Abstract
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.
Highlights
Floods are among the most destructive natural disasters, threatening lives as well as properties.Generally, fluvial floods are formed in the following series of processes: runoff generation, runoff concentration, streamflow propagation and floodplain inundation [1,2]
There have been attempts to integrate other types of remote sensing products into forecasting models, e.g., remotely sensed terrestrial water storage (TWS) [16,23], leaf area index (LAI) and/or evapotranspiration (ET) [18]; they have not been used for operational purposes
Despite the high efficiency of data-based models, remote sensing products such as soil moisture often cannot be directly used as a constraint, as there may be no comparable variables in the model
Summary
Floods are among the most destructive natural disasters, threatening lives as well as properties. Fluvial floods are formed in the following series of processes: runoff generation, runoff concentration, streamflow propagation and floodplain inundation [1,2]. Most operational fluvial flood forecasting systems only simulate the first three processes, to provide water level and streamflow forecasts [3]. Rainfall-runoff models, which simulate the rainfall infiltration and runoff generation followed by streamflow concentration and propagation processes, form the core of a rainfall-driven flood forecasting system [12]. These models are all driven by data to different extents [8].
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