Influence of DEM resolution on landslide simulation performance based on the Scoops3D model

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.

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.

<div> <p>The Hydrologic Simulation Program-FORTRAN (HSPF) model is widely used to develop management strategies for water resources and to evaluate the hydrologic effect of various management scenarios. The spatial resolution of the input data used to parameterize HSPF model may induce uncertainty in model outputs. In this study, the impact of spatial resolutions of Digital Elevation Model (DEM) and land use map on the uncertainty of HSPF predicted flow and sediment were evaluated. DEM resolution can affect stream length, watershed area and average slope, whereas land use data resolution can lead to redistribution of land use information. Results showed that finer resolution DEM and land use maps can generate higher flow volumes and sediment loads compared to modelling scenarios using inputs of coarse resolution. The relative change in model performance between the baseline scenario (high-resolution) and scenarios of coarser resolution described uncertainties due to DEM and land use spatial information, and the probability density function of these uncertainties was used to estimate these uncertainties. Modelled flow and sediment uncertainty due to DEM resolution seems to follow a log-normal and a general extreme value distribution respectively, whereas modelled flow and sediment uncertainty due to land use resolution seems to both follow a general extreme value distribution. Overall, results highlight the need for a high-resolution DEM and land use maps in the application of the HSPF model while they provide useful information for reducing the model uncertainties.</p> </div> <p> </p>

Worldwide, landslides claim many lives each year, with an average of 162.6 deaths reported in Japan from 1945 to 2019. There is growing concern about a potential increase in this number due to climate change. The primary source of shallow and rapid landslides within watersheds is the 0-order basins, which are located above the 1st order drainage system. These active geomorphological locations govern the frequency of mass movement. Despite the recognition of their importance, little attention has been paid to the role of 0-order basins in initiating landslides. Drainage systems can be extracted using the Digital Elevation Model (DEM) in GIS software. However, the effect of DEM resolution on the extraction of 1st order basins remains unexplained. This research develops an algorithm to assess the impact of DEM resolution on the extraction of first-order basins, channel head points, and the identification of approximate 0-order basins. The study includes algorithms to evaluate the correlation between DEM resolution and 1st order drainage system extraction using fuzzy classification techniques for approximate 0-order basins. The algorithm was applied in Toho Village, Fukuoka, Japan, defining the most appropriate DEM and stream definition threshold with an 86.48% accuracy and ±30 m error margin for channel head points. Critical slip surfaces were identified inside the 0-order basins and validated with a landslide inventory map with a 91% accuracy. The developed algorithms support hazard management and land use planning, providing valuable tools for sustainable development.

Rainfall-induced landslides are driven by intense storms in mountainous regions. However, drainage basin-scale modeling faces challenges due to uncertainties in input parameters and computational limitations. Particularly, the Digital Elevation Model (DEM) resolution used in the hydrological and slope stability models can impact the prediction of landslides. Hence, understanding the influence of the domain’s resolution on the results is critical. This study uses a pseudo-three-dimensional slope stability approach coupled with the Parallel Integrated Hydrological model (ParFlow) to predict failures by Hurricane Maria in a watershed in Utuado, Puerto Rico. A 1-m resolution pre-hurricane DEM is used as the base scenario for both ParFlow and the slope stability analyses. The DEM resolution was then varied, and the results were compared to the base scenario with a focus on the landslide area density, timing of failures, and associated failure mechanisms. Results demonstrated that a coarser DEM for the slope stability analysis significantly affected the predicted landslides, whereas it had minimal impact on the hydrologic model. Resampling the DEM for the hydrological model while maintaining a high-resolution DEM for the slope stability model reduced the required computational resources significantly without compromising the validity of the results in regional-scale applications.

Understanding the terrain and its impact on watershed characteristics, streamflow, and sediment loading is crucial for effective water resource management. This study investigates the influence of resampled Digital Elevation Models (DEM) on the prediction of watershed characteristics, streamflow, and sediment loading upstream of Oyun River Watershed, Nigeria. Various DEM resolutions, ranging from 30-meter to 90-meter, were analysed to assess their effects on hydrological predictions. To delineate the watershed, a DEM of 90-meter resolution was sourced from the space Shuttle Radar Topography Mission (SRTM), and the ASTER global DEM data sources. The 90-meter resolution was resampled to four different resolutions which are 75-meter, 60-meter, 45-meter, and 30-meter resolutions. The watershed and streamline were delineated, and the hydrologic simulation was performed using Soil and Water Assessment Tool (SWAT). The research findings revealed that changes in DEM resolution had a negligible impact on streamflow predictions within the Oyun River Watershed. However, a noticeable impact was observed in the prediction of sediment concentration. The 90-meter resolution DEM yielded the lowest predicted sediment concentration, measuring 2.28 mg/l, while the 30-meter resampled DEM produced the highest value at 5.21mg/l. Similarly, the sediment yield (SYLD t/ha) exhibited considerable variation across the different DEM resolutions, with the 90-meter DEM demonstrating the lowest value of approximately 528.90 t/ha, and the 30-meter DEM registering the highest at 2145.57 t/ha. Overall, this research highlights the necessity of careful DEM selection in hydrological modelling to ensure a comprehensive understanding of watershed dynamics, particularly in regions where sediment transport and water quality are of paramount concern.

Soil erosion and nutrient loss from surface runoff and subsurface leaching are critical problems for cultivated land. Conservation initiatives show a persistent need for field-scale cropland vulnerability assessments to inform farm management options and prioritize efforts at watershed or regional scales. The Soil Vulnerability Index (SVI) was developed by USDA9s Natural Resources Conservation Service (NRCS) to assess inherent vulnerability of cropland to surface runoff and leaching using readily available soil and topographic inputs: hydrologic soil group, slope, erodibility K-factor, coarse fragments, and organic carbon (C). The SVI has been evaluated in a few watersheds but requires further evaluation across a wider range of physiographic and climatic conditions. The objective of this study was to evaluate the ability of the SVI to correctly identify vulnerability class based on slope, digital elevation model (DEM) resolution, hydrologic soil group, and soil erodibility across 13 of USDA9s Conservation Effects Assessment Project (CEAP) watersheds. The SVI classification was consistent with model output classification with a similarity rate of more than 70% when the SVI component corresponded to the primary route of loss for nutrients or sediment. Results showed that SVIs were consistent with local scientific expertise about the site vulnerability to runoff and leaching, and were particularly useful in areas with mixed slopes and hydrologic soil groups. In watersheds with uniform C or D hydrologic soil groups, the SVI was primarily driven by slope. In these cases, it was important to use a digital elevation map with 10 m resolution or higher to more finely distinguish vulnerability. In areas with uniform slopes and hydrologic soil group, and in areas with uniformly steep slopes, the SVI was not able to identify fields with greater or lower vulnerability than others. In these cases, vulnerability assessments required additional factors: depth of restrictive layer, clay content, slope length, and landscape position. While the SVI was able to categorize vulnerability correctly in mixed soil and slope conditions, findings from this project highlight the need for incorporating DEM-sourced slope and other factors like depth of restrictive layer, clay content, slope length, and landscape position into the SVI to ensure that the SVI is applicable to the broad range of geomorphic conditions found in the United States.

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.

In China, flood inundation modeling is often limited by the lack of high quality topographic dataset, especially in small cities where hydrologic informatization is relatively backward. Coarse-resolution digital elevation models (DEMs) often lead to lower accuracy in simulation results due to the loss of small-scale building features in terrain modeling. Increasing the DEM resolution by resampling techniques may be an available solution to building representation problems in data-sparse areas, which has not yet received much attention and needs to be tested in conjunction with urban inundation models. This paper evaluated the impact of DEM resolution and resampling techniques on the building treatment method (BTM) and the output of urban inundation model to determine whether satisfactory inundation simulation can be achieved based on resampled DEMs with fine resolution. Using the 2016 flood event in Wuhan as the study case, a detailed comparison among 19 modeling scenarios differing in DEM resolutions, resampling techniques and BTMs was subsequently conducted. The outcome of the comparison analysis revealed the sensitivity of the simulated inundation depth to the resampled DEM resolution. Furthermore, the scenario using resampled DEMs with high resolution showed better performance in urban inundation modeling because the building features and the flow paths within building gaps were adequately represented by the BTM. The findings of this study may facilitate the application of BTMs for urban inundation modeling to predict (with useful accuracy) the inundation extent and depth in areas where fine topography data are unavailable.

There is a growing interest in investigating the accuracy of digital elevation model (DEM). However people usually have an unbalanced view on DEM errors. They emphasize DEM sampling errors, but ignore the impact of DEM resolution and terrian roughness on the accuracy of terrain representation. This research puts forward the concept of DEM terrain representation error (Et) and then investigates the generation, factors, measurement and simulation of DEM terrain representation errors. A multi-resolution and multi-relief comparative approach is used as the major methodology in this research. The experiment reveals a quantitative relationship between the error and the variation of resolution and terrain roughness at a global level. Root mean square error (RMS Et) is regressed against surface profile curvature (V) and DEM resolution (R) at 10 resolution levels. It is found that the RMS Et may be expressed asRMS Et=(0.0061·V+0.0052)·V+0.2415. This result may be very useful in forecasting DEM accuracy, as well as in determining the DEM resolution related to the accuracy requirement of particular application.

The principles and methods of automatic extraction of river network based on raster digital elevation model (DEM) were briefly introduced. The process to extract river network using the Arc Hydro Tools includes: depression filling, building flow direction matrix, building flow accumulation matrix, building river network. In order to study the effects of different resolution DEM on extraction of river network, Qing jiang River, for instance, the experiment results on the extraction of drainage network from the 10m, 90m, 160m resolution DEM data were compared. It can be concluded that on the Arc GIS platform, the best observable resolution is 90m, which's extraction results can both meet the requirements on the accuracy and efficiency. but in flat area deviation is existed in the extraction result. Therefore, from the overall automatic extraction of basin river network based on DEM is feasible.

Topography plays a critical role in controlling water dispersion and soil movement in hydrologic modeling for water resources management with raster-based digital elevation model (DEM). This study aims to model effects of DEM resolution on runoff simulation through coupling fuzzy analysis technique with a topography based rainfall–runoff model (TOPMODEL). Different levels of DEM grid sizes between 30 m and 200 m are examined, and the results indicate that 30 m DEM resolution is the best for all catchments. Results demonstrate that the DEM resolution could have significant influence on the TOPMODEL rainfall–runoff simulation. Fuzzy analysis technique is used to further examine the uncertain DEM resolution based on considering Nash, sum of squared error, and sum of absolute error values of TOPMODEL. The developed model is calibrated and validated against observed flow during the period 2010–2012, and generally performed acceptably for model Nash–Sutcliffe value. The proposed method is useful for studying hydrological processes of watershed associated with topography uncertainty and providing support for identifying proper water resources management strategy.

Application of an inappropriate Digital Elevation Model (DEM) might lead to uncertainty in modelling of the hydrological cycle. The novelty of this work is the development of a comprehensive framework to evaluate the effect of DEM resolution (12 to 500 m), source (TanDEM-X, SRTM, AW3D30 and ASTER GDEM2), resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority) and area threshold (1000 to 50,000 ha) on Soil and Water Assessment Tool (SWAT) outputs based on five criteria: (1) river network extraction, (2) streamflow simulation, (3) topography, slope and basin characteristics, (4) hydrological and (5) water quality simulations. Kelantan River Basin, a tropical basin in Peninsular Malaysia was selected as study area. The major findings are summarized as follows: (1) TanDEM-X had better river network extraction capability than ASTER GDEM2, (2) better monthly streamflow simulations were obtained between 20 m and 60 m DEM resolutions, with the smallest area threshold (1000 ha), (3) TanDEM-X and SRTM DEMs outperformed ASTER GDEM2 on monthly streamflow simulation, (4) DEM resolution, source and resampling technique were insensitive to most of the hydrological components, except the lateral flow, (5) area threshold was sensitive to SWAT-simulated surface runoff, soil water content and evapotranspiration, (6) DEM scenarios had a larger impact on sediment yield simulations compared to the total nitrogen and total phosphorus simulations. We recommend a preliminary assessment of DEM uncertainties on SWAT outputs to obtain more reliable modelling outputs.

As flood inundation risk maps have become a central piece of information for both urban and risk management planning, also a need to assess the accuracies and uncertainties of these maps has emerged. Most maps show the inundation boundaries as crisp lines on visually appealing maps, whereby many planners and decision makers, among others, automatically believe the boundaries are both accurate and reliable. However, as this study shows, probably all such maps, even those that are based on high-resolution digital elevation models (DEMs), have immanent uncertainties which can be directly related to both DEM resolution and the steepness of terrain slopes perpendicular to the river flow direction. Based on a number of degenerated DEMs, covering areas along the Eskilstuna River, Sweden, these uncertainties have been quantified into an empirically-derived disparity distance equation, yielding values of distance between true and modeled inundation boundary location. Using the inundation polygon, the DEM, a value representing the DEM resolution, and the desired level of confidence as inputs in a new-developed algorithm that utilizes the disparity distance equation, the slope and DEM dependent uncertainties can be directly visualized on a map. The implications of this strategy should benefit planning and help reduce high costs of floods where infrastructure, etc., have been placed in flood-prone areas without enough consideration of map uncertainties.

ABSTRACTClassification of land surface to landforms is fundamental to interpretation of various environmental processes. The heterogeneous landform descriptions and classification approaches, in combination with the scale dependence of digital elevation models (DEMs) and their products, defy the development of an interoperable and transferable automated landform classification approach. A theoretical framework has proposed that land surface should be regionalised to morphologic meaningful objects, delimited by discontinuities (i.e. slope breaks and inflections) and subsequently classified with morphometric and contextual criteria. However, an automated methodology meeting these conditions is still lacking. This study is an attempt to automate this framework through the investigation of a modified version of a spatially adaptive pattern-based approach and its potential to produce morphologic meaningful objects of various shapes and sizes, present at the given DEM resolution. These objects were classified to 15 landform element classes based on semantic descriptions, including criteria of morphometry, relative topographic position and topological relations. Results were visually analysed by draping them over DEMs and contours and quantitatively assessed with fuzzy classification tools. The modified pattern-based approach was proven to be efficient for delineation of morphologic meaningful objects in DEMs. The classification approach was transferable to various landscapes and DEM resolutions, given that it uses spatially flexible fuzzy criteria.