Mountainous Areas Research Articles

Investigating climatic drivers of snow phenology by considering key-substage heterogeneity

Investigating the main climatic drivers responsible for changes in snow cover phenology (SCP) is crucial for making scientific countermeasures to ensure water resources security in global mountainous regions. However, most studies have explored drivers of SCP changes using a fixed substage division scheme and correlation analysis (referred to as the traditional method), potentially limiting reliability and accuracy in mountainous areas with complex terrain and climate. Here, a novel method is developed to efficiently identify main climatic drivers of SCP changes. This method employs a flexible scheme to account for the spatial heterogeneity of the dominant sub-period (the sub-period with the major climatic effect) in combination with regression analysis. Using the arid region of China as a case study, the new method was applied to three SCP parameters including snow cover days, snow start date, and snow end date, based on a seamless snow cover dataset from 2002 to 2019. The method’s effectiveness was evaluated by comparing it with the traditional method. The results indicate significant spatial heterogeneity in the dominant sub-period(s), closely associated with local temperature and elevation. The traditional method failed to accurately identify the main drivers of SCP changes, as evidenced by sub-region and elevation zone analyses showing adjusted coefficient of determination (R2) of < 0.5 in most cases. This inadequacy is attributed to its fixed and inappropriate scheme of substage division. In contrast, the new method, with its flexible scheme, achieved much higher adjusted R2 values (mostly > 0.5) and exhibited better performances in predicting SCP changes. Thanks to the new method, climatic causes of SCP changes were successfully identified in 12 hotspots (regions with significant SCP changes), all with adjusted R2 > 0.5. The climatic causes were found to vary significantly across different hotspot regions and SCP parameters. The proposed method holds significant potential to enhance the reliability of analyses concerning main climatic drivers of SCP changes in mountainous regions globally.

A Novel Strategy Coupling Optimised Sampling with Heterogeneous Ensemble Machine-Learning to Predict Landslide Susceptibility

The accuracy of data-driven landslide susceptibility prediction depends heavily on the quality of non-landslide samples and the selection of machine-learning algorithms. Current methods rely on artificial prior knowledge to obtain negative samples from landslide-free regions or outside the landslide buffer zones randomly and quickly but often ignore the reliability of non-landslide samples, which will pose a serious risk of including potential landslides and lead to erroneous outcomes in training data. Furthermore, diverse machine-learning models exhibit distinct classification capabilities, and applying a single model can readily result in over-fitting of the dataset and introduce potential uncertainties in predictions. To address these problems, taking Chenxi County, a hilly and mountainous area in southern China, as an example, this research proposes a strategy-coupling optimised sampling with heterogeneous ensemble machine learning to enhance the accuracy of landslide susceptibility prediction. Initially, 21 landslide impact factors were derived from five aspects: geology, hydrology, topography, meteorology, human activities, and geographical environment. Then, these factors were screened through a correlation analysis and collinearity diagnosis. Afterwards, an optimised sampling (OS) method was utilised to select negative samples by fusing the reliability of non-landslide samples and certainty factor values on the basis of the environmental similarity and statistical model. Subsequently, the adopted non-landslide samples and historical landslides were combined to create machine-learning datasets. Finally, baseline models (support vector machine, random forest, and back propagation neural network) and the stacking ensemble model were employed to predict susceptibility. The findings indicated that the OS method, considering the reliability of non-landslide samples, achieved higher-quality negative samples than currently widely used sampling methods. The stacking ensemble machine-learning model outperformed those three baseline models. Notably, the accuracy of the hybrid OS–Stacking model is most promising, up to 97.1%. The integrated strategy significantly improves the prediction of landslide susceptibility and makes it reliable and effective for assessing regional geohazard risk.

Estimating high-resolution snow depth over the North Hemisphere mountains utilizing active microwave backscatter and machine learning

While ground meteorological stations provide accurate snow depth data, their limited spatial coverage results in observational gaps. Satellites offer long-term, large-scale observations, addressing these gaps. Existing snow depth retrieval algorithms mainly use passive microwave remote sensing data with a 25 km resolution, insufficient for capturing snow depth variability in mountainous areas. This paper introduces active microwave backscatter data and machine learning techniques for high-resolution snow depth estimation. We conducted a preliminary exploration of the relationship between Sentinel-1 backscatter coefficient σ0 and snow depth. Due to factors such as vegetation coverage and underlying soil properties, the relationship between σ0 and snow depth is complex and nonlinear. Consequently, six machine learning models were trained to learn this relationship using σ0 and auxiliary data as input features, with in-situ snow depth serving as the target variable. After extensive validation, the Extreme Random Trees (ERT) model was selected for its high accuracy and stability. Using the ERT model, we generated 500 m-resolution snow depth data for northern hemisphere mountains, then analyzed temporal snow depth variations and altitudinal stratification.

Secondary contact zone and genetic introgression in closely related haplodiploid social spider mites.

How frequently hybridisation and gene flow occur in the contact zones of diverging taxa is important for understanding the speciation process. Stigmaeopsis sabelisi and Stigmaeopsis miscanthi high-aggression form (hereafter, S. miscanthi HG) are haplodiploid, social spider mites that infest the Chinese silver grass, Miscanthus sinensis. These two species are closely related and parapatrically distributed in Japan. In mountainous areas, S. sabelisi and S. miscanthi HG are often found in the highlands and lowlands, respectively, suggesting that they are in contact at intermediate altitudes. It is estimated that they diverged from their common ancestors distributed in subtropical regions (south of Japan) during the last glacial period, expanded their distribution into the Japanese Archipelago, and came to have such a parapatric distribution (secondary contact). As their reproductive isolation is strong but incomplete, hybridisation and genetic introgression are expected at their distributional boundaries. In this study, we investigated their spatial distribution patterns along the elevation on Mt. Amagi using male morphological differences, and investigated their hybridisation status using single-nucleotide polymorphisms by MIG-seq. We found their contact zone at altitudes of 150-430 m, suggesting that their contact zone is prevalent in the parapatric area, which is in line with a previous study. Interspecific mating was predicted based on the sex ratio in the contact zone. No obvious hybrids were found, but genetic introgression was detected although it was extremely low.

Operation, Expansion, and Improvement of the Snow Load Alert System “YukioroSignal”

The “YukioroSignal” system, which provides snow load alerts, was developed to inform decision-making regarding snow removal from house roofs. It was launched in Niigata Prefecture in 2018 and expanded to cover all special heavy snowfall areas in Japan, including the Hokkaido, Tohoku, and Hokuriku regions in 2024. The system uses the SNOWPACK model to estimate high-accuracy snow weight from real-time snow depth data published online at observation points. At locations where snow depth gauges are not installed, such as in mountainous areas, snow weight is estimated using inverse distance-weighted interpolation, but accuracy is reduced. To overcome this problem, this study attempted to integrate this information with the snow water equivalent distribution calculated using the simple-layer snow distribution model. To validate this improvement, manual observations of snow weight were performed at 98 sites and compared with simulation results. The accuracy of snow weight estimation at distances far away from snow depth stations was improved. The six-year operation of YukioroSignal showed the additional required information that is vulnerable to damage even with less snowfall, such as vacant houses, and caution of changes in hazard levels by an increase in snowburst in a short period.

The effect of climate change on the variation of low flow rates within mountainous basins: Case study of the Srou basin-Middle Atlas-Morocco (1976-2016)

Abstract The study of hydro-climatic variability over extended periods is crucial for comprehending the effects of climate variability on water resources and the incidence of droughts. In Morocco, drought has historically afflicted the plains, eastern, and southern regions of the country. However, the impact of drought in mountainous areas has often been overlooked. Climate change has exacerbated this situation, as evidenced by significant water deficits experienced by Morocco in 1982 and 2007. These events underscore the vulnerability of regions to drought, especially during periods of low-flow. This study focuses on examining rainfall variability within the Srou River basin, and its impact on fluctuations in low-flow rates in the context of a changing climate. The analysis utilizes the Cumulative Sum method to assess these dynamics. Besides, rainfall data and monthly low-flow rates were analysed and assessed. The obtained results reveal a significant variability in rainfall across the watershed, with a general trend of decline punctuated by occasional brief wet periods. These drought episodes have led to diminished surface and groundwater resources within the area. This research sheds light on the pressing need to address hydro-climatic challenges and underscores the urgency of adaptation measures to safeguard water security in Morocco’s diverse geographical contexts.

Regeneration of the Austrian forests and browsing impact – Insights from the latest National Forest Inventory

Abstract The initial development stage of forests forms the basis for the future tree generation. Various driving and interacting factors determine the regeneration process at small and large scale. Based on the latest Austrian National Forest Inventory 2016–2021 the regeneration assessments and estimation methods were collated. Analyses were conducted to estimate the forest area regarding regeneration necessity and occurrence, the causes of regeneration deficits, the origin from natural and artificial regeneration, tree species occurrence as well as browsing impact. The results show a sea level gradient of increasing shares of necessary but missing regeneration, ranging between 15% at the lowest and 60% at the highest altitudes. Accordingly, high shares can be found in protective forest without yield (50%) and in the growth regions of the mountainous areas of the Alps (up to 40%). Due to the multitude of driving and interacting factors, the assessed causes of regeneration deficits show divers patterns. Light as a limiting factor appears to be more relevant at low altitudes, and competition through ground vegetation is highest at low and high altitudes. The influence of the humus layer on the seed bed was assessed to increase with altitude, as well as the impact of forest pasture, while game browsing as cause of regeneration deficits show no clear tendency over the analysed strata. About 91% of the necessary and occurring regeneration originates from natural regeneration and the remaining from artificial or a mixture of both regeneration types. The number of tree species in the ground vegetation layer decreases with altitude. Considering browsing impact the results show percentages of affected areas between 40% and 80%. The highest browsing impacts were found at intermediate altitudes between 600 and 1,500 m above sea level. From a large-scale perspective as provided by NFIs, the results show divers patterns of regeneration necessity and occurrence and impacts on the regeneration. Representative large-scale monitoring by NFIs provides the opportunity to identify and quantify the adverse impacts and forest strata that are affected by regeneration deficits, and therefore support the development of forest management strategies that aim at the establishment of the future tree generation under changing climate conditions.

Effects of plantations on rainfall redistribution in a rocky mountain area of North China

AbstractRainfall redistribution plays a crucial role in the water cycle. However, the main factors affecting the redistribution of rainfall remain uncertain. We chose three different plantations—cork oak (Quercus variabilis Bl.), oriental arborvitae (Platycladus orientalis L.) and black locust (Robinia pseudoacacia L.)—to investigate the role of plantations in rainfall redistribution and to determine the main factors influencing rainfall redistribution. The results indicated that cork oak exhibited the highest stemflow (0.34%) and the lowest canopy interception (12.58%), whereas black locust had the lowest stemflow (0.21%), and oriental arborvitae displayed the greatest canopy interception (32.8%). Under different density conditions for cork oaks, the stemflow was highest (0.39%) in low‐density forests with 750 trees ha−2 and lowest (0.34%) in medium‐density forests with 1100 trees ha−2. Meanwhile, the highest canopy interception (17.68%) was observed in high‐density forests (1300 trees ha−2), while the lowest interception rate (9.22%) was found in low‐density forests. The main factors affecting rainfall redistribution and their contribution rates were as follows: bark roughness index (35%), wind speed (18.6%), tree species (14.2%), diameter at breast height (11.2%), stand density (9.6%) and rainfall amount (5.4%). Our findings suggested that structural characteristics of trees are the primary factors affecting rainfall redistribution. Planting cork oak in the rocky mountain regions of North China is recommended because of its substantial stemflow production, particularly under low‐density growth conditions. Therefore, this study has significant guiding implications for the selection of afforestation tree species in similar rocky mountain areas globally.

Vegetation and environmental changes on the Northeast China Plain during warm periods since MIS 3

Understanding past terrestrial ecosystem responses to climate changes is vital for future predictions, but research in densely populated plains is limited due to insufficient materials. This study focuses on vegetation and environmental changes in the Northeast China Plain since Marine Isotopic Stage (MIS) 3 using pollen analysis from core WCZK03. The findings reveal significant shifts in vegetation that correspond to climatic events. During MIS 3 (52–29 cal ka BP), the region was predominantly a lake environment with vegetation transitioning from grassland dominated by Poaceae, Chenopodiaceae and Artemisia to conifer-broadleaf mixed forests as the climate ameliorated. The Last Glacial Maximum (29–17 cal ka BP) was characterized by loess deposits, followed by drought-tolerant grassland (17.0–11.3 cal ka BP) dominated by Artemisia and Chenopodiaceae in the plain. The onset of the Holocene witnessed the expansion of conifer and broad-leaved deciduous forests in hilly areas and the retreat of grassland in the plain. The sedimentary sequence shows transitions from fluvial-lacustrine deposits to loess-like and black soil deposits, showing significant environmental changes. This study suggests that changes in vegetation on the Northeast China Plain were closely related to regional climate patterns and were more responsive to climate changes than the surrounding mountainous areas.

Rupture behaviors of the southern Xianshuihe fault and seismicity around Mt. Gongga: Insights from the 2022 MW 6.6 Luding (China) earthquake sequence

The 2022 MW 6.6 Luding earthquake occurred on the Moxi segment of the Xianshuihe fault at the southeast margin of Tibetan Plateau, China. To assess the seismic potential of the Moxi segment, we examine the rupture process of the mainshock and aftershock sequence, along with historical seismicity. Our preferred slip model inverted from teleseismic body waves and regional GNSS static displacements shows a dominant southeastward rupture consisting of two distinct, prominent slip patches along strike extending by ∼15 km, with a peak slip of ∼2.8 m, approximately balancing the slip deficit since the last major earthquake in 1786. The northern section of the Moxi segment experienced minor coseismic slip, which, together with the significant slip deficits and positive Coulomb failure stress change induced by the 2022 mainshock indicates a high seismic potential. Several aftershock clusters are distributed along or near the Moxi segment, with strike-slip focal mechanisms around the downdip edge of the coseismic slip area at ∼8‐12 km. At the eastern flank of Mt. Gongga, another cluster of normal faulting aftershocks is located at shallower depths of ∼3‐7 km, with high seismicity rate over ∼9 months including two other M5 sequences in January and February 2023. Similar intense shallow normal faulting activity had occurred after the impoundment of the nearby Dagangshan reservoir in 2015. We speculate that some NW-SE trending normal faults were initially developed by the gravitational collapse of Mt. Gongga underneath the eastern flank, further weakened by fluid flow, as supported by the existence of hot springs and water impoundment, and reactivated by the tensional stress change induced by the 2022 mainshock. These results have important implications for assessing the seismic hazard in and around the Moxi segment, and the potential interplay between strike-slip fault and nearby mountain areas.

Re-geomorphic Mapping of Unroofed Cave of Crystal Mountain Area, Bahariya – Farafra Depressions, Western Desert of Egypt

Re-geomorphic Mapping of Unroofed Cave of Crystal Mountain Area, Bahariya – Farafra Depressions, Western Desert of Egypt

Relationships between pollen assemblages and modern vegetation of the southern Gaoligong Mountains region, southwest China

Comprehensive understanding of the modern pollen–vegetation relationship is crucial for utilizing fossil pollen to reconstruct palaeovegetation, especially in mountainous areas of southwestern China. In this study, we present forty-five modern surface samples collected from five different vegetation communities of the southern Gaoligong Mountains region, and the vegetation composition was investigated at each sample site. The modern pollen assemblages were analyzed through multivariate analysis to evaluate the relationships between vegetation types and pollen assemblages, and to investigate the representation of major pollen types in different vegetation types. The results indicate that the surface pollen assemblages of different vegetation types reliably represent the modern vegetation. The median R-values of dominant pollen taxa in the Gaoligong Mountains region can be ordered as follows: Pinus > Alnus > Poaceae > evergreen Quercus. The over-representation of Pinus pollen, attributed to its high productivity and strong dispersal ability, makes it the dominant component of exotic pollen. Conversely, the under-representation of evergreen Quercus suggests that the presence of its pollen within a sample might indicate the growth of the local parent plant in the study area. The human activities in the region have a recognizable effect on the surface pollen spectra and also exert a significant negative impact on pollen diversity. Our study supplements the modern pollen database of the mountain regions in southwestern China, and provides representative modern pollen analogues to interpret fossil pollen records from southwestern China and similar regions.

Influence of soil organic carbon fractions on the soil priming effect under different vegetation restoration modes

AbstractThe soil priming effect is a key mechanism influencing carbon (C) cycling processes between the forest soil organic carbon (SOC) pool and the atmosphere. Different vegetation restoration modes have different SOC pool compositions, and it is not clear whether such differences affect the soil priming effect. Therefore, we selected soil from six typical vegetation restoration modes Platycladus orientalis (PO), Pinus sylvestris (PS), Quercus acutissima (QA), shrub (SH) and wasteland (WL) in the soil and rocky mountainous areas of northern China and measured the soil properties, microbial communities, microbial necromass carbon (MNC), SOC fractions and the soil priming effect. The SOC content decreased in the order of PO (21.33 g kg−1), PS (22.00 g kg−1), QA (13.67 g kg−1), SH (13.33 g kg−1) and WL (10.33 g kg−1), and the trends of the mineral‐associated organic carbon (MAOC) and fungal necromass carbon (FNC) content were the same as those of the SOC content. The soil priming effect was greater in both forests and shrublands than in wastelands, with the greatest effect occurring in PO forests, where the soil priming effect reached 159.91 mg CO2‐C kg−1 soil after 30 days of incubation, which was 1.4 times greater than that in WL. The soil priming effect was mainly determined by the difference in MAOC content. In addition, the soil C/N ratio and bacterial community diversity also indirectly affected the soil priming effect by influencing the soil MNC and SOC fractions. Overall, afforestation increased the SOC content, fungal necromass contribution and mineral conservation, increasing the soil priming effect. This theoretical foundation supports the enhancement of SOC sequestration capacity by implementing various modes of vegetation restoration in the future.

Analysis of resting status reveals distinct elevational variation in metabolisms of lizards.

Animals spend a considerable proportion of their life span at rest. However, resting status has often been overlooked when investigating how species respond to environmental conditions. This may induce a large bias in understanding the local adaptation of species across environmental gradients and their vulnerability to potential environmental change. Here, we conducted an empirical study on montane agamid lizards, combined with mechanistic modeling, to compare elevational variations in body temperature and metabolisms (cumulative digestion and maintenance cost) between resting and active status. Our study on three populations of an agamid lizard along an elevational gradient revealed a trend of decreasing body temperature toward higher elevations, the main contributor of which was resting status of the lizards. Using population-specific reaction norms, we predicted greater elevational variation in hourly and cumulative digestion for resting lizards than for active lizards. Climate-change impacts, estimated as the change in cumulative digestion, also show greater elevational variation when resting status is factored into the analysis. Further, our global analysis of 98 agamid species revealed that in about half of their combined distributional range, the contribution of resting status in determining the elevational variation in cumulative digestion and maintenance cost of lizards was greater than the contribution made by a lizard's active status. Our study highlights the importance of considering resting status when investigating how species respond to environmental conditions, especially for those distributed over tropical and subtropical mountain areas.

Future Change in the Contribution of Riming and Depositional Growth to the Surface Solid Precipitation in Hokkaido, Japan

Abstract This study examined future changes in the microphysical properties of surface solid precipitation over Hokkaido, Japan. A process-tracking model that predicts the mass of the hydrometeors generated by each cloud microphysical process was implemented in a meteorological model. This implementation aimed to analyze the mass fraction of hydrometeors resulting from depositional growth and the riming process to the total mass of surface solid precipitation. Results from pseudo–global warming experiments suggest two potential future changes in the characteristics of surface solid precipitation over Hokkaido. First, the rimed particles are expected to increase and be dominant over the west and northwest coast of Hokkaido, where heavy snowfall occurs primarily due to the lake effect. Second, the mass fraction from depositional growth under relatively higher temperatures is expected to increase. This increase is anticipated to be dominant over the eastern part and mountainous area of Hokkaido. Additionally, the fraction of liquid precipitation to total precipitation is expected to increase in the future. These results suggest that the microphysical properties of solid precipitation in Hokkaido are expected to be similar to those observed in the current climate over Hokuriku, the central part of Japan even in warmer climate conditions. Significance Statement This study examines potential future changes in the growth processes contributing to surface precipitation particles in Hokkaido, Japan. The surface solid precipitation particles in the western and eastern regions of Hokkaido are mainly generated through depositional growth that occurs within the temperature ranges −36° to −20°C and −20° to −10°C, respectively. A future shift is anticipated, with riming becoming the primary process. This shift suggests that snowfall particles will be heavier than those in the current climate, which would increase the snow-removal workload. The change in precipitation characteristics could influence adaptation and mitigation strategies for climate change in cold regions.

Hydrology and trophic flexibility structure alpine stream food webs in the Teton Range, Wyoming, USA

AbstractUnderstanding biotic interactions and how they vary across habitats is important for assessing the vulnerability of communities to climate change. Receding glaciers in high mountain areas can lead to the hydrologic homogenization of streams and reduce habitat heterogeneity, which are predicted to drive declines in regional diversity and imperil endemic species. However, little is known about food web structure in alpine stream habitats, particularly among streams fed by different hydrologic sources (e.g., glaciers or snowfields). We used gut content and stable isotope analyses to characterize food web structure of alpine macroinvertebrate communities in streams fed by glaciers, subterranean ice, and seasonal snowpack in the Teton Range, Wyoming, USA. Specifically, we sought to (1) assess community resource use among streams fed by different hydrologic sources, (2) explore how variability in resource use relates to feeding strategies, and (3) identify which environmental variables influenced resource use within communities. Average taxa diet differed among all hydrologic sources, and food webs in subterranean ice‐fed streams were largely supported by the gold alga Hydrurus. This finding bolsters a hypothesis that streams fed by subterranean ice may provide key habitat for cold‐water species under climate change by maintaining a longer growing season for this high‐quality food resource. While a range of environmental variables associated with hydrologic source (e.g., stream temperature) were related to diet composition, hydrologic source categories explained the most variation in diet composition models. Less variable diets within versus among streams suggest high trophic flexibility, which was further supported by high levels of omnivory. This inherent trophic flexibility may bolster alpine stream communities against future changes in resource availability as the mountain cryosphere fades. Ultimately, our results expand understanding of the habitat requirements for imperiled alpine taxa while empowering predictions of their vulnerability under climate change.

Morphostructural evidence of crustal-scale, active along-strike segmentation of the Umbria-Marche Apennines, Italy

This paper discusses the response of topography and river networks to non-uniform lithology and tectonic forcing in the Umbria-Marche sector of the Apennines fold and thrust belt. We ruled out the role of variable erosion of rock types and interpret channel steepness data in terms of rock uplift, discovering a southward increase in the total amount of uplift. Such a trend appears as the large-scale response to uneven vertical motions of different sectors of the mountain ridge and foothills. The general coincidence between sector boundaries and transversal, NE-SW striking faults mapped by seismic interpretation in the outer zone of the fold and thrust belt, suggests that such faults extend to the SW, beneath the allochthonous thrust sheets of the mountainous area. Therefore, it may be inferred that such transversal faults represent long-lived, deeply rooted basement structures compartmentalizing both the axial and the outer zones of the fold and thrust belt. We suggest that differential uplift was essentially controlled by variable amounts of basement thrust displacement characterizing the compartmentalized belt. This interpretation deviates from a more conventional view that uplift of the central Apennines, particularly prominent in the south, is dynamically supported. Our results, besides shedding new light into the active tectonic behavior of a large portion of the Italian peninsula, also provide general insights into the surface response to the differential behavior of crustal blocks produced by along-strike segmentation of active mountain belts.

Distributional response of the rare and critically endangered Ilex nanchuanensis to climate change in East Asia

Abstract Global climate change, dominated by climate warming, is seriously affecting the balance of global ecosystems, but the risk of species extinction is particularly high in low-altitude mountain areas. To clarify the response of the endemic and critically endangered species Ilex nanchuanensis to climate change, this study used the MaxEnt model to simulate and predict the potential habitat of I. nanchuanensis during the Last Interglacial, Last Glacial Maximum, the current period, and two future periods (the 2050 s and 2070 s). The results showed that the hottest monthly minimum temperature is the most important climatic factor affecting the geographical distribution of I. nanchuanensis. Furthermore, I. nanchuanensis will be at risk of population shrinkage and extinction in the future, with the center of mass moving further northwest as concentrations of greenhouse gases increase, especially in the 2070 s, when its geographical distribution shrinks the most under the RCP6 scenario. Therefore, to actively respond to the impacts of climate change, protected areas should be established around the geographical distribution centers of species, and core, buffer, and experimental areas should be scientifically and rationally delineated for the conservation and cultivation of germplasm resources.

Spatiotemporal evolution and influencing factors of flood resilience in Beibu Gulf Urban Agglomeration

In the context of global climate change and rapid urbanization, enhancing flood resilience is essential for mitigating urban flood risk. However, few studies have conducted long-term, cross-scale dynamic evaluations of flood resilience and analyzed its influencing factors and mechanisms. Taking the Beibu Gulf urban agglomeration as the study area, a long-term, cross-scale dynamic evaluation index system based on the “Robustness-Resistance-Recovery” (3Rs) framework were adopted to assess the spatiotemporal evolution of flood resilience from 2000 to 2020. Furthermore, the optimal parameters-based geographical detector model is employed to identify the key influencing factors and mechanisms. The results reveal that pre-flood robustness is lower in coastal areas and higher in inland regions. In the during-flood stage, cities with greater comprehensive power exhibit stronger resistance. Post-flood recovery is higher in city centers and marginal mountainous areas, while coastal and inland low-lying regions show lower recovery. The flood resilience of urban agglomerations has improved in recent years, largely due to the enhancement of urban flood control infrastructure and healthcare capacity. However, disparities between cities persist. From 2000 to 2020, economic factors have been the primary drivers of improved flood resilience, while ecological factors have gained increasing importance over the past decade. These findings provide valuable insights for flood prevention, mitigation, and resilience management in urban agglomerations. The proposed dynamic evaluation index system offers a reference framework for evaluating flood resilience in other regions.

An enhanced neighborhood differential method for potential landslide identification from stacking-InSAR results

Stacking-InSAR is widely used for identifying potential landslide. However, in Stacking-InSAR results, there are often challenges to clearly identify the potential landslide due to wide-scale system errors, especially the elevation-related atmospheric disturbances, leading to low precision of potential landslide identification. This paper introduces an enhanced neighborhood differential method that employs a concept of step size to identify potential landslide from Stacking-InSAR. Applied to the mountainous regions in the northwestern part of Sichuan Province, China, this method successfully identified 117 potential landslides, verified through field investigations. Compared to traditional Stacking-InSAR, this method additionally identified 34 potential landslides. Further analysis indicates that the optimal step size in the neighborhood differential method should ensure that one differential pixel is located inside the potential landslide area while the other is outside, thereby making the step size closely related to the spatial extent of the potential landslide. The results indicate that this method could effectively reduce wide-scale system error in Stacking-InSAR results, providing an important technical reference for the rapid and efficient identification of potential landslide over a wide mountainous area in the future.