The Gravity Recovery and Climate Experiment (GRACE) dataset has emerged as a pivotal tool for quantifying terrestrial water storage (TWS) anomalies at regional scales. However, its coarse spatial resolution (similar to 3 degrees) introduces substantial uncertainties in localized hydrological analyses. To overcome this limitation, we developed a spatiotemporal deep learning framework that synergistically integrates Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory networks (BiLSTM), enhanced by a time-space attention mechanism. Applied to Yunnan Province, China, this framework achieved a tenfold resolution enhancement (1 degrees-0.1 degrees), preserving high consistency with raw GRACE data (cc = 0.94). Validation against independent datasets demonstrated a 6-15 % improvement in Coefficient of Determination (R-2) over conventional downscaling methods, while maintaining moderate to strong correlations (r = 0.53-0.74) with WGHM products and river-lake water level data. Multivariate analysis revealed statistically significant couplings between downscaled TWS variations and key environmental drivers, including soil moisture (SoilMoi), land surface temperature (LST), evapotranspiration (E), the Normalized Difference Vegetation Index (NDVI), and precipitation (TP). The refined GRACE Drought Severity Index (GRACE-DSI) exhibited enhanced synchronization with the Standardized Precipitation Evapotranspiration Index (SPEI), showing a >10 % increase in correlation coefficients compared to predownscaling values. This methodological advancement enabled precise spatiotemporal characterization of drought dynamics during the 2002-2023 period, particularly capturing the 2009-2012 extreme drought and 2019-2021 pluvial anomalies with sub-basin spatial fidelity. Our framework provides an operational solution for high-resolution hydrological monitoring, offering critical insights for adaptive water resource management in topographically complex regions.

Measuring the rural development level of counties is helpful for identifying regional differences and proposing targeted strategies. In this study, 44,743-point data were selected from 17 types of rural lists in China, and a four-dimensional indicator system was constructed following the “point-to-area” principle, to analyse the spatial differentiation and factors influencing rural development levels. The results reveal that the characteristic points of rural development present a “polarized” pattern along the Hu Huanyong Line, with four agglomeration cores in Southeast China. The third quadrant, delineated by the Hu Huanyong Line and the Botai Line, has the lowest rural development level. Meanwhile, the eastern coastal area and the northern coastal area lead in average rural development level among eight socioeconomic zones. Beijing, Tianjin, and Shanghai have either the highest or second highest levels in overall and dimensional development, whereas Tibet has the lowest level. Moreover, the cultural level in most counties remains at a relatively low level or below. The key factors influencing this pattern include the natural environment and economic production, especially river density, cultural resources, and general public budget expenditure per capita. This study deepens the understanding of rural development, aiming to inform global sustainable rural development assessment.

The Longtantian coal mine is a nationally significant large-scale coal mine in China and is listed as one of the Yunnan Province's coal mines for supply assurance and capacity increase, but extensive mining has caused significant surface deformation, posing safety hazards. High-precision monitoring and prediction of surface deformation are essential. This study used the small baseline subset interferometric synthetic aperture radar technique to extract surface deformation time-series data from Sentinel-1A images between 4 January 2020 and 19 January 2024. Considering the spatiotemporal heterogeneity, dynamic time warping based K-means clustering analysis was applied to divide the area into subspaces with similar deformation trends. A convolutional neural network optimized bidirectional long short-term memory (CNN-BiLSTM) prediction model was constructed using lithological properties (described by internal friction angle and cohesion), fault distance, rainfall, and deformation data. Results showed an annual deformation rate of -86.96-50.42 mm/year. The F2, F3, and F4 faults, the third section of the Longtan Formation (P(2)l(3)) and the Quaternary (Q) lithological units, rainfall and historical deformation have all had an impact on the surface deformation of the mining area. The experimental results show that the CNN-BiLSTM architecture has significantly improved prediction accuracy compared to the basic LSTM model. The prediction error distribution of the original LSTM model is in the range of [-196.55, 106.91] mm, while the improved CNN-BiLSTM model reduces the error range to the range of [-58.22, 74.33] mm, with extreme error values reduced by 70.40% and 30.47%, respectively. Therefore, the research results can provide scientific data support for disaster prevention and reduction of Longtantian Coal mine, and provide reference for monitoring and forecasting of other mining areas.

Hydrothermal minerals intimately associated with mineralization processes are pivotal in deciphering the genesis of porphyry deposits. A meticulous examination of these hydrothermal minerals serves to quantitatively delineate the progression of ore-forming hydrothermal fluids and to scrutinize the myriad factors influencing porphyry Cu-Mo mineralization. In this study, we employed a multifaceted approach encompassing mineral geochemistry, geochronology, and diffusion chronology to probe into the time scales of mineralization, as well as to characterize and trace the evolution of ore-forming fluids within the Yulong porphyry Cu-Mo deposits located in eastern Tibet.Quartz extracted from hydrothermal veins displays cathodoluminescence (CL) images characterized by diverse brightness and textures. Trace element analyses reveal a robust correlation between the intensity of CL and the titanium (Ti) content within the quartz. This study takes advantage of diffusion chronology to determine time scales of multistage magma-related hydrothermal events. The pronounced Ti concentration gradients observed in CL images, in conjunction with Ti diffusion modeling for distinct quartz generations, suggest that the majority of mineralization at the Yulong deposit occurred within a relatively brief interval ranging from 880,000 to 16,000 years. The complex, multi-stage hydrothermal stockwork veins and the relatively short time scales indicate that fluid pulses at Yulong developed rapidly, within spans of tens of thousands of years. These research outcomes underscore that delineating the time scale of a singular mineralization pulse is instrumental in constructing a more precise chronological framework for porphyry deposits, thus facilitating the quantification of extensive metal enrichment processes. In comparison with other globally recognized giant porphyry deposits, this study identifies the mineralization rate, magma injection rate, and fluid flux as critical determinants influencing the magnitude of porphyry mineralization.Hydrothermal rutile (TiO2), a common accessory mineral found in hydrothermal veins and alteration assemblages of porphyry deposits, offers significant insights into the characteristics of hydrothermal fluids. In the Yulong deposit, TiO2 polymorphs have been identified through Raman spectroscopy, textural analysis, and chemical characterization. Brookite and anatase pseudomorphs are indicative of low-temperature hydrothermal fluids that destabilize primary Ti-bearing minerals during argillic alteration processes. Rutile intergrown with sulfides in veins exhibits well-defined patchy and sector zoning, with notable tungsten enrichment in the backscattered bright patches and sector zones. The enrichment of tungsten is effectively facilitated by halogen-rich (F, Cl) aqueous fluids during the hydrothermal mineralization. Consequently, the chemical and isotopic compositions preserved in rutile provide comprehensive information that enhances our understanding of the hydrothermal fluids active during the formation of porphyry deposits. This enhanced understanding may potentially aid in delineating vectors that lead to the localization of porphyry deposits. Moreover, precise identification of TiO2 polymorphs is crucial for a deeper comprehension of hydrothermal processes, especially when employing rutile geochemistry as an indicator for mineralization.

The identification of hydrothermal alteration minerals using remote sensing technology is an important hot spot in mineral exploration. However, the influence of spatial resolution and spectral resolution on the identification of hydrothermal alteration minerals is an important scientific issue worth studying. Therefore, WorldView-3 data with high spatial resolution and GF-5 data with high spectral resolution were selected to carry out a hydrothermal alteration minerals identification comparative study in the Duobuza porphyry copper mining area in Tibet. Principal component analysis (PCA), spectral angle mapper (SAM) and mixed tuned matched filter (MTMF) methods were employed for WorldView-3 data, while SAM and spectral information divergence (SID) methods were used for GF-5 data. The results of comparative analysis based on field verification data show that: (1) For WorldView-3 data, the accuracy of hydrothermal alteration mineral identification using SAM, PCA, and MTMF methods were 65.18%, 87.38%, and 78.63%, respectively, with the PCA method achieving the highest accuracy; (2) For GF-5 data, the accuracy of hydrothermal alteration mineral identification using SAM and SID methods were 91.38% and 65.18%, respectively, with the SAM method achieving the highest accuracy; (3) The optimal recognition accuracy of hydrothermal alteration minerals in GF-5 data is superior to that in WorldView-3 data, indicating that the high spectral resolution offers greater advantages than the high spatial resolution; (4) Both GF-5 data and WorldView-3 data have advantages in the identification of hydrothermal alteration minerals, GF-5 data can provide richer spectral information to help distinguish minerals with similar spectral characteristics while the higher spatial resolution of WorldView-3 data is helpful to distinguish boundaries. These results demonstrate that GF-5 excels in mineral discrimination, while WorldView-3 is advantageous for spatial mapping. Integrating these datasets in future studies could provide optimal hydrothermal alteration mapping results.

Gold is a vital strategic resource for many countries. The Laozhaiwan area is an important gold resource base in Yunnan Province and even nationwide. Conducting mineral resource exploration in this region to increase gold reserves is of great significance. The application of remote sensing technology in mineral resource exploration is a green and efficient technical approach, which has been widely utilized in the field of mineral resource prospecting. This study selects the Laozhaiwan area in the southeastern part of Yunnan Province as the research region. Linear and ring structures were extracted using the remote sensing visual interpretation method based on Sentinel-2A multispectral data. Additionally, Sentinel-2A, ASTER, and ZY1-02D data were used to extract iron-stained, hydroxyl, silicification, and limonite alteration information through Principal Component Analysis (PCA) and Spectral Angle Mapper (SAM) methods. Additionally, 50 linear structures and 12 ring structures were extracted. A comprehensive analysis of geological data reveals that alteration minerals and linear-ring structures are closely related to mineralization, providing valuable indicators for mineral resource exploration. By comprehensively analyzing the alteration information and remote sensing interpretation results of the linear-ring structures, two prospective areas for mineral exploration were delineated. Field investigations and petrographic studies confirmed the reliability of remote sensing technology in mineral exploration. The mineral exploration method based on multi-source remote sensing technology can clearly reflect various alteration information and linear-ring structural data. It provides remote sensing geological insights for geological survey work and has great application potential in the field of mineral resource exploration.

The structural integrity of high-efficiency plate-fin heat exchangers (PFHE) in fourth-generation nuclear reactors faces challenges from creep-fatigue interactions and multi-source uncertainties. This paper proposes an integrated framework for probabilistic creep-fatigue assessment that incorporates direct methods and artificial neural networks (ANN). The developed probabilistic Linear Matching Method (pLMM) framework is employed for the reliability assessment and reliability-based design optimization of PFHE, with three levels of creep-fatigue interactions considered. In detail, the deterministic creep-fatigue responses of the structure under cyclic thermal-mechanical loadings are obtained utilizing the extended Direct Steady Cyclic Analysis, which accounts for creep-fatigue interactions at both the mechanical behaviour and damage levels. Observations of the damage composition at the critical locations indicate that fatigue damage plays a dominant role in the creep-fatigue failure of PFHE, while a transition in the dominant mode occurs when parameter random fluctuations are introduced. For probabilistic analysis, a robust ANN-based surrogate model is established to relate input parameters to key responses, following the quantitative description of the individual impacts of multiple parameter uncertainties on lifespan. Combined with Monte Carlo simulation, the statistical distributions of key responses, including damage and lifespan, are obtained. In terms of investigating the dominant creep-fatigue modes under uncertainties, the probability of each mode is quantitatively displayed, and the creep-fatigue interaction mechanism at the statistical level is clarified. On the other hand, the lifespan evaluation curve based on multigrade reliability is created for engineering applications. Furthermore, the study of parameter distributions shows that a higher coefficient of variation significantly reduces the structural reliability of PFHE. Lastly, a double-loop procedure within the pLMM framework is proposed for the reliability-based structural optimization. The optimized structure exhibits satisfactory reliability performance while achieving lightweighting goals.

Gold mining plays a vital role in the economic development of many countries, and Myanmar is known for its rich mineral deposits. Preliminary exploration suggests that the northern region of Wa State in Myanmar is a prospective area for gold mineralization. However, detailed geological investigations are currently lacking, and the target range for mineral exploration remains unclear. Remote sensing technology can provide significant guidance for cross-border mineral exploration. This study developed a multi-source remote sensing methodology to delineate Carlin-type gold deposits in northern Wa State, Myanmar. Landsat 8 OLI and ASTER data were used to interpret fault information. ASTER data were employed to extract iron-stained and silicified alteration information using the iCrosta method. Based on the Google Earth Engine (GEE) platform, lithological classification information was obtained using the random forest method. By comprehensively analyzing the fault, alteration, and lithological information extracted from remote sensing data, two gold exploration target areas were delineated. Taking the No. 1 exploration target area as an example, the pyrite information was further extracted by China’s ZY1-02D hyperspectral data based on spectral angle method, the scope of exploration target area is further narrowed. Through field investigations and petrographic analysis conducted in the narrowed exploration target areas, gold mineralization was indeed identified, demonstrating the effectiveness of the mineral exploration methodology based on multi-source remote sensing data. This study can provide an important reference for the exploration of mineral resources in other similar geological settings worldwide.

With the rapid increase in global lithium demand, the exploration of newly discovered lithium in the bauxite of the Wenshan area in southeastern Yunnan has become increasingly important. However, the current research on clay-type lithium in the Wenshan area has primarily focused on local exploration, and large-scale predictive metallogenic studies remain limited. To address this, this study utilized multi-source remote sensing data from ZY1-02D and ASTER, combined with ALOS 12.5 m DEM and Sentinel-2 imagery, to carry out remote sensing mineral identification, structural interpretation, and prospectivity mapping for clay-type lithium in the Wenshan area. This study indicates that clay-type lithium in the Wenshan area is controlled by NW, EW, and NE linear structures and are mainly distributed in the region from north of the Wenshan–Malipo fault to south of the Guangnan–Funing fault. High-value areas of iron-rich silicates and iron–magnesium minerals revealed by ASTER data indicate lithium enrichment, while montmorillonite and cookeite identification by ZY1-02D have strong indicative significance for lithium. Field verification samples show the highest Li2O content reaching 11,150 μg/g, with six samples meeting the comprehensive utilization criteria for lithium in bauxite (Li2O ≥ 500 μg/g) and also showing an enrichment of rare earth elements (REEs) and gallium (Ga). By integrating stratigraphic, structural, mineral identification, geochemical characteristics, and field verification data, ten mineral exploration target areas were delineated. This study validates the effectiveness of remote sensing technology in the exploration of clay-type lithium and provides an applicable workflow for similar environments worldwide.

Study region: Jiangxi Province, China Study focus: Canopy interception is influenced by both structural and rainfall characteristics. yet, the relative contributions remain unclear due to their complex interactions. A process-based experimental study was conducted to quantify rainwater storage on canopies and assess how rainfall and crown structural characteristics influence key interception metrics: maximum and minimum canopy interception weight (Smax and Smin, g), interception per unit crown projected area (Cmax and Cmin, mm), and interception to gross precipitation ratio (Rmax and Rmin, %) for broadleaf and needleleaf species. New hydrological insights: Needleleaf species exhibit greater interception capacity than broadleaf species, due to morphological differences. Canopy interception is strongly influenced by both crown structure and rainfall parameters, with crown structure being critical for Cmax, Cmin, Smax, Smin, Rmax and Rmin, while rainfall characteristics primarily affect Rmax and Rmin. Three-dimensional canopy variables (leaf biomass density, leaf area density, leaf count density) have a stronger effect on Cmax and Cmin than two-dimensional variables (leaf area index, leaf biomass index, leaf count index). For Rmax, Rmin, Cmax and Cmin, leaf-related traits contributed more than branch-related traits. Within both leaf- and branch-related variables, their influence ranked as biomass, count, and area. Overall, Our findings indicate that tree characteristics exert a greater influence on rainfall interception than rainfall characteristics, whereas rainfall characteristics play a more role in shaping the interception ratio.

Altered precipitation regimes due to climate change influence plant–water interactions through shifts in soil moisture dynamics, highlighting the need for a mechanistic understanding of diverse water-use strategies and plant adaptations. In this study, we adopted an integrated approach combining measurements of stable hydrogen and oxygen isotopes in soil, groundwater, and xylem water, alongside sap flow and tree growth using dendrometers, to investigate the water-use strategies of Chinese fir (Cunninghamia lanceolata) under varying drought intensities. The experimental design included a control (C) and three precipitation reduction treatments (−30%, −50%, and −80%). This study analyzed data collected from both the wet and dry seasons of 2022, with precipitation exclusion devices installed and functioning since October 2021. The results indicated that during the wet season, Chinese fir primarily used shallow soil water (0–20 cm), with uptake proportions of 54.30%, 87.90%, 86.00%, and 63.70% under the C, −30%, −50%, and −80% treatments, respectively. In the dry season, as shallow soil water became increasingly scarce, water uptake gradually shifted toward deeper soil layers (40–60 cm), accounting for 49.30%, 79.10%, 68.50%, and 32.40%, respectively, and to groundwater sources, with 37.60%, 6.90%, 21.30%, and 61.50%, respectively. As expected, all precipitation reduction treatments reduced growth and water consumption (transpiration) compared with the C group. Notably, Chinese fir under the extreme drought treatment maintained adequate transpiration by relying heavily on groundwater throughout both seasons. This enabled increased growth during the wet season, though it also induced early growth cessation during the dry season. These findings suggest that Chinese fir exhibits substantial plasticity in its water acquisition strategies, allowing dynamic adjustment of water uptake between soil layers and groundwater sources depending on moisture availability. Our 1-year study demonstrates that Chinese fir can regulate water use and maintain radial growth under varying precipitation reduction treatments and seasonal conditions. Continuous long-term monitoring is essential to assess the sustained effects of drought on these ecohydrological processes.

Thermal infrared remote sensing (TIRS) technology is intriguing for geothermal anomaly detection due to its time efficiency and cost-effectiveness. Land surface temperature (LST) derived from TIRS indicates potential geothermal anomalies. However, LST on different natural features, exhibiting relatively cold/hot anomalies in daytime and nighttime, affected the identification of abnormal areas. The aim of this study was to highlight geothermal anomalous areas by integrating multi-view daytime and nighttime LST data. Specifically, the winter LST time series of drillings on 2013–2023 was processed based on Landsat-8 TIRS in diurnal scenarios. An information aggregation classification method about Dempster-Shafer evidence theory was presented to target recognition at nighttime by the ASTER LST product. This day-night information fusion analysis effectively highlighted potential areas with geothermal anomalies. The results demonstrated that the geothermal anomalies were mainly distributed along the Ruili-Longling Fault and Wanding Fault in a Northeast (NE) – Southwest (SW) direction, and the NE side was more significant than the SW side. These findings suggested that the east side was closer to the magma heat source, and the presumed magma heat source location aligned closely with the detected geothermal anomalies. Geological data were employed for geological interpretation of the LST anomaly area, eliminating non-geothermal influences. Along with drilling data verification, this method was successfully used to identify geothermal anomaly areas in Ruili City, Yunnan Province. Overall, this study provided valuable insights into the detection of geothermal anomalies through TIRS, contributing to the successful development of geothermal resources.

Summer low flows are critically important for community water supply and various aquatic functions in the interior of British Columbia (BC), Canada. There is a critical need to determine forest disturbance thresholds on summer low flows, particularly in the context of climate change and increasing forest disturbance. The forest disturbance threshold is defined as the disturbance level above which significant changes in flow variables are detected. In this study, we developed a seasonal hydrological response curve to determine forest disturbance thresholds for significant hydrological impacts on summer low flows in 20 forested watersheds in the BC interior. Based on the proposed method, the results suggest that forest disturbance thresholds varied from 8 to 52 % of CECA (cumulative equivalent clear-cut area) with an average of 18 %, which were not significantly different from thresholds on annual streamflow. Watersheds characterized by more distinct dry summers, lower annual energy availability, higher snow contributions, larger sizes, and greater topographic gradients (represented by greater slope, elevation difference, and downslope distance gradient, and lower water retention index) had lower disturbance thresholds on summer low flows. Both positive (increasing) and negative (decreasing) impacts of forest disturbance on summer low flows were detected. These results can greatly support practical watershed management for sustaining water supply in the local communities. Our methodology can potentially be applied in any individual watershed to assess forest disturbance thresholds on summer low flows where long-term data on forest disturbance, climate, and hydrology are available.

With the continuous development of thermal infrared remote sensing technology and the maturation of remote sensing inversion algorithms based on surface temperatures, identifying high-temperature anomalous areas by inverting surface temperatures has become an crucial approach to finding geothermal potential areas. The eastern region of Longyang in western Yunnan Province is renowned for geothermal resources, though the distribution area of geothermal potential remains unknown. Therefore, this study used Landsat-8 TIRS data and four surface temperature inversion algorithms, namely, mono-window algorithm, single-channel algorithm, Du split window algorithm (SWD), and Jiménez-Muñoz split window algorithm (SWJ), to explore the astern region of Longyang. The inversion results were compared with Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST) results for analysis and cross-validation to select the optimal algorithm. A multi-view remote sensing temperature anomaly information extraction method was adopted. Moreover, the overall threshold method, the fracture structure buffer method, and the joint analysis of diurnal temporal data were combined for the reduction of the thermal anomaly area as well as for comprehensively defining the geothermal prospective area in the study area. The results demonstrated that the mono-window algorithm had the highest accuracy with a Pearson coefficient of 0.77, which is more suitable for the surface temperature inversion in Longyang area. Furthermore, three geothermal anomalies (A, B, and C) were identified in the study area, with larger thermal anomaly in A and C, but a smaller one in B. All three areas had hot spring points verified, with A and C exhibiting more significant development potential. The research results provide a reliable methodological basis for the development of geothermal resources in the region.

The conversion of natural forests to planted forests has become a global trend, and the practice has wide-ranging effects on soil. This study aimed to explore the differences in soil water movement after the conversion of evergreen and deciduous broad-leaved mixed forests (natural forest, NF) to Chinese fir (Cunninghamia lanceolate (Lamb.) Hook.) plantations (CFP, 20–21 years old). Soil samples from five layers (0–5, 5–10, 10–20, 20–30, and 30–50 cm) were collected from NF and CFP before and after rainfall event in the Peng Chongjian watershed, Jiangxi Province. The physical properties of the soils, including the mean and coefficient of variation (CV) of soil moisture content and the soil particle composition, were determined in both forest types. The δD of soil water and the litter water-holding capacity were also measured. The results showed that the variation ranges of moisture content in each soil layer after the rainfall was 21.13%–49.40% in CFP and 21.33%–43.87% in NF. There were no significant differences in soil bulk density or porosity; the clay and silt contents were significantly increased in topsoil, while the sand was significantly decreased (P < 0.05). After the rainfall, soil water in CFP responded more promptly than NF. In the process of infiltration, the contribution of rainfall to soil moisture gradually decreased with increasing soil depth. Topsoil (0–5 cm) in NF responded promptly to rainfall, but the response showed a lag effect with the increase of soil depth. With the extension of infiltration time, the contribution of precipitation to deep soil gradually increased. The results showed that the soil did not degrade after the conversion of NF to CFP, a significant guiding result for plantation cultivation.

On 2 January 2022, an earthquake of Ms 5.5 occurred in Ninglang County, Lijiang City, the earthquake-prone area of northwestern Yunnan. Whether this earthquake caused significant deformation and thermal anomalies and whether there is a relationship between them needs further investigation. Currently, multi-source remote sensing technology has become a powerful tool for long-time-series monitoring of earthquakes and active ruptures which mainly focuses on single crustal deformation and thermal anomaly. This study aims to reveal the crustal deformation and thermal anomaly characteristics of the Ninglang earthquake by using both Interferometric Synthetic Aperture Radar (InSAR) and Robust Satellite Techniques (RST). First, Sentinel-1A satellite SAR data were selected to obtain the coseismic deformation field based on Differential InSAR (D-InSAR), and the Small Baseline Set InSAR (SBAS-InSAR) technique was exploited to invert the pre- and post-earthquake displacement sequences. Then, RST was used to extract the thermal anomalies before and after the earthquake by using Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST). The results indicate that the seismic crustal deformation is dominated by subsidence, with 23 thermal anomalies before and after the earthquake. It is speculated that the Yongning Fault in the deformation area is the main seismogenic fault of the Ninglang earthquake, which is dominated by positive fault dip-slip motion. Meanwhile, the seismic fault system composed of NE- and NW-oriented faults is an important factor in the formation of thermal anomalies, which are accompanied by changes in stress at different stages before and after the earthquake. Moreover, the crustal deformation and seismic thermal anomalies are correlated in time and space, and the active rupture activities in the region produce deformation accompanied by changes in thermal radiation. This study provides clues from remote sensing observations for analyzing the Ninglang earthquake and provides a reference for the joint application of InSAR and RST for earthquake monitoring.

Environmental variables are crucial factors affecting the development and distribution of landslides, and they also provide vitally important information for statistically-based landslide susceptibility mapping (SLSM). The acquisition and utilization of appropriate and the most influential environmental variables and their combinations are crucial for improving the quality of SLSM results. However, compared with the construction of SLSM models based on machine learning, the acquisition and utilization of high-quality environmental variables have received very little attention. In order to further clarify the research status of the application of environmental variables and possible development directions in future research, this study systematically analyzed the application of environmental variables in SLSM. To this end, a literature database was constructed by collecting 261 peer-reviewed articles (from 2002 to 2021) on SLSM from the Web of Science and CNKI platform (www.cnki.net) based on the keywords of “landslide susceptibility” and “environmental variable.” We found that existing methods for determining environmental variables do not consider the regional representativeness and geomorphological significance of the variables. We also found that at present, environmental variables are utilized generally without the realization and understanding of their spatial heterogeneity. Accordingly, this study raises two major scientific issues: 1) Effective identification of important environmental variables required in SLSM. 2) Effective representation of the spatial heterogeneity of environmental variables in SLSM modeling. From the perspective of the identification of dominant variables and their geospatial pattern of heterogeneity, targeted solutions for future research are also preliminarily discussed, including the method for identifying dominant variables from qualitative and quantitative perspectives and SLSM model construction considering the specific geospatial patterns. In addition, the applicability and limitation of the mentioned methods are discussed.

Moso bamboo (Phyllostachys edulis) expansion into adjacent forests has been reported to alter the local water cycle. Transpiration is the most important component of the water budget. However, the changes in transpiration within Japanese cedar forests following moso bamboo expansion, as well as the primary factors driving these changes, remain unclear. In this study, we conducted an investigation in a mixed plantation of Japanese cedar and moso bamboo, a pure Japanese cedar forest, and a Japanese cedar forest after the removal of moso bamboo, from July 2017 to June 2018. We measured leaf area index (LAI), root biomass (Root), and sap flow density (Js). The annual transpiration of Japanese cedar significantly decreased from 521.48 mm in the pure Japanese cedar forest to 293.34 mm in the mixed plantation of Japanese cedar and moso bamboo. Likewise, the leaf area index decreased from 5.26 to 3.96, and the root biomass decreased from 252.78 g/m2 to 160.24 g/m2. In Japanese cedar forest, the transpiration in summer (189.47 mm) is greater than the sum of transpiration in autumn and winter (143.36 mm). The partial least squares path modeling (PLS-PM) analysis demonstrated that the decrease in root biomass was the direct cause of the reduced transpiration, while the decrease in leaf area index primarily led to the decline in root biomass. The results imply that the expansion of moso bamboo changed the above-ground canopy structure of Japanese cedar through the competition of above-ground mechanical damage, which reduced the leaf area and photosynthetic capacity of Japanese cedar, and then affected the below-ground root biomass, finally leading to the reduction of transpiration and the variation of ecohydrological process.

Forest litter plays an important role in the hydrological process of an ecosystem, but the mechanism of inter-ception is not well understood and has not been fully evaluated. We conducted a process-based experiment under five simulated rainfall intensities. We directly quantified the interception of leaf litter and determined the effects of litter mass (MA), rainfall characteristics (RI) and leaf type (SP) on the interception of six common subtropical tree species, including two coniferous (Cunninghamia lanceolata (Lamb.) Hook. and Pinus massoniana Lamb.) and four broadleaf deciduous (Phoebe bournei (Hemsl.) Yang, Schima superba Gardn. et Champ., Liquidambar for-mosana Hance and Cinnamomum camphora (L.) Presl) species. The results demonstrated the following. (1) The interception process of litter can be divided into three phases: the wetting phase, the saturation phase, and the postrainfall drainage phase. The first phase of which arrived approximately 95% of the maximum interception storage (Cmax) and the duration was longer in broadleaf species than in coniferous species. This phase was significantly different among the species and rainfall characteristics, but it was not affected by litter mass. (2) Cmax and the minimum interception storage capacity (Cmin)of litter were not significantly different between the two forest types, but among the six species, especially between P.bournei and each of the two coniferous species, these values showed the greatest differences. (3) Cmax and Cmin increased significantly with litter mass and rain intensity. (4) Based on commonality analysis, the interaction between MA and RI largely outweighed the other variables in terms of their contribution to Cmax; however, MA surpassed all the other variables in its contribution to Cmin. We also found that the effects of the individual variables on Cmax and Cmin as well as their interactions, were significantly different; the three most influential variables for Cmax ranked in the descending order of their contributions were the interaction between MA and RI (41.07%), RI alone (23.69%), and MA (20.81%), and those for Cmin were MA alone (52.59%), the interaction between MA and RI (25.81%) and leaf types (14.58%). The quantitative and mechanistic explanations regarding the effects of leaf types, rainfall characteristics, litter mass and their interactions on litter Cmax and Cmin presented in this study are expected to increase our under-standing of the mechanism underlying litter interception.