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Susceptibility areas identification and risk assessment of debris flow using the Flow-R model: a case study of Basu County of Tibet

Susceptibility areas identification and risk assessment of debris flow using the Flow-R model: a... Background: Uncertainties exist in the magnitude and outbreak of debris flow disasters, resulting in significant loss of lives and property to human society. Improved identification of debris flow susceptibility areas can help to predict the location and sphere of influence of debris flow disaster, thus accurately assessing the risk of debris flow disaster and reducing losses caused by such a disaster. The dry-hot valleys of Basu County in the Eastern Qinghai-Tibet Plateau are typical areas of high debris flow incidence, mapping of debris flow susceptibility identification and regional risk assessment is needed in this area. Results: The parameters improved Flow-R model was first applied to identify debris flow susceptibility areas in Basu county using the digital elevation model, flow accumulation, slope, plan curvature, and land use data, followed by debris flow risk assessment. The Flow-R model can output high result accuracy of high-resolution susceptibility to debris flow identification on a regional scale with less data, and its accuracy value is 87.6%, indicating that the suscep - tibility to regional debris flow disaster is credible. This study provides a useful basis for effective prevention of regional debris flow disasters in the future, and provides a useful method for effectively identifying the debris flow susceptibil- ity areas and assessing the related risk in large-scale areas. Conclusions: (1) The debris flow susceptibility areas in Basu County covered 97.04 km (0.79% of the study area), dis- tributed mainly in the Nujiang River Valley, Lengqu tributaries, and both sides of National Highway 318. (2) The debris flow susceptibility areas were dominant in zones characterized by an altitude range of 3000–4000 m, a plane curva- −1 −1 ture of − 2/100 m to 1/100 m , and a low slope of 20°–40°. In addition, the susceptibility areas were dominant in the unused land and less prevalent in the water area. The highest and lowest susceptibility values were observed for cultivated and unused lands, respectively. (3) The debris flow risk in the study areas accounted for 0.82 km and revealed a distribution of high-risk debris flow along roads. The areas with a high debris flow risk were mainly distrib - uted along the mainstream of the Nujiang River, which is the main future protected area. Keywords: Flow-R model, Debris flow, Susceptibility areas, Basu County Introduction Debris flow is a common geological disaster in moun - tainous areas, with complex causes and high suddenness. Under favorable terrains, high loose materials and water amounts flow through gullies under the force of gravity, damaging the surrounded traffic roads, buildings, vegeta - *Correspondence: qhchenqiong@163.com tion, and cultivated lands (Iverson 1997; Tang and Liang College of Geographic Sciences, Qinghai Normal University, Xining 810008, 2008). Every year, debris flows cause thousands of deaths China Full list of author information is available at the end of the article and huge economic losses worldwide (Dowling and Santi © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 2 of 21 2014). China is one of the countries where debris flow models requires extensive field survey data, which are disasters are most severe. Indeed, about 2/3 of the moun- costly and difficult to obtain, particularly at a regional tainous areas in China are affected by debris flow (Cui scale. Although machine learning techniques can be used et  al. 2000). Hengduan, in the southeast of the Qinghai- to assess debris-flow susceptibility at regional scales, but Tibet Plateau, is a mountainous area characterized by the model is complex to build, extremely dependent on high mountains, intense tectonic activity, complex and the quantity and quality of data, with some uncertainty diverse geological and geomorphological environment, in weight assignment, and the training samples need to vagaries of climate and concentrated precipitation time, be representative, etc. their results may be inaccurate as and frequent mountain disasters. According to Bian et al. they are incapable of differentiating susceptibility within about 932 mountain disasters occurred in this region the same debris flow gully (Qing et  al. 2020). The Flow- from 2006 to 2015, causing 1,373 casualties and 2.5 bil- R model is a software model for automatic identification lion yuan in direct economic losses, constituting a seri- of debris flow source areas and estimation of debris flow ous threat to the safety of human life and property in this spread based on GIS tools. Indeed, this model can run region (Bian et al. 2018). With the frequent occurrence of using fewer data requirements (Horton et al. 2008, 2011). extreme rainfall events caused by global climate change, It is not an encapsulated model, and users can adjust its the frequency, scale, and complexity of debris flow in algorithms and parameters according to the character- mountain areas may continue to increase in the future, istics of the study area, achieving good results. In addi- presenting an increased risk of debris flow disasters (Cui tion, the fewer data input requirements allow for carrying et  al. 2015, 2019). Therefore, accurate identification of out a regional-scale assessment before the occurrence the location, path, and extent of potential debris flow dis - of debris flow disasters (Llanes 2016; Sturzenegger et  al. asters can help in effective debris-flow monitoring and 2019). Numerous studies have applied the Flow-R model implementation of policies, which are crucial for accurate in debris flow assessment and showed satisfactory results risk assessment of debris flow disasters. Moreover, they (Blahut et  al. 2010; Baumann et  al. 2011; Horton et  al. can also help in implementing specific and effective pro - 2013; Blais-Stevens and Behnia 2016; Park et  al. 2016; tective measures to, directly or indirectly, reduce or pre- Kang and Lee 2018), These studies mostly compared dif - vent losses caused by debris flow disasters. ferent data resolutions, methods, and parameters, com- Identification of areas susceptible to debris flows is pare the susceptibility values and ranges under different an important approach for qualitative and quantitative parameter configurations. However, in China, relevant assessment of the potential regional debris flow disas - studies on this model have only been carried out in a ters (Fell et al. 2008). Susceptible areas are more likely to few debris flow gullies (Hou et al. 2019; Nie and Li 2019). experience debris flow events. However, it doesn’t neces - u Th s, its applicability needs further exploration. sarily imply a higher frequency of occurrence. Regional Due to the complexity of debris flow, high precision susceptibility mapping allows determining the spatial dis- regional scale susceptibility identification research meth - tribution of debris-flow risk with fewer data requirements ods, large amount of demanded data, not easy to obtain, based on DEM (Horton et al. 2013). It is usually relatively long calculation time and other limitations, regional difficult to identify the site of a debris flow disaster using scale susceptibility research results are often low resolu- ground surveys (Cama et  al. 2017). Therefore, combin - tion, and some high resolution data are only applicable ing debris flow source area detection with debris flow to small regional scale. The Flow-R model is fast and effi - spread prediction is a fast and effective method to assess cient, with a small amount of data input to obtain debris regional debris flow susceptibility (Horton et  al. 2011; flow susceptibility, and is more suitable for application at Pastorello et  al. 2017). There are numerous methods for a large scale. Previous studies related to Flow-R model assessing debris flow susceptibility. Traditional methods mostly compare the threshold results of different param - are based on field disaster investigation, quantitative sta - eters at a small scale, and rarely attempt the susceptibility tistical analysis using mathematical models, and qualita- results at a large regional scale. In this study, Basu County tive analysis (Xu et  al. 2013; Kritikos and Davies 2015; of Tibet was selected as the study area. It is located in the Wang et al. 2017; Xia et al. 2017; Wu et al. 2019). In addi- West Hengduan Mountain area, susceptible to the occur- tion, several researchers have assessed the debris flow rence of natural debris flow disasters. The Flow-R model susceptibility using machine learning algorithms (Zhang was used to determine the identification index thresh - et al. 2019; Hu et al. 2019; Xiong et al. 2020), while others old of critical conditions, such as sediment availability, have combined factors determining debris flow suscepti - water input, and terrain slope, followed by identifying the bility in empirical models (Gomes et  al. 2013; Blais-Ste- debris flow susceptibility in the study area using the flow vens and Behnia 2016; Gong et  al. 2017; Kang and Lee spreading algorithm and energy calculation of motion 2018). Compared with empirical models, mathematical simulation, and an attempt is made to provide higher Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 3 of 21 Fig. 1 The geographic location of the study area resolution susceptibility mapping results while selecting elevation is 2545  m, the terrain in the northeastern is a large regional scale, which is representative and at the high, while in the southwestern part is relatively low. It same time the results are applied to regional risk assess- can be divided into three different geomorphic structure ment, providing a reference for the future development regions: Northwestern Plateau, Central Nujiang deep of regional debris flow disaster prevention and control cut, and Southeastern high mountain wide valley regions strategies. (Local Chorography Compilation Committee of Basu County, 2012). Study area Due to the complicated geological tectonic movement In this study, Basu County of Tibet was selected as the and the heavy erosion by surface water, rivers are gath- study area. Basu county of Qamdo city is located in the ered in the study area. Consisting of three major river southwest of China, the southeast of the Tibetan Plateau, systems, namely Nujiang River, Yuqu River, and Lengqu and the west of the Hengduan mountains, covering an River, with over 100 tributaries. The rivers are recharged 4 2 area of 1.23 × 10 km , with an average altitude of around mainly from rainfall and snow meltwater. On the other 4640 m (Fig.  1). In terms of the geological structure, the hand, the region is dominated by a temperate semi-arid northern, central, and southern parts of the study area plateau monsoon climate, with an average annual pre- belong to the patchwork area of the Leiwuqi terrane, cipitation of 254.5  mm and an average annual amount Jiayuqiao terrane, and Gangdisi massif, respectively. In of rainy days of 75  days, and high evaporation, with an addition to the high degree of metamorphism, the study average annual evaporation of 3000  mm. Precipitation area is characterized by complicated geological structures is mainly distributed from May–September every year, with developed fold fractures and outcropped strata. The accounting for 70% of the annual precipitation. The cli - highest elevation of the region is 6840 m, and the lowest mate in the winter and spring seasons is cold and dry, Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 4 of 21 with limited precipitation, while in summer and autumn, Committee of Basu County 2012), where very typical dry temperature and rainfall increase as a result of southwest and hot valleys are developed. Due to the restriction of monsoon influence. The average annual temperature in the natural environment, Basu County is sparsely popu- the county and valley is 10.4 ℃, while that in the alpine lated (about 40,000 people), with a low level of social and region is below 3 ℃, the temperature is low throughout economic development, focusing on agriculture and ani- the year. Due to the dual effects of regional climate and mal husbandry activities. In addition, the Sichuan-Tibet geomorphic conditions, the overall vegetation cover- Line, which is a famous tourist transport line in China, age in the region is low, showing significant vertical ter - passes through Basu County. rain characteristics. From the valley to the plateau, the sequence of major land cover types are as follows: dry- Characteristics of the regional debris flow disaster hot valley temperate steppe, temperate meadow steppe, The neotectonic movement in the study area is strong, mountain meadow, subalpine meadow, dark coniferous with high and marked elevation differences between forest (dominated by western Sichuan spruce), alpine mountains. Indeed, the study area is characterized by meadow, alpine shrub meadow, alpine sparse vegeta- complex typical deep-cut valleys, with unstable pre- tion, alpine sub-ice and snow, and ice and snow (Local cipitation and glacier movement effects, intense ero - Chorography Compilation Committee of Basu County sion from the crisscrossing rivers, a large longitudinal 2012). drop of the gully bed, and steep terrain. All these On the other hand, the complex and diverse topo- characteristics contribute to the occurrence of geo- graphic and geomorphologic features of the region have logical disasters (eg., mountain collapse, landslide, and significant effects on the redistribution of water and debris flow), threatening the safety of residents and heat conditions. The Foehn effect is very common in traffic roads (Fig.  2). The area presents a high risk of some deep gorge areas (Local Chorography Compilation debris flow development. Indeed, the area is located Fig. 2 Photographs of debris flows in the the study area (upper left: 29° 59′ 15.81″ N, 97° 10′ 55.78″ E; Upper right: 30° 3′ 37.09″ N, 96° 54′ 50.61″ E; Lower left: 30° 2′ 20.89″ N, 96° 45′ 10.50″ E; Lower right: 30° N, 1′ 38.77″ 32.90″ 7′ 97° E) Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 5 of 21 in a relatively complicated plot split zone, with devel- events. Glaciers and snow cover are widely distributed oped fold fracture. The geological structure results in in high, extremely high, and plain mountains of the study joints and fissures development, breaking up rocks. In area. Indeed, with seasonal change and global warming, fact, poor mechanics proprieties of rock mass result in a large amount of water is produced by intensified snow weak weathering resistance, forming a large amount of and ice melt processes, thus increasing the debris flow rock debris in the gully under the influence of gravity. disaster risk (Lv et al. 1999). In addition, the region belongs to the transition zone Due to the complexity of the regional terrain, canyon of the southeast edge of the Tibetan Plateau, where development, rock metamorphism, mountain fragmenta- river erosion is heavy. In the region, gullies are well tion, sparse vegetation, loose soil, unstable precipitation, developed, while valley slopes are steppe and unstable. and snow and ice melt processes, this region provides a Collapse, landslide, and rockfall occur frequently, form- natural disaster-prone environment for debris flow (Lv ing a large deposit amount. Besides the low vegeta- et al. 1999). The residual slope gravel, glacial till, and drift tion cover in the valley, caused by the Foehn effect and pebble soils with poor stability are accumulated, along dry-hot climate, that contributes to debris flows, the with water movement through the slope gradient, at the extremely low temperatures in the high mountain areas foot of the slope, the bottom of the mountain, and the significantly promote the freezing-thaw weathering outlet of secondary gullies, resulting in debris flow event. process. The bare rock of mountains consists of a large The debris flow types in this region include precipitation number of detrital materials (e.g., forming rock-flow - and glacial debris flows, with a prevalence of the precipi - ing hillsides, stone curtains, and Stone River), which tation debris flow type. Glacial debris flow occurs mainly transport a large amount of rock detrital materials to in the quaternary erosion gully area of high-altitude the gullies and provide a large number of loose detrital mountains. Debris flow gullies are mostly pear-shaped, materials (Lv et al. 1999). scoop-shaped, and fan-shaped, with small to medium On the other hand, unstable meteorological conditions scale. and snow meltwater in this region provide water source Debris flow disaster in this region not only threatens conditions for debris flow disasters. The annual and inter - the safety of residents but also the National Highway annual precipitation in the study area is extremely vari- 318, which passes through the study area. Indeed, debris able. Along with concentrated precipitation during the flows often destroy roads and form roadblocks (Luo et al. flood season, most debris flow disasters occur frequently 1996), making this area one of the most endangered sec- over several months. According to the climate station tions of the National Highway 318 (Yang et al. 2012). information of Basu County from 1980 to 2002, the maxi- mum monthly precipitation (183  mm) was observed in Data and methods July 2002, while the longest continuous rainfall dura- Data source tion is 11  days, and the maximum daily precipitation is Obtaining debris flow susceptibility data needs and data 71.1  mm (October 4, 1993). The maximum (375  mm) accessibility according to Flow-R model, the data used and minimum (105.8  mm) annual precipitations were in this study consist of geographic information, remote observed in 1990 and 1983, respectively. The precipita - sensing image, and ground survey data (Table  1). Geo- tion fluctuations can significantly promote debris flow graphic information data includes 12.5  m × 12.5  m Table 1 Data source Data type Source Data resolution DEM ALOS PALSAR 12.5 m*12.5 m https:// asf. alaska. edu/ data- sets/ deriv ed- data- sets/ alos- palsar- rtc/ alos- palsar- radio metric- terra in- corre ction/ Land cover data Global land cover map data products 10 m*10 m http:// data. ess. tsing hua. edu. cn/ Spatial distribution of disaster points Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences – http:// www. resdc. cn/ Landsat-8 OLI remote sensing image USGS 30 m*30 m https:// earth explo rer. usgs. gov/ Building information OpenStreetMap – https:// www. opens treet map. org 2020–2021 Field survey data Debris flow disaster site – Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 6 of 21 Digital Elevation Model (DEM), flow accumulation, Afterward, the cumulative vectorial value of the flow slope, plan curvature, land use with 10 m × 10  m resolu- was obtained by solving multiple equations. The tion, reclassification of land cover data based on different cumulative vectorial values in the study area ranged land use patterns as land use input. Building information from 0 to 37856764. obtained from Map street, while remote sensing image 4. Plane curvature refers to the direction perpendicular data include Landsat-8 OLI remote sensing images, with to the maximum slope. Plane curvature is related to 30  m of resolution. The ground survey data consist of the convergence and dispersion of the flow through geological disaster information of the study area and the the ground surface. In this study in Arcgis software geological disaster data obtained during two field investi - using Curvature tool to obtain: the slope of each gations in the 2020–2021 period. grid was first calculated by referring to the adja - cency matrix using DEM data, and then the second Data preprocessing derivative of the slope was calculated by fitting the DEM, flow accumulation, slope, plan curvature, and pixel with eight adjacent pixels to determine plane land use data were processed in ArcGIS software. DEM curvature. Positive and negative values of plane cur- data were obtained by advanced land observing satellite vature indicate that the surface of the pixel is convex phased array type L-band synthetic aperture radar (ALOS upward and concave upward, respectively. The plane PALSAR) dataset, with a data resolution of 12.5 m. These curvature of the study area ranges from − 104/100 to −1 data were used to determine flow accumulation, slope, 104.344/100  m . and plane curvature data in ArcGIS10.2 software. In 5. In order to obtain an accurate land use map of the addition, land use data were obtained by processing the study area, the 2017 global land cover data, devel- 2017 global land cover mapping performed by Gong et al. oped by Gong et  al. were considered in this study. (2019). Two scenes of images of the study area were first mosaicked under the same projection (WGS_1984_ 1. For DEM processing, 15 scenes of images within UTM_Zone_47N), and then reclassified 10 land the study area were downloaded from the website cover types based on the corresponding attribute and combined under the same projection condi- table using the Reclassify tool in Arcgis software. tion (The output projection coordinate system is set The wetland, tundra/shrub, and bare/glacier snow to: ‘WGS_1984_UTM_Zone_47N’ according to the lands were classified as water, grassland, and unused zone location): According to the properties of the land, respectively, while other land types remained first scene of the image, mosaic of all 15 scenes of unchanged. In total, six types of land use data were DEM data, with consistent attributes, the study area obtained, namely cultivated land, grassland, forest DEM is cropped according to the zone extent and land, artificial surface, water area, and unused land. projections to facilitate further computation and use. Use resample tool to convert 10 m higher resolution 2. Slope refers to the degree of steepness of the ground to lower 12.5  m resolution, output range consistent surface. The slope is defined as the ratio of vertical with dem and aligned, resampling method using the height to horizontal distance. In this study, the slope default method (NEAREST). tool was used in Arcgis software to obtain: the eleva- tion value matrix was calculated using the DEM data Through the above preprocessing, the following data synthesized, and then the slope was obtained from results are obtained (Fig. 3): the proximity matrix. The slope of the study area Regarding data fusion of geological disaster points, two ranges from 0° to 84.169°. geological disaster datasets were considered in this study, 3. Flow Accumulation refers to the accumulated flow obtained from the data center of the Institute of Geo- at each point of the regional terrain, which can be graphic Environment and Natural Resources Research, obtained by the flow simulation method on the Chinese Academy of Sciences, and a field survey in the regional terrain surface. In this study, the Fill tool was region during the 2020–2021 period. The two datasets used in Arcgis software to fill the pits first, and then were merged to create a new geological disaster informa- the Flow Accumulation tool was used to obtain: the tion database. In total, 89 debris flow disaster informa - elevation value matrix was first calculated using the tion points were obtained in the study area. synthetic DEM data, and then the adjacent matrix Remote sensing image preprocessing: In order to obtain was used to calculate the slope value and the accu- high-resolution remote sensing images, two scenes of mulation direction. The flow direction was derived remote sensing images, acquired on November 17 and from the maximum values of the calculated slope August 13 in 2013, with cloud covers of less than 5% in the eight directions, with a range value of 1–128. were downloaded (Path/Row:134/40; 134/39). The data Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 7 of 21 Fig. 3 Data preprocessing results. DEM (a); Slope (b); Plane curvature (c); Flow accumulation (d); Land use (e) processing included geometric and radiometric correc- of landsat8_oli, and the resampling method of cubic Con- tion, mosaic and cropping in ENVI software, followed by volution to fuse the multispectral image (30 m) with the the Gram-Schmidt Pan sharpening tool, sensor selection higher resolution panchromatic band (15  m) to obtain a Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 8 of 21 Fig. 4 Flow chart of Flow-R model higher resolution multispectral image with a synthetic www. flow-r. org/). Horton et al. developed Flow-R model image resolution of 15  m, acquired high-resolution for disaster susceptibility identification (Horton et  al. remote sensing imgaes for subsequent result validation. 2008), which consists of two parts: identification of the potential source area and simulation of debris flow move - Flow‑R model ment (Fig. 4). The model output results in the area under The Flow-R model is a GIS-based simulation model of the debris flow spread range and the associated qualita - regional-scale gravity disaster path, combining debris tive probability of being vulnerable to the potential risk flow source area detection with debris flow spread pre - of debris flow, with higher values indicating a higher diction. The purpose is to locate hazardous processes and probability of debris flow arrival. The potential spread gain insight into existing or potential susceptible areas, indicates the worst-case scenario, so the area of suscepti- mainly involving areas with conditions for debris flow bility results is often larger than the actual area of the site to occur, and reflecting the extent to which debris flows (Horton et al. 2008). may spread (Horton et al. 2008). The model was obtained The identification of potential source area in this model from the Flow-R website of Lausanne University (https:// is based on three critical factors of debris flow occur - rence: sediment availability, water input, and terrain slope. The input variables include DEM, slope, plane curvature, and flow accumulation. Users can add lithol - Table 2 Input parameters used in the Flow-R model ogy, elevation, surface curvature, land use variables as Parameters Method Value input, and limit source region identification conditions Slope – 15°–40° to improve the simulation accuracy (Horton et al. 2008). −1 Plane curvature – − 0.5/100 m By dividing the threshold of each element for source area Flow accumulation Rare event 10 m DEM identification, values in the layer of each input dataset are Direction algorithm Holmgren (1994) dh = 2 Exp = 04.0 divided into three types: source area, non-source area, modified and uncertain area. The potential source area of debris Inertial algorithm Weights Gamma_2000 flow is defined as an area whose superimposed layers Friction loss function Travel angle 11.0_deg were divided into source areas at least once and were Energy limitation Velocity 15_mps never divided into non-source areas. Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 9 of 21 The movement of debris flow is simulated using a the slope range of 15°–40° (Takahashi, 1981; Ricken- spreading algorithm and an energy calculation. Among mann and Zimmermann, 1993; Horton et  al. 2013). them, the spreading algorithm of debris flow is deter - The plane curvature is the curvature perpendicular to mined based on the flow directions algorithm and the the steepest slope, and the negative value is generally inertial algorithm, while the energy calculation of debris the gullies prone to debris flow (Horton et  al. 2013). flow is determined based on the friction loss function Indeed, the value increase with increasing DEM reso- and the energy limitaion. Users can select and adjust the lution, and the values generally range from − 2/100 to −1 thresholds of calculation methods and parameters pro-0.01/100  m (Baumann et  al. 2011). Park et  al. used vided by the model according to the requirements. DEM with 10  m resolution and a threshold value of −1 − 1/100  m to achieve high accuracy of simulation results (Park et  al. 2016). In this study, the DEM data Selection of Flow‑R model parameters resolution was 12.5  m, and the threshold value was set Due to the large scale of the study area and the different −1 to − 0.5 m/100  m . The cumulative amount is the flow environmental factors driving natural disasters, global accumulation of each grid point obtained using the susceptibility distribution from a macro perspective flow simulation algorithm. was considered in this study, selecting input parameters Rare/extreme precipitation events with different based on data availability. Thresholds were fixed based resolutions can be selected in the model. The rare pre - on previous studies (Table 2). cipitation event algorithm was selected in this study. The slope is an important factor in identifying sus - Geological lithology or land use data can be used as ceptible areas to debris flow. According to previ - input providing provenance information (Horton et  al. ous studies, debris flow is more likely to occur under 2011). The geological conditions of the study area are Fig. 5 Comparison between actual debris flow disaster points and Flow-R model results Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 10 of 21 complex, while the accuracy of the publicly available flow, providing targeted monitoring and early warning geological data is low. Therefore, the geological param - reference for accurate assessment of debris flow disaster eter was ignored in this study, defining the geological risk. condition of the study area as an uncertain source area. Disaster risk assessment is the analysis and evaluation Land use data were considered to reflect provenance of life, property, livelihood, and the vulnerability of the input conditions, according to the stability differences disaster-causing factors and exposure bodies that may of debris flow under different land-use types (Xie and cause potential threats or injuries to human society. Dis- Wei, 2011). Indeed, water/grassland, artificial surface/ aster risk per unit area is closely related to disaster sus- cultivated land, forest/unused land were set as source ceptibility and vulnerability of the body exposure within areas, uncertain, non-source areas, respectively. In the a given area, which can be obtained by combining sus- Flow-R model, the flow direction algorithm, inertial ceptibility and vulnerability. In this study, debris flow dis - algorithm, friction loss coefficient, and energy limita - aster risk was determined using the following formula: tion method adopted parameters given by Horton et al. Debris flow disaster risk = Debris flow susceptibility areas∗ (2008). Vulnerability of risk exposure body (1) Methodology of debris flow disaster risk assessment Debris flow disaster in the study area is mainly affected Human society is the most exposed to debris flow disas - by roads, settlements, residential buildings, farmland, ters and related damage. Predicting potential debris flow and engineering facilities. In this study, the selection of susceptibility areas and combining them with exposure risk exposure body factors considered surface objects bodies (artificial buildings, roads…) can help managers related to human society, including artificial buildings, to better evaluate hazard-affected risk. Indeed, estab - roads, houses, and cultivated lands, which are often lishing preventive strategies and implementing effective vulnerable to disaster, resulting in economic losses. measures to reduce human activities in high-risk areas Although forest, grassland, unused land, water body, and can effectively reduce the economic losses caused by other natural environment are also vulnerable to damage, natural debris flow disasters in the region (Shi 2018). In they have some capacity for self-regulation, resulting in this study, the assessment of debris flow disaster risk was low social and economic losses. Indeed, these land types carried out based on susceptibility identification of debris Fig. 6 Random partial validation using Landsat-8 OLI_TIRS images. a1, b1, c1, and d1 represent the susceptibility results of debris flow superimposed on four remote sensing images; a2, b2, c2, and d2 represent base maps of remote sensing images Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 11 of 21 Fig. 7 Disaster occurrence points that fall outside the predicted area Table 3 Data of disaster points not located in susceptibility areas Bodies at risk of exposure along potential debris flow paths in the study area were identified to determine the Points Villages and towns Longitude Latitude area and extent of regional disaster risk exposure. Vec- 1 Gokyim 96° 46′ 31″ 30° 37′ 15″ tor data of buildings, residential areas, agricultural lands, 2 Lagen 97° 1′ 51.1″ 30° 1′ 26.5″ parks, and roads were first extracted from OpenStreet - 3 Jidar 96° 40′ 41.2″ 29° 55′ 34.8″ Map data, and then the completeness of the risk expo- 4 Baima 96° 55′ 16.90″ 30° 3′ 13.28″ sure body at random locations was checked using Google 5 Tongka 96° 32′ 42.48″ 30° 31′ 21.02″ Earth before being assessed. 6 Bangda 97° 16′ 28.7″ 30° 8′ 0.7″ 7 Bangda Results 97° 18′ 11.61″ 30° 7′ 12.38″ Verification of the debris flow susceptibility results 8 Bangda 97° 18′ 12.45″ 30° 7′ 11.07″ The range of susceptibility results output by Flow-R 9 Gyizhong 97° 14′ 35.8″ 30° 19′ 48.8″ is consistent with each layer, and the data resolution 10 Gyizhong 97° 10′ 11.87″ 30° 27′ 18.8″ is 12.5  m × 12.5  m. There are 78947809 raster cells in 11 Yiqen 97° 7′ 31.5″ 30° 33′ 16″ the study area, among which, 6211045 raster cells have susceptibility result value distribution, and the rest of the raster cells are NoData (− 9999). According to the exhibit low vulnerability to flow-debris disasters. The dis - Flow-R model results, the highest and lowest debris- aster risks of artificial facilities, cultivated land, and other flow susceptibility values were 1 and 0.0003, respectively highly vulnerable bodies were determined to evaluate the (Fig.  5), The higher the susceptibility value, the greater regional debris flow disaster risk. the possibility of debris flow spreading, the lower the Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 12 of 21 Fig. 8 Susceptibility distribution of debris flow at different scales. a Susceptibility of debris flow in Basu County at large scale; b Susceptibility of debris flow in part of Nujiang River basin in Eastern Basu County; c Susceptibility of debris flow at the township scale; d Susceptibility of debris flow at the village level; e Debris flow susceptibility of a single debris gully susceptibility value, the lower the possibility of debris verification points was limited and mainly located along flow spreading, and the non-distribution of susceptibility the highway. Indeed, some susceptibility areas identified value means that there are no conditions for debris flow using the Flow-R model were located on slopes and gul- disaster and the debris flow does not spread. The higher lies at higher elevations, making it difficult to validate the susceptibility value, the lower the area range. In this them using field data. Therefore, remote sensing image study, the susceptibility results from the model are veri- data were used to further verify the result. Landsat-8 fied using the distribution of disaster points list data. In OLI_TIRS satellite remote sensing images were used to Arcgis software, the suspensitivity results are extracted to perform random local verification, the main judgment the disaster distribution point data, and the points with is based on whether the part with susceptibility value suspensitivity values are the correct points for the model has almost no vegetation cover, serious surface damage, debris flow simulation, and the points with the value of exposed soil, rough texture, irregular perimeter, incon- − 9999 are the wrong simulation points. The susceptibil - sistent with the overall texture of the surrounding area ity results obtained by the model were validated using the and whether there is a formation area, circulation area, actual debris flow disaster points (89 points). The results accumulation area, these debris flow traces or debris flow showed 78 disaster points in the susceptibility areas iden- ditch, etc. as the basis for judging whether the results are tified by the model, while 11 points were located outside reasonable (Fig.  6). Four groups of susceptibility areas the identified areas, indicating a good simulation accu - (Fig.  6a1, b1, c1, d1) were randomly selected and com- racy value of 87.6%. pared with those identified using remote sensing images The actual debris flow disaster points were identi - (Fig.  6a2, b2, c2, d2). The results revealed that high sus - fied from field investigations. Thus, the number of ceptibility areas included obvious debris flow gullies in Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 13 of 21 Fig. 9 Debris flow susceptibility classes and altitude ranges in Basu County Table 4 Statistics of different debris flow susceptibility classes in Basu County Type Area/km Proportion of susceptibility areas of debris Percentage of flow/% the area in Basu County/% Low susceptibility 72.61 74.8 0.59 Medium-susceptibility 18.49 19.1 0.15 High susceptibility 5.94 6.1 0.05 Total 97.04 100 0.79 the mountain, while areas of low susceptibility exhibited susceptibility information near the disaster occurrences scattered loose debris flow. The overall comparison and points. Indeed, by referring to the susceptibility identifi - verification results suggest good simulation results. cation results of debris flow and local Chronicles (Local The verification results showed 11 disaster points out - Chorography Compilation Committee of Basu County side the susceptibility areas (Fig.  7 and Table  3). This 2012), it was observed that the debris flow disaster in the finding may be due to several reasons. First, the accu - Yuqu river tributary basin in the study area has not devel- racy of input data can affect the identification accuracy oped. As the formation of debris flow disaster is complex of the susceptible areas for debris flow, as the Flow-R is to a certain extent, geological, lithology, soil, and other an empirical model. Second, there were 7 debris flow intrinsic parameters were not used to further restrict the points in the non-susceptible areas in the tributaries of selection of source area. Therefore, the disaster points Yuqu River (points 1, 7, 8, 9, 10, and 11), with a lack of may exhibit local favorable accumulation conditions for Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 14 of 21 20% Low susceptibility Medium -susceptibility High susceptibility 16% 12% 8% 4% 0% altitude / m Fig. 10 Relationship between susceptibility of debris flow and altitude 20% Low susceptibility Medium -susceptibility High susceptibility 16% 12% 8% 4% slope / e 0% 0~55~10 10~15 15~20 20~2525~30 30~3535~40 40~4545~50 50~5555~60 60~6565~70 70~7575~80 Fig. 11 Relationship between debris flow susceptibility and slope Susceptibility areas identification debris flow occurrence, forming debris flow gullies under Debris-flow susceptibility mapping can help to deter - sufficient precipitation (Fig.  7a, c, d). Third, due to the mine and thoroughly assess the most likely affected areas large scale of the study area and the obvious differences in by the flow debris disaster. The susceptible areas are the the natural environment, disaster thresholds may be spa- areas likely to be affected by debris flow disasters, which tially different, resulting in discrepancies in the results. may not occur due to vegetation covering debris flow Fourth, the disaster points come from manual records, gullies. Obtaining accurate data is a labor-intensive task, some debris flows occur in high mountains, which are making it difficult to carry out disaster identification and observable but inaccessible, and the recorded informa- inspection on a regional scale. tion may not represent the exact location of the debris The Flow-R model can rapidly provide the debris-flow flow, leading to discrepancies in the results. Finally, some susceptibility results at a large county scale, with fewer disaster points were recorded as hillslope debris flow input data and a resolution of 12.5  m (Fig.  8). As shown (Table  3, points 3–11), while the plane curvature value in Fig.  8a, the results were not conclusive in the macro- was greater than the threshold set when identifying the scopic scale of the study area, showing small debris-flow source area, potentially causing non-identification of dif - susceptibility areas without being able to examine the fuse slopes (Fig. 7b, c). proportion proportion Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 15 of 21 14% Low susceptibility Medium -susceptibility 12% High susceptibility 10% 8% 6% 4% 2% flow accumulation 0% Fig. 12 Relationship between debris flow susceptibility and flow accumulation 50% Low susceptibility Medium -susceptibility High susceptibility 40% 30% 20% 10% plane curvature 0% -1 / 100m Fig. 13 Relationship between debris flow susceptibility and plane curvature spatial differences of susceptibility. u Th s, by adjusting the 45% Low suscepbility scale, the distribution and differences between the flow- Medium -suscepbility debris susceptibility areas were well identified (Fig.  8b–e). High suscepbility National Highway 318 has great importance and influ - 30% ence on the development of southwest China. Previous studies on debris flow disasters in this region mainly focused on the route along Highway 318 (Zou et  al. 15% 2013), without considering other areas in the study region. In addition, most of the research results were segmented according to debris flow hazard zoning, 0% landuse even some areas are not at risk. Thus, these results have cultivated forest land grass land water land unused land building land land not revealed the change of debris flow risk in different Fig. 14 Relationship between debris flow susceptibility and land use regions at the microscale of the study area and identi- fied the debris flow risk area for subsequent disaster proportion proportion proportion Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 16 of 21 prevention and mitigation. Our study showed that the susceptibility areas of debris flow are not only distributed along National Highway 318 and Lentqu river tributary but also on both sides of Nujiang River valley. Indeed, the susceptibility areas along the Nujiang River valley are large, while no risk was observed in the adjacent Yuqu river tributary. Debris flow susceptibility areas are prone to disasters, presenting a high-risk degree with a small range, distributed mainly along the valley. While areas presenting low-risk are large, spreading around the chan- nel depending on the terrain. Classification and distribution characteristics of debris flow susceptibility areas The debris flow susceptibility area was 97.04 km , accounting for about 0.79% of the study area. The suscep - tibility results were divided into three classes using the natural breaks (Jenks) method provided In Arcgis soft- Fig. 15 A schematic diagram of overlapping parts of debris-flow ware, namely low-susceptibility, medium-susceptibility, susceptibility identification and infrastructure is used to distinguish and high-susceptibility classes (Fig. 9). debris flow risk ( The points refer to the physical environment of the According to the results of the Flow-R model, the spa- study area) tial distribution of susceptibility classes was analyzed. The area of low susceptibility areas was about 72.61 km , accounting for 0.59 and 74.8% of the study area and the total surface of debris flow area, respectively. The reaches 4640 m, and the range of debris flow susceptibil - medium susceptibility area covered about 18.49 km , ity is 2599–5279  m. The debris flow susceptibility areas accounting for 0.15 and 19.1% of the study area and the are mainly distributed in the study area below 4000  m total debris flow susceptibility areas, respectively. The in altitude, and the distribution is more concentrated on high susceptibility area was about 5.94 km , accounting both sides of the river valley at 3000–4000  m in altitude for 0.05 and 6.1% of the study area and the total debris (Fig.  9).The results indicate that deep canyons with high flow area, respectively (Table  4). In addition, the area of elevation differences provide favorable topographic con - medium–high susceptibility region was relatively small, ditions for the occurrence of debris flow disasters, The which is related to debris flow circulation area. Over 70% urban land and roads in the study area are mostly dis- of the areas revealed low susceptibility to debris flow, tributed in the low altitude area of the region, suggesting mainly in the accumulation area, which is the attenuation that regional debris flow disasters may cause significant and diffusion zone of debris flow. Therefore, although damages. debris flow occurs frequently in the study area, the area The results of the relationship between debris flow sus - affected by debris flow disaster is small, while the area’s ceptibility and slope (Fig. 11) showed that the range slope high susceptibility to debris flow accounted for a small with debris flow susceptibility was 0°–75.44°, and the surface. overall distribution was relatively concentrated between The raster file of susceptibility results is converted 20° and 40°. Below 35°, the percentage of susceptibility into Points element shapefiles in Arcgis, and the values areas increases step by step with the increase of slope are extracted to points, then altitude, slope, plane curva- grade; above 35°, the percentage of susceptibility areas ture, flow accumulation and land use values are extracted decreases step by step with the increase of slope grade. to points, and the area is classified into different classes The medium and high level susceptibility areas are mainly according to different feature values, so that the analysis concentrated in the range of slope grades below 40°. This can calculate the percentage of the area of different sus - result suggests that the low slope (between 20° and 40°) ceptibility classes in each feature class. of the study area provides favorable topographic condi- The results of the relationship between debris flow tions for the occurrence of debris flow disasters, while susceptibility and altitude (Fig.  10)show that we did not higher slopes of the study area are more favorable for low restrict the altitude at which debris flow occurs in the debris susceptibility. In the study area, villages, buildings, model, although the average altitude of the study area and farmland are more distributed in low-sloped areas, Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 17 of 21 making debris flow more damaging to society and the economy. The results of the relationship between debris flow sus - ceptibility and flow accumulation (Fig.  12) show that the range of flow accumulation values for debris flow suscep - tibility is 0–37856056, and the susceptibility is mainly distributed in the range of lower flow accumulation. The higher the flow accumulation, the lower the distribution range of debris flow susceptibility areas, but with a higher flow accumulation, the higher the debris flow susceptibil - ity. Susceptibility values were mainly distributed in the range of low flow accumulation rates. However, the grid unit upstream peripheral convergence may exhibit high cumulants under rainfed conditions, resulting in low debris flow diffusion in this grid and gradual downward diffusion. The results of the relationship between debris flow sus - ceptibility and plane curvature (Fig.  13) show that the plane curvature of debris flow susceptibility is − 21.822/ −1 −1 100  m to 20.257/100  m , and the distribution of debris flow susceptibility is relatively concentrated in the plane −1 −1 curvature of − 2/100  m to 1/100  m , among which the medium to high susceptibility is mainly distributed at −1 −1 − 2/100  m to 0/100  m . This indicates that the study −1 −1 area The plane curvature of − 2/100  m to 1/100  m in the study area provides more favorable topographic con- ditions for debris flow spreading and propagation. The debris flow downstream accumulation area is more dis - tributed within the concave terrain with negative values −1 −1 of − 2/100  m to 0/100  m , and the medium to high susceptibility is more easily formed by diffuse accumula - Fig. 16 Classification of debris-flow risk levels in Basu County tion and damage to the downstream. Land use types were extracted by masking in Arcgis software using susceptibility results, and then suscep- Debris flow disaster risk assessment tibility ratios were calculated for each land use type The vector risk exposure body information obtained from (Fig.  14). The susceptibility areas of cultivated land OpenStreetMap was rasterized using ArcGIS software, accounted for 20.00%, with an average susceptibil- with a data resolution of 12.5 m, to ensure that each pixel ity value of 0.1478. In, addition, the proportion values of the grid is aligned with the susceptibility result pixels of susceptibility areas in woodland, grassland, water, obtained by the Flow-R model, the area of the exposure unused, and building lands were 20.61, 14.22, 1.09, 2 body in the region is about 34.07km , and building area 38.15, and 5.93%, with average values of 0.1349, 0.1185, in the study area is relatively small and concentrated, 0.1402, 0.1054, and 0.1068, respectively. The debris- and the main exposeure bodies are roads. The overlap flow susceptibility areas were abundant in the unused between the risk exposure body map and the susceptibil- land and less prevalent in the water area. In addi- ity result of debris flow refers to the risk exposure body tion, the highest and lowest susceptibility values were with high vulnerability located in the debris-flow suscep - observed in cultivated and unused lands, respectively. tibility area. The area and extent of potentially exposed to It should be noted that the proportion of building lands debris flow disaster risk were determined (Fig. 15). in the study area is not high (about 6%), but the propor- The debris flow disaster risk in the region can be tion of building lands with susceptibility reaches nearly obtained by identifying the debris flow susceptibility 6% of the susceptibility types, indicating that the dis- areas that are prone to high property losses and vulner- tribution range of debris flow susceptibility on build - ability to disaster. The potential risk areas of debris flow ing lands is high and can have far-reaching effects on disaster in the study area are mostly located on roads, human activities in the area. 2 covering a total area of about 0.82 km . The potential Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 18 of 21 debris flow risk areas accounted for 0.84% of the total prevent and mitigate debris flow hazards. In risk-free surface of debris-flow susceptibility. In addition, the area areas, there is generally no risk of debris flow disaster. of low-susceptibility, medium-susceptibility, and high- Human activities can, therefore, be carried out normally. susceptibility risks were about 0.64 and 0.14, and 0.04 Investment in disaster prevention and control in the km , respectively. These results indicate that this method non-risk areas can be moderately reduced, thus allow- can reduce the extent of debris flow disaster monitoring ing additional disaster prevention and control resources and warning, target specific areas of the road and build - to be allocated to areas of high debris flow risk. Human ing before planned reinforcement, and allows disaster activities can also be carried out in low-risk areas, but it prevention measures, thus reducing the economic loss is necessary to strengthen disaster warning and detection caused by debris flow disaster. in areas of low debris flow risk and appropriately invest in disaster prevention and mitigation forces. In medium risk areas, artificial construction facilities and agricultural Disaster risk zoning activities need to be reduced, and existing facilities and The risk levels of debris flow in the study area were clas - buildings should be strengthened and relocated to reduce sified based on the risk of debris flow on a unit grid. Sev - 2 the risk of debris flow disaster, improve the awareness of eral regular hexagonal grids, with a unit area of 4 km , disaster prevention among regional residents, strengthen were established using Generate Tessellation tool in Arc- the level of disaster warning and monitoring, and contin- GIS software, while the small marginal areas (less than 2 2 uously detect regional disaster risk. In high-risk areas, it km ) were eliminated. The study area is large, and when is suggested to avoid the construction of artificial facili - the unit area is selected, too small will lead to overly ties and relocate the existing facilities and residential fragmented division results, and too large will lead to areas, and strengthen resource investment in effective difficulty reflecting the differences in the region. After 2 reinforcement management of roads in the study area, to comprehensive consideration and comparison, 4km reduce disaster losses. was finally chosen as the unit area. The regular hexa - The above suggestions can effectively reduce and con - gon is closer to the circle than the regular quadrilateral, trol the regional debris flow disaster, improve the level exhibiting a smaller area/circumference ratio. Indeed, of debris flow warning, particularly in the rainy season, the regular hexagon is a similarly shaped polygon that enhance disaster risk forecasting, implement disaster can be arranged uniformly in space, thus significantly prevention and mitigation related works, thus effectively minimizing the result deviation caused by the boundary reducing the risk of life and property safety caused by effect and better reflecting the internal information and debris flow disaster in Basu County. condition of regional space. The sum of debris flow risk in each hexagonal grid was calculated before classifying Discussion the regional debris flow risk level in the same unit area. It is difficult to accomplish high-resolution susceptibil - The susceptibility values were superimposed on each ity identification at the regional scale, so the Flow-R unit area and the results ranged from 0 to 881.9858, the model, a method in which a small amount of data can susceptibility values per unit area were equal interval quickly obtain more accurate results, is used to identify method classified into four classes, namely no risk (0), the potential spread of debris flow at the regional scale. low risk (0–300), medium risk (300–600), and high risk Because the susceptibility results of the Flow-R model (600–900). The potential disaster risk was visualized to are constrained by the quality of DEM, so in the range reveal the spatial variation of debris flow risk in the study of 12300km , we choose 12.5  m resolution on the basis area (Fig. 16). of not destroying the original data resolution, consider- The debris flow risk map revealed 3 082 regional disas - ing the data accessibility and model operation, to obtain ter risk level grids, including 2  534, 493, 49, and 6 grids the highest possible data resolution and susceptibility showing no risk, low risk, medium risk, and high risk result quality. Of course, the model is limited and does areas, respectively. Therefore, most parts of the study not reflect the local control factors and specific condi - area have exhibited no debris flow risk. Moreover, most tions. The results are often larger than the actual extent debris flow risk areas are of low risk, while the areas of of debris flow occurrence, but the output can be consid - medium and high debris flow risks are small and spatially ered accurate for the purpose of susceptibility mapping scattered. (Horton et al. 2011). According to the results of risk zoning, it is neces- The validation of the results is an important reflection sary to improve strategies to prevent regional debris of the accuracy and validity of the model. In this paper, flow disasters, particularly in areas presenting medium the results are validated based on the disaster points list to high susceptibility areas of debris flow disaster, and and the visual comparison of remote sensing images, reduce human activities in susceptible areas to effectively Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 19 of 21 and the accuracy of the validation points reaches 87.6%. the study area was assessed at on a large scale. The results According to the comparison of remote sensing images, of this study were not compared and discussed by adjust- the debris flow channels in the susceptibility area have ing data resolution, model methods, and parameters due obvious characteristics, and the overall results of the to the running time of the model, but by referring to the model in the study area are ideal, and the results have a parameter selection in related studies. The susceptibil - high degree of confidence. However, for the accurate por - ity acquisition based on Flow-R is characterized by easy trayal of the debris flow extent for validation, this paper access to data, relatively reliable results (according to the obviously did not do enough. Due to the limitation of comparison and validation of this paper), high resolu- image quality and technical level, the actual occurrence tion of results (generally large scale results are difficult of regional large scale debris flow is difficult to obtain. to apply to road risk evaluation, and the roads in this Because it is difficult to extract debris flow extent infor - study area are critical), and the ability to distinguish local mation directly from the images, firstly, the rainfall is scale debris flow susceptibility changes, based on which concentrated before and after the occurrence of debris regional scale risk evaluation is conducted, and suscep- flow and the cloud cover is large, so the real images are tibility information extraction based on regional debris not easy to obtain. Moreover, the debris flow accumula - flow related Although there may be some deviations tion will be covered by the natural changes of the ground between the results and the actual situation, the large surface with time changes, so it is often impossible to scale high-resolution results can be applied to the over- directly obtain the actual location of debris flow occur - all regional risk assessment and zoning, so that the high rence, and the remote sensing image resolution itself has susceptibility and risk areas can be prevented in advance. some limitations on the results. Due to the difference of natural conditions on a regional scale, the texture fea- Conclusions tures and so on of debris flow are not consistent on the In this study, the 12.5 m resolution DEM data were used images of different regions. Remote sensing interpreta - in the Flow-R model to identify the regional debris flow tion of debris flow information on a large scale is obvi - susceptibility. The results were first validated using the ously more difficult, but the validation of a small scale actual disaster point data combined with remote sensing single debris flow ditch can be carried out by overlaying images, and then the regional disaster risk was further research results with images if high-resolution images evaluated. The main conclusions reached are follows: can be obtained, and such validation can only be carried (1) There was a lack of susceptible debris flow areas out after the occurrence of a disaster, which is undesir- in most parts of the study area. The debris flow suscep - able in areas with frequent human activities. Therefore, tibility areas were mainly observed in the Nujiang River the debris flow risk in the region can be effectively esti - valley, the tributaries of the Lengqu river, and both sides mated before disasters occur, providing references for of National Highway 318, covering a total area of about debris flow prevention and control policies, specific 2 97.04 km (0.79% of the study area). Moreover, low, regional construction planning, as well as disaster predic- medium, and high susceptibility areas covered 0.59, 0.15, tion and early warning systems, thus reducing financial and 0.05% of the study area, respectively. In the Nuji- and economic losses and protecting people’s lives. ang River valley, the debris flow susceptibility was more Debris flow hazards are influenced by the complex sur - widely distributed than that along National Highway 318. rounding environment, and the uncertainty of regional Although the high susceptibility value of debris flow are scale data and the model itself is inevitable, meanwhile, prone to disasters, their area may be small. These areas there must be errors between such nonlinear research were distributed along the valley channel. The debris flow analysis and the actual situation, and it is often neces- in the low susceptibility zone is not easy to occur but has sary to make a lot of adjustment work on parameters to a greater range and extends around the channel, depend- compare with the actual local occurrence to reduce simu- ing on the terrain characteristics. lation errors. Some algorithms in the Flow-R model are (2) The debris flow susceptibility in the study area was mainly derived from empirical algorithms, explaining the mainly distributed in areas with altitude values below multiple different choices that have been made in terms 4000  m, particularly on both sides of the river valley, of method and parameter considered in the model. Hor- at an altitude range of 3000–4000  m. In addition, the ton et  al. (2013), Kang and Lee (2018), and Park et  al. results revealed the distribution of the susceptibility val- (2016), compared different data resolutions, methods, ues within the low-slope range of 20–40°, thus providing and parameters, compared the susceptibility values and favorable terrain conditions for the occurrence of debris ranges under different parameter configurations, and flow disasters. The susceptibility is mainly distributed in explored the most reasonable values of parameters in dif- the range where the flow accumulation is low, and the ferent regions. In this study, the debris flow disaster in higher the flow accumulation, the higher the range of Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 20 of 21 Bian JH, Li XZ, Hu KH (2018) Study on distribution characteristics and dynamic debris flow susceptibility distribution is about less, but evolution of mountain hazards in Hengduan mountains area. In: China with the higher flow accumulation, the debris flow sus - engineering geology annual conference on 2018. Xi’an 2018 ceptibility is much higher. The areas with plane curva - Blahut J, Horton P, Sterlacchini S, Jaboyedoff M (2010) Debris flow hazard −1 modelling on medium scale: Valtellina di Tirano, Italy. Nat Hazard ture from − 2/100 to 1/100  m were more susceptible 10(11):2379–2390 to debris flow, providing favorable terrain conditions for Blais-Stevens A, Behnia P (2016) Debris flow susceptibility mapping using a debris flow diffusion. On the other hand, the debris flow qualitative heuristic method and Flow-R along the Yukon Alaska Highway Corridor, Canada. Nat Hazard 16(2):449–462 susceptibility areas were most abundant in the unused Cama M, Lombardo L, Conoscenti C, Rotigliano E (2017) Improving transfer- land and less prevalent in the water area. In addition, the ability strategies for debris flow susceptibility assessment: application highest and lowest susceptibility values were found in to the Saponara and Itala catchments (Messina, Italy). Geomorphology 288:52–65 cultivated and unused lands, respectively. Chen HK, Tang HM (2011) Evaluation of geological disaster fatalness along (3) The risk exposure body such as buildings, residen - Sichuan–Tibet highway. Chin Highw 09:17–23 tial areas and roads in the susceptible areas was employed Cui P, Liu SJ, Tan WP (2000) Progress of debris flow forecast in China. Chin J Nat Disasters 02:10–15 to determine the potential debris flow disaster risk. Most Cui P, Su FH, Zou Q, Chen NS, Zhang YL (2015) Risk assessment and disaster of the risk exposure bodies were located on the road sur- reduction strategies for mountainous and meteorological hazards in face, covering a total area of 0.82 k m (0.84% of debris- Tibetan Plateau. Chin Sci Bull 60(32):3067–3077 Cui P, Guo XJ, Jiang TH, Zhang GT, Jin W (2019) Disaster effect induced by flow susceptibility areas). The risk level of the study area asian water tower change and mitigation strategies. Bull Chin Acad Sci was classified, taking into account 4 km as a unit area. 34(11):1313–1321 The results revealed a low overall debris flow risk level in Dowling CA, Santi PM (2014) Debris flows and their toll on human life: a global analysis of debris-flow fatalities from 1950 to 2011. Nat Hazards the study area, with few and scattered areas of medium– 71(1):203–227 high risk. Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Acknowledgements Eng Geol 102(3–4):99–111 This work was supported by Second Tibetan Plateau Scientific Expedition and Gomes RAT, Guimarães RF, De Carvalho J, Fernandes NF, Do Amaral J (2013) Research Program (STEP) [Grant No. 2019QZKK0906] and China’s National Key Combining spatial models for shallow landslides and debris-flows predic- Research and Development Project (NKPs) [Grant No. 2019YFA0606902]. tion. Remote Sens 5(5):2219–2237 Gong K, Yang T, Xia C, Yang Y (2017) Assessment on the hazard of debris flow Author contributions based on FLO - 2D: a case study of debris flow in Cutou Gully, Wenchuan, Huange Xu and Peng Su designed this study in consultation with Qiong Chen, Sichuan. Chin J Water Resour Water Eng 28(06):134–138 Huange Xu handled the analysis of the data and completed the manuscript Gong P, Liu H, Zhang M et al (2019) Stable classification with limited sample: with the support of other authors. All authors read and approved the final transferring a 30-m resolution sample set collected in 2015 to mapping manuscript. 10-m resolution global land cover in 2017. Sci Bull 64(6):370–373 Horton P, Jaboyedoff M, Zimmermann M, Mazottf B, Longchamp C (2011) Funding Flow-R, a model for debris flow susceptibility mapping at a regional This work was supported by Second Tibetan Plateau Scientific Expedition and scale-some case studies. Ital J Eng Geol 2:875–884 Research Program (STEP) [Grant No. 2019QZKK0906] and China’s National Key Horton P, Jaboyedoff M, Rudaz B, Zimmermann M (2013) Flow-R, a model for Research and Development Project (NKPs) [Grant No. 2019YFA0606902]. susceptibility mapping of debris flows and other gravitational hazards at a regional scale. Nat Hazards 13(4):869–885 Availability of data and materials Horton P, Jaboyedoff M, Bardou E (2008) Debris flow susceptibility mapping All data and materials are available from the corresponding author upon at a regional scale. In: Proceedings of the 4th Canadian Conference on reasonable request. Geohazards: From Causes to Management. Presse de l’Université Laval, Québec Hou YL, Hu SJ, Peng QY, Xu JK, Wu XG (2019) Analysis of debris flow suscepti- Declarations bility in loess gully region: a case study of Laolang Gully in Lanzhou. Chin Ecol Environ Monit Three Gorges 4(04):48–56 Competing interests Hu XY, Qin SW, Dou Q, Liu F, Qiao SS, Dong D (2019) Susceptibility analysis of The authors declare that they have no competing interests. debris flow based on GIS and random forest—a case study of a moun- tainous area in northern Taonan City, Jilin Province. Chin Bull Soil Water Author details Conserv 39:204–210 College of Geographic Sciences, Qinghai Normal University, Xining 810008, Iverson RM (1997) The physics of debris flows. Rev Geophys 35(3):245–296 China. Academy of Plateau Science and Sustainability, Xining 810008, China. Kang S, Lee S (2018) Debris flow susceptibility assessment based on an Institute of Geographic Sciences and Natural Resources Research, CAS, empirical approach in the central region of South Korea. 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Susceptibility areas identification and risk assessment of debris flow using the Flow-R model: a case study of Basu County of Tibet

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10.1186/s40677-022-00216-3
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Abstract

Background: Uncertainties exist in the magnitude and outbreak of debris flow disasters, resulting in significant loss of lives and property to human society. Improved identification of debris flow susceptibility areas can help to predict the location and sphere of influence of debris flow disaster, thus accurately assessing the risk of debris flow disaster and reducing losses caused by such a disaster. The dry-hot valleys of Basu County in the Eastern Qinghai-Tibet Plateau are typical areas of high debris flow incidence, mapping of debris flow susceptibility identification and regional risk assessment is needed in this area. Results: The parameters improved Flow-R model was first applied to identify debris flow susceptibility areas in Basu county using the digital elevation model, flow accumulation, slope, plan curvature, and land use data, followed by debris flow risk assessment. The Flow-R model can output high result accuracy of high-resolution susceptibility to debris flow identification on a regional scale with less data, and its accuracy value is 87.6%, indicating that the suscep - tibility to regional debris flow disaster is credible. This study provides a useful basis for effective prevention of regional debris flow disasters in the future, and provides a useful method for effectively identifying the debris flow susceptibil- ity areas and assessing the related risk in large-scale areas. Conclusions: (1) The debris flow susceptibility areas in Basu County covered 97.04 km (0.79% of the study area), dis- tributed mainly in the Nujiang River Valley, Lengqu tributaries, and both sides of National Highway 318. (2) The debris flow susceptibility areas were dominant in zones characterized by an altitude range of 3000–4000 m, a plane curva- −1 −1 ture of − 2/100 m to 1/100 m , and a low slope of 20°–40°. In addition, the susceptibility areas were dominant in the unused land and less prevalent in the water area. The highest and lowest susceptibility values were observed for cultivated and unused lands, respectively. (3) The debris flow risk in the study areas accounted for 0.82 km and revealed a distribution of high-risk debris flow along roads. The areas with a high debris flow risk were mainly distrib - uted along the mainstream of the Nujiang River, which is the main future protected area. Keywords: Flow-R model, Debris flow, Susceptibility areas, Basu County Introduction Debris flow is a common geological disaster in moun - tainous areas, with complex causes and high suddenness. Under favorable terrains, high loose materials and water amounts flow through gullies under the force of gravity, damaging the surrounded traffic roads, buildings, vegeta - *Correspondence: qhchenqiong@163.com tion, and cultivated lands (Iverson 1997; Tang and Liang College of Geographic Sciences, Qinghai Normal University, Xining 810008, 2008). Every year, debris flows cause thousands of deaths China Full list of author information is available at the end of the article and huge economic losses worldwide (Dowling and Santi © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 2 of 21 2014). China is one of the countries where debris flow models requires extensive field survey data, which are disasters are most severe. Indeed, about 2/3 of the moun- costly and difficult to obtain, particularly at a regional tainous areas in China are affected by debris flow (Cui scale. Although machine learning techniques can be used et  al. 2000). Hengduan, in the southeast of the Qinghai- to assess debris-flow susceptibility at regional scales, but Tibet Plateau, is a mountainous area characterized by the model is complex to build, extremely dependent on high mountains, intense tectonic activity, complex and the quantity and quality of data, with some uncertainty diverse geological and geomorphological environment, in weight assignment, and the training samples need to vagaries of climate and concentrated precipitation time, be representative, etc. their results may be inaccurate as and frequent mountain disasters. According to Bian et al. they are incapable of differentiating susceptibility within about 932 mountain disasters occurred in this region the same debris flow gully (Qing et  al. 2020). The Flow- from 2006 to 2015, causing 1,373 casualties and 2.5 bil- R model is a software model for automatic identification lion yuan in direct economic losses, constituting a seri- of debris flow source areas and estimation of debris flow ous threat to the safety of human life and property in this spread based on GIS tools. Indeed, this model can run region (Bian et al. 2018). With the frequent occurrence of using fewer data requirements (Horton et al. 2008, 2011). extreme rainfall events caused by global climate change, It is not an encapsulated model, and users can adjust its the frequency, scale, and complexity of debris flow in algorithms and parameters according to the character- mountain areas may continue to increase in the future, istics of the study area, achieving good results. In addi- presenting an increased risk of debris flow disasters (Cui tion, the fewer data input requirements allow for carrying et  al. 2015, 2019). Therefore, accurate identification of out a regional-scale assessment before the occurrence the location, path, and extent of potential debris flow dis - of debris flow disasters (Llanes 2016; Sturzenegger et  al. asters can help in effective debris-flow monitoring and 2019). Numerous studies have applied the Flow-R model implementation of policies, which are crucial for accurate in debris flow assessment and showed satisfactory results risk assessment of debris flow disasters. Moreover, they (Blahut et  al. 2010; Baumann et  al. 2011; Horton et  al. can also help in implementing specific and effective pro - 2013; Blais-Stevens and Behnia 2016; Park et  al. 2016; tective measures to, directly or indirectly, reduce or pre- Kang and Lee 2018), These studies mostly compared dif - vent losses caused by debris flow disasters. ferent data resolutions, methods, and parameters, com- Identification of areas susceptible to debris flows is pare the susceptibility values and ranges under different an important approach for qualitative and quantitative parameter configurations. However, in China, relevant assessment of the potential regional debris flow disas - studies on this model have only been carried out in a ters (Fell et al. 2008). Susceptible areas are more likely to few debris flow gullies (Hou et al. 2019; Nie and Li 2019). experience debris flow events. However, it doesn’t neces - u Th s, its applicability needs further exploration. sarily imply a higher frequency of occurrence. Regional Due to the complexity of debris flow, high precision susceptibility mapping allows determining the spatial dis- regional scale susceptibility identification research meth - tribution of debris-flow risk with fewer data requirements ods, large amount of demanded data, not easy to obtain, based on DEM (Horton et al. 2013). It is usually relatively long calculation time and other limitations, regional difficult to identify the site of a debris flow disaster using scale susceptibility research results are often low resolu- ground surveys (Cama et  al. 2017). Therefore, combin - tion, and some high resolution data are only applicable ing debris flow source area detection with debris flow to small regional scale. The Flow-R model is fast and effi - spread prediction is a fast and effective method to assess cient, with a small amount of data input to obtain debris regional debris flow susceptibility (Horton et  al. 2011; flow susceptibility, and is more suitable for application at Pastorello et  al. 2017). There are numerous methods for a large scale. Previous studies related to Flow-R model assessing debris flow susceptibility. Traditional methods mostly compare the threshold results of different param - are based on field disaster investigation, quantitative sta - eters at a small scale, and rarely attempt the susceptibility tistical analysis using mathematical models, and qualita- results at a large regional scale. In this study, Basu County tive analysis (Xu et  al. 2013; Kritikos and Davies 2015; of Tibet was selected as the study area. It is located in the Wang et al. 2017; Xia et al. 2017; Wu et al. 2019). In addi- West Hengduan Mountain area, susceptible to the occur- tion, several researchers have assessed the debris flow rence of natural debris flow disasters. The Flow-R model susceptibility using machine learning algorithms (Zhang was used to determine the identification index thresh - et al. 2019; Hu et al. 2019; Xiong et al. 2020), while others old of critical conditions, such as sediment availability, have combined factors determining debris flow suscepti - water input, and terrain slope, followed by identifying the bility in empirical models (Gomes et  al. 2013; Blais-Ste- debris flow susceptibility in the study area using the flow vens and Behnia 2016; Gong et  al. 2017; Kang and Lee spreading algorithm and energy calculation of motion 2018). Compared with empirical models, mathematical simulation, and an attempt is made to provide higher Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 3 of 21 Fig. 1 The geographic location of the study area resolution susceptibility mapping results while selecting elevation is 2545  m, the terrain in the northeastern is a large regional scale, which is representative and at the high, while in the southwestern part is relatively low. It same time the results are applied to regional risk assess- can be divided into three different geomorphic structure ment, providing a reference for the future development regions: Northwestern Plateau, Central Nujiang deep of regional debris flow disaster prevention and control cut, and Southeastern high mountain wide valley regions strategies. (Local Chorography Compilation Committee of Basu County, 2012). Study area Due to the complicated geological tectonic movement In this study, Basu County of Tibet was selected as the and the heavy erosion by surface water, rivers are gath- study area. Basu county of Qamdo city is located in the ered in the study area. Consisting of three major river southwest of China, the southeast of the Tibetan Plateau, systems, namely Nujiang River, Yuqu River, and Lengqu and the west of the Hengduan mountains, covering an River, with over 100 tributaries. The rivers are recharged 4 2 area of 1.23 × 10 km , with an average altitude of around mainly from rainfall and snow meltwater. On the other 4640 m (Fig.  1). In terms of the geological structure, the hand, the region is dominated by a temperate semi-arid northern, central, and southern parts of the study area plateau monsoon climate, with an average annual pre- belong to the patchwork area of the Leiwuqi terrane, cipitation of 254.5  mm and an average annual amount Jiayuqiao terrane, and Gangdisi massif, respectively. In of rainy days of 75  days, and high evaporation, with an addition to the high degree of metamorphism, the study average annual evaporation of 3000  mm. Precipitation area is characterized by complicated geological structures is mainly distributed from May–September every year, with developed fold fractures and outcropped strata. The accounting for 70% of the annual precipitation. The cli - highest elevation of the region is 6840 m, and the lowest mate in the winter and spring seasons is cold and dry, Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 4 of 21 with limited precipitation, while in summer and autumn, Committee of Basu County 2012), where very typical dry temperature and rainfall increase as a result of southwest and hot valleys are developed. Due to the restriction of monsoon influence. The average annual temperature in the natural environment, Basu County is sparsely popu- the county and valley is 10.4 ℃, while that in the alpine lated (about 40,000 people), with a low level of social and region is below 3 ℃, the temperature is low throughout economic development, focusing on agriculture and ani- the year. Due to the dual effects of regional climate and mal husbandry activities. In addition, the Sichuan-Tibet geomorphic conditions, the overall vegetation cover- Line, which is a famous tourist transport line in China, age in the region is low, showing significant vertical ter - passes through Basu County. rain characteristics. From the valley to the plateau, the sequence of major land cover types are as follows: dry- Characteristics of the regional debris flow disaster hot valley temperate steppe, temperate meadow steppe, The neotectonic movement in the study area is strong, mountain meadow, subalpine meadow, dark coniferous with high and marked elevation differences between forest (dominated by western Sichuan spruce), alpine mountains. Indeed, the study area is characterized by meadow, alpine shrub meadow, alpine sparse vegeta- complex typical deep-cut valleys, with unstable pre- tion, alpine sub-ice and snow, and ice and snow (Local cipitation and glacier movement effects, intense ero - Chorography Compilation Committee of Basu County sion from the crisscrossing rivers, a large longitudinal 2012). drop of the gully bed, and steep terrain. All these On the other hand, the complex and diverse topo- characteristics contribute to the occurrence of geo- graphic and geomorphologic features of the region have logical disasters (eg., mountain collapse, landslide, and significant effects on the redistribution of water and debris flow), threatening the safety of residents and heat conditions. The Foehn effect is very common in traffic roads (Fig.  2). The area presents a high risk of some deep gorge areas (Local Chorography Compilation debris flow development. Indeed, the area is located Fig. 2 Photographs of debris flows in the the study area (upper left: 29° 59′ 15.81″ N, 97° 10′ 55.78″ E; Upper right: 30° 3′ 37.09″ N, 96° 54′ 50.61″ E; Lower left: 30° 2′ 20.89″ N, 96° 45′ 10.50″ E; Lower right: 30° N, 1′ 38.77″ 32.90″ 7′ 97° E) Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 5 of 21 in a relatively complicated plot split zone, with devel- events. Glaciers and snow cover are widely distributed oped fold fracture. The geological structure results in in high, extremely high, and plain mountains of the study joints and fissures development, breaking up rocks. In area. Indeed, with seasonal change and global warming, fact, poor mechanics proprieties of rock mass result in a large amount of water is produced by intensified snow weak weathering resistance, forming a large amount of and ice melt processes, thus increasing the debris flow rock debris in the gully under the influence of gravity. disaster risk (Lv et al. 1999). In addition, the region belongs to the transition zone Due to the complexity of the regional terrain, canyon of the southeast edge of the Tibetan Plateau, where development, rock metamorphism, mountain fragmenta- river erosion is heavy. In the region, gullies are well tion, sparse vegetation, loose soil, unstable precipitation, developed, while valley slopes are steppe and unstable. and snow and ice melt processes, this region provides a Collapse, landslide, and rockfall occur frequently, form- natural disaster-prone environment for debris flow (Lv ing a large deposit amount. Besides the low vegeta- et al. 1999). The residual slope gravel, glacial till, and drift tion cover in the valley, caused by the Foehn effect and pebble soils with poor stability are accumulated, along dry-hot climate, that contributes to debris flows, the with water movement through the slope gradient, at the extremely low temperatures in the high mountain areas foot of the slope, the bottom of the mountain, and the significantly promote the freezing-thaw weathering outlet of secondary gullies, resulting in debris flow event. process. The bare rock of mountains consists of a large The debris flow types in this region include precipitation number of detrital materials (e.g., forming rock-flow - and glacial debris flows, with a prevalence of the precipi - ing hillsides, stone curtains, and Stone River), which tation debris flow type. Glacial debris flow occurs mainly transport a large amount of rock detrital materials to in the quaternary erosion gully area of high-altitude the gullies and provide a large number of loose detrital mountains. Debris flow gullies are mostly pear-shaped, materials (Lv et al. 1999). scoop-shaped, and fan-shaped, with small to medium On the other hand, unstable meteorological conditions scale. and snow meltwater in this region provide water source Debris flow disaster in this region not only threatens conditions for debris flow disasters. The annual and inter - the safety of residents but also the National Highway annual precipitation in the study area is extremely vari- 318, which passes through the study area. Indeed, debris able. Along with concentrated precipitation during the flows often destroy roads and form roadblocks (Luo et al. flood season, most debris flow disasters occur frequently 1996), making this area one of the most endangered sec- over several months. According to the climate station tions of the National Highway 318 (Yang et al. 2012). information of Basu County from 1980 to 2002, the maxi- mum monthly precipitation (183  mm) was observed in Data and methods July 2002, while the longest continuous rainfall dura- Data source tion is 11  days, and the maximum daily precipitation is Obtaining debris flow susceptibility data needs and data 71.1  mm (October 4, 1993). The maximum (375  mm) accessibility according to Flow-R model, the data used and minimum (105.8  mm) annual precipitations were in this study consist of geographic information, remote observed in 1990 and 1983, respectively. The precipita - sensing image, and ground survey data (Table  1). Geo- tion fluctuations can significantly promote debris flow graphic information data includes 12.5  m × 12.5  m Table 1 Data source Data type Source Data resolution DEM ALOS PALSAR 12.5 m*12.5 m https:// asf. alaska. edu/ data- sets/ deriv ed- data- sets/ alos- palsar- rtc/ alos- palsar- radio metric- terra in- corre ction/ Land cover data Global land cover map data products 10 m*10 m http:// data. ess. tsing hua. edu. cn/ Spatial distribution of disaster points Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences – http:// www. resdc. cn/ Landsat-8 OLI remote sensing image USGS 30 m*30 m https:// earth explo rer. usgs. gov/ Building information OpenStreetMap – https:// www. opens treet map. org 2020–2021 Field survey data Debris flow disaster site – Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 6 of 21 Digital Elevation Model (DEM), flow accumulation, Afterward, the cumulative vectorial value of the flow slope, plan curvature, land use with 10 m × 10  m resolu- was obtained by solving multiple equations. The tion, reclassification of land cover data based on different cumulative vectorial values in the study area ranged land use patterns as land use input. Building information from 0 to 37856764. obtained from Map street, while remote sensing image 4. Plane curvature refers to the direction perpendicular data include Landsat-8 OLI remote sensing images, with to the maximum slope. Plane curvature is related to 30  m of resolution. The ground survey data consist of the convergence and dispersion of the flow through geological disaster information of the study area and the the ground surface. In this study in Arcgis software geological disaster data obtained during two field investi - using Curvature tool to obtain: the slope of each gations in the 2020–2021 period. grid was first calculated by referring to the adja - cency matrix using DEM data, and then the second Data preprocessing derivative of the slope was calculated by fitting the DEM, flow accumulation, slope, plan curvature, and pixel with eight adjacent pixels to determine plane land use data were processed in ArcGIS software. DEM curvature. Positive and negative values of plane cur- data were obtained by advanced land observing satellite vature indicate that the surface of the pixel is convex phased array type L-band synthetic aperture radar (ALOS upward and concave upward, respectively. The plane PALSAR) dataset, with a data resolution of 12.5 m. These curvature of the study area ranges from − 104/100 to −1 data were used to determine flow accumulation, slope, 104.344/100  m . and plane curvature data in ArcGIS10.2 software. In 5. In order to obtain an accurate land use map of the addition, land use data were obtained by processing the study area, the 2017 global land cover data, devel- 2017 global land cover mapping performed by Gong et al. oped by Gong et  al. were considered in this study. (2019). Two scenes of images of the study area were first mosaicked under the same projection (WGS_1984_ 1. For DEM processing, 15 scenes of images within UTM_Zone_47N), and then reclassified 10 land the study area were downloaded from the website cover types based on the corresponding attribute and combined under the same projection condi- table using the Reclassify tool in Arcgis software. tion (The output projection coordinate system is set The wetland, tundra/shrub, and bare/glacier snow to: ‘WGS_1984_UTM_Zone_47N’ according to the lands were classified as water, grassland, and unused zone location): According to the properties of the land, respectively, while other land types remained first scene of the image, mosaic of all 15 scenes of unchanged. In total, six types of land use data were DEM data, with consistent attributes, the study area obtained, namely cultivated land, grassland, forest DEM is cropped according to the zone extent and land, artificial surface, water area, and unused land. projections to facilitate further computation and use. Use resample tool to convert 10 m higher resolution 2. Slope refers to the degree of steepness of the ground to lower 12.5  m resolution, output range consistent surface. The slope is defined as the ratio of vertical with dem and aligned, resampling method using the height to horizontal distance. In this study, the slope default method (NEAREST). tool was used in Arcgis software to obtain: the eleva- tion value matrix was calculated using the DEM data Through the above preprocessing, the following data synthesized, and then the slope was obtained from results are obtained (Fig. 3): the proximity matrix. The slope of the study area Regarding data fusion of geological disaster points, two ranges from 0° to 84.169°. geological disaster datasets were considered in this study, 3. Flow Accumulation refers to the accumulated flow obtained from the data center of the Institute of Geo- at each point of the regional terrain, which can be graphic Environment and Natural Resources Research, obtained by the flow simulation method on the Chinese Academy of Sciences, and a field survey in the regional terrain surface. In this study, the Fill tool was region during the 2020–2021 period. The two datasets used in Arcgis software to fill the pits first, and then were merged to create a new geological disaster informa- the Flow Accumulation tool was used to obtain: the tion database. In total, 89 debris flow disaster informa - elevation value matrix was first calculated using the tion points were obtained in the study area. synthetic DEM data, and then the adjacent matrix Remote sensing image preprocessing: In order to obtain was used to calculate the slope value and the accu- high-resolution remote sensing images, two scenes of mulation direction. The flow direction was derived remote sensing images, acquired on November 17 and from the maximum values of the calculated slope August 13 in 2013, with cloud covers of less than 5% in the eight directions, with a range value of 1–128. were downloaded (Path/Row:134/40; 134/39). The data Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 7 of 21 Fig. 3 Data preprocessing results. DEM (a); Slope (b); Plane curvature (c); Flow accumulation (d); Land use (e) processing included geometric and radiometric correc- of landsat8_oli, and the resampling method of cubic Con- tion, mosaic and cropping in ENVI software, followed by volution to fuse the multispectral image (30 m) with the the Gram-Schmidt Pan sharpening tool, sensor selection higher resolution panchromatic band (15  m) to obtain a Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 8 of 21 Fig. 4 Flow chart of Flow-R model higher resolution multispectral image with a synthetic www. flow-r. org/). Horton et al. developed Flow-R model image resolution of 15  m, acquired high-resolution for disaster susceptibility identification (Horton et  al. remote sensing imgaes for subsequent result validation. 2008), which consists of two parts: identification of the potential source area and simulation of debris flow move - Flow‑R model ment (Fig. 4). The model output results in the area under The Flow-R model is a GIS-based simulation model of the debris flow spread range and the associated qualita - regional-scale gravity disaster path, combining debris tive probability of being vulnerable to the potential risk flow source area detection with debris flow spread pre - of debris flow, with higher values indicating a higher diction. The purpose is to locate hazardous processes and probability of debris flow arrival. The potential spread gain insight into existing or potential susceptible areas, indicates the worst-case scenario, so the area of suscepti- mainly involving areas with conditions for debris flow bility results is often larger than the actual area of the site to occur, and reflecting the extent to which debris flows (Horton et al. 2008). may spread (Horton et al. 2008). The model was obtained The identification of potential source area in this model from the Flow-R website of Lausanne University (https:// is based on three critical factors of debris flow occur - rence: sediment availability, water input, and terrain slope. The input variables include DEM, slope, plane curvature, and flow accumulation. Users can add lithol - Table 2 Input parameters used in the Flow-R model ogy, elevation, surface curvature, land use variables as Parameters Method Value input, and limit source region identification conditions Slope – 15°–40° to improve the simulation accuracy (Horton et al. 2008). −1 Plane curvature – − 0.5/100 m By dividing the threshold of each element for source area Flow accumulation Rare event 10 m DEM identification, values in the layer of each input dataset are Direction algorithm Holmgren (1994) dh = 2 Exp = 04.0 divided into three types: source area, non-source area, modified and uncertain area. The potential source area of debris Inertial algorithm Weights Gamma_2000 flow is defined as an area whose superimposed layers Friction loss function Travel angle 11.0_deg were divided into source areas at least once and were Energy limitation Velocity 15_mps never divided into non-source areas. Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 9 of 21 The movement of debris flow is simulated using a the slope range of 15°–40° (Takahashi, 1981; Ricken- spreading algorithm and an energy calculation. Among mann and Zimmermann, 1993; Horton et  al. 2013). them, the spreading algorithm of debris flow is deter - The plane curvature is the curvature perpendicular to mined based on the flow directions algorithm and the the steepest slope, and the negative value is generally inertial algorithm, while the energy calculation of debris the gullies prone to debris flow (Horton et  al. 2013). flow is determined based on the friction loss function Indeed, the value increase with increasing DEM reso- and the energy limitaion. Users can select and adjust the lution, and the values generally range from − 2/100 to −1 thresholds of calculation methods and parameters pro-0.01/100  m (Baumann et  al. 2011). Park et  al. used vided by the model according to the requirements. DEM with 10  m resolution and a threshold value of −1 − 1/100  m to achieve high accuracy of simulation results (Park et  al. 2016). In this study, the DEM data Selection of Flow‑R model parameters resolution was 12.5  m, and the threshold value was set Due to the large scale of the study area and the different −1 to − 0.5 m/100  m . The cumulative amount is the flow environmental factors driving natural disasters, global accumulation of each grid point obtained using the susceptibility distribution from a macro perspective flow simulation algorithm. was considered in this study, selecting input parameters Rare/extreme precipitation events with different based on data availability. Thresholds were fixed based resolutions can be selected in the model. The rare pre - on previous studies (Table 2). cipitation event algorithm was selected in this study. The slope is an important factor in identifying sus - Geological lithology or land use data can be used as ceptible areas to debris flow. According to previ - input providing provenance information (Horton et  al. ous studies, debris flow is more likely to occur under 2011). The geological conditions of the study area are Fig. 5 Comparison between actual debris flow disaster points and Flow-R model results Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 10 of 21 complex, while the accuracy of the publicly available flow, providing targeted monitoring and early warning geological data is low. Therefore, the geological param - reference for accurate assessment of debris flow disaster eter was ignored in this study, defining the geological risk. condition of the study area as an uncertain source area. Disaster risk assessment is the analysis and evaluation Land use data were considered to reflect provenance of life, property, livelihood, and the vulnerability of the input conditions, according to the stability differences disaster-causing factors and exposure bodies that may of debris flow under different land-use types (Xie and cause potential threats or injuries to human society. Dis- Wei, 2011). Indeed, water/grassland, artificial surface/ aster risk per unit area is closely related to disaster sus- cultivated land, forest/unused land were set as source ceptibility and vulnerability of the body exposure within areas, uncertain, non-source areas, respectively. In the a given area, which can be obtained by combining sus- Flow-R model, the flow direction algorithm, inertial ceptibility and vulnerability. In this study, debris flow dis - algorithm, friction loss coefficient, and energy limita - aster risk was determined using the following formula: tion method adopted parameters given by Horton et al. Debris flow disaster risk = Debris flow susceptibility areas∗ (2008). Vulnerability of risk exposure body (1) Methodology of debris flow disaster risk assessment Debris flow disaster in the study area is mainly affected Human society is the most exposed to debris flow disas - by roads, settlements, residential buildings, farmland, ters and related damage. Predicting potential debris flow and engineering facilities. In this study, the selection of susceptibility areas and combining them with exposure risk exposure body factors considered surface objects bodies (artificial buildings, roads…) can help managers related to human society, including artificial buildings, to better evaluate hazard-affected risk. Indeed, estab - roads, houses, and cultivated lands, which are often lishing preventive strategies and implementing effective vulnerable to disaster, resulting in economic losses. measures to reduce human activities in high-risk areas Although forest, grassland, unused land, water body, and can effectively reduce the economic losses caused by other natural environment are also vulnerable to damage, natural debris flow disasters in the region (Shi 2018). In they have some capacity for self-regulation, resulting in this study, the assessment of debris flow disaster risk was low social and economic losses. Indeed, these land types carried out based on susceptibility identification of debris Fig. 6 Random partial validation using Landsat-8 OLI_TIRS images. a1, b1, c1, and d1 represent the susceptibility results of debris flow superimposed on four remote sensing images; a2, b2, c2, and d2 represent base maps of remote sensing images Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 11 of 21 Fig. 7 Disaster occurrence points that fall outside the predicted area Table 3 Data of disaster points not located in susceptibility areas Bodies at risk of exposure along potential debris flow paths in the study area were identified to determine the Points Villages and towns Longitude Latitude area and extent of regional disaster risk exposure. Vec- 1 Gokyim 96° 46′ 31″ 30° 37′ 15″ tor data of buildings, residential areas, agricultural lands, 2 Lagen 97° 1′ 51.1″ 30° 1′ 26.5″ parks, and roads were first extracted from OpenStreet - 3 Jidar 96° 40′ 41.2″ 29° 55′ 34.8″ Map data, and then the completeness of the risk expo- 4 Baima 96° 55′ 16.90″ 30° 3′ 13.28″ sure body at random locations was checked using Google 5 Tongka 96° 32′ 42.48″ 30° 31′ 21.02″ Earth before being assessed. 6 Bangda 97° 16′ 28.7″ 30° 8′ 0.7″ 7 Bangda Results 97° 18′ 11.61″ 30° 7′ 12.38″ Verification of the debris flow susceptibility results 8 Bangda 97° 18′ 12.45″ 30° 7′ 11.07″ The range of susceptibility results output by Flow-R 9 Gyizhong 97° 14′ 35.8″ 30° 19′ 48.8″ is consistent with each layer, and the data resolution 10 Gyizhong 97° 10′ 11.87″ 30° 27′ 18.8″ is 12.5  m × 12.5  m. There are 78947809 raster cells in 11 Yiqen 97° 7′ 31.5″ 30° 33′ 16″ the study area, among which, 6211045 raster cells have susceptibility result value distribution, and the rest of the raster cells are NoData (− 9999). According to the exhibit low vulnerability to flow-debris disasters. The dis - Flow-R model results, the highest and lowest debris- aster risks of artificial facilities, cultivated land, and other flow susceptibility values were 1 and 0.0003, respectively highly vulnerable bodies were determined to evaluate the (Fig.  5), The higher the susceptibility value, the greater regional debris flow disaster risk. the possibility of debris flow spreading, the lower the Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 12 of 21 Fig. 8 Susceptibility distribution of debris flow at different scales. a Susceptibility of debris flow in Basu County at large scale; b Susceptibility of debris flow in part of Nujiang River basin in Eastern Basu County; c Susceptibility of debris flow at the township scale; d Susceptibility of debris flow at the village level; e Debris flow susceptibility of a single debris gully susceptibility value, the lower the possibility of debris verification points was limited and mainly located along flow spreading, and the non-distribution of susceptibility the highway. Indeed, some susceptibility areas identified value means that there are no conditions for debris flow using the Flow-R model were located on slopes and gul- disaster and the debris flow does not spread. The higher lies at higher elevations, making it difficult to validate the susceptibility value, the lower the area range. In this them using field data. Therefore, remote sensing image study, the susceptibility results from the model are veri- data were used to further verify the result. Landsat-8 fied using the distribution of disaster points list data. In OLI_TIRS satellite remote sensing images were used to Arcgis software, the suspensitivity results are extracted to perform random local verification, the main judgment the disaster distribution point data, and the points with is based on whether the part with susceptibility value suspensitivity values are the correct points for the model has almost no vegetation cover, serious surface damage, debris flow simulation, and the points with the value of exposed soil, rough texture, irregular perimeter, incon- − 9999 are the wrong simulation points. The susceptibil - sistent with the overall texture of the surrounding area ity results obtained by the model were validated using the and whether there is a formation area, circulation area, actual debris flow disaster points (89 points). The results accumulation area, these debris flow traces or debris flow showed 78 disaster points in the susceptibility areas iden- ditch, etc. as the basis for judging whether the results are tified by the model, while 11 points were located outside reasonable (Fig.  6). Four groups of susceptibility areas the identified areas, indicating a good simulation accu - (Fig.  6a1, b1, c1, d1) were randomly selected and com- racy value of 87.6%. pared with those identified using remote sensing images The actual debris flow disaster points were identi - (Fig.  6a2, b2, c2, d2). The results revealed that high sus - fied from field investigations. Thus, the number of ceptibility areas included obvious debris flow gullies in Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 13 of 21 Fig. 9 Debris flow susceptibility classes and altitude ranges in Basu County Table 4 Statistics of different debris flow susceptibility classes in Basu County Type Area/km Proportion of susceptibility areas of debris Percentage of flow/% the area in Basu County/% Low susceptibility 72.61 74.8 0.59 Medium-susceptibility 18.49 19.1 0.15 High susceptibility 5.94 6.1 0.05 Total 97.04 100 0.79 the mountain, while areas of low susceptibility exhibited susceptibility information near the disaster occurrences scattered loose debris flow. The overall comparison and points. Indeed, by referring to the susceptibility identifi - verification results suggest good simulation results. cation results of debris flow and local Chronicles (Local The verification results showed 11 disaster points out - Chorography Compilation Committee of Basu County side the susceptibility areas (Fig.  7 and Table  3). This 2012), it was observed that the debris flow disaster in the finding may be due to several reasons. First, the accu - Yuqu river tributary basin in the study area has not devel- racy of input data can affect the identification accuracy oped. As the formation of debris flow disaster is complex of the susceptible areas for debris flow, as the Flow-R is to a certain extent, geological, lithology, soil, and other an empirical model. Second, there were 7 debris flow intrinsic parameters were not used to further restrict the points in the non-susceptible areas in the tributaries of selection of source area. Therefore, the disaster points Yuqu River (points 1, 7, 8, 9, 10, and 11), with a lack of may exhibit local favorable accumulation conditions for Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 14 of 21 20% Low susceptibility Medium -susceptibility High susceptibility 16% 12% 8% 4% 0% altitude / m Fig. 10 Relationship between susceptibility of debris flow and altitude 20% Low susceptibility Medium -susceptibility High susceptibility 16% 12% 8% 4% slope / e 0% 0~55~10 10~15 15~20 20~2525~30 30~3535~40 40~4545~50 50~5555~60 60~6565~70 70~7575~80 Fig. 11 Relationship between debris flow susceptibility and slope Susceptibility areas identification debris flow occurrence, forming debris flow gullies under Debris-flow susceptibility mapping can help to deter - sufficient precipitation (Fig.  7a, c, d). Third, due to the mine and thoroughly assess the most likely affected areas large scale of the study area and the obvious differences in by the flow debris disaster. The susceptible areas are the the natural environment, disaster thresholds may be spa- areas likely to be affected by debris flow disasters, which tially different, resulting in discrepancies in the results. may not occur due to vegetation covering debris flow Fourth, the disaster points come from manual records, gullies. Obtaining accurate data is a labor-intensive task, some debris flows occur in high mountains, which are making it difficult to carry out disaster identification and observable but inaccessible, and the recorded informa- inspection on a regional scale. tion may not represent the exact location of the debris The Flow-R model can rapidly provide the debris-flow flow, leading to discrepancies in the results. Finally, some susceptibility results at a large county scale, with fewer disaster points were recorded as hillslope debris flow input data and a resolution of 12.5  m (Fig.  8). As shown (Table  3, points 3–11), while the plane curvature value in Fig.  8a, the results were not conclusive in the macro- was greater than the threshold set when identifying the scopic scale of the study area, showing small debris-flow source area, potentially causing non-identification of dif - susceptibility areas without being able to examine the fuse slopes (Fig. 7b, c). proportion proportion Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 15 of 21 14% Low susceptibility Medium -susceptibility 12% High susceptibility 10% 8% 6% 4% 2% flow accumulation 0% Fig. 12 Relationship between debris flow susceptibility and flow accumulation 50% Low susceptibility Medium -susceptibility High susceptibility 40% 30% 20% 10% plane curvature 0% -1 / 100m Fig. 13 Relationship between debris flow susceptibility and plane curvature spatial differences of susceptibility. u Th s, by adjusting the 45% Low suscepbility scale, the distribution and differences between the flow- Medium -suscepbility debris susceptibility areas were well identified (Fig.  8b–e). High suscepbility National Highway 318 has great importance and influ - 30% ence on the development of southwest China. Previous studies on debris flow disasters in this region mainly focused on the route along Highway 318 (Zou et  al. 15% 2013), without considering other areas in the study region. In addition, most of the research results were segmented according to debris flow hazard zoning, 0% landuse even some areas are not at risk. Thus, these results have cultivated forest land grass land water land unused land building land land not revealed the change of debris flow risk in different Fig. 14 Relationship between debris flow susceptibility and land use regions at the microscale of the study area and identi- fied the debris flow risk area for subsequent disaster proportion proportion proportion Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 16 of 21 prevention and mitigation. Our study showed that the susceptibility areas of debris flow are not only distributed along National Highway 318 and Lentqu river tributary but also on both sides of Nujiang River valley. Indeed, the susceptibility areas along the Nujiang River valley are large, while no risk was observed in the adjacent Yuqu river tributary. Debris flow susceptibility areas are prone to disasters, presenting a high-risk degree with a small range, distributed mainly along the valley. While areas presenting low-risk are large, spreading around the chan- nel depending on the terrain. Classification and distribution characteristics of debris flow susceptibility areas The debris flow susceptibility area was 97.04 km , accounting for about 0.79% of the study area. The suscep - tibility results were divided into three classes using the natural breaks (Jenks) method provided In Arcgis soft- Fig. 15 A schematic diagram of overlapping parts of debris-flow ware, namely low-susceptibility, medium-susceptibility, susceptibility identification and infrastructure is used to distinguish and high-susceptibility classes (Fig. 9). debris flow risk ( The points refer to the physical environment of the According to the results of the Flow-R model, the spa- study area) tial distribution of susceptibility classes was analyzed. The area of low susceptibility areas was about 72.61 km , accounting for 0.59 and 74.8% of the study area and the total surface of debris flow area, respectively. The reaches 4640 m, and the range of debris flow susceptibil - medium susceptibility area covered about 18.49 km , ity is 2599–5279  m. The debris flow susceptibility areas accounting for 0.15 and 19.1% of the study area and the are mainly distributed in the study area below 4000  m total debris flow susceptibility areas, respectively. The in altitude, and the distribution is more concentrated on high susceptibility area was about 5.94 km , accounting both sides of the river valley at 3000–4000  m in altitude for 0.05 and 6.1% of the study area and the total debris (Fig.  9).The results indicate that deep canyons with high flow area, respectively (Table  4). In addition, the area of elevation differences provide favorable topographic con - medium–high susceptibility region was relatively small, ditions for the occurrence of debris flow disasters, The which is related to debris flow circulation area. Over 70% urban land and roads in the study area are mostly dis- of the areas revealed low susceptibility to debris flow, tributed in the low altitude area of the region, suggesting mainly in the accumulation area, which is the attenuation that regional debris flow disasters may cause significant and diffusion zone of debris flow. Therefore, although damages. debris flow occurs frequently in the study area, the area The results of the relationship between debris flow sus - affected by debris flow disaster is small, while the area’s ceptibility and slope (Fig. 11) showed that the range slope high susceptibility to debris flow accounted for a small with debris flow susceptibility was 0°–75.44°, and the surface. overall distribution was relatively concentrated between The raster file of susceptibility results is converted 20° and 40°. Below 35°, the percentage of susceptibility into Points element shapefiles in Arcgis, and the values areas increases step by step with the increase of slope are extracted to points, then altitude, slope, plane curva- grade; above 35°, the percentage of susceptibility areas ture, flow accumulation and land use values are extracted decreases step by step with the increase of slope grade. to points, and the area is classified into different classes The medium and high level susceptibility areas are mainly according to different feature values, so that the analysis concentrated in the range of slope grades below 40°. This can calculate the percentage of the area of different sus - result suggests that the low slope (between 20° and 40°) ceptibility classes in each feature class. of the study area provides favorable topographic condi- The results of the relationship between debris flow tions for the occurrence of debris flow disasters, while susceptibility and altitude (Fig.  10)show that we did not higher slopes of the study area are more favorable for low restrict the altitude at which debris flow occurs in the debris susceptibility. In the study area, villages, buildings, model, although the average altitude of the study area and farmland are more distributed in low-sloped areas, Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 17 of 21 making debris flow more damaging to society and the economy. The results of the relationship between debris flow sus - ceptibility and flow accumulation (Fig.  12) show that the range of flow accumulation values for debris flow suscep - tibility is 0–37856056, and the susceptibility is mainly distributed in the range of lower flow accumulation. The higher the flow accumulation, the lower the distribution range of debris flow susceptibility areas, but with a higher flow accumulation, the higher the debris flow susceptibil - ity. Susceptibility values were mainly distributed in the range of low flow accumulation rates. However, the grid unit upstream peripheral convergence may exhibit high cumulants under rainfed conditions, resulting in low debris flow diffusion in this grid and gradual downward diffusion. The results of the relationship between debris flow sus - ceptibility and plane curvature (Fig.  13) show that the plane curvature of debris flow susceptibility is − 21.822/ −1 −1 100  m to 20.257/100  m , and the distribution of debris flow susceptibility is relatively concentrated in the plane −1 −1 curvature of − 2/100  m to 1/100  m , among which the medium to high susceptibility is mainly distributed at −1 −1 − 2/100  m to 0/100  m . This indicates that the study −1 −1 area The plane curvature of − 2/100  m to 1/100  m in the study area provides more favorable topographic con- ditions for debris flow spreading and propagation. The debris flow downstream accumulation area is more dis - tributed within the concave terrain with negative values −1 −1 of − 2/100  m to 0/100  m , and the medium to high susceptibility is more easily formed by diffuse accumula - Fig. 16 Classification of debris-flow risk levels in Basu County tion and damage to the downstream. Land use types were extracted by masking in Arcgis software using susceptibility results, and then suscep- Debris flow disaster risk assessment tibility ratios were calculated for each land use type The vector risk exposure body information obtained from (Fig.  14). The susceptibility areas of cultivated land OpenStreetMap was rasterized using ArcGIS software, accounted for 20.00%, with an average susceptibil- with a data resolution of 12.5 m, to ensure that each pixel ity value of 0.1478. In, addition, the proportion values of the grid is aligned with the susceptibility result pixels of susceptibility areas in woodland, grassland, water, obtained by the Flow-R model, the area of the exposure unused, and building lands were 20.61, 14.22, 1.09, 2 body in the region is about 34.07km , and building area 38.15, and 5.93%, with average values of 0.1349, 0.1185, in the study area is relatively small and concentrated, 0.1402, 0.1054, and 0.1068, respectively. The debris- and the main exposeure bodies are roads. The overlap flow susceptibility areas were abundant in the unused between the risk exposure body map and the susceptibil- land and less prevalent in the water area. In addi- ity result of debris flow refers to the risk exposure body tion, the highest and lowest susceptibility values were with high vulnerability located in the debris-flow suscep - observed in cultivated and unused lands, respectively. tibility area. The area and extent of potentially exposed to It should be noted that the proportion of building lands debris flow disaster risk were determined (Fig. 15). in the study area is not high (about 6%), but the propor- The debris flow disaster risk in the region can be tion of building lands with susceptibility reaches nearly obtained by identifying the debris flow susceptibility 6% of the susceptibility types, indicating that the dis- areas that are prone to high property losses and vulner- tribution range of debris flow susceptibility on build - ability to disaster. The potential risk areas of debris flow ing lands is high and can have far-reaching effects on disaster in the study area are mostly located on roads, human activities in the area. 2 covering a total area of about 0.82 km . The potential Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 18 of 21 debris flow risk areas accounted for 0.84% of the total prevent and mitigate debris flow hazards. In risk-free surface of debris-flow susceptibility. In addition, the area areas, there is generally no risk of debris flow disaster. of low-susceptibility, medium-susceptibility, and high- Human activities can, therefore, be carried out normally. susceptibility risks were about 0.64 and 0.14, and 0.04 Investment in disaster prevention and control in the km , respectively. These results indicate that this method non-risk areas can be moderately reduced, thus allow- can reduce the extent of debris flow disaster monitoring ing additional disaster prevention and control resources and warning, target specific areas of the road and build - to be allocated to areas of high debris flow risk. Human ing before planned reinforcement, and allows disaster activities can also be carried out in low-risk areas, but it prevention measures, thus reducing the economic loss is necessary to strengthen disaster warning and detection caused by debris flow disaster. in areas of low debris flow risk and appropriately invest in disaster prevention and mitigation forces. In medium risk areas, artificial construction facilities and agricultural Disaster risk zoning activities need to be reduced, and existing facilities and The risk levels of debris flow in the study area were clas - buildings should be strengthened and relocated to reduce sified based on the risk of debris flow on a unit grid. Sev - 2 the risk of debris flow disaster, improve the awareness of eral regular hexagonal grids, with a unit area of 4 km , disaster prevention among regional residents, strengthen were established using Generate Tessellation tool in Arc- the level of disaster warning and monitoring, and contin- GIS software, while the small marginal areas (less than 2 2 uously detect regional disaster risk. In high-risk areas, it km ) were eliminated. The study area is large, and when is suggested to avoid the construction of artificial facili - the unit area is selected, too small will lead to overly ties and relocate the existing facilities and residential fragmented division results, and too large will lead to areas, and strengthen resource investment in effective difficulty reflecting the differences in the region. After 2 reinforcement management of roads in the study area, to comprehensive consideration and comparison, 4km reduce disaster losses. was finally chosen as the unit area. The regular hexa - The above suggestions can effectively reduce and con - gon is closer to the circle than the regular quadrilateral, trol the regional debris flow disaster, improve the level exhibiting a smaller area/circumference ratio. Indeed, of debris flow warning, particularly in the rainy season, the regular hexagon is a similarly shaped polygon that enhance disaster risk forecasting, implement disaster can be arranged uniformly in space, thus significantly prevention and mitigation related works, thus effectively minimizing the result deviation caused by the boundary reducing the risk of life and property safety caused by effect and better reflecting the internal information and debris flow disaster in Basu County. condition of regional space. The sum of debris flow risk in each hexagonal grid was calculated before classifying Discussion the regional debris flow risk level in the same unit area. It is difficult to accomplish high-resolution susceptibil - The susceptibility values were superimposed on each ity identification at the regional scale, so the Flow-R unit area and the results ranged from 0 to 881.9858, the model, a method in which a small amount of data can susceptibility values per unit area were equal interval quickly obtain more accurate results, is used to identify method classified into four classes, namely no risk (0), the potential spread of debris flow at the regional scale. low risk (0–300), medium risk (300–600), and high risk Because the susceptibility results of the Flow-R model (600–900). The potential disaster risk was visualized to are constrained by the quality of DEM, so in the range reveal the spatial variation of debris flow risk in the study of 12300km , we choose 12.5  m resolution on the basis area (Fig. 16). of not destroying the original data resolution, consider- The debris flow risk map revealed 3 082 regional disas - ing the data accessibility and model operation, to obtain ter risk level grids, including 2  534, 493, 49, and 6 grids the highest possible data resolution and susceptibility showing no risk, low risk, medium risk, and high risk result quality. Of course, the model is limited and does areas, respectively. Therefore, most parts of the study not reflect the local control factors and specific condi - area have exhibited no debris flow risk. Moreover, most tions. The results are often larger than the actual extent debris flow risk areas are of low risk, while the areas of of debris flow occurrence, but the output can be consid - medium and high debris flow risks are small and spatially ered accurate for the purpose of susceptibility mapping scattered. (Horton et al. 2011). According to the results of risk zoning, it is neces- The validation of the results is an important reflection sary to improve strategies to prevent regional debris of the accuracy and validity of the model. In this paper, flow disasters, particularly in areas presenting medium the results are validated based on the disaster points list to high susceptibility areas of debris flow disaster, and and the visual comparison of remote sensing images, reduce human activities in susceptible areas to effectively Xu  et al. Geoenvironmental Disasters (2022) 9:13 Page 19 of 21 and the accuracy of the validation points reaches 87.6%. the study area was assessed at on a large scale. The results According to the comparison of remote sensing images, of this study were not compared and discussed by adjust- the debris flow channels in the susceptibility area have ing data resolution, model methods, and parameters due obvious characteristics, and the overall results of the to the running time of the model, but by referring to the model in the study area are ideal, and the results have a parameter selection in related studies. The susceptibil - high degree of confidence. However, for the accurate por - ity acquisition based on Flow-R is characterized by easy trayal of the debris flow extent for validation, this paper access to data, relatively reliable results (according to the obviously did not do enough. Due to the limitation of comparison and validation of this paper), high resolu- image quality and technical level, the actual occurrence tion of results (generally large scale results are difficult of regional large scale debris flow is difficult to obtain. to apply to road risk evaluation, and the roads in this Because it is difficult to extract debris flow extent infor - study area are critical), and the ability to distinguish local mation directly from the images, firstly, the rainfall is scale debris flow susceptibility changes, based on which concentrated before and after the occurrence of debris regional scale risk evaluation is conducted, and suscep- flow and the cloud cover is large, so the real images are tibility information extraction based on regional debris not easy to obtain. Moreover, the debris flow accumula - flow related Although there may be some deviations tion will be covered by the natural changes of the ground between the results and the actual situation, the large surface with time changes, so it is often impossible to scale high-resolution results can be applied to the over- directly obtain the actual location of debris flow occur - all regional risk assessment and zoning, so that the high rence, and the remote sensing image resolution itself has susceptibility and risk areas can be prevented in advance. some limitations on the results. Due to the difference of natural conditions on a regional scale, the texture fea- Conclusions tures and so on of debris flow are not consistent on the In this study, the 12.5 m resolution DEM data were used images of different regions. Remote sensing interpreta - in the Flow-R model to identify the regional debris flow tion of debris flow information on a large scale is obvi - susceptibility. The results were first validated using the ously more difficult, but the validation of a small scale actual disaster point data combined with remote sensing single debris flow ditch can be carried out by overlaying images, and then the regional disaster risk was further research results with images if high-resolution images evaluated. The main conclusions reached are follows: can be obtained, and such validation can only be carried (1) There was a lack of susceptible debris flow areas out after the occurrence of a disaster, which is undesir- in most parts of the study area. The debris flow suscep - able in areas with frequent human activities. Therefore, tibility areas were mainly observed in the Nujiang River the debris flow risk in the region can be effectively esti - valley, the tributaries of the Lengqu river, and both sides mated before disasters occur, providing references for of National Highway 318, covering a total area of about debris flow prevention and control policies, specific 2 97.04 km (0.79% of the study area). Moreover, low, regional construction planning, as well as disaster predic- medium, and high susceptibility areas covered 0.59, 0.15, tion and early warning systems, thus reducing financial and 0.05% of the study area, respectively. In the Nuji- and economic losses and protecting people’s lives. ang River valley, the debris flow susceptibility was more Debris flow hazards are influenced by the complex sur - widely distributed than that along National Highway 318. rounding environment, and the uncertainty of regional Although the high susceptibility value of debris flow are scale data and the model itself is inevitable, meanwhile, prone to disasters, their area may be small. These areas there must be errors between such nonlinear research were distributed along the valley channel. The debris flow analysis and the actual situation, and it is often neces- in the low susceptibility zone is not easy to occur but has sary to make a lot of adjustment work on parameters to a greater range and extends around the channel, depend- compare with the actual local occurrence to reduce simu- ing on the terrain characteristics. lation errors. Some algorithms in the Flow-R model are (2) The debris flow susceptibility in the study area was mainly derived from empirical algorithms, explaining the mainly distributed in areas with altitude values below multiple different choices that have been made in terms 4000  m, particularly on both sides of the river valley, of method and parameter considered in the model. Hor- at an altitude range of 3000–4000  m. In addition, the ton et  al. (2013), Kang and Lee (2018), and Park et  al. results revealed the distribution of the susceptibility val- (2016), compared different data resolutions, methods, ues within the low-slope range of 20–40°, thus providing and parameters, compared the susceptibility values and favorable terrain conditions for the occurrence of debris ranges under different parameter configurations, and flow disasters. The susceptibility is mainly distributed in explored the most reasonable values of parameters in dif- the range where the flow accumulation is low, and the ferent regions. In this study, the debris flow disaster in higher the flow accumulation, the higher the range of Xu et al. Geoenvironmental Disasters (2022) 9:13 Page 20 of 21 Bian JH, Li XZ, Hu KH (2018) Study on distribution characteristics and dynamic debris flow susceptibility distribution is about less, but evolution of mountain hazards in Hengduan mountains area. In: China with the higher flow accumulation, the debris flow sus - engineering geology annual conference on 2018. Xi’an 2018 ceptibility is much higher. The areas with plane curva - Blahut J, Horton P, Sterlacchini S, Jaboyedoff M (2010) Debris flow hazard −1 modelling on medium scale: Valtellina di Tirano, Italy. Nat Hazard ture from − 2/100 to 1/100  m were more susceptible 10(11):2379–2390 to debris flow, providing favorable terrain conditions for Blais-Stevens A, Behnia P (2016) Debris flow susceptibility mapping using a debris flow diffusion. On the other hand, the debris flow qualitative heuristic method and Flow-R along the Yukon Alaska Highway Corridor, Canada. Nat Hazard 16(2):449–462 susceptibility areas were most abundant in the unused Cama M, Lombardo L, Conoscenti C, Rotigliano E (2017) Improving transfer- land and less prevalent in the water area. In addition, the ability strategies for debris flow susceptibility assessment: application highest and lowest susceptibility values were found in to the Saponara and Itala catchments (Messina, Italy). Geomorphology 288:52–65 cultivated and unused lands, respectively. Chen HK, Tang HM (2011) Evaluation of geological disaster fatalness along (3) The risk exposure body such as buildings, residen - Sichuan–Tibet highway. 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Bull Chin Acad Sci was classified, taking into account 4 km as a unit area. 34(11):1313–1321 The results revealed a low overall debris flow risk level in Dowling CA, Santi PM (2014) Debris flows and their toll on human life: a global analysis of debris-flow fatalities from 1950 to 2011. Nat Hazards the study area, with few and scattered areas of medium– 71(1):203–227 high risk. Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Acknowledgements Eng Geol 102(3–4):99–111 This work was supported by Second Tibetan Plateau Scientific Expedition and Gomes RAT, Guimarães RF, De Carvalho J, Fernandes NF, Do Amaral J (2013) Research Program (STEP) [Grant No. 2019QZKK0906] and China’s National Key Combining spatial models for shallow landslides and debris-flows predic- Research and Development Project (NKPs) [Grant No. 2019YFA0606902]. tion. Remote Sens 5(5):2219–2237 Gong K, Yang T, Xia C, Yang Y (2017) Assessment on the hazard of debris flow Author contributions based on FLO - 2D: a case study of debris flow in Cutou Gully, Wenchuan, Huange Xu and Peng Su designed this study in consultation with Qiong Chen, Sichuan. Chin J Water Resour Water Eng 28(06):134–138 Huange Xu handled the analysis of the data and completed the manuscript Gong P, Liu H, Zhang M et al (2019) Stable classification with limited sample: with the support of other authors. All authors read and approved the final transferring a 30-m resolution sample set collected in 2015 to mapping manuscript. 10-m resolution global land cover in 2017. Sci Bull 64(6):370–373 Horton P, Jaboyedoff M, Zimmermann M, Mazottf B, Longchamp C (2011) Funding Flow-R, a model for debris flow susceptibility mapping at a regional This work was supported by Second Tibetan Plateau Scientific Expedition and scale-some case studies. Ital J Eng Geol 2:875–884 Research Program (STEP) [Grant No. 2019QZKK0906] and China’s National Key Horton P, Jaboyedoff M, Rudaz B, Zimmermann M (2013) Flow-R, a model for Research and Development Project (NKPs) [Grant No. 2019YFA0606902]. susceptibility mapping of debris flows and other gravitational hazards at a regional scale. Nat Hazards 13(4):869–885 Availability of data and materials Horton P, Jaboyedoff M, Bardou E (2008) Debris flow susceptibility mapping All data and materials are available from the corresponding author upon at a regional scale. In: Proceedings of the 4th Canadian Conference on reasonable request. Geohazards: From Causes to Management. Presse de l’Université Laval, Québec Hou YL, Hu SJ, Peng QY, Xu JK, Wu XG (2019) Analysis of debris flow suscepti- Declarations bility in loess gully region: a case study of Laolang Gully in Lanzhou. 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Geoenvironmental DisastersSpringer Journals

Published: Jun 6, 2022

Keywords: Flow-R model; Debris flow; Susceptibility areas; Basu County

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