Effects of soil type and rainfall intensity on sheet erosion processes and sediment characteristics along the climatic gradient in central-south China

Soil type regulates the divergent loss characteristics of sediment associated carbon and nitrogen in different size classes during rainfall erosion on cultivated lands

Sediment associated carbon and nitrogen loss under rainfall, an important cause of soil quality degradation and water eutrophication, strongly depends on the intrinsic properties of original soil types. Relative to total loss, the transport behaviors of organic carbon and nitrogen among sediment size classes and response to soil types remain poorly understood. The concentrations of organic carbon (OC) and total nitrogen (TN) in different sediment size classes (>1, 0.25-1, 0.10-0.25, and <0.10 mm) and their contributions to total sediment load during rainfall erosion were determined under field plot rainfall simulation (at 90 mm h-1) on three contrasting soil types (Luvisol, Alisol and Ferralsol) with increased aggregate stability. During rainfall erosion, the concentrations of OC and TN in total and different sized sediments decreased first and then reached a steady state. The variability of OC and TN concentrations (coefficient of variations in 4.2-53.1% and 6.6-41.9%) among sediment size classes decreased from Luvisol to Ferralsol. Compared to original soils, sediments exhibited larger C/N ratios for Luvisol, and smaller values for Alisol, indicating the more selective transport of labile organic matter for weaker aggregated soils. Among sediment size classes, fine particles (<0.10 mm) accounted 69-88% of total OC and TN losses for Luvisol, and decreased to 30-39% for Ferralsol; and the main transport mechanisms of sediment associated OC and TN shifting from suspension-saltation (<0.10 mm) to rolling (>0.25 mm) with increased aggregate stability. Among original soil properties, inorganic cementing agents (including amorphous iron oxides and clay minerals) showed closer relationships with sediment OC and TN losses (|r| = 0.61-0.89, p < 0.001) than organic matter properties (|r| = 0.55-0.87, p < 0.001), further implying the important role of soil aggregate stability across soil types. This study provides an in-depth understanding on soil carbon and nitrogen losses and their divergent characteristics among soil types deserves consideration in the development of erosion model and land management in agricultural systems.

Response of sediment transport capacity to soil properties and hydraulic parameters in the typical agricultural regions of the Loess Plateau

The sediment transport capacity by overland flow (T_c) is a key parameter in process-based soil erosion models and T_c variation is sensitive to changes in soil properties. This study was undertaken to investigate T_c variations with respect to soil properties and establish a universal relationship to predict T_c. The test soils were collected from typical agricultural regions (Guanzhong basin-Yangling (YL), Weibei Dry plateau-Chunhua (CH), Hilly and gully region-Ansai (AS), Ago-pastoral transition zone along the Great Wall-Yuyang (YY), and Weiriver floodplain-Weicheng (WC)) of the Loess Plateau, and subjected to 36 different combinations of slope gradients (S, 5.24-44.52 %) and flow discharge (q, 0.00033-0.00125 m² s^-1) in a hydraulic flume. The results showed that the mean T_c values for WC were 2.15, 1.38, 1.32, and 1.16 times greater than those for YL, CH, AS, and YY, respectively. T_c significantly decreased with clay content (C), mean weight diameter (MWD), and soil organic matter content (SOM). T_c for different soil types increased with S and q as a binary power function, and T_c variation was more sensitive to S than to q. Stream power (w) was the most appropriate hydraulic variable to express T_c for different soils. T_c for different soil types could be satisfactorily simulated using a quaternary power function of S, q, C, and MWD (R² = 0.94; NSE = 0.94) or a ternary power function of w, C, and MWD (R² = 0.94; NSE = 0.94). The new T_c equation can reflect the effect of soil properties on it and facilitate the development of a process-based soil erosion model.

Laws Governing Nitrogen Loss and Its Numerical Simulation in the Sloping Farmland of the Miyun Reservoir

Surface flow (SF) and subsurface flow (SSF) are important hydrological processes occurring on slopes, and are driven by two main factors: rainfall intensity and slope gradient. To explore nitrogen (N) migration and loss from sloping farmland in the Miyun Reservoir, the characteristics of total nitrogen (TN) migration and loss via SF and SSF under different rainfall intensities (30, 40, 50, 60, 70, and 80 mm/h) and slope gradients (5°, 10°, and 15°) were studied using indoor stimulated rainfall tests and mathematical models. Nitrogen loss via SF and SSF was found to increase exponentially and linearly with time, respectively, with SSF showing 14-78 times higher loss than SF. Under different rainfall intensities, SSF generally had larger TN loss loading than SF, thereby indicating that SSF was the main route for TN loss. However, the TN loss loading proportion via SF increasing from 14.03% to 35.82% with increasing rainfall intensity is noteworthy. Furthermore, compared with the measurement data, the precision evaluation index Nash-Suttcliffe efficient (NSE) and the determination coefficient (R2) of the effective mixing depth model in the numerical simulation of TN loss through SF in the sloping farmland in the Miyun Reservoir were 0.74 and 0.831, respectively, whereas those of the convection-dispersion equation for SSF were 0.81 and 0.811, respectively, thus indicating good simulation results. Therefore, this paper provides a reference for studying the mechanism of N migration and loss in sloping farmland in the Miyun Reservoir.

Comparisons of soil organic carbon enrichment and loss in sediments among red soil, black soil, and loess in China

Water erosion could cause wide and serious soil organic carbon (SOC) loss, but differences in SOC loss and enrichment in sediments among red soil, black soil, and loess in China have received less attention. This study investigates the transport of sediments and generation regulation of runoffs during the erosion process by collecting data from indoor or outdoor artificial simulated rainfall experiments and selecting typical regional rainfall intensity and slope gradient for bare cultivate soil slopes as well as 5–8 m length and 1.5–2 m width runoff plots or soil pans. Then, the change in SOC loss for the three widely distributed and seriously eroded soils, from south to north in China, is clarified. Results show that the stable value and growth rate of soil and SOC loss rates followed the following order: black soil < red soil < loess. The SOC loss rate of loess was more sensitive to rainfall intensity and slope gradient than those of the two other soils. The SOC enrichment ratio (ERocs) of the sediments of the red soil and loess soil is higher than that of the black soil, and this difference increases as the soil loss rate decreases. ERocs generally has a negative exponential relationship with soil loss, but it has a negative logarithmic relationship with soil loss for the loess soil with high aggregate and clay contents. SOC and clay content determine the SOC enrichment in sediments for different soils. In addition, this study provides recommendations for improving SOC dynamic models for soil under water erosion.

Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

In Brazil, the development of gullies constitutes widespread land degradation, especially in the state of South Mato Grosso, where fighting against this degradation has become a priority for policy makers. However, the environmental and anthropogenic factors that promote gully development are multiple, interact, and present a complexity that can vary by locality, making their prediction difficult. In this framework, a database was constructed for the Rio Ivinhema basin in the southern part of the state, including 400 georeferenced gullies and 13 geo-environmental descriptors. Multivariate statistical analysis was performed using principal component analysis (PCA) to identify the processes controlling the variability in gully development. Susceptibility maps were created through four machine learning models: multivariate discriminant analysis (MDA), logistic regression (LR), classification and regression tree (CART), and random forest (RF). The predictive performance of the models was analyzed by five evaluation indices: accuracy (ACC), sensitivity (SST), specificity (SPF), precision (PRC), and Receiver Operating Characteristic curve (ROC curve). The results show the existence of two major processes controlling gully erosion. The first is the surface runoff process, which is related to conditions of slightly higher relief and higher rainfall. The second also reflects high surface runoff conditions, but rather related to high drainage density and downslope, close to the river network. Human activity represented by peri-urban areas, construction of small earthen dams, and extensive rotational farming contribute significantly to gully formation. The four machine learning models yielded fairly similar results and validated susceptibility maps (ROC curve > 0.8). However, we noted a better performance of the random forest (RF) model (86% and 89.8% for training and test, respectively, with an ROC curve value of 0.931). The evaluation of the contribution of the parameters shows that susceptibility to gully erosion is not governed primarily by a single factor, but rather by the interconnection between different factors, mainly elevation, geology, precipitation, and land use.

Frequency analysis of storm-scale soil erosion and characterization of extreme erosive events by linking the DWEPP model and a stochastic rainfall generator

Soil erosion affects agricultural landscapes worldwide, threatening food security and ecosystem viability. In arable environments, soil loss is primarily caused by short, intense rainstorms, typically characterized by high spatiotemporal variability. The complexity of erosive events challenges modeling efforts and explicit inclusion of extreme events in long-term risk assessment is missing. This study is intended to bridge this gap by quantifying the discrete and cumulative impacts of rainstorms on plot-scale soil erosion and providing storm-scale erosion risk analyses for a cropland region in northern Israel. Central to our analyses is the coupling of (1) a stochastic rainfall generator able to reproduce extremes down to 5-minute temporal resolutions; (2) a processes-based event-scale cropland erosion model (Dynamic WEPP, DWEPP); and, (3) a state-of-the-art frequency analysis method that explicitly accounts for rainstorms occurrence and properties. To our knowledge, this is the first study in which DWEPP runoff and soil loss are calibrated at the plot-scale on cropland (NSE is 0.82 and 0.79 for event runoff and sediment, respectively). We generated 300-year stochastic simulations of event runoff and sediment yield based on synthetic precipitation time series. Based on this data, the mean annual soil erosion in the study site is 0.1 kg m^-2 [1.1 t ha^-1]. Results of the risk analysis indicate that individual extreme rainstorms (>50 return period), characterized by high rainfall intensities (30-minute maximal intensity > ~60 mm h^-1) and high rainfall depth (>~200 mm), can trigger soil losses even one order of magnitude higher than the annual mean. The erosion efficiency of these rainstorms is mainly controlled by the short-duration (≤30 min) maximal intensities. The results demonstrate the importance of incorporating the impact of extreme events into soil conservation and management tools. We expect our methodology to be valuable for investigating future changes in soil erosion with changing climate.

Optimizing the soil conservation service curve number model by accounting for rainfall characteristics: a case study of surface water sources in Beijing

The upper catchment of the Miyun reservoir is an important drinking water source in Beijing. In recent years, researchers have used the soil conservation service curve number (SCS-CN) model to calculate surface runoff for the district. Although the runoff forecasting accuracy was unsatisfactory, the lack of understanding of rainfall processes and their influence on runoff may explain the observed deviations. Our study sought to optimize and assess the SCS-CN model simulation accuracy for the district by proposing an SCS-CN calculation method for each runoff event (CN_t) based on observation data for 253 rainfall and runoff events from 7 plots in the Miyun Shixia watershed. This study elucidated a significant positive correlation between the ratio of CN_t and the average SCS-CN (CN₁), as well as the ratio of the maximum X-minute rainfall amount (P_X) to the total rainfall amount for each rainfall event (P). Furthermore, a calculation method involving power function equations between CN_t/CN₁ and P_X/P was proposed for CN_t. When X = 5 min and the initial abstraction ratio (λ) = 0.01, the simulation performance of the optimized model was the highest, with a Nash-Sutcliffe efficiency coefficient of 0.791, which was significantly higher than that of the non-optimized SCS-CN model. The simulation performance for bare and cultivated land was higher than that of other land uses, with E_f values of 0.831 and 0.828, respectively. Future research should focus on improving the prediction accuracy of runoff events resulting from high-intensity and short-duration rainfall events.

Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques

Soil erosion determines landforms, soil formation and distribution, soil fertility, and land degradation processes. In arid and semiarid ecosystems, soil erosion is a key process to understand, foresee, and prevent desertification. Addressing soil erosion throughout watersheds scales requires basic information to develop soil erosion control strategies and to reduce land degradation. To assess and remediate the non-sustainable soil erosion rates, restoration programs benefit from the knowledge of the spatial distribution of the soil losses to develop maps of soil erosion. This study presents Support Vector Machine (SVM), Random Forest (RF), and adaptive boosting (AdaBoost) data mining models to map soil erosion susceptibility in Kozetopraghi watershed, Iran. A soil erosion inventory map was prepared from field rainfall simulation experiments on 174 randomly selected points along the Kozetopraghi watershed. In previous studies, this map has been prepared using indirect methods such as the Universal Soil Loss Equation to assess soil erosion. Direct field measurements for mapping soil erosion susceptibility have so far not been carried out in our study site in the past. The soil erosion rate data generated by simulated rainfall in 1 m2 plots at rainfall rate of 40 mmh−1 was used to develop the soil erosion map. Of the available data, 70% and 30% were randomly classified to calibrate and validate the models, respectively. As a result, the RF model with the highest area under the curve (AUC) value in a receiver operating characteristics (ROC) curve (0.91), and the lowest mean square error (MSE) value (0.09), has the most concordance and spatial differentiation. Sensitivity analysis by Jackknife and IncNodePurity methods indicates that the slope angle is the most important factor within the soil erosion susceptibility map. The RF susceptibility map showed that the areas located in the center and near the watershed outlet have the most susceptibility to soil erosion. This information can be used to support the development of sustainable restoration plans with more accuracy. Our methodology has been evaluated and can be also applied in other regions.

Dual benefits of long-term ecological agricultural engineering: Mitigation of nutrient losses and improvement of soil quality

Soil erosion of sloped farmland in the Three Gorges Reservoir area (TGRA) has led to the serious loss of nutrients, soil quality degradation and the downstream water quality being threatened. Thus, a series of ecological agricultural engineering measures was established in 2011, as a field experiment using citrus (navel orange) plants to reduce soil erosion, which was monitored from 2011 to 2018. These ecological agricultural engineering measures included three treatments: 1) citrus intercropped with white clover (WC), 2) citrus orchard land mulched with straw (SM) and 3) citrus intercropped with hemerocallis (Hemerocallis flava) contour hedgerows (CH). The conventional citrus orchard management was regarded as control (CK). The results show, that compared with CK, nutrient loss from the experiments were reduced by the following amounts: for nitrogen - WC (35.5%), SM (44.0%) and CH (52.0%); for phosphorus - WC (40.0%), SM (51.7%) and CH (58.3%). Therefore, the ecological agricultural engineering measures effectively mitigate the nutrient loss loads of the navel orange citrus gardens. The citrus intercropped with the hemerocallis hedgerows is the most effective measure for the control of nutrient loss. After 8 years of experiment, the soil quality represented by average soil quality index (SQI) in these three treatments, are significantly higher than that of the CK (and the beginning of the experiment). This is because the application of these measures prevented the loss of: soil organic matter, bulk density and total phosphorus. It is predicted that the soil qualities of these three treatments will remain in the range of soil grade II and I for the next 5 years but the soil quality of CK will decrease to soil quality grade II and III. These results show that ecological agricultural engineering measures are a long-term promising and feasible method to reduce soil erosion and enhance soil quality.

Modelling the impacts of climate and land use changes on soil water erosion: Model applications, limitations and future challenges

The world is experiencing serious soil losses. Soil erosion has become an important environmental problem in certain regions and is strongly affected by climate and land use changes. By selecting and reviewing 13 extensively used soil water erosion models (SWEMs) from the published literature, we summarize the current model-based knowledge on how climate factors (e.g., rainfall, freeze-thaw cycles, rainstorms, temperature and atmospheric CO₂ concentrations) and land use change impact soil erosion worldwide. This study also provides a critical review of the application of these 13 SWEMs. By comparing model structures, features, prediction accuracies, and erosion processes, we recommend the most suitable SWEMs for different regions of the globe (Asia, Europe, Africa and the America) based on the evaluations of 13 SWEMs. Future soil erosion could be simulated using the RUSLE, LISEM, WEPP v2010.1, SWAT, EPIC, KINEROS and AGNPS models in Asia; the RUSLE, WEPP v2010.1, SWAT, EPIC, WATEM-SEDEM, MEFIDIS, AGNPS and AnnAGNPS models in Europe; the RUSLE, LISEM, SWAT, and AGNPS models in Africa; and the WEPP v2010.1, SWAT, EPIC, KINEROS, AGNPS and AnnAGNPS models in America. Finally, the limitations and challenges of the 13 SWEMs are highlighted.

A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China

Increasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for mountainous regions. The objective of this research was to develop an approach that can effectively employ remotely-sensed and ancillary data, to map soil erosion risks in an agroforestry ecosystem in a mountainous region. This research employed field survey data, soil-type maps, digital elevation model data, weather station data, and Landsat imagery, for extraction of potential variables. It used the random forest approach to identify eight key variables—slope, slope of slope, normalized difference greenness index at leaf-on season, soil organic matter, fractional vegetation at leaf-on season, fractional soil at leaf-off season, precipitation in June, and percent of soil clay—for mapping soil erosion risk distribution in hickory plantations in Western Zhejiang Province, China. The results showed that an overall accuracy of 89.8% was obtained for three levels of soil erosion risk. Approximately one-fourth of hickory plantations were at high-risk, requiring the owners or decision makers to take proper measures to reduce the soil erosion problem. This research provides a new approach to predict soil erosion risk, based on the primary variables that can be extracted directly from remotely-sensed data and ancillary data. This proposed approach will be valuable for other agroforestry and plantations, such as Torreya grandis, eucalyptus, and the rubber tree, that are playing important roles in improving economic conditions for the local farmers but face soil erosion problems.