Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

Effects of Content of Soil Rock Fragments on Soil Erodibility in China

Soil erosion is serious in China—the soil in plateau and mountain areas contain a large of rock fragments, and their content and distribution have an important influence on soil erosion. However, there are still no complete results for calculating soil erodibility factor (K) that have corrected rock fragments in China. In this paper, the data available on rock fragments in the soil profile (RFP); rock fragments on the surface of the soil (RFS); and environmental factors such as elevation, terrain relief, slope, vegetation coverage (characterised by normalised difference vegetation index, NDVI), land use, precipitation, temperature, and soil type were used to explore the effects of content of soil rock fragments on calculating of K in China. The correlation analysis, typical sampling area analysis, and redundancy analysis were applied to analyse the effects of content of soil rock fragments on calculating of K and its relationship with environment factors. The results showed that (1) The rock fragments in the soil profile (RFP) increased K. The rock fragments on the surface (RFS) of the soil reduced K. The effect of both RFP and RFS reduced K. (2) The effect of rock fragments on K was most affected by elevation, followed by terrain relief, NDVI, slope, soil type, temperature, and precipitation, but had little correlation with land use. (3) The result of redundancy analysis showed elevation to be the main predominant factor of the effect of rock fragments on K. This study fully considered the effect of rock fragments on calculating of K and carried out a quantitative analysis of the factors affecting the effect of rock fragments on K, so as to provide necessary scientific basis for estimating K and evaluating soil erosion status in China more accurately.

Spatial variability of soil erodibility in pastures and forest areas in the municipality of Porto Velho, Rondônia

“Erodibility” is a characteristic of the soil that represents the susceptibility with which its particles from the most superficial layer are taken and transported to lower places by erosive agents, causing environmental and economic damages. This work estimated soil erodibility in pastures and forest areas in the municipality of Porto Velho-Rondônia. In the field, three areas with different types of vegetation were selected, one with brachiaria, another with mombaça grass, and a third in native forest. In areas with pastures, a sampling mesh of equal sizes was outlined (90 m x 60 m), and in the forested area an approximate sampling mesh (90 m x 50 m), with a regular spacing of 10 m between the samples points for both areas. The sampling was done at the crossing points of the mesh at a depth of 0.0-0.2 m, composing 70 sample points in the areas with pastures and 60 sample points in the forest area, totaling 200 samples. Then, laboratory analyzes were carried out to determine the texture followed by the fractionation of the sand, and the organic carbon followed by the estimate of the organic matter of the soil. The erodibility factors were calculated using indirect prediction models, and then, univariate, geostatistical and multivariate techniques were applied. The pastures’ environments differed from the forest environment. However, the mombaça grass area functions as an intermediate environment between the forest and the brachiaria, being closer to the forest environment. Keywords: erodibility, factors, kriging, principal components.

Soil Erodibility Analysis and Mapping in Gilgel Gibe-I Catchment, Omo-Gibe River Basin, Ethiopia

The soil erosion factor, erodibility, measures the susceptibility of soil particles to transport and detachment by erosive agents. Soil erosion and sedimentation models use soil properties and erodibility as the main input. However, in developing countries such as Ethiopia, data on soil erosion and soil-related properties are limited. For this reason, different researchers use different data sources that are adopted from a large scale and come with very different results. For this reason, the study was proposed to analyze and map the soil erodibility of the catchment area using primary data. 80 mixed soil samples were taken from the catchment with GPS coordinates and analyzed in the laboratory for soil texture class and soil organic matter. Accordingly, sandy clay loam is a dominant soil texture class covering 65% of the catchment area with 2.46% average soil organic matter, which is high in the mountainous part and lower in the lower valley of the catchment area. Most of the catchment area, which accounts for more than 78% of the area, was dominated by medium- or coarse-grained soil structure, and in the upper parts of the catchment area, 21% of the catchment area was covered with fine-grained soil structure. Similarly, 66% of the catchment area was covered with slow to moderate soil permeability, followed by slow soil permeability covering 21% of the area. Finally, the soil erodibility value of the Gilgel Gibe-I catchment was determined to be 0.046 ton h·MJ−1·mm−1 with a range of 0.032 to 0.063 ton·h·MJ−1·mm−1. In general, soils with slow permeability, high silt content, and medium- to fine-grained soil structures are the most erodible. They are conveniently separate; they tend to crust and form high drainage. Knowing this, the catchment has a moderate soil erodibility value. Thus, the study recommends evidence of land cover and the protection of arable land through suitable soil and water protection measures to improve soil permeability and soil structure.

Comparison of the Applicability of Different Soil Erosion Models to Predict Soil Erodibility Factor and Event Soil Losses on Loess Slopes in Hungary

Climate change induces more extreme precipitation events, which increase the amount of soil loss. There are continuous requests from the decision-makers in the European Union to provide data on soil loss; the question is, which ones should we use? The paper presents the results of USLE (Universal Soil Loss Equation), RUSLE (Revised USLE), USLE-M (USLE-Modified) and EPIC (Erosion-Productivity Impact Calculator) modelling, based on rainfall simulations performed in the Koppány Valley, Hungary. Soil losses were measured during low-, moderate- and high-intensity rainfalls on cultivated soils formed on loess. The soil erodibility values were calculated by the equations of the applied soil erosion models and ranged from 0.0028 to 0.0087 t ha h ha−1 MJ−1 mm−1 for the USLE-related models. EPIC produced larger values. The coefficient of determination resulted in an acceptable correlation between the measured and calculated values only in the case of USLE-M. Based on other statistical indicators (e.g., NSEI, RMSE, PBIAS and relative error), RUSLE, USLE and USLE-M resulted in the best performance. Overall, regardless of being non-physically based models, USLE-type models seem to produce accurate soil erodibility values, thus modelling outputs.