Gorges Reservoir Area Research Articles

Evaluating the influences hedgerow on soil erosion and nitrogen loss of purple soil sloping farmland under simulated rainfall

Hedgerow is a vital soil and water conservation technology for sloping farmland that can significantly reduce erosion and nutrient loss via their above- and below-ground parts. The effect of hedgerow on erosion and nutrient loss has been widely investigated, while the respective effects of their above- and below-ground parts are still unclear. Therefore, the purple soil from the Three Gorges Reservoir Area was used, and 3 slope conditions of control check, whole hedgerow and hedgerow roots only combined with 2 slope gradients (15° and 25°) were constructed and the simulated rainfall tests were researched at 3 rainfall intensities (60, 90 and 120 mm h−1). The runoff initiation time, runoff and erosion rates, and loss of nitrogen via runoff and sediment were analysed. In comparison to the control check slope condition, it was indicated that hedgerow increased the runoff initiation time by 43.38 %, decreased runoff and erosion by 15.59 % and 78.37 %, and decreased nitrogen loss via runoff by approximately 40 % and via sediment by approximately 70 % on average, respectively. The average contribution rates of the below-ground part of the hedgerow was 49.89 % for the increase in runoff initiation time, 33.99 % for runoff reduction, and 39.91 % for erosion reduction. In addition, more than 2/3 and 58.49 % of nitrogen loss reduction via runoff and sediment, respectively, was contributed by the above-ground part of the hedgerow. These results are beneficial for comprehending the control mechanism of hedgerow on soil erosion and nitrogen loss, thereby provide a scientific foundation for the sustainable and efficient utilization of soil and water resources on sloping farmland.

Probing multi-physical process and deformation mechanism of a large-scale landslide using integrated dual-source monitoring

The implementation of isolated heterologous monitoring systems for spatially distant borehole deployments often comes with substantial equipment costs, which can limit the effectiveness of geohazard mitigation and georisk management efforts. To address this, we have developed a novel monitoring system that integrates fiber Bragg grating (FBG) and microelectromechanical system (MEMS) techniques to capture soil moisture, temperature, sliding resistance, strain, surface tilt, and deep-seated inclination. This system enables real-time, simultaneous data acquisition and cross-validation analyses, offering a cost-effective solution for monitoring critical parameters in geohazard-prone areas. We successfully applied this integrated monitoring system to the Xinpu landslide, an active super-large landslide located in the Three Gorges Reservoir Area (TGRA) of China. The resulting strain profile confirmed the presence of two shallow secondary sliding surfaces at depths of approximately 7 m and 12 m, respectively, in addition to the deep-seated sliding surface at a depth of ∼28 m. The lower secondary sliding surface was activated by extreme precipitation, while the upper one was primarily driven by significant changes in reservoir water levels and secondarily triggered by concentrated rainfalls. Anti-slide piles have remarkably reinforced the upper moving masses but failed to control the lower ones. The gap between the pile heads and the soil amplified the rainwater erosion effect, creating a preferential channel for rainwater infiltration. Multi-physical measurements revealed a mixture of seepage-driven and buoyancy-driven behaviors within the landslide. This study offers an integrated dual-source multi-physical monitoring paradigm that enables collaborative management of multiple crucial boreholes on a large-scale landslide, and contributes to the evaluation and improvement of engineering measures in similar geological settings.

Prediction and countermeasures of heavy metal copper pollution accident in the Three Gorges Reservoir Area

In order to mitigate the hazards of water pollution in drinking water source areas (DWSAs), developing applicable models and proposing effective solutions is of paramount significance. The study developed the Heshangshan Drinking Water Source Area (HDWSA) Hydrodynamic Model, integrating Geographic Information System (GIS) into a two-dimensional hydrodynamic water quality model using FORTRAN. TECPLOT360 software (Software Tools for Numerical Simulation with Visualization) visualized contamination transportation and diffusion. The model's relative error is less than 6%, indicating its strong stability and high reliability. The HDWSA in the Three Gorges Reservoir Area (TGRA) was used as a case study, focusing on copper (Cu) as a pollutant. By regulating the flow downstream from the Xiangjiaba Reservoir, Scheduling Group 1 and Scheduling Group 2 respectively increased the flow by 4000 m3/s and 8000 m3/s. The study analyzed the spatio-temporal variations of Cu concentration following pollution accident and flow scheduling. Under accident conditions, it took 71, 61, 49, and 56 min for the Cu concentration in the study area to decrease to below the standard value (1 mg/L) during dry, falling, flood, and storage periods. Scheduling Groups 1 and 2 reduced the pollutant exceedance duration by 19–26 min and 12–18 min across the four water periods.

Particle Swarm Optimization of Support Vector Machine Inversion Model for Overhead Upright Piers Damage-Inducing Factor

In the Three Gorges reservoir area, the overhead upright pier is the primary structural form. For intelligent monitoring of existing terminals, this research chooses Chongqing Xintian Port as the study object and proposes a support vector machine (SVM) damage-inducing factor (DIF) inversion model based on particle swarm optimization (PSO). To apply the finite element method to analyze the stress distribution characteristics of quay pile groups under three main DIFs, including the stacking effect, ship impact load effect, and bank slope effect. After characterizing the stress data, it becomes evident that there exists a correlation between stress and each DIF parameter. Before generating the training sample set, principal component analysis is employed to reduce dimensionality and eliminate a substantial amount of redundant data. The model has an accuracy of 0.999 for the identification of the type of DIF and 0.975 for the identification of the location of the action of the DIF with F1 coefficients of 0.999 and 0.978, respectively. For the strength of DIF predictions, MAE and MSE were 4.871 and 1.202, respectively, R2 was 0.986, NSE was 0.986, WI was 0.996, and PBIAS was 0.095. After extracting every sample, the relative error for the ship impact load effect is 0.05, and the highest relative error for the bank slope effect is 0.02; the error for the stacking effect is limited to 0.08. The results suggest that the damage inducement inversion model of the SVM optimized by the PSO algorithm can effectively identify the DIF of the overhead upright pier.

Evaluating the soil hydraulic functions of vegetation restoration in a subtropical hilly catchment: Insights from continuous soil moisture monitoring

The global trend of vegetation “greening” in the context of ecological restoration necessitates an urgent assessment of ecosystem services. As essential components of ecosystem services, the hydraulic functions of soil in infiltrating and retaining water following vegetation restoration remain unclear, especially in subtropical mountainous and hilly areas with complex topographies. From 2018 to 2021, soil moisture data collected at five-minute intervals were monitored for three restoration strategies in a hilly catchment of China’s Three Gorges Reservoir area. The restoration strategies included planted forest (PF) and natural restoration (naturally regenerated forest, NF; deforested pasture, DP). The soil moisture response to rainfall under these strategies was evaluated using several metrics, including the time difference between peak rainfall intensity and peak soil moisture response (Tp2p), cumulative infiltration, and occurrence frequency of preferential flow (PRF). The results showed that the average soil moisture content (SMC) of PF was significantly (p < 0.05) lower than that of NF and DP in both dry and wet seasons, regardless of upslope or downslope location. However, the topographic position affected the difference in average SMC between DP and NF. At the downslope location, the average SMC of DP (0.39 cm3 cm−3) was significantly higher than that of NF (0.33 cm3 cm−3). Conversely, at the upslope location, the average SMC of DP (0.27 cm3 cm−3) was lower than that of NF (0.30 cm3 cm−3). These findings suggested that PF had a lower amount of soil water storage than NF and DP, which was supported by the lowest cumulative infiltration in PF during storm events. The response of soil moisture to storms in PF (Tp2p = 3.1 h) was slower than that in NF (Tp2p = 1.9 h) and DP (Tp2p = 2.5 h). This was consistent with the lower occurrence frequency of preferential flow in PF (PRF = 19.2 %) than NF (PRF = 39.2 %) and DP (PRF = 32.9 %). Therefore, longer response time and less preferential flow indicated that the PF had a relatively poor soil moisture responsiveness to storm events. Accordingly, this study highlights the insufficiency of afforestation in soil water infiltration and retention compared to natural restoration, meriting consideration when assessing soil hydraulic functions in vegetation restoration areas.

Assessing the imperative of conditioning factor grading in machine learning-based landslide susceptibility modeling: A critical inquiry

Current machine learning approaches to landslide susceptibility modeling often involve grading conditioning factors, a method characterized by substantial subjectivity and randomness. The necessity and rationality of such grading have sparked continued debate. Recognizing the potential profound impact of this grading on the results of models, we conducted an in-depth study focusing on four townships within the Wanzhou section of the Three Gorges Reservoir area. A comprehensive assessment was conducted using three traditional machine learning models, five ensemble learning models, and four deep learning models to evaluate the implications of continuous factor grading. Three grading strategies were explored: non-grading, equal intervals, and natural breaks. Further investigation was conducted to determine how various grade levels (e.g., 4, 6, 8, 12, 16, 20) affect model efficacy. Our analysis reveals that the Support Vector Machine (SVM) model performs optimally with an 8-level grading using natural breaks. In contrast, a decision tree (DT) and its associated ensemble models are more effective without grading. For Multi-Layer Perceptron Neural Network (MLPNN) and Convolutional Neural Networks (CNN) models, a natural breaks grading exceeding 8 levels is advisable. Gated Recurrent Unit (GRU) and Deep Neural Networks (DNN) models benefit from an equidistant grading strategy of over 12 levels, while Long Short-Term Memory Neural Networks (LSTM) models thrive with an equidistant grading surpassing 16 levels. This study is pioneering in introducing grading guidelines for machine learning models in landslide susceptibility modeling. Our findings offer invaluable insights for future research, setting a path towards more standardized practices in this field. This enhances the bridge between theoretical knowledge and its real-world application, promoting a more rigorous and systematic grading approach and advancing the standardization of landslide susceptibility modeling.

Step-like displacement prediction and failure mechanism analysis of slow-moving reservoir landslide

Landslides triggered by extreme rainfall due to global climate change are becoming more frequent. The Earth surface processes activity and landform evolution caused by landslide movement seriously affect human survival and the environment. In recent years, the reservoir slope located in the Three Gorges reservoir (TGR) area has been subject to the combined effects of extreme rainfall and reservoir regulation, which have been highly susceptible to inducing landslides. Reservoir landslides usually show step-like slow movement characteristics. The causes of slow-moving landslides are complex, the mechanisms behind them are difficult to grasp, and it may evolve into violent landslide disasters. Therefore, accurately predicting step-like displacement in slow-moving landslides is an effective solution for risk reduction. To achieve the accurate prediction of the step-like displacement of slow-moving landslides and the analysis of the failure mechanism, this study establishes a prediction model applicable to step-like displacement prediction of slow-moving reservoir landslides is developed based on 16 years of continuously monitored cumulative displacement of the Jiuxianping landslide, combined with the Empirical Mode Decomposition (EMD) method and the Improved Particle Swarm Optimization (IPSO) optimized the Long short-term memory (LSTM) neural network. This approach can effectively improve step-like displacement prediction accuracy. In particular, this approach can better represent the association between the step displacement of slow-moving landslides with environmental characteristics (e.g., rainfall, reservoir level). The study reveals that there are differences in the mechanisms and influencing conditions that cause the deformation of different areas of the landslide. Additionally, the reservoir water level fluctuation and seasonal rainfall are identified as the primary reasons contributing to the stepped displacement of the Jiuxianping landslide. The method provides a basis for the step-like displacement prediction, instability mechanism and landform evolution study of slow-moving reservoir landslides and provides valuable guidance for the prevention and control of similar reservoir geomorphic hazards worldwide.

Mixed cropping enhances the nitrogen and phosphorus purification efficiency of sewage in wetland ecosystems

Although the water purification performance of individual species and their removal mechanisms have been thoroughly documented, the potential enhancement of sewage nitrogen and phosphorus removal rates through the combined effects of different plant species has yet to be comprehensively investigated. This study opted for Canna indica and Thalia dealbata, two characteristic wetland plants from the Three Gorges Reservoir Area, as the test subjects. The impact of cropping patterns (single-cropping and mixed-cropping) on the efficiency of nitrogen and phosphorus removal from sewage at varying concentrations was explored. The findings unveiled that the mixed-cropping pattern considerably heightened the total nitrogen (TN) absorption of plants in sewage environments, though it did not appreciably affect total phosphorus absorption. Moreover, sewage concentration influenced the nitrogen and phosphorus absorption patterns of plants, potentially exhibiting a threshold phenomenon. Mixed cropping significantly augmented the removal rates of sewage nitrogen and phosphorus. Under the single cropping arrangement, C. indica displayed superior pollutant removal performance in comparison to T. dealbata. Furthermore, the wetland plants significantly diminished the TN content of the soil matrix. The results indicated that the mixed planting pattern enhanced the purification efficiency of effluent nitrogen and phosphorus in the artificial wetland, probably due to the better purification by mixed species under competition for nutrients or the dilution effect of multiple species on effluent nitrogen and phosphorus nutrient salts following mixed planting. These results should guide planting selection for efficient and sustainable wastewater purification in wetland ecosystems.

An integrated numerical model for landslide-generated waves: a case study of the Gongjiafang landslide, Three Gorges Reservoir, China

ABSTRACT Landslide is a frequent geological hazard in the Three Gorges reservoir area of China. Impulse waves could be created and cause substantial damage if the landslide collides with a water body. A nonlinear shallow water equation in combination with a progressive landslide model is proposed to simulate this catastrophic disaster. The proposed approach is distinguished by rapid calculation, less computational burden, and high practical value. By simulating the 2008 Three Gorges Reservoir Gongjiafang landslide and the subsequent impulse waves, we illustrated and validated this methodology. The initiation and motion processes of the landslide, as well as the generation, propagation, and run-up processes of impulse waves, were thoroughly performed. The simulation results were in good agreement with the field survey data, demonstrating the viability of the proposed approach for simulating the impulse waves triggered by landslide events. This numerical approach is beneficial for engineers to perform a preliminary estimation of landslide-generated waves and further facilitate the prevention and mitigation of geological hazards.

Automated Machine Learning-Based Landslide Susceptibility Mapping for the Three Gorges Reservoir Area, China

Automated Machine Learning-Based Landslide Susceptibility Mapping for the Three Gorges Reservoir Area, China

Spatiotemporal Variations in Actual Evapotranspiration Based on LPJ Model and Its Driving Mechanism in the Three Gorges Reservoir Area

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.

Monitoring the Area Change in the Three Gorges Reservoir Riparian Zone Based on Genetic Algorithm Optimized Machine Learning Algorithms and Sentinel-1 Data

Riparian zones play a critical role in ecosystems. Accurately extracting the area of a riparian zone in open water is challenging due to human activities and climate change. This study used Sentinel-1 satellite data to investigate the capabilities of the support vector machine, extreme gradient boosting, and random forest methods, which were optimized by genetic algorithms for the detection of area changes in the riparian zone in the heart region of the Three Gorges Reservoir area of China. A total of 29 images were collected in 2020, and three models were created for each image, which were then transferred to other phases. The models’ performance metrics were validated using all of the images. The results indicated that the SVM method achieved the best performance with an accuracy of 0.945, an F1_Score of 0.950, and a kappa coefficient of 0.889. The optimal model was then used to monitor the area changes in the riparian zone over the study area in 2020. It was calculated that the area of the riparian zones was the smallest on 26 December and the largest on 17 June, with a maximum riparian zone of 31.97 km2. Overall, this study demonstrates that an SVM is the most stable method for detecting area changes in a riparian zone when using Sentinel-1 data compared to the RF and XGB methods. The findings are anticipated to provide a feasible plan for detecting the area dynamics in open-water riparian zones and to provide valuable information for the rational utilization of land resources and the ecological safety of the riparian zone in the Three Gorges Reservoir.

Landslide Dynamic Susceptibility Mapping Base on Machine Learning and the PS-InSAR Coupling Model

Complex and fragile geological conditions combined with periodic fluctuations in reservoir water levels have led to frequent landslide disasters in the Three Gorges Reservoir area. With the development of remote sensing technology, many scholars have applied it to landslide susceptibility assessment to improve model accuracy; however, how to couple these two to obtain the optimal susceptibility assessment model remains to be studied. Based on Sentinel-1 data, relevant data, and existing research results, the information value method (IV), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) models were selected to analyze landslide susceptibility in the urban area of Wanzhou. Models with superior performance will be coupled with PS-InSAR deformation data using two methods: joint training and weighted overlay. The accuracy of different models was assessed and compared with the aim of determining the optimal coupling model and the role of InSAR in the model. The results indicate that the accuracy of different landslide susceptibility prediction models is ranked as RF &gt; SVM &gt; CNN &gt; IV. Among the coupled dynamic models, the performance ranking was as follows: InSAR jointly trained RF (IJRF) &gt; InSAR weighted overlay RF (IWRF) &gt; InSAR jointly trained SVM (IJSVM) &gt; InSAR weighted overlay SVM (IWSVM). Notably, the IJRF model, which combines InSAR deformation data through joint training, exhibited the highest accuracy, with an AUC value of 0.995. In the factor importance analysis within the IJRF model, InSAR deformation data ranked third after hydrological distance (0.210) and elevation (0.163), with a value of 0.154. A comparison between landslide dynamic susceptibility mapping (LDSM) and landslide susceptibility mapping (LSM) revealed that the inclusion of InSAR deformation data effectively reduced false positives around the landslide areas. The results suggest that joint training is the most suitable coupling method, allowing for the optimal expression of InSAR deformation data and enhancing the predictive accuracy of the model. This study serves as a reference for future research and provides a foundation for landslide risk management.

Study on the deformation mechanism of anti-piles in Majiagou landslide in Three Gorges Reservoir Area, China

Study on the deformation mechanism of anti-piles in Majiagou landslide in Three Gorges Reservoir Area, China

Mechanism of strength deterioration of red sandstone on reservoir bank slopes under the action of dry–wet cycles

To investigate the micro-scale mechanism of strength deterioration under different times of dry–wet cycles, laboratory tests of physical properties, triaxial compression, X-ray diffraction (XRD) and scanning electron microscopy (SEM) were conducted on red sandstone on reservoir bank slopes. The research results showed that when the 5th dry–wet cycle ended, the dry mass and peak strength started to rapidly decline, while the porosity and saturated mass increased rapidly. In general, all of these behaviours become relatively stable when the number of cycles increased. Similarly, cohesion and internal friction angle changed most significantly from 0 to 10 cycles and then became stable. In addition, the physical expansion of the adsorbed water film and the dissolution and expansion of mineral particles increased the number of internal microcracks and pores and the porosity and saturated mass of the sample. In addition, the pore fluid effect and fracture flow effect made the microcracks in the red sandstone grow and connect; thus, the peak strength of the sample decreased. Moreover, during the dry–wet cycles, the change in the relative content of mineral particles and the pore fluid effect weakened the clay cementation, and then the dry mass and cohesion of the samples decreased. The research reported in this paper will play a very significant role in the scientific analysis of slope stability in the Three Gorges Reservoir area.

Ecological zoning and dynamic assessment of effectiveness in the Three Gorges Reservoir Area, China

Human activities render ecological conservation a formidable challenge. The rational zoning and dynamic evaluation of ecological regions are critical for the protection of regional biodiversity and the promotion of ecological sustainability. Here, the InVEST-HFI model was used to evaluate both the habitat quality (HQ) and the human footprint index (HFI) of the Three Gorges Reservoir Area (TGRA), a representative ecologically vulnerable area in southwest China. Superimposition analysis was employed to identify eco-regional zoning types and the PLUS model was applied to simulate and evaluate the benefits of ecological zoning. The results showed that the overall HQ of the TGRA area is satisfactory, with high-grade HQ areas covering 50.16 % of the entire reservoir area, mainly distributed in the Hubei section. The HFI displays a spatial distribution pattern, with higher values concentrated in the southwest and lower values in the northeast; there was also a concentration of high-value areas in Chongqing. Based on the HQ and HFI, the TGRA can be divided into three ecological protection zone types—a strict ecological protection zone, accounting for 57.18 % of the total area and distributed in the head and belly of the reservoir; a habitat restoration zone, accounting for 17.45 % of the total area and distributed in the tail of the reservoir; and a moderate development zone, which is scattered along the main stream of the Yangtze River in the middle of the reservoir. Simulation projections of land use in the TGRA for natural development and zoning protection scenarios until 2035 revealed that the ecosystem service value (ESV) of the latter scenario is 2.25 billion yuan higher than that of the former. Ecological zoning management can improve the ESV of the TGRA. We demonstrated the effectiveness of utilizing the HQ and HFI measures in ecological zoning, thus providing a valuable reference for assessing its benefits.

Relationship of construction land expansion and ecological environment changes in the Three Gorges reservoir area of China

With the rapid development of urbanization, the disorderly construction land expansion takes up too much ecological space, leading to the exacerbation of the regional internal ecosystem. Therefore, exploring the coordinated relationship between construction land expansion and ecological environment change has become a key issue for regional sustainable development. Based on remote sensing technology, the evaluation system of construction land expansion and ecological environment change in the Three Gorges Reservoir area was constructed. The spatial–temporal evolution of construction land expansion and ecological environment change from 1995 to 2020 and their coordination were analyzed, and they were divided into coupling and coordinated development types. The research shows the following findings: Firstly, in the year 2020, the comprehensive value of construction land expansion reached 0.8635, an increase of more than 8 times compared to 1995, indicating a significant trend of rapid expansion. Secondly, the comprehensive value of ecological environment change from 1995 to 2020 remained between 0.4 and 0.6 and continued to change in a good direction. And improving the remote sensing ecological indicators has expanded the difference in ecological environment quality in the Three Gorges Reservoir Area. Thirdly, the coupled model increased from 0.0534 to 0.7951 from 1995 to 2020, which is in a low-level and slow-growing state, and social and economic development does not completely depend on destroying the ecological environment quality. Finally, three types of coordinated development are divided: ecological environment protection type, development and protection in tandem type, and ecological environment consumable type. Differentiated regulation strategies and suggestions are also proposed.

Dissolution feature differences of carbonate rock within hydro-fluctuation belt located in the Three Gorges Reservoir Area

The stability of geological environment in the Three Gorges Reservoir Area (TGRA) has experienced severe challenges due to the 30 m hydro-fluctuation belt (HFB). The vertical dissolution features vary with the mechanism of water and rock interaction. Although dissolution features have been adequately explained by physical and mechanical parameters, the characterization of chemical changes in dissolution is vital but frequently neglected information. For this purpose, the hyperspectral characteristics and mineralogical composition variation of rock HFB in different areas of the Triassic Daye Formation are tested to elucidate the dissolution behaviors. Then, the deterioration degree of dissolution is quantified by a multi-objective data integration based on Random Forest Regression (RFR) model. The spatial distribution of dissolution and the seasonal deterioration characteristics are identified depending on the spectral response of carbonate. Specifically, the dissolution degree is proportional to elevation, and inverse to the bedding angle at 0–40° in the investigated stratum. Combined with the hydrological characteristics of study area, the corresponding regional phenomena can be distinguished as reservoir water dominated, rainfall dominated, and coupled action. Affected by reservoir water and acid rainfall, the HFB may deteriorate most severely from January to April. In addition, the contribution of RFR model integrated spectral and geometric data to the deterioration evaluation of the HFB is discussed, which can effectively enhance the identification of rock mass boundary in a single spectral data model. Thus, these results are conducive to the rapid evaluation of regional geomorphological characteristics and the monitoring of geological disasters in the TGRA.

Research on the Spatiotemporal Dynamic Relationship between Human Activity Intensity and Ecosystem Service Value in the Three Gorges Reservoir Area

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.

Simulating the Land Use and Carbon Storage for Nature-Based Solutions (NbS) under Multi-Scenarios in the Three Gorges Reservoir Area: Integration of Remote Sensing Data and the RF–Markov–CA–InVEST Model

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 &gt; reservoir centre &gt; 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.