Assessment and mapping of flood potential in the Slănic catchment in Romania

Abstract

Flood risk assessment is an important component of risk management. Given this context, this paper aims to identify and map areas with high potential for flash-floods and flooding occurrence, at different spatial scales (from catchment to local scale), in order to estimate the flood/flooding vulnerability. The paper is based on three main methods, which were applied in the Slȃnic River catchment (427 km 2), located in the external curvature region of the Romanian Carpathians: (i) statistical analyses; (ii) determination and mapping of some indices to assess the flash-flood and flooding potential (FFPI and respectively FPI) and (iii) hydraulic modelling. The data used mainly include hydrological statistics (maximum monthly and annual discharges, flood-related data) and spatial data on catchment geographical characteristics (hypsometry, geology, soils, land use) obtained or derived from various sources (maps, aerial images, digital databases, field measurements) which were integrated into the GIS environment. The aforementioned methods helped to (i) highlight specificities of floods in the Slȃnic catchment (magnitude, frequency, flood waves characteristics); (ii) identify areas with high potential for flash-floods and flooding at the catchment spatial scale; (iii) assess the structural vulnerability in the Cernȃteşti village, by simulating flood-prone areas for flood peaks with exceedance probability of 1%, 5% and 10%. The results could lead to a better knowledge and understanding of flood characteristics in the study area, in order to mitigate the flood risk through a more effective management, both at the catchment scale, as well as local scale (in the Cernȃteşti village).

Identification of the Potential Risk Areas Regarding the Floods Occurrence Within Small Mountain Catchments

“…The land-usehas been extracted from Corine Land Cover database of 2012 (European Environmental Agency) in vector format. The evaluation rates were given after rasterization depending on the impact of each class on the flood occurrence events [9]. Thus, the areas dominated by forests (58% of the Suceviţa catchment surface) received the evaluation rate of 1, while the built-up space (11.12%), the watercourses (0.56%) and the industrial or commercial units(0.13%) received the maximum evaluation rate.…”

Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques

“…The highest share (58%) of the study area corresponds to the plan curvature values from 0.1 to 0.5 (Figure 2e). Modified Fournier Index (MFI), as another flash-flood conditioning factor, describes the spatial variability of rainfall intensity within a specific region [37,49]. The following Equation (1) can be used to calculate the MFI values [50]:…”

“…This morphometric flash-flood predictor was derived from the DEM (Figure 3a). The following classes were established for slope angle: [49]. Although the slope class between 15 • and 25 • contains almost 40% of the torrential areas, the highest density of torrential pixels was recorded in the class higher than 25 • .…”

Accessing the capability of TRMM 3B42 V7 to simulate streamflow during extreme rain events: Case study for a Himalayan River Basin

“…Therefore, hydrological simulation of these extreme events is critical to understand their severity by suitable structural and non-structural measures (Zaharia et al 2015).…”