Search Results (387)

Phyllostachys praecox is a valuable tree species in karst ecosystems, but improper mulching practices can worsen soil degradation. Understanding soil nutrient limitations is crucial for successful restoration and sustainable development. However, it remains unclear whether and how mulching management of Phyllostachys praecox affects soil enzyme stoichiometry and nutrient limitation in karst areas. Here, we conducted a field experiment in Chongqing karst bamboo forest ecosystems with four mulching treatments: 1-year (T1), 2-years (T2), 1-year and recovery and 1-year (T3), and no mulching (CK). We investigated the activities of the C-acquiring enzyme β-1,4-glucosidase (BG), N-acquiring enzymes L-leucine aminopeptidase (LAP) and β-1,4-N-acetylglucosaminidase (BNA), as well as P-acquiring enzyme phosphatase activity (AP), to assess the limitations of C, N or P and identify the main factors influencing soil microbial nutrient limitation. Compared with the CK treatment, both the T2 and T3 management treatments significantly increased the SOC, TN, MBC, and MBN. Furthermore, the soil enzyme stoichiometric ratio in the karst bamboo forests deviated from the global ecosystem ratio of 1:1:1. T1 > T3 > CK > T2 presented higher values of C/(C + N) and C/(C + P), with T1 having values that were 1.10 and 1.12 greater than those of T2, respectively. Additionally, there was a significant negative correlation between microbial C and N limitations and total nutrients, but a positive correlation with microbial biomass ratios. In conclusion, changes in mulching management of Phyllostachys praecox affect soil enzyme stoichiometry activities and their ratios by influencing total nutrients and microbial biomass ratios. This study suggests an alternate year cover pattern (mulching in one year and resting in the next) as a scientific management approach for bamboo forests, contributing to a better understanding of nutrient limitation mechanisms in karst bamboo forest ecosystems. Full article

The operation of large reservoirs significantly modifies flow–sediment regimes, and the reaches immediately downstream of the dams are the first to undergo responsive channel adjustments. Considering that the geomorphological responses are directly related to the flood control safety, channel stability and other sustainable functions of rivers, this paper explores the similarities and dissimilarities of the channel adjustments in the two reaches with gravel–sand beds immediately downstream of the Xiangjiaba reservoir and the Three Gorges Dam, respectively. The results show that major erosion primarily occurred during the initial years of reservoir impoundment. And then with the prominent reduction in washable sediment on the riverbed, the erosion intensity further weakened. It takes 6 to 13 years for the two reaches to reach a new state of relative equilibrium. In comparison, after the equilibrium state has been achieved, the reach with significant tributary sediment inflows exhibits alternating erosion and deposition dynamics, while the other remains relatively stable. The tributaries that transport a large amount of sediment during floods are the main sources of sediment deposition in the downstream reaches of the Xiangjiaba reservoir. However, the tributary inflow of the Qing River with low sediment concentrations has little impact on the riverbed evolution of the reaches from Yichang to Zhicheng immediately downstream of the Three Gorges Dam. These findings contribute to a deeper understanding of geomorphic adjustments near dams in response to upstream damming. Full article

The topographies of soil erosion and deposition are critical factors that significantly influence soil quality, subsequently impacting the erodibility of soils in karst regions. However, the investigation into the effects of erosion and deposition topographies on soil erodibility across different stages of vegetation succession in karst trough valleys is still at a preliminary stage. Therefore, three distinct topographic features (dip slopes, anti-dip slopes, and valley depressions) were selected at erosion (dip/anti-dip slope) and deposition sites (valley) to investigate the spatial heterogeneity of soil physicochemical properties, infiltration capacity, aggregate stability, and erodibility in karst trough valleys. Additionally, five different stages of vegetation succession in karst forests were considered: Abandoned land stage (ALS), Herb stage (HS), Herb-Shrub stage (HES), Shrub stage (SHS), and Forest stage (FS). Additionally, the relationships among these factors were analyzed to identify the key driving factors influencing soil erodibility. The results revealed that soil physicochemical properties and soil aggregate stability at the deposition site were significantly superior to those at the erosion site. The FS resulted in the best soil physicochemical properties, whereas the HS resulted in the highest soil aggregate stability within the deposition site. However, the soil infiltration capacity at the erosion site was significantly greater than that at the deposition sites. The ALS had the strongest soil infiltration capacity at both the erosion and deposition sites. The soil erodibility at erosion sites (0.064) was significantly greater than that at deposition sites (0.051), with the highest soil erodibility observed on anti-dip slopes during the HES at erosion sites (0.142). The structural equation model reveals that erosion and deposition topographies, vegetation succession, soil physicochemical properties, soil aggregates, and soil infiltration characteristics collectively account for 88% of the variation in soil erodibility under different conditions. Specifically, both direct and indirect influences on soil erodibility are most significantly exerted by soil aggregate stability and vegetation succession. This study provides scientific evidence to support the management of soil erosion and ecological restoration in karst trough valleys while offering technical assistance for regional ecological improvement and poverty alleviation. Full article

With the expansion of cities and the development of industries, heavy metal pollution has caused a serious negative impact on the growth and development of animals and plants, which has become a global economic and social problem. Cadmium (Cd) is one of the main heavy metals that threaten the growth and development of plants, and it can lead to the imminent extinction of plants in severe cases. The part of upper reaches of the Yangtze River in China from Yibin to the Three Gorges Reservoir has been contaminated with varying degrees of Cd, and a rare and endangered plant called Myricaria laxiflora also lives in this area. The stress of heavy metal Cd on M. laxiflora populations is still unknown. In this study, we used the seedlings of M. laxiflora as materials, and adopted conventional physiological and biochemical analyses to characterize the morphological and physiological responses of M. laxiflora under different concentrations of Cd, and analyzed its response to Cd stress at the transcriptional level. The results showed that the wild population of M. laxiflora was stressed by the heavy metal Cd. High concentrations of Cd can inhibit the growth of M. laxiflora. M. laxiflora responded to the Cd stress through resistance substances such as malondialdehyde (MDA), hydrogen peroxide (H₂O₂), superoxide dismutase (SOD), catalase (CAT), and phytohormones such as auxin (IAA), gibberellin (GA) and abscisic acid (ABA). Transcriptome analysis was carried out on M. lasiflora seedlings exposed to 24 h, 48 h, and 72 h of Cd stress. Compared with 0 h (control), 2470, 11,707, and 11,733 differential expressed genes (DEGs) were identified, respectively. Among them, the number of down-regulated genes is more than the number of up-regulated genes. Transcriptome analysis showed that the upregulated genes were mainly enriched in MAPK signaling pathway, ethylene-induced pathway, ABA response pathway and other pathways, and the downregulated genes were mainly enriched in photosynthesis related pathways. Cd stress affected photosynthesis of M. laxiflora, and M. laxiflora may activate the MAPK signaling pathway through ethylene and ABA to improve the ability of Cd stress tolerance. These results reveal morphological changes, physiological and biochemical reactions and related key response pathways of M. laxiflora during Cd stress. It can provide a reference basis for habitat restoration and selection of wildlife environments for M. laxiflora. Full article

Perilous rock mass disasters are typical forms of collapse disasters. Perilous rock masses are widely distributed in mountainous areas around the world and often pose a great threat to residents and line engineering. The correct evaluation of the stability and disaster-causing ability of perilous rock is important for the guarantee of sustainable development for human beings living in mountainous areas. The dynamic disaster effects of perilous rock collapse have always been a hot topic in the field of engineering geological disaster research. This study takes typical #WY8 and #WY47 perilous rock masses in a zone called the Jiaohua rock perilous rock zone in Chongqing, China, as a case study. The Jiaohua perilous rock mass is located in the Kaizhou District of the Three Gorges Reservoir area in China, which is mainly distributed in a ‘long strip’. The initial deformation and collapse of the perilous rock zone occurred in September 2004, and many local collapses have occurred since. In this study, the basic characteristics of the perilous rock belt of Jiaohua rock were first analyzed, and the failure mechanism of the perilous rock mass of Jiaohua rock was then summarized. Then, a numerical model of the perilous rock mass was established by DAN-W, and the disaster process of perilous rock collapse was analyzed. According to the characteristics of perilous rock and cliffs, considering the collapse partition, the collapse path of debris flow can be divided into three sections: the collapse section, slip section, and accumulation section. The calculation results show that the maximum velocity of the front edge of the #WY8 debris flow is 27.26 m/s, the maximum velocity of the trailing edge is 16.71 m/s, the maximum sliding distance is 437 m, and the impact force of the debris flow on the building is up to 52.29 kPa. The maximum velocity of the front edge of the #WY47 debris flow is 31.05 m/s, the maximum velocity of the trailing edge is 21.99 m/s, the maximum sliding distance is 194.31 m, and the impact force of the debris flow on the building is 241.15 kPa. Civil buildings within the scope of collapse are at risk of being completely destroyed. The research results of this study provide a certain theoretical basis for disaster prevention and mitigation work in the hidden danger area of rock avalanche disasters in the Three Gorges Reservoir area. Full article

Conservation agriculture practices (CAs) are important under the increasingly serious soil quality degradation of sloping farmlands worldwide. However, little is known about how the long-term application of CAs influences soil quality at different slope positions. We conducted field experiments for a watershed sloping farmland’s mainstream planting systems in the Three Gorges Reservoir area of China. Orchard plots were treated with a conventional citrus planting pattern (C-CK), citrus intercropped with white clover (WC), citrus orchard soil mulched with straw (SM) and citrus intercropped with Hemerocallis flava contour hedgerows (HF). Crop field plots were treated with a conventional wheat–peanut rotation (W-CK), a wheat–peanut rotation intercropped with Toona sinensis contour hedgerows (TS), a wheat–peanut rotation intercropped with alfalfa contour hedgerows (AF) and a ryegrass–sesame rotation (RS). We collected soil samples from the plots at the upper, middle and lower slope positions and measured their soil properties after a nine-year experiment. We found that (1) CAs improved the soil properties at the three slope positions; (2) the effect of the CAs on the soil properties was more significant than that on the slope position; and (3) the soil quality index at the upper, middle and lower slope positions increased by 29.9%, 45.8% and 33.3%, respectively, for WC; 48.7%, 39.5% and 27.1%, respectively, for SM; and 21.7%, 25.5% and 21.6%, respectively, for HF compared to C-CK; as well as 18.7%, 23.7% and 20.4%, respectively, for TS; 16.9%, 18.6% and 16.5%, respectively, for AF; and 16.1%, 13.0% and 13.9%, respectively, for RS compared to W-CK. These findings suggest that long-term CA application enhances the soil quality of the slope position, of which SM and TS applied to orchards and crop fields, respectively, are the most effective. Full article

Accurate landslide displacement prediction is an essential prerequisite for early warning systems aimed at mitigating geological hazards. However, the inherent nonlinearity and dynamic complexity of landslide evolution often hinder forecasting performance. Previous studies have frequently combined signal decomposition techniques with individual machine learning methods to enhance prediction reliability. To address the limitations and uncertainties associated with individual models, this study presents a hybrid framework for displacement forecasting that combines variational mode decomposition (VMD) with multiple deep learning (DL) methods, including long short-term memory neural network (LSTM), gated recurrent unit neural network (GRU), and convolutional neural network (CNN), using a cloud model-based weighted strategy. Specifically, VMD decomposes cumulative displacement data into trend, periodic, and random components, thereby reducing the non-stationarity of raw data. Separate DL networks are trained to predict each component, and the forecasts are subsequently integrated through the cloud model-based combination strategy with optimally assigned weights. The proposed approach underwent thorough validation utilizing field monitoring data from the Baishuihe landslide in the Three Gorges Reservoir (TGR) region of China. Experimental results demonstrate the framework’s capacity to effectively leverage the strengths of individual forecasting methods, achieving RMSE, MAPE, and R values of 12.63 mm, 0.46%, and 0.987 at site ZG118, and 20.50 mm, 0.52%, and 0.990 at site XD01, respectively. This combined approach substantially enhances prediction accuracy for landslides exhibiting step-like behavior. Full article

Design floods are traditionally estimated based on the at-site annual maximum flood series, including historical information of hydraulic structures. Nevertheless, the construction and operation of upstream reservoirs undermine the assumption of stationarity in the downstream flood data series. This paper investigates non-stationary design flood estimation considering historical information from the Three Gorges Reservoir (TGR) in the Yangtze River. Based on the property that the distribution function of a continuous random variable increases monotonically, we proposed a novel time-varying P-III distribution coupled with the curve fitting method (referred to as the Tv-P3/CF model) to estimate design floods in the TGR operation period, and we comparatively studied the reservoir indices and parameter estimation methods. The results indicate that: (1) The modified reservoir index used as a covariate can effectively capture the non-stationary characteristics of the flood series; (2) The Tv-P3/CF model emphasizes the fitness of historical information, yielding superior results compared to time-varying P-III distribution estimated by the maximum likelihood method; (3) Compared to the original design values, the 1000-year design peak discharge Q_m and 3-day and 7-day flood volumes in the TGR operation period are reduced by approximately 20%, while the 15-day and 30-day flood volumes are reduced by about 16%; (4) The flood-limited water level of the TGR can be raised from 145 m to 154 m, which can annually generate 0.32 billion kW h more hydropower (or increase by 6.8%) during flood season without increasing flood prevention risks. Full article

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. Full article

Most river deltas worldwide are located in well-developed, densely populated lowland regions that face challenges from accelerated sea level rise. Deltas with morphological equilibrium are the foundation for associated prosperous economies and societies, as well as for preserving ecological fragile environments. And for deltas to be in morphological equilibrium, sufficient fluvial sediment supplies are fundamental. Severe droughts have significant impacts on the sediment load discharged to the sea, but this is considerably less studied compared to flooding events. This study examines the characteristics of Yangtze River sediment flowing toward the East China Sea during severe droughts. The effect of the Three Gorges Dam (TGD) was investigated by comparing the difference before and after its construction in 2003. Results indicate that the sediment load from the Yangtze River to the sea has experienced a more pronounced decrease during severe drought years since 2003. The primary cause is a substantial reduction in sediment supply from the upper reaches, resulting from the impoundment of the Three Gorges Reservoir created in 2003 and the construction of additional major reservoirs in the upper reach thereafter. Simultaneously, this is accompanied by the fining of sediment grain size. The fining of sediment and considerably reduced sediment load discharged to the sea during severe droughts after 2003 are likely to accelerate the erosion of the Yangtze subaqueous delta. The rating parameter values during severe drought years fall within the range observed in normal years, indicating that these drought events do not align with extreme rating parameter values. Less than 30% of the average discrepancy between measured and reconstructed sediment loads in severe drought years before 2003, and approximately 10% of the discrepancy after 2003, demonstrate the feasibility of reconstructing sediment loads for severe drought events using a sediment rating curve. This rating curve is based on daily water discharge and sediment concentration data collected during the corresponding period. These findings indicate that the rating curve-based reconstruction of sediment load performs well during severe droughts, with relative error slightly exceeding the average error of normal years prior to 2003 and approaching that observed after 2003. This study provides insights on sediment management of the Yangtze River system, including its coastal zone, and is valuable for many other large river systems worldwide. Full article

Nitrate is among the most widely occurring contaminants in groundwater on a global scale, posing a serious threat to drinking water supplies. With the advancement of urbanization and mountainous agriculture, the nitrate in the groundwater of Wanzhou District in the Three Gorges Reservoir Area has formed a complex combination of pollution sources. To more accurately identify the sources of nitrate in groundwater, this study integrates hydrochemical methods and environmental isotope techniques to analyze the sources and transformation processes in shallow groundwater nitrate under different land-use types. Furthermore, the Bayesian isotope mixing model (MixSAIR) is employed to calculate the contribution rates in various nitrate sources. The results indicate that nitrate is the primary form of inorganic nitrogen in shallow groundwater within the study area, with nitrate concentrations in cultivated groundwater generally higher than those in construction land and forest land. The transformation process of nitrate is predominantly nitrification, with little to no denitrification observed. In cultivated shallow groundwater, nitrate mainly originates from chemical fertilizers (36.3%), sewage and manure (35.4%), and soil organic nitrogen (24.7%); in forested areas, nitrate primarily comes from atmospheric precipitation (35.3%), chemical fertilizers (31.3%), and soil organic nitrogen (22.1%); while in constructed areas, nitrate mainly derives from chemical fertilizers (46.0%) and sewage and manure (32.2%). These results establish a scientific foundation for formulating groundwater pollution control and management strategies in the region and serve as a reference for identifying nitrate sources in groundwater in regions with comparable hydrogeological features and pollution profiles. Full article

The effect of the dry–wet cycle, characterized by periodic water level changes in the Three Gorges Reservoir, will severely degrade the bearing performance of rock formations. In order to explore the effect of the dry–wet cycle on the mesoscopic damage mechanism of jointed sandstone, a list of meso-experiments was carried out on sandstone subjected to dry–wet cycles. The pore structure, throat features and mesoscopic damage evolution of jointed sandstone with the action of the dry–wet cycle were analyzed using a-low-field nuclear magnetic resonance (NMR) technique. Subsequently, the impact on the mineral content of dry–wet cycles was studied by small angle X-ray scattering (SAXS). Based on this, the mesoscopic damage mechanism of sandstone subjected to dry–wet cycles was revealed. The results show that the effects of the drying–wetting cycle can promote the development of porous channels within sandstone, resulting in cumulative damage. Besides, with an increase in dry–wet cycles, the proportion of small pores and pore throats decreased, while the proportion of medium and large pores and pore throats increased. The combined effects of extrusion crush, tensile fracture, chemical reaction and dissolution of minerals inside the jointed sandstone contributed to the development of mesoscopic pores, resulting in the increase of porosity and permeability of rock samples under the dry–wet cycles. The results provide an important reference value for the stability evaluation of rock mass engineering under long-term dry–wet alternation. Full article

Building extraction in landslide-affected scattered mountainous areas is essential for sustainable development, as it improves disaster risk management, fosters sustainable land use, safeguards the environment, and bolsters socio-economic advancement; however, this process entails considerable challenges. This study proposes a Res-Unet-based model to extract landslide-affected buildings from unmanned aerial vehicle (UAV) data in scattered mountain regions, leveraging the feature extraction capabilities of ResNet and the precise localization abilities of U-Net. A landslide-affected, scattered mountainous region within the Three Gorges Reservoir area was selected as a case study to validate the model’s performance. Experimental results indicate that Res-Unet displays high accuracy and robustness in building recognition, attaining accuracy (ACC), intersection-over-union (IOU), and F1-score values of 0.9849, 0.9785, and 0.9892, respectively. This enhancement can be attributed to the combined model, which amalgamates the skip connections, the symmetric architecture of U-Net, and the residual blocks of ResNet. This integration preserves low-level detail during recovery at higher levels, facilitating the extraction of multi-scale features while also mitigating the vanishing gradient problem prevalent in deep network training through the residual block structure, thus enabling the extraction of more complex features. The proposed Res-Unet approach shows significant potential for the accurate recognition and extraction of buildings in complex terrains through the efficient processing of remote sensing images. Full article

Computational models enable accurate, timely prediction of landslides based on the monitoring data on-site as the development of artificial intelligence technology. The most existing prediction methods focus on finding a single prediction algorithm with excellent performance or an integrated and efficient hyperparameter optimization algorithm with a highly accurate regression prediction algorithm. In order to break through the limitation of generalization of prediction models, this paper proposes an ensemble model that combines deep learning algorithms, with a stacking framework optimized with the sliding window method. Multiple deep learning algorithms are set as the first layer of the stacking framework, which is optimized with the sliding window method to avoid confusion in the time order of datasets based on time series analysis. The Shengjibao landslide in the Three Gorges Reservoir is used as a case study. First, the cumulative displacement is decomposed into a trend and a periodic term using a moving average method. A single-factor and a multi-factor superposition model based on multiple deep learning algorithms are used to predict the trend and periodic term of the displacement, respectively. Finally, the predicted values of the trend and periodic terms are added to obtain the total predicted landslide displacement. For monitoring point ZK2-3, the values of RMSE and MAPE of the total displacement prediction with the stacking model are 15.93 mm and 0.54%, and the values of RMSE and MAPE of the best-performing individual deep learning model are 20.00 mm and 0.64%. The results show that the stacking model outperforms other models by combining the advantages of each individual deep learning algorithm. This study provides a framework for integrating landslide displacement prediction models. It can serve as a reference for the geological disaster prediction and the establishment of an early warning system in the Three Gorges Reservoir Area. Full article

The distribution characteristics of the microbial community in rhizosphere soils of different altitudinal gradients were explored to uncover ecological factors affecting microbial community composition. In this study, the community variations of bacteria and fungi in the rhizosphere soil of Chrysanthemum indicum L. were analyzed. Samples were distributed along an altitudinal gradient of 300–1500 m above sea level in the Fuling watershed of the Three Gorges Reservoir area, China. The analysis was conducted using Illumina MiSeq high-throughput sequencing and bioinformatics analyses. Through correlation analysis with ecological factors, the altitude distribution pattern and driving factors of soil microbial diversity in the mountainous and hilly region of Chongqing were explored. According to the results, the richness and diversity of rhizosphere soil bacteria increased with altitude, while fungi were the richest and most diverse at an altitude of 900 m. The composition of the microbial community differed among different altitudes. Actinobacteria, Proteobacteria, Acidobacteriota, Chloroflexi, Bacteroidota, Ascomycota, unclassified_k_Fungi, Basidiomycota, and Mortierellomycota dominated the microbial community in rhizosphere soil. Correlation analysis showed that the distribution of rhizosphere soil microbial communities correlated with soil ecological factors at different altitudes. Moisture, pH, total nitrogen, total potassium, available potassium, urease, and catalase were significantly positively correlated with rhizosphere soil bacterial α-diversity, while their correlations with fungi were not significant. Variation partition analysis showed that the combined effects of soil physical and chemical factors, enzyme activity, and microbial quantity regulated bacterial community structure and composition. Their combined contributions (19.21%) were lower than the individual effects of soil physical and chemical factors (48.49%), enzyme activity (53.24%), and microbial quantity (60.38%). The effects of ecological factors on fungal communities differed: While the soil physical and chemical factors (44.43%) alone had a clear effect on fungal community structures, their combined contributions had no apparent effect. The results of this study not only contribute to a deeper understanding of the impact mechanism of altitude gradient on the diversity of rhizosphere soil microbial communities, but also provide a scientific basis for the protection and management of mountainous and hilly ecosystems. It lays a foundation for the future exploration of the relationship between microbial communities and plant–soil interactions. Full article