NBS Indicators

1. Soil Erosion
Soil erosion is a slow and naturally occurring process that affects all landforms. In the agricultural field, the
topsoil gets worn away by the natural forces of water and wind or due to farming activities such as tillage. The
average rates of soil erosion throughout the world are estimated to be around 12 to 15 tons per hectare per year
which is equivalent to annual loss of 0.90-0.95 mm of soil (FAO, 2015). Soil erosion involves three main
processes: soil detachment, movement, and deposition. Soil erosion reduces crop productivity and contributes
to pollution of waterbodies nearby through the deposition of topsoil. Soil compaction, low organic matter, loss
of soil structure, poor internal drainage, salinization and soil acidity problems are other serious soil degradation
conditions that can accelerate the soil erosion process.
Universal Soil Loss Equation (USLE)
The USLE was developed by Wischmeier and Smith in 1958 which was later modified by Renard et al. in 1994.
USLE is widely used for the study of soil erosion because of its simplicity, despite some extensive requirements
of data. However, most of the input data are freely available global datasets.
The Universal Soil Loss Equation (USLE) predicts the long-term average annual rate of erosion on a field slope
based on rainfall pattern, soil type, topography, crop system and management practices. USLE only predicts
the amount of soil loss that results from sheet or rill erosion on a single slope and does not account for
additional soil losses that might occur from gully, wind or tillage erosion. This erosion model was created for
use in selected cropping and management systems but is also applicable to non-agricultural conditions such as
construction sites. The USLE can be used to compare soil losses from a particular field with a specific crop
and management system to "tolerable soil loss" rates. Alternative management and crop systems may also be
evaluated to determine the adequacy of conservation measures in farm planning.
Five major factors are used to calculate the soil loss for a given site. Each factor is the numerical estimate of a
specific condition that affects the severity of soil erosion at a particular location. The erosion values reflected
by these factors can vary considerably due to varying weather conditions. Therefore, the values obtained from
the USLE more accurately represent long-term averages.
USLE is described by the following equation:
A = R x K x LS x C x P
Where, A is the average annual soil loss (tonsha-1 year-1), R is the rainfall erosivity (MJmmha-1 h-1 year), K is the
soil erodibility factor (tons ha-1 R unit-1), LS is the topographic factor (dimensionless), C is the cropping
management factors (dimensionless), and P is the practice support factor (dimensionless).
Methodology
1. Rainfall erosivity (R)
Rainfall erosivity is defined as the product of the total kinetic energy multiplied by the maximum 30 min rainfall
intensity (Wischmeier & Smith, 1978). The rainfall erosivity index based on mean annual EI30 is given by
equation:
R= EI30/100
Where, E is in MJ/haand I30 is in mm/h.
In several cases, countries have their specific empirical equations. For example, in Vietnam, Nguyen (1996)
suggested the following equation based on annual precipitation over 54 years from 253 meteorological stations
throughout the country.

2. R = 0.548257*P – 59.9
where, P is the yearly precipitation (mm).
The global Rainfall erosivity layer is available from the website https://esdac.jrc.ec.europa.eu/content/global-
rainfall-erosivity which has a spatial resolution of ~1 km. Similarly, the precipitation data can be obtained from
remotely sensed data such as WorldClim (https://www.worldclim.org/data/index.html) and CRU
(https://crudata.uea.ac.uk/cru/data/hrg/). The spatial resolution of the WorldClim and CRU data is ~1 km
and ~55 km respectively.
2. Soil erodibility (K)
Soil erodibility is the measure of the effect of soil properties and soil profile characteristics on soil loss. Many
variables influence the erodibility of the soil, such as particle size, organic content and structure, and percentage
of sand, silt and clay. K is the most challenging factor in USLE which requires substantial time, cost and
resources for detailed field surveys. Several countries have developed their own look-up table for the K values
of their dominant soil types.
If the K data is not available, remotely sensed data can be used to estimate the K value. The soil class maps can
be downloaded from SoilGrids (https://soilgrids.org/), having resolution of 250 m. With reference from FAO
Harmonized World Soil Database (FAO-HWSD) (https://webarchive.iiasa.ac.at/Research/LUC/External-
World-soil-database/HTML/), the percentage of silt, sand and clay, permeability, organic matter content and
structure for each soil class were obtained. Then, the soil erodibility factor was calculated using Renard et al.
(1997), which is expressed as follows:
K=2.1 X 10-4(12-a)M1.14+3.25(b-2)+2.5(c-3)/759
where, M = (silt%+very fine sand%)(100-clay%), a=organic matter%, b=structure code:(1) very structured or
particulate, (2) fairly structured, (3) slightly structured and (4) solid c = profile permeability code: (1) rapid, (2)
moderate to rapid, (3) moderate, (4) moderate to slow, (5) slow and (6) very slow.
Alternatively, K value can be estimated using FAO Harmonized World Soil Database and look up table from
Roose (1996), as shown in Table 11. The raster maps and database containing metadata of each soil class can
be downloaded in a GIS software. Once the soil classes are known, the values of the percentage of sand, silt
and clay as well as organic carbon, can be assigned. The percentages of soil texture and organic carbon material
are then looked up in the table provided by Roose (1996) containing the K values. The organic carbon is
converted into organic material using a conversion factor, taking reference from IPCC-AFOLU 2006 and is
expressed as:
OM= 1.72 X OC
where, OM is organic matter, OC is organic content obtained from FAO-HWSD

3. Table 1: Soil composition and mean erodibility values for different soil texture
3. Topographic factor (LS)
The topographic factor is the combination of slope length (L) and slope steepness (S). An increase in the slope
length causes increase in erosion due to the progressive accumulation of run-off in the downslope direction.
Similarly, increase in slope steepness increase soil erosion because of the increasing velocity of run-off. Many
studies estimated the topographic factor using the equation suggested by Moore & Wilson (1992) expressed as:
LS = (
As
22.13
)m
X (
sinβ
0.09
)n
Where, As is the upslope contributing area per unit width (m), β is the steepest slope angle (radian), and m and
n are slope length exponent and slope steepness exponent, respectively. The values of exponents range for m
= 0.2-0.6 and n = 1.0-1.3, where lower values are used for prevailing sheet flow and higher values for prevailing
rill flow. The values 22.13 m (72.6 ft.) and 0.09 rad (5.14°) are the length and slope of the standard USLE plot,
respectively.
Currently, GIS tools or functions are used to measure the LS in soil erosion studies using Digital Elevation
Model (DEM) and can be expressed as:
LS = (
FA∗cell size
22.13
)m
∗ (
sinβ∗0.01745
0.09
)n
Where, FA is flow accumulation, cell size is the size of the DEM data and slope angle is in percentage.
4. Cropping management factor (C)

4. C reflects the effect of cropping and management practices on soil erosion rates. Generally, the C-factor ranges
from 0 to 1, 0 indicating very strong cover effects and well-protected soil and 1 indicating no cover present or
barren land. C can be estimated using the Normalized Difference Vegetation Index (NDVI) from satellite
images or simply use the look-up tables associated with the landcover of the study area. Several reports and
literatures have values of C associated with different landcovers such as RUSLE handbook by Renard et al.
(1997), Land husbandry by Roose (1996) and USLE Fact Sheet
(http://www.omafra.gov.on.ca/english/engineer/facts/12-051.htm). Land use/landcover maps can be
downloaded from global datasets such as ESA CCI (https://www.esa-landcover-cci.org/), Sentinel-2 LULC
(https://www.arcgis.com/apps/instant/media/index.html?appid=fc92d38533d440078f17678ebc20e8e2) or
use the locally available land use/landcover maps. The average annual C-factor values for different crops and
land cover is shown in Table 12.
Table 12: C-factor values for the USLE ((Source: Wischmeier and Smith (1978), Roose (1977), Singh et al. (1981), El-Swaify et al. (1982), Hurni (1987),
Hashim and Wong (1988)) in Morgan (2005)
5. Support practice factor (P)
P factor is defined as the support or land management practice factor. The P factor adjusts the potential erosion
by water runoff by implementing the effects of contouring, strip cropping, and terraced contour farming
(Wischmeier & Smith, 1978). P value is assigned 1.0 if there is no erosion control solution. P is considered the
most uncertain value (Morgan & Nearing, 2011) due to difficulties in its estimation, such as the need for direct
observations at the specific land plot to determine the land use type and identify the specific farming system is
notably time intensive and costly. The P-factor values for different erosion control practices is shown in Table
13.

5. All the USLE factor maps can be prepared in GIS software and the Raster Calculator tool can be used to
produce the final soil loss map.
Alternatively, there is a global dataset of soil erosion having a resolution of 25 km produced by Joint Research
Center of the European Commission (JRC) (https://esdac.jrc.ec.europa.eu/content/global-soil-erosion) which
can be used to visualize the soil loss in 202 countries.
Table 2: P-factor values for the USLE (Wischmeier and Smith (1978), Roose (1977), Chan (1981a) in Morgan (2005)
The input data for estimating soil erosion is shown in the Table 14.
Table 3: Input data table for estimating the soil erosion
Variable Units Data Data Source Spatial
Resolution
Rainfall
erosivity (R)
MJmmha-
1hyear-1
Mean annual
precipitation
Global Rainfall Erosivity
(https://esdac.jrc.ec.europa.eu/content/global-rainfall-erosivity)
1km
Soil
erodibility
(K)
tonsha-
1hyear
Soil map Soil map (https://soilgrids.org/)
With reference of FAO Harmonized World Soil Database
(https://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-
database/HTML/), the silt, sand, clay, permeability and structure
values for each soil class were obtained. Then, soil erodibility
factor was calculate using Renard et al. (1997) equation.
250 m
Topographi
c factor
(LS)
dimensionl
ess
Digital
elevation
model
SRTM (https://earthexplorer.usgs.gov/) 30 m
Cropping
managem
ent factor
(C)
dimensionl
ess
LULC map Land use/landcover from ESA CCI (https://www.esa-landcover-
cci.org/)
Sentinel-2 LULC
(https://www.arcgis.com/apps/instant/media/index.html?appid=f
c92d38533d440078f17678ebc20e8e2) or use the locally available
land use/landcover maps.
Values used from USDA (RUSLE Handbook, Renard et al. ,1997)
OMAFRA: USLE Fact Sheet
(http://www.omafra.gov.on.ca/english/engineer/facts/12-
051.htm )
Depends on
LULC map
resolution
Practice
support
factor (P)
dimensionl
ess
LULC map Land use/landcover from ESA CCI (https://www.esa-landcover-
cci.org/)
Sentinel-2 LULC
(https://www.arcgis.com/apps/instant/media/index.html?appid=f
c92d38533d440078f17678ebc20e8e2) or use the locally available
land use/landcover maps.
Values used from USDA (RUSLE Handbook, Renard et al. ,1997)
OMAFRA: USLE Fact Sheet
(http://www.omafra.gov.on.ca/english/engineer/facts/12-
051.htm )
Depends on
LULC map
resolution