Chapter 5 : Estimation of Soil Erosion 110 5.1 INTRODUCTION Soil erosion is caused by detachment and removal of soil particles from land surface. It is a natural physical phenomenon, which has helped in shaping the present form of earth’s surface. With the advent of modern civilization, the pressure on land increased, which lead to its overexploitation, and subsequently, its degradation. This triggered a very fast pace of erosion of soil from land surface due to the action of two fluids, wind and water. Soil erosion caused due to natural phenomena is termed geological erosion, and that triggered due to overexploitation of land surface is called accelerated erosion. Evaluation of loss of soil from watersheds is required while assessing the severity of soil erosion and its effects on agricultural production. Soil loss is determined by either theoretical estimation based on values of watershed parameters or actual measurements in the field. Fig. 5.1 Soil Erosion CHAPTER-5 ESTIMATION OF SOIL EROSION
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Chapter 5 : Estimation of Soil Erosion
110
5.1 INTRODUCTION
Soil erosion is caused by detachment and removal of soil particles from land
surface. It is a natural physical phenomenon, which has helped in shaping the
present form of earth’s surface. With the advent of modern civilization, the
pressure on land increased, which lead to its overexploitation, and subsequently,
its degradation. This triggered a very fast pace of erosion of soil from land surface
due to the action of two fluids, wind and water. Soil erosion caused due to natural
phenomena is termed geological erosion, and that triggered due to
overexploitation of land surface is called accelerated erosion. Evaluation of loss of
soil from watersheds is required while assessing the severity of soil erosion and its
effects on agricultural production. Soil loss is determined by either theoretical
estimation based on values of watershed parameters or actual measurements in the
field.
Fig. 5.1 Soil Erosion
CHAPTER-5
ESTIMATION OF SOIL EROSION
Chapter 5 : Estimation of Soil Erosion
111
Soil erosion is the removal of soils by water and/or wind. Erosion is slight from
soil well covered by dense grasses or forest, but is enormous from steep, poorly
covered soil that are exposed to heavy rainfall or strong winds. Well-aggregated
soils resist erosion but pulverized silts and very fine sands are the most easily
eroded.
Problems associated with soil erosion, movement and deposition of sediment in
rivers, lakes, and estuaries persist through the geologic ages in almost all parts of
the earth. But the situation is aggravated in recent times with man’s increasing
intervention with the environment. Scientific management of soil, water and
vegetation resources on water shed basis is very important to arrest erosion and
rapid siltation in rivers, lakes and estuaries.
The land area of our Country has been widely affected by water and wind erosion
that are 32.8 M ha and 10.8 M ha respectively. So, soil erosion is the severe
problem and there should be given suitable measures. Soil erosion is recognized
as a serious threat to man’s-being worldly wide. Accelerating soil erosion also has
adverse economic and environmental impacts on sustainable development.
The Universal Soil Loss Equation is an empirical model that is widely used all
over the world for the assessment and prediction of soil erosion due to water
runoff. When the equation was originally developed, it was not intended to be
valid for a large area. However various researchers who used it on a large scale for
watersheds reported satisfactory results. One was Mellerowicz et. al (1994), who
comments that it is still by far the most widely used method ,but it is necessary to
adjust the USLE factors to a specific location for reliable results.
Soil loss can be estimated as a function of parameters of watersheds. There have
been sincere attempts to develop soil loss estimation models, beginning from the
sixties of the twentieth century. Wischmeier and Smith presented the most
effective model on soil loss, popularly known as the Universal Soil Loss Equation
(USLE). This also opened a new chapter for research in this field. This model
formed the basic structure of most of the soil loss models, which came after this
period. The notable amongst these are, the Soil Loss Equation Model for Southern
Chapter 5 : Estimation of Soil Erosion
112
Africa (SLEMSA) of Elwell (1978) and the Modified Universal Soil Loss
Equation (MUSLE) of Williams (1975).
Geographical Information Systems (GIS) is a modern tool, which provides
information on all geographical variables and has been frequently used in soil
erosion studies. Remotely sensed satellite images are also helpful for generating
up-to-date land use/cover maps of earth Surface facilitate the identification of
erosion-prone areas.
In this study, it has been planned to develop a GIS and Remote Sensing based
spatial model using USLE model for assessing soil erosion prone areas in Idar
watershed, located in the Sabarkantha district of Gujarat. The various steps for
the implementation of USLE model under GIS environment have been automated
by developing computer programs of ArcGIS 9.1 software. The thematic maps
used as the factors of USLE model have been analyzed simultaneously to assess
total soil erosion which finally has been divided into four soil erosion classes from
very slight (0-5 t/he/year), slight (5-10 t/he/year), moderate (10-30 t/he/year)
classes to high (30-61t/he/year) one using GIS.
The appropriate soil conservation measures have been proposed for the high soil
erosion prone areas depending upon prevailing terrain conditions.
5.2 LITERATURE REVIEW
Soil erosion assessment for watershed management is one of the major concerns,
some approaches used by researcher is presented below:
Morgan and Finney (1984) developed this model to predict annual soil loss from
field-sized areas on hill slope. The model is a process-based model, which means
that it runs in water phase and sediment phase. These primary layers were
integrated in the GIS environment for generating the secondary maps. The erosion
maps showing the intensity of the erosion process were prepared. The value
ranges from 0.1 to 3.8 kg/m2.
Chapter 5 : Estimation of Soil Erosion
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Suri & Cebecauer (1996) presents an assessment of potential and actual soil
erosion at a regional scale (1:500,000) covering the whole area of Slovakia by the
soil data integration and analysis. Potential soil erosion indicates the inherent
susceptibility of land to erosion irrespective of contemporary existing land
cover/management. Actual soil erosion refers that modify potential erosion.
Calhoun (1999) determined the sediment yield of the 54.4 km2 Hanalei River
basin, using three methods: 1) The Universal Soil Loss Equation USLE, which
uses natural characteristics of the basin such as the amount of rain, slope steepness
and length values, and soil types to predict sediment erosion in a basin; 2) The
thickness and calibrated radiocarbon age of fluvial deposits cored from the coastal
plain; and 3) Field measurement of suspended sediment in the river. USLE
provided a model prediction of sediment yield that tested with observational data
of methods 2 and 3. Several cures, including one by the US Soil Conservation
service, predicted a sediment delivery ratio of measured sediment yield: gross
erosion between approximately 15 % and 50%. Here delivery of sediment was
higher than predicted yield.
C. V. Srinivas et al. (2002) used the soil loss in Nagpur district of Maharashtra
employing USLE method and by adopting integrated analysis in GIS to prioritize
the tahsils for soil conservation and for delineation of suitable conservation units.
Remote Sensing techniques were applied to delineate the land cover of district and
to arrive at annual cover factors. Results indicated that potential soil loss of very
slight (>5-10 tonnes/ha/year) exist in the valley in North Western, Northern and in
the plains of Central and Eastern parts of the district. Moderate to moderately
severe erosion rates (10 to 20 tonnes/ha/year) was noticed in the South Eastern
and some Central parts. Severe, very severe and extremely severe erosion rates
(20 to 80 tonnes/ha/year) were noticed in the Northern, Western, South Western
and Southern parts of the district.
Goel (2004) investigated to control erosion and conserve water to meet the
requirements of supplemental and pre-sowing irrigation for major cereal crops in
the area and to maximize agricultural productivity. Benefit/ cost ratios ranging
Chapter 5 : Estimation of Soil Erosion
114
from 0.41 to 1.33 were obtained for water harvesting structures of different sizes
with estimated life of 25 and 40 years respectively, by taking into account
different crop return from maize and wheat.
Moehansyah (2004) used Areal Non Point Source Watershed Environment
Response Simulation (ANSWERS), Universal Soil Loss Equation (USLE) and
Adapted Universal Soil Loss Equation (AUSLE) was evaluated for their
performance under the field conditions of the Riam Kanan catchments in South
Kalimantan province of Indonesia. While ANSWERS was evaluated for its
accuracy to predict both runoff and soil loss, USLE and AUSLE were evaluated
for soil loss only. The study was carried out in the context of sedimentation
concerns for the Muhammad Nur reservoir an important source of drinking and
irrigation water supply for the catchment. The models were evaluated using field
data collected under four different land uses and during 2 years of field
experiments. The land uses considered were cropland with minimum tillage,
cropland with conventional tillage, grassland and areas reforested with rubber
trees. The ANSWERS model in general has a tendency to over predict runoff
values. The ANSWERS model also was relatively better for predicting soil loss
followed by the AUSLE and USLE models. Overall, the ANSWERS model
proved superior for predicting soil loss in the Riam Kanan catchment. However,
given that the AUSLE model produced sufficiently reliable results and is
relatively easy to use, the AUSLE model would also appear to be a useful tool for
predicting soil erosion in the catchment.
Ozhan (2005) applied USLE to forestlands in Turkey. This regional application of
USLE and its reliability was tested against measured data, especially for forest
ecosystems. The objective was to compute the cropping management (C) and the
support practice (P) factors of the equation together in a single numerical value as
a cover and management factor (CP) for forest and pseudo-maqui ecosystems
using the local watershed and plot experiments carried out in the vicinity of
Istanbul. CP factors were computed using known ( rainfall erosivity factor, R) and
estimated numerical values of other factors (average annual soil loss, A; soil-
erodibility factor K; combined slope length and slope-steepness factor,LS) The CP
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115
factors are found to be 0.021 for old-growth oak-beech forest ecosystem in
watershed-1 and pseudo-maqui ecosystem and 0.011 for forest ecosystem in
watershed-2.
Love (2006) explores a weight of evidence approach for sediment calibration as a
part of overall watershed model calibration, using both graphical and statistical
measures, based on recent experience with U.S.EPA Hydrological Simulation
program-FORTRAN (HSPF). Model parameterization and calibration procedures
were described, using simple model results, to demonstrate recommended
graphical and statistical procedures to assess model performance for sediment
loading, concentrations and budget within a watershed modeling framework.
Although the results were found specific to the EPA HSPA model, the approach
and procedure for sediment calibration are applicable to other watershed model
that represents sediment process and behavior at the watershed scale.
R. C. Izaurralde & J. R. Williams &W. M. Post & A. M. Thomson &W. B.
McGill & L. B. Owens & R. Lal (2006) - The soil C balance is determined by
the difference between inputs (e.g., plant litter, organic amendments, depositional
C) and outputs (e.g., soil respiration, dissolved organic C leaching, and eroded C).
The objective of this paper is to discover the long-term influence of soil erosion
on the C cycle of managed watersheds near Coshocton, OH. the erosion
productivity impact calculator (EPIC) model to evaluate the role of erosion–
deposition processes on the C balance of three small watersheds (∼1 ha) was
applied.
Ariel C. BLANCO and Kazuo NADAOKA (2006) - In this study, three spatially
distributed-type models - Universal Soil Loss Equation (USLE), Unit Stream
Power Erosion/Deposition (USPED), and CASC2D - implemented in GIS were
used to assess changes in the relative magnitude and pattern of soil erosion as a
result of land use/land cover changes determined from Landsat images (1993-
2002) and to examine their utility in identifying “hot spots”, where soil
conservation measures are most needed. GIS analysis is used to discover
relationship between watershed characteristics, erosion estimates and lake
Chapter 5 : Estimation of Soil Erosion
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sedimentation pattern. The spatial pattern of erosion generated by USLE and
USPED are compared to CASC2D results to determine whether the models are
applicable for tropical environments.
P. P. Dabral & Neelakshi Baithuri & Ashish Pandey (2008) - Soil erosion
assessment of Dikrong river basin of Arunachal Pradesh (India) was carried out.
The Arc Info 7.2 GIS software and RS (ERDAS IMAGINE 8.4 image processing
software) provided spatial input data and the USLE was used to predict the spatial
distribution of the average annual soil loss on grid basis. The average annual soil
loss of the Dikrong river basin is 51 t ha−1 year−1. About 25.61% of the
watershed area is found out to be under slight erosion class. Areas covered by
moderate, high, very high, severe and very severe erosion potential zones are
26.51%, 17.87%, 13.74%, 2.39% and 13.88% respectively. Therefore, these areas
need immediate attention from soil conservation point of view.
Alejandra M. Rojas-González (2008) - This work uses the USLE equation to
calculate and evaluate these zones in Puerto Rico, basically in Río Grande de
Arecibo basin. Some model inputs such as cover factor and conservation practice
factor can also be successfully derived from remotely sensed data. The LS factor
map was generated from slope map; and aspect map derived from DEM. The K
factor map was prepared from soil map, which it was obtained from SURGO data.
The K factor values from a Soil Survey of United States and Virgin Islands
(1998). Maps covering each parameter (R, K, LS, C and P) were integrated to
generate a composite map of potential erosion intensity based on advanced GIS
functionality.
Li Hui, Chen Xiaoling, Kyoung Jae Lim, Cai Xiaobin, Myung Sagong (2010) -
Assessment of Soil Erosion and Sediment Yield in Liao Watershed, Jiangxi
Province, China, Using USLE, GIS, and RS had been done. A geographic
information system (GIS) was used to generate maps of the USLE factors, which
include rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS),
cover (C), and conservation practice (P) factors. By integrating these factors in a
GIS, a spatial distribution of soil erosion over the Liao watershed was obtained. A
Chapter 5 : Estimation of Soil Erosion
117
spatially distributed sediment delivery ratio (SDR) module was developed to
account for soil erosion and deposition.
Pascal Dumas, Julia Printemps (2010), described the implementation of the
Universal Soil Loss Equation (USLE) for the mapping and quantification of the
potential soil erosion in the South Pacific Islands. The USLE model, commonly
used to calculate average annual soil loss per unit land area resulting from sheet
and rill erosion, can be written as A=R*E*L*S*C*P. A is the soil loss, R is the
rainfall-run off erosivity factor, E is a soil erodibility factor, L is a slope length
factor, S is a slope steepness factor, C is a cover management factor and P is a
supporting practice factor. The specialization of this model is implemented using
the data processing and mapping functionalities of a Geographical Information
System (GIS) from input data which included a digital elevation model, a soil
map, a land cover map and precipitation data.
Ahmet Karaburun (2010) - The study was done to estimate C factor values for
Buyukcekmece watershed using NDVI derived from 2007 Landsat 5 TM Image.
The final C factor map was generated using the regression equation in Spatial
Analyst tool of ArcGIS 9.3 software. It is found that north part of watershed has
higher C factor values and almost 60% of watershed area has C factor classes
between 0.2 and 0.4.
Vipul Shide, K. N. Tiwari and Manjushree Singh (2010) applied Universal Soil
Loss Equation (USLE) interactively with raster-based geographic information
system (GIS) to calculate potential soil loss at micro watershed level in the Konar
basin of upper Damodar Valley Catchment of India. The main advantage of the
GIS methodology is in providing quick information on the estimated value of soil
loss for any part of the investigated area. The rainfall erosivity R-factor of LISLE
was found as 293.96 and the soil erodibility K-factor varies from 0.325 - 0.476.
Slopes in the catchment varied between 0 and 83% having LS factor values
ranging from 0 - 6.7. The C-factor values were computed from existing cropping
patterns in the catchment and support practice P-factors were assigned by studying
land slope. Average annual soil erosion at micro watershed level in Konar basin
Chapter 5 : Estimation of Soil Erosion
118
having 961.4 km2 areas was estimated as 1.68 t/ha/yr. Further, micro watershed
priorities have been fixed on the basis of soil erosion risk to implement
management practices in micro watersheds which will reduce soil erosion in