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<p>The monitoring and modeling of riverine floods have been covered extensively in the scientific literature with a substantial number of scientific contributions related to calibration/validation of hydraulic and hydrological models and assimilation of Earth Observation (EO) data into them. These models, when used for flood forecasting purposes, rely heavily on ground-based hydrological networks along with numerical weather models which, particularly in data-scarce regions, are often challenged by data sparsity. In these situations, EO data offer a viable solution to enhance the skill of these flood forecasting systems by providing global-scale observations of key hydrological variables such as precipitation, soil moisture, river discharge, water levels, and flood extent. This manuscript reviews and discusses the capability of these EO data in enhancing flood forecasting systems, by analyzing their accuracy, lead time, and reliability, while at the same time highlighting key challenges such as data latency, spatial–temporal resolution trade-offs, and model assimilation constraints. By leveraging recent advancements in remote sensing, data assimilation techniques, and artificial intelligence, EO-based flood forecasting has the potential to bridge existing observational gaps, particularly in vulnerable regions. The paper also outlines future research directions and technological developments needed to maximize the impact of satellite data in operational flood forecasting systems.</p>

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The Potential of EO Data for Enhanced Flood Monitoring and Forecasting: A Consortium Assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月15日

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  • Angelica Tarpanelli,
  • Christian Massari,
  • Beatriz Revilla-Romero,
  • Mohammad J. Tourian,
  • Peyman Saemian,
  • Omid Elmi,
  • Daniel Scherer,
  • Vanessa Pedinotti,
  • Cecile Kittel,
  • Jérôme Benveniste,
  • Peter Bauer-Gottwein,
  • Luca Ciabatta,
  • Connor Chewning,
  • Silvia Barbetta,
  • Paolo Filippucci,
  • Èlia Cantoni,
  • Denise Dettmering,
  • Jafet Andersson,
  • Laetitia Gal,
  • David Gustafsson,
  • Yeshewatesfa Hundecha,
  • Gilles Larnicol,
  • Kevin Larnier,
  • Karina Nielsen,
  • Adrien Paris,
  • Malak Sadki,
  • Christian Schwatke,
  • Paolo Tamagnone,
  • Artemis Vrettou,
  • Karim Douch,
  • Espen Volden,
  • Guy Schumann

摘要

The monitoring and modeling of riverine floods have been covered extensively in the scientific literature with a substantial number of scientific contributions related to calibration/validation of hydraulic and hydrological models and assimilation of Earth Observation (EO) data into them. These models, when used for flood forecasting purposes, rely heavily on ground-based hydrological networks along with numerical weather models which, particularly in data-scarce regions, are often challenged by data sparsity. In these situations, EO data offer a viable solution to enhance the skill of these flood forecasting systems by providing global-scale observations of key hydrological variables such as precipitation, soil moisture, river discharge, water levels, and flood extent. This manuscript reviews and discusses the capability of these EO data in enhancing flood forecasting systems, by analyzing their accuracy, lead time, and reliability, while at the same time highlighting key challenges such as data latency, spatial–temporal resolution trade-offs, and model assimilation constraints. By leveraging recent advancements in remote sensing, data assimilation techniques, and artificial intelligence, EO-based flood forecasting has the potential to bridge existing observational gaps, particularly in vulnerable regions. The paper also outlines future research directions and technological developments needed to maximize the impact of satellite data in operational flood forecasting systems.