Changes In Soil Erosion Research Articles

Exploring the inclusion of soil management practices in erosion models towards the improvement of post-fire predictions

Wildfires are recognized for having a strong impact on forest soils, a situation aggravated by inadequate pre-fire land management practices. Land management operations, such as plowing, are routinely carried out for cultural reasons and can impact soils for decades after their implementation. Therefore, it is crucial to take into account the pre-fire land management history when predicting post-fire sediment losses in burnt areas. This consideration is critical for a realistic assessment of soil erosion risk and, consequently, for effectively implementing emergency stabilization and/or rehabilitation measures.The aim of the study was to integrate pre-fire land management practices into erosion models, to enhance post-fire sediment losses predictions at slope scale. To accomplish this goal, both Multiple Linear Regression (MLR) and the revised-Morgan-Morgan-Finney model (revised-MMF) were applied in the Colmeal burnt area (Central Portugal). These models were adapted to account the impacts of different management options, specifically no plowing, downslope-plowing and contour-plowing, on the erosive response following a wildfire.The results revealed fluctuations in the performance of both models across different soil management, and over time since the wildfire. Despite the observed variability, it is important to highlight the positive outcomes achieved with the revised-MMF model over the three monitoring years where contour-plowing was applied. These results demonstrate that the best model performances are achieved when soil management is individualized and analyzed independently. Similarly, the MLR model exhibited improved performance when incorporating management practices into its predictions. This study confirms that disturbances on topsoil, whether caused by wildfires or soil management operations, play key roles in driving change in soil erosion. Hence, integrating these factors into models is essential for providing relevant information for the development of mitigation and/or restoration strategies in areas at high risk of erosion.

Landscape and Soil Erosion Changes Along Different Types of Road Over the Past 30 Years in the Largest Loess Tableland of China

ABSTRACTRoad construction, as a product of modern civilization, can alter landscape patterns and accelerate erosion, especially in the loess tableland area of China with fragile eco‐environment. Different types of road could change land surface and increased soil erosion in different ways, while knowledge about this topic is limited. In this study, our objective is to evaluate the landscape and soil erosion changes along different types of road in the largest loess tableland of China. Results showed that: (1) The land use type became more diverse along the three types of road in 1990–2020, leading to increased landscape fragmentation and spatial heterogeneity. (2) Different types of road had different proportions of soil erosion values. In the expressway and provincial road, the proportion of low soil erosion values (below 100 t ha−1 yr−1) was > 70% in 1990–2020; in the national road, the proportion of low erosion values (below 100 t ha−1 yr−1) was > 50%. (3) In 1990–2020, soil erosion increased with the increase of road‐induced landscape fragmentation along the three types of road. For the expressway, soil erosion value increased from 91.5 to 98.5 t ha−1 yr−1 between 1990 and 2020; for the national road, soil erosion value increased from 174.5 to 184.8 t ha−1 yr−1; and for the provincial road, soil erosion value increased from 93.7 to 97.1 t ha−1 yr−1. (4) Effective soil and water conservation programs could mitigate erosion. Compared with the simulated soil erosion value, the actual soil erosion value decreased along the expressway, national road, and provincial road by 1.21, 3.67, and 2.96 t ha−1 yr−1, respectively, between 2005 and 2020. This study emphasizes the importance of road in aggravating erosion and the effectiveness of soil and water conservation programs in mitigating soil erosion within the loess area.

Assessment of forest disturbance and soil erosion in wind farm project using satellite observations

The construction of wind farms, involving road construction and wind turbine installation, severely disrupts natural landscapes. Wind energy expansion in global forested areas has unclear impacts on local forests and ecosystem services. Due to a lack of information on internal road distribution and deployment dates, few studies have assessed forest disturbances caused by wind farms. Environmental issues like vegetation destruction and soil erosion may be overlooked. To address this, we integrated multi-source spaceborne observations to identify deployment dates and road distributions of forest wind farms and mapped related forest disturbances and soil erosion changes. Six global locations were tested, showing over 80 % accuracy. Disturbance intensity ranged from 1.5 to 6.5 ha/MW, with NDVI decreasing by 0.03 to 0.33 in disturbed forest regions. The average soil erosion increase per unit area due to road construction ranged from 24.74 to 274.33 t/hm−1a−1, while wind turbine construction caused an average soil erosion increase ranging from 26.52 to 26.52 to 263.46 t/hm−1a−1. Road construction is the primary cause of forest disturbance, with greater soil erosion increases in mountainous than in plain forests. This method enhances monitoring and understanding of wind farms' environmental impacts.

Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method

The source region of the Yellow River (SRYR), known as the “Chinese Water Tower”, is currently grappling with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes. Moreover, conventional methods for assessing soil erosion do not adequately address the variety of erosion types present in the SRYR. Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. By leveraging soil erosion data measured by 137Cs from 521 locations and employing the random forest (RF) algorithm, a new soil erosion model was formulated. Key findings include that: (1) The RF soil erosion model significantly outperformed the revised universal soil loss equation (RUSLE) model and revised wind erosion equation (RWEQ) model, achieving an R2 of 0.52 and an RMSE of 5.88. (2) The RF model indicated that from 2001 to 2020, the SRYR experienced an average annual soil erosion modulus (SEM) of 19.32 t·ha−1·y−1 with an annual total erosion in the SRYR of 225.18 × 106 t·y−1. Spatial analysis revealed that 78.64% of the region suffered low erosion, with erosion intensity declining from northwest to southeast. (3) The annual SEM in the SRYR demonstrated a downward trend from 2001 to 2020, with 83.43% of the study area showing improvement. Based on these findings, measures for soil erosion prevention and control in the SRYR were proposed. Future studies should refine the temporal analysis to better understand the influence of extreme climate events on soil erosion, while leveraging high-resolution data to enhance model accuracy. Insights into the drivers of soil erosion in the SRYR will support more effective policy development.

Drivers and Consequences of Land Degradation on Livestock Productivity in Sub-Saharan Africa: A Systematic Literature Review

Land degradation is a major threat to sub-Saharan Africa rangelands, which are crucial for livestock farming and the livelihood of millions of people in the region. This systematic review aims to provide a comprehensive understanding of the causes and effects of land degradation, as well as to evaluate the effectiveness of different mitigation strategies. Following the PRISMA guidelines, we identified, screened, and analyzed 25 peer-reviewed articles published before 30 June 2024 using Scopus. The review highlights key factors that drive land degradation, such as soil erosion, drought, deforestation, and climate change, as well as socio-economic factors like poverty, land tenure issues, population pressure, and economic instability. These factors have serious implications, as land degradation can lead to poor quality of forage, an increased incidence of diseases, higher mortality rates among livestock, and a significant decline in livestock productivity. The socio-economic consequences of this degradation are significant, leading to reduced household income, increased poverty, and heightened food insecurity. Furthermore, the review assesses various mitigation strategies and concludes that practices such as rotational grazing and soil conservation techniques demonstrate high effectiveness, while agroforestry practices show only moderate success. An integrated approach that combines environmental, economic, and policy measures is crucial to addressing the complex challenge of land degradation in sub-Saharan Africa. Strengthening land tenure, improving economic stability, and promoting sustainable agricultural practices are essential steps toward improving the health of rangelands, increasing livestock productivity, and supporting the resilience and well-being of rural communities.

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.

Spatial assessment of soil resources on different land uses and slope gradient as a planning tool, in Ferenjua watershed, Ethiopia

Soil erosion, land degradation and climate change are the major environmental threats attributed to low agricultural productivity in Ethiopia. To reduce soil erosion and restore degraded lands, integrated soil and water conservation interventions have been implemented. However, the impacts of the intervention measures were not sufficiently studied due to the lack of baseline information. Therefore, this study was carried out to evaluate and understand the current conditions of biophysical resources in Ferenjua watershed, which is located in Gondar zuria district. In this study, more emphasis was given to the levels of total nitrogen (TN), available phosphorous (P), organic matter (OM), bulk density (BD), pH, moisture, texture and soil erosion. To collect the biophysical data, the watershed was divided into 144 grid-based sampling points with a resolution of 250 m by 250 m. In addition, the data were collected in 8 representative profile pits. Three primary land uses were identified from supervised land use and land cover classification and described as cultivated land (55.08 %), forestland (30.80 %), and grassland (14.12 %). Erosion hotspot map shows that >40 % of the watershed is moderately and highly susceptible to soil erosion. The highest BD (1.36 gm cm−3) was observed at Vertisols-dominated cultivated lands located in the flatlands, whereas, the lowest BD (1.28 gm cm−3) was observed under forestland. Regarding the results of TN, P, and OM, >60 % of the watershed was classified as having low to very low soil nutrient levels. Consequently, development interventions should be considered to restore degraded lands for the sustained use of resources in the watershed.

137Cs radiotracer in investigating influence of hillslope positions and land use on soil erosion and soil organic carbon stock—A case study in the Himalayan region

AbstractThe topography and land use/land cover (LULC) of the hillslope play a significant influence on soil erosion because of water, which is considered as a principal factor for the reduction of soil organic carbon content. Reliable information on the impact of erosion mechanism on soil organic carbon stock (SOCS) is essential for effectively accounting for the carbon flux that influences climate change. The main objectives of this study were to determine soil erosion based on the variation of 137Cs (Radiocesium) radionuclide activity at various hillslope positions and LULC in a hilly and mountainous region of the north‐western Himalayas. Additionally, the relationship between 137Cs concentration, soil erosion rate and SOCS were examined. Fallout radionuclide‐137Cs have emerged as a suitable method for assessing soil erosion in hilly and mountainous regions where rugged topography and extreme weather events restrain the conventional soil erosion assessment. The study revealed very high soil erosion rates of 32.89 and 30.70 t ha−1 year−1 in the lower hillslope positions with cultivated fields. The lowest soil erosion was obtained with a mean of 0.47 t ha−1 year−1 from the ridge with grassland, followed by the upper hillslope (5.50 t ha−1 year−1 under deodar forest and 14.07 t ha−1 year−1 under pine forest), and the middle hillslope (1.58 t ha−1 year−1 for deodar and 7.77 t ha−1 year−1 for pine forest). The soil erosion rates differ significantly between cultivated and forested regions, and there is also a significant difference between deodar and pine forests. Moreover, a significant difference was found between topographic positions concerning 137Cs, SOCS and soil redistribution rate. This difference was more pronounced at hillslope positions with different LULC. In both disturbed (cultivated) (r2 = .111) and undisturbed (forested and grassland) (r2 = .356) soils, positive and statistically significant (p < .005) poor relationships were found between SOCS and 137Cs inventory. This indicates the presence of various factors influencing the soil organic carbon stock (SOCS) mechanism or the indirect contribution of soil erosion‐induced carbon loss. This suggests that forest cover can enhance SOCS in the soil, mitigating the adverse effects of soil erosion and climate change. Consequently, 137Cs could be effectively used to quantify the SOC stock in soil redistribution over the hillslope affected by soil erosion. Statistical analyses indicated that the 137Cs inventory, SOCS and erosion were significantly affected by various hillslope positions and LULC types.

Predicting Soil Erosion Using RUSLE and GeoSOS-FLUS Models: A Case Study in Kunming, China

Revealing the relationship between land use changes and soil erosion provides a reference for formulating future land use strategies. This study simulated historical and future soil erosion changes based on the RULSE and GeoSOS-FLUS models and used a random forest model to explain the relative importance of natural and anthropogenic factors on soil erosion. The main conclusions are as follows: (1) From 1990 to 2020, significant changes in land use occurred in Kunming, with a continuous reduction in woodland, grassland, and cropland, being converted into construction land, which grew by 195.18% compared with 1990. (2) During this period, the soil erosion modulus decreased from 133.85 t/(km²·a) in 1990 to 130.32 t/(km²·a) in 2020, with a reduction in soil loss by 74,485.46 t/a, mainly due to the conversion of cropland to construction and ecological lands (woodland, grassland). (3) The expansion of construction land will continue, and it is expected that by 2050, the soil erosion modulus will decrease by 3.77 t/(km²·a), 4.27 t/(km²·a), and 3.27 t/(km²·a) under natural development, rapid development, and ecological protection scenarios, respectively. However, under the cropland protection scenario, the soil erosion modulus increased by 0.26 t/(km²·a) compared with 2020. (4) The spatial pattern of soil erosion is influenced by both natural and anthropogenic factors, and as human activities intensify in the future, the influence of anthropogenic factors will further increase. Traditionally, the expansion of construction land is thought to increase soil loss. Our study may offer a new perspective and provide a reference for future land use planning and soil loss management in Kunming.

The impact of vegetation reconstruction on soil erosion in the Loess plateau

Vegetation restoration not only extensively reshapes spatial land use patterns but also profoundly affects the dynamics of runoff and sediment loss. However, the influence of vegetation restoration on runoff and sediment yield from a regional perspective are scarce. This study therefore focused on 85 sites within the “Grain for Green” Project (GGP) region on the Loess Plateau, to investigate the impacts of the GGP on soil erosion. The results revealed a notable reduction in sediment loss and runoff due to vegetation restoration. Since the inception of the GGP in 1999, approximately 4.1 × 106 ha of degraded lands have been converted into forestlands, shrublands, and grasslands, resulting in an average annual reduction of 1.4 × 109 m3 in runoff and a decrease of 3.6 × 108 t in annual sediment loss on the whole Loess Plateau, with the GGP contributing approximately 26.7% of the sediment reduction in the Yellow River basin. The reduced soil erosion has mainly been regulated by vegetation cover, soil properties (clay, silt, and sand), slope, and precipitation on the Loess Plateau. The insights gained offer valuable contributions to large-scale assessments of changes in soil erosion in response to vegetation reconstruction and enhance our understanding of the spatial configurations associated with soil erosion control measures.

Half of the soil erosion in the Alps during the Holocene is explained by transient erosion crises as a consequence of rapid human land clearing

Human land use changes have altered soil erosion for millennia with extensive consequences on terrestrial and aquatic ecosystems as well as on biogeochemical cycles along the land-ocean continuum. Despite their great importance, past erosion trends have high uncertainties limiting quantitative estimates of long-term erosion dynamics. Here, we applied a new approach combining well-dated paleo-records of soil erosion from lake sediments and a spatially distributed semi-empirical model to simulate annual soil erosion in six lake watershed systems in the Northwestern Alps during the Holocene. Progressive and abrupt changes in soil erosion are detected in the six watersheds. Progressive erosion explains most of the soil exports observed during the Early to Mid-Holocene period (from 11,700 to 3000 cal. yr. BP), while transient erosion crises (i.e., periods of abrupt increase in the erosion rates spanning approximately 1000 ± 500 years) led to massive soil losses during the Late-Holocene period (from 3000 to 1000 cal. yr. BP). Our coupled approach of proxy-model reconstruction shows that the transient erosion crises represent the half of the total soil erosion exports during the Holocene. These estimates defy current representations of large-scale soil erosion during the Holocene that do not consider transient erosion crises, hence potentially underestimating the anthropogenic perturbation of lateral fluxes and fate along the land-ocean continuum. Our results further suggest that erosion and/or land cover proxies need to be consistently integrated into model approaches when attempting to estimate past variations in mass exports from terrestrial to aquatic ecosystems over centennial to millennial timescales.

Changes in soil erosion caused by wildfire: A conceptual biogeographic model

Soil erosion rates after wildfire are strongly controlled by intrinsic properties such as topography, weather, climate, soil, and vegetation. These landscape and hydroclimatic properties are important in determining post-fire erosion rates; however, their influence on post-fire erosion and their interaction with the intensity of a wildfire remains uncertain. A key limitation in resolving this uncertainty is the lack of conceptual models and frameworks for organising data related to the geomorphic sensitivity of landscapes to wildfire. Our aim is to develop a framework for consolidating understanding of post-fire erosion in the context of hydroclimatic conditions which contribute to system states, for example soil and vegetation properties, and wildfire regime. The framework is developed around a simple conceptual model where the change in erosion due to wildfire is a product of change in runoff generation and sediment supply, which is strongly related to landscape net primary productivity (NPP). We hypothesised that geomorphic sensitivity to wildfire should vary as a unimodal humped relationship across a gradient of NPP, peaking at an intermediate level. To develop this framework and to test the hypothesis, we first review intrinsic soil and vegetation properties related to the supply and transport of sediment from burned and unburned hillslopes. Net primary productivity is systematically related to these intrinsic properties because it integrates many processes involved in soil and vegetation development. Empirical data indicate a trend in the change in surface runoff generation with NPP after wildfire, peaking at an NPP of approximately 15 Mg C ha−1 y−1. A simple model of fuel availability and soil heating are correlated with a similar “humped” trend in sediment supply. These results are consistent with our conceptual model, which indicates that sediment supply and runoff contribute towards a distinct peak in wildfire effects on erosion at an intermediate level of NPP. We propose that landscapes of intermediate NPP typically have the highest quantity of fuel available to burn, which cause large changes to the soil surface properties. Landscapes at intermediate NPP also tend to produce intrinsic soil and vegetation properties that promote erosion after wildfire. The interplay between these short and long-term landscape characteristics is strongest at intermediate levels of NPP. Our proposed biogeographic model of geomorphic sensitivity to wildfire was supported by erosion data from burned hillslope and zero-order catchments studies from a range fire-prone landscapes in Australia and North America. Our proposed conceptual model will help identify areas most vulnerable to post-fire erosion changes.

Correlation between flood couplet-based sediment yield and rainfall patterns in a small watershed on the Chinese Loess Plateau

Understanding how rainfall patterns impact sediment yields during rainfall events is important to explore soil erosion changes and to prevent watershed flood disasters. However, few studies identified sediment yields variation affected by rainfall patterns at continuous deposited flood couplet scale. This study analyzed sediment yields under different rainfall patterns and correlated particle depositional features on the condition that the erosive environment was obviously changed by anthropogenic activities in a check dam-controlled small watershed on the Chinese Loess Plateau. Results showed that erosive rainfall events, having formed 40 flood couplets behind the check dam between 1978 and 2010, can be grouped into three rainfall patterns (i.e., rainfall patterns I, II, and III) according to changes in rainfall amount, erosivity, and duration. Rainfall pattern III, characterized by high rainfall amount and erosivity within a short duration (i.e., rainstorm) accounted for only 15.0 % of erosive rainfall events while yielding 44.7 % of the total sediment. Conversely, rainfall pattern I (the most common, 55.0 %), characterized by its long duration and relatively low rainfall amount and erosivity, only contributed 25.3 % of the total sediment. Both sediment yields and thickness proportions of coarse particle layer in flood couplets exhibited a decreasing trend under all three rainfall patterns. Meanwhile, the main factor that impacted sediment yields changed from rainfall in the 1980s and 1990s to the “Grain for Green” program dominated anthropogenic activities since 1999. For a small watershed with a few terraces and only one check dam at the outlet, artificial vegetation restoration significantly reduced coarse soil particle loss and sediment yield. Findings showed that sediment yield and deposition feature variations in flood couplets under different rainfall patterns are helpful in improving watershed soil and water conservation measures against flood disasters, particularly under rainstorms.

Soil erosion prediction and spatiotemporal heterogeneity in driving effects of precipitation and vegetation on the northern slope of Tianshan Mountain

Soil erosion changes become more unclear in the future due to climate change and increased human activity. However, the spatiotemporal heterogeneity in the driving effects of key factors on soil erosion remains inadequately understood on the northern slope of the Tianshan Mountains (NSTM). In this paper, the spatiotemporal evolution of soil erosion on the NSTM was evaluated in the historical period (2000–2015) by the Revised Universal Soil Loss Equation (RUSLE) model. Subsequently, the FLUS-CMIP6-RUSLE model was employed to predict soil erosion in the future period (2030–2050). Then, the GTWR model was used to dissect the spatiotemporal heterogeneity in the driving effects of precipitation and vegetation. The results indicated that soil erosion on the NSTM fluctuated from 2000 to 2015, increasing by 1.77 t ha −1 yr −1. Soil erosion areas mainly occurred in Changji City, Fukang City, Hutubi County, Mulei County, Qitai County, and Turpan City. The soil erosion modulus in 2030, 2040, and 2050 under different climate scenarios will be 26.56,28.83, and 37.49 t ha −1 yr −1, respectively. The spatial pattern of soil erosion is similar to the historical period. Precipitation was the main factor intensifying soil erosion in the eastern and western counties from 2000 to 2020, while the soil erosion change in central counties was affected by vegetation. In the future, soil erosion change in the counties (i.e., Karamay, Yili, Tacheng, Shihezi, Manas, Qitai, and Mulei) will be driven by precipitation. The vegetation will be the main driver of soil erosion change in central and eastern counties (i.e., Urumqi, Turpan, Fukang City, Jimsar County, and Changji City). This study firstly attempts to explore the spatiotemporal heterogeneity in the driving effects of precipitation and vegetation using Spatial-Temporal Geographically Weighted Regression (GTWR) in urban agglomerations of NSTM. It is meaningful to explore erosion-prone regions and to implement rational soil conservation measures in the study region and similar regions in the world.

Rainstorm erosion difference and topographical changes induced by heavy rainfall between afforestation and grassland restoration catchments on the Chinese Loess Plateau

The frequency and intensity of heavy rainfall on the Chinese Loess Plateau (CLP) are increasing due to global climate change, which has caused severe soil erosion. However, precise evaluation of heavy rainfall-induced soil erosion, deposition, and related topographical changes at the catchment scale is lacking, and there is uncertainty as to how effectively trees can inhibit erosion, especially during heavy rainfall. In this study, we selected a pair of catchments with afforestation and natural grassland restoration on the CLP. Precise evaluation of erosion and topographic changes induced by heavy rainfall was conducted based on airborne light detection and ranging (LiDAR) data. In the grassland catchment, the gully bed area increased by 17.6 %, the gully slope and inter-gully land area decreased by 1.4 %, and the length of the gully shoulder line decreased by 4.4 %. Conversely, the forestland catchment experienced a decrease in gully bed area of 8.8 %, an increase in gully slope area of 5.3 %, a decrease in inter-gully land area of 9.9 %, and a decrease in the length of the gully shoulder line of 11.4 %. Following heavy rainfall, both catchments experienced a substantial increase in terrain relief. Soil erosion in the gully slope accounted for >90 % of the total erosion in both catchments, while deposition in the same area contributed to >75 % of the total deposition. The grassland catchment experienced a unit area erosion of 233,908 m3 km−2, which was 4.7 times greater than that of the forestland catchment (49,768 m3 km−2). In both catchments, erosion predominantly transpired in the steep slope region adjacent to the gully shoulder line, accounting for >15 % of the gully slope erosion area. The grassland catchment exhibited a continuous distribution pattern of erosion and deposition, whereas the forestland catchment exhibited a scattered and intertwined distribution pattern. This study is essential for further research on erosion and topographic changes induced by extreme weather events and to determine appropriate measures.

Identifying and Mapping the Spatial Factors That Control Soil Erosion Changes in the Yellow River Basin of China

The Yellow River Basin has been considered to have the most serious soil erosion in the world, and identifying and mapping the spatial controlling factors would be of great help in adopting targeting strategies for soil erosion prevention. This study used the Universal Soil Loss Equation (USLE) to estimate the spatial and temporal changes in soil erosion from 1985 to 2020 and analyzed the controlling factors. The results indicated that from 1985 to 2020, the average erosion modulus in the Yellow River Basin was 1160.97 t∙km−2∙yr−1, and the erosion modulus in the middle reach was significantly greater than in the lower and upper reaches. Changes in vegetation coverage, rainfall and land use controlled 38.95%, 40.87% and 9.21% of soil erosion changes, respectively. Among them, the area in which soil erosion was decreased due to increased vegetation coverage accounted for 70.77% of the area controlled by vegetation coverage, while the area in which soil erosion was increased due to increased rainfall accounted for 86.62% of the area controlled by rainfall. These results prove the effectiveness of vegetation restoration projects in controlling soil erosion in the Yellow River Basin, but more attention needs to be paid to the impact of rainfall on soil erosion in the future.

Analysis of soil erosion changes and influencing factors based on the CSLE model and GeoDector in Dongjiang River Basin of China

AbstractSoil and water conservation and protection are of great importance to China, as the source of the Pearl and Xiang Rivers, and the main rare earth mining area, the ecology of Dongjiang River Basin, is very fragile. In this paper, the Chinese soil loss equation and the Geodetector method are used to analyze the spatial and temporal patterns and the impact factors of soil erosion in this area from 2016 to 2020. The results show that (1) Soil erosion intensity in the Dongjiang River Basin from 2016 to 2020 is mainly mild, with the soil erosion intensity in the Dongjiang River Basin in 2016 being the largest in five years. (2) The erosion modulus shows an increasing trend with the increase of slope. In terms of vegetation, when the vegetation cover is <30%, the soil erosion modulus is the largest, which is easy to induce soil erosion. When the vegetation cover is >60%, the soil erosion modulus is the smallest, which effectively slows down the occurrence of soil erosion. Rainfall is proportional to the soil erosion modulus and soil erosion. (3) The results of Geodetector indicate that the single factor sizes are land use > vegetation > slope > precipitation, and interaction factors all show a nonlinear enhancement. (4) The proper allocation of land use types, the prohibition of steep slopes for cultivation, and the restoration of forestry can effectively prevent and control soil erosion in the area.

Soil Erosion Characteristics in Tropical Island Watersheds Based on CSLE Model: Discussion of Driving Mechanisms

Previous research has primarily focused on soil erosion issues in arid and semi-arid regions, with a limited understanding of soil erosion mechanisms in tropical areas. Additionally, there is a lack of a holistic perspective to determine the spatial attribution of soil erosion. The conversion of tropical rainforests into economically driven plantations, like rubber and pulpwood, has resulted in distinct soil erosion characteristics in specific regions. To enhance our knowledge of soil erosion patterns and mechanisms in tropical regions, it is necessary to examine soil erosion in the three major watersheds of Hainan Island from 1991 to 2021, which encompass significant geographical features such as tropical island water sources and tropical rainforest national parks. The study employed the China Soil Loss Equation (CSLE) model, slope trend analysis, Pearson correlation analysis, land-use transfer matrix, and spatial attribution analysis to examine soil erosion under different scenarios. The research results indicate that scenarios driven by the combination of natural and human factors have the greatest impact on soil erosion changes in the entire study area. Co-driven increases affected 53.56% of the area, while co-driven decreases affected 21.74%. The 31-year soil erosion showed an overall increasing trend. Human factors were identified as the primary drivers of increased soil erosion in the Nandu River basin, while a combination of climate and anthropogenic factors influenced the decrease in soil erosion. In the Changhua River basin, climate and human activities contributed to the soil erosion increase, while human activities primarily caused the decrease in soil erosion. In the Wanquan River basin, climate intensified soil erosion, whereas human activities mitigated it. This study underscores the significant combined impact of human activities and natural factors on soil erosion in tropical regions. It emphasizes the importance of considering human-induced factors when implementing soil erosion control measures in tropical regions.

Estimating the CSLE Biological Conservation Measures’ B-Factor Using Google Earth’s Engine

The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of the B-factor at the regional scale is fundamental in predicting regional soil erosion and dynamic changes. In this study, we developed an optimal computational procedure for estimating and mapping the B-factor in the Google Earth Engine (GEE) cloud computing environment using multiple data sources through data suitability assessment and image fusion. Taking the Yanhe River Basin in the Loess Plateau of China as an example, we evaluated the availability of daily precipitation data (CHIRPS, ERA5, and PERSIANN-CDR data) against the data at national meteorological stations. We estimated the B-factor from Sentinel-2 data and proposed a new method, namely the trend migration method, to patch the missing values in Sentinel-2 data using three other remote sensing data (MOD09GA, Landsat 7, and Landsat 8). We then calculated and mapped the B-factor in the Yanhe River Basin based on rainfall erosivity, vegetation coverage, and land use types. The results show that the ERA5 precipitation dataset outperforms the CHIRPS and PERSIANN-CDR data in estimating rainfall and rainfall erosivity, and it can be utilized as an alternative data source for meteorological stations in soil erosion modeling. Compared to the harmonic analysis of time series (HANTS), the trend migration method proposed in this study is more suitable for patching the missing parts of Sentinel-2 data. The restored high-resolution Sentinel-2 data fit nicely with the 10 m resolution land use data, enhancing the B-factor calculation accuracy at local and region scales. The B-factor computation procedure developed in this study is applicable to various river basin and regional scales for soil erosion monitoring.

Assessing soil erosion in a semiarid ecosystem in Central Argentina using 137Cs and 7Be measurements

ABSTRACT Argentina is a Latin American country which encounters soil degradation problems. The most productive regions have implemented conservative land practices (no-till). However, agricultural frontier has been displaced to marginal lands with arid and semiarid climates, with the consequent disappearance in many areas of native forest and land degradation. In this work, the fallout of gamma-emitting radionuclides, 137Cs and 7Be, was jointly used to assess changes in soil erosion in a recently converted semiarid ecosystem into agricultural land. 137Cs was utilized to estimate the erosion over the past 60 years, whereas 7Be was employed to estimate the erosion after the conversion of the area to cultivated land and soil tillage. For 137Cs the Proportional Model (PM), the Mass Balance Model II (MBMII) and the MODERN model were used, for 7Be the Profile Distribution Model (PDM) and the MODERN model were used. 137Cs indicates mean erosional rates of 8.2, 10.5 and 6.5 Mg ha−1 a−1, using MBMII, PM and MODERN, respectively, and that a soil layer between 0.5 and 0.8 mm was annually lost by erosion. By applying a 7Be tracer, we measured erosion rates of 2.4 and 3.3 Mg ha−1 (with PDM and Modern, respectively), indicating the loss of the upper 0.2 mm of soil. This erosion can be attributed to a few heavy rainfalls that occurred within the past 90 days. The results suggest that current land management practices have led to an increase in soil erosion. This could be attributed to the fact that the soil remains bare after crop harvest, which may compromise its conservation and future productivity.