Study on Soil Erosion Model Under Different Slopes in Southwest Karst Mountain Area

The aim was to further research soil erosion characteristics and accurately predict soil erosion amount in karst areas. Based on field surveys and research achievements available, yellow soils, which are widely distributed, were chosen as test soil samples and slope, rain intensity, vegetation coverage and bare-rock ratio were taken as soil erosion factors. Artificial rain simulation instruments （needle-type） were made use of to simulate correlation of rain intensity, vegetation coverage, and bare-rock ratio with soil erosion quantity. Furthermore, multiple-factor linear regression analysis, stepwise regression analysis and multiple-factor non-linear regression analy- sis were made to establish a multiple-factor formula of soil erosion modulus with dif- ferent slopes and select regression models with high correlation coefficients. The re- sults show that a non-linear regression model reached extremely significant level or significant level （0.692〈FF〈0.988） and linear regression model achieved significant lev- el （0.523〈FF〈0.634）. The effects of erosion modulus changed from decreasing to in- creasing and the erosion factors from high to low were rain intensity, vegetation cov- erage and bare-rock ratio when slope gradient was at 6~, 16~, 26~ and 36~. The mod- el is of high accuracy for predicting gentle slope and abtupt slope, which reveals correlation of erosion modulus with erosion factors in karst areas.

An estimation method of soil wind erosion in Inner Mongolia of China based on geographic information system and remote sensing

Studies of wind erosion based on Geographic Information System（GIS） and Remote Sensing（RS） have not attracted sufficient attention because they are limited by natural and scientific factors.Few studies have been conducted to estimate the intensity of large-scale wind erosion in Inner Mongolia,China.In the present study,a new model based on five factors including the number of snow cover days,soil erodibility,aridity,vegetation index and wind field intensity was developed to quantitatively estimate the amount of wind erosion.The results showed that wind erosion widely existed in Inner Mongolia.It covers an area of approximately 90×104 km2,accounting for 80% of the study region.During 1985–2011,wind erosion has aggravated over the entire region of Inner Mongolia,which was indicated by enlarged zones of erosion at severe,intensive and mild levels.In Inner Mongolia,a distinct spatial differentiation of wind erosion intensity was noted.The distribution of change intensity exhibited a downward trend that decreased from severe increase in the southwest to mild decrease in the northeast of the region.Zones occupied by barren land or sparse vegetation showed the most severe erosion,followed by land occupied by open shrubbery.Grasslands would have the most dramatic potential for changes in the future because these areas showed the largest fluctuation range of change intensity.In addition,a significantly negative relation was noted between change intensity and land slope.The relation between soil type and change intensity differed with the content of Ca CO3 and the surface composition of sandy,loamy and clayey soils with particle sizes of 0–1 cm.The results have certain significance for understanding the mechanism and change process of wind erosion that has occurred during the study period.Therefore,the present study can provide a scientific basis for the prevention and treatment of wind erosion in Inner Mongolia.

Uncertainty Assessment of Soil Erosion Model Using Particle Filtering

Recent advances in computer with geographic information system(GIS) technologies have allowed modelers to develop physics-based models for modeling soil erosion processes in time and space.However, it has been widely recognized that the effect of uncertainties on model predictions may be more significant when modelers apply such models for their own modeling purposes.Sources of uncertainty involved in modeling include data, model structural, and parameter uncertainty.To deal with the uncertain parameters of a catchment-scale soil erosion model(CSEM) and assess simulation uncertainties in soil erosion, particle filtering modeling(PF) is introduced in the CSEM.The proposed method, CSEM-PF, estimates parameters of non-linear and non-Gaussian systems, such as a physics-based soil erosion model by assimilating observation data such as discharge and sediment discharge sequences at outlets.PF provides timevarying feasible parameter sets as well as uncertainty bounds of outputs while traditional automatic calibration techniques result in a time-invariant global optimal parameter set.CSEM-PF was applied to a small mountainous catchment of the Yongdamdam in Korea for soil erosion modeling and uncertainty assessment for three historical typhoon events.Finally, the most optimal parameter sets and uncertainty bounds of simulation of both discharge and sediment discharge at each time step of the study events are provided.

Comparison of Slope Length Factor Extraction in Hillslope Soil Erosion Model with Different DEM Resolutions

In this study, non-cumulative slope length(NCSL) calculation method and spatial analytical calculation(SAC) method were respectively applied to extract slope length and slope length factor from 10 sample areas, which are located in Ansai County, north Shaanxi Province. The comparison of computation precision between variable DEM resolutions showed that NCSL was superior to SAC entirely. And the results were best when the DEM resolutions were 5 and 10 m. Besides, the results of slope length factor were nearly the same under the two conditions. So DEM of 10 m resolution can be used to extract slope length.

Assessment of SoilGrids data for soil erosion estimation at watershed scale: A case study in northern Thailand

Soil erosion has been identified as one of the most destructive forms of land degradation,posing a threat to the sustainability of global economic,social and environmental systems.This underscores the need for sustainable land management that takes erosion control and prevention into consideration.This requires the use of state-of-the-art erosion prediction models.The models often require extensive input of detailed spatial and temporal data,some of which are not readily available in many developing countries,particularly detailed soil data.The soil dataset Global Gridded Soil Information(SoilGrids)could potentially fill the data gap.Nevertheless,its value and accuracy for soil erosion modelling in the humid tropics is still unknown,necessitating the need to assess its value vis-à-vis field-based data.The major objective of this study was to conduct a comparative assessment of the value of SoilGrids and field-based soil data for estimating soil loss.Soil samples were collected from five physiographic positions(summit,shoulder,back slope,foot slope,and toe slope)using the soil catena approach.Samples were collected using a 5-cm steel sample ring(undisturbed)and a spade(disturbed).Data of the landform,predominant vegetation types,canopy cover,average plant height,land use,soil depth,shear strength,and soil color were recorded for each site.The soil samples were subjected to laboratory analysis for saturated hydraulic conductivity,bulk density,particle size distribution,and organic matter content.Pedotransfer functions were applied on the SoilGrids and field-based data to generate soil hydrological properties.The resultant field-based data were compared with the SoilGrids data for corresponding points/areas to determine the potential similarities of the two datasets.Both datasets were then used as inputs for soil erosion assessment using the revised Morgan-Morgan-Finney model.The results from both datasets were again compared to determine the degree of similarity.The results showed that with respect to point-based comparison,both datasets were significantly different.At the hillslope delineation level,the field-based data still consistently had a greater degree of variability,but the hillslope averages were not significantly different for both datasets.Similar results were recorded with the soil loss parameters generated from both datasets;point-based comparison showed that both datasets were significantly different,whereas the reverse was true for parcel/area-based comparison.SoilGrids data are certainly useful,especially where soil data are lacking;the utility of this dataset is,however,dependent on the scale of operation or the extent of detail required.When detailed,site-specific data are required,SoilGrids may not be a good alternative to soil survey data in the humid tropics.On the other hand,if the average soil properties of a region,area,or land parcel are required for the implementation of a particular project,plan,or program,SoilGrids data can be a very valuable alternative to soil survey data.

Assessing the USLE Crop and Management Factor C for Soil Erosion Modeling in a Large Mountainous Watershed in Central China

Due to the impoundment of the Yangtze River, the Three Gorges Dam in China fosters high land-use dynamics. Soil erosion is expected to increase dramatically. One of the key factors in soil erosion control is the vegetation cover and crop type. However, determining these factors adequately for the use in soil erosion modeling is very time-consuming especially for large mountainous areas, such as the Xiangxi （香溪） catchment in the Three Gorges area. In our study, the crop and management factor C was calculated using the fractional vegetation cover （CFvc） based on Landsat-TM images from 2005, 2006, and 2007 and on literature studies （CLIT）. In 2007, the values of CFvc range between 0.001 and 0.98 in the Xiangxi catchment. The mean CFVC value is 0.05. CLIT values are distinctly higher, ranging from 0.08 to 0.46 with a mean value of 0.32 in the Xiangxi catchment. The mean potential soil loss amounts to 120.62 t/ha/a in the Xiangxi catchment when using CLIT for modeling. Based on CFVC, the predicted mean soil loss in the Xiangxi catchment is 11.50 t/ha/a. Therefore, CLIT appears to bemore reliable than the C factor based on the fractional vegetation cover.

Cogitation on developing a dynamic model of soil wind erosion

Studies on soil wind erosion began with single factors affecting soil wind erosion; with increasing quantities of data being accumulated,the wind erosion equation(WEQ),the revised wind erosion equation(RWEQ),the wind erosion prediction system(WEPS),and other soil wind erosion models have been successively established,and great advances have been achieved.Here we briefly review the soil wind erosion research course and analyze the advantages and disadvantages of the current soil wind erosion models.From the perspective of the dynamics of wind erosion,we classified the factors affecting soil wind erosion into three categories,namely,wind erosivity factors(WEF),soil antierodibility factors(SAF),and roughness interference factors(RIF).We proposed the concept of a standard plot of soil wind erosion to solve the problem of uncertainty of the soil wind erosion modulus on a spatial scale,and provided methods to set similarity conditions in wind tunnel simulation experiments and to convert the spatial scale of the wind erosion modulus from the standard plot to a large scale field.We also proposed a conceptual model on the basis of the dynamics of soil wind erosion with the theoretical basis that wind produces a shear force on the soil surface.This shear force is partitioned by barely erodible soil surfaces and roughness elements on the ground,and the amount of soil loss by wind should be calculated by comparing the shear force of the wind on barely erodible soil surfaces with the anti-erosion force of the surface soil.One advantage of this conceptual model is that the calculated soil wind erosion modulus is not subject to changes of spatial scale.Finally,we recommended continual improvement of the existing models while also establishing new models.

Applying seepage modeling to improve sediment yield predictions in contour ridge systems

Contour ridge systems may lead to seepage that could result in serious soil erosion. Modeling soil erosion under seepage conditions in a contour ridge system has been overlooked in most current soil erosion models. To address the importance of seepage in soil erosion modeling, a total of 23 treatments with 3 factors, row grade, field slope and ridge height, in 5 gradients were arranged in an orthogonal rotatable central composite design. The second-order polynomial regression model for predicting the sediment yield was improved by using the measured or predicted seepage discharge as an input factor, which increased the coefficient of determination(R^2) from 0.743 to 0.915 or 0.893. The improved regression models combined with the measured seepage discharge had a lower P(0.007) compared to those combined with the predicted seepage discharge(P=0.016). With the measured seepage discharge incorporated, some significant(P<0.050) effects and interactions of influential factors on sediment yield were detected, including the row grade and its interactions with the field slope, ridge height and seepage discharge, the quadratic terms of the field slope and its interactions with the row grade and seepage discharge. In the regression model with the predicted seepage discharge as an influencing factor, only the interaction between row grade and seepage discharge significantly affected the sediment yield. The regression model incorporated with predicted seepage discharge may be expressed simply and can be used effectively when measured seepage discharge data are not available.

An approach to estimating sediment transport capacity of overland flow

Estimating sediment transport capacity of overland flow is essential to the development of physically based soil erosion models.Correlation analysis indicates that stream power is a dominant factor for sediment transport in overland flows and a new sediment transport capacity equation is proposed based on dimensional analysis.The coefficients of the new equation are calibrated using the published laboratory data,and rainfall impact is taken into consideration by adding an empirical factor on the dimensionless critical stream power.The new sediment transport capacity equation is a function of stream power,rainfall impacted critical stream power and slope.The new equation is applied in a one-dimensional soil erosion model to simulate field data of a runoff plot and the simulation results are reliable.