Soil moisture remote-sensing applications for identification of flood-prone areas along transport infrastructure

Abstract

The expected increase in precipitation and temperature in Scandinavia, and especially short-time heavy precipitation, will increase the frequency of flooding. Urban areas are the most vulnerable, and specifically, the road infrastructure. The accumulation of large volumes of water and sediments on road-stream intersections gets severe consequences for the road drainage structures. This study integrates the spatial and temporal soil moisture properties into the research about flood prediction methods by a case study of two areas in Sweden, Västra Götaland and Värmland, which was affected by severe flooding in August 2014. Soil moisture data are derived from remote-sensing techniques, with a focus on the soil moisture-specific satellites ASCAT and SMOS. Furthermore, several physical catchments descriptors (PCDs) are analyzed and the result shows that larger slopes and drainage density, in general, mean a higher risk of flooding. The precipitation is the same; however, it can be concluded that more precipitation in most cases gives higher soil moisture values. The lack, or the dimensioning, of road drainage structures seems to have a large impact on the flood risk as more sediment and water can be accumulated at the road-stream intersection. The results show that the method implementing soil moisture satellite data is promising for improving the reliability of flooding.

Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran

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GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

“…The use and processing of numerous spatial data, satellite images, and aerial photographs in geographic information systems (GIS) for the selection of appropriate check dam sites over large watershed scales are computationally costly [25,26]. Straightforward site identification for a more efficient management support may be facilitated by an analytic hierarchy process (AHP) in GIS [27,28] based on some geomorphometric and topo-hydrological watershed factors [29,30] that, to our best knowledge, yet remain to be considered for this purpose.…”

An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping

“…Because of lack of reasonable and trustworthy techniques, most spatial modeling studies reported in the literature use a simple random method to select absence samples in different fields, for example, groundwater potential [3,5,10,[13][14][15][16], landslide susceptibility [1,[17][18][19][20][21], gully erosion susceptibility [22][23][24][25], land subsidence susceptibility [26][27][28][29][30][31], and flood susceptibility [18,[32][33][34][35][36][37]. However, the random sampling method has some drawbacks.…”