Construction of Evaluation Model of Surface Soil Erodibility Factors in Black Soil Area

2021 ◽  
Vol 7 (6) ◽  
pp. 6283-6302
Author(s):  
Wenfeng Gong ◽  
Tiedong Liu ◽  
Yan Jiang
Keyword(s):  
Soil Erosion ◽  
Surface Soil ◽  
Evaluation Model ◽  
Black Soil ◽  
Soil Erodibility ◽  
K Value ◽  
Spatial Distribution Characteristics ◽  
Quantitative Performance ◽  
Performance And Evaluation ◽  
Soil Area

The increasing soil erosion in black soil area has caused widespread concern from all walks of life. Based on this background, the evaluation model of surface soil erodibility factor in black soil area is studied and constructed. The data of erosion gully is from the general survey data of surface soil erosion gully in black soil area. After quantifying the morphological characteristics of the data, the vector data of erosion gully are gridded by fractal theory. The number of non-empty grids is calculated by the attribute query function of ArcGIS, and the pixel size is transformed in turn to obtain different coverage grids and corresponding fractal parameters. The surface soil runoff and soil erosion process in black soil area are simulated by PESERA model. To build the surface soil erosion model of black soil area, in the process of building the user-defined model, it needs to carefully select the parameters used for modeling, and it needs to consider all the factors that may play a role in the whole process of soil erosion. The factors of surface soil erodibility in black soil area are analyzed, including spatial distribution characteristics of soil erodibility’s K value, semivariance function analysis of soil erodibility’s K value, and spatial distribution characteristics analysis of soil erodibility’s K value. Finally, the evaluation model of surface soil erodibility factor in black soil area is constructed. By testing the quantitative performance and evaluation accuracy of erodibility factors, it is proved that this method has good quantitative performance and evaluation accuracy of erodibility factors, and has strong practicability.

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

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Xiaofang Huang ◽  
Lirong Lin ◽  
Shuwen Ding ◽  
Zhengchao Tian ◽  
Xinyuan Zhu ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Variability ◽  
Influencing Factors ◽  
Soil Type ◽  
Soil Survey ◽  
Purple Soil ◽  
Soil Erodibility ◽  
K Value ◽  
K Factor

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.

scholarly journals Pemetaan Erodibilitas Tanah Dan Korelasinya Terhadap Karakteristik Tanah Di Das Serang, Kulonprogo

2018 ◽  
Vol 2 (1) ◽  
pp. 135
Author(s):  
Efrinda Ari Ayuningtyas ◽  
Ainul Fahmi Nur Ilma ◽  
Rindhang Bima Yudha
Keyword(s):  
Soil Erosion ◽  
Soil Properties ◽  
Spatial Information ◽  
Organic Soil ◽  
Soil Erodibility ◽  
Soil Permeability ◽  
K Value ◽  
Gis Tools ◽  
Soil Structures ◽  
K Factor

Soil erosion was happened caused by many factors, such as rainfall intensity, soil erodbility, steepness and length of slope, land cover, and conservation practices. In other case, the soil properties also influence the vulnerability of soil to be detached. This soil properties characteristics is classified as soil erodibility. Erodibility factor (K) from the Universal Soil Loss Equation (USLE) in this study was the result of soil erodibility estimation or soil capability to be dispersed by rain. K factor was affected by soil organic, soil permeability, soil structures, and soil textures. This study was contributed in Serang Watershed because of the main fuction of this watershed to supply water resources especially in Sermo Reservoir in Ngrancah Subwatershed. This reservoir was used to distribute water and irrigation to all Kulonprogo District and especially to keep the sustainability of sedimentation of soastal area di Glagah Beach. All of soil properties was collected in each landform of Serang Watershed and was analyzed by laboratory measurement. By using K factor formula, the K value can be estimated. Geographic Information System (GIS) tools were used to map and represent the spatial information of soil erodibility of Serang Watershed. The result of this study showed that the high value of K factor was distributed in the area which has genesis of structural, denudated structural, and sedimented denudational. Furthermore, this study can be strived to analyze soil erosion hazard which was influenced by soil erodibility.

scholarly journals Analysis of Soil Erodibility Factor for Hydrologic Processes in Kereke Watershed, North Central Nigeria

2021 ◽  
Vol 25 (3) ◽  
pp. 425-432
Author(s):  
G.A. Songu ◽  
R.D. Abu ◽  
N.M. Temwa ◽  
S.T. Yiye ◽  
S. Wahab ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Erosion ◽  
Bulk Density ◽  
Soil Management ◽  
Soil Samples ◽  
Hydrological Processes ◽  
Soil Erodibility ◽  
K Value ◽  
North Central ◽  
Soil Erodibility Factor

: Soil erodibility factor plays essential role in determining how susceptible soil is to hydrological processes such as detachment and removal by rainfall and runoff that could influence soil erosion and sediment entrainment by flooding in an area. This paper therefore determined the erodibility k-value of soil in the Kereke watershed with the purpose of assessing its susceptibility to hydrological processes. Data was collected on some soil properties such as soil texture, soil structure, soil organic matter content, soil carbon content, soil porosity, soil bulk density, soil moisture content and soil erodibility k-value. The systematic sampling procedure was used to select thirty-two settlements which served as catchment areas for data collection; from which thirty-two soil samples were collected for analysis. Tables and graph were used to present the data, and percentages were used to depict variations in the data set. Results of the study shows that the soil samples have high percent sand particles (71.6%), moderate amount of clay particles (15.7%), silt(12.7%), organic carbon (0.6%), organic matter (1.1%), bulk density (1.4 gcm-3 ), porosity (45.2%), moisture content (14.0%) and low soil erodibility k-value of 0.018. The soil erodibility k-value in the study area was considered to be low, and therefore the soils are moderately erodible. This probably accounts for the moderate intensity of soil erosion channels and entrained sediments by flooding observed in the study area. It is therefore recommended that soil management practices should be encouraged by farmers especially planting of cover crops, shifting cultivation and fallowing system. This will allow nutrients gain and improved bulk density to limit soil erodibility capacity and good soil management in the area. Key words: Soil erodibility factor, hydrological processes, Kereke watershed, North Central Nigeria

Research of Spatial Distribution of Cr and Ni in a Typical Black Soil on the GIS

2014 ◽  
Vol 651-653 ◽  
pp. 1231-1235
Author(s):  
Jing Jing Zhang ◽  
Jing Shuang Liu ◽  
Yang Wang
Keyword(s):  
Spatial Distribution ◽  
Surface Soil ◽  
Black Soil ◽  
Geostatistical Analysis ◽  
Corn Belt ◽  
Pollution Level ◽  
Distribution Characteristics ◽  
Spatial Distribution Characteristics ◽  
Close Relationship ◽  
Relationship Of

ArcGIS analysis was applied to study the concentrations and spatial distribution characteristics of Cr and Ni in surface soil (0~20cm) of corn belt in Dehui—the typical area of black soil in Jilin province. The average concentrations of Cr and Ni were 49.85 mg·kg-1 and 20.85 mg·kg-1, which were both lower than the corresponding background value and thus in no pollution level. The trends for soil Cr and Ni were similar with higher concentrations trending N-S across the centre of the study area, while the other hotspots were located in the southwest of Dehui. There was prominent correlation between Cr and Ni in 0.01 level, indicating the close relationship of them. The method based on the geostatistical analysis in ArcGIS can exactly reflect the character of spatial distribution of heavy metals.

scholarly journals A Quantitative Analysis of Factors Influencing Organic Matter Concentration in the Topsoil of Black Soil in Northeast China Based on Spatial Heterogeneous Patterns

2021 ◽  
Vol 10 (5) ◽  
pp. 348
Author(s):  
Zhenbo Du ◽  
Bingbo Gao ◽  
Cong Ou ◽  
Zhenrong Du ◽  
Jianyu Yang ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Matter ◽  
Soil Erosion ◽  
Soil Type ◽  
Northeast China ◽  
Black Soil ◽  
Factors Affecting ◽  
Soil Zone ◽  
Rainfed Farming ◽  
Study Results

Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China’s commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited.

COMPUTER ASSISTED MAPPING OF SOIL EROSION POTENTIAL

1985 ◽  
Vol 65 (3) ◽  
pp. 411-418 ◽  
Author(s):  
T. VOLD ◽  
M. W. SONDHEIM ◽  
N. K. NAGPAL
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Surface Soil ◽  
Mineral Soil ◽  
Weighted Average ◽  
Bare Soil ◽  
Computer Assisted ◽  
Additional Information ◽  
Potential Map ◽  
Fraser Valley

Soil erosion potential maps and summary statistics can be produced from existing information with relative ease with the aid of computers. Soil maps are digitized and survey information is stored as attributes for each soil. Algorithms are then prepared which evaluate the appropriate data base attributes (e.g. texture, slope) for each interpretation. Forty surface soil erosion potential maps were produced for the Lower Fraser Valley which identify the most erosion-prone areas and indicate average potential soil losses to be expected under assumed conditions. The algorithm developed follows the universal soil loss equation. Differences across the landscape in the R, K, and S factors are taken into account whereas the L factor is considered as a constant equal to 1.0. Worst conditions of bare soil (no crop cover, i.e. C = 1.0) and no erosion control practices (i.e. P = 1.0) are assumed. The five surface soil erosion potential classes are determined by a weighted average annual soil loss value based both on the upper 20 cm of mineral soil and on the proportion of the various soils in the polygon. A unique polygon number shown on the erosion potential map provides a link to computer tables which give additional information for each individual soil within that polygon. Key words: Erosion, computer mapping, USLE

scholarly journals Distribution of soil nutrients and erodibility factor under different soil types in an erosion region of Southeast China

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11630
Author(s):  
Man Liu ◽  
Guilin Han
Keyword(s):  
Soil Erosion ◽  
Soil Nutrients ◽  
Soil Ph ◽  
Major Elements ◽  
Soil Types ◽  
Soil Erodibility ◽  
Soil Evolution ◽  
Southeast China ◽  
Red Soils ◽  
K Factor

Background Soil erosion can affect the distribution of soil nutrients, which restricts soil productivity. However, it is still a challenge to understand the response of soil nutrients to erosion under different soil types. Methods The distribution of soil nutrients, including soil organic carbon (SOC), soil organic nitrogen (SON), and soil major elements (expressed as Al2O3, CaO, Fe2O3, K2O, Na2O, MgO, TiO2, and SiO2), were analyzed in the profiles from yellow soils, red soils, and lateritic red soils in an erosion region of Southeast China. Soil erodibility K factor calculated on the Erosion Productivity Impact Calculator (EPIC) model was used to indicate erosion risk of surface soils (0∼30 cm depth). The relationships between these soil properties were explored by Spearman’s rank correlation analysis, further to determine the factors that affected the distribution of SOC, SON, and soil major elements under different soil types. Results The K factors in the red soils were significantly lower than those in the yellow soils and significantly higher than those in the lateritic red soils. The SON concentrations in the deep layer of the yellow soils were twice larger than those in the red soils and lateritic red soils, while the SOC concentrations between them were not significantly different. The concentrations of most major elements, except Al2O3 and SiO2, in the yellow soils, were significantly larger than those in the red soils and lateritic red soils. Moreover, the concentrations of major metal elements positively correlated with silt proportions and SiO2 concentrations positively correlated with sand proportions at the 0∼80 cm depth in the yellow soils. Soil major elements depended on both soil evolution and soil erosion in the surface layer of yellow soils. In the yellow soils below the 80 cm depth, soil pH positively correlated with K2O, Na2O, and CaO concentrations, while negatively correlated with Fe2O3 concentrations, which was controlled by the processes of soil evolution. The concentrations of soil major elements did not significantly correlate with soil pH or particle distribution in the red soils and lateritic red soils, likely associated with intricate factors. Conclusions These results suggest that soil nutrients and soil erodibility K factor in the yellow soils were higher than those in the lateritic red soils and red soils. The distribution of soil nutrients is controlled by soil erosion and soil evolution in the erosion region of Southeast China.

scholarly journals Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Veera Narayana Balabathina ◽  
R. P. Raju ◽  
Wuletaw Mulualem ◽  
Gedefaw Tadele
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Soil Erodibility ◽  
Lake Tana ◽  
Erosion Rates ◽  
Soil Erosion Rate ◽  
Soil Loss Equation

Abstract Background Soil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment. Results The total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment. Conclusions Use of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.

Advances in techniques to assess soil erodibility

2021 ◽  
pp. 175-214
Author(s):  
R. J. Rickson ◽  
◽  
E. Dowdeswell Downey ◽  
G. Alegbeleye ◽  
S. E. Cooper ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Test Procedure ◽  
Aggregate Stability ◽  
Analytical Techniques ◽  
Soil Biology ◽  
Soil Erodibility ◽  
Erosion Rates ◽  
Erosion Processes ◽  
Assessment Techniques ◽  
Soil Erosion By Water

Soil erodibility is the susceptibility of soil to the erosive forces of rainsplash, runoff and wind. It is a significant factor in determining present and future soil erosion rates. Focusing on soil erosion by water, this chapter shows that erodibility is determined by static and dynamic soil properties that control a range of sub-processes affecting soil erosion, but there is no standardised test procedure, making comparison of erodibility assessment techniques and their results challenging. Most researchers agree that aggregate stability is the best indicator of soil erodibility. Selection of techniques to measure aggregate stability need to consider the type of disruptive forces and breakdown processes to which field aggregates are subjected. New indices must incorporate spatial and temporal variabilities in erodibility; the different erosion processes operating; the impact of climate change; and the role of soil biology. New analytical techniques such as computer aided tomography show promise in considering soil erodibility as a dynamic continuum operating over 3 dimensions.

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