Resolution DEM Research Articles

Monitoring and Evaluation of Debris Flow Disaster in the Loess Plateau Area of China: A Case Study

The Loess Plateau area, with complex geomorphological features and geological structure, is highly prone to geologic disasters such as landslides and debris flow, which cause great losses. To investigate the initiation mechanism of landslide and debris flow disasters and their spreading patterns, historical satellite images in the Laolang gully were collected and digitized to generate three-dimensional topographic and geomorphological maps. Typical landslides were selected for landslide thickness measurement using a standard penetrometer and high-density electrical method. Numerical models were established to simulate the occurrence and development of landslides under different working conditions and to evaluate the spreading range based on the propagation algorithm and friction law. The results show that the 10 m resolution DEM data are well matched with the potential hazard events observed in the field site. The smaller the critical slope threshold, the greater the extent and distance of landslide spreading. The larger the angle of arrival, the greater the energy loss, and therefore the smaller the landslide movement distance. The results can provide scientific theoretical guidance for the prevention and control of rainfall-induced landslide and debris flow disasters in the Loess Plateau area.

FLOOD INUNDATION MAPPING OF GODAVARI RIVER BY USING 2D HEC-RAS MODEL

Due to increasing urbanization, there are various drastic changes taking place in environment these changes are hazardous for human being as well as animal and environment. Effect of environmental degradation can create environmental issues such as flood, drought etc. Out of this flood is the most common and disastrous hazard in the world. Present study utilizes 2D Unsteady flow modelling for flood inundation mapping in the lower Godavari basin for a reach length of 70 km from Sambaigudem in Telanagana district of Ramanujavaram to Damaracherla in Andhra Pradesh district of Nalgonda. For analysis of this 2D flow modelling and inundation mapping 30m resolution DEM is used. From ancient time majority of cities are developed near or on the banks of the river and these cities were affected due to flood during rainy season or due to the high intensity rainfall. As India is among the most vulnerable to flooding because of its geographical features. Therefore, this study is performed to analyze the flood plain and the extent of the affected areas present on or near the bank of Godavari River and provide mitigation measures for it.

On detailed representation of flood defences and flow-wave coupling in coastal flood modelling

Coastal flooding is projected to become more severe over the 21st century, necessitating effective adaptation, which in turn requires detailed local scale information that can only be provided by detailed numerical modelling. The current lack of information on flood protection measures and the high resource requirements of traditional hydrodynamic models presents concurrent challenges for detailed coastal flood modelling. But how comprehensive do the representation of coastal flood defences and hydrodynamic forcing need to be for adequately accurate modelling of coastal flooding? Here, we attempt to answer this question through strategic numerical simulations of the flooding that occurred at Île de Ré (France) during the Xynthia storm (2010), using the flexible mesh model Delft3D FM, with an over-land grid resolution of ~10 m. The model is validated against the flood extents observed in Île de Ré during Xynthia. We use three levels of detail in flood defence representation: a 5 m resolution DEM (i.e. base case DEM), the same 5 m DEM augmented with defences extracted from a 1 m DEM and Google Earth images (i.e. moderately augmented DEM), and the moderately augmented DEM further augmented with in-situ measurements of flood defences (i.e. highly augmented DEM). Simulations with these three DEMs are performed with and without flow-wave coupling (thus, 6 simulations in total), and results are analysed in terms of four flood indicators: maximum flood depths, flood extents, flood current velocities and flood damages. Our analysis indicates that both detailed representation of flood defences and the inclusion of waves have substantial effects on coastal flood modelling at local scale, with the former having a more pronounced effect. The return on the investment in implementing highly detailed in-situ measurements to represent flood defences appears to be low in this case, and adequately accurate results are obtained with a moderately augmented DEM. The combined effect of using the moderately augmented DEM together with waves, relative to using the base case DEM without waves, is to decrease maximum flood depths (up to 2 m), flood extent (by ~10%), maximum current velocities (in ~50% flooded area) and total flood damage (by ~27% or ~€ 188 million).

UAV based comprehensive modelling approach for flood hazard assessment and mitigation planning

The assessment of flood hazard in semi-arid region under the scare data situation is a challenging task under the changing climatic situation. Therefore, the present research is carry out for Rel river catchment, Gujrat, India as case study and presented the comprehensive approach to identify the possible flood extend and flood mitigation measures for better flood assessment. The UAV based high resolution (3.6 cm × 3.6 cm) DEM is generated for most flood vulnerable area (i.e. Dhanera city), whereas scare precipitation data under ungauged condition is generated using GEE and National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) system. The HEC-HMS based hydrologic model is utilized for simulation of stream flow under the observed data, however, the 2D HEC-RAS based open-source model is used for flood assessment in 2D under extreme flood, dam break and levees condition. The flood decision making maps i.e water surface elevation, flood inundation, flood arrival time, and flood velocity are prepared for flood event 2017. The six flood hazard classes were selected from H1 (Low) to H6 (High) categories. The hazard maps of flood for different scenario i.e., 2D hydrodynamic analysis, 2D dam breach, 2D analysis with extended levees, and Dam break analysis with extended levees are derived. The model was validated with observed river gauge (water level) and discharge data at Dhanera City of flood 2017. The developed UAV based comprehensive modelling approach for flood hazard and mitigation planning provides the systematic approach for flood hazard assessment and would be adopted as a system framework for flood risk assessment for any similar case worldwide.

Qualitative and quantitative comparisons of high resolution DEMs from two different VHR satellite sensors

Qualitative and quantitative comparisons of high resolution DEMs from two different VHR satellite sensors

Assessment of water shortage in underground aquifer in Abuja during dry season

Water shortage is a complex issue with severe implications for human well-being, agriculture, and ecosystems. Climate change, population growth, and inadequate water management practices contribute to this global problem. This study is to identify the causes of ground water scarcity and determine groundwater potential zones in the Abuja community. The research area's borehole coordinates, Landsat 8 satellite imagery, AsterDEM 30m resolution DEM data, and vertical electrical sounding (VES) data were all used to do this. Unsupervised classification of Landsat imagery was used to create a layer of land use and cover, image filtering techniques were used to improve the visualization of lineament extraction, and density analysis was used to quantify lineament density. AsterDEM data was subjected to surface analysis to acquire elevation data, while VES data were interpolated to produce spatial distributions of the resistivity and thickness parameters. To identify groundwater potential zones, multiple criterion analysis using the analytical hierarchical process (AHP) was utilized to reclassify variables such as land use, elevation, lineament density, resistivity, and thickness. To create the zones, these characteristics were weighted and layered. According to the findings, resistivity levels varied from 6.25 m to 676 m, and the geologic ground structure's thickness ranged from 0.27 m to 92.37 m. Elevation varied from 41 meters to 720 meters, while lineament density ranged from 0 to 0.04. 74% of the land was categorized as vegetated, 21% as built up, 2.7% as water bodies, and 2.3% as rock formations. Within Abuja, zones of groundwater potential were identified as being very low (5%), low (26.7%), marginally high (26.3%), moderately high (28.5%), and very high (13.4%)

Automated Avalanche Terrain Exposure Scale (ATES) mapping – local validation and optimization in western Canada

Abstract. The Avalanche Terrain Exposure Scale (ATES) is a system for classifying mountainous terrain based on the degree of exposure to avalanche hazard. The intent of ATES is to improve backcountry recreationist's ability to make informed risk management decisions by simplifying their terrain analysis. Access to ATES has been largely limited to manually generated maps in high-use areas due to the cost and time to generate ATES maps. Automated ATES (AutoATES) is a chain of geospatial models which provides a path towards developing ATES maps on large spatial scales for relatively minimal cost compared to manual maps. This research validates and localizes AutoATES using two ATES benchmark maps which are based on independent ATES maps from three field experts. We compare the performance of AutoATES in two study areas with unique snow climate and terrain characteristics: Connaught Creek in Glacier National Park, British Columbia, Canada, and Bow Summit in Banff National Park, Alberta, Canada. Our results show that AutoATES aligns with the ATES benchmark maps in 74.5 % of the Connaught Creek study area and 84.4 % of the Bow Summit study area. This is comparable to independently developed manual ATES maps which on average align with the ATES benchmark maps in 76.1 % of Connaught Creek and 84.8 % of Bow Summit. We also compare a variety of DEM types (lidar, stereo photogrammetry, Canadian National Topographic Database) and resolutions (5–26 m) in Connaught Creek to investigate how input data type affects AutoATES performance. Overall, we find that DEM resolution and type are not strong indicators of accuracy for AutoATES, with a map accuracy of 74.5 % ± 1 % for all DEMs. This research demonstrates the efficacy of AutoATES compared to expert manual ATES mapping methods and provides a platform for large-scale development of ATES maps to assist backcountry recreationists in making more informed avalanche risk management decisions.

Prediction of debris flow initiation points using high resolution DEM and GIS analysis in South Korea

This study presents a GIS analysis model that utilizes high-resolution Digital Elevation Map data to predict the initiation points of debris flow through the overlap of slope stability analysis techniques and rainfall flow analysis. The debris flow that occurred on Mount Woomyun in Seoul, South Korea in 2011 was selected as the case study for comparing and analyzing the prediction model against the actual case. In South Korea, occurrences of debris flow mostly happen during the summer monsoon season. When a substantial amount of rainfall accumulates and heavy rainfall events occur, the pore water pressure within slopes reaches saturation. Consequently, flow is initiated along “temporary streams.” In this study, areas with a combination of low slope stability and abrupt changes in velocity vectors were assumed to have the highest probability of debris flow occurrence due to the overlapping of these factors. The research model was conducted under this assumption. To evaluate the performance of the model, 3D coordinates of the model’s result point, and actual occurrence points were obtained, and a Pearson correlation analysis was conducted to compare them. With these results, R2 values of X = 0.9147, Y = 0.8625, and H = 0.8942 were obtained. These high R2 indicate a strong correlation between the model’s predictions and actual occurrences.

Impacts of DEM type and resolution on deep learning-based flood inundation mapping

Impacts of DEM type and resolution on deep learning-based flood inundation mapping

Floodplain mapping of an ungauged river: A case study on Seti River in Pokhara, Nepal

Settlements and infrastructures along the banks of Seti River in Pokhara, Nepal are at high risk of flood. Floodplain mapping for ungauged Seti River is not straightforward like the one for gauged rivers. Main goal of this study was to prepare floodplain maps along the ungauged Seti River in Pokhara, as a case study, using one-dimensional HEC-RAS model. First, catchment area ratio (CAR) method was applied to find annual flow in ungauged Seti River based on flow data of gauged Mardi station. Once the annual maximum flow was estimated for sufficient time length (i.e., 42 years), peak flood was predicted using Gumbel method for various reaches of Seti River within Pokhara. Thus, estimated peak floods were also compared with the peak floods predicted using Gumbel method based on the annual flow data of Tanahu station. As the specific discharge observed to be comparable with each other (difference ≤ 2.68%), CAR method found be a reliable one that is useful for ungauged river. Then, Cowan’s approach was applied to estimate Manning’s roughness coefficient (n) and used it for calibration of HEC-RAS model. Cowan approach found to be a best alternative for ungauged river as the comparison of modelled flow depth with measured flow depth yielded only 3.82% difference. Finally, 1D hydraulic modelling was performed using calibrated HEC-RAS model with available 12.5 m resolution DEM terrain data. Floodplain maps were prepared based on the HEC-RAS simulation results coupled with Google Earth map. The flood inundation area within Pokhara was found to be 2.76, 3.05, and 3.59 Km2 for the peak flood of 20, 50, and 100 years return periods, respectively. Moreover, Laltin Bazar and Gaighat areas were identified to be at high risk of flood such that these areas found to be inundated with 20 or greater years return period floods, compared to Ramghat area which was observed to be flooded with the peak flood of 50 or more years return period. Floodplain maps of this study could be used for preparing flood hazard maps, planning infrastructures, and flood management.

Subsidence of a Coal Ash Landfill in a Power Plant Observed by Applying PSInSAR to Sentinel-1 SAR Data

We analyzed ground subsidence at the coal ash disposal sites of Stanton Energy Center, a power plant located in Orlando, Florida, USA, by applying 157 Sentinel-1 SAR images obtained between May 2017 and December 2022 in ascending orbit to the PSInSAR technique. A LiDAR DEM with 1 m posting was used for the DInSAR and StaMPS processing for PSInSAR. The results showed significant ground subsidence on the area where solar panels were installed on top of the coal ash landfill. The coal ash landfill was divided into three sites (A, B, and C) according to the landfill sequence. The spatially averaged PSInSAR showed subsidence rates of 7.3 mm/year, 6.2 mm/year, and 8.8 mm/year in sites A, B, and C, respectively. In particular, relatively newly deposited sites A and B showed a decreasing trend in subsidence rate with higher quadratic components in regression function, indicating a stabilization of the subsidence. On the other hand, the oldest site C exhibited the highest (and a relatively constant) subsidence rate, suggesting that the settlement occurred earlier and is now at a constant rate. It is also suspected that new dumping activity near C might have caused a higher subsidence rate than in sites A and B. No subsidence occurred at other solar panel installations on the ground outside the landfill, suggesting that the subsidence was caused by the gravitational compaction of the landfill materials rather than by the instability of the solar facilities. Comparison of PSInSAR results with lower resolution DEMs, such as SRTM and Copernicus DEM, showed range errors of the PS positions proportional to the height deviation from LiDAR DEM, highlighting the importance of accurate DEMs for the time-series analysis of SAR data.

Modification of the RUSLE slope length factor based on a multiple flow algorithm considering vertical leakage at karst landscapes

Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing area. The existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is constant at any slope length. The objective of this study was to modify the L-equation by establishing the functional relationship between surface-runoff intensity and karst slope length, and to evaluate its predictive capability at different resolution DEMs. Transfer grid layers were generated based on the area rate of surface karstification and considered the runoff transmission percentage at the exposed karst fractures or conduits to be zero. Using the multiple flow direction algorithm united with the transfer grid (MFDTG), the flow accumulation of each grid cell was simulated to estimate the average surface-runoff intensity over different slope lengths. The effectiveness of MFDTG algorithm was validated by runoff plot data in Southwestern China. The simulated results in a typical peak-cluster depression basin with an area rate of surface karstification of 6.5% showed that the relationship between surface-runoff intensity and slope length was a negative power function. Estimated by the proposed modified L-equation ((alx(b+1)/22.13)m), the L-factor averages of the study basin ranged from 0.35 to 0.41 at 1, 5, 25 and 90 m resolutions respectively. This study indicated that the modified L-equation enables an improved prediction of the much smaller L-factor and the use of any resolution DEMs on karst landscapes. Particular attention should be given to the variation of surface-runoff intensity with slope length when predicting L-factor on hillslopes with runoff scale effect.

Performance assessment of global open‐access digital elevation models in China mainland coastal region

AbstractDigital elevation models (DEMs) are the fundamental datasets for coastal ecosystem monitoring, and several global open‐accessed DEMs have recently been reported. In coastal regions, a comprehensive vertical accuracy assessment of these DEMs has not yet been carried out. In this study, eight open‐access DEM datasets, including SRTM‐3, SRTM‐1, TanDEM‐X, ASTER GDEM v3, MERIT DEM, AW3D30, NASADEM, and CoastalDEM, were investigated across the coastal region of the Chinese mainland using high accuracy ICESat‐2 data as a reference. Statistical tools including mean absolute error (MAE) and root mean square error (RMSE) were selected to describe the data error/uncertainty and spatial distribution. Moreover, the effects of elevation ranging, slope degree, geomorphogenesis and landuse on vertical accuracy were further analyzed to assess their applicability. The assessment results revealed that the CoastalDEM and NASADEM datasets had the highest accuracy, with MAE values of 1.68 and 1.88 m and RMSE values of 2.55 and 2.61 m, for 3‐arc second and 1‐arc second resolution DEMs, respectively. Other DEMs with close accuracies include AW3D30, SRTM‐1, MERIT, and SRTM‐3 DEM. The results proved that the CoastalDEM outperformed other datasets, indicating its applicability in coastal regions.

Defining multi-scale surface roughness of a coral reef using a high-resolution LiDAR digital elevation model

A decline in coral reef surface roughness may indicate that a coral reef ecosystem is approaching functional collapse. This is because surface roughness underpins many critical ecosystem metrics, such as, live coral cover and high fish biomass. Yet, we understand very little about how surface roughness changes at ecosystem scales due to the limited spatial extents of coral reef ecological surveys. Here, we investigate the surface roughness of a coral reef across multiple spatial scales on a high-resolution Light Detection and Ranging (LiDAR) derived digital elevation model (DEM, 0.25 m cells). We found that complex features dominated regions with high roughness (e.g., spur and groove systems on the reef slope) and increased the average roughness of geomorphic zones that are otherwise flat and featureless (e.g., lagoonal patch reefs in the sandy deep lagoon). Each geomorphic zone had a unique signature with high values of roughness usually expressed over finer spatial scales and, conversely, lower values of roughness observed over broader spatial scales. We allocated each geomorphic zone into one of four roughness equivalent habitats (REHs 1–4) that share similar surface roughness properties but are not necessarily geographically contiguous regions. These results identify fine scale features and geomorphic zones that are important for providing roughness to coral reef systems, such as spur and groove systems and the fore-reef slope respectively. They also suggest that morphodynamic processes operating at broader spatial scales influence the physical structure of coral reef ecosystems. The quantification of coral reef surface roughness is becoming increasingly important due to the wider availability of high (<1 m) and hyper (<0.1 m) resolution DEMs. Our approach and findings here can be used to provide greater surface textural information for common coral reef geomorphic zones and aid future management and research efforts, such as, monitoring of coral reef ecosystem response to environmental change and reef restoration and adaptation programs.

A hybrid machine learning-based multi-DEM ensemble model of river cross-section extraction: Implications on streamflow routing

Accurate representation of river channel and floodplain geometries is of prime concern for streamflow routing and flood inundation modelling under inadequate surveyed river cross-section data-condition, in many rivers across the globe. Due to global coverage and easy accessibility, the public domain medium resolution (1-arc second) DEMs such as SRTM, ASTER, and ALOS-AW3D30, with a linear vertical bias-correction approach, are widely used for hydrodynamic modelling of upland river networks. In this context, this study, for the first time, developed a hybrid machine learning-based Multi-DEM Ensemble (MDE) approach to simulate bias-corrected cross-sections using open source DEMs-extracted river cross-sections. Additionally, the hydraulic suitability of the simulated cross-sections and associated uncertainties were analysed. For this, the routing of streamflow and water level was performed using MIKE11-HD for a low-gradient flood-prone deltaic river basin of Eastern India (∼9000 km2) consisting of 19 river distributary systems. A comprehensive evaluation of the developed hybrid machine learning-based MDE models revealed an excellent performance of the Random Forest-Particle Swarm Optimisation-MDE (RF-PSO-MDE) model (NSE: 0.89–0.95; RMSE: 1.75–2.23 m) and Deep Neural Network-Particle Swarm Optimisation-MDE (DNN-PSO-MDE) model (NSE: 0.89–0.93; RMSE: 2.09–2.27 m) followed by the Support Vector Machine-Particle Swarm Optimisation-MDE (SVM-PSO-MDE) model (NSE: 0.87–0.92 and RMSE: 2.22–2.50 m). The MIKE11-HD simulations using RF-PSO-MDE and DNN-PSO-MDE model-simulated cross-sections were in close agreement with the observed streamflow and water level hydrographs, together with excellent matching of the flood peak, time to flood peak, and streamflow regimes. Predictive uncertainties analysis by the quantile regression technique confirmed an acceptable range of the Mean Prediction Interval and Percentage Interval Coverage Probability for the MIKE11-HD-RFMDE and MIKE11-HD-DNNMDE model variants including the MIKE11-HD-S model. Conclusively, the developed MDE models have potential for generating river cross-sections under sparse bathymetric conditions for the accurate simulation of streamflow and water level estimates using public domain digital elevation models.

Experiences with the RTM Method in Local Quasi-Geoid Modeling

In local quasi-geoid modeling, the residual terrain modeling (RTM) method is often used to remove short-wavelength gravity field signals from the measured gravity on the ground in order to obtain a regularized and smooth gravity field that is suited for field interpolation and modeling. Accurate computation of RTM corrections plays a crucial role in computing an accurate local quasi-geoid, and it requires a set of fine-tuned parameters, including the combination of DEMs with different resolutions for suitably representing the real topography, the choice of integration radius for properly defining the extent of the computation zone, and the determination of reference topography to properly describe the RTM-reduced Earth’s surface. To our knowledge, this has not been systematically documented, despite its obvious importance. This study aims to systematically investigate the impact of these factors on RTM correction computation and, consequently, on local quasi-geoid modeling to provide practical guidelines for real-world applications. The tesseroid-based gravity forward modeling technique is employed to investigate the following issues existing in the practical use of the RTM method: ① Can the combination of a high-resolution DEM and a DEM with a lower resolution replace the single use of the high-resolution DEM for RTM correction computation while maintaining accuracy and improving efficiency? If it does, how do I properly choose the resolution of this coarse DEM as well as the integration radius r1 for the inner zone and r2 for the outer zone? ② How large would the differences between the RTM corrections computed by three types of reference topographies, which are obtained from the direct averaging (DA) approach, the moving averaging (MA) approach, and the spherical harmonic (SH) approach, be, and how large would their impact on quasi-geoid modeling be? To obtain objective findings, two research regions were selected for this investigation. One is the Colorado test area (USA) with rugged terrain, and the other is the Auvergne test area (France) with moderate terrain. The main numerical findings are: (1) the combination of the 3” resolution DEM (inner zone) and the 30″ resolution DEM (outer zone) is sufficient for accurate and efficient RTM correction computation; (2) if the resolution of the reference topography is 5′ or slightly lower, all three types of reference topographies are able to obtain local quasi-geoid models at a similar accuracy level, while the values of r1 and r2 are preferred to be at least 20 km and 111 km, respectively; (3) if the reference topography has a resolution of 30′ or lower, the MA or SH reference topography is recommended, especially for the latter one, and the values of r1 and r2 are suggested to be at least 20 km and 222 km, respectively. The above numerical findings can be taken as a reference for local quasi-geoid determination in areas with different topographic regimes than the two selected test areas.

Comparison of different hydrological and stability assumptions for physically-based modeling of shallow landslides

Physically-based models are reliable techniques for landslide susceptibility assessment, therefore, several models have been proposed in the literature. In this study, two different hydrological models (FSLAM and TRIGRS) and two slope stability calculation methods (infinite slope and limit equilibrium method with spherical failure surface in SCOOPS3D) were compared. The rainfall-triggered shallow landslides that occurred in Kaptanpaşa, Rize (Turkey) in 2017 were selected as a case study. The main focus is investigating the impact of landslide morphology on the success of the slope stability models. For this purpose, landslides in the inventory were grouped according to their morphological characteristics, such as the landslide depth, length, width and their ratios, and their compatibility with model assumptions (e.g. the infinite slope assumption). Then, the model performances in different landslide types were evaluated separately. To calibrate the model parameters a new time-saving, semi-automated calibration strategy, based on a sensitivity analysis, is proposed. Concretely, stability parameters c′∅′Z were calibrated via a MATLAB code by considering the area under the curve (AUC) and extreme conditions: saturated and initial conditions. Overall, SCOOPS3D is deduced to have better performance than infinite slope analysis. However, the accuracy of SCOOPS3D is revealed to decrease for landslides with a high ratio of length/depth and width/depth. From the comparison of hydrological models, FSLAM yielded more plausible results than TRIGRS with the available data. It is demonstrated that the morphological features of the landslides significantly influence the performance of the physically-based stability models. Furthermore, it is highlighted that the resolution of the Digital Elevation Model in comparison to the width and length of the landslides in an inventory can be influential in the performance of the physical model. 10 m resolution of DEM is deemed to be suitable for the study area judging the predictive performance in small landslides.

How does high resolution topography affect flood simulation at watershed scale: A case study in a small watershed based on tilt photography

The data source and resolution of DEM will affect the extraction of watershed features and flood simulation precision. In this paper, based on the 7 cm resolution DEM of the Liulin watershed obtained by tilt photography technology, the nearest neighbor interpolation, bilinear interpolation, and cubic convolution methods were used to resample DEMs with 1m and 30m resolution, and 6 resampled DEMs were obtained. The Aster 30m resolution DEM was downloaded from the geospatial data cloud. The watershed features were extracted from the 7 DEMs and compared. Based on these DEMs, the HEC-HMS model was established. 20 flood events were selected in the Liulin watershed for model calibration and verification. The results showed that the resampling method has little effect on the topographic features, but there were some differences between the resampling and downloaded DEM topographic features. The flood simulation results of the 1m resolution DEM-based HEC-HMS model are slightly better than that of the 30m resolution DEM-based model. The resampled 30m resolution DEM-based model performed identically with Aster 30m resolution DEM-based model. Therefore, an Aster 30m DEM can be used for flood simulation in a watershed that lacks measured high-precision topographic data.

Assessment of the combined effects of Manning roughness and DEM resolution for the water surface elevation prediction using the HEC-RAS model: a case study of Moudjar River in the Northeast of Algeria

Assessment of the combined effects of Manning roughness and DEM resolution for the water surface elevation prediction using the HEC-RAS model: a case study of Moudjar River in the Northeast of Algeria

An approach to select optimum inputs for hydrological modeling to improve simulation accuracy in data-scarce regions

Study regionThe source regions of the Yellow River and Yangtze River in the central-eastern part of the Tibetan Plateau. Study focusHydrological model is an important tool in the simulation of watershed hydrology. However, as more and more basic geographic data becomes publicly available and shared globally, researchers are developing a 'symptom' of difficulty in choosing data, so a systematic comparative analysis for model input data selection is necessary. We tested the effects of different types, sources, and resolutions of input data on the model output results based on the SWAT model, and focused on the mechanism of the role of different input data in the model and how to select an appropriate input data for similar studies. New hydrological insights for the regionThe results show that: the meteorological data is crucial in the model's runoff simulation, and ground meteorological observation station data outperforms reanalysis data such as CFSR. Optimizing CFSR significantly improves the model's performance. The DEM resolution minimally impacts runoff simulation, as the difference in results is primarily due to the topographic characteristics of the watershed. DEM selection should consider TWI complexity and its compatibility with the watershed network. The selection of LULC data has little effect on the simulation, and the best input data combination is OBS + 90 m DEM + CNLULC. These findings assist input data selection for similar watersheds using the SWAT model.