Search Results (19)

As key headwater regions of the upper Yangtze River, the Yalong and Dadu River basins are expected to experience highly uncertain hydrological responses under climate warming. However, the nonlinear and spatially heterogeneous evolution of streamflow across multiple time-frequency scales remains insufficiently understood. In this study, a SWAT model driven by CMIP6 climate projections under four shared socioeconomic pathways (SSP1-2.6 to SSP5-8.5) was coupled with multivariate wavelet coherence, spatial wavelet transform, and change-point detection methods to investigate the spatiotemporal evolution of streamflow and extreme risks during 2017–2100. Results indicate that precipitation is the primary driver of streamflow variability, with streamflow responding rapidly, while air temperature mainly regulates seasonal intensity via snowmelt. Streamflow seasonal intensity exhibits a northwest-southeast gradient, with low variability upstream and high sensitivity downstream, reflecting precipitation-concentrated, forested canyons where rapid lateral flow and dry-season evapotranspiration amplify flow contrasts. Moreover, hydrological nonstationarity and extreme risks are projected to intensify, with structural regime shifts emerging in the 2040s–2050s and extreme high-flow magnitudes doubling under SSP5-8.5, accompanied by more frequent drought-flood alternations. These findings highlight an upstream buffering-downstream sensitivity pattern, emphasizing the need for spatially differentiated water resources management under nonstationary climate conditions. Full article

Water Resource Carrying Capacity (WRCC) serves as a critical indicator for assessing the supportive capacity of water resource systems. This study selects 28 districts and counties within the Dadu River Basin as case studies, constructs a WRCC evaluation framework based on the four-dimensional collaborative perspective of “water resources–society–economy–ecology,” proposes a combined weighting method integrating the CRITIC and entropy weight approaches to optimize index weight calculation, and applies the obstacle degree model to investigate the spatio-temporal heterogeneity of regional WRCC and its underlying driving mechanisms. The results show the following: (1) From 2011 to 2020, the WRCC in the Dadu River Basin increased by 17% as a whole. All districts and counties showed an upward trend. (2) The spatial differentiation of WRCC is significant. The downstream regions are approaching the fourth-level threshold, driven by the adoption of water-saving technologies and the agglomeration effects of economic activities. In contrast, the upstream regions face inherent constraints arising from the conflict between ecological conservation and resource exploitation, leading to a relatively slower growth rate. (3) The driving mechanism of WRCC shows the transformation characteristics of “shifting from being dominated by the social economy to the synergy of the economy and ecology”. Based on this analysis, a strategy of “zonal regulation–structural optimization–management upgrade” is proposed. Full article

Under the pressures of global climate change, the sustainable management of plant resources in alpine gorge regions faces severe challenges. P. aquilinum var. latiusculum is widely harvested and utilized by residents in the upper reaches of the Dadu River–Min River basin due to its high edible and medicinal value. This study employed ensemble models to simulate the potential distribution of P. aquilinum var. latiusculum in this region, predicting the impacts of future climate change on its distribution, the centroid migration of suitable habitats, and niche dynamics. A production dynamics model was also constructed to identify current and future potential cultivation areas by integrating ecological suitability and nutritional component synergies. The results show that current high-suitability areas and core cultivation zones of P. aquilinum var. latiusculum are predominantly distributed in patchy, fragmented patterns across the Wenchuan, Li, Mao, Luding, and Xiaojin Counties and Kangding City. Under climate change, the “mountain-top trap effect” drives a significant increase in high-suitability areas and core cultivation zones, while moderate-to-low-suitability areas and marginal cultivation zones decrease substantially. Meanwhile, suitable habitats and cultivation areas exhibit a northward migration trend toward higher latitudes. The most significant changes in suitable area and cultivation zone extent, as well as the most pronounced niche shifts, occur under high-emission climate scenarios. This research facilitates the development of suitability-based management strategies for P. aquilinum var. latiusculum in the study region and provides scientific references for the sustainable utilization of montane plant resources in the face of climate change. Full article

Argentina anserina (Rosaceae), a perennial herb, forms enlarged tuberous roots (commonly referred to as “ginseng fruit”) exclusively in the Qinghai–Tibet Plateau, making it a unique medicinal and edible plant resource in this region. The upper reaches of the Dadu River and Minjiang River are one of its primary production areas in China. This study employs an ensemble model to simulate the potential distribution of A. anserina in this region, predicting the impacts of future climate change on its distribution, ecological niche, and centroid migration patterns. Additionally, a cultivation productivity evaluation model integrating ecological suitability and nutritional components was developed to delineate potential cultivation areas. Results indicate that high-suitability habitats span 0.37 × 10⁴ km² (7.39% of the total suitable area), exhibiting a patchy and fragmented distribution in Aba County, Rangtang County, Jiuzhi County, and Banma County. Core cultivation areas cover 3.78 × 10⁴ km², distributed across Aba County, Rangtang County, Jiuzhi County, Seda County, Banma County, Hongyuan County, and Markam City. Under future climate scenarios, the suitable distribution area of A. anserina will gradually decline with rising temperatures, migrating to higher-latitude northern regions, accompanied by increased niche migration. By the 2090s under the SSP5-8.5 scenario, the centroid demonstrates the largest migration amplitude, with high-suitability habitats showing a “collapsing” polarization pattern and near-complete niche separation from the previous period, indicating significant changes. Collectively, these results provide a theoretical basis for the sustainable utilization of A. anserina in the upper Dadu River and Minjiang River basin. Full article

Landslides on reservoir slopes are one of the key geologic hazards that threaten the safe operation of hydropower plants. The aim of our study was to reduce the limitations of the existing methods of landslide risk assessment when dealing with complex nonlinear relationships and the difficulty of quantifying the uncertainty of predictions. We established a multidimensional system of landslide risk assessment that covers geological settings, meteorological conditions, and the ecological environment, and we proposed a model of landslide risk assessment that integrates Bayesian theory and a random forest algorithm. In addition, the model quantifies uncertainty through probability distributions and provides confidence intervals for the prediction results, thus significantly improving the usefulness and reliability of the assessment. In this study, we adopted the Gini index and SHAP (SHapley Additive exPlanations) value, an analytical methodology, to reveal the key factors affecting slope stability and their interaction. The empirical results obtained show that the model effectively identifies the key risk factors and also provides an accurate prediction of landslide risk, thus enhancing scientific and targeted decision making. This study offers strong support for managing landslide risk and providing a more solid guarantee of the safe operation of hydropower station sites. Full article

Precipitation is a key component of the water cycle. Different precipitation data sources have strengths and weaknesses. To combine these strengths and achieve accurate precipitation data, this study introduces a dual-layer neural network (D-ConvLSTM) based on a convolutional long short-term memory neural network (ConvLSTM) that integrates ground station data (1 h interval) and grid precipitation data generated by the China Meteorological Administration Multi-source merged Precipitation Analysis System (CMPAS, 1 h interval, 0.05° × 0.05°) through a two-layer network for precipitation identification and correction. To evaluate the performance of the proposed model, D-ConvLSTM, optimal interpolation (OI), and a single-layer ConvLSTM model are evaluated in the Dadu River Basin, China. The results show that D-ConvLSTM outperforms the CMPAS in all the metrics compared with the OI and ConvLSTM, with improvements of 18.9% and 19.8% in the critical success index (CSI) and Kling–Gupta efficiency (KGE), respectively. D-ConvLSTM enhances gridded precipitation under various conditions, including areas without station data, different intensities, and regions. Furthermore, this study analyzes the impact of training data distribution on the performance of the D-ConvLSTM model and enhances model performance by adjusting the training data distribution. The analysis reveals that the ratio of dry to wet data in the training set affects the model’s identification performance. The ratio of overestimation to underestimation of gridded data compared with station observations influences value correction. This study offers a new model for merging station and gridded precipitation data and provides insights for enhancing the accuracy of neural network merging. Full article

The eastern slope of Gongga Mountain is located in the mountainous region of Southwestern China, which has strong geologic tectonics that leads to frequent landslide hazards. A large number of such landslides were induced by the 2022 Luding Ms 6.8 earthquake. Therefore, it is necessary to identify the spatial distribution of landslides in the region. In this paper, the Google Earth platform and GF-1 and GF-6 satellite imagery were used to construct new pre-earthquake and co-seismic landslides. Then, we analyzed the relationship between the conditioning factors of the pre-earthquake and co-seismic landslide inventories and the spatial distribution of landslides, as well as the main controlling factors of landslide development. The main conclusions are as follows: (i) Through remote-sensing interpretation and field investigation, 1198 and 4284 landslides were recognized before and after the earthquake, respectively, and the scale was mainly small- and medium-sized. (ii) In two kinds of inventories, landslides are primarily distributed along the banks of the Dadu River basin, within elevations of 1200–1400 m and slopes of 30–50°. (iii) The distribution of pre-earthquake and co-seismic landslides was influenced by engineering geological layer combinations and earthquake intensity, with these two factors being the most significant. This paper plays an important role in hazard prevention and reconstruction planning in the Gongga Mountains. Full article

Studying the spatiotemporal evolution and driving forces behind construction land amidst the intricate ecological and geological setting on the eastern edge of the Qinghai–Tibet Plateau offers invaluable insights for local sustainable development in a landscape transition zone and ecologically fragile area. Using construction land data from four phases, spanning 1990 to 2020, in Mianning County, this study employs methodologies like the Landscape Expansion Index (LEI) and land use transfer matrix to delineate the spatiotemporal evolution characteristics of construction land. A comprehensive set of 12 influencing factors across five categories—geomorphology, geological activity, climate, river and vegetation environment, and social economy—were examined. The Geographically Weighted Regression (GWR) model was then employed to decipher the spatial distribution pattern of construction land in 1990 and 2020, shedding light on the driving mechanisms behind its changes over the three decades. The research reveals distinct patterns of construction land distribution and evolution in Mianning County, shaped by the ecological and geological landscape. Notably, the Anning River wide valley exhibits a concentrated and contiguous development mode, while the Yalong River deep valley showcases a decentralized development pattern, and the Dadu River basin manifests an aggregation development mode centered around high mountain lakes. Over the study period, all three river basins witnessed varying degrees of construction land expansion, transitioning from quantitative expansion to qualitative enhancement. Edge expansion predominantly characterizes the expansion mode, complemented by leapfrog and infilling modes, accompanied by conversions from cropland and forest land to construction land. An analysis of the spatial pattern and drivers of construction land change highlights human-induced factors dominating the Anning River Basin, contrasting with natural factors prevailing in the Yalong River Basin and the Dadu River Basin. Future efforts should prioritize climate change considerations and environmental capacity, aiming for an ecologically resilient spatial pattern of construction land. Full article

Geological disasters in large alpine reservoirs primarily take the form of landslide occurrences and are predominantly induced by slope instability. Presently, risk monitoring and assessment strategies tend to prioritize sudden alerts overlooking progressive trajectories from the onset of creeping deformations within the slope to its critical state preceding landslides. Hence, analyzing landslide safety risks over time demonstrates a significant degree of hysteresis, highlighting the necessity for a comprehensive approach to risk assessment that encompasses both gradual and sudden precursors to landslide events. This study analyzes the factors affecting slope stability and establishes a slope evaluation indicator system that includes terrain morphology, meteorological conditions, the ecological environment, soil conditions, human activity, and external manifestation. It proposes a quantitative model for slope landslide risk assessment based on a fuzzy broad learning system, aiming to accurately assess slopes with different risk levels. The overall assessment accuracy rate reaches 92.08%. This multi-dimensional risk assessment model provides long-term monitoring of slope conditions and scientific guidance on landslide risk management and disaster prevention and mitigation on a long time scale for risky slopes in reservoir areas. Full article

With the large-scale development and grid connection of renewable energy, hydropower faces more intense and frequent peak shaving and frequency regulation, giving rise to water level fluctuations and frequently forced sluice adjustments at hydropower stations. This paper proposes a model that combines “offline calculation” and “online search”. First, feasible sluice opening combinations for different water levels at each hydropower station are calculated offline, and a sluice operation strategy table is constructed. Subsequently, an optimal sluice operation strategy is searched online according to the real-time water level and various regulatory requirements. As an example, we select three hydropower stations in the middle reach of the Dadu River in China, namely, Pubugou, Shenxigou, and Zhentouba. The results show that the total number of adjustments of the sluices of the cascade hydropower stations was reduced from 1195 to 675, a reduction of 43.5%, and the leading hydropower station, Pubugou, met water level control requirements, whereas the fluctuations in the water level of the two downstream daily regulating hydropower stations, Shenxigou and Zhentouba, were reduced by 1.38 m and 0.55 m, respectively. The results indicate that the sluices of hydropower stations were optimally used under high-intensity peak shaving and frequency regulation. Full article

Debris flow is a typical natural disaster in the middle reaches of the Dadu River, which seriously threatens the safety of life and property of local residents. However, there is currently a lack of a comprehensive analysis methods applicable to the blockage of river channels by debris flow in the Dadu River basin, limiting disaster prevention and mitigation in this area. Based on previous large-scale model tests carried out in the middle reaches of the Dadu River, the debris flows are divided into dam-type debris flows and submerged debris flows. The calculation formulas for the maximum travel distance of the two kinds of debris flows entering the river are obtained via theoretical derivation. The formulas for calculating the length and volume of debris flow accumulation are derived, and the relationship between the debris flow loss coefficient and river blocking degree in the middle part of the Dadu River is analyzed. An identification method of river blocking by debris flow is put forward in this study. By calculating the maximum blocking degree, S (the ratio of the maximum driving distance of the debris flow to the width of the river), and the volume of the source materials needed to form a debris flow dam under the conditions that the debris flow does not reach the opposite bank (V₁), reaches the opposite bank but does not block the river (V₂), and reaches the opposite bank (V₃), the form of debris flow blocking the river is distinguished. When S = 1, V > V₃, complete blockage occurs; when S = 1, V > V₂, the river is mostly blocked; when S < 1, V > V₁, the river is half-blocked. This study established an identification method of river blocking by debris flow, providing a basis for early warning for river blocking and disaster prevention in the middle reaches of the Dadu River. Full article

Analysis of spatial patterns and driving factors of different ethnic villages under regional integration is important for the conservation and development of ethnic villages. This article takes seven multi-ethnic villages in the Dadu River Basin of Ganzi Prefecture as an example; we employ a quantitative model of spatial syntax based on the theory of figure–ground relationship to link the tangible and intangible spaces. The results reveal the logical context and formation mechanism among the overall layout, residential architecture, and public space of the villages. The findings of this study are as follows: (1) The site of different ethnic villages reflects commonality. (2) The spatial configurations of the villages are significantly influenced by the surrounding natural environment, with significant differences. Cluster-concentrated villages exhibit the smallest expansion trend, rich spatial levels, and strong ethnic territoriality; strip-intensive villages have the largest scale and the strongest permeability; and radiation-dispersion villages have variable expansion directions and architectural relationships among residents. (3) Both of the participants’ selection behaviors and residential spaces constitute the internal structure for the ethnic culture, which is driven by the humanistic spirit and force of social order, making the spatial morphology a diversified and multi-layered characteristic. (4) The rural space has gradually changed from a single residential unit to a complex unit with multiple functions. The findings extend the scope of research to ethnic villages in watersheds and provide a theoretical and practical basis for the development of other similar rural villages. Full article

In the context of global warming, climate change in river headwater regions and its drivers have attracted increasing attention. In this study, tree-ring width (TRW) chronology was constructed using tree-ring samples of fir (Abies faxoniana) in Dadu River Basin in the central part of the western Sichuan Plateau, China. Correlation analysis with climatic factors implies that the radial growth of trees in the region is mainly limited by temperature and has the highest correlation with the mean minimum temperature in summer (June and July) (R = 0.602, p < 0.001). On this basis, the TRW chronology was adopted to reconstruct variations in the mean minimum temperatures in summer from 1733 to 2010 in the upper reaches of Dadu River. The reconstruction equation was stable and reliable and offered a variance explanation rate of 36.2% in the observed period (1962~2010). In the past 278 years, the region experienced nine warm periods and ten cold periods. The warmest and coldest years occurred in 2010 and 1798, respectively, with values of 13.6 °C and 11.0 °C. The reconstruction was highly spatiotemporally representative and verified by temperatures reconstructed using other tree-ring data in surrounding areas. A significant warming trend was found in the last few decades. Moreover, the multi-taper method (MTM) analysis indicated significant periodic changes in quasi-2-year and 21–35-year periods, for which the El Niño Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) could be the key controlling factors. Full article

The Dadu River travels in the mountainous areas of southwestern China, one of regions with the most hazards that has long suffered from frequent geohazards. The early identification of landslides in this region is urgently needed, especially after the recent Luding earthquake (MS 6.8). While conventional ground-based monitoring techniques are limited by the complex terrain conditions in these alpine valley regions, space interferometric synthetic aperture radar (InSAR) provides an incomparable advantage in obtaining surface deformation with high precision and over a wide area, which is very useful for long-term and slow geohazard monitoring. In this study, more than 500 Sentinel-1 SAR images with four frames acquired during 2017~2022 were collected to detect the hidden landslide regions from the Jinchuan to Ebian Section along the Dadu River, based on joint-scatterer InSAR (JS-InSAR) and small baseline subset (SBAS) techniques. The results showed that our method could be successfully applied for landslide monitoring in complex mountainous regions. Furthermore, 143 potential landslide regions spreading over an 800 km area along the Dadu River were extracted by integrating the deformation measurements and optical images. Our study can provide a reference for large-scale geological hazard surveys in mountainous areas, and the InSAR technique will be encouraged for the local government in future long-term monitoring applications in the Dadu River Basin. Full article

The eastern margin of the Qinghai-Tibet Plateau is an extreme topography transition zone, and characterized by significant vegetation zonation, in addition to geographic features (such as enormous topographic relief and active tectonics) that control the occurrence of debris flows, which are rapid, surging flows of water-charged clastic sediments moving along a steep channel and are one of the most dangerous mountain hazards in this region. There is thus an urgent need in this region to conduct a regional-scale debris flow susceptibility assessment to determine the spatial likelihood of a debris flow occurrence and guarantee the safety of people and property, in addition to the smooth operation of the Sichuan-Tibet transport corridor. It is, however, a challenging task to estimate the region’s debris flow susceptibility while taking into consideration the comprehensive impacts of vegetation on the occurrence of debris flows, such as the positive effect of root anchoring and the negative effect of vegetation weight loads. In this study, a novel regional-scale susceptibility assessment method was constructed by integrating state-of-the-art machine learning algorithms (such as support vector classification (SVC), random forest (RF), and eXtreme Gradient Boosting (XGB)) with the removing outliers (RO) algorithm and particle swarm optimization (PSO), allowing the impacts of vegetation on debris flow initiation to be integrated with the topographical conditions, hydrological conditions, and geotechnical conditions. This method is finally applied to assess the regional-scale susceptibility of debris flows in the Dadu River basin on the eastern margin of the Qinghai-Tibet Plateau. The study results show that (i) all hybrid machine learning techniques can effectively predict the occurrence of debris flows in the extreme topography transition zone; (ii) the hybrid machine learning technique RO-PSO-SVC has the best performance, and its accuracy (ACC) is 0.946 and the area under the ROC curve (AUC) is 0.981; (iii) the RO-PSO algorithm improves SVC, RF, and XGB performance (according to the ACC value) by 3.84%, 2.59%, and 5.94%, respectively; and (iv) the contribution rate of ecology-related variables is almost only one-tenth that of topography- and hydrology-related factors, according to the factor important analysis for RO-PSO-SVC. Furthermore, debris flow susceptibility maps for the Dadu River basin were created, which can be used to assess and mitigate debris flow hazards. Full article