Debris Flow Occurrence Research Articles

Debris Flow Susceptibility Assessment of Sichuan-Tibet Railway Based on GIS and CF-LR Coupling Model

Sichuan-Tibet Railway is an important transportation system in China, which is of great significance to promote the communication between Tibet and the mainland. This paper takes the lin-chang section of the Sichuan-Tibet railway with a width of 2km on both sides as the research area. The occurrence of debris flow must have appropriate solid source, water source and topographic conditions. In this paper, ten disaster-causing factors are selected, including relief degree of land surface, profile curvature, silty soil, distance to the rive, distance to the fault, elevation, slope aspect, NDVI, average annual rainfall and land utilization rate. The information of disaster-causing factors was extracted from Arcgis and evaluated by the certainty factor model. The probability of debris flow in the study area was calculated by the coupling model of binary Logistic regression model combined with certainty factor model. ROC curve analysis of the CF-LR model shows that AUC is 0.89, indicating that the model has a good effect. The distribution map of debris flow susceptibility in the study area was obtained by using grid calculator in GIS. The results show that under the CF-LR model, extremely low, low, medium and high prone areas cover 2547km₂, 235km₂, 246km₂ and 1099km₂ respectively, accounting for 61.72%, 5.69%, 5.96% and 26.63% of the total area of the study area, respectively. The high risk areas are mainly in Milin, Nyingchi and Bomi counties. This study provides an important reference for assessing the risk of debris flow geological disaster on Sichuan-Tibet Railway.

SUSCEPTIBILITY ASSESSMENT OF RAINFALL-INDUCED DEBRIS FLOW IN THE LOWER REACHES OF YAJIANG RIVER BASED ON GIS AND CF COUPLING MODEL

Abstract. The lower reaches of the Yajiang River are high in the east and low in the west, with abundant rainfall, and contain a large amount of hydropower resources that have not been exploited and utilized. Nonetheless, due to the unique geographical environment in southeast Tibet, rainfall debris flow is one of the frequent geological disasters in this area. In this research, 42 debris flow points were collected, ten disaster-causing factors were selected, and satellite elevation data were analyzed to evaluate the disaster susceptibility of the study area. The disaster-causing factors information is extracted from Arcgis. The certainty factor model (CF) was used to calculate the coefficient of certainty of 10 factors including fault distance, elevation, normalized difference vegetation index (NDVI), average annual rainfall, profile curvature, relief, silt content, TWI, SPI, and slope aspect. The Analytic Hierarchy Process (AHP), Binary Logistic Regression (LR), Random Forest (RF) and CF model were used to analyze and predict the possibility of debris flow occurrence. The results show that the accuracy of the CF-LR model is the highest under the verification of the ROC curve. In the prediction model, the high-risk areas of debris flow are mainly concentrated in the first half of the lower reaches of the Yajiang River and distributed along both sides of the river bank. After bringing the data of different annual rainfall into the model, it is found that the saturation critical value of debris flow water source in the study area is within the range of 600–700mm annual rainfall.

Debris Flow Hazard Assessment in District Chitral, Eastern Hindu Kush, Pakistan

The aim of this study is debris flow risk zonation using geological and hydrometeorological indicators in district Chitral, Hindu Kush Region Northwest Pakistan. The research is based on secondary data. Multi criteria Analysis (MCA) in Geographical Information System (GIS) environment was used to achieve the objective of the study. The geological and hydrometeorological parameters were analyzed by making five classes of each parameter. The classes are ranked as most favorable and least favorable with numerical weights. The weights were assigned in accordance to their importance in debris flow occurrence. Then weighted overlay analysis techniques were applied to develop composite map representing the importance of each factor. Debris flow risk zonation map was resulted into four classes very high risk zones, high risk zone, moderate zone, low risk zone. The geology of the study area is diverse with frequents earthquakes. Similarly the forest cover is decreasing due to anthropogenic activities. The area is also characterized by long cold winters with frost action. These factors are destabilizing the slope. During summer season rain storm event results high surface runoff and peak discharge in the perennial and non-perennial channels which results flood and debris flow. These events result human life loss and disruption. The main villages located in very high risk zone are Mulkoh, Mastuj, Reshun, Shegram, Terich Gol, Rogar, Asurat, Boni, Brep and Rech Tockhow. They have been frequently affected by hazard in the past decade. Out of the total area, very high risk zone is expanded over 8%, high risk zone is expanded over16%, moderate risk zone is 29% and the rest is low risk zone. This study has highlighted the risk zones which will help disaster management authorities and policy makers to reduce the risk of debris flow in future.

Historical activity of debris flows in the medium-high mountains: Regional reconstruction using dendrogeomorphic approach

Detailed knowledge of the occurrence of debris flows in the past is key to understanding their linkage to changing climatic variables and their occurrence in the future. For a comprehensive understanding of the origin of these processes, regional reconstruction is optimal rather than detailed analysis of isolated catchments. This study presents the results of a dendrogeomorphic reconstruction of debris flows across an entire medium-high mountain range in Central Europe covering more than 500 km2. The tree-ring data allowed the reconstruction of 96 debris flow events at 21 sites. The average frequency of events was 6.8 per decade, which is comparable or higher compared to alpine valleys. A detailed analysis of potential precipitation triggers was also performed in the paper, whose magnitude significantly influenced not only the number of debris flows but also their magnitude. Debris flows occur in two forms in the study area, with channelized debris flows showing significantly higher magnitude but lower frequency than fan debris flows. The differences between the two types are probably due to the different source of material that is reactivated during precipitation events of different duration and magnitude.

Debris flow susceptibility assessment of Leh Valley, Ladakh, based on concepts of connectivity, propagation and evidence-based probability

The Leh Valley which lies within the Trans Himalayan state of Ladakh, India, is known to be affected almost annually by debris flows ranging from minor to catastrophic scale events. The effect has been getting magnified due to increased urbanization and rapid growth in tourism industry. Though these flows are triggered by intense and abnormal rainfall events the conditioning factor has always been the topography and sediment availability. A lucid acknowledgement of the terrain condition and the degree of vulnerability of such events is required. For this a detail investigation of sediment availability, topographic conditions and their relation with known events becomes crucial. This study utilizes index of connectivity (IC) model to understand the sediment source-sink relationship and farther applied Flow-R model to simulate the probable scenario of events through predefined algorithms. We then use the Weights of evidence (WOE) method to compute the statistical probability of debris flow occurrence. This paper demonstrates the application of these three independent techniques and their implementation in a highly rugged terrain of Ladakh which is a region of frequent debris flows onslaught. The IC and Flow-R models are found to be counter supportive and effective in delineating areas which could be affected by flows that will solely originate in upstream areas where high angle channels directly connected to sediment sources are present. WOE-based model determines the probability of the rare and extensive flows that results from downward integration of other drainage networks in an open fan area.

Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods

Debris flows, triggered by dual interferences extrinsically and intrinsically, have been widespread in China. The debris-flow susceptibility (DFS) assessment is acknowledged as the benchmark for the mitigation and prevention of debris flow risks, but DFS assessments at the national level are lacking. The role of human activities in the DFS assessment has always been overlooked. On the basis of a detailed inventory of debris-flow sites and a large set of environmental and human-related characteristics, this research presents the comparative performance of the well-known information value (IV), logistic regression (LR) and random forest (RF) models for DFS assessments in China. Twelve causative factors, namely, elevation, slope, aspect, rainfall, the normalized difference vegetation index (NDVI), land use, landform, geology, distance to faults, density of villages, distance to rivers and distance to roads, were considered. Debris-flow susceptibility maps were then generated after the nonlinear relationship between the debris-flow occurrence and the causative factors was captured. Finally, the predictive performance of the three maps was evaluated through receiver operating characteristic (ROC) curves, and the validation results showed that areas under the ROC curves were 81.98%, 79.96% and 97.38% for the IV, LR and RF models, respectively, indicating that the RF model outperformed the other two traditional statistical methods. The importance ranking of the RF model also revealed that distance to roads, slope and rainfall dominated the spatial distribution of debris flows. This is the first experiment to compare between the traditional statistical and machine learning methods in DFS studies for the whole of China. Our results could provide some empirical support for China’s policymakers and local practitioners in their efforts to enable residents to be less vulnerable to disasters.

Debris-Flow Channel Headwater Dynamics: Examining Channel Recharge Cycles With Terrestrial Laser Scanning

Debris-flows present a natural hazard to the safe operation of linear infrastructure in mountainous environments. The most significant contributor to debris-flow occurrence is a supply of readily erodible material, often created by rockfalls and other shallow landslides. The spatial distribution and total volume of storage are also critical factors, controlling the initiation location, predominant flow type, and termination location of debris-flow surges. Therefore, there is a need to be able to systematically incorporate debris recharge processes and timeframes into the monitoring and characterization of debris-flow hazards. In this work, the authors present the results of 7 years of terrestrial laser scanning (TLS) captured at the White Canyon. The White Canyon represents an analog to large scale, steep catchments to investigate the role of sediment supply on debris-flow processes. The TLS dataset was collected at monthly to quarterly intervals, providing a basis for analysis of debris transfer processes occurring on the study slope. A rockfall database of over 72,000 events was generated from 52 change detection analyses and is linked to catchment recharge and transfer processes. The results indicate that the 17 channels analyzed in the White Canyon do not directly match the conceptual models proposed from the supply theory. The channels display a variety of behaviors when exposed to the same climate signature. The temporal data acquisition rate was found to have a significant influence on the dynamics of movement that can be interpreted from TLS change detection analysis. The work highlights the need for higher frequency monitoring and the integration of climate data into the analysis, in order to better understand these dynamic processes.

Rainfall thresholds of debris flows based on varying rainfall intensity types in the mountain areas of Beijing

Three strong storms that occurred on July 21, 2012, July 20, 2016, and July 16, 2018 caused severe flooding and debris flows in the mountain areas of Beijing. The detailed records of these storms and the actual occurrence time of the debris flows provide an opportunity for evaluating the thresholds derived from different types of rainfall intensities. Herein, a new rainfall threshold of debris flows is derived from the real-time rainfall intensities in Beijing. In addition, the thresholds are estimated based on the average rainfall intensities over the interval from the beginning of the rainfall to the debris flow occurrence and over the entire rainfall duration. The results show that the various types of rainfall thresholds display significantly different capabilities in terms of separating the storms with positive debris-flow response from those with negative debris-flow response, and the real-time rainfall threshold exhibits the best performance. Moreover, our data indicate that the debris flows in Beijing are triggered by the combined work of rainfall intensity and cumulative precipitation. A debris flow is initiated only when the cumulative precipitation and rainfall intensity simultaneously reach a threshold level.

A Comparative Analysis of Certainty Factor-Based Machine Learning Methods for Collapse and Landslide Susceptibility Mapping in Wenchuan County, China

After the “5·12” Wenchuan earthquake in 2008, collapses and landslides have occurred continuously, resulting in the accumulation of a large quantity of loose sediment on slopes or in gullies, providing rich material source reserves for the occurrence of debris flow and flash flood disasters. Therefore, it is of great significance to build a collapse and landslide susceptibility evaluation model in Wenchuan County for local disaster prevention and mitigation. Taking Wenchuan County as the research object and according to the data of 1081 historical collapse and landslide disaster points, as well as the natural environment, this paper first selects six categories of environmental factors (13 environmental factors in total) including topography (slope, aspect, curvature, terrain relief, TWI), geological structure (lithology, soil type, distance to fault), meteorology and hydrology (rainfall, distance to river), seismic impact (PGA), ecological impact (NDVI), and impact of human activity (land use). It then builds three single models (LR, SVM, RF) and three CF-based hybrid models (CF-LR, CF-SVM, CF-RF), and makes a comparative analysis of the accuracy and reliability of the models, thereby obtaining the optimal model in the research area. Finally, this study discusses the contribution of environmental factors to the collapse and the landslide susceptibility prediction of the optimal model. The research results show that (1) the areas prone to extremely high collapse and landslide predicted by the six models (LR, CF-LR, SVM, CF-SVM, RF and CF-RF) have an area of 730.595 km2, 377.521 km2, 361.772 km2, 372.979 km2, 318.631 km2, and 306.51 km2, respectively, and the frequency ratio precision of collapses and landslides is 0.916, 0.938, 0.955, 0.956, 0.972, and 0.984, respectively; (2) the ranking of the comprehensive index based on the confusion matrix is CF-RF>RF>CF-SVM>CF-LR>SVM>LR and the ranking of the AUC value is CF-RF>RF>CF-SVM>CF-LR>SVM>LR. To a certain extent, the coupling models can improve precision more over the single models. The CF-RF model ranks the highest in all indexes, with a POA value of 257.046 and an AUC value of 0.946; (3) rainfall, soil type, and distance to river are the three most important environmental factors, accounting for 24.216%, 22.309%, and 11.41%, respectively. Therefore, it is necessary to strengthen the monitoring of mountains and rock masses close to rivers in case of rainstorms in Wenchuan county and other similar areas prone to post-earthquake landslides.

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.

Debris flow initiation in postglacial terrain: Insights from shallow landslide initiation models and geomorphic mapping in Southeast Alaska

AbstractDebris flows pose persistent hazards and shape high‐relief landscapes in diverse physiographic settings, but predicting the spatiotemporal occurrence of debris flows in postglacial topography remains challenging. To evaluate the debris flow process in high‐relief postglacial terrain, we conducted a geomorphic investigation to characterize geologic, glacial, volcanic, and land use contributions to landslide initiation across Southeast Alaska. To evaluate controls on landslide (esp. debris flow) occurrence in Sitka, we used field observation, geomorphic mapping, landslide characteristics as documented in the Tongass National Forest inventory, and a novel application of the shallow landslide model SHALSTAB to postglacial terrain. A complex geomorphic history of glaciation and volcanic activity provides a template for spatially heterogeneous landslide occurrence. Landslide density across the region is highly variable, but debris flow density is high on south‐ or southeast‐facing hillslopes where volcanic tephra soils are present and/or where timber harvest has occurred since 1900. High landslide density along the western coast of Baranof and Kruzof islands coincides with deposition of glacial sediment and thick tephra and exposure to extreme rainfall from atmospheric rivers on south‐facing aspects but the relative contributions of these controls are unclear. Timber harvest has also been identified as an important control on landslide occurrence in the region. Focusing on a subset of geo‐referenced landslides near Sitka, we used the SHALSTAB shallow landslide initiation model, which has been frequently applied in non‐glacial terrain, to identify areas of high landslide potential in steep, convergent terrain. In a validation against mapped landslide polygons, the model significantly outperformed random guessing, with area under the curve (AUC) = 0.709 on a performance classification curve of true positives vs. false positives. This successful application of SHALSTAB demonstrates practical utility for hazards analysis in postglacial landscapes to mitigate risk to people and infrastructure.

Some Considerations for Using Numerical Methods to Simulate Possible Debris Flows: The Case of the 2013 and 2020 Wayao Debris Flows (Sichuan, China)

Using a numerical simulation method based on physical equations to obtain the debris flow risk range is important for local-scale debris flow risk assessment. While many debris flow models have been used to reproduce processes after debris flow occurrence, their predictability in potentially catastrophic debris flow scenarios has mostly not been evaluated in detail. Two single-phase flow models and two two-phase models were used to reproduce the Wayao debris flow event in 2013. Then the Wayao debris flow event in 2020 was predicted by the four models with the same parameters in 2013. The depth distributions of the debris source and deposition fan were mapped by visual interpretation, electric resistivity surveys, field measurements, and unmanned aerial vehicle (UAV) surveys. The digital elevation model (DEM), rainfall data, and other simulation parameters were collected. These models can reproduce the geometry and thickness distribution of the debris flow fan in 2013. However, the predictions of the runout range and the deposition depth are quite different from the actuality in 2020. The performance and usability of these models are compared and discussed. This could provide a reference for selecting physical models to assess debris-flow risk.

Susceptibility Assessment of Debris Flows Coupled with Ecohydrological Activation in the Eastern Qinghai-Tibet Plateau

The eastern margin of the Qinghai-Tibet Plateau is an extreme topography transition zone, and characterized by significant vegetation zonation, in addition to geographic features (such as enormous topographic relief and active tectonics) that control the occurrence of debris flows, which are rapid, surging flows of water-charged clastic sediments moving along a steep channel and are one of the most dangerous mountain hazards in this region. There is thus an urgent need in this region to conduct a regional-scale debris flow susceptibility assessment to determine the spatial likelihood of a debris flow occurrence and guarantee the safety of people and property, in addition to the smooth operation of the Sichuan-Tibet transport corridor. It is, however, a challenging task to estimate the region’s debris flow susceptibility while taking into consideration the comprehensive impacts of vegetation on the occurrence of debris flows, such as the positive effect of root anchoring and the negative effect of vegetation weight loads. In this study, a novel regional-scale susceptibility assessment method was constructed by integrating state-of-the-art machine learning algorithms (such as support vector classification (SVC), random forest (RF), and eXtreme Gradient Boosting (XGB)) with the removing outliers (RO) algorithm and particle swarm optimization (PSO), allowing the impacts of vegetation on debris flow initiation to be integrated with the topographical conditions, hydrological conditions, and geotechnical conditions. This method is finally applied to assess the regional-scale susceptibility of debris flows in the Dadu River basin on the eastern margin of the Qinghai-Tibet Plateau. The study results show that (i) all hybrid machine learning techniques can effectively predict the occurrence of debris flows in the extreme topography transition zone; (ii) the hybrid machine learning technique RO-PSO-SVC has the best performance, and its accuracy (ACC) is 0.946 and the area under the ROC curve (AUC) is 0.981; (iii) the RO-PSO algorithm improves SVC, RF, and XGB performance (according to the ACC value) by 3.84%, 2.59%, and 5.94%, respectively; and (iv) the contribution rate of ecology-related variables is almost only one-tenth that of topography- and hydrology-related factors, according to the factor important analysis for RO-PSO-SVC. Furthermore, debris flow susceptibility maps for the Dadu River basin were created, which can be used to assess and mitigate debris flow hazards.

The rainfall thresholds and soil characteristics of large geological disasters in Zhejiang, China

Based on observed minute precipitation data during Lekima influencing Zhejiang (from 2019–08-07 00:00:00 to 2019–08-12 23:55:00), the rainfall thresholds for debris flow and landslide are investigated. The rainfall intensity and duration (I-D) thresholds are I = 1247.73D−403.44 + 19.09 and I = 142.63D−0.58–3.37 for debris flow and landslide, respectively. Hourly meteorological data and soil data reveal that (1) the soil volume water content (SVWC) at deep soil layers (50–100 cm) fluctuated continuously during the occurrence of debris flow and landslide. At the end of the landslide period, SVWC at deep soil layers returned stable. (2) At the same soil layer, there was little difference between the upper adjacent values of SVWC in the periods of debris flow and landslide, but the lower adjacent values in the period of landslide were smaller. However, different from the distribution of soil moisture, the lower adjacent values of the soil temperature at all layers were basically the same when debris flow and landslide occurred, while the upper adjacent values were higher when landslide occurred than when debris flow occurred. (3) Compared with the whole typhoon influencing period and debris flow occurrence period, ground surface temperature and surface air temperature, soil temperature, and surface air temperature showed better correlations in landslide occurrence period. (4) Both during debris flow and landslide occurrence time, the soil temperature at all layers did not change with the change of soil depth. Soil temperature and moisture at shallow soil layers owned better correlations than at deep soil layers.

New insights into the occurrence of the catastrophic Zhaiban slope debris flow that occurred in a dry valley in the Hengduan Mountains in southwest China

New insights into the occurrence of the catastrophic Zhaiban slope debris flow that occurred in a dry valley in the Hengduan Mountains in southwest China

Differences in the occurrence of debris flows in tropical and temperate environments: field observations and geomorphologic characteristics in Serra do Mar (Brazil) and British Columbia (Canada)

Debris flows are among the most destructive types of mass movements throughout the world and are not restricted to certain climate zones or geological environments. In opposite corners of the American continent, Canada and Brazil have experienced several historic debris flows. As part of a one-year international exchange program, the lead author compared morphological evidence related to debris flows and the morphometry of watersheds at sites in Canada and Brazil, with the ultimate goal of improving the understanding of debris flows in Brazil. Field surveys carried out in both areas in 2019 and 2020 permitted observation of the debris-flow signatures, as well as the physical aspects of the surrounding areas and morphometric mapping of watersheds. Both areas exhibit similar typical features of debris flows, and the morphometric results indicate differences that may influence the recurrence of events at the sites in Canada as compared to Brazil due to their higher values for the parameters area > 25° (A25), relief ratio (Rr), and Melton ratio (Mr) at the Canadian sites; however, this dataset is limited. Compared to results in the literature from around the world, values of morphometric parameters at Brazilian sites are within the ranges observed in other tropical climates.

Effects of Glacier and Geomorphology on the Mechanism Difference of Glacier‐Related Debris Flow on the South and North Banks of Parlung Zangbo River, Southeastern Tibetan Plateau

Alpine glaciers are vulnerable to climate changes, and their recession due to warming has already induced a large number of geohazards closely related to the glacial motion, such as debris flow. Strong coupling of the geology, geomorphology, climate, and glacial action controls the type, size, development, and frequency of debris flow along the Parlung Zangbo River, Southeastern (SE) Tibetan Plateau. Field investigation in recent years indicates that the north bank is prone to much more frequenter debris flows than the south bank. The sharp contrast between the two river banks is due to the different formation conditions of debris flows, especially glaciers and geomorphology. The present paper examines the differences in the glacier and geomorphology conditions for glacier‐related debris flows to occur, through a combination of field investigation, interviews with local residents, and geomorphological parameter, and glacier distribution analysis in the ArcGIS platform. The result indicates that, compared to the south bank of the Parlung River, the north bank has more favorable conditions in the glacier and geomorphology south, which is more conducive to the occurrence of debris flow. In conclusion, three kinds of mechanisms due to different formation conditions, especially glacier and geomorphology, are analyzed. The glacier‐related debris flows on the south and north banks occur through different mechanisms due to different formation conditions. The debris flows on the north bank primarily occur in modes I and III, while the south bank is dominated by modes II and III debris flows. Based on the result, effective measures are proposed for mitigating damage to roads and railways within this area.

Watershed landslide evolution combined with topographic factors to delineate the spatio-temporal distribution of sediment sources in central Taiwan

This study uses topographical factors to quantitatively describe the uplift amplitude of the plate movement, collects and analyzes the long-term series of rainfall, earthquakes, landslides and debris flows in watersheds from 1993 to 2019, explores the evolution mechanism of landslides, and uses conceptual models to map the areas prone to occurrence of potential debris flows in central Taiwan. Results show that the topographic factors of a watershed can effectively reflect the concavity index (θ) of the crustal uplift, and the spatial distribution of the concavity index in the watershed can be easily estimated. Combining the spatial distribution of concavity index and stream order, a watershed-scale potential debris flow map can be obtained. According to the interpretation rate change of the relationship between the concavity index and the watershed collapse rate, we can understand the occurrence time of landslides and/or debris flows, as well as the sediment transport dynamics of the watershed. The change of the interpretation rate of the concavity index to the collapse rate can be used as a guideline for the establishment of a debris flow warning system.

Assessment of the Potentiality to the Debris-Flow Occurrence from Physiographic and Morphometrics Parameters: a Case Study in Santo Antônio Basin (Caraguatatuba, São Paulo State, Brazil)

This work aims to evaluate the potential for the debris-flow triggering from Santo Antônio hydrographic basin, located in the Serra do Mar region on North Coast of the State of São Paulo, Brazil, based on physiographic attributes, rainfall data, and morphometric parameters. For this purpose, hydrographic basin techniques were applied, assessing the vulnerability to the debris flow from geomorphological, geological, climatic, and anthropic aspects, and morphometric parameters relevant to the triggering of these processes in watersheds were calculated. Seven physiographic units were identified, which supported the understanding of geological and geomorphological aspects of the basin: coastal plains; river plains; colluvium and talus ramps; escarpments of Serra do Mar; upland of Paraitinga; mountainous relief and hillocks domain. The sub-basins located in steep sections of the relief, with high slopes, valleys, and channels docked, high drainage densities present higher values in the morphometric parameters, indicating a greater potential for triggering and occurrence of debris-flow processes. The joint analysis of physiographic compartmentalization with the identification of relief features, slope, amplitude, valley, slope shapes and morphometric parameters, is extremely relevant to recognize hydrographic basins susceptible to debris flows, as it integrates, and correlates aspects of the physical environment considered to trigger in the occurrence of these processes.

Changes in debris-flow susceptibility after the Wenchuan earthquake revealed by meteorological and hydro-meteorological thresholds

After the 2008 Wenchuan earthquake (Mw 7.9), an increase in rainfall-induced debris flows was initially observed in the earthquake-hit region (Sichuan, China). In the following years, the frequency of debris flows gradually reduced, indicating a recovery of stability of debris deposits originated from the earthquake. To assess these dynamically changing conditions, empirical thresholds have been identified to predict post-seismic debris flow occurrences with two approaches: a meteorological approach based only on precipitation characteristics, and a hydrometeorological approach that also considers the hydrologic conditions before the onset of rainfall. Both used the available record of precipitations and debris flows that occurred in the years 2008–2015 in Wenchuan county. Hydrometeorological thresholds for debris flows have been identified at the catchment scale by assessing the water balance with a simplified lumped hydrological model, based on the Budyko framework, and using catchment water storage as a proxy of the moisture state of the slopes. The results indicate that both meteorological and hydrometeorological thresholds allow catching the progressive recovery of stability of the debris deposits. In particular, the assessment of water balance at the catchment scale highlights the role played by the hydrological processes affecting the slopes, leading to the definition of reliable and robust thresholds. The hydrometeorological approach is thus recommended to define thresholds for early warning of debris flows at the catchment scale.