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 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 Digital mapping of soil erodibility for water erosion in New South Wales, Australia

Soil Research ◽  
2018 ◽  
Vol 56 (2) ◽  
pp. 158 ◽  
Author(s):  
Xihua Yang ◽  
Jonathan Gray ◽  
Greg Chapman ◽  
Qinggaozi Zhu ◽  
Mitch Tulau ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Soil Properties ◽  
Soil Loss ◽  
New South ◽  
New South Wales ◽  
Field Measurements ◽  
Soil Erodibility ◽  
South Wales ◽  
Australian Continent ◽  
K Factor

Soil erodibility represents the soil’s response to rainfall and run-off erosivity and is related to soil properties such as organic matter content, texture, structure, permeability and aggregate stability. Soil erodibility is an important factor in soil erosion modelling, such as the Revised Universal Soil Loss Equation (RUSLE), in which it is represented by the soil erodibility factor (K-factor). However, determination of soil erodibility at larger spatial scales is often problematic because of the lack of spatial data on soil properties and field measurements for model validation. Recently, a major national project has resulted in the release of digital soil maps (DSMs) for a wide range of key soil properties over the entire Australian continent at approximately 90-m spatial resolution. In the present study we used the DSMs and New South Wales (NSW) Soil and Land Information System to map and validate soil erodibility for soil depths up to 100 cm. We assessed eight empirical methods or existing maps on erodibility estimation and produced a harmonised high-resolution soil erodibility map for the entire state of NSW with improvements based on studies in NSW. The modelled erodibility values were compared with those from field measurements at soil plots for NSW soils and revealed good agreement. The erodibility map shows similar patterns as that of the parent material lithology classes, but no obvious trend with any single soil property. Most of the modelled erodibility values range from 0.02 to 0.07 t ha h ha–1 MJ–1 mm–1 with a mean (± s.d.) of 0.035 ± 0.007 t ha h ha–1 MJ–1 mm–1. The validated K-factor map was further used along with other RUSLE factors to assess soil loss across NSW for preventing and managing soil erosion.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Gizaw Tesfaye ◽  
Tolesa Ameyu
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Erosion ◽  
Catchment Area ◽  
Soil Structure ◽  
Soil Erodibility ◽  
Soil Permeability ◽  
Fine Grained ◽  
Fine Grained Soil ◽  
Texture Class

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.

scholarly journals Spatial Variability Modeling of Soil Erodibility Index in Relation to Some Soil Properties at Field Scale

2016 ◽  
Vol 6 (2) ◽  
pp. 16 ◽  
Author(s):  
C. Gyamfi ◽  
J. M. Ndambuki ◽  
R.W. Salim
Keyword(s):  
Organic Matter ◽  
Soil Erosion ◽  
Soil Properties ◽  
Bulk Density ◽  
Particle Density ◽  
Significant Variable ◽  
Soil Erodibility ◽  
Field Scale ◽  
Undisturbed Soil ◽  
Variability Modeling

<p class="1Body">Soil erosion is a major land degradation issue affecting various facets of human lives. To curtail soil erosion occurrence requires understanding of soil properties and how they influence soil erosion. To this end, the soil erodibility index which gives an indication of the susceptibility of soils to erosion was examined. In particular, we aimed to determine soil erodibility index at field scale and establish relationships that exist between selected soil properties and soil erodibility index. It was hypothesized that for soil erodibility index to vary spatially, then the existing soil properties should have varying spatial structure. Hundred disturbed and 100 undisturbed soil samples were collected from a 7.3 ha gridded area. The samples were analyzed for particle size distribution, bulk density, particle density, organic matter content and porosity. All soil analyses were conducted following standard procedures. Data were analyzed statistically and geostatistically on the basis of semivariograms. Sandy clay loam was the dominant soil texture in the studied field. Results indicate significant negative relationship between<strong> </strong>sand content, bulk density, particle density and organic matter with soil erodibility index. Silt correlated significantly with a positive relation with soil erodibility. Estimated erodibility for the sampled field ranged from 0.019 t.ha.hr/ha.MJ.mmto 0.055 t.ha.hr/ha.MJ.mm. The order of dominance of erodibility ranges were 0.038-0.042 t.ha.hr/ha.MJ.mm&gt; 0. 036-0.08 t.ha.hr/ha.MJ.mm&gt; 0.032-0.036 t.ha.hr/ha.MJ.mm&gt; 0.019-0.032 t.ha.hr/ha.MJ.mm&gt; 0.042-0.055 t.ha.hr/ha.MJ.mm. Regression analysis revealed silt to be the most significant variable that influences soil erodibility. The best regression of soil properties on soil erodibility index gave an R<sup>2 </sup>of 0.90. A comparison of the regression equation with other studies indicated good performance of the equation developed.</p>

scholarly journals ASSESSMENT OF SOIL EROSION BY SIMULATING RAINFALL ON AN EQUATORIAL ORGANIC SOIL

2017 ◽  
Vol 8 (2) ◽  
pp. 72-81
Author(s):  
Johari A.H ◽  
Law P.L. ◽  
Taib S.N.L. ◽  
Yong L.K.
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Organic Soil ◽  
Coarse Sand ◽  
Soil Samples ◽  
Soil Erodibility ◽  
Simulated Rainfall ◽  
Construction Sites ◽  
The Impact ◽  
Soil Loss Equation

Soil erosion occurs on construction sites partly due to site clearing that exposes the land to the erosive power of rainfall. A proposed construction project requires the submission of an Environmental Impact Assessment EIA) to assess the impact of the project on the environment. Assessment of soil erosion is included in the EIA, but the equation to estimate soil erosion known as the Universal Soil Loss Equation (USLE) is only applicable to a soil containing up to four percent organic matter. This limitation of USLE requires an alternative that can predict soil erosion on an organic soil. This study attempts to assess erosion that occurs on an organic soil by simulated rainfall. Field soil samples were reconstructed into three shapes and exposed to simulated rainfall. Results indicate that the amount of organic soil loss decreases with increasing duration of rainfall. Particle size distribution shows that particles with sizes finer than coarse sand (1.7 mm) remained on the slopes. Equations were developed from the graphs of soil loss versus duration of simulated rainfall to estimate soil loss occurring on slopes covered by an organic soil. The outcome of this study can be a precursor to developing an equation to estimate soil erodibility of a slope overlain by an organic soil.

Effects of Content of Soil Rock Fragments on Calculating of Soil Erodibility

2021 ◽  
Author(s):  
Miaomiao Yang ◽  
Qinke Yang ◽  
Keli Zhang ◽  
Yuru Li ◽  
Chunmei Wang ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Soil Erosion ◽  
Soil Profile ◽  
Joint Effect ◽  
Saturated Hydraulic Conductivity ◽  
Soil Erodibility ◽  
Soil Permeability ◽  
Land Area ◽  
Rock Fragments ◽  
Global Land

&lt;p&gt;&amp;#12304;Objective&amp;#12305;Rock fragments (&gt;2mm diameter) are an important component of soil, and its presence has a significant impact on soil erosion and sediment yield. So it is essential to take into full account content of the rock fragments for accurate calculation of soil erodibility factor (K). &amp;#12304;Method&amp;#12305;In this paper, based on the data available of the content of rock fragments and classes of soil texture with a resolution of 30 arc-second, influence of the content of rock fragments, including rock fragments in the soil profile (RFP) and gravels on the surface of the soil (SC), on K was assessed at a global scale, using the equation (Brakensiek, 1986) of the relationship between saturated hydraulic conductivity and grade of soil permeability, and the equation (Poesen) of soil erodibility attenuation under a rock fragment cover. &amp;#12304;Result&amp;#12305;Results show: (1) The existence of rock fragments in the soil increased K by 4.43% and soil permeability by 5.68% on average in grade and lowering soil saturated hydraulic conductivity by 11.57% by reducing water infiltration rate of the soil and increasing surface runoff. The gravels on the surface of the mountain land and desert/gobi reduced K by 18.7% by protecting the soil from splashing of rain drops and scrubbing of runoff; so once the content of rock fragments in the soil profile and gravels on the surface of the land are taken into account in calculation, soil K may be 5.52% lower; (2)In the areas dominated with the effect of rock fragments, about 62.7% of the global land area, soil K decreased by 0.0091( t&amp;#8226;hm&lt;sup&gt;2&lt;/sup&gt;&amp;#8226;h)&amp;#8226;( hm&lt;sup&gt;-2&lt;/sup&gt;&amp;#8226;MJ&lt;sup&gt;-1&lt;/sup&gt;&amp;#8226;mm&lt;sup&gt;-1&lt;/sup&gt;) on average, while in the area affected mainly by rock fragments in profile, about 31.1% of the global land area, soil K increased by 0.0019( t&amp;#8226;hm&lt;sup&gt;2&lt;/sup&gt;&amp;#8226;h)&amp;#8226;( hm&lt;sup&gt;-2&lt;/sup&gt;&amp;#8226;MJ&lt;sup&gt;-1&lt;/sup&gt;&amp;#8226;mm&lt;sup&gt;-1&lt;/sup&gt;); and (3)The joint effect of rock fragments in profile and gravels on the surface reduced the soil erosion rate by 11.8% in the 6 sample areas. &amp;#12304;Conclusion&amp;#12305; The presence of RFP increases soil K while the presence of SC does reversely. The joint effect of the two leads to decrease in soil erosion. In plotting regional soil erosion maps, it is essential to take both of the two into account so as to improve accuracy of the mapping.&lt;/p&gt;

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

scholarly journals Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion

2019 ◽  
Vol 11 (9) ◽  
pp. 1106 ◽  
Author(s):  
Dimitrios D. Alexakis ◽  
Evdokia Tapoglou ◽  
Anthi-Eirini K. Vozinaki ◽  
Ioannis K. Tsanis
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Erosion ◽  
Soil Properties ◽  
Satellite Remote Sensing ◽  
Soil Loss ◽  
Assessment Model ◽  
Landsat 8 ◽  
Field Spectroscopy ◽  
K Factor

Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.

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