Impact of Resolution and Source of Digital Elevation Model on Topological Attributes and Simulated Runoff by SWAT Model

The Digital Elevation Model (DEM) of a watershed is one of the most important inputs in most hydrological analyses and plays a key role in the accurate prediction of various hydrological processes. Comprehensive knowledge of the impact of different DEM sources on the performance of a model is essential before utilizing the model. In this study, we evaluated the influence of TOPO1:25000, ASTER, and SRTM DEMs, as input, on the performance of the Soil and Water Assessment Tool (SWAT) model for the prediction of surface runoff. We also investigated the effect of the resolution of the studied DEM sources on the accuracy of the SWAT model in the estimation of runoff. The second objective of this study was to identify the most influential and the least impactful input parameters on the performance of the SWAT model. We studied the Zarrineh River watershed in Iran as a case study to compare the effect of the aforementioned DEM types and DEM resolution on the output of the SWAT model. The outcomes of the study demonstrated that influential parameters on predicted runoff as well as a few watershed parameters, such as reach lengths, reach slopes, number of sub-basins, and the number of hydrologic response units (HRU), differs noticeably when the DEM source and resolution changes. It was also observed that simulated results over-predict the runoff during low precipitation periods and under-predict the runoff during high precipitation months, and the accuracy of the simulated results decreases by reducing the DEM resolution. The results showed that the SWAT model had the best performance when the TOPO1:25000 DEM was used as the input source. Low-resolution DEMs are available to a wider range of researchers. The outcomes of the current study can be employed to estimate the impact of low-resolution input data on the simulated result as well as substantially reduce the computation time by decreasing the input DEMresolution with only a minor reduction of accuracy.

A hydrodynamically consistent "slope-area" relationship for analysing fluvial landscape with wide rivers.

<p>Landforms and channel networks have long been analysed through co-variation between topographic slope and drainage area, which is derived from easy-to-implement flow routing algorithms (D8 or Dinf) relying on topographic slopes. The slope-area relationship has been successful to identify morphologic regions in landscapes likely reflecting the erosion and transport processes that shape them. But the implicit assumption for using the slope-area relationship is that channels are narrower than the DEM resolution and that, at this scale, the flow is correctly routed. These assumptions are no more valid for very high-resolution DEM (HRDEM, <2 m) that are now widely available with unprecedented level of vertical accuracy (< 20 cm). In wide rivers, the drainage area algorithm puts the total river discharge in one of the pixel of each channel section and let the others with unrealistically low areas. In other words, D8 or Dinf algorithms are not adapted to resolve the lateral extent of rivers.</p><p>In this study, we propose a new topographic analysis relying on realistic hydraulic simulations of surface flow. For this, we use a particle-based hydraulic model, Floodos, which solves the 2D shallow water equations, and we present an analysis of the 1m LiDAR DEM of the Elder creek watershed in California, for which channels are up to ten meters wide. By simulating channel flows with water depth, hydraulic slope, specific discharge and bed shear stress, the hydraulic model reveals landscape patterns that are not described by the slope-area relationship. Additionally, the flow model handles very well the small irregularities of the topography.</p><p>We introduce new geomorphic descriptors: the hydraulic slope and the specific drainage area (or specific discharge). The catchment organization is then analysed through a new framework called the hydraulic slope-area diagram. This diagram has several benefits over the classical slope-area diagram. It correctly classifies pixels located in the river for a given discharge in the fluvial domain leading to a sharper transition between the colluvial and fluvial domain. The hillslope-to-valley transition is also insensitive to the DEM resolution. Channel width can also be automatically calculated based on a joint analysis of Dinf and 2D shallow water simulation. Finally, the capability to perform the hydraulic slope-area for various discharges brings a richer description of landscape organization by highlighting discharge-dependent regions such as floodplain areas and fluvial terraces.</p>

Sediment connectivity in the Koiyavgan glacier's cirques (Adyl-Su river basin, Caucasus, Russia)

<p>The highland cirques mostly created by nivation and glacial exaration take large areas in mountains and have a significant role in the sediment transit of the basins. The approximate view on the connection of cirques and low levels in the sediment flow could be given with the sediment connectivity index analysis. We study the spatial distribution of the index for typical ice cirque – the Koiyavgan cirque near the join of the Main Caucasus Range and its offshoot (the Gumachy range). This area is located in the tops of the Adyl-Su valley (left side of the Baksan river basin). In August 2020, we got a high-resolution orthophoto image (13+ cm) and digital elevation model (27+ cm) from aerial photography. The territory located in the elevation range from 3230 to 4022 m. Geological conditions: gneiss, metamorphic shale and basic dark coloured igneous rocks. There is no developed vegetation cover. Typical post-glacial cirques topography includes (top-down): mountain tops, very steep bedrock slopes, colluvial footslopes and fans, cirques bottom (moraine ridges with dividing valleys, craters from melting of the in-moraine covered ice etc.) with fluvial, avalanche and creep post-shaping, and bottom surface break as analogue of riegels in glacial trough valleys. The connectivity index (CI) after Cavalli et al. [2013] is very dependent on initial DEM resolution, from the method for filling mistaken depressions, from window size for computing intermediate geomorphometric variables (e.g. roughness index), from choice in flow impedance variable, from area coverage and terrain diversity and others. We compute connectivity index with the parameters: 1) DEM resolution – 27 cm; 2) impedance variable – terrain roughness index (standard deviation of elevation) with window 7*7 cells; 3) standard filling method used in the ArcMap (filling local depression without any limitations on maximum depth); 4) range of impedance values before normalization (partially related to area coverage) is from 0 to 72 m. In the some buffers from the channel network the connectivity index generally grows in the top-down direction. Greatest spurt of the CI values relates to the cirques low border - the riegel (3300 m asl). There are two levels characterised with low values of the CI: 3550 m and 3750 m. The first one is backside of cirques bottom with relatively low flow accumulation area and low-moderate slopes (0-25°), the second one is mountain tops with high steep slopes, but with lowest flow accumulation. For different geomorphodynamical zones the threshold of IC where sediment transit turns into sediment accumulation has differ values: for example, -2.3 for colluvial fans and -2.5 for alluvial fans (p-value for differences significance « 0.01). Maximum values of CI (quantile: the top-95%) for accumulative positions again are -1.27 and -0.72. Its means, those accumulative processes areas with different mechanics of the deposition may be delineated with using non-constant CI values only. The potential of sediment flow connectivity modelling for high mountain isn’t exhausted, but its application needs wide discussion and calibration.<br>The study was supported by the Russian Science Foundation (project No. 19-17-00181).</p>

Overland flow (dis)connectivity in a new vineyard under steep slope conditions in the Spanish Pyrenees: Effect of DEM resolution and terrain preparation

<p>The ability of identifying –based on numerical analysis– disconnected areas –in terms of overland flow pathways– depends on the digital elevation model (DEM) resolution, type of flow accumulation algorithm and DEM accuracy. On the other hand, tillage practices (in lowlands) and terrain preparation (at any slope gradient) may condition the occurrence of permanent/ temporal disconnected areas. In this study, the effect of DEM resolution and the presence of a drainage ditch and forest trails on the number, location and characteristics of disconnected areas is evaluated in a steep (mean slope gradient of 29%) farmland area of the Spanish Pyrenees. A new vineyard plantation (3785 m<sup>2</sup> and 5120 m<sup>2</sup> including the transit area; espalier system) and its upslope drainage area are evaluated. This site is located near Barbenuta village (Huesca province), at high elevation (1184-1260 m a.s.l.). Abandoned terraced fields and patches of natural vegetation (trees and shrubs) occupy the upslope area, where several forest trails cross from east to west. To protect soil against water soil erosion, farmers built a drainage ditch (total length of 137 m; ca. 0.30 m width; ca. 0.15 m depth) upslope the vineyard boundary, which minimizes runoff entrance into the field. A professional drone (senseFly© eBee X) was used to obtain –after point cloud processing– Structure-from-Motion (SfM)-derived DEMs at different spatial resolution, namely: 1, 0.5, 0.2, 0.1 and 0.05 m. We used combined information of the DEMs before and after filling the local sinks. As expected, the number (n=34, 341, 1079, 1272 and 1907) and size (mean=500, 60, 21, 18 and 12 m<sup>2</sup>; median=68, 15, 5, 4 and 2 m<sup>2</sup>; σ=920, 178, 69, 71 and 49 m<sup>2</sup>) of sub-basins increased and decreased, respectively, with decreasing the pixel size, due to fractal geometry and higher influence of micro-topography components (e.g. soil roughness, random local sinks) –higher ratios of 'residual topography (σ of slope) / pixel size': 0.2 (at coarser resolution), 1.8, 20.3, 113.6 and 636.8 (at finer resolution)–. The total area also varied with the different DEMs: 17010, 20514, 22398, 22852 and 22807 m<sup>2</sup>. The number (n=21, 292, 903, 928 and 1283) and area (41, 143, 118, 58 and 44 m<sup>2</sup>) of disconnected areas increased and decreased, respectively, with decreasing the pixel size, representing 0.24%, 0.70%, 0.53%, 0.25% and 0.19% of the total drainage area. Similar differences were observed in other topographic metrics like the drainage-boundary perimeter and maximum flow length. These results prove the impossibility of defining a unique overland flow pattern. Further research should be focused on the role of runoff depth and how the effect of man-made landscape elements (drainage ditch, forest trail) and practices (tillage) on disconnectivity may depend on rainfall depth and intensity, and indirectly on plant growth.</p>

Flow directions of shallow groundwater in a boreal catchment

<p>For most catchments, there is insufficient data to determine the location of the groundwater surface. For humid climates, it is, therefore, often assumed that the groundwater-surface follows the surface topography. This assumption allows using digital elevation models (DEMs) to estimate the flow directions and catchment boundaries. However, high-resolution elevation data also include many small-scale features that are unlikely to affect the direction of groundwater flow, or only affect it during specific conditions. Furthermore, flow directions may change during events or depending on the water level.</p><p>The optimal resolution of the DEM for determining groundwater flow directions is not known yet. Therefore, we studied how much DEM derived flow directions and catchment boundaries are affected by the resolution or smoothing of the elevation data for the Krycklan catchment in northern Sweden. We also measured the groundwater levels in two small sub-catchments to determine what DEM resolution best describes the actual groundwater-surface and flow directions.</p><p>For the topographic analyses, the LiDAR-based elevation data were first smoothed with various filters (e.g., Gaussian filters) and resampled to obtain lower resolution elevation data. We then determined the flow directions for these different DEMs. The aim was to determine where in the catchment the calculated flow directions are most sensitive to the resolution of the topographic data. The results of the topographic analyses show that for some areas, particularly flat areas, ridges, streambanks and locations where the local slope differs from the general slope, the calculated flow directions depend strongly on the resolution and smoothing of the elevation data.</p><p>To test how well the DEM based groundwater flow directions represent actual flow directions, we installed a dense (5-20 m spacing) network of shallow (1 to 6 m deep) groundwater wells (75 wells in total) in a 1 ha and a 2 ha gauged sub-catchment. The triangular nested design of the groundwater well network allowed us to determine the smaller (5 m) and larger scale (20 m) groundwater gradients. The recorded water levels were augmented and validated by manual measurements during the summers of 2018 and 2019. The high spatial and temporal resolution data allowed us to study the response of the groundwater level and the flow directions to different meteorological situations (e.g., large precipitation events after dry and wet conditions and during a very dry period in summer 2018). These observations indicate that the degree to which the groundwater-surface is a subdued copy of the surface topography varies throughout the year, and provides information on which DEM resolution most accurately represents the groundwater-surface and flow directions.</p>

DEM Resolutions for Landslide Susceptibility Modeling

In landslide susceptibility mapping, the digital elevation model (DEM) is one of the most essential data sets, which is frequently used. Therefore, evaluate the effects of the spatial resolution of DEM on the landslide susceptibility model is very important. Hence, this paper is analyzed only the effects of the spatial resolution of DEM, Advanced Spaceborne Thermal Emission, and Reflection (ASTER) was used for DEM data source. The ASTER DEM was resampled to 45, 60, 75, and 90 m spatial resolutions. A set of geodatabases were built using Geographic Information System (GIS), which contains landslide governing factors and landslide inventory. Frequency ratio (FR) and certainty factor (CF) statistical methods were employed to generate a landslide susceptibility map. Landslide density and area under the curve (AUC) were applied to evaluate the model's performance for each DEM resolution. The results of the predictive rate curve value of AUC showed a coarser DEM resolution (90 m) produced the best performance and prediction accuracy. This indicated that a coarser DEM resolution produced higher predictive accuracy than fine resolution. Concerning the statistical models, the frequency ratio model produced very good accuracy at the coarser DEM resolutions (75 and 90 m). The predictive rate curve value of AUC ranges from 86-92% for the FR model and 81-89% for the CF model which indicating very good accuracy of the models to predict future landslide incidence in the study area. Therefore, it is possible to endorse statistical methods (frequency ratio, and certainty factor) respect with to DEM resolution, which is satisfactory to landslide susceptibility mapping.

DEM Resolutions for Landslide Susceptibility Modeling

In landslide susceptibility mapping, the digital elevation model (DEM) is one of the most essential data sets, which is frequently used. Therefore, evaluate the effects of the spatial resolution of DEM on the landslide susceptibility model is very important. Hence, this paper is analyzed only the effects of the spatial resolution of DEM, Advanced Spaceborne Thermal Emission, and Reflection (ASTER) was used for DEM data source. The ASTER DEM was resampled to 45, 60, 75, and 90 m spatial resolutions. A set of geodatabases were built using Geographic Information System (GIS), which contains landslide governing factors and landslide inventory. Frequency ratio (FR) and certainty factor (CF) statistical methods were employed to generate a landslide susceptibility map. Landslide density and area under the curve (AUC) were applied to evaluate the model's performance for each DEM resolution. The results of the predictive rate curve value of AUC showed a coarser DEM resolution (90 m) produced the best performance and prediction accuracy. This indicated that a coarser DEM resolution produced higher predictive accuracy than fine resolution. Concerning the statistical models, the frequency ratio model produced very good accuracy at the coarser DEM resolutions (75 and 90 m). The predictive rate curve value of AUC ranges from 86–92% for the FR model and 81–89% for the CF model which indicating very good accuracy of the models to predict future landslide incidence in the study area. Therefore, it is possible to endorse statistical methods (frequency ratio, and certainty factor) respect with to DEM resolution, is satisfactory to landslide susceptibility mapping.

Sensitivity Assessment of Spatial Resolution Difference in DEM for Soil Erosion Estimation Based on UAV Observations: An Experiment on Agriculture Terraces in the Middle Hill of Nepal

Soil erosion in the agricultural area of a hill slope is a fundamental issue for crop productivity and environmental sustainability. Building terrace is a very popular way to control soil erosion, and accurate assessment of the soil erosion rate is important for sustainable agriculture and environmental management. Currently, many soil erosion estimations are mainly based on the freely available medium or coarse resolution digital elevation model (DEM) data that neglect micro topographic modification of the agriculture terraces. The development of unmanned aerial vehicle (UAV) technology enables the development of high-resolution (centimeter level) DEM to present accurate topographic features. To demonstrate the sensitivity of soil erosion estimates to DEM resolution at this high-resolution level, this study tries to evaluate soil erosion estimation in the Middle Hill agriculture terraces in Nepal based on UAV derived high-resolution (5 × 5 cm) DEM data and make a comparative study for the estimates by using the DEM data aggregated into different spatial resolutions (5 × 5 cm to 10 × 10 m). Firstly, slope gradient, slope length, and topographic factors were calculated at different resolutions. Then, the revised universal soil loss estimation (RUSLE) model was applied to estimate soil erosion rates with the derived LS factor at different resolutions. The results indicated that there was higher change rate in slope gradient, slope length, LS factor, and soil erosion rate when using DEM data with resolution from 5 × 5 cm to 2 × 2 m than using coarser DEM data. A power trend line was effectively used to present the relationship between soil erosion rate and DEM resolution. The findings indicated that soil erosion estimates are highly sensitive to DEM resolution (from 5 × 5 cm to 2 × 2 m), and the changes become relatively stable from 2 × 2 m. The use of DEM data with pixel size larger than 2 × 2 m cannot detect the micro topography. With the insights about the influencing mechanism of DEM resolution on soil erosion estimates, this study provides important suggestions for appropriate DEM data selection that should be investigated first for accurate soil erosion estimation.