Surveys in Geophysics

, Volume 37, Issue 5, pp 977–1034 | Cite as

Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges

  • Stefania GrimaldiEmail author
  • Yuan Li
  • Valentijn R. N. Pauwels
  • Jeffrey P. Walker


Accurate, precise and timely forecasts of flood wave arrival time, depth and velocity at each point of the floodplain are essential to reduce damage and save lives. Current computational capabilities support hydraulic models of increasing complexity over extended catchments. Yet a number of sources of uncertainty (e.g., input and boundary conditions, implementation data) may hinder the delivery of accurate predictions. Field gauging data of water levels and discharge have traditionally been used for hydraulic model calibration, validation and real-time constraint. However, the discrete spatial distribution of field data impedes the testing of the model skill at the two-dimensional scale. The increasing availability of spatially distributed remote sensing (RS) observations of flood extent and water level offers the opportunity for a comprehensive analysis of the predictive capability of hydraulic models. The adequate use of the large amount of information offered by RS observations triggers a series of challenging questions on the resolution, accuracy and frequency of acquisition of RS observations; on RS data processing algorithms; and on calibration, validation and data assimilation protocols. This paper presents a review of the availability of RS observations of flood extent and levels, and their use for calibration, validation and real-time constraint of hydraulic flood forecasting models. A number of conclusions and recommendations for future research are drawn with the aim of harmonising the pace of technological developments and their applications.


Hydraulic modelling of floods Remote sensing Flood extent and level Data assimilation Real-time forecast 



This study is financially supported by the Bushfires and Natural Hazards CRC project—Improving flood forecast skill using remote sensing data. Valentijn Pauwels is funded by ARC Future Fellow grant FT130100545. The authors would like to acknowledge the Australian Bureau of Meteorology and Geoscience Australia for their valuable comments and support.


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© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Civil EngineeringMonash UniversityClaytonAustralia

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