SRTM Research Articles

Urban waterlogging vulnerability assess using SAR imagery and integrated terrain analysis

Waterlogging is a significant concern in urban areas and can result in severe disruptions and damage and it’s an urban problem. This study is conducted in Thoothukudi and Tamil Nadu, which are particularly sensitive to waterlogging because of their geographical and meteorological circumstances. Using synthetic aperture radar (SAR) images from 2015 to 2022, topographical analysis, land use/land cover (LULC) data, and geological insights, this research intends to identify and assess areas prone to water logging. The data source for this study comprises rainfall records from the Indian Meteorological Department (IMD), Sentinel-1 SAR imagery, Sentinel-2 multispectral images from the European Space Agency (ESA), and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM).Terrain analysis was undertaken using DEM to generate elevation, slope, and aspect maps, while SAR data were processed to extract water pixels, which included the extraction of water pixels from SAR data for each year and overlaying them. The overlaid image was correlated with topographic maps to identify the high-risk regions. Key places such as Muthayapuram, Milavittan, Bryant Nagar, and Thalamuthunagar were constantly highlighted as prone to floods. Additionally, the saltpan regions, defined by low-lying water table levels, endure continuous flooding, demonstrating the usefulness of combining SAR imaging with topographic analysis for urban water management. These findings provide useful insights for urban planners and policymakers, underlining the need for deliberate steps to reduce waterlogging, maintain public health, and minimize infrastructure damage, thus enabling sustainable development in Thoothukudi.

Spatial variability of soil organic carbon in Tamil Nadu: Integrating remote sensing and machine learning approaches

Aim: The experiment was carried out to estimate the geographical distribution of soil organic carbon (SOC) in Tamil Nadu, India, using advanced machine learning models. Methodology: High-resolution remote sensing data from Landsat 8, climate indices from TerraClimate, and terrain indices from the Shuttle Radar Topography Mission (SRTM) were integrated to construct reliable SOC estimation models. The models employed in this study included Random Forest (RF), Quantile Neural Network (QNN), Cubist, and Bootstrapped Random Forest (BRF). These models were selected for their ability to capture complex relationships between SOC and environmental covariates. Results: The QNN and Cubist models displayed superior accuracy with mean absolute error (MAE) values of 0.48 and 0.47, respectively. Variogram analysis indicated moderate spatial dependence across all models, with the Cubist model exhibiting the highest partial sill and sill values. Interpretation: These findings demonstrate the effectiveness of combining machine learning with remote sensing for SOC estimation. The results suggest that these models can be valuable tools in improving the accuracy of SOC distribution maps, which are essential for sustainable crop production. By identifying potential SOC sequestration areas, this research contributes to the development of targeted soil management strategies that prioritize soil health and support sustainable agricultural practices. Key words: Carbon sequestration, Digital soil mapping, Estimation models, Environmental covariates, Land management

The impact of dam management and rainfall patterns on flooding in the Niger Delta: using Sentinel-1 SAR data

This study explored how dam operation and rainfall patterns interact to influence flooding in the Niger Delta, Nigeria. This study utilizes Sentinel-1 Synthetic Aperture Radar (SAR) data to analyze the flood events from 2018 to 2022, focusing on key locations such as Obiafu (downstream), Kainji/Jebba, and Lagdo dams (upstream). Analyzed Sentinel-1 satellite data [European Space Agency (ESA), 2018–2022], shuttle Radar Topography Mission, alongside rainfall data [Center for Hydrometeorology and Remote Sensing (CHRS), 2018–2022] and surface water level to assess flood variations between 2018 and 2022. The flood extent was differentiated from that of permanent water bodies using Sentinel-1 images with an RGB band (Snap 7.0), and the rainfall patterns were examined via inverse distance weighting (IDW) interpolation (ArcGIS 10.5). The findings reveal that the intensity and timing of flooding vary significantly across these regions. Obiafu experienced high flooding in October 2020 and 2022, while Kainji/Jebba and Lagdo dams had contrasting flood severities during these periods. Additionally, a comparative analysis of hydrograph data from these locations shows a delayed downstream response, highlighting the need for coordinated water release strategies to mitigate flood risks. The results also indicate that upstream dam operations, particularly water releases, contribute to downstream flooding, although local rainfall patterns remain a crucial factor. By aligning SAR data with real-time water levels, this study emphasizes the importance of integrating satellite-based monitoring with hydrological models to improve flood prediction accuracy and minimize socio-economic impacts in the Niger Delta. The study concludes that enhanced flood management strategies, incorporating both rainfall forecasting and dam operation schedules, are critical for reducing flood-related vulnerabilities in the region.

Habitat Suitability Modelling for the Red Dwarf Honeybee (Apis florea (Linnaeus)) and Its Distribution Prediction Using Machine Learning and Cloud Computing

Apis florea bees were recently identified in Egypt, marking the second occurrence of this species on the African continent. The objective of this study was to track the distribution of A. florea in Egypt and evaluate its potential for invasive behaviour. Field surveys were conducted over a 2-year period, resulting in the collection of data on the spatial distribution of the red dwarf honeybees. A comprehensive analysis was performed utilizing long-term monthly temperature and rainfall data to generate spatially interpolated climate surfaces with a 1-km resolution. Vegetation variables derived from Terra MODIS were also incorporated. Furthermore, elevation data obtained from the Shuttle Radar Topography Mission were utilized to derive slope, aspect, and hillshade based on the digital elevation model. The collected data were subject to resampling for optimal data smoothing. Subsequently, a random forest model was applied, followed by an accuracy assessment to evaluate the classification output. The results indicated the selection of the mean temperature of coldest quarter (bio11), annual mean temperature (bio01), and minimum temperature of coldest month (bio06) as temperature-derived parameters are the most important parameters. Annual precipitation (bio12) and precipitation of wettest quarter (bio16) as precipitation parameters, and non-tree vegetation parameter as well as the elevation. The calculation of the Habitat Suitability Index revealed that the most suitable areas, covering a total of 200131.9 km2, were predominantly situated in the eastern and northern regions of Egypt, including the Nile Delta characterized by its fertile agricultural lands and the presence of the river Nile. In contrast, the western and southern parts exhibited low habitat suitability due to the absence of significant green vegetation and low relative humidity.

Real-Time Wind Speed Analysis at Wind-Damaged Tower of a Transmission Line

Affected by the Super Typhoon “Mangkhut,” a total of five base towers of a transmission line in the mountainous area of China collapsed. In this paper, a mathematical model is established based on the Shuttle Radar Topography Mission (SRTM) data near the accident tower. The measured wind speed in the plain area under the mountain is used as the calculation boundary condition. The wind speed at the top of the mountain is calculated by using a numerical simulation method. The design wind speed and calculated wind speed at the tower site are compared, and the influence of wind speed on tower position in this wind disaster accident is analyzed.

Climate Change Effects on Land Use and Land Cover Suitability in the Southern Brazilian Semiarid Region

Climate change is expected to alter the environmental suitability of land use and land cover (LULC) classes globally. In this study, we investigated the potential impacts of climate change on the environmental suitability of the most representative LULC classes in the southern Brazilian semiarid region. We employed the Random Forest algorithm trained with climatic, soil, and topographic data to project future LULC suitability under the Representative Concentration Pathway RCP 2.6 (optimistic) and 8.5 (pessimistic) scenarios. The climate data included the mean annual air temperature and precipitation from the WorldClim2 platform for historical (1970–2000) and future (2061–2080) scenarios. Soil data were obtained from the SoilGrids 2.1 digital soil mapping platform, while topographic data were produced by NASA’s Shuttle Radar Topography Mission (SRTM). Our model achieved an overall accuracy of 60%. Under the worst-case scenario (RCP 8.5), croplands may lose approximately 8% of their suitable area, while pastures are expected to expand by up to 30%. Areas suitable for savannas are expected to increase under both RCP scenarios, potentially expanding into lands historically occupied by forests, grasslands, and eucalyptus plantations. These projected changes may lead to biodiversity loss and socioeconomic disruptions in the study area.

Assessing open‐access digital elevation models for hydrological applications in a large scale plain: Drainage networks, shallow water bodies and vertical accuracy

AbstractThis study evaluated six open‐access digital elevation models (DEMs) for the Del Azul Creek Basin in the Argentine Chaco‐Pampean Plain: Shuttle Radar Topography Mission, Advanced Land Observing Satellite Phased Array L‐Band Synthetic Aperture Radar, TerraSAR‐X Add‐On for Digital Elevation Measurements (TanDEM‐X), NASADEM Global DEM, Forest and Building height biases were removed from Copernicus GLO 30 DEM V1‐0 (FABDEM), and TanDEM‐X 30 m Edited DEM (EDEM). Statistical metrics were calculated for (i) residuals between DEMs and the Ice, Cloud and Land Elevation Satellite‐2 (ICESat‐2); (ii) the minimum distance between DEM‐derived drainage networks and those from the Buenos Aires Provincial Water Authority; and (iii) DEM‐derived slopes in shallow water bodies compared with the Joint Research Centre's Global Surface Water Mapping product. Analyses were performed for four elevation and seven slope bands. TanDEM‐X had the smallest errors compared to ICESat‐2 (median 0.19 m, NMAD 0.38 m), followed by FABDEM (median 0.31 m, NMAD 0.23 m). EDEM performed best in drainage networks (median 99.45 m, NMAD 117.16 m), followed by FABDEM. In general, the vertical error increased with elevation and the accuracy of the drainage network estimates improved. The vertical accuracy decreased with steeper slopes, with FABDEM performing the best across all slope ranges. FABDEM exhibited the best performance in determining seasonally dispersed shallow water bodies, demonstrating its overall usefulness for hydrological applications in large‐scale plains characterized by aeolian geoforms of lowland accumulation and erosion. Assessing freely available products provides valuable resources for researchers and professionals and can guide decision making for managing hydrological resources, including flood risk and infrastructure development.

Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data

Preprocessing Synthetic Aperture Radar (SAR) data is a crucial initial stage in leveraging SAR data for remote sensing applications. Terrain correction, both radiometric and geometric, and the detection of layover/shadow areas hold significant importance when SAR data are collected over mountainous regions. This study aims at investigating the impact of the Digital Elevation Model (DEM) used for terrain correction (radiometric and geometric) and for mapping layover/shadow areas on windthrow detection using COSMO SkyMed SAR images. The terrain correction was done using a radiometric and geometric terrain correction algorithm. Specifically, we evaluated five different DEMs: (i–ii) a digital terrain model and a digital surface model derived from airborne LiDAR flights; (iii) the ALOS Global Digital Surface Model; (iv) the Copernicus global DEM; and (v) the Shuttle Radar Topography Mission (SRTM) DEM. All five DEMs were resampled at 2 m and 30 m pixel spacing, obtaining a total of 10 DEMs. The terrain-corrected COSMO SkyMed SAR images were employed for windthrow detection in a forested area in the north of Italy. The findings revealed significant variations in windthrow detection across the ten corrections. The detailed LiDAR-derived terrain model (i.e., DTM at 2 m pixel spacing) emerged as the optimal choice for both pixel spacings considered.

Enhancing the performance of runoff prediction in data-scarce hydrological domains using advanced transfer learning

Accurate hydrological predictions are often hindered by the lack of stream gauges in data-scarce regions, where traditional transfer learning (TL) models like Long Short-Term Memory (LSTM) networks often face limitations due to reduced accuracy and adaptability. To enhance runoff prediction in such regions, we developed DAformer, a novel TL approach that integrates domain adversarial neural networks with the Informer model. Trained on comprehensive runoff data from U.S. basins, DAformer was applied to three basins in Chile and the Chaersen basin in China, demonstrating an effective transfer from data-rich to data-scarce environments. Results show that DAformer significantly outperforms LSTM-based models, improving forecast accuracy by 16.1% for 1-day lead time and by 100.5% for 5-day lead time. These improvements indicate that the DAformer model not only enhances prediction accuracy but also holds substantial practical implications for flood risk management and water resource planning in regions with limited data availability. By clustering basins based on Shuttle Radar Topography Mission (SRTM) and other geographical data, we found that relying on multiple source basins further enhances the performance. DAformer, therefore, serves as a robust and scalable method for enhancing runoff prediction for regions with limited data.

Morphometric analysis and watershed delineation of the Karnaphuli river basin: A comparative study using different DEMs in Chittagong, Bangladesh

AbstractThe present study aimed to delineate a robust watershed boundary and extract its morphometric parameters in the Karnaphuli watershed, Bangladesh, using different digital elevation models (DEMs). Two DEMs, the shuttle radar topographic mission (SRTM) and the terra advanced spaceborne thermal emission and reflection radiometer (ASTER), were employed to delineate watershed boundaries and evaluate various morphometric characteristics. Raster data such as fill sinks, flow direction, and flow accumulation were utilized to delineate the watershed. The morphometric analysis included the estimation of linear, areal, and relief parameters. The findings revealed a noticeable difference between the datasets; ASTER delineated a larger watershed area than SRTM. Regarding stream order, there were streams ranging from 1st to 6th order, encompassing numerous small, medium, and main channels of the river. Drainage density was calculated as 0.52 km/km² for SRTM and 0.51 km/km² for ASTER, both having a spatial resolution of 30 m. The bifurcation ratio ranged from 1.94 for SRTM to 2.45 for ASTER, indicating varying degrees of structural disturbance influenced by geological factors. The form factor indicated an elongated shape of the study area. The overall dimensions of the streams and the watershed extent suggest moderate mean annual rainfall discharge. The watershed exhibited relatively high denudation rates, attributed to the basin relief. A low drainage density indicated the importance of infiltration processes. The findings of this study provide valuable insights into the spatial variability of watershed characteristics, highlighting the need for tailored management strategies for the Karnaphuli River. Implementing targeted conservation measures and watershed management practices based on these morphometric parameters can enhance water resource sustainability and ecological health in the region.

A Global Evaluation of Radar‐Derived Digital Elevation Models: SRTM, NASADEM, and GLO‐30

AbstractThis study evaluates global radar‐derived digital elevation models (DEMs), namely the Shuttle Radar Topography Mission (SRTM), NASADEM and GLO‐30 DEMs. We evaluate their accuracy over bare‐earth terrain and characterize elevation biases induced by forests using global Lidar measurements from the Ice, Cloud, and Land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS), the Global Ecosystem Dynamics Investigation (GEDI) and the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) instruments collected on locally flat terrain. Our analysis is based on error statistics calculated for each DEM tile, which are then summarized as global error percentiles, providing a regional characterization of DEM quality. We find NASADEM to be a significant improvement upon the SRTM V3. Over bare ground areas, the mean elevation bias and root mean square error (RMSE) improved from 0.68 to 2.50 m respectively to 0.00 and 1.5 m as compared to ICESat/GLAS. GLO‐30 is more accurate with bare ground elevation bias and RMSE were below 0.05 and 0.55 m. Similar improvements were observed when compared to GEDI and ICESat‐2 measurements. The DEM biases associated with the presence of vegetation vary linearly with canopy height, and more closely follow the percentile of Lidar Relative Height (RH50). Other factors such as canopy density, radar frequency and Lidar technology also contribute to observed elevation biases. This global analysis highlights the potential of various technologies for mapping of Earth's topography, and the need for more advanced remote sensing observations that can resolve vegetation structure and sub‐canopy ground elevation.

Predictive Modelling of Land Cover Changes in the Greater Amanzule Peatlands Using Multi-Source Remote Sensing and Machine Learning Techniques

The Greater Amanzule Peatlands (GAP) in Ghana is an important biodiversity hotspot facing increasing pressure from anthropogenic land-use activities driven by rapid agricultural plantation expansion, urbanisation, and the burgeoning oil and gas industry. Accurate measurement of how these pressures alter land cover over time, along with the projection of future changes, is crucial for sustainable management. This study aims to analyse these changes from 2010 to 2020 and predict future scenarios up to 2040 using multi-source remote sensing and machine learning techniques. Optical, radar, and topographical remote sensing data from Landsat-7, Landsat-8, ALOS/PALSAR, and Shuttle Radar Topography Mission derived digital elevation models (DEMs) were integrated to perform land cover change analysis using Random Forest (RF), while Cellular Automata Artificial Neural Networks (CA-ANNs) were employed for predictive modelling. The classification model achieved overall accuracies of 93% in 2010 and 94% in both 2015 and 2020, with weighted F1 scores of 80.0%, 75.8%, and 75.7%, respectively. Validation of the predictive model yielded a Kappa value of 0.70, with an overall accuracy rate of 80%, ensuring reliable spatial predictions of future land cover dynamics. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, they do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the understudied GAP region, offering critical insights for developing targeted conservation strategies. Additionally, the methodological framework, which combines optical, radar, and topographical data with machine learning, provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands that is applicable to other regions facing similar environmental challenges.

Subsurface basement structures of the Usangu basin, East African rift system, with implications for basin structural configuration and hydrocarbon potential

The Usangu basin is a rift basin developed along the Eastern arm of the East African rift system trending in the NE-SW direction. Although the general structures of the basin have been well studied, the structural configuration of the basin and the spatial and depth variations of sediment thickness are still not well known. This study investigates the structures related basement configuration and the variation of sediment thickness within the basin using the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), aeromagnetic and gravity data. Results from DEM data indicate a few lineaments on the basin flanks representing the Usangu and Chimala border faults with no structures in the central part of the basin. The aeromagnetic and gravity data highlight three sets of normal and strike-slip faults, most of which trend NE-SW and others NNE-SSW, while a few trends WNW-ESE or NW-SE. Structures on the southwest of the basin reveal complex patterns attributed to the concentration of important tectonic and seismic activities in the study area. The Euler deconvolution and gravity models used to calculate the depth to the basement show that the basement is shallow in the north and south to southwest, and the basin deepens in the northeastern, northwestern and western parts. The findings also reveal that the basin has two grabens, troughs, depression and intrabasinal basement trending in the same direction as the basin configuration. The general thickness of sediments filling the basin ranges from 3 to 4.5 km, with the maximum accumulation of sediments reaching up to 4.8 km in the two depocenters at the south and southwest of the basin. The depth range of the sediments obtained implies that the basin has potential for hydrocarbon exploration with the possibility of natural gas occurrence.

On the origin of the Azzel-Matti circular structure (southern Algeria): Insights from remote sensing, geological and geophysical data

The Azzel-Matti circular structure (25°51′ N, 0°35’ E) is located on the Tanezrouft plateau at the boundary between the West African Craton and the Bled El Mass compartment. Its morphology exhibits a diameter of about 6.5 km, with a raised rim that stands above the surrounding terrain. This study is based on a combination of remote sensing data (Landsat-8-OLI images and Shuttle Radar Topography Mission (SRTM) images), aeromagnetic and seismic data, and field investigations in and around the Azzel-Matti circular structure to analyze its morphology and ascertain its origin. The investigation of the area surrounding the structure did not reveal any evidence of magmatism, diapirism or impact. However, it did confirm the presence of new faults of a deeper character, as highlighted by remote sensing, aeromagnetic, and seismic analysis. This suggests that tectonic deformation is a probable factor contributing to the formation of this circular structure. The morphology of the structure is explained by the counter-clockwise rotation of the strata, likely resulting from the interplay between movements along the sub-meridian faults marking the eastern and western boundaries and the ESE-WNW oblique reverse fault marking the northern boundary. These faults are coherently arranged to facilitate the counter-clockwise rotation of the strata. The movement of the faults and their depth indicate an NE-SW compressional event corresponding to the Late Paleozoic Variscan (Hercynian) orogeny. This is supported by the presence of horizontal lacustrine carbonate formations, presumably of Jurassic age, which are discordant with the Carboniferous strata, thus providing evidence for the age of the Azzel-Matti circular structure between the Moscovian and the Jurassic.

Evaluation of GOCE/GRACE and combined global geopotential models using GNSS/levelling data over Nigeria

AbstractGlobal Geopotential Models (GGMs) provide valuable information about Earth’s gravity field functionals, such as geoid heights and gravity anomalies. However, ground-based datasets are required to validate these GGMs at the regional and local scales. In this study, we validated the accuracy of GGMs by comparing them with ground-based Global Navigation Satellite System (GNSS)/levelling data for the first time in Nigeria. We employed two validation scenarios: with and without considering spectral consistency using the spectral enhancement method (SEM) to incorporate high and very high frequencies of the gravity field spectrum from the combined global gravity field model (XGM2019e_2159) and the residual terrain model (RTM) derived from the Shuttle Radar Topography Mission (SRTM) data, respectively. The results of this evaluation confirmed that the application of SEM improved the assessment of the GGM solutions in an unbiased manner. Integrating XGM2019e_2159 and SRTM data to constrain the high-frequency component of geoid heights in Gravity Field and Steady-State Ocean Circulation Explorer (GOCE)-based GGMs led to an improvement of approximately 10% in reducing the standard deviation (STD) relative to when SEM was not applied. GO_CONS_GCF_2_TIM_R6 at spherical harmonics (SH) of up to degree and order 260 demonstrated the lowest STD when compared to GO_CONS_GCF_2_DIR_R6 and GO_CONS_GCF_2_SPW_R5, with a reduction from 0.380 m without SEM application to 0.342 m with SEM implementation. In addition, four transformation models, namely, linear, four-parameter, five-parameter, and seven-parameter models, were evaluated. The objective is to mitigate the reference system offsets between the GNSS/levelling data and the GGMs and to identify the particular parametric model with the smallest STD across all GGMs. This effort reduced the GGMs misfits to GNSS/levelling to 0.30 m, representing a 15.3% decrease in STD. Notably, the XGM2019e_2159 model provides this improvement.

Improving the accuracy of honey bee forage class mapping using ensemble learning and multi-source satellite data in Google Earth Engine

In semi-arid agro-pastoral environments of Africa, beekeeping is widely recognized as an important activity to improve and diversify livelihoods. Although the scientific identification of suitable honey bees (Apis mellifera ssps.) forages may guide beekeepers to set up apiaries or to timely move honey bee colonies to exploit bee forage resources available in various landscapes, the characterization and mapping of bee forage classes is challenging. We evaluated how various data sources and classification algorithms in Google Earth Engine (GEE) affect the accuracy of honey bee forage class mapping in a semi-arid region of Ethiopia. Predictors derived from multi-source satellite data, such as high-resolution Planet imagery (P), Sentinel 1 RADAR (S1), Sentinel 2 multispectral (S2), and Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) were tested and best predictors were identified using Forward Feature Selection (FFS). Four machine learning algorithms (Gradient Tree Boost (GTB), Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machine (SVM)), all available in GEE, were compared and ensembled for honey bee forage class mapping. The results show that the highest accuracy is obtained by all four algorithms when combining P, S1, S2, and DEM compared to using predictors from a single data source or any other combinations. GTB had higher overall accuracy (90.9 %) than RF (88.2 %), CART (85.5 %), or SVM (79.9 %). Nonetheless, the highest overall accuracy (94.7 %) was obtained when integrating the four machine learning algorithms in an Ensemble Learning Approach (ELA). Applying ELA improved the classification accuracy by 3.8 %, 6.5 %, 9.2 %, and 14.8 % compared to single learner classification algorithms (i.e., GTB, RF, CART, and SVM, respectively). This study demonstrates an improved classification accuracy for honey bee forage class mapping in tropical rangeland by applying ELA, which can provide a better approach for monitoring and managing bee forage resources.

Water reservoir techniques using remote sensing and GIS: a literature survey

Abstract The issue of storing flood waters caused by rains and river floods is very important to address the problems of drought. This study reviewed the latest research to address the problems of water scarcity and conservation with advanced methods. these study methodologies range from employing Geographic Information Systems Programs to handle and analyses remote sensing data to incorporating contemporary techniques to enhance morphometric analysis, where researchers employed various analytical techniques. To determine if these watersheds are appropriate for storing water, some studies have preferred Multi-Criteria Decision-Making models (MCDM), more especially the fuzzy analytical hierarchy (FAHP) Fuzzy Analytical Hierarchy Process. Others have employed techniques for hydrological analysis, such as the ArcGIS program’s Arc hydrology tools. Researchers reached a consensus regarding the fundamental parameters required for morphometric analysis in the demarcation of watersheds, notwithstanding methodological variances. Additionally, many researchers have argued the importance of using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM) mission and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER DEM) in morphometric and hydrologic studies.

Spatial planning of the Bueno Brandão (MG) municipality with emphasis on hydrographic sub-basins

Territorial planning must be carried out with the objective of organizing human activities so to promote sustainable development, reconciling human well-being with environmental preservation. In this context, this study was developed with an initial proposal for the spatial planning of the municipality of Bueno Brandão (MG), using the hydrographic sub-basins as environmental planning units. For this purpose, the Geographic Information System software QGIS was used to structure a database composed of hydrographic, altimetric information, municipal limits and spatial distribution of rural clusters and the urban area of the municipality. The Digital Elevation Model, obtained from the Shuttle Radar Topography Mission project, was also used. Google Earth images were used to verify that the areas of population concentration coincided with the limits of the sub-basins. The initial proposal for Bueno Brandão spatial planning resulted in the identification of 43 rural clusters and the delimitation of the urban area, distributed in 12 hydrographic sub-basins.

Effects of Resampled DEM on Watershed Characteristics and Prediction of Sediment Load in Oyun Watershed, Kwara, Nigeria

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.

Flooding Hazard Vulnerability Assessment Using Remote Sensing Data and Geospatial Techniques: A Case Study from Mekkah Province, Saudi Arabia

Flash floods are catastrophic phenomena that pose a serious risk to coastal infrastructures, towns, villages, and cities. This study assesses the risk of flash floods in the ungauged Mekkah province region based on specific and effective morphometric and topographic features characterizing the study region. Shuttle Radar Topography Mission (SRTM) data were employed to construct a digital elevation model (DEM) for a detailed analysis, and the geographical information systems software 10.4 (GIS) was utilized to assess the linear, area, and relief aspects of the morphometric parameters. The ArcHydro tool was used to prepare the primary parameters, including the watershed border, flow accumulation, flow direction, flow length, and stream ordering. The study region’s flash flood hazard degrees were assessed using several morphometric characteristics that were measured, computed, and connected. Two different and effective methods were used to independently develop two models of flood vulnerability behaviors. The integrated method analysis revealed that most of the eastern and western parts of the studied province provide high levels of flood vulnerability. Due to it being one of the most helpful topographic indices, the integrated flood vulnerability final map was overlayed with the topographic position index (TPI). The integrated results aided in understanding the link between the general basins’ morphometric characteristics and their topographical features for mapping the different flood susceptibility locations over the entire studied province. Thus, this can be applied to investigate a surface-specific reduction plan against the impacts of flood hazards in the studied landscape.