The RUSLE model and SDR method have been applied to fulfil the objective of this study. The models are designed based on the area and input of the suitable data for soil loss prediction. The universal soil loss equation (USLE) is an empirically based model proposed by Wischmeier and Smith in 1978. It was the most widely used model for soil loss estimation. The equation of RUSLE soil erosion estimation described as,
Where,
A is the estimated average soil loss in ton/ha/year,
R is the erosivity of rainfall in mj mm/ha h year,
K is the soil erodibility factor in ton ha h/ha mj mm,
LS is the topographic factor integrating slope length and steepness (LS) dimensionless,
C is the cover-management factor, dimensionless, and
P is the support practice factor, dimension.
All the above factors are converted from Polygon to raster form values using the conversion tool of the Arc tool box in ArcGIS 10.5.
Rainfall erosivity factor (R):
The daily rainfall data of the study area has been obtained from the Global Weather Data for SWAT (GWDS),to calculate the rainfall erosivity (R factor)[1] of the study area. GWDS website allow us to download daily CFSR (Climatic Forecast System Reanalysis) data for a given location, therefore, the daily rainfall data of the study area were collected from six weather stations (Table1), located within the district. In total of 20 years of rainfall data (from 1980-2000) are used for the 1985 rainfall erosivity map, and for the 2005 and 2014 maps, the rainfall data used are from 1998 to 2014, a total of 17 years’ data.
The average annual rainfall (mean annual precipitation) of each weather stations of the study area was calculated (Table 1) to generate the rainfall erosivity (R factor) map.
Table 1
Annual Mean Precipitation (mm) of Morena District, (Duration of Rainfall Years1980-2014)
Stations | Latitude | Longitude | Elevation | Annual Mean Precipitation (mm) (For 1985 R-factor map) (Rainfall data 1980–2000) | Annual Mean Precipitation (mm) (For 2005 & 2014 R-factor map) (Rainfall Data 1998–2014) |
1 | 77°29'E | 26°22’N | 185 | 780.41 | 782.76 |
2 | 77°30'E | 26°4'N | 265 | 826.73 | 843.92 |
3 | 77°48’E | 26°23'N | 185 | 853.18 | 860.54 |
4 | 78°7'E | 26°23'N | 193 | 927.99 | 933.89 |
5 | 78°7'E | 26°41'N | 137 | 953.64 | 949.92 |
6 | 78°26'E | 26°41'N | 151 | 962.87 | 976.53 |
The average annual station wise rainfall data (Table 1) are converted into shapefile point data in ArcGIS10.5. Than using the IDW tool (Interpolates a raster surface from points using an Inverse Distance Weighted (IDW)) of the Arc tool box in ArcGIS 10.5, the shapefile (vector) point rainfall data of the study area are converted into raster format. Now, each pixel of the raster data of the district represents the mean annual rainfall value or Average Annual Precipitation (AAP) value of the district.
The rainfall erosivity map (R-factor) map of the study area has been calculated using the empirical equation developed by Singh (1981). This equation is applicable for the entire country. The equation to calculation the Rainfall erosivity (R factor) can be expressed as follows:
$$R=79+0.363*AAP \left(Average Annual Precipitation\right)$$
The values of R-factor of the study area are shown in Table 2.
Table 2
Rainfall Erosivity Factor of Morena District (R factor)
Stations | R factor (1985) | R-factor (2005 & 2014) |
1 | 362.29 | 363.14 |
2 | 374.24 | 377.20 |
3 | 385.67 | 391.27 |
4 | 402.03 | 405.34 |
5 | 417.87 | 419.41 |
6 | 428.52 | 433.48 |
Soil erodibility factor (K):
The FAO-UNESCO (Food and Agriculture Organization) Soil Map of the World has been used to understand the textural information of soil in the study area. FAO-UNESCO provides Digitized Soil Map of the World (DSMW), at 1:5,000,000 scales, is in the geographic projection. This DSMW data set is linked to commonly used soil parameters, namely, sand, silt, clay, organic carbon, pH, water storage capacity, soil depth, cation exchange capacity of the soil and the clay fraction, total exchangeable nutrients etc. The FAO DSMW data permits soil and its associated configurations to be displayed or queried in terms of user-selected soil parameters, and it postulates a geographical tool to query and visualise the database (Chadli, 2016).The soil map of the study area was extracted using the clip tool in ArcGIS 10.Soil map of the study area was classified into three soil texture group namely Eutric Cambisols (BE), Chromic Luvisols (LC), and Lithosols (I).
In the study area to calculation of the K factor, Wiliams (1995) equation has been used. The following equation for estimating \({K}_{USLE}\) values is given by Williams’ (1995):
\({K}_{USLE}= {K}_{w }\) =\({f}_{csand }\bullet {f}_{cl-si }\bullet {f}_{orgc }\bullet {f}_{hisand}\)
Where,
\({f}_{csand}\) is a factor, that lowers the K indicator in soils with high coarse-sand content and higher for soils with little sand; \({f}_{cl-si}\) gives low soil erodibility factors for soils with high clay-to-silt ratios; \({f}_{orgc}\) reduces K values in soils with high organic carbon content, while \({f}_{hisand}\) lowers K values for soils with extremely high sand content:
$${f}_{csand }= \left(0.2+0.3\bullet exp\left[-0.256\bullet {m}_{s}\bullet \left(1-\frac{{m}_{slit}}{100}\right)\right]\right)$$
$${f}_{cl-si}={\left(\frac{{m}_{slit}}{{m}_{c}-{m}_{slit}}\right)}^{0.3}$$
$${f}_{orgc}=\left(1-\frac{0.25\bullet orgC}{orgC+exp\left[3.72-2.95\bullet orgC\right]}\right)$$
$${f}_{hisand}= \left(1-\frac{0.7\left(1-\frac{{m}_{s}}{100}\right)}{\left(\left(1-\frac{{m}_{s}}{100}\right)+exp\left[-5.51+22.9\bullet \left(1-\frac{{m}_{s}}{100}\right)\right]\right)}\right)$$
Where
\({m}_{s}\) = the sand fraction content (0.05-2.00 mm diameter) [%];
\({m}_{slit}\) = the slit fraction content (0.002–0.05 mm diameter) [%];
\({m}_{c}\) = the clay fraction content (< 0.002 mm diameter) [%];
\(orgC\) = the organic carbon (SOC) content [%].
The assumptions used to calculate \({K}_{w}\) and \({K}_{ws}\) values are presented in Table 3.
Parameters of model micro-plot, assumed in the estimation of \({K}_{USLE}\) in simulated conditions (\({K}_{d}\)): \({L}_{hill}\)=2m, \({\alpha }_{hill}\)=6o, \({C}_{USLE}\)= 1, \({P}_{USLE}\)=1.
The K factor of each soil texture group of the district has been calculated using this William’s formula and the K values of district soil are presented in Table 3.
Table 3
Soil K Factor values of Morena District
Dominant Soils | K value |
EutricCambisols | 0.154 |
Chromic Luvisols (LC) | 0.142 |
Lithosols (I) | 0.134 |
Topography LS factor:
The slope length factors (L) and slope steepness factors (S) are considered as topographic factor. The ASTER DEM (2013) (30 m resolution)[2] has been used to generate the LS factor. The slope of the study area has been calculated from ASTER DEM using the Slope tool of the spatial analysis tools in ArcGIS 10.5and the slope map has been prepared.
The LS-factor was computed by the help of the Raster Calculation tool of Spatial analyst extension in ArcGIS 10.5, using the following the Eq. (1), proposed by Moore & Burch, 1972).The computation of LS requires factors such as flow accumulation and slope steepness.
$$LS={\left(flow accumulation*cell size/22.13\right)}^{0.4}*({sin slope/0.0896)}^{1.3}$$
1
……....
Where, flow accumulation denotes the accumulated upslope contributing area for a given cell, LS = combined slope length and slope steepness factor, cell size = size of grid cell and sin slope = slope degree value in sin (Prasannakumar et al., 2012).
Land Use Land Cover (LU LC) of the Study Region
The land use and land cover datasets for the present study area has been used the decadal land use and land cover classification across India (Roy et al., 2016) This data set provides land use and land cover (LULC) classification products at 100-m resolution for India at decadal intervals for 1985, 1995 and 2005.The used data procured and interpreted from various sources of Landsat series like Landsat 4 and 5 Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Multispectral (MSS) data along with Indian Remote Sensing satellites (IRS) Resourcesat Linear Imaging Self-Scanning Sensor-1 and III (LISS-I, LISS-III) data. The interpreted data were verified with ground truth surveys, and visual interpretation also opted for the analysis. The data also were classified based on International Geosphere-Biosphere Programme (IGBP) classification scheme[3].
The land use land cover data of the study area extracted from the decadal land use data of India for the year 1985 and 2005.The extracted land use data of the study are classified in nine land use categories for the year 1985 and the land use class classified in ten land use categories for year 2005. The land use data is classified into eight categories for the year 2014. The land use classes of the Morena district have been identified as water bodies, deciduous forest, mixed forest, scrub land, plantation, crop land, fallow land, barren land, waste land and built up land respectively. Table 4 showing the land use land cover classes of the study area for the year 1985, 2005 and 2014, and Figure 2, shows the decadal (year 1985, 2005, and 2014), land use and land cover of the study district.
Table 4 Land Use and Land Cover of the Study Region, Year 1985, 2005, and 2014
Sl. No
|
Land Use Classes
|
1985
|
2005
|
2014
|
Area (sq km)
|
Area %
|
Area (sq km)
|
Area %
|
Area (sq km)
|
Area %
|
1
|
Water bodies
|
125.68
|
2.52
|
111.18
|
2.23
|
53.49
|
1.05
|
2
|
Deciduous forest
|
385.53
|
7.72
|
295.27
|
5.91
|
873.34
|
17.93
|
3
|
Mixed forest
|
604.97
|
12.12
|
644.49
|
12.91
|
4
|
Scrub land
|
243.54
|
4.88
|
195.22
|
3.91
|
5
|
Plantation
|
Nill
|
Nill
|
1.05
|
0.02
|
6
|
Crop land
|
2675.59
|
53.61
|
2876.13
|
57.60
|
2412.14
|
48.10
|
7
|
Fallow land
|
94.05
|
1.88
|
220.72
|
4.42
|
335.24
|
8.07
|
8
|
Barren land
|
0.71
|
0.02
|
0.27
|
0.01
|
85.33
|
1.61
|
9
|
Waste land
|
852.52
|
17.08
|
629.44
|
12.61
|
1100.52
|
22.62
|
10
|
Built up land
|
8.69
|
0.17
|
19.43
|
0.39
|
33.70
|
0.67
|
Total Area
|
4991.28
|
100.00
|
4993.20
|
100.00
|
4993.76
|
100.00
|
Crop management C factor:
Surface cover management is a key factor in soil erosion estimation model; which shows how land cropping and surface vegetation management affect surface soil erosion rate (Renard et al., 1997).Surface covers defend the high intensity of rainfall and protect the soil surface. Hence, surface cover management factor (C-factor) defined as the ratio in between soil loss from the clean tilled land and soil loss from agricultural cropland (Wischmeier & Smith, 1978). In the RUSLE model, the C-factor is considered as the value of 0 to 1, which is based on the land surface features. The value near or above to 1, indicates the barren land, water bodies, where the value near to 0 denotes the dense vegetation.
The value of the C factor can be taken from the USLE guide handbook, or it can be understood through the extensive field observation. NDVI (Normalized Deviation Vegetation Index) calculation is another method that helps to understand the cover management of the area. In the present study the values of C-factor are derived from the USLE handbook and various literature reviews (Table 5).
Conservation support and practice factor (P):
Conservation support and practice factor (P) is defined as the ratio between soil loss in topographic tillage slope and soil loss under conservation support condition (Renard et al., 1997).Land support practice including contour farming, strip cropping, and land terracing reduces the soil erosion rates and conserving the soil's qualitative characteristics.
P-factor value in the RUSLE model varies from 0 to 1. 0 implies that the area introduced by perfect conservation practice, including built-up land, plantation area, contour cropping. Where, 1 indicates to the area which has no support practice (Ebrahimzadeh et al., 2018; Prasannakumar et al., 2012). In the study area, the value of P factor of each land features ware taken from the previous literature Table 5.
Table 5
Morena District Land Use C and P factor
Land Use Classes | C factor | P factor | Sources |
Waterbodies | 0 | 0 | (Wischmeier & Smith, 1978) |
Deciduous forest | 0.4 | 1 | (Wischmeier & Smith, 1978) |
Mixed forest | 0 | 1 | (Renard et al., 1997) |
Scrub land | 0.01 | 1 | (Prasannakumar et al., 2012) |
Plantations | 0.5 | 0.5 | (Prasannakumar et al., 2012) |
Crop land | 0.28 | 0.5 | (Biswas & Pani, 2015) |
Fallow land | 1 | 0.9 | (Pandey et al., 2007) |
Barren land | 1 | 1 | (Biswas & Pani, 2015) |
Waste land | 0.18 | 1 | (Pandey et al., 2007) |
Built up land | 1 | 0 | (Wischmeier & Smith, 1978) |
[1] Global weather data for SWAT (GWDS) (https://globalweather.tamu.edu/), (The daily CFSR (Climate Forecast System Reanalysis) data(precipitation, wind, relative humidity, and solar) over the 36 years (1979-2014) for a given location and time period.
[2]Downloaded from Earth data search (https://search.earthdata.nasa.gov/search).
[3](https://daac.ornl.gov/VEGETATION/guides/Decadal_LULC_India.html).