Search Results (15)

Under the background of global climate change, shallow landslide clusters induced by extreme rainfall are occurring with increasing frequency, causing severe casualties and economic losses. To address this challenge, this study proposes an integrated approach to support both emergency response and long-term mitigation for rainfall-induced shallow landslides. The workflow includes (1) rapid landslide detection based on time-series image fusion and threshold segmentation on the Google Earth Engine (GEE) platform; (2) numerical simulation of landslide runout using the R.avaflow model; (3) landslide susceptibility assessment based on event-driven inventories and machine learning; and (4) delineation of high-risk slopes by integrating simulation outputs, susceptibility results, and exposed elements. Applied to Qugaona Township in Zhouqu County, Bailong River Basin, the framework identified 747 landslides. The R.avaflow simulations captured the spatial extent and depositional features of landslides, assisting post-disaster operations. The Gradient Boosting-based susceptibility model achieved an accuracy of 0.870, with 8.0% of the area classified as highly susceptible. In Cangan Village, high-risk slopes were delineated, with 31.08%, 17.85%, and 22.42% of slopes potentially affecting buildings, farmland, and roads, respectively. The study recommends engineering interventions for these areas. Compared with traditional methods, this approach demonstrates greater applicability and provides a more comprehensive basis for managing rainfall-induced landslide hazards. Full article

Terrestrial ecosystems are vital carbon sinks that can effectively restrain the rise in CO₂ in the atmosphere. How ecosystem carbon storage (CS) in semi-arid watershed areas with slow urbanization is affected by comprehensive factors of the environment and land use, along with its temporal and spatial changes has still not been fully explored. Notably, there is a paucity of research on the temporal and spatial changes and development trends of CS in the rapid deformation belt of slopes from the eastern margin of the Qinghai–Tibet Plateau to the Loess Plateau. Taking Bailong River Basin (BRB) as an example, this study combined GeoSOS-FLUS, the InVEST model, and localized “social–economic–nature” scenario to simulate the long-term dynamic evolution of CS. The aim was to study how topographic factors and land use change, and their interactions impact carbon sinks and gradient effects in steep-slope areas, and then find out the relationship between carbon sinks and topographic factors to explore strategies to improve regional carbon sink capacity. The results showed that the following: (1) CS in BRB increased year by year, with a total increase of 558 tons (3.19%), and showed significant spatial heterogeneity, mainly due to the conversion of woodland and arable land; (2) except for land use type, the relationship between CS and topographic gradient is inverted U-shaped, showing a complex spatial response; and (3) it is estimated that by 2050, under the arable land protection and natural development scenarios, CS will decrease by 0.07% and 0.005%, respectively, encroachment on undeveloped mountain areas, while the ecological protection scenario gives priority to protecting the carbon sinks of woodland and grassland, and CS will increase by 0.37%. This study supports the implementation of targeted ecological protection measures through topographic gradient zoning, provides a reference for policy makers in similar topographic regions to effectively manage the spatial heterogeneity of CS, and helps further strengthen global and regional climate change mitigation efforts. Full article

Land use/land cover changes (LUCCs) significantly reshape ecosystem services (ESs) within the framework of climate change. Studying LUCC and its impact on ESs is crucial for a comprehensive understanding of the impact of human activities on ecosystems. The InVEST model coupled with the predicted land use data were used to analyze the spatiotemporal characteristics of four ESs (soil conservation (SC), water yield (WY), carbon storage (CS), and habitat quality (HQ)) under three scenarios from 2040 to 2100 and quantified trade-offs/synergies and bundles of these ESs within the Bailong River Basin (BRB). The results indicated that (1) under the SSP1-2.6 scenario, there is an anticipated increase in forestland, a concurrent decrease in grassland, farmland, and built-up land, and an enhancement in four ESs from 2040 to 2100. The forestland and farmland in the SSP2-4.5 scenario showed a gradual decrease, with an expansion of grassland and built-up land. Except for HQ, the other three ESs were reduced. Both forestland and grassland decreased. Built-up land and farmland increased, and ESs decreased significantly under the SSP5-8.5 scenario. (2) Synergistic effects were identified among the ESs, with the most pronounced synergy observed between CS and HQ. Spatially, six pairs of ESs under the SSP1-2.6 scenario showed synergistic effects. Under the SSP2-4.5 and SSP5-8.5 scenarios, most of the ESs present trade-off effects. (3) The characterization of ES bundles revealed that the balanced enhancement of the four ESs predominantly occurred in the southern region of the basin. Among the scenarios, SSP1-2.6 had the highest representation, followed by the SSP2-4.5, while the SSP5-8.5 had the lowest proportion. The findings facilitate the sustainable and balanced development of diverse ESs and offer theoretical and technical insights for devising spatial regulation policies and ecosystem-based management strategies. Full article

The eastern margin of the Tibetan Plateau is one of the regions with the most severe landslide disasters on a global scale. With the intensification of seismic activity around the Tibetan Plateau and the increase in extreme rainfall events, the prevention of landslide disasters in the region is facing serious challenges. This article selects the Bailong River Basin located in this region as the research area, and the historical landslide data obtained from high-precision remote sensing image interpretation combined with field validation are used as the sample library. Using machine learning algorithms and data-driven landslide susceptibility assessment as the methods, 17 commonly used models and 17 important factors affecting the development of landslides are selected to carry out the susceptibility assessment. The results show that the BaggingClassifier model shows advantageous applicability in the region, and the landslide susceptibility distribution map of the Bailong River Basin was generated using this model. The results show that the road and population density are both high in very high and high susceptible areas, indicating that there is still a significant potential landslide risk in the basin. The quantitative evaluation of the main influencing factors emphasizes that distance to a road is the most important factor. However, due to the widespread utilization of ancient landslides by local residents for settlement and agricultural cultivation over hundreds of years, the vast majority of landslides are likely to have occurred prior to human settlement. Therefore, the importance of this factor may be overestimated, and the evaluation of the factors still needs to be dynamically examined in conjunction with the development history of the region. The five factors of NDVI, altitude, faults, average annual rainfall, and rivers have a secondary impact on landslide susceptibility. The research results have important significance for the susceptibility assessment of landslides in the complex environment of human–land interaction and for the construction of landslide disaster monitoring and early warning systems in the Bailong River Basin. Full article

In order to safeguard the health of river ecosystems and maintain ecological balance, it is essential to rationally allocate water resources. This study utilized continuous runoff data from 1967 to 2020 at the Zhouqu Hydrological Station on the Bailong River. Five hydrological methods, tailored to the hydrological characteristics of the Zhouqu hydrological cross-section, were employed. These methods included the improved dynamic calculation method, the NGPRP method, the improved monthly frequency computation method, the improved RVA method, and the Tennant method. Ecological flow calculations were conducted to determine the ecological flow, with analysis carried out through the degree of satisfaction, economic benefits, and the nonlinear fitting of the GCAS model. We established an ecological flow threshold and early warning program for this specific hydrological cross-section. Ecological flow values calculated using different methods for each month of the year were compared. The improved RVA method and Tennant method resulted in small values ranging from 4.05 to 36.40 m³/s and 7.65 to 22.94 m³/s, respectively, with high satisfaction levels and economic benefits, but not conducive to ecologically sound development. In contrast, the dynamic calculation method, NGPRP method, and improved monthly frequency calculation method yielded larger ecological flow values in the ranges of 21.79–97.02 m³/s, 23.90–137.00 m³/s, and 28.50–126.00 m³/s, respectively, with poor fulfillment and economic benefits. Ecological flow thresholds were determined using the GCAS model, with values ranging from 16.72 to 114.58 m³/s during the abundant water period and from 5.03 to 63.63 m³/s during the dry water period. A three-level ecological warning system was proposed based on these thresholds, with the orange warning level indicating optimal sustainable development capacity for the Zhouqu Hydrological Station. This study provides valuable insights into the scientific management of water resources in the Bailong River Basin to ensure ecological security and promote sustainable development. Full article

The Bailong River Basin is situated at the northeastern edge of the Qinghai–Tibet Plateau and the western transition zone of the Loess Plateau, characterized by steep terrain and heavy rainfall. This area experiences frequent occurrences of debris flows, posing serious threats to towns and construction projects. Focusing on the Huaiyazigou debris flow in the Bailong River Basin, numerical simulations of debris flow processes were conducted using Digital Surface Model (DSM) data with a resolution of 5 m × 5 m for various recurrence periods. The simulation results indicate that the debris flow develops rapidly along the gully after formation, decelerating and beginning to deposit upon reaching the cement plant area near the mouth of the gully, eventually merging into the Bailong River. The primary destructive modes of debris flow disasters encompass impact and burial. When encountering buildings, their flow characteristics manifest as deposition and diversion. A debris flow hazard classification model, based on intensity and recurrence periods, was established according to Swiss and Austrian standards, dividing the hazard into low, medium, and high levels. This method generated a debris flow hazard zone map, offering guidance for risk prevention and monitoring. This research demonstrates that using high-precision Digital Surface Models (DSM) can accurately represent the digital information of debris flow gully terrains and buildings. During the simulation process, it realistically reflects the characteristics of the debris flow movement, allowing for the more precise delineation of hazard zones. Full article

We coupled the global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Future Land Use Simulation model (FLUS) to evaluate land use change in the Bailong River Basin (BRB) under three shared socioeconomic pathway and representative concentration pathway scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5). Additionally, we used calibrated soil and water assessment tools (SWATs) to evaluate the streamflow in the BRB from 2008 to 2100 under the combined influence of climate and land use changes. The results indicate that (1) under the SSP126-EP scenario, forests have been well preserved, and there has been an increase in the combined area of forests and water bodies. The SSP245-ND scenario has a similar reduction pattern in agricultural land as SSP126-EP, with relatively good grassland preservation and a moderate expansion rate in built-up land. In contrast, the SSP585-EG scenario features a rapid expansion of built-up land, converting a significant amount of farmland and grassland into built-up land. (2) From 2021 to 2100, the annual average flow increases under all three scenarios, and the streamflow change is most significant under SSP5–8.5. (3) Compared to the baseline period, the monthly runoff increases, with the most significant increase occurring during the summer months (June to August). This study offers a thorough assessment of potential future changes in streamflow. Its findings are expected to be applied in the future to improve the management of water resources at a local level. Full article

The Bailong River Basin is one of the most developed regions for debris flow disasters worldwide, often causing severe secondary disasters by blocking rivers. Therefore, the early identification of potential debris flow disasters that may block the river in this region is of great significance for disaster risk prevention and reduction. However, it is quite challenging to identify potential debris flow disasters that may block rivers at a regional scale, as conducting numerical simulations for each debris flow catchment would require significant time and financial resources. The purpose of this article is to use public resource data and machine learning methods to establish a relationship model between debris flow-induced river blockage and key influencing factors, thereby economically predicting potential areas at risk for debris flow-induced river blockage disasters. Based on the field investigation, data collection, and remote sensing interpretation, this study selected 12 parameters, including the basin area, basin height difference, relief ratio, circularity ratio, landslide density, fault density, lithology index, annual average frequency of daily rainfall exceeding 40 mm, river width, river discharge, river gradient, and confluence angle, as critical factors to determine whether debris flows will cause river blockages. A relationship model between debris flow-induced river blockage and influencing factors was constructed based on machine learning algorithms. Several machine learning algorithms were compared, and the XGB model performed the best, with a prediction accuracy of 0.881 and an area under the ROC curve of 0.926. This study found that the river width is the determining factor for debris flow blocking rivers, followed by the annual average frequency of daily rainfall exceeding 40 mm, basin height difference, circularity ratio, basin area, and river discharge. The early identification method proposed in this study for river blockage disasters caused by debris flows can provide a reference for the quantitative assessment and pre-disaster prevention of debris flow-induced river blockage chain risks in similar high-mountain gorge areas. Full article

The land use and ecological environment of the Bailong River Basin (BRB) have undergone significant changes in the context of developing urban–rural integration and ecological conservation in western China. As a key ecologically fragile area in the west region, a landscape ecological risk (LER) assessment can reflect the extent to which human activities and environmental changes threaten the ecosystems in the BRB. This study aims to explore the empowerment of indicator weights in an LER assessment. Landscape index weights and LER were analyzed based on land use data for three periods using objective and combined empowerment methods. It was found that the weighting results had apparent scale dependence, and the entropy weight method had the best results in indicator empowerment. From 2000–2020, the LER presented reduced risk, increased heterogeneity, and reduced aggregation. The shift from a medium-risk area to a lower-risk area was the primary transfer type of LER in the study area, and the LER showed a decreasing development trend. So far, research on weight empowerment in LER evaluations has been urgent. This study improved the landscape ecological risk assessment system by selecting an empowerment method that optimally takes into account scale dependence while providing valuable insights into the sustainability of the landscape in this watershed. Full article

To solve grid-scale problems and evaluation indicator selection in landscape ecological risk index (LERI) evaluation, this paper takes the Bailong River Basin in Gansu Province (BLRB) as an example. The LERI evaluation formulae and optimal grid scales were determined by screening landscape indices and area changes in the LERI at different grid scales. The evaluation indices were finally obtained according to the landscape characteristics and the correlation analysis of the landscape index value. Through the statistical analysis of the area of the LERI at the grid scale of 1–6 km, the optimal grid scale was determined to be 5 km. There was little change in land use patterns, with the most significant increases in artificial surfaces at 3.29% and 3.58%, respectively. Cultivated land was the only land use type to decrease by 184.3 km². The LERI drops with the reduced cultivated land area; the landscape ecological medium risk area and cultivated land keep the same spatial distribution. Due to the limitation of the topography, cultivated land is generally distributed below 2500 m altitude, so 2500 m becomes the turning point in the spatial distribution of the LERI. The medium risk below 2500 m dominates the LERI type. Reduced cultivated land was the leading cause of reduced ecological risk according to an overlay analysis. The study of LERI evaluations provides a theoretical basis for sustainable and ecological environmental protection in the BLRB. Full article

In the mapping and assessment of mountain hazard susceptibility using machine learning models, the selection of model parameters plays a critical role in the accuracy of predicting models. In this study, we present a novel approach for developing a prediction model based on random forest (RF) by incorporating ensembles of hyperparameter optimization. The performance of the RF model is enhanced by employing a Bayesian optimization (Bayes) method and a genetic algorithm (GA) and verified in the Wudu section of the Bailong River basin, China, which is a typical hazard-prone, mountainous area. We identified fourteen influential factors based on field measurements to describe the “avalanche–landslide–debris flow” hazard chains in the study area. We constructed training (80%) and validation (20%) datasets for 378 hazard sites. The performance of the models was assessed using standard statistical metrics, including recall, confusion matrix, accuracy, F1, precision, and area under the operating characteristic curve (AUC), based on a multicollinearity analysis and Relief-F two-step evaluation. The results indicate that all three models, i.e., RF, GA-RF, and Bayes-RF, achieved good performance (AUC: 0.89~0.92). The Bayes-RF model outperformed the other two models (AUC = 0.92). Therefore, this model is highly accurate and robust for mountain hazard susceptibility assessment and is useful for the study area as well as other regions. Additionally, stakeholders can use the susceptibility map produced to guide mountain hazard prevention and control measures in the region. Full article

Debris flow risk comprehensively reflects the natural and social properties of debris flow disasters and is composed of the risk of the disaster-causing body and the vulnerability of the carrier. The Bailong River Basin (BRB) is a typical mountainous environment where regional debris flow disasters occur frequently, seriously threatening the lives of residents, infrastructure, and regional ecological security. However, there are few studies on the risk assessment of mountainous debris flow disasters in the BRB. By considering a complete catchment, based on remote sensing and GIS methods, we selected 17 influencing factors, such as area, average slope, lithology, NPP, average annual precipitation, landslide density, river density, fault density, etc. and applied a machine learning algorithm to establish a hazard assessment model. The analysis shows that the Extra Trees model is the most effective for debris flow hazard assessments, with an accuracy rate of 88%. Based on socio-economic data and debris flow disaster survey data, we established a vulnerability assessment model by applying the Contributing Weight Superposition method. We used the product of debris flow hazard and vulnerability to construct a debris flow risk assessment model. The catchments at a very high-risk were distributed mainly in the urban area of Wudu District and the northern part of Tanchang County, that is, areas with relatively dense economic activities and a high disaster frequency. These findings indicate that the assessment results provide scientific support for planning measures to prevent or reduce debris flow hazards. The proposed assessment methods can also be used to provide relevant guidance for a regional risk assessment of debris flows in the BRB and other regions. Full article

Understanding the spatiotemporal evolution and driving mechanisms of landslides following a mega-earthquake at the catchment scale can lead to improved landslide hazard assessment and reduced related risk. However, little effort has been made to undertake such research in the Wenchuan earthquake-affected region, outside Sichuan Province, China. In this study, we used the Goulinping valley in the Bailong River basin in southern Gansu Province, China, as an example. By examining the multitemporal inventory, we revealed various characteristics of the spatiotemporal evolution of landslides over the past 13 years (2007–2020). We evaluated the activity of landslides using multisource remote-sensing technology, analyzed the driving mechanisms of landslides, and further quantified the contribution of landslide evolution to debris flow in the catchment. Our results indicate that the number of landslides increased by nearly six times from 2007 to 2020, and the total volume of landslides approximately doubled. The evolution of landslides in the catchment can be divided into three stages: the earthquake driving stage (2008), the coupled driving stage of earthquake and rainfall (2008–2017), and the rainfall driving stage (2017–present). Landslides in the upstream limestone area were responsive to earthquakes, while the middle–lower loess–phyllite-dominated reaches were mainly controlled by rainfall. Thus, the current landslides in the upstream region remain stable, and those in the mid-downstream are vigorous. Small landslides and mid-downstream slope erosion can rapidly provide abundant debris flow and reduce its threshold, leading to an increase in the frequency and scale of debris flow. This study lays the foundation for studying landslide mechanisms in the Bailong River basin or similar regions. It also aids in engineering management and landslide risk mitigation under seismic activity and climate change conditions. Full article

Debris flows are a major geological hazard in mountainous regions. For improving mitigation, it is important to study the spatial distribution and factors controlling debris flows. In the Bailong River Basin, central China, landslides and debris flows are very well developed due to the large differences in terrain, the complex geological environment, and concentrated rainfall. For analysis, 52 influencing factors, statistical, machine learning, remote sensing and GIS methods were used to analyze the spatial distribution and controlling factors of 652 debris flow catchments with different frequencies. The spatial distribution of these catchments was divided into three zones according to their differences in debris flow frequencies. A comprehensive analysis of the relationship between various factors and debris flows was made. Through parameter optimization and feature selection, the Extra Trees classifier performed the best, with an accuracy of 95.6%. The results show that lithology was the most important factor controlling debris flows in the study area (with a contribution of 26%), followed by landslide density and factors affecting slope stability (road density, fault density and peak ground acceleration, with a total contribution of 30%). The average annual frequency of daily rainfall > 20 mm was the most important triggering factor (with a contribution of 7%). Forest area and vegetation cover were also important controlling factors (with a total contribution of 9%), and they should be regarded as an important component of debris flow mitigation measures. The results are helpful to improve the understanding of factors influencing debris flows and provide a reference for the formulation of mitigation measures. Full article

Previous studies have shown that the mechanical effects of vegetation roots on slope stability can be classified as additional cohesion effects and anchorage effects. The present study investigated the combined mechanical effects (additional cohesion effects and anchorage effects) of vegetation on a slope with coarse-grained soil in the mountainous region (significantly prone to slope failure) of Gansu Province, China. A detailed survey of tree density, root system morphology and slope profiles was conducted, and we also assessed the soil cohesion provided by the root systems of monospecific stands of Robinia pseudoacacia growing in different locations on the slope. The measured data were incorporated into a numerical slope model to calculate the stability of the slope under the influence of trees. The results indicated that it was necessary to consider the anchoring effect of coarse roots when estimating the mechanical effects of trees on slope stability. In particular, the FoS (factor of safety) of the slope was increased by the presence of trees. The results also demonstrated that vegetation increased slope stability. The reinforcing effects were most significant when the trees were planted along the entire slope. Although the reinforcing effects contributed by trees were limited (only 4–11%), they were essential for making optimal use of vegetation for enhancing slope stability. Overall, vegetation development can make a major contribution to ecosystem restoration in the study region. Full article