Tarpanelli et al. (2026) The Potential of EO Data for Enhanced Flood Monitoring and Forecasting: A Consortium Assessment

Identification

• Journal: Surveys in Geophysics
• Year: 2026
• Date: 2026-02-15
• Authors: Angelica Tarpanelli, Christian Massari, Beatriz Revilla-Romero, Mohammad J. Tourian, Peyman Saemian, Omid Elmi, Daniel Scherer, Vanessa Pedinotti, Cécile Marie Margaretha Kittel, Jérôme Benveniste, Peter Bauer‐Gottwein, Luca Ciabatta, Connor Chewning, Silvia Barbetta, Paolo Filippucci, Èlia Cantoni, Denise Dettmering, Jafet Andersson, Laëtitia Gal, David Gustafsson, Yeshewatesfa Hundecha, Gilles Larnicol, Kévin Larnier, Karina Nielsen, Adrien Paris, Malak Sadki, Christian Schwatke, Paolo Tamagnone, Artemis Vrettou, Karim Douch, Espen Volden, Guy J.-P. Schumann
• DOI: 10.1007/s10712-026-09935-w

Research Groups

• Research Institute for the Geo‑Hydrological Protection, National Research Council (CNR), Perugia, Italy
• GMV, Remote Sensing and Geospatial Analytics, Madrid, Spain
• Institute of Geodesy, University of Stuttgart, Stuttgart, Germany
• Deutsches Geodätisches Forschungsinstitut der Technischen Universität München (DGFI-TUM), Munich, Germany
• Magellium, Ramonville‑Saint‑Agne, France
• DHI-GRAS, Hørsholm, Denmark
• COSPAR, C/O CNES, Paris, France
• DTU Space, The Technical University of Denmark, Kgs. Lyngby, Denmark
• Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
• SMHI Sveriges Meteorologiska Och Hydrologiska Institut, Norrköping, Sweden
• Hydro Matters, Le Faget, France
• RSS-Hydro, Kayl, Luxembourg
• Serco Italia SPA C/O ESRIN, Frascati, Italy
• European Space Agency-ESRIN, Frascati, Italy

Short Summary

This paper provides a consortium assessment reviewing the capabilities of Earth Observation (EO) data to enhance riverine flood monitoring and forecasting systems, evaluating their accuracy, lead time, and reliability while addressing key challenges and outlining future advancements. It concludes that despite significant scientific progress, EO data remain largely under-exploited in operational flood forecasting, particularly in data-scarce regions.

Objective

• To review and discuss the capability of Earth Observation (EO) data in enhancing riverine flood forecasting systems, analyzing their accuracy, lead time, and reliability.
• To highlight key challenges such as data latency, spatial–temporal resolution trade-offs, and model assimilation constraints.
• To outline future research directions and technological developments needed to maximize the impact of satellite data in operational flood forecasting systems, especially in data-scarce regions.

Study Configuration

• Spatial Scale: Global, continental, basin, regional, and local scales (e.g., river networks, small water bodies, urban areas).
• Temporal Scale: Short- to medium-term predictions, real-time, near-real-time (NRT), daily, sub-daily, monthly, seasonal, and long-term (decadal) observations.

Methodology and Data

• Models used:
  • Hydrological and Hydraulic models (1D, 2D)
  • Rainfall-runoff models
  • Land Surface Models (LSMs)
  • Numerical Weather Prediction (NWP) models
  • Data-driven models: Statistical models, Machine Learning (ML), Deep Learning (e.g., Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN))
  • Physical process-driven models (lumped-conceptual, fully distributed physically based)
  • Hybrid models (integrating dynamical and statistical approaches)
  • Data assimilation techniques (e.g., Kalman filter, sequential filtering, smoothing, particle batch smoother, model conditional processor (MCP))
  • Operational systems: GloFAS, EFAS, HYPE (WWH), Google's Flood Hub, Iber-PEST
• Data sources:
  • Satellite/Earth Observation (EO):
    • Precipitation: Integrated Multi-satellite Retrievals for GPM (IMERG), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Soil Moisture-to-Rain-ASCAT (SM2RAIN_ASCAT), Tropical Rainfall Measuring Mission (TRMM), Meteosat.
    • Snow: Copernicus Land Monitoring Service (Fractional Snow Cover, Snow Cover Extent, Snow Water Equivalent), Sentinel-2, Sentinel-1 (C-band radar).
    • Soil Moisture: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced SCATterometer (ASCAT).
    • Total Water Storage (TWS): Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GRACE-FO), Gravity field and steady-state Ocean Circulation Explorer (GOCE).
    • Surface Water Height (Altimetry): TOPEX/Poseidon, Jason series (Jason-1, 2, 3, Sentinel-6 Michael Freilich), ERS-1/2, ENVISAT, SARAL/AltiKa, CryoSat-2, Sentinel-3A/B, Surface Water and Ocean Topography (SWOT), SMall Altimetry Satellites for Hydrology (SMASH), HydroGNSS (GNSS reflectometry, CYGNSS).
    • Surface Water Extent (Imagery/SAR): MODIS, Landsat, Sentinel-2, PlanetScope, Synthetic Aperture Radar (SAR) (TerraSAR-X, COSMO-SkyMed, Sentinel-1, ALOS-PALSAR), Visible Infrared Imaging Radiometer Suite (VIIRS).
    • Derived products/databases: Hydroweb.Next, Copernicus Global Land Monitoring Service, Database for Hydrological Time Series of Inland Waters (DAHITI), HydroSat, Global River Radar Altimeter Time Series (GRRATS), Bluedot Observatory, Water Area Tracking from Satellite imagery (WaTSat), SRTM Water Body Data (SWBD), Global Inundation Extent from Multiple Satellites (GIEMS), Global Surface Water Dataset (GSW), Global WaterPack, FloodSENS.
  • In-situ/Ground-based: Hydrological networks, rain gauges, river gauges, discharge records, meteorological variables, ground truth data.
  • Reanalysis: ERA5.
  • Auxiliary data: Digital Elevation Models (DEM), topography, land cover classification, Height Above Nearest Drainage (HAND), Slope of Nearest Drainage (SND).

Main Results

• Earth Observation (EO) data significantly improve flood forecasting by providing global observations of key hydrological variables (precipitation, soil moisture, river discharge, water levels, flood extent), particularly in data-scarce regions.
• Satellite data assimilation enhances model accuracy and reduces predictive uncertainties, especially when integrated into hydrological and hydraulic models.
• Key challenges for operational integration include data latency (ranging from hours to months), spatial-temporal resolution trade-offs, and complexities in defining observation operators for data assimilation due to representativity issues.
• Future advancements from new satellite missions (e.g., SWOT, NISAR, SMASH, HydroGNSS, MTG, CubeSats) and advanced AI/ML techniques (e.g., LSTMs, CNNs, hybrid models) hold significant potential to bridge observational gaps, extend lead times, and improve forecast reliability, especially for extreme events and ungauged basins.
• Despite scientific literature demonstrating improved performance, the use of satellite data in operational flood forecasting systems remains largely under-exploited due to technical and institutional barriers.
• EO data contribute to flood forecasting by serving as: (1) forcing input data, (2) sources for setting initial conditions (e.g., soil moisture, snow cover, reservoir levels), (3) data for model calibration, and (4) observations within data assimilation frameworks.

Contributions

• Provides a comprehensive, expert-driven consortium assessment of the strengths, limitations, and future potential of current and upcoming Earth Observation (EO) missions and methodologies for riverine flood forecasting.
• Systematically reviews the role of various EO-derived hydrological variables (precipitation, snow, soil moisture, total water storage, surface water height, surface water extent, river discharge, flood extent) in enhancing flood prediction.
• Identifies critical technical and institutional barriers hindering the operational uptake of EO data in flood forecasting systems, particularly in data-scarce regions.
• Outlines a clear roadmap for future advancements, emphasizing the integration of new satellite missions, advanced Machine Learning and AI approaches, and hybrid modeling techniques to improve forecast accuracy, lead time, and reliability.
• Highlights the importance of multi-sensor integration and data fusion to overcome spatial and temporal resolution limitations and integrate complementary observables for comprehensive flood monitoring.

Funding

• EO4FLOOD Project, European Space Agency, grant number ESA 4000145540/24/I-EB.
• Open access funding provided by Consiglio Nazionale Delle Ricerche (CNR) within the CRUI-CARE Agreement.

Citation

@article{Tarpanelli2026Potential,
  author = {Tarpanelli, Angelica and Massari, Christian and Revilla-Romero, Beatriz and Tourian, Mohammad J. and Saemian, Peyman and Elmi, Omid and Scherer, Daniel and Pedinotti, Vanessa and Kittel, Cécile Marie Margaretha and Benveniste, Jérôme and Bauer‐Gottwein, Peter and Ciabatta, Luca and Chewning, Connor and Barbetta, Silvia and Filippucci, Paolo and Cantoni, Èlia and Dettmering, Denise and Andersson, Jafet and Gal, Laëtitia and Gustafsson, David and Hundecha, Yeshewatesfa and Larnicol, Gilles and Larnier, Kévin and Nielsen, Karina and Paris, Adrien and Sadki, Malak and Schwatke, Christian and Tamagnone, Paolo and Vrettou, Artemis and Douch, Karim and Volden, Espen and Schumann, Guy J.-P.},
  title = {The Potential of EO Data for Enhanced Flood Monitoring and Forecasting: A Consortium Assessment},
  journal = {Surveys in Geophysics},
  year = {2026},
  doi = {10.1007/s10712-026-09935-w},
  url = {https://doi.org/10.1007/s10712-026-09935-w}
}