Spatial Probability Research Articles

Landslide Hazard Prediction Based on UAV Remote Sensing and Discrete Element Model Simulation—Case from the Zhuangguoyu Landslide in Northern China

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies.

A global analysis of nearshore and submarine springs: spatial distribution, controlling factors, and probability of presence

Coastal springs act as bi-directionally preferential flow paths between coastal aquifers and oceans. While these springs can supply coastal ecosystems with nutrients, they also present vulnerabilities such as contamination and seawater intrusion. Despite their significance, substantial knowledge gaps exist regarding coastal springs due to their complex hydrogeological nature. This study provides a comprehensive global assessment of coastal springs, focusing on their distribution, controlling factors, and likelihood of occurrence. A global dataset of known coastal springs was compiled and analyzed, revealing 66 % of identified springs in the Mediterranean region, mainly linked to karst systems. In contrast, fewer springs were noted in Africa, South America, and South Asia. Key factors influencing spring occurrence were examined using geostatistical methods and integrated into a multi-criteria decision-making framework to develop a Coastal Spring Probability Index (CSPI) along coastline. High-potential areas for coastal springs were identified in regions characterized by significant carbonate and volcanic rock formations, wetter climates, and active tectonic margins, such as southern Europe, the Caribbean, tropical islands, and eastern Asia. Conversely, regions with dry climates and high water demand, such as North Africa and South America, exhibited a lower likelihood of spring presence. These findings will serve as a baseline for local-scale studies and aimed at improving coastal spring inventories and establishing monitoring networks. It will contribute to vulnerability assessment studies, thereby enhancing the management of coastal groundwater resources. The study emphasizes the importance of multidisciplinary approaches to understand coastal spring dynamics and advocates for a strategic planning in groundwater management and conservation. Ongoing efforts to inventory and monitor coastal springs, coupled with targeted conservation measures, are essential for ensuring the long-term sustainability of coastal water resources and ecosystems.

Three-dimensional vegetation structure drives patterns of seed dispersal by African hornbills.

Three-dimensional (3D) vegetation structure influences animal movements and, consequently, ecosystem functions. Animals disperse the seeds of 60%-90% of trees in tropical rainforests, which are among the most structurally complex ecosystems on Earth. Here, we investigated how 3D rainforest structure influences the movements of large, frugivorous birds and resulting spatial patterns of seed dispersal. We GPS-tracked white-thighed (Bycanistes albotibialis) and black-casqued hornbills (Ceratogymna atrata) in a study area surveyed by light detection and ranging (LiDAR) in southern Cameroon. We found that both species preferred areas of greater canopy height and white-thighed hornbill preferred areas of greater vertical complexity. In addition, 33% of the hornbills preferred areas close to canopy gaps, while 16.7% and 27.8% avoided large and small gaps, respectively. White-thighed hornbills avoided swamp habitats, while black-casqued increased their preference for swamps during the hottest temperatures. We mapped spatial probabilities of seed dispersal by hornbills, showing that 3D structural attributes shape this ecological process by influencing hornbill behaviour. These results provide evidence of a possible feedback loop between rainforest vegetation structure and seed dispersal by animals. Interactions between seed dispersers and vegetation structure described here are essential for understanding ecosystem functions in tropical rainforests and critical for predicting how rainforests respond to anthropogenic impacts.

Construction of landslide warning by combining rainfall threshold and landslide susceptibility in the gully region of the Loess Plateau: A case of Lanzhou City, China

Regional forecasting of the landslide rainfall threshold is complex, and the empirical prediction of the rainfall threshold relies on historical statistical data. To obtain the intensity I ∼ duration (D) rainfall threshold, Lanzhou City was selected as the research object, and a field investigation, 3S technology, mathematical statistics and a Python language environment were employed to develop a set of automatic rainfall threshold calculation programs. Factor elimination, the information value method, weight of evidence, the frequency ratio, the density method and receiver operating characteristic (ROC) curve were applied to obtain landslide susceptibility, and the I ∼ D rainfall threshold (temporal probability) and landslide susceptibility (spatial probability) were combined to construct regional landslide rainfall threshold warning levels (red, orange, yellow, and blue). The results showed the rainfall duration of historical landslides ranged from 0.99 to 216 h, thereinto, it was accounted for 92.78 % with that of between 6 and 96 h, and the rainfall intensity was between 0.11 and 23.6 mm/h, and the α and β of the rainfall threshold curve was gradually increased with an increase in the rainfall threshold probability. Additionally, when the rainfall condition probability was the same, landslides occurrence was more frequent in Chenguan District, where there are large areas of bare land, and slope and annual rainfall are less than 10 °and 300 mm, respectively. Fifteen, nine, and six factors were used to obtain landslide susceptibility, and the accuracy of the ROC ranged from 0.78 to 0.80 under different methods and different factor combinations. The spatial distribution of landslide susceptibility was very similar. There were 35, 12, 25, and 108 landslides associated with red, orange, yellow, and blue levels, respectively, under the spatial–temporal probability, but 30, 26, 54, and 70, respectively, under temporal probability. Under the different conditions, there were 89, 69, and 22 landslide points with unchanged, increased, and decreased warning levels, respectively. The ability and accuracy of rainfall threshold warning and prediction were effectively improved by integrating the temporal and spatial probabilities of the rainfall threshold.

Nonseparable wave evolution equations in quantum kinetics

A nonseparable wave-like integro-differential equation for the time evolution of the Wigner distribution function in phase space is educed from the corresponding separable kinetic equation. By employing the quantum hydrodynamical description, a non-local evolution wave equation is also derived by synthesizing the Hamilton‐Jacobi equation with that of continuity, which predicts the generation of nonlocal and quadrupole quantum phenomena in the propagation of the spatial probability density.

Assessing the influence of temperature on slope stability in a temperate climate: A nationwide spatial probability analysis in Italy

Among landslide controls, the role of temperature in temperate regions remains poorly understood. Experiments revealed thermo-hydro-mechanical effects in geomaterials; however, field evidence of temperature-controlled landsliding is scarce. This complexity hinders the formulation of a temperature-related variable, useable in modelling across scales. Here, we identified spatial correlations between temperature and shallow landslides in gentle clay slopes. Notably, the temperature in the shallow underground is controlled by that of the atmosphere, and clays are the most sensitive to temperature among all geomaterials. Exploiting the Italian Landslide Inventory, we constructed a slope unit-based Generalised Additive Model and utilised Land Surface Temperature (LST) data from MODIS, accessible in Google Earth Engine. Interestingly, we observed a stronger positive correlation between landslides and LST, particularly in southern Italy, where categorised widespread shallow instabilities are common. Although more experiments and site-specific studies are warranted, the observed pattern appears consistent with thermal soil weakening, which may enhance landslide mobility.

Strategic placement of volunteer responder system defibrillators.

Volunteer responder systems (VRS) alert and guide nearby lay rescuers towards the location of an emergency. An application of such a system is to out-of-hospital cardiac arrests, where early cardiopulmonary resuscitation (CPR) and defibrillation with an automated external defibrillator (AED) are crucial for improving survival rates. However, many AEDs remain underutilized due to poor location choices, while other areas lack adequate AED coverage. In this paper, we present a comprehensive data-driven algorithmic approach to optimize deployment of (additional) public-access AEDs to be used in a VRS. Alongside a binary integer programming (BIP) formulation, we consider two heuristic methods, namely Greedy and Greedy Randomized Adaptive Search Procedure (GRASP), to solve the gradual Maximal Covering Location (MCLP) problem with partial coverage for AED deployment. We develop realistic gradually decreasing coverage functions for volunteers going on foot, by bike, or by car. A spatial probability distribution of cardiac arrest is estimated using kernel density estimation to be used as input for the models and to evaluate the solutions. We apply our approach to 29 real-world instances (municipalities) in the Netherlands. We show that GRASP can obtain near-optimal solutions for large problem instances in significantly less time than the exact method. The results indicate that relocating existing AEDs improves the weighted average coverage from 36% to 49% across all municipalities, with relative improvements ranging from 1% to 175%. For most municipalities, strategically placing 5 to 10 additional AEDs can already provide substantial improvements.

Quantifying the effects of direct human activities and climate change on the spatial propagation of hydrological drought in the Yellow River Basin, China

The spatio-temporal evolution and propagation of flood and drought events are important in predicting their occurrences. This study attempted to investigate into the spatial propagation characteristics of hydrological droughts and their dynamics by identifying the spatial propagation probability (PP) and propagation time (PT) of different drought levels in the Yellow River Basin (YRB), China, based on copula functions and conditional probability. Then, the effects of direct human activities and climate change on the hydrological drought propagation were quantified by comparing the propagation characteristics of hydrological drought under observed and natural runoff. Results indicated that: (1) overall, the propagation of hydrological drought in the YRB showed a generally decreasing trend in PP and an increasing trend in PT with the increase of distance, presenting a gradually attenuating propagation along the river channel, the spatial propagation process of hydrological drought is similar to the domino effect; (2) when extreme hydrological drought occurred in the area represented by the Tangnaihai station located in the upstream basin, the probability of downstream areas experiencing moderate to severe hydrological drought showed an approximately decreasing trend along the river channel, with the PP of the observed sequence being slightly less than the natural sequence and the fluctuation of the hydrological drought propagation being more apparent; (3) taking 1986 as the dividing year, the average relative contribution rates of direct human activities to the changes in drought PT in spring, summer, autumn, and winter were 39.7%, 62.9%, 55.3%, and −67.2%, respectively, indicating a significant regulatory effect of reservoirs on hydrological drought propagation. In general, this study provided new insights into the spatial propagation mechanism of hydrological drought and offered a promising pathway for drought early warning and mitigation.

InSAR-Driven Dynamic Landslide Hazard Mapping in Highly Vegetated Area

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management.

Probabilistic modeling of energy transfer in disordered organic semiconductors

The non-radiative energy transfer process governs the transport of excitons in organic semiconductors, directly affecting the performance of organic optoelectronic devices. Successful models describe this transfer in terms of energy donor–acceptor pair sites, in contrast to experimental photophysical properties, which reflect the average behavior of the molecular ensemble. In this study, an energetic and spatial probability density function is proposed to determine the average non-radiative energy rate for homotransfer processes. This approach considers the energetic-spatial distribution typical of disordered semiconducting polymers. The average homotransfer rate is significantly dependent on the energy of the donor site, allowing the identification of the photophysical process most likely to occur. Values of the order of 1011 s−1 were predicted and are consistent with experimental results. This approach was used to evaluate how the energy transfer efficiency in heterostructures is affected by the energy and position of the energy donor site. The model presented in this study can be explored in other organic systems to investigate exciton transport mechanisms in new organic optoelectronic device architectures.

Carbon metabolism mechanisms and evolution characteristics analysis of the food-water-energy nexus system under blue-green infrastructure changes

Food, water, and energy comprise a complex system (FWE nexus) that generates much carbon emissions during operation. At the same time, urban blue-green infrastructure (BGI) has a critical carbon sequestration function. This paper combines the functions of the FWE nexus and BGI and uses ecological network analysis (ENA) and the Markov model to measure the carbon metabolism (CM) mechanisms and evolutionary characteristics of BGI and FWE nexus (BGI-FWE nexus) complex systems. The results show that Guangzhou has high carbon emissions, and Zhaoqing and Huizhou have high carbon sequestration. Resident land and industrial and transportation land transfers to different land uses are more likely to produce positive carbon flows, while BGI transfers to other types are more likely to produce negative carbon flows. The study of CM mechanisms reveals a high proportion of competition relationships and a low proportion of mutualism relationships. The ecological utility index (EUI) tends to fall initially and then increase, peaking at 0.84 in 2015–2020, the highest value for the study period. The CM network has less system robustness (SR) and is in an unsustainable state of high redundancy and low efficiency. The mechanism evolution characterization study’s findings show a decreased likelihood of remaining original and less stability in the spatial transfer probability matrices of EUI and SR. In this study, we constructed a BGI-FWE nexus research framework based on the different CM functions of BGI and FWE nexus. The research framework contributes to the realization of carbon reduction in the FWE nexus system and is essential for the planning and management of urban BGI.

Breaking the mold of simulation-optimization: Direct forward machine learning methods for groundwater contaminant source identification

Groundwater Contaminant Source Identification (GCSI) is important for addressing environmental concerns. Currently, it is widely achieved using the Simulation/Optimization (S/O) method. However, the utilization of optimization techniques may cause high computation costs and parameter equifinality issues. We introduce two innovative GCSI methods, Direct Forward Machine Learning (DFML) and One-Hot Machine Learning (OHML), utilizing the classical Artificial Neuro Network (ANN) model in the machine learning field. Both new methods eliminate the need for an optimization algorithm in GCSI, thus reducing the construction effort and improving efficiency. The first method, DFML can directly estimate eight parameters, providing valuable insights into the contaminant location, release history, and aquifer properties. The second method, OHML can estimate the spatial probability distribution of the contaminant location through one-hot encoding, addressing uncertainties in the contaminant source location estimations realized by DFML.Evaluations demonstrate that both methods exhibit satisfying performances. DFML can estimate contaminant location and aquifer properties with high accuracy; and estimate the release history information with moderate accuracy. The OHML correctly assigns the higher contaminant probabilities to regions containing true contaminant locations. The combination of DFML and OHML offers a comprehensive framework. This study contributes valid methodologies to the GCSI field.

Feasibility of Coseismic Landslide Prediction Based on GNSS Observations: A Case Study of the 2022 Ms 6.8 Luding, China, Earthquake

Abstract On 5 September 2022, an M 6.8 earthquake struck Luding County in Sichuan Province, China, triggering extensive landslides and causing severe damages. In this study, taking this event as an exemplary case study, we test the feasibility of fast earthquake-induced landslide prediction utilizing Global Navigation Satellite System (GNSS) observations. Particularly, we construct finite-slip models based on static offsets and 1 Hz displacement waveforms. Employing these slip models, physics-based simulation (PBS) is applied separately to obtain peak ground velocity (PGV). The PGVs are then integrated into landslide spatial distribution probability prediction based on the Deep Forest algorithm. Our results show that the predicted landslides probability distribution of fast inversion models using static and high-rate GNSS data align well with the landslide catalog. Furthermore, high-rate GNSS data can improve the model performance by providing the evolution information of rupture. In addition, we also derive PGV from the empirically regressed ground-motion prediction equations (GMPEs) and incorporate it into landslide prediction. The GMPEs exhibits an advantage in terms of prediction recall for landslides and a relatively reduced accuracy compared with the PBS. Despite the inherent uncertainties in this study, based on the case study of the 2022 M 6.8 Luding earthquake, we utilize GNSS data and present a set of methods for real-time landslide prediction. The achieved model performance is relatively satisfactory, considering the challenges and uncertainties involved.

Morphological patterns and spatial probability maps of the inferior frontal sulcus in the human brain.

The inferior frontal sulcus (ifs) is a prominent sulcus on the lateral frontal cortex, separating the middle frontal gyrus from the inferior frontal gyrus. The morphology of the ifs can be difficult to distinguish from adjacent sulci, which are often misidentified as continuations of the ifs. The morphological variability of the ifs and its relationship to surrounding sulci were examined in 40 healthy human subjects (i.e., 80 hemispheres). The sulci were identified and labeled on the native cortical surface meshes of individual subjects, permitting proper intra-sulcal assessment. Two main morphological patterns of the ifs were identified across hemispheres: in Type I, the ifs was a single continuous sulcus, and in Type II, the ifs was discontinuous and appeared in two segments. The morphology of the ifs could be further subdivided into nine subtypes based on the presence of anterior and posterior sulcal extensions. The ifs was often observed to connect, either superficially or completely, with surrounding sulci, and seldom appeared as an independent sulcus. The spatial variability of the ifs and its various morphological configurations were quantified in the form of surface spatial probability maps which are made publicly available in the standard fsaverage space. These maps demonstrated that the ifs generally occupied a consistent position across hemispheres and across individuals. The normalized mean sulcal depths associated with the main morphological types were also computed. The present study provides the first detailed description of the ifs as a sulcal complex composed of segments and extensions that can be clearly differentiated from adjacent sulci. These descriptions, together with the spatial probability maps, are critical for the accurate identification of the ifs in anatomical and functional neuroimaging studies investigating the structural characteristics and functional organization of this region in the human brain.

ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning.

Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with varying shapes. In this study, we propose ST-CellSeg, an image-based machine learning method for spatial transcriptomics that uses manifold for cell segmentation and is novel in its consideration of multi-scale information. We first construct a fully connected graph which acts as a spatial transcriptomic manifold. Using multi-scale data, we then determine the low-dimensional spatial probability distribution representation for cell segmentation. Using the adjusted Rand index (ARI), normalized mutual information (NMI), and Silhouette coefficient (SC) as model performance measures, the proposed algorithm significantly outperforms baseline models in selected datasets and is efficient in computational complexity.

Stochastic characterization of navigation strategies in an automated variant of the Barnes maze.

Animals can use a repertoire of strategies to navigate in an environment, and it remains an intriguing question how these strategies are selected based on the nature and familiarity of environments. To investigate this question, we developed a fully automated variant of the Barnes maze, characterized by 24 vestibules distributed along the periphery of a circular arena, and monitored the trajectories of mice over 15 days as they learned to navigate towards a goal vestibule from a random start vestibule. We show that the patterns of vestibule visits can be reproduced by the combination of three stochastic processes reminiscent of random, serial, and spatial strategies. The processes randomly selected vestibules based on either uniform (random) or biased (serial and spatial) probability distributions. They closely matched experimental data across a range of statistical distributions characterizing the length, distribution, step size, direction, and stereotypy of vestibule sequences, revealing a shift from random to spatial and serial strategies over time, with a strategy switch occurring approximately every six vestibule visits. Our study provides a novel apparatus and analysis toolset for tracking the repertoire of navigation strategies and demonstrates that a set of stochastic processes can largely account for exploration patterns in the Barnes maze.

A Novel Approach for the Enhancement of Security through Defining the Spatial Secrecy Outage Probability in the Industrial Internet of Things

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment, but has also posed significant challenges to network security, especially physical layer security, which is crucial for the reliable transmission of messages among interconnected devices and sensors. Focusing on enhancing the physical layer security in IIoT communication, this paper proposes a mechanism for the Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate devices is known, while the eavesdropper devices have a random distribution of locations, forming the SSOP model related to the device locations from the perspective of insecure regions (ISRs) and secure regions (SRs), and the closed-form expression for its upper bound is derived. Subsequently, under the constraint of the SSOP conditions, we establish an optimization model with the objective of system throughput maximization based on the Spatial Secrecy Outage Probability (SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of the system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in MATLAB, we verified the accuracy of the definition and derived the upper bound of the SSOP. The experimental results also show that the SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that, when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. This performance is crucial for understanding and optimizing the security performance of IIoT communication systems.

Use of remote sensing, spatial and geophysical modeling, and real recharging capabilities to identify suitable areas for groundwater exploitation in dry coastal areas

Accurate identification of groundwater potential areas in arid regions is an important task for groundwater management and sustainability. As a result, this study used the innovative integration of remote sensing (RS), geographic information system (GIS), watershed modeling system (WMS), geophysical survey, and water mass balance equation to identify potential groundwater areas in the W. Dara, Eastern Desert, Egypt. A weighted spatial probability model (WSPM) of groundwater potential based on eight regulatory factors was implemented within ArcGIS software. Drainage density (DD), precipitation (P), net groundwater recharge (NGR), terrain slope (TS), lineament density (LD), lithologic group (LG), water quality (TDS), and depth to groundwater level (DGW) are the aspects considered. The Analytical hierarchy process (AHP) method was used to assign weights to these parameters, and their accuracy was estimated using the consistency ratio (CR). The resulting groundwater potential map classified W. Dara study area into five categories, ranging from very low to very high potential. A geophysical survey, in the form of Vertical Electrical Sounding (VES) and Transient Electromagnetic (TEM), was conducted along W. Dara to validate the results of the WSPM, which identified areas of high groundwater potential. The 1D inversion of VES/TEM shows that the central and western parts of W. Dara are considered the most promising areas for groundwater occurrence, and are located in areas of high and very high potential classes derived from WSPM. Moreover, the results of VES and TEM surveys showed that the proposed aquifers (Nubian Sandstone, Miocene, and Quaternary) in the study area are horizontally and vertically connected through a set of normal faults traversing NW-SE. Ten sites have been proposed for drilling additional exploitative wells in W. Dara area based on the WSPM and geophysical survey with the aim of sustainable development. Thus, the integrated techniques applied in this study proved effective in accurately determining the development strategy for arid and semi-arid coastal areas, especially those that suffer from scarcity of rainfall and increased agricultural reclamation requirements in remote areas.

Martian Dust Storm Spatial‐Temporal Analysis of Tentative Landing Areas for China's Tianwen‐3 Mars Mission

AbstractChina's first Mars sampling return mission (Tianwen‐3) is designed to launch and retrieve samples around 2030. Three tentative landing areas (TLAs) (Amazonis, Chryse and Utopia Planitiae, i.e., TLA‐A, TLA‐C and TLA‐U) are selected based on elevation <−2,000 m and latitude between 17° and 30°N. As a dominant feature of Martian meteorology, dust storms manifest in all seasons and affect the accuracy and safety of Mars exploration missions. Tianwen‐3's landing, sampling and ascent phases are in the dust storm season. Therefore, analyzing dust storm activity around landing areas is significant for the Tianwen‐3 mission. According to Mars Daily Global Maps taken by Mars Orbiter Camera spanning Martian years 24–28, 2,476 dust storm events around the three TLAs were identified in this research. Dust storm temporal probabilities within TLA‐A, TLA‐C and TLA‐U were calculated as 0%–44.69%, 0%–66.29% and 0%–33.64%, separately. Dust storm spatial probabilities around the TLA‐A, TLA‐C and TLA‐U were computed, with ranges of 0%–10.71%, 0%–14.55% and 0%–19.96% during the T1 period (Ls = 161–309°), and 0%–6.75%, 0%–7.65% and 0%–8.26% during the T2 period (Ls = 342‐55°), respectively. Finally, considering the temporal and spatial distribution of dust storms, we recommend the T2 period as the launch scenario. Three safe periods (Ls = 2–18°, 4–12°, and 356–4°) were assigned for the entry‐descent‐landing (EDL) phase, along with one period (Ls = 45–55°) for the take‐off and ascent phase. Five circular landing zones with dust storm spatial probability <3% were selected for the Tianwen‐3 mission.

A Comprehensive Approach to Quantitative Risk Assessment of Rockfalls on Buildings Using 3D Model of Rockfall Runout

Rockfalls are incidents of nature that take place when rocks or boulders break from a steep slope and fall to the ground. They can pose considerable threats to buildings placed in high-risk zones. Despite the fact that the impact of a rockfall on a building can cause structural and non-structural damage, few studies have been undertaken to investigate the danger associated with this event. Most of these studies indicated that the risk resulting from rockfall hazards is hard to forecast and assess. A comprehensive quantitative risk assessment approach for rockfalls on buildings is developed and described in this paper and applied for the Mtein village in Mount Lebanon. This method employs a 3D model to simulate the rockfall trajectories using a combination of digital elevation data, field surveys, and orthorectified aerial photographs. The spatial and temporal probability of rockfalls were evaluated using the analysis of historical data in two triggering-factor scenarios: earthquake and precipitation. The findings show that, during the period of 1472 years between the years 551 (the first observed large earthquake in Lebanon) and the current year of the study (2023), the temporal probability will potentially be equal to 0.002 and 0.105 in the cases of earthquake- and rainfall-triggered rockfalls, respectively, while the maximal damage values are expected to be 232 USD and 10,511 USD per year, respectively. The end result is a final map presenting the risk values assigned to each building that could be damaged by rockfalls.