Soil Loss Prediction Research Articles

Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin

Rainfall erosivity is a crucial factor in the evaluation of soil erosion, significantly influencing the complex relationships among water, soil, and the environment. Understanding its attributes and variations in space and time is essential for effective water resource management, erosion mitigation, and land-use planning. This paper utilizes daily precipitation data from 123 grid points in the Niyang River Basin, spanning from 2008 to 2016, to calculate rainfall erosivity using a straightforward algorithmic model. Ordinary Kriging was used to examine the spatial and temporal variations in rainfall erosivity, while Spearman’s correlation analysis was employed to examine the relationships between annual rainfall erosivity and various factors, including multi-year average precipitation, erosive rainfall, dry-season rainfall, wet-season rainfall, temperature, and elevation. The results indicate a year-by-year increase in rainfall erosivity in the basin, with a trend towards stabilization. The average annual rainfall erosivity over the years is 711 MJ·mm·hm−2·h−1, peaking at 1098 MJ·mm·hm−2·h−1 in 2014. A significant 93.9% of rainfall erosivity is concentrated in the wet season, with a maximum of 191 MJ·mm·hm−2·h−1 in July. The left bank of the mainstream, especially the central and lower sections of the main river and its tributaries, experiences the highest rainfall erosivity. Rainfall factors predominantly influence erosivity, with erosive rainfall showing the strongest correlation (rho = 0.93), while temperature and elevation have relatively minor effects. This study enhances the understanding of rainfall erosive forces in the plateau region and provides a scientific basis for predicting soil loss, developing effective erosion control measures, and ensuring sustainable land use.

Soil loss estimation using RUSLE model: Comparison of conventional and digital soil data at watershed scale in central Iran

Sustainable agriculture and eco-environment conservation face constant threats from soil erosion in mountainous regions. This study aimed to predict soil loss in the mountainous watershed by applying the Revise Universal Equation Soil Loss (RUSLE) and Sediment Delivery Ratio (SDR) models and to evaluate the efficacy of the RUSLE model through the observed data in a hydrometric station. Comparing a polygonal, and a digital soil map (prepared by the Kriging method) for deriving the K factor to estimate soil loss was another objective of the recent research. In this study, two scenarios were examined for the calculation of spatial variation of the K-factor; scenario I comprised soil data derived from a legacy soil map with eight soil units and the data from their representative soil profiles (i.e., traditional soil survey), and scenario II comprised K-factor derived from 100 studied sites using kriging technique (i.e., digital soil survey). The results of RUSLE modeling showed that there was no significant difference between scenario I (7.97 t/(ha. yr)) and scenario II (7.93 t/(ha. yr)) for predicting soil loss with the RUSLE model. This finding confirms that the RUSLE model with limited and legacy data can provide a reliable prediction of soil loss in the given watershed. Moreover, long and short-term periods were used to estimate soil loss, while in the long-term period, estimated soil loss had higher accordance with actual soil loss. Sediment delivery ratio was calculated using models of Vanoni, Boyce, USDA-SCS, and Slope-based models. The results indicated that the Slope-based model had the highest fitness (R2=0.946, ME=0.27, and RMSE=0.275 for scenario I and R2=0.9443, ME=0.26 and RMSE=0.40 for scenario II), with observed sedimentation rate at the hydrometric station at the outlet of the watershed. Overall, predicted soil loss severity map using the traditional soil map by RUSLE model could provide trustworthy information for decision makers and governors at the given and similar watersheds in semiarid regions.

Comparison of predictive modeling approaches to estimate soil erosion under spatially heterogeneous field conditions

The accuracy of soil erosion models in agroecosystems with heterogeneous field conditions is challenging due to uncertainties from soil water fluxes and crop growth. In this study, we coupled two modeling methods (Freebairn and Rose) to represent soil erosion with a process-based crop and runoff models within the SIMPLACE framework. Their accuracy was compared to a statistical model developed using 16 erosion plots (each of 625 cm2) within the same field. Uncertainty analysis showed that runoff and slope angle were the most critical components for predicting sediment yield in both models, followed by soil erodibility in the Freebairn model and entrainment efficiency in the Rose model. However, due to plot size constraints, slope-length effects were not examined. The Freebairn model had a slightly higher accuracy (RMSE = 0.69 t ha−1 d−1) of sediment yield predictions than the Rose model (RMSE = 0.83 t ha−1 d−1). Both models are effective for predicting soil loss with appropriate parameter values.

Advances in a measurement method of rainfall kinetic power and momentum affecting soil erosion processes

AbstractRainfall is one of the factors affecting soil erosion, and rainfall kinetic energy or momentum is used to represent the capability of the precipitation to erode soil, named rainfall erosivity. Accurate measurements of rainfall erosivity are useful for a reliable prediction of soil loss. Rainfall momentum and kinetic energy, both calculated per unit time and area, can be obtained using terminal raindrop velocity and the drop size distribution (DSD) measured by disdrometers, which are instruments expensive and not suitable for large scale use. An innovative patented method to measure the rainfall energy is based on the estimation of DSD by simultaneous detection, in a given time interval, of the rainfall intensity I and the number N of raindrops that hit a specific piezoelectric surface. In this paper, advances of this method are presented. In particular, two new theoretical procedures to estimate the parameters μ and Λ of Ulbrich's distribution, that allow for the calculation of the rainfall kinetic energy and momentum, are proposed. Both procedures (Scenario 1 and 2) are based on the frequency distribution of the momentum M(D) of raindrops detected in a sampling time interval. Specifically, in the Scenario 1, μ and Λ are estimated by using I, N and the standard deviation, s(D), of the drop diameters obtained from the measured momentum distribution. In the Scenario 2 the parameters are estimated using I, N and the mean value, m(D), of the drop diameters deriving from the momentum distribution. The reliability of the proposed procedures was tested using DSD measurements recorded in three different experimental sites. The developed analysis demonstrated that Scenario 2 is the best method to estimate μ and Λ, and to reproduce the DSD, accordingly. The proposed method, associated with a patented device, not yet build, will allow the direct measurement of the rainfall energy characteristics, which are usually roughly estimated from rainfall intensity. The possibility to easily measure these energy variables can support the development of research in the field of soil erosion and, in general, of hydrogeological instability. In particular, the proposed measurement method and the construction of the device could stimulate the scientific community to deepen the study of the effect of the rainfall energy on soil erosion for improving the predictive capability of water erosion models.

Climate change impairs the effects of vegetation improvement on soil erosion control in the Qinghai-Tibetan Plateau

Water erosion is a wide ecosystem degradation threat for the Qinghai-Tibetan Plateau (QTP) region and surrounding areas. Climate wetting and vegetation greening in the QTP could have complex impacts on water erosion. However, there is limited information about the erosion dynamics under climate change and the effects of vegetation improvement on erosion control in this alpine region. By calibrating the Revised Universal Soil Loss Equation (RUSLE) model, we estimated water erosion in the QTP and simulated future changes using different Shared Socioeconomic Pathway-Representative Concentration Pathway (SSP) scenarios. We showed intensified erosion and that the benefits of vegetation improvement to erosion control was impaired under climate change. Under the SSP585 scenario, annual water erosion in the QTP was predicted to increase by 0.35 t∙ha−1∙yr−1 and be dominated by an accelerative rise of rainfall erosivity (R factor of RUSLE). Compared with historical observations, the C factor of RUSLE under the SSP585 scenario was believed to reduce by 4.35 % on average during the 2081–2100 period, but predicted soil loss will increase by more than 90 %. Although more considerable vegetation improvement could occur under high climate forcing, there may also be more intensified rainfall erosivity and water erosion across the QTP than those under SSP126 and SSP245 scenarios. Our results suggest that mitigating climate change rather than vegetation improvement is essential to address climate-dominated ecosystem degradation threats such as water erosion in the QTP.

Erodibility Indices and Soil Loss from Different Land uses in Owukpa and Okaba Bioregion of Lower Coal Measures Geological Sediments of the Anambra Basin, Nigeria

The study on erodibility indices and soil loss from different land uses in Owukpa and Okaba bioregion of lower coal measures geological sediments of the Anambra Basin, Nigeria was conducted forested, cultivated and residential lands. Thirty-nine (39) representative soil samples were collected at a depth of 0 – 20 cm using auger, labeled and preserved to the laboratory for standard laboratory procedures. The percentage of different grain sizes Moisture content, organic carbon, organic matter content, soil pH, bulk density, particle density, porosity, soil structural index, specific gravity, particle size and permeability were verified. The three land uses were predominantly sandy soil and classified as sandy loam, moderate Permeability, low moisture and clay contents. Dispersion, Erosion, Clay and Modified Clay ratios where high, while Water Stable Aggregates was low. Cultivated land use has the highest value of erodibility factor K (0.12) and predicted soil loss was 18.22 tons/ha/yr. Residential land use value of erodibility factor K was 0.10 and the predicted soil loss of 14.80 Tons/ha /yr while forested land use K was lowest (0.08) with predicted soil loss of 11.84 tons/ha/yr. Using one way ANOVA at p ? 0.05 the different land use patterns did not significantly influence erodibility factor and soil loss in the study area.

Assessing the Influence of Multiresolution DEMs on Soil Loss Prediction using the RUSLE Model in Central Morocco

Abstract The topography plays a significant role in determining the rate of soil loss. The Digital Elevation Model (DEM) retrieved from earth observation products can be used to calculate the slope length and steepness, which are important factors in the Revised Universal Soil Loss Equation (RUSLE) used to estimate soil erosion. In the present study, the effect of two DEM resolutions (30m and 90 m) on RUSLE model results was assessed and used to quantify the rate of soil loss in Settat province (Morocco), specifically in certain abandoned quarries. As a result, DEM-30 and DEM-90 soil erosion rates vary from 0 to 282.77 t.ha-1.yr-1 and 0 to 13.79 t.ha-1.yr-1, respectively. At the quarry level, the abandoned quarries showed soil loss rates ranging from 0 to 7.92 t.ha-1.yr-1 for DEM-30. Contrary to this, the soil loss rate for DEM-90 data was zero. To summarize, for more relevant and detailed conclusions, high-resolution data must be used to collect more information and describe the erosion process with as much detail as possible, especially for small areas.

Estimation of agricultural productivity loss due to soil loss in Buayan-Malungon River Basin, Philippines: A foundation for watershed protection and agricultural development

The Buayan-Malungon River Basin (BMRB) is currently experiencing intolerable soil loss due to improper agricultural practices, and unsustainable land uses. This problem disrupts important ecosystem services such as provisioning, regulating, cultural, and supporting. This article provides an estimate of the agricultural crop productivity losses due to soil loss. The study estimates productivity losses through regression analysis by using nitrogen (N) fertilizer as a proxy for soil quality. The result revealed that N-fertilizer had a significant effect on corn yield. The predicted soil loss in BMRB in 2020 reached 41,308 tons/ha. Hence, the total soil loss cost in the river basin reached PhP 218,023,624 in 2020. This cost can be minimized if farmers practice a contour farming system as it helps to improve soil quality and control soil loss, which leads to sustainable farming and environmental sustainability.

Modeling soil loss under rainfall events using machine learning algorithms

Soil loss is an environmental concern of global importance. Accurate simulation of soil loss in small watersheds is crucial for protecting the environment and implementing soil and water conservation measures. However, predicting soil loss while meeting the criteria of high precision, efficiency, and generalizability remains a challenge. Therefore, this study first used three machine learning (ML) algorithms, namely, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to develop soil loss models and predict soil loss rates (SLRs). These soil loss models were constructed using field observation data with an average SLR of 1756.48 t/km2 from rainfall events and small watersheds in the hilly-gully region of the Loess Plateau, China. During training, testing and generalizability stages, the average coefficients of determination from the RF, SVM, and ANN models were 0.903, 0.860, and 0.836, respectively. Similarly, the average Nash-Sutcliffe coefficients of efficiency from the RF, SVM and ANN models were 0.893, 0.791 and 0.814, respectively. These results indicated that MLs have superior predictive performance and generalizability, and broad prospects for predicting SLRs. This study also demonstrated that the RF model outperformed better than the SVM and ANN models. Therefore, the RF model was used to simulate the SLR of each small watershed in the Chabagou watershed. Our results showed the four-year (2017–2020) average annual SLR of the small watersheds ranged from 0.73 to 1.63 × 104 t/(km2∙a) in the Chabagou watershed. Additionally, the results also indicated the SLR of small watersheds under the rainstorm event with a 100-year recurrence interval was 4.4–51.3 times that of other rainfall events.Furthermore, this study confirmed that bare land was the predominant source of soil loss in the Chabagou watershed, followed by cropland land and grassland. This study helps to provide the theoretical basis for deploying soil and water conservation measures to realize the sustainable utilization of soil resources in the future.

Spatial soil loss prediction impacted by long-term land use/land cover change: a case study of Swat District.

Soil erosion is a destructive consequence of land degradation caused by deforestation, improper farming practices, overgrazing, and urbanization. This irreversible effect negatively impacts the limited renewable soil resource, causing soil truncation, reduced fertility, and unstable slopes. To address the anticipation of erosion modulus resulting from long-term land use and land cover (LULC) changes, a study was conducted in the Swat District of Khyber Pakhtunkhwa (Kpk), Pakistan. The study aimed to predict and evaluate soil erosion concerning these changes using remote sensing (RS), geographic information systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE) model. We also evaluated the impact of the Billion Tree Tsunami Project (BTTP) on soil erosion in the region. Model inputs, such as rainfall erosivity factor, topography factor, land cover and management factor, and erodibility factor, were used to calculate soil erosion. The results revealed that significant soil loss occurred under 2001, 2011, and 2021 LULC conditions, accounting for 67.26%, 61.78%, and 65.32%, falling within the category of low erosion potential. The vulnerable topographical features of the area indicated higher erosion modulus. The maximum soil loss rates observed in 2001, 2011, and 2021 were 80 t/ha-1/year-1, 120 t/ha-1/year-1, and 96 t/ha-1/year-1, respectively. However, the observed reduction in soil loss in 2021 as compared to 2001 and 2011 suggests a positive influence of the BTTP on soil conservation efforts. This study underscores the potential of afforestation initiatives like the BTTP in mitigating soil erosion and highlights the significance of environmental conservation programs in regions with vulnerable topography.

Quantifying interactions between slope gradient, slope length and rainfall intensity on sheet erosion on steep slopes using Multiple Linear Regression

Understanding the interactions between influencing factors on sheet erosion is crucial for model development and mechanism elucidation. Simulation rainfall tests using a full-factorial design were conducted in 0.4 m wide flumes with five slope lengths (0.4, 0.8, 1.2, 1.6, and 2 m), five slope gradients (17.63, 26.79, 36.40, 46.63 and 57.74 %) and five rainfall intensities (48.0, 62.4, 102.0, 148.5, and 170.1 mm·h−1) to quantity interactions between these factors on sheet erosion and runoff rate by using Multiple Linear Regression. The results revealed that adding all two-way interaction terms into the equation, such as Rainfall intensity ∗ Slope length (IL), Slope gradient ∗ Rainfall intensity (SI) and Slope gradient ∗ Slope length (SL), significantly improved the interpretation of erosion and runoff rate. Under various slope gradients, the interaction item IL had a negative impact on erosion rate with an average slope of −0.86, but a positive impact on runoff rate with an average slope of 4.33, which improved the interpretation of regression equations for erosion and runoff rate by 0.86 %–1.83 % and 0.21 %–0.47 %. SI exhibited a positive effect on both erosion and runoff rate with an average slope of 4.36 and 8.44, and enhanced the interpretation for erosion and runoff rate by 0.70 %–3.54 % and 0.02 %–0.32 % under various slope lengths. SL had a negative effect on both erosion and runoff rate with an average slope of −3.55 and −6.93, and increased the interpretation of erosion and runoff rate by 1.15 %–6.84 % and 0.10 %–2.26 % under various rainfall intensities. The roles of I and S on erosion and runoff are both mutually promoting, of S and L are both mutually hindering, and of I and L on erosion are mutually hindering but on runoff are mutually promoting. Additionally, the interactions between I, L and S were even higher than that of a single influencing factor in some cases. Our findings highlight the need to consider the two-way interactions between influencing factors when predicting soil loss and provide methodological guidance for quantifying the interactions on soil erosion.

Estimation of Soil Loss by Revised USLE Model using Geospatial Techniques: A Case Study of Sainj Valley, Northwestern Himalaya, India

Soil erosion is a severe environmental problem. The current study is being conducted in Sainj valley of Himachal Pradesh. An increase in soil erosion rates has significant effects on land degradation, biodiversity loss, productivity and other factors. The Revised Universal Soil Loss Equation (RUSLE) model along with, Geographic Information System (GIS) and Remote Sensing (RS), was used in current study to quantify soil loss in the Sainj valley. Five important parameters like, Rainfall erosivity factor (R), Soil erodibility factor (K), Slope length and steepness (LS), Cropping management factor (C), and support practice factor (P) have been used to estimate the amount of soil loss in the study area. All these maps were created in GIS software using a variety of data preparation methods. The rainfall erosivity factor ranged between 501.19 to 1097.23 mt ha-1 cm-1. Spatial distribution of conservation support practice on soil loss indicated the variability (0–1) where lower value represents the higher conservation practice. The results show that average annual predicted soil loss is between 0 to 541.52tons/ha/yr and the predicted Average soil erosion rate was 11.15 tons/ha/Yr. Predicted soil loss was classified into six Erosion intensity classes Negligible (0-5), Low Erosion (5-10), Moderate Erosion (10-25), Moderately high (25-75), High Erosion (75-100), Extremely High Erosion (>100) From soil loss estimation it was observed that 62 % of the area is having very low erosion intensity and 0.37% of the study area having Extremely High erosion intensity. These findings can help the decision makers further in developing a suitable conservation program to prevent soil erosion.

Erosivity and seasonal rainfall for Pelotas-RS, Brazil with the RainfallErosivityFactor package and Modified Fournier Index

One of the main factors affecting water erosion is rainfall erosivity. Knowledge of this factor can contribute to the prediction of soil losses and to the identification of the most suitable periods for agricultural cultivation operations. The use of software, based on current calculation tools, can contribute to the quick and accurate analysis of long series of rainfall data. The objective of this work is to determine the rainfall volume and rainfall erosivity (R), in different seasons of the year, through the correlation between the EI30 index, the rainfall coefficient (Rc) and the Fournier Index modified (MFI), for rainfall data from Pelotas-RS-Brazil. Rainfall data was used with an interval of 10 min, from 1993 to 2015, evaluated with the RainfallErosivityFactor package of the R-Project software. In the package, the R-factor is obtained by averaging the monthly EI30 values from the data series. Data was evaluated on a monthly, annual and seasonal scale. It was found that the average annual rainfall is 1369.46 mm, with February being the month with the highest rainfall. The series of annual precipitation data did not show a significant trend according to the Mann-Kendall test and the average annual erosivity is 8166.8 MJ mm h−1 ha−1 yr−1. Erosivity in summer is significantly higher than in winter, with the summer-autumn period accounting for 64.4% and winter represented only 17% of annual erosivity. There is a strong positive correlation between the erosivity and the rainfall coefficient, for monthly average data. Erosivity and MFI showed a very strong significant correlation for summer and autumn. The rainfall coefficient tended to show lower values in March and higher values in February. The results obtained are important to identify the periods of most critical erosivity, in which management systems must prioritize surface coverage and minimum soil disturbance.

“Estimating soil surface roughness by proximal sensing for soil erosion modeling implementation at field scale”

Soil Surface Roughness (SSR) is a physical feature of soil microtopography, which is strongly influenced by tillage practices and plays a key role in hydrological and soil erosion processes. Therefore, surface roughness indices are required when using models to estimate soil erosion rates, where tabular values or direct measurements can be used. Field measurements often imply out-of-date and time-consuming methods, such as the pin meter and the roller chain, providing inaccurate indices. A novel technique for SSR measurement has been adopted, employing an RGB-Depth camera to produce a small-scale Digital Elevation Model of the soil surface, in order to extrapolate roughness indices. Canopy cover coverage (CC) of the cover crop was also detected from the camera's images. The values obtained for SSR and CC indices were implemented in the MMF (Morgan-Morgan-Finney) model, to validate the reliability of the proposed methodology by comparing the models' results for sediment yields with long-term soil erosion measurements in sloping vineyards in NW Italy. The performance of the model in predicting soil losses was satisfactory to good for a vineyard plot with inter-rows managed with recurrent tillage, and it was improved using spatialized soil roughness input data with respect to a uniform value. Performance for plot with permanent ground cover was not so good, however it was also improved using spatialized data. The measured values were also useful to obtain C-factor for RUSLE application, to be used instead of tabular values.

Erosivity Factor of the Revised Universal Soil Loss Equation (RUSLE) - A Systematized Review

The Revised Universal Soil Loss Equation (RUSLE) is a globally accepted erosion model which has gained good acceptability. Among the five influences of the RUSLE method of soil erosion estimation, the erosivity factor (R) represents rainfall event’s ability to produce erosion. It is mainly affected by rainfall intensity and kinetic energy of the rain. The erosion index represented by EI30 is the most common R-factor estimation method. Due to the non-availability of rainfall intensity data in many watersheds, researchers have developed methods for erosivity estimation using rainfall depth. The Modified Fournier Index method has gained popularity. Recently, different models using machine learning techniques and ANN are also being set up to establish the R-factor for soil loss estimation. These models can estimate the R-factor quickly and more accurately. They can even predict the R-factor for the future to predict soil loss and plan conservation measures accordingly. An attempt has been made here to review different methodologies proposed by scientists across the globe for arriving at the R-factor for soil loss estimation using RUSLE model.

A modified RUSLE model to simulate soil erosion under different ecological restoration types in the loess hilly area

Soil erosion is mainly affected by the rainfall characteristics and land cover conditions, and soil erosion modelling is important for evaluating land degradation status. The revised Universal Soil Loss Equation (RUSLE) have been widely used to simulate soil loss rate. Previous studies usually considered the general rainfall characteristics and direct effect of runoff with the event rainfall erosivity factor (Re) to produce event soil loss (Ae), whereas the fluctuation of rainfall intensity within the natural rainfall profile has rarely been considered. In this study, the relative amplitude of rainfall intensity (Ram) was proposed to generalize the features of rainfall intensity fluctuation under natural rainfall, and it was incorporated in a new Re (Re=RamEI30) to develop the RUSLE model considering the fluctuation of rainfall intensity (RUSLE-F). The simulation performance of RUSLE-F model was compared with RUSLE-M1 model (Re=EI30) and RUSLE-M2 model (Re=QREI30) using observations in field plots of grassland, orchard and shrubland during 2011–2016 in a loess hilly catchment of China. The results indicated that the relationship between Ae and RamEI30 was well described by a power function with higher R2 values (0.82–0.96) compared to QREI30 (0.80–0.88) and EI30 (0.24–0.28). The RUSLE-F model much improved the accuracy in simulating Ae with higher NSE (0.55–0.79 vs −0.11∼0.54) and lower RMSE (0.82–1.67 vs 1.04–2.49) than RUSLE-M1 model. Furthermore, the RUSLE-F model had better simulation performance than RUSLE-M2 model under grassland and orchard, and more importantly the rainfall data in the RUSLE-F model can be easily obtained compared to the measurements or estimations of runoff data required by the RUSLE-M2 model. This study highlighted the paramount importance of rainfall intensity fluctuation in event soil loss prediction, and the RUSLE-F model contributed to the further development of USLE/RUSLE family of models.

Rill and Interrill Soil Loss Estimations Using the USLE-MB Equation at the Sparacia Experimental Site (South Italy)

A reliable prediction of event soil loss at the plot scale can be obtained by Universal Soil Loss Equation (USLE)-type models. For the Sparacia site (South Italy), the USLE-MB model was recently developed, in which the effect of the erosive agent is modeled using the rainfall erosivity index of the USLE by a power b1 > 1 of the runoff coefficient QR. In this investigation, the model is parameterized separately using plot data collected for rill and interrill events that occurred in the Sparacia experimental area. The values b1 = 1.406 and b1 = 1.012 were obtained for the interrill and rill databases, respectively, which revealed a different effect of the runoff coefficient on soil loss due to the two erosive processes. Different relationships expressive of topographic factors were also deduced. The USLE-MB estimation performance significantly improved when operating the distinction between the two databases compared with the model parameterized on the complete database. The model was particularly reliable in estimating the highest event soil loss values, which were associated with the occurrence of rills. Finally, the proposed parameterization procedure lends itself to being tested in the framework of empirical soil loss modeling.

Application of Geo-informatics for Soil Erosion Mapping

Soil erosion is a most severe environmental problem in hilly area. The study is carried out on Upper Bagmati River basin, North of Kathmandu valley having an area of 61 Sq.km. (approx). Universal Soil Loss Equation (USLE) model, with Geographic Information System (GIS) has been used to quantify the soil loss. Erosion modelling requires huge amount of information and data, usually coming from different sources and available in different formats and scales and for management of these data, GIS was used, which helped considerably in organizing the spatial data representing the effects of each factor affecting soil erosion. Five essential parameters of USLE Rainfall erosivity factor (R), Soil erodibility Factor (K), Slope length and steepness (LS) factor, Cropping management factor (C) and Support practice factor (P) have been used to estimate soil loss amount in the study area. All of these layers have been prepared in Arc GIS using various data sources and data preparation methods. DEM was prepared from the contour data with the interval of 20m which was used to generate LS factor. The monthly rainfall data (2010) of 17 rain gauge stations within the catchment area have been used to predict the R factor. K, C and P factors in basin area are adopted from the literature. The spatial distribution map of soil loss of the basin has been generated and classified into six categories depending on the calculated soil erosion amount. The annual predicted soil loss ranges between 0 and 292.878 t/ha/y. Low soil loss (mean 9.7 t/ha/y) have been recorded under forested areas. The high rate (mean 40.4 t/ha/y) of soil erosion was found in the cultivation area.

Analysis of the sediment sources of flood driven erosion and deposition in the river channel of the Fu River Basin

Studying the characteristics of runoff and sediment processes and revealing the sources of sediment provide key guidance for the scientific formulation of relevant soil erosion protection measures and water conservancy development plans. In the current study, the flow and sediment data of five hydrological stations on the main stream of the Fu River Basin (FRB) from 2007 to 2018 were selected to identify flood events, explore the variation of sediment transport along the FRB, and clarify the sediment sources. The results found that the Jiangyou–Fujiangqiao section is the main source of sediment in the FRB during the flood season. The runoff volume and sediment load during flood events in the Jiangyou–Fujiangqiao section accounted for 35% and 145% respectively of that of Xiaoheba station. These results combined with the change of the sediment load before and after the 2008 Wenchuan Earthquake (May 12) show that the sediment in this section mainly comes from the Fu River tributary–the Tongkou River watershed. The calculation results for the sediment carrying capacity of the Fu River show that the main stream was in a state of erosion in theory. However, according to the calculation results for the interval sediment yield during flood events, the sediment load at the Xiaoheba station was smaller than that at the Shehong station upstream. The analysis indicates that this was not because of sediment deposition in the river channel, but because of sand mining in the river channel and sediment interception by water conservancy projects. If heavy rainfall occurs in the FRB, the sediment accumulated upstream will move downstream with the resulting flood, and the sediment yield in the FRB may further increase. These research conclusions can provide reference information for improving the prediction and management ability of soil and water loss in the FRB and scientific regulation of the Three Gorges Reservoir.

Soil erosion assessment under different land use types using modified Gerlach trough in North-Western Thailand Highland

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