Regular-grid Digital Elevation Model (DEM) data are fundamental for viewshed analysis, with direct effects on both computational accuracy and efficiency. High-resolution DEMs yield more detailed results, but computational costs can be prohibitively high, creating a dilemma of balancing computational accuracy and efficiency. To address this, we propose a multi-resolution viewshed (MRVd) method that couples varying resolutions of terrain data based on distance from the viewpoint: high-resolution DEM data are used near the viewpoint, while low-resolution DEM data are used further away. Experiments conducted across mountainous, plain, and urban terrains show that MRVd can achieve better performance than fixed-resolution viewshed approaches with only marginal additional computation time. Accuracy improvements were 2-3 times on average and up to 6-7 times in some cases. Furthermore, MRVd effectively mitigates chunk distortion by leveraging the localized high-resolution DEM data, producing results similar to those obtained using uniformly high-resolution DEM data. These findings suggest that coupling multi-resolution terrain data provides a scalable, resource-efficient computational strategy to balance accuracy and efficiency in viewshed computation. The MRVd approach enhances the robustness and reliability of viewshed calculation for practical applications in landscape assessment, spatial planning, archaeology, and related fields.

Landslides are a major geological hazard causing significant casualties and economic losses. Reliable risk assessment requires high-quality spatiotemporal event data, yet no publicly available landslide catalogue with fine-grained precision exists for China. To address this, we developed a landslide event catalogue for mainland China from 2008-2024 based on news reports. The dataset was generated via large-scale web crawling, information extraction using an open-source large language model (LLM), event deduplication, geocoding, and multi-stage validation. It contains 1,582 events with detailed spatiotemporal attributes, some with minute-level temporal precision and spatial resolution down to the county, village, or specific reported sites. Evaluation shows that, while casualty-related information is less accurate, the LLM reliably captures key attributes such as time, location, and triggering factors. This demonstrates the feasibility of using LLMs to extract critical landslide data from news reports. Compared with existing catalogues, our dataset offers more events and improved spatiotemporal accuracy, providing a valuable resource for landslide hazard assessment, early warning model development, and disaster risk management in China.

The rapid development of information technologies provides a new carrier for digitizing, integrating, and synthesizing multiple scales and sources of information. This paradigm shift enables holistic modeling, analysis, and representation of the complex real world. Crucially, information space has emerged as a critical bridge, harmonizing geography's traditional dual dimensions—physical space and human-social space—thereby redefining modern geographic research frameworks. This evolution necessitates a conceptual transition from binary space to ternary space, where information space becomes an integral third dimension. To formalize this paradigm, this paper introduces information geography (InfoGeo), a nascent discipline centered on information space as both its theoretical foundation and research domain. We rigorously define InfoGeo's conceptual architecture, delineate its research objectives, disciplinary system, and methodological framework, and critically compare its epistemological distinctions from geographic information science (GIScience). Through systematic analysis, we identify key challenges and propose a forward-looking research agenda. This study aims to establish InfoGeo as a foundational framework for understanding geospatial phenomena in the digital age and to provide a theoretical roadmap for advancing interdisciplinary geographic research.

The coastal zone, situated at the land-ocean interface, represents a complex regional system in which physical, ecological, and social subsystems are highly coupled. Under the dual impacts of global change and human activities, shoreline dynamics and ecological degradation have intensified, and the system may be approaching or crossing tipping thresholds. This study addresses the mathematical representation and classification of tipping points in coastal zone systems. Based on dynamical systems theory, we developed a unified mathematical framework that incorporates land-sea interactions and multi-scale processes. We establish a six-category classification system encompassing bifurcation-driven, noise-driven, shock-driven, rate-driven, space-driven, and information-driven tipping points, and further elucidate the spatiotemporal characteristics and triggering mechanisms of each category. Furthermore, a classification model of tipping points was constructed using large language models and literature-based big data, and 91 typical cases worldwide are analyzed, through which the heterogeneous spatial distribution of such events is revealed. The results demonstrate that coastal tipping point research possesses both representativeness and particularity. Future research should focus on key issues of “classification-identification-response” and advance integrated efforts in data fusion, model development, and adaptive management.

Quaternary climate change on the Tibetan Plateau is vital for understanding environmental evolution both globally and in the Northern Hemisphere. The Paleo-Daocheng Ice Cap (PDIC), located on the southeastern Tibetan Plateau (TP), plays a crucial role in exploring the coupling mechanism between TP uplift and climate change. This study sampled granules, pebbles, boulders, and coarse-grained sand from the surfaces of moraines and bedrock samples in the PDIC area. The new 10Be exposure ages of 23 samples from three different glacial remnants of the PDIC ranged from 71.0 f 4.6 to 15.6 f 1.0 ka. Subsequently, a comprehensive analysis of both new and previously-compiled 10Be ages from the Shaluli Mountains was conducted. This analysis confirmed that the area entered the cryosphere around 0.8-0.6 Ma. Probabilistic cosmogenic age analysis tool (P-CAAT) analysis on the 10Be ages since Marine Isotope Stage (MIS) 6 was also conducted. This identified at least four significant glacial fluctuations (at 153.5 f 17.6 ka, 109.7 f 18.6 ka, 54.0 f 14.4 ka, and 19.4 f 9.2 ka). These glacial fluctuations represent a comprehensive response to temperature, precipitation, and insolation, with these climate determinants making varying contributions across different stages since MIS 6.

The deformation degree and failure mode of the slope is always different under the influence of different factors. In this paper, the geological characteristics of the consequent slope failure happened in Xuan'en County are statistically analyzed, the sensitivity of several factors to the deformation degree of the slope is orthogonally studied. Further, the influence pattern of the main controlling factors is also discussed by response surface method. The results show that slope dip and strata dip are the main contributing factors, and the difference in their combination forms leads to the difference in slope failure modes. When the strata dip of the rock is greater than the slope dip, the slope shows slipping-bending failure under gravity pressure, otherwise, it is mostly slipping-rupture failure which develops gradually from the toe to the top of the slope.

BackgroundIn the last three years, COVID-19 has caused significant harm to both human health and economic stability. Analyzing the causes and mechanisms of COVID-19 has significant theoretical and practical implications for its prevention and mitigation. The role of meteorological factors in the transmission of COVID-19 is crucial, yet their relationship remains a subject of intense debate.MethodsTo mitigate the issues arising from short time series, large study units, unrepresentative data and linear research methods in previous studies, this study used counties or districts with populations exceeding 100,000 or 500,000 as the study unit. The commencement of local outbreaks was determined by exceeding 100 cumulative confirmed cases. Pearson correlation analysis, generalized additive model (GAM) and distributed lag nonlinear model (DLNM) were used to analyze the relationship and lag effect between the daily new cases of COVID-19 and meteorological factors (temperature, relative humidity, solar radiation, surface pressure, precipitation, wind speed) across 440 counties or districts in seven countries of the Americas, spanning from January 1, 2020, to December 31, 2021.ResultsThe linear correlations between daily new cases and meteorological indicators such as air temperature, relative humidity and solar radiation were not significant. However, the non-linear correlations were significant. The turning points in the relationship for temperature, relative humidity and solar radiation were 5°C and 23°C, 74% and 750 kJ/m2, respectively.ConclusionThe influence of meteorological factors on COVID-19 is non-linear. There are two thresholds in the relationship with temperature: 5°C and 23°C. Below 5°C and above 23°C, there is a positive correlation, while between 5°C and 23°C, the correlation is negative. Relative humidity and solar radiation show negative correlations, but there is a change in slope at about 74% and 750 kJ/m2, respectively.

Empirical orthogonal function (EOF) analysis is widely adopted for identifying spatial patterns in regular latitude-longitude gridded geographical data. However, a standard EOF analysis could underestimate variances from low latitudes due to the increasing grid cell areas when moving toward the equator. A broadly adopted compensating approach is the area-weighted EOF, where the grid data are multiplied by a factor proportional to the grid area, thereby forcing each datum to represent identical areas. In this article, we revisit the area-weighting scheme and discuss its potential drawbacks. In particular, we show that along with compensating the unequal areas, another unaddressed issue is which region of the data is more relevant to our focused problem. We propose a regional attentive EOF (RA-EOF) method, which resolves area-compensation and region selection simultaneously. We conduct case studies of the North Atlantic Oscillation (NAO) analysis and perform RA-EOF to the sea level pressure variability in the North Atlantic sector. Experiments show that our method evidently detects the NAO pattern in the leading mode of sea level pressure variability and suggests the southwestward movement of the southern action centre of NAO during the summers. Our findings clarify the ambiguity about the summer NAO in previous studies. The proposed methodology provides a potent instrument for discerning the spatial distribution and analysing the temporal variability of atmospheric teleconnections and oscillations, particularly in cases characterized by weak signal strength. The first mode of sea level pressure variability revealed by regional attentive empirical orthogonal function (EOF) evidently detects the North Atlantic Oscillation (NAO) pattern and suggests the southwestward movement of the southern action centre of NAO during the summers. This result avoids confusion from multiple statistically significant but interference modes.image

Long-wavelength（＞500 km） magnetic anomalies originating in the lithosphere were first found in satellite magnetic surveys.Compared to the striking magnetic anomalies around the world, the long-wavelength magnetic anomalies in China and surrounding regions are relatively weak. Specialized research on each of these anomalies has been quite inadequate; their geological origins remain unclear, in particular their connection to tectonic activity in the Chinese and surrounding regions. We focus on six magnetic high anomalies over the（1） Tarim Basin,（2） Sichuan Basin（3） Great Xing’an Range,（4） Barmer Basin,（5） Central Myanmar Basin, and（6） Sunda and Banda Arcs, and a striking magnetic low anomaly along the southern part of the Himalayan-Tibetan Plateau. We have analyzed their geological origins by reviewing related research and by detailed comparison with geological results. The tectonic backgrounds for these anomalies belong to two cases: either ancient basin basement, or subduction-collision zone. However, the geological origins of largescale regional magnetic anomalies are always subject to dispute, mainly because of limited surface exposure of sources, later tectonic destruction, and superposition of multi-phase events.

The COVID-19 pandemic has significantly impacted human health and daily life. Meteorological factors play a crucial role in virus transmission. Understanding these factors is critical for pandemic control, yet debates continue due to variations in research methods and data. This study uses a global dataset and focuses on administrative units smaller than 10,000 km². The relationship between daily COVID-19 incidence rates and meteorological factors—including temperature, relative humidity (RH), ultraviolet radiation (UVR), and diurnal temperature range (DTR)—was analyzed across 173 units in 62 countries. The results revealed significant nonlinear responses and lag effects between incidence rates and the meteorological factors. Key inflection points were observed at 0°C and 24°C for temperature, 63% for RH, 102 KJ/m² for UVR, and 15°C for DTR. Below 0°C, infection risk increased with rising temperatures; from 0°C to 24°C, risk decreased; and above 24°C, risk escalated rapidly. RH below 63% correlated inversely with infection risk, while above 63%, the correlation was positive. Infection risk decreased with UVR below 102 KJ/m² but increased beyond this threshold. These nonlinear responses are primarily driven by the differential effects of these factors on virus survival, transmission, and human immunity. Conditions around 24°C, 63% RH, 102 KJ/m² UVR, and DTR below 15°C appear optimal for human immunity, correlating with lower infection risk. High temperature and low UVR prompted immediate infection responses, while low temperatures, high UVR, and larger DTR showed delayed effects. These findings provide new, more reliable evidence for understanding the mechanisms driving COVID-19 pandemic dynamics and for preventing the future normalization of the epidemic.

As the largest global carbon pool system, terrestrial ecosystems have an important role to maintain the stability of ecosystems. Human activities affect the structural changes in the ground surface and interfere with terrestrial ecosystem evolution, and consequently, carbon stock is changed in the region. Therefore, forecasting future carbon stock changes under different land use scenarios has important research implications for promoting stable evolution and cycling of terrestrial ecosystems. This study is conducted with the land use data from 2000 to 2020, and incorporates the Patch-generating Land Use Simulation model with the Integrated Valuation of Ecosystem Services and Trade-Offs model to analyze the changes in land use in Hefei, and its influence on the carbon stock in Hefei in various scenarios. During the study period, the mutual conversion between different land types in Hefei City made the land structure change within the study area more significant. The rapidly evolving surface structure is the reason for the decrease of carbon storage in terrestrial ecosystems, with a cumulative decrease of 1.2 x 108 t. The spatial distribution in carbon storage in the study area shows a distribution pattern of low in the north and high in the south. The high carbon storage area is obviously banded in the study area. The area with obvious changes in surface structure has more obvious changes in carbon storage. Compared with the natural development scenario, the downward trend of carbon storage in the ecological protection scenario and the comprehensive development scenario has slowed down due to the restrictions on the structural trans-formation of land types and the implementation of relevant ecological protection policies. Therefore, this study will support the future management and policy making of Hefei City with the background of China's "double carbon" target and the significant position of Hefei City.

Geometric algebra serves as the unified language of mathematics, physics, and engineering in the 21st century. Coinciding with the era of artificial intelligence, the utilization of a Large Language Model (LLM) can significantly benefit the learning and application of geometric algebra. This study develops a representative application called PrivateGPT, based on the ggml-ggml-nous-gpt4-vicuna-13b model, to explore the integration of geometric algebra and LLM by building a knowledge base of geometric algebra expertise. The Geometric Algebra Knowledge Base was created by collecting 20,711 papers and data, categorizing them by topics. This application possesses the capability of iterative refinement, enhancing its understanding and reasoning of geometric algebra knowledge. It accomplishes the textual summarization of research content, methods, innovations, and conclusions. It facilitates the development of tailored learning plans for students from diverse fields to acquire knowledge of geometric algebra in their specific domains. Additionally, we compared the performance of PrivateGPT and ChatGPT in providing personalized learning paths for the same group of learners and evaluated their responses through a questionnaire survey. The results showed that PrivateGPT has an advantage in devising tailored learning plans for learners from various disciplines.

The life cycle of a typhoon is mainly divided into three stages: far away from the coast, offshore, and on land. The disaster bodies and typhoons affected are different at every stage. The traditional assessment methods could not consider the risk from the complete typhoon life cycle to cover the effects of both land and sea. This paper proposes a Bayesian network risk assessment model to evaluate the navigational ship risk and damage risk of coastal structures during the different typhoon life cycles. The disaster memory database of the typhoon is also constructed in this paper, which deconstructs the typhoon risk factors and forms a unified description semantics.

The accurate simulation of climate is always critically important and also a challenge. This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian networks based on the concept of a coarse-fine model. The improved method constructs a coarse-fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing a Bayesian network constructed on the interrelationships between internal climate variables of the GREB model to achieve local optimization. To objectively validate the performance and generalization of the improved method, the method is applied to the simulation of surface temperature and temperature of the atmosphere based on the 3.75 degrees x 3.75 degrees global data sets by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) from 1985 to 2014. The results demonstrate that the improved model exhibits higher average accuracy and lower spatial differentiation than the original GREB model and is robust in long-term simulations. This approach addresses issues with the accuracy of the GREB model in local areas, which can be attributed to an overreliance on boundary and initial conditions, as well as a lack of fully usable observed data. Additionally, the model overcomes the challenge of poor robustness in statistical models due to ambiguous climate inclusions. Thus, the improved method provides a promising way to give a reliable and stable simulation of climate.

The Hengduan Mountains is in the transitional zone between the Qinghai-Tibet Plateau (QTP) and the Yunnan-Guizhou Plateau in China, and a key area for elucidating the Quaternary environmental changes in Asia. The paleo-Daocheng ice cap was located on the Shaluli Hilly Plateau in the northeastern Hengduan Mountains, the oldest moraines in the Hengduan Mountains region were found in the ice cap area. Such glacial landforms provide key evidence to study the timing when this area entered the cryosphere with the uplift of the QTP. However, it is difficult to collect suitable glacial boulders from these moraines for traditional terrestrial in-situ cosmogenic nuclide (TCN) exposure dating because of long-term severe moraine degradation. Here, we collected clast samples from the moraine surface and depth profile to constrain the age of the oldest moraine in Kuzhaori (moraine E) using TCN 10 Be dating technique. The minimum 10 Be ages of five clast samples from the moraine surface range from 187.4±1.5 to 576.8±4.3 ka, implying that the moraine has been seriously degraded since deposition. Based on the TCN 10 Be concentrations of the samples from a depth profile and simulations, the exposure-erosion-inheritance history of the profile was obtained. By fitting to the profile 10 Be concentrations using the chi-square test, the simulations yielded a reliable age of 626.0±52.5 ka for the moraine. Therefore, the oldest moraine (moraine E) in Kuzhaori was most likely formed at about 0.63 Ma ago, corresponding to the marine isotope stage (MIS) 16. This glaciation represents the maximum Quaternary glaciation after the QTP was elevated into the cryosphere by the Kunlun-Yellow River Tectonic Movement.

As an essential data-driven model, machine learning can simulate runoff based on meteorological data at the watershed level. It has been widely used in the simulation of hydrological runoff. Considering the impact of snow cover on runoff in high-altitude mountainous areas, based on remote sensing data and atmospheric reanalysis data, in this paper we established a runoff simulation model with a random forest model and ANN (artificial neural network) model for the Xiying River Basin in the western Qilian region The verification of the measured data showed that the NSE (Nash–Sutcliffe efficiency), RMSE (root mean square error), and PBIAS (percent bias) values of the random forest model and ANN model were 0.701 and 0.748, 6.228 m3/s and 4.554 m3/s, and 4.903% and 8.329%, respectively. Considering the influence of ice and snow on runoff, the simulation accuracy of both the random forest model and ANN model was improved during the period of significant decreases in the annual snow and ice water equivalent in the Xiying River Basin from April to May, after the snow remote sensing data were introduced into the model. Specifically, for the random forest model, the NSE increased by 0.099, the RMSE decreased by 0.369 m3/s, and the PBIAS decreased by 1.689%. For the ANN model, the NSE increased by 0.207, the RMSE decreased by 0.700 m3/s, and the PBIAS decreased by 1.103%. In this study, based on remote sensing data and atmospheric reanalysis data, the random forest model and ANN model were used to effectively simulate hydrological runoff processes in high-altitude mountainous areas without observational data. In particular, the accuracy of the machine learning simulations of snowmelt runoff (especially during the snowmelt period) was effectively improved by introducing the snow remote sensing data, which can provide a methodological reference for the simulation and prediction of snowmelt runoff in alpine mountains.

摘要: 川藏铁路在波密跨越嘉黎断裂，研究其周边地应力场对认识嘉黎断裂对川藏铁路廊道地应力场的影响至关重要.通过数值模型计算了通古地区及其邻近鲁朗、多康地区地应力场，通过对比3个模型的加载条件和力学参数差异，分析了沿嘉黎断裂带地应力场与周边区域地应力场的不同.结果表明：（1）总体来看，随深度变化的泊松比，能更好地拟合实测地应力数据；（2）地应力场区域差异明显，多康地区地应力较高，挤压明显，而鲁朗地区挤压程度仅为多康地区1/3左右，通古地区跨越嘉黎断裂带，应力场显示挤压很弱，可能应力已经释放；（3）基于实测数据和合适的地质力学参数，可以较为有效地预测地壳浅层的局部应力场，而预测地壳深部应力场则需要精确的泊松比上限值. 关键词: 川藏铁路 / 地应力场 / 嘉黎断裂 / 泊松比 / 工程地质

太阳黑子磁场磁极性指数作为衡量太阳磁场磁性变化的重要指标,在研究太阳活动方面发挥重要作用.本文根据1749-2019年的太阳黑子磁场磁极性指数时间序列(SMFPI),利用索周法获得其周期性特征并成功构建了太阳黑子磁场磁极性指标.通过对近270年太阳黑子磁极性数据的索周分析,发现存在显著的准22.16年、18.20年的主周期规律,其中以准22.16年的周期性最强.对太阳黑子磁场磁极性指标进行数值模拟,重建了近270年的太阳黑子磁场磁极性时间序列;通过对比太阳黑子磁场极性的重建数据和观测数据,发现构建的太阳黑子磁场磁极性指标能够准确反映太阳黑子磁场磁极性的变化规律.利用功率谱分析和小波分析方法,分别对太阳黑子磁场磁极性观测数据和重建数据进行周期性分析:功率谱分析表明两组数据均具有平均22.54年的主周期和18.13年的次周期;小波分析表明两组数据在1749-2019年期间近270年整个时段内具有17-43.5年的周期区间.因此,两种周期分析结果保持良好的一致性,也同索周分析呈现的结果保持一致.太阳黑子磁场磁极性指标能够有效重建太阳黑子磁场磁性时间序列,准确反映太阳活动的变化趋势;进而为研究太阳黑子磁场磁极性循环、太阳活动、全球气候变化提供有效途径和奠定理论基础.

Cosmogenic nuclide exposure dating is one of the most intensively applied dating methods with which to study glacial geomorphology.Glacial erratics have been the major dating objective in many studies.Some research has proposed that glacial erratics may undergo rollover and re-transportation during the late exposure stage,which can affect the dating results.However,there is no direct evidence to confirm this possibility.In this study,we collected seven samples from a vertical section inside a glacial erratic in the paleo-Daocheng ice cap in the southeastern Tibetan Plateau,measuring their contents of the cosmogenic nuclides 10 Be and 26 Al.The results show that from the top to the bottom,the concentrations of 10 Be were（1.21 ± 0.05） × 10~6,（1.00 ± 0.02） × 10~6,（0.88 ± 0.03） × 10~6,（0.77 ± 0.02） × 106,（0.75 ± 0.03） × 10~6,（0.95 ±0.03） × 10~6 and（1.46 ± 0.04） × 10~6 atoms/g.The 10 Be concentrations decreased from（1.21 ± 0.05） × 10~6 atoms/g to（0.75 ±0.03） × 10~6 atoms/g and then increased to（1.46 ± 0.04） × 10~6 atoms/g,which is not consistent with the theoretical prediction of a gradual decrease.This phenomenon indicates that the glacial erratic may have rolled over at least once.The lower surface of the erratic could have been on top at some time in the past.Therefore,its exposure age was greater than the exposure age that was expected,based on its current orientation.This study provides numerical evidence for an erratic rollover event.