Natural flood management (NFM) involves the use of natural processes and environments to mitigate flood risk. Large tracts of upland areas in Wales are used for commercial forestry. Appropriate management of the forest structure, species and age diversity, and harvesting schedules in these areas has the potential to provide significant NFM benefits, which have not yet been fully explored. In this study, we sought to develop a digital twin of the forests in the Afon Pennal catchment, located near Machynlleth in mid-Wales, through a novel instrumental setup to collect the canopy throughfall data in real-time and combine it with satellite-derived forest parameters to simulate the impact of forest management on the river’s streamflow response. The Afon Pennal catchment is owned and managed by Natural Resources Wales and consists of both managed and unmanaged forest areas at differing stages of maturity. We attached LoRaWAN® sensors to 22 tipping bucket rain gauges placed under different types of forest canopy at five locations within the catchment, which enabled real-time data collection at a 5-minute interval. Remotely sensed data from the European Space Agency’s Sentinel-2 satellite was used to obtain the Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) values at a daily temporal resolution. These data were used to train an integrated model representing the forest canopy interception and catchment hydrological processes and then simulate river streamflow under various forest-type configurations and harvesting scenarios. Our results show that the integrated model has the capability to model streamflow based on remotely sensed LAI and FVC values, making it a potentially valuable tool for aiding and informing forest management planning in the future.
How to cite: Patil, S. and Cooper, M.: A digital twin of forests for natural flood management in Wales, UK, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2496, https://doi.org/10.5194/egusphere-egu25-2496, 2025.
River ice dynamics significantly impact hydrological processes, ecological systems, and water resource management, particularly in boreal sub-Arctic regions such as Finland. Accurate river ice extent and phenology monitoring is crucial for understanding climate change impacts, improving water management, and mitigating ice-related risks. Field-based river ice monitoring methods face limitations in spatiotemporal coverage and accuracy. This study integrates multi-source remote sensing data and advanced analytical techniques to improve river ice monitoring and prediction capabilities.
Utilizing both optical and synthetic aperture radar (SAR) satellite imagery, combined with in-situ observations from fixed cameras, we develop high-resolution algorithms to detect river ice extent and assess its phenological characteristics. Furthermore, we explore the potential of using lake ice monitoring as a proxy for river ice dynamics, leveraging proximal lake-river systems to predict river ice conditions. Advanced digital twin frameworks will derive critical ice parameters, such as freeze-up, break-up, and ice thickness, enabling real-time monitoring and decision-making.
This research addresses the challenges of monitoring river ice variations by integrating multi-source observational data into predictive models. By enhancing river ice observations' spatial and temporal resolution, this study contributes to sustainable water resource management and supports global adaptation strategies aligned with Sustainable Development Goals (SDGs). The findings highlight the transformative potential of digital solutions in hydrological research and river basin management.
How to cite: Qiu, J. and Torabi Haghighi, A.: High-Resolution Mapping of Boreal Fluvial Ice Extent Variations from the Perspective of Lake Ice Changes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6120, https://doi.org/10.5194/egusphere-egu25-6120, 2025.
Monitoring total suspended solids (TSS) is critical for understanding water quality and managing pollution in river ecosystems. However, traditional methods face challenges in achieving real-time estimates in resource-constrained environments. This study aims to develop an optimized framework for convolutional neural network (CNN) to estimate TSS concentrations using Sentinel-2 multispectral data, with a focus on lightweight architecture and quantization techniques for real-time applications. Neural Architecture Search (NAS) combined with Pareto optimization was used to identify lightweight CNN models, ensuring high performance with minimal computational cost. Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) were applied to further compress model sizes while maintaining accuracy. Performance was evaluated using metrics such as Nash-Sutcliffe Efficiency (NSE) and Root Mean Squared Error (RMSE).
As a result, the lightweight Mobilenet (8.11 MB) attained an NSE of 0.828, and quantization further reduced the model size by 91%, yielding a compact 0.74 MB model with an enhanced NSE of 0.832. This quantized TSS estimation model showed the potential for real-time TSS estimation on mobile and edge devices. The proposed lightweighting and quantization framework provides a scalable solution for real-time TSS monitoring, connecting advanced machine learning methods with practical environmental applications. This approach enables efficient, real-time water quality assessment in a variety of environmental conditions, making it suitable for use on resource-constrained platforms such as drones, unmanned aerial vehicles and satellites.
How to cite: Moon, J., Jung, S., Suh, S., Baek, J. H., Lee, S., Ok, C., and Pyo, J.: Study on the Framework for Real time Total Suspended Solids Monitoring using Sentinel-2 and Edge Artificial Intelligence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3403, https://doi.org/10.5194/egusphere-egu25-3403, 2025.
Rivers serve as primary conveyors of sediment, continuously reshaping landscapes through fluvial dynamics where hydroclimatic conditions play crucial role in governing the seasonality and magnitude of sediment transport, sediment connectivity and morphological adjustment. In high-latitude regions snowmelt-driven spring floods have traditionally been the major contributor to sediment fluxes. However, climate change is altering the hydroclimatic conditions, leading to hydroclimatic regime shifts with significant implications for sediment transport dynamics, river morphology, and landscape stability. This study assess the impacts of hydroclimatic shifts on sediment transport dynamics in high-latitude rivers, with a focus on seasonality and variability of sediment transport events, and functional sediment connectivity. Using a combination of (1) hydrological, meteorological, and geomorphological time-series data from in situ field measurements, remote sensing, gauging stations, and historical aerial imagery, (2) computational morphodynamic modelling with high spatiotemporal resolution, and (3) Index of Connectivity and sediment budgeting approaches, this research provides new insights into how climate-driven hydrological changes reshape fluvial sediment transport processes. The study focuses on two meandering rivers in boreal and subarctic environments, both expected to experience contrasting hydrological changes due to climate change. The findings provide critical insights into the seasonality, variability, and long-term trends of sediment transport dynamics and morphological responses in river systems transitioning from Nival control to Pluvial control. In addition, the results emphasize the need for continuous monitoring and advanced modelling, such as digital twins, to capture evolving patterns and thresholds of sediment transport. Future research should integrate real-time data with predictive multimethod approaches to improve understanding of short- and long-term morphodynamic feedback.
How to cite: Blåfield, L.: High-latitude fluvial dynamics under hydroclimatic shift – Insights on sediment transport and river morphology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15584, https://doi.org/10.5194/egusphere-egu25-15584, 2025.
Riverbed topography in boreal rivers plays a critical role in shaping fluvial and ecological processes. The spatial variation in riverbed elevation and in channel morphology influence for example the flow dynamics, sediment transport and deposition, nutrient cycling, and ecological dynamics providing habitats for variety of aquatic species. Thus, understanding riverbed topography and its change is essential for managing riverine systems and evaluating the impact of climate change and anthropogenic land use. However, quantifying the riverbed topography presents numerous challenges due to the highly dynamic environment (hydraulic conditions, substrate variability and temporal changes) of boreal rivers experiencing extreme events annually (e.g. floods and ice). The advancements in geospatial technologies, such as high-density laser scanning and multibeam sonar mapping, can enable detailed characterization of riverbed characteristics, however there is a lack of understanding the capabilities and limitations of these novel technologies in boreal river conditions. Therefore, the aim of this study is to investigate the capabilities of high density underwater and above water green LiDAR and multibeam sonar data to characterize the riverbed and riverbank topography and to develop methodologies to create seamless high detail digital terrain model (DTM) for the whole river channel. Following main research questions (RQs) were investigated: RQ1: What are the limitations in regards spatial resolution and data comprehensiveness between measurements systems? RQ2: Are the riverbed topography characteristics consistent between measurement systems?
Field surveys were conducted in the Oulankajoki River, located in northeastern Finland during autumn 2024. High-density point cloud data was acquired with the following systems: novel underwater LiDAR (ULi, green wavelength, Fraunhofer IPM) mounted into autonomous surface vehicle (Otter, Maritime Robotics), airborne bathymetric LiDAR (ABS, Fraunhofer IPM) mounted into high payload capacity drone and multibeam sonar (Baywei M4) mounted into Otter. In addition, in-situ control point measurements (VRS-GNSS) as well as water level and flow velocity (ADCP) information were collected to be used as auxiliary information in the analysis. To investigate the set RQs, following two analysis steps were conducted: (1) the point cloud density and coverage was assessed through varying grid size from 10 cm to 2 meter to identify the possible limitations in spatial resolution and coverage of the point clouds (RQ1), (2) the differences of the created DTMs were assessed with varying grid size and the following topographical characteristics were evaluated: elevation and slope variation, shape of river cross-sectional and longitudinal profiles, and bed roughness (small-scale variations of the riverbed surface characterized as the standard deviation of elevation or roughness indices).
How to cite: Kankare, V., Kaartinen, H., Hakala, T., Kukko, A., Linnea, B., Lintunen, K., Kasvi, E., and Alho, P.: Assessing the capabilities of the high-density green wavelength LiDAR point cloud and multibeam sonar data to quantify riverbed topography, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16838, https://doi.org/10.5194/egusphere-egu25-16838, 2025.
Climate change is affecting the global water, energy and carbon cycle resulting in more severe hydrometeorological events with more societal impact (e.g. precipitation, floods, droughts). Decision support systems for operational or planning purposes are essential to accurately predict and monitor environmental disasters, and optimally manage water and environmental resources now and in the future. The Digital Twin Component (DTC) Hydrology Next project focuses on solutions for monitoring and simulations and forecasting, it requires high-resolution (1 km, 1 hour-day) satellite Earth Observation (EO) data, fully integrated with advanced and spatially distributed modelling systems. Within this scope, we aim to improve operational reservoir monitoring to obtain reliable estimates of surface area, water level and volume (i.e. storage). Secondly, we aim to enhance predictions by data assimilation using wflow_sbm (Imhoff et al., 2020, Eilander et al., 2021, van Verseveld et al., 2024, Imhoff et al., 2024). The focus will be on the Rhine catchment focusing on flooding in co-creation with RWS (Dutch ministry of traffic and waterways) and Zambia focusing on reservoir monitoring and flood management working together with WARMA (Water Resources Management Authority). We also consider including 2D hydraulic flood simulations using SFINCS (Leijnse et al., 2021) driven by outputs from wflow_sbm.
Eilander, D., van Verseveld, W., Yamazaki, D., Weerts, A., Winsemius, H. C., and Ward, P. J. (2021) A hydrography upscaling method for scale invariant parametrization of distributed hydrological models, Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021.
Imhoff, R. O., van Verseveld, W. J., Osnabrugge, B., and Weerts, A. H. (2020) Scaling Point-Scale (Pedo)transfer Functions to Seamless LargeDomain Parameter Estimates for High-Resolution Distributed Hydrologic Modeling: An Example for the Rhine River, Water Resour. Res., 56, https://doi.org/10.1029/2019WR026807.
Imhoff, Ruben and Buitink, Joost and van Verseveld, Willem and Weerts, Albrecht, A fast high resolution distributed hydrological model for forecasting, climate scenarios and digital twin applications using wflow_sbm. Environmental Modelling & Software,179,https://doi.org/10.1016/j.envsoft.2024.106099, 2024
Leijnse, T. W. B. et al. (2021). Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes. Coastal Engineering, 163, Article 103796. https://doi.org/10.1016/j.coastaleng.2020.103796
van Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B.: Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications, Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, 2024.
How to cite: Weerts, A., Haag, A., and Tsiokanos, A.: Digital twin developments in DTC Hydrology Next: reservoirs and flooding, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16787, https://doi.org/10.5194/egusphere-egu25-16787, 2025.
Flooding is one of the most destructive natural disasters, leading to widespread damage to infrastructure, loss of life, and major disruptions to urban economies. Rapid urbanization, inadequate drainage systems, and the rising frequency of extreme rainfall events are among the main drivers of flooding. The vulnerabilities of urban systems to flooding necessitate an integrated, simulation-based approach to analyse and compare various flood conditions, fostering proactive flood risk management.
This study aims at developing a River-System Digital Twin (DT) framework for flood hazard mapping and risk reduction. The proposed DT combines advanced hydrodynamic modelling, geospatial analysis, and 3D city modelling to accurately simulate flood scenarios. High-resolution Digital Elevation Models (DEMs), river cross-section data, historical and projected land use and land cover (LULC) maps, and rainfall data for various return periods (e.g., 25, 50, and 100 years) are among the datasets that are used. These datasets are incorporated into a geospatial framework that facilitates both scenario-based analysis and simulation.
The methodology involves building a 3D City Information Model (CIM) by extracting building and vegetation parcels from satellite imagery and using procedural modelling techniques in CityEngine software. This CIM is linked dynamically to flood inundation results obtained from 2D hydrodynamic simulation models. The results obtained from the DT, which include spatially dispersed water depths, flow velocities, and hazard intensity zones, can elucidate the possible effects of flooding on urban infrastructure, such as buildings in flood-prone areas and transit networks.
Preliminary results of the study, conducted in the Adyar River Basin in Chennai, India, indicate that the Digital Twin (DT) effectively captures the geographical variation in inundation patterns across different rainfall scenarios Thus, by integrating 3D City Information Models (CIM) with hydrodynamic simulations for an urban system, the study aims to create a powerful tool for predicting, visualizing, and planning to mitigate the potential impacts of flooding on urban infrastructure and communities.
How to cite: Shravani, K. and Srivastav, R.: River-System Digital Twin for Flood Hazard Mapping, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15018, https://doi.org/10.5194/egusphere-egu25-15018, 2025.
Northern peatlands have a key role in global carbon exchange but at the same time they are expected to face severe climate change driven alterations. A substantial fraction of the peatland carbon exchange occurs through lateral flux of dissolved organic carbon (DOC), and due to the strong seasonality DOC dynamics in northern environments are prone to ongoing hydrologic and climatic changes. Changes in snow accumulation and melt can alter DOC leaching patterns and thus a detailed understanding of the processes occurring during the spring melt period is required. Although recently more attention has been drawn to exploring the high-resolution temporal dynamics in lateral carbon flux, spatial processes in peatland DOC transport are still not adequately documented and understood. In addition, further effort is needed to combine high-resolution spatial and temporal data. We aim to address this gap by using high-resolution unmanned aircraft system (UAS) monitoring of snow cover and melt during the peak melt period with daily UAS mapping in spring 2024. The spatial mapping is combined with high-frequency in-situ DOC and hydrological monitoring in Puukkosuo fen located next to Oulanka Research Station, northeastern Finland. Our installations include a stream gauging station in the peatland outlet with continuous UV-Vis spectroscopy based water quality measurements (DOC, nitrate, turbidity) along with pH and water isotope monitoring. We also monitor other key hydrological parameters in groundwater wells. The objective was to document spatial variations in snow cover melt and hydrological activation, link these spatial processes to DOC export, and identify the key spatiotemporal processes occurring during the spring melt period. With this approach, we work towards a better understanding of peatland ecohydrology, develop and test methods to use novel monitoring techniques for peatland research and management, and provide framework for predictions of peatland carbon dynamics in response to changing snow regimes.
How to cite: Korhonen, P., Ala-Aho, P., Kløve, B., and Marttila, H.: Springtime melting and DOC leaching from northern fen – how changing snow conditions impact spatial hydrological processes in peatlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6175, https://doi.org/10.5194/egusphere-egu25-6175, 2025.
In more than half of Earth’s rivers, ice cover is observed at some point during the hydrological year. Ice cover influences rivers' hydrological and geomorphological processes, with altered flow properties leading to reduced sediment transport capacity. The duration of river ice cover has been observed to be shortening, a trend predicted to continue in the future. As a result of climate change, freezing and breakup times, as well as the length of river ice cover periods, are shifting. Consequently, sediment transport processes are also changing, with relatively unknown effects on environments where ice cover has historically been a recurring phenomenon.
This study aims to quantify the effects of shorter river ice cover periods and complete ice cover loss on sediment transport in seasonally freezing rivers. To achieve this, we employ hydraulic modelling to analyse these impacts. We use known discharge and weather event data to model how wintertime sediment transport is influenced by changing freeze-thaw cycles and the shortening or complete disappearance of permanent ice cover periods. To assess the potential for sediment erosion, transport, and deposition under changing river ice conditions, we use HECRAS 1D and 2D models to gain insights into sediment transport processes. Preliminary modelling results are presented to evaluate current sediment dynamics and predict future scenarios under evolving river ice conditions.
The study focuses on three Finnish watersheds to represent regional differences. The first site, the Tana River in northern Finland, is a boreal sub-Arctic, snow-dominated watershed with an ice-covered period from October to May or June. The second site, the Oulanka River in northeast Finland, currently changing from a snow-dominated to a rain-dominated regime, with ice cover from November to early May. The third site, the Uskela River in southern Finland, is a hemiboreal, rain-dominated watershed with varying ice cover depending on seasonal frost and thaw cycles. The rivers differ in sediment properties: Tana has a gravel bed, Oulanka has a sand bed, and Uskela has a clay bed. Field campaigns at these sites collected data for hydraulic modelling, including airborne laser scanning, discharge measurements, and water level monitoring.
How to cite: Lintunen, K., Kasvi, E., and Alho, P.: Impact of ice cover loss on sediment transport processes in seasonally frozen rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9611, https://doi.org/10.5194/egusphere-egu25-9611, 2025.
Modern fishery management and fish stock conservation are often based on estimated biological reference points or conservation limits. For Atlantic salmon, the reference points, defined as spawning targets, are based on the definition of spawning areas. Based on previous studies water depth, flow velocity and substrate type (particle size) are considered the most important instream habitat variables in determining the spawning habitat selection of salmon. The large subarctic Tana River, located in northern Fennoscandia, is one of the most biologically diverse salmon rivers in the world. The importance of salmon fishing to the local community is significant. The catchment area of the Tana River system is c. 16.400 km2, the main stem is approximately 200 km long and has several tributaries. More than 90 % of the catchment area is subarctic tundra, forest, swamp and wetlands, practically remote wilderness.
The location and size of spawning areas in the Tana River system relies on coarse resolution maps and subjective habitat evaluation (local knowledge and expert judgement). Remote sensing and hydraulic modeling offer a quantitative, objective, spatially continuous and high-resolution approach for identifying the spawning areas. Water depths and flow velocities have been studied by hydraulic modeling for decades. The hydraulic model offers the opportunity to simulate different flow conditions and make predictions. Remote sensing is increasingly used for mapping fluvial habitats, and by utilizing the novel multispectral (including green wavelength) airborne laser scanning (ALS), it is possible to collect high detailed spatial data simultaneously from the riparian zone and underwater environment characteristics.
The aim of this study is to use quantitative and data-based approach to identify potential salmon spawning areas in the Tana River, and to examine how multispectral ALS, aerial photogrammetry and hydraulic modeling can be used to characterize hydromorphological variables important to salmon spawning areas. By integrating high-resolution digital elevation model (DEM) obtained from multispectral ALS and hydraulic data, the hydraulic model will be generated to identify the ideal depth and velocity areas for spawning, and simulate different water levels and discharge scenarios. The substrate type is defined based on ALS data and aerial photographs. The model is also used for studying the effects of extremes, such as floods and droughts, on the spawning areas. The validation will be done by comparing modeling results with habitat and hydraulic data collected from the river system with conventional means and results of long-term electrofishing surveys. The research is carried out as part of the Digital Waters (DIWA) flagship and the DIWA PhD pilot.
How to cite: Kärkkäinen, E., Kankare, V., Calle, M., Kaartinen, H., Erkinaro, J., and Alho, P.: Identifying Atlantic salmon spawning areas based on multispectral airborne laser scanning, and hydraulic modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8005, https://doi.org/10.5194/egusphere-egu25-8005, 2025.
Observations over recent decades in cold regions, including delayed freeze-up, earlier snowmelt, and rapidly increasing precipitation and runoff, underscore the dynamic nature of groundwater (GW)-surface water (SW) interactions. These changes result in distinct spatiotemporal exchange flow patterns, which in turn enhance the variability of biogeochemical processes, with critical implications for carbon cycle and water-resource budgets. Despite their significance, our current understanding of GW-SW interactions and their biogeochemical implications remains limited. Therefore, it is essential to integrate multidimensional analyses, spatiotemporal scales, and interface hydraulic characterization into modelling frameworks. Our target is to provide a comprehensive representation of the interconnections between GW-SW interactions and diverse river valley scale dissolved carbon transport in the north boreal aquifer, at the Oulanka Research Station, Finland. This target can be further divided into three major categories: i) Development of a conceptual model for the entire river valley. This includes understanding GW-SW processes, particularly changes in aquifer permeability during frozen and thawed periods, and devising tailored methods to investigate the flow paths, exchange rates, and associated hydrogeochemical processes. ii) Linking GW-SW dynamics to carbon cycling through hydrogeochemical analyses. This involves identifying spatiotemporal variability in GW geochemistry via statistical analysis and improving this conceptual framework using supplementary data on various redox conditions and stable isotopes (δ2H and δ18O) as environmental tracers. iii) Under the Digital Waters (DIWA) flagship initiative, we undertake site-specific digitalization and modelling of GW-SW processes across river valleys. This effort aims to accurately simulate water movement and carbon cycling within diverse environments, with a specific focus on examining the connectivity and distinctions between river-aquifer, hillslope-aquifer, and peatland-aquifer systems. The research hypothesis will be tested through hydrological analyses, supported by high-resolution 3D GW flow modelling. This process encompasses data collection and interpretation, hydrogeological and hydrogeochemical characterization, conceptual model development, and numerical simulations using the GMS software MODFLOW package and the Amanzi-ATS software under varying boundary conditions and disturbances.
How to cite: Yang, Y., Okkonen, J., Korkka-Niemi, K., Ala-Aho, P., and Marttila, H.: How are groundwater-surface water interactions and carbon transport interconnected on a river valley scale?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8149, https://doi.org/10.5194/egusphere-egu25-8149, 2025.
Long-distance open-channel water diversion projects, such as China’s South-to-North Water Diversion Project, have significantly mitigated regional water supply-demand imbalances. However, the hydraulic behavior of open channels during water conveyance is highly complex, particularly under abnormal conditions like extreme weather or equipment failures, which can cause abrupt hydraulic changes, rapid water level rises, and even local overtopping or other safety hazards. Therefore, global, real-time, and accurate monitoring of open-channel hydraulics is essential to ensure the project's safe and efficient operation. Hydraulic characteristics of open channels are typically obtained through hydrological monitoring systems and numerical simulations. The reasonable placement and number of monitoring sections in a hydrological system are crucial for balancing monitoring accuracy and construction costs across the entire open channel. Numerical simulation accuracy and reliability depend on clear boundary conditions, precise Manning roughness coefficients, and other key parameters. However, these parameters can vary over time and are often difficult to determine in practical applications. Physics-Informed Neural Networks (PINNs) provide an effective solution to these challenges. This study develops a PINN model to predict the hydraulic characteristics of unsteady flow in open channels by integrating sparse hydrological data with physical laws. The study also examines how the number and placement of monitoring sections affect the accuracy of hydraulic predictions for the entire channel. Results demonstrate that PINNs can achieve high-precision hydraulic predictions along the channel using data from only three optimally placed monitoring sections, with average relative L2 errors below 0.5%. PINNs exhibit strong generalization across diverse boundary conditions, accurately predicting complex flow scenarios and demonstrating significantly higher noise resistance compared to traditional methods. Even with Gaussian noise levels of 10%, PINN predictions maintain relative L2 errors within 3%. Furthermore, PINNs show substantial potential for inverting key parameters such as the Manning roughness coefficient. PINNs offer an efficient and rapid approach to hydraulic predictions for long-distance water conveyance projects, aiding in the design and optimization of monitoring systems while minimizing the number of sensors, equipment, and costs.
How to cite: Feng, J., Li, Z., Mu, T., Li, X., Li, P., and Qian, S.: Physics-Informed Neural Networks for Hydraulic Monitoring in Water Diversion Projects with Limited Cross-Section Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11123, https://doi.org/10.5194/egusphere-egu25-11123, 2025.
Hydrodiversity, an emerging scientific concept, represents the variability and diversity of water-related systems, processes, and resources. It encompasses hydrological features such as flow regimes, water levels, and groundwater interactions, and interacts dynamically with geo- and biodiversity. Despite its significance in maintaining ecosystem resilience, hydrodiversity remains underexplored, with no standardized definition or comprehensive framework for measurement.
In river systems, particularly in transition zones (riparian, littoral, and hyporheic areas), hydrodiversity plays a vital role in regulating connectivity, enhancing water and sediment transport, and supporting biodiversity. These zones act as ecological hotspots, influenced by seasonal hydrological processes like snowmelt-driven flooding and ice cover. However, climate change and anthropogenic pressures, such as land use changes and water management, threaten hydrodiversity, leading to ecosystem degradation and biodiversity loss.
Advancements in geospatial technologies, including multispectral lidar, unmanned surface vehicles, and hydrological modelling, provide new opportunities to quantify hydrodiversity. These tools enable precise mapping of water pathways, sediment transport, and habitat dynamics, offering insights into the interactions between geo-, bio-, and hydrodiversity. Coupled surface-groundwater models further enhance the understanding of hydrodiversity’s temporal and spatial variability.
We aim to establish a working definition of hydrodiversity and develop methodologies for its quantification. By leveraging cutting-edge technologies and interdisciplinary approaches, we seek to bridge knowledge gaps, support sustainable river management, and align with EU initiatives such as the Biodiversity Strategy for 2030 and the Nature Restoration Law.
Hydrodiversity research has the potential to transform our understanding of ecosystem processes, providing critical tools for predicting and mitigating the impacts of geomorphological changes in river systems. It highlights the importance of integrating geo-, bio-, and hydrodiversity for the preservation and restoration of river systems in a changing world.
How to cite: Alho, P., Marttila, H., and Kankare, V.: Hydrodiversity: A Concept to Understanding and Preserving River System Resilience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12814, https://doi.org/10.5194/egusphere-egu25-12814, 2025.
Comprehensive, large sample hydrological datasets, such as CAMELS (Catchment Attributes and MEteorology for Large-sample Studies), have provided the basis for advances in many aspects of hydrological research in recent years. They can be utilized for several purposes, such as training data driven hydrological models, comparisons between regions dominated by different types of hydrological processes and testing of general validity of hydrological theories. The value of these datasets is in combining multitude of data sources into one, easily accessible and usable, harmonized and high quality package. We present CAMELS-FI, an extensive hydro-meteorological dataset for over 160 catchments, which adheres to the blueprint established by the previous CAMELS-datasets. It combines hydrological and meteorological time series with static catchment attributes in a format that enables comparisons between catchments within the dataset but also between different CAMELS-datasets.
CAMELS-FI provides up to 30 years of daily data, containing variables similar to previous CAMELS-datasets, such as streamflow observations, rainfall, temperature, evapotranspiration and snow. In addition, static attributes describing among others the catchment’s soil type, land use and topography are provided. The selected catchments are either not impacted or only marginally impacted by actively managed reservoirs, and have observations from at least five years. We also intend to compare the differences between the hydrological and meteorological signatures in different catchments, as well as compare the regional variability of soil, land cover and topography in order to give deeper insights on the properties of Finnish catchments.
We are planning to use CAMELS-FI to train and test a deep learning neural network to make river flow predictions in unagauged catchments more accurate in Finland.
How to cite: Seppä, I., Gonzales Inca, C., and Alho, P.: CAMELS-FI: Large scale catchment attributes and hydrometeorological time series in Finland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18296, https://doi.org/10.5194/egusphere-egu25-18296, 2025.
For a thorough understanding our water resources and enabling stakeholders to make informed decisions, it is vital to have a detailed and always up to date knowledge on all components of the water cycle with their interactions. To be able to achieve this, it is important to meet certain prerequisites:
- having an extensive monitoring of all components of the water cycle with a high degree of automation
- having the data stored centrally in a homogeneous way, thereby assuring immediate access from all stakeholders to exactly the same dataset
- being able to quickly and easily visualize and analyze all relevant data in an integrated way
However, current monitoring systems and workflows often do not meet these prerequisites. Commonly, data storage is decentralized and inconsistent while software solutions are fragmented to data management and analysis tools with rigid data exchange formats. As a result, experts and researchers are regularly forced to waste numerous hours on formatting and organizing dataset before being able to approach decision makers. In addition, time is wasted by the necessity of manual data exchange.
To address these challenges, we are developing ENOLA, a web-based water information system. ENOLA is built upon the mentioned prerequisites: It facilitates the monitoring of all components of the water cycle by enabling automated raw data processing and plausibility checks, centralizes data storage in a cloud solution and integrates analysis tools into a user centered interface. The service is designed for collaboratively working on one common dataset, no matter if directly from the field, the office or a conference. Yet, detailed control over access permissions accommodates security needs. It is possible to grant access to your data to externals, as well as including readily available public datasets into your analysis. Standart as well as highly-specialized visualization and analysis tools enable users to quickly assess the system state. Furthermore, data can also be directly be accessed in external software via an API.
Our solution provides a modern and efficient approach to water data monitoring, tailored to meet the needs of research institutes, authorities, water supply companies and environmental consultancies. With ENOLA, organizations can enhance water resource management and decision-making as everyone is immediately on the same page.
How to cite: Knöll, P., Strom, A., Confal, J. M., and Pagès, L.: ENOLA – facilitating decision making with a web based water information system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17976, https://doi.org/10.5194/egusphere-egu25-17976, 2025.
Urban areas face escalating risks from localised extreme precipitation events, characterised by unprecedented rainfall volumes and increasing frequency of short-duration, high-intensity storms, posing significant challenges to urban infrastructure and public safety.
The intricate nature of urban hydro-meteorology presents significant scientific and practical challenges due to the strongly nonlinear characteristics of urban dynamics, of the embedding geophysical fields, and their associated extreme variability across a wide range of spatial and temporal scales.
The Fresnel platform, an advanced urban hydro-meteorological observatory, merges conceptual models and field observations. It has been purposely set-up to provide the concerned communities with the necessary observation data thanks to the deployment of numerous high resolution sensors, that easily yield Big Data. Additionally, it offers appropriate software tools to analyse and simulate this data across a wide range of spatial and temporal scales.
Part of this platform, RadX SaaS provides a graphical interface for Multi-Hydro, an in-house fully distributed and physically-based hydrological model developed at École nationale des ponts et chaussées (ENPC). This interface allows users to seamlessly launch simulations, leveraging the resources of dedicated high-performance computing infrastructure. Multi-Hydro integrates four open-source software applications developed by the scientific community, simulating various aspects of the urban water cycle, including surface flow, sewer flow, ground flow, and precipitation. Directly from the RadX web interface, user can set up different scenarios on a given catchment, adjust land use parameters, and analyse their impact on discharge within the drainage system. Users can select either actual rainfall events recorded by the dual X-band weather radar located at ENPC campus East of Paris. Alternatively, for educational purposes, they can input their own custom synthetic rainfall data.
RadX also now provides real-time and historical data from the newly acquired Micro Rain Radar, part of the TARANIS (exTreme and multi-scAle RAiNdrop parIS observatory) platform. This radar profiler offers unique meteorological insights by providing Doppler spectra of hydrometeors. In the context of the France-Taiwan Ra2DW (Radar Rainfall Drop size distribution and Wind) project, this instrument will be used to evaluate and quantify the impact of wind drift effect and DSD variability on ground rainfall estimation. Eventually, this research work will enable updated radar rainfall estimates and associated uncertainties, which are then to be applied to the Multi-Hydro hydrological model.
Additional components can be integrated into RadX to meet specific requirements using visual tools and forecasting systems, including those from third parties. The platform continues to evolve through an iterative development process, driven by ongoing feedback and requests from both ENPC students, scientific researchers and industry professionals.
Authors acknowledge the France-Taiwan Ra2DW project, supported in France by the French National Research Agency (grant number ANR-23-CE01-0019-01).
How to cite: Drouen, G., Chikhaoui, E., Schertzer, D., and Tchiguirinskaia, I.: RadX: Urban Resilience SaaS, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8770, https://doi.org/10.5194/egusphere-egu25-8770, 2025.
Understanding spatiotemporal processes in boreal peatlands is essential to understanding ongoing changes across peatland ecosystem and functions in the northern conditions. We have implemented a new peatland digitalization research program in Puukkosuo fen peatland close to Oulanka Research Station, northeast Finland. Using the latest monitoring technologies, we plan systematically identifying key spatiotemporal processes in this peatland. Our mission is to obtain a detailed understanding of ecohydrological processes coupled with atmospheric gas exchange and peatland ecosystem dynamics on a detailed spatiotemporal scale. Specifically, our program at the Puukkosuo fen consists of: 1) continuous high-frequency in-situ DOC and ecohydrological monitoring of surface and groundwater, 2) extensive and continuous drone mapping to measure spatial variability in hydrological connectivity, vegetation, and snow accumulation and cover, 3) eddy-covariance and chamber measurements of greenhouse gas exchanges, 4) detailed 3D mapping of peat and underlying geology, and 5) 3D fully integrated modelling approaches allowing future projections. Using this integrated monitoring and modelling approach, we build digital platform for Puukkosuo peatland and pave the way to developing and testing Digital Twin approaches for peatland research and management needs.
How to cite: Marttila, H., Christensen, T. R., Ala-Aho, P., Kløve, B., and Paavola, R. and the Puukkosuo team: Digitalization of boreal peatland - digital solutions for identifying key spatiotemporal processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17116, https://doi.org/10.5194/egusphere-egu25-17116, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot A
EGU25-13200 | ECS | Posters virtual | VPS9
A NeuralFAO56 Python Package for data-driven Irrigation Demand CalculationTue, 29 Apr, 14:00–15:45 (CEST) | vPA.12
The accurate estimation of crop evapotranspiration (ETc), root zone soil moisture depletion, and irrigation demands is critical for optimizing water resource management and enhancing sustainability in precision agriculture. The FAO-56 model serves as a foundational tool for these predictions; however, its conventional workflow necessitates the manual acquisition of essential inputs such as climatic data and soil moisture from disparate external sources. This process can be time-intensive, cost-prohibitive, and susceptible to human error. Furthermore, the deterministic nature of FAO56 can lead to inaccuracies if reference evapotranspiration and crop coefficients are not meticulously estimated. This study introduces NeuralFAO56, a Python package that integrates advanced machine learning models and real-time data acquisition with the FAO-56 framework to automate and improve the estimation of ETc and irrigation demands. By leveraging application programming interfaces (APIs) to automatically collect real-time climatic data from meteorological stations and NASA’s Soil Moisture Active Passive (SMAP), NeuralFAO56 dynamically updates model inputs. The package incorporates a range of machine learning models, including Long Short-Term Memory (LSTM) and transformer architectures, to generate data-driven ETc estimations, thereby enhancing the accuracy and adaptability of irrigation predictions. NeuralFAO56 is designed with a modular architecture, enabling users to customize its functionalities for diverse agro-hydrological contexts. This tool provides a robust, user-friendly platform for researchers, water resource managers, and agricultural professionals, facilitating intelligent irrigation decision-making, improving water-use efficiency, and contributing to sustainable agricultural practices.
How to cite: Neupane, A. and Samadi, V.: A NeuralFAO56 Python Package for data-driven Irrigation Demand Calculation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13200, https://doi.org/10.5194/egusphere-egu25-13200, 2025.
