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AbstractRiver ecosystems are intricately connected to the biogeochemical processes mediated by sediment bacterial communities. The Three Gorges Reservoir, a critical freshwater resource, has seen limited research on bacterial community composition, particularly in comparative studies between its mainstream and tributaries. High-throughput sequencing was utilized to examine bacterial diversity and community structure in the sediments of the Wanzhou section. The analysis uncovered significant spatiotemporal heterogeneity and revealed similar community structures between the mainstream and tributaries, with Proteobacteria, Actinobacteria, and Bacteroidetes as the predominant phyla. Although no significant phylum-level differences were observed among the top five dominant species, their relative abundances showed notable variations. These differences are attributed to the reservoir’s cyclical water storage and release, which alter nutrient concentrations and induce seasonal variations in bacterial communities. The intrinsic growth characteristics of microorganisms and their adaptive responses to environmental changes are identified as key factors influencing bacterial community differentiation. These insights enhance the understanding of bacterial dynamics in freshwater sediments and contribute to ecological assessments and environmental management in the Three Gorges Reservoir area. [ABSTRACT FROM AUTHOR]

Water color is an essential indicator of water quality assessment, and thus water color remote sensing has become a common method in large-scale water quality monitoring. The satellite-derived Forel–Ule index (FUI) can actually reflect the comprehensive water color characterization on a large scale; however, the spatial distribution and temporal trends in water color and their drivers remain prevalently elusive. Using the Google Earth Engine platform, this study conducts the Landsat-derived FUI to track the complicated water color dynamics in a large reservoir, i.e., the Three Gorges Reservoir (TGR), in China over the past decade. The results show that the distinct patterns of latitudinal FUI distribution are found in the four typical TGR tributaries on the yearly and monthly scales, and the causal relationship between heterogeneous FUI trends and natural/anthropogenic drivers on different temporal scales is highlighted. In addition, the coexistence of phytoplankton bloom and summer flood in the TGR tributaries has been revealed through the hybrid representation of greenish and yellowish schemes. This study is an important step forward in understanding the water quality change in a river–reservoir ecosystem affected by complex coupling drivers on a large spatiotemporal scale. [ABSTRACT FROM AUTHOR]

Accurately predicting river water levels is crucial for managing water resources and controlling floods. In this study, we propose a water level prediction model based on a deep learning method (Transformer model) to improve the accuracy and efficiency of predicting inland river water levels. Water level data from seven hydrological stations were collected from the downstream area of the Three Gorges Reservoir, which confirmed the effectiveness of the model. The proposed model was improved by three main algorithms: the wavelet thresholding denoising algorithm, the maximum information coefficient (MIC) algorithm, and the linear exponential (LINEX) loss function. The results show that the proposed MIC-TF-LINEX model has achieved superior performance in predicting water levels compared to other models, such as traditional Transformer, Back Propagation Neural Network, and Bi-directional Long Short-Term Memory. Furthermore, extending the forecast period will also affect the accuracy of the water level forecasting model. When the prediction duration is 8 h, the R2 value is 0.9989, the MAE is 0.1020, the MSE is 0.0166, and the MAPE is 0.0060. When the prediction timeframe is within 56 h, the MSE of the prediction result is still less than 0.1 m. This study provides a highly accurate and well-suited method for predicting water level. [ABSTRACT FROM AUTHOR]

Understanding the biodiversity–ecosystem multifunctionality relationship is critical for predicting the consequences of species loss on the sustainable provision of ecosystem services. Both theoretical and empirical studies generally demonstrate a positive biodiversity–ecosystem multifunctionality relationship. However, the underlying mechanisms linking soil multifunctionality (SMF) to plant diversity remain unclear, particularly in dynamic riparian habitats. In this study, we investigated the plant community, 10 soil functions, and their drivers within the riparian zone regulated by the Three Gorges Dam in China. Our results showed that taxonomic, phylogenetic, and functional diversity affect SMF at alpha and beta scales in both positive and negative ways. Notably, most diversity metrics are negatively correlated with SMF, especially at lower elevations and in areas near the dam. Alpha and beta diversity contribute equally to SMF, whereas functional diversity explains SMF better than taxonomic or phylogenetic diversity. Furthermore, abiotic variables explain 24% of the variance in SMF, significantly exceeding the 3% explained by biotic variables. Dam inundation has both direct effects on SMF and indirect effects mediated by soil pH, bulk density, and functional dispersion, all of which are critical variables in elucidating SMF changes. Our findings indicate that dam inundation modulates the effect of plant diversity on SMF and underscore the roles of biotic factors and functional diversity in mediating this effect. This study challenges the prevalent notion that biodiversity universally positively affects ecosystem multifunctionality and broadens our understanding of the linkages between plant diversity and SMF, as well as its drivers under dam‐induced hydrological changes. [ABSTRACT FROM AUTHOR]

Since its impoundment in 2003, more than 5,000 landslides have been identified in the Three Gorges Reservoir, and more than 600 slides have apparent activity, which causes significant damage and threats to residents and water infrastructure. Understanding the kinematic behavior and velocity characteristics, mechanisms, trigger factors, and dynamic models of landslides contribute to their instability evaluation and prevention. However, landslide stability analysis is challenging because of complex influencing factors and unclear structural features. The primary objectives of this study were to investigate the kinematic, mechanical, and dynamic characteristics of the Chuwangjing landslide and to identify the trigger factors. We applied multi-resource remote sensing techniques, including satellite Tri-Stereo, unmanned aerial vehicle (UAV) surveys, light detection and ranging (LiDAR) point clouds, and interferometric synthetic aperture radar (InSAR) techniques, to analyze morphological, kinematic, and dynamic features, combined with meteorological and hydrological data. The increased velocity during periods of intense rainfall and prolonged water function, particularly during periodic rapid drawdown periods at high water levels, indicates that deformation is primarily governed by these two factors. The composition of cracks and scrapes detected by LiDAR and satellite Tri-Stereo technology and the deformation distribution on the slope indicated a retrogressive model. We analyzed the landslide's kinematic model and dynamic conditions by considering characteristics such as step-like deformation, influencing factors, and geological composition. Furthermore, by comparing the application effects of multi-remote sensing technology combinations in landslide analysis, this study proved the usefulness of an integrated method for landslide analysis and trending evaluation. [ABSTRACT FROM AUTHOR]

Precise and reliable displacement prediction is essential for preventing landslide disasters, but the evolution of landslides is a dynamic process influenced by diverse factors at different stages. Despite advances in the application of machine learning models to landslide displacement prediction, these models struggle to dynamically capture triggers during the prediction process. This limitation not only fails to capture the characteristics of the short-term fast deformation area, thus affecting the overall prediction accuracy, but also fails to establish a connection between the data relationships and the physical mechanism, thereby limiting the understanding of the physical mechanism of the landslide and resulting in low reliability of the prediction results. In this study, we establish a new model for landslide displacement prediction that combines double exponential smoothing (DES), variational mode decomposition (VMD), and evolutionary attention-based long short-term memory (EA–LSTM). The prediction process is as follows: (i) VMD is used to extract trend, periodic, and random displacement from cumulative displacement; (ii) DES is utilized for forecasting trend displacement, and periodic and random displacements are predicted by EA–LSTM; and (iii) these individual predictions are combined to produce the total displacement prediction. The proposed model is validated using monitoring data collected from the Baishuihe and Bazimen landslides in the Three Gorges Reservoir area. The results indicate that, compared with other models, the proposed model demonstrates higher predictive accuracy. In addition, the real-time dynamic weights of historical information revealed by the model on different time stamps are consistent with the actual historical evolution of landslides. These results verify that the proposed model is a promising tool for the high-quality prediction of landslides and can inform landslide treatment-related decision-making. [ABSTRACT FROM AUTHOR]

Sediment trapping significantly influences the comprehensive benefits of reservoirs and hinders the connection between sediment and nutrients in the upstream and downstream of rivers. Quantifying the role of sediment trapping by dams is important because this operation controls fluvial geomorphology, aquatic ecology, and water quality, particularly for flood processes with highly variable inflows. This study developed a new model to calculate the sediment trap efficiency (TE) of flood events during the flood season in the Three Gorges Reservoir (TGR) using a generalized theoretical approach. The TE of 55 flood events that occurred since the impoundment of the TGR were calculated and ranged from 16.0% to 99.8%, with a mean of 77.8%. Contrary to the previous understanding that large reservoirs have consistently large TE values based on long‐term data, our results show that large reservoirs can have a wide range of TE values during short‐term flood events. The proposed model estimates TE using three variables: inflow discharge, outflow discharge, and reservoir water level. Furthermore, the additional sedimentation risk to the TGR by optimized scheduling during the flood season is discussed. The results provide a reference for sedimentation management and optimal operation in the TGR. Key Points: A new framework to estimate reservoir sediment trapping efficiency (TE) was introducedThe Three Gorges Reservoir (TGR) has a wide TE range during short‐term flood eventsThe operation mode of "low level during large inflow, high level during small inflow " can be adopted during the flood season to improve comprehensive benefits of the TGR [ABSTRACT FROM AUTHOR]

Since the impoundment of Three Gorges Reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is the Outang landslide, a large-scale and active landslide, on the south bank of the Yangtze River. The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics. Data mining technology, including the two-step clustering and Apriori algorithm, is then used to identify the dominant triggers of landslide movement. In the data mining process, the two-step clustering method clusters the candidate triggers and displacement rate into several groups, and the Apriori algorithm generates correlation criteria for the cause-and-effect. The analysis considers multiple locations of the landslide and incorporates two types of time scales: long-term deformation on a monthly basis and short-term deformation on a daily basis. This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors. The data mining results reveal different dominant triggering factors depending on the monitoring frequency: the monthly and bi-monthly cumulative rainfall control the monthly deformation, and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide. It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslide-prone areas.

This study presents a long-term reactivation monitoring approach using multi-source satellite SAR and optical data. It also explores the relationship between landslide deformations and hydrological factors for reactivation assessment and management. More specifically, this approach is applied to investigate the long-term reactivation evolution of the Huangtupo landslide under the local engineering work conducted in four different phases. The spatial–temporal evolutions of the Huangtupo landslide were mapped by the multi-temporal optical images from the PlanetScope satellite, revealing that the building areas were gradually replaced by the vegetation covers. The long-term reactivated deformations were explored by the multi-source satellite SAR data from ALOS-2 and Sentinel-1A/B based on time-series InSAR analysis. Cross-validation between the multi-source InSAR results showed that the ALOS-2 and Sentinel-1A/B measurements are highly correlated. Long-term spatial–temporal deformations were investigated using the Sentinel-1A/B Track 11 data between 2016 and 2022. A long stack of SAR datasets was divided into four consecutive branches based on the local engineering work plan. A general slowing down deformation trend can be observed from the time series analysis, indicating the effectiveness of local measures for maintaining the slope stability. Most importantly, seasonal fluctuations caused by changes in rainfall and Three Gorges Reservoir (TGR) water level can be clearly observed from the time series evolutions of deformation during each phase. Through the gray correlation analysis and cross wavelet analysis, it is found that the lower part of the Huangtupo landslide shows a higher correlation with the TGR water level changes, while the upper slope mainly responds to rainfall. Importantly, it is found that the lowest common power was observed at P4 since August, 2020, suggesting that the upper part of the slope has been changed to be more rainfall erosion resistant due to the effort of the drainage system and vegetation restoration from the ecological restoration project. [ABSTRACT FROM AUTHOR]

Sediment deposition is critical in maintaining riparian plant communities by providing essential nutrients and posing growth challenges. This study focuses on Cynodon dactylon, a dominant clonal species in the riparian zones of the Three Gorges Reservoir, and its interaction with sediment deposition over three years. Results indicated an average sediment deposition depth of 2.85 cm in the lower riparian regions. Observations revealed that C. dactylon coverage increased progressively at lower elevations despite its dominance diminishing with rising elevation levels. Additionally, positive linear correlations between C. dactylon coverage and sediment deposition depths were identified during flood periods, underscoring the species' role in enhancing sediment deposition. These findings suggest that C. dactylon plays a significant role in sediment accumulation, which may bolster its growth and survival prospects during subsequent growing cycles. The study highlights the importance of riparian vegetation, mainly perennial clonal species like C. dactylon, in promoting sediment accumulation and contributing to the stability and functionality of riparian ecosystems. [ABSTRACT FROM AUTHOR]

After the construction of cascade reservoirs in the upper reaches of the Three Gorges Reservoir (TGR), the sediment load outflow of the upper Yangtze River Basin (YRB) has been significantly altered, decreasing from 491.8 Mt/yr (1956–2002) to 36.1 Mt/yr (2003–2017) at Yichang station. This has widely affected river hydrology, suspended sediment grain size distribution, and channel morphology. This study analyzed hydrological variations in water discharge and sediment load of the upper YRB over the past 62 years (1956–2017) by employing a double mass curve. The variations in the source areas of sediment yielding for the upper YRB were quantified, and field measurement data of the cross-channel profile were collected to investigate the sedimentation process in the TGR from 2003 to 2017. More than 90% of the sediment load reduction in the upper YRB may be explained by human activities. The Jinshajiang River was no longer the largest sediment source area for the Zhutuo station (accounting for 5.23%) in the 2013–2017 time span, and the sediment rating rates for the inflow and outflow of the TGR shifted to negatively correlated. A longitudinal fining trend was revealed in the suspended sediment size. Still, the mean median grain size of suspended sediment in the TGR had an increasing trend in the 2013–2017 period. This result may be closely related to sediment regulation in reservoirs and incoming sediment load reduction. Sedimentation in the TGR decreased sharply from 299.8 Mt/yr in 2003–2012 to 47.2 Mt/yr in 2013–2017, but the sedimentation rate of the TGR remained at > 80% annually. Moreover, some cross sections in the fluctuating backwater zone experienced scouring. [ABSTRACT FROM AUTHOR]

Affected by climatic factors and human activities, the conditions of the sediment deposition of the Three Gorges Reservoir (TGR) in the upper Yangtze River have undergone significant changes, the understandings of the sedimentation in TGR and its factors remain unclear. In this paper, the regime for sediment deposition in TGR from 2003 to 2017 is analyzed based on field data, its influence factors are discussed, and the empirical formulas governing the relationships are presented. The results show that the sedimentation pattern of the TGR is discontinuous and that the perennial inundated area is the key sedimentation area, while the reaches of Xiangxizhen, Wushan, Fengjie, Wanxian, Huanghuacheng, Lanzhuba, and Tunaozi are the main deposition reaches in the reservoir, with the same characteristics of wider cross-sections. Sediment deposition in the TGR is affected by the incoming water and sediment, the operation rule of the reservoir, and by the characteristics of different reaches. Compared with the factors influencing the total sediment trapping ratio of the reservoir, local reach conditions are also the main factors influencing the sediment trapping ratio of the reaches due to the difference of sediment carrying capacity between wider reach and narrow reach. The sediment trapping ratios for reservoir scale and reach scale can be good indicators that reflect sedimentation in the TGR; the calculated sediment trapping ratios agree well with field observations. With the continuous construction and operation of the main and tributary reservoirs in the upper Yangtze River, the existing sedimentation regime of the TGR will persist for many years. [ABSTRACT FROM AUTHOR]

Human‐induced disturbances such as dam construction and regulation have led to widespread alterations in hydrological processes and thus substantially influence plant characteristics in the hydro‐fluctuation zones (HFZs). To reveal utilization of limited resources and mechanisms of inter‐specific competition and species co‐existence of plant communities based on niche breadth and overlap under the different HFZs of the Three Gorges Reservoir (TGR) in China, we conducted a field investigation with 368 quadrats on the effects of hydrological alterations on plant diversity and niche characteristics. The results showed anti‐seasonal flooding precipitated the gradual disappearance of the original diverse niches, resulting in the reduction of plant species richness and functional diversity and more obvious competition among plant species with similar resource requirements. Annuals, perennials and shrubs accounted for 71.23%, 27.39% and 1.37%, respectively, suggesting that annuals and flood‐tolerant riparian herbs were favored under such novel flooding conditions. A consistent increase in species number, Shannon‐Wiener diversity index and Simpson dominance index with altitude was inconsistent with hump‐shaped diversity–disturbance relationship of the intermediate disturbance hypothesis, while the opposite trend was observed for the Pielou evenness index. This species distribution pattern might be caused by several synergetic attributes (e.g., the submergence depth, plant tolerant capacity to flooding, life form, dispersal mode and inter‐specific competition). Vegetation types shifted from xerophytes to mesophytes and eventually to hygrophytes with the increasing flooding time in the HFZs. Hydrological alterations proved to be the paramount driver of vegetation distribution in the different HFZs. The niche analysis provided the first insights on the mechanisms of resource utilization and inter‐specific competition, of which annuals could germinate quickly after soil drainage to achieve the greatest competitive advantages and occupy a larger niche space than other plants. Vegetation was still in the early stage of primary succession in the novel riparian forests. Therefore, vegetation restoration strategies should be biased towards herbaceous plants, due to annuals with better environmental adaptability, supplemented by shrubs and small trees. To establish a complete reference system for vegetation restoration, natural vegetation monitory plots in the different succession stages should be established in the different HFZs of the TGR, and their environmental conditions, community structures and inter‐specific relationships further analyzed. [ABSTRACT FROM AUTHOR]

The construction of large reservoirs causes shoal- channel adjustments downstream of dams, which significantly affects river regimes, navigation, flood control and ecology. To explore the characteristics and mechanism of shoal- channel adjustment in the Wuhan reach, water and sediment observation data from 1952 to 2022 and topographic observation data from 1959 to 2021 were used to analyse the effects of the bed sediment composition, runoff and sediment load and regulation projects. The results revealed the following: ① After the operation of the Three Gorges Dam (TGD), the flushing volume in the low-water channel in the Wuhan reach accounted for 90. 4% of the total flushing volume in the channel, the channel section became increasingly narrow, and the river phase coefficient decreased. Different adjustments occurred based on scouring and silting in different central bars and branches. From 2001 to 2021, the continental areas of the Tieban bar and Baisha bar decreased by 63. 1% and 61. 5%, respectively, whereas the continental areas of the Qian bar and Tianxing bar increased by 212. 3% and 14. 5%, respectively. After 2013, the left and right branches of the Tieban bar were scoured, the left branch of the Tianxing bar was silted, and the right branch was scoured. ② When the amount of incoming sand decreased sharply, the riverbed was mostly fine sand, with grain sizes of 0. 125 mm≤d < 0. 25 mm, and the antiscourability was weakened, which is a prerequisite for increased erosion. The peak of erosion and dry replenishment of the TGD intensified the amplitude of erosion of the low-flow branch. Regulation projects has changed the transverse distribution of erosion and deposition in the shoal channel, which is the dominant factor influencing the adjustment of central bars and significantly explains why the left branch of the Tianxing bar was silted and the right branch was scoured. Before the operation of the TGD, the evolutionary linkages between shoals were strong. After the operation of the TGD, the decrease in sediment inflow and the prolongation of the period of moderate water led to erosion of the shoal, and the evolutionary linkage between the shoals weakened. After the implementation of regulation projects, the stability of the central bar was maintained, and the slow flow and sediment retention effect improved erosion resistance. The sedimentation of the central bar limited the development of the side beach. The sand source was also reduced by the operation of the TGD. The evolutionary linkage between the side beaches was further weakened, and the evolutionary linkage between the central bars was also further weakened. In contrast, the evolutionary linkage between the side beach and the central bar was strengthened again. [ABSTRACT FROM AUTHOR]

[Display omitted] • Different types of landslides generate the corresponding impact factors. • Dataset replenishment strategy reduces uncertainty of landslide susceptibility. • SHAP and Apriori algorithm provide insights into factor contribution to landslides. Landslide susceptibility mapping (LSM) warrants considerable attention as a prerequisite for risk assessment and prevention in response to the increasing global occurrences of landslides. However, refined LSM at the township scale still faces the problem of the limited number of landslides and the oversight of relevant factors associated with different landslide types. This work is dedicated to propose a novel dataset replenishment strategy designed to address landslide sample scarcity, and to identify dominant factors in high landslide susceptibility zones to refine LSM at township scale for decision makers. Herein, the Wuling County in the Three Gorges Reservoir area of China was taken as taken as the research case. Initially, field investigation and calibration of 21 landslides yielded accumulation and rock areas, and the corresponding impact factors were therefore proposed to construct input features for machine learning facilitated by factor diagnosis using Pearson correlation coefficient (PCC) and multicollinearity test. Subsequently, a training and validation dataset split of 8: 2 was randomly generated, enabling the application of ensemble machine learning model for LSM prediction. The study culminated in the feasibility of dataset replenishment strategy, which was employed to explore uncertainties and identify governing factors in LSM. The results indicate that the bagging-ANN model exhibits superior performance in both accumulation and rock areas, with Areas Under Curve (AUC) of 0.987 and 0.994. Furthermore, the dataset replenishment strategy emerges as instrumental in enhancing the reliability of LSM, as evidenced by an 8.97 % reduction in average value (AVG) and a 11.00 % increase in standard deviation (STD). The governing factors for landslides in accumulation and rock areas are designated as river erosion and height, respectively. Together with the excellent performance, this study is expected to provide a promising reference for LSM in other townships worldwide. [ABSTRACT FROM AUTHOR]

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Soil aggregate stability measurement is essential to determine soil health status under various conditions. The Mean Weight Diameter (MWD) is the most applied index to express aggregate stability particularly for wet and dry sieving. However, the MWD could generally present results affected by pre-stress aggregate size distribution when remained aggregates after stress are considered for calculation. Therefore, the objective of the present study was to eliminate the similarities between the MWD results and pre-stress aggregate size distribution, which largely affects treatments differentiation. Samples from 145-160 m (lower), 160–169 m (middle), and 169–175 m (upper) elevations differently affected by the Water Level Fluctuation in Three Gorges Reservoir (TGR) and control (>175 m) were exposed to wet shaking stress. Aggregates remained at each sieve opening, aggregates disintegrated/passed through the sieve opening, and small macroaggregates and microaggregates accumulated were recorded. These aggregates were used to determine and compare two indexes (1) using remained aggregates (MWD) and (2) using disintegrated/accumulated aggregates (MWDd/a) based on the differences among treatments. The results for both pre-stress aggregates and remained aggregates after stress were showed consistent significant differences (p

Reservoirs exert a profound influence on the cycling of dissolved organic matter (DOM) in inland waters by altering flow regimes. Biological incubations can help to disentangle the role that microbial processing plays in the DOM cycling within reservoirs. However, the complex DOM composition poses a great challenge to the analysis of such data. Here we tested if the interpretable machine learning (ML) methodologies can contribute to capturing the relationships between molecular reactivity and composition. We developed time‐specific ML models based on 7‐day and 30‐day incubations to simulate the biogeochemical processes in the Three Gorges Reservoir over shorter and longer water retention periods, respectively. Results showed that the extended water retention time likely allows the successive microbial degradation of molecules, with stochasticity exerting a non‐negligible effect on the molecular composition at the initial stage of the incubation. This study highlights the potential of ML in enhancing our interpretation of DOM dynamics over time. Plain Language Summary: As a comprehensive man‐made infrastructure, reservoirs significantly influence the chemical composition, reactivity, and turnover time of dissolved organic matter (DOM) within inland waters. However, it remains elusive how DOM molecules respond to microbial processing over different time scales. Besides the well‐recognized predictive power of machine learning (ML) methodologies, we delved into the processes of tuning the ML models to acquire additional interpretability. We used an under‐sampling strategy to improve model performance and simultaneously observed the variations in model performance metrics for different biological reactivity pools over incubations with different durations. We find that shorter incubation periods result in a broader range of molecules disappearing, with a greater contribution of stochasticity, while the longer incubation allows the successive biodegradation of oxygen‐poor compounds, with a greater contribution of directed degradation. As a complement to traditional geochemical methods, we unveiled a novel perspective in understanding the DOM dynamics over time using ML. Key Points: Machine learning (ML) models were built to correlate the molecular composition and biological reactivity at the world's largest reservoirShorter incubations result in a broader range of molecules disappearing, with a greater contribution of stochasticityTuning the ML model contributes to yield additional interpretability beyond its well‐recognized predictive power [ABSTRACT FROM AUTHOR]

The Three Gorges Project is the largest hydraulic hub project in the world, and its hydrological management has altered the hydrological environment of the reservoir area, affecting the carbon emission and absorption of the reservoir water. In this study, representative hydrological stations in the Three Gorges Reservoir area were selected as research sites to monitor the CO2 and CH4 fluxes of the reservoir water and nine environmental factors during the drainage and impoundment periods in 2022. The study aimed to explore the mechanisms of hydrological management and environmental factors on greenhouse gas emissions. The results showed that the mean CO2 fluxes of the reservoir water during the drainage and impoundment periods were (103.82 ± 284.86) mmol·m−2·d−1 and (134.39 ± 62.41) mmol·m−2·d−1, respectively, while the mean CH4 fluxes were (1.013 ± 0.58) mmol·m−2·d−1 and (0.571 ± 0.70) mmol·m−2·d−1, respectively, indicating an overall "carbon source" characteristic. Through the evaluation of the characteristic importance of environmental factors, it was found that the main controlling factors of CO2 flux during the drainage period were total phosphorus (TP) and chlorophyll a (Chl_a), while total nitrogen (TN) was the main controlling factor during the impoundment period. Dissolved organic carbon (DOC) was the main controlling factor of CH4 flux during the different periods. Based on these findings, a "source-sink" mechanism of CO2 and CH4 in the Three Gorges Reservoir water under reservoir regulation was proposed. This study is of great significance for revealing the impact of reservoir construction on global ecosystem carbon cycling and providing scientific support for formulating "emission reduction and carbon sequestration" plans and achieving "dual carbon" goals. [ABSTRACT FROM AUTHOR]

This study used multi-platform remote sensing datasets to assess the impacts of land use and land cover (LULC) change on forest biomass density across the Three Gorges Reservoir (TGR) region along the Yangtze River. This study maps land use and land cover (LULC) change and forest aboveground biomass estimation (AGB) using Sentinel-2A and GEDI-L4B data based on observations between 2019 and 2022. The data analysis results of monitoring revealed a strong association frequency between land-use change and forest land cover since the significant relationship with forest aboveground biomass density has also been explored. The total deforestation decreased significantly in 2022. The accuracy assessment results show a classification accuracy of between 90.4% and 94.1%, with a kappa coefficient of 82.4%–92.1%. For the TGR region forest, the minimum to maximum mean above-ground biomass values are from 15.81 to 373.66 Mgha-1, respectively. Moreover, the fitting performance of the statistical analysis of aboveground biomass density of forest mean for the forest was quite decent with training (R2: 0.92, RMSE: 12.30 Mg/ha) and testing (R2: 0.79, RMSE: 18.07 Mg/ha) datasets. The results highlight LULC change impacts, particularly forest land class over forest AGB stands, and the effectiveness of the GEDI-L4B product in regional biomass estimation in the TGR region. [ABSTRACT FROM AUTHOR]

The ecosystem of the water level fluctuation (WLF) zone of the Three Gorges Reservoir (TGR) is highly vulnerable and sensitive due to its unique cyclical flooding and drought conditions. The ecological impact of biological invasion in this area is particularly severe, making it crucial to study the differences in resource utilization strategies between invasive plants (IPs) and native plants (NPs) using functional traits to explore the mechanisms of invasion. We selected the WLF zone of Pengxi River in the TGR area and conducted a multi-scale study along the elevation gradient. The results reveal that at the regional scale, IPs have a larger height and specific leaf area, smaller leaf tissue density, and specific root length compared to NPs, showing a preference for enhancing aboveground resource acquisition over leaf defense capabilities. They allocate more tissue construction resources to their roots to withstand environmental pressures, which may be the key to their successful intrusion, highlighting the role of niche differentiation. On the community scale, the H and SLA of IPs and NPs are positively correlated with elevation, while the LTD of IPs shows a negative correlation. At elevations of 175 m and below, IP and NP exhibit functional convergence, while above 175 m, functional divergence was observed. This indicates that although the different resource utilization strategies are crucial for successful IP invasion, the environmental filtering from periodic floods and drought pressures play a significant role in community assembly in the WLF zone, allowing IP to integrate into habitats with similar functional characteristics already inhabited by NP and establish their own communities. [ABSTRACT FROM AUTHOR]

Riverine water temperature (WT) is a crucial factor affecting habitat quality and ecological effect of aquatic ecosystems. To accurately quantify and classify WT variation features caused by climate change and reservoir construction and operation, a framework was developed that integrates multivariate vine copula model for accurately reconstructing the WT process and general evaluation indicators for comprehensively characterizing of WT variation. In this framework, month‐wise R‐vine copula models were employed to depict the multivariate dependence structure between WT and related hydrometeorological factors, and the change of WT process in the fluctuation range and thermal deviation was analogized as the change of simple harmonic wave in amplitude and phase. A testing‐oriented application of this framework in Yichang section of the Yangtze River highlighted that climate change and the Three Gorges Reservoir (TGR) dominated or participated in the fluctuation range changing and phase deviation of different monthly WT processes, as the ratios of affected months were 1.08:1 and 1.25:1 during the construction phase, and 1:2 and 1:1.28 during the operation phase. WT process also exhibited diverse monthly variation trends during construction and operation phases of the TGR. Therefore, it is inappropriate to neglect the impact of the TGR construction phase and climate change on WT variation. The proposed framework achieved systematic quantification and attribution analysis of WT variation, thereby providing an enhanced understanding of the variation characteristics of river thermal regimes under the individual and combined effects of climate change and artificial reservoir. Considering the significant influence of WT variation on aquatic organism reproduction, the identification of the sources and categories of monthly WT variation can also serve as a foundation for future targeted thermal and hydrological regime regulation, aiming to protecting aquatic species and preventing biodiversity loss. [ABSTRACT FROM AUTHOR]

The Three Gorges Reservoir (TGR) is one of the world's largest hydropower projects and plays an important role in water resources management in the Yangtze River. For the sake of disaster prevention and catchment management, it is crucial to understand the regulation capacity of the TGR on extreme hydrological events and its impact on flow regime in a changing climate. This study obtains historical inflows of the TGR from 1961 to 2019 and uses a distributed hydrological model to simulate the future inflows from 2021 to 2070. These data are adopted to drive a machine learning‐based TGR operation model to obtain the simulated outflow with TGR operation, which are then compared with the natural flow without TGR operation to assess the impact of TGR. The results indicate that the average flood peaks and total flooding days in the historical period could have been reduced by 29.2% and 53.4% with the operation of TGR. The relative declines in drought indicators including duration and intensity were generally less than 10%. Faced with more severe extreme hydrological events in the future, the TGR is still expected to alleviate floods and droughts, but cannot bring them down to historical levels. The impact of TGR operation on flow regime will also evolve in a changing climate, potentially altering the habitats of river ecosystems. This study proposes feasible methods for simulating the operation of large reservoirs and quantifying the impact on flow regime, and provides insights for integrated watershed management in the upper Yangtze River basin. Plain Language Summary: The Three Gorges Reservoir (TGR) is located in the upper Yangtze River basin and is one of the world's largest hydropower projects. This study combines hydrological modeling and machine learning methods to quantify the effects of TGR operation on flow regime in the historical and future periods. The results indicate that the operation of TGR could play an important role in mitigating historical droughts and floods. Specifically, the flood peak in 1998 could have been reduced by 24.7% if the TGR was in operation. In the future, the extreme hydrological events will become more severe. While the TGR will continue to play a significant role, it cannot fully control floods and droughts to the same extent as it did in the historical period. In a changing climate, the Yangtze River basin will still face higher flood and drought risks with the operation of TGR. Under future climate scenarios, the operation of TGR will also alter the flow regime metrics and have new implications on the river ecosystem environment. Key Points: An ensemble learning model based on two neural networks is constructed to simulate Three Gorges Reservoir (TGR) operationsTGR can mitigate floods and droughts, but this capability might be weakened by more severe future extreme eventsTGR can offset most flow regime changes caused by climate change, but adaptive operation strategies are still needed [ABSTRACT FROM AUTHOR]

Dissolved organic matter (DOM) is one of the active soil carbon fractions. The characteristics of DOM in the riparian zone are closely related to the occurrence of eutrophication. However, there is no scientific consensus on how flooding intensity on DOM distribution in the riparian zone of the Three Gorges Reservoir (TGR). In this study, 99 soil riparian samples were collected from three water level elevations (145–155 m asl, 155–165 m asl and 165–175 m asl) in both mainstream and tributaries of the Yangtze River within the TGR. DOM characteristics were investigated using ultraviolet–visible absorption spectra and three‐dimensional fluorescence excitation–emission matrix‐parallel factor analysis as well as principal component analysis (PCA). The result showed that dissolved organic carbon (DOC), absorption coefficient at 254 nm (a254), absorption coefficient at 350 nm, specific ultraviolet absorbance at 254 nm (SUVA254), and humification index (HIX) decreased while freshness index (β:α) and biological index (BIX) increased with the decline of the water level elevations. The trend in the DOM characteristics along the riparian zone was mainly attributed to the variation in the bioavailability of DOM and fluorescence components (C1, C3 and C4). Overall, our study highlights the newly formed DOM during the dry season, indicated by β:α and BIX, might be more accumulated at the lower elevation. Furthermore, because of the greater lability during the reservoir impoundment, the newly formed DOM can be easily utilized by algae and may raise water eutrophication under re‐flooding and longer flooding, thus enforced strategic mitigation measures could help to achieve the goal of riparian restoration in TGR. [ABSTRACT FROM AUTHOR]

The Three Gorges Reservoir (TGR) is the largest artificial water resources regulation and storage center in China. Thereby the evaporation in the reservoir area has profound impact on the regional eco- hydrological process. Analysis on the differences of pan evaporation characteristics between water surface and land surface is of great importance for understanding regional hydrological circulation and climate change. This work applied observations from both floating site and land site in Badong hydrometeorological station and the radiation data from ERA5 dataset, found the difference of seasonality characteristic of pan evaporation and energy budget between water surface and land surface, and preliminarily analyzed the reasons on the differences. The main results are as follows: ① Significant difference of seasonal pan evaporation characteristics was detected between water surface and land surface, with high peak appeared in August over land surface while appeared in December over water surface; ② The energy budget analysis indicating TGR area generally accumulated heat during summer season while released heat and significantly increased available energy in water during winter season, which resulted peak value appeared four months later in water site than that of the land site; ③ The controlling factors analysis indicated that the difference of evaporation and thermal characteristics was controlled by nonlinear comprehensive influences from multi micrometeorological factors, which was dominated by air temperature and saturated water pressure deficit during accumulating heat period (March to September), while by water temperature and temperature difference between air temperature and water temperature during releasing heat period (October to February of next year). [ABSTRACT FROM AUTHOR]

Carbon (C), nitrogen (N), and phosphorus (P) stoichiometry serve as valuable indices for plant nutrient utilization and biogeochemical cycling within ecosystems. However, the allocation of these nutrients among different plant organs and the underlying drivers in dynamic riparian ecosystems remain inadequately understood. In this study, we gathered plant samples from diverse life forms (annuals and perennials) and organs (leaves, stems, and roots) in the riparian zone of the Three Gorges Reservoir Region (TGRR) in China--a novel ecosystem subject to winter flooding. We used random forest analysis and structural equation modeling to find out how flooding, life forms, plant communities, and soil variables affect organs C, N, and P levels. Results showed that the mean concentrations of plant C, N, and P in the riparian zone of the TGRR were 386.65, 19.31, and 5.27 mg/g for leaves respectively, 404.02, 11.23, and 4.81 mg/g for stems respectively, and 388.22, 9.32, and 3.27 mg/g for roots respectively. The C:N, C:P and N:P ratios were 16.15, 191.7 and 5.56 for leaves respectively; 26.98, 273.72 and 4.6 for stems respectively; and 16.63, 223.06 and 4.77 for roots respectively. Riparian plants exhibited nitrogen limitation, with weak carbon sequestration, low nutrient utilization efficiency, and a high capacity for nutrient uptake. Plant C:N:P stoichiometry was significantly different across life forms and organs, with higher N and P concentrations in leaves than stems and roots, and higher in annuals than perennials. While flooding stress triggered distinct responses in the C, N, and P concentrations among annual and perennial plants, they maintained similar stoichiometric ratios along flooding gradients. Furthermore, our investigation identified soil properties and life forms as more influential factors than plant communities in shaping variations in C:N:P stoichiometry in organs. Flooding indirectly impacts plant C:N:P stoichiometry primarily through alterations in plant community composition and soil factors. This study underscores the potential for hydrologic changes to influence plant community composition and soil nutrient dynamics, and further alter plant ecological strategies and biogeochemical cycling in riparian ecosystems. [ABSTRACT FROM AUTHOR]

This study conducts an in-depth exploration of the efficacy of deep learning and ensemble learning techniques for slope-unit-based landslide susceptibility prediction within the context of the Three Gorges Reservoir area in China, with a specific focus on Wanzhou District. Leveraging a dataset comprising twelve distinct landslide factors and 1909 Slope Units, the research evaluates three deep learning models (Long Short-Term Memory, Recurrent Neural Network, and Gated Recurrent Unit) as well as three ensemble learning models (LightGBM (LGBM), Extra Trees, and Random Forest) using five performance metrics. Central to this endeavor is the adept utilization of remote sensing technology, including Landsat 8 OLI images, Digital Elevation Model (DEM) data, and Google Earth Pro images. The Landsat 8 OLI images offer a panoramic view of the study area, capturing essential landscape features and variations. The DEM data, providing detailed elevation information, empowers the analysis of terrain morphology crucial for landslide susceptibility assessment. The findings conclusively showcase that ensemble learning models harnessed in this study, augmented by the integration of diverse remote sensing data, exhibit exceptional predictive capabilities in accurately anticipating landslide susceptibility. These models outperform their deep learning model counterparts, attributing their success to the multi-faceted insights derived from the synergy between remote sensing imagery and advanced machine learning algorithms. The ensemble models' enhanced performance metrics, such as F1-score, recall, precision, and area under the curve (AUC) values, underscore their potential utility in real-world landslide prediction scenarios. Especially noteworthy among the ensemble models is LGBM, which emerges as the most promising candidate with the highest F1-score (0.561) and Recall (0.524), indicating that the LGBM model possesses a more robust predictive capability for landslide samples. In-depth interpretability analysis using SHapley Additive exPlanations (SHAP) values and Partial Dependence Plots (PDP) assessments delves into the mechanics of LGBM's predictive prowess. This analysis, reliant on remote sensing data, provides clarity into the contributions of various evaluation factors, emphasizing the roles of attributes such as proximity to the river, rainfall, and elevation. The correlation patterns revealed between these factors and landslide susceptibility add layers of understanding, while the intricate interplay of distance to the river unveils the complex interactions between geological and climatic variables. [ABSTRACT FROM AUTHOR]

Reservoir sedimentation affects lifespan, hydropower generation, water resource use, and ecosystem sustainability. In this study, using measured data from the worldrenowned Three Gorges Reservoir (TGR), sediment transport capacity was analysed from 2002 to 2020 at four TGR gauging stations (Cuntan, Qingxichang, Wanxian, and Miaohe). The result indicated that: (1) a significant decrease in sediment transport capacity occurred in the TGR before and after dam construction, with an overall reduction of 82% in the perennial backwater zone. Notably, Qingxichang, Wanxian, and Miaohe experienced reductions of 40%, 91%, and 59%, respectively. Since the initial operation phase, their maximum monthly sediment transport capacity has consistently occurred in July. (2) Changes in the water level in front of the dam, the incoming flow, and sediment influxes were the primary factors driving the annual variations in sediment transport capacity. Among the factors affecting the variation of sediment transport capacity along the TGR, the influence of the cross-sectional morphology was more pronounced than that of the distance from the dam. Sediment transport capacities in the wide and shallow sections were considerably lower than those in the narrow and deep sections. (3) A sediment transport amplitude index (Fd) was proposed to assess the sediment dynamics within the TGR, which effectively characterized the sedimentation conditions based on the relative importance of these influencing factors. These findings provide a theoretical basis for operational strategies in the TGR and offer insights into the patterns of sediment transport capacity changes in reservoir areas before and after dam construction in other river-type reservoirs. [ABSTRACT FROM AUTHOR]

Purpose: The water-level-fluctuating zone (WLFZ) is the buffer zone of energy and material exchange between terrestrial and aquatic ecosystems. Artificial vegetation restoration of WLFZ can improve the interception capacity of P pollution. The purpose of this study is to explore the effect of artificial vegetation restoration on the bioavailability of soil phosphorus (P) in the WLFZ. Material and methods: Soil samples from different spatial locations (natural vegetation zone, artificial vegetation restoration zone) and different altitudes of the WLFZ were collected in the Three Gorges Reservoir (TGR) region, Chongqing, China. Soil P fraction, microbial biomass P (MBP), and phosphatase activity were measured. Results and discussion: Artificial vegetation restoration changed the spatial distribution patterns of soil bioavailable P (Bio-P) in the WLFZ. The soil bioavailable inorganic P (Bio-Pi) in the artificial vegetation restoration zone was significantly higher than those at the natural vegetation zone (p < 0.05) and its content decreased with the decrease of altitude. The content of bioavailable organic P (Bio-Po) in the two transects was not significantly different in general, but was different at different altitudes. Phosphodiesterase (PDE) activity was negatively correlated with Bio-Po in artificial vegetation restoration zone (p < 0.01, R2 = 0.21), but significantly positively correlated with in natural vegetation zone (p < 0.05, R2 = 0.17); this suggests that the relationship between Bio-Po and PDE activity was altered by vegetation restoration. Moreover, the factors controlling the bioavailability of P in the WLFZ are discussed. Conclusion: Artificial vegetation restoration and altitude are the control factors of soil P fractions and bioavailability in WLFZ. Vegetation restoration can increase soil TP and Bio-Pi in general but has little effect on Bio-Po. [ABSTRACT FROM AUTHOR]

Purpose: The Three Gorges Reservoir (TGR) is the largest water conservation project in the world but suffers from harmful algal blooms (HABs) currently. A large amount of phosphorus (P) has accumulated in the sediment due to the construction of the Three Gorges Dam. Phosphorus release from sediment may provide an important P source for overlying water that further triggers HABs. This study aimed to evaluate the contribution of sediment internal P and reveal the mechanisms controlling sediment P release. Material and methods: Chemical sequential extraction approach and diffusive gradients in thin films (DGT) techniques were employed to determine the P fractions in sediments and the vertical distribution of P, iron (Fe), and sulfur (S) at the sediment‒water interface (SWI). Results and discussion: Results indicated that the total P content in the sediments of the TGR is high, with a mean content of 1368 mg kg−1. The P concentration of different fractions in sediments followed the order HCl-P > NaOH-P > BD-P. The averaged P release flux at the SWI was estimated at 0.42 mg m−2 day−1, suggesting that sediment P release is a potential P source for the overlying water. Significant positive relationships between DGT-P and DGT-Fe concentrations from sampling sites Wushan County (WS), Zigui County (ZG), and Xiangxi River (XX) were observed with correlation coefficients (R2) of 0.91, 0.39, and 0.29, respectively. Furthermore, DGT-P and DGT-S concentrations were also significantly positively correlated at the sampling sites WS, ZG, and XX, with R2 of 0.71, 0.87, and 0.50, respectively. Conclusion: The internal P load is severe in the TGR. The reductive dissolution of Fe–P is likely one of the main mechanisms causing P release in the sediments. Furthermore, sulfate reduction associated coprecipitation with Fe promotes the release of Fe–P. These results provide important scientific and technical support for the mitigation of internal P pollution in large deep-water reservoirs. [ABSTRACT FROM AUTHOR]

Abstract Human‐induced disturbances such as dam construction and regulation have led to widespread alterations in hydrological processes and thus substantially influence plant characteristics in the hydro‐fluctuation zones (HFZs). To reveal utilization of limited resources and mechanisms of inter‐specific competition and species co‐existence of plant communities based on niche breadth and overlap under the different HFZs of the Three Gorges Reservoir (TGR) in China, we conducted a field investigation with 368 quadrats on the effects of hydrological alterations on plant diversity and niche characteristics. The results showed anti‐seasonal flooding precipitated the gradual disappearance of the original diverse niches, resulting in the reduction of plant species richness and functional diversity and more obvious competition among plant species with similar resource requirements. Annuals, perennials and shrubs accounted for 71.23%, 27.39% and 1.37%, respectively, suggesting that annuals and flood‐tolerant riparian herbs were favored under such novel flooding conditions. A consistent increase in species number, Shannon‐Wiener diversity index and Simpson dominance index with altitude was inconsistent with hump‐shaped diversity–disturbance relationship of the intermediate disturbance hypothesis, while the opposite trend was observed for the Pielou evenness index. This species distribution pattern might be caused by several synergetic attributes (e.g., the submergence depth, plant tolerant capacity to flooding, life form, dispersal mode and inter‐specific competition). Vegetation types shifted from xerophytes to mesophytes and eventually to hygrophytes with the increasing flooding time in the HFZs. Hydrological alterations proved to be the paramount driver of vegetation distribution in the different HFZs. The niche analysis provided the first insights on the mechanisms of resource utilization and inter‐specific competition, of which annuals could germinate quickly after soil drainage to achieve the greatest competitive advantages and occupy a larger niche space than other plants. Vegetation was still in the early stage of primary succession in the novel riparian forests. Therefore, vegetation restoration strategies should be biased towards herbaceous plants, due to annuals with better environmental adaptability, supplemented by shrubs and small trees. To establish a complete reference system for vegetation restoration, natural vegetation monitory plots in the different succession stages should be established in the different HFZs of the TGR, and their environmental conditions, community structures and inter‐specific relationships further analyzed.

The bank slope rock mass below the flood control restricted water level (145 m) in the Three Gorges Reservoir area has experienced long-term immersion in the process of reservoir water lifting and lowering; the water level changes lead to the rock mass being subjected to two working conditions, tangential loading shearing and normal unloading shearing. The difference in rock shearing characteristics directly affects the stability evaluation of bank slopes in different reservoir operation stages. Tangential loading and normal unloading shear tests were carried out on the typical feldspar quartz sandstone after different soaking days; then the characteristics changes of sandstone shear under the two stress conditions were obtained. The microscopic mechanism of the differences was revealed by solution test, SEM test, and nuclear magnetic resonance test. The results show that: (1) compared with the initial sample, after soaking for 80 days, the cohesion loss of the sample is greater than the loss of internal friction angle. The cohesion of the sample under tangential loading is reduced by 40.5%, and the internal friction Angle is only reduced by 2%, while the cohesion of the sample under normal unloading shear is reduced by 31%, and the internal friction Angle is reduced by 8%. (2) The long-term immersion of the sample results in the dissolution of the cement minerals, the gradual development and penetration of the secondary pores, and the increase of porosity. After 60 days of immersion, basically， the water content, porosity, and pore structure of the sample reach a stable state; the particle skeleton overcoming the shear action is almost no longer affected by the soaking water. This is the reason why the shear properties of the samples with long-term water saturation gradually weaken and become stable. (3) Under normal unloading shear conditions, the deviation between the main crack surface and the theoretical shear surface increases; the fracture surface is more inclined to form “S” and “M” types. The increase of the actual shear plane increases the peak shear stress that the rock can bear. Because the biggest contribution to the rock is the skeleton particle, the internal friction angle is larger, and the filling cementing material that provides cohesion makes less contribution to the tensile shear failure. The cohesion obtained by normal unloading is also lower. This study can provide basic information for the stability evaluation of wading slope in reservoir area with the fluctuation of water level and the selection of test method considering the actual working conditions.

Abstract Channel-forming discharge (D cf) is an important parameter in river management and reservoir flood regulation. Applying the methods for calculating D cf to reaches downstream reservoirs characterized by drastic changes in water and sediment conditions and long-term scouring status is difficult. Based on the riverbed-shaping principle of sediment-laden water flow, while simultaneously considering the active action of water flow and response of the riverbed, this study proposes a new method for calculating D cf by identifying the extreme value of the suspended sediment-carrying capacity index. The application of this method to the middle and lower reaches of the Yangtze River showed that after the impoundment of the Three Gorges Reservoir, D cf in this section was reduced by an amplitude between 2500 and 4700 m3/s. The results can be used to guide the operation of the Three Gorges Reservoir and the management of the middle and lower reaches of the Yangtze River, thus providing reference for other river channels downstream of the reservoir.

Water color is an essential indicator of water quality assessment, and thus water color remote sensing has become a common method in large-scale water quality monitoring. The satellite-derived Forel–Ule index (FUI) can actually reflect the comprehensive water color characterization on a large scale; however, the spatial distribution and temporal trends in water color and their drivers remain prevalently elusive. Using the Google Earth Engine platform, this study conducts the Landsat-derived FUI to track the complicated water color dynamics in a large reservoir, i.e., the Three Gorges Reservoir (TGR), in China over the past decade. The results show that the distinct patterns of latitudinal FUI distribution are found in the four typical TGR tributaries on the yearly and monthly scales, and the causal relationship between heterogeneous FUI trends and natural/anthropogenic drivers on different temporal scales is highlighted. In addition, the coexistence of phytoplankton bloom and summer flood in the TGR tributaries has been revealed through the hybrid representation of greenish and yellowish schemes. This study is an important step forward in understanding the water quality change in a river–reservoir ecosystem affected by complex coupling drivers on a large spatiotemporal scale.

Exploring the effects of climate oscillations on the population diversity and structure of endangered organisms in the Three Gorges Reservoir (TGR) area is essential for hydrological environment changes on endangered organism evolution. Myricaria laxiflora is an endemic and endangered shrub restricted to the TGR along the banks of Yangtze River, China. Recently, six natural populations of this species were newly found upstream and downstream of the TGR, whose habitats have been dramatically changed by the summer flooding regulated by large dams. To study the water level fluctuations and climatic shifts on the genetic diversity and genetic differentiation of the six natural populations, 303 individuals from six populations were analyzed based on one nuclear DNA (ITS) and four chloroplast fragments (trnL-F, psbA-trnH, rps16, and rpl16). The phylogenetic tree and significant genetic divergence identified in the cpDNA and ITS with genetic isolation and limited gene flow among regions suggested that the six populations separated well to two groups distributed upstream and downstream. The MaxEnt modeling results indicated that obvious unidirectional eastward migration via Yangtze River gorges watercourse mediated from Last Interglacial to Last Glacial Maximum were showed with the narrow scale distributions of six remnant populations and nine extirpated populations. The initial habitat fragmentation could be triggered by the accumulation of local habitat loss of the impoundment of the TGR during the Present period and might remain stable restoration with bidirectional diffusion in the Future. Divergences among M. laxiflora populations might have been induced by the drastic changes of the external environment and limited seed/ pollen dispersal capacity, as the results of long-term ecological adaptability of summer flooding stress. The haplotypes of nuclear gene could be used for population's differentiation and germplasm protection. This identified gene flow and range dynamics have provided support for the gene-flow and geology hypothesis. It is also crucial for rescuing conservation to understand the impact of environmental dynamics on endangered organism evolution. [ABSTRACT FROM AUTHOR]

Flooding and climate warming lead to the increase of low‐molecular‐weight organic acid (LMWOA) inputs to soil. However, it is unclear what the effects of flooding and climate warming on soil nitrogen fraction transformation under LMWOA input are. Fluvisol was collected in the riparian zone of the Three Gorges Reservoir to conduct an incubation experiment with the treatments of three LMWOAs at five contents, two hydrological environments and two temperatures. Compared with LMWOA input of 0 mg kg−1, the soil nitrogen in ion exchangeable fraction (IEF‐N), strong oxidant extractable fraction (SOEF‐N) and non‐transformable nitrogen (NTF‐N) were increased by 35.77, 54.71 and 25.49 mg kg−1, respectively, under LMWOA input of 40 mmol kg−1. Soil nitrogen in the weak acid extractable fraction (WAEF‐N) was decreased by 117.41 mg kg−1. Microbial biomass nitrogen (MBN) was increased by 25.29 mg kg−1, and calcium carbonate (CaCO3) was decreased by 8.03 g kg−1. The contents of IEF‐N, NTF‐N and amorphous iron oxides were higher under the flooding environment than under the drying environment. In contrast, opposite results were observed for soil nitrogen in the strong alkali extractable fraction (SAEF‐N), SOEF‐N and MBN. IEF‐N and NTF‐N contents were higher at 20°C than at 30°C, and the contents of SOEF‐N and MBN were higher at 30°C than at 20°C. WAEF‐N was dissolved by LMWOA through CaCO3 dissolution. The dissolved WAEF‐N was partly transformed into SOEF‐N and NTF‐N by microbial assimilation and humification. Flooding desorbed SAEF‐N through the crystalline transformation of iron oxides. Flooding inhibited the transformation of IEF‐N to SOEF‐N and promoted the formation of NTF‐N, while warming did the opposite. The results provide a perspective of the bound state between soil and nitrogen for understanding the soil nitrogen cycling and help to assess the impacts of climate warming and flooding on soil nitrogen loss from soil to the surrounding water. [ABSTRACT FROM AUTHOR]

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The quality of landslide susceptibility assessment is effected by landslide inventory and raster resolution. The point landslide inventory and polygon landslide inventory are usually two spatial representations. The uncertainty to landslide susceptibility result will be induced by different raster resolution and landslide spatial representation. To explore the uncertainty caused by differences in landslide spatial representation and raster resolution, 2 spatial representations (point and polygon) in landslide inventory and 4 raster resolutions (25 m, 50 m, 75 m and 100 m) were introduced in the landslide susceptibility model. A total of 489 landslides with accurate boundary information were obtained in Wanzhou District, Chongqing, China. 5 geo-environmental predictors and random forest model as a data-driven model were used in the landslide susceptibility assessment. The research results demonstrate that (i) in terms of model performance, the prediction performance of polygon models was higher than that of point models by comparing reliability and accuracy. In addition, under the conditions of different base map resolution in the study area, the 25m resolution has a relatively good prediction performance. (ii) In terms of model robustness analysis, the lowest uncertainty was the 25 m resolution model, which has more stable prediction performance. Therefore, the research is useful for selecting the raster resolution and spatial representations in the data-driven landslide susceptibility assessment. [ABSTRACT FROM AUTHOR]

Hydrodynamic pressure caused by a decline in reservoir water-level is one of the dominant factors inducing the deformation of reservoir landslides in mountainous areas, which can lead to the revival of some hydrodynamic pressure landslides. Rear-slope cutting and front-slope pressing are the most common emergency treatment methods for hydrodynamic pressure landslides. To evaluate the effects of emergency treatment and predict the development trend of landslide deformation, we selected the Shuping landslide, which is a typical hydrodynamic pressure landslide in the Three Gorges Reservoir. Based on the surface deformation surveys and field monitoring data collected over the past 20 years, the deformation characteristics before and after the emergency treatment of the Shuping landslide were analyzed, and changes in the seepage field, displacement field, and stability coefficient before and after the landslide emergency treatment were simulated. The results revealed that a decrease in the reservoir water-level was the primary factor governing the deformation of the Shuping landslide. The implementation of front-slope pressing magnified the hydrodynamic pressure effect but increased the anti-sliding force. Simultaneously, slope cutting significantly reduced the sliding force of the landslide. Therefore, the landslide improved from an unstable state to a quasi-stable state. However, the continuous development of the landslide front bank collapse and heavy rainfall will reduce the stability of the landslide. Therefore, it is necessary to conduct long-term monitoring and design follow-up prevention and control projects. [ABSTRACT FROM AUTHOR]

Freshwater microplastic pollution is an urgent issue of global concern, and research on the distribution in reservoirs is lacking. We investigated the microplastic pollution levels in wet sediments collected from the Three Gorges Reservoir, the largest reservoir of China. Results show that microplastics were ubiquitous in the sediments of the Three Gorges Reservoir, and their abundance ranged from 59 to 276 pp/kg (plastic particles per kg, dry weight). Economic development and total population were important factors affecting the spatial heterogeneity of microplastic abundance, and the contribution of large cities along the reservoir to microplastic pollution should be paid with more attention. Fibrous microplastics were the most abundant type of microplastic particles in reservoir sediments, whereas polystyrene, polypropylene, and polyamide were the main types of polymers. The apparent spatial heterogeneity in morphology and color of microplastics is attributed to different anthropogenic or land-based pollution sources. Moreover, the accumulation of microplastics near the inlet of tributaries reflects the role of potential contributors of tributaries. More importantly, multiple bisphenols (BPs) and heavy metals detected at the microplastic surfaces indicate that microplastics can act as carriers of these pollutants in the environment in the same way as sediments did, which may alter the environmental fate and toxicity of these pollutants. Therefore, we conclude that the Three Gorges Reservoir had been contaminated with microplastics, which posed a stress risk for organisms who ingest them along with their associated pollutants (BPs, heavy metals). [ABSTRACT FROM AUTHOR]

Reservoir sedimentation leads to a loss of storage capacity, thereby affecting the ability of a reservoir to fulfil its designated purpose. Accordingly, it is important to investigate the regularity of sediment delivery to constrain reservoir sedimentation. On the basis of data recorded at Zhutuo, Beibei, Wulong, Cuntan, and other hydrological stations between 2003 and 2021, individual flood events in the Three Gorges Reservoir (TGR) were identified. The regularity of sediment delivery during each flood event was then analyzed. The impacts of factors such as the water level in front of the dam, the rate of sediment transport into and out of the reservoir, and flood detention time on sediment delivery during flood events were investigated. The results indicate that it is more reasonable to classify the flood events in the TGR by considering the asynchronous propagation of flood and sediment peaks on the basis of a unified quantitative standard. Sediment delivery in the TGR occurred mainly during flood events, accounting for 67. 0% of the annual sediment delivery volume. The sediment delivery ratio ( SDR) during the flood events was inversely proportional to the water level in front of the dam and proportional to the sediment transport rate into and out of the reservoir. However, the correlations were weaker than that between the SDR and flood detention time, whereby the SDR was inversely proportional to the flood detention time. The correlations between the SDR and other factors were strongest during flood events when the average inflow was greater than 30 000 m³ / s or the average water level in front of the dam was less than 150 m. This study provides an important reference for optimizing the operation of the TGR and reducing sediment deposition. [ABSTRACT FROM AUTHOR]

Global warming has changed the thermal and dynamic environment of climate effects, further exacerbating the uneven distribution of water in China within the year, and the threat of drought will continue to intensify. Flood control and drought resistance are equally important at present. How to reasonably utilize the flood control capacity of reservoirs and raise the flood control level is the key to alleviating the supply-demand contradiction of water resources in China. In the face of new situations, combining the existing operation regulations of the Three Gorges Reservoir and long series of historical runoff data, this paper takes the Three Gorges Reservoir as the object and formulates different flood control level schemes to analyze the benefits and risks of raising the flood control level of the Three Gorges Reservoir. The results indicate that relying on the current forecast level and the joint operation of upstream reservoirs, the flood control risk of the Three Gorges Reservoir flood control level rising to 155-157 m is controllable. It will not lower the flood control standards of Jingjiang and increase the risk of backwater flooding in the reservoir area. When the flood control level of the Three Gorges is raised to 153 m, the average annual power genertion reached its maximum value of 102.9 billion kWh, an increase of 16.7% compared to the initial design value. Consistent with years of statistical law, the Three Gorges Reservoir reaches its maximum power generation capacity in both wet and normal years when the flood control level increases to 153 m. Higher flood control level in dry years, resulting in better power generation efficiency. The flood control benefits are negatively correlated with the flood control level, but the average annual flood detention volume of different schemes varies by 1.5 billion m3. It has changed little. The benefits of water resources are positively correlated with the flood control level. The starting water level of the Three Gorges Reservoir increases synchronously with the rise in the flood control level. When the flood control level rises to 147 m, the full storage rate of the Three Gorges Reservoir can reach 100% by the end of October. For every 1m increase in flood control level, the average annual outflow of water during the storage period can increase by about 500 million m3. Relying on the current forecast level and the joint operation of upstream reservoirs, the flood control risk of the Three Gorges Reservoir s flood control water level rising to 155- 157 m is controllable. Considering both benefits and risks, the flood control level of the Three Gorges Reservoir can be raised to 153 m at present. [ABSTRACT FROM AUTHOR]

The historical sedimentary and evolutionary characteristics of persistent organic pollutants and endocrine disruptors in typical regions of the Three Gorges Reservoir are scarcely studied. Herein, the 96-year data on contaminated sediment history were reconstructed using Caesium 137 isotope dating. Polychlorinated biphenyl concentrations in the involved sediment cores ranged from non-detected (ND) to 11.39 ng/g. The concentrations of polycyclic aromatic hydrocarbons ranged from ND to 2075.20 ng/g and peaked in the 1970s owing to natural, agricultural and human activities. Further, phthalate esters (PAEs) and heavy metals (HMs) were detected at concentrations ranging from ND to 589.2 ng/g and 12.10–93.67 μg/g, respectively, with highest values recorded in the 1980s owing to rapid industrialisation and insufficient management during China's early reform and development stages. PAE and HM concentrations have increased in recent years, suggesting the need to focus on industrial and agricultural activities that have caused this impact. Although current pollutant concentrations in sediments do not pose a risk to the aquatic ecosystem, they should be continuously monitored.