applied to 2020–2025 PlanetScope scenes to generate metre-scale PPUT maps. A 26 July 2024 case demonstrates that >97% of the lake surface remains below 10 µg/L, while rare (<1%) but spatially coherent hotspots >20 µg/L coincide with tributary mouths and narrow channels, highlighting priority areas for management intervention. Although demonstrated here for phosphorus, the PlanetScope–KD framework is model-agnostic with respect to the target variable and can be retrained for other water-quality parameters with optical or hydro-meteorological controls, such as chlorophyll-a, dissolved oxygen, and surface water temperature. This opens a pathway toward unified, high-resolution, multi-parameter lake water-quality prediction to support adaptive monitoring and lake-basin management.

How to cite: Deng, Y., Pan, D., Yang, S., and Gharabaghi, B.: Knowledge Distillation of PlanetScope Imagery for Metre-Scale Lake Water-Quality Mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8119, https://doi.org/10.5194/egusphere-egu26-8119, 2026.

How to cite: Cheung, P. K.: Is blue-green infrastructure effective in reducing urban flood depth and area?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8693, https://doi.org/10.5194/egusphere-egu26-8693, 2026.

Impact of Organoclay Content on Hydraulic Performance of Filter Strips to Treat Urban Runoff

Yaren Ozturk ¹, Derya Ayral Cınar ² 

¹ Marmara University, Istanbul, Turkiye

² Gebze Technical University, Kocaeli, Turkiye  

Abstract. 

Due to urbanization and climate change, it has become common for urban runoff to carry pollutants to surface water bodies, wastewater treatment plants, infrastructure systems and groundwater. Pollutants transported include heavy metals, solids, nutrients, pathogens and various organic substances such as pesticides and polycyclic aromatic hydrocarbons (PAH). It is proposed to manage this pollutant load at source before it reaches receiving environments.  Nature-based solutions such as filter ditches, infiltration ponds or rain gardens are considered more efficient to manage urban runoff. Among these methods, filter ditches have the highest potential to treat pollutants. It is thought that the use of organoclays, synthesized by the integration of surfactants into the clay mineral structure, as filter material may increase a common contaminant in urban runoff -PAH- removal compared to conventional clay minerals. In addition to treatment efficiency, another important parameter in designing filter ditches is the hydraulic permeability of the filter material. It is desirable that the infiltration rate of the surface flow is slow enough to allow time for pollutant removal and fast enough to prevent ponding on the filter. This study investigated how organoclays, which are proposed to enhance PAH removal from urban runoff, affect the hydraulic permeability of the filter material. Organoclay synthesized by Ca-montmorillonite and HDTMA is used at different percentages in the filter material mixture and hydraulic permeability was determined. Hydraulic conductivity of sand was 4.5x10^-4 cm/s and it dropped to 2.4x10^-5 cm/s and 2.3x10^-5 cm/s when 10% and 20% clay was used, respectively. On the contrary, organoclay at 10% and 20% did not decrease the hydraulic conductivity significantly (to 1.5x10^-4 cm/s and 1.4x10^-4 cm/s, respectively). As hydraulic conductivity is suggested to be 0.3-1.4 x 10^-4 cm/s for surface runoff treatment systems, it appeared that using 20% organoclay is promising to treat emerging pollutants such as PAHs without comprimising the hydraulic performance of the filter system.

Keywords: Nature based solutions, urban runoff, climate change, filter strips, organoclay

How to cite: Ozturk, Y. and Ayral Cınar, D.: Impact of Organoclay Content on Hydraulic Performance of Filter Strips to Treat Urban Runoff, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8965, https://doi.org/10.5194/egusphere-egu26-8965, 2026.

To address climate change and population growth, Central Asia must urgently adopt a holistic water resource management strategy, moving beyond traditional sectoral approaches to embrace a water-energy-food-ecosystems (WEFE) nexus approach.  The WEAP model, a key research tool, integrates hydrological data and socioeconomic factors to create scenarios considering glacier melt, irrigation expansion, and energy generation. WEAP, unlike hydrological models, highlights unmet demand, demonstrating the sectoral impacts of water scarcity for decision-makers. The Nexus approach uses the WEAP model to optimize the Vakhsh hydropower cascade (Nurek and Rogun plants), balancing energy security, environmental flows, and predictable agricultural water supply. The WEAP model assesses innovative irrigation technologies in the Zarafshon basin to enhance food security and cross-border cooperation between Tajikistan and Uzbekistan. The scenario analysis shows that modernizing irrigation systems reduces the burden on the ecosystem and ensures stable harvests even in dry years. Integrating climate forecasts into WEAP allows for water availability scenarios, enabling adaptation measures like optimized cropping and expanded runoff management. WEAP modeling in the Vakhsh and Zarafshon basins highlights the importance of cross-sectoral considerations for water resource management in Tajikistan, providing a basis for sustainable water system decisions.

How to cite: Niyazov, J.: A Nexus-Based Approach to Water Resources Assessment: Practical Application of the WEAP Model in Tajikistan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9585, https://doi.org/10.5194/egusphere-egu26-9585, 2026.

The primary goal of hydrological modeling is to generate reliable forecasts of future changes in water resources. In the arid conditions of Central Asia, where irrigated agriculture demands significant water resources during summer, early forecasting is crucial for planning water allocation between upstream and downstream regions.

The WEAP model offers a flexible and user-friendly framework for addressing various water resource management challenges. It supports decision-makers and experts in constructing and selecting optimal solutions for water management. Accurate hydrological forecasts of water availability during the growing season are essential for effective water resource planning. National hydrometeorological services in Central Asia are adopting and adapting modern, effective methods for hydrological forecasting. The primary goal of developing a methodology for forecasting river water content in the Kyrgyz Republic, using the WEAP model, is to create a calculation algorithm, simulate a water management model, and implement this methodology into the practices of the Kyrgyz Republic's National Hydrometeorological Services. This approach will be applied to forecast water availability in the Naryn River during the growing season, monitor changes in the Toktogul Reservoir's water volumes, support hydroelectric power production, and facilitate agricultural irrigation. Advanced forecasts of low water availability during the growing season are vital for implementing preventive measures to ensure efficient water use by water and energy management organizations.

The WEAP model allows for the use of various scenarios, such as climatic ones, with a focus on the national level, while introducing various innovative technologies for irrigation and energy conservation in the upcoming years. This is significant for long-term planning in water management activities and the energy strategy of both the country and the region.

How to cite: Kalashnikova, O.: Predictive WEAP modeling for NEXUS management in the Naryn River basin (Kyrgyzstan), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9627, https://doi.org/10.5194/egusphere-egu26-9627, 2026.

Inter-state agricultural trade plays a critical role in redistributing water resources across India, particularly for water-intensive crops such as rice. This study examines the structure of inter-state rice virtual water trade in India using a directed, weighted complex network approach. Physical inter-state rice trade data covering all Indian states were obtained from the Directorate General of Commercial Intelligence and Statistics (DGCIS) and transformed into virtual water flows using crop-specific virtual water content coefficient for rice (m³/ ton), assumed to be uniform across states. This transformation enables an assessment of trade relationships in terms of embodied water transfers rather than physical commodity volumes. States are represented as nodes and directed edges denote rice virtual water flows from exporting to importing states, weighted by total virtual water volumes (m³). Network properties were analysed using strength-based measures to quantify import and export intensities, betweenness centrality to identify states functioning as key intermediaries in trade pathways, and PageRank to assess systemic importance within the national virtual water trade system. These metrics jointly allow differentiation between dominant exporting states, import-dependent states, and structurally central states influencing the overall redistribution of water through trade. The analysis reveals a highly centralized rice virtual water trade network, characterised by a small group of states accounting for a disproportionate share of total virtual water exports. States such as Punjab, Haryana, Andhra Pradesh, Chhattisgarh, Uttar Pradesh, Odisha, and Madhya Pradesh emerge as major exporters, while several other states rely predominantly on inter-state imports to meet rice demand. The concentration of virtual water exports among a limited number of producing regions indicates strong structural dependencies within the national trade network. Several major exporting states like Punjab are also subject to increasing pressure on water resources, the observed trade patterns raise concerns regarding the sustainability of current production-trade configurations. By integrating crop-specific virtual water accounting with complex network analysis, this study provides a quantitative framework for identifying key contributors, dependencies, and structural vulnerabilities in India’s inter-state agricultural water redistribution system. The methodology is transferable to other crops, years, and regional contexts and offers a basis for informing discussions on sustainable agricultural trade and water resource management.

How to cite: Badoni, A. and Reddy, M. J.: Mapping Inter-State Rice Virtual Water Trade in India Using Complex Network Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11912, https://doi.org/10.5194/egusphere-egu26-11912, 2026.

Accurate drought forecasting is crucial for effective agricultural risk management in semi-arid regions, particularly in drought-prone regions of Maharashtra, India, where the majority of the population relies on farming. This study develops a one-month-ahead drought forecasting using random forest regression, an ensemble tree-based machine-learning algorithm, using the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple temporal scales. Random Forest regression models were trained to forecast SPEI-3, SPEI-6, and SPEI-12, incorporating rainfall, temperature, and derived hydro-climatic predictors. Model performance exhibits clear timescale-dependent predictability, with skill increasing for longer accumulation periods: SPEI-3 (R² = 0.55, RMSE = 0.81), SPEI-6 (R² = 0.65, RMSE = 0.69), and SPEI-12 (R² = 0.87, RMSE = 0.38). Corresponding generalization ratios of 62.4%, 71.8%, and 90.5% indicate improved robustness and reduced overfitting at short (SPEI-3) to long (SPEI-12) timescales. Feature importance analysis consistently highlights the current SPEI state, contributing approximately 35–40% of the total importance, followed by the precipitation minus potential evapotranspiration (PPET) balance and other hydro-climatic variables, reflecting the dominant role of drought persistence and climatic memory in one-month-ahead forecasting. The models successfully capture spatial drought patterns, though reduced accuracy is observed for extreme drought magnitudes at shorter timescales, likely due to inherent climate non-stationarity and rapidly evolving predictor relationships. Overall, this study demonstrates the effectiveness of machine-learning-driven, one-month-ahead drought forecasting across multiple SPEI time scales, enabling near-real-time monitoring and early warning depending on the selected accumulation period. The proposed framework provides a scalable foundation for operational drought early-warning systems in Maharashtra and other drought-prone hydro-climatic regions worldwide.

Keywords: SPEI, Drought forecasting, Random Forest

How to cite: Ganjir, G., Reddy, M. J., and Karmakar, S.: Multi-Timescale SPEI Drought Forecasting Using Random Forest Regression over Maharashtra, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12410, https://doi.org/10.5194/egusphere-egu26-12410, 2026.

Water scarcity across the Mediterranean is increasingly forcing economies that are deeply integrated into global markets to balance export performance with long-term water sustainability. Research on “virtual water” and trade-related water footprints has grown rapidly, yet it remains fragmented: studies rely on diverse frameworks (physical accounting, MRIO, life-cycle assessment, and hybrids), use non-uniform scarcity metrics, and often treat climate projections and adaptation only implicitly. This review asks: which methods are currently used to estimate the water footprint of trade under climate constraints, what limits their comparability, and what methodological protocol is needed for a robust, policy-relevant application to Morocco? By framing trade-related water footprints as part of coupled human–water systems, the review highlights how economic structures, trade choices, and climate-driven water scarcity interact and generate feedbacks relevant for water governance and policy design.

We follow a PRISMA-type workflow based on systematic searches in Scopus and Web of Science, with explicit inclusion/exclusion criteria and standardized data extraction. Studies are coded along five dimensions: (i) data type (monetary vs. physical); (ii) modelling approach (IO/MRIO, LCA, hybrid); (iii) treatment of scarcity (stress factors, availability indicators, scarcity-adjusted footprints); (iv) integration of climate change (scenarios, downscaling, hydrological modelling); and (v) potential to inform policy (efficiency improvements, reallocation options, abstraction caps, and economic or trade-related instruments). Institutional sources are used in a complementary way to document indicator frameworks and datasets, without replacing the peer-reviewed evidence base.

The review delivers (1) an operational typology of methods used to quantify trade-related water footprints under climate stress; (2) a diagnosis of key comparability barriers (spatial resolution, upstream embodied water through inputs, and the limited use of dynamic approaches); and (3) a practical empirical agenda linking hydrological projections, water-extended input–output frameworks, and decision-relevant scarcity metrics. Outputs will be shared through a reusable coding grid and an analytical framework diagram to support country studies and comparative work across the Mediterranean.

How to cite: Tasra, F. and Mafamane, D.: How Much Water Is Embedded in Trade? A Systematic Review and Research Roadmap for Morocco under Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14286, https://doi.org/10.5194/egusphere-egu26-14286, 2026.

Accurate flood forecasting in complex river basins depends on the effective use of high-resolution hydro-meteorological information, physically based hydrological models, and appropriate calibration procedures. This work describes the development of an event-based flood modelling framework for the Arno River basin (Italy), designed to enhance the simulation of flood hydrographs and support operational flood forecasting activities.

Spatially distributed rainfall data were obtained from raster-based precipitation products and transformed into event-specific time series suitable for use within the distributed hydrological model MOBIDIC. Observed discharge records from several gauging stations were retrieved from raw monitoring archives and reorganized into event-based datasets, allowing a coherent and consistent comparison between simulated and observed hydrographs. A unified processing workflow was established to ensure proper temporal synchronization among rainfall inputs, model outputs, and discharge observations.

The proposed framework was tested on major flood events that occurred in November 2023. Model performance was assessed using standard evaluation metrics, including the Nash–Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), correlation measures, and time-lag analysis. Results from the initial simulations show that the model is able to capture flood timing satisfactorily, while differences in peak discharge magnitude and recession dynamics indicate the necessity for targeted parameter calibration.

A preliminary manual sensitivity analysis was carried out to identify key soil and hydraulic parameters influencing runoff generation and channel routing processes. Building on these results, an automated calibration approach based on PEST++ is currently being developed to systematically optimize the most sensitive parameters and improve model performance across multiple flood events.

Overall, the presented framework offers a reproducible and scalable methodology for event-based flood modelling and calibration in complex catchments. It provides a solid basis for multi-event analyses, automated calibration, and the future incorporation of data assimilation and artificial intelligence techniques into operational flood forecasting systems.

How to cite: Abbasi, H. K. J., Castelli, F., and Masi, M.: Event-Based Calibration of a Physically-Based Hydrological Model for Flood Simulation in the Arno River Basin Tuscany Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14773, https://doi.org/10.5194/egusphere-egu26-14773, 2026.

Urban water pollution remains a major challenge for sanitation management in Brazil and other tropical regions. In areas served by separate sewer systems, illicit domestic sewage connections to stormwater drainage networks represent a significant source of contamination of urban runoff and receiving water bodies. Conventional inspection techniques for identifying such contributions are often operationally complex, spatially limited, and therefore rarely applied. Distributed temperature sensing techniques have been successfully used in temperate regions to detect sewage inputs based on thermal contrasts; however, their applicability under tropical conditions remains poorly explored.

This study investigates the thermal signature of domestic sewage in a tropical urban environment and evaluates the detectability of illicit sewage discharges in stormwater systems using a simplified thermal mixing model. Sewage temperature was monitored using thermocouples connected to a data logger with 1-minute temporal resolution in a sewer interceptor located at the São Carlos School of Engineering, University of São Paulo, Brazil, in an area characterized by student housing and food service facilities. Two monitoring campaigns were conducted. Mean sewage temperatures of 27.45 ± 0.45 °C (November 2024–April 2025) and 24.21 ± 0.54 °C (September–November 2025) were observed. A moderate Pearson correlation between sewage temperature and local air temperature (r = 0.58, p < 0.05, n = 140) indicates that atmospheric conditions partially influence sewage thermal variability.

Based on the monitored sewage temperatures (T₂) and stormwater temperature data (T₁) from the literature, a preliminary theoretical model was developed using an instantaneous energy balance approach. The model relates the detectable temperature variation (ΔT) to the sewage fraction (f), defined as the ratio between sewage discharge (Q₂) and stormwater flow (Q₁). Results indicate an exponential relationship between f and ΔT for different thermal contrasts (T₂ − T₁). The minimum detectable sewage discharge was found to be highly sensitive to ΔT, associated with the thermal resolution of the sensing system, while showing direct proportionality to stormwater flow and inverse proportionality to the thermal contrast between sewage and runoff. Future work will focus on model validation under field conditions and its extension to non-stationary flow regimes.

How to cite: de lima neto, E., Bertotto, L. E., and Wendland, E. C.: Applicability of distributed thermal sensing for identifying illicit sewage connections in urban drainage networks under tropical climates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15359, https://doi.org/10.5194/egusphere-egu26-15359, 2026.

The Vu Gia – Thu Bon River Basin (VGTBRB), Central Vietnam’s largest river basin (about 10,350 km²), flows through Quang Nam Province and Da Nang City. It supplies water for multiple purposes, including hydropower generation (with around 20 operational upstream hydropower plants), irrigation, and domestic use (accounting for almost 70% of domestic use for Da Nang). While this multifunctional role supports the regional socio-economic development, the basin is increasingly challenged by intensifying water, energy, and food (WEF) demands driven by population growth, urban expansion, tourism development, and salinity intrusion, highlighting the need for an integrated Water-Energy-Food nexus approach.

Despite growing global research on the WEF nexus, no comprehensive statistical WEF nexus models have been developed for the VGTBRB. Previous studies in the region have largely focused on individual sectors, overlooking the role of salinity intrusion and its implications for water demand, food production, and tourism-related resource use. This study addresses this gap by employing a WEF nexus framework combined with System Dynamics Modelling (SDM) to capture sectoral interactions, feedback mechanisms, and trade-offs in water allocation under future climate and socio-economic scenarios. The analysis incorporates historical data from 2010 to 2024 for model calibration and validation, and projections for 2025–2050 aligned with climate change scenarios and the regional Master Plan for 2021–2030 with a vision to 2050.

Results indicate pronounced seasonal variability in water demand, critical feedback between temperature and domestic water use, and interactions between rainfall and water use that influence the risks of salinity intrusion at downstream water supply intakes. In addition, a positive relationship is identified between tourism growth and water demand, particularly during dry seasons, which exacerbates water stress.

By explicitly integrating salinity and tourism dynamics, this study pioneers a WEF nexus-based modelling approach for the VGTBRB. The findings provide policy-relevant insights to enhance water system resilience under climate and socio-economic change, support progress towards the Sustainable Development Goals, and inform integrated resource governance in a tourism-dependent, salinity-affected river basin.

How to cite: Hoang, L., Shamseldin, A. Y., Henning, T. F. P., Latu, K., Zorn, C., and Dong, S.: Assessing the Water-Energy-Food Nexus under Climate and Socio-economic Change in Vu Gia – Thu Bon River Basin, Vietnam, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15614, https://doi.org/10.5194/egusphere-egu26-15614, 2026.

Deep learning models, particularly LSTMs, have transformed large-sample hydrology by achieving high streamflow predictive performance, yet they remain largely black-box approaches with limited physical interpretability and no explicit representation of multiphysical hydrological processes. Differentiable, learnable process-based models (or δ-models) overcome these limitations by embedding neural networks within differentiable physics frameworks. While existing benchmarks like HBV-δ have proven this concept across 671 US basins, they rely on conceptual foundations (e.g., empirical beta-functions) that approximate, rather than resolve, underlying soil physics. This study introduces MILC-δ (Modular Differentiable Physic-Informed Learning), designed to bridge this gap. The MILC model utilizes continuous soil water retention curves and physically derived drainage laws, which can aid in more accurate hydrological flux simulation. Thus, we developed a MILC-δ - a hydrologic model embedded with neural networks and trained in a differentiable programming framework. Consequently, MILC-δ is anticipated to match or exceed HBV-δ by leveraging neural networks to map static catchment attributes directly to physically measurable properties (e.g., pore size distribution, hydraulic conductivity) rather than abstract calibration parameters. Initial testing of the developed model shows that the model performs at par in some basins and better than HBV-δ in other basins. This approach gives LSTM-level accuracy and generalizability as well as the clear physical story stakeholders actually need to explain the decline in baseflow, threats to the groundwater recharge, etc.

How to cite: Sharma, V., Barbhuiya, S., and Gupta, V.: Differentiable, Learnable MILC: Balancing Predictive Skill and Physical Interpretability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16119, https://doi.org/10.5194/egusphere-egu26-16119, 2026.

Accurate forecasting of reservoir inflow is crucial for managing water resources, maintaining a balance between water supply and demand, preventing floods, supporting hydropower production, and planning irrigation. India, ranking third globally, with more than 5,000 dams, faces challenges in reservoir operations due to hydrological variability caused by the monsoon. While ensuring demand, supply, and flood security requires high water levels, the reservoir also needs to maintain a certain amount of free storage to accommodate high inflows. While Long-Short Term Memory (LSTM) models have been widely used for inflow forecasting, traditional LSTM models often limit their ability to capture sudden hydrological extremes and accurately represent peak timings. Therefore, a comparative evaluation of various advanced LSTM variants is necessary to identify architectures that are more reliable for modelling nonlinear inflow dynamics. Our study introduces a specialised type of recurrent neural network, specifically the LSTM framework, for forecasting daily reservoir inflow. Our methodology uses a structured feature engineering strategy that integrates hydrometeorological forcings, hydrological state variables, and outputs from the CaMa-Flood hydrodynamics model. A permutation-based feature importance analysis, in terms of the increase in mean absolute error, highlights that antecedent precipitation and lagged upstream reservoir outflow are the main influencing factors for the inflow forecast within a multivariate sequence-to-one LSTM framework. Overall, this framework provides a strong, scalable, and practical solution for inflow forecasting. By supporting timely operational decisions for water release, flood preparedness and storage optimisation, the framework serves as an effective tool for managing reservoirs.

How to cite: Mani, D. and Mishra, V.: Data-Driven LSTM Architectures for Reservoir Inflow Forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16207, https://doi.org/10.5194/egusphere-egu26-16207, 2026.

Streamflow provides critical support for the biodiversity of aquatic and riparian ecosystems, sediment transport, and nutrient cycling. Therefore, a minimum streamflow in rivers is crucial for sustaining the proper functioning of aquatic habitats. However, lean or low-flow regimes have been significantly altered by various human activities, such as dam construction, flow diversion for irrigation purposes, industrialisation, and urbanisation. Moreover, changing climate is causing erratic monsoons, increased temperatures, and more prolonged droughts, thereby maintaining ecological flow has become increasingly challenging and urgent to preserve the riverine ecosystems. Our aim is to develop a robust, data-driven framework for estimating environmental flows (E-flows) across 55 stations in major Indian river basins. The primary objective is to assess the quantity and timing of streamflow required to sustain the various river ecosystems, utilising hydrological indicators and long-term datasets, such as temperature and precipitation from the Indian Meteorological Department (IMD). Changes in streamflow characteristics are assessed by comparing observed and machine learning-based naturalised flows, enabling the isolation of reservoir-induced impacts on the streamflow regime, magnitude, duration, and seasonal timing. The study hypothesises that, with the use of observed streamflow data, naturalised streamflow reconstruction and a multi-indicator hydrologic approach, integrating Indicators of Hydrological Alteration (IHA), the Range of Variability Approach (RVA), and Flow Duration Curve (FDC) analysis, can provide reliable E-Flow estimates at regional and national scales. By comparing indicator-based benchmarks derived from observed and naturalised streamflow, the stations are classified according to the degree of hydrologic alteration, thereby supporting scientifically informed river management and policy decisions. 

How to cite: Sahil, S. and Mishra, V.: Estimation of Ecological Flow for Major Indian River Basins under Changing Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16490, https://doi.org/10.5194/egusphere-egu26-16490, 2026.

Lakes and associated hydrological processes are sensitive indicators of environmental change and climate variability. Variations in lake water level and storage reflect the combined effects of atmospheric forcing (precipitation, evaporation, and temperature regime) and anthropogenic interventions, including irrigation, drainage, and hydraulic infrastructure development. Continuous monitoring of lake level dynamics is therefore essential for water resources management, evaluation of regional climate impacts, and assessment of environmental risks in arid and semi-arid regions.

Central Asia has experienced pronounced hydrological transformations over recent decades as a result of climate warming, altered precipitation patterns, and intensified human water use. These changes are manifested in contrasting lake responses, ranging from the dramatic desiccation of the Aral Sea to the expansion of endorheic water bodies receiving anthropogenic inflows. Lake Sarikamish, one of the largest lowland lakes in the region, is located along the Uzbekistan–Turkmenistan border near the escarpment of the Ustyurt Plateau and represents a key example of such coupled natural–human system dynamics.

This study investigates water level variability of Lake Sarikamish over the period 2001–2024 using satellite altimetry observations from the Global Reservoirs and Lakes Monitor (G-REALM) database. The dataset, provided at a 10-day temporal resolution in NetCDF format, was processed to construct a continuous long-term time series. Short data gaps were filled using linear interpolation, a method previously shown to yield robust performance for altimetric lake level records. Descriptive statistics and trend analyses were applied to quantify intra-annual variability, interannual fluctuations, and long-term tendencies.

The minimum lake level during the observation period was recorded in February 2002 (4.23 m), while the maximum level occurred in April 2018 (8.84 m). The time series exhibits substantial interannual variability, with a standard deviation of 0.91 m. Four distinct phases of lake level evolution were identified: (i) a rapid increase during 2001–2007 at a rate of +0.56 m yr⁻¹, (ii) a short-term decline in 2008–2009 (−0.60 m yr⁻¹), (iii) a prolonged period of moderate increase during 2010–2020 (+0.15 m yr⁻¹), and (iv) a renewed decrease during 2021–2024 (−0.36 m yr⁻¹). Despite the recent downward trend, the overall period is characterized by a net positive trend of +0.16 m yr⁻¹.

The observed post-2020 decline suggests an increasing influence of regional climate change, particularly rising air temperatures and reduced effective precipitation. Continued water level lowering may have negative consequences for local ecosystems, biodiversity, and environmental stability. The results highlight the value of satellite altimetry for long-term lake monitoring and emphasize the need for integrated assessments of climatic and anthropogenic drivers of lake hydrological change in Central Asia.

How to cite: Umirzakov, G., Kalabaev, S., Gafurov, A., and Turgunov, D.: Remote Sensing–Based Monitoring of Lake Sarikamish Water Level Dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17089, https://doi.org/10.5194/egusphere-egu26-17089, 2026.

The present study investigates the hydrological performance of two Nature Based Solutions (NBS) realised within the urban requalification project of the former military area “Caserma Gavoglio” (now public park), in one of the most heavily urbanized districts of the city of Genoa (Italy). The rapid expansion of urbanization has led to an increase in impervious surfaces and a consequent increase in runoff generation, flood volume and flood peak. Since the required expansion of the stormwater drainage capacity is neither economically nor environmentally sustainable, innovative stormwater management strategies are required. In this context, NBSs represent effective solutions to mitigate runoff generation and peak flows and restore natural infiltration processes.

A resin gravel permeable pavement (PP) was used for the paving of about 40% of the park surfaces while a bioswale was realised alongside the sport field to manage stormwater excess.  

The PP was preliminarily tested in the laboratory by monitoring the outflow from a standardized test bed under various rainfall input and slope conditions. The results of the tests were interpreted mathematically using the analogy of the step response function of first- and second-order dynamic systems. This allows to transfer the laboratory results for comparison with field conditions, even if these were not precisely reproduced in the laboratory tests.

Both NBSs were monitored in the field with the objective to measure the outflow rate, representing the inflow to the urban drainage system, and to compare it with the corresponding rainfall input.

Two hydrometric measurement stations and one rain gauge station were installed. Since the stormwater drainage system was already in place, water stage probes were housed inside existing manholes equipped with suitable “V-shaped” weirs. Due to non-standard operational conditions, the measurement stations were preliminarily tested in the laboratory to verify their accuracy prior to field installation.

From the monitored rainfall events, direct comparisons between the measured precipitation and the outflow hydrographs were performed. These analyses enabled the quantification of the retention and detention effects due to the NBSs and their improvement relative to typical impervious paving solutions. The following performance indicators were derived for each significant precipitation event that exceeded the retention capacity of the NBS: (i) the outflow coefficient, defined as the ratio between total outflow and rainfall volumes, (ii) the peak reduction coefficient, i.e. the ratio between peak discharge and peak rainfall intensity and (iii) the system response delay, i.e. the time lag between the centre of mass of the flow hydrograph and that of the rainfall.

Acknowledgements

This work was conducted in the framework of the Urban Nature LABs (UNALAB) project, under the “HORIZON 2020” programme, Smart and sustainable Cities-SCC-02-2016-2017, as a collaboration between the University of Genova (DICCA) and the Municipality of Genova (project partner).

How to cite: Ferro, M., Chinchella, E., Cauteruccio, A., and Lanza, L. G.:  Laboratory testing and in-situ monitoring of the hydrological response of a resin gravel permeable pavement and a bioswale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18311, https://doi.org/10.5194/egusphere-egu26-18311, 2026.

Climate change, rising water demand, and ecosystem stress are intensifying the reliance on groundwater while limiting the capacity of many basins to effectively monitor and manage subsurface water resources. In data-scarce and conflict-affected regions where monitoring networks are sparse, decision-makers increasingly require reliable, high-resolution information to support drought preparedness, climate adaptation, and sustainable groundwater governance. The present study proposes an evidence-based machine-learning framework for the purpose of enhancing the monitoring of groundwater storage anomaly (GWSA) through the process of downscaling GRACE and GRACE-FO observations from ~3° to 0.1°. The reconstruction of monthly GRACE/GRACE-FO gaps was performed using a Seasonal-Trend Decomposition based on Loess (STL), and a Random Forest model was trained with hydroclimatic and land-surface predictors, including soil moisture, snow water equivalent, evapotranspiration, precipitation, land-surface temperature, and the normalized difference vegetation index (NDVI). The performance of the model was evaluated by comparing the model's results with the existing in-situ groundwater-level observations in the Kabul River Basin. The results indicate that satellite-inferred groundwater losses in Afghanistan are persistent, with a rate of −0.71 cm yr^-1, ranging from basin-scale depletion of −0.77 cm yr^-1 in the Helmand River Basin to −0.40 cm yr^-1 in the Northern River Basin. Recent conditions indicate intensified depletion during 2018–2022, with year-sum GWSA declines reaching ~145 cm in the Harirod–Murghab River Basin, while the Northern River Basin shows comparatively lower losses (~80 cm). The 0.1° downscaled product improves agreement with observations (root mean square error (RMSE) reductions up to 77.8%) and reveals spatially heterogeneous hotspots that are not detectable at coarse GRACE resolution. Generally, the proposed framework translates coarse satellite gravimetry into actionable, basin-relevant information for climate-resilient groundwater management, while underscoring the necessity for uncertainty-aware, multi-source monitoring under increasing hydroclimatic extremes. The approach enables the early detection of emerging depletion hotspots, thereby supporting proactive planning for future water security. This includes targeted demand management, drought response, and adaptation investments in groundwater-dependent regions.

How to cite: Azizi, A. H., Akhtar, F., Borgemeister, C., and Tischbein, B.: Assessing Groundwater Storage Changes in Data-Scarce Basins of Afghanistan: A Machine-Learning Based Downscaling of GRACE(-FO) Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18956, https://doi.org/10.5194/egusphere-egu26-18956, 2026.

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) is a well-established technology to determine water heights in reservoirs, rivers, and lakes. A big advantage of GNSS-IR over traditional level measurements is that it is a non-contact flood-proof method. So far, GNSS-IR has been applied through off-site processing, necessitating a good internet connection for near-real-time monitoring. In Africa, where the TEMBO project seeks to develop in situ monitoring of weather and water, such connections are often not available, especially in more remote river valleys. Although a live satellite uplink would be possible, these tend to be costly and very energy-hungry. For this reason, equipment was developed that allowed local processing (edge processing). The advantage is that only water levels and some system information need to be communicated, which can be done with a simple satellite modem at very moderate costs. Existing gnssrefl code (https://gnssrefl.readthedocs.io/), written in Python, was rewritten in Rust to facilitate running the code on a PICO 2.  By reducing unneeded lines of code, the runtime was reduced from three minutes with the original Python code to less than three seconds. In all, energy use was minimized to avoid the need for large solar panels. With power cycling and uploads four times per day, the average power consumption was 44mW, which translates into a small solar panel of 1.2 W (66mm x 113mm). Water level measurement accuracy depended on integration time or, better, the number of satellites captured and was about 8cm when five or more satellites were captured. Total material costs, excluding the satellite modem, were about EU 50. The satellite modem and antenna were, at EU 360, the most expensive parts.

TEMBO Africa: The work leading to these results has received funding from the European Horizon Europe Programme (2021-2027) under grant agreement n° 101086209. The opinions expressed in the document are of the authors only and in no way reflect the European Commission’s opinions. The European Union is not liable for any use that may be made of the information.

How to cite: van de Giesen, N., De Laere, T., van Driel, J., van der Bijl, W., and Loen, S.: A Low-Cost Flood-Proof Water Level Measurement System, Using GNSS Reflectrometry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-75, https://doi.org/10.5194/egusphere-egu26-75, 2026.

Wells and boreholes have long served as critical sources of freshwater in the semi-arid and arid regions of Africa. Despite their importance, effective monitoring of these water points remains limited due to the high cost of establishing and maintaining dedicated observation wells, resulting in sparse and unreliable datasets. This study explores a cost-effective approach to groundwater monitoring by equipping operational wells and boreholes with low frequency acoustic sensors integrated into a scalable wireless sensor network. The system enables continuous acquisition of time-series data on water levels, discharge rates, and recharge dynamics. The major innovation here is that we use existing and operational water infrastructure as monitoring points. The presentation will demonstrate the principles, advantages, and obstacles that still need to be overcome. The proposed method improves data availability and supports more sustainable groundwater management across data-scarce regions in Africa.

How to cite: Geofrey, A., Hut, R., and van de Giesen, N.: Acoustic Sensor–Based Borehole Monitoring in Semi-Arid African Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1566, https://doi.org/10.5194/egusphere-egu26-1566, 2026.

Measuring open-channel hydraulics is crucial, for example, for deriving discharges from stage observations, estimating travel times for pollutant plumes, and assessing riverbed dynamics. State-of-the-art surveying approaches are typically conducted along predefined cross-sections of the river course, either manually using a flow meter or with instrumented boats. The latter are technologically advanced platforms equipped with electric propulsion, ADCP sensors, and high-precision RTK-GPS and may cost several tens of thousands of US dollars (or euros). To fill data gaps between cross-sections, surveys often rely on longitudinal boat campaigns, which are generally feasible only in larger streams without hydraulic barriers.

To support water authorities with limited budgets, particularly to survey smaller streams, we developed MONIKA, a low-cost surveying catamaran. In accordance with its acronym, MONIKA is comprised of three primary functions: MO - Monitoring (continuous tracking of water parameters), NI - Navigation (movement along and across the stream), and KA -  Kartography (mapping of the riverbed morphology). The platform is equipped with a castable sonar, GPS, and two CTD (Conductivity-Temperature-Depth) dataloggers. As an additional payload, a commercial high-precision inclination sensor is deployed to monitor the water surface slope. All data-processing steps are implemented in an object-oriented framework within an open-source Python package.

After extensive testing and design optimization, the engine-less boat can be deployed in two operational modes: (1) bank-guided operation using an aluminum rod and snap hook, and (2) free-floating operation in which the boat is retrieved with a net installed at the downstream end of the study reach. The free-floating mode is particularly suited for surveying riverbed slope, as it avoids operator-induced interference with inclination measurements.

As an initial application, MONIKA, was deployed at two sections of the Spree River (Germany) to support the placement of new sampling stations downstream of a river confluence. MONIKA was used to determine the minimum downstream distance required for complete mixing. Future applications will extend this approach to open-channel surveys in small rivers, with a particular emphasis on data-scarce catchments.

How to cite: Nixdorf, E., Böhmeke, M., and Gatzke, F.: Development of a low-cost water vehicle for surveying river bed elevation and chemo-physical changes along the river course , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4963, https://doi.org/10.5194/egusphere-egu26-4963, 2026.

Reliable radon-222 measurements are essential for a wide range of hydrological, geological, and environmental applications, including the study of surface water - groundwater interactions and the quantification of groundwater discharge. Despite the widespread use of ²²²Rn detectors, routine verification of instrument performance and measurement stability remains limited, particularly in laboratories with constrained financial and technical resources. This study presents the development and evaluation of a cost-effective rock sample unit designed to support quality control, calibration checks, and inter-laboratory comparison of ²²²Rn measurements.

The system is based on acidic plutonic igneous rock purchased from commercial suppliers, selected for their naturally elevated and stable ²²²Rn production. The rocks were enclosed in a simple, airtight container assembled using readily available components, including a standard garden filter and plastic tubing. This configuration allows ²²²Rn generated within the rock matrix to accumulate in a closed volume and be circulated through commonly used ²²²Rn detectors without the need for specialized or commercial equipment. Equal amounts of material were placed in each rock sample unit, which were then sealed and stored for 21 days to allow ²²²Rn to reach secular equilibrium with its parent radionuclides. Initial characterization of the rock units was performed at the IAEA Isotope Hydrology laboratory. Each unit was analysed three times using a standardized protocol consisting of six measurement cycles of 30 minutes each. Measurements were conducted using RAD7 and RAD8 ²²²Rn detectors from Durridge, which are widely applied in environmental and hydrological studies. The results demonstrated stable and reproducible ²²²Rn concentrations across repeated measurements, confirming the suitability of the rock units as reference sources for quality control purposes.

Following this initial validation, the previously measured rock sample units were distributed to participating laboratories in Argentina, France, Malaysia, and Morocco. Each laboratory applied the same measurement protocol and used their routinely operated ²²²Rn detectors (RAD7 and RAD8).

To support the interpretation of the observed variability, contextual information was considered, including the age of the instrument, the date of last recalibration, the intensity of use, the type of water typically analysed (saline or non-saline; surface water or groundwater), and the range of ²²²Rn concentrations normally encountered. This approach enabled the assessment of the significance of deviations under different operating conditions and allowed the evaluation of the robustness of measurements obtained with calibrated versus non-calibrated instruments.

This exercise showed that even simple comparison of ²²²Rn responses obtained from the rock units provides valuable insight into the performance of the instrument and detect the potential measurement drift related to the lack of calibration. The results demonstrate that these cost-effective rock sample units represent a practical and accessible tool for strengthening ²²²Rn measurement quality assurance. Their simplicity, low resource requirements, and reproducibility make them particularly suitable for routine checks, contributing to the improved comparability of ²²²Rn data.

How to cite: Huneau, F., Grondona, S., Santoni, S., Poh, S. C., El Ghali, T., Terzer-Wassmuth, S., and Vital, M.: A cost-effective rock sample unit for quality control and intercomparison of 222Rn measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5086, https://doi.org/10.5194/egusphere-egu26-5086, 2026.

The terrestrial water and nutrient cycle is of crucial importance, influencing the climate, ecosystems, and related services. In many climates, non-rainfall water inputs (NRWIs) play a significant role in the water cycle. These inputs stem from various processes, including dew, fog, and soil water vapour adsorption. Weighable lysimeters are ideal tools for quantifying such water inputs to ecosystems, as their surfaces are either plant- or soil-covered, which is relevant for their formation processes, compared to devices with artificial surfaces. However, the nutrient inputs from dew and fog, apart from wet and dry deposition, are yet to be overlooked, as it is difficult to monitor these hidden nutrient inputs to ecosystems without adequate sampling devices.

We present a newly developed dew collector for the regular collection and analysis of dew samples, for example for stable isotopes, nutrients, and other substances. The lack of automated methods for collecting dew samples represents a significant bottleneck to account for these hidden nutrient inputs. Using a comprehensive measurement setup with weighable lysimeters, wet and dry deposition, and dew and fog water collectors, we show how NRWIs introduce nutrients into ecosystems with different land uses (grassland and cropland) in a temperate climate.

How to cite: Groh, J., Lücke, A., Pütz, T., Engels, F., Funk, R., Sitnikow, A., Beysens, D., and Amelung, W.: Automated sampling of dew water to identify hidden nutrient inputs to ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11522, https://doi.org/10.5194/egusphere-egu26-11522, 2026.

Palmes diffusion tubes are widely used as a low-cost method for measuring ambient nitrogen dioxide (NO₂) air pollution. Based on the principle of molecular diffusion, ambient NO₂ accumulates as nitrite at the closed end of the tube. After a typical four-week exposure period, the tubes are returned to a laboratory, where the nitrite is dissolved in water, reacted with a colorimetric reagent, and quantified by measuring the resulting color change using a spectrophotometer.

Despite their effectiveness and affordability, Palmes diffusion tubes are still rarely used in Africa. A major reason is that tube preparation and analysis are typically must be carried out in laboratories outside the continent. One key barrier to establishing local Palmes laboratories is the high upfront cost of spectrophotometers required for sample analysis.

While conventional spectrophotometers can measure absorbance across a wide range of wavelengths, most reagent-based colorimetric analyses require only a single wavelength. For Palmes tube analysis, absorbance is measured at 540 nm, corresponding to the maximum absorption of the Griess reagent. Since green LEDs emit light within a narrow waveband close to this absorption peak, they offer a low-cost alternative light source.

We present and will live-demonstrate a simple device that replaces the spectrophotometer in the Palmes tube analysis workflow. The device consists of a 3D-printed light-tight cuvette holder housing a green LED for illumination and a photodiode to measure transmitted light. Measurement results are displayed directly on the device. The system can determine nitrite concentrations with an accuracy of 3 µg/L, corresponding to approximately 0.1 µg/m³ of ambient NO₂ for a four-week exposure period—well below the intrinsic uncertainty of the Palmes diffusion method.

Costing only a fraction of a conventional spectrophotometer, this device has the potential to greatly expand in-situ monitoring of NO₂ pollution in sub-Saharan Africa without substantially increasing costs. Moreover, it provides a promising proof of concept for developing similar low-cost instruments for other air and water quality applications based on colorimetric measurements.

How to cite: Mijling, B. and Hut, R.: Low-cost spectrophotometer for measuring nitrogen dioxide (NO2) air pollution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12528, https://doi.org/10.5194/egusphere-egu26-12528, 2026.

One of the Earth's natural physical fields is the geoelectric field. The conductivity of subterranean medium and the locations of pollution sources, among other things, may be examined by tracking variations in the geoelectric field signal over time. The success rate of resource exploration and the accuracy of geological structure inversion are closely correlated with signal quality. Conventional geoelectric field measurement techniques use electrochemical non-polarizing electrodes to detect the potential difference between two electrodes that are far apart in order to acquire the geoelectric field signal. The potential difference value that exists between the electrodes for their own causes is called the "range difference."  Environmental conditions will influence the electrodes' range difference, and the range difference variation amplitude will be greater than the amplitude of the actual geoelectric field signal. Non-polarizing electrodes must be buried deep below during the actual measuring procedure, and electrolyte solutions must be poured to lower the grounding impedance. The electrolyte solution is prone to evaporation or loss in unique environments like deserts and the Gobi, which might result in an abrupt rise in the grounding impedance of the non-polarizing electrodes. This will impact the precision of the geoelectric field signal measurement findings.

This design, which is based on the charge induction principle, aims to create a new kind of electric field sensor that can continuously measure the geoelectric field signal without range differences and does not require the electrodes to be buried. The viability of this sensor is confirmed using physical models and circuit simulations, as well as by contrasting the geoelectric field signal measurement findings of the physical product with those of solid non-polarizing electrodes.

How to cite: Lei, D. and Zhen, Q.: A novel geophysical electric field sensor design and testing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16212, https://doi.org/10.5194/egusphere-egu26-16212, 2026.

Nitrate contamination of surface and groundwater is a serious environmental and public health issue.  Identifying the source of this pollutant is an important step in addressing the problem. Nitrogen and oxygen isotopes (δ¹⁵N and δ¹⁸O values) are a powerful tool for tracing the source(s) of nitrate and understanding processes that impact its cycling in the environment.  Traditionally nitrate isotopes are measured via isotope ratio mass spectrometry (IRMS) but in recent years laser spectroscopy has become a practical option.

We evaluated Picarro’s new PI5131-i isotopic and gas concentration analyser for determining bulk δ¹⁵N and δ¹⁸O values of N₂O converted from dissolved nitrate using the Titanium III chloride method. The PI5131-i analyser is based on a robust mid-infrared, laser-based cavity ring-down spectrometry (CRDS) technology. This system when combined with Picarro’s Sage gas autosampler allowed us to analyse the isotopic composition of dissolved nitrate to a level matching IRMS precision and at concentrations as low as 0.05 mg/L NO3-N. 

In 40 mL reaction vials, Ti (III) chloride was added to 10 mL sample at a 1:20 ratio (v/v, reagent to sample). After 24 hours of reaction time enough N₂O was produced for laser spectroscopy analysis. Prior to analysis, the headspace N₂O was transferred into 12 mL exetainers to fit in the Sage autosampler. We compared a direct transfer protocol where 2 mL N₂O from the reaction vial is injected into exetainers and a 2-steps protocol where the N₂O is injected into purged exetainers (evacuated and pressurized with synthetic air).

Both transfer methods performed well in a blind nitrate intercomparison exercise (NICO).  The direct transfer workflow required fewer preparation steps but required a blank correction, whereas the two-step protocol was more labour-intensive due to the purge and fill process.

How to cite: McKay, J., Douence, C., Hofmann, M., Woźniak, J., and Bhattacharya, J.: Analysis of Nitrate Stable Isotopes by Cavity Ring-Down Spectroscopy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18536, https://doi.org/10.5194/egusphere-egu26-18536, 2026.

Rewetted peatlands exhibit strong small-scale, spatio-temporal variability in their greenhouse gas (GHG; CO₂, CH₄ and N₂O) emissions. Those are shaped by water table dynamics, vegetation structure, and microclimate. Capturing “hotspots” and “hot moments” across heterogeneous peatlands typically requires dense instrumentation. However, conventional monitoring solutions remain expensive, difficult to scale, and often depend on commercial, vendor-locked systems. We present the Environmental Variables Explorer (EVE) as a low-cost, modular, open-source alternative that enables researchers to build, repair, adapt, and self-host their monitoring stack without vendor lock-in.

EVE is a platform blueprint rather than a single device. It combines low-power microcontroller nodes with power-saving duty cycling and two interoperable end-to-end, full user controlled workflows. The first, offline workflow, provides robust timestamped local storage (RTC + FRAM) with Bluetooth retrieval via a custom Android app - suited for remote sites. The second, online workflow, uses an ESP32 IoT node to upload measurements via Wi-Fi to a self-hosted PHP/MySQL backend that provides a web dashboard, API access, data visualization and data export (as CSV file) on inexpensive shared hosting. Critically, the offline-online duality provides a “fallback” logic for intermittently connected peatland environments and supports gradual scaling from single devices to multi-site networks.

Building on EVE’s user-controlled pipeline, we present a pathway toward transferable near-real-time analytics by adding chamber-based GHG modules (low-cost CO₂/CH₄ sensing and chamber automation/sampling workflows. Integrating data-driven models (Random Forest and related methods) to estimate flux dynamics and annual budgets across 2-3 sites. Explicitly comparing high-end versus minimal low-cost inputs. By releasing hardware designs, firmware, backend code, and build documentation, this work aims to lower barriers for peatland and other scientists to deploy reproducible monitoring networks and to move toward shared, community-driven approaches for scalable GHG observation and modeling.

How to cite: Kretzschmar, M. S., Dubbert, M., Lück, M., Asante, M., Sossa, G., and Hoffmann, M.: EVE: a low-cost, modular, end-to-end monitoring pipeline for environmental variables and GHG in rewetted peatlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19260, https://doi.org/10.5194/egusphere-egu26-19260, 2026.

How to cite: Rowan, T., Noriega Gimenez, J., Jia, Y., Tang, Y., Howard, B., Kelleher, L., Tumelty, L., Packman, A., Paschalis, A., Krause, S., and Buytaert, W.: Low-Cost Water Quality Buoys: Open-Source Design and AI-Enhanced Monitoring , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21344, https://doi.org/10.5194/egusphere-egu26-21344, 2026.

The emergence of Unmanned Aerial Systems (UAS) has revolutionized environmental monitoring by bridging the gap between stationary ground-based stations and coarse-resolution satellite imagery. However, integrating high-fidelity sensors into lightweight platforms remains a challenge due to strict Size, Weight, and Power (SWaP) constraints. This study presents the development and deployment of an advanced, portable sensing payload designed for high-resolution environmental data collection.

The integrated payload consists of a suite of low-cost yet calibrated sensors capable of measuring Isme PM2.5, CO2, NH4, Smoke and O3 at high temporal frequencies. To ensure data integrity, the system incorporates an onboard microprocessor for real-time data fusion, GPS-tagging, and active aspiration systems to mitigate the effects of rotor wash and thermal interference.

Preliminary field campaigns were conducted across two locations (Dehradun, Uttarakhand and New Delhi) to evaluate the system’s performance. Results indicate that the payload provides vertical and horizontal spatial resolutions previously unattainable with traditional methods. This work highlights the potential of modular UAS payloads to provide actionable insights into boundary layer dynamics and pollutant dispersion in complex terrains.

To ensure data integrity, the platform integrates active aspiration systems designed to decouple sensor readings from the effects of rotor wash and localized thermal artifacts. Initial experiments demonstrate that the payload achieves high-granularity in vertical and horizontal spatial resolutions.

How to cite: Natoo, A.: Developing a Lightweight UAS sensing Payload for Environmental Data collection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21545, https://doi.org/10.5194/egusphere-egu26-21545, 2026.

Grassed waterways (GWW) are a nature-based solution in agricultural catchments to reduce surface runoff and soil erosion. However, continuous measurements of surface runoff in a GWW remain challenging, limiting knowledge of how to construct a measurement station to obtain reliable data. Furthermore, these limitations restrict our understanding of hydrological processes and the effectiveness of GWWs. In this study, we present a monitoring station designed to measure surface runoff and quantify soil erosion from a 6 ha agricultural sub-catchment, and discuss the opportunities and limitations of monitoring runoff, sediments, and nutrients in a managed GWW. This study is part of the overall 66 ha catchment at the HOAL (Hydrological Open-Air Laboratory), Austria.  

We developed an H-Flume-like structure that reliably quantifies flow without disturbing the GWW’s function. Non-contact radar probes measure the height and velocity of runoff in the structure, allowing discharge calculations during runoff events. When a specified runoff height is detected, an automatic water sampler collects water for further analysis, such as sediment quantification. Thermal and optical cameras are mounted on the structure to capture images from upslope, the structure itself, and downslope, providing several perspectives for visual documentation of runoff processes and sediment transport.

While complementary measurements and modelling support the understanding of the overall effectiveness of the GWW in the HOAL catchment, this station provides valuable information on the timing of runoff, peak flow reduction, and catchment connectivity. The integrated sensor network at this station and throughout the HOAL - including rain gauges, soil moisture sensors, and additional runoff stations - enables a process-based understanding of how grassed waterways affect surface runoff, pluvial floods, and sediment and nutrient transport towards the stream.

This methodology remains under active development, and we encourage community input on improvements to the current methodologies and suggestions for additional observations. This presentation aims to share our current design, present preliminary results, and foster collaborative discussion on advancing monitoring of vegetated, nature-based erosion control structures.

How to cite: Konzett, M., Strauss, P., Thoma, C., Marjanovic, D., Szeles, B., Blöschl, G., and Schmaltz, E.: In a Grassed Waterway the grass is always greener – and surface runoff a challenge to measure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23048, https://doi.org/10.5194/egusphere-egu26-23048, 2026.

Water sampling is essential for assessing the quality of rural and urban water systems. As part of the NERC-NSFGEO SMARTWATER project we aim to diagnose pollution “hot spots” and “hot moments” within watersheds defined as locations and times of pollution transport. To understand and diagnose pollutant dynamics we are forming a smart monitoring network consisting of offline and online sensors, low-cost proxy sensor measurements, and event-based sample collection using autosamplers.

To address existing autosampler constraints, we have developed a smart online autosampler that can be triggered either by a float switch or remotely through a LoRa network. The system is optimised for low-power operation using 12V electronics, light and smaller lithium-based batteries, power optimised Arduino controller, LoRa shield, commercial solenoid values and relays. Laboratory testing has validated the system operation and effective flushing of water between sampling bottle fills. Field deployment along our urban observatory, the Birmingham Urban River Observatory, a UNESCO Intergovernmental Hydrological Programme site, demonstrated performance comparable to standard systems.

This open-source design enables scalable, cost-effective monitoring of river water quality, facilitating improved spatial and temporal assessment across multiple catchments. SMARTWATER: https://www.smart-water.org.uk/

How to cite: Kelleher, L. and Khamis, K. and the SMARTWATER Team: SMARTWATER: low-cost, open-source portable water autosampler for environmental monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23272, https://doi.org/10.5194/egusphere-egu26-23272, 2026.

Whether we talk about safety reasons, energy production or regulation, water resources management is one of EDF’s (Électricité De France, French hydropower company) main concerns.

The range of water-based activities is steadily increasing : paddleboarding, canoeing, float-tube fishing, these floating crafts are now widely available at low cost and are becoming popular on rivers. EDF’s hydroelectric facility operators are regularly faced with the intrusion of swimmers and watercraft near the structures. This occurs, for example, beyond the restricted zone upstream of hydroelectric installations. This zone, usually marked by a line of buoys, is intended to prevent drowning accidents that could result from the start-up of a turbine or any other system capable of creating a suction current. Similar risks exist downstream of the structures, where currents and depths attract swimmers and floating crafts.

Given the risky behavior of river users, EDF’s challenge is to secure the vicinity of its installations by assessing the danger and proposing appropriate countermeasures. In this context, EDF aims at determining current thresholds beyond which the swimming ability and maneuvering capacity of floating crafts (paddleboards, canoes, float-tubes) for different user profiles (children, adults, athletes) are no longer sufficient to escape the danger of being sucked in or swept away. This study therefore only concerns areas where users cannot stand in the river. Such work will enable the company to implement the necessary measures to secure zones considered hazardous near hydroelectric structures.

To carry out this work, tests in collaboration with members of the SDIS 81 Water Rescue team were conducted at the Millau whitewater stadium to determine the current speeds beyond which swimmers and light watercraft can no longer escape danger. These trials, carried out under controlled and safe conditions, involved scenarios of swimming and maneuvering floating crafts in currents ranging from weak to strong at the Millau water sports facility. The objective was to assess the swimming and mobility capacities of swimmers and non-motorized watercraft (paddleboards, canoes, float-tubes) across different current speed ranges.

All the tools routinely used by EDF hydrometric teams to measure flow velocities were deployed (ADCP, current meter, SVR radar, LSPIV) to better characterize the different current speeds tested.

The results obtained made it possible to identify the threshold values sought, based on current speed and the presence or not of turbulence. Finally, a theoretical approach based on Newton’s second law helped corroborate the empirical results obtained during the tests.

How to cite: Morlot, T., Belleville, A., and Le Brun, M.: Risk near hydroelectric structures – Determination of critical velocity thresholds for river users (swimmers and floating crafts), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3833, https://doi.org/10.5194/egusphere-egu26-3833, 2026.

River discharge is a key indicator for water resources management and flood forecasting; however, the traditional single stage–discharge rating curve used for its estimation produces systematic errors under unsteady flow conditions due to hysteresis. In this study, field-measured Manning’s roughness coefficients (n) are first estimated at Naju Bridge on the Yeongsan River by combining H-ADCP–measured discharges with water-surface slopes derived from upstream and downstream stage observations, using the continuous slope–area (CSA) framework in inverse form. The resulting 10-min n time series for the 2020 flood events is then segmented by stage to represent cross-sectional controls on roughness. These stage-wise n segments are subsequently applied to the CSA method to compute discharge time series for the 2019 and 2021 flood events, and the estimates are validated against observations. The estimated n exhibits a consistent stage-dependent pattern, including a rapid decrease at low stages, convergence at intermediate stages, an inflection point near the onset of rapid cross-sectional expansion, and an increase at high stages, reaching n ≈ 0.08–0.09 during extreme floods—values higher than those from conventional empirical formulas and design criteria. Using the measured and stage-segmented n, the CSA-based discharge estimates successfully reproduce hysteresis across six flood events, achieving R² ≥ 0.94 and peak error ≤ 3%, although nRMSE exceeds 10% under low-flow conditions. Overall, applying field-derived roughness substantially improves CSA discharge estimation and supports the practical use of roughness monitoring and CSA-based computation in rivers subject to unsteady flow.

How to cite: Ikhan, L., Dongsu, K., Dohyeon, K., Gibeom, S., and Jonghyeon, Y.: Field Estimation of Manning’s Roughness and Its Application to CSA Discharge Computation during Flood Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4747, https://doi.org/10.5194/egusphere-egu26-4747, 2026.

Sediment transport is a key process in fluvial geomorphology, particularly in gravel-bed rivers, as it controls channel morphology and has important implications for river management and restoration. This process occurs in three main phases: entrainment, movement, and deposition, and is influenced by factors such as particle size, shape, density, angularity, imbrication, and the ratio between transported sediment size and bed material.  Although field data on sediment transport are essential, they are often difficult to obtain. To overcome this, a wide range of monitoring techniques has been developed, including direct samplers such as the Helley-Smith sampler and bedload traps, acoustic sensors such as geophones and hydrophones, and laboratory experiments that allow sediment dynamics to be studied under controlled conditions. In parallel, sediment transport models rely strongly on grain size distribution, either using the full distribution or representative metrics such as D50 or D84. Since the 1990s, sediment tracking has become increasingly important, with gravel tagging emerging as a widely used method for analysing sediment mobility and travel distances. Technological advances have significantly improved recovery rates, particularly through the use of electronic tags. Passive Integrated Transponder (PIT) tags are now commonly used due to their small size, low cost, long lifespan, and passive operation. Active tags, such as VHF and UHF, enable continuous tracking via fixed antennas but are larger and more expensive. In this work, we used active tags to estimate the volume of sediment mobilised by deploying RFID‑tagged gravels, thereby improving our understanding of sediment movement through the concept of virtual velocity. Tag seeding was carried out at four sites upstream of the first fixed antenna. Preliminary results show that gravels in the 45–181 mm size range (85% of all seeded material) have been mobilised over distances of approximately 700 m and detected by the antennas, whereas gravels in the coarsest size classes have not yet been recorded. In conclusion, higher‑magnitude events are required for the coarsest particle sizes to become mobile and be detected by the first antenna. Although several low‑magnitude events have occurred, very few of the mobilised gravels have been detected by the second antenna located downstream of the first, suggesting that they may have become buried or trapped in pools.

ACKNOWLEDGMENTS: This work is funded by the European Research Council (ERC) through the Horizon Europe 2021 Starting Grant program under REA grant agreement number 101039181 - SEDAHEAD.

How to cite: Juez, C. and Rabanaque, M. P.: Sediment mobility in a gravel-bed river (Aragón Subordán River, Central Spanish Pyrenees) assessed using active RFID tags, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7104, https://doi.org/10.5194/egusphere-egu26-7104, 2026.

River flow conditions at high latitudes show strong seasonal variability due to changes in hydrological conditions throughout the year. During winter, ice cover reduces flow velocity. In spring, snowmelt increases discharge and often results in flooding. During summer and autumn, rainfall can cause temporary increases in discharge. High-latitude fluvial environments are particularly sensitive to climate change, which has been found to have a stronger impact in these regions than in rivers at lower latitudes. This study investigated seasonal flow dynamics in a single meander bend of the Oulankajoki River in Finland during winter, spring, and autumn over one hydrological year using an Acoustic Doppler Current Profiler (ADCP). Vertical ADCP surveys provided detailed cross-sectional velocity profiles and flow directions throughout the water column in each field campaign.

Results showed significant variation between seasons. Winter ice cover significantly reduced near-surface velocities and shifted the high-velocity core (HVC) to mid-depth, whereas in open-water conditions the highest velocities occurred closer to the surface. In open-channel conditions, peak velocities were observed in the shallow upstream and deep downstream sections of the bend, while in winter flow decelerated downstream of the bend apex.  During winter and autumn, the HVC was located near the inner bank in the upstream section of the meander bend and gradually shifted to the outer bank before the bend apex. Downstream of the apex, the HVC migrated from the outer bank toward the center of the channel. Unlike in winter and autumn, the spring flood caused the HVC to migrate from the upstream of the meander, flow directly across the point bar, and shift toward the outer bank at the apex.

The study is now being extended by integrating continuous monitoring of flow conditions using a side-looking Doppler current meter. This enables long-term, high-resolution observations, including ice-covered periods. Combining vertical and horizontal ADCP measurements is expected to provide a rare, spatially comprehensive and temporally continuous dataset. This integration enables improved characterization of flow variability and seasonal dynamics in high latitude rivers, thereby enhancing hydrological analyses and process-based modeling.

How to cite: Korkiakoski, K.: From short-term vertical ADCP measurements to continuous Side-Looking current meter monitoring in a high-latitude river, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7246, https://doi.org/10.5194/egusphere-egu26-7246, 2026.

Ensuring the safety of spillways during flood events is a critical challenge for dam operators and public authorities. Accurate knowledge of flow velocities along spillways and downstream of ski jumps is essential for assessing the erosive potential of high‑velocity flows and preventing structural damage. However, intrusive velocity measurement techniques are unsuitable in such configurations due to limited accessibility and the very high flow speeds involved. Image‑based velocimetry offers an attractive alternative, providing instantaneous, spatially distributed velocity fields from a single viewpoint. Yet, conventional image-based techniques rely on the assumption of a planar free surface, which becomes invalid for curved flows such as those encountered on spillway chutes or nappe flows. Surface curvature induces geometric distortions in ortho‑rectified images, leading to significant velocity errors.

Stereo‑vision systems can be used to reconstruct non‑planar free surfaces, but their deployment on full‑scale spillways is complex and costly as it requires synchronized cameras with high spatial and temporal resolution. To overcome these limitations, we propose OrthoCyd, a novel single‑camera orthorectification method dedicated to flows whose free‑surface geometry is known a priori and corresponds to a right‑cylindrical surface (i.e., a planar surface which is curved along the longitudinal dimension). This approach is well suited to spillway chutes, with the assumption that the free surface follows the underlying curved geometry. OrthoCyd enable consistent displacement measurements with any image-velocimetry method (block matching, tracking, optical flow, spatio-temporal approach). This method extends the applicability of image‑based velocimetry to non‑planar free‑surface flows while maintaining the simplicity and practicality of single‑camera acquisition systems.

Two applications illustrate the method: a laboratory experiment on a free‑nappe flow, and a field application on an operating spillway. These examples demonstrate that OrthoCyd provides reliable velocity measurements in both controlled and full‑scale conditions.

How to cite: Bodart, G., Hauet, A., Le Coz, J., and Jodeau, M.: Applying image velocimetry on curved free‑surface geometries of known shape such as spillways or free-nappe: the OrthoCyd method., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7926, https://doi.org/10.5194/egusphere-egu26-7926, 2026.

Recently, non-contact current meters (radar, image-based) are widely used to measure the discharge of rivers, and research and cases of calculating discharge by applying surface velocity-based index velocity method and velocity distribution method are increasing. To apply index velocity or velocity distribution methods based on surface velocity, the relationship between the surface velocity used as the index velocity and the cross-sectional average velocity must be known. In general, in straight channels with sufficiently large channel width, the ratio of the cross-sectional average velocity to the maximum velocity (ϕ(M)) is known to be between 0.6 and 0.7.

In this study, the relationship between the maximum surface velocity and the cross-sectional average velocity was analyzed. Using 179 flow measurement data from 60 sites, the ratio of the cross-sectional average velocity to the maximum surface velocity (≈ϕ(M)) was calculated. As a result, ϕ(M) was analyzed to have an average of 0.64(correlation coefficient R=0.86). When the relationship between the two elements was established as a linear equation, the slope was calculated to be 0.6306 (R=0.74). The ratio of ϕ(M) and the velocity coefficient α (ratio of reference discharge/surface velocity discharge (α=1 applied)) was calculated to be 0.75 on average (range 0.51 to 0.91), and when the relationship between the two elements was established as a linear equation, the slope was calculated to be 0.7586 (R= 0.68). The correlation coefficient between ϕ(M) and the maximum surface velocity/average surface velocity of the cross-section was calculated to be -0.84, indicating that ϕ(M) shows a strong correlation with the distribution characteristics of the surface velocity. When the relationship between the two factors was established as an exponential equation, the coefficient of determination was calculated to be 0.76, confirming that the value of ϕ(M) can be estimated and used through surface velocity measurements.

keyword : average velocity, surface velocity, index velocity, ϕ(M)

How to cite: Lee, S., Jang, B., and Lee, J.: Relationship between Cross-Sectional Mean Velocity and Surface Velocity in Rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8486, https://doi.org/10.5194/egusphere-egu26-8486, 2026.

Non-contact river discharge measurement techniques, such as radar-based surface velocity sensors, are increasingly applied in hydrological observations due to their advantages in operational safety and accessibility during flood events. However, these sensors directly measure only surface velocity, and reliable discharge estimation therefore requires conversion to depth-averaged velocity using an accurately estimated velocity index (α). In practice, α is often treated as a constant or determined empirically, which can lead to substantial uncertainty, particularly under unsteady flow conditions. 

This study proposes a regression-based framework to quantify the velocity index as a function of hydraulic and flow variability characteristics, using field observations from natural rivers. Both Acoustic Doppler Current Profiler (ADCP) measurements and radar-based surface velocity data are employed. First, reliable depth-averaged velocities and velocity profiles are obtained from ADCP observations, from which reference velocity index values are derived. Subsequently, corresponding α values for radar observations are generated using stage–discharge relationships, and the regression dataset is expanded by integrating both ADCP- and radar-based cases.

The velocity index is formulated using a hybrid multiplicative regression model incorporating water surface slope, channel aspect ratio, and the rate of water level change (dH/dt). In particular, the inclusion of the water level change rate explicitly accounts for unsteady flow effects occurring during rising and falling stages of flood events. Model performance and robustness are comprehensively evaluated using adjusted coefficient of determination, root mean square error, mean absolute percentage error, and variance inflation factor to assess both predictive accuracy and multicollinearity.

Results indicate that the three-variable model consisting of water surface slope, channel aspect ratio, and water level change rate achieves the most favorable balance, exhibiting the lowest prediction errors while maintaining low multicollinearity. The incorporation of dH/dt is shown to effectively represent hysteresis effects in the relationship between surface velocity and depth-averaged velocity during flood conditions, significantly improving model stability.

The regression model proposed in this study is developed based on an integrated dataset combining ADCP and radar observations and provides a velocity index formulation that is applicable across a wide range of hydraulic conditions, including unsteady flood flows, without dependence on a specific sensor type. The results confirm that the proposed model contributes to improving the reliability and consistency of depth-averaged velocity estimation in non-contact river discharge measurements.

Keywords : Non-contact river discharge measurement, Velocity index (mean velocity conversion coefficient), Surface velocity, Unsteady flow conditions, Regression model

How to cite: Lee, T. H., Chun, J. H., Park, S. H., Ok, T. W., and Kim, W. J.: An Integrated Regression Model for Estimating the Velocity Index in Non-Contact River Discharge Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8722, https://doi.org/10.5194/egusphere-egu26-8722, 2026.

Accurate measurement of river surface velocity is essential for hydrological monitoring, flood forecasting, and water resource management. In contrast with traditional in-situ point measurements using electromagnetic current meters, remote sensing techniques offer significant advantages for river surface velocity estimation, including rapid data acquisition, lower operational costs, and contactless operation. Based on Unmanned Aerial Systems (UAS) equipped with Doppler radar becomes more attractive due to it is suitable for real-time velocity determination and has fewer limitations.

The UAS-borne Doppler radar estimates river surface velocity by detecting the frequency shift of backscattered microwaves, with the drone operating at a controlled hover altitude of approximately 4 meters above the water surface during field measurements. However, the propeller wash (propwash) generated by the UAS distorts the radar return signal, resulting in a composite velocity measurement that combines the true river surface flow with the locally induced airflow velocity. To isolate the true river velocity, we introduce a bidirectional observation scheme in which the same surface footprint is measured from two opposing directions. The Continuous Wavelet Transform (CWT) algorithm is employed to extract the mixed velocity components from each directional dataset. By analytically reconciling these bidirectional measurements, the downwash contribution is effectively removed, thereby yielding a refined estimate of the true river surface velocity.

In this study, river surface velocity was analysed across more than 50 cross-sections spanning four distinct rivers, covering a broad velocity range from 20 cm/s to 250 cm/s. To validate the velocity estimates obtained from the UAS-Doppler radar and CWT method, comparisons were made against in-situ measurements collected using instruments such as the OTT MF Pro, Acoustic Doppler Current Profilers (ADCP), and flow trackers. Quantitative analysis confirmed that the UAS-Doppler radar system provides reliable river flow velocity measurements while offering enhanced efficiency in post-processing workflows.

How to cite: Zhou, Z., Hu, X., Merk, F., Disse, M., Caccamo, E., Barbetta, S., Giordan, D., Tarpanelli, A., Flendsted Jensen, V., Andreas Pedersen, M., Nielsen, S., Wennerberg, D., Fagerström, V., Gustafsson, D., and Bauer-Gottwein, P.: River surface velocity estimation using UAS-borne Doppler radar and continuous wavelet transform, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9919, https://doi.org/10.5194/egusphere-egu26-9919, 2026.

Ensuring high-quality river discharge measurements is fundamental for flood protection, forecasting and assessing water availability under changing climate conditions. While modern technologies such as Acoustic Doppler Current Profilers (ADCP), camera-based surface velocimetry and emerging sensor technologies offer advanced capabilities, they also present new challenges in terms of quality assurance compared to traditional instruments such as rotating-element current meters.

Although the modern measurement methods mentioned above are already in use in many countries, standardized testing procedures are still lacking. Coupled with the complexity of device application and data evaluation, and the absence of standardized operating protocols in these areas, the accuracy of these methods may be compromised compared to conventional techniques. Addressing these issues requires innovative, internationally coordinated approaches to quality assurance that can adapt to future developments.

Several strategies have proven effective in improving data reliability:

• Routine device checks and maintenance to guarantee functionality and detect early signs of malfunction.
• Regular intercomparison measurements to identify systematic and random errors, highlight operational differences, and ensure comparability of results.
• Continuous training and experience exchange to reduce operational errors and strengthen technical expertise.
• Development of independent software solutions and adoption of open data principles to reduce black box solutions, enable robust data analysis and foster innovation.
• Creation of guidelines and institutionalization of standards for measurement methods, intercomparison programs and device testing.
• Promotion of research and development to advance measurement techniques and adapt to emerging technologies.

Our work highlights effective measures and underscores the need for coordinated international initiatives to establish common standards. By combining research, operational experience, and international collaboration, we can collectively strengthen the reliability of river monitoring worldwide.

How to cite: Kainz, S., Hasenhündl, M., and Büchen, S.: Ensuring Data Quality in River Discharge Measurements: Strategies and Future Directions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10110, https://doi.org/10.5194/egusphere-egu26-10110, 2026.

Salt Dilution (SD) is an accurate, safe, relatively inexpensive, and easily employed method to measure water flow in turbulent streams and rivers. Often, Temperature Compensated Electrical Conductivity (EC.T) sensors are deployed continuously in Automated Salt Dilution (AQ) flow measurement systems. Recent studies (Cano-Paoli, K., Chiogna, G., and Bellin, A. 2019) have examined how well a continuous EC.T record can be used in regression analysis to estimate the Discharge. EC.T has the benefit of not requiring a stable Pressure Transducer (PT) elevation and avoids other complications of a stage-discharge station, such as shifting hydraulic controls. However, EC.T can be impacted by sediment fouling, aeration, and seasonal/storm event-related changes to the relationship with Q. This study examines how robust a combined stage-discharge-EC.T time series can be for the generation of a maximum likelihood flow hydrograph. This relationship can be useful for infilling missing data and determining hydraulic control shifts in the stage-discharge relationship. Examples are presented from several mountainous catchments.

How to cite: Sentlinger, G., Hofmeister, F., Combatti, M., Gentile, A., Chiogna, G., Weijs, S., Hauet, A., Mindham, D., and Mahannah, R.: Using Electrical Conductivity as a Proxy for Q: A Madman’s Delusion or Elusive Science, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11565, https://doi.org/10.5194/egusphere-egu26-11565, 2026.

This study investigates surface-to-depth-averaged velocity relationships for discharge estimation across a wide range of river scales and flow conditions. The analysis focuses on conversion factors used in surface-velocity-based discharge estimation and their hydraulic controls. A total of 172 ADCP datasets collected from Korean rivers between 2016 and 2018 were reprocessed using an updated version of QRev to obtain consistent and reliable velocity and discharge estimates. Surface velocities were estimated using QRev-based extrapolation and power-law velocity profile methods, and conversion factors were calculated as the ratio of depth-averaged velocity to surface velocity.

The mean conversion factor was 0.88 for the extrapolation method and 0.85 for the power-law method, with most values ranging between 0.80 and 0.90. The estimated uncertainty was approximately 7–8%. Analysis of hydraulic variables showed that conversion factors increased with water surface width and mean depth, whereas weak negative trends were observed with mean velocity and shape factor. Correlation coefficients were generally below 0.5, indicating substantial scatter and limitations in generalizing conversion factors based on single hydraulic parameters.

How to cite: Lee, K., Lee, S., and Yang, S.: Characteristics of Surface-to-Depth-Averaged Velocity Conversion Factors Based on ADCP Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15987, https://doi.org/10.5194/egusphere-egu26-15987, 2026.

We present RUHM (Rating curve Uncertainty estimation using Hydraulic Modelling), a new open-source tool for rapid estimation of stage–discharge rating curves and their uncertainty at river discharge monitoring stations. A rating curve models the relation between stage (water level) and discharge at a river location and is used to derive discharge time series from water level at most discharge monitoring stations worldwide. However, many stage-discharge field gaugings are needed to estimate rating curves and their uncertainty using traditional approaches. RUHM combines a 1D hydraulic model with Bayesian inference and uncertainty estimation techniques to more rapidly estimate a rating curve and its associated uncertainty with a minimum of three low to middle flow gaugings. The data needed to use RUHM can be effectively collected using drone/UAV (Unmanned Aerial Vehicle) surveys, reducing field efforts compared with traditional approaches. Our open-source implementation of RUHM is written in Python and features a graphical user interface, a user manual, two workflows for pre-processing UAV-derived and/or traditionally surveyed data to generate the RUHM input data files, and example datasets with UAV data. Applications of RUHM to Swedish stations show well-constrained and robust uncertainty estimates where the 1D-flow assumption holds, and wider uncertainty intervals where it does not.

How to cite: Westerberg, I., Karlsen, R., Mansanarez, V., and Lavenius, A.: RUHM: An open-source tool for rapid stage–discharge Rating curve Uncertainty estimation using Hydraulic Modelling and UAV data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17351, https://doi.org/10.5194/egusphere-egu26-17351, 2026.

Discharge observations mainly rely on gauged water levels through rating curves (RC), whose reliable establishment requires long term measurements that are often unavailable due to high maintenance costs, complex terrain, and political reasons. As a result, many basins worldwide remain ungauged, making RC estimation particularly challenging. Recent advances in remote sensing, including satellite altimetry, provide new opportunities for discharge and RC estimation in ungauged basins. However, several challenges remain, including parameter equifinality in discharge inversion from water level, oversimplified assumptions of channel resistance and cross-sectional instability in morphologically active rivers. While Unmanned Aerial Systems (UAS) enable retrieval of channel geometry in complex and hard-to-reach river reaches which imposes an important constraint to mitigate parameter equifinality in hydrodynamic modeling, a systematic assessment of how UAS and remote sensing observations can be combined to reliably estimate rating curves in fully ungauged basins remains lacking.

Funded by European Union's Horizon Europe project UAWOS (Unoccupied Airborne Water Observing System), this study presents a RC estimation framework specifically for ungauged basins using multisource remote sensing, UAS data, and a coupled lumped rainfall–runoff and one-dimensional hydrodynamic model. The model is fully forced and calibrated using remote sensing and UAS observations only. To address parameter equifinality, we first perform a temporal-scale dependent parameter sensitivity analysis to assess the identifiability of model parameters given availability of different remote sensing observations. Based on the sensitivity results, a multi-staged Bayesian calibration strategy is introduced, in which each observation type constrains only the parameter subspace supported by its information content. Isar River, Germany was chosen to test and evaluate the feasibility of the proposed methodology.

Overall, the proposed framework provides a transferable theoretical and technical pathway for estimating RC in ungauged river basins, demonstrating the potential of combining UAS and remote sensing data to derive RC, without relying on prior discharge measurements and offering implications for estimation of ungauged catchments.

How to cite: Hu, X., Zhou, Z., Anwar, F., Tuo, Y., Nielsen, S., Merk, F., Bauer-Gottwein, P., and Disse, M.: Rating curve estimation in ungauged basins using coupled hydrological–hydraulic modelling and multi-source remote sensing and UAS data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18005, https://doi.org/10.5194/egusphere-egu26-18005, 2026.

Most existing developments in camera gauges focus on single-camera configurations supported by ancillary information, such as detailed three-dimensional (3D) channel geometry and ground control points (GCPs). In these approaches, the water surface is delineated from images and reprojected onto a predefined 3D terrain model. However, acquiring accurate and up-to-date 3D models is often challenging or impractical, particularly in dynamic river environments where channel geometry evolves over time. As a result, frequent model updates are required to maintain measurement accuracy. Another key limitation of conventional camera gauges is the limited quantification of uncertainty in water level estimation. Surface velocity is typically derived using image velocimetry or particle tracking velocimetry. While these methods can provide accurate velocity measurements, they are contingent upon the selection of several parameters that must be meticulously chosen for each monitoring site. Furthermore, the performance of these methods can be degraded by challenging camera poses, varying illumination conditions, and flow regimes.

To address these limitations, we introduce a novel low-cost camera gauge system that integrates stereo photogrammetry with artificial intelligence (AI). The system comprises two low-cost cameras connected to a microcomputer capable of capturing, storing, and transmitting images and short video sequences to an online server. An AI-assisted multi-epoch stereo photogrammetry workflow is then applied to estimate camera pose and reconstruct dense 3D model. This process eliminates the need for predefined 3D data of the cross-section and allows us to compute a new and updated 3D model for each image pair. The updated 3D models are the key component of our methodology, from each water level and water surface velocity can measured in scaled values. Additionally, geomorphologic process can be also measured comparing subsequently 3D models. River water surface segmentation is performed using two foundation models, Grounding DINO and the Segment Anything Model (SAM). River waterlines from both images are then matched and projected into the 3D model, from which the water level is retrieved. This approach enables explicit assessment of errors in water level measurements. Particles tracked in video sequences, using a robust AI model, in both images are further projected into the 3D model, enabling scaled estimation of water surface velocity and, subsequently, river discharge.

The proposed methodology provides a robust and scalable remote sensing solution for river monitoring, enabling the observation of hydrological variables and geomorphological processes. Its low cost and reduced reliance on site-specific ancillary data make it well suited for addressing observational data gaps and for densifying hydrological monitoring networks. Moreover, with an appropriate setup, the system can be used for real-time monitoring, making it a valuable tool in scenarios such as flash floods.

How to cite: Zamboni, P., Krüger, R., Bertalan, L., and Eltner, A.: A novel low-cost stereo camera system for river monitoring., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19389, https://doi.org/10.5194/egusphere-egu26-19389, 2026.

Fluvial ice significantly impacts river hydrodynamics by increasing flow resistance, which leads to altered water levels, flow velocities, and turbulence characteristics. River ice also has socio-economic implications as it impacts energy production, flood risk management, and transportation. These effects have remarkable spatial extent: approximately one third of the Earth’s landmass is drained by seasonally freezing rivers. At the same time, the Earth system is experiencing rapid and dramatic changes due to changing climate and these changes are intense in northern areas due to even faster warming and fragile ecosystems. Despite the importance of understanding these changes, obtaining detailed information of ice-covered hydrodynamics remains challenging and potentially dangerous, resulting in limited data availability even under current conditions.

In this research, more comprehensive insights into fluvial ice and ice-covered hydrodynamics in Finland are achieved by integrating novel approaches in physical modelling, numerical modelling, and field data acquisition and processing. Information of current conditions in the studied subarctic rivers is gathered using conventional methods, such as flow velocity measurements, as well as emerging technologies including underwater drones. The study includes ice growth calculations under projected climate and flow conditions. Flume experiments in an indoor flume with proxy ice and bed topography and pressurised conditions are conducted to observe ice-covered hydraulics in a controlled environment. New methodologies and integration of different approaches allow for gathering more comprehensive information on seasonally freezing rivers and help in predicting future changes in response to climate change.

How to cite: Vaahtera, R., de Vet, M., Veijalainen, N., and Lotsari, E.: River ice under climate change: integrating modelling and data acquisition methods for detecting changes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19665, https://doi.org/10.5194/egusphere-egu26-19665, 2026.

Global warming has increased the amount of deadwood in forests due to wildfires, insect outbreaks, and droughts. Deadwood and fresh wood are mobilised by erosion into river systems as driftwood, forming the largest organic carbon sink due to its slow decomposition rate. Thus, event-based driftwood transport is crucial for disaster management and assessing carbon storage. Here, we applied YOLOv8 to detect driftwood using images from surveillance cameras and a drone. Three types of driftwood, i.e., instream, riverbank, and nearshore, were used from the database of Swiss, Arctic Data Center, and our own drone surveys to train the model of object detection and instance segmentation. To estimate the volume of driftwood, we compared the detected image areas with radio-frequency identification (RFID) tagged logs of known dimensions, establishing an area-to-volume conversion. Our models achieved an mAP50 of 0.96 for in-stream object detection. Applying this model to Typhoon Kong-rey in the Liwu River, we estimated an in-stream driftwood volume of 3.5×10⁵ m³, with a carbon stock of 8.24×10¹⁰ g C, representing 0.11% of Taiwan’s annual carbon export. Furthermore, we observed that driftwood flux increases nonlinearly with river discharge. Our analysis suggests that driftwood accumulation along the outer bends of the riverbank may lead to pulsed driftwood flux. These findings highlight the significance of event-scale driftwood transport as a quantifiable component of green carbon and demonstrate the feasibility of integrating deep learning-based detection with hydrological monitoring for carbon budget assessments.

Keywords: Driftwood flux, YOLOv8, RFID, drone, green carbon

How to cite: Kong, Q.-Y., Yang, C.-J., Tsai, C.-H., and Lee, M.-Y.: Integrating deep learning detection and hydrological monitoring for driftwood flux and carbon stock estimation in a steep tropical basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4783, https://doi.org/10.5194/egusphere-egu26-4783, 2026.

In this study, we present the continuation of the novel robotic mechanism introduced at the EGU General Assembly Conference 2025 (I. S. Tsimpidi, 2025) for autonomous soil moisture data collection. Soil moisture is vital for irrigation, flood and drought forecasting, and hydrological studies, yet shows strong spatial and temporal variability; therefore accurate measurements are required. We conduct field experiments to improve the fully autonomous robotized procedure with AgriOne, reducing sampling time and enhancing repeatability.
As the AgriOne robot, Figure 1, enables in situ, high-precision, spatially dense data collection across the field, we conducted additional field experiments to collect soil moisture data, both autonomously and manually. The AgriOne robot autonomously executes soil moisture data-collection missions, with sampling positions defined by georeferenced waypoints. The waypoints were generated in ArcGIS software using a grid creation tool, with the centre of each grid square as the selected position. The size of each grid cell was defined as 8m * 8m. In the sequel, these waypoints feed the robotic autonomous navigation system, which combines satellite positioning and motion sensors to continuously estimate the robot’s position and plan its trajectory and the sampling points, to meet the initially planned sampling protocol. For the robotic navigation, a hierarchical control architecture generates velocity commands to guide the robot to each target location with centimeter-level positioning accuracy. Upon reaching each waypoint, the system autonomously triggers a probing mechanism to collect and log soil moisture measurements before continuing to the next mission point. Manual data collection was performed by a human carrying a handheld TEROS 12 sensor connected to a Bluetooth sensor interface for instant readings in a mobile application. The positions for taking the measurement were selected using an empirical sampling method.

Figure 1: AgriOne robot with description of its components.

The first experiment was executed successfully in mid-July in a flat field with no vegetation cover, no precipitation, relative air humidity of 52%, air temperature of 20 °C and wind speed of 2 Bft. The autonomous data collection yielded data from 69 of the 73 waypoints where the robot stopped, and the manual data collection yielded data from 50 waypoints, both covering an area of 4.800m2. The second experiment was successfully conducted in mid-October in an area with low elevations and dense grass cover. On the experimental day, precipitation was absent; air temperature was 12°C, relative air humidity was 66% and wind speed was 1 Bft. In this experiment, AgriOne autonomously collected soil moisture data from 63 of the 72 waypoints where stopped, and from 41 waypoints we collected soil moisture data manually, covering an area of 4.700 m2. The results of the conducted experiments are presented on a satellite map of the tested areas, with proportioning the points based on the soil moisture values and are shown in Figure 2.

Figure 2:Presentation of the SM collected data autonomously and manually in both testing areas.

Tsimpidi, I. S. (2025). Large-scale Soil Moisture Monitoring: A New Approach. EGU General Assembly Conference Abstracts, pp. EGU25-1910.

How to cite: Tsimpidi, I., Labra Caso, F., Sumathy, V., Soulis, K., and Nikolakopoulos, G.: Autonomous Robotised Repeatable Soil Moisture Sampling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5265, https://doi.org/10.5194/egusphere-egu26-5265, 2026.

Planetary boundaries for many legacy and emerging contaminants are exceeded. Moving beyond the “safe operating space” for handling these pollutants means increased risks of tipping points which may irreversibly change the functioning of ecosystems and the services they provide, resulting in severe environmental and public health impacts.

In particular the monitoring and prediction of the strongly nonlinear behaviour of many contaminants, including pollution hotspots (locations) and hot moments (events) that disproportionally affecting catchment water quality when a significant proportion of the contaminant load is mobilised withing river catchments and transported to the river network and then further downstream, remains a significant challenge for state of the art water quality monitoring.

We here present the SMARTWATER environmental sensing platform, integrating sensor technology, network and data science innovations with and mathematical modelling with stakeholder catchment knowledge to we diagnose, understand, predict, and manage the emergence and evolution of water pollution hotspots and hot moments. We highlight how innovations in fluorescence and UV absorbance optical sensing technologies can be utilised for instance to track the drivers of extreme hypoxia events through urban and rural observatories and how the combination of easy to sense water quality proxies widely dispersed across the catchment can help optimising high-utility observational networks with regards to the placements of multi-sensor platforms as well as guiding their operation. Deploying data-science approaches including hysteresis and flushing indexes across a range of low- to higher monitoring locations revealed not only divergences in the sources and their mobilisation of different pollutant types (nutrients, DOM, metals) but also differences in their downstream evolution and spatial footprints through complex (and managed) river networks. Integrating information of the different behaviours of pollutants and functional markers such as tryptophan-like fluorescence and Chlorophyll a helped to identify pollutant specific activated source areas and mobilisation mechanisms, supporting also the development of automated event-triggered in-situ sampling solutions for analysis of emerging pollutants (including microplastics) and microbial analyses that are currently not possible to sense in-situ. Integrating this information highlights drastic differences in the contaminant specific emergence of pollution hotspots and hot moments including their large-scale footprint and longer-term relevance for catchment water pollution.

How to cite: Krause, S. and the SmartWater Team: Smart sensor networks for tracking the evolution of water pollution hotspots and hot moments through river networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7650, https://doi.org/10.5194/egusphere-egu26-7650, 2026.

Within river deployments that combine Acoustic Doppler Current Profiler (ADCP) hydrodynamics with high-frequency water quality sensing now offer unprecedented detail regarding instream physical processes, chemical mixing, and nutrient transformations, but a key barrier is translating complex, high-volume datasets into process-based interpretations.  Here, we present the Reactivity Index – Hydrodynamic Index (RI - H) workflow, an approach that combines standard hydrodynamic and water-quality sensor data into diagnostic behavioural classes describing hydrochemical behaviours, demonstrated at the Kennet-Thames confluence (Reading, UK). 

The workflow was developed and tested using a remote-controlled moving-boat platform (ArcBoat) equipped with a SonTek M9 (ADCP) and a YSI EXO2 multiparameter sonde.  We collected simultaneous near-surface measurements of velocity, nutrients (NH₄⁺-N, NO₃⁻-N), fluorescent dissolved organic matter (fDOM), turbidity, and specific conductivity across a quasi-synoptic transect design (upstream controls, repeated cross-sections, and diagonal transects) on three days. The study reach was segregated into 14 spatial zones to monitor the chemical and physical changes from upstream end-members (of the Rivers Kennet and Thames), how the end-members interact and evolve downstream of the confluence, and the shift in chemical and physical behaviour under different hydrological conditions. 

RI and H were derived from solute concentrations and flow velocities. Plotting the two indices enabled each observation to be classified into one of five process-based behavioural categories (e.g., Retentive, Reactive, Low-energy depletion, Attenuating, and Conservative). Across three campaigns (including a rainfall-impacted survey), zonal contrasts in RI were consistently strong (Kruskal–Wallis ε² = 0.28–0.81; p < 0.0001), identifying zones with distinct behavioural signatures (nitrate reaction or ammonium retention zones). Extending the same logic to turbidity yielded complementary particulate-transport classes (Local Input, Advective Input, Sediment Deposition, Advective Dilution and Conservative mixing), demonstrating that the workflow was applicable for solutes and particulates. 

Our high-frequency transect sampling captured the hydrological and biogeochemical response to sequential rainfall events on 26 February 2025. Following morning rainfall, we identified a pollutant pulse characterised by elevated NH₄⁺ and fDOM, indicative of sewage or wastewater influence in the River Kennet, which diluted progressively downstream. A late-afternoon high-intensity rain and hail event triggered a distinct second wave, marked by a sharp spike in nitrate NO₃^- and turbidity, characteristic of surface run-off. The rapid succession of these pulses reveals differing pollutant sources and pathways activated under varying rainfall intensities. Statistically strong spatial contrasts in reactivity persisted even during this dynamic event (Kruskal-Wallis ε² > 0.28 - 0.76 for all solutes). This outcome demonstrates the workflow can resolve within-event functional shifts, translating sensor data into a real-time diagnostic of a river's response to rainfall. The RI – H framework provides a standardised approach by enabling event-scale diagnosis of solute and sediment behaviour that cannot be resolved by fixed or point-based monitoring alone. By classifying how rivers transport and process materials in space and time using deployable sensors, the workflow offers a diagnostic, process-informed water quality assessments relevant to better understanding pollutant dispersal, chemical transformations and biota in fluvial systems. 

How to cite: Pesso, C., Rameshwaran, P., Wade, A. J., and Everard, N.: Resolving Sequential Storm-Driven Pollutant Pulses using Novel Hydrochemical Measurements within a Reactivity-Hydrodynamic workflow., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7732, https://doi.org/10.5194/egusphere-egu26-7732, 2026.

Autonomous camera-based systems combined with image velocimetry analyses, enables operational river flow measurements in rapidly responding rivers. The open-source OpenRiverCam (ORC) software stack supports edge and cloud video processing, time-series generation, and rating-curve development, enabling fully operational, scalable non-contact water level and discharge estimation with relatively affordable camera systems.

Despite these advances, image calibration remains a major bottleneck for broad uptake, as it typically requires high-precision surveying of non-collinear ground control points to constrain the camera's pose. This process is often complex and relies on instruments that are not readily available to many users.

We investigate a photogrammetry-based alternative workflow for camera pose estimation for possible integration in ORC: during camera installation, users collect a set of smartphone photographs from multiple viewpoints near the camera location. A photogrammetric reconstruction using these photos together with a sample video from the installed camera, jointly estimates the camera pose and lens parameters. The resulting camera pose is then used to orthorectify videos in operational data collection. Using controlled experiments and field experiments in New Zealand, Zambia and The Netherlands, we assess here (i) the accuracy of reconstructed 3D coordinates compared to traditional calibration, (ii) methods to robustly constrain the horizontal plane, (iii) the number of photographs required, and (iv) the influence of GPS accuracy on the solution.

This approach aims to significantly simplify calibration workflows and lower the barrier to deploying camera-based river monitoring systems.

How to cite: Winsemius, H., Samboko, H., Peña-Haro, S., Mather, S., and Biggs, H.: Calibration of oblique image-based monitoring systems using photogrammetric principles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12962, https://doi.org/10.5194/egusphere-egu26-12962, 2026.

One of the major difficulties in studying and protecting non-perennial rivers is the lack of knowledge about the occurrence of dry periods and their duration. Traditional flow measurement systems are not reliable in measuring zero or near-zero flows and do not provide any information on the presence of isolated ponds during periods of no flow. In this context, satellite observations can make a crucial contribution thanks to their global coverage and high observation frequency, especially if freely available data can be exploited. In the field of multispectral satellite data, the free datasets provided by Landsat (USGS/NASA) and Sentinel-2 (ESA) are particularly useful. Another dataset with interesting features is that of PlanetScope, but unfortunately this data is not free, although available on request for research purposes. In this study, we present an analysis of the potential and limitations of these three datasets in observing the intermittency regime of non-perennial rivers. The differences in their spatial, temporal and spectral resolution make them more or less suitable for monitoring specific rivers with given characteristics and observation requirements. It emerged that thanks to a long archive of observations (more than 40 years), Landsat is particularly useful for analyzing changes in the intermittent flow regime over time, enabling the detection of climatic trends over the standard climatological period of 30 years but due to coarse spatial resolution (30 m) it only allows observation of rivers with sufficiently wide active riverbeds (around 90 m or more). Thanks to the finer spatial resolution (10 or 20 m depending on the band), Sentinel-2 allows observation of water features greater than 6-15 m in rivers larger than approximately 30 m for an observation period that currently stands at 9 years. Thanks to the short revisit time of 5 days or less and the free availability of data, this dataset is particularly useful for continuous observations and obtaining the annual intermittency regime in a larger set of rivers of adequate size. PlanetScope provides data with spatial resolution of around 3 m and a revisit time up to 1 day. Although the spatial resolution is significantly higher than that of Sentinel-2, the ability to observe small water surfaces is not improved proportionally. In fact, we have found that this data allows for the observation of water features greater than 4-10 m in rivers larger than approximately 20 m. This is likely due to the different spectral characteristics of the acquired data. Another factor affecting performance is related to the acquisition time, which for Sentinel-2 is the same for all acquisitions of the same scene, while for PlanetScope images it is variable. This leads to inconsistency in the dataset, making it more challenging to identify water surfaces.

For all considered datasets, the “flowing,” “ponding,” and “dry” phases can be distinguished in a supervised manner using false-color images or automatically by exploiting the reflectance characteristics of water. The performances of both supervised and unsupervised classification are analyzed for different datasets and in various case studies.

How to cite: Papa, M. N., Cavallo, C., Iantorno, L., Brichetto, I., Manfreda, G., Negro, G., and Vezza, P.: Potential and limitation of Landsat, Sentinel-2 and Planet datasets in monitoring the intermittency regime of non-perennial rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13018, https://doi.org/10.5194/egusphere-egu26-13018, 2026.

Recent developments in hydrometric practices enable the measurements of continuous maximum surface velocity of flow passing at a river section and the corresponding water level using a combined surface velocity and water level sensors installed across river bridges. These measurements enable continuous discharge estimation at these river section by employing the well-studied entropy methods. However, the cost of equipping these combined radars at many gauging stations of a river may be prohibitive. But the use of standalone water level radars at many stations may not be cost-wise prohibitive. Taking into consideration of this aspect, the current study proposes a novel method of routing the upstream estimated velocity hydrograph to a desired downstream station of a river reach, which is equipped with a water level sensor, and using the routed velocity hydrograph and the water levels measured at that station, one can estimate the corresponding discharge hydrograph. The proposed study establishes the equation governing the velocity hydrograph propagation in a channel reach which is of the same form as that of the weak-diffusive wave equations governing the discharge and flow depth hydrographs propagation. The derived velocity routing equation is of the same form as the Muskingum routing equation. The parameters of the routing method are estimated using the channel and velocity characteristics of the propagating velocity hydrograph. The proposed velocity routing method is tested by routing the hypothetical velocity hydrographs arrived at by routing a given hypothetical discharge hydrograph defined by Pearson Type-III mathematical function at the inlet of 25 uniform trapezoidal channel reaches each characterised by a unique combination of bed slope and Manning’s roughness characteristics. The benchmark solutions were arrived using the HEC-RAS model. The routed velocity hydrographs enable the close reproduction of the corresponding estimated benchmark velocity hydrographs and, thus, proving the appropriateness of the proposed velocity hydrograph routing method.  

How to cite: Perumal, M. and Rao, C. M.: Velocity Hydrograph Routing to Enable Discharge Estimation at a Channel Section, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13052, https://doi.org/10.5194/egusphere-egu26-13052, 2026.

Rivers play a key role in the transport and retention of floating debris, including plastics. Reliable and scalable monitoring of riverine plastic accumulation is essential for identifying hotspots, understanding debris movement, and supporting mitigation strategies. However, conventional in situ monitoring approaches are often labor-intensive, spatially limited, and difficult to deploy consistently across large or remote river systems. This study presents a semi-automated, image-based monitoring framework that integrates satellite remote sensing and machine learning to detect and map riverine plastic accumulation hotspots at a global scale.

The methodology integrates high spatial resolution imagery for precise manual annotation of accumulation areas and multispectral Sentinel-2 data for classification of litter hotspots using Random Forests in Google Earth Engine. The workflow combines the most influential spectral bands with targeted spectral indices, including NDVI, PI, FDI, and SI13, to enhance class separability between plastic, water, and vegetation.

The methodology is evaluated across three highly polluted river systems in Indonesia, Guatemala, and Ghana. These sites represent a wide range of hydrological and environmental conditions, including floating vegetation, canopy shading, and narrow urban channels affected by pixel mixing. Results demonstrate high within-river classification performance, with overall accuracies up to 99.5% on independent sections of the same river, and robust cross-river generalization when spectral indices are incorporated, achieving plastic F1-scores up to 79%.

In addition to image classification, the workflow supports multi-temporal analysis to generate hotspot frequency maps, enabling the identification of persistent plastic accumulation zones linked to river morphology and infrastructure. Feature-importance analysis highlights the relevance of specific spectral bands and indices across different environmental conditions and supports the development of reduced, generalizable models.

To facilitate reproducibility and large-scale application, the methodology is operationalized in an open-access Google Earth Engine application that enables users to apply the trained model to rivers worldwide using Sentinel-2 imagery. The proposed framework contributes to the advancement of environmental monitoring and provides a foundation for future developments toward global, long-term assessment of river plastic dynamics.

More information: https://doi.org/10.1016/j.isci.2025.114570

How to cite: Pérez-García, Á., Amanda, G., López, J. Fco., Russwurm, M., and van Emmerik, T. H. M.: Identifying River Plastic Hotspots from Space, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13155, https://doi.org/10.5194/egusphere-egu26-13155, 2026.

Accurate representation of the spatial and temporal variability of precipitation is a fundamental requirement for reliable flood modelling, especially if semi-distributed/fully-distributed models are used. However, official rain gauge networks often exhibit limited spatial coverage and low density, leading to substantial uncertainty in the representation of rainfall at the sub-basin scale. Recently, opportunistic precipitation observations derived from personal weather stations (PWS) have attracted increasing attention as a potential complementary data source, offering unprecedented spatial coverage. At the same time, PWS networks are characterized by heterogeneous data quality, inconsistent maintenance, frequent outages, incomplete records, and a dynamically changing network structure. Despite the attention that PWS have gained, their applicability in hydrological modelling, especially within semi-distributed modelling frameworks, has been explored in only a limited number of studies.

This study evaluates the feasibility of PWS rainfall data for semi-distributed hydrological flood modelling and outlines the conditions under which their application is appropriate. The Lambro catchment in northern Italy is used as a case study. PWS rainfall observations obtained from the Meteonetwork platform (https://www.meteonetwork.it/, Giazzi et al., 2022) and official rainfall data provided by the Lombardy Regional Environmental Protection Agency (ARPA) are used in this study. Different PWS-based rainfall datasets are created: namely, raw PWS data (PWSraw), quality-controlled PWS data (PWSqc), and data from persistent PWS stations, implying those PWS that were active over all considered storm events (PWSqc_p), and their combinations with the ARPA observations (denoted by ARPA + PWSraw, ARPA + PWSqc, ARPA + PWSqc_p, respectively).

Each set is compared to the ARPA rain gauge measurements, which are used a reference dataset. The evaluation is performed by comparing rainfall features at the point- and sub-basin scales, as well as through semi-distributed hydrological flood simulations by analyzing the impact of the rainfall input on simulated peak discharge and timing of its occurrence, and runoff volume at the basin outlet. The hydrological modelling with every rainfall dataset is performed by using the semi-distributed model developed by Politecnico di Milano (Cazzaniga et al., 2022).

The results demonstrate that quality-controlled and persistent PWS datasets (PWSqc and PWSqc_p), as well as their combination with ARPA observations, generally enhance hydrological model performance. This indicates that PWS data can provide added value for semi-distributed flood modelling when appropriately controlled and integrated with reference datasets from the official networks.

References

Cazzaniga, G., De Michele, C., D’Amico, M., Deidda, C., Antonio Ghezzi, A., and Nebuloni, R.: Hydrological response of a peri-urban catchment exploiting conventional and unconventional rainfall observations:  the case study of Lambro Catchment, Hydrology and Earth Sysem. Sciences, 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, 2022.

Giazzi, M., Peressutti, G., Cerri, L., Fumi, M., Riva, I. F., Chini, A., Ferrari, G., Cioni, G., Franch, G., Tartari, G., Galbiati, F., Condemi, V., and Ceppi, A.: Meteonetwork: An Open Crowdsourced Weather Data System, Atmosphere, 13, 928, https://doi.org/10.3390/atmos13060928, 2022.

https://www.arpalombardia.it/   

Acknowledgments

The authors would like to thank the COST Action “OpenSense” (CA20136) for supporting collaboration opportunities among the co-authors through the STSM program.

How to cite: Kovačević, R., Ceppi, A., De Michele, C., Nebuloni, R., and Todorović, A.: Personal weather station rainfall data for semi-distributed flood modelling: Feasibility and limitations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15052, https://doi.org/10.5194/egusphere-egu26-15052, 2026.

How to cite: Weldon, S.: A novel approach to automated field saturated hydraulic conductivity measurements at depth, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15282, https://doi.org/10.5194/egusphere-egu26-15282, 2026.

The double whammy of increasing human water consumption and the effects of warming world is poised to further shrink large lakes, especially in arid and semi-arid regions. Further, reservoir sedimentation remains a critical challenge to global water security, causing a progressive loss in storage capacity and disrupting the ecological balance of downstream river systems. The motivation of the present study is based on the fact that at present reservoir managers are dependent upon expensive hydrographic surveys to locate the sedimentation impacted area, which cannot be conducted as and when required due to financial constraints. Further, the recent floods in the Indian state of Punjab (August 2025) were related to intense rainfall, sudden sediment inflow, resulting in drastic reduction in the storage capacity of the reservoirs.

This study presents a comprehensive geospatial framework on assessment of elevation-area-capacity relationships of Ukai reservoir. The method uses multi-temporal optical and SAR satellite imageries (Landsat-9, Sentinel-2 and Sentinel-1) and corresponding altimetry water level data to delineate the water spread areas (contours) at varying elevations. Two different time-periods (historical, 2008-2010 and 2021-2022) were compared to assess the change in contours. The results of sedimentation assessment clearly show an expansion in water boundary extent in recent years compared to that in the past decade. Additionally, the findings reveal that over the last decade Ukai reservoir’s live storage capacity has significantly declined by ~200 MCM, indicating ~20 MCM annual sedimentation rate. The spatial analysis distinctly maps the geographical areas of sediment accumulation or erosion and show that the sediment change is not uniform.

How to cite: Ghosh Natoo, N., Kumar Gupta, P., and Ramchandra Nikam, B.: Rapid Sedimentation Impact Assessment of Ukai Reservoir using Geospatial methodology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16174, https://doi.org/10.5194/egusphere-egu26-16174, 2026.

Proximal remote sensing represents an effective approach for water quality monitoring, enabling the estimation of turbidity and suspended sediment concentration (SSC) through spectral indices, such as red–green band ratios. Low-cost RGB cameras are widely adopted for this purpose, however, their measurements are strongly affected by variations in illumination, shadows, surface glint, and ambient environmental conditions, which can compromise data consistency and reliability. Extending the spectral coverage into the near-infrared (NIR) domain has the potential to enhance sensitivity to suspended sediments and reduce the influence of variable lighting conditions. Although hyperspectral sensors remain costly and impractical for routine monitoring, the analysis of hyperspectral data provides valuable insights into the most informative wavelengths and supports the targeted integration of RGB imagery with selected NIR bands for future field applications.

In this laboratory study, proximal hyperspectral sensing was employed to investigate SSC under both artificial and ambient illumination conditions, using two sediment types with contrasting optical properties (yellowish soil and white China clay). The experiments assess the influence of illumination conditions and sediment characteristics on spectral signatures, and compare the performance of reflectance information derived from the RGB and NIR spectral ranges. The results offer initial insights into sediment–reflectance interactions and contribute to the development of more robust and cost-effective proximal remote sensing strategies for water quality monitoring in real-world environments.

Keywords: Proximal remote sensing; hyperspectral data; suspended sediment concentration; laboratory experiments

How to cite: Moe, A. C., Miglino, D., Zhuang, R., Saddi, K. C., Viscido, L., Methaprayun, M., Shareen, N., Budrach, T., Mapiam, P. P., Bogaard, T., and Manfreda, S.: Assessment of Optical and Near-Infrared Proximal Remote Sensing for Suspended Sediment Concentration Estimation under Artificial and Ambient Illumination, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16213, https://doi.org/10.5194/egusphere-egu26-16213, 2026.

Deployment and upscale of traditional monitoring systems have different challenges, specially in developing countries, because of high investment costs, difficult operation and maintenance. In the last years there have been several low-cost, open-source products developed as well as initiatives with the objective of tackling down those issues.

Herein we present the setup and first leanings of the project L-DaaS “Local people for Discharge monitoring as a Service” where we proposed a scheme of using an open-source software for flow monitoring and a low-cost hardware which use standard components with a business model centred around a local enterprise in charge of the operation and maintenance. The project was executed in Zambia with a locally-driven environmental monitoring company based in the same country. Key stakeholders where the Water Resource Management Authority of Zambia and hydropower operators.

Open-source and low-cost system are not by themselves a solution, it is also needed to create a sustainable and scalable business model which fosters affordable, efficient, and locally supported water management solutions.

How to cite: Peña-Haro, S., Samboko, H. T., and Winsemius, H. C.: Towards a Low-Cost River Monitoring Setup, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19490, https://doi.org/10.5194/egusphere-egu26-19490, 2026.

Arid regions such as Oman are increasingly susceptible to severe flash floods driven by climate change and rapid urbanization. Accurate water level measurements are vital for flood preparedness and the development of early warning systems required to mitigate severe socio-economic impacts, including substantial property damage and the recurring loss of life. Beyond disaster mitigation, recording runoff is essential for sustainable water management and for enhancing the understanding of hydrological processes in small-scale, ephemeral catchments that remain largely ungauged. However, traditional water level monitoring via pressure gauges or radar sensors is often hindered by high infrastructure costs, physical vulnerability to high-flow events, and the changing morphology of wadi channels. To address these limitations, we present a robust photogrammetric workflow integrated into a low-cost, Raspberry Pi-based optical monitoring system for measuring water level, surface velocities and discharge assessment.

The workflow relies on the synergy between a single fixed low-cost camera and high-resolution Digital Terrain Models (DTMs) generated through UAV-based Structure-from-Motion (SfM-MVS). To convert 2D image measurements into 3D object space, both the camera and the DTM must be referenced in a shared coordinate system. Traditionally, this is established using permanent Ground Control Points (GCPs) measured with RTK GNSS; however, establishing and maintaining such markers in adverse wadi conditions is logistically challenging and the physical markers are prone to being lost during flood events. We address this by employing the GIRAFFE (Geospatial Image Registration And reFErencing) workflow. This approach replaces physical markers by performing an image-to-geometry registration that aligns the real 2D camera view with a synthetic image rendered from the UAV-based 3D pointcloud. Using the AI-based LightGlue matching algorithm, the system automatically identifies homologous points between the views to create 2D–3D correspondences. These correspondences function as pseudo-control points, allowing for the precise determination of the camera’s 3D pose and orientation via spatial resection.

For the hydrological monitoring, the workflow further employs two AI-driven stages:

Water Level Estimation: Convolutional Neural Networks (CNNs) segment the water area in time-lapse images. The resulting waterlines are projected into 3D space and intersected with the DTM to derive accurate water levels.

Discharge Assessment: Surface flow velocities are measured using the PIPs++ (Persistent Independent Particle tracker) technique. Unlike traditional frame-by-frame methods, PIPs++ tracks particles across multiple time steps jointly, providing enhanced temporal smoothness and robustness against illumination changes or partial occlusions. Based on these surface velocities, the mean velocity is determined and combined with the wetted cross-section from the DTM to estimate total discharge.

Initial results from deployments in Wadi Al-Hawasinah, Oman, demonstrate that this solar-powered, remote system successfully captures ephemeral flow events. By leveraging GIRAFFE for automated localization and PIPs++ for robust surface velocity estimation, this workflow provides a scalable and cost-effective solution for enhancing flood early warning systems in complex, ungauged terrains.

How to cite: Krüger, R., Zamboni, P., Grundmann, J., Al-Rawas, G., and Eltner, A.: AI-Driven Photogrammetric Workflow for Low-Cost Wadi Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19511, https://doi.org/10.5194/egusphere-egu26-19511, 2026.

Granitic hard rock aquifers dominate much of semi-arid peninsular India and are characterized by highly heterogeneous, fracture-controlled flow systems with limited storage. In these settings, conventional indicators such as static water levels and standard well drawdown equations provide poor insight into actual aquifer stress and pumping sustainability. This study presents an integrated hydrological monitoring approach that combines flow meters, borewell camera scans, and continuous camera-based observations to directly understand aquifer behaviour under pumping. We deploy non-invasive flow measurement to quantify real-time abstraction, alongside step-drawdown tests and downhole camera surveys to identify active fracture zones, their depth-wise contribution to yield, and their dynamic response during sustained pumping. Continuous camera scans during pumping cycles enable direct visualization of drawdown, fracture inflows, and the rapid transition from borehole storage to fracture-limited supply, revealing why prolonged pumping from deeper depths often leads to high energy use with marginal water gains. By linking pumping rates, energy consumption, and observed subsurface flow processes, the study demonstrates how mismatches between pump capacity and fracture-controlled yields drive inefficiency and accelerated aquifer stress. The results highlight the value of image-based and sensor-driven monitoring for developing context-specific indicators of groundwater stress and for identifying optimal pumping regimes in hard rock aquifers. This integrated methodology offers a scalable pathway to improve hydrological understanding, support adaptive groundwater management, and inform incentive-based interventions aimed at conserving both water and energy in data-scarce, remote settings.

How to cite: N r, L., Vikram Jain, A., Jain, A., Misquitta, K., Grewal, V., and Srinivasan, V.: Understanding Pumping Dynamics in Granitic Hard Rock Aquifers Using Integrated Flow Metering and Camera-Based Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21112, https://doi.org/10.5194/egusphere-egu26-21112, 2026.

Precipitation and evaporation are the two fluxes coupling the atmospheric and terrestrial compartments of the hydrologic cycle. Accurate and robust observations of the spatial and temporal variability of these two fluxes over the Earth’s continents is crucial to help understand the intricacies of land surface – atmosphere interactions. Improving our understanding and our ability to quantify these interactions is not only important for scientific purposes (such as developing better earth system models) but also for societally relevant applications (such as flood and drought forecasting). Here, we demonstrate the potential and address the limitations of microwave links from cellular communication networks for estimating both precipitation and evaporation.

Previous research has shown that attenuation of microwave signals propagating through rainfall from the transmitting to the receiving antennas of microwave links can be related to the average rainfall intensity along the path between transmitter and receiver. Over the past two decades, this notion has been successfully applied to retrieve rainfall fields from existing microwave links which are part of cellular communication networks. Rain-induced signal loss due to absorption and scattering of microwave signals by raindrops, a source of “noise” for mobile network operators, has turned out to be a “signal” for hydrometeorological science and applications. The approach of using existing cellular communication infrastructure for environmental monitoring (in this case rainfall measurement) has been dubbed “opportunistic sensing”.

However, atmospheric constituents between the transmitters and receivers of microwave links do not only affect signal propagation when it rains. When it is dry, refractive index fluctuations induced by temperature and water vapor variations resulting from rising turbulent eddies in the atmospheric boundary layer between transmitters and receivers cause received signals to “scintillate”. The variance of these scintillations has been shown to be related to the structure parameter of the refractive index, which in turn can be related to sensible and latent heat fluxes across the microwave link path using Monin-Obukhov Similarity Theory (and the aid of auxiliary information). This principle is used by microwave scintillometers, commercially available instruments for observing turbulent fluxes in the atmospheric boundary layer.

Recent research results show that microwave links from cellular communication networks can, under certain conditions, also be employed as boundary layer scintillometers. Combining this notion with the previous finding that such microwave links can also be used as path-average rain gauges suggests that there is potential to use each of the roughly five million backhaul links from cellular communication networks worldwide as combined precipitation-evaporation sensors. Hence, gaining access to received signal level data from this enormous number of microwave links would allow large-scale rainfall and evaporation mapping, also for regions across the globe which are currently poorly served in terms of dedicated meteorological stations.

We present both the physical basis of this approach and empirical results from previous and ongoing measurement campaigns to discuss the potential and challenges of opportunistic sensing of two hydrologic fluxes with one single instrument: precipitation and evaporation.

How to cite: Uijlenhoet, R., Walraven,  ., van der Valk, L., Coenders, M., Hut, R., Overeem, A., and Hartogensis, O.: Opportunistic Sensing of Precipitation and Evaporation Using Microwave Links From Cellular Communication Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23288, https://doi.org/10.5194/egusphere-egu26-23288, 2026.

Mechanistic soil water and heat transport models are important tools for quantifying and predicting exchange fluxes between the atmosphere, vegetation, soils, and aquifers. For instance, these models are essential for assessing how soils help mitigate flood and heatwave risks, particularly in urban environments. Mechanistic soil water and heat transport models rely on parameters characterising the soils' hydraulic and thermal properties. The estimation of these parameters through inverse modelling and the quantification of associated uncertainties is challenging due to the non-linear nature of the processes and computational demands of the simulations. However, recent advances in inverse modelling algorithms such as Iterative Ensemble Smoothers (IES) allow us to handle highly parameterised, non-linear models while keeping the number of model runs relatively small (~1000).  These advances not only keep the computational demand for inverse modelling and parameter estimation tractable, but in addition allow for the quantitative assessment of data worth of available or planned observations, which can be used to increase the efficiency of experimental designs. In this work, we tested a Levenberg-Marquardt form of IES, a relatively novel method increasingly used in reservoir and groundwater modelling, with a mechanistic soil water and heat transport model. We firstly generated reference values of soil moisture, temperature and fluxes using three synthetic models representing different characteristic soil profiles with contrasting parameter values. We simulated a calibration period representing our planned field experiments, consisting of two infiltration tests with warm and cold water, and a prediction period including a heat wave and an extreme rainfall event. Secondly, we used the IES algorithm to history-match the model-generated “observations” of soil moisture and temperature at six depths for the calibration period. We finally evaluated the algorithm's ability to estimate the reference parameters, as well as predict soil moisture, temperature, and fluxes. The results show that the posterior distributions obtained with the IES algorithm are consistent with the reference values for all parameters and predictions considered. Furthermore, the relatively small number of runs required (< 10,000) allowed us to perform parameter estimation and uncertainty quantification across different experimental scenarios, thereby quantifying their data worth and optimising our experimental design. The synthetic approaches formed the basis for simulating water and heat transport in three real-world urban soils and assessing the worth of temperature measurements in tracing water and heat fluxes. IES algorithms have strong potential to become standard tools for vadose zone modelling, and the insights gained from our study offer a solid foundation for their effective application.

How to cite: Di Ciacca, A., Delottier, H., Coudène, M., Narth, T., Halloran, L., Bullinger, G., and Brunner, P.: Parameter estimation, uncertainty analysis and data worth assessment of soil water and heat transport models using an Iterative Ensemble Smoother, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1851, https://doi.org/10.5194/egusphere-egu26-1851, 2026.

Two-dimensional (2D) hydraulic models are essential tools for flood hazard assessment, yet their calibration remains computationally demanding and strongly constrained by data availability. This study presents a Bayesian calibration framework that integrates a convolutional neural network (CNN) surrogate model to efficiently infer spatially distributed Manning’s roughness coefficients while explicitly accounting for model structural uncertainty.

The approach is applied to a reach of the Lower Piura River (Peru), a flood-prone basin characterized by limited in situ observations. An ensemble of TELEMAC-2D simulations is generated using Latin Hypercube Sampling over multiple roughness configurations, and a CNN is trained to emulate spatial water depth fields with high fidelity. To focus learning on hydraulically relevant regions, a weighted loss function based on roughness–depth sensitivity is employed.

The trained emulator is embedded within a Bayesian inference scheme that incorporates a Gaussian Process discrepancy term to represent systematic model–reality deviations. Posterior distributions of Manning’s coefficients and uncertainty parameters are estimated using Markov Chain Monte Carlo sampling. Synthetic experiments demonstrate accurate parameter recovery in hydraulically sensitive areas, while a real-case application based on optical satellite imagery confirms the method’s ability to reproduce observed flood depth patterns under data scarcity.

The proposed framework significantly reduces computational cost compared to conventional calibration approaches and provides a probabilistic characterization of parameter uncertainty. These results highlight the potential of CNN-based surrogate models as scalable tools for Bayesian inference in large-scale hydraulic modeling and flood risk assessment.

How to cite: Zevallos Ruiz, J. A.: Bayesian calibration of a 2D hydraulic model using a CNN-based surrogate emulator under data-scarce conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1947, https://doi.org/10.5194/egusphere-egu26-1947, 2026.

The Finite‑Volume Neural Network (FINN) merges the rigor of classical numerical discretizations with the flexibility of artificial neural networks (ANNs) to uncover unknown terms or parameters in partially unknown partial differential equations (PDEs). While this hybrid framework enhances flexibility and interpretability, the highly parameterized ANN makes uncertainty quantification (UQ) of the identified PDE components both demanding and computationally expensive, especially when conventional Bayesian approaches rely on costly Markov Chain Monte Carlo sampling. To address this, we introduce a computationally efficient, Machine Learning (ML)‑assisted inference‑with‑UQ scheme that yields confidence intervals for the PDE components learned by FINN. The procedure consists of data‑driven bootstrapping of the available observations and repeated training of FINN on each resampled set. We illustrate the method on the retardation factor of a diffusion‑sorption PDE, showing that it produces trustworthy interval estimates while markedly lowering the computational burden.

How to cite: Scheurer, S., Frenner, R., Brünnette, T., Oladyshkin, S., and Nowak, W.: Efficient Uncertainty Quantification for Physics-Aware Machine Learning of Diffusion-Sorption Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3076, https://doi.org/10.5194/egusphere-egu26-3076, 2026.

It is unrealistic to build independent alternative models to constitute the model space of Bayesian model aver­ aging (BMA) in groundwater/surface water modeling. Using uniform prior weights can lead to overweighting models with similar structures, as well as biased posterior model weights and BMA predictions. This study applied a correlation matrix R to measure the correlations among alternative models. And two weighting schemes based on R, namely the cos-square (CS) and capped eigenvalue (CE), were used to dilute models’ prior weights. Additionally, the effective model number (Neff) metric derived from R was proposed to measure the effectiveness of BMA model set. Based on two real-world cases (snowmelt runoff modeling and groundwater modeling), and a synthetical groundwater case, we validated the importance of prior weight dilution and the important value of the R-based methods in improving BMA prediction. The results demonstrated that the prior weight dilution schemes redistribute models’ prior weights by penalizing highly correlated models while rewarding those with relatively independent structures. The BMA predictive performance is improved using the weight dilution schemes, with the CS scheme outperforming the CE scheme. In addition, the correlation matrix provides insight into the rationality of the model structures in the BMA model set. The metric of Neff can serve as an effective tool for quantifying the effectiveness of the model set, which provides an important reference for updating the model set and improving BMA predictions with prior weight dilution schemes.

How to cite: Haoxin, H.: Prior weight dilution in Bayesian model averaging for groundwater modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3699, https://doi.org/10.5194/egusphere-egu26-3699, 2026.

Estimates of irrigation water withdrawals (IWW) by global irrigation models (GIM) depend on assumptions about which features (irrigated lands, crop types, schedules and water sources) are represented and how they are formalised. While uncertainty and sensitivity analyses (UA/SA) routinely interrogate parameter uncertainty, many assumptions are qualitative or pragmatic, resist numerical characterisation and thus lie beyond conventional quantitative approaches.

We addressed this gap by subjecting 100 irrigation modelling assumptions, drawn from c. 50 papers, to an expert elicitation process involving eleven scientists and five irrigators. Experts were asked to rank the assumptions in terms of influence and then assess the first ten based on their situational limitations, plausibility, choice space, peer agreement and influence on model outputs.

Scientists identified irrigated area, irrigation efficiency and water availability, often represented by single datasets, as primary drivers of global IWW estimates. Both scientists and irrigators judged these assumptions to be highly influential with weak pedigree (quality of the knowledge base), exhibiting limited empirical support, derivation under practical constraints, multiple plausible alternatives, and low peer agreement, and thus placing them in the NUSAP "danger zone". 

By linking assumption influence with epistemic strength, this study extends conventional UA/SA, demonstrating that extended peer engagement can reveal overlooked uncertainties, enhance transparency and strengthen the robustness of global IWW assessments under deep uncertainty.

How to cite: Linga, S. N., Aguiló-Rivera, C., Richards, O., Flinders, S., Larsen, J., Massimi, M., De Grandis, G., and Puy, A.: Extended peer community finds limited epistemic justification for key assumptions in global irrigation models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4054, https://doi.org/10.5194/egusphere-egu26-4054, 2026.

In coastal aquifers, a main objective of groundwater management is often to determine sustainable pumping rates that avoid seawater intrusion and well salinization. Models can be used to understand and forecast the behavior of such aquifers. A necessary step for modeling is calibration, and such a process contains uncertainty. Understanding how uncertainties affect water management is crucial for providing water utilities with the necessary information about well salinization risk. Uncertainty in the model can be reduced by new observation types and locations. One of the most common observation types is groundwater head observation in an observation well. However, the number of observation wells is limited by constraints, such as budget. A FOSM approach was chosen for data worth analysis considering parameter and observation uncertainty. It was implemented in a real-world island aquifer in the Pwales CA (Malta). The flow model was developed using MODFLOW6, and Linear Uncertainty analysis was done via PEST. FOSM was conducted, aiming to find optimum groundwater head observation locations to reduce forecast uncertainty. The workflow used scripts for reproductibility and it adapted to high-dimensional model.

How to cite: Ekiz, M. Ç., Caputo, M. C., De Carlo, L., Turturro, A. C., Sapiano, M., Galea, L., and Schembri, M.: Utilizing Linear Uncertainty Analysis for Potential Observation Well Locations in the Pwales Aquifer, Malta, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4306, https://doi.org/10.5194/egusphere-egu26-4306, 2026.

Connected, highly permeable subsurface features act as preferential flow paths and strongly influence solute transport. Transport models are frequently used for risk assessment and mitigation and should properly account for these connected structures to provide unbiased and informative predictions.

The parameterization of hydraulic properties typically relies on multi-Gaussian distributions and poorly integrate geological knowledge in the prior. They allow for a flexible and efficient data assimilation of process variables (heads, concentrations), but may lead to unrealistic geology and insufficient description of connectivity. Alternatively, detailed descriptions of heterogeneities can be obtained with advanced geostatistical methods, such as multiple-point statistics. They account for geological knowledge and can be conditioned to geological or geophysical data. Unfortunately, they are hardly compatible with a data assimilation process and therefore usually fail to match observed data.

To address this issue, we compared several approaches capable of integrating both geological knowledge and observation data. We employed different parameterization strategies by using pilot points (de Marsily, 1978), adopting a facies-like representation of the subsurface with truncated pluri-Gaussian simulations (Matheron et al., 1987), or inserting structures whose positions can be adjusted during parameter estimation (Khambhammettu et al., 2020). We also implemented data space inversion (Delottier et al, 2023), which bypasses parameter estimation and focuses on the link between observations and forecasts.

The approaches are tested with a transport model considering a contaminant migration scenario in a synthetic alluvial aquifer with permeable channels. The predictions of interest are the mass flow, peak time, and total mass of contaminant reaching the river. Results show that there is a compromise to find between a simple but effective parameterization, which can struggle to represent connectivity, and a more detailed one, which is more difficult to make consistent with observations.

How to cite: Petitjean, Z., Pryet, A., Atteia, O., Kham, M., Couplet, M., Lamouroux, R., and Lalbat, F.: Adding geological knowledge in transport models to improve their predictive capacity for risk mitigation in heterogeneous aquifers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5697, https://doi.org/10.5194/egusphere-egu26-5697, 2026.

Infiltration is a quantitative expression of water loss in the urban hydrological cycle, but current hydrological models often use empirical or semi-empirical equations. The inherent uncertainties in these equations (often simplifying boundary conditions or water content expression) are not accurately conveyed to end users via hydrological models. As well, these empirical equations introduce model-form uncertainty that is often ignored before model calibration. This research focuses on analyzing the uncertainty in the infiltration process by constructing a quantitative framework based on uncertainty propagation from a true, physical model (Richards Equation) to conceptually simpler models (Green-Ampt and Horton’s model) that uses entropy to track the uncertainty’s magnitude change. Firstly, we conducted sensitivity analyses using various designed rainfalls (time series as well as IDF curve) in a watershed over varying spatial-temporal scales to isolate the uncertainty propagation in the infiltration equations arising from different spatial-temporal scales. This uncertainty propagation framework for infiltration answers the question of how changes in the structural assumptions of the infiltration equation affect peak flowrate errors or volume estimation errors. It adopts entropy as a quantitative index to describe the amount of information loss in the infiltration process, as well as how the uncertainty propagates over time and space. Furthermore, this entropy uncertainty framework can help in decision making related to when a more physically-based approach must be used, or when a simplified equation is still acceptable.

How to cite: Zhang, Z. and Valeo, C.: Entropy-Based Quantification of Infiltration Model-Form Uncertainty, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5846, https://doi.org/10.5194/egusphere-egu26-5846, 2026.

Hydrological models are a fundamental tool for supporting water resources management; yet, model predictions are often affected by high uncertainty. Among the other sources of uncertainty, snow-dominated catchments must also cope with modeling the snow accumulation and melting processes. Snow controls discharge by storing winter precipitation and releasing it during melt periods, thereby regulating the timing and magnitude of the streamflow. 

A widely used approach for representing snow processes is the degree-day model, which estimates snow accumulation and melt based on air temperature thresholds and a melting factor. Due to the scarcity of in situ snow observations, the degree-day parameters are commonly inferred by calibrating the discharge within a hydrological model, thereby exacerbating parameter equifinality.

In this study, we quantify the impact of constraining degree-day model parameters with MODIS, a multispectral satellite sensor that provides near-daily global observations of snow cover extent. The constrained calibration framework results in an overall improvement in discharge performance and a significant reduction in parameter uncertainty, including for the non-snow-related parameters of the other model. These results underscore the importance of integrating satellite-based snow information to mitigate equifinality and enhance the robustness of hydrological modeling in alpine environments.

How to cite: Di Dato, M., Zanoni, M. G., Avesani, D., Di Marco, F., and Bellin, A.: Reducing parameter uncertainty in hydrological modeling using MODIS-derived constraints, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7856, https://doi.org/10.5194/egusphere-egu26-7856, 2026.

Climate change and rapid urbanization are reshaping insect phenology and spatial occurrence patterns, leading to increasingly frequent nuisance insect outbreaks in urban environments. In dense metropolitan areas, sudden mass emergence of nuisance insects-such as mayflies, red-backed lovebugs, and non-biting midges-can cause sanitation concerns, disruption of urban infrastructure, and surges in public complaints, placing growing pressure on local environmental management. Despite these challenges, most current management practices remain reactive, relying on complaint-driven responses after outbreaks occur, which limits timely and efficient allocation of monitoring and management resources.

This study presents a predictive environmental modelling framework designed to support municipal decision-making by forecasting monthly nuisance insect outbreak risk at the district (gu) level in Seoul, South Korea. Rather than pursuing nationwide prediction, the study focuses on a single metropolitan system where environmental heterogeneity, administrative demand, and operational feasibility are closely aligned. By fixing the spatial analysis unit at the municipal district level, the framework delivers risk information directly compatible with urban monitoring plans, prioritization of management efforts, and allocation of limited resources.

A GIS-based spatial database was constructed by integrating nuisance insect occurrence history derived from citizen-science platforms and open biodiversity databases, monthly climate variables (mean temperature and cumulative precipitation), and district-level land cover composition. Occurrence records were subjected to quality control procedures, including coordinate validation and spatial de-duplication, and aggregated into monthly district-level counts as a proxy for outbreak intensity. Climate predictors were selected for interpretability and relevance to insect life cycles, while land cover metrics emphasized water and wetland areas, green spaces, and urbanized land.

To characterize baseline spatial tendencies, species distribution modelling was applied to derive habitat suitability indices for each target taxon. These indices were incorporated as auxiliary predictors to support interpretation of spatial risk patterns rather than serving as standalone forecasts. A predictive model integrating climate variables, land cover composition, occurrence history, and habitat suitability indices was then developed to estimate one-month-ahead outbreak risk scores for each district. Continuous risk scores were translated into ordinal risk classes using objective threshold rules to facilitate interpretation and identification of priority districts.

The predicted results indicate clear seasonal and spatial heterogeneity in outbreak risk across Seoul. Elevated risk tends to concentrate within specific seasonal windows, while district-level patterns vary according to local environmental conditions. Species-specific differences suggest that the relative importance of spatial drivers differs among taxa, with some showing stronger associations with water-related land cover and others responding more strongly to urban–green space configurations.

By delivering interpretable, one-month-ahead risk information at an administrative scale, the proposed framework provides a practical basis for shifting nuisance insect management from reactive responses toward anticipatory, risk-informed planning. The workflow is implemented as a reproducible, GIS-based pipeline that can be updated as new climate or occurrence data become available, demonstrating how predictive environmental modelling can function as an operational decision-support tool for urban environmental management under increasing climate and ecological uncertainty.

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (RS-2021-NR060142).

How to cite: Lee, J. H., Cho, D. H., Kim, D. G., Wee, J., Lee, S. C., and Lee, J. A.: A Predictive Environmental Modelling Framework for Decision Support:Monthly Municipal-Level Forecasting of Nuisance Insect Outbreak Risk in Seoul, South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8668, https://doi.org/10.5194/egusphere-egu26-8668, 2026.

For beverage manufacturers that utilize local water resources, it is crucial to understand future physical water risks caused by climate change to ensure business continuity and appropriate disclosure to investors, customers, and other stakeholders. Recently, free and widely used water risk screening tools have become available, such as Aqueduct 4.0 (WRI, 2023) and Water Risk Filter (WWF, 2024). However, because of their global-scale design, they may not accurately represent site-specific hydrological water cycle processes, including local water use conditions and surface water-groundwater interactions. Thus, their outputs may not be consistent with historical experience or local perceptions.

Watershed modeling is a useful tool for representing local hydrological water cycle processes and quantitatively evaluating future water risks. Nevertheless, watershed model parameters are inherently uncertain, and future prediction simulations with a single parameter set may lead to either underestimation or overestimation of water risk metrics. Therefore, in order to assess water risk more effectively, it is necessary to develop a framework that can identify feasible parameter combinations (multiple solutions) while taking into account parameter uncertainty.

In this study, we developed a watershed model that considers parameter uncertainty to quantify future physical water risks in the Phu Sai River Basin, Rayong Province, Thailand (approximately 280 km²), which is a concern due to the increase in water risks based on Aqueduct 4.0. A watershed modeling tool GETFLOWS was applied, which can simulate surface water and groundwater flow simultaneously.

The required data for watershed modeling was classified into hydrological observations, meteorology, land use/land cover, topography, geology, and water use. Our primary source of data was public data, including global datasets for meteorology, land use/land cover, and topography, as well as Thai government datasets for geology and water use. Regarding hydrological observation data used for model validation, in addition to existing data released by the Thai government, field measurements of river discharge and groundwater levels were conducted to improve model accuracy. Model performance for 2015–2025 was evaluated using the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and correlation coefficient.

The first step was to identify a parameter set that can accurately reproduce the hydrological observations through manual calibration. The next step was to create realistic parameter ranges and conduct sensitivity analyses to extract parameters that have a significant impact on simulated river discharge and groundwater levels. For the selected parameters, we generated 100 parameter combinations using Latin hypercube sampling and ultimately identified two parameter sets that showed high agreement with hydrological observations based on the NSE, RMSE, and correlation coefficient. Obtaining multiple solutions could help us evaluate the spread of future water risk predictions caused by parameter uncertainty.

In future work, we plan to conduct future prediction simulations using climate projection datasets such as NEX-GDDP-CMIP6 v2.0 (NASA, 2025) and to quantitatively evaluate future physical water risks based on risk assessment metrics such as required river discharge and groundwater level thresholds.

How to cite: Kajita, M., Saito, M., Tawara, Y., Kurihara, S., and Okamoto, H.: Watershed modeling considering parameter uncertainty to evaluate future physical water risks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8930, https://doi.org/10.5194/egusphere-egu26-8930, 2026.

Event Mean Concentration (EMC) is widely used to estimate stormwater pollutant loads due to its simplicity and low data requirements. However, conventional deterministic approaches typically use a single representative EMC for a catchment to estimate loads, thereby neglecting temporal variability across seasons and storm events, and potentially biasing event-level load estimates. To address this limitation, we present a Bayesian hierarchical linear mixed model that explicitly quantifies seasonal and inter-event variability in EMCs by estimating full posterior distributions, rather than a single deterministic EMC value, while retaining the operational simplicity of the traditional EMC approach. The model decomposes EMC into three hierarchical components: a global fixed effect, a seasonal random effect, and an event-level random effect. This structure enables EMC variability to be partitioned across multiple temporal scales and propagated into predictive uncertainty. The approach is demonstrated using soluble reactive phosphorus (SRP) data from a mixed urban catchment in London, Canada, comprising 18 monitored storm events across summer and fall seasons. A suite of models is developed to systematically evaluate methodological choices, including models with increasing levels of EMC variability representation (from global-only to full hierarchical structures) and models considering alternative land-use representations (lumped versus distributed). Results indicate that the full hierarchical model consistently outperforms simplified structures that exclude key variability components, as evaluated using leave-one-out cross-validation (LOO). Models that explicitly represent distinct land-use types demonstrate improved predictive performance compared to lumped representations; however, spatial disaggregation increases marginal variance, reflecting additional uncertainty. For the full hierarchical, land-use-distributed model, seasonal-level effects account for the largest share of marginal variability (median 63%), indicating that EMC variability for SRP manifests mainly as seasonal changes. Overall, these findings demonstrate that a single representative EMC is insufficient to characterize intrinsic temporal variability. By explicitly propagating uncertainty across hierarchical levels, the proposed Bayesian framework improves the reliability of stormwater load predictions and provides a more robust basis for management decisions.

How to cite: Dong, Z., Goel, P., and Robinson, C.: A Bayesian Hierarchical Model to Simulate Temporal Variability in Urban Stormwater Event Mean Concentrations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12048, https://doi.org/10.5194/egusphere-egu26-12048, 2026.

Understanding how uncertainty propagates in hydrological modelling, from precipitation inputs to streamflow simulations, is essential for improving model sensitivity and strengthening the interpretation of model outputs. While gridded precipitation products are increasingly developed and widely used in different applications worldwide, their impacts on the uncertainty of simulated streamflows remain largely unexplored. In this work, we tested a variety of state-of-the-art precipitation datasets to explore how their uncertainty cascades through a process-based hydrological model and influences streamflow predictions using the Reno River basin in Italy as a case study. Our results indicate that, although precipitation patterns are broadly consistent across datasets, substantial differences emerge at seasonal and annual scales especially in complex terrains. Moreover, precipitation uncertainties are propagated and also amplified to the streamflow, on average 3.5 times for the dry season. The opposite occurs for the wet season, where uncertainty slightly decreases. The subsequent analysis reveals that the influence of precipitation uncertainty differs among subbasins. As such, our work emphasises the substantial impact of precipitation forcing in hydrological modelling and the significance of evaluating and quantifying uncertainty propagation

How to cite: Cenobio-Cruz, O. and Di Baldassarre, G.: From precipitation datasets to streamflow simulations: Tracing the propagation of uncertainty in hydrological modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12725, https://doi.org/10.5194/egusphere-egu26-12725, 2026.

The traditional groundwater modeling approach of manual calibration with a handful of parameters and ad hoc one-at-a-time sensitivity analysis is giving way to formal data assimilation and uncertainty estimation, where the natural very-high dimensionality of the inverse problem is embraced.  In theory, this is an improvement for applied groundwater modeling, and, more importantly, the management of groundwater resources.  However, this transition is not without hardship.  Many new concepts, skills, and techniques must be learned to effectively and efficiently assimilate many kinds of information and to ultimately provide robust estimates of predictive uncertainty in an applied groundwater modeling setting, where time and budget pressures are real. 

This talk will present some foundational concepts surrounding predictive groundwater modeling, including the roles of model complexity, data, and uncertainty. The talk will include discussion of apparent trends in the groundwater modeling industry, with a few examples of modern applied predictive groundwater modeling.

How to cite: White, J.: Predictive groundwater modeling and uncertainty estimation in practice, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14178, https://doi.org/10.5194/egusphere-egu26-14178, 2026.

The recent passing of Ilya M. Sobol’ marks the loss of one of the most influential figures in the development of global sensitivity analysis (GSA). Sobol’s work fundamentally shaped how uncertainty in model outputs is attributed to uncertain inputs, providing a rigorous and widely adopted framework that has become a cornerstone of uncertainty and sensitivity analysis across the Earth, environmental, and hydrological sciences.

This contribution first offers a brief tribute to Sobol’s scientific legacy and a concise review of the conceptual foundations of GSA. We revisit the primary question that motivated Sobol’s work—How much of the uncertainty in the model output is caused by each uncertain input?—and discuss why this question remains central for the analysis of complex, nonlinear, and high-dimensional models. We also emphasize that the principles underpinning GSA are increasingly relevant in the context of artificial intelligence (AI), where complex and high-dimensional models demand robust and transparent methods for attributing influence and uncertainty.

Building on this foundation, we highlight recent developments around variogram analysis of response surfaces (VARS), and in particular X-VARS, which extend GSA concepts to settings relevant for explainable AI (XAI). By leveraging paired perturbations and scale-explicit analysis, X-VARS enables efficient and robust attribution of uncertainty and influence in complex models, making GSA practical for modern AI-driven applications. Compared to established explainability methods such as SHAP, X-VARS offers substantial gains in computational efficiency while providing diagnostically richer insight into nonlinearity, interactions, and scale dependence.

We conclude by highlighting some key challenges and opportunities for the next generation of GSA methods in complex modelling and AI applications.

How to cite: Razavi, S., Panigrahi, B., and Abbasnezhad, H.: Ilya M. Sobol’ (1926–2025): A Tribute and Overview of the Foundations of Global Sensitivity Analysis, Recent Advances, and Extensions toward Explainable Artificial Intelligence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14866, https://doi.org/10.5194/egusphere-egu26-14866, 2026.

How to cite: Eythorsson, D. and the Comphyd team: On the Architecture of Integration in Hydrological Modelling — Orchestrating Reproducible, Scalable, and Transparent Workflows with SYMFLUENCE , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15704, https://doi.org/10.5194/egusphere-egu26-15704, 2026.

The optimization of flood risk mitigation measures necessitates the estimation of flood risk without and with a wide range of combinations of mitigation measures. This flood risk estimation is a complex task, involving climate, hydrological, hydraulic and economic process components. Moreover, these components do not form a linear process chain but interact - for example through local protection measures and potential flood protection failures. Additionally, flood risk assessment is accompanied by significant natural and model uncertainties.

We developed a probabilistic model capable of efficiently estimating the flood risk at river scale [1]. It explicitly models the interplay among flood process components and mitigation measures, making it well suited to estimate the benefit of individual mitigation measures or combinations thereof. In the latter case, the model captures the joint effect of mitigation measures, rather than summing their independent benefits.

Decision making in flood risk management involves numerous possible mitigation measures, multiple conflicting objectives (such as minimizing costs versus maximizing risk reduction), and generally very high uncertainties. In this contribution, we present how the flood risk model can support decision making for the selection of flood mitigation measures. Identifying pareto-optimal combinations of mitigation measures at river scale poses different challenges: a combinatorially large design space, partly discrete optimization variables, substantial natural and model uncertainties and large variance in the sample-based risk estimates. We present an optimization framework tailored for these settings, which balances between the robustness of sample-based flood estimates and the convergence behavior of the optimization given computational constraints. 

How to cite: Ruf, M. and Straub, D.: Optimizing flood risk mitigation measures at river scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18127, https://doi.org/10.5194/egusphere-egu26-18127, 2026.

Hydrological models often fail to fully capture catchment responses, which leaves systematic residuals between modelled and observed flow. Here, we present a comprehensive state-dependent, two-stage residual modelling framework that separates residuals into modelling error, arising from structural and parametric limitation, and observation error, coming from measurement and forcing data uncertainty. The residuals are estimated conditional on hydrological states, allowing the error dynamics to vary across flow regimes rather than assuming stationarity. The residual model is trained on 124 CAMELS-AU catchments from diverse climatic regions across Australia and is tested on independent catchments from the continent. The results demonstrate improved correction across all flow regimes, specifically for high (>95th percentile) and extremely high flow (>99th percentile). To enhance the generalizability, Minimum Redundancy Maximum Relevance (mRMR) feature selection is employed to identify the most important catchment attributes, which are used as static inputs alongside the dynamic model states and hydrological forcings. The framework is applicable to fully calibrated, partially calibrated, and uncalibrated hydrological models, and remains effective under limited or absent streamflow data. By explicitly modelling residuals as separable state-dependent processes, the proposed framework provides a robust method for improved streamflow correction, with particular relevance for peak flow estimation and applications in data-scarce environments. 

How to cite: Hammad, M., Mehrotra, R., and Sharma, A.: Modelling Hydrologically-Conditioned Residuals For Improved Error Correction Across Different Flow Regimes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18152, https://doi.org/10.5194/egusphere-egu26-18152, 2026.

How to cite: Giboulot, A. and Ancey, C.: Relating free-surface flow and discharge in mountain streams, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18396, https://doi.org/10.5194/egusphere-egu26-18396, 2026.

Uncertainty in hydrological modeling has an important effect on the reliability of predictions, such as droughts and floods. Uncertainty quantification can expose parameter sensitivities and structural flaws, enabling better calibration and robust risk assessments. However, the use and implementation of suitable methods often hinder good research and thus good model results.

Building on a decade of community work, SPOTPY has evolved into a widely used tool for covering a wide range of peer-reviewed hydrological calibration, uncertainty, and sensitivity analysis techniques. Here, the latest advances in research and practice are presented, featuring an expanded set of optimization algorithms, hydrological performance metrics, and high-throughput workflows that make rigorous parameter exploration accessible, ranging from desktop studies to large computing clusters.

The new release strengthens SPOTPY’s role as a “single entry point” for testing alternative calibration strategies for any hydrological or ecohydrological model. A redesigned model interface simplifies the coupling of external models (from simple conceptual bucket models to fully distributed land‑surface models), while improved I/O handling and database backends streamline storage of millions of simulations for posterior analysis. The availability of global and local optimization methods has been extended and harmonized: alongside classic algorithms such as SCE‑UA, DREAM, ROPE and Monte Carlo sampling, users can now flexibly switch between multi‑objective and single‑objective formulations and customize stopping criteria to balance convergence and computational cost.

For performance evaluation, SPOTPY now offers an enriched library of objective functions and hydrological signatures tailored to discharge and ecohydrological time series, from classical Kling–Gupta Efficiency (parametric and non‑parametric) to hydrological signature-based flow percentile‑based indicators. All metrics are fully integrated into calibration, sensitivity analysis, and uncertainty assessment workflows so that users can, for example, calibrate to traditional goodness‑of‑fit while simultaneously tracking regime‑oriented diagnostics that are critical for low‑flow, flood, or water‑quality applications. Recent case studies demonstrate how these capabilities help quantify trade‑offs between parameter identifiability and process realism in hydrological models under changing climate and land‑use conditions, for which an overview will be presented.

Furthermore, the new features will be illustrated through real-world hydrological applications, highlighting practical guidance on algorithm choice, the diagnostic use of hydrological signatures, and robust uncertainty communication. However, as not every model produces the expected result on the first try, a discussion ground will be provided for problems that are frequently encountered in hydrological modeling.

How to cite: Houska, T.: There is sense in every model: Discover it with SPOTPY, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18621, https://doi.org/10.5194/egusphere-egu26-18621, 2026.

The optimal sensor placement (OSP) within water distribution networks (WDNs) is a critical research area, driven by the need for effective leak detection, localization, and data-driven decision support. Due to the large scale, complex topology, and uncertain hydraulic behavior of WDNs, identifying sensor locations that are both informative and spatially representative remains a challenging task. This study proposes an information-entropy and clustering-based framework for optimal sensor placement, aimed at enhancing leak detection and localization through machine learning in smart water networks.

Hydraulic pressure data are generated using EPANET by introducing a single leak at a time, modeled as an additional demand at each demand node in the network. The pressure signal at each node is treated as a random variable, and its probability distribution is estimated using a histogram-based approach. Shannon information entropy is employed to quantify the uncertainty and sensitivity of nodal pressure responses, where nodes with higher entropy are considered more informative and responsive to system disturbances such as leaks. Mutual information is incorporated to compute shared information between candidate sensor locations. By penalizing nodes that exhibit high redundancy with previously selected sensors, the proposed framework ensures that each sensor contributes unique and complementary information. Furthermore, to guarantee spatially distributed sensors and network-wide coverage, spectral clustering is applied using nodal coordinates and elevation (X, Y, Z), partitioning the network into geographically coherent clusters. Sensors are placed within each cluster at the nodes with maximum penalized entropy score. A greedy search algorithm is employed to maximize this score. Consequently, this integrated framework effectively balances information maximization, redundancy reduction, and spatial representativeness.

The methodology is validated on the benchmark Modena network, a medium-sized gravity-fed WDN consisting of 268 demand nodes, 317 pipes, and four reservoirs. The performance of the information-entropy and clustering-based sensor placement framework was evaluated using spatial classification accuracy metrics at varied distance tolerances (0m to 500m). The multilayer perceptron (MLP) based leak localization model trained using data from the information-entropy and clustering-based OSP at nodes {7, 42, 162, 228, 257} achieved training accuracy of 97.62% and test accuracy of 85.73%. Spatial accuracy results further validate robustness, with localization accuracies of 93.94% within 100 m, improving to 97.95% and 99.72% within 200 m and 500 m tolerance, respectively. Robust performance was maintained even after introducing noise into the data; however, under noisy conditions, the use of spatial accuracy metrics is recommended to effectively predict the leak zone rather than exact node locations. The high spatial accuracies demonstrate the frameworks effectiveness for machine learning–based predictive analytics. The framework supports informed decision-making and provides an efficient solution for smart water network monitoring and management.

How to cite: Jain, P. and Reddy, M. J.: Information entropy and clustering-based sensor placement in water distribution networks for leak detection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18895, https://doi.org/10.5194/egusphere-egu26-18895, 2026.

How to cite: Lombardo, L. and Viglione, A.: Beyond deterministic hydrological modelling: a copula-based uncertainty framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20073, https://doi.org/10.5194/egusphere-egu26-20073, 2026.

Practical constraints often force modellers to rely on legacy data rather than targeted new data collection relying tailored sampling design for subsurface modelling. While these pre-existing datasets enable model development and gap identification, their spatial density and distribution may not always meet the desired resolution or precision. Consequently, strategic subsampling for calibration and validation is essential to ensure a robust and accurate performance assessment of the resulting models. While cross-validation techniques are commonly applied to maximize data utility, their application in spatial modelling yields overoptimistic performance estimates with high variance, particularly when data are clustered. Probabilistic-based sampling is known to tackle bias, but its effectiveness remains poorly understood for spatially sparse and clustered legacy data.
This research evaluates the impact of subsampling methods on the validation of spatial interpolation techniques. Conditional versus random subsampling is compared for different subsample sizes in terms of actual model performance with particular attention to geostatistical concepts that additionally take into account spatial autocorrelation within subsurface data. Legacy boreholes spanning over a century with sparse and clustered spatial distribution were queried to model peat content in 3D. Conditioning relied on 2D legacy attributes such as age, spatial coordinates, and target feature statistics. We also investigated how the complexity of spatial variation (represented in different models with varying anisotropic autocorrelation) influenced performance by populating the existing borehole configuration with three 3D target features: two more spatially continuous synthetic and one heterogeneous, real field dataset. First results suggest that variance of validation results reduced exclusively in the heterogeneous case, provided the validation subset was large enough (35%) to incorporate the cumulative peat content within a borehole as a 2D attribute. These results underscore the resilience of conditioned probabilistic subsampling over alternative validation methods for legacy-based modelling.

How to cite: De Weerdt, P., Luca, S., and Van De Vijver, E.: Conditional Subsampling of Legacy Boreholes for Subsurface Model Validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20968, https://doi.org/10.5194/egusphere-egu26-20968, 2026.

The sustainable yield of aquifer systems in sedimentary basins can theoretically be derived by long-term model simulations considering the adverse effects of pumping, such as streamflow depletion, subsidence, or contamination induced by flow reversals. The implementation of a model-based approach for sustainable yield estimation is often challenged by the lack of knowledge on system properties and (paleo)-recharge rates. Specifically, leakage flows through aquitards generally drive the hydrodynamic response of confined aquifers to pumping, but their properties are poorly constrained by typical observational datasets. Dating methods such as radiocarbon have been widely used to infer residence time in confined aquifer systems. However, their use to constrain flow hydrodynamics in multi-layer systems with a state-of-the-art inverse modeling approach is scarce.

In this study, we investigated the flow dynamics of the Aquitaine Basin located in Southwest France, with an extensive repository of hydrologic and geochemical data spanning several decades. A 2D cross-sectional numerical flow model was developed and extended to simulate reactive transport of radiogenic carbon. An inverse modeling approach was then implemented to estimate model parameters using observed hydraulic heads and ¹⁴C activity. This paves the way to a more rational quantification of the sustainable yield of critical resources for the resilience of water supply in a changing world.

How to cite: Pryet, A., Marín Rivera, C. F., Fernandez, N., Saltel, M., Atteia, O., Franceschi, M., Goncalves, J., Hamelin, B., Deschamp, P., Claveau, A., and Marlin, C.: How can inverse reactive transport modelling of radiocarbon improve the estimation of sustainable yield in confined aquifer systems?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21104, https://doi.org/10.5194/egusphere-egu26-21104, 2026.

Observations are key for development of earth and environmental systems models. They can be used for initialisation, verification and evaluation, forcing, constraining and benchmarking. Providing the observed variables required by modellers in a useful format with necessary metadata, including uncertainty characterisation, has traditionally not been seen as the responsibility of the observation providers. This has caused a mismatch between what is provided and what is required, leading to proxy climate data such as reanalyses being widely used in place of true observations. To enhance the uptake and usability of observational datasets, they should be provided with easy access, in easy to use format, clearly documented and with detailed metadata.

The Working Group on Observations for Researching Climate (WGORC) was set up in 2025 by the World Climate Research Program (WCRP) Earth System Modelling and Observations (ESMO) core project. WGORC kicked off in December 2025 and is focused on improving the use of observations throughout climate science, addressing the mismatch mentioned above and ensuring correct use and application of observations to model development.

ESMO working groups function through the activities of panels and task teams which are set up in response to identified needs. WGORC has one existing panel and will be looking to set up at least two more based on the outcomes of the scoping activities currently underway.

The initial WGORC focus areas include:

* Characterisation and communication of observational uncertainties 

* Use of observations in machine learning applications for climate 

* Observations to better understand and model extreme weather and climate events 

* Data rescue and recovery of historical climate observations 

The existing panel, obs4MIPs, is an ongoing community-driven initiative to provide observational datasets in the format to support model benchmarking and evaluation of Earth System Models as part of the Climate Model Intercomparison Projects (CMIP). 

This presentation will describe WGORC in detail and outline the scoping activities in the four focus areas and the ongoing activities of the existing panel obs4MIPs which is currently investigating how to expand its offering to provide point observations as well as gridded, and the provision of associated uncertainty data with each dataset. It will also discuss opportunities on how the community can stay engaged with WGORC and ESMO activities and be consulted during user requirements gathering for climate observations.

How to cite: Doherty, A., Woolliams, E., and Rao, D.: Community needs for Observations to constrain Earth System Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21485, https://doi.org/10.5194/egusphere-egu26-21485, 2026.

How process knowledge can improve flood forecasting: A stochastic and deterministic perspective (Invited)

Svenja Fischer

Hydrology and Environmental Hydraulics, Wageningen University & Research, Wageningen, the Netherlands

Flood prediction remains challenging. With changing climate and environment, predictions are becoming more important because the impacts are increasing, while at the same time, they are becoming more challenging because flood-generating mechanisms change. Floods can be triggered by different processes, such as heavy rain, long-duration rain or melting snow. With changing climate, these processes are expected to change in frequency and magnitude. However, in current flood prediction models, the different flood-generating mechanisms are not explicitly considered and all flood events are treated equally. While in stochastic hydrology, process knowledge has been shown to be able to improve flood estimation and reduce uncertainty, this is less well studied for physical models. This can introduce uncertainty into the estimation.

The first step is to identify the relationship between atmospheric and catchment characteristics, flood-generation processes and the flood hydrograph. The identified relations are then integrated in the hydrological models by directly tailoring the physical relations to each flood type. In combination with an dynamic weighting approach, this enables a non-stationary and flexible flood prediction that can capture the changing frequency and magnitude of flood types and provide different flood scenarios with assigned probabilities. This approach does not only reduce the error in flood peak prediction but also improves the link of the model parameters to physical processes and thus increases our understanding of flood processes. Moreover, the uncertainty of the considered process can be directly quantified by a probability-based evaluation.

How to cite: Fischer, S.: How process knowledge can improve flood forecasting: A stochastic and deterministic perspective (Invited), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22244, https://doi.org/10.5194/egusphere-egu26-22244, 2026.

Our work has aimed to deal with the long-term groundwater response to understand the natural and human roles at the continental scale, with a time window ranging from 2003 to 2024. A comprehensive mass balance of the terrestrial water storage (TWS) approach was used for the determination of monthly groundwater storage change to attribute its variability to both climatic and human-induced drivers. Our findings show a long-term upward pattern of groundwater storage anomaly (GWSA), demonstrating a dual reality of groundwater response to precipitation, where the flux of the groundwater system (recharge/discharge) is quickly responsive to inter-annual extremes of precipitation, with a response time of 0 months. However, the state (total storage) is slow and integrated with a multi-year response time peaking approximately 29 months after a precipitation event. There is a significant hydro-climatic regime shift that developed after 2018, where groundwater was being replenished to levels never seen before. This surge occurred because of positive precipitation anomalies caused by a prolonged multi-year La Niña phase, and there is a shift within the regime of the hydrogeological system of the continent from negative human contribution at the beginning (2003) to positive human intervention toward the end of the study time range. This is primarily driven by sustained decreasing groundwater withdrawal, mainly for agricultural activities, at a slower rate than the continent's total annual groundwater renewal. Although the continent has recently benefited from a period of intense, climate-driven recharge (post-2019), this has occurred in defiance of a massive and growing human-induced withdrawal signal. This positive continental trend also masks severe concurrent regional droughts, such as the catastrophic 2020-2022 La Niña-induced drought in the Horn of Africa. These findings present the first complete picture of the African groundwater system, underscoring its susceptibility to significant climatic phenomena such as the El Niño-Southern Oscillation (ENSO). It also highlights the critical need to incorporate regional variations and diverse climatic factors in all future water security assessments carried out on water security. Moreover, continental groundwater storage anomaly is a robust proxy for the integrated impact of major climate teleconnections like ENSO. This work measures these opposing forces and finds that the current positive balance of groundwater storage is vulnerable and relies on the proper maintenance of the positive wet climate. This indicates the need for sustainable policy interventions in the groundwater sector based on long-term agricultural water productivity to maintain the value of the most significant African source of freshwater.

How to cite: Brhane, G. K., Ni, C.-F., and Liu, C.-Y.: Continental-Scale Groundwater Response Dynamics to Climate Variability in Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-193, https://doi.org/10.5194/egusphere-egu26-193, 2026.

African countries' economic growth and sustainable development is being constrained by their capability to adapt to climate change, in particular regarding water availability and distribution. Hydrological processes exhibit a high degree of temporal and spatial variability, and their modelling is affected by issues of nonlinearity of physical processes, conflicting spatial and temporal scales, and uncertainty in parameter estimates. In addition, conventional hydrometeorological data has long suffered from data breaks due to changes in reporting methods and from gaps (missing information), especially in Africa.

This study focuses on modelling the impact of climate change and variability on the long-term distribution of water-balance components in East Africa through the evaluation of historical patterns and future projections.

The methodology uses an integrated Soil and Water Assessment Tool (SWAT) with a machine learning model to examine historical data, to capture nonlinear hydrological patterns, and to generate accurate projections. The modelling analysis is partitioned into two phases: (1) a land-phase module, where SWAT simulates processes from the event of raindrops onto the land surface to the stream, and (2) a climate-phase module, where Regional Climate Models (RCMs) will be used to produce time series for a set of climatic variables under different scenarios. Machine learning algorithms that closely align with RCM will be used to impact assessments on water resources. Based on this, we aim to predict the impact of drought related to the water resources for sustainable agricultural production potential across the region.

At the EGU General Assembly, we will present the spatio-temporal variability of seasonal water balance in East Africa, and the modelling framework for climate change projection and drought prediction.

Acknowledgments:  This research is supported by Eni S.p.A. through the Eni Award “Debut in Research: Young Talents from Africa”. We thank our company tutors Alessandro Nardella and Alessandra Bertoli for their invaluable guidance and technical expertise.

How to cite: Kebede, T. A., Laio, F., and Viglione, A.: Hydrological Response to Climate Change and Variability in East Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-531, https://doi.org/10.5194/egusphere-egu26-531, 2026.

In recent years, artificial reservoirs have attracted increasing attention, not only for their essential role in mitigating hydrological and meteorological extremes but also for their vulnerability to climate variability in region like West Africa. This growing interest has been supported by advances in remote sensing, which now allow near-real-time monitoring of reservoir surface extent (RSE) dynamics. However, regional-scale research quantifying and communicating trends and variability in reservoir surface dynamics remains limited. Additionally, while the effects of large-scale climate indices on hydrological processes have been largely investigated, any study has specifically examined how RSE respond to these climate indices across West Africa. At the same time, regression-based machine learning approaches, frequently used to assess multiple teleconnections while addressing multicollinearity issues, often lack systematic evaluations of their robustness and reliability. These gaps constitute important challenges for both local and regional efforts to monitor hydroclimatic shift impacts and anticipate water resource stress under ongoing global warming. In this study, we addressed these challenges by assessing the spatiotemporal variability and trends in RSE dynamics from 1985 to 2022 for 482 reservoirs across West Africa in relation to ten Sea Surface Temperature Anomaly (SSTA) indices. We further evaluated the performance of three supervised machine learning methods, Ridge Regression, Elastic Net, and Partial Least Squares (PLS) to identify the most suitable for modeling and predicting the effects of SSTA indices on RSE. Finally, we identified the dominant oscillations influencing RSE dynamics and highlighted the regional response patterns of West African reservoirs to the ten SSTA indices. We found strong interannual and decadal variability in both the SSTA indices and RSE, underscoring a dynamic coupling between oceanic conditions and terrestrial hydrology in West Africa. Among the ten indices, the Western Mediterranean Index (WMED) shows the strongest and statistically significant upward trend (p < 0.05). At the reservoir level, 43.26% of the 485 reservoirs exhibit significant long-term trends, with 31.07% showing declines. Of the three algorithms tested, PLS delivers the best generalizability and the most stable out-of-sample predictive performance, but only when using PCA-filtered and low-collinearity predictors. WMED emerges as the most influential driver, with moderate contributions from the Atlantic modes (AMO and AMM). Finally, SSTA regression coefficients vary widely across reservoirs, with minimal spatial clustering, indicating uneven reservoir sensitivity to oceanic oscillations likely shaped by local factors.

How to cite: Kouassi, V. B. S., Chun, K. P., Yao, B. A. F., Soro, G. E., Agbenorhevi, A. E., Goula, A. B. T., Kelome-Ahouangnivo, N. C., and Klaus, J.: Influence of Large-Scale Climate Indices on Reservoir Surface Extent Variability in West Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-667, https://doi.org/10.5194/egusphere-egu26-667, 2026.

Parameter equifinality remains a central challenge in hydrological modeling, limiting the reliability of process-based tools such as the Soil and Water Assessment Tool (SWAT). This study evaluates how multi-variable calibration strategies that combine in-situ streamflow with five remote sensing global actual evapotranspiration (RSAET) products (GLEAM v3.6, GLEAM v4.2, ETMonitor, PML, and SSEBop) can reduce equifinality, using the Essaouira watershed (Morocco) as a study case. A total of 10,000 Monte Carlo simulations were performed, from which the 100 best-performing parameter sets were selected for posterior uncertainty assessment. A Composite Identifiability Score (CIS) was developed by integrating normalized metrics of standard deviation, entropy, peak-to-width ratio, and Kullback-Leibler divergence to quantify parameter identifiability.

Results show that streamflow-only calibration (S0) yields the highest CIS, confirming the strong constraining power of discharge on routing and runoff parameters. However, multi-variable calibration further reduces equifinality for several soil–plant–atmosphere parameters, with the Streamflow + GLEAM v3.6 configuration achieving the highest multi-source CIS, followed by SSEBop and GLEAM v4.2. In terms of performance, streamflow-only scenarios achieve the highest NSE and lowest PBIAS, while hybrid streamflow–AET calibrations maintain strong predictive skill and improve the physical consistency of ET-related processes. In contrast, AET-only calibrations exhibit poor runoff-volume accuracy and large water-balance inconsistencies.

Overall, integrating complementary AET datasets with discharge observations enhances parameter identifiability, constrains key hydrological processes, and mitigates equifinality. This demonstrates a practical pathway to strengthen SWAT model robustness in data-scarce regions, as is the case for many African basins. These results are preliminary, and ongoing work aims to consolidate them by extending the calibration and identifiability framework to include soil-moisture remote-sensing products, with the goal of further constraining soil-water dynamics and reducing remaining model uncertainties.

How to cite: Ouarani, M., Alitane, A., Manyari, Y., Mulla, D., and Ait Brahim, Y.: Reducing Hydrological Uncertainty: A Multi-Variable SWAT Calibration Using Global AET Products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1029, https://doi.org/10.5194/egusphere-egu26-1029, 2026.

Near-surface rainfall estimates from Commercial Microwave Links (CMLs) are a viable source of rainfall information in data scarce regions, notably Low and Middle-Income countries in the tropics. CMLs are point-to-point radio links commonly used in cellular telecommunication networks. When it rains, the radio signal between two cell phone towers is (partially) attenuated, and this rain-induced attenuation can be used to infer the average rainfall intensity along the path. Typically, every 15 minutes the minimum and maximum received signal levels are stored in network management systems by mobile network operators for quality monitoring purposes. Based on these signal levels it is possible to estimate path-averaged rainfall intensities, which can be interpolated to produce high-resolution rainfall maps.

In this study we investigate the use of several thousands of CMLs, predominantly located in heavily urbanized areas, during one rainy season in Nigeria. We use 32 hourly rain gauges (12 from Nigeria’s Meteorological Agency, and 20 from the Trans-African Hydro-Meteorological Observatory) as a reference to compare with the path-averaged rainfall intensities from CMLs within 5 km of a gauge. To quantify the uncertainties in CML rainfall estimates we compare the performance of these links with different frequency and polarization across the same path. We make a similar comparison by comparing interpolated rainfall maps from CMLs to available gridded (satellite) rainfall products on a seasonal basis. As such, this study aims to highlight the added value of using CMLs as an opportunistic source of rainfall estimation in a region where reference rainfall information from dedicated ground-based sensors is very limited. It offers a balanced outlook for the use of these near-surface rainfall estimates with their associated uncertainties as input for hydrometeorological applications at the kilometer scale, ranging  from numerical weather prediction to calibration of satellite precipitation products, and hydrological modelling.

How to cite: Walraven, B., Droste, A., Overeem, A., Coenders, M., Hut, R., and Uijlenhoet, R.: Opportunities for opportunistic sensing of rainfall with Commercial Microwave Links in Nigeria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6809, https://doi.org/10.5194/egusphere-egu26-6809, 2026.

How to cite: Gabella, D., Pimentel, R., Nieto, H., Burchard-Levine, V., Dube, T., and Andreu, A.: How to assess drought in data scarcity areas? A case study in Kruger National Park (South Africa) , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12394, https://doi.org/10.5194/egusphere-egu26-12394, 2026.

Coastal erosion is a persistent and growing challenge along West African shorelines, posing serious risks to livelihoods, infrastructure, and coastal ecosystems. In Ghana, erosion along the eastern coastline is driven by strong wave action, sea-level rise associated with climate change, unregulated coastal development, and sediment retention resulting from dam construction. In response, a range of hard engineering interventions, such as sea walls, breakwaters, armour rock revetments, and groyne systems were implemented in 2017 to reduce shoreline retreat. However, systematic and cost-effective monitoring is required to assess the effectiveness and long-term impacts of these interventions. This study documents the development of a digital coastal erosion monitoring tool using the Digital Earth Africa (DE Africa) platform, focusing on Blekusu, a highly erosion-prone coastal community in eastern Ghana, where major sea-defense structures have been constructed. Relevant national stakeholders, including the Ghana Hydrological Authority, the Ghana Meteorological Agency (GMet), and the Water Research Institute (WRI), were identified through an IWMI–DE Africa stakeholder training workshop and engaged to co-create the coastal erosion use case. An ecosystem of DE Africa tools was used, including DE Africa Explorer, DE Africa Maps, and DE Africa Sandbox. The Explorer served as a unified interface to query and retrieve analysis‑ready coastline datasets and metadata (2010–2024), spanning both pre‑ and post‑intervention periods, derived from 30 m Landsat imagery and tidal modeling. The DE Africa Sandbox served as an integrated development environment for Python-based analysis, enabling users to access, process, and visualize coastline dynamics without relying on multiple external tools. Existing coastal erosion notebooks within the Sandbox were adapted, significantly reducing the learning curve and development time. Code sharing among team members facilitated collaborative development and aligned with open-access data-sharing principles. IWMI and DE Africa jointly reviewed the use case and developed online training materials that allow users to enroll, build capacity, and earn certification. Shoreline extraction and change-detection analyses revealed persistent accretion exceeding 30 m in several sections following the intervention, although localized stabilization was observed near engineered structures. These results demonstrate the reliability of DE Africa as a scalable, accessible, and cost-effective platform for coastal monitoring and support its integration into national coastal management and planning strategies. Continued investment in DE Africa and its integration into university curricula would further expand its impact across Africa.

How to cite: Mortey, E., Agyekum, J., Yiadom, A., Kumah, M., Tilahun, S., Haile, A. T., and Seid, A.: Digital Earth Africa as a Platform for Coastal Erosion Monitoring along Ghana’s Eastern Coastline, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13916, https://doi.org/10.5194/egusphere-egu26-13916, 2026.

Southern Africa is increasingly vulnerable to long-term drought, particularly in arid to semi-arid regions. However, insufficient monitoring makes the timing and extent of groundwater response to droughts difficult to quantify. Here, we combine evidence of past drought in the satellite record with water table modeling to evaluate the spatio-temporal relationship between agricultural and hydrological drought across Southern Africa from 1981-2025. We use the normalized difference vegetation index (NDVI) from the STFLNDVI dataset as a proxy for agricultural drought. We simulate monthly water table depth (WTD) at 30 arcsecond resolution using the Water Table Model (WTM), which dynamically couples surface and subsurface hydrologic processes. 

By defining standardized anomalies derived from NDVI and simulated WTD, we aim to examine spatio-temporal drought propagation characteristics between agricultural and hydrological drought. Drought periods are compared against documented droughts to evaluate whether the human experience of drought  is reflected in simulated groundwater changes. The goal of this study is to provide a regional assessment of drought propagation into groundwater across Southern Africa and to identify where groundwater systems may be vulnerable to drought. 

How to cite: Quigley, L. and Callaghan, K.: A multi-proxy approach to evaluating  Drought in Southern Africa: climate, vegetation, and water table, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15327, https://doi.org/10.5194/egusphere-egu26-15327, 2026.

The African monsoon is governed by the complex interplay of large-scale atmospheric and oceanic processes. Understanding how these drivers influence rainfall variability can improve the prediction of hydro-meteorological extremes (e.g. heavy rainfall, droughts) for this vulnerable region. This study examines the role of dominant climate modes, such as the El Niño–Southern Oscillation (ENSO) and the Atlantic Niño, in modulating rainfall variability and extremes over West Africa. Unlike previous studies, this investigation is conducted across seven objectively defined rainfall zones in West Africa, describing typical rainfall regimes (e.g. Sahelian) for the region. Moreover, the analysis builds on an advanced quality-controlled station-based rainfall dataset, namely the West African Historical Precipitation Database, compiled over the past decade to improve the coverage and quality of data from rain gauges in this region. To describe the state of ENSO, the Atlantic Niño and other climate modes, various state-of-the art indices (e.g. MEIv2, ATL3, DMI, AMM, SOI) and indices specifically established for West African Monsoon (e.g. the African Southwesterly Index ASWI) are used. The statistical relationships between climate modes and rainfall variability are assessed for the seasonal rainfall amount and other rainfall statistics (e.g. onset, rainfall probability, mean-wet day amount) for the main monsoon phases over a period of 50 years (e.g. 1960 to 2010). Preliminary results show that JAS-rainfall for the Sahelian and Sudan savanna region is controlled by both, ENSO and Atlantic Niño, and low-level wind dynamics. The ASWI alone can explain up to 50% of the rainfall variability in this region compared to 20% for MEIv2 and ALT3. Moving southwards to the coastal regions with two monsoon peaks (MJJ and SON), ENSO becomes the dominant driver for MJJ-rainfall with lagged impacts between 1 to 3 months. Notably, ATL3 displays regime-dependent sign reversals, highlighting the contrasting impacts of Atlantic Niño across the region. Our findings indicate that WAM is strongly influenced by various drivers whose dependence structure with monsoonal rainfall varies in space and time. This reflects shifts in teleconnection dynamics and emphasizes the development of modelling approaches that can capture this non-stationarity in a suitable way.   

How to cite: Bliefernicht, J., Kloos, L., Sawadogo, W., Sy, S., Ndiaye, A., Jagdhuber, T., and Kunstmann, H.: The role of ENSO and Atlantic Niño on rainfall variability and extremes in West Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15382, https://doi.org/10.5194/egusphere-egu26-15382, 2026.

Coastal megacities like Lagos, Nigeria, face major water security challenges. Rapid population growth and urbanization are placing enormous pressure on water resources. Lagos domestic water supply is almost entirely sourced from its underlying multi-layered aquifer. Sustainable groundwater management requires a robust conceptual model of that Lagos coastal aquifer system that resolves subsurface heterogeneity, hydrostratigraphy, hydraulic connectivity, degree of confinement, saltwater intrusion, and anthropogenic contamination. Such model is currently hampered by fragmented subsurface data and prevalence of undifferentiated lithologies. The coastal aquifer system has been typically described as comprising three main aquifers units separated by aquitards of variable thickness and discontinuous lateral extent, and underlain by a thick clay aquitard found between ~150-250+ m depth below surface, with a general dipping toward the ocean. This study develops a data-driven framework using novel integration of borehole geophysical datasets with machine learning (ML) techniques to improve Lagos aquifer system conceptualisation and quantify uncertainty.

We compiled and synthesized an extensive database, including over 100 borehole gamma-ray and resistivity logs. The well-logs were processed using unsupervised ML clustering to objectively delineate the aquifer lithology and hydrostratigraphy. Gamma-ray log responses revealed pronounced vertical and lateral heterogeneity, with distinct clay-rich and sand-dominated horizons that allow clearer differentiation of previously undifferentiated aquifer/aquitard units across the aquifer system. Resistivity patterns further delineated the saline water occurrence in southern Lagos, revealing the clearer saline intrusion extent.

Building upon the lithological and hydrological delineations, we further constructed a high-resolution 3D aquifer model using a novel machine learning (ML) technique. Unlike conventional geostatistical methods like Ordinary Kriging, which can under-perform for the non-stationary processes inherent to complex coastal sedimentary geology, we developed a specialized artificial neural network (ANN) scheme. This architecture used a series of distance-based basis functions as covariates to directly predict spatial interpolation weights. Critically, the training process incorporates ridge regression and an entropy-based regularization, enabling the model to capture both smooth regional trends and abrupt lithological variations observed in boreholes. Furthermore, the framework provides robust uncertainty quantification by differentiating between data noise (aleatoric uncertainty) and model uncertainty (epistemic uncertainty addressed using ensemble methods and Monte Carlo dropout). The ML technique was validated against synthetic benchmarks and applied to generate a probabilistically constrained 3D model by transforming discrete, irregular borehole observations into a continuous, uncertainty-aware volumetric representation.

The 3D model offers an unprecedented view of aquitard continuity and aquifers hydraulic connectivity, potential recharge pathways, and areas vulnerable to over-abstraction or saline intrusion, creating a robust framework for groundwater assessment and sustainable management in Lagos. This technique offers a transferable framework for hydrogeological studies in data-limited coastal megacities across Africa.

How to cite: Olabode, O., Giannakis, I., and Comte, J.-C.: Data-driven conceptualisation of the complex multilayered coastal aquifer system underlying Lagos megacity, Nigeria: Integrating borehole geophysics and machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18298, https://doi.org/10.5194/egusphere-egu26-18298, 2026.

Understanding groundwater recharge processes in dryland and seasonally dry subhumid environments is central to improving water‑resource resilience under increasing climatic variability and land‑use change. This study integrates multi‑year groundwater level observations from crystalline basement aquifers in an unpumped agricultural setting about 30 km north of Harare, Zimbabwe (2018-2025) with recent groundwater‑level data from eight boreholes monitored in the pumped urban area of the city (2022–2025). Together, these high-resolution (30-min frequency) datasets provide insight into both natural and urbanised dryland systems, where recharge is highly sensitive to rainfall variability and human pressures.

Recharge in the agricultural sites was characterised using water‑table fluctuation (WTF) methods, chloride mass balance (CMB), water‑stable isotopes, and dissolved gas residence time tracers. The effect of variation in land use—such as tilled land, land under conservation agriculture, and woodland—on the responses to cumulative rainfall and rainfall events of varying magnitude was studied by integrating daily rainfall data collected at the research site. Recharge was observed for most years across all sites and was controlled by hydrogeological settings, rainfall totals and antecedent conditions, i.e. the groundwater level at the end of the preceding dry season. No measurable recharge occurred at most of the agricultural sites during a year of poor rainfall (380 mm total), highlighting the strong climatic dependency of basement‑aquifer recharge in subhumid drylands. Annual groundwater level variations were mostly limited to 2 to 3 m.

In contrast, the urban groundwater dataset from Harare revealed markedly different recharge behaviour. Groundwater‑level fluctuations were strongly influenced by nearby pumping, producing hydrographs that diverged from the smoother, rainfall‑controlled signals seen in natural settings and showing much stronger annual variation of groundwater levels up to about 15 m. However, pumping did not mask the overall annual recharge pattern, though at some sites, groundwater capture was markedly increased. A subset of the boreholes had similar hydrographs to those in the unpumped, agricultural setting. The variability in drawdown and recovery responses across the eight urban boreholes underscores the need for well‑designed, spatially distributed groundwater‑monitoring networks to evaluate the sustainability of abstraction and to detect changes in recharge availability under increasing urban water demand. In addition, capturing and integrating existing datasets—such as drilling logs, pump tests, and historical abstraction records—can provide valuable baseline information to support groundwater management in Harare.

Taken together, these findings advance understanding of recharge processes in both natural and urban dryland basement aquifers, emphasise the sensitivity of recharge to climatic variability, and highlight the implications for sustainable groundwater management under changing land use and rainfall regimes.

How to cite: Brauns, B., Kativhu, O. T., Lapworth, D. J., MacDonald, A. M., Mudimbu, D., Owen, R. J. S., Shumba, S., and Souta, M.: Recharge in natural and urbanised subhumid dryland basement aquifers in Sub-Saharan Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18605, https://doi.org/10.5194/egusphere-egu26-18605, 2026.

Water stress and groundwater overexploitation constitute critical challenges to water sustainability in semi-arid regions. In Morocco, the Saïss Basin represents a strategic agricultural area facing increasing pressure on groundwater resources due to intensive irrigation, fragmented governance, and limited coordination among water stakeholders. This study assesses the effectiveness of stakeholder’s integration within the Integrated Water Resources Management (IWRM) framework, with a focus on governance, participation, and decision-making processes. A mixed-methods approach was adopted, combining stakeholder surveys, institutional analysis, and a SWOT-based evaluation to address both technical and socio-institutional dimensions of water management. Key stakeholders’ groups, including public authorities, water agencies, agricultural users, and local organizations, were analyzed in terms of their roles, interactions, and influence on groundwater management. The results reveal a persistent gap between IWRM principles and their practical implementation, characterized by limited stakeholder coordination, uneven participation, and sectoral fragmentation. While institutional frameworks for IWRM exist, their operationalization remains constrained by power asymmetries, insufficient data sharing, and weak integration of local actors in decision-making. The SWOT analysis highlights opportunities for improving stakeholder engagement through participatory platforms, capacity building, and the integration of scientific knowledge into policy processes.

This study provides evidence-based insights into the governance barriers hindering effective IWRM implementation in groundwater-dependent regions. The findings contribute to ongoing debates on adaptive water governance and offer practical recommendations to strengthen stakeholder integration as a pathway toward sustainable groundwater management and the achievement of SDG 6 in arid and semi-arid regions.

How to cite: Ijlil, S., Essahlaoui, A., Hssaisoune, M., Essahlaoui, N., Van Rompaey, A., Mili, E. M., Ait Lahssaine, I., Faouzi, E., and Bouchaou, L.: Stakeholders engagement in Integrated Water Resources Management in the Saïss Basin, Morocco: Challenges and Responses under climate change conditions , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19169, https://doi.org/10.5194/egusphere-egu26-19169, 2026.

In 2019, scientists from African countries called for more research on drought and better drought forecasting and management (Padma, 2019). Between 2020 and 2023, the Horn of Africa had experienced the worst drought in 40 years, with severe consequences related to reduced agricultural productivity and high food prices (Okoth, 2024). In this presentation, we will showcase the drought research done within the DOWN2EARTH project with a case study in Kenya.

Agro-pastoral livelihoods in the Horn of Africa (HoA) are acutely exposed to climate variability due to the predominance of rain-fed systems. Yet drought risk emerges from more than rainfall deficits—it reflects interacting biophysical processes, socio-economic vulnerability, and institutional response capacity. We advance an integrated, impact-based and adaptation-informed framework by combining statistical risk modelling across Kenya’s arid and semi-arid lands (ASALs) with a coupled socio-hydrological and agent-based simulation of human–water interactions.

First, using Spearman correlations and Random Forest regression, we link drought hazards to observed societal impacts and identify distinct timescale sensitivities: short (2–6 months) precipitation deficits align with increased household water trekking distances, while medium-to-long drought indices (5–24 months) better explain declines in milk production and increases in malnutrition. Clustering counties by vulnerability profiles improves predictive skill. Socio-economic clustering best captures water access outcomes, whereas environmental clustering better explains agricultural and nutrition impacts. Extending to probabilistic risk via Random Forest hindcasts (1984–2014) yields Average Annual Loss (AAL) and Probable Maximum Loss (PML) estimates, highlighting spatial heterogeneity: high water-access risk in northwestern Kenya and elevated livestock, milk, and malnutrition risk in eastern and southeastern counties. Priority adaptation pathways include sanitation and safe water access, poverty reduction, and small-scale water infrastructure.

Second, the ADOPT‑AP framework couples the DRYP hydrological model with a behavioural agent model to simulate bounded-rational adaptation and policy scenarios. Sensitivity analysis identifies irrigation abstraction as the dominant driver of both drought hazard and adaptation uptake. Replacing upstream commercial farms with communities or forests increases downstream streamflow and groundwater, modestly improving water access and production in drought years. During the 2020–2023 drought, doubling extension access marginally boosts low-cost measure adoption but not capital-intensive options, underscoring finance constraints; scaling water harvesting improves milk and reduces water trekking but has mixed crop effects and downstream hydrological trade-offs.

Together, these results demonstrate how vulnerability-informed, spatially targeted interventions and dynamic adaptation modelling can be used to strengthen early warning, guide equitable water governance, and build long-term resilience. However, improving drought management requires more than research. Early warnings are for example often not acted upon because of cultural values or limited resources. We therefore advocate for more transdisciplinary research, co-creation of drought adaptation solutions, and strengthening connections between communities and formal governance actors.

References

Padma, T. V. (2019). African nations push UN to improve drought research. Nature, 573(7774).

Okoth, D. (2024). The cost of African drought. Nature Africa, doi.org/10.1038/d44148-024-00075-0.

How to cite: Odongo, R. A., Streefkerk, I., Van Loon, A. F., Wasonga, O., De Moel, H., Wens, M., De Bruijn, J., and Aerts, J.: Advancing drought risk analysis and management: a case study of Kenya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19778, https://doi.org/10.5194/egusphere-egu26-19778, 2026.

In climate-vulnerable and data-scarce countries like Ethiopia, developing an understanding of groundwater–surface water (GW–SW) interactions is essential to understanding hydrological and hydrogeological functioning, ecological resilience, water security, and sustainable water resource management. Water resources in Ethiopia support rain-fed and irrigated agriculture, domestic supply, urban centers, hydropower, and water-dependent ecosystems. Current understanding of GW–SW processes in Ethiopia is fragmented, with most existing groundwater and surface water studies conducted independent of one another. The result is substantial knowledge gaps pertaining to effects of widespread irrigation, land-use change, and climate change on connected water systems, capacity of aquifers during droughts, and seasonal GW–SW exchange fluxes and GW contribution to stream flow.  A literature  analysis of GW–SW interactions in Ethiopia highlights methodological and scientific limitations, and indicates opportunity to improve GW–SW understanding through fully-integrated GW–SW modeling. GW–SW interaction investigations in Ethiopia are primarily local and clustered geographically. The dynamic feedbacks between surface water, unsaturated zones, and aquifers are still not well quantified, with most studies relying on point-scale hydrogeochemical indicators, baseflow separation methods, or groundwater potential mapping. Predictive understanding hydroclimatic datasets and hydrogeological processes is limited by lack of long-term monitoring data, inconsistent conceptual models, and minimal representation of land-use change and unknown human water abstraction.  There are no fully-integrated GW–SW modeling studies at a basin/watershed scale in Ethiopia, although loosely coupled models have been used.  As part of a Natural Resources Canada Technical Assistance Program with Wollo University, a HydroGeoSphere fully-integrated GW–SW model for the Borkena watershed, located on the northeastern margin of the Rift Valley, has been created for educational and training purposes. The development of the HGS model followed the framework laid out in the Canada1Water initiative for national scale water resources assessment. As part of the model construction process, requisite data has been assembled from globally extensive sources, including geology, hydrology, soil, landcover and vegetation, and climatology; thus demonstrating that state-of-the-art models can be deployed in perceived data sparse regions. Following construction, model application can be demonstrated towards long-term water security planning, climate resilience, and sustainable water management in Ethiopia. The framework developed in this project is broadly applicable to other Sub-Saharan African nations, where process-based understanding of GW–SW interactions is indispensable but still lacking.

How to cite: Adamu, A. Y., Nigussie, A. B., Frey, S. K., Ejigu, A. A., and Russell, H. A. J.:  Groundwater–Surface Water Interactions in Ethiopia: A Review of Knowledge Gaps, Emerging Opportunities, and the Role of Integrated HydroGeoSphere Modeling under Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21954, https://doi.org/10.5194/egusphere-egu26-21954, 2026.

The Caatinga, the largest tropical dry forest in South America, holds significant yet often unrecognized potential for carbon sequestration and ecosystem functioning despite its highly seasonal and water-limited environment. However, carbon dynamics in this biome remain poorly quantified, especially regarding how vegetation structure, climate variability, and land-use interventions influence net primary productivity (NPP). This knowledge gap is particularly concerning given that the Caatinga is the Brazilian biome most severely affected by land degradation. Approximately 12% of its territory is classified within the two highest degradation classes, characterized by vegetation loss, low productivity, and depleted soil organic matter. This degradation process disproportionately affects traditional populations, such as Indigenous Peoples, Quilombola communities, and smallholder farmers, who rely directly on natural resources for their livelihoods.

To address this gap, we integrate satellite-based remote sensing, eddy-covariance observations, and ecological modeling to investigate spatial and temporal patterns of NPP under contrasting vegetation conditions and management regimes. First, we estimate NPP using a Light Use Efficiency (LUE) model driven exclusively by remote sensing inputs and compare these outputs with flux tower-derived NPP calculated from flux measurements collected during both a dry year and a wet year. This comparison enables the assessment of how semi-arid constraints, such as recurrent droughts, elevated temperatures, and soil water scarcity, shape photosynthetic efficiency and biomass accumulation. Once validated, the LUE model is applied to characterize spatial and temporal patterns of NPP in two contrasting socio-ecological contexts: a degraded area and a recovering area. This approach allows us to evaluate how contrasting management conditions influence the capacity of Caatinga vegetation to assimilate carbon.

Preliminary results indicate a strong sensitivity of NPP to rainfall variability and canopy structure, with degraded areas showing reduced carbon sequestration compared to conserved areas. These findings contribute to broader discussions on sustainable land management in the Brazilian Semi-Arid region and the urgent need for inclusive public policies to mitigate land degradation, protect ecosystems, and support the livelihoods of local communities.

How to cite: Oliveira, S., Bezerra, U., Lourenço, A., Valente, F., and Cunha, J.: Advancing NPP modelling to support sustainable management in Brazil’s semi-arid ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1220, https://doi.org/10.5194/egusphere-egu26-1220, 2026.

Just in the last four decades, ~5 million sq.km of humid land has transformed into a dryland. A rapidly warming climate is expected to further accelerate this dryland expansion. Consequently, not only will societies face permanent water insecurity, but plant and animal biodiversity will be under threat. Despite these consequences, very little is known about the physical mechanisms which cause irreversible drying of humid regions. As a result, our ability to predict the future expansion of drylands and its impact remains limited. Learning how drylands have expanded historically from humid regions could hold the key to predicting how they might expand in the future. So far, dryland expansion has been attributed to shifts in atmospheric circulation, topography, and orbital cycles. However, these changes occur at timescales reaching up to millions of years and thus do not fully explain the current rate of expansion. In this regard, the role of vegetation response to atmospheric drying and the consequent changes in land–atmosphere interactions have been largely ignored as possible mechanistic pathways of dryland expansion. Here, by tracking the air flowing over drylands, I show that the warming and drying of that air by changes in dryland vegetation-driven land–atmosphere feedback contributes to dryland expansion in the downwind direction. As they dry, drylands contribute less moisture and more heat to downwind humid regions, reducing precipitation and increasing atmospheric water demand, which ultimately causes their aridification. In ~40% of the land area that recently transitioned from a humid region into a dryland, self-expansion accounted for >50% of the observed aridification. Our results highlight the urgent need for climate change mitigation measures in drylands and provide a scientific basis for land-based interventions to prevent irreversible drying of humid regions

How to cite: Koppa, A.: Land–atmosphere feedbacks a mechanism of dryland expansion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4955, https://doi.org/10.5194/egusphere-egu26-4955, 2026.

Streamflow forecasting is an essential component of effective water resources management, particularly in regions highly vulnerable to extreme hydroclimatic events, such as the Brazilian semi-arid region, which is characterized by pronounced spatial and temporal variability of precipitation, frequent droughts, and occasional flood events. The scarcity, irregularity, and limited duration of hydrological data in many watersheds of this region pose significant challenges to traditional hydrological modeling approaches, restricting the ability to make informed decisions in water resources planning and operational management. In recent years, machine learning–based models, particularly Long Short-Term Memory (LSTM) recurrent neural networks, have shown considerable potential for streamflow modelling due to their ability to capture complex nonlinear relationships and long-term temporal dependencies between precipitation, catchment storage, and runoff generation processes. However, the modelling performance is highly dependent on the availability of extensive and continuous historical records, which limits their direct applicability in data-scarce watersheds. In this context, transfer learning has emerged as a promising strategy to overcome these limitations by enabling the transfer of knowledge learned in well-monitored source sub-basins to improve predictions in target watersheds with limited data availability. This study aims to evaluate the transferability of deep learning models for streamflow modelling among watersheds of the Brazilian semi-arid region, considering different scenarios of data availability. The study also seeks to identify the main physical and hydrological parameters that influence both the performance and transferability of the models. LSTM models were initially pre-trained on watersheds with longer historical records and subsequently fine-tuned for watersheds with varying levels of available local data. Performance evaluation, conducted using widely adopted hydrological metrics, demonstrated that knowledge transfer is effective, allowing significant gains in predictive accuracy even when local datasets are limited. Furthermore, it was observed that certain hydrological and physiographic attributes exert a direct influence on the models’ ability to generalize to new basins. The application of eXplainable Artificial Intelligence (XAI) techniques further reinforced the physical consistency of the streamflow modelling, enhancing both interpretability and reliability of the results. Overall, the use of transfer learning proved to be a highly promising strategy for improving hydrological modelling in data-scarce semi-arid regions, reducing dependence on long-term monitoring, supporting more effective water resources management, and contributing to risk mitigation and sustainability in these vulnerable environments.

How to cite: Fonte Boa de Campos Maia, T., Marcela de Paula Kolanski, M., Rodrigues, A., and Brentan, B.: Transfer Learning for Streamflow Modelling Among Sub-Basins of the Brazilian Semi-Arid Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5965, https://doi.org/10.5194/egusphere-egu26-5965, 2026.

Groundwater recharge is one of the least constrained hydrological fluxes in dryland catchments, particularly where thick vadose zones, ephemeral streamflow, and high evaporative demand decouple rainfall from aquifer replenishment. Recharge is commonly attributed to rare, high-magnitude floods, yet this perspective rarely accounts for event sequencing and vadose-zone memory. Here, we synthesise multi-year hydrometric observations and process-based simulations from an ephemeral dryland stream in the arid zone of Australia (Fowlers Gap, NSW) to show that temporal clustering of moderate streamflow events can enable focused recharge where thick vadose zones impose strong percolation thresholds. Field data indicate that several historic floods produced substantial vadose-zone wetting but no sustained groundwater response, whereas sequences of closely spaced, moderate flows generated delayed, yet persistent water-table rise beneath the channel.

Numerical simulations demonstrate that event clustering progressively wets the vadose zone, suppresses evapotranspiration losses, and non-linearly increases unsaturated hydraulic conductivity, resulting in sufficient flow via the streambed to measurably recharge the aquifer. These results show that groundwater recharge in dryland catchments emerges from the interaction between event sequencing, vadose-zone properties, transmission losses, and evapotranspiration, rather than from rainfall or streamflow magnitude alone. Under projected climate change, shifts toward more intense but less frequent rainfall may reduce recharge by disrupting event clustering, even where total precipitation remains unchanged. Explicitly accounting for vadose-zone memory and event sequencing is therefore essential for recharge estimation, model calibration, and dryland water-resource assessments.

How to cite: Rau, G. C., Bastías Espejo, J., Acworth, I., Andersen, M. S., Irvine, D., Bernardi, T., and Cuthbert, M. O.: Streamflow event clustering and vadose-zone memory as coupled controls on focused groundwater recharge in dryland catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5983, https://doi.org/10.5194/egusphere-egu26-5983, 2026.

Drought propagation in dryland environments is often conceptualised as a sequential transfer of meteorological deficits into soil moisture, streamflow, and groundwater drought with the propagation rate controlled by catchment and aquifer properties. However, quantifying the spatial and temporal propagation of rainfall deficits remains challenging because few hydrological models explicitly connect climate forcing to all relevant hydrological stores and fluxes, or represent their interactions across spatial scales. Here, we investigate how meteorological drought propagates through overland flow, streamflow, and soil moisture into groundwater storage across dryland catchments in the Horn of Africa. We hypothesise that drought propagation will be related to aridity, catchment scale, and lateral groundwater connectivity. To investigate these ideas, we use the 1 km resolution DRYland water Partition (DRYP) hydrological model to simulate daily water-balance components across the Horn of Africa from 2000–2023. To examine the influence of basin size, catchments are delineated using a range of stream orders (4–8); for example, stream order 5 yields ~1,300 basins with a mean area of ≈900 km² spanning hyper-arid to humid conditions. We focus on regionally widespread meteorological drought events defined using SPEI-12 (derived from CHIRPS rainfall and hPET); catchment-mean SPEI is used to identify basins experiencing prolonged meteorological drought (> 12 months). We then use basin-scale total water storage anomaly (TWSA) and the standardised total water storage index (STWSI) to explore groundwater dynamics in basins experiencing prolonged drought. Our analysis reveals substantial spatial heterogeneity in groundwater response to meteorological drought. Even during prolonged drought events, many basins exhibit neutral or increasing TWSA trends, with coherent spatial clusters of both declining and increasing storage observed within the same drought period. These results demonstrate that meteorological drought does not consistently translate into groundwater drought, even over multi-year timescales. We explore how aridity, basin size, and lateral groundwater contributions can decouple groundwater dynamics from atmospheric water deficits, leading to enhanced drought resilience in some regions (and longer recovery in others). This work highlights the limitations of assuming fixed drought-propagation pathways in drylands and demonstrates the value of high-resolution modelling for improving drought monitoring and water-resource management under increasing climate variability.

How to cite: Blake, G., Michaelides, K., Singer, M., Quichimbo, E. A., and Cuthbert, M.: Controls on drought propagation in drylands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7899, https://doi.org/10.5194/egusphere-egu26-7899, 2026.

The Qaidam Basin, located on the northern margin of the Qinghai-Tibet Plateau in China, is the second-largest inland basin in the country. Hosting numerous salt lakes rich in mineral resources, with particularly prominent lithium reserves. The Nalenggele River, the largest inland river within the Qaidam Basin, hosts the most extensive brine-type lithium deposit in China in its terminal salt lake region. With lithium resources reaching 2.3 million tons, this deposit accounts for approximately 87% of Chinese total lithium reserves. The genetic mechanism of brine in this terminal salt lake area is closely linked to hydrogeochemical processes. Previous studies have primarily focused on the qualitative analysis of hydrogeochemical processes, while relatively few have quantitatively assessed the impact of different hydrogeochemical processes. In this study, river water and groundwater from the mountainous areas to the basin within the watershed are selected as the research objects. Multiple isotopic tracers (δ²H, δ¹⁸O, ⁸⁷Sr/⁸⁶Sr, δ¹¹B and δ⁷Li) are employed to trace the material sources and evolutionary processes of elements. The material sources, controlling factors and evolutionary mechanisms of hydrochemistry in the Nalenggele River Basin are clarified. The Positive Matrix Factorization model is applied to quantitatively identify the recharge sources of different water bodies, trace the material sources of major ions, and elucidate the evolutionary processes of the watershed hydrological cycle. The results show that: (1) The hydrochemical compositions of both river water and groundwater are dominated by Na⁺ and Cl⁻. The hydrochemical types evolve from mixed Cl·HCO₃·SO₄-Na·Ca type in the upstream rivers to Cl-Na type in the downstream waters. Analyses of ionic ratios and strontium isotope data confirm that water-rock interaction is the primary controlling factor of hydrochemical compositions, which is characterized by silicate weathering as the dominant process, supplemented by carbonate weathering and evaporite (halite, gypsum, mirabilite) dissolution. Cation exchange exhibits spatial heterogeneity: forward exchange (Ca²⁺/Mg²⁺ vs. Na⁺/K⁺) occurs in the upstream and downstream areas, while reverse exchange takes place in the midstream area. (2) Evidence from δ²H and δ¹⁸O indicates that river water is mainly recharged by atmospheric precipitation from the southern mountainous areas with the elevation of 4700m. Groundwater has close hydraulic connectivity with river water, showing bidirectional recharge-discharge interactions. (3) The observed B and Li isotopic footprints in the Nalenggele River Catchment are significantly depleted in heavy isotopes compared with those in other geological systems dominated by natural weathering processes. In the upper reaches of the Nalenggele River, the concentrations of B and Li increase sharply, while the δ¹¹B and δ⁷Li values decrease gradually. The mechanism responsible for the B and Li enrichment is mainly associated with the Li-B supply potential of material sources, favorable tectonic conduits for water circulation, and high evaporation rates. (4) The Positive Matrix Factorization model quantitatively reveals the contribution rates of different hydrogeochemical processes during the hydrological cycle, specifically: evaporite mineral dissolution (28%), mixed evaporite dissolution (25%), agricultural activity (17%), and silicate weathering (30%). This study provides a comprehensive framework for integrating multi-isotope tracers and statistical models to quantify hydrochemical processes in arid inland basins.

How to cite: Zhang, S., Zhao, C., Liu, K., and Zhang, Y.: Revealing hydrochemical characteristics and evolution process of river and groundwater in the Nalenggele Basin, northwest China: insights from major ions, multi-isotopes (H、O 、Sr、B and Li) tracers, and positive matrix factorization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8648, https://doi.org/10.5194/egusphere-egu26-8648, 2026.

Runoff responses to precipitation can vary widely across catchments and climates, shaped by hillslope water storage and release dynamics and by the transmission of hydrological signals through channel networks. Understanding these controls is critical for interpreting hydrological behavior and informing water resource management.

Here, we apply ensemble rainfall–runoff analysis (ERRA) to characterize runoff responses across 189 Iranian catchments spanning diverse landscapes and climates. ERRA quantifies the increase in lagged streamflow attributable to each unit of additional precipitation while accounting for nonlinear catchment behavior.

Peak runoff response, as quantified by ERRA across Iran, is higher in more humid climates, in steeper and smaller catchments, and in catchments with shallower water tables. The direction and approximate magnitude of these effects persist after accounting for correlations among the drivers (e.g., deeper water tables are more common in more arid regions).

These findings highlight the importance of catchment attributes in shaping runoff behavior, particularly in arid and semi-arid regions, where climatic variability and groundwater dynamics play a crucial role in sustainable water resource management and effective flood risk mitigation.

How to cite: Eslami, Z., Seybold, H., and Kirchner, J. W.: Climatic, topographic, and groundwater controls on runoff response to precipitation: evidence from a large-sample data set, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9343, https://doi.org/10.5194/egusphere-egu26-9343, 2026.

Climate seasonality, characterized by alternating drought and rainfall, drives the degradation and recovery of soil properties that shape hydrological functioning. These shifts can trigger ecohydrological responses such as increased runoff, reduced soil water availability, and vegetation decline, while subsequent recovery may enable partial or full restoration of soil hydraulic properties. The objective of this study was to determine how the interaction of these mechanisms, driven by stochastic climatic inputs, shapes the soil water balance, with particular emphasis on runoff generation.

We developed a conceptual model in which soil hydraulic properties are represented as a dynamic variable subject to degradation, recovery, or no change, formulated using discrete-state logic. In a specific implementation, state-dependent thresholds determine system behavior: degradation occurs as a discrete jump triggered by abrupt wetting of the soil, characterized by rapid wetting from a dry state to an upper moisture threshold; recovery follows a time-dependent trajectory when soil moisture is maintained within this threshold for a sufficient duration; and outside these conditions, no change occurs. This variable dynamically scales infiltration capacity, allowing rainfall under degraded conditions to generate surface runoff.

Resulting runoff time series were normalized by annual evaporation and classified using agglomerative hierarchical clustering with dynamic time warping. Three characteristic runoff patterns emerged: (i) rapid recovery with sub-decadal runoff decline; (ii) an intermediate transitional pattern with slower recovery and moderate runoff persistence; and (iii) permanent degradation associated with multi-decadal runoff regimes. To identify the drivers of these patterns, we analyzed a dimensionless set of parameters using linear discriminant analysis. The dominant control leading to degradation was the temporal clustering of rainfall, defined as the relative duration of rainfall events compared to the expected interarrival time between events.

Using dimensionless parameter combinations that express state-shifting forcings associated with each regime, we investigated clusters of runoff patterns. The results indicate that temporally isolated rainfall events can trigger irreversible state shifts leading to runoff-dominated regimes, whereas sustained or closely spaced rainfall events have the potential to initiate recovery processes spanning multiple decades.

How to cite: Hinz, C., Monhasser, A., and Wachsmut, G.: Event-based changes in hydraulic properties of surface soils in semi-arid regions may generate surface runoff regimes at decadal timescales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9834, https://doi.org/10.5194/egusphere-egu26-9834, 2026.

Flow Duration Curves (FDCs) are used to describe streamflow variability and support water-resources planning. In Mediterranean climates, intermittent rivers challenge FDC regionalization because zero-flow periods influence the lower part of the curve and discharge observations are scarce or fragmented in gauged basins. Southern Italy is a representative case where historical hydrological information is discontinuous and uncertain, so regional tools are needed to infer flow behavior in unmonitored catchments [1].

This study utilizes historical daily discharge from 58 gauging stations across three regions in southern Italy, covered by the Departments of Napoli (1925–1994), Bari (1950–1996), and Catanzaro (1950–1984). Stations were retained if they provide at least 15 years of records. Streamflow intermittency is quantified using the Intermittency Ratio (τ), defined as the fraction of non-zero daily discharge observations over the total number of daily observations. In the selected dataset, 20 stations exhibit τ < 1, indicating zero-flow days and intermittent behavior; these stations are considered in the intermittency analysis and regionalization.

For each basin, a hydrologically connected DEM was built by integrating an authoritative 20 m × 20 m DTM with the available river network and basin boundaries, enabling extraction of physiographic and morphologic descriptors in a GIS environment, supported by land-cover and geological and rainfall information [2,3]. A set of physiographic, topographic, and climatic covariates was analyzed, starting with collinearity assessment to reduce redundancy among predictors. The dependence of τ on catchment descriptors was investigated through stepwise regression using a power-law formulation. Results show that a parsimonious model based on two predictors—catchment area (A) and a catchment-shape descriptor (SF)—is sufficient to describe a significant portion of the observed variability in τ across the study region (as seen in figure 1).

Model performance is evaluated using standard statistical indices, including R², PBIAS, RMSE, MAE, and MAPE, which summarize explained variance, bias, and absolute/relative errors. The approach supports practical estimation of intermittency in ungauged Mediterranean catchments and provides a basis to incorporate intermittency into FDC regionalization, improving low-flow and zero-flow representation in data-constrained basins.

Figure 1. Observed vs predicted intermittency ratio (τ). Labels: B = Bari, N = Napoli, C = Catanzaro. Dashed line = 1:1.

Keywords: Intermittency ratio, Flow Duration Curve, regionalization, regression, stepwise selection, Mediterranean rivers, Southern Italy, GIS

References:

[1] Viola, F., Noto, L. V., Cannarozzo, M., & La Loggia, G. (2011). Regional flow duration curves for ungauged sites in Sicily. Hydrology and Earth System Sciences, 15(1), 323–331.

[2] Mendicino, G., & Senatore, A. (2013). Evaluation of parametric and statistical approaches for the regionalization of flow duration curves in intermittent regimes. Journal of Hydrology, 480, 19–32.

[3] Burgan, H. I., & Aksoy, H. (2022). Daily flow duration curve model for ungauged intermittent subbasins of gauged rivers. Journal of Hydrology, 604, 127249.

How to cite: Moradi, F., Padulano, R., and Del Giudice, G.: Regionalization of streamflow intermittency in Mediterranean catchments (Southern Italy), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11211, https://doi.org/10.5194/egusphere-egu26-11211, 2026.

Ephemeral river systems are key hydrological components of dryland catchments, particularly in semi-arid regions where surface water availability is highly episodic and closely linked to short-lived rainfall events. Shallow channel depressions temporarily store water and extend its availability beyond individual flow events. However, the extent to which sedimentation affects their long-term storage capacity remains poorly constrained, especially in data-scarce regions of southern Africa. This knowledge gap is increasingly relevant under rising water demand, pronounced climate variability, and more frequent droughts.

This study focuses on the Iishana system, a transboundary network of ephemeral channels and shallow depressions in northern Namibia and southern Angola. The system constitutes the primary rural water resource for a densely populated semi-arid region, with surface water availability largely restricted to short periods following rainfall events. Consequently, the capacity of channel depressions to retain water over extended periods is critical for domestic use, livestock, and small-scale agriculture. Understanding sediment accumulation processes in these depressions is therefore essential for improving water resource management in dryland catchments. The Iishana system is characterized by very low gradients, episodic runoff, and highly variable hydrological connectivity, making it representative of semi-arid environments where event-driven processes, storage, and transmission losses dominate.

The objective of this study is to characterize sediment properties and quantify sedimentation rates in selected depressions in order to assess their influence on surface water storage. Sediment cores were collected from multiple depressions and analyzed using physical and geochemical methods. Chronologies were established using ²¹⁰Pb and ¹³⁷Cs radionuclides, supported by radiocarbon dating. Sedimentation rates were calculated using Constant Flux-Constant Sedimentation (CFCS) and Constant Rate of Supply (CRS) models.

Sediments are predominantly fine-grained sand and silt, with weak pedogenic development, indicating limited and discontinuous deposition. The CFCS model results show low accumulation rates ranging from 0.017 to 0.12 cm yr^-1, while CRS-derived mass accumulation rates range between 0.05 and 0.07 g cm^-2yr^-1. Below approximately 10 cm depth, sediment ages commonly exceed 150 years. In contrast, ¹³⁷Cs activities were very low and lacked identifiable peaks, rendering this radionuclide unsuitable for chronologies in the study area.

The consistently low sedimentation rates indicate that natural sediment infill currently plays a negligible role in reducing the surface water storage capacity of ephemeral depressions within the Iishana system. From a hydrological perspective, this suggests that storage limitations are primarily controlled by hydrological connectivity, event-driven runoff generation, and infiltration rather than by progressive sediment accumulation. The results provide an empirical basis for evaluating the potential of selected depressions for targeted deepening or lateral expansion to enhance short-term surface water storage during episodic flow events.

Furthermore, spatial variability in sediment properties and accumulation rates highlights the importance of site-specific characteristics such as channel connectivity, flow velocity, and local catchment conditions, which are key controls on hydrological processes in dryland systems. By linking sediment dynamics with surface water storage, this study contributes to hydrological modeling, scenario-based planning, and sustainable water management in semi-arid catchments under increasing climatic stress.

How to cite: Fahrenberg, M., Reinhardt-Imjela, C., Katte, V., Pires, E., Jüpner, R., and Schulte, A.: Do sediments matter? Assessing sedimentation effects on surface water storage in a semi-arid ephemeral river system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11411, https://doi.org/10.5194/egusphere-egu26-11411, 2026.

Shallow, temporary salt lakes, known as ephemeral playas, are considered among the hydrogeological most sensitive systems to climatic extreme perturbations and flood extent. With a drainage basin exceeding 1,000,000 km² and a lake depth of less than 6.5 m, Kati Thanda-Lake Eyre (KT-LE) in Southern Australia is characterized by a highly variable water balance and large water level fluctuations. It has reached its maximum level only once in the past 150 years, during the 1974-1977 “Great Filling”, emphasizing its status as one of the most unpredictable systems.

During the project, we aim to understand how hydro-climate variability across event magnitudes drives episodic lake filling and drying, and which basin-scale processes (inflow generation, transmission losses, evaporation, and potential groundwater interactions) control the water-level dynamics of KT-LE. We use the one-dimensional General Lake Model (GLM), forced with hourly ERA5 meteorological and hydrological reanalysis inputs over the 1974–2022 period to simulate daily lake level variations through time.

Initially, in this study, our model, calibrated in terms of surface energy balance and driven by a basin-averaged surface runoff, produces overestimated lake levels compared to satellite measurements. This suggests that basin-scale precipitation signals, largely driven by the northern catchment, do not necessarily reflect hydrological conditions farther south near the lake. To quantify water losses and the processes controlling them, we first define the fraction of inflow reaching the lake that reproduces the observed lake-level evolution with satisfactory agreement. We find that losses are strongly non-linear through time, exceeding 70–90% during minor floods, when precipitation affects only limited portions of the Lake Eyre Basin, and decreasing toward 0% during major floods, indicating a saturated and fully interconnected basin state. To identify the processes driving these non-linear losses, accounting for where precipitation occurs and which sub-basins are active, a river-focused approach using GLOFAS v4.0 (a channel-routing system) suggests that transmission losses estimated from spatial-mean ERA5 runoff may be overestimated in years when not all sub-basins contribute simultaneously to downstream flow.

Given the large surface area of the basin and the limited in-situ monitoring, these multi-scale results underline the importance of assessing and continuously evaluating the limitations of reanalysis-based climatic and hydrological forcing when applied to arid environments.

How to cite: Mama, A. N., Fluteau, F., Le Hir, G., May, J.-H., Faraon, T., Storms, J., and Schuster, M.: A modeling perspective on hydro-climate variability in dryland lakes : What is the impact of low- to high-frequency and intensity hydro-climate variability on the rise, development, persistence and demise of Lake Eyre - Kati Thanda ?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13776, https://doi.org/10.5194/egusphere-egu26-13776, 2026.

Mediterranean basins are increasingly exposed to sequences of dry and wet years that challenge reliable estimates of water availability and ecosystem resilience^1,2.In karst catchments, strong surface–groundwater coupling and large subsurface storage fundamentally alter how precipitation is partitioned and how long hydrological anomalies persist. Although the physical behavior of karst systems is well known, most hydrological assessments and management-oriented studies still rely on models that do not explicitly represent karst processes. Consequently, it remains unclear how much water-balance estimates and management-relevant fluxes would change if karst dynamics were properly accounted for under persistent climatic extremes.
Here we address this gap using a counterfactual modeling framework that directly compares karst and non-karst realizations of the same basin under identical climate and land-use forcing. Our central research question is how climatic persistence (consecutive dry and wet years) controls precipitation partitioning in a Mediterranean karst basin, and how different those responses would be if the basin behaved as non-karst. We hypothesize that explicit representation of karst processes increases hydrological memory, making multi-year climatic sequences more influential than isolated extreme years and leading to substantially different estimates of key water fluxes.
We applied a physically based, distributed hydrological model to the 4,818 km² Mijares River basin in eastern Spain, a heterogeneous karst system with strong surface–groundwater interaction. The model was forced with meteorological data for 2000–2014 and a land-use map derived from the SIOSE 2014 classification. Using the same forcing and experimental design, we generated a counterfactual non-karst scenario in which karst-specific subsurface processes were suppressed.
At the basin scale, results show that karst-induced subsurface storage and delayed water transfer strongly amplify the impact of climatic persistence. In the karst configuration, sequences of dry or wet years exert a stronger control on the partitioning of precipitation into evapotranspiration, infiltration, runoff, subsurface flow, and percolation than do isolated extreme years. In contrast, the non-karst scenario exhibits weaker hydrological memory, with more immediate and climate-proportional responses. In several flux components, the karst and non-karst simulations diverge not only in magnitude but also in their implied hydrological functioning.
At the land-use class level, forests and shrublands in karst terrain promote infiltration and evapotranspiration with negligible surface runoff, reinforcing delayed subsurface responses during dry periods. Agricultural areas show higher interannual variability, while artificial surfaces generate disproportionate increases in runoff, particularly during persistent wet sequences. These contrasts are markedly attenuated in the non-karst experiment.
Overall, this paired karst–non-karst modeling approach demonstrates that omitting or oversimplifying karst processes can lead to substantial errors in both the magnitude and interpretation of water fluxes under persistent climatic conditions, providing a robust basis for evaluating water-resource decisions in karst regions facing increasing climate variability.
References
1 Rivas-Tabares, D., Tarquis, A. M., Willaarts, B., & De Miguel, Á. (2019). An accurate evaluation of water availability in sub-arid Mediterranean watersheds through SWAT: Cega-Eresma-Adaja. Agricultural Water Management, 212, 211-225.
2 Rivas-Tabares, D. A., Saa-Requejo, A., Martín-Sotoca, J. J., & Tarquis, A. M. (2021). Multiscaling NDVI series analysis of rainfed cereal in Central Spain. Remote Sensing, 13(4), 568.

How to cite: Vivas Betancourt, L. D., Rivas Tabares, D., Neumann, M. B., Herrero, J., and Sanz, M. J.: Paired karst–non-karst modeling reveals hidden sensitivity to climatic persistence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13964, https://doi.org/10.5194/egusphere-egu26-13964, 2026.

Rapid urban expansion in arid cities is transforming surface hydrology and amplifying flood risks under increasingly variable rainfall conditions. This study examines land use and land cover (LULC) change and its hydrologic implications in Al Ain, UAE, a transboundary basin influenced by orographic forcing from the Oman Mountains. Multi-temporal Landsat imagery from 1987–2023 was classified into five dominant surface types-sand, compacted sand, rocky terrain, built-up, and green areas using supervised Maximum Likelihood classification. Accuracy assessment yielded overall accuracies of 68–86% and kappa values up to 0.81, consistent with regional arid-environment benchmarks.

LULC analysis revealed a substantial shift from natural to managed surfaces, with built-up and green areas increasing by 274% and 1,667%, respectively, at the expense of rocky and sandy terrains. These changes were incorporated into a distributed GSSHA model to simulate rainfall–runoff responses across five major storm events between 2007 and 2024. Model results show that progressive urbanization markedly increased peak discharge (up to 78%) and runoff volume, particularly under low- to moderate-intensity storms where infiltration-excess (Hortonian) processes dominate. Under extreme events, the flood response became primarily governed by rainfall intensity, diminishing the relative impact of LULC change.

Spatial analysis emphasized strong localization of flood hazards within newly urbanized areas, with flood depths intensifying along residential and roadway corridors. While the expansion of irrigated green spaces enhanced infiltration locally, their spatial distribution limited broader runoff mitigation. The findings highlight storm condition dependent urban flood response in arid environments and emphasize the need for hydrologically informed urban design, including permeable pavements, vegetated buffers, and managed aquifer recharge systems.

This integrated approach, combining multi-temporal remote sensing and distributed hydrologic modeling, offers a transferable framework for evaluating urban flood dynamics in data-scarce arid regions, supporting policy efforts toward climate-resilient urban planning in rapidly developing desert cities.

How to cite: Bose, A. and Sharif, H.: Hydrologic consequences of rapid urbanization in an arid environment: Multi-temporal remote sensing and distributed flood modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14306, https://doi.org/10.5194/egusphere-egu26-14306, 2026.

Global climate change is reshaping precipitation regimes worldwide and intensifying aridity across many dryland regions. These changes also impact the dry subtropics of northwestern Australia. In this region, the strong spatial and temporal variability of rainfall complicates the characterisation of hydrologic processes, making it challenging to assess groundwater recharge, manage water resources effectively, and protect vulnerable ecosystems.

The region is marked by a distinct dry winter season (April-October) and a wet summer season (November-March), and it receives a far greater proportion of cyclone-driven rainfall than most other parts of Australia. These cyclonic systems contribute to pronounced seasonal and interannual variability, producing intense but short-lived flash-flooding events separated by extended droughts. During the study period (2015–2024), more than 80 % of total rainfall occurred between December and March across the five monitored weather stations. Mean annual rainfall ranged from 288 to 366 mm, while mean relative humidity remained low (30-37 %). Consequently, potential evaporation rates were extremely high, often exceeding 3000 mm/y.

To better understand the atmospheric processes governing precipitation formation and moisture sourcing in this environment, we analysed ten years of rainfall stable hydrogen and oxygen isotope compositions (δ¹⁸O and δ²H) from five sites. We also analysed 1,101 air parcel trajectories corresponding to rain events at five sites over the study period. Rainfall with low δ²H and δ¹⁸O values occurred predominantly during high-rainfall months, demonstrating a strong ‘amount effect’ at most locations. Both, stable hydrogen and oxygen isotope compositions were positively correlated with the stratiform fraction of total precipitation, indicating substantial sub‑cloud evaporation during stratiform events. On average, ~30 % of rainfall was lost to sub‑cloud evaporation, and back‑trajectory analysis showed that up to 47 % of wet-season moisture originated from recycled land evapotranspiration.

Differences between arithmetic and volume-weighted monthly isotope means highlight the seasonal importance of small-volume rainfall events. To address this bias, we introduce a new “cut‑off” method designed to reduce the disproportionate influence of low‑volume rainfall on monthly isotope compositions and on the construction of Local Meteoric Water Lines.

How to cite: Skrzypek, G., Wan, C., Dogramaci, S., Gleeson, J., Hedley, P., Grierson, P., and Gibson, J.: What the stable isotope composition of precipitation reveals when it rarely rains - a decade of observations from northwestern Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16315, https://doi.org/10.5194/egusphere-egu26-16315, 2026.

Hydrological rainfall-runoff models are essential tools for simulating and predicting watershed responses to meteorological forcings by linking precipitation and climate inputs with catchment characteristics such as land cover, topography, and soil properties. By representing key hydrological processes, these models support water resources management, extreme event forecasting, and infrastructure design. In semi-arid and data-scarce environments, modelling becomes particularly challenging due to high hydroclimatic variability and ephemeral flow regimes, often associated with limited or discontinuous observational records. Under these conditions, reliable rainfall-runoff simulations are essential not only for understanding catchment dynamics but also for supporting water resource management, including reservoir operation, allocation strategies, and drought risk mitigation. Regardless of their structure, all models require parameter calibration using observed data to ensure reliable reconstruction of hydrological response; this calibration represents a critical step, especially in data-limited contexts, where parameter uncertainty and equifinality pose significant challenges.

This study investigates the impact of different calibration strategies on model performance and parameter estimation under conditions of limited and incomplete observational data in a semi-arid region. The analysis was carried out using the IHACRES model (Identification of unit Hydrographs And Components from Rainfall, Evaporation and Streamflow; Jakeman, 1990), a parsimonious conceptual rainfall-runoff model specifically designed for applications in data-scarce environments. IHACRES consists of a non-linear loss module that converts rainfall into effective precipitation and a linear routing module that simulates both fast and delayed runoff components. The model was slightly modified and applied to several gauged catchments in Sicily (Italy), encompassing a wide range of climatic conditions and including many ephemeral streams. Calibration experiments were performed using a Monte Carlo approach and evaluated using both single- and multi-objective frameworks. Four complementary performance metrics were adopted as objective functions: Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), relative cumulative volume error (RVE), and an error metric based on flow duration curves signatures (D*). Single-objective calibration optimized individual metrics, whereas multi-objective configurations combined time series accuracy, water balance consistency, and flow regime representation in bi-, tri-, and tetra-objective setups. Multi-objective calibration explicitly incorporated equifinality through Pareto dominance theory, identifying non-dominated parameter sets and quantifying trade-offs among competing objectives.

Results indicate that single-objective calibration may reproduce specific hydrograph features but can misrepresent overall water availability and flow regime characteristics. In contrast, multi-objective calibration approaches can jointly constrain hydrograph dynamics, cumulative water balance, and flow regime behavior as represented by flow duration curves, leading to more reliable estimates of both high and low flows. Pareto-optimal analysis also revealed functional relationships among model parameters, suggesting opportunities to reduce parameter dimensionality and derive empirical relationships to estimate one parameter from another. This study demonstrates that multi-objective approaches offer significant advantages in explicitly addressing equifinality-driven parameter uncertainty, and that integrating Pareto-based optimization with uncertainty quantification improves the robustness and interpretability of hydrological simulations.

How to cite: Alongi, F., Alonzo, C., Francipane, A., and Noto, L. V.: Calibration Strategies for IHACRES in Data-Scarce Environments: Addressing Equifinality and Parameter Uncertainty, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17863, https://doi.org/10.5194/egusphere-egu26-17863, 2026.

Mediterranean coastal areas are prone and increasingly exposed to hydrological stress driven by water scarcity, climate change, increasing agricultural pressure, groundwater exploitation, and water quality degradation. These drivers manifest differently across regions but often result in common challenges and issues such as salinization, saltwater intrusion, and competition between agricultural and domestic or industrial water uses, thus impacting water management in irrigation districts. 

In this context, the AI4Water PRIMA project investigates hydrological issues and water management challenges in four Mediterranean coastal irrigation districts and aims to apply Artificial Intelligence optimization and prediction techniques to improve water management. The four study areas are the Ras Jebel Coastal area (Tunisia), the Coastal Constantinois and Seybouse Basins (Algeria), the Capitanata Irrigation District (Italy), and the Nile Delta Basin (Egypt). Although these areas are all located in the Mediterranean region, they differ in population (from 50 thousands up to 3.5 million people), hydrogeological characteristics, water sources, irrigation practices, and water management policies. 

This contribution, after introducing the AI4Water project, presents a preliminary comparison of the main hydrological and irrigation issues in the selected case studies, with a broader Mediterranean perspective. The comparison highlights both shared vulnerabilities and site-specific drivers of hydrological stress, emphasizing the need for context-dependent management strategies. By framing the different case studies within a common perspective, the project provides a basis for cross-district comparison and discussion, supporting the development of adaptive and transferable water management approaches for Mediterranean coastal systems. This comparative approach is intended to stimulate discussion and critical feedback from the scientific community working on similar or related case studies.

How to cite: Colosio, P., Bouguerra, H., Elsayed, S., Hanif, M. F., Khlifi, S., Mohammad, H., Onaindia, E., Ounaies, S., Peli, M., Ranzi, R., Serina, I., Signoroni, R., Tachi, S. E., Trablesi, F., and Barontini, S.: Hydrological issues in Mediterranean coastal semiarid irrigation districts in the context of the AI4Water PRIMA project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21452, https://doi.org/10.5194/egusphere-egu26-21452, 2026.

Dryland catchments are highly sensitive to climatic variability, with evapotranspiration dominating the water balance and strongly constraining water availability. In irrigated drylands, understanding how climate change and land-use transformations jointly affect crop water consumption is critical for sustainable water management. This study analyses long-term changes in agricultural water use in the Amu Darya Basin, the largest transboundary river basin in Central Asia and one of the world’s most water-stressed regions.

We classified annual crop cover and cropping practices from 1987 to 2019 using the Landsat archive at 30 m spatial resolution. These crop cover maps served as a consistent input for estimating crop water consumption using satellite-based estimates of actual evapotranspiration, again derived from Landsat imagery, and computed with the Operational Simplified Surface Energy Balance (SSEBop) model, a water–energy balance approach well suited for dryland environments. A decomposition approach was applied to disentangle the relative contributions of climate change and land-use change to observed evapotranspiration dynamics.

Results show that total crop water consumption increased by about 10% over the study period, while average water use per unit area rose by 18%. Rising temperatures and increasing atmospheric evaporative demand alone would have pushed up water consumption by 21%. In contrast, shifts toward less water-intensive cropping practices, most notably from water-intensive summer cotton to winter wheat, offset only around 3% of this increase. Climate-driven effects intensified after the early 2000s and were strongest in downstream areas, where water stress, salinity, and ageing irrigation infrastructure limit adaptive capacity.

The findings demonstrate that, in irrigated dryland catchments, land-use change and cropping adjustments alone cannot counteract the accelerating impacts of climate change on evapotranspiration. All evapotranspiration and land-use datasets generated in this study are openly accessible, supporting transparency, reproducibility, and future research in data-scarce dryland regions. Our results underscore the need to combine improvements in water-use efficiency with climate mitigation and basin-scale management to strengthen hydrological resilience under continued warming.

How to cite: Müller, D., Senay, G., Umirbekov, A., Tarasova, L., Rufin, P., Pulatov, B., and Peña-Guerrero, M. D.: Climate-driven increases in crop water consumption in a Central Asian dryland catchment despite less water-intensive cropping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21807, https://doi.org/10.5194/egusphere-egu26-21807, 2026.

In addition to supplying the water that people need daily, groundwater also affects agricultural methods, preserves natural balance, and promotes industrial development. The 108,300 km2 Shabelle River Basin served as the site of the current study. Monitoring, evaluating, and conserving groundwater supplies for water resource management and development is made possible by the effective integration of remote sensing data and GIS in hydro-geological research. The Shabelle basin area's Ground Water Potential Zones were defined by combining seven thematic layers—geology, land use/land cover, drainage density, slope, lineament density, rainfall distribution map, and soil map—into a GIS platform using the spatial analyst tool in Arc GIS 10.8. The analytical hierarchy process (AHP) technique is used to find the weighted values for each parameter and its sub-parameters based on the relative importance of the influencing elements for groundwater recharge. Four groups were identified on the final groundwater potential zonation map of the study area: low potential zones of 1,548.7 km² (1.43%), moderate potential zones of 25,786.23 km² (23.81%), high potential zones of 22,353.12 km² (20.64%), and very high potential zones of 55,341.3 km² (54.10%). According to this study, high and very high groundwater potential zones dominate in the basin in 75% of the entire studied region. These zones are found in the basin's northern and central regions, where low slopes, fractured geological formations, and porous soil are present. However, because to their steep slopes, strong geological formations, and low rainfall zones, the south and southwest regions of the basin have poor potential zones. When well data was utilized to validate the accuracy of this data, there was a high degree of agreement between the expected and observed well performance. The Shabelle river basin's water management policies, effective use of natural resources, physical design, and sustainable groundwater development should all benefit greatly from the findings, particularly as the adverse effects of climate change on human life become closer. Anywhere else in the world, the study's methodologies can be used. The findings of this study can be applied to future research on agriculture, basin management, sustainable groundwater, and the interaction between groundwater and climate change.

How to cite: Alasow, I. M. A., Hussein, M. A., Tiwari, S. K., and Bhatla, R.: Identification of Groundwater Potential Zones Using GIS and Multi-Criteria Decision-Making Techniques: A Case Study of the Shabelle River Basin (Somalia), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-95, https://doi.org/10.5194/egusphere-egu26-95, 2026.

Accurate streamflow monitoring is essential for water resources management, yet many Brazilian watersheds lack sufficiently long historical records to support effective decision-making. This challenge is particularly critical in the Metropolitan Region of Belo Horizonte (RMBH), which depends on major reservoirs located within its territory – such as Rio Manso, Serra Azul, Vargem das Flores, and the Ibirité (REGAP) reservoir – for industrial and domestic water supply. Several of these strategic systems suffer from limited or inconsistent hydrological monitoring, complicating operational planning, increasing the risk of water shortages and of compromising reservoirs flow outcome capacity. Transfer Learning (TL) with Long Short-Term Memory (LSTM) networks emerges as a promising strategy to overcome this limitation, enabling the development of hydrological models in watersheds with little or no historical data. This study investigates the application of TL to enhance daily streamflow prediction in data-scarce basins of the Metropolitan Region of Belo Horizonte (RMBH), while assessing the optimal length of local streamflow records required to improve hydrological modelling through fine-tuning of a regional TL model. For this, 23 watersheds with similar hydrological behaviour and geomorphological characteristics were previously selected in the RMBH to evaluate the feasibility of reconstructing streamflow time series in data-scarce regions. Satellite-derived products and reanalysis datasets were employed as inputs to overcome limitations in hydrometeorological data availability. Furthermore, eXplainable Artificial Intelligence (XAI) methods are employed to explore the physical feasibility of knowledge transfer, with the potential to identify which watershed attributes – such as drainage area, elevation, soil-moisture dynamics, land-use composition, and climatic seasonality – most strongly influence whether hydrological behaviour learned in source basins can be meaningfully transferred to target basins. Significant performance gains can be achieved with only one to two years of local data, allowing accurate models to be developed rapidly even in newly monitored watersheds. This improves considerably the decision-making in data scarce regions, primarily those ones with some water conflicts. XAI analyses confirmed the physical soundness of the predictions, supporting more reliable streamflow reconstruction. However, further methodological improvements are required, as some watersheds were unable to benefit from transfer learning. Overall, TL represents a powerful direction for streamflow modelling in regions with limited monitoring, while XAI provides a framework to understand the physical consistency of the transferred knowledge and to determine the minimum monitoring effort required to build reliable local models.

How to cite: Rodrigues, A., Maia, T., Macedo, M., Perdigão, R., Eleutério, J., and Brentan, B.: Transfer Learning for Hydrological Modelling and XAI-Based Physical Consistency Assessment in Reconstructing Streamflow Time Series in Data-Scarce Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-841, https://doi.org/10.5194/egusphere-egu26-841, 2026.

Physics-Informed Neural Networks (PINNs) offer a promising framework for groundwater modeling in regions where hydrogeological data are limited. However, their performance significantly depends on the choice of constraint weights associated with governing equations and derivative-based regularizations. In this study, we develop a constraint-weight selection strategy for PINNs to simulate groundwater head dynamics in data-sparse environments where aquifer properties such as hydraulic conductivity (K) and specific yield/storativity (S) are unavailable. The proposed formulation incorporates first-, second-, and third-order spatial and temporal derivatives of hydraulic head and aquifer properties into the PINN loss function, enabling the model to capture fine-scale spatiotemporal variations without explicit knowledge of subsurface parameters. The approach is applied to a small section of the Varuna River Basin, using groundwater-level observations collected from 37 monitoring stations between 2022 and 2024. The dataset contains several missing values that the PINN framework handles seamlessly, unlike conventional simulation models such as MODFLOW, which require complete and continuous input fields for stable execution. An iterative optimization scheme is employed to balance data fidelity, physical constraints, and derivative-based regularization during training. The proposed method achieves a training R² of 0.986 and a testing R² of 0.947, with corresponding RMSE values of 0.721 and 1.416 meters, respectively. These results demonstrate that adaptive constraint weighting significantly improves prediction accuracy, robustness, and convergence compared to fixed-weight PINN formulations. Overall, the study highlights the potential of derivative-enhanced PINNs for groundwater modeling in data-sparse aquifers and provides a generalized framework for physics-guided learning under missing or incomplete observations.e data scarcity.

How to cite: Bajpai, M., Gaur, S., and Singh, K.: Derivative-Enhanced Constraint Weights for PINNs in Groundwater Flow Modeling Under Unknown Aquifer Properties, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-896, https://doi.org/10.5194/egusphere-egu26-896, 2026.

In West Africa, limited access to hydrometric data remains a major challenge for advancing surface water research and improving water management. Since the early 1980s, many gauging stations have been decommissioned, leaving gaps in reliable streamflow records across numerous catchments. Parameter regionalization of hydrological models is commonly employed to enable runoff prediction in ungauged catchments. This study represents an assessment of rainfall-runoff model regionalization across West Africa. We used an unprecedented dataset of 189 near-natural catchments to compare two contrasting approaches: (i) a benchmark conceptual modeling framework using the GR4J model, regionalized with three parameter-transfer techniques (spatial proximity, physiographic similarity, and Random Forest), and (ii) a data-driven framework based on Long Short-Term Memory (LSTM) neural networks. Using a leave-one-out resampling approach, regionalization approaches were evaluated using different performance metrics: (i) the Kling-Gupta Efficiency (KGE), calculated between simulated and observed streamflows, (ii) the relative bias (rBias) on several hydrological signatures computed with observed or simulated discharge and (iii) the difference between observed and simulated flood quantiles. Results show that the conceptual modeling approach with traditional parameter-transfer techniques consistently underperforms compared to the LSTM, failing to reproduce key hydrological signatures. In contrast, the LSTM model showed better generalization performance, accurately simulating streamflow with a median KGE of 0.67 and reliably capturing hydrological signatures and flood quantiles across West Africa’s diverse climates and landscapes with lower biases. These findings highlight the potential of data-driven approaches to enhance hydrological prediction in data-scarce regions, supporting more effective flood risk management and water resource planning.

How to cite: Tramblay, Y., Diop, S. B., Kate, F., Souassi, I., Dieppois, B., Bodian, A., Guerin, J., Hostache, R., Johannet, A., Kratzert, F., Oudin, L., Sivelle, V., and Traoré, K.: Large-scale streamflow regionalization in ungauged West African catchments: How do classical and deep learning approaches compare?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1536, https://doi.org/10.5194/egusphere-egu26-1536, 2026.

Intensive groundwater abstraction, land-use changes, and climate variability have significantly altered natural discharge and flow patterns within groundwater systems, threatening long-term groundwater sustainability. These disruptions increase the risk of degradation in ecosystems that rely directly or indirectly on groundwater discharge, i. e. groundwater-dependent ecosystems (GDEs).

Mexico is particularly vulnerable to declining water table levels, a situation accelerated by gaps in groundwater management that fail to incorporate GDEs into decision-making processes. This issue is especially critical in northeastern Michoacán, home to two of the country’s largest lakes: Pátzcuaro and Cuitzeo lakes, which represent a key study area for studying growing threats to GDEs caused by pollution, climate change, and intensive groundwater abstraction. In order to preserve GDEs, along with their associated biodiversity and ecosystem services, accurate mapping is essential to secure their future integration into groundwater sustainability policies and conservation initiatives.

To address this issue, we compared four methods usually used in geospatial mapping: the Analytical Hierarchy Process (AHP), Weights of Evidence (WoE), and two machine learning models: Logistic Regression (LR) and Random Forest (RF), using environmental variables associated with GDE presence obtained from geospatial data and remote sensing products.

Model performance was evaluated using a validation dataset derived from local inventories and fieldwork conducted in 2024, applying Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC) metric. Results showed that RF (AUC = 0.82) and LR (AUC = 0.70) outperformed WoE (AUC = 0.61) and AHP (AUC = 0.59), with RF demonstrating the highest predictive accuracy and best performance in cross-validation folds.

The GDEs prediction map derived from RF highlights areas primarily along the shores of both lakes, where volcanic lithology contacts with lacustrine deposits, inducing groundwater discharge through springs that sustain wetlands. Additional GDEs areas occur along fault zones that enhance discharge within volcanic lithology near Morelia City and in perennial streams located at intermediate elevations.

The study faces limitations related to varying spatial resolutions, independent errors in geospatial datasets, and uneven data quality across local zones within the study area. Furthermore, the absence of direct field verification for areas with the highest predicted GDE potential constrains the overall impact of the study. Nevertheless, this research provides significant evidence of the advantages of using machine learning approaches in regions lacking detailed hydrogeological information, supporting the integration of GDEs into groundwater sustainability management.

How to cite: Salgado-Albiter, A. C., Olea-Olea, S., Ramírez-Serrato, N. L., Morales-Casique, E., Ramírez-González, L., and Llanos-Solis, A. G.: Comparison of expert-knowledge and machine learning approaches for mapping groundwater-dependent ecosystems in a regional setting in Central Mexico, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4669, https://doi.org/10.5194/egusphere-egu26-4669, 2026.

Accurate prediction of river water quality is essential for effective watershed management, yet it is often hindered by practical monitoring constraints, including infrequent grab sampling (e.g., monthly observations) and the lack of reliable streamflow data. These limitations restrict the applicability of conventional process-based water-quality models and necessitate alternative analytical tools. In this study, we propose a graph-based machine learning framework that integrates prediction and diagnostic analyses of river water quality, with chromaticity prediction in the Hantan River Basin, Republic of Korea, as a case study. Graph-based models outperformed purely temporal baselines, with the Graph Sample-and-Aggregate (GraphSAGE) model achieving a test R² of 0.82. Its sampling-based spatial aggregation integrates localized and distributed upstream information across the river network, allowing the model to capture nonlinear relationships mediated by implicit flow connectivity. Graph explanation analyses using PGExplainer identify the SC sub-watershed as the dominant pollution source and primary intervention area. In addition, feature attribution analyses distinguish persistent long-term drivers (e.g., TOC associated with major wastewater treatment plant discharges) from short-term episodic influences linked to facility-specific effluent spikes. Overall, these results demonstrate that graph-based machine learning can serve as a useful framework for both prediction and diagnostic interpretation of key water-quality drivers in data-limited river systems.

How to cite: Choi, S., Jung, E., Shin, H.-S., Song, J.-H., You, H., Son, H., Choi, I., Yang, J., and Moon, H.-C.: Graph-based machine learning approach for river water quality prediction under data limitations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4811, https://doi.org/10.5194/egusphere-egu26-4811, 2026.

Regional frequency analysis relies heavily on robust goodness-of-fit (GOF) testing for selecting an appropriate probability distribution, which directly influences the accuracy of estimated quantiles. However, existing statistical approaches often involve strong assumptions and computational overheads that limit their effectiveness, particularly for large regional datasets. The widely used L-moment-based approach requires scaling each site’s data by its own mean, which raises concerns about potential distortion of the original distributional characteristics. To overcome this limitation, the present study proposes a novel Deep Learning (DL)-based GOF test that identifies the regional distribution without performing mean-based scaling. The proposed methodology employs a Deep Neural Network (DNN) trained to classify regional distributions based on the distinctive behavior of Generalized Extreme Value, Generalized Pareto, Generalized Logistic, Generalized Normal, and Pearson Type III distributions under specific mathematical transformations. These transformations yield distribution-specific signatures that form the basis of the DNN training process. For a given dataset, the transformations are applied, and kernel density estimates derived from the transformed data are used as inputs to a pre-trained DNN model to identify the most suitable regional distribution. The DNN classifier achieved an accuracy of 95.09% on the training dataset and 94.86% on the test dataset. A comprehensive simulation study was conducted for multiple regional configurations to assess the performance of the proposed DL-based GOF test. The results were compared against the conventional L-moment-based GOF approach. The proposed method demonstrated comparable classification accuracy for smaller region sizes and marginally improved accuracy for larger datasets. The proposed DL-based GOF framework shows significant promise, particularly due to its substantially lower computational cost compared to the conventional L-moment methodology. The findings suggest that this approach can facilitate accurate and efficient estimation of quantiles, thereby supporting informed decision-making planning, management and risk assessment.

How to cite: Kaur, S. and Chavan, S. R.: Proposing a Deep Learning based Regional Goodness-of-Fit test for identification of regional distribution , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5433, https://doi.org/10.5194/egusphere-egu26-5433, 2026.

Lakes are the essential asset for the inhabitants of our planet since these are vital sources of water. It is understood that these lakes become more crucial in the regions where water is not easily available such as in Himalayas, drought-prone, and arid regions. However, it has been noticed that the dual problems have arisen at the same time due to climate change, i.e., water scarcity in the arid or drought-prone regions due to rapid extinction of some of the lakes and flood devastations in Himalayan due to overtopping of water from the vulnerable lakes. Climate Change extremes cannot be blamed alone for the extinction of these lakes while overexploitation, improper maintenance and non-civic senses have also exaggerated the process. While the catastrophic events due to these lakes called Glacier Lake Outburst Floods (GLOFs) are mostly occurring due to extremes rainfall events causing regular expansion and contraction of the lakes. However, these extreme events are more intense and frequent due to climate change and tends to increase in the future, making these lakes more vulnerable and responsible for such events.  It is essential to monitor the lake water dynamics not only for sustainable water resources management but also for mitigating future catastrophic event risk arising due to these lakes. While the monitoring of lakes is not always easy either due to data-scarcity in the catchments or impossible in-situ measurements due to inaccessible catchment terrain like in Himalayas. The availability and accessibility of advanced remote satellite sensing data such as altimeter, and space-borne Light Detection and Ranging (LiDAR) have been enabled us lake monitoring, however, their processing demands modern approaches. Hence, the present study aims to develop a machine learning model integrated with geospatial approach to process these advance remote sensing data for the spatio and temporal monitoring of water dynamics of lakes. The present study utilizes Icesat-2 as space-borne LiDAR and Surface Water and Ocean Topography (SWOT) as wide swath altimeter data. The study provides a reliable and precise remote sensing derived Water Surface Elevation (WSE) for the lakes at spatial and temporal scales. The derived WSE for lakes would help us to identify the vulnerable lakes and to evolve robust policies to solve dual lake problems at greater extent, i.e., water scarcity in drought or arid-prone regions as well as in the regions like Himalayas for mitigating catastrophic events due to glacier lakes. Further, the developed model would be easily applicable to any lake while the finer adjustment may be required due to different topographic conditions. 

Keywords: Lake water dynamics, Space-borne LiDAR, Altimeter, Machine Learning, and Geospatial.

How to cite: Ranjan, R., Rai, A. K., Dhote, P. R., and Keshari, A. K.: Leveraging Advanced Remote Sensing with Machine Learning and Geospatial Techniques for Spatio-Temporal Monitoring of Lake Water Dynamics in Inaccessible and Data-Scarce Catchments , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6020, https://doi.org/10.5194/egusphere-egu26-6020, 2026.

Water quality monitoring in subsurface environments is often limited by sparse, irregular, and uncertain measurements, complicating the calibration process and reliability of transport models. In this study, we propose a Finite Volume (FV) residual Physics Informed Neural Network (PINN) framework for contaminant transport through subsurface media governed by the advection-dispersion equation (ADE), with a focus on generating predictions considering parameteric uncertainty for data-scarce environments. The core idea is to replace the strong-form PDE residual typically used in PINNs with a control-volume conservation imbalance derived from a discrete FV balance. Neural network predictions are used to evaluate advective and dispersive numerical fluxes at cell faces, and training minimizes the resulting cell-wise flux imbalance while enforcing initial and boundary conditions. This conservative formulation enables transport-specific numerical flux treatments (e.g., upwind/TVD advection and consistent boundary fluxes), and we assess performance for advection-dominated systems with sharp concentration fronts. 

To represent heterogeneity and uncertainty in dispersion, we parameterize the dispersion coefficient as a strictly positive random field using a low-dimensional basis. Uncertainty is propagated through the learned surrogate using Monte Carlo sampling to obtain prediction intervals and monitoring-relevant risk metrics such as threshold exceedance probabilities at selected locations. We outline two uncertainty workflows: (i) an ensemble strategy that trains FV-PINN models across sampled dispersion realizations, and (ii) a prospective conditional FV-PINN that takes random-field coefficients as additional inputs, enabling efficient Monte Carlo evaluation after a single training stage. The application of the methodology is demonstrated on simple benchmark examples designed to represent sparse monitoring data, showing how conservative learning and random-field uncertainty propagation can support reliable transport predictions when observations are limited.

How to cite: Jain, S., Dey, S., and Chahar, B. R.: A Conservative FV-Residual PINN Framework for Solute Transport through Subsurface Media with Dispersion Uncertainty for Data-Scarce Environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6256, https://doi.org/10.5194/egusphere-egu26-6256, 2026.

Pharmaceuticals, ubiquitous in human, veterinary and agricultural use, are prevalent emerging contaminants in Chinese surface waters. Although not highly persistent, their low removal in conventional wastewater treatment leads to continuous discharge, creating "pseudo-persistence." This chronic exposure poses significant ecological and human health risks, including hormonal disruption of female reproduction and antibiotic-induced gut microbiota alterations and antimicrobial resistance in aquatic biota.

Numerous pharmaceuticals (>100) have been detected in China's surface waters. However, clear regulatory priorities are lacking, and nationwide monitoring is insufficient, leaving many regions without concentration or risk data. This study aims to: (1) identify pharmaceuticals posing the highest human and environmental hazards; (2) develop nationwide predictive concentration models using machine learning; and (3) generate a health risk map for pharmaceuticals in China's surface waters.

Through systematic keyword searches in Web of Science and CNKI, we compiled data from 227 peer-reviewed articles (2010-2023), covering approximately 13,000 sampling sites across China's nine major river basins. Pharmaceutical concentrations, detection frequencies, and sampling metadata were extracted. To assess environmental behavior and risks, four key indicators were selected: octanol-water distribution coefficient (LogDow) for bioaccumulation potential, degradation half-life (T1/2) for persistence, predicted no-effect concentration for aquatic ecosystems (PNECeco) for ecotoxicity, and predicted no-effect concentration for human exposure (PNEChum) through drinking water and fish consumption.

Principal component analysis (PCA) integrated four indicators into a composite hazard score (HP) and to combine concentration and detection frequency into an exposure potential score (EP). Pharmaceuticals were preliminarily screened based on reference thresholds for HP and EP values, and then ranked by the product of HP and EP to establish priority control lists for each river basin. Roxithromycin and erythromycin, exhibiting high toxicity and extensive data, ranked highest across all basins. Antibiotics were consistently high-priority in all nine basins. In densely populated basins (Haihe, Yangtze, Pearl), bezafibrate, indomethacin, and ibuprofen require additional attention. Hormones (estrone, estriol, ethinylestradiol) showed elevated concentrations and risks in Songhua/Liao basins. Increased monitoring is strongly recommended for data-scarce inland basins.

Four representative pharmaceuticals (erythromycin, ciprofloxacin, norfloxacin, carbamazepine), selected based on high toxicity or exposure potential, were modeled nationally. Predictors included 27 variables across five categories: Socioeconomic, Healthcare, Agricultural and aquacultural, Natural environmental, and Water quality indicators. Seven machine learning algorithms were evaluated (DT, ExtraTrees, GB, KNN, RF, SVM, XGBoost). RF demonstrated superior performance and was selected for feature selection (via weighted backward stepwise regression) and hyperparameter tuning (grid search with 10-fold CV). The optimal model was chosen based on R² and RMSE.

Predicted concentrations were then input into the USEPA-recommended human health risk assessment model. Carbamazepine, ciprofloxacin, and norfloxacin exhibited low risks nationwide (HQ < 1). Erythromycin exceeded safe levels (HQ > 1) in eastern regions (Yangtze River Delta, Bohai Rim, Pearl River Delta). Spatially, erythromycin and norfloxacin risks displayed a distinct east-west gradient (higher east), while carbamazepine and ciprofloxacin showed minimal spatial variation.

How to cite: Li, J.: Nationwide Prioritization and Machine Learning-Based Risk Prediction of Pharmaceuticals in China's Surface Waters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6360, https://doi.org/10.5194/egusphere-egu26-6360, 2026.