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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.