Detecting Flash Flood Susceptible Areas Using MultiCriteria Decision Making Model: A Case Study of Thai Nguyen Province, Vietnam45244

Detecting Flash Flood Susceptible Areas Using Multi-Criteria Decision Making Model: A Case Study of Thai Nguyen Province, Vietnam Duong Thi Loi(1)(*) Phạm Anh Tuan(2), Nguyen Van Manh(3)(4) (1) Hanoi National University of Education, Hanoi, Vietnam Tay Bac University, Son La, Vietnam (3) National Cheng Kung University, Tainan, Taiwan (4) VAST Institute of Geography, Hanoi, Vietnam *Correspondence: duongloi1710@gmail.com (2) Abstract: In this study, Flash Flood Potential Index (FFPI) integrated with Geographic Information System (GIS) to determine the susceptible areas based on the pre-event characteristics in the study area Five different physical characteristics that relate to flash flood potentials such as slope, land use land cover, soil texture, forest canopy density, and drainage density were selected to calculate the index maps Each index was classified from to 10 by identifying their influence levels with the presence or absence of flash flood As a result, the most susceptible areas were given value at 10 while the least susceptible areas were assigned a value at These indices were then mapped and integrated into a weighted linear model Analytic Hierarchy Process (AHP) was used to determine the weighted correlation among elements based on their importance to this phenomenon Weighted Flash Flood Potential Index (WFFPI) was generated based on the individual indices from Slope index, Soil index, Land use cover index, Forest Canopy Density index, and Drainage Density index The final results described visually the spatial distribution of flash flood potential in the study area Accordingly, the susceptible areas with this phenomenon were divided into four levels including very high, high, moderate and low Keywords: Flash flood; Flash Flood Potential Index (FFPI); GIS; run-off Introduction Floods are considered as one of the most common types of natural disasters in Vietnam Like many countries in the world, the number of flood events has increased significantly in the last few decades and has caused many negative effects on the environment and society (Gioti et al, 2013) In which flash flood is the top weather-related killer (Jeffrey, 2013) Flash floods are defined as rapid-onset hydrologic events of shortduration, hence forecasting is difficult Moreover, in almost the developing countries, the serious shortage of flood warning system causes could limit the abilities in flood prevention The complex variations in climate, land use and other anthropogenic interventions also lead the changes in flood risk and make more complicated the problem (Nektarios et al, 2011) Along with the development of science technology, the understanding of flash flood causes has improved in recent years, but most of these researchers concentrate simulating the process of flash floods based on the recorded data from the meteorological stations (Evangelia et al, 2011) In addition, the need for specific detail data of flash flood from the stations for such models is a big challenge for many developing countries where there is a shortage of warning monitoring systems, especially in mountainous areas Flash floods are known as a natural phenomenon, however, they are directly affected by on-site hydrologic factors such as soil component, slope and land cover These factors could promote or inhibit the runoff process, main cause of the flash flood Besides, physical factors are often stable and less variable Therefore, assessing the flash flood potential areas based on the pre-event characteristics is a reliable basis for disaster research and flash flood prediction It contributes importantly to natural disaster prevention and environmental protection One of the previous methods for estimating the run-off potential is used flash flood potential index (FFPI) This index was made and developed by Greg Smith (2003) for the Colorado river basin, in the National Weather Service (USA) To calculate the FFPI values at Colorado River basin, four input factors (slope, vegetation, soil type, and land use) were selected as parameters and processed in GIS environment Some authors modified the original Smith version of the FFPI for implementation and applied in other study areas, typically Brewster (2009), Kruzdlo (2010), Ceru (2012), Zogg and Deitsch (2013) These studies improved by considering and changing of the weighing input factors in the final equation of FFPI Obviously, the selection of input factors and weight estimation keep a crucial role in this methodology According to conventional reasoning, rainfall is often considered as the most important factor for forecasting floods, however, what happens to the rain when it is combined with the conditions on the ground can sometimes be more important In addition, a flash flood can even occur with the drought condition and ground is not saturated (Christopher et al, 2010) Therefore, in some cases, runoff production processes were selected to study instead of rainfall characteristics In this paper, the modified-FFPI model was used to assess the risk of a flash flood The geographic factors at study area have a major impact on the timing of runoff, amount of infiltration, and severity of flash flooding The major factors selected as the index to assess the flash flood potential areas include slope, soil, land use, forest canopy density, and drainage density Each of these indices contains the value from to 10 corresponding to the probability of flash flood from least to most and then added in a weighted linear model to create the FFPI map Weights are evaluated based on their influence on the runoff - a direct expression of flash floods With the support of ArcGIS and ENVI software, digital maps and a satellite image are collected to build the database for studying Moreover, GIS is also a useful tool for integrating multiple indices of influence for flash flood hazard susceptibility mapping (Chen et al, 2016) This research focuses on identifying the flash flood potential areas in the study area and it can be used as a useful material for helping decision-makers and research about the natural disaster in the local area Methodology 2.1 Study area Thai Nguyen is a province located in the Northeast region of Vietnam The geographical coordinate stretches across from 20020’ to 22025’North and 105025’ to 106016’ East Thai Nguyen shares its border with six provinces including Bac Can, Vinh Phuc, Tuyen Quang, Lang Son, Bac Giang, and Hanoi and its terrain has many mountain ranges running from the north to the south The study area is covered by 3541.5 sq km (Fig 1) Thai Nguyen is considered as the gateway for socio-economic exchange between the Northeast Area and the Red River Delta However, Thai Nguyen has been affected by flash floods in recent years with serious damages to humans and properties From 1994 till now, there are about four floods each year in the average and affected areas around 10 - 40 sq.km (Nguyen et al 2009) Thai Nguyen province becomes one of the most hard-hit with many deaths and reported injuries caused by flash floods in the north of Vietnam Figure Thai Nguyen administrative map 2.2 Data collection In this research, the spatial data such as administrative, hydrology, soil and land use cover data were collected from different sources to calculate the index maps corresponding with input geographic factors More specifically, topographic map and soil map in 2010 provided by the Thai Nguyen Department of Natural Resources and Environment were used to calculate slope index, soil texture index and drainage density index The land use land cover map in 2010 was also provided by Thai Nguyen Department of Natural Resources and Environment, but the attributes were updated based on fieldwork and compared to Landsat taken in 2016 This satellite data was also used to calculate Forest Canopy Density Although the data derived from different sources and time, this does not affect much to the accuracy of result Because both topography and soil texture are little changed elements, especially being topography, a relatively inherent factor Therefore, there is almost no difference in these factors in a short time Moreover, land use land cover is the element that easily changes in a short time under human activities, so this data was updated to 2016 from the remote sensing data and information from fieldwork to ensure the update of research results Five factors were considered to select including slope, soil, land use cover, forest canopy density, and drainage density Each index map was consequently given FFPI values (from to 10) according to each data type The value of 10 means the highest potential for a flash flood to occur and means the lowest (Ziyue et al 2015) To derive flash flood potential index maps, all maps were overlaid and weighted based on the AHP (Analytic Hierarchy Process) method Based on the outcome, the FFPI values were classified and mapped The final result was divided into four classes consisted of very high, high, moderate and low potential flash flood Low potential flash flood areas had the value from to 2.5 Similarly, moderate potential areas, high potential areas, and very high potential areas were given the value from 2.5 to 5, from to 7.5 and more than 7.5, respectively The methodology was illustrated in the flowchart (Figure 2) Figure Methodological flow chart 2.3 Data processing  Identify the slope index The slope was generated from the Digital Elevation Model (DEM) with 30 m cells, obtained by interpolating from the Thai Nguyen topographic map provided by Thai Nguyen Department of Natural Resources and Environment of scale 1: 50.000 Many scientists indicated that the variation of the slope is one of the important factors affecting the timing of runoff and amount of infiltration and infiltration rate decreases with the increase of slope angle by the experimental projects such as GreenHill et al, 1983; Fox et al, 1997 and Akbarimehr et al, 2012 In general, the rain with high intensive at slope exceeding 30 percent leads to extremely quick runoff and rapid response in local creeks and stream (Jeffrey, 2013; Wenbin et al 2015) Slope values were classified from to 10 Percent slope greater than 30% were given a 10 on the scale and the slope values in the range from 0% to 30% were divided from to (Smith, 2003) The classification of the slope was described in table The processing was done with the support of ArcGIS software and the result was shown in figure Table Classification of Slope by FFPI value Slope (%) FFPI value Slope (%) FFPI value Above 30 10 From 12 to 15 From 27 to 30 From to 12 From 24 to 27 From to From 21 to 24 From to From 18 to 21 Under From 15 to 18 (a) (b) Figure Slope map (a) and slope  Identify the soil texture index Soil map at scale 1: 250 000 provided by Thai Nguyen Department of Natural Resources and Environment was used to carry out the soil texture index The basis for dividing the soil map by FFPI value is based on the soil texture which is determined by the proportions be weight of clay, silt, and sand A soil texture containing more sand will have better infiltration but it is hard to create the surface runoff, while more clay soils will restrict the infiltration but it can promote the runoff (Jeffrey, 2013) According to the soil map, Thai Nguyen province was covered by five soil texture types, such as Sand, Sandy Clay Loam, Silt Loam, Clay Loam, Water body, and Rock Mountain In addition, the soil textures with different thickness affect significantly to infiltration Accordingly, a soil texture with thick sand layer will infiltrate better than others, but it will be the opposite of thick clay soils In the study area, soil textures were distributed with different thicknesses and divided into three levels: from to 70 centimeter (cm), from 70 to 100 cm and above 100cm As a result, the higher the infiltration is, the smaller the FFPI value is Based on the real data on soil texture integrated with classification method by Smith 2010, the FFPI values corresponding soil textures were shown in table The combination of soil texture and soil layer thickness was applied to build the soil index and it was described in figure Table Classification of Soil texture by FFPI value Soil texture Soil layer thickness (centimeter) FFPI Sand From to 70 From to 70 From 70 to 100 Above 100 From to 70 From 70 to 100 Sandy Clay Loam Silt Loam Clay Loam (a) Above 100 From to 70 From 70 to 100 Above 100 10 (b) Figure Soil texture map (a) and Soil texture index (b)  Identify the land use land cover index The spatial data of land use land cover map 2010 provided by the Thai Nguyen Department of Natural Resources and Environment was used to generate the land use land cover index map Accordingly, the study area was divided into 10 types of land use land cover (Fig 5a) The FFPI values were given for the different types of land use land cover based on the storage ability on the foliage and roof of vegetation (Pei-Jun et al, 2007; Ebrahimian et al, 2009; Lincoln et al, 2016) and classified in table The residential lands, especially the urban areas have a high flash flood potential due to the domination of impermeable surfaces and compacted soils as well as the shortage of natural vegetation This speeds up the runoff and causes the flood In the opposite manner, in areas with better storage capacities such as forest, paddy, the risk of flash floods will be limited The result of land use land cover index was mapped and shown in Figure 5b Table Classification of Land use land cover by FFPI value Types of land FFPI Types of land FFPI Water Evergreen Forest Residential land Mixed Forest Short-term industrial crop Shrub/Scrub Long-term industrial crop Grass Paddy Rock Mountain Figure Land use land cover map (a) and Land use land cover index (b)  Identify the forest canopy density index Forest canopy density (FCD) is considered as an important index to identify the flood potential because it can intercept rainfall, slowing its fall to the ground Therefore, it has a positive impact on preventing floods Besides, it also helps in regulating the interchange of heat, water vapor, and atmospheric gases - the main factors lead to weather variation FCD index was calculated based on the reflective value from bands of Landsat OLI (Operational Land Imager) Landsat taken in October 2016 and provided by United States Geological Survey (USGS) was selected and processed in this paper In the first step, bands of Landsat images were converted from DN (Digital Number) to TOA (Top of Atmosphere) reflectance The purpose of this work is to eliminate the negative effects of the atmosphere on image quality This process has been done by using equation (1) Lλ = MLQcal + AL (1) where, Lλ = TOA spectral radiance (Watts/( m2 * srad * μm)) ML = Band-specific multiplicative rescaling factor from the metadata AL = Band-specific additive rescaling factor from the metadata Qcal = Quantized and calibrated standard product pixel values (DN) In the second step, FCD was generated based on the indices including advanced vegetation index (AVI), bare soil index (BI) and canopy shadow index (SI) These indices were calculated as equation (2), (3), (4) The last step, VD (Vegetation density) - which was derived from the combination of AVI and BI, and SSI - which was derived from SI by using a linear transformation were used to calculate the FCD This process was carried out as equation (5) This process was done with the support of ArcGIS and ENVI software (Duong et al, 2017) (2) AVI  ( B5  1) * (65536  B4) * ( B5  B4) Where, B5 is near infrared band and B4 is red band BI  ( B6  B 4)  ( B5  B 2) *100  100 ( B6  B 4)  ( B5  B 2) (3) Where, B2 is green band, B4 is red band, B5 is near infrared band and B6 is shortwave infrared band SI  ( 65536  B ) * ( 65536  B 3) * ( 65536  B ) (4) Where, B2 is green band, B3 is blue band and B4 is red band (5) FCD  VD * SSI   In this case, the values of FCD were given in value range from 1.44625% to 90.7236 % FCD Index was reclassified then by FFPI value from FCD map with the values range from to 10 and mapped in figure As a result, the high flash flood potential corresponds with the low FCD and vice versa (Table 4) Table Classification of Forest Canopy Density by FFPI value Forest Canopy (%) FFPI Forest Canopy (%) FFPI From to 10 From 50 to 60 From 10 to 20 From 60 to 70 From 20 to 30 From 70 to 80 From 30 to 40 From 80 to 90 From 40 to 50 Above 90 10 (a) (b) Figure Forest Canopy Density (a) and Forest Canopy Density Index (b)  Identify the drainage density index Drainage density was determined based on the total length of stream per basin area by Roberte Horton 1945 DEM data 30 meters used first to determine the boundary of river basins by the interpolation method with the support of ArcGIS software According to the interpolation result, the study area was divided into five river basin, those are Cau river basin, the Cong river basin, Cho Chu, Nghing Tuong, and Du river basin In the next step, hydrographic system maps taken from Thai Nguyen Department of Natural Resources and Environment and river basins map were combined to take out the drainage density map As the result, the drainage density values corresponding with the Nghing Tuong river basin, Du river basin, Cau river basin, Cong river basin, and Cho Chu river basin were 0.68, 0.82, 1.43, 2.0 and 2.75 respectively (figure 7) After that, this result was classified into FFPI value and mapped A drainage basin with a large number of tributaries has a higher stream density than a basin with very few streams (Pallard et al, 2009; Gregogy, 2010), therefore the FFPI value increased corresponding with increasing of drainage density (a) (b) Figure Drainage Density (a) and Drainage Density Index (b)  Generation of Weighted Flash flood potential index (WFFPI) Analytic Hierarchy Process (AHP) was used for determining the weights of the individual index This method was developed by Thomas Saaty in 1990 and become one of the most famous methods for making multi-criteria decisions Saaty’s method describes the level of importance of parameters and their relationship on a scale of to After the computation of weights using Saaty’s pairwise comparison method, the Consistency Ratio (CR) in this case was 0.018992 The Slope was considered as the most important index and given the weight at 0.47 and the Drainage Density was determined as the least important index and weighted at 0.04 The Soil index, Land use land cover index and Forest Canopy Density index were weighted with 0.09; 0.3; and 0.1 respectively Therefore, Weighted Flash Flood Potential Index (WFFPI) was generated based on the individual indices from Slope index, Soil index, Land use land cover index, Forest Canopy Density index, and Drainage Density index and computed as given (6): WFFPI  0.47( M )  0.3( L)  0.09( S )  0.1( F )  0.04( D) N Where, WUGSI: Weighted Flash Flood Potential Index M: Slope index L: Land use land cover index S: Soil texture index F: Forest canopy density index D: Drainage density index N: Sum of weightings (6) Result Following the methodology described above, the WFFPI was the result of the synthesis from five indices (Slope index, Soil index, Land use land cover index, Forest canopy density index, and Drainage density index) The WFFPI values in the final result were ranged from to and were classified into four classes: very high, high, moderate and low Very high flash flood potential responds with the WFFPI above In a similar manner, the WFFPI values from to 7, from to and less than are given the high, medium and low flash flood potential and it was shown in table The result from the WFFPI map indicated the areas susceptible to flash floods As a result, most of the areas in the study area were at the high and medium flash flood potential with the percentage at 17.54 % and 81.06 % respectively Especially, the areas with very high flash flood potential occupied 0.27% and distributed along the upper streams of the high mountain (figure 8) Table Area and percentage of WFFPI distribution Class Area (hectare) Percentage (%) Low 3954.31 1.13 Moderate 284141.43 81.06 High 61498.05 17.54 Very high 946.0 0.27 Figure Weight Flash Flood Potential Index (WFFPI) The statistics from the map shown that the locations at high and very high flash flood potential were distributed in Vo Nhai, Dai Tu, and Dong Hy district Vo Nhai was considered as the most serious susceptible district because the areas at high and very high potential with the flash flood reach 24237.7 and 635.4 respectively In addition, Dai Tu and Dong Hy were worrisome districts for flash floods In which, Dai Tu had 12855.44 at the high level and 78.5 at a very high level with flash flood potential and Dong Hy was 7992.2 and 131.6 respectively These districts were located at the upstream of the big river such as Nghing Tuong and Cong River The other districts such as Phu Binh, Thai Nguyen, and Song Cong, the flash flood potentials were less serious than others The statistics were described in Fig These results were verified then based on the statistics about flash flood events in the study area from 1969 to 2013 collected by authors According to these statistics, the areas having the high flash flood frequency happened at some points belong to Dai Tu district, Dinh Hoa district, Dong Hy district, and Vo Nhai district It coincided with the results of the classification map The results proved that the integration of GIS and FFPI model brings a reliable tool for evaluating the flash flood potential area Figure Distribution of Flash Flood Potential Index (FFPI) by districts in Thai Nguyen Conclusions Flash flood is one of the natural disasters interested especially It is not only by its severity with aspects of the environment, society, and economy, but also its complex and multifaceted nature The studying the flash flood is an important respect of the water resources management Identifying the susceptible areas to flash flood contributes to mitigate the related risks and make decisions This paper introduces a methodology to evaluate the flash flood potential areas based on the integration of WFFPI and GIS environment By the determining weights of five physical factors (slope, soil texture, forest canopy density, land use land cover, and drainage density), this research also indicates the close relationship between the physical characteristic of study area and flash flood potential Compared to the FFPI method used in previous studies, the FFPI was modified with the determining weights of factors by the consultation of experts and using AHP method to fit with features of the study area in this paper Moreover, selecting the weights for each factor keeps an important role in this research and changing it will affect significantly to the results of the study Therefore, this work needs to consider carefully and to be based on the actual characteristics of the study area as well as the consultation of experts Besides being dependent on physical elements such as slope, soil, forest canopy, land use, and drainage density, flash flood risk is also affected by the various human activities such as building structures: roads, highways and hydroelectric, changing of land use structure (Kaan et al, 2010) This will be added in the next research of author It is very necessary to help the research results to be more comprehensive In addition, it should be noted that the results could be subjected to errors and uncertainties because of the errors of spatial data, the complicated variation of the phenomenon and limitations on the actual database at the measurement stations Therefore, it is necessary to improve more other information in real time from the station to verify the results of the study and evaluate the accuracy Acknowledgments This research was supported by Hanoi National University of Education References Akbarimehr M, 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Li, Yuebo Xie, Jiyang Tian, Jia Liu, Nana Zhao (2015) Effects of Rainfall Intensity and Slope Gradient on Runoff and Soil Moisture Content on Different Growing Stages of Spring Maize Water, 7, 2990-3008 Ziyue Zeng, Gouqiang Tang, Di Long, Hui Xu, Yun Chen, Yang Hong (2015) Development of GIS-based FFPI for China's Flash Flood Forecasting 23rd International Conference on Geoinformatics, China IEEE, Available at: https://ieeexplore.ieee.org/document/7378697 ... the flash flood potential areas in the study area and it can be used as a useful material for helping decision- makers and research about the natural disaster in the local area Methodology 2.1 Study. .. consisted of very high, high, moderate and low potential flash flood Low potential flash flood areas had the value from to 2.5 Similarly, moderate potential areas, high potential areas, and very... vapor, and atmospheric gases - the main factors lead to weather variation FCD index was calculated based on the reflective value from bands of Landsat OLI (Operational Land Imager) Landsat taken