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Chapter 5 : Estimation of Soil Erosion

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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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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


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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.


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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


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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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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


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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


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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


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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

Konar basin.

Reshma Parveen, Uday Kumar (2012) - Integrated Approach of Universal Soil

Loss Equation (USLE) and Geographical Information System (GIS) for Soil Loss

Risk Assessment in Upper South Koel Basin, Jharkhand had been done. The

present study area is a part of Chotanagpur plateau with undulating topography,

with a very high risk of soil erosion. In the present study an attempt has been

made to assess the annual soil loss in Upper South Koel basin using Universal Soil

Loss Equation (USLE) in GIS framework. Such information can be of immense

help in identifying priority areas for implementation of erosion control measures.

The soil erosion rate was determined as a function of land topography, soil

texture, land use/land cover, rainfall erosivity, and crop management and practice

in the watershed using the Universal Soil Loss Equation (for Indian conditions),

remote sensing imagery, and GIS techniques.

S. Baby Shwetha, P. Madesh, R. Suresh and P. Lokesh Bharani, (2012) - The

GIS-based Sediment Assessment Tool for Effective Erosion Control (SATEEC)

was developed to estimate soil loss and sediment yield for any location within a

watershed using RUSLE and a spatially distributed sediment delivery ratio.

SATEEC was enhanced in this study by developing new modules to: 1) simulate

the effects of sediment retention basins on the receiving water bodies, 2) estimate

the sediment yield from a single storm event and 3) prepare input parameters for

the Web-based sediment decision support system using a GIS interface. The

enhanced SATEEC system was applied to study the watershed to demonstrate

how the enhanced system can be effectively used for soil erosion control.

Supakij Nontananandh and Burin Changnoi, (2012) - Based on USLE

Modeling, for Assessment of Soil Erosion in Songkhram Watershed, Northeastern

of Thailand, GIS-based methods were proposed and applied to data from the

Songkhram sub basin in the Songkhram watershed. ArcGIS software was used to


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derive land use, land cover and topographical data for the watershed. An open

source GIS (QGIS) and the Geographic Resources Analysis Support System

(GRASS) package were used to carry out geographical data analysis and database

management system (DBMS) implementation, both of which were implemented

by Postgres Plus software. The watershed was mapped into topographically and

geographically homogeneous grid cells to capture watershed heterogeneity. The

soil erosion in each cell was calculated using the universal soil loss equation by

carefully determining its various parameters and classifying the watershed into

different levels of soil erosion severity.

Tevfik ERKAL, Unal YILDIRIM (2012) - This paper contains research in

which the authors applied the Universal Soil Loss Equation (USLE), Geographical

Information Systems (GIS), and remote sensing to the mapping of the soil erosion

risk in the Sincanlı sub-watershed area of the Akarcay Basin, Afyonkarahisar,

Turkey. The rainfall-runoff erosivity factor (R) was developed from annual

precipitation data and previous studies, soil map and soil survey data was used to

develop the soil erodibility factor (K), and a digital elevation model image was

used to generate the topographic factor (LS). The cover-management factor (C)

was developed based on vegetation, shade, and soil fraction images derived from

spectral mixture analysis of a Landsat Thematic Mapper image.

Péter CSÁFORDI, Andrea PŐDÖR, Jan BUG and Zoltán GRIBOVSZKI

(2012) - The analysis of soil erosion with the USLE in a GIS environment, a new

workflow has been developed with the ArcGIS Model Builder. The aim of this

four-part framework is to accelerate data processing and to ensure comparability

of soil erosion risk maps. The first submodel generates the stream network with

connected catchments, computes slope conditions and the LS factor in USLE

based on the DEM. The second submodel integrates stream lines, roads,

catchment boundaries, land cover, land use, and soil maps. This combined dataset

is the basis for the preparation of other USLE-factors. The third submodel

estimates soil loss, and creates zonal statistics of soil erosion. The fourth

submodel classifies soil loss into categories enabling the comparison of modeled


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and observed soil erosion. The framework was applied in a small forested

catchment in Hungary.

Hasan Raja Naqvi, Laishram Mirana Devi, Masood Ahsan Siddiqui (2012)

carried out study to identify the soil loss estimation, to prioritize the micro

watersheds on the basis of mean soil loss values and to suggest best conservation

measures for the Nun Nadi watershed in Doon Valley employing Revised

Universal Soil Loss Estimation (RULSE) model. Approximately 23 km2 area

comprising 7 micro watersheds was classified as very high and high priority risk

zones. These micro watersheds demand immediate attention in terms of

management and planning perspective. This micro level study provides accurate

results in the context of soil loss prediction.

Kapil Ghosh, Sunil Kumar De, Shreya Bandyopadhyay, Sushmita Saha

(2013) assessed Soil Loss of the Dhalai River Basin, Tripura, India Using USLE.

The present study aims at estimating potential and actual soil loss (t·h-1·y-1) as

well as to indentify the major erosion prone sub-watersheds in the study area.

Average annual soil loss has been estimated by multiplying five parameters, i.e.: R

(the rainfall erosivity factor), K (the soil erodibility factor), LS (the topographic

factor), C (the crop management factor) and P (the conservation support practice).

Such estimation is based on the principles de- fined in the Universal Soil Loss

Equation (USLE) with some modifications.

5.3 SCOPE AND OBJECTIVE OF THE PRESENT STUDY

Soil erosion is a growing problem especially in areas of agricultural activity where

soil erosion not only leads to decrease agricultural productivity but also reduces

water availability. Soil erosion is a natural process that varies according to natural

and anthropogenic factors (Wischmeier and Smith 1978) but accelerated soil

erosion occurs as a result of the effects of the disrespectful use of soil by human-

beings. It is a serious problem of concern worldwide and it is difficult to

accurately assess its economic and environmental impacts because of its extent,

magnitude, and rate and the complex processes associated with it (Lal 1994).

Many human-induced activities, such as mining, construction, and agriculture,


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disturb land surfaces, resulting in accelerated erosion. Soil erosion from cultivated

areas is typically higher than that from uncultivated areas (Brown 1984). The

United Nations Environmental Program reported that crop productivity is reduced

and becomes uneconomic on about 20 million ha/year due to soil erosion and

degradation (Anonymous 1991). Erosion may also be exacerbated in the future in

many parts of the world because of climatic change towards a more vigorous

hydrologic cycle (Amore et al. 2004, Pandey et al. 2007).

Soil erosion is an environmental crisis in the world today that threatens natural

environment and also the agriculture. Accelerated soil erosion also adversely

impacts economy and environment (Lal, 1998). Evidently, the developing

countries suffer more because of the inability of their farming population to

replace lost soils and nutrients (Erenstein, 1999). India is a developing country

and agriculture is a backbone of the Indian economy. Therefore, sustainable land

management practices are urgently required to preserve the production potential.

The soil erosion rate in the northern Himalayan region ranged from 2000 to 2500

ton/km2/yr which is highly erosion prone (Garde and Kothyari, 1987) and

according to Singh et al., 1992, the Shiwalik hills, north western Himalayan

region, ravines and shifting cultivations are under severe erosion- more than 20

Mg/ha/yr. Catchments and watersheds have been identified as planning units for

administrative purpose to conserve the land and water resources (Honore, 1999).

Kiflu Gudeta (2010) has also utilized the watershed management approach and

employed RS and GIS as a tool for soil loss estimation.

The objective of this present study is to estimate soil erosion using USLE, RS and

GIS and to suggest the soil conservation measures for Idar watershed of

Sabarkantha district, Gujarat, India.

5.4 MODELING SOIL EROSION

Field studies for prediction and assessment of soil erosion are expensive, time-

consuming and need to be collected over many years. Though providing detailed

understanding of the erosion processes, field studies have limitations because of

complexity of interactions and the difficulty of generalizing from the results. Soil


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erosion models can simulate erosion processes in the watershed and may be able

to take into account many of the complex interactions that affect rates of erosion.

Modeling of soil erosion is depends upon the factors which effecting the soil

erosion.

Soil erosion prediction and assessment has been a challenge to researchers since

the 1930s' and several models have been developed (Lal, 2001). These models are

categorized as empirical, semi-empirical and physical process-based models.

Empirical models are primarily based on observation and are usually statistical in

nature. Semi-empirical model lies somewhere between physically process-based

models and empirical models and are based on spatially lumped forms of water

and sediment continuity equations. Physical process-based models are intended to

represent the essential mechanism controlling erosion. They represent the

synthesis of the individual components which affect erosion, including the

complex interactions between various factors and their spatial and temporal

variabilities.

Some of the widely used erosion models are:

Empirical Models:

Universal Soil Loss Equation (USLE)

Revised Universal Soil Los Equation (RUSLE)

Semi Empirical Models:

Modified Universal Soil Loss Equation (MUSLE)

Morgan, Morgan and Finney (MMF) Model

Physical Process-based Model

Water Erosion Prediction Project (WEPP) Model


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5.5 UNIVERSAL SOIL LOSS EQUATION (USLE)

The Universal Soil Loss Equation (USLE) by Wischmeier and Smith (1978) is a

simplified model and was found to be used most commonly throughout the

literature because of its simplicity (Bähr, 1999). Problems often encountered with

this model include that it was originally developed for agricultural applications

therefore, its application to urban settings is limited and it cannot predict single

storm event soil erosion data (Stone, 2000). The shortcomings of the USLE

model have been accounted for within the Revised Universal Soil Loss Equation

(RUSLE).

The Revised Soil Loss Equation (RUSLE) erosion prediction model (Renard et

al. 1997) has been adapted to include the benefits of the USLE model as well as

eliminate its short-comings (United States Department of Agriculture, 2003). The

RUSLE model has adapted to include non-agricultural areas (Stone,

2000). However, much of the necessary data was unavailable for the study site

including modified R-factors that are able to calculate the significance of pooling

or puddle water from rainfall events (The Soil Erosion Site, 2004). For this

reason, despite the limitations of USLE, it proved to be the best prediction model

for the project as it offered the most accurate results with the simplest application.

Recently Ahmet Karaburan (2010), Vipul Shinde, K. N. Tiwari and

Manjushree Singh (2010), Reshma Praveen and Uday Kumar (2012), Tevfik

ERKAL and Unal YILDIRIM (2012), Hasan Raja Naqvi, Laishram Mirana

Devi and Masood Ahsan Siddiqui (2012), Supakij Nontananandh and Burin

Changnoi (2012) and Kapil Ghosh, Sunil Kumar De, Shreya Bandyopadhyay,

Sushmita Saha (2013) have used Universal Soil Loss Equation (USLE) to predict

longtime average soil losses in runoff from watershed areas.


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The Universal Soil Loss Equation is as follows:

A = R*K*L*S*C*P ... (5.1)

Where,

A = estimate gross soil erosion, t/ha/year

R = rainfall erosivity factor, joules/(ha/year), (t-m/ha) (mm/h) per year

K = Soil erodibility factor (t/ha)/erosivity factor (R), t/joules, t/ha-year

L = Slope length factor

S = Slope gradient factor

C = Crop cover or crop management factor

P = Supporting conservation practice factor

5.5.1 Rainfall Erosivity Factor (R)

The rainfall erosivity factor (R) measures the erosive force of rainfall and runoff.

A heavy annual precipitation received in a number of gentle rains may cause little

erosion, while a lower yearly rainfall descending in a few torrential downpours

may result in severe damage. This account for the marked erosion recorded in

semiarid regions. The R factor, sometimes called the rainfall erosion index, takes

into account the erosive effects of storms. The total kinetic energy of each storm

(related to intensity and total rainfall) plus the average rainfall during the 30-min

period of greatest intensity is considered. The sum of the indexes for all storms

occurring during a year provides an annual index. An average of indexes for

several years is used in USLE. Rainfall erosivity factor R is given in Table 5.2.

The following equation is used for calculation of R-factor,

30log893.210

100

1maxIhiliR

...(5.2)

Where,

Ii = Intensity of rain in a given period (cm/hr).

hi = Amount of precipitation in that period (cm).

Imax30 = Maximum intensity during a 30 minutes period (cm/hr).


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5.5.2 Soil Erodibility Factor (K)

Soil erodibility factor (K), is the rate of susceptibility of soil particles to erosion

per unit of rain erosivity factor (R). This factor represents both susceptibility of

soil to erosion and the rate of runoff. Although soil resistance to erosion depends

in part on topographic position, slope steepness, cover and the amount of

disturbances created by man (e.g. during the tillage), physical and chemical

properties of soil are also the most important determinates. Erosivity varies with

soil texture, aggregate stability, shear strength infiltration capacity and organic

matter content. Values of K are given in Table 5.3 for different soil groups. The

value of K (Foster et al., 1981) can be determined from nomogram or it can be

calculated by the following regression equation:

K = 2.8 * 10-7 M1.14 (12-a) + 4.3 * 10-3 (b-2) + 3.3*10-3 (c-3) ...(5.3)

Where,

M = Particle size parameter (% silt + % very fine sand) (100-% clay)

a = % organic matter

b = Soil structure code (very fine granular,1; medium or coarse granular,3;

blocky, platy or massive, 4),

c = Profile permeability class (rapid, 1; moderate rapid, 2; moderate, 3;

slow to moderate 4; slow, 5; very slow, 6).

5.5.3 Slope Length Factor (L)

The length of the slope, on which the overland flow occurs, affects the rate of soil

erosion. On larger slope lengths, there is a higher concentration of overland flow,

and also a higher velocity of flow which triggers a higher rate of soil erosion.

Zingg (1940) found that soil loss has a non-linear relationship with the land slope

length, that is, soil loss α (Lp) m, where Lp is the actual slope length, and m is the

ratio of the soil loss from the field plot length to the soil loss from the unit with a

slope length of 22.13 meters. Average value of LS is given in Table 5.5. The

slope length factor is determined by using the following formula.


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m

p

13.22

LL ...(5.4)

Where,

Lp = The actual unbroken length of the slope (meters) measured up to the point

where the overland flow terminates, and m is an exponent which is equal to 0.5

for slopes ≥ 5%, 0.4 for 4%, 0.3 for 3%, and 0.2 for 1%. Dvorak and Novak

(1994) have recommended the values for m as 0.5 for 10% and 0.6 for 10%.

5.5.4 Slope Gradient Factor (S)

On steep slope the flow velocity is high, which causes showering and cutting of

soil. Also soil erosion due to splash is high, because splashed particles on steep

slopes are thrown to larger distances down the slope on an inclined plane and the

damage due to raindrop impact is greater on soil crust. The slope gradient

factor(S) expresses the ratio of soil loss from a plot of known slope to soil loss

from a unit plot under identical conditions. Wischmeier and Smith (1965) used

the following formula for determined of the factor S:

613.6

s043.0306.043.0S

2 ...(5.5)

Where,

S = the slope of the field plot (%)

5.5.5 Cover and Management Factor (C)

The C-factor is used to reflect the effect of cropping and management practices on

erosion rates. It is the factor used most often to compare the relative impacts of

management option on conservation plants. The C-factor indicates how the

conservation, plan will affect the average annual soil loss how and that soil-loss

potential will be distributed in time during construction activities, crop relations or

other management schemes. “C” represents the effects of plants, soil biomass and

soil disturbing activities on erosion.


128

The best cover for minimum erosion is dense forest or tall, thick grass with dead

residue ground cover 4-5 cm. thick. Plant material in contact with the surface

protects the soil from raindrop splash and erosion flowing water. In contrast,

continuous cotton cropping would result in 40-60 times more erosion than thick

grass residue cover. C-factor for various land use/ land cover classes is given in

(Table 5.6).

5.5.6 Practice Factor (P)

The P-factor reflects the impact of support practices on the average annual erosion

rate. It is the ratio of soil loss with contouring and / or strip cropping to that with

straight row forming up-and-down slope. As with the other factors, the P-factor

differentiates between cropland and rangeland or permanent pasture. Both option

allow for terracing or contouring, but the cropland options contains a strip

cropping routine whereas the rangeland/permanent pasture option contains another

mechanical disturbance routine. The P-factor values on different slope gradients

are given in (Table 5.8).

5.6 CLOSURE

The soil assessment technique is used in the present study. This technique is

helpful to evaluate the influence of different land cover and soil management

factors in quantitative estimations of soil loss of the study area. The remotely

sensed data has been found to be highly valuable in delineation of the land cover

with greater precision of type and extent and to evaluate the appropriate annual

cover factors. Implementation of universal soil loss equation using integration

procedure of GIS enabled the prediction of soil loss in the sub-watersheds.


129

Table 5.2: Rainfall Erosivity Factor (R) For Idar Raingauge Station.

Sr.No. Year Rainfall Erosivity Factor

Idar Raingauge Station

1 2000 99.23

2 2001 107.99

3 2002 641.35

4 2003 142.27

5 2004 80.37

6 2005 52.1

7 2006 109.08

8 2007 43.36

9 2008 146.42

10 2009 108.57

Mean 153.07

Table 5.3: Soil Erodibility Factor (K) For Different Soil Groups

Sr. No. Order Soil Sub Group K

factor

1 Inceptisols Lithic Ustorthents 0.2635

2 Inceptisols Vertic Ustochrepts 0.3386

3 Entisols Typic Ustifluvents 0.1261

4 Inceptisols Typic Ustochrepts 0.3378


130

Table 5.4: Average Values of K Factors For Different Sub-Watersheds.

Table 5.5: Average Values of LS Factors For Different Sub-Watersheds.

Sub-Watershed Code K- Factor

SWS-1 0.2771

SWS-2 0.2878

SWS-3 0.3161

SWS-4 0.3199

SWS-5 0.3123

SWS-6 0.3048

Sub-Watershed Code LS - Factor

SWS-1 0.3139

SWS-2 0.3071

SWS-3 0.3457

SWS-4 0.3213

SWS-5 0.5487

SWS-6 0.3956


131

Table 5.6: Values of cover and management factor(C) under different land use/ land

covers classes

Table 5.7: Average Values of C-Factors for Different Sub-Watersheds.

Sr.No Level-I Level-II C-Factor

1 Agriculture Double Crop 0.4

Single crop 0.5

2 Waste Land

Land Without Scrub 0.8

Land With Scrub 0.95

3 Settlement Urban & Rural 0.2

4 Water Bodies River 0

Reservoir/Stream 0

5 Plantation Plantation 0.1

Sub-Watershed Code C - Factor

SWS-1 0.4307

SWS-2 0.4523

SWS-3 0.4676

SWS-4 0.4123

SWS-5 0.3998

SWS-6 0.4503


132

Table 5.8 : Conservation Practice Factor (P) on Different Slope Gradients

Table 5.9: Soil Erosion for Each Sub-Watershed

• • •

Sr.No Slope Percentage P-Factor

1 1.0-2.0 0.6

2 3.0-5.0 0.5

3 6.0-8.0 0.5

4 8.0-12.0 0.6

5 13.0-16.0 0.7

6 17.0-20.0 0.8

7 21.0-25.0 0.9

Sub-Watershed Code Soil Erosion

(Tonnes/Ha/Year)

SWS-1 3.412

SWS-2 3.672

SWS-3 4.537

SWS-4 3.730

SWS-5 5.978

SWS-6 4.924

Mean 4.376

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