Search Results (51)

In cold-region rock engineering, freeze–thaw cycle-induced crack propagation in fractured rock masses serves as a major cause of disasters such as slope instability. Existing studies primarily focus on the influence of individual fissure parameters, yet lack a systematic analysis of the crack propagation mechanisms under the coupled action of multiple parameters. To address this, we establish three groups of slope models with different rock bridge distances (d), rock bridge angles (α), and fissure angles (β) based on the PFC2D discrete element method. Frost heave loads are simulated by incorporating the volumetric expansion during water–ice phase change. The Parallel Bond Model (PBM) is used to capture the mechanical behavior between particles and the bond fracture process. This reveals the crack evolution laws under freeze–thaw cycles. The results show that, at a short rock bridge distance of d = 60 m, stress concentrates in the fracture zone. This easily leads to the rapid penetration of main cracks and triggers sudden instability. At a long rock bridge distance where d ≥ 100 m, the degree of stress concentration decreases. Meanwhile, the stress distribution range expands, promoting multiple crack initiation points and the development of branch cracks. The number of cracks increases as the rock bridge distance grows. In cases where the rock bridge angle is α ≤ 60°, stress is more likely to concentrate in the fracture zone. The crack propagation exhibits strong synergy, easily forming a penetration surface. When α = 75°, the stress concentration areas become dispersed and their distribution range expands. Cracks initiate earliest at this angle, with the largest number of cracks forming. Cumulative damage is significant under this condition. When the fissure angle is β = 60°, stress concentration areas gather around the fissures. Their distribution range expands, making cracks easier to propagate. Crack propagation becomes more dispersed in this case. When β = 30°, the main crack rapidly penetrates due to stress concentration, inhibiting the development of branch cracks, and the number of cracks is the smallest after freeze–thaw cycles. When β = 75°, the freeze–thaw stress dispersion leads to insufficient driving force, and the number of cracks is 623. The research findings provide a theoretical foundation for assessing freeze–thaw damage in fractured rock masses of cold regions and for guiding engineering stability control from a multi-parameter perspective. Full article

The Southwest Region (SWR) is one of Nigeria’s six geo-political zones and comprises six distinct states. It holds considerable significance due to its unique geographical features, economic vibrancy, pastoral heritage, and fragile natural ecosystems. These ecosystems are becoming increasingly susceptible to human activities and the adverse impacts of climate change. This study analyzed the temporal and spatial variations of the Normalized Difference Vegetation Index (NDVI) in relation to key influencing factors in the SWR from 2001 to 2020. The analytical methods included Sen’s slope estimator, the Mann–Kendall trend test, and the Geographical Detector Model (GDM). The analysis revealed significant spatial variability in vegetation cover, with dense vegetation concentrated in the eastern part of the region and low vegetation coverage overall, reflected by an average NDVI value of 0.45, indicating persistent vegetation stress. Human activities, particularly land use and land cover (LULC) changes, were identified as major drivers of vegetation loss in some states such as Ekiti, Lagos, Ogun, and Ondo. Conversely, Osun and Oyo exhibited signs of vegetation recovery, suggesting the potential for restoration. The study found that topographic factors, including slope and elevation, as well as climatic variables like precipitation, influenced vegetation patterns. However, the impact of these factors was secondary to LULC dynamics. The interaction detection analysis further highlighted the cumulative effect of combined anthropogenic and environmental factors on vegetation distribution, with the interaction between LULC and topography being particularly significant. These findings provide essential insights into the biological condition of the SWR and contribute to advancing the understanding of vegetation patterns with critical implications for the sustainable management and conservation of tropical forest ecosystems. Full article

Water–sediment evolution and attribution analysis in watersheds is one of the research focuses of hydrogeology. An in-depth investigation into the spatiotemporal variation of water and sediment at multiple spatial scales within the basin, along with a systematic assessment of the respective impacts of climate change and human activities, provides a scientific foundation for formulating effective soil and water conservation practices and integrated water resource management strategies. This research holds significant implications for the sustainable development and ecological management of the basin. In this study, the Mann–Kendall nonparametric test method, double cumulative curve method, cumulative anomaly method, and cumulative slope change rate analysis method were used to quantitatively study the effects of climate change and human activities on runoff and sediment load changes at different spatial scales in the Huangfuchuan River basin. The results show that (1) from 1966 to 2020, the annual runoff and annual sediment load discharge in the Huangfuchuan River basin showed a significant decreasing trend. Among them, the reduction in runoff and sediment in the control sub-basin of Shagedu Station in the upper reaches was more obvious than that in the whole basin. The mutation points of runoff and sediment load in the two basins were 1979 and 1998. The water–sediment relationship exhibits a power function pattern. (2) After the abrupt change, in the change period B (1980–1997), the contribution rates of climate change and human activities to runoff and sediment load reduction in the Huangfuchuan River basin were 24.12%, 75.88% and 20.05%, 79.95%, respectively. In the change period C (1998–2020), the contribution rates of the two factors to the runoff and sediment load reduction in the Huangfuchuan River basin were 18.91%, 81.09% and 15.61%, 84.39%, respectively. Among them, the influence of precipitation in the upper reaches of the Huangfuchuan River basin on the change in runoff and sediment load is higher than that of the whole basin, and the influence on the decrease of sediment load discharge is more significant before 1998. There are certain stage differences and spatial scale effects. (3) Human activities such as large-scale vegetation restoration and construction of silt dam engineering measures are the main reasons for the reduction in runoff and sediment load in the Huangfuchuan River basin and have played a greater role after 1998. Full article

Global assessments of landslide impact on critical communication infrastructure have become urgent because of rising occurrences related to human activities and climate change. The landslide and glacial slide susceptibility along the Karakoram Highway poses a significant threat to the infrastructure ecosystem, local communities, and the critical China–Pakistan Economic Corridor. This research paper utilized the Small Baseline Subset InSAR technique to monitor the deformation patterns over the past 5 years, yielding high-resolution insights into the terrain instability in this geologically active region. The SBAS time series results reveal that the substantial cumulative deformation in our study area ranges from 203 mm to −486 mm, with annual deformation rates spanning from 62 mm/year to −104 mm/year. Notably, the deformation that occurred is mainly concentrated in the northern section of our study area. The slope’s aspect is responsible for the maximum deformed material flow towards the Karakoram Highway via steep slopes, lost glacial formations, and the climate variations that cause the instability of the terrain. The given pattern suggests that the northern area of the Karakoram Highway is exposed to a greater risk from the combined influence of glacial slides, landslides, and climatic shifts, which call for the increased monitoring of the Karakoram Highway. The SBAS-InSAR method is first-rate in deformation monitoring, and it provides a scientific basis for developing real-time landslide monitoring systems. The line of sight limitations and the complexity and imprecision of weather-induced signal degradation should be balanced through additional data sources, such as field surveys to conduct large slide and glacial slide susceptibility evaluations. These research results support proactive hazard mitigation and infrastructure planning along the China–Pakistan Economic Corridor by incorporating SBAS-InSAR monitoring into the original planning. The country’s trade policymakers and national level engineers can enhance transport resilience, efficiently manage the landslide and glacial slide risks, and guarantee safer infrastructure along this strategic trade route. Full article

As an important drinking water source for Beijing, the capital of China, the water inflow of Miyun Reservoir has been decreasing year by year, which has affected the urban water supply security. To understand the variation trend of the inflow and analyze the main factors influencing the runoff change, this research focused on the watershed of Miyun Reservoir as the target. Based on the runoff data from 1984 to 2020 at the outlet of the basin, as well as the precipitation, potential evaporation intensity, NDVI (normalized difference vegetation index), population, and GDP (Gross Domestic Product) data, combined with correlation analysis methods, empirical statistical methods, the SCRCQ (Slope Change Ratio of Cumulative Quantity) method, and the GIS, the interannual variation characteristics of various elements in the basin were analyzed, the correlation between runoff and other factors was studied, and the influencing degrees of precipitation, water surface evaporation intensity, human activities, and other factors on the runoff change in the basin were quantitatively separated. The research results showed that the runoff exhibited a distinct decreasing trend, and there were two mutation points in the basin runoff from 1984 to 2020, which were 1995 and 2014, respectively. The runoff change was divided into three stages: 1984–1995 (upward trend in T1), 1995–2014 (downward trend in T2), and 2014–2020 (stable trend in T3). Runoff was significantly correlated with four indicators: the summer leaf area index of the Chaohe River and Baihe River, the regional GDP and population, among which the correlation of the summer leaf area index was the largest. Compared with the period T1, the contribution rates of climate change to the runoff reduction in T2 and T3 were 6.38% and 5.73%, and the contribution rates of human activities to the runoff reduction were 93.62% and 94.27%, respectively. Therefore, the change in annual runoff in the Miyun Reservoir watershed is mainly affected by human activities, and the contribution of climate change to the runoff attenuation is weak. This study is significant in the maintenance and enhancement of runoff in typical watershed. Full article

This research proposed the method of using cumulative positive and negative elevation increment indicators based on road segment to identify the slope characteristics of mountain city roads. Furthermore, it proposed the adoption of these indicators, combined with driving dynamics and emission theory, to analyze the correlation mechanism between the road slope and the actual driving fuel consumption and emissions. Three routes with different slope characteristics were selected in the mountain city of Chongqing, and six road driving tests were conducted using a Class N2 heavy-duty diesel vehicle. Finally, a comprehensive and in-depth study on fuel consumption and emission characteristics was carried out. The results show that the cumulative positive and negative elevation increment indicators based on road segment can correctly identify the complex slope characteristics of mountain city roads. Moreover, using the above indicators, the research method based on the theory of driving dynamics and emission successfully revealed the correlation mechanism between the slope of mountain city roads and the fuel consumption and emissions. Overall, the changes in fuel consumption factor and pollutants CO, NO_X, and PN are positively correlated with the change in slope. The increase in slope leads to a rise in load, thereby increasing the required power, fuel consumption, and rich combustion conditions, ultimately leading to an increase in pollutants. It should be noted that driving dynamics also affect fuel consumption and emissions, leading to the specific rate of change between slope and fuel consumption not being consistent and a significant increase in the PN (Particulate Number) on some road sections. In addition, exhaust gas temperature may have a certain impact on emissions. Full article

The consequence of climatic change and anthropogenic environmental modifications is a notable diminution in runoff across arid and semi-arid regions. For the sustainable management of regional water resources, it is crucial to comprehend the impacts of climatic and anthropogenic factors on runoff patterns. The Zuli River was designated as the study area for this study, and the Mann–Kendall test, double cumulative curve method, slope change ratio of cumulative quantity method, and elasticity coefficient method were employed to identify mutation points and to quantify the relative impacts of climatic variation and human activities on runoff. The results revealed a statistically insignificant downward trend in mean annual precipitation, a significant declining trend in runoff, and an evident increasing trend in potential evapotranspiration and temperature between the years 1957 and 2019. The analysis revealed that the point of sudden change in runoff at Huining station occurred in 1992, whereas the mutation point at Guo Chengyi station was identified in 1985 and that at Jingyuan station in 1995. The contribution of climate change to runoff was found to range from 28.7% to 58.5%, while the contribution of human activities to runoff ranged from 41.5% to 71.3%, based on different methodologies. Therefore, human activities were recognized as the main factor affecting the variations in runoff within the Zuli River Basin, while climate change acts as a secondary contributor. The results of the study hold considerable importance for enhancing the scientific understanding of hydrological processes within the basin and for guiding regional water administration strategies. Full article

Rainfall infiltration affects permafrost-related slope stability by changing the pore water pressure in soil. In this study, the infiltration responses under rainfall conditions were elucidated. The instantaneous profile method and filter paper method were used to obtain the soil–water characteristic curve (SWCC) and hydraulic conductivity function (HCF). During the rainfall infiltration test, the vertical patters of volumetric moisture contents, total hydraulic head or suction and wetting front were recorded. Advancing displacement and rate of the wetting front, the cumulative infiltration, the instantaneous infiltration rate, and the average infiltration rate were determined to comprehensively assess the rainfall infiltration process, along with SWCC and HCF. Additionally, the effects of dry density and runoff on the one-dimensional vertical infiltration process of soil columns were evaluated. The results showed that the variation curve of wetting front displacement versus time obeys a power function relationship. In addition, the infiltration rate–time relationship curve and the unsaturated permeability curve could be roughly divided into three stages, and the SWCC and HCF calculated by volumetric moisture content are more sensitive to changes in dry density than to changes in runoff or hydraulic head height. Full article

A better understanding of the spatiotemporal variation characteristics of rainfall erosivity and effects of extreme rainfall events on soil erosion is the basis for improved water resource planning, protection, and ecological restoration in the Qinling Mountains. Using long-term daily precipitation data from 19 national standard meteorological stations from 1957 to 2018, the spatiotemporal variation trend of rainfall erosivity was explored. A linear regression analysis method was used to detect trends in rainfall erosivity. The spatial pattern of rainfall erosivity, which is based on annual, seasonal, and extreme rainfall indices, was analyzed via a geospatial interpolation method. Effects of natural factors and human activities on soil erosion at different stages were examined via the double cumulative curve method. The average annual rainfall erosivity in the Shangluo area is 2306 MJ mm ha⁻¹ h⁻¹ year⁻¹ and generally displays a gradual decreasing trend from southeast to northwest. Over the last 60 years, the annual R exhibited a nonsignificant increasing trend (p > 0.05). Overall, rainfall erosivity showed a phased trend with an increasing trend after 2000. Rainfall erosivity from June to September accounts for 78.5% of the annual total, while the annual R is mainly determined by a few rainfall events during the year. RX1d and RX5d account for 20–40% and 60–80%, respectively, of the total annual R and are likely to result in severe soil erosion in sloping cultivated land areas, agricultural lands, and dirt roads with continued climate change. Implementation of the National Natural Forest Protection Project and the ‘Grain for Green’ Project significantly reduced the intensity and scope of soil erosion in the area. This study aids in understanding the ecohydrological processes and soil erosion and sediment transport characteristics in the Qinling Mountains and promotes water resource protection and management along the middle route of the South-to-North Water Diversion Project. Full article

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research. Full article

Land Surface Temperature (LST) products obtained by thermal infrared (TIR) remote sensing contain considerable blank areas due to the frequent occurrence of cloud coverage. The studies on the all-time reconstruction of the cloud-covered LST of geostationary meteorological satellite LST products are relatively few. To accurately fill the blank area, a hybrid method for reconstructing hourly FY-4A AGRI LST under cloud-covered conditions was proposed using a random forest (RF) regression algorithm and Savitzky-Golay (S-G) filtering. The ERA5-Land surface cumulative net radiation flux (SNR) reanalysis data was first introduced to represent the change in surface energy arising from cloud coverage. The RF regression method was used to estimate the LST correlation model based on clear-sky LST and the corresponding predictor variables, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), surface elevation and slope. The fitted model was then applied to reconstruct the cloud-covered LST. The S–G filtering method was used to smooth the outliers of reconstructed LST in the temporal dimension. The accuracy evaluation was performed using the measured LST of the representative meteorological stations after scale correction. The coefficients of determination derived with the reference LST were all above 0.73 on the three examined days, with a bias of −1.13–0.39 K, mean absolute errors (MAE) of 1.46–2.4 K, and root mean square errors (RMSE) of 1.77–3.2 K. These results indicate that the proposed method has strong potential for accurately restoring the spatial and temporal continuity of LST and can provide a solution for the production and research of gap-free LST products with high temporal resolution. Full article

Climate change and human activities exert significant impact on the mechanism of runoff generation and confluence. Comprehending the reasons of runoff change is crucial for the sustainable development of water resources. Taking the Upper Shule River as the research area, the M-K test and the moving t test were used to diagnose the runoff mutation time. Furthermore, the slope changing ratio of cumulative quantity method (SCRCQ), climate elasticity method, and Budyko equation were utilized to quantitatively evaluate the impacts and contribution rates of climate change and human activities. The following results were obtained: (1) The Upper Shule River experienced a significant increase in runoff from 1972 to 2021, with 1998 marking the year of abrupt change. (2) The runoff sensitivity showed a downward trend from 1972 to 2021. The main factor affecting the decrease in runoff sensitivity was the characteristic parameters of underlying surface (n), followed by precipitation (P), while the influence of potential evapotranspiration (ET₀) was the weakest. (3) The response of runoff changes to runoff sensitivity and influencing factors were 90.32% and 9.68%, respectively. (4) The results of three attribution methods indicated that climate change was the primary factor causing the alteration of runoff in the Upper Shule River. The research results supplement the hydrological change mechanisms of the Upper Shule River and provide a scientific basis for future water resources management and flood control measures. Full article

Climate change and human activities exert significant influence on the water–sediment relationship in arid and semi-arid regions. Therefore, comprehending the underlying mechanisms is crucial for the effective management of water and soil resources, as well as integrated watershed management. This research focuses on the Kuye River watershed (KYH_W) in the middle reaches of the Yellow River in China, along with its sub-watersheds Wangdaohengtazi (WDHT_SW) and Xinmiaosi (XM_SW). This paper utilizes the Mann–Kendall non-parametric test and the double cumulative curve method to examine the interannual trends of runoff, sediment transport, precipitation, temperature, and NDVI factors. Furthermore, the method of the slope change ratio of cumulative quantity (SCRCQ) is utilized to quantitatively evaluate the impacts and contribution rates of climate change and human activities on water–sediment changes within each watershed. The results are as follows: (1) From 1969 to 2019, the entire watershed experienced a significant decrease in both runoff and sediment transport, with 1997 marking the year of abrupt change. However, following 2012, the KYH_W and WDHT_SW exhibited a noticeable rebound in runoff. (2) Human activities predominantly contribute to the reduction in water and sediment in the watershed. (3) After the abrupt change, between 1998 and 2011, the contribution rates of climate change and human activities to the annual runoff reduction in the entire KYH_W reached 33% and 64%, respectively. Moreover, these rates for sediment transport reduction reached 26% and 74%, respectively. Subsequently, after 2012, the contribution rates of both factors to the increase in watershed runoff reached 29% and 71%, respectively. Factors other than the NDVI, within human activities, played a dominant role in augmenting the watershed’s runoff. (4) Prior to 2011, changes in vegetation cover resulting from the Grain for Green Program, as measured by the NDVI, emerged as the primary factor responsible for reduced runoff in the watershed. Conversely, factors other than the NDVI assumed dominance in reducing sediment transport. The SCRCQ method offers a quantitative approach to assessing water–sediment changes. Based on this method, the study further underscores the substantial impacts of climate change and human activities on variations in runoff and sediment transport within the KYH_W in the middle reaches of the Yellow River. Notably, the water–sediment changes in the KYH_W exhibit distinct stage-wise and spatial discrepancies, which warrant increased attention in future research endeavors. Full article

Climate change has intensified the risk of extreme precipitation, while mountainous areas are constrained by complex disaster mechanisms and difficulties in data acquisition, making it challenging for existing critical rainfall threshold accuracy to meet practical needs. Therefore, this study focuses on Yunnan Province as the research area. Based on historical flash flood events, and combining remote sensing data and measured data, 12 causative factors are selected from four aspects: terrain and landforms, land use, meteorology and hydrology, and population and economy. A combined qualitative and quantitative method is employed to analyze the relationship between flash floods and triggering factors, and to calibrate the parameters of the RTI (Rainfall Threshold Index) model. Meanwhile, machine learning is introduced to quantify the contribution of different causative factors and identify key causative factors of flash floods. Based on this, a parameter η coupling the causative mechanism is proposed to optimize the RTI method, and develop a framework for calculating county-level critical rainfall thresholds. The results show that: (1) Extreme rainfall, elevation, slope, and other factors are direct triggers of flash floods, and the high-risk areas for flash floods are mainly concentrated in the northeast and southeast of Yunnan Province. (2) The intraday rainfall has the highest correlation with the accumulated rainfall of the previous ten days; the critical cumulative rainfall ranges from 50 mm to 400 mm. (3) The county-level critical rainfall threshold for Yunnan Province is relatively accurate. These findings will provide theoretical references for improving flash flood early warning methods. Full article

The China–Nepal Transportation Corridor is vital to the country’s efforts to build a land trade route in South Asia and promote the Ring-Himalayan Economic Cooperation Belt. Due to the complex geological structure and topographical environment of the Qinghai–Tibet Plateau, coupled with the impact of climate change, the frequent occurrence of geological disasters has increased the operational difficulty of the China–Nepal Highway and the construction difficulty of the China–Nepal Railway. However, to date, there has been no systematic study of the spatial distribution of landslides along the entire route within the area, the factors influencing landslides at different scales, or the causes of landslides under different topographic backgrounds. There is an even greater lack of research on areas threatened by potential landslides. This study comprehensively applies remote sensing, mathematical statistics, and machine learning methods to map landslides along the China–Nepal transportation corridor, explore the influencing factors and causes of different types of landslides, and investigate the distribution characteristics of potential landslides. A total of 609 historic landslides have been interpreted in the study area and were found to be distributed along faults and locally concentrated. The strata from which landslides develop are relatively weak and are mainly distributed within 2 km of a fault with a slope between 20° and 30°. The direction of slope for the majority of landslides is south to south-west, and their elevation is between 4000 and 5000 m. In addition, we discovered a power law relationship between landslide area and volume (V_L = 2.722 × A_L^1.134) and determined that there were 47 super-large landslides, 213 large landslides, and 349 small and medium-sized landslides in the area, respectively. Slope is the most significant influencing factor for the development of landslides in the area. Apart from slope, faults and strata significantly influence the development of large and medium-small landslides, respectively. We have identified 223 potential landslides in the region, 15 of which directly threaten major transport routes, mainly in the Renbu Gorge section of the China–Nepal Highway and the proposed China–Nepal Railway section from Peikucuo to Gyirong County. In addition, we also discussed the causes of landslides within three geomorphic units in the region. First, the combined effects of faulting, elevation, and relatively weak strata contribute to the development of super-large and large landslides in the Gyirong basin and gorge. Second, the relatively weak strata and the cumulative damaging effects of earthquakes promote the development of small and medium-sized landslides in the Xainza-Dinggye rift basin. Third, under the combined effect of the hanging wall effect of thrust faults and the relatively weak material composition, landslides of various types have developed in the Nagarzê mountain. It is worth noting that potential landslides have developed in all three geomorphic units mentioned above. This study provides data and theory to assist in the accurate mitigation and control of landslide hazards in the corridor. Full article