Flash-flood Areas Research Articles

Application of SWAT Model for Assessment of Surface Runoff in Flash Flood Areas

In addition to changes in the amount of rain, changes in land use upstream are considered a factor that directly affects the maximum runoff flow in a basin, especially in areas that have experienced floods and flash floods. This research article presents the application of the SWAT model to assess runoff in areas that have experienced flash floods, in order to analyze the proportion of land use change to the maximum runoff. Study areas that experienced flash floods were in three basins in Thailand (Lam Saphung Basin, Phrom River Basin, and Chern River Basin Part 1, which is a sub-basin of the Nam Chi River Basin). This study analyzed two main factors that influenced runoff in the river basin by considering two simulation situations: (1) changes in land use affecting runoff assessed by considering land use maps in 2006, 2008, 2010, 2015, 2017, 2019, and 2021 when using rainfall data only in the year 2021 for all cases and (2) changes in the amount of rainfall influencing runoff by considering the rainfall records in 2006, 2008, 2010, 2015, 2017, 2019, and 2021 when using the land use data only in the year 2021 for all cases. The results of the study found that the SWAT model can be effectively applied to estimate annual runoff in areas that have experienced flash floods under eight parameters with R2 values of 0.74, 0.82, and 0.74 for the Lam Saphung River Basin, Phrom River Basin, and Chern River Basin Part 1, respectively. In addition, it was found that the proportion of land use changes that involved changes from forested areas to residential areas was the greatest from 2008 to 2010 in the Phrom River Basin and Chern River Basin Part 1. This resulted in an increase in the maximum runoff amounts of 77.78% and 46.87%, respectively. When land use was constant, it was found that the rainfall in 2010, which was the highest, also had the greatest impact on the runoff in all three areas.

POTENSI BANJIR BANDANG MENGGUNAKAN ANALISIS MORFOMETRI DI DAERAH ALIRAN SUNGAI KLAGISON KOTA SORONG

Flash flood is water level increases rapidly in the river beyond flood level and occurs due to extreme weather. It's happened quickly without warning. The Klagison Watershed in Sorong City has grown rapidly and often had flooded. Settlements dominate almost on watershed areas and there are many hills that are deforested due to mining activities upstream. This study aims to examine the morphometric conditions and to analyze the spatial distribution of the potential flash flood in the Klagison Watershed of Sorong City based on morphometric analysis. GIS is used to obtain morphometric parameters such as linear, relief and aerial parameters. The correlation coefficient between each morphometric parameter was calculated using Pearson Correlation. Determination of potential flash flood areas using the priority zone formula. The result shows that the Klagison watershed has high erosion potential and large runoff based on morphometric analysis. This can be seen in the high value of the texture ratio (T) and low Constant channel maintenance (C) so that the runoff volume is also large. The Lof value in all sub-watersheds is small, which means that surface runoff will quickly to the channel. The flash flood potential in the Klagison watershed is very low (Sub-watershed 1 and Sub-watershed 5), moderate (Sub-watershed 3 and Sub-watershed 4), high (Sub-watershed 2) and very high (Sub-watershed 6 and Sub-watershed 7). It means that Sub-watershed 2, Sub-watershed 6 and Sub-watershed 7 need management priorities related to disaster mitigation.
 Keywords: flash flood; morphometry; watershed

Flash Flood Susceptibility Mapping at Andungbiru Watershed, East Java Using AHP-Information Weighted Method

Flash floods are among the most frequent natural disasters caused by heavy rain associated with a severe thunderstorm, which leads to social and economic losses in infrastructure and agriculture. Therefore, this research aims to map flash flood potential susceptibility (FFPS) in the Pekalen watershed, using Geographic Information System (GIS) technology and statistical analysis to reduce the risk of flooding. The opinion and experience of an expert on the weight assessment method were carried out using the Analytical Hierarchy Process (AHP). Furthermore, the probability statistical methods and GIS were used in flash flood areas in the Pekalen watershed in Andungbiru, Probolinggo village. This study was carried out using geomorphological factors, namely elevation, slope, stream power index, and topographic wetness index, with a resolution of 30 m. Thematic map scale of the land use, river density, distance to the river, rainfall, and geology is in the ratio of is in a ratio of 1:25.000. Imagery processing was carried out using Landsat 8 30 m x 30 m resolution imagery, such as the Normalized Difference Vegetation Index. The result showed that the model map of FFPS obtained low 8%, low 23%, moderate 27%, moderate to high 26%, high 13%, and very high 2% index values. The next stage of modeling analysis led to validation using statistic receiver operating Characteristic Curve (ROC) of area Under Curve (AUC) with a value of 90.15. In conclusion, the factors that significantly trigger flash floods are distance to the river, land use, and slope.
 
 Keywords: AHP-weighted; information content; FFSP; GIS; Geomorphology
 Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember
 
 This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Geohazard characterization using remote sensing to model flash floods of the southeast Sinai, Egypt

Flash floods have led to disruptions of human activities and the destruction of properties particularly in the Nuweiba region of southeast Sinai. Despite the arid nature of Sinai, flash floods still pose a great hazard to the region. Using remote sensing characterization, geohazard models were developed to identify flash flood areas; delineate and discriminate morphological features, active channels areas, and soil physiography with a view to categorize risk areas exposed to flash floods hazards in a hazard map; and proffer measures for mitigation. The characterization was achieved using a workflow model developed from spatial data sets of the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) and the red-green-blue (RGB) composites from Landsat bands 7 Enhanced Thematic Mapper (ETM+). The data were used to investigate and assess flash flood areas for morphological attributes, watershed basin characterization, and generation of false color composite from the Landsat 7 ETM+ 7, 4, and 2 RGB bands, and the principal component analysis band from a composite of 742 RGB. Multivariate analysis of generated raster layer attributes provided enhancements and attribute discrimination for delineating areas of active flood channels, upslope zones, and soil physiography discrimination from their spectral reflectance. The results are presented in an integrated approach of remote sensing with geographic information systems (GIS) and indicate that the soil types and geologic units contribute greatly toward activation of these flash floods, which is triggered by intense rainstorms. Components of the generated map attributes of SRTM DEM, gradient of DEM, and Landsat 7 ETM+ composite of 742 bands in GIS were used to generate a hazard map using spatial analysis to depict the nature and scale of the issues identified. Due to the flash flood vulnerabilities, the study area was classified into stable/low-, moderate-, and high-risk areas. Mitigation measures to control flash floods were proposed to enable adequate preparations to mitigate impending flash flood disasters.

A New Machine Learning Approach for parameter regionalization of Flash Flood Modelling in Henan Province, China

China is one of the countries in the world that seriously affected by flash floods disasters. The flash flood caused by extreme rainfall occurred at mountainous small-sized watersheds in China often leads to serious economic damages and obstructs the social development. Setting up an efficient forecasting system for flash flood has been widely accepted as one of the key non-structural measures to improve the control and prevention capability of China. However, due to the data limitation, establishing forecast models in those flash flood areas is challenged by the lack of parameter references. This paper proposed a new machine learning approach based on the Random Forest (RF) algorithm for model parameter regionalization. Integrated with distributed deterministic hydrological models of 20 small-sized watersheds in Henan province, the RF algorithm has been applied for defining the watersheds’ similarity and further transferring the parameters from sample watersheds to the objective watershed. Validated through leave-one-out approach, the RF model is able to effectively improve the simulation accuracy of flash floods in Henan province. The presented approach showed high-levelled applicability to be extended in other flash flood areas in China for providing effective reference for parameter regionalization.

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In this regard, a geospatial database of the flood with 178 flood locations and with 10 flash-flood predictors was prepared and used for this proposed approach. AHP and FR were used for processing and coding the predictors into a numeric format, whereas DNN, which is a powerful and state-of-the-art probabilistic machine leaning, was employed to build an inference flash-flood model. The reliability of the models was verified with the help of Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), and several statistical measures. The result shows that the two proposed ensemble models, DNN-AHP and DNN-FR, are capable of predicting future flash-flood areas with accuracy higher than 92%; therefore, they are a new tool for flash-flood studies.

Structural Durability Assessment of Stilt Houses to Flash Flooding: Case Study of Flash Flood-Affected Sites in Thailand

The ‘stilt house’ is found in many flood-prone areas and represents local wisdom regarding building construction to coexist with floodwaters. Most academic research projects have studied stilt houses based on two types of flood: inundation and coastal flooding. The study of pillar houses in flash floods is very limited. This research investigated whether the main structure of a stilt house could withstand strong water current to determine the suitability of the stilt house for flash flood sites. The study explored the physical appearance of stilt houses in five flash flood areas in Thailand. The styles of stilt houses in each area were simplified to generate models and to then test their tolerance toward moving water. The main findings were: 1) the main structure of the stilt house can resist flood loads at 1.00 m depth with a waterflow speed at 3.05 m/s; 2) the most vulnerable points on the main structure if struck by more rapid, deeper flows of water are the base of the column and the joint between the column and beams; and 3) the horizontal or diagonal bracing members perpendicular to the flow and not above the flood level become water blockades that increase the reactive force to the main structure.

Variation in Expression of <i>Sub</i>1 Gene and Association with Submergence Stress Related Traits in Advance Rice (<i>Oryza sativa</i> L.) Genotypes as a Tool of Climate Change Adaptation

Floods have now become most detrimental natural catastrophe worldwide due to radical climatic fluxes. Therefore, there is a dire necessity to develop a high yielding rice lines to deal with this scenario. For this purpose, a large scale experiment was conducted including one hundred and fifteen (115) rice genotypes having SUB1 gene imported from International Rice Research Institute (IRRI) Philippines, six local cultivars/approved varieties and three high yielding rice varieties i.e. Sabitri, IR6 and NSICRC222 being used as potential varieties in different countries of Asia as susceptible check and IR64-SUB1 as tolerant check. The genotypic screening was performed using two PCR-based DNA markers i.e. ART5 and SC3. Phenotypic screening was conducted in a natural pond to assess the interaction of SUB1 gene in natural stagnant flood water as well as the suitability of introgression of SUB1 gene into approved varieties and elite rice lines. The genotypes were assessed in terms of plant survival percentage, submergence tolerance index, physical condition, stem elongation, number of grains per panicle, thousand grain weight, grain yields and deviations in these traits after submergence stress. The PCR results suggested that both the primers ART5 and SC3 may be used as potential PCR-based markers for molecular screening of rice genotypes for SUB1 QTL. Furthermore, it confirmed the presence of SUB1 gene in all the lines imported from IRRI, while it was absent in all the local cultivars studied. All the genotypes with submergence tolerant gene (SUB1) showed significantly greater tolerance level in submergence stress of 14 days, as compared to other local cultivars/varieties, authenticating the effectiveness of SUB1QTL in conferring submergence tolerance. Significantly different performances of all the SUB1 genotypes in terms of all the studied traits indicate high Genotypic and Genotypic Environment Interaction (GEI) of SUB1QTL. Employment of SUB1 lines such as R105479:149-18, IR64-SUB1 and Rl05469:81-22-3 in breeding programs for developing flood tolerant rice varieties might further upsurge rice yields in flash flood areas. Correlation analysis revealed that plant survival percentage after submergence, reduced stem elongation during submergence and submergence tolerance index are very important traits for developing submergence tolerant lines.

Assessment of vulnerability in agricultural sector in disaster prone areas of Bangladesh

A study was carried out in 24 Upazillas of 12 districts of Bangladesh with a view to identifying risks, vulnerabilities and impacts of different types of natural disasters commonly occurring in Bangladesh with respect to agricultural production and sustainable agricultural development. Three districts were selected from each category of the disaster viz., drought, saline, river flood and flash flood prone. Two Upazillas were selected randomly from each district by considering the homogeneity (climate, agricultural practices, agricultural production, disaster risks, livelihood system, population, etc.) of the particular disaster affected areas. Of the four disasters, drought prone area covers the highest net cultivable area (NCA) measuring about 121 ha. Flash flood, salinity and river flood prone area covers approximately 115, 60 and 30 ha, respectively. The dominant cropping pattern in drought areas is Fallow-T.Aman-Wheat. In saline areas, it is Fallow-T. Aman- Pulse while in flood areas, it is Fallow-T.Aman- HYV Boro. Trend analysis shows that overall cropped area decreased by 1% in all disaster prone areas from 1984 - 2013. The highest decrease in cropped area was found for pulse in both drought (13%) and river flood (14%) areas. In saline and flash flood areas, it was for spice and potato, respectively. Among 72 farmers, 85% is vulnerable to drought, 90% to salinity, 69% to river flood and 95% to flash flood. Medium high land was found to be the most vulnerable for agricultural production in all disaster prone areas. Due to vulnerability to disasters, medium high land remains fallow in saline (83%), river flood (51%) and flash flood (31%) areas. In case of drought regions, medium low land (37%) remains fallow.Bangladesh J. Sci. Res. 28(2): 91-101, Dec-2015

Tekanan Ekonomi dan strategi Koping Keluarga Petani di Daerah Rawan Banjir

This study aimed to analyze the influence of family characteristics and economic pressure on coping strategies of farmer families in flash flood areas, Kebumen District. Farmer families that involved in the study were 100 families consisting of 50 families from the village of Kemujan and 50 families from the village of Tegalsari. Data were analyzed using descriptive analysis, different t test, and multiple linear regression. Farmer family perceived the economic pressure of their family in the medium category. Economic coping strategies which were done by farmer family are also classified as moderate, but the intensity of coping strategies done by farmer family was categorized into low level. Economic pressure of family increases with the number of family burden and the type of government assistance that has been received. In contrast, the economic pressure decreases as the increase of home-grounds and also the change of ownership status of the house. Economic pressure of family affects the coping strategies both in number and intensity. Another factor that determines coping strategies of family is the age of the husband, the husbands’ side job, and the wife's work. The number and intensity of coping strategies that family has will increase in line with the age of husband and a condition when wife has a job. The number of coping strategies done by family also increases if husband has side job. This research shows the close relationship between economic pressure and coping strategies of farmer family in flash flood areas.

Hydrometeorological modelling for flash flood areas: the case of the 2002 Gard event in France

AbstractIn the context of flash flood forecasting, this paper proposes a few advances in our understanding of the hydrometeorological processes and their associated modelling requirements that may be useful to introduce within an operational forecasting chain. The study is focused on the September 2002 storm that produced more than 600 mm of rainfall in <24 h and triggered a series of flash floods in the South of France. This catastrophic event took 23 human lives in 16 distinct subcatchments. This paper proposes a combined detailed analysis of the meteorological event and hydrological simulations of the response of four small‐ungauged catchments. The meteorological analyses are based on observations and results of simulation of rain fields obtained with the MesoNH model. These analyses explained the steadiness of the storms that led to a locally intense precipitation: the role of the orography and favourable synoptic conditions. The hydrological model is set up without any calibration and the soil parameter specification is based on an existing soil database. Radar rainfall estimations are used. Simulated specific peak discharges are found to be in agreement with estimations from a postevent in situ investigation. Based on the model results, a cartography of the dominant process is proposed for the four selected catchments.