Patterns of remotely sensed floodplain saturation and its use in runoff predictions

Abstract

Abstract:Principal components analysis (PCA) is applied to a time series of European Remote Sensing (ERS) synthetic aperture radar (SAR) scenes of the Alzette River floodplain (Grand-Duchy of Luxembourg). These images cover markedly different hydrological conditions during several winter seasons in order to enable the examination of the decrease of the radar backscattering signal during drying-up phases following important flood events. At the floodplain scale, with homogeneous land use and constant topography, the first principal components (PCs) are mainly dominated by the variance related to the changing areas. The PCs are thus mainly controlled by subsurface and surface water dynamics. The field observations of a densely equipped piezometric network in the floodplain are used to calculate a mean soil saturation index (SSI) continuously. A classification scheme, based on the PCs and k-means algorithm, leads to the segmentation of the floodplain into several hydrological behaviour classes with distinctive responses versus changing moisture conditions. To validate this classification method with ground-based estimations, the relation between the mean backscattering values of microplots within each PCA-derived hydrological class and the water table measurements, expressed by means of the SSI, is evaluated. Results show that each class of microplots is characterized by the slope of the 'backscattering-SSI' function and by the SSI threshold value at which groundwater resurgence appears. The water ponding implies very low signal return due to the specular backscattering effect on the water surface. Based on established relationships between measured initial water table depths, runoff coefficients and rainfall-induced water table rises, these results are used to discuss the potential of SAR-derived information in flood management applications.

Soil loss, water ponding and sediment deposition variations as a consequence of rainfall intensity and land use: a multi‐criteria analysis

“…However, these models are inaccurate when applied over wide regions and they aim at evaluating soil loss quantitatively, neglecting both the accumulating sediments downslope (Boardman, 2006) and water ponding. Water ponding is often modeled through the use of infi ltration modeling techniques, such as the GreenAmpt equation (Liu et al, 2008), or it is directly observed and mapped with remote sensing techniques (Matgen et al, 2006). Models that actually predict sediment yield downslope are very rare (see the discussion in Nearing et al, 2000 andCrosson, 2000a,b).…”

On the value of surface saturated area dynamics mapped with thermal infrared imagery for modeling the hillslope-riparian-stream continuum

“…Moreover, most of the existing studies have focused on using conceptual models and analyzing the performance of diverse topographic and wetness indices as predictive tools [e.g., Franks et al ., ; Güntner et al ., ; Grabs et al ., ; Birkel et al ., ; Ali et al ., ] and not on including surface saturation patterns in multicriterion validation [cf., Doppler et al ., ]. One reason for this is that current research still focuses on finding suitable methods to map surface saturation and its dynamics based on direct assessment according to the squishy boot method [e.g., Rinderer et al ., ], hydrometric data [e.g., Kulasova et al ., ], vegetation patterns [e.g., Kulasova et al ., ], soil morphology [e.g., Doppler et al ., ], or remote sensing data [e.g., Matgen et al ., ].…”

Analysis of flood pulse dynamics in the lower basin of the San Pedro River (northwestern Mexico) using remote sensing

“…To overcome the technical limitations of RS, synthetic aperture radar (SAR) is currently used (Matgen et al, 2006;Martinez & Le Toan, 2007;Rebelo et al, 2012). However, the cost and availability of SAR data cannot compete yet with optical multispectral imagery, which additionally integrates the largest time series for landscape change analysis (Frazier & Page, 2009;Qi et al, 2009;Chormanski et al, 2011;Pricope, 2012).…”