Soil Erosion Intensity Research Articles

Quantifying the Geomorphological Susceptibility of the Piping Erosion in Loess Using LiDAR-Derived DEM and Machine Learning Methods

Soil piping erosion is an underground soil erosion process that is significantly underestimated or overlooked. It can lead to intense soil erosion and trigger surface processes such as landslides, collapses, and channel erosion. Conducting susceptibility mapping is a vital way to identify the potential for soil piping erosion, which is of enormous significance for soil and water conservation as well as geological disaster prevention. This study utilized airborne radar drones to survey and map 1194 sinkholes in Sunjiacha basin, Huining County, on the Loess Plateau in Northwest China. We identified seventeen key hydrogeomorphological factors that influence sinkhole susceptibility and used six machine learning models—support vector machine (SVM), logistic regression (LR), Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), random forest (RF), and gradient boosting decision tree (GBDT)—for the susceptibility assessment and mapping of loess sinkholes. We then evaluated and validated the prediction results of various models using the area under curve (AUC) of the Receiver Operating Characteristic Curve (ROC). The results showed that all six of these machine learning algorithms had an AUC of more than 0.85. The GBDT model had the best predictive accuracy (AUC = 0.94) and model migration performance (AUC = 0.93), and it could find sinkholes with high and very high susceptibility levels in loess areas. This suggests that the GBDT model is well suited for the fine-scale susceptibility mapping of sinkholes in loess regions.

Research on the spatiotemporal distribution and control degree of soil erosion in Dali River Basin

The Dali River basin as the representative areas of the middle reaches of the Yellow River. Accurately evaluating the degree of soil erosion control is very important for soil erosion control. In response to the problems existing in the current research on soil erosion control degree (SECD) methods, the maximum possible soil erosion modulus is introduced into the evaluation model. Thereby eliminating the uncertainty of the assumed state of the original model. This paper calculates the SECD for 2020 based on this method. The results show: (1) From the soil erosion modulus spatial distribution, it can be seen that the soil erosion intensity is stronger in the eastern and southwestern parts. (2) Based on the SECD spatial distribution, it was found that eastern and southwestern regions were well controlled. This is consistent with the actual situation. (3) As the slope increases, the area proportion of SECD within the 0.2-0.3 range in each slope zone shows an increasing trend, while that of SECD within the 0.5-1 range gradually decreases; (4) In the case of vegetation coverage greater than 80%, SECD is still concentrated between 0.3-0.5, which indicates that vegetation coverage is not the only factor affecting soil erosion control. This research provided a new perspective for the evaluation of SECD.

Research on identification and zoning control of territorial spatial risk pattern based on deep learning: A case study of Shenzhen, China

In the era of globalization, digitalization, and diversification, currently societal cities face risks intertwined by uncertainty and complexity. Thus, it is crucial to address risks concerns to achieve modernization of spatial management. Moreover, how to scientifically identify the risk pattern of territorial space and how to map and control it in space becomes a critical and urgent issue to be addressed for achieving modernization of spatial management. Understanding the intricate relationship between land use, environmental factors, and population density is essential for identifying risk patterns that can inform effective disaster management strategies. By employing advanced analytical methods, this study aims to provide a comprehensive framework for recognizing and mitigating territorial spatial risks in an increasingly complex urban landscape. This study used a case study to conduct the unsupervised algorithm of stack autoencoder self-organizing map to analyze the distribution and spatial clustering of Shenzhen's land spatial risk pattern and reveal the driving mechanism of its risk pattern formation in detail. It was found that: (1) The overall territorial spatial risk pattern of Shenzhen presents a distribution trend of "high in the west and low in the east, high in the south and low in the north"; that is, I and II risk areas are clustered in the hilly areas in the northeast and IV and V risk areas in the coastal areas in the southwest; (2) Population density, land use intensity, seawater intrusion, geological disasters, relief amplitude, soil type, soil erosion intensity are the main driving forces for the formation of Shenzhen's territorial spatial risk pattern. In particular, seawater intrusion, population density and land use intensity, soil type were found having significantly synergistic impact on the risk pattern.

Analysis of the coupling and coordination between soil erosion and land use in the Northeastern black soil region of China: a case study of Lishu County

Lishu County, which is located in the black soil region of Northeast China, represents a key site for the analysis of soil erosion intensity. This study offers a scientific foundation for the development of targeted soil and water conservation strategies within the region. The Revised Universal Soil Loss Equation (RUSLE) was employed to compute the soil erosion modulus in Lishu County, with the objective of conducting a quantitative analysis of the temporal and spatial distribution patterns of soil erosion. Additionally, the changing characteristics of soil erosion were examined from the perspectives of land use types and slope variations. The Generalized Connectivity Causality Model (GCCM) was utilized to identify the causal relationship between soil erosion and land use types through the reconstruction of state space and cross-mapping predictions. (1) Soil erosion in Lishu County between 2000 and 2020 predominantly exhibited mild to moderate levels, characterized by patchy and sporadic erosion, with relatively severe occurrences in the northern and central regions. (2) Soil erosion was correlated with land use and slope variations, with more than 90% of erosion incidents transpiring in cultivated land areas. The 3°-5° slope range in Lishu County emerged as a focal point for erosion, necessitating targeted prevention and control measures. (3) The GCCM model illustrated a discernible causal relationship between soil erosion and land use, revealing mutual influences between the two factors. Between 2000 and 2020, both the area and intensity of soil erosion in Lishu County exhibited an initial increase, followed by a subsequent decrease. This suggests an overall trend of amelioration in soil erosion conditions. However, notable spatial disparities persist in the erosion distribution across the region.

A Probabilistic Statistical Risk Assessment Method for Soil Erosion Using Remote Sensing Data: A Case Study of the Dali River Basin

Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity ignores the random nature of the occurrence of erosion; therefore, neither method accurately reflects the risk of soil erosion. In order to address this issue, this study proposes a soil erosion risk assessment method that integrates the outcome and the probability of occurrence of soil erosion by means of a probabilistic statistical model. Subsequently, experimental research is conducted in the Dali River Basin. On the basis of long time-series data, using mathematical statistics as a tool and drawing on the empirical frequency formula, the probabilistic statistical risk assessment model is combined with the Modified Universal Soil Loss Equation (RUSLE) model to account for the probability of regional soil erosion at different intensity levels in the long time-series, which is combined with the intensity of erosion to carry out soil erosion risk assessment. The results of our study show the following: (1) The central and southwestern regions of the Dali River Basin (DRB) present medium and high levels of soil erosion risk, with the proportion of low-risk areas increasing annually, accounting for 78.97% of the DRB in 2020, while extremely high-risk areas account for only 0.40% of the DRB. (2) The major components impacting soil erosion risk in the DRB, as revealed by the geodetector, are the normalized difference vegetation index (NDVI) and slope, where the interaction between the two dominated the spatial variation in soil erosion risk. (3) Comparing the soil erosion risk and its status in the coming years, the proposed assessment method based on the occurrence probability can reveal the future soil erosion risk better than the traditional assessment method.

Analysis of Temporal and Spatial Carbon Stock Changes and Driving Mechanism in Xinjiang Region by Coupled PLUS-InVEST-Geodector Model

Based on the goal of "dual-carbon" strategy, it is important to explore the impacts of land use change on carbon stock and the drivers of spatial differentiation of carbon stock in Xinjiang. Here, we predicted the land use types in Xinjiang in 2035 under different scenarios and analyzed the impacts of land use on carbon stock, which is of great theoretical and practical importance for policy formulation, land use structure adjustment, and carbon neutrality target achievement in Xinjiang. The coupled PLUS-InVEST-Geodector model was used to explore the spatial and temporal patterns of carbon stock change under the scenarios of rapid development, natural change, arable land protection, and ecological protection in Xinjiang in 2035 and to quantitatively reveal the attribution of influences on the changes in carbon stock from the perspectives of land use change and the combination of nature-socioeconomic-accessibility. The results showed that： ① From 1990 to 2020, the area of arable land and construction land in Xinjiang increased, and in terms of the transfer direction, it was mainly shifted from unutilized land to grassland. ② On the time scale, the carbon stock in Xinjiang showed the fluctuation of "decrease-increase-decrease," with an overall increasing trend. The transfer of unutilized land to grassland was the main reason for the increase in carbon stock； on the spatial scale, the carbon stock in the Altai Mountains in the north, the Tianshan Mountains in the middle, and the Kunlun Mountains in the south was higher, whereas the carbon stock in the Tarim Basin and the Junggar Basin was lower. ③ In 2035, the carbon stock of the natural development and rapid development scenarios decreased by 27.24 Tg and 71.17 Tg compared with 2020, respectively, and the ecological protection and arable land protection scenarios increased by 492.55 Tg and 46.67 Tg. The ecological protection scenario could significantly increase the carbon stock of the Xinjiang Region compared with that in the other scenarios, and the distribution pattern of the carbon stock in the four scenarios was more or less the same as that in 2020. In addition to land transformation, soil erosion intensity was the main driver of spatial differentiation of carbon stocks in Xinjiang （q value of 0.3501）, followed by net primary productivity of vegetation. The results of multifactor interactions showed that the spatial differentiation of carbon stocks in Xinjiang was the result of the joint action of multiple factors. All the factors had a synergistic enhancement under the interactions. The interaction between soil erosion intensity and the net primary productivity of vegetation was the main driver of the spatial differentiation of carbon stocks in Xinjiang.

Landslide susceptibility prediction based on landform predisposing indexes − An example from the Beiluo River Basin

The Beiluo River basin, which flows through the central part of the Loess Plateau, has experienced intense soil erosion and significant geomorphic change, which has provided favorable conditions for the occurrence of a large number of landslides. Landform indexes, which can express geomorphologic development state and internal rules, can transfer the development process information of surface morphology into the evaluation of landslide susceptibility, and help get more accurate landslide susceptibility prediction results. Taking the Beiluo River Basin as an example, a landslide susceptibility prediction model based on landform index is proposed by comparing the importance of landform index. In order to improve the accuracy of LSP, 10 kinds of general predictors indexes, 5 kinds of landform predisposing indexes and 1821 landslide points were compiled, and the geographic information system of Beiluo River Basin was constructed. Through the correlation test and CF model, the environmental indexes were evaluated to obtain the sensitive index results, and the combination of different environmental predictors indexes were classified according to the sensitive index results. Based on the combined classification results, the Max Entropy (MaxEnt) model was used to evaluate the Landslide susceptibility prediction (LSP), while the calculated results were evaluated and compared using the receiver operating characteristic (ROC) curve and landslide density. The results show that the vertical erosion factor, elevation, rainfall, horizontal erosion factor, slope angle and NDVI play a key role in controlling the spatial distribution of landslides in the study area. At the same time, the accuracy of landslide susceptibility is compared by AUC value. According to the calculation results, the Group5 (AUC = 0.803) with reasonable terrain index performs better in the training and test stages, and the relative accuracy is improved by 6.22 % compared with the non-introduction of terrain index and the omission rate difference is the best (omission rate difference = 0.0005), indicating that the introduction of landform index can effectively improve the landslide susceptibility prediction. The distribution of different sensitive areas was observed. The high sensitive areas and very high sensitive areas are mainly distributed in the southern Luochuan loess tableland and the northern Wuqi loess hilly area. The research results provide a scientific basis for landslide susceptibility prediction with rational introduction of landform indexes and regional infrastructure construction.

Integrated risk assessment of landslide in karst terrains: Advancing landslides management in Beiliu City, China

Beiliu City, Guangxi Zhuang Autonomous Region, China, like other karst landscape areas, is also suffering from the threat of landslides. This research applied the circular variance method to delineate 31,465 slope units and then combined it with a geographic information system and a well-established risk assessment system to synthesize three perspectives-susceptibility, hazard, and vulnerability to realize the risk assessment of karst landslides in Beiliu City. Using perennial field surveys, aerial images-derived landslide inventories, slope unit investigations, and a literature review combined with a Fully Convolutional Neural Networks approach, we present a culminating in the derivation of landslide susceptibility assessment outcomes within the study area. Because Beiliu City is located in a karst area, this research employs the quantification of soil erosion intensity as a metric to assess landslide hazard. In this study, based on a comprehensive analysis of the basic characteristics of landslides and their associated disaster-bearing bodies, a robust landslide vulnerability assessment index system was constructed for risk assessment of karst landslides in Beiliu City. The landslide risk assessment model in karst area is constructed by combining the landslide susceptibility and the vulnerability in the danger range. Detailed field surveys verified the scientific validity and accuracy of the assessment results. This research extends beyond merely furnishing scientific data support for local governmental efforts in disaster prevention and mitigation. It impressively enhances the methodological framework for conducting landslide risk assessments within karst landscapes, thereby offering a robust foundation for both immediate and strategic responses to the inherent geological vulnerabilities of these areas.

Research on spatial patterns of soil erosion in wind erosion region based on the revised wind erosion equation and partial least squares regression

Understanding the complex influencing factors of soil erosion is critical for maintaining regional ecological security. The revised wind erosion equation (RWEQ) and partial least squares regression (PLSR) model were used to reveal the primary factors affecting soil erosion. Based on the results of the RWEQ model, the wind erosion area was 8,439.3 km2 in 2018, accounting for 54.3% of the total planning area, and mild erosion, moderate erosion, intense erosion, very intense erosion, and fierce erosion accounted for 18.22%, 11.43%, 11.78%, 10.16%, and 2.74% of the total planning area, respectively. Based on the results of the PLSR model, the results indicate that the proportion of mining land, meteorological indicators, and mean square deviation of slope difference were positively correlated with the soil erosion area, intensity, and severity, respectively. The proportion of grassland and forest land was negatively correlated with the soil erosion area, intensity, and severity. The mean square deviation of slope difference, the mean square deviation of difference in elevation, and the proportion of urban land use contributed the most to the soil erosion severity index, with variable importance in projection (VIP) scores of 1.55, 1.44 and 1.43, respectively. The mean square deviation of slope difference, the proportion of urban land, and the mean square deviation of difference in elevation contributed the most to the soil erosion area, with VIP scores of 1.53, 1.26 and 1.24, respectively. The results provide new insights into wind erosion and the influences of weather conditions and human influences, which can guide regional soil and water conservation planning in wind erosion regions.

Anthropogenic forest degradation and its impact on soil erosion in the mountainous region of northern China

Soil erosion has significant negative effects on the eco-environment, and it also threatens the sustainability of agricultural production. However, studies on soil erosion in northern China during the historical period, and its relationship with climate change and human activities, are rare. Here we present a 2900-year pollen and geochemical record from a sediment core from Lingganhai Lake, an alpine freshwater lake on the Chinese Loess Plateau in northern China. We observed an abrupt increase in the influx of detrital elements at ∼ 750 CE, accompanied by decreased tree pollen and increased cereal pollen percentages, which suggest severe soil erosion induced by intensified human activities. Coeval pollen and fungal spore records from other sites in the vicinity of Lingganhai Lake also indicate reduced forest cover, intensified agricultural and grazing activity, and increased dust storm frequency. During 1250–1380 CE, decreased soil erosion at Lingganhai Lake was accompanied by forest recovery and a reduction in the intensity of human activities. However, after ∼ 1380 CE, intensive soil erosion was renewed, likely caused by enhanced human activity, which partially overlapped with intensified dust activity in northern China during the Little Ice Age. Overall, our results demonstrate the close link between vegetation cover and soil erosion, and they highlight the importance of the maintenance of montane forest in northern China for soil preservation.

How has carbon storage changed in the Yili-Tianshan region over the past three decades and into the future? What has driven it to change?

Predicting future land use changes and assessing carbon storage remain challenging. Nowadays, how nature and socioeconomics drive changes in carbon storage is a hot topic in research. In this study, through the projection of land use type and the integration of the PLUS, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Geodetector models, we constructed a framework for assessing carbon storage in different land use scenarios. Utilizing this framework, it is possible to project land use change and estimate carbon storage based on different development scenarios. We applied the framework to the Yili Tianshan region and identified the main driving forces for carbon storage change. Further, we estimated the carbon storage in the Yili Tianshan region in 2035 under four scenarios (RE, NE, EP, and CLP). The results showed the following: 1) Between 1990 and 2020, there was an increase in the forest area and water bodies in the Yili-Tianshan region, mainly from bare land. 2) As shown on the time scale, carbon storage increases in the Yili-Tianshan region with a W-shaped fluctuation by converting grasslands and bare land into forests. On a spatial scale, the carbon storage was lower in the center and higher on both sides in the Yili-Tianshan region. 3) In 2035- RE, 2035-ND, and 2035-EP scenarios, the carbon storage was increased by 4.30 Tg, 6.67 Tg, and 12.08 Tg; in the 2035-CLP scenario, it was decreased by 14.63 Tg. The Yili-Tianshan region experienced a notable rise in carbon storage under the 2035-EP scenario compared to the other three scenarios. 4) Soil type played a significant role in the spatial differentiation of carbon storage in Yili-Tianshan (q value 0.5958), followed by population density (0.5394). The changes in carbon storage in the Yili-Tianshan region are the result of synergistic effects of multiple factors, in which the soil type∩soil erosion intensity are the most important. This research could provide a reference method for improving regional carbon storage.

Evaluation of Soil Quality of Pingliang City Based on Fuzzy Mathematics and Cluster Analysis

Pingliang City has a complex topography and diverse soil types. To realize the improvement of soil according to local conditions and the reasonable and sustainable use of soil resources, an evaluation of soil quality in Pingliang City was carried out, based on the soil distribution situation in Pingliang City, adopting a method combining fuzzy mathematics and cluster analysis of the main evaluation factors, such as soil organic matter, topsoil depth, soil erosion intensity, soil moisture regime, effective soil thickness, soil texture, soil profile structure, soil nutrient status and topographical parts, to carry out a comprehensive evaluation. A comprehensive evaluation of soil quality was conducted in seven counties under the jurisdiction of Pingliang City, and the evaluation results were compared and analyzed against the national standard, “Cultivated land quality grade”, to provide a basis for the selection of scientific soil improvement methods. The results of the arable land quality grades indicate that the quality of farmland in Pingliang City is divided into three to ten grades, and the average quality grade of farmland is 6.83, which is in the middle–lower level, and the overall grade distribution shows the characteristics of low in the middle and high in the east and west. The results of fuzzy mathematics combined with cluster analysis indicated the following trends in soil quality for the 12 soil genera: Chuan black gunny soil > yellow moist soil > sandy soil > silt soil > mulching helilu soil> loessal soil> loamy soil > slope loessal soil > arenosol > tillage leaching gray cinnamon soil > calcareous gray cinnamon soil > red clay soil. The results of the combination of fuzzy mathematics and clustering were significantly correlated with the results of the evaluation of the soil quality of arable land; the correlation coefficient was 0.884. This indicates that the method can accurately and objectively review the advantages and disadvantages of arable land soil and can be effectively applied to the evaluation of the soil quality of agricultural soils in other regions. It is a complement to the existing evaluation of the soil quality of arable land and at the same time provides a reference for the improvement of soil quality in agricultural regions.

East Asian summer monsoon and early anthropogenic impacts on the lacustrine carbon cycle in the Otindag Sandy Land since the middle Holocene

Lake systems play a pivotal role in the global carbon cycle, yet the biogeochemical processes in alkaline and saline lakes have received little attention. In this study, we present a comprehensive analysis of δ13Ccarb and δ18Ocarb records from Lake Chagan in the Otindag Sandy Land, located in North China, spanning approximately 7000 years. Our aim is to explore how the lacustrine inorganic carbon cycle responded to both climate change and human activity. The findings reveal a notable highstand in Lake Chagan around 6.9–6.0 cal kyr BP, followed by a gradual regression leading to an unstable state over the past 1.6 cal kyr BP. The hydroclimate variations inferred from Lake Chagan align with other regional records, suggesting that long-term East Asian summer monsoon (EASM) precipitation trends were influenced by decreasing insolation and variations in solar irradiance on a centennial timescale. The sources of dissolved inorganic carbon (DIC) in lake water generally mirrored regional hydroclimate changes since the middle Holocene. Our evidence indicates that during periods of strong EASM activity, more allochthonous organic matter was converted to lake water DIC, coinciding with high vegetation cover and intense soil erosion. While hydroclimatic changes significantly impacted the lacustrine carbon cycle, the isotopic evidence also suggests substantial anthropogenic influences post-1.6 cal kyr BP, particularly in terms of the input of allochthonous organic matter into the lakes of the Otindag Sandy Land. These findings underscore the potential for anthropogenic-driven terrestrial carbon cycle feedback to global climate change over millennia.

Assessing hydrological interactions, soil erosion intensities, and vegetation dynamics in Nyabarongo River tributaries: a SWAT and RUSLE modeling approach

Assessing hydrological interactions, soil erosion intensities, and vegetation dynamics in Nyabarongo River tributaries: a SWAT and RUSLE modeling approach

High-resolution soil erosion mapping in croplands via Sentinel-2 bare soil imaging and a two-step classification approach

Erosion-induced lateral soil redistribution leads to spatially heterogenous soil composition, which can be captured through the distinctive spectral reflectance of soils under varying levels of erosion influence. This points to the potential of using remotely sensed soil spectra to detect severe erosion and deposition hotspots in exposed croplands and, importantly, further differentiate the intra-class spectral variability of moderate erosion that often occupies the largest proportion. Here, we aim to develop a two-step erosion classification and mapping approach based on multitemporal compositing of Sentinel-2 bare soil images of a typical agricultural region (11,500 km2) of northeast China. A random forest classifier was firstly trained against the ground-truth data derived from very high resolution (VHR) imagery in Google Earth, with an overall accuracy of 91 % that allowed for clear delineation of severe erosion and deposition areas based on their distinct topographic and spectral features particularly in the red and red-edge bands. In the second step, the remaining area of moderate erosion (60.30 %) was further differentiated using Iterative Self-Organizing cluster unsupervised classification to yield a five-class soil erosion map at 10 m spatial resolution. The accuracy of the predicted map was successfully validated by independent Caesium-137 (137Cs) and soil organic carbon observations at catchment and regional scales, as revealed by significant inter-class differences in soil redistribution rates estimated from 137Cs inventory. The severe erosion class had a soil loss rate of 5.5 mm yr−1, suggesting that previous assessments have underestimated erosion severity. The spatial accordance of crop growth with soil erosion intensity, particularly in localized settings, further highlighted the potential of bare soil imaging for mapping the spatiotemporal development of soil erosion and its response to targeted sustainable cropland management efforts.

Assessment of Soil Erosion within the Gayathripuzha River Basin Using RUSLE Model and GIS Tools

Soil erosion, a natural phenomenon accelerated by human activities, became a major problem for agricultural land use and for water quality concerns. Soil erosion by water is influenced by various factors like climate, nature of land surface, slope steepness, soil properties, land cover, and management practices. Estimation of soil erosion is essential to know the nature and quantity of soil loss and create specific strategies for soil conservation. This research paper thoroughly examines the susceptibility of soil erosion in the Gayathripuzha river basin in Kerala, India, which is encompassing a wide area of around 961 km². Employing the Revised Universal Soil Loss Equation (RUSLE), this investigation assesses the influential factors affecting soil erosion, which comprise rainfall (R Factor), soil erodibility (K Factor), topography (LS Factor), land usage (C Factor), and the impact of conservation measures (P Factor). The study was conducted using Geographic Information System (GIS) tools like ArcGIS 10.8 Desktop and integrating data on rainfall, soil properties, land topographic features, and land use. Rainfall erosivity is a major factor causing erosion in Gayathripuzha river basin and it ranges from 746.03 to 1222.35 MJ mm ha−1 h−1 y−1 in the river basin. Soil erodibility factor also varies widely within the region. Slope length and steepness factor showed that highland ecosystems are present only in a small area of the river basin. Variation in land use and land cover also influenced the soil erosion within the area. The estimated average annual soil erosion ranges from 0 to 133.18 t ha-1 yr-1 within the research site. The research found that soil erosion varied greatly across the study area. Regions with slopes over 50% had much higher erosion rates, while most of the study area had low erosion levels. Notably, the intensity of soil erosion demonstrated a positive correlation with slope percentage, with areas classified as bare ground demonstrating the highest erosion rates. These findings emphasize the necessity for targeted and comprehensive conservation strategies tailored to the diverse landscape of the region to effectively manage soil erosion and mitigate its adverse impacts.

Rare‐earth element phytoextraction efficiency in southern Chinese mines under different clipping intensities using a co‐cropping system (Dicranopteris pedata and Pinus massoniana)

AbstractOur understanding of how varying clipping intensities affect the efficiency of phytoextraction remains limited, particularly with co‐cropping systems. This study focuses on assessing the effects of different clipping intensities on phytoextraction efficiency within co‐cropping systems dominated by the key species Dicranopteris pedata and Pinus massoniana in rare‐earth mines of southern China. Our results confirmed the following. Firstly, the compensation index, dry aboveground biomass, and rare‐earth elements (REE) accumulation of clipped D. pedata per unit area were highest under 100% clipping (p < 0.05). Meanwhile, the bioabsorption coefficient, bioconcentration factor, and translocation factor of P. massoniana were less than 1. Secondly, the soil catalase, sucrase, and α‐diversity indices of bacteria and fungi separately indicated no significant variation between the clipping treatments and control. The REE content in tree rings increased, and the total REE contents in thicker tree rings were higher than those in the thinner tree rings of P. massoniana from 1997 to 2016. Additionally, the soil erosion intensity diminished significantly in the growth area of P. massoniana from 1995 to 2019. Thirdly, both D. pedata and P. massoniana, when subjected to 100% clipping, could effectively remove excess REEs from the 0–20 cm soil layer within 18.57–61.19 years. In contrast, P. massoniana alone achieved a removal rate of excess REEs in the 20–60 cm layer, within 923.16–1899.62 years. Thus, D. pedata with 100% clipping can phytoextract the maximum amount of REEs. It is noted that P. massoniana cannot be used for REEs phytoextraction or phytostabilization. Co‐cropping with 100% clipping would have a less negative impact on growth and REEs extraction, and the co‐cropping system can phytoextract excess REEs from the surface soil within decades. However, it is not efficient for the phytoextraction of excess REEs from deep soil layers.

Human activities caused lake ecological transitions in the Chinese Loess Plateau over the past 1400 years

The combined effects of climate change and human disturbance has degraded lake ecosystems worldwide in recent times, but there are few studies that explore the relative impact of these factors over long timescales. In this paper, we use sedimentary pigment data to reveal variations in algal abundance and the lake ecological environment of a typical subalpine lake (Beilianchi Lake) in the southwestern Chinese Loess Plateau, to distinguish the relative effects of natural climate change and human disturbance over the past 5000 years. Our data show that between 5000 and 1400 cal yr BP, algal abundance exhibited a slow decline without obvious fluctuations, mainly driven by a gradually drying and cooling climate. After 1400 cal yr BP, algal abundance showed large and rapid fluctuations characterized by a pattern of a decline, then increase, and then decline again. During this period, human activities began to affect lake ecology and gradually overshadowed the role of climatic changes as the main driving factor. Enhanced human disturbances caused a rapid and synchronous decline in algal abundance due to increases in soil erosion and reductions in water transparency. Algal biomass levels recovered when the soil erosion intensity decreased during 800–500 cal yr BP corresponding to the period from Southern Song to Yuan Dynasties. Thereafter (during the period of Ming and Qing Dynasties), as human activities became further enhanced and the soil erosion intensity became very high, the lake ecosystem rapidly transitioned to a stable state of low algal growth. This indicates that the lake ecosystem may have had the capacity to recover from moderate external disturbances in the early stages of human activity, but the resilience of the lake ecosystem began to decrease under stronger human disturbances. Our findings provide important empirical evidence for evaluating the current state of lakes and for sound lake management in the future.

Quantitative estimation of dust transport in the desert region of northwest China by plutonium isotopes

Dust is an important source of atmospheric pollution, and quantitative estimation of desert dust transport is crucial for air pollution control. In this study, five typical sandy soil profiles in the Tengger Desert were collected and analyzed for 239,240Pu concentration and 240Pu/239Pu atomic ratios in order to identify the source of 239,240Pu in this area and explore the sedimentary characteristics of dust in different profiles. The results revealed that the concentrations of 239,240Pu in the soil profiles were between 0.002 and 0.443 mBq/g with an exception of the deep layer soil at one site. The measured atomic ratios of 240Pu/239Pu are at the global atmospheric fallout level with a mean of 0.184 ± 0.020, indicating that global fallout is the dominant source of plutonium in this region. The total inventories of 239,240Pu in the reference sites in this area were estimated to be 39.2–44.6 Bq/m2, this is in agreement with the value from the global fallout of atmospheric nuclear weapon tests at the similar latitude (30–40 °N: 42 Bq/m2). The estimated erosion rate in the erosion profile utilizing soil erosion intensity mode is 2491 t/km2/yr and the soil erosion depth is 9.86 cm, While, the stacking rate of the accumulation profile is 1383 t/km2/yr, and the depth of accumulation is estimated to be 5.48 cm. The difference between the erosion and accumulation profiles indicated that approximately 1107 t/km2/yr of dust was exported from the Gobi landform area of the Tengger Desert, which might be transported long distance in the downwind direction.

Impact of watershed management practices on vegetation, land use changes, and soil erosion in River Basins of the Atlas, Morocco

Soil erosion, a land degradation process triggered by natural and anthropogenic factors, seriously impacts landscapes and water resources. The influence of vegetation cover and land use changes on intensity of soil erosion of two catchments in mountainous regions of Morocco is evident, as it alters hydrologic response and sediment dynamics. This research aims to analyze the interactions among plants, soil, geology, meteorology, and orography, assessing soil erosion responses using the process-oriented IntErO model - Erosion Potential Method to determine erosion rates. The obtained results indicate that the Tiguert river basin experiences higher soil losses (Ggod = 5184.47 m³/god) and soil losses per square kilometre (Ggod/km² = 508.28 m³/km² god) compared to the Wanmroud catchment (Ggod = 2555.66 m³/god, Ggod/km² = 381.44 m³/km² god), confirming the theory that areas with denser and more effective vegetation cover experience lower soil erosion rates. Furthermore, the Wanmroud basin exhibits a more regular shape and lower watershed development coefficient, implying lower human impact. This study has shown the relationships between land use changes, vegetation cover, and soil erosion dynamics, offering valuable insights for sustainable land management practices in mountainous regions of Morocco.