Application of remote sensing data to constrain operational rainfall-driven flood forecasting: A review

Yuan Li, Stefania Grimaldi, Jeffrey Walker, Valentijn Rachel Noel Pauwels

Research output: Contribution to journalArticleResearchpeer-review

70 Citations (Scopus)


Fluvial flooding is one of the most catastrophic natural disasters threatening people’s lives and possessions. Flood forecasting systems, which simulate runoff generation and propagation processes, provide information to support flood warning delivery and emergency response. The forecasting models need to be driven by input data and further constrained by historical and real-time
observations using batch calibration and/or data assimilation techniques so as to produce relatively accurate and reliable flow forecasts. Traditionally, flood forecasting models are forced, calibrated and updated using in-situ measurements, e.g., gauged precipitation and discharge. The rapid
development of hydrologic remote sensing offers a potential to provide additional/alternative forcing and constraint to facilitate timely and reliable forecasts. This has brought increasing interest to exploring the use of remote sensing data for flood forecasting. This paper reviews the recent advances
on integration of remotely sensed precipitation and soil moisture with rainfall-runoff models for rainfall-driven flood forecasting. Scientific and operational challenges on the effective and optimal integration of remote sensing data into forecasting models are discussed.
Original languageEnglish
Pages (from-to)1 - 29
Number of pages29
JournalRemote Sensing
Issue number6
Publication statusPublished - 28 May 2016


  • Remote sensing
  • Flood forecasting
  • Soil moisture
  • Precipitation
  • Batch calibration
  • Data assimilation

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