Digital Elevation Model Research Articles

A unique Ruleset for Saltmarsh and Mangrove monitoring using Sentinel-2

Abstract. Mangrove and saltmarsh vegetation are two important communities in the wetland ecosystem that require continuous monitoring considering their ecological threats and status. Remote Sensing observation is one of the tools to monitor this community. However algorithms are also important for the best sensor to map it accurately. Although there is some research about eCognition-based mapping, a unique rule set is important to monitor this community. Considering this research gap, a unique threshold-based ruleset has been generated in the Random Forest Model environment. A range of vegetation indices, individual bands of Sentinel 2 and Digital Elevation Model (DEM) data were tested in the feature selection method to find the best features for a Random Forest (RF) model. Top three variables were selected and those are (a) reNDVI_A, (b) VIRE_A, and (c) SWIR1 which gave 98.37% accuracy for the test data. A similar trend was found for the other three sites when they were compared with observed data. For site 1, Mangrove was 84.73%, Saltmarsh was 60.19%, and Mixed was 70.12% accurate. A similar trend was found for other three sites with overall accuracy 87.74 % for site-2, 66.23% for site 3, and 72.98% for site 4. A unique ruleset for wetland extent estimation will help to map a wide range of areas with a very minimum level of fieldwork. It will also reduce the cost of mapping done by visual observation, measurement and extensive fieldwork. The results of this work will provide the necessary insight and motivation for ecologists, and environmentalists.

Identification of Paleovolcanic Centers in the Bima District, East Sumbawa Island (Indonesia) as Guidance for Future Exploration of Cu-Au Deposits

The formation of Cu-Au mineralization, such as porphyry and epithermal deposits, is strongly associated with volcanic processes in specific tectonic settings, such as subduction zones. The identification of the presence of ancient volcanoes is one of the important steps to finding mineral deposits. This study aims to identify the presence of ancient volcanoes in the Bima District, eastern part of Sumbawa Island, as a step toward determining the potential indication of Cu-Au mineralization. The methods used in this research consist of a literature study, image analysis and remote sensing, field survey and data collection, and petrographic analysis. Image analysis using DEMNAS (Digital Elevation Model), including texture and pattern analysis using the concept of volcanic anatomy, aims to identify the remaining forms of ancient volcanoes. Field surveys and data collection include volcano geomorphology, lithology and sampling, and also geological structures. Petrographic analysis is conducted to qualitatively characterize the texture, structure, and mineralogy of volcanic rocks. The identification results show that there are at least ten volcanoes (crown) identified through image analysis, namely Doro Mbangga, Doro Baku, Doro Donggo Masa, Doro Rompo, Doro Sape, Doro Kowo, Doro Jia, Doro Sambori, Doro Mangge, and Doro Lambu. Each of these volcanoes has one or more eruption center (hummock). The eruption center identified in the central, proximal, to distal facies of the volcano, even superimposing one volcano product with another, and spread around 80-90% in the study area. The volcanic facies in the study area are characterized by the central part being composed of lithologies such as intrusive rocks, lava, and diatreme breccia, while the proximal and distal facies are composed of breccia, volcanic breccia, and tuff. Hydrothermal alteration zones are identified in the central and proximal facies of the volcano. These alterations were associated with the presence of eruption centers, where the abundance of eruption centers means that hydrothermal alterations are particularly well developed and pervasively formed. Identified argillic and advanced argillic alteration associated with stockworks forming a lithocap environment. In addition, the presence of intrusive rocks such as diorite and dacite with chloritic and sericitic alteration in the central facies of Doro Baku can be associated with the presence of deposits such as porphyry and epithermal, so the identification of ancient volcanic eruption centers in the Bima district has implications for the potential discovery of Cu-Au mineralization, such as porphyry and epithermal deposits.

Understanding future water-carbon-land coupled systems in the era of COP 27: The case of the Hanjiang river Basin, China

The 27th Conference of the Parties (COP 27) emphasized addressing this century's interconnected challenges of food, water, and energy. This study proposes a coupled ecosystem service and multi-scenario land-use change simulation model designed to investigate the future dynamics of water-carbon-land coupled systems in the Hanjiang River Basin (HJRB), the primary objective is to provide valuable insights that can inform strategic spatial management decisions, aligning with the ambitious objectives outlined in COP 27. Specifically, this study utilized the PLUS model, InVEST model, and redundancy analysis to comprehensively analyze the interplay between ecosystem services and land-use changes, with a specific focus on water, carbon, and land dynamics. The results showed that regardless of the simulated scenarios, there was a consistent pattern observed in the changes of land use types within the HJRB, with a decrease in farmland and an increase in forest. However, the water area showed an increasing trend in all scenarios, especially in the ecological land protection (ELP) and sustainable development scenarios. Furthermore, the ELP scenario effectively suppressed the expansion of building land and the erosion of ecological land. Ecosystem services under different scenarios showed similar spatial distribution patterns but presented varying degrees of change related to the impact of future land use and urban development on ecosystem services. The water yield (WY), carbon storage, and soil conservation in the upstream areas increased to varying degrees, while those in the downstream areas decreased. In conclusion, precipitation, land use/land cover change, DEM (Digital Elevation Model), and NDVI (Normalized Difference Vegetation Index) were the main driving factors affecting ecosystem services, with precipitation having the most significant and enduring impact on WY. This study supports the adoption of targeted spatial management measures to promote sustainable development and enhance human well-being.

Pixel-level Terrain Processing of the Martian Surface from Moderate Resolution Images of Tianwen-1

The moderate-resolution camera (MoRIC) of the Tianwen-1 orbiter, China’s first Mars exploration mission, is a frame-imaging scheme color camera. However, generating a precise digital elevation model (DEM) from these images faces challenges due to the presence of featureless and repetitive regions on the Martian surface. Traditional photogrammetric methods often struggle to achieve pixel-level resolution in these areas. To address this, we employ a shape from shading (SFS) algorithm that leverages shading patterns to reconstruct fine-scale terrain details, optimizing photogrammetric initial DEM. This paper presents a novel global bisection search algorithm, based on the Pearson correlation coefficient to determine the smoothing parameter, a crucial element in the SFS process. Comparisons with higher-resolution data from the Mars Express high-resolution stereo camera and Mars Reconnaissance Orbiter CTX images validate our approach, highlighting the potential of SFS to significantly enhance the scientific value of Tianwen-1 MoRIC data by generating high-resolution, three-dimensional maps of Mars.

An Entropy-based Method to Evaluate the Appropriate Spatial Resolution

Abstract. Geospatial data is available at increasingly finer spatial resolutions. However, such data can also be problematic because it may result in increased financial, resource and time cost. It might also be unnecessary if the phenomenon of interest does not vary at this scale or if the scientific application does not require it. This paper explores the scale effects associated with increased spatial resolution using the entropy-based local indicator of spatial association (LISA-ELSA). Results are illustrated for two datasets a digital elevation model (30 m ASTER GDEM) and for a raster PM2.5 air pollution map (1000 m). It is shown that increasing the size of the local window for the LISA can be used to explore the effect of reducing the spatial resolution. It is possible to identify areas where the spatial association is invariant with increasing window size and which could be represented with coarser pixels.

From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland

Flood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities by investigating flood susceptibility in rapidly urbanising regions prone to extreme weather events, focusing on Gdańsk, Poland. Three popular ML techniques, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), were evaluated for handling complex, nonlinear data using a dataset of 265 urban flood episodes. An ensemble filter feature selection (EFFS) approach was introduced to overcome the single-method feature selection limitations, optimising the selection of factors contributing to flood susceptibility. Additionally, the study incorporates explainable artificial intelligence (XAI), namely, the Shapley Additive exPlanations (SHAP) model, to enhance the transparency and interpretability of the modelling results. The models’ performance was evaluated using various statistical measures on a testing dataset. The ANN model demonstrated a superior performance, outperforming the RF and the SVM. SHAP analysis identified rainwater collectors, land surface temperature (LST), digital elevation model (DEM), soil, river buffers, and normalized difference vegetation index (NDVI) as contributors to flood susceptibility, making them more understandable and actionable for stakeholders. The findings highlight the need for tailored flood management strategies, offering a novel approach to urban flood forecasting that emphasises predictive power and model explainability.

Automatic Detection of Flood Extent and Volume Estimation from Sentinel-2 Satellite Images using Deep Learning Techniques in India

Floods are unexpected natural disasters that can have a major impact on human life, soil along bank erosion, damage vital infrastructure, road closures, economy standard, and society of various affected regions. An initial step of proper assessment is necessary for flood damage along with accurate measurements to easily restore essential damage of infrastructure, relief, and mitigation as quickly as possible. Nowadays, the rapid development of remote sensing images using deep learning as a most positive tool for accurately estimating the extent of overall flood detection surfaces. The monitoring of flood detections from remote sensing images still extends a few issues due to mostly varying from different weather changes conditions, cloud coverage areas that can have a limit to use of level of visible remote sensing satellite collected data. Moreover, Remote Sensing Satellite based observations may not always be mapped to the distribution's flood point peak, also it is very essential for both the flood extent and flood volume estimation. To overcome this challenge, we have presented a new remote sensing technology that integrates with a high resolution multi-spectral satellite data/information by using an advanced Deep Learning to accurately analyze remote sensing based observations. In our experiment, we use the European Space Agency (ESA) launched Sentinel type-1, Sentinel type-2 data and Digital Elevation Model (DEM) to accurately measure flood monitoring results. In our study, we reviewed a real example of the flood situation that happened in 2019 in Kolhapur. In our results, we evaluated a flood volume estimation at 0.0010 km3 in Kolhapur district. Finally, the proposed methodology provides an effective way to accurately motoring floods using low-cost satellite data and deep learning approaches. This project has the potential to improve the more accurate flood detection and mapping which can prevent an exactly timely response and immediate recovery efforts for flood surrounding affected areas.

Multi-Source Fusion Enhanced Feature Segmentation in Remote Sensing Imagery

Abstract. With deepening application of deep learning technology in the field of remote sensing, several challenges persist in the segmentation of remote sensing optical images. These challenges include: (1) insufficient availability of deep learning-based remote sensing semantic segmentation datasets; (2) inadequate utilization of multi-source remote sensing data in the field of semantic segmentation; (3) limited sample size for effective model training, as well as the need to enhance both the speed and accuracy of model training. To address these challenges, this study introduces a multi-source remote sensing dataset consisting of 15000 data pairs, each comprising remote sensing multispectral data, synthetic aperture radar(SAR) images, land use and land cover(LULC) data, digital elevation model (DEM) data as well as the analysis data including Slope, Aspect, and Hillshade. Through the application of an end-to-end network based on pix2pix, effective fusion and feature enhancement of multi-source remote sensing data were achieved. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and Spectral Angle Mapper(SAM) values reached 0.84, 23.14, and 0.19, respectively, representing significant improvements over the baseline Pix2pix model’s performance metrics of 0.62, 18.84, and 0.23. In downstream semantic segmentation applications, the enhanced dataset was utilized to train semantic segmentation models for remote sensing image analysis. This approach effectively improved the training speed and segmentation accuracy of the models, with the mean intersection over union (mIoU) increasing from 0.467 to 0.481 and accuracy rising from 0.734 to 0.746. Moreover, the visual representation of remote sensing image segmentation demonstrated noticeable enhancements.

DSM Reconstruction from Uncalibrated Multi-View Satellite Stereo Images by RPC Estimation and Integration

In this paper, we propose a 3D Digital Surface Model (DSM) reconstruction method from uncalibrated Multi-view Satellite Stereo (MVSS) images, where Rational Polynomial Coefficient (RPC) sensor parameters are not available. While recent investigations have introduced several techniques to reconstruct high-precision and high-density DSMs from MVSS images, they inherently depend on the use of geo-corrected RPC sensor parameters. However, RPC parameters from satellite sensors are subject to being erroneous due to inaccurate sensor data. In addition, due to the increasing data availability from the internet, uncalibrated satellite images can be easily obtained without RPC parameters. This study proposes a novel method to reconstruct a 3D DSM from uncalibrated MVSS images by estimating and integrating RPC parameters. To do this, we first employ a structure from motion (SfM) and 3D homography-based geo-referencing method to reconstruct an initial DSM. Second, we sample 3D points from the initial DSM as references and reproject them to the 2D image space to determine 3D–2D correspondences. Using the correspondences, we directly calculate all RPC parameters. To overcome the memory shortage problem while running the large size of satellite images, we also propose an RPC integration method. Image space is partitioned to multiple tiles, and RPC estimation is performed independently in each tile. Then, all tiles’ RPCs are integrated into the final RPC to represent the geometry of the whole image space. Finally, the integrated RPC is used to run a true MVSS pipeline to obtain the 3D DSM. The experimental results show that the proposed method can achieve 1.455 m Mean Absolute Error (MAE) in the height map reconstruction from multi-view satellite benchmark datasets. We also show that the proposed method can be used to reconstruct a geo-referenced 3D DSM from uncalibrated and freely available Google Earth imagery.

Participatory multi-criteria decision making for optimal siting of multipurpose artificial reservoirs

Water resources management is a crucial activity due to climate change concerns and increasing water scarcity. The increase of water storage by building new artificial reservoirs is one of the most common solutions to fulfill the community’s needs for potable water, energy, irrigation, and prevention of flood risk. The siting of the reservoirs is usually assessed based on topographical and hydrological considerations. However, there are additional crucial factors, often disregarded in common engineering top-down design approaches, that should be considered to determine suitable locations, such as bio-physical, socio-economic, regulatory, and environmental factors, especially where there are competing interests in the use of territory. Involving the communities in the initial stages of the decision-making process is therefore crucial. In this work, we present a methodology, based on multi-criteria decision-making analysis (MCDA), for identifying the optimal locations for new reservoirs while simultaneously addressing all the aforementioned factors through community engagement. The presented method aims at overcoming two major challenges in existing works, i.e., retrieving quantitative information from the participatory approach and going beyond standard hydro-morphological suitability of a dam site. In a first step, an algorithm assesses a large number of sites using a Digital Elevation Model (DEM). Then, MCDA is conducted to rank all potential locations. The selection criteria are defined quantitatively, based on a territorial analysis combined with hydrological modelling. The criteria include: geometric and morphological aspects (reservoir volume, etc.), hydrological indicators (water balance, potential flood mitigation), anthropization (population, infrastructures, etc.), landscape, archaeological heritage, ecology, environmental components and natural hazards. A web-based survey tool based on the pairwise comparison is developed to engage the community in the decision-making process, allowing them to express their perspectives on the relative significance of each criterion. The application of this methodology is demonstrated through a case study in the Pesa river watershed, Italy, showcasing its effectiveness in identifying the most suitable reservoir locations while targeting the highest environmental preservation and community acceptance. The results show that sole engineering criteria might not reflect the sensibility of the community and that variations of the size of the dam can lead to distinct trade-offs between costs and benefits where interference with impact elements occurs.

Multi-Approaches for Flash Flooding Hazard Assessment of Rabigh Area, Makkah Province, Saudi Arabia: Insights from Geospatial Analysis

Flash flood hazard assessment is a critical component of disaster risk management, particularly in regions vulnerable to extreme rainfall and climatic events. This study focuses on evaluating the flash flood susceptibility of the Rabigh area, located along the Red Sea coast in Makkah province, Saudi Arabia. Using advanced GIS tools and a spatial multi-criteria analysis approach, the research integrates a variety of datasets, including remotely sensed satellite data, the SRTM Digital Elevation Model (DEM), and topographic indices. The main goal was to produce detailed flood susceptibility maps based on the morphometric characteristics of the region’s drainage basins. These basins were delineated and assessed for their flood vulnerability using three distinct modeling techniques, each highlighting different aspects of flood behavior. The results show that the northern basin (Dulaidila) and the central basins (Rabigh, Algud, and Al Nuaibeaa) exhibit the highest flood risk, with significant susceptibility also observed in the southern basins (Ofoq and Saabar). Other basins in the region display moderate susceptibility levels. A key aspect of this analysis was the overlay of the integrated flood susceptibility map with the Topographic Position Index (TPI), a crucial topographic indicator, which helped refine the understanding of flood-prone areas by linking basin morphometry with in-situ topographic features. This study’s comprehensive approach offers valuable insights that can be applied to other coastal regions where hydrological and climatic data are scarce, contributing to more effective flood risk mitigation and strategic planning.

Morphometric and Soil Erosion Characterization Based on Geospatial Analysis and Drainage Basin Prioritization of the Rabigh Area Along the Eastern Red Sea Coastal Plain, Saudi Arabia

Soil erosion is one of the most significant problems in global environmental development. Assigning, analyzing, and quantifying the main physical characteristics of drainage basins are powerful keys in identifying regions where there is a higher risk of soil erosion and where prompt mitigation actions are needed. Drainage basins and their drainage systems are ideally defined using the analysis morphometric parameters and their quantitative description. The present study aims to analyze morphometric parameters to prioritize drainage basins that are prone to erosion. Topographic sheets and remotely sensed digital elevation model (DEM) datasets have been prepared and analyzed using geospatial techniques to delineate drainage basins of different sizes and extract different ordered drainage systems. Based on the analysis of morphometric parameters, the Rabigh area was divided into 12 drainage basins, which significantly contribute to determining soil erosion priority levels. The present study selected and applied the most effective morphometric parameters to rank and prioritize the drainage basins of the study area after considering the crucial quantitative characteristics, such as linear, relief, and areal aspects. For each single basin, the compound factor was assigned from several morphometric parameters and applied to rank the Rabigh area. The results confirm that Basins 1, 4, 11, and 12 require a high level of soil erosion priority over an area of 2107 km2; however, Basins 3, 8, 9, and 10 have little degradation and a low level of soil erosion priority. Therefore, in the regions where high soil erosion is a factor, mitigation techniques such as terracing, filter strips, contouring, and other effective and useful structural and non-structural methods should be applied.

Forgotten Ecological Corridors: A GIS Analysis of the Ditches and Hedges in the Roman Centuriation Northeast of Padua

Studying historical rural landscapes beyond their archaeological and cultural significance, as has typically been addressed in previous research, is important in the context of current environmental challenges. Some historical rural landscapes, such as Roman land divisions, have persisted for more than 2000 years and may still contribute to sustainability goals. To assess this topic, the hydraulic and vegetation network of the centuriation northeast of Padua were studied, emphasising their multiple benefits. Their length, distribution, and evolution over time (2008–2022) were vectorised and measured using available digital terrain models and orthophotographs in a geographic information system (GIS). The results revealed a significant decline in the length of water ditches and hedgerows across almost all examined areas, despite their preservation being highlighted in regional and local spatial planning documents. These findings indicate the need for a better understanding of the local dynamics driving such trends and highlight the importance of adopting a more tailored approach to their planning. This study discusses the GIS metrics utilised and, in this way, contributes to landscape monitoring and restoration actions. Finally, a multifunctional approach to the sustainable planning of this area is proposed here—one that integrates the cultural archaeological heritage in question with environmental preservation and contemporary climate adaptation and mitigation strategies.

The Holocene evolution of the fluvial system of the southern Hessische Ried (Upper Rhine Graben, Germany) and its role for the use of the river Landgraben as a waterway during Roman times

Abstract. Intensive human intervention in the natural drainage system of the Hessische Ried (Upper Rhine Graben, Germany) resulted in the transformation of a large wetland into an intensively used cultural landscape. At least since the first century CE, when Romans conducted early river regulation and water management, the natural water network has experienced extensive anthropogenic re-organisation. The LandGraben project focuses on the reconstruction of the natural and anthropogenic watercourse systems along the river Landgraben, a tributary to the river Rhine in the northern Hessische Ried. Several rivers from the southern Hessische Ried could have fed the river Landgraben during Roman times via the meandering Palaeo-Neckar depression, thus increasing both length and discharge of the Roman waterway. In this study, we present results of our investigations within the southern Hessische Ried to reconstruct the former channel network that was used by Romans for the transportation of troops, wares and border security. Our approach integrates the use of high-resolution digital elevation models (DEMs) as a base for geophysical measurements (electrical resistivity tomography, ERT), direct push sensing and sediment coring in combination with sedimentological and geochemical analyses of the encountered sedimentary units. Local chronostratigraphies were established based on radiocarbon dating. Based on our results, we are able to differentiate a palimpsest of several abandoned fluvial channels throughout the southern Hessische Ried and to reconstruct the spatio-temporal development of the corresponding fluvial systems. Chronostratigraphic data show that the Palaeo-Neckar stopped flowing through the Hessische Ried around 10 000 cal BCE. Moreover, we found that the river Weschnitz, the largest of the possible Landgraben tributaries, stopped flowing through the Palaeo-Neckar depression at ca. 3000 cal BCE. Instead, it took a right-angled shortcut westwards to the river Rhine, north of the city of Lorsch. For the first time, we present geomorphological evidence that the river Weschnitz was not diverted by the Romans as speculated so far. This is corroborated by numerous wooden posts of human-made construction crossing an E–W running water course at the modern Weschnitz knee, with the oldest posts being dendrochronologically dated to the fourth and third millennia BCE. We further argue that the river Winkelbach/Lauter formed its knee towards the river Rhine and thus left the Palaeo-Neckar depression not later than the second century CE but most probably even contemporaneously with the formation of the Weschnitz diversion. In the case of the Winkelbach/Lauter, a high-energy flood event presumably related to strong rainfall and/or meltwater processes in the Odenwald Mountains is assumed to be responsible for the initiation of the new, diverted water course. With regard to the Landgraben and its use as a waterway within the Roman fluvioscape of the Hessische Ried, we therefore conclude that the Romans successfully collected water from several smaller rivers, such as from the rivers Modau and Darmbach and from even smaller tributaries, to make the river Landgraben a navigable waterway. The rivers Weschnitz and Winkelbach/Lauter, however, did not contribute any water to the Landgraben system during Roman times.

3D Modelling and Measuring Dam System of a Pellucid Tufa Lake Using UAV Digital Photogrammetry

The acquisition of the three-dimensional (3D) morphology of the complete tufa dam system is of great significance for analyzing the formation and development of a pellucid tufa lake in a fluvial tufa valley. The dam system is usually composed of the dams partially exposed above-water and the ones totally submerged underwater. This situation makes it difficult to directly obtain the real 3D scene of the dam system solely using an existing measurement technique. In recent years, unmanned aerial vehicle (UAV) digital photogrammetry has been increasingly used to acquire high-precision 3D models of various earth surface scenes. In this study, taking Wolong Lake and its neighborhood in Jiuzhaigou Valley, China as the study site, we employed a fixed-wing UAV equipped with a consumer-level digital camera to capture the overlapping images, and produced the initial Digital Surface Model (DSM) of the dam system. The refraction correction was applied to retrieving the underwater Digital Elevation Model (DEM) of the submerged dam or dam part, and the ground interpolation was adopted to eliminate vegetation obstruction to obtain the DEM of the dam parts above-water. Based on the complete 3D model of the dam system, the elevation profiles along the centerlines of Wolong Lake were derived, and the dimension data of those tufa dams on the section lines were accurately measured. In combination of local hydrodynamics, the implication of the morphological characteristics for analyzing the formation and development of the tufa dam system was also explored.

Identification of optimal locations for green space initiatives through GIS-based multi-criteria analysis and the analytical hierarchy process

Urban green spaces play a vital role in enhancing the well-being of communities and mitigating environmental challenges such as air pollution and global warming. Despite their importance, effective models to allocate these green spaces are often overlooked, particularly in developing countries. This study utilises GIS-based Multi-Criteria Analysis and the Analytical Hierarchy Process to recommend optimal locations for green space interventions in Lilongwe City, Malawi, based on nine factors: population density, proximity to roads, slope, Digital Elevation Model (DEM), Normalized Difference Vegetative Index (NDVI), land cover, existing green space, proximity to water bodies, and nitrogen dioxide concentration. The results show that 0.57% (23,776 hectares) of Lilongwe city is highly suitable while 14.50% (604,596 hectares) is unsuitable for green space interventions, where population density was the most determining factor. The suitability varied across the city, with highly suitable areas predominantly located in the southern part. The study highlights the importance of informed decision-making in urban green space planning, setting a standard for equitable access to green spaces and sustainable urban development.

Sequential bias-corrected weighted least squares solution of mixed additive and multiplicative error models

In the era of big data, the number of observations in adjustment calculations may reach tens or even hundreds of thousands. When dealing with these large dataset problems, many matrix operations are often required. At this time, the dimensions of the matrix will be large, which will generate a great computational burden. At present, no research results have been published on the computational efficiency of bias-corrected weighted least squares (bcWLS) for mixed additive and multiplicative error models (MAMEM). Sequential adjustment (SEA) groups the observations for calculation and can provide the same computational precision while greatly improving computational efficiency. This paper applies the idea of SEA to the calculation of bcWLS and proposes an iterative solution for sequential bcWLS (SEbcWLS). Using three simulation experiments to verify the effectiveness of our method, it was found that when the number of observations is 10000, the effect is better when the number of groups does not exceed 100, achieving the same precision as the original method while having high computational efficiency. The calculation results of line fitting and plane fitting are not affected by the number of grouping groups. For DEM (Digital elevation model) experiments with strong nonlinearity, when the number of grouping groups is too large, the effect is not very good, but the calculation efficiency is also higher than the original method, and the difference in calculation results is not significant.

Presenting a transdisciplinary robust approach for comprehensive assessment of large-scale underground water resources in western Indo-Gangetic Basin

Overexploitation, pollution, and anthropogenic activities threaten the sustainability of groundwater resources in the western Indo-Gangetic Basin. Meanwhile, distinguishing regions prone to contamination and understanding the natural and anthropogenic factors affecting groundwater quality is challenging due to the heterogeneous nature of aquifer systems and the lack of high-resolution spatial and temporal data on aquifer protective hydrogeological layers. This study presents a transdisciplinary robust approach combining regional electrical resistivity surveys, hydrogeological data, physicochemical analyses, and geospatial datasets to identify regions prone to contamination and understand the impacts of natural and anthropogenic factors on groundwater resources. This approach involves three key steps: evaluating the geohydraulic nature of aquifer protective hydrogeological layers, mapping the aquifer vulnerability index (AVI), and conducting comparative analyses of potentially vulnerable areas with groundwater quality index (GWQI) and hydrological factors. Firstly, model-based inversion of ID Vertical Electrical Sounding (VES) data provides insights into geoelectrical indices such as depth, thickness, apparent resistivity, longitudinal conductance, transverse resistance, and longitudinal resistivity of aquifer protective hydrogeological layers. Second, the Artificial Neural Network (ANN) model is used as a multilayer perceptron network to simulate hydraulic conductivity (K) using geoelectrical indices of aquifer protective hydrogeological layers. Subsequently, by considering ANN-derived K and VES-derived h of aquifer protective hydrogeological layers, the dynamic hydraulic resistance to the vertical flow of wastewater through the protective hydrogeological layers evaluated the index of the potentially vulnerable areas. Comparative analyses of potentially vulnerable areas with GWQI and hydrological factors (e.g., digital elevation model, soil, drainage density, lineament density, slope) enhance understanding regions prone to contaminants and land surface stress. Findings show that the ANN approach to simulate K, reducing effort with costs associated with slug testing is significant for AVI assessment. Furthermore, the geohydraulic characteristics, vulnerability indexing, and comparative analyses assist in identifying contamination-prone areas, improving groundwater resource protection and exploration activities.

Creating Sustainable Flood Maps Using Machine Learning and Free Remote Sensing Data in Unmapped Areas

This study leverages a Random Forest model to predict flood hazard in Arizona, New Mexico, Colorado, and Utah, focusing on enhancing sustainability in flood management. Utilizing the National Flood Hazard Layer (NFHL), an intricate flood map of Arizona was generated, with the Random Forest Classification algorithm assessing flood hazard for each grid cell. Weather variable predictions from TerraClimate were integrated with NFHL classifications and Digital Elevation Model (DEM) analyses, providing a comprehensive understanding of flood dynamics. The research highlights the model’s capability to predict flood hazard in areas lacking NFHL classifications, thereby supporting sustainable flood management by elucidating weather’s influence on flood hazard. This approach aligns with sustainable development goals by aiding in resilient infrastructure design and informed urban planning, reducing the impact of floods on communities. Despite recognizing constraints such as input data precision and the model’s potential limitations in capturing complex variable interactions, the methodology offers a robust framework for flood hazard evaluation in other regions. Integrating diverse data sources, this study presents a valuable tool for decision-makers, supporting sustainable practices, and enhancing the resilience of vulnerable regions against flood hazards. This integrated approach underscores the potential of advanced modeling techniques in promoting sustainability in environmental hazard management.

Characteristics and Tectonic Implications of the Geomorphic Indices of the Watersheds around the Lijiang–Jinpingshan Fault

The Lijiang–Jinpingshan fault (LJF) is an important secondary boundary fault that obliquely cuts the Sichuan–Yunnan rhombic block. It is of great significance for understanding the tectonic evolution of the Sichuan–Yunnan rhombic block and even the southeastern margin of the Tibet Plateau. Based on a digital elevation model (DEM), this work combines ArcGIS with MATLAB script programs to extract geomorphic indices including slope, the relief degree of the land surface (RDLS), hypsometric integral (HI), and channel steepness index (ksn) of 593 sub–watersheds and strip terrain profiles around the LJF. By analyzing the spatial distribution characteristics of the geomorphic indices and combining the regional lithology and precipitation conditions, the spatial distribution of the geomorphic indices around the study area was analyzed to reveal the implications of the LJF’s activity. The results of this work indicate that (1) the distribution of geomorphic indices around the LJF may not be controlled by climate and lithological conditions, and the LJF is the dominant factor controlling the geomorphic evolution of the region. (2) The spatial distribution patterns of geomorphic indices and strip terrain profiles reveal that the vertical movement of the LJF resulted in a pronounced uplift on its northwest side, with tectonic activity gradually diminishing from northeast to southwest. Furthermore, based on the spatial distribution characteristics of these geomorphic indices, the activity intensity of the LJF can be categorized into four distinct segments: Jianchuan–Lijiang, Lijiang–Ninglang, Ninglang–Muli, and Muli–Shimian. (3) The activity of the LJF obtained from tectonic geomorphology is consistent with the conclusions obtained in previous geological and geodesic studies. This work provides evidence of the activity and segmentation of the LJF in tectonic geomorphology. The results provide insight for the discussion of tectonic deformation and earthquake disaster mechanisms in the southeastern margin of the Tibet Plateau.