- Novel observation techniques enabling improved parametrization of hydrologic processes
- Integration and fusion of next-generation hydrologic observations into modeling frameworks
- Digital twins and other advanced digital representations of hydrologic systems
- Real-time forecasting, early-warning, and decision-support systems for water resources and hydrologic hazards
- Case studies demonstrating the operational use and societal impact of hydrologic digitization
Orals: Wed, 6 May, 16:15–18:00 | Room 2.15
Digital Water Twins (DWTs) are increasingly adopted to support sustainable and efficient management of complex water systems. While process-based models form the analytical core of the DWTs, the literature lacks a coherent, model-centric synthesis that links DWT architectures and operational objectives to explicit interoperability requirements and systematic framework evaluation. Here we synthesize the literature to distil interoperability requirements for process-based models within multi-scale DWT architectures and to evaluate established interoperability frameworks (i.e., BMI/CSDMS, OpenMI, HydroCouple, ESMF and OMS3) against operational demands. The synthesis identifies core interoperability requirements for process-based DWTs, including semantic consistency, modular coupling, coordinated time–space execution, operational robustness, and traceability. Comparative analysis shows that framework suitability is primarily determined by where coupling and semantics sit in the DWT architecture. BMI/CSDMS favours rapid model wrapping and flexible model–data exchange. OpenMI and HydroCouple support tighter, time-synchronized and spatially explicit coupling, making them suitable for operational and high-performance contexts. ESMF excels at parallel coupling and regridding for Earth-system–scale applications but requires substantial integration effort. OMS3 is best suited to modular, calibration-focused workflows, with more limited applicability to spatially detailed or HPC-driven models. Consistent with these trade-offs, application studies continue to rely on ad-hoc pipelines, while successful large-scale initiatives converge on layered interoperability strategies that combine lightweight interfaces, targeted runtime couplers, and platform services. These syntheses establish a model-centric evaluation basis for selecting and combining interoperability frameworks and provide actionable guidance for designing scalable, robust, and decision-relevant DWTs.
Acknowledgement
This research has been conducted with Flagship Programme funding granted by the Research Council of Finland for Digital Waters Flagship (decision no. 359248).
How to cite: Omer, A., Pal, D., Olsman, J., Carter, E., and Koivusalo, H.: Interoperability Strategies for Process-Based Models in Digital Water Twins: An Architectural and Framework-Based Synthesis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17115, https://doi.org/10.5194/egusphere-egu26-17115, 2026.
How to cite: Hosseini, S. H., Zolghadr-Asli, B., Tenkanen, H., Madani, K., Matin, M. A., Demir, I., Ostfeld, A., Singh, V. P., and Savic, D.: Large Language Model-Based Multi-Agent Systems: The Next Frontier in Digital Water Engineering , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18353, https://doi.org/10.5194/egusphere-egu26-18353, 2026.
How to cite: Vasiuta, M. and Mäkinen, V.: Adaptive geospatial data search engine for river basin observation systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17695, https://doi.org/10.5194/egusphere-egu26-17695, 2026.
Water resources management has evolved from traditional monofunctional approaches toward integrated water resources management, aiming to better understand and represent the complex interactions between social and hydrological components. These interactions form a coupled and dynamic socio-hydrological system. Significant advances have been made in developing concepts and theories related to socio-hydrological systems, sustainable water resources management, resilience enhancement, restoration, and vulnerability assessment.
In cold environments, substantial climate variability occurs, and the effects of climate change have shown strong impacts on catchment hydrology and biogeochemistry. At the same time, several policy initiatives and legal frameworks have been implemented in recent years—such as the EU Water Framework Directive (WFD) and the European Green Deal—to improve freshwater protection and achieve good ecological status. Furthermore, changes in agricultural practices, including fertilization, drainage, and cropping systems, are observed at the local scale. Consequently, these system properties exhibit strong spatial and temporal variability. Assessing such variability requires advanced and objective indicators based on reliable, continuous data and information to describe the different properties of the system.
Recent advances in physics-based and AI-based hydrological modeling enable more accurate and spatially distributed representations of hydrological processes and patterns. These models can provide richer data and insights for the quantitative assessment of the vulnerability–resilience–sustainability nexus in socio-hydrological systems. This study presents a case study of two managed catchments in southern Finland, using the process-based SWAT+ model in combination with Python-based composite indicators to spatially assess vulnerability, resilience, and sustainability within the socio-hydrological system, thereby providing tools to support green and digital transitions in water resources management.
How to cite: Gonzales-Inca, C., Kasvi, E., and Alho, P.: Spatial modeling of the vulnerability-resilience-sustainability nexus in complex socio-hydrological system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13323, https://doi.org/10.5194/egusphere-egu26-13323, 2026.
Understanding river channel changes induced by floods is crucial for effective flood risk management, as floods dramatically reshape rivers through erosion and deposition impacting infrastructure and ecosystems. Factors like landslide-channel interactions, channel confinement or human induced channel modifications (e.g. sediment extraction) significantly amplify flood risks. Predicting future hazards with this knowledge helps manage development in floodplains, design better flood defenses, and adapt to climate change impacts on river systems.
Thus, we need efficient methods for monitoring the dynamics of the river channel, especially after flood events. Also, as use of Digital Twins is emerging for river management, ability to update the 3D topography of the river Digital Twin rapidly becomes a necessity. Mobile laser scanning (MLS) technology offers a way of direct 3D measurements of the environment, regardless of ambient light. We have developed and tested multi-platform mobile laser scanning (MLS) systems for riverine environment mapping for more than a decade, including deployments since 2012 at the Rambla de la Viuda in Spain. Rambla de la Viuda is an ephemeral gravel bed channel that remains dry for most of the year, but infrequent flash floods can significantly reshape its geomorphology. During the past years such flooding occasions have become more frequent and powerful, not forgetting the extreme flooding in October 2024.
Three different laser scanning systems were used to obtain 3D point cloud data of the dry river bed in Rambla de la Viuda after a major flood event in March 2025. A 8 km long reach of the river channel was scanned for the first time with drone operated laser scanning system (Riegl VUX-120 scanner and NovAtel CPT7 GNSS-IMU navigation system), and smaller parts of the channel were scanned with two different backpack systems: commercial SLAM system Faro Orbis and FGI developed Akhka system with Riegl miniVUX-3 scanner and NovAtel Pwrpak 7/ISA-100C GNSS-IMU navigation system.
In addition to the aforementioned data acquisition, we have collected time series data of the same area with backpack and ATV based MLS systems in years 2012, 2013, 2016 and 2019, and we present some examples of change in selected parts of the reach.
We compare the operability of these systems, and their feasibility for detailed river bed topography mapping in terms of area coverage, level of detail and ease of operation. Time series data is used for change detection, i.e. erosion and deposition, examples of which are depicted discussed in the presentation.
How to cite: Kukko, A., Kaartinen, H., Alho, P., Kankare, V., Calle, M., and Benito, G.: Multi-source approach for reach scale river bed modeling - a case study on Rambla de la Viuda, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9698, https://doi.org/10.5194/egusphere-egu26-9698, 2026.
Riverbank erosion and deposition are fundamental drivers of fluvial geomorphological change yet their long-term interannual dynamics remain poorly quantified at high spatial resolutions, particularly in sub-arctic environments. Existing studies typically rely on short monitoring periods or coarse-resolution remote sensing data, limiting the ability to resolve interannual variability, cumulative change, and the influence of hydroclimatic extremes. This study addresses these limitations by exploiting a unique over a decade long time series of high-resolution terrestrial laser scanning (TLS) point cloud data collected from the Pulmanki River in northern Finland. The main aims of this research are (1) to quantify the decadal riverbank erosion and deposition, and their interannual variability, (2) to investigate the key drivers of the observed geomorphic change, and (3) to evaluate how effectively long-term TLS data can capture these changes and their controlling mechanisms.
The study focuses on a single 18 meters high bank on one compound asymmetric meander bend of the Pulmanki River, where the experimental design was initially established already in 2012. The surface angle of the bank is 36° at the apex and it consists of horizontally bedded fluvio-lacustrine sediments. Annual TLS point cloud data have been collected using Riegl VZ-400i laser scanner, with consistent data acquisition geometry and robust georeferencing using real time kinetic global navigation satellite system (RTK-GNSS) measured reference points. Point cloud data have been acquired from the riverbank during spring and autumn field campaigns, enabling the assessment of both interannual and decadal-scale changes. Point cloud data were collected using multiple scanning locations and merged into a composite point cloud to ensure comprehensive data of the whole riverbank at centimeter-scale resolution. Three-dimensional geomorphic change will be quantified using a direct point cloud to point cloud comparison while accounting for surface orientation and measurement uncertainty. This will enable detection of both gradual bank retreat and episodic mass failures, as well as localized sediment accumulation. Particular emphasis is placed on the uncertainty quantification, including levels of detectable change and the robustness of volumetric erosion and deposition estimates over long monitoring periods. Finally, to investigate and interpret the drivers of the observed geomorphic change and its variability, long-term auxiliary information of river flow characteristics (using acoustic doppler current profiler, ADCP) and water level collected during field surveys together with climatic data (e.g., precipitation and temperature) will be analyzed.
How to cite: Kankare, V., Blåfield, L., Markkanen, V., Lintunen, K., Kaartinen, H., Kukko, A., Kasvi, E., and Alho, P.: Capturing the geomorphological change of a sub-arctic riverbank through unique long term terrestrial laser scanning time series, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15890, https://doi.org/10.5194/egusphere-egu26-15890, 2026.
Ice cover significantly alters river flow dynamics by introducing friction at the ice-water interface, shifting the high-velocity core closer to the riverbed, and modifying shear forces that influence sediment transport. Climate change further complicates these processes as winter base flow increases and ice conditions change. Rising temperatures reduce the extent and stability of river ice, making discharge patterns more variable and impacting under-ice sediment transport processes. Therefore, real-time, continuous monitoring of under-ice flow and sediment dynamics is essential. As part of an experimental study, a new measurement approach was tested against the traditional, labour-intensive method in a Finnish river under mid-winter conditions. Instead of conventional cross-sectional stationary measurements taken from ice-auger holes at one-metre intervals once or twice per winter, the new approach used side-looking acoustic Doppler sensors mounted on aluminium frames, with three sensors per frame evenly spaced. These frames were placed vertically into the river along the outer bank through large holes in the ice. This enabled the sensors to measure flow across the entire horizontal cross-section and at three depths: near the ice surface, mid-water column, and near the riverbed. The sensors remained in the river for several weeks, continuously recording under-ice flow and sediment transport conditions. This pilot deployment aimed to assess whether these sensors could replace the conventional method for long-term, enable continuous monitoring of under-ice flow conditions and possibly reveal previously unresolved temporal variability in under-ice hydraulics and sediment transport, including short-lived flow pulses, vertical velocity redistribution, and event-scale sediment mobilisation that are not captured by episodic winter measurements. Traditional cross-sectional measurements were conducted at sensor locations to compare flow dynamics, data accuracy and spatiotemporal resolution. The results will help evaluate the feasibility of continuous hydrological monitoring in ice-covered conditions, which remains challenging today.
How to cite: Blåfield, L. and Alho, P.: From Intermittent Observations to Continuous Monitoring: Advancing Under-Ice River Flow Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9969, https://doi.org/10.5194/egusphere-egu26-9969, 2026.
Accurate Digital Elevation Models (DEMs) at high spatial resolution are a critical prerequisite for terrain-aware hydrologic Digital Twins, where topographic errors directly compromise flow routing, inundation mapping, and hazard prediction. Within the scope of hydrologic digitization, improving the reliability of DEMs derived from spaceborne Interferometric Synthetic Aperture Radar (InSAR) remains a key challenge.
InSAR is a widely used technique for DEM generation, which exploits phase differences and coherence information from two or more SAR acquisitions. While spaceborne InSAR enables large-scale and weather-independent observations, its performance is strongly constrained by sensor geometry, temporal and perpendicular baselines, and surface dynamics. In particular, coherence degradation caused by vegetation cover, soil moisture variability, atmospheric effects, and the presence of wetlands or water bodies leads to noisy interferograms and reduced DEM accuracy in hydrologically relevant environments.
This study investigates a learning-assisted InSAR framework to enhance interferometric data quality and mitigate coherence-related limitations in SAR-derived DEMs. Deep learning based generative and representation-learning models, including diffusion models, generative adversarial networks, and variational autoencoders, are evaluated to support coherence enhancement and artifact suppression in SAR image pairs. The learning components are integrated with established InSAR processing pipelines to improve interferometric phase stability and DEM quality without compromising the physical consistency of the interferometric observables.
Our methodology leverages daily repeat-track ICEYE and bistatic TerraSAR-X/TanDEM-X satellite acquisitions, with high-resolution reference DEMs from the National Land Survey of Finland enabling robust validation beyond open global DEM products. Initial experiments using Sentinel-1 image pairs show consistent improvements in interferogram quality and spatial coherence patterns relative to baseline InSAR processing, particularly in vegetated and mixed land-cover areas affected by decorrelation. Quantitative aspect of the methodology focuses on improvements in interferogram quality, elevation accuracy, and uncertainty patterns relevant for hydrologic Digital Twin applications. The workflow is designed for scalability across different SAR sensor configurations.
By addressing coherence limitations through the integration of physics-aware deep learning with InSAR pipelines, this work aims to enable more reliable, high-resolution DEM generation for terrain-sensitive hydrologic Digital Twins within the Digital Waters (DIWA) framework.
How to cite: Humayun, M. F., Demil, G., Westerlund, T., Oussalah, M., and Heikkonen, J.: Learning-assisted InSAR DEM Enhancement for High-Resolution, Terrain-Aware Hydrologic Digital Twins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16726, https://doi.org/10.5194/egusphere-egu26-16726, 2026.
Lakes cover only about three percent of the Earth’s land surface, yet they are a critical component of the hydrosphere and provide substantial ecosystem services. Sustained monitoring and modeling of lake ecological status are therefore essential. Within the European Union, the Water Framework Directive provides a harmonized framework for assessing lake ecological status using biological quality elements such as phytoplankton, aquatic flora, benthic invertebrates, and fish, classifying lakes into five status classes: high, good, moderate, poor, and bad. However, sparse and infrequent field-based ecological measurements limit spatial and temporal coverage, particularly for near-real-time assessments.
We present a national-scale machine-learning framework for ecological status classification of 2,487 Finnish lakes using routinely available water-quality and morphometric variables, including total nitrogen, total phosphorus, turbidity, conductivity, pH, color, dissolved oxygen, Secchi depth, maximum depth, and lake surface area. Multiple classification models were evaluated, including Random Forest, XGBoost, Support Vector Machine, Artificial Neural Network, and TabNet. Model uncertainty was explicitly quantified using a Bayesian neural network. An ensemble of models achieved a macro F1 score of 0.67 and a Matthews correlation coefficient of 0.50 under five-fold cross-validation.
The Bayesian neural network achieved the lowest Brier score of 0.44 and Expected Calibration Error of 0.04, indicating superior probabilistic calibration compared to other models. Mean total predictive uncertainty across all lakes was 0.12, with the lowest uncertainty observed for high and bad ecological status classes and the highest uncertainty associated with intermediate classes, reflecting transitional ecological conditions and class overlap. These results demonstrate that data-efficient machine-learning models, combined with explicit uncertainty quantification, can support cost-effective and scalable ecological status assessment for lakes with limited monitoring data.
The proposed framework enhances national-scale reporting, supports prioritization of restoration efforts, and provides uncertainty-aware decision support for lake management, particularly in Arctic–Boreal regions.
Keywords: Ecological status assessment, Water quality, Finnish lakes, Machine learning, Near-real-time monitoring.
How to cite: Mahdian, M., Abolfathi, S., Kukkonen, J., and Kolehmainen, M.: Uncertainty-Aware Data-Driven Framework for Near Real-Time Lake Ecological Status Assessment under the EU Water Framework Directive , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23205, https://doi.org/10.5194/egusphere-egu26-23205, 2026.
The amount of drainage from the aquifer is one of the most important components of the groundwater balance in aquifers situated in humid and extremely humid climates. To manage groundwater and modify the groundwater balance, modeling this interaction is essential. However, it has rarely been taken into consideration thus far, and no comprehensive approach has been put out due to the complexity of simulating and forecasting the two systems. There are two methods for draining the amount of drainage from the aquifer: in the first, a portion of the river serves as a natural drain. In the second, artificial drains are excavated to a depth of one to three meters to regulate the amount of groundwater rise. It regulates groundwater levels, keeps agricultural areas from draining, stops land salinization and groundwater contamination, and shields plant roots. Based on the outcomes of numerical modeling, machine learning models can forecast the amount of drainage from the aquifer. This strategy led to the development of a numerical modeling and machine learning method that simulates the drainage system and aquifer and then estimates drainage from the aquifer. The Guilan Plain in northern Iran was used to evaluate such an approach. The drainage-aquifer system was simulated using MODFLOW in GMS software. The drained amount of the aquifer was then predicted using Gaussian process regression (GPR), a probabilistic and Gaussian machine learning technique. Features such as surface recharge, groundwater level, topography, and aquifer discharge were extracted from the simulated model and later used as inputs to machine learning models to predict the amount of aquifer drainage. The MODFLOW simulation's results showed that drains discharge a significant quantity of groundwater each year, which could not be disregarded when evaluating the groundwater balance. In addition, GPR performed the best in predicting the volume of aquifer drainage with RMSE, MAE, and NSE of 1.772 thousand cubic meters per month, 0.70 thousand cubic meters per month, and 0.78, respectively. The proposed approach can be investigated in other comparable locations to forecast the amount of aquifer draining and modify the groundwater balance based on the results obtained.
How to cite: Ghordoyee Milan, S., Rasti, M., and Torabi Haghighi, A.: Hybrid Numerical–Machine Learning Framework for Predicting Aquifer Drainage, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17470, https://doi.org/10.5194/egusphere-egu26-17470, 2026.
Peatland restoration is increasingly implemented as a nature-based solution to recover ecosystem functions degraded by historical drainage, yet the temporal dynamics of hydrological and thermal recovery remain poorly understood, particularly at fine time scales. This study investigates the effects of restoration on water table (WT) and porewater temperature (Tpw) using high-resolution (30-min) data across 43 boreal peatlands in Finland, including drained, restored, and pristine sites, based on a long-term before-after control-impact experiment. Wavelet coherence analysis is applied to examine the temporal similarity between restored and pristine sites in terms of WT and Tpw dynamics across hourly to monthly scales.
Our results show that restoration significantly increases the global wavelet coherence of WT across all time scales, with the strongest recovery observed at daily and longer scales. However, WT coherence at sub-daily scales remains low, indicating incomplete recovery of short-term hydrological buffering. In contrast, Tpw dynamics show limited restoration effects, with significant coherence increases only at monthly scales. Differences in restoration response are evident among peatland types and trophic levels, with open mires and intermediate-nutrient sites exhibiting the highest post-restoration WT coherence. Temporal analysis reveals a gradual increase in WT coherence over the first six years post-restoration, followed by stabilization, while Tpw coherence remains variable. These findings highlight the importance of multi-scale temporal analysis in evaluating restoration success and underscore the need for long-term monitoring to capture the full trajectory of hydrological recovery in peatlands.
How to cite: Lu, X., Päkkilä, L., Räsänen, A., Ronkanen, A.-K., and Marttila, H.: Can water table and thermal regimes in boreal peatlands recover temporal dynamics in a decade after restoration?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3452, https://doi.org/10.5194/egusphere-egu26-3452, 2026.
Snow drought is characterized by an abnormally low snowpack, resulting generally from reduced precipitation, warmer surface air temperature (SAT), or a combination of both. However, there is still a limited understanding of how snow drought patterns change across space and time. Hence, this study investigates the influence of precipitation and SAT on the spatio-temporal patterns of snow droughts in Fennoscandia during 1980-2022. In general, the results show strong spatial variations in dry (reduced precipitation) and warm (elevated SAT) snow drought types across Fennoscandia over time. Dry snow droughts were more frequent in northern and mountainous parts of Fennoscandia, indicating that precipitation deficits are the primary driver under persistent cold conditions. In contrast, warm snow droughts were more spatially extensive, affecting both northern and southern regions, and showing considerably higher frequencies in coastal zones and lower-latitude areas. This sheds particular light on the increasing occurrence of SAT-driven snow droughts across Fennoscandia. These findings indicate a transition from precipitation-driven snow droughts in high-latitude regions to SAT-driven events in southern and maritime areas in response to global warming and climate change. Accordingly, this study lays a solid foundation for developing climate-adaptive water resources management strategies in Fennoscandia, where snowpack plays a crucial role in water security and regional sustainable development.
How to cite: Parvin, S., Marttila, H., Carter, E., and Irannezhad, M.: Historical Patterns of Snow Drought in Fennoscandia: 1980-2022 , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6607, https://doi.org/10.5194/egusphere-egu26-6607, 2026.
Urban flood monitoring requires timely and dependable decisions in fast-evolving, partially observed settings. Sensor-network faults can degrade awareness and delay response, with substantial human and economic consequences. We introduce a Nowcasting Physics-Informed (NPI) framework for detecting faults in streamflow sensors using a 100-min sliding window sampled every 2 min. The approach combines measured sensor signals with outputs from the Storm Water Management Model (SWMM), forms a fused feature set, and feeds it to a stacked long short-term memory (LSTM) model to estimate the probability of a fault at the end of each window. We assess the benefit of coupling physical-model information with data-driven learning by comparing non-physics baselines. Over five cross-validation folds, the physics-informed fusion improves F1 by 3.7 to 12.5 percentage points, raising performance from 0.75 for a data-only LSTM to 0.88 for the complete NPI model. The pipeline is causal, yields auditable predictions via explicit physical features, and generates binary alerts that operators can use directly. Overall, the method offers a practical blueprint for robust warning systems that maintain performance under unseen conditions.
How to cite: Mousavi, F., Torabi Haghighi, A., Silander, J., Monemi, M., and Rasti, M.: When Physics Meets Machine Learning for Nowcasting of Hydrological Sensors Fault Detection , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7330, https://doi.org/10.5194/egusphere-egu26-7330, 2026.
We introduce a comprehensive observation and measurement framework designed to monitor ecosystem-level changes in a former peat extraction area using advanced instrumentation and digitalisation tools. This system aims to deliver accurate scientific data to support peatland management decisions and provide reference measurements for carbon emission calculations in the land use sector. In Finland, peat extraction for energy is being phased out as part of the green transition. Former extraction sites are commonly restored through rewetting, yet the short- and long-term impacts on water quality, greenhouse gas (GHG) emissions, and terrestrial and aquatic ecosystems remain poorly understood. To address this knowledge gap, we have established an intensive monitoring site at the Turvesuo–Miehonsuo peat extraction area in the Sanginjoki catchment near Oulu, Finland. Peat extraction ended in 2023, and rewetting is planned for 2025–2026. Our monitoring integrates online and cloud-based data transfer, model input, and visualisation from: 1) continuous high-frequency water quality and aquatic gas measurements, 2) eddy covariance and chamber-based GHG flux monitoring, 3) drone surveys for spatial variability assessment, 4) hydrological monitoring of surface and groundwater, 5) microbial and algal community analyses, and 6) detailed vascular plant and bryophyte inventories. This approach will provide a robust dataset for evaluating the environmental impacts of peatland rewetting using latest technological advances.
How to cite: Marttila, H., Lohila, A., Liimatainen, M., Ronkanen, A.-K., and Postila, H. and the VISIO project team for Turvesuo-Miehonsuo: Ecosystem-level Monitoring of Environmental Impact after Peatland Rewetting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7978, https://doi.org/10.5194/egusphere-egu26-7978, 2026.
Groundwater (GW) seepage into streams plays a crucial role in sustaining streamflow and regulating thermal regimes in boreal and arctic headwaters. However, spatial variation of seepage and interactions with the stream network remain difficult to observe, especially in snow-dominated catchments with seasonal freezing. This study used high-resolution distributed temperature sensing (DTS) to identify groundwater and surface-water contributions along a sub-arctic headwater stream in northern Finland. Stream temperature was continuously monitored at 2 m spatial and 30 min temporal resolution over a 2 km reach. Seasonal slope-based thermal patterns were used to distinguish stream sections dominated by groundwater inflow. In addition, melt-active nights with observed snow cover and elevated stream discharge were used to capture surface and near-surface inputs to the stream. These DTS-derived signals were compared with commonly used terrain-based predictors, including upslope contributing area (UCA) and topographic wetness index (TWI), as well as with lateral inflow simulations from the SpaFHy-2D hydrological model. The results show that topography-based indices captured broad-scale surface convergence but failed to consistently identify local groundwater discharge zones. SpaFHy-2D reproduced the general distribution of major groundwater-influenced reaches but shows local mismatches, particularly in esker-controlled sections. This study highlights the value of in-stream temperature observations and hydrological modeling for detecting groundwater–surface water interactions in cold-region, where strong seasonality and snow-dominated hydrology limit traditional field methods.
How to cite: Parvizi, P., Marttila, H., Launiainen, S., Ala-aho, P., Nousu, J.-P., Croghan, D., and Martinkauppi, I.: Detection of Groundwater–surface water Contributions in a Subarctic Stream using Distributed Temperature Sensing, Topographic Indices, and Spatial Hydrological Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9308, https://doi.org/10.5194/egusphere-egu26-9308, 2026.
The Fram Strait is a key region for Arctic freshening and Atlantification, shaped by the interaction between warm, saline Atlantic Water inflow and the export of cold, fresh Polar Surface Waters, with Atlantic heat transport exerting a strong control on regional sea ice conditions. While the Fram Strait has been extensively studied using hydrographic transects and deep water profiles via mooring arrays, open surface waters and those beneath sea ice and non-summer periods, when sea-ice cover limits accessibility, are comparatively under sampled. We present one of the first Fram Strait dedicated, ultra high resolution surface water geochemistry datasets from the I/B Oden as part of the ARTofMELT 2023 expedition (May–June). We have collected continuous measurements of temperature, salinity, δ¹⁸O, and d-excess in surface waters at 8 m depth along the expedition cruise track during the transition from winter to spring. Our stable water isotope measurements provide a powerful tool to distinguish surface water provenance and freshwater modification in this region of the Arctic, where temperature–salinity (T-S) properties often converge under sea ice. We observed pronounced gradients across the Fram Strait, with fresher and isotopically depleted surface waters in the west, consistent with influence from the East Greenland Current, and more saline, isotopically enriched waters in the east. Conventional T-S frameworks would classify most observations as Polar Surface Water, inherently indicating surface waters being derived and exported from the Arctic. However, the isotopic composition suggests that waters with an Atlantic provenance intrude substantially farther beneath the sea ice in the eastern Fram Strait than previously appreciated. By combining continuous surface water isotope measurements with isotopic observations from sea ice and precipitation, we further show that the contribution of local sea ice melt to surface waters increases during the second half of the expedition, preceding the observed surface melt onset by approximately two weeks. Our results demonstrate that T-S relationships alone are insufficient to resolve surface water provenance and freshwater modification, whereas seawater isotopes provide critical constraints on the sources and evolution of surface water masses and freshwater in the seasonally ice-covered Fram Strait.
How to cite: den Ouden, F., Welker, J., and Kopec, B.: Continuous Surface Water Isotopes Reveal Fram Strait Water Mass Interactions during the Onset of Sea Ice Melt, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18755, https://doi.org/10.5194/egusphere-egu26-18755, 2026.
Winter remains the most understudied season across northern latitudes, despite its growing importance in the context of rapid warming of the north. Winters are changing, for example, via processes related to the Arctic water cycle, such as increased rainfall and extreme temperature fluctuations, which disrupt the previously more predictable hydrological regime of northern stream systems. We show how a large panarctic scale climate-driven winter event triggered biological activity in a typical subarctic stream at the Pallas Atmosphere-Ecosystem Supersite in northern Finland. By using simultaneous high-frequency measurements of water vapor, precipitation, and stream water isotopes, along with water chemistry, discharge, and bacterial community attributes, we captured two exceptionally warm Atlantic air intrusion events in early winter 2020–2021. These two closely spaced rain-on-snow events caused complete snowpack melt and triggered a high discharge and dissolved organic carbon pulse in our study stream, which in turn reactivated the already receding summer-like aquatic bacterial community and initiated their primary production despite the prevailing cold and dark conditions. Our findings reveal how large-scale climate anomalies can abruptly disrupt local-scale hydrology and trigger biological activity in wintering stream ecosystems, highlighting the sensitivity of aquatic ecosystems and biogeochemical processes to shifting weather and climate patterns. Capturing this event underscores the need for continuous high-frequency observations that span seasons and years. Overall, this study reinforces winter as a critical season for ecological research and reveals the vulnerability and responsiveness of northern stream ecosystems to climate change.
How to cite: Mustonen, K.-R., Bailey, H., Croghan, D., Lehosmaa, K., Tauriainen, J., Ala-Aho, P., Marttila, H., Hyöky, V., and Welker, J.: Climate-driven extreme winter thaw triggers summerlike microbial activity in a subarctic stream, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16563, https://doi.org/10.5194/egusphere-egu26-16563, 2026.
The United States Corps of Engineers (USACE) maintains over 30,000 km (25,000 mi) of coastal and inland waterways to ensure safe navigation for commercial, recreational, and military traffic. The USACE leverages hydrographic surveying with SONAR echosounders to generate bathymetric surfaces used to identify where and how much material needs removal for channel maintenance. Bathymetric survey data is archived within the USACE eHydro database. However, since these hydrographic survey operations are contracted externally, the data retained have discrepancies impacting end-use cases (e.g., single-beam vs. dual-beam echosounders, survey density, projection system). For operational use in hydrologic and hydrographic applications, SONAR data must be reprocessed into a standard raster data format. Since small errors in bathymetric surface estimation can translate to economically impactful sediment volumes, robust surface generation and uncertainty quantification is crucial.
The USACE currently uses Triangulated Irregular Networks (TIN) as the default surface generation algorithm; this deterministic method has limited capability to capture spatial correlation structure in SONAR data. This study compares bathymetric surfaces created with TIN, Nearest Neighbor (NEAN), and Natural Neighbor (NATN) interpolation and two robust geostatistical interpolation methods—isotropic Ordinary Kriging (OK), and isotropic Regression Kriging (RK) based on spline trend residuals, with a goal of creating an automatic SONAR-to-bathymetric surface data processing pipeline.
The analysis uses 100 independent SONAR surveys collected from across the USACE civil works districts representing diverse spatial extents, sampling densities, and channel morphologies. 10-foot spatial resolution bathymetric surfaces are generated using each of the five interpolation methods for each SONAR dataset. To ensure reproducible kriging models for OK and RK approaches, multiple empirical semivariogram shape functions were fit using a weighted least squares solution with final shape function selection based on maximum Coefficient of Determination. A 5-fold cross-validation using Root Mean Squared Error (RMSE) selects the optimal spline trend surface for RK.
Once bathymetric surfaces are generated, a 10-fold cross-validation scheme for each SONAR dataset compares the five interpolation methods. Normalized Median Absolute Deviation (NMAD) and RMSE assess each method’s accuracy across all surveys. Across the 100 surveys, the OK approach proved best, yielding a 39.1% and 54.6% decrease in RMSE and NMAD, respectively, compared to TIN. The RK approach produced 8.1% decrease in RMSE and 31.4% decrease in NMAD compared to TIN. Conversely, neighbor-based approaches produced worse bathymetric surfaces with a 15.0% increase in RMSE and 5.2% increase in NMAD for the NEAN approach, and an 11.1% increase in RMSE and 11.3% increase in NMAD for the NATN approach.
The preliminary results of the study indicate the importance of accounting for spatial autocorrelation between points in generating accurate bathymetric surface estimates with unbiased uncertainty. Simple deterministic interpolation (TIN) cannot reliably account for complex topography that manifests from dredging and tidal response. However, methods modeling semivariance across the dataset (OK and RK) can account for spatial structure to better model seabed morphologies. In practice, employing geostatistical methods to generate accurate bathymetric surfaces could improve coastal morphological modeling and dredge planning.
How to cite: Caldwell, W., Carter, E., and Asborno, M.: Geostatistical interpolation methods to create robust bathymetric surfaces across the USACE dredging portfolio, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15218, https://doi.org/10.5194/egusphere-egu26-15218, 2026.
The Matilija Creek watershed in southern California, USA, is characterized by pronounced vulnerability to post-wildfire debris flow and sediment-laden flood hazards which are challenging to predict since they occur as a result of the confluence of diverse but interconnected physical mechanisms. These events are a cascading hazard, in that wildfire increases susceptibility to mass movements. Southern California is prone to wildfires due to its dry climate during the summer months. The fires in turn cause changes in hydrologic response, including increased runoff and decreased soil cohesion. The region has also experienced severe drought in the mid-2010s. Stationarity is often an assumption of both statistical and physically-based hydrologic models, but in the case of Matilija Creek watershed it is likely that the best hydrologic parameters vary as a result of both drought and fire. Changes in hydrologic response can be detected through a wide variety of statistical analyses, including traditional methods for detecting changes in water yield, double-mass analysis and flow-duration curves. Data assimilation is a promising approach for dynamically capturing post-disturbance changes in hydrologic response over time. This study aims to assess the utility of data assimilation with a physically-based hydrologic model to detect changes in hydrologic parameters during a drought and following a fire. In our previous work on this method, over-parameterization has likely caused inconsistent results between runs. If many parameters are allowed to change too drastically, different parameter shifts can cause the same results. This new analysis will therefore focus on a small set of infiltration-related parameters. Data assimilation is also compared to a statistical method – a performance of a linear model of runoff-ratio over time - for detecting post-wildfire changes in the hydrologic response. In choosing a data assimilation algorithm, this study seeks to provide a more objective and data-driven assessment of the wildfire-driven changes in hydrologic parameters than would be possible with other methods.
How to cite: Culler, E. and Livneh, B.: An investigation of post-wildfire changes in hydrologic parameters using data assimilation in a southern California watershed , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22445, https://doi.org/10.5194/egusphere-egu26-22445, 2026.
Immersive digital twin platforms are emerging as a key paradigm for advancing the understanding and management of hydrologic systems, by integrating interfaces and workflows within a unified conceptual architecture. This contribution presents the I-DT Hydro Framework, a conceptual model that defines the core components, interactions, and design principles of immersive digital twin platforms for water resources applications. The framework functions as a human-facing integration layer for a hydrologic digital twin architecture that includes data integration, multi-scale modeling, and simulation pipelines, continuously supported by real-time monitoring from remote sensing, in-situ observations, and crowdsourced data, and made accessible through immersive and interactive interfaces. A central element of the I-DT Hydro Framework is the role of immersive interfaces, enabled through extended reality (XR), which mediates between complex computational processes and human interpretation and decision-making. Hydrologic systems and the models used to represent them are inherently complex and often inaccessible to non-specialist users. Immersive experiences provide a new method for decision-makers with diverse domain expertise to access, explore, and interpret the knowledge generated by hydrologic data and models. The framework further illustrates how hybrid modeling and machine learning can be embedded within autonomous pipelines to support adaptive decision support systems under dynamic environmental conditions. By explicitly linking platform layers, workflows, and user interaction, the I-DT Hydro Framework provides a shared reference for the design and evaluation of immersive digital twin platforms, supporting scalability, interoperability, and stakeholder engagement in hydrologic digitization.
How to cite: Bazzaz, S., Carter, E., Rasti, M., and Kløve, B.: The I-DT Hydro Framework: Immersive Digital Twin Platforms for Hydrologic Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17002, https://doi.org/10.5194/egusphere-egu26-17002, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot A
EGU26-20425 | Posters virtual | VPS9
Revisiting the observational strategy: Toward more robust inference of compound water level extreme processes in estuariesWed, 06 May, 14:30–14:33 (CEST) vPoster spot A
Most studies on estuarine compound flooding patterns and processes either utilize paired station data of streamflow and sea level (or simulated data), readily available at national, continental and global scales, or are confined to one estuary system and specific events, where detailed water level observations exist. These approaches do not provide a full understanding of the processes underlying the compound events, as reflected in observed spatial patterns and temporal variation.
We call for revised observational strategies for integrating estuary-scale process and the manifestation of compound water level extremes in the estuary with larger-scale patterns and trends. Proving methodological insights from a review of existing compound flooding literature and our multi-scale analysis in Europe, we establish recommendations for such a setup.
As a baseline, our analysis uses regional-scale patterns and long-term trends in compound flooding potential in three contrasting countries in Europe (Norway, Finland and Spain, differing in terms of tidal amplitude, seasonality and relative sea level trends), quantified with state-of-the-art methods. We then document the availability of direct estuarine water level data for validating the inferred flooding potential. Finally, three different field setups in northern Norway, central Finland and northern Spain are used to test how compounding streamflow and sea level conditions inferred from national stations are visible in different parts of the estuary. Unlike previous studies, we also examine the manifestation of low water conditions to maximize the usefulness of the acquired data for extreme-event studies. Moreover, we test how robust the state-of-the-art infrastructure is in extreme conditions, including meso-tidal variation, river ice, snowmelt-induced flooding and drying up of estuaries.
This multi-scale study allows us to present recommendations for robust observational strategies that allow inference of processes governing compound water level extremes at multiple scales, and explaining the observed patterns and trends. Such strategies have potential in improving our understanding of current and future compound hazards. Accommodating low-water conditions and hydrometeorological extreme conditions facilitates continental and global comparative studies.
How to cite: Nylén, T. and Tolvanen, H.: Revisiting the observational strategy: Toward more robust inference of compound water level extreme processes in estuaries, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20425, https://doi.org/10.5194/egusphere-egu26-20425, 2026.
EGU26-21040 | ECS | Posters virtual | VPS9
Identifying Scale-Dependent Snow patterns from learned fusion of Multi-modal, Multi-resolution satellite observationsWed, 06 May, 15:12–15:15 (CEST) vPoster spot A
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.
