Applying Debris Flow Simulation for Detailed Hazard and Risk Mapping

This paper study the effect of the Digital Elevation Model (DEM) data parameters ( type, source, and resolution) on a slope unit and landslide susceptibility analysis using Geographic Information System (GIS). The Landslide susceptibility analysis and slope unit delineation is done using six DEM data from three different sources (Bige Map, SRTM, and Wietu) at varying resolutions and pixel sizes. The Frequency Ratio Method is then used to analyze Ghana’s susceptibility to seismic landslides whiles; a pseudo-static critical slip method (Factor of safety method) is used to check the accuracy and also indicate the specific slopes to fail at specific seismic loading. The research aimed to achieve the goal since the method allowed for the attainment of an optimum slope unit and landslide map for each resolution of data regardless of the source. The procedure permitted and informed a decision making on the best DEM DATA and resolution for slope unit and Landslide analysis. The result showcases a DEM Resolution of 18 and 30 m and pixel 30m*30m size to be the best DEM to be used for landslide susceptibility mapping. The results further prove Bige Map and STRM DEM to give a more precise effect than weitu.

The resolution of digital elevation model (DEM) used for watershed delineation and data extracted from the delineated watershed relevant to hydrography plays an important role in the calibration of hydrological models. The effect of DEM resolution on the drainage length, watershed area and average slope are studied in many earlier works. DEM resolution can have impact on hydrologic modeling parameters, water quality simulation results. The impacts of DEM resolution on results of the watershed response and water quality modeling are studied using HSPF (Hydrologic Simulation Program Fortran) model applied to a watershed in Kentucky in this study. Three major streams in this watershed are impaired due to pathogen pollution according to the current state of Kentucky water quality standard set for recreation. The effects of DEM resolution on water quality modeling and calibration for fecal coliform bacteria are studied using a HSPF model. Results from this study indicate that the DEM resolution affects hydrography parameters of the delineated watershed, influence the calibration process, and the non-point and point source loadings to the streams in the watershed.

Micro accelerometers are crucial tools for monitoring and providing early warning of geological disasters. This study focuses on the potential debris flow in Wangjiayuan, Fang County, Hubei Province, aiming to explore the relationship between clay content and debris flow motion characteristics. Physical model experiments were designed with varying clay content to simulate debris flow motion, and micro accelerometers were employed to monitor and record vibration acceleration changes during debris flow initiation and motion. Frequency spectra were generated using Fourier transform to analyze the motion characteristics in the frequency domain. The results showed that the predominant period of debris flow initiation ranged from 15 Hz to 29 Hz, while the period during motion in the middle section of the flow area ranged from 36 Hz to 54 Hz. Higher clay particle content was associated with a decrease in both the frequency range and mean of the predominant period, as well as a reduction in the Fourier amplitude extremum. These findings provide a foundation for monitoring and predicting debris flows, and the vibration signal characteristics of debris flow initiation and motion can be used to infer the clay content, offering insights into early warning systems for such geological hazards.

BackgroundUncertainties 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.ResultsThe 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 susceptibility 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 susceptibility areas and assessing the related risk in large-scale areas.Conclusions(1) The debris flow susceptibility areas in Basu County covered 97.04 km2 (0.79% of the study area), distributed 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 curvature of − 2/100 m−1 to 1/100 m−1, 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 km2 and revealed a distribution of high-risk debris flow along roads. The areas with a high debris flow risk were mainly distributed along the mainstream of the Nujiang River, which is the main future protected area.

To accurately assess landslide susceptibility and model debris flow paths, acquisition of high-resolution elevation data is essential. These data enable precise topographical analysis, considering factors like slope and curvature. But most of the time such high-resolution data will have inaccuracies due to vegetation, especially in tropical region. This study proposes a comprehensive approach that integrates field data and Structure from Motion (SfM) technology to rectify such DEM inaccuracies caused by dense vegetation. The goal is to enhance the accuracy of landslide simulations and volumetric analysis for effective post-disaster management. The Western Ghats, running parallel to India's western coast, has experienced a surge in rainfall-induced landslides, resulting in significant casualties in recent years. Notably, the Pettimudi village landslide in 2020 caused 70 deaths. The unique geomorphological features of the Western Ghats, such as concave curvature, colluvium deposits, and evidence of paleo landslides, contribute to the area's susceptibility. The study emphasizes the need for detailed assessment and mitigation strategies. The proposed method aims to improve the post-event high-resolution DEM accuracy by integrating field-collected elevation values and utilizing Agisoft Metashape software with the SfM algorithm. The rectification process involves combining elevation differences measured during a field study with photogrammetric elevation data. The field-collected elevation differences are crucial for rectifying these points, and enhancing the Digital Elevation Model (DEM) accuracy. The inaccessible source region is improved using a SfM-created DEM from drone footage, resulting in a more accurate post-event DEM. Correcting the 1 m DEM using an SfM-generated DEM proves challenging but significantly improves detail and accuracy, especially over the landslide source area. The impact of the rectification on accuracy is assessed by comparing volumes calculated from the initial DEM and the newly corrected DEM. The difference in volumes of debris depletion and accumulation, computed using initial and corrected DEMs, highlights variations, with depleted volumes being significantly larger due to the extensive depth increase over the landslide source region. These volumes are then utilized in Rapid Mass Movement Simulation (RAMMS) to validate the rectification process and enhance landslide impact predictions. Debris flow simulations in RAMMS, utilizing the rectified 1 m DEM, show specific outcomes at the landslide source region and toe of the landslide. The study emphasizes the importance of integrating SfM technology with field data to improve DEM accuracy, acknowledging the significance of additional field data for further refinement. The potential adoption of highly precise aerial imagery from Unmanned Aerial Vehicle (UAV) surveys is suggested to further enhance the SfM DEM. Debris flow modeling with RAMMS serves as a vital step in validating the accuracy and reliability of the rectified elevation model. Keywords: Landslides, Western Ghats, Pettimudi, Digital elevation model (DEM), Structure-from-Motion (SfM) technology, Rapid Mass Movement Simulation (RAMMS)

Landslides are one of the most damaging geological hazards in mountainous regions such as the Himalayas. The Himalayan region is, tectonically, the most active region in the world that is highly vulnerable to landslides and associated hazards. Landslide susceptibility mapping (LSM) is a useful tool for understanding the probability of the spatial distribution of future landslide regions. In this research, the landslide inventory datasets were collected during the field study of the Kullu valley in July 2018, and 149 landslide locations were collected as global positioning system (GPS) points. The present study evaluates the LSM using three different spatial resolution of the digital elevation model (DEM) derived from three different sources. The data-driven traditional frequency ratio (FR) model was used for this study. The FR model was used for this research to assess the impact of the different spatial resolution of DEMs on the LSM. DEM data was derived from Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) ALOS-PALSAR for 12.5 m, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global for 30 m, and the Shuttle Radar Topography Mission (SRTM) for 90 m. As an input, we used eight landslide conditioning factors based on the study area and topographic features of the Kullu valley in the Himalayas. The ASTER-Global 30m DEM showed higher accuracy of 0.910 compared to 0.839 for 12.5 m and 0.824 for 90 m DEM resolution. This study shows that that 30 m resolution is better suited for LSM for the Kullu valley region in the Himalayas. The LSM can be used for mitigation and future planning for spatial planners and developmental authorities in the region.

Exploring LiDAR data for mapping the micro-topography and tidal hydro-dynamics of mangrove systems: An example from southeast Queensland, Australia

The formation and breaching of a short-lived landslide dam at Hsiaolin Village, Taiwan — Part II: Simulation of debris flow with landslide dam breach

The authors predicted mudflow and debris flow velocities with 350 field and laboratory measurements and obtained a slight trend for V=u to increase with h=d50, and the ratio of V=u is approximately 10 and rarely exceeds 30. The logarithmic relationship of turbulent resistance agrees reasonably well with the measurements. Good results also are obtained with the Manning-Strickler approach. The dispersive stress equation also compares well with the measurements, but only when h=d50 < 50. For individual series data such as Hashimoto and Puerco, there is a trend for V=u to increase with h=d50. But for other individual series data, such as Davies, Rickenmann, Paris, St. Helens, Nevado del Ruiz, Wanglin, Wang, and Wenhai, there is a trend for V=u to decrease with h=d50 (Fig. 1 of the original paper). The discussers have some field data that also show the same phenomena, which are presented in Fig. 1. The database includes a total of 119 flow velocity measurements, in which each point includes flow depth, density (volume concentration), median grain diameter d50, grain size of less than 10% d10, and slope. The data of Jiangjia were obtained from the field measurements in Jiangjia Ravine, Yunnan, China, in 1999; the data of Hunshui were obtained from the field measurements in Hunshui Ravine, Yunnan, China, in 1976–1978; and the data of Liuwan were obtained from the field measurements in Liuwan Ravine, Gansu, China, in 1963–1964. Debris flow is a gravity flow, and the gravity force plays an important role in the movement of debris flow. So debris flow moves on a large slope and deposits on a small slope. Fei and Su (2004) showed that the main driving force of debris flow is provided by particles, not by water. When the volume concentration C 1⁄4 0:27 (or density ρ 1⁄4 1:46 g=cm3), the particle driving force is equal to the water driving force in debris flow. They defined that the minimum density of debris flow is 1:46 g=cm3, and 1:46–1:80 g=cm3 is the range of less viscous debris flow. Therefore, the minimum density of viscous debris flow is 1:80 g=cm3 (C 1⁄4 0:47) (Fei and Su 2004; Yu 2008a). Two types of shear stresses describe these two kinds of debris flows: (1) the viscous stress for viscous debris flow, and (2) the turbulent stress for less viscous debris flow (Fei and Su 2004). As the driving force of debris flow is provided primarily by particles, the coarse particle plays an important role in the movement of debris flow, especially for viscous debris flow with a large volume concentration. So the larger particle diameter, the larger is the velocity. There is a trend for V to increase with d50, and there is a trend for V=u to increase with d50=h. Two empirical equations of mean velocity of viscous debris flow described this relationship (Wu et al. 1993; Yu 2001): V u 1⁄4 27:57 d50 h 0:245 ð1Þ

High-Resolution Topography (HRT) data sets are becoming increasingly available, improving our ability and opportunities to monitor geomorphic changes through multi-temporal Digital Terrain Models (DTMs). The use of repeated topographic surveys enables inferring the sediment dynamics of hazardous geomorphic processes such as floods, debris flows, and landslides, and allows us to derive important information on the risks often associated with these processes. The topographic surveying platforms, georeferencing systems, and processing tools have seen important developments in the last two decades, in particular Light Detection And Ranging (LiDAR) technology used in Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Moreover, HRT data, produced through these techniques, changed a lot in terms of point cloud density, accuracy and precision over time. Therefore, old “legacy” data sets and recent surveys can often show comparison problems, especially when multi-temporal data are not homogeneous in terms of quality and uncertainties. In this context, data co-registration should be used to guarantee the coherence among multi-temporal surveys, minimizing, on stable areas, the distance between corresponding points acquired at different epochs. Although several studies highlight that this process is fundamental to properly compare multi-temporal DTMs, it is often not addressed in LiDAR post-processing workflows. In this paper we focus on the alignment of multi-temporal surveys in a topographically complex and rugged environment as the Moscardo debris-flow catchment (Eastern Italian Alps), testing various co-registration methods to align multi-temporal ALS point clouds (i.e. years 2003, 2009 and 2013) and the derived DTMs. In particular, we tested the pairwise registration with manual correspondences, the Iterative Closest Point (ICP) algorithm and a mathematical model that allows aligning simultaneously a generic number of point clouds, the so-called Generalized Procrustes Analysis (GPA), also in its GPA-ICP variant. Then, to correct the possible small inaccuracies generated from the gridding interpolation process, a custom-developed DTM co-registration tool (GRD-CoReg) was used to align gridded data. Both alignment phases (i.e. at point cloud and DTM level) proved to be fundamental and allowed us to obtain proper and reliable DTMs of Difference (DoDs), useful to quantify the debris mobilized and to detect the spatial and temporal patterns of catchment-scale erosion and deposition. The consistency of DoDs data was verified through the comparison between the erosion estimate of DoDs and the volumes of debris-flow events measured by the monitoring station close to the Moscardo torrent catchment outlet. The GPA-ICP algorithm followed by the GRD-CoReg tool proved to be the most effective solution for improving DoDs results with a decrease of systematic trend due to vertical and horizontal uncertainties between surveys, especially at steep slopes. The net volume difference (i.e. the sediment output from the catchment) of the 2003–2013 period changed from 3,237,896 m3 to 135,902 m3 in DoDs obtained from not co-registered and co-registered DTMs. The volume of debris flows measured at the catchment outlet during the same time interval amounts to 169,660 m3. The comparison with debris-flow volume measures at the monitoring station shows, therefore, that the DTMs obtained from the co-registration processes generate more reliable DoDs than those obtained from the raw DTMs (without the alignment).

Exploring debris flow deposit morphology in river valleys: Insights from physical modeling experiments

Flood incidents that often occur in Karawang regency need to be mitigated. These expectations exist on technologies that can predict, anticipate and reduce disaster risks. Flood modeling techniques using Digital Elevation Model (DEM) data can be applied in mitigation activities. High accuracy DEM data used in modeling, will result in better flooding flood models. The result of high accuracy DEM data processing will yield information about surface morphology which can be used to identify indication of flood hazard area. The purpose of this study was to identify and describe flood hazard areas by identifying wetland areas using DEM data and Landsat-8 images. TerraSAR-X high-resolution data is used to detect wetlands from landscapes, while land cover is identified by Landsat image data. The Topography Wetness Index (TWI) method is used to detect and identify wetland areas with basic DEM data, while for land cover analysis using Tasseled Cap Transformation (TCT) method. The result of TWI modeling yields information about potential land of flood. Overlay TWI map with land cover map that produces information that in Karawang regency the most vulnerable areas occur flooding in rice fields. The spatial accuracy of the flood hazard area in this study was 87%.

Using the physical deterministic model to analyze landslide stability has become a hotspot of landslide disasters research all over the world. The Digital Elevation Model (DEM) resolution has a great influence on the simulation effect of 3 D physical models. However, few researchers have studied the prediction performance of the 3 D models under different DEM resolutions. Therefore, based on the 3 D model Scoops3D, the spatial distribution of landslides was simulated and predicted under five different DEMs resolutions (2.5 m, 5 m, 10 m, 20 m, and 30 m). The optimal parameters of the model were obtained through field investigations and laboratory experiments, and then, the simulation results were compared with the actual landslides distribution. Receiver operating characteristic (ROC) analysis and %LRclass index were used to quantitatively evaluate the prediction performance of the 3 D model under five different DEMs resolutions. The results show that Scoops3D has good performance in landslide spatial distribution prediction. In addition, we also found that the simulation results of high-resolution DEM were not ideal, while the prediction results of medium resolution DEMs (i.e., 5 m and 10 m) were more accurate. Therefore, this study provide a reference to select the most suitable DEM resolution for landslide stability analysis.

Debris flow was mass movement such as; clay, fine sand, organic waste and coarse material such as; gravel, rocks, organic waste, logs mixed with water. Based on historical information the slope of the ancient caldera of Mount Batur that were potentially threatened for the village with debris flows hazards including; Trunyan, Abang Batudinding and Buahan Village. Modeling with DFLOWZ software was supported by spatial data with input parameters namely; Digital Elevation Model (DEM) data and Polyline data. Modeling was an initial approach in predicting the debris inundation area. Research results to get the type of material and debris flow direction. The observed flow anatomy was debris flow in valleys and rivers at an altitude of ± 1500 msl, curved in the valleys of the hills and the direction of flow is to the west. The DFLOWZ analysis shows that the retained deposit area that has the potential to cause debris avalanches is 49,830 m2, with an inundation height based on 5-7 m thick slope valley. The results of the modeling serve as a form of early warning for communities affected by the threat of inundation of debris flows.

Using low resolution digital elevation model (DEM) data, reconstruction of cartographic contours is focused on. With a small number of control points, we aim to draw a smooth curve as well as to compute the area and perimeter of a closed contour accurately. The input data forms an oriented contour, each control point of which consists of three values: x, y, and tangent angle. An interpolation method based on arc spline is developed. The method can reproduce G1 continuous curves,in which the exact area and perimeter can be calculated. In a numerical experiment for analytically defined curves, accurate computation of the area and perimeter was achieved with a smaller number of control points. In another experiment using a DEM data, the reconstructed contours were smoother than those by a conventional method.