On the Use of Satellite Remote Sensing to Detect Floods and Droughts at Large Scales

Abstract

Hydrological extremes, in particular floods and droughts, impact all regions across planet Earth. They are mainly controlled by the temporal evolution of key hydrological variables like precipitation, evaporation, soil moisture, groundwater storage, surface water storage and discharge. Precise knowledge of the spatial and temporal evolution of these variables at the scale of river basins is essential to better understand and forecast floods and droughts. In this article, we present recent advances on the capability of Earth observation (EO) satellites to provide global monitoring of floods and droughts. The local scale monitoring of these events which is traditionally done using high-resolution optical or SAR (synthetic aperture radar) EO and in situ data will not be addressed. We discuss the applications of moderate-to low-spatial-resolution space-based observations, e.g., satellite gravimetry (GRACE and GRACE-FO), passive microwaves (i.e. SMOS) and satellite altimetry (i.e. the JASON series and the Copernicus Sentinel missions), with supporting examples. We examine the benefits and drawbacks of integrating these EO datasets to better monitor and understand the processes at work and eventually to help in early warning and management of flood and drought events. Their main advantage is their large monitoring scale that provides a "big picture" or synoptic view of the event that cannot be achieved with often sparse in situ measurements. Finally, we present upcoming and future EO missions related to this topic including the SWOT mission. Keywords Floods • Droughts • Large scale • Terrestrial water storage • GRACE • SMOS • Satellite altimetry • SWOT Water Storage on the Continents: General RemarksFreshwater represents less than 3% of the total amount of water on Earth. On land, freshwater is stored in various reservoirs such as ice caps, snow, glaciers, groundwater, soil moisture (in the unsaturated soil and root zone, i.e. in the upper few metres of the soil (e.g., Hillel 1998)) and surface water bodies (rivers, lakes, man-made reservoirs, wetlands and inundated areas) (Fig. 1). These different storage compartments are in direct interactions with the atmosphere. For example, in the tropical Pacific, long-term droughts and floods are under the influence of the El Niño-Southern Oscillation (ENSO) events (e.g., Ward et al. 2014; Fok et al. 2018 and references therein).

Remote Sensing Methods for Flood Prediction: A Review

“…Airborne SAR was studied for real-time flood area observation as well. Mason et al [40] studied a method for selecting a subset automatically and in near real-time, which would allow the SAR water levels to be used in a forecasting model. Distributed water levels may be estimated indirectly along the flood extents in SAR images by intersecting the extents with the floodplain topography.…”

Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

“…However, the distribution of hydrological stations is sparse, and the number of observation stations worldwide has decreased continuously due to considerable financial and material costs [6,7]. Considering the unique advantages that covering inaccessible regions and ones not affected by extreme hydrological events, the remote sensing technologies provide a preferable tool to evaluate river discharge, particularly in poorly gauged areas [8]. In this context, multiple remote sensing platforms have been used to return adequate streamflow observations.…”

Synergistic Calibration of a Hydrological Model Using Discharge and Remotely Sensed Soil Moisture in the Paraná River Basin

“…More recently, the first missions dedicated to estimating global soil moisture from L-Band observations were launched: the Soil Moisture Ocean Salinity Mission (SMOS) [8], launched in 2009, which provides soil moisture information every three days with a spatial resolution of 40 km; and the Soil Moisture Active and Passive (SMAP) [9] mission, launched in 2015. These advances have contributed to the development of global multidecadal datasets of soil moisture [10], fostering the hydrological monitoring of agricultural lands [11] and extreme events such as droughts and floods [12,13], as well as offering the potential to better inform and constrain hydrological models [14], particularly for explicitly accounting soil moisture models, where the soil water balance is the core of the model structure.…”