Global Multi-resolution Terrain Elevation Data Research Articles

Estimation of monthly sunshine duration using satellite derived cloud data

Sunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Türkiye using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.

Geometry and topology of a Polish Outer Carpathian digital-elevation-model-interpreted lineament network in the context of regional tectonics

Abstract. The Polish part of the Western Outer Carpathian lineament network was analysed based on the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) digital elevation model. Lineaments were identified in the visual screening of the hillshade model. To the best of our knowledge, no one has studied the geometrical properties of the network in relation to the topological ones. The NetworkGT QGIS toolbox was applied to identify the nodes and branches of the network as well as to calculate the topology parameters. Our aim was to find differences between the western and eastern parts of the Western Outer Carpathians; therefore, the analyses were carried out in six sectors chosen based on the geographical subdivision in the geological context: three in the north, mainly the Silesian unit, and three in the south, mainly the Magura unit. We found general agreement of the identified network with the photo-lineament map; however, some of the photo-lineaments are not confirmed by a digital elevation model (DEM). We found that the topological parameters of the networks change from west to east but not from north to south. There are areas of increased interconnectivity, especially the Nowy Sącz Basin, where the lineament network may reflect a complicated system of cross-cutting, deep-rooted fault zones in the basement.

VERTICAL ACCURACY ASSESSMENT FOR OPEN-SOURCE DIGITAL ELEVATION MODEL: A CASE STUDY OF BASRAH CITY, IRAQ

Abstract. In the diverse domains of earth observation, elevation data are essential for a wide range of applications with various technical requirements and use cases. The Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Shuttle Radar Topography Mission (SRTM), and other projects have made a large number of global Digital Elevation Model (DEM) datasets for environmental modelling and studies freely available. Global DEMs have undergone an accuracy review to measure their inherent vertical uncertainty to show how accurate information should be considered while planning and analysing. Comparing the DEMs with highly accurate geodetic control points as the independent reference data one of the best methods in the evaluation process. SRTM 30m, SRTM 90m, ALOS World 3D-30, Aster-GDEM, GMTED2010, and NASADEM are among the worldwide DEMs that were examined. Comparisons are made between 793 geodetic control points values and those from SRTM 30m, SRTM 90m, ALOS World 3D-30, Aster-GDEM, GMTED2010, and NASADEM. The statistical analysis of global DEMs from GPS reference elevations gave us that the accuracy of the ALOS World 3D-30m is much better than other models with RMSE and STD values of 1.2497 and 1.235 m, respectively. In contrast, Aster-GDEM exhibited the highest RMSE and residual error of STD values of 5.793 m and 3.394 m, respectively.

A Review on Global and Localised Coverage Elevation Data Sources for Topographic Application

As the need for elevation data grows, it is more vital than ever for users to match the data degree of dependability, precision, and spatial resolution to their specific uses to produce a useful and cost-effective product. This article will describe several sources of elevation data, ranging from space-based to aerial-based techniques, and classify the data according to its respective quality and accuracy. The elevation data sources can be classified into two namely localised or can also be referred to as regional, and global coverage. Among the example of localised sources of elevation data are Light Detection and Ranging (LiDAR) and Interferometry Synthetic Aperture Radar (InSAR). The global sources of elevation data are Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER), Advanced Land Observing Satellite (ALOSW3D), Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010), TerraSAR-X add on for daily Digital Elevation Measurement (TanDEM-X), The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), Radar Satellite (RADARSAT) Constellation Mission (RCM) and Satellite-Derived Bathymetry (SDB). The characteristics of each elevation data source were discussed in terms of its launch date, period of observation, spatial resolution, horizontal and vertical datum, and coverage. Its reliability was described in detail for future topographic applications.

Accuracy assessment of open-source global digital elevation models (GDEMs) with global navigation satellite system (GNSS) levelling

Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Advanced Land Observing Satellite World-3D (ALOSW3D), and TerraSAR-X Digital Elevation Measurement (TanDEM-X) are open-source Digital Elevation Model (DEM) datasets for environmental modelling and studies. The spatial resolution and vertical accuracy of DEM data sources play a significant role, particularly in dealing with land inundation, periodic flooding, and coastal erosion. In this study, the comparison between orthometric height, H from Global Navigation Satellite System (GNSS) observation, and DEM is performed to evaluate the accuracy of each DEM in terms of their Root Mean Square Error (RMSE) and correlation coefficient, R2 for monitoring the coastline. The result has indicated that TanDEM-X shows the smallest RMSE of 2.574 m compared to SRTM30, SRTM90, ASTER, GMTED10, and ALOSW3D with RMSE of 2.968 m, 3.006 m, 3.217 m, 2.975 m, and 2.876 m respectively. Furthermore, TanDEM-X illustrates the largest correlation coefficients, R2 of 0.959 m, compared to SRTM30, SRTM90, ASTER, GMTED10, and ALOSW3D at 0.891 m, 0.899 m, 0.590 m, 0.888 m 0.913 m respectively. Hence, the result has indicated that TanDEM-X is the best option among all DEMs for any topographic applications.

A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets

ABSTRACT Accurately quantifying the aboveground biomass (AGB) of forests is crucial for understanding global change-related issues such as the carbon cycle and climate change. Many studies have estimated AGB from multiple remotely sensed datasets using various algorithms, but substantial uncertainties remain in AGB predictions. In this study, we aim to explore whether diverse algorithms stacked together are able to improve the accuracy of AGB estimates. To build the stacking framework, five base learners were first selected from a series of algorithms, including multivariate adaptive regression splines (MARS), support vector regression (SVR), multilayer perceptron (MLP) model, random forests (RF), extremely randomized trees (ERT), stochastic gradient boosting (SGB), gradient-boosted regression tree (GBRT) algorithm, and categorical boosting (CatBoost), based on diversity and accuracy metrics. Ridge and RF were utilized as the meta learner to combine the outputs of base learners. In addition, six important features were selected according to the feature importance values provided by the CatBoost, ERT, GBRT, SGB, MARS and RF algorithms as inputs of the meta learner in the stacking process. We then used stacking models with 3–5 selected base learners and ridge or RF to estimate AGB. The AGB data compiled from plot-level forest AGB, high-resolution AGB data derived from field and lidar data and the corresponding predictor variables extracted from the satellite-derived leaf area index, net primary production, forest canopy height, tree cover data, and Global Multiresolution Terrain Elevation Data 2010, as well as climate data, were randomly split into groups of 80% for training the model and 20% for model evaluation. The evaluation results showed that stacking generally outweighed the optimal base learner and provided improved AGB estimations, mainly by decreasing the bias. All stacking models had relative improvement (RI) values in bias of at least 22.12%, even reaching more than 90% under some scenarios, except for deciduous broadleaf forests, where an optimal algorithm could provide low biased estimations. In contrast, the improvements of stacking in R2 and RMSE were not significant. The stacking of MARS, MLP, and SVR provided improved results compared with the optimal base learner, and the average RI in R2 was 3.54% when we used all data without separating forest types. Finally, the optimal stacking model was used to generate global forest AGB maps.

Land-Use Improvements in the Weather Research and Forecasting Model over Complex Mountainous Terrain and Comparison of Different Grid Sizes

Weather forecasts over mountainous terrain are challenging due to the complex topography that is necessarily smoothed by actual local-area models. As complex mountainous territories represent 20% of the Earth’s surface, accurate forecasts and the numerical resolution of the interaction between the surface and the atmospheric boundary layer are crucial. We present an assessment of the Weather Research and Forecasting model with two different grid spacings (1 km and 0.5 km), using two topography datasets (NASA Shuttle Radar Topography Mission and Global Multi-resolution Terrain Elevation Data 2010, digital elevation models) and four land-cover-description datasets (Corine Land Cover, U.S. Geological Survey land-use, MODIS30 and MODIS15, Moderate Resolution Imaging Spectroradiometer land-use). We investigate the Ortles Cevadale region in the Rhaetian Alps (central Italian Alps), focusing on the upper Forni Glacier proglacial area, where a micrometeorological station operated from 28 August to 11 September 2017. The simulation outputs are compared with observations at this micrometeorological station and four other weather stations distributed around the Forni Glacier with respect to the latent heat, sensible heat and ground heat fluxes, mixing-layer height, soil moisture, 2-m air temperature, and 10-m wind speed. The different model runs make it possible to isolate the contributions of land use, topography, grid spacing, and boundary-layer parametrizations. Among the considered factors, land use proves to have the most significant impact on results.

Assessment and comparison of Digital Elevation Model (DEM) products in varying topographic, land cover regions and its attribute: a case study in Shikoku Island Japan

Digital Elevation Model (DEM) is the model of Earth’s surface and is significantly variable for science application. The DEMs based on free provided are the 10 m DEM produced by the Geographical Survey Institute of Japan (GSI-DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM, Shuttle Radar Topography Mission (SRTM), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales (HydroSHEDS), and Global 30 Arc-Second Elevation (GTOPO30) that are normally used in scientific researches. These terrain data have been generated by different methods, such as Stereoscopic Photogrammetry, RADAR-SAR interferometry, LIDAR, and GPS. The generated data contain an error from collecting and processing methods that have related to morphology relief and land cover type. In this study, the six open-source DEMs and their attributes are evaluated by the referent elevation points observed by GPS, in Shikoku Island Japan. It was revealed that the error of DEMs is an effect on the terrain characteristic. Overall, the accuracy values of fine-resolution DEMs based on the RMSE value are 5.9 m, 9.9 m, and 10.1 m for GSI-DEM, ASTER, and SRTM, respectively. The coarse-resolution DEMs revealed the accuracy of RMSE as 18.2 m, 69.4 m, and 61.8 m for GMTED2010, HydroSHEDS, and GTOPO30, respectively. The accuracy of the slope and river network is also evaluated. This approach might be used to recommend for a new generation of DEM and revealed for the accuracy of the Earth’s surface.

Comparing global passive microwave freeze/thaw records: Investigating differences between Ka- and L-band products

The NASA L-Band Soil Moisture Active Passive (SMAP) satellite mission launched in 2015 has produced soil moisture and freeze thaw (FT) products at a global scale. While the use of L-band (1.41 GHz) passive microwave radiometry (P-MW) has proven useful in detecting changes in the surface FT state, these classifications have not been comprehensively assessed against similar existing FT products, such as the global FT record from the Special Sensor Microwave/Imager (SSM/I, Ka-band, 37.0 GHz) as part of the FT Earth System Data Record (FT-ESDR). In order to fill in this gap, this study investigates regions in which FT classifications diverge and identifies potential sources of classification variability. The SMAP and SSM/I FT records are compared over an extended period covering multiple seasonal cycles from April 2015 through December 2017. The spatially and temporally varying relationship between these products is examined in relation to climate (Köppen-Geiger climate classes and air temperature), MODIS (MoDerate Resolution Imaging Spectrometer) land cover, and topography (using Global Multi-resolution Terrain Elevation Data). SMAP and SSM/I FT product agreement proportion (Ap) was corrected for seasonality and then separated by land cover classes and compared to the global Ap mean. The agreement between these products vary most notably during freeze and thaw onset and in areas near abundant surface water, snow and ice, and wetlands. Relative to other vegetation types, reduced agreement between FT products is also observed over grasslands, sparsely vegetated lands, as well as mixed and evergreen forests. Distinct seasonal differences in FT classification agreement were also detected between products over cold arid regions and between continental and temperate classes. Similarly, as topographic complexity increases, a decreasing trend in agreement between L- and Ka-band FT products is observed. While reiterating challenges in FT classifications identified by prior studies, this work also contributes new insights by providing detailed geospatial and seasonal analyses into the factors contributing to FT product divergence.

Characterizing spatial patterns of pine bark beetle outbreaks during the dry and rainy season’s in Honduras with the aid of geographic information systems and remote sensing data

Coniferous and mixed forests cover approximately 42% of Honduras forested areas, however; pine bark beetle (PBB) (Dendroctonus spp.) outbreaks are an environmental hazard that has caused incalculable ecological and economic impacts in Honduras. In this research, in order to plan more focalized measures for controlling the PBB outbreaks, it is essential to identify those areas that have a high susceptibility to a PBB outbreak during the dry and rainy seasons. For this purpose, we require to associate the historical PBB outbreak points (2017–2019), with a series of environmental and anthropogenic variables that according to the literature review have affected the initiation and the spread of PBB outbreaks. To assess the current climatic variables we used MODIS land surface temperature product (LST-MOD11A2), wind speed, precipitation, and temperature acquired from the WorldClim data. To assess vegetation vigor, we use the Normalized drought moisture index estimated from the MODIS surface reflectance product (MOD09A3). To give us an understanding of the density of the forest, we compare two products, the MODIS Leaf Area Index (LAI-MOD15A2H), and the MODIS Vegetation Continuous Fields (VFC-MOD44B). We included elevation, aspect, and slope as variables and acquired this data from the Global multiresolution terrain elevation data (GMTED-2010). Finally, we used geographical information systems data to derive proximity to different types of roads as anthropogenic data and wildfire density. We do a preliminary analysis of the variables and eliminated those which show the least importance. Furthermore, we integrated the most relevant variables identified with the PBB points by using the Random Forest (RF) algorithm to fit the model and then predicted the current PBB outbreak susceptibility for the dry and rainy season. Results indicated that climatic variables weigh heavily in determining high susceptibility areas. Our prediction results show high and very high susceptibility in the North-eastern and Central parts of the country especially. The results acquired, can lead to improved preventive and control measures to reduce the negative ecological effects that are caused by PBB outbreaks.

Relationship between Environmental Covariates and Ceylon Tea Cultivation in Sri Lanka

How the current distribution of tea cultivation is influenced by specific environmental conditions in Sri Lanka is yet to be explored. Therefore, this study aims to assess the differences between tea and non-tea growing areas with respect to climatic and topographic covariates, and to determine the major covariates that control tea distributions. Climatic data of temperature and rainfall were extracted from WorldClim-Global Climate Data; the elevation, slopes, and aspects were obtained from Global Multi-resolution Terrain Elevation Data; and the solar radiation data was computed using a clear-sky solar radiation model. Random points were created on rasterised environmental layers for tea-growing and non-tea growing areas, stratified into low, mid, and high regions, using ArcGIS version 10.4.1 (Environmental Systems Research Institute: ESRI Redlands, CA, USA).Correlations were derived between covariates and tea and non-tea growing areas. According to the logistic regression analysis, there was no significant influence of the south-west, west, and north-west aspect compared to the north aspect when all other covariates were held constant. The odds ratio indicated that an area with a one-unit higher solar radiation was 1.453 times more likely to be a tea growing area. Similarly, a per unit increase in slope increases the likelihood of an area being suitable for tea cultivation by 1.039 times. When the annual mean temperature increased, the suitability of tea cultivation decreased, but an increased rainfall had increased the suitability of an area for tea cultivation. Areas with a north facing slope had the highest suitability for tea cultivation. This research demonstrated that tea growing could be expanded into a variety of locations as long as these variables are either found or managed in order to obtain the critical levels. In addition, it is proposed that the results of this study could be utilised in the assessment of the climate or/and land suitability for tea.

Comparison of digital elevation models and relevant derived attributes

The digital elevation model (DEM) and its derivative attributes are important parameters for evaluating any process using digital terrain analysis. Five freely available global DEM products including Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model version 2 (ASTER GDEM2), Shuttle Radar Topographic Mission version 4.1 (SRTM V4.1), Global Multiresolution Terrain Elevation Data 2010 (GMTED2010), EarthEnv-DEM90, and Global 30 Arc-Second Elevation (GTOPO30) were assessed in this study. The objective of this study was to compare the differences of elevations, slopes, and topographic wetness indices (TWIs) derived from these five DEM products. SRTM V4.1 showed a better accuracy [root mean square error (RMSE)=4.87 m] than ASTER GDEM2 (RMSE=7.08 m) based on ICESat/GLAS (the Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System) laser altimetry points. ICESat/GLAS data were then selected as the benchmark to rectify the SRTM V4.1 data using the simple kriging (SK) interpolation method. The corrected high-accuracy SRTM V4.1 data (RMSE=1.14 m) were then regarded as the reference data. EarthEnv-DEM90 displayed the best accuracy in the DEM and slope, whereas the TWI accuracy of GMTED2010 was best. The accuracy of topographic attributes was sensitive to the roughness of the terrain. DEM and slope displayed a larger error variance as the elevation increased. DEM was sensitive to the data source and slope was sensitive to the data source and spatial resolution. TWI was influenced by data source and spatial resolution. As the spatial resolution decreased, the differences of topographic attributes tended to decrease.

Urban roughness parameters estimation from globally available datasets for mesoscale modeling in megacities

Approaches to understanding urban atmospheric phenomena in cities include mesoscale models coupled with urban canopy models (UCMs). To improve the accuracy of these models, urban morphological parameters of higher spatial resolution are necessary. In this paper, we introduce an approach to derive 1-km scale urban parameters from globally available satellite images of Landsat 8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and nighttime light (NL) images from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) for use in numerical weather models. Empirical equations derived from linear fittings were used to calculate urban geometric parameter plane area index from satellite images, which were then compared with values derived from real plane area index. To calculate urban geometric parameters average building height, an artificial neural network fitting was conducted. From the basic urban geometric parameters, other necessary urban parameters were derived. After validating the method, a weather simulation was conducted using the derived parameters for Jakarta, Indonesia. Results showed that urban parameters made from global datasets could improve the performance of the single-layer urban canopy model; and these estimated parameters can be used as a substitute when actual building information are lacking.

Control quality of open source Digital Elevation Models (DEMs) in Tunisia

Digital Elevation Models (DEMs) are an invaluable source of information in large area studies. Some of the DEMs such as Advanced Spaceborne Thermal Emission Radiometer-Global Digital Elevation Model (ASTER GDEM), shuttle radar topography mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available for the scientific community worldwide. Prior to any application, global datasets of DEMs should be evaluated using reference data of higher accuracy. Therefore, the main objective of this study is to assess the quality of the ASTER GDEM (version 2), SRTM (version 4) and systematic subsample GMTED2010 in Tunisia. The validation process, adopted here, is based on two main approaches: internal and external validations. The internal validation is achieved by performing visual inspection of shaded relief images extracted from the three DEMs. At this level, results show that SRTM is essentially similar to ASTER GDEM2 in term of relief features representation. In the second alternative, the vertical accuracy of each DEM is evaluated using 60 Global Positioning System (GPS) validation points. The overall vertical accuracy shows RMSE error of 11.96 m, 8.65 m and 10.86 m for ASTER GDEM2, SRTM and GMTED2010 DEM respectively, in comparison with GPS elevation points.

OPEN-SOURCE DIGITAL ELEVATION MODEL (DEMs) EVALUATION WITH GPS AND LiDAR DATA

Abstract. Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available Digital Elevation Model (DEM) datasets for environmental modeling and studies. The quality of spatial resolution and vertical accuracy of the DEM data source has a great influence particularly on the accuracy specifically for inundation mapping. Most of the coastal inundation risk studies used the publicly available DEM to estimated the coastal inundation and associated damaged especially to human population based on the increment of sea level. In this study, the comparison between ground truth data from Global Positioning System (GPS) observation and DEM is done to evaluate the accuracy of each DEM. The vertical accuracy of SRTM shows better result against ASTER and GMTED10 with an RMSE of 6.054 m. On top of the accuracy, the correlation of DEM is identified with the high determination of coefficient of 0.912 for SRTM. For coastal zone area, DEMs based on airborne light detection and ranging (LiDAR) dataset was used as ground truth data relating to terrain height. In this case, the LiDAR DEM is compared against the new SRTM DEM after applying the scale factor. From the findings, the accuracy of the new DEM model from SRTM can be improved by applying scale factor. The result clearly shows that the value of RMSE exhibit slightly different when it reached 0.503 m. Hence, this new model is the most suitable and meets the accuracy requirement for coastal inundation risk assessment using open source data. The suitability of these datasets for further analysis on coastal management studies is vital to assess the potentially vulnerable areas caused by coastal inundation.

Integrating Landsat Imageries and Digital Elevation Models to Infer Water Level Change in Hoover Dam

The Thematic Mapper onboard Landsat 4, 5, and Enhanced Thematic Mapper Plus ( $\text{TM/ETM}+$ ) onboard Landsat 7 have frequency bands (green and SWIR) to effectively measure water body extents and their changes via the Modified Normalized Difference Water Index (MNDWI). Here, we developed a technique, called the thematic imagery-altimetry system (TIAS), to infer the vertical water changes from MNDWI horizontal water extent changes by integrating long-term $\text{TM/ETM}+$ imageries with available digital elevation models (DEMs). The result is a technique to quantify water level changes of natural or artificial water bodies over two decades. Several DEMs were used to compute intersects with $\text{TM/ETM}+$ water extent time series to evaluate the robustness of the technique. These DEMs include: the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map version 2 (ASTER-GDEM2, at 1 arcsec resolution), the Shuttle Radar Topography Mission version 2 (SRTM C-band at 1 arcsec), and the Global Multiresolution Terrain Elevation Data (GMTED2010 at 7.5 arcsec). We demonstrated our technique near Hoover Dam (HD) in Lake Mead to quantify its respective decadal water level changes. The dammed water had experienced extraordinary level variation in the past 20 years due to natural decline from intake or artificial impoundments. The discrepancy of the HD water level changes from an analysis of 32-year (1984–2015) time series, including 584 Landsat scenes, using the GMTED2010 DEM, has a RMSE reached $0.85 \pm 0.63\; \text{m}$ (91% of data) as compared with in situ stage record.

Digital elevation models on accuracy validation and bias correction in vertical

Digital Elevation Model (DEM) is used to represent the terrain of the earth. A free provided DEMs are the 10 m DEM produced by the Geographical Survey Institute of Japan (GSI-DEM), Advanced Space Borne Thermal Emission and Reflection Radiometer-Global DEM, Shuttle Radar Topography Mission, Global Multi-resolution Terrain Elevation Data 2010, Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales, and Global 30 Arc-Second Elevation that are actually used in scientific studies. DEMs have made a high accuracy to assess an error using an observation elevation point. The DEMs in this study at an original spatial resolution of the Shikoku Island, Japan were collected that were evaluated and corrected by using the referent elevation points observed by global position system. The evaluation and correction method of the DEMs were based on the statistical measures and linear transformation algorithm respectively. The results reveal that the GSI-DEM has higher accuracy than the five DEMs, and these DEMs have gotten more accuracy after corrected by the transform’s parameters. This approach will be used to recommend for a new DEM in a future, and it can be applied for making a high accuracy DEM to model the earth’s terrain.

High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data

Abstract. Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP) modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL), is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF) model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30), the Shuttle Radar Topography Mission (SRTM), and the Global Multi-resolution Terrain Elevation Data set (GMTED2010) terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.

Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow

The sensitivity of streamflow simulated with the Soil and Water Assessment Tool (SWAT) model to Digital Elevation Model (DEM) resolution, DEM source and DEM resampling technique is still poorly understood. The objective of this study is to compare SWAT model streamflow estimates in the Johor River Basin (JRB), Malaysia for DEMs differing in resolution (from 20 to 1500 m), sources (Shuttle Radar Topography Mission: SRTM v4.1, Advanced Space-borne Thermal Emission and Reflection Radiometer: ASTER GDEM2, EarthEnv-DEM90 and Global Multi-resolution Terrain Elevation Data 2010: GMTED2010) and resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority). The key findings were as follows: (1) SRTM v4.1 (Root Mean Square Error (RMSE) = 11.16 m) and EarthEnv-DEM90 (RMSE = 12.4 m) had better vertical accuracy over the JRB compared to the ASTER GDEM2 (RMSE = 16.95 m); (2) Accurate annual streamflow simulations were obtained by using nearly all of the DEM resolutions, as pointed out by a relative error (RE) lower than 7% from 20 to 50 m and from 100 to 800 m DEMs; (3) Prediction errors were the lowest for ASTER GDEM2 (RE = 3.9%), followed by SRTM v4.1 (RE = 5.4%), EarthEnv-DEM90 (RE = 6.3%), and GMTED2010 (RE = 7.3%); (4) the majority and nearest neighbour resampling techniques performed the best (RE of 6.0%), followed by bilinear interpolation (RE of 7.2%) and cubic convolution (7.5%). The study indicates that DEM resolution is the most sensitive SWAT model DEM parameter compared to DEM source and DEM resampling technique for streamflow simulation within SWAT.

Accuracy validation and comparison of global digital elevation models over Croatia

Accuracy assessment of digital elevation models (DEMs) plays an important role in facing their use in geoscience applications. This study investigates the vertical accuracy of most recently published versions of global DEMs over Croatia: 1 arc-minute global relief model (ETOPO1), Global 30 Arc-Second Elevation (GTOPO30), SRTM30+, Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Altimeter Corrected Elevations 2 DEM (ACE2 DEM), Shuttle Radar Topography Mission GDEM (SRTM GDEM), and Advanced Spaceborne Thermal Emission and Reflection GDEM (ASTER GDEM). Geodetic ground control points (benchmarks) allowed the comparison in terms of vertical accuracy. Additionally, the differences between models in the identical points were determined and analysed after adjusting the models to the same resolution. The results have yielded information about the overall accuracy of models; the accuracy depending on height, land cover, and slope; presence of large and systematic errors; and mutual agreement between the models.Overall vertical accuracies according to the root mean square error over Croatia are: ETOPO1 27.6 m, GTOPO30 21.6 m, SRTM30+ 21.3 m, GMTED2010 7.4 m, ACE2 DEM 4.5 m, SRTM GDEM 3.8 m, and ASTER GDEM 7.1 m. The highest accuracy was shown by SRTM GDEM version 3, which is far better than the previously released versions. All models have proved to be worse in the rough and forest areas, whereas in flat and barelands, new-generation global DEMs SRTM GDEM, and ASTER GDEM are highly accurate.