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Developing an effective emergency collaborative distribution system is critical to improve on-time response performance, especially considering secondary disasters. To address the challenge, this paper investigates a bi-objective robust optimisation model on relief collaborative distribution among three echelons of authorities, the province, the municipality, and the county, which aims to minimise the total travel time and the total humanitarian logistics cost simultaneously. The optimal location of relief supply facilities and the relief distribution schemes will be determined by the optimisation model, which considers uncertain demand and travel time. Moreover, two robust optimisation methods are utilised to deduce the robust counterparts of the proposed model. An epsilon-constraint-based approach is used to solve the bi-objective optimisation model. A real-world case study based on an earthquake and aftershocks with different magnitudes in Yunnan Province is provided. The results show that incorporating secondary disaster scenarios contributes to reducing the total travel time and cost. For making full use of emergency resources and preventing situations from worsening, the centralised decision scheme is more effective than the decentralised one. The uncertainty of demand of primary disaster relief has a higher impact on the optimal solution than that of travel time. [ABSTRACT FROM AUTHOR]

The catastrophic flood of 1813 that occurred in the Western Carpathians region was the largest natural disaster ever recorded here. The 1812/1813 winter season was very cold with abundant snow cover. Spring and summer months were also rich in liquid precipitation, further increasing soil saturation and catchment runoff. Torrential rains were reported to occur from 24 to 26 August when two deep low-pressure systems met over the Western Carpathians above the headwaters of large rivers like Váh and Hron. This resulted in a flood with the flood wave being 10 m above the long-term river stage. In this study we summarize the findings from our archival research to report on the causes, severity and extent of the 1813 flood. Using three-dimensional terrain modelling we have reconstructed the culmination water level in two towns affected by the flood from the rivers Váh and Hron using historical maps and preserved flood marks. [ABSTRACT FROM AUTHOR]

The objective of this article is to present a novel GIS plugin for classifying and georeferencing high‐resolution oblique imagery with the intention of creating landcover datasets for spatial analysis. The Mountain Image Analysis Suite (MIAS) is a newly released plugin for the open‐source software, QGIS. MIAS was developed with images from Mountain Legacy Project, the world's largest systematic collection of high‐resolution mountain images. It works with both grayscale and color imagery, including historical images that predate aerial and satellite imagery. MIAS encompasses four tools for classifying and georeferencing oblique images. The plugin accesses pretrained deep learning models from a PyTorch‐based segmentation network to automate the classification of landcover in oblique images. Monoplotting is accomplished through the construction of a virtual photograph simulating the view from the camera using a shaded relief model. Once the virtual photograph is produced, the user aligns the classified image to the virtual photograph using a set of control points. This allows the creation of a classified and georeferenced raster representing the landcover for the area visible in the original oblique image. Similar workflows to the one contained in MIAS have been used for landcover mapping with oblique images to a high level of accuracy. However, MIAS is the first piece of software to bring all stages of image analysis into a single platform. MIAS has many applications across diverse fields such as mountain research, ecological restoration, community‐based mapping, environmental planning, and more. [ABSTRACT FROM AUTHOR]

The current research focus on visualizing terrain features emphasizes quantification and detailed simulation, without adequately considering the impact of spatial–temporal variations in the terrain on human cognition. However, advancements in visualization technology, such as efficient and rapid construction of large‐scale three‐dimensional (3D) terrain scenes, real‐time dynamic display, and free‐roaming from any viewpoint, currently provide ample technical support for visualizing spatial–temporal information. Therefore, this article proposes a 3D terrain viewing model that considers the spatial–temporal changes in light intensity and incident direction in a terrain scene, based on the principles of radiometry and computer graphics theory and supported by the physically based rendering techniques. This model aims to accurately represent the subtle variations in real‐world terrain surfaces and highlight the key elements of hill terrain. Theoretically, this model provides a foundation for the virtual reconstruction of real‐world terrain. [ABSTRACT FROM AUTHOR]

Aim: In continuous environments, niche limits of species often determine their distribution limits. However, when these limits spatially coincide with a perceived dispersal barrier, the determinants of species' ranges may be confounded. We investigate the distribution pattern of the Brown Hornbill (Aves: Bucerotidae), which spans significant riverine barriers, but stops south of the Brahmaputra River. Considering its preference for low‐elevation evergreen forests, we posit that the lack of sufficient habitats north of the Brahmaputra prevents dispersal of the Brown Hornbill, and not the river itself. Location: The Brahmaputra valley and the Indo‐Burma hotspot. Taxa: Anorrhinus austeni, Aceros nipalensis. Methods: We analysed citizen‐science occurrence data on hornbill presence against a suite of climatic, canopy‐specific and topographical predictors to model the environmental niche of the Brown Hornbill. We used presence‐only maximum entropy modelling in an information theoretic framework, in conjunction with constructing binary logistic regression models using presence and pseudoabsence data. We compared niche models of the Brown Hornbill and the Rufous‐necked hornbill, a close relative with a similar distribution, but which has spanned the Brahmaputra River. Results: Despite the presence of suitable wet‐evergreen forests north of the Brahmaputra River, the hilly terrains in the region act as a biogeographic barrier for the Brown Hornbill, which prefers lowland evergreen forests. Further, highly suitable regions for the Brown and the Rufous‐necked Hornbills precisely delineate low and high‐elevation evergreen forests respectively, indicating that these birds are separated along an elevational axis. Main Conclusions: The Brahmaputra River lies at the cusp of two subtly different environmental regimes. It may therefore serve as the niche limit for certain organisms and not as a physical obstacle to their dispersal. Our study implicitly predicts how widespread deforestation prevalent in the lowland evergreen forests of this region adversely impacts the distributions of organisms that depend on them, such as the Brown Hornbill. Further, our study proffers an approach to ascertain determinants of species distributions in a hypothesis testing framework. [ABSTRACT FROM AUTHOR]

Ticks represent important vectors of a number of bacterial and viral disease agents, owing to their hematophagous nature and their questing behavior (the process in which they seek new hosts). Questing activity is notably seasonal with spatiotemporal dynamics that needs to be understood in detail as part of mediating and mitigating tick-borne disease risk. Models of the geography of tick questing activity developed to date, however, have ignored the temporal dimensions of that behavior; more fundamentally, they have often not considered the sampling underlying available occurrence data. Here, we have addressed these shortfalls for Amblyomma americanum, the most commonly encountered tick in the central Great Plains, via (1) detailed, longitudinal sampling to characterize the spatiotemporal dimensions of tick questing activity; (2) randomization tests to establish in which environmental dimensions a species is manifesting selective use; and (3) modeling methods that include both presence data and absence data, taking fullest advantage of the information available in the data resource. The outcome was a detailed picture of geographic and temporal variation in suitability for the species through the two-year course of this study. Such models that take full advantage of available information will be crucial in understanding the risk of tick-borne disease into the future. [ABSTRACT FROM AUTHOR]

Boundary recognition visually delineates the horizontal extent of subsurface anomalies, providing a foundational basis for interpreting gravity data. Compared to gravity observations, gravity gradient data offers the benefits of multiple components and the enhancement of shortwave information in graphical representations. In our study, we investigated the delineation of geological structure boundaries in the lunar Oceanus Procellarum region using gravity gradient techniques. First, based on the observations from the Gravity Recovery and Interior Laboratory (GRAIL) mission, we obtained high-precision synthetic full tensor values of the gravity gradient as our primary research data. Moreover, to reduce curvature errors during large-scale terrain forward modeling, we applied the spherical prism of tesseroid to eliminate the effects of near-surface topography. Grounded on the Bouguer anomalies of gravity gradient, four distinct boundary recognition methods have been employed to explore the geological structure of the lunar Oceanus Procellarum region. It includes the Theta map method, the directional Theta map method, the combination of total horizontal derivative and the modulus of full tensor gravity gradient, and the improved edge detection method based on the Theta map method.The common feature of these methods is the utilization of the multi-component data combination from gravity gradient tensors, which can enhance the accuracy of edge detection. From the investigation results presented in the paper, we have found the following: 1) The improved edge detection method, based on the Theta map principle, enables better identification of the center of subsurface anomaly bodies during boundary recognition, utilizing a consistent single color in graphic displays. 2) According to the results of boundary recognition, numerous strip anomalies exist in the Oceanus Procellarum area that show less correlation with topographic relief. One possible explanation for these graphical anomalies of gravity gradient is that they serve as channels for the upwelling of mantle material during lunar evolution. [ABSTRACT FROM AUTHOR]

ObjectiveData/MethodsFindingsConclusionPolice officers often find themselves responding to calls that do not involve criminal behavior, but calls related to individuals experiencing emotional distress or mental illness that requires police response. This research aims to identify high-risk places of mental health calls for service (MHCFS) in an urban setting and compare place features of these locations by police response (locations where a CIT-trained officer is dispatched versus a non-trained officer dispatched).Our research uses Atlantic City Police Department’s calls for service data from 1 January 2019 through 30 December 2022, including the incidents of mental incompetence with location components. After removing the calls that were not coded as mental health, a total of 2229 incidents involving perceived mental illness were geocoded and compared. We used RTMDx software to generate risk terrain models for MHCFS in Atlantic City, NJ.The RTM analysis yielded no association between places of interest and MHCFS for calls involving non-CIT-trained officers. Yet, we identified five high-risk activity spaces of MHCFS where officers with CIT training were involved. Grocery stores (RRV = 2.873), places of leisure (RRV = 2.764), Health & Wellness Facilities (RRV = 2.624), Eateries & Restaurants (RRV = 2.547), and Convenience & Variety Stores (RRV = 2.280) are the risk factors identified by the RTM analysis. Given the characteristics of places identified by risk-terrain modeling, we reason that the characteristics of these places indicate that individuals experiencing perceived or actual mental health problems hang out in spaces with ease of access and in spaces where they can stay relatively longer with minimal attention.Our research suggests that there is an association between spatial factors and MHCFS that are responded to by CIT-trained officers. City officials should consider deploying additional resources and mental health staff to these places to address the issue of mental health in urban settings. Future research should consider testing whether the places we identified in our analysis are also high-risk places for MHCFS in other urban environments. [ABSTRACT FROM AUTHOR]

Objectives: In 3 dimensional (3D) scenes, the bottom surface of 3D models is usually horizontal, and terrain models are mostly irregular surfaces with continuous undulations, which leads to problems such as gaps or terrain flooding when matching 3D models with terrain models, etc. In the current research on matching 3D models with terrain, there is a lack of scene complexity constraint, so some matching methods are not applicable to 3D scenes with multiple complexities. Therefore, how to complete mode terrain matching while taking into account the scene complexity constraint and retaining certain terrain integrity is the current problem to be considered and solved. Methods: Through research on 3D model data organization, scene complexity evaluation, and grid reconstruction, etc., a 3D model and terrain model matching method that takes into account the scene complexity is proposed to achieve a seamless matching between the model and the terrain. First, the method improves the data organization of multi-source heterogeneous 3D model by combining the characteristics of 3D model. Then, it constructs the 3D scene complexity evaluation model by combining the scene complexity evaluation factors, and calculates and classifies the 3D scene complexity level. Finally, according to the scene complexity classification results, it realizes the accurate matching of multi-source heterogeneous 3D models and terrain considering the scene complexity. Results: Different 3D scenes contain different amounts of information, and their corresponding scene complexity is also different. For the 3D scenes with low scene complexity, the elevation value fitting method is used, and the 3D scenes with high scene complexity use the grid reconstruction method. The visual perception and realtime frame rate are used as reference indicators to compare and analyze this method with the traditional method. The frame rate is stable at 58 frames per second, and it can be seen that the scene complexity constraint of this method can optimize the system loading smoothness and improve the visualization effect. Con⁃ clusion: We construct a scene complexity evaluation model based on scene information entropy, and design a model and terrain adaptation algorithm adapted to the scene complexity level. We organize and express the 3D scene model data with different scene complexity more efficiently while ensuring the real-time frame rate when rendering the 3D model, and effectively optimizes the problem of accurate matching between model and terrain. [ABSTRACT FROM AUTHOR]

In addition to geometric accuracy, topological information, appearance and georeferenced data, semantic capabilities are key strengths of digital 3D city models. This provides the foundation for a growing number of use cases, far beyond visualization. While these use cases mostly focused on models of buildings or the terrain so far, the increasing availability of data on roads and other transportation infrastructure opened up a range of emerging use cases in the field of semantic 3D streetspace models. While there are already a number of implemented examples, there is also a potential for new use cases not yet established in the field of 3D city modeling, which benefit from detailed representations of roads and their environment. To ensure clarity in our discussions, we introduce an unambiguous distinction between the terms 'application domain', 'use case', 'functionality' and 'software application'. Based on these definitions, use cases are categorized according to their primary application domain and discussed with respect to relevant literature and required functionalities. Furthermore, requirements of functionalities towards semantic 3D streetspace models are determined and evaluated in detail with regard to geometric, semantic, topological, temporal and visual aspects. This article aims to give an overview on use cases in the context of semantic 3D streetspace models and to present requirements of respective functionalities, in order to provide insight for researchers, municipalities, companies, data providers, mapping agencies and other stakeholders interested in creating and using a digital twin of the streetspace. [ABSTRACT FROM AUTHOR]

The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and wall function formulations are concerned with maintaining the fully developed wind profiles specified at the inlet of CFD domains. The impact of these related configurations is worth discussing in engineering applications, especially when commercial codes restrict the internal modifications. This study evaluates the numerical performances of open-source OpenFOAM 2.3.0 and commercial Fluent 17.2 codes as supplementary scientific comparisons. This contribution focuses on the modified turbulence closures to incorporate turbulent profiles produced from mesoscale PBL parameterizations and the modified wall treatments relating to the roughness length. The near-ground flow features are evaluated by selecting the flat terrains and the classical Askervein benchmark case. The improvement in near-ground wind flow under the downscaled framework shows satisfactory performance in the open-source CFD platform. This contributes to engineers realizing the micro-siting of wind turbines and quantifying terrain-induced speed-up phenomena under the scope of wind-resistant design. [ABSTRACT FROM AUTHOR]

AbstractDeep learning-based super-resolution methods have been successfully applied to digital elevation model (DEM) downscaling studies by designing structures and loss functions of the model. However, little attention has been paid to the design of super-resolution models that can maintain the hydrological characteristics of the DEM, which is important for hydrological studies. This study introduces a super-resolution model that integrates hydrologic knowledge (HKSRCGAN), with the aim to effectively maintain topographic features as well as the hydrologic connectivity of the DEM. The hydrological knowledge derived from surface flow direction and hydrological features are integrated into a deep learning algorithm to guide model training. The 30 m spatial resolution FABDEM is used to demonstrate the utility of the proposed method. Results show that the HKSRCGAN outperforms the bicubic interpolation, SRCNN, SRGAN, SRResNet and TfaSR methods in reducing topographic errors and maintaining hydrologic characteristics. In the test area, the entropy difference analysis shows that the DEM generated by HKSRCGAN is similar to the information contained in the reference DEM. Furthermore, super-resolution models integrating hydrological knowledge are valuable for modeling terrain primarily shaped by gravity and surface water flows. In the future, deep learning-based models integrating hydrologic knowledge are expected to be applied in DEM upscaling to maintain consistent hydrological characteristics. [ABSTRACT FROM AUTHOR]

While it is clear that space matters when it comes to explaining crime, there is a lack of research exploring whether such spatial effects remain significant if victim characteristics are introduced into the analysis. This paper addresses this gap by applying risk terrain modeling to predict assaults for victims of different racial backgrounds and local versus non-local residents. The results show spatial risk factors have different impact on the likelihood of victimization for individuals of different racial backgrounds and those who live in the city where the crime occurs versus those who are visiting. The findings suggest that more attention should be given to integrating the victim characteristics with the characteristics of space when predicting violent crime using spatial analysis. [ABSTRACT FROM AUTHOR]

The Nari Caldera on Ulleung Island, an oceanic intraplate volcano, is a significant repository of information on the latest volcanic activity. To interpret the characteristics of the latest volcanic activity, it is essential to understand the caldera structures formed by the most explosive eruption of the Ulleung volcanic edifice. This study presents a three-dimensional (3-D) resistivity model based on audio-magnetotelluric (AMT) data and interprets the caldera structure of Ulleung Island. New land and ocean terrain models were used for the 3-D inversion of the AMT data, and a finer, nonuniform grid was generated for the caldera area. Subsequently, 3-D inversion and imaging were conducted on the AMT data at 25 stations. In the caldera area of Ulleung Island, the 3-D resistivity model is divided into two bodies: a high resistivity body located in the south and a low resistivity body located in the north. This structure is consistent with the location of the two calderas, Seongin Caldera and Nari Caldera, as inferred from geological studies. Furthermore, the high resistivity body located in the south exhibited a bowl shape in the 3-D space. Therefore, we suggest that the high resistivity body located south of the caldera on Ulleung Island is a structure of the Seongin Caldera. The Seongin Caldera has a diameter of approximately 1.5 km and a caldera fill height of approximately 0.8 km, as measured from the resistivity model. Based on the stratigraphy of Ulleung Island and the physical properties of the rock types, the interior of the Seongin Caldera was considered to have been filled with trachytic lavas of the Seonginbong Group. From the high geothermal gradient of Ulleung Island and the stratigraphy of the GH-4 borehole, the low resistivity body extending from the shallow depths of the Nari Caldera to the lower part of the Seongin Caldera could be interpreted as trachytic rocks that underwent hydrothermal alteration. In addition, a low resistivity body contains highly porous and/or weathered rock. This study presents information on the calderas of Ulleung Island that can aid in interpreting the characteristics of the latest volcanic activity. We expect this information to contribute to the preparation for potential volcanic hazards. [ABSTRACT FROM AUTHOR]

The increasing role of unmanned aerial vehicle (UAV) swarms in modern warfare poses a significant challenge to ground and air defense systems. Considering complex terrain environments and multi-sensor resources including radar and photoelectric systems constraints, a novel multi-sensor dynamic scheduling algorithm is proposed in this paper. Firstly, a transmission model with Fresnel zone under complex terrain and sensor models for radar/photoelectric systems are established. Considering the constraints of 6 factors, such as pitch angle, array scanning angle and threat levels, a detection model is developed subsequently. Secondly, to meet the real-time requirements of ground and air defense systems, a fast calculation method for Fresnel zone clearance using adaptive buffer is achieved. Thirdly, an improved Hungarian algorithm is proposed to solve the combinatorial optimization problem of sensor scheduling. Finally, simulation experiments are conducted to evaluate the algorithm performance under different conditions. The results demonstrate that the proposed approach significantly reduces the sensor switching rate while achieving a high sensor-UAV matching rate and high-threat matching rate. Furthermore, the simulation results verify the effectiveness of the proposed algorithm when applied to multi-sensor scheduling for defending UAV swarms. • Modern militaries need advanced sensor scheduling solutions to defend UAV. • Considering the Fresnel zone under complex terrain is crucial. • Adaptive buffer enables rapid calculation of Fresnel zone clearance. • Multiple sensor constraints make scheduling problems realistic. • Improved algorithm effectively enhances matching rates while reducing switching. [ABSTRACT FROM AUTHOR]

Flood hazard assessment of cities gained significance globally due to rise in frequency of flood events and rapid urbanisation. Uncertainties in flood inundation models largely depend on the quality of input datasets, among which topography plays a vital role. This study demonstrates the effectiveness of global terrain models in simulating accurate flooding and generating hazard maps while considering the influence of land-use dynamics focussing on data-scarce regions. Open-source forest and building removed digital elevation model (FABDEM) and a terrain model TDX-12 DTM derived from TanDEM-X 12 DSM using a simple-morphological-filtering technique are considered for comparing their performance in simulating a flood event occurred in Surat city during the year 2006. Spatially varying short-term urban growth scenario for the year 2035 is developed by utilizing historical land-use maps of the study area and urban growth indicators. These are combined using multi-criteria-decision-making techniques and Cellular-Automata-Markov-Chain model. The FABDEM based hydrodynamic model performed better (Root-Mean-Squared-Error RMSE of 1.59 m) than TDX-12 DTM based model (RMSE: 1.88 m). Intercomparison of hazard maps of the FABDEM and TDX-12 DTM with ground-surveyed TopoDEM based-model showed an overall accuracy of 71.8% and 72.8%; for the future scenario 71.8% and 75.5% respectively. In a span of 29 years, a notable increase in hazard magnitude of 7.5% is solely attributed to change in land dynamics. In this study, though the FABDEM based-model showed better RMSE than TDX-12 DTM, the model is relatively less successful in capturing high-hazard regions. The DEMs processed for removal of non-ground objects yield accurate models than globally trained models. [ABSTRACT FROM AUTHOR]

A major threat is posed to both human society and the environment by geological hazards, necessitating the need for accurate and comprehensive investigation and application in this field. Traditional geological hazard monitoring technologies are not only inefficient, but also fail to accurately delineate affected areas and provide comprehensive disaster data sets, preventing researchers from conducting precise investigations, detection and prevention. Therefore, this article summarizes the application of oblique photography technology through examples, especially its application in the investigation of local geological disasters such as debris flows and landslides, and the use of orthogonal-based corrected single-view oblique photography technology in the investigation of regional geological disasters such as earthquakes. Through summary, it is found that compared with traditional methods, UAV oblique photography technology is more excellent in the accuracy of investigation, detection, prevention, terrain information identification and geological disaster risk factor analysis in geological disasters. UAV oblique photography technology plays a vital role in the investigation, detection and prevention of geological disasters due to its versatility, efficiency and accuracy. It can help create detailed maps and 3D models of terrain by capturing high-resolution images from multiple angles to help identify potential geohazards. It can also detect changes in landforms, vegetation or waterways to provide warning signs for landslides, sinkholes or provide early warning signals for potential geological hazards such as erosion. High-resolution imagery captured by drones allows rescuers to quickly assess impacts on infrastructure, settlements and natural resources, thereby facilitating the efficient allocation of resources for rescue and recovery efforts. Drone oblique photography technology significantly enhances the ability to investigate, detect and prevent geohazards by providing timely, detailed spatial information critical for informed decision-making and proactive disaster management. Additionally considering that drone oblique photography technology may be combined with artificial intelligence algorithms and virtual reality (VR) imaging technology in the future, this integration is expected to improve the efficiency and accuracy of geological disaster management, thus ensuring the safety of human life and property. [ABSTRACT FROM AUTHOR]

The Shuttle Radar Topography Mission (SRTM) is a digital representation of the terrain surface morphology that contains rich terrain information and is widely used in environmental analyses. However, SRTM is adversely affected by mixed noise, which typically include random and stripe noise. Mixed noise results in the significant loss of topographic information, which reduce the validity of related research. To eliminate mixed noise in SRTM data, we propose an adaptive low‐rank group sparse model based on edge preservation (ALGS_EP) to remove mixed noise from datasets. The method relies on a low‐rank group sparse model that considers the gradient features of the terrain. It calculates a terrain factor to adapt the noise elimination model to terrain changes. Additionally, it integrates with the edge structure of elevation data and applies a double‐gradient constraint to preserve the structural details of the elevation data. The proposed model, built upon the alternating direction multiplier method framework, enhances the traditional weighted kernel paradigm minimization algorithm by introducing variable weights that adjust according to the gradient of elevation data during iterations. Additionally, it incorporates the correlation between strip noise and residual data blocks when computing the iteration count, ensuring an iterative solution approach that converges to the optimal solution. We used ALGS_EP to process global SRTM 1 data and published a higher‐quality and higher‐precision elevation dataset. The elevation data noise before and after noise elimination were statistically analyzed. Simulated and empirical results show that the model is highly robust and more effective than existing methods in both visual and quantitative evaluations. The noise elimination rate was 97.6%, compared to the original data. Therefore, this research was valuable for applications that use digital elevation model as an important data layer. [ABSTRACT FROM AUTHOR]

I investigate the impact of economic integration, in terms of trade costs and information technology, on the interregional location patterns of retail firms by introducing three features into a new economic geography model: individuals consume only a restricted number of varieties; firms compete for customer acquisition; and the advancement of information technology enables consumers to easily perceive and buy products sold by firms in distant regions. I consider two possibilities regarding competition for attention: relative competition, in which a firm can attract more consumers by advertising more than the average level, and absolute competition, in which each firm competes with another to enhance the share of its advertisement in the market. I show that consumption variety is richer in the agglomerated region, but spatial consumption inequality shrinks as information technology develops. A reduction in trade costs may cause the redispersion of economic activities under sufficiently low trade costs only in the case of absolute competition. This is more likely when competition for attention is fierce and information technology is not fully developed. The results not only support the complementarity between information technology and cities but also provide a new insight that competition among firms is an important factor governing the impact of information technology on economic geography. [ABSTRACT FROM AUTHOR]

The parabolic equation (PE) model is effective for predicting signal propagation over irregular terrains. The shift map method of the PE model is highly accurate and widely used for terrain propagation predictions. The maximum propagation angle is a crucial parameter of the shift map model. Here, unreasonable values may result in incorrect results or further computations. However, the prediction formula used in existing literature is limited, thereby leading to inaccurate results. In this study, a maximum propagation angle prediction method was developed. The maximum propagation angle with terrain should be calculated as the sum of the maximum propagation angle without terrain and twice the maximum terrain slope angle. Hence, the selection criteria for the different models in the hybrid terrain model were reanalyzed. An accurate maximum propagation angle is key for the PE model over irregular terrains, as it ensures prediction accuracy and improves calculation speed. [ABSTRACT FROM AUTHOR]

Seamless integration of three-dimensional (3D) terrain models and hydraulic structure models is a technical challenge in the construction of 3D virtual scenes for irrigation areas. This study proposes a level-of-detail (LOD)-based dynamic classification integration method for hydraulic structure models and 3D terrain models, called CM-D-LOD. Hydraulic structures are classified according to their point, line, and surface morphologies, as well as their dependence on or independence of the terrain into four categories: point-like hydraulic structures independent of terrain, line-like hydraulic structures dependent on terrain, surface-like hydraulic structures dependent on terrain, and surface-like hydraulic structures independent of terrain. By utilizing the proposed model classification integration method, a visualization management platform for virtual geographical environments of irrigation areas is developed, and experiments are conducted in the Zhuluo Ba Irrigation Area within the large economic zone along China's eastern coast. Results demonstrate that the integration accuracy can be controlled between 0.2 and 0.7 m and that the 3D virtual scene of the irrigation area can be updated in real time. The proposed classification integration method transforms the traditional global model integration approach into a more efficient one, significantly improving the efficiency of constructing virtual geographical scenes for irrigation areas. [ABSTRACT FROM AUTHOR]

The denser the Lidar data, the more faithfully it models the terrain. This property of dense Lidar data allows a detection of changes but also their quantification at a level of precision that can highlight even displacements of objects in all three spatial directions. The point cloud has different densities and accuracies depending on the platform on which the scanning system is installed (manned aircraft, drones or ground stations). Density and precision parameters drive different approaches in change detection algorithms. For completing these tasks, two independent applications were developed to highlight both major landscape changes and smaller changes such as displacement of objects, construction settlements, landslides. [ABSTRACT FROM AUTHOR]

Revit is a widely used architectural design and model creation software, but its control and performance of terrainhas been carried out in a "surface" way, not a real solid model, so it is difficult to meet the needs of some projects on terrain.Aiming at the pain point that internal functions of Revit cannot build 3D terrain entities, this paper takes forward designapplication of actual engineering projects as the research background, conducts research on solid terrain modeling of Revitbased on Dynamo, introduces five application methods, and proposes a optimization method to solve problems in practicalapplication, and finally forms a feasible solution. It provides some ideas for creating and using solid terrain models in Revitconveniently and quickly [ABSTRACT FROM AUTHOR]

Despite the fatal impact of heavy precipitation on people's lives and the social economy, its accurate estimating remains challenging. In this study, we show how to address this issue by kilometer-scale simulations and how to reduce computational costs in Northeast China with the complex terrain and distribution of land and sea. Three typical heavy precipitation events are simulated at 3 km horizontal resolution, and each event is simulated with 24 combinations of schemes (with or without a scale-aware cumulus scheme, three microphysics schemes, and four planetary boundary layer schemes), which are evaluated against gauge observations. Compared to gauge observations, the ensemble mean of simulations of hourly maximum precipitation and average accumulated precipitation outperforms three widely accepted satellite products in the cold vortex and the snowstorm case, and is of comparable accuracy in the typhoon case. Overall, the microphysics scheme significantly impacts the maximum hourly precipitation, whereas the planetary boundary layer scheme has a strong control over the accumulated precipitation. The similarity among different simulations is linked to the level of convective instability's impact on heavy precipitation in each case, which also indicates that conducting 24 simulations can be not necessary. This study uses an ensemble performance estimation technique assuming the impact of different schemes is additive and finds that performing 13 rather than 24 simulations allows finding the best-performing combination of parameterization schemes, which allows for saving almost 50% of computational costs. [ABSTRACT FROM AUTHOR]

Landslides in Alpine valleys pose significant hazards due to potential impacts on inhabited areas and landslide-dam formation. These valleys feature diverse glacial deposits (moraine, debris talus, and glacio-fluvio-lacustrine materials) with varying technical traits. A comprehensive study investigated a Carnic Alps landslide in northeastern Italy, focusing on moraine deposits. This entailed geological-geomorphological analyses, slope deformation monitoring, and utilisation of 2D and 3D stability models. The 19th-century landslide occurred in Tolmezzo, Friuli Venezia Giulia, and affected the Cazzaso village. Geological-geomorphological studies involved LiDAR-derived terrain models, borehole-based slope stratigraphy, and inclinometer-derived landslide characterisation. Deformation data combined GPS measurements and multi-temporal interferometric synthetic aperture radar. Reconstructing the slope's geological model, stability assessments employed 2D and 3D limit equilibrium methods, factoring in groundwater and seismicity. Validation occurred against monitoring data and geologicalgeomorphological observations. The 3D models further identified most landslidesusceptible zones. [ABSTRACT FROM AUTHOR]

This article explores the overlooked history and significance of physical relief models in the development of geographical knowledge and education. By examining their use in academic, educational and public settings, it argues for a broader appreciation of these models as integral to the discipline's material culture. Historical debates around their function and purpose are highlighted amidst developments in modelling techniques, materials, and instructional guides. A deeper investigation into the models, their creators, and their influence on geographical learning and public engagement is advocated for and illustrated through the production and impact of the 1951 book The Earth's Crust. • Relief models are crucial components of geography's material culture. • School geography employed models and modelling exercises to advance pedagogy. • Popular books used relief models as visual aids to attract public interest in geography. [ABSTRACT FROM AUTHOR]

The isle of Gavdos, and its wider area, is one of the few places worldwide where the calibration and validation of altimetric satellites has been carried out during the last, more than, two decades using dedicated techniques at sea and on land. The sea-surface calibration employed for the determination of the bias in the satellite altimeter's sea-surface height relies on the use of a gravimetric geoid in collocation with data from tide gauges, permanent global navigation satellite system (GNSS) receivers, as well as meteorological and oceanographic sensors. Hence, a high-accuracy and high-resolution gravimetric geoid model in the vicinity of Gavdos and its surrounding area is of vital importance. The existence of such a geoid model resides in the availability of reliable, in terms of accuracy, and dense, in terms of spatial resolution, gravity data. The isle of Gavdos presents varying topographic characteristics with heights larger than 400 m within small spatial distances of ~7 km. The small size of the island and the significant bathymetric variations in its surrounding marine regions make the determination of the gravity field and the geoid a challenging task. Given the above, the objective of the present work was two-fold. First, to collect new land gravity data over the isle of Gavdos in order to complete the existing database and cover parts of the island where voids existed. Relative gravity campaigns have been designed to cover as homogenously as possible the entire island of Gavdos and especially areas where the topographic gradient is large. The second focus was on the determination of a high-resolution, 1 ′ × 1 ′ , and high-accuracy gravimetric geoid for the wider Gavdos area, which will support activities on the determination of the absolute altimetric bias. The relative gravity campaigns have been designed and carried out employing a CG5 relative gravity meter along with geodetic grade GNSS receivers to determine the geodetic position of the acquired observations. Geoid determination has been based on the newly acquired and historical gravity data, GNSS/Leveling observations, and topography and bathymetry databases for the region. The modeling was based on the well-known remove–compute–restore (RCR) method, employing least-squares collocation (LSC) and fast Fourier transform (FFT) methods for the evaluation of the Stokes' integral. Modeling of the long wavelength contribution has been based on EIGEN6c4 and XGM2019e global geopotential models (GGMs), while for the contribution of the topography, the residual terrain model correction has been employed using both the classical, space domain, and spectral approaches. From the results achieved, the final geoid model accuracy reached the ±1–3 cm level, while in terms of the absolute differences to the GNSS/Leveling data per baseline length, 28.4% of the differences were below the 1 c m S i j   [ k m ] level and 55.2% below the 2 c m S i j   [ k m ] . The latter improved drastically to 52.8% and 81.1%, respectively, after deterministic fit to GNSS/Leveling data, while in terms of the relative differences, the final geoid reaches relative uncertainties of 11.58 ppm (±1.2 cm) for baselines as short as 0–10 km, which improves to 10.63 ppm (±1.1 cm) after the fit. [ABSTRACT FROM AUTHOR]

Lunar laser retroreflectors are a crucial target for lunar laser ranging (LLR). During LLR operations at all lunar laser ranging stations, the considerable distance between the Earth and the Moon makes it impossible to visually identify the position of the lunar laser retroreflectors within the camera's field of view (FOV). Typically, we use the camera to observe various lunar surface features illuminated by sunlight, and then we align the telescope with the corresponding area for accurate positioning. Based on lunar digital elevation model (DEM) data and INPOP19a planetary ephemeris, our research presents an illumination model for the lunar terrain around the lunar laser retroreflectors, suitable for the 1' camera FOV constraint at the TianQin laser ranging station. The illumination model is linked to universal time coordinated (UTC) time and DEM data. By inputting the UTC time, the illumination conditions of the terrain surrounding the five lunar laser retroreflectors can be determined for that specific time. The terrain surrounding the Apollo 15 and Lunokhod 2 retroreflectors exhibits distinctive terrain features under illumination, making them easier to locate compared to other retroreflectors. This illumination model provides a better solution to the problem of aligning laser ranging stations to lunar laser retroreflectors. [ABSTRACT FROM AUTHOR]

Predicting soil behavior under dynamic load due to earthquakes is pivotal for engineering structures and human life. Due to various limitations, such as insufficient computers and difficulties in generating models, the third-dimension effect is generally neglected in many studies. Conversely, the third-dimension effect in regions with high topographic differences, deep basins, three-dimensional heterogeneous and anisotropic environments, and alluvium is at a level that cannot be neglected. This study created a three-dimensional model of the northwest of Turkey for the first time by including surface topography. Soil properties were added to this model, and dynamic analysis was performed. This new model aims to increase the accuracy of ground motion predictions in Northwest Turkey. The accuracy of this model was analyzed using real earthquake data recorded in the study area. In addition, a new software (SiteEffect3D) with various features has been developed to create a three-dimensional mesh with topography using digital elevation model data and to perform dynamic analysis more effectively. This software has been tested comparatively with "Plaxis 3D" software using synthetic terrain models. The importance of this study is that in addition to its contributions to site response analysis and seismic hazard assessment, new software has been developed that can be used in similar studies. The findings will provide valuable information for seismic design and construction practices and facilitate the development of more effective strategies to reduce the potential damage from earthquakes in the region. [ABSTRACT FROM AUTHOR]

• This paper identified the innovation networks and innovation environment as the core components of the regional innovation system. • A two-region, three-sector New Economic Geography model is developed by integrating the digital economy, innovation cooperation, R&D elements flow, and innovation output into a unified framework. • Digital economy promotes innovation output through promoting intra-regional and inter-regional cooperation and attracting the flow of R&D elements personnel and capital. • Underdeveloped cities benefit more from digital economy, while large central cities function as conduits and generate spillover effects to interconnected regions. This paper explored the paradigm shift in spatial innovation dynamics driven by the rapid emergence of the digital economy (DIGE) through the lens of the regional innovation system (RIS). By integrating DIGE, RIS, and innovation output into a unified analytical framework, this study examined the impact and mechanisms of DIGE on innovation output across 330 Chinese cities from 2011 to 2020. Results show that DIGE development significantly contributes to innovation output. This contribution is mediated by DIGE's strengthening of the RIS, primarily through its facilitation of integration into innovation networks and enhancement of the innovation environment. Regional heterogeneities are evident in the innovation-promoting influence of DIGE, with less developed cities, cities with underdeveloped infrastructure, and smaller cities benefiting more significantly. While the direct impact of DIGE on large central cities is subtle, their pivotal positions enable them to radiate and disseminate innovation spillovers to connected cities through extensive cooperative networks. This study underscored the potential of leveraging DIGE, especially in disadvantaged regions, to cultivate robust and synergistic RIS and act as catalysts for augmented innovation output. [Display omitted] [ABSTRACT FROM AUTHOR]

Monitoring and protecting flowing watercourses is a complex and challenging task that requires the collaboration and coordination of various stakeholders such as governments, industries, farmers, consumers and environmental groups. The study of the dynamics of the concentration of polluting factors and especially the concentrations of heavy metals and highlighting a seasonal variation is a necessary element from this point of view. In this article, we present the results of our analyses carried out in two measurement campaigns executed in 10 monitoring points along the Danube River, between Braila city and Isaccea city in the pre-deltaic area, during the summer season and autumn season 2022. The importance of this area is given by the fact that the Danube Delta is part of the UNESCO heritage, and the monitoring of polluting factors is a necessity in the desire to protect this area. The data measured during the July and August 2022 campaign cover a wide range of chemical species: Phosphate, CCO, CBO5, NH4+, N-NO2, N-NO3−, N-Total, P-PO4 3−, SO42−, Cl−, phenols, as well as metals with a harmful effect: Al, As, Cd, Cr, Fe. The study includes an evaluation based on the statistical approach of the results to highlight the significant correlations and differences identified between the two data sets. Next, to highlight the obtained results, a numerical model was considered using HEC-RAS and ESRI ArcGIS applications in a two-dimensional unsteady flow model in order to obtain the non-homogenous concentrations' distributions in the studied area. These two-dimensional models have been less studied in the specialized literature. In this way, interesting results could be obtained, and prediction methods regarding the dynamics of metal concentrations could be structured. The data obtained were used for the terrain model from the USGS service, and the flows of the Danube and its two tributaries were simulated using the data provided by the national services. In this work, we present the results obtained for the dynamics of the concentrations of the metals Al, As, Cd, Cr and Fe and the evaluation of the specific absorption coefficients for the explanation and correlation with the results of the measurements. Except for the numerical model presented, we would like to highlight the existence of some contributions of the main tributaries of the Danube in the study area. Such a systematic study has not been carried out due to conditions imposed by the border authorities. From this point of view, this study has an element of originality. The study is part of a more complex project in which the spatio-temporal distribution of the polluting factors in the water was evaluated, and the habitats in the study area were inventoried—especially those of community interest. In this way, we were able to expose the self-purification capacity of the Danube and highlight the existence of a concentration reduction gradient along the course of the river. The aspects related to the influence of the distribution of polluting factors on the state of health will be the subject of another article. [ABSTRACT FROM AUTHOR]

Hate crime has exhibited an alarming increase in many cities over the past few years. As a nation, the United States recorded 7759 in 2020, the highest number in 12 years according to the FBI. Researchers and police practitioners have long relied on "place-based" approaches to study and respond to crime, exploiting the strong and consistent concentration of crime within a small number of places. One tool that has proven utility directing place-based approaches is Risk Terrain Modeling (RTM). This study quantifies the degree to which hate crime is concentrated within place, and assesses whether micro-level risk factors identified by RTM, combined with mezzo-level neighborhood features, predict future hate crime. Results suggest these place-based tools can identify factors with a meaningful influence on the risk of hate crime. [ABSTRACT FROM AUTHOR]

Spatial autoregressive (SAR) models are often used to explicitly account for the spatial dependence underlying geographic phenomena. However, traditional SAR models are specified using a single SAR coefficient, assuming constant spatial dependence over space. This assumption oversimplifies the situation where the true spatial autoregressive process varies in strength; the consequences of ignoring heterogeneous autocorrelation remain to be discussed. This study proposes a heterogeneous spatial autocorrelation model by extending the spatial lag model (SLM). The new model includes change point detection for identifying patterns of spatially varying autocorrelation strengths, a SAR coefficient matrix for representing heterogeneous spatial autocorrelation, and maximum likelihood estimation for determining multiple SAR coefficients. Monte Carlo simulations demonstrate that the proposed method is effective in modeling SAR processes with heterogeneous autocorrelation patterns, while traditional SLM inflates uncertainties in the regression coefficients when a heterogeneous autocorrelation structure is not accounted for. We further applied the new method to an empirical analysis of traffic crashes in the Greater Perth Area, Australia. The heterogeneous spatial autocorrelation model reduces model RMSE by 42% (compared with traditional SLM). Results from both simulation and empirical studies indicate that spatially varying autocorrelation strengths should be considered for SAR processes and relevant applications. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to understand the socioeconomic impact of flood events on households, especially household welfare in terms of changes in consumption and coping strategies to deal with flood risk. This study is based on Bihar, one of the most frequently flood-affected, most populous and economically backward states in India. Design/methodology/approach: Primary data were collected from 700 households in the seven most frequently flood-affected districts in Bihar. A total of 100 individuals from each district were randomly selected from flood-affected villages. Based on a detailed literature review, an econometric (probit) model was developed to test the null hypothesis of the availability of consumption insurance, and the multivariate probability approach was used to analyze the various coping strategies of these households. Findings: The results of this study suggest that flood-affected households maintain their consumption by overcoming various losses, including income, house damage and livestock loss. Households depend on financial transfers, borrowings and relief, and migrate to overcome losses. Borrowing could be an extra burden as the government compensates for house damage and crop loss late to the affected households. Again, there is no compensation to overcome livelihood loss and deal with occurrences of post-flood diseases, which further emphasizes the policy implications of strengthening the health infrastructure in the state and generating alternative livelihood opportunities. Originality/value: This study discusses flood risk in terms of changes in household welfare, identifies the most effective risk-coping capabilities of rural communities and contributes to the shortcomings of the government insurance and relief model. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0569 [ABSTRACT FROM AUTHOR]

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High-resolution wind datasets are crucial for ultra-short-term wind forecasting. Penetration of WT installations near urban areas that are constantly changing will motivate researchers to understand how to adapt their models to terrain changes to reduce forecasting errors. Although CFD modelling is not widely used for ultra-short-term forecasting purposes, it can overcome such difficulties. In this research, we will spatially extrapolate vertical profile LIDAR wind measurements into a 3D wind velocity field over a large and relatively complex terrain with the use of stationary CFD simulations. The extrapolated field is validated with measurements at a hub height of three WTs located in the area. The accuracy of the model increases with height because of the terrain anomalies and turbulence effects. The maximum MAE of wind velocity at WT hub height is 0.81 m/s, and MAPE is 7.98%. Our model remains accurate even with great simplifications and scarce measurements for the complex terrain conditions of our case study. The models' performance under such circumstances establishes it as a promising tool for the evolution of ultra-short-term forecasting as well as for the evaluation of new WT installations by providing valuable data for all models. [ABSTRACT FROM AUTHOR]

Navigation system performance degrades significantly in complex environments. It is important to analyze satellite visibility through 3D terrain modelling and separate the satellite signals propagated by NLOS to suppress the NLOS error. However, the traditional 3D terrain modelling visibility analysis method based on the pure terrain cover angle is only suitable for determining the visibility of GNSS satellites and may incorrectly separate LOS propagate measurement signals from members with low relative ranges and elevation angles under air–ground swarm conditions. To this end, this paper proposes a belief-propagating cooperative navigation method based on air–ground visibility analysis, which avoids mistakenly separating close-range LOS cooperative navigation signals by simultaneously considering the distances, elevation angles, and azimuths of the signal sources relative to the air–ground swarm members. The simulation shows that the cooperative navigation NLOS identification method based on air–ground visibility analysis proposed in this paper can more accurately realize the separation of NLOS signals under cooperative conditions than the traditional pure angular 3D terrain modelling visibility analysis method can, and the localization error of the members to be assisted is significantly reduced. [ABSTRACT FROM AUTHOR]

Snow avalanches are the primary mountain hazard for mechanized skiing operations. Helicopter and snowcat ski guides are tasked with finding safe terrain to provide guests with enjoyable skiing in a fast-paced and highly dynamic and complex decision environment. Based on years of experience, ski guides have established systematic decision-making practices that streamline the process and limit the potential negative influences of time pressure and emotional investment. While this expertise is shared within guiding teams through mentorship, the current lack of a quantitative description of the process prevents the development of decision aids that could strengthen the process. To address this knowledge gap, we collaborated with guides at Canadian Mountain Holidays (CMH) Galena Lodge to catalogue and analyze their decision-making process for the daily run list, where they code runs as green (open for guiding), red (closed), or black (not considered) before heading into the field. To capture the real-world decision-making process, we first built the structure of the decision-making process with input from guides, and then used a wide range of available relevant data indicative of run characteristics, current conditions, and prior run list decisions to create the features of the models. We employed three different modelling approaches to capture the run list decision-making process: Bayesian Network, Random Forest, and Extreme Gradient Boosting. The overall accuracies of the models are 84.6 %, 91.9 %, and 93.3 % respectively, compared to a testing dataset of roughly 20,000 observed run codes. The insights of our analysis provide a baseline for the development of effective decision support tools for backcountry avalanche risk management that can offer independent perspectives on operational terrain choices based on historic patterns or as a training tool for newer guides. [ABSTRACT FROM AUTHOR]

In this study, a variable‐resolution version of the Community Earth System Model (VR‐CESM) with mesh refinement (∼0.125°) over East Asia is used to simulate the regional climate in this region. For the evaluation of model performance and sensitivity to model resolution, the simulated near‐surface temperature and precipitation are compared with observations and simulation results from a globally quasi‐uniform (∼1°) CESM (UN‐CESM). Results show that VR‐CESM better simulates the spatial patterns and seasonal variations of mean temperature and precipitation than UN‐CESM over China. For extreme events, VR‐CESM improves the simulation of the occurrence frequency of wintertime daily minimum temperature and heavy precipitation. In regions with complex terrains, VR‐CESM better resolves the topographic forcing and captures the observed fine‐scale spatial patterns of temperature and precipitation, although precipitation is still overestimated. For East Asian summer monsoon precipitation, both UN‐CESM and VR‐CESM tend to overestimate (underestimate) the precipitation over northern (southern) China, which is associated with too strong meridional water vapor transport in the models and biases in the large‐scale circulation in the middle and upper troposphere. Different from previous studies with different physics parameterizations and refined domains, as the model resolution increases, simulated monsoon precipitation evolution is not obviously improved, and convective precipitation intensity decreases over eastern China. Despite this, our results indicate that VR‐CESM simulates regional climate, topographical forcing, and large‐scale circulations over East Asia reasonably well, and thus it can be applied for the future climate projection in the region. Plain Language Summary: Horizontal resolution is one of the key factors that affect the climate model's fidelity. Current global climate models (GCMs) are usually run at a coarse resolution due to the limitation of computational resources. To compromise, variable‐resolution GCM with higher resolution over domains of interest is developed to reduce the computational costs of high‐resolution modeling. This study evaluates the impact of model horizontal resolution on simulating regional climate in terms of temperature and precipitation using the model with a regionally‐refined high resolution over East Asia. Compared to observational data sets and the coarse‐resolution model, the regionally‐refined model better simulates spatial distributions and seasonal variations of climatological mean temperature and precipitation over China, as well as the occurrence frequency of extreme cold events and heavy precipitation. The regionally‐refined model with high‐resolution terrain improves precipitation simulation in regions with complex terrains. Our results demonstrate the regionally‐refined model's capability in simulating the regional climate over East Asia. Key Points: Variable‐resolution CESM2 (VR‐CESM2) performs better in simulating seasonal variation of precipitation over China but overestimates its magnitude than the quasi‐uniform 1° CESM2VR‐CESM2 reasonably simulates frequency of cold temperature extremes and heavy precipitationPrecipitation improvements at higher resolution depend on model physics parameterizations and refined domain size [ABSTRACT FROM AUTHOR]

Based on the results of field measurements of methane concentrations in water and its specific fluxes from the water surface, the emission of methane in the warm season from the Zeya Reservoir, one of the largest artificial hydropower facilities in Russia, has been assessed for the first time. Data were obtained during field surveys carried out in September 2021 and July 2022. Hydrological and hydrochemical surveys have made it possible to obtain information about the thermal, oxygen, and chemical structure of the reservoir water column, as well as carry out a comprehensive zoning of its water area. A digital model of the relief of the Zeya reservoir bed is developed, which, together with zoning, has made it possible to perform detailed calculations of the total methane emission from the Zeya Reservoir. It has been found that, for the reservoir, the main sources of organic matter and methane are swampy tributaries and runoff of organic matter from the shores, which enters the water area coastal areas. These areas accumulate allochthonous organic matter and are characterized by high methane fluxes. The total CH4 flux from the Zeya Reservoir surface is significantly higher in summer (when maximum heating of shallow waters is observed) than in the autumn. The CH4 emission coefficients obtained by the authors from the Zeya Reservoir (8.6–17.2 kgCH4/ha) correspond to the coefficients presented in the Supplements to the 2019 IPCC Guidelines for Boreal Reservoirs. [ABSTRACT FROM AUTHOR]

Marine geomorphological maps are useful to understand seafloor structure for example in the context of ecological studies, resources management or conservation planning. Although techniques to build such maps are increasingly sophisticated, manual techniques are still largely used. Automated approaches are needed to get reproducible maps in a reasonable time. This work provides statistical learning approaches based framework to build automatically geomorphological maps. We used bathymetric data to build Digital Bathymetric Model (DBM) and compute terrain attributes characteristic of seafloor geomorphology. Then, we used clustering based algorithms to select automatically ground truth locations from a reference geomorphological map manually made. Finally a supervised classification model random forest based was used to build predictive models for seafloor geomorphology typologies. Subsequently we studied the effect of DBM resolution, sample size and sampling method of the ground truth locations, in the quality of map production via a series of simulations. Results showed that the proposed framework allowed to build efficiently relevant seafloor geomorphological maps. The best compromise between the sampling effort and the quality of the resulting maps was obtained with 100 m DBM resolution, 200 data points sample size and using a complexity-dependent sampling method and led to a map matching at 90% the reference one. [ABSTRACT FROM AUTHOR]

The consequences of a catastrophic windthrow in a old-growth hemiboreal stands of the Kologrivsky Forest Reserve were investigated. The degree of damage to tree stands was assessed by interpretation of the Sentinel-2 satellite images. Data from the GBIF portal, SRTM global terrain height models, and tree stand heights were used for the quantitative analysis of factors affecting the presence of wind damage. It was found that tree stands on an area of 277.9 hectares (40.5% of the entire massif) were damaged by windthrow. The results of the analysis of height models and regression models showed that spruce stands are more vulnerable to wind damage, as well as stands of greater height or those growing at higher elevation on the ground. [ABSTRACT FROM AUTHOR]

In this paper, we investigate population agglomeration in a long narrow economy, in which an odd number of places are evenly distributed over a line segment. The bifurcation analysis of this economy elucidates the mechanism of the emergence of twin cities around the central city. The validity and usefulness of this analysis are confirmed using several well-known economic geography models that display various kinds of bifurcation behaviors. By this analysis, we investigate the historical change in the population distribution in a chain of cities on Japan's Main Island. [ABSTRACT FROM AUTHOR]

When legged robots perform complex tasks in unstructured environments, falls are inevitable due to unknown external disturbances. However, current research mainly focuses on the locomotion control of legged robots without falling. This paper proposes a comprehensive decision-making and control framework to address the falling over of quadruped robots. First, a capturability-based fall prediction algorithm is derived for planar single-contact and 3D multi-contact locomotion with a predefined gait sequence. For safe fall control, a novel contact-implicit trajectory optimization method is proposed to generate both state and input trajectories and contact mode sequences. Specifically, incorporating uncertainty into the system and terrain models enables mitigating the non-smoothness of contact dynamics while improving the robustness of the resulting trajectories. Furthermore, a model-free deep reinforcement learning-based approach is presented to achieve fall recovery after the robot completes a fall. Experimental results demonstrate that the proposed fall prediction algorithm accurately predicts robot falls with up to 95% accuracy approximately 395ms in advance. Compared to classical locomotion controllers, which often struggle to maintain balance under significant pushes or terrain perturbations, the presented framework can autonomously switch to the fall controller approximately 0.06s after the perturbation, effectively preventing falls or achieving recovery with a threefold reduction in touchdown impact velocity. These findings highlight the effectiveness of the proposed framework in enhancing the stability and safety of legged robots in unstructured environments. • The proposed fall prediction approach exhibits significantly enhanced reliability and flexibility. • The proposed controller can autonomously generate contact sequences, reducing damage from falls. • The designed reinforcement learning-method improves efficiency and robustness in fall recovery. [ABSTRACT FROM AUTHOR]

This study aims to reveal the spatial structural characteristics of 1,652 Ethnic-Minority Villages (EMV) in China and to analyze the mechanisms driving their spatial heterogeneity. EMV are a special type of settlement space that preserve a large number of historical traces of the ethnic culture of ancient China. They are important carriers of China's excellent traditional culture and are key to the implementation of rural revitalization strategies. In this study, 1652 EMV in China were selected as the research subjects. The Nearest Neighbor Index, kernel density, and spatial autocorrelation index were employed to reveal the spatial structural characteristics of minority villages. Neural network models, spatial lag models, and geographical detectors were used to analyze the formation mechanism of spatial heterogeneity in EMV. The results indicate that: (1) EMV exhibit significant spatial differentiation characterized by "single-core with multiple surrounding sub-centers," "polarization between east and west," "decreasing quantity from southwest to east coast to northeast to northwest," and "large dispersion with small agglomeration." (2) EMV are mainly distributed in areas rich in intangible cultural heritage, with high vegetation coverage and low altitude, far from central cities, and having limited arable land and an underdeveloped economy and transportation, particularly in shaded or riverbank areas. (3) Distance from the nearest river (X3), distance from central cities (X8), national intangible cultural heritage (X9), and NDVI (X10) were the main driving factors affecting the spatial distribution of EMV, whereas elevation (X1) and GDP (X5) had the weakest influence. As EMV are a relatively unique territorial spatial unit, the identification of their spatial heterogeneity characteristics not only deepens the research content of settlement geography, but also involves the assessment, protection, and development of Minority Villages, which is of great significance for the inheritance and utilization of excellent ethnic cultures in the era. [ABSTRACT FROM AUTHOR]

Methods for soil water conditions assessment are often highly localized or data demanding. In this study we propose a new scalable approach to assess soil water conditions. The main goal is to test whether the approach can be used to provide information about local conditions, without the need of extensive data sets. The approach utilizes a combination of normalized topography derived from the HAND terrain model (Height Above the Nearest Drainage) and hypsometric curves to identify wet and saturated areas for any given geographical extent. The study was conducted through a case study in the Lagan River catchment in the southwest of Sweden. To analyze the performance of the approach, a non‐linear regression analysis was performed to assess the relationship between the fraction of wet area and the normalized terrain. This was followed by a correlation analysis, in which the correspondence of the derived output was validated against the national Soil Moisture Map provided by the Swedish University of Agricultural Sciences. The results show a strong, and statistically significant, negative exponential relationship between the fraction of wet area, and the maximum heights within the studied area. The approach also corresponds well with the spatial variations highlighted in the Soil Moisture Map, although better in predicted wetter areas than under dry conditions. Going forward, we believe the integration of hypsometric curves and the HAND model could not only improve water balance calculations but assist in the assessments of flood and drought‐prone areas. Key Points: Hypsometric curves in combination with normalized terrain data can be used to assess soil moisture with limited data requirementsThe inclusion of flexible and scalable parameters facilitates local adaptation and allow a broad application of the proposed approachThe proposed approach could prove useful for water balance calculations, flood management and agriculture [ABSTRACT FROM AUTHOR]

A precise gravimetric geoid model is computed utilising Stokes’s formula, supposing an absence of topography above the geoid. Subsequently, the geoid model undergoes a simple correction for topographic masses, the constant density is taken as 2670 kg/m3. Notably, the true density of topographical mass deviates by approximately ±20% from this constant value. Recently, the University of New Brunswick in Canada released a global topographical density model at a 30 arc-second resolution. This paper investigates the impact of incorporating this model on the precision of the gravimetric geoid within a mountainous region in the Colorado test region. Numerical findings reveal that variations in geoid undulation attributable to this model can extend to a few decimetres, a discrepancy that cannot be neglected in geoid modelling with one-centimetre precision. It is therefore recommended that the considerable impact of topographic density fluctuations on geoid determination be taken into account, particularly in mountainous regions. [ABSTRACT FROM AUTHOR]

Indonesia released a new regional geoid model in 2020—the Indonesian Geoid 2020 (INAGEOID2020). It covers the Indonesian region with a spatial resolution of 0.01 × 0.01 degree with the unit in meters. The model was generated through a series of data and computations. Three components of gravity data, i.e., the observed free-air anomaly, the long-wave from the global geoid model, and the short-wave from the terrain model, were employed. The computation was performed using the Remove-Compute-Restore technique with the Fast Fourier Transformation approach. The output was then fitted to the geoid at tide stations by adding a fitting plane to the geoid model. The fitting plane was constructed based on the difference between the geoid model and each tide gauge benchmark. The final geoid model was evaluated by comparing the model with the reference data. Based on quality metrics, the accuracy of INAGEOID2020 varied between 6 cm to 29 cm. Any interested user can use this gridded geoid model to convert geodetic to orthometric heights and vice versa. [ABSTRACT FROM AUTHOR]

In the referred loess tunnel, there was no groundwater during the preliminary investigation phase, but there were water seepage, cracks and other hazard-related problems during the construction. Based on investigation of the relationship between the tunnel and the surrounding water system, this paper developed a three-dimensional terrain model and revealed the relationship between the groundwater and the tunnel in combination with the drilling and geophysical data. This study confirmed that there was a close correlation between tunnel structural cracking and water seepage as well as groundwater level variations. Even with large tunnel depth (i.e., 40–116 m), the soil fissures can still provide a fast infiltration channel for surface water and upper stagnant water, leading to a rise in the groundwater level and causing regular hazards of the tunnel. These problems can be solved by improving the design, strengthening construction control and drainage in the tunnel site. [ABSTRACT FROM AUTHOR]