Delineation Of Flood-prone Areas Research Articles

Delineation of flood-prone areas using Digital Elevation Model (DEM) in Kuala Krai, Kelantan

Kelantan is one of the states that experience flood events annually including the Kuala Krai district. Flooding has a severe impact on roads, villages, farms, and people’s livelihoods. Early detection of flood-prone areas is essential in detecting the specific area. This study aimed to map a flood-prone area based on evaluation of terrain characteristics include elevation, aspect, slope, contour and hill shade of the Kuala Krai area. The raster data of DEM with 50 m resolution and hydrological data were evaluated to create a potential flood map. A flood-prone map is created by overlaying terrain features with flood criteria of a gentle slope with contour < 250 m and degree of slope less than 10, flat surface in aspect and consideration of hill shade factors. The map was also supported by vector data of rivers network that cross the Kuala Krai area. 56% of the Kuala Krai area facing a moderate risk of flood occurrence, while 41 % of the areas are in a dangerous state and are predicted to be at a high risk of flood. This can be concluded that the areas with low elevation, adjacent to rivers, are the places where actions need to be taken by the experts to overcome the disaster in the future.

Delineation of flood-prone areas using modified topographic index for a river basin

Abstract The modified topographic index () based on digital elevation models (DEMs) was employed to delineate flood-prone areas in Mahanadi basin, India. and flood inundation maps were compared to obtain the threshold () beyond which the area is assumed to be inundated by flood and the exponent of the . Scale dependence was also investigated to evaluate the sensitiveness of spatial resolution of the DEMs. DEMs of five resolutions, namely, ASTER global, SRTM, GMTED2010 (30 arc-seconds), GMTED 2010 (15 arc-seconds), and GMTED 2010 (7.5 arc-seconds), were used and ASTER global was preferred due to its low error compared to the remainder. Flood frequency analysis was conducted to obtain the relationship between flood-prone areas and flood magnitude. It was observed that (i) the exponent in the showed little variation, (ii) is reduced with reducing spatial resolution of the DEM, and (iii) error is also reduced as the DEMs' resolution is reduced.

A GIS Tool for Mapping Dam-Break Flood Hazards in Italy

Mapping the delineation of areas that are flooded due to water control infrastructure failure is a critical issue. Practical difficulties often present challenges to the accurate and effective analysis of dam-break hazard areas. Such studies are expensive, lengthy, and require large volumes of incoming data and refined technical skills. The creation of cost-efficient geospatial tools provides rapid and inexpensive estimates of instantaneous dam-break (due to structural failure) flooded areas that complement, but do not replace, the results of hydrodynamic simulations. The current study implements a Geographic Information System (GIS) based method that can provide useful information regarding the delineation of dam-break flood-prone areas in both data-scarce environments and transboundary regions, in the absence of detailed studies. Moreover, the proposed tool enables, without advanced technical skills, the analysis of a wide number of case studies that support the prioritization of interventions, or, in emergency situations, the simulation of numerous initial hypotheses (e.g., the modification of initial water level/volume in the case of limited dam functionality), without incurring high computational time. The proposed model is based on the commonly available data for masonry dams, i.e., dam geometry (e.g., reservoir capacity, dam height, and crest length), and a Digital Elevation Model. The model allows for rapid and cost-effective dam-break hazard mapping by evaluating three components: (i) the dam-failure discharge hydrograph, (ii) the propagation of the flood, and (iii) the delineation of flood-prone areas. The tool exhibited high accuracy and reliability in the identification of hypothetical dam-break flood-prone areas when compared to the results of traditional hydrodynamic approaches, as applied to a dam in Basilicata (Southern Italy). In particular, the over- and under-estimation rates of the proposed tool, for the San Giuliano dam, Basilicata, were evaluated by comparing its outputs with flood inundation maps that were obtained by two traditional methods whil using a one-dimensional and a two-dimensional propagation model, resulting in a specificity value of roughly 90%. These results confirm that most parts of the flood map were correctly classified as flooded by the proposed GIS model. A sensitivity value of over 75% confirms that several zones were also correctly identified as non-flooded. Moreover, the overall effectiveness and reliability of the proposed model were evaluated, for the Gleno Dam (located in the Central Italian Alps), by comparing the results of literature studies concerning the application of monodimensional numerical models and the extent of the flooded area reconstructed by the available historical information, obtaining an accuracy of around 94%. Finally, the computational efficiency of the proposed tool was tested on a demonstrative application of 250 Italian arch and gravity dams. The results, when carried out using a PC, Pentium Intel Core i5 Processor CPU 3.2 GHz, 8 GB RAM, required about 73 min, showing the potential of such a tool applied to dam-break flood mapping for a large number of dams.

Delineation of flood-prone areas using geomorphological approach in the Mekong River Basin

For many river basins, flood maps have not been developed, especially in developing countries. Generally, flood hazard maps are generated by a modeling process based on past flood events and require a large amount of input data for the basin. The present study aims to delineate flood-prone areas in the Mekong River Basin from geomorphological features by using linear binary classifiers and receiver operating characteristics (ROC) analysis. Our method investigates the performance of five single features and six composite indices associated with flood hazards. The results indicate that elevation difference to the nearest river network has the best performance among all single features and composite indices with predicted abilities success rate SR = 63.15% and modified success rate MSR = 78.21%. This study shows that the combination of linear binary classifiers and ROC analysis can be used to detect flood-prone areas not only in small basins but also in a large basin such as the Mekong River Basin. This approach is advantageous for basins that lack observation data because the method does not require a large amount of basin information (i.e. all features and indices are derived from a digital elevation model). The outcomes of this study can provide useful information for identifying areas that are prone to flooding.

Investigation on the use of geomorphic approaches for the delineation of flood prone areas

Three different geomorphic approaches to the identification of flood prone areas are investigated by means of a comparative analysis of the input parameters, the performances and the range of applicability. The selected algorithms are: the method proposed by Manfreda et al. (2011) based on a modified version of the Topographic Index (TIm); the linear binary classifier proposed by Degiorgis et al. (2012), which uses different geomorphic features related to the location of the site under exam with respect to the nearest hazard source; the hydro-geomorphic method by Nardi et al. (2006) simulating inundation flow depths along the river valley with the associated extent of surrounding inundated areas. Comparison has been carried out on two sub-catchments of the Tiber River in Central Italy. The simulated flooded areas, obtained using the selected three methods, are evaluated using as a reference the Tiber River Basin Authority standard flood maps. The aim of the research is to deepen our understanding on the potential of geomorphic algorithms and to define new strategies for prompt hydraulic risk mapping and preliminary flood hazard graduation. This is of foremost importance when detailed hydrologic and hydraulic studies are not available, e.g., over large regions and for ungauged basins.

Suggested Procedures for Delineation of Flood-Prone Areas by the Use of Natural Features

Suggested Procedures for Delineation of Flood-Prone Areas by the Use of Natural Features