Search Results (44)

The Three Gorges Reservoir Area (TGRA) is an important ecological barrier and cultural intermingling zone in the upper reaches of the Yangtze River, and its traditional villages carry unique information about natural changes and civilisational development, but face the challenges of conservation and development under the impact of modernisation and ecological pressure. This study takes 112 traditional villages in the TGRA that have been included in the protection list as the research objects, aiming to construct a cultural landscape identification framework for the traditional villages in the TGRA. Through field surveys, landscape feature assessments, GIS spatial analysis, and multi-source data analysis, we systematically analyse their cultural landscape type systems and spatial differentiation characteristics, and then reveal their cultural landscape types and spatial differentiation patterns. (1) The results of the study show that the spatial distribution of traditional villages exhibits significant altitude gradient differentiation—the low-altitude area is dominated by traffic and trade villages, the middle-altitude area is dominated by patriarchal manor villages and mountain farming villages, and the high-altitude area is dominated by ethno-cultural and ecologically dependent villages. (2) Slope and direction analyses further reveal that the gently sloping areas are conducive to the development of commercial and agricultural settlements, while the steeply sloping areas strengthen the function of ethnic and cultural defence. The results indicate that topographic conditions drive the synergistic evolution of the human–land system in traditional villages through the mechanisms of agricultural optimisation, trade networks, cultural defence, and ecological adaptation. The study provides a paradigm of “nature–humanities” interaction analysis for the conservation and development of traditional villages in mountainous areas, which is of practical value in coordinating the construction of ecological barriers and the revitalisation of villages in the reservoir area. Full article

Globally, ecosystems are affected by climate change, human activities, and natural disasters, which impact ecosystem quality and stability. Vegetation plays a crucial role in ecosystem material cycle and energy transformation, making it important to monitor its resilience under disturbance stress. The Critical Slowing Down (CSD) indicates that as ecosystems near collapse, the autocorrelation of lag temporal increases and resilience decreases. We used the lag Temporal Autocorrelation (TAC) of long-term remote sensing Leaf Area Index (LAI) to monitor vegetation resilience in the Three Gorges Reservoir Area (TGRA). The Disturbance Event Model (DEM) was used to validate the CSD. The results showed the following: (1) The eastern TGRA exhibited high and increasing vegetation resilience, while most areas showed a decline. (2) Among the various vegetation types, forests demonstrated higher resilience than other vegetation types. (3) Precipitation, temperature, and soil moisture significantly influenced vegetation resilience dynamics within the TGRA. (4) For model accuracy, the CSD’s results were consistent with the DEM, confirming its applicability in the TGRA. Overall, the CSD when applied to long-term remote sensing data, provided valuable quantitative indicators for vegetation resilience. Furthermore, more CSD-based indicators are needed to analyze vegetation resilience dynamics and better understand the biological processes determining vegetation degradation and restoration. Full article

Understanding the impact of human activities on regional water resources is essential for sustainable basin management. This study examines long-term terrestrial water storage anomalies (TWSA) in the Three Gorges Reservoir Area (TGRA) over two decades, from 2003 to 2023. The analysis utilizes data from the Gravity Recovery and Climate Experiment (GRACE) and its successor mission (GRACE-FO), complemented by Global Land Data Assimilation System (GLDAS) models and ECMWF Reanalysis v5 (ERA5) datasets. The research methodically explores the comparative contributions of natural factors and human activities to the region’s hydrological dynamics. By integrating the GRACE Drought Severity Index (GRACE-DSI), this study uncovers the dynamics of droughts during extreme climate events. It also reveals the pivotal role of the Three Gorges Dam (TGD) in mitigating these events and managing regional water resources. Our findings indicate a notable upward trend in TWSA within the TGRA, with an annual increase of 0.93 cm/year. This trend is largely due to the effective regulatory operations of TGD. The dam effectively balances the seasonal distribution of water storage between summer and winter and substantially reduces the adverse effects of extreme droughts on regional water resources. Further, the GRACE-DSI analysis underscores the swift recovery of TWSA following the 2022 drought, highlighting TGD’s critical role in responding to extreme climatic conditions. Through correlation analysis, it was found that compared with natural factors (correlation 0.62), human activities (correlation 0.91) exhibit a higher relative contribution to TWSA variability. The human-induced contributions were derived from the difference between GRACE and GLDAS datasets, capturing the combined effects of all human activities, including the operations of the TGD, agricultural irrigation, and urbanization. However, the TGD serves as a key regulatory facility that significantly influences regional water resource dynamics, particularly in mitigating extreme climatic events. This study provides a scientific basis for water resource management in the TGRA and similar large reservoir regions, emphasizing the necessity of integrating the interactions between human activities and natural factors in basin management strategies. Full article

Identifying the relationship between extreme precipitation (EP) and large-scale climate circulation is of great significance for extreme weather management and warning. Previous studies have effectively revealed the influence of single climate circulation on EP, although the influence characteristics of multiple climate circulation are still unclear. In this study, seasonal spatiotemporal changes in decadal anomalies of daily EP were analyzed based on quantile perturbation method (QPM) within the Three Gorges Reservoir Area (TGRA) for the period from 1960 to 2020. Sea surface temperature (SST)- and sea level pressure (SLP)-related climate circulation factors were selected to examine their interaction influences on and contributions to EP. The results showed that: (1) Summer EP anomalies exhibited greater temporal variability than those in other seasons, with the cycle duration of dry/wet alternation shortening from 15 years to 5 years. Winter EP anomalies showed pronounced spatial homogeneity patterns, especially in the 1970s. (2) According to the analysis based on a single driver, the Southern Oscillation Index (SOI), the North Atlantic Oscillation (NAO), and the Indian Ocean Dipole (IOD) had prolonged correlations with seasonal EP anomalies. (3) More contributions can be obtained from multiple climate circulations (binary and ternary drivers) on seasonal EP anomalies than from a single driver. Although difference existed in seasonal combinations of ternary factors, their contributions on EP anomalies were more than 60%. This study provides an insight into the mechanisms of modulation and pathways influencing various large-scale climate circulation on seasonal EP anomalies. Full article

Displacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by precipitation and fluctuations in reservoir water level, and displacement deformation shows a step-like curve. Landslide displacement in TGRA is related to its geology and is affected by external factors. Hence, this study proposes a novel landslide displacement prediction model based on variational mode decomposition (VMD) and a Harris Hawk optimized kernel extreme learning machine (HHO-KELM). Specifically, VMD decomposes the measured displacement into trend, periodic, and random components. Then, the influencing factors are also decomposed into periodic and random components. The feature data, with periodic and random data, are input into the training set, and the trend, periodic, and random term components are predicted by HHO-KELM, respectively. Finally, the total predicted displacement is calculated by summing the predicted values of the three components. The accuracy and effectiveness of the prediction model are tested on the Shuizhuyuan landslide in the TGRA, with the results demonstrating that the new model provides satisfactory prediction accuracy without complex parameter settings. Therefore, under the premise of VMD effectively decomposing displacement data, combined with the global optimization ability of the HHO heuristic algorithm and the fast-learning ability of KELM, HHO-KELM can be used for displacement prediction of step-like landslides in the TGRA. Full article

Rainfall-induced landslides are a major hazard in the Three Gorges Reservoir area (TGRA) of China, encompassing 19 districts and counties with extensive coverage and significant spatial variation in terrain. This study introduces the Gradient Boosting Decision Tree (GBDT) model, implemented on the Google Earth Engine (GEE) cloud platform, to dynamically assess landslide risks within the TGRA. Utilizing the GBDT model for landslide susceptibility analysis, the results show high accuracy with a prediction precision of 86.2% and a recall rate of 95.7%. Furthermore, leveraging GEE’s powerful computational capabilities and real-time updated rainfall data, we dynamically mapped landslide hazards across the TGRA. The integration of the GBDT with GEE enabled near-real-time processing of remote sensing and meteorological radar data from the significant “8–31” 2014 rainstorm event, achieving dynamic and accurate hazard assessments. This study provides a scalable solution applicable globally to similar regions, making a significant contribution to the field of geohazard analysis by improving real-time landslide hazard assessment and mitigation strategies. Full article

Non-point source (NPS) pollution poses a significant threat to the water environment, yet controlling it at the watershed scale remains a formidable challenge. Understanding the characteristics and drivers of nitrogen (N) and phosphorus (P) outputs at the watershed scale, along with identifying cost-effective best management practices (BMPs), is crucial for effective pollution control. In this study, we utilized the Wangjiaqiao watershed within the Three Gorges Reservoir Area (TGRA) as a case study to explore the characteristics of N and P load outputs and their dominant drivers by combining the SWAT model and a geographic detector. Based on our analysis of N and P loads within the watershed, we employed the entropy weight method to evaluate the reduction efficiency and cost-effectiveness of 64 BMP scenarios, encompassing seven measures (vegetative filter strips, parallel terraces, 10% fertilizer reduction, 30% fertilizer reduction, residue cover tillage, grass mulching, and returning farmland to forest) and their combinations. Our findings revealed the following: (1) spatial heterogeneity in NPS loads within the watershed, primarily influenced by land use, fertilizer application, and surface runoff, with interactive enhancement effects among driving factors; (2) the differential effectiveness of BMPs at the watershed level, with structural measures, particularly terracing, exhibiting higher efficacy and achieving reduction rates of 28.12% for total nitrogen (TN) and 37.69% for total phosphorus (TP); the combined BMPs showed improved reduction efficiency, but not merely additive; and (3) in terms of cost-effectiveness, 30% fertilizer reduction emerged as the most beneficial among the individual measures. Moreover, a combination of vegetative filter strips, parallel terraces, and 30% fertilizer reduction demonstrated significant improvements in TN and TP reductions (48.05% and 61.95%, respectively), suggesting their widespread applicability. Overall, our study provides insights into developing a cost-effective BMP strategy for the Wangjiaqiao watershed and offers valuable guidance for NPS pollution management in similar small watersheds within the TGRA. Full article

The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in the uncertainty of InSAR results, where some deformations cannot be calculated, and some are not true deformations. This uncertainty of InSAR results will lead to errors in landslide hazard assessment. Here, we attempt to evaluate landslide hazards by considering combined rainfall and surface deformation. The main objective of this research was to mitigate the impact of bias and explore the accurate landslide hazard assessment method. A total of 201 landslides and 11 geo-environment factors were utilized for landslide susceptibility assessment by support vector machine (SVM) model in Wanzhou District, Three Gorges Reservoir Area (TGRA). The preliminary hazard is obtained by analyzing the statistical data of landslides and rainfall. Based on the SAR image data of Sentinel-1A satellites from September 2019 to October 2021, the SBAS-InSAR method was used to analyze surface deformation. The correlation between surface deformation and rainfall was analyzed, and the deformation factor variables were applied to landslide hazard assessment. The research results demonstrate that the error caused by the uncertainty of InSAR results can be effectively avoided by analyzing the relationship between rainfall and surface deformation. Our results can effectively adjust and correct the hazard results and eliminate the errors in the general hazard assessment. Our proposed method can be used to assess the landslide hazard in more detail and provide a reference for fine risk management and control. Full article

Under the influence of climate change and human activities, the ecohydrological processes in the Three Gorges Reservoir Area (TGRA) present new evolution characteristics at different temporal and spatial scales. Research on the evolution and driving mechanism of key ecohydrological element in the TGRA under the changing environment has important theoretical and practical values for correctly understanding the ecohydrological situation in the reservoir area and guiding the coordinated development of water and soil resources. In this study, the LPJ (Lund–Potsdam–Jena) model was used to simulate and analyze the spatiotemporal variations in evapotranspiration (AET) from 1981 to 2020. Sen’s slope and sensitivity analysis methods were used to quantify individual contributions of climate and human factors to changes in AET in different periods. The results indicate the following: (1) The simulation accuracy of the LPJ model for AET in the TGRA was high, with a certainty coefficient (R2), Nash efficiency coefficient (NSE), and mean relative error (MRE) of 0.89, 0.76, and 4.32%, respectively. (2) The multiyear average AET was 650.71 mm and increased at a rate of 21.63 mm/10a from 1981 to 2020. The annual distribution of AET showed a unimodal seasonal variation trend. The peak value occurred in July, reaching 113.02 mm, and the valley value occurred in January and December, less than 13 mm. (3) AET increased by 5.60% and 6.28% before and after impoundment, respectively. The contribution rate of human activities increased significantly from −3.75% before impoundment to 26.95% after impoundment, and the contribution ratios of climate change were 89.39% and 73.09%, respectively, during these two periods. From 1981 to 2020, AET increased by 5.28%, in which the contribution ratios of climate and human factors were 89.39% and 10.61%, respectively. Full article

The Three Gorges Dam project and other human activities, including regional urbanization and industrialization, have had a substantial influence on the biological environment of the Three Gorges Reservoir Area (TGRA). They have changed the surface land use pattern, disrupted ecosystem structure and function, and influenced changes in the value of ecosystem services. The human activity intensity (HAI) assessment model, the ecosystem services value (ESV) assessment model, and the bivariate spatial autocorrelation model were used based on the spatiotemporal evolution data of towns along the Yangtze River in the TGRA in 1995, 2000, 2005, 2010, 2015, and 2020. At the same time, the spatiotemporal impact of the HAI on land use patterns was evaluated and the magnitude of the spatiotemporal influences on the ESV was investigated. The findings demonstrate the following: (1) The TGRA’s higher reaches are occupied by forested land, while the middle and lower portions are characterized by agricultural land. Land change in the reservoir region has mostly featured transitions between wooded land, agricultural land, grassland, and building land during the last 25 years. Because of differences in natural geography and administrative divisions, the intensity of human activity in the TGRA changes throughout the Yangtze River, with higher intensity in Chongqing and lower intensity in Hubei. By comparing the ESV and the HAI and validating with Moran scatter plots, it was determined that there is a negative relationship between the value of ecosystem services and the intensity of human activities. (2) The ESV rose from CNY 1017.16 × 10⁸ in 1995 to CNY 1052.73 × 10⁸ in 2020, suggesting that the policies of converting farmland back into forests, eliminating outdated production capacity, and developing green industries, among other ecological conservation measures, are effective. (3) In the research area, the effect coefficient of HAI on ESV ranges from −0.02 to −0.032 to −0.031. This coefficient represents the correlation between the HAI and ESV and can preliminarily judge the change in the degree of correlation between the HAI and ESV. The increase in HAI leads to a decrease in the value of ecosystem services, and there is a clear negative spatial correlation between the two. The low human activity area and low ecosystem service value area in the Chongqing section have been transformed into a high ecosystem service value area through years of returning farmland to forest and ecological management measures for sustainable development. Full article

Rapid industrialisation and urbanisation have moved contemporary civilization ahead but also deepened clashes with nature. Human society’s long-term evolution faces a number of serious problems, including the climate issue and frequent natural disasters. This research analyses the spatiotemporal evolution features of land use remote sensing data from 2005, 2010, 2015, and 2020. Under the Nature-based Solutions (NbS) idea, four scenarios are established: Business as Usual (BAU), Woodland Conservation (WLC), Arable Land Conservation (ALC), and Urban Transformation and Development (UTD). The RF–Markov–CA model is used to simulate the spatiotemporal patterns of land use for the years 2025 and 2030. Furthermore, the InVEST model is utilised to assess and forecast the spatiotemporal evolution features of carbon storage. The findings show that (1) the primary land use categories in the Three Gorges Reservoir Area (TGRA) from 2005 to 2020 are arable land and woodland. Arable land has a declining tendency, whereas woodland has an increasing–decreasing trend. (2) The WLC scenario exhibits the greatest growth in woodland and the lowest drop in grassland from 2020 to 2030, indicating a more stable ecosystem. (3) The TGRA demonstrates substantial geographic variation in carbon storage from 2005 to 2030, with a broad distribution pattern of “higher in the north, lower in the south, higher in the east, lower in the west, with the reservoir head > reservoir centre > reservoir tail”. (4) In comparison to the other three scenarios, the WLC scenario sees a slower development of construction and arable land from 2020 to 2030, whereas the ecological land area rises the highest and carbon storage increases. As a result, the WLC scenario is the TGRA’s recommended development choice. The study’s findings have substantial implications for the TGRA’s ecological preservation and management, as well as for the optimisation of ecosystem carbon cycling and the promotion of regional sustainable development. Full article

Landslide displacement prediction has garnered significant recognition as a pivotal component in realizing successful early warnings and implementing effective control measures. This task remains challenging as landslide deformation involves not only temporal dependency within time series data but also spatial dependence across various regions within landslides. The present study proposes a landslide spatiotemporal displacement forecasting model by introducing attention-based deep learning algorithms based on spatiotemporal analysis. The Maximal Information Coefficient (MIC) approach is employed to quantify the spatial and temporal correlations within the daily data of Global Navigation Satellite System (GNSS) observations. Based on the quantitative spatiotemporal analysis, the proposed prediction model combines a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture spatial and temporal dependencies individually. Spatial–temporal attention mechanisms are implemented to optimize the model. Additionally, we develop a single-point prediction model using LSTM and a multiple-point prediction model using the CNN-LSTM without an attention mechanism to compare the forecasting capabilities of the attention-based CNN-LSTM model. The Outang landslide in the Three Gorges Reservoir Area (TGRA), characterized by a large and active landslide equipped with an advanced monitoring system, is taken as a studied case. The temporal MIC results shed light on the response times of monitored daily displacement to external factors, showing a lagging duration of between 10 and 50 days. The spatial MIC results indicate mutual influence among different locations within the landslide, particularly in the case of nearby sites experiencing significant deformation. The attention-based CNN-LSTM model demonstrates an impressive predictive performance across six monitoring stations within the Outang landslide area. Notably, it achieves a remarkable maximum coefficient of determination (R²) value of 0.9989, accompanied by minimum values for root mean squared error (RMSE), absolute mean error (MAE), and mean absolute percentage error (MAPE), specifically, 1.18 mm, 0.99 mm, and 0.33%, respectively. The proposed model excels in predicting displacements at all six monitoring points, whereas other models demonstrate strong performance at specific individual stations but lack consistent performance across all stations. This study, involving quantitative deformation characteristics analysis and spatiotemporal displacement prediction, holds promising potential for a more profound understanding of landslide evolution and a significant contribution to reducing landslide risk. Full article

The Three Gorges Reservoir Area (TGRA) in China has extensive non-timber product plantations (NTPP), in which integrated management based on intensive fertilization and weeding were required to maintain and improve yields for a long time. Uncertainties still existed regarding the compound effects of environment and the long-term integrated management on soil organic carbon content (SOC) in NTPP. Data from 341 sampling plots covering six primary NTPP types were collected to investigate the influence of environment and management on topsoil (0–10 cm) SOC of NTPP using a coupled algorithm based on machine learning and structural equation modeling. Results showed significant differences and spatial variabilities in SOC content among different types of NTPP. Integrated management accounted for approximately 53% of the accumulation of topsoil organic carbon, surpassing the total contribution of topography, climate, vegetation, and soil properties in NTPP of TGRA. SOC content increased with available nitrogen for NTPP at all altitudes in TGRA. The study highlighted the potential of enhancing SOC storage through adaptive integrated management in NTPP of vast areas. Improving soil organic carbon stock in large area of non-timber production plantations would benefit the realization of carbon neutralization in next decades. Full article

Landslides are geological events that frequently cause major disasters. Research on landslides is essential, but current studies mostly use historical landslide data and do not reflect dynamic, real-time research results. In this study, landslide deformations and land-use changes were used to analyze the landslide distribution in Fengjie County and Wushan County in the Three Gorges Reservoir Area (TGRA) by using interferometric and polarimetric SAR. In this study, the mean annual rate of landslide deformations was obtained using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) for the ALOS-2 (2014–2019) data. Land-use changes were based on the 2007 and 2017 land-use results from dual-polarization ALOS-1 and ALOS-2 data, respectively. To address the problem of classification accuracy reduction caused by geometric distortion in mountainous areas, we first used texture maps and pseudocolor maps synthesized with dual-polarization intensity maps to perform classification with random forest (RF), and then we used coherence and slope maps to run the K-Means algorithm (KMA). We named this the secondary classification method. It is an improvement on the single classification method, exhibiting a 94% classification accuracy, especially in rugged areas. Combined with land-use changes, GIS spatial analysis was used to analyze the spatial distribution of landslides, and it was found that the landslide rate was significantly correlated with the type after change, with a correlation coefficient of 0.7. In addition, land-use types associated with human activities, such as cultivated vegetation, were more likely to cause landslide deformation, which can be used to guide local land-use planning. Full article

Habitat quality (HQ) is an important indicator to characterize the level of biodiversity and ecosystem services, and can reflect the quality of the human living environment. Changes in land use can disturb regional HQ. Current research mostly focuses on assessing the good or bad quality of regional habitats, and less on the spatial response relationship between land use change and HQ, and even fewer studies on finely distinguishing the impact of land use types on HQ. Therefore, taking Three Gorges Reservoir Area (TGRA) of China as the study area, this paper first analyzes the land use change of study area by using the land use transfer matrix, land use rate model and landscape pattern index, and then combines the InVEST model with the multi-scale geographically weighted regression (MGWR) model to build a refined assessment framework to quantitatively assess the spatial and temporal evolution patterns of HQ, and then analyse in detail the spatial response relationship of each land use type change on the impact of HQ. The results showed that from 2000 to 2020, the land use in the TGRA shows a changing state of “urban expansion, cultivated land shrinkage, forest land growth, and grassland degradation”. With the change in land use, the habitat quality index (HQI) in the study area showed an “ increase first and then decline” change characteristics, and the HQ degradation was more obvious in the areas with intense human activities. The impact of land use change over the past 20 years on HQ in the TGRA has significant spatial and temporal heterogeneity, with changes in paddy and dryland having mainly negative impacts on HQ, and changes in sparse land, shrubland, and medium-cover grassland having mainly positive impacts on HQ. This paper mainly provides a research framework for refined assessment, and the results can provide scientific support for land planning and ecological protection in the TGRA, and the research methods and ideas can provide references for similar research. Full article