SOIL EROSION MODELING USING GIS AND RUSLE ON THE EURAJOKI WATERSHED FINLAND

SOIL EROSION MODELING USING GIS AND RUSLE ON THE EURAJOKI WATERSHED FINLAND
TAMPERE UNIVERSITY OF APPLIED SCIENCES
DEGREE PROGRAM IN ENVIRONMENTAL ENGINEERING
Bachelor´s Thesis
SOIL EROSION MODELING USING GIS AND
RUSLE ON THE EURAJOKI WATERSHED
FINLAND
Gossa Wordofa
Thesis Supervisor: Eeva Sundström
Commissioned by:
TAMK, University of Applied Sciences
Department of Environmental Engineering
Tampere, June 2011
Tampere University of Applied Sciences
Department of Environmental Engineering
ii
Tampere University of Applied Sciences
Degree Program in Environmental Engineering
Name of report: Soil Erosion Modeling Using GIS and RUSLE on the Eurajoki
Watershed, Finland
Bachelor‟s thesis 36 pages, appendixes 3 pages
Supervised by: Eeva Sundstöm
June 2011
ABSTRACT
This thesis study applied Geographical Information System (GIS) and the Revised
Universal Soil Loss Equation (RUSLE) to predict the annual average soil loss rate
from the Eurajoki watershed (South-West Finland). To achieve the goals of the
thesis, the RUSLE factors were calculated using the local data that was collected
from National Land Survey of Finland and Finnish Metrological Institute. The soil
survey data were used to develop the soil erodibility factor (K), and a digital
elevation model of the catchment was used to generate the topographic factor (LS).
The values of cover-management (C) factor and support practice (P) factor were
collected from literature due to lack of satellite image and soil index map. Usually
C and P factors determine from land cover and land use classes respectively. The
rainfall–runoff erosivity (R) was derived from monthly rainfall data and Fournier
index.
The results indicate that the average annual soil loss (A) within the catchment is
about 5 Mg/ha/yr (5 metric ton per hectare per year). This is highly depend on R
value which ranges between 299 and 307 MJ/ha.mm/h with the highest values
being in the lower part of the catchment and the lowest values in the higher part of
the catchment. Slopes in the catchment varied with steep slopes having higher
values of slope length and mean LS factor is 1.34.
It is also important to note that the steepest slopes show high risk of soil erosion, it
is therefore recommended that further study be undertaken to establish the suitable
soil and water conservation measures that should be implemented in these areas as
well as the whole catchment.
Keywords: Soil Erosion, RUSLE, GIS, Eurajoki Watershed
iii
ACKNOWLEDGEMENT
Firstly, I would like to thank God whose grace has been sufficient throughout the
study period. Secondly, it is important to note that a research based thesis such as
this is never accomplished without the collaboration and cooperation of many
people and organizations.
To begin with I would like to thank Pirkko Karlsson from Finnish Meteorological
Institute and National Land Survey of Finland for their support in this research
based thesis by providing the necessary data. I am also grateful to all TAMK
lecturers, supervisor and students for their unlimited support during the study
period.
Lastly, I offer my regards and blessings to all of those who supported me in any
respect during the completion of the thesis.
Gossa Wordofa
June 2011
v
TABLE OF CONTENTS
ACKNOWLEDGEMENT ................................................................................................. iii
ACKNOWLEDGEMENT ................................................................................................. iii
LIST OF SYMBOLS AND ACRONYMS ....................................................................... vii
LIST OF FIGURES ......................................................................................................... viii
LIST OF TABLES ............................................................................................................. ix
1
2
INTRODUCTION ...................................................................................................... 1
1.1
Background ......................................................................................................... 1
1.2
Problem Statement .............................................................................................. 2
1.3
Objective of the Study ........................................................................................ 2
LITERATURE REVIEW ........................................................................................... 4
2.1
3
Soil Erosion......................................................................................................... 4
2.1.1
Sheet Erosion .............................................................................................. 5
2.1.2
Rill and Gully Erosion ................................................................................ 5
2.1.3
Stream Bank Erosion .................................................................................. 5
2.1.4
River Erosion .............................................................................................. 6
2.2
Soil Erosion Measurement .................................................................................. 6
2.3
Revised Universal Soil Loss Equation (RUSLE) ............................................... 7
2.4
RUSLE Factors ................................................................................................... 8
2.4.1
Rainfall/runoff erosivity (R-factor)............................................................. 9
2.4.2
Soil Erodibility Index (K factor) ............................................................... 10
2.4.3
Slope and Slope Length (LS) Factors ....................................................... 10
2.4.4
Cover management factor (C) ................................................................... 11
2.4.5
The Support Practice (P Factor) ................................................................ 11
MATERIALS AND METHODOLOGY .................................................................. 12
3.1
Site Description................................................................................................. 12
3.1.1
Location and Extent .................................................................................. 12
3.1.2
People, Topography and Climate .............................................................. 14
3.1.3
Land Cover and Soil Types....................................................................... 14
3.1.4
Agricultural System .................................................................................. 15
3.2
Data Sources ..................................................................................................... 16
vi
3.2.1
The digital elevation model (DEM) .......................................................... 16
3.2.2
Soil Data ................................................................................................... 17
3.2.3
Precipitation Data...................................................................................... 17
3.2.4
Land use and Land Layer .......................................................................... 17
DATA ANALYSIS ................................................................................................... 18
4
4.1
Calculation of RUSLE Factors ......................................................................... 19
4.1.1
Rainfall Erosivity Factor (R Factor) ......................................................... 19
4.1.2
K Factors .................................................................................................. 25
4.1.3
LS Factor................................................................................................... 29
4.1.4
Cover (C) Factor ....................................................................................... 30
4.1.5
Support Practice (P) Factor ....................................................................... 31
RESULTS AND DISCUSSION ............................................................................... 33
5
5.1
Rainfall and runoff erosivity R-factor............................................................... 33
5.2
Soil erodibility K Factor ................................................................................... 33
5.3
Slope Length and Steepness LS Factor ............................................................. 34
5.4
C and P Factors ................................................................................................. 34
5.5
Annual Average Soil Loss ................................................................................ 34
CONCLUSIONS ...................................................................................................... 36
6
REFERENCES
APPENDIXES
A.
Precipitation data in mm from Köyliö Yttilä rain station
B.
Precipitation data in mm from Pöytyä Yläne rain station
C.
Ten years precipitation data from ten different stations
D.
Additional information of rain gauge stations
vii
LIST OF SYMBOLS AND ACRONYMS
A
RUSLEs Equation Average Annual Soil Loss
AAP
Average Annual Precipitation
C
RUSLEs Equation cover-management factor
DEM
Digital Elevation Model
E
Storm Energy
EI
storm erosivity
EI30
30-min storm erosivity
Elsat
Elevation of station from sea level
GIS
Geographic Information System
Grlat
Grid 27E coordinates of the station, latitude
Grlon
Grid 27E coordinates of the station, longitude
I
precipitation intensity (mm/h)
I30
maximum 30-min intensity (mm/h)
K
RUSLEs Equation soil erodibility factor
L
RUSLEs Equation slope length factor
Lat
Latitude, degrees*100 + minutes
Lat-sec
Lat_sec= latitude seconds
Lpnn
Number of the Station
LS
Slope length and steepness factor
MAR
Mean Annual Rainfall
MFI
Modified Founier Index
OM
Organic matter
P
Support Practice Factor
R
RUSLEs Equation Rainfall-runoff erosivity factor
Rrmon
Monthly Precipitation
RUSLE
Revised Universal Soil Loss Equation
S
RUSLEs Equation slope steepness factor
USLE
Universal Soil Loss Equation
viii
LIST OF FIGURES
FIGURE 1. Soil erosion process (Sharma, Partha Das. 2009).................................6
FIGURE 2. Flow chart for modeling of soil erosion loss caused by water .............9
FIGURE 3. Eurajoki watershed together with its 306-km2 catchment, serves as the
pilot area of the project (Lepistö 2010)..................................................................13
FIGURE 4. Main soil types in the Eurajoki catchment .........................................15
FIGURE 5. Main land use classes in the Eurajoki catchment ...............................15
FIGURE 6. Overall Methodology for generating RUSLE factors ........................18
FIGURE 7. The average annual precipitation (AAP) as related to elevation ........19
FIGURE 8. Annual rainfall data for both Köyliö Yttila and Pöytyä Yläne rain
guage stations .........................................................................................................20
FIGURE 9. Ten years monthly rainfall data for both Köyliö Yttila and Pöytyä
Yläne rain guage stations .......................................................................................21
FIGURE 10. Distribution of soil types on the entire study area (Kronvang 2005)27
FIGURE 11. Eurajoki catchment soil textural classes (Suresh 2000, Arora 2003)28
FIGURE 12. 25mX25m DEM of Eurajoki area ....................................................30
FIGURE 13. Land use map in the Eurajoki catchment (Kronvang 2005) .............31
ix
LIST OF TABLES
TABLE 1. Rainfall Data from Different Rain Gauge ............................................20
TABLE 2. Shows the equations and the reference where they can be found ........23
TABLE 3. Relationships between the Mean Annual Rainfall (MAR) and Rainfall
Erosivity (R) Given by Kassam et al., 1992 ..........................................................24
TABLE 4. Selected R factor Models ....................................................................25
TABLE 5. Calculation of R-factor.........................................................................25
TABLE 6. Land use and C and P Factors ..............................................................32
1
1
INTRODUCTION
According to recent assessments over 80% of the world‟s agricultural land suffers
from moderate to severe erosion which induced loss of productivity. Because of
this and population growth, the global per capita food supply is currently
declining. In many areas of the world, on-site impacts of increased soil loss are
frequently coupled with serious off-site impacts related to the increased
mobilization of sediment and its delivery to rivers. These off-site impacts include
water pollution, reservoir sedimentation, the degradation of aquatic habitats and
the increased cost of water treatment. (Ritchie et al., 2003)
1.1 Background
The limitations of current measurement techniques and models to provide
information on the spatial and temporal patterns of soil and water degradation
across catchments restrict ability to develop cost-effective land management
strategies. However, the advent of new techniques of erosion assessment and
recent developments in the application of remote sensing and geographic
information systems (GIS) to the study of erosion and sediment delivery offer
considerable potential for meeting these requirements.
Since disturbed lands in watersheds are significant source of sediment, a
systematic rating of their potential for erosion would be useful in soil
conservation planning. Most importantly mapping and assessment of erosion
prone areas enhances soil conservation and watershed management.
Maps
showing the spatial distribution of natural and management related erosion factors
are of great value in the early stages of land management plans, allowing
identification of preferential areas where action against soil erosion is more urgent
or where the remediation effort will have highest revenue. (Mbugua W. 2009)
2
1.2 Problem Statement
Lake Pyhäjärvi is a large (155 km2) but shallow mesotrophic lake in southwest
Finland has a great meaning locally, nationally and internationally and also
considered as the most important lake in terms of water supply, recreational use.
The two rivers Yläneenjoki and Pyhäjoki and the four main ditches, which are a
part of Eurajoki watershed, discharge into Lake Pyhäjärvi. The soils in the river
valley are mainly clay and silt, whereas tills and organic soils dominate elsewhere
in the catchment. (Tattari and Rekolainen, 2006)
The soil erosion in the vicinity of Lake Pyhäjärvi and its main tributaries is the
major cause for the degradation land and loss of its fertilities. For example, over
60% of the external nutrients into the Lake Pyhäjärvi come from Eurajoki
catchment.
Erosion rates in Yläneenjoki area vary owing to local natural
conditions and management practices. Agricultural field plots and catchments
dominated by agriculture produce higher erosion rates than forested areas. The
clayey soil of the south-western coastal plains is more susceptible to erosion than
inland predominantly sandy and till soils. (Tattari and Rekolainen, 2006)
Therefore, the aim of the thesis is to use GIS techniques to determine RUSLE‟s
parameters and to estimate the annual average soil loss by erosion from the entire
watershed.
1.3 Objective of the Study
The overall objectives of the thesis are:

To evaluate the application of Geographic Information System (GIS) and
Revised Universal Soil Loss Equation (RUSLE) to determine the soil loss.

To predict the amount soil loss from the Eurajoki watershed by using
ArcGIS and Revised Universal Soil Loss Equation.
3
Its specific objectives are:

To identify and describe the six components of the RUSLE soil loss
equation.

To familiarize with the digital elevation model (DEM) to generate the
slope length and steepness of Eurajoki watershed
4
2
LITERATURE REVIEW
The subject of soil erosion has been a global concern and numerous research
works have been undertaken. In this chapter, soil erosion has been discussed
briefly including the model that has been preferred in its assessment and the
importance of GIS application
2.1 Soil Erosion
Soil erosion is a three phase phenomena consisting of the detachments of
individual soil particles from the soil mass and their transport by erosive agents,
such as running water and wind. When sufficient energy is no longer available
with erosive agents to transport the particles then the third phase is called a
„deposition‟ takes place. The potential for soil erosion varies from watershed to
watershed depending on the configuration of the watershed (topography, shape),
the soil characteristics, the local climatic conditions and the land use and
management practices implemented on the watershed (Arora K. 2003 and Suresh
R. 2000). The removal of topsoil by water is takes place in the following ways:

Sheet erosion

Rill erosion

Gully erosion

Stream bank erosion

River erosion
5
2.1.1
Sheet Erosion
Sheet erosion is more or less is the removal of a uniform thin layer or „sheet‟ of
soil by flowing water from a given width of sloping land. The amount of soil
removed by this type of erosion is small, but as it flows down the slope, it
increases in size and develops into rill erosion. (Arora K. 2003 and Suresh R.
2000)
2.1.2
Rill and Gully Erosion
With rill erosion the erosive effect of flowing water suddenly increases at a
location where a confluence of surface water occurs. Due to low infiltration rates
and the occurrence of rainfall, the excess water collects very slowly over the land
surface and into the rill. As this gathering of water continues the depth of water
together with the velocity, kinetic energy, and the soil particle carrying capacity
of the water increases. Then the rill erosion develops into gully erosion. (Arora K.
2003 and Suresh R. 2000)
2.1.3
Stream Bank Erosion
The removal of soil from the stream of the stream bank occurs due to either water
flowing over the sides of the stream from overland runoff or the water flowing in
the stream and scouring the banks. Stream bank erosion is a continuous process in
perennial streams and is caused by the souring and undercutting of the soil below
the water surface caused by wave action during normal stream flow events.
(Arora K. 2003 and Suresh R. 2000)
6
2.1.4
River Erosion
This type of erosion occurs particularly in rivers in which permanent water flow
takes place, usually with varying rate. River erosion is likely to be more effective
in the water courses of smaller catchment area and those having less favorable
conditions for draining discharge. (Arora K. 2003 and Suresh R. 2000)
Figure 1 shows the development or stage of soil erosion by water (Sharma, Partha
Das. 2009)
FIGURE 1. Soil erosion process (Sharma, Partha Das. 2009)
2.2 Soil Erosion Measurement
The adverse influences of widespread soil erosion on soil degradation,
agricultural production, water quality, hydrological systems, and environments,
have long been recognized as severe problems for human sustainability.
However, estimation of soil erosion loss is often difficult due to the complex
interplay of many factors, such as climate, land cover, soil, topography, and
7
human activities.
Accurate and timely estimation of soil erosion loss or
evaluation of soil erosion risk has become an urgent task. (Mbugua W. 2009)
In many situations, land managers and policy makers are more interested in the
spatial distribution of soil erosion risk than in absolute values of soil erosion loss.
To address this need the combined use of Geographic Information System (GIS)
and erosion models has been shown to be an effective approach to estimating the
magnitude and distribution of erosion (Mitasova et al., 1996; Yitayew et al.,
1999). Among numerous mathematical models used to estimate, or, simulate soil
erosion, the Revised Universal Soil Loss Equation (RUSLE) is widely used and
accepted model to predict the average soil erosion rate from certain area.
2.3 Revised Universal Soil Loss Equation (RUSLE)
The RUSLE is a revision of the Universal Soil Loss Equation (USLE) which was
originally developed to predict erosion on croplands in the US. Following the
revision, the equation can be employed in a variety of environments including,
agricultural
site,
rangeland,
mine
sites,
construction
sites,
etc.
(ENVIRONMENTAL GIS: Lab 10)
The Revised Universal Soil Loss Equation (RUSLE), which is greatly accepted
and has wide use, is simple and easy to parameterize and requires less data and
time to run than most other models dealing with water erosion. GIS on the other
hand facilitates efficient manipulation and display of a large amount of georeferenced data. (ENVIRONMENTAL GIS: Lab 10)
The model represents how climate, soil, topography, and land use affect rill and
gully soil erosion caused by raindrop impact and surface runoff. It has been
extensively used to estimate soil erosion loss, to assess soil erosion risk, and to
guide development and conservation plans in order to control erosion under
8
different land-cover conditions, such as croplands, rangelands, and disturbed
forest lands. The RUSLE is expressed as:
A = R K LS C P
(1)
Where;
A = Average annual soil loss in in Mg/ha/yr
R = Rainfall/runoff erosivity (MJ.mm.ha-1.h-1.yr-1)
K = Soil erodibility (Mg h/MJ/mm)
LS = Slope Length and Steepness Factor
C = Cover-management
P = Support practice factor
Source: (ENVIRONMENTAL GIS: Lab 10)
2.4 RUSLE Factors
Rainfall erosivity factor (R), Soil erodibility factor (K), Slope Length and
Steepness Factor (LS), Cover-management (C) and Support practice (P) factor are
the major parameters in the application of RUSLE.
9
Precipitation data
Rainfall Raster
R
Elevation Model
Satellite Image
Vegitation Cover
C
Inventory Data
Contour Lines
Soil Loss
Topographic Raster
LS
Stream
Satelite Image
Land Use
P
Conservation Practice
Soil Map
Soil Erodibility Raster
K
Soil eroibility
FIGURE 2. Flow chart for modeling of soil erosion loss caused by water
2.4.1
Rainfall/runoff erosivity (R-factor)
R is a measure of erosivity of rainfall which is the product of storm kinetic energy
and maximum 30-minute intensity EI30. When other factors are constant, storm
losses from rainfall are directly proportional to the product of the total kinetic
energy of the storm (E) times its maximum 30-minute intensity (I30). (Arnoldus,
1978)
Most of the time rainfall intensity and storm kinetic energy data are not available
at national meteorological stations. By the absence rainfall intensity and storm
kinetic energy data for this study area, mean annual and monthly rainfall data
have been used to estimate the R factor. (Arnoldus, 1978)
10
2.4.2
Soil Erodibility Index (K factor)
Soil erodibility factor represents both susceptibility of soil to erosion and the rate
of runoff, as measured under the standard unit plot condition. The value of this
factor is affected by infiltration capacity and structural stability of the soil. So, the
K values run from 1.0 to 0.01 with the highest values for soils with high content
of silt or very fine sand. For example, soils high in clay have low K values, about
0.05 to 0.15, because they resistant to detachment. Coarse textured soils, such as
sandy soils, have low K values, about 0.05 to 0.2, because of low runoff even
though these soils are easily detached. Medium textured soils, such as the silt
loam soils, have a moderate K values, about 0.25 to 0.04, because they are
moderately susceptible to detachment and they produce moderate runoff. Soils
having high silt content are most erodible of all soils. They are easily detached;
tend to crust and produce high rates of runoff. Values of K for these soils tend to
be greater than 0.4. (Weesies A.)
2.4.3
Slope and Slope Length (LS) Factors
L and S are factors representing the topography of the land and they define the
effects of slope length and slope angle on sheet and rill erosion. The slope length
factor L is defined as the distance from the source of runoff to the point where
deposition begins, or runoff becomes focused into a defined channel.
The
interaction of angle and length of slope has an effect on the magnitude of erosion.
For example, soil losses from plots on irregular slopes may be dependent on the
slope immediately above the point of measurement. As a result of this interaction,
the effect of slope length and degree of slope should always be considered
together. (Edwards, 1987)
11
2.4.4
Cover management factor (C)
Cover management factor is the crop or land cover management factor and
measures the combined effect of all the interrelated vegetative cover and
management variables. In other word, this factor measures the protection of the
soil surface from raindrop impact by vegetative material at some height above the
soil surface and the additional protection from raindrop impact and overland flow
by cover in contact with the soil surface (surface cover). It is defined as the ratio
of soil loss from land maintained under specified conditions to the corresponding
loss from continuous tilled bare fallow. Values can vary from 0 for very well
protected soils to 1.5 for finely tilled, ridged surfaces that produce much runoff,
leaving it susceptible to rill erosion. (Van der Knijff et al., 2000)
2.4.5
The Support Practice (P Factor)
The Support Practice is the support or land management practice factor. In
RUSLE, the support practice factor is generally applied to disturbed lands and
represents how surface and management practices such as contouring, terracing
and strip cropping are used to reduce soil erosion. For areas where there is no
support practice the P factor is set to 1.0 (Simms A.D 2003)
12
3
MATERIALS AND METHODOLOGY
Determining the intensity, amount and distribution of erosion has a big import for
environmental management specialist to make an informed decision on the
suitable soil and water conservation measures that should be installed in a given
area. The Universal Soil Loss Equation (Wischmeier, 1978) or the Revised Soil
Loss Equation (Renald et al., 1997) is often used to predict rainfall erosion in
landscapes/watersheds using GIS.
3.1 Site Description
3.1.1
Location and Extent
The site is situated at latitude of 61, 2000 (6112'0.000"N) and longitude of 21,
7333
(2143'59.880"E)
in
the
region of Satakunta
which
is located
in
the province of South-Western Finland. The total area of the catchment, which
includes: lake Pyhäjärvi, area between the lake and the sea, and the two major
rivers (Yläneenjoki and Pyhäjoki), is 1336 km2 (figure 3).
2
From this the
2
municipality and the lake covers 643.78 km and 154 km respectively. The two
rivers and the four main ditches (catchment areas between 6-20 km2), which is
located in the nearby catchment area, flow directly into the lake Pyhäjärvi.
(Lepistö 2010)
Yläneenjoki river basin is considerably larger (233 km2) than that of Pyhäjoki
River (78 km2). The river mouths of both Yläneenjoki and Pyhäjoki have been
regularly monitored and hence the loading estimates to the lake are easily
available. On the contrary, the diffuse load from direct, nearby catchments is
much more difficult to assess due to the contingency of water level, sediment and
nutrient measurements. (Lepistö 2010)
13
N
W
E
Irjanne
Panelia
Eurajoki
Ñ
Eu
ra
S
jok
i
Eurakoski
Ju
ÑKiukainen
jo
va
yli
Kö
ki
i
jok
ön
Ñ RAUMA
Turajärvi
Eura Ñ
Köyliö
Kauttua
Ñ
Köyliönjärvi
Säkylä
Ñ
Pyhäjärvi
Py
hä
jo
ki
water
field
forest + others
5
0
5
Ñ
Yläne
10 km
Southwest Finland Regional Environment Centre
National Land Survey of Finland Permission number 7/MYY/02
FIGURE 3. Eurajoki watershed together with its 306-km2 catchment, serves as the
pilot area of the project (Lepistö 2010)
14
3.1.2
People, Topography and Climate
The estimated population density for entire municipalities partly extending
outside catchment is 63,300 inhabitants. The average annual precipitation is
estimated to be 599 mm (1990-2000). Ten years (1991–2000) average discharge
measured in the Yläaneenjoki main channel is 2.2 m3/s and 0.7 m3/s in Pyhäjoki,
respectively. The highest discharges typically occur during the spring and late
autumn months. The portion of groundwater flow is not measured but according
to typical annual water balances groundwater accounts for less than 20% of
annual rainfall. (Hyvärinen, 2003)
The total number of lakes which are included in the study catchment is about 23.
The stream network density is 0.58 km/km2. (Hyvärinen, 2003)
3.1.3
Land Cover and Soil Types
The predominantly soil types of the area are clay and moraine. For example, the
soils in the Yläneenjoki river valley are mainly clay and silt. Forests and natural
wetlands cover 65% of the catchment the rest being agricultural (34%) and urban
(1%) of the entire catchment (fig. 4& 5).
15
Bedrock
10%
Soil Types Other
0%
Silt
4%
Clay
30%
Moraine
31%
Sand
and
gravel
12%
Esker
formatio
n
2%
Organic
11%
FIGURE 4. Main soil types in the Eurajoki
catchment
Deciduou
s forest
1%
Spruce
forest
14%
Freshwate
r
13%
Mixed
forest
13%
Land Cover
Other
4%
Moorland
32%
Arable
land
23%
FIGURE 5. Main land use classes in the
Eurajoki catchment
The land cover of the study area is mainly moorland (uncultivated hill land),
unmanaged grassland, arable land and agricultural land which cover an area of
308.4 km2 (Kronvang 2003).
3.1.4
Agricultural System
Agriculture in the catchment area consists mainly of cereal production and poultry
husbandry and is relatively intensive for Finland (Pyykkönen et al., 2004).
According to surveys performed in 2000–2002, 75% of the agricultural area is
planted for spring cereals and 5–10% for winter cereals (Pyykkönen et. al. 2004).
The livestock of the study area is: 7,260 cattle, 64,200 pigs and 2,316,000 poultry
(Kronvang 2003).
16
3.2 Data Sources
The quantitative evaluation of the soil erosion loss by RUSLE is based on its
component factors; such as: rainfall data, digital elevation model (DEM), soil type
map, land cover map, and satellite map. Those data were obtained from
Meteorological Institute, Environment Institute and National Land Survey of the
country. For example, all precipitation data are obtained from metrological
institute; topographic maps from national land survey and all results of other
relevant studies are from Environment Institute. These different data sources may
have different data formats, projections, data quality, and spatial resolution. The
use of GIS provides the tools to manage and analyze these data. However, the
evaluation of these data is necessary before they are used. The uncertainties
regarding data sources may introduce larger uncertainties in soil erosion
estimates. Great attention should be paid to the evaluation and preprocessing of
data sources, such as data interpolation, conversion, and registration.
3.2.1
The digital elevation model (DEM)
Digital elevation model (DEM) is a digital file consisting of terrain elevations for
ground positions at regularly spaced horizontal intervals. In other word, digital
elevation model (DEM) data are digital representations of cartographic
information.
The DEM data files of study area are available from National Land Survey of
Finland and PaITuli-paikkatietopalvelu – CSC. The DEM data was added to
ArcGIS 10 to calculate the flow length and slope steepness.
17
3.2.2
Soil Data
The soil data for this study is obtained from Finnish Environmental Institute
library and information center. The soil types of the study area are Moraine 31%,
Clay 30%, Organic matter 11%, Sand and Gravel 12%, Bedrock 10%, Silt 4% and
Esker formation 2% (figure 4).
3.2.3
Precipitation Data
The rainfall data used in this study is from two rainfall stations namely Köyliö
Yttilä and Pöytyä Yläne Stations. Köyliö Yttilä rain gauge is located at the upper
part of the catchment (Grlat: 6786717 and Grlon: 3250885) while as the Pöytyä
Yläne rain gauge is located at the lower part of the catchment (Grlat: 6760108 and
Grlon: 3249760). In order to increase the accuracy of the result additional rainfall
data from eight rain gauge stations, which are not located in the study area but
close enough to it, were used. All those precipitation data of these stations were
obtained directly from Finland Metrological Institute.
3.2.4
Land use and Land Layer
The role of land use and land cover category has been immense particularly in
estimating the C and P factors of the RUSLE model. Thus their influence on soil
loss would be to some extent decisive, however, slope length and slope gradient
have put strong reflection of their pattern at final result of the RUSLE model.
(Hudad B. 2010). Usually, C and P factors determine from satellite map, aerial
photos and filed observation. But in this study, due to absence of satellite map and
other necessary information, the values of C and P factors were obtained from
literatures (previous studies).
18
4
DATA ANALYSIS
Data analysis was undertaken using RUSLE model, ArcGIS 9.3, Microsoft Office
Excel 2007 and different equations proposed by many authors. The slope length
and steepness (LS factor) is drived from DEM by application of ArcGIS. In
summary figure 6 shows the methodology applied so as to achieve the intended
objectives
Soil
Map
Satellite
Map
Topographic
Map
Rainfall
Data
Digitizing
Contour
Rainfall
Map
Fournier
Index
DEM
K
C& P
Slope
Map
Slope
Length
R
LS
RUSLE
FIGURE 6. Overall Methodology for generating RUSLE factors
19
4.1 Calculation of RUSLE Factors
4.1.1
Rainfall Erosivity Factor (R Factor)
To calculate the value of R factor of the study area, the relationship between
rainfall and elevation of the rain gauge stations was developed from table 1 as
shown in figure 7. From the figure, equation 2 was derived in order to
interpolating the mean annual rainfall (MAR) values across the catchment
Average Annual Rainfall (mm)
AAR Vs Elevation
700.00
680.00
y = -0.3967x + 642.31
660.00
R² = 0.1428
640.00
620.00
600.00
580.00
0.0
20.0
40.0
60.0
Elevation (m)
80.0
100.0
120.0
FIGURE 7. The average annual precipitation (AAP) as related to elevation
The derived equation is:
Y= -0.3967X + 642.31
(2)
Where;
Y is the mean annual precipitation (MAR) in mm and x is the elevation in
meters of the point where the rainfall data is being determined.
20
By using equation 2, the values of MAR are calculated for ten rain gauge stations
as shown on table 1 below
TABLE 1. Rainfall Data from Different Rain Gauge
1106
46
53
1111
1117
1201
Name
KAARINA YLTÖINEN
LAITILA HAUKKA
TURKU ARTUKAINEN
KOKEMÄKI PEIPOHJA
HYRKÖLÄ
LIETO TAMMENTAKA
KÖYLIÖ YTTILÄ
PÖYTYÄ YLÄNE
HUITTINEN SALLILA
ORIPÄÄ TEINIKIVI
JOKIOINEN
OBSERVATORIO
Elevation (m)
AAP (mm)
MAR = -0.3967X + 642.31
6.0
21.0
28.0
37.0
657.3
605.47
689.79
606.06
639.9298
633.9793
631.2024
627.6321
39.0
46.0
53.0
70.0
82.0
104.0
635.74
590.88
602.9
597.81
633.05
611.36
626.8387
624.0618
621.2849
614.541
609.7806
601.0532
The following graphs (figure 8 and 9) were developed to show the annual and
monthly rainfall data of both Köyliö Yttila and Pöytyä Yläne Rain gauges for a
period of 10 years.
Rainfall Data
1000
Rainfall (mm)
No.
103
1007
118
1104
800
600
400
KÖYLIÖ YTTILÄ
200
PÖYTYÄ YLÄNE
0
Year
FIGURE 8. Annual rainfall data for both Köyliö Yttila and Pöytyä Yläne rain
guage stations
21
Total Monthly Data in Ten Years
800.0
Rainfall (mm)
700.0
600.0
500.0
400.0
300.0
köyliö yttilä
200.0
Pöytyä yläne
100.0
0.0
Month
FIGURE 9. Ten years monthly rainfall data for both Köyliö Yttila and Pöytyä
Yläne rain guage stations
From the above graphs it is observed that the amount of rainfall vary in both rain
gauge stations and throughout the year. This is a clear indication that the rainfall
runoff erosivity would also vary from the lower part of the catchment to the upper
part of the catchment. Also, it is important to note that the rainfall varies with
elevation variation.
Model
The Rainfall erosivity factor R is often determined from rainfall intensity if such
data are available. So, the Rainfall-Runoff erosivity (R) can be defined as an
aggregate measure of the amounts and intensities of individual rain storms over
the year (Hudson 1981; Wenner 1981).
∑ [∑ (
)]
( )
22
Where;
R- Rainfall/runoff erosivity (MJ.mm.ha-1.h-1.yr-1)
E - Total storm kinetic energy (MJ/ha)
I30 - Maximum 30-min rainfall intensity (mm/h)
j - Index of number of years used to produce the average
k - Index of number of storms in a year
n - Number of years used to obtain average R
m - Number of storms in each year
In majority of cases rainfall intensity data are very rare. As such, if there is no
station with rainfall intensity data, the R factor is determined using monthly and
mean annual rainfall. According to different author, monthly precipitation data pi,
mean annual precipitation data p and Fournier Index F area the basic terms used
to calculate erosivity R in the absence of storm kinetic energy and rainfall
intensity (Anoldus 1980). Monthly and mean annual precipitation data of the
study area is found directly from Finnish metrological institute, but the Fournier
Index F is calculated by using equation 4.
∑( ∑
)
( )
Where:
pi – Monthly rainfall (mm)
p – Mean annual rainfall (mm)
l – Number of month (which is 12 months)
There are many equations which are derived to determine the value of R for a
certain location; but there‟s no guarantee that if those equations would work
somewhere else. To circuit this problem, several but tested relationships were
used and the resulting rainfall erosivity indexes averaged. To determine the
23
suitable equations that could be used at Eurajoki catchment, nine equations given
in table 2 were tested based on the relationships between the Mean Annual
Rainfall (MAR) and Rainfall Erosivity (R) given by Kassam et al.,1992 (table 3).
TABLE 2. Shows the equations and the reference where they can be found
Case References/Source
Equations
of
the R and P or F Relationship
1
Arnoldous (1980)
R = 4.17F – 152
2
YU & Rosewell, 1996
R = 3.82 F1.41
3
Arnoldus – Exponential, 1977
R = 0.302 F1.93
4
Renald & Freimun – F, 1994
R = 0.739F1.847
5
Renald & Freimun – P, 1994
R = 0.0483P1.61
6
Roose in Morgan and Davidson R = P x 0.5
(1991)
7
Kassam et al.,1992
R = 117.6 (1.00105(MAR)) for <
2000mm
8
Singh et al., 1981
Rfactor = 79 + 0.363R
9
Freimund (1994)
R = 0.6120 F1.56; Sicily-Italy
R = 0.264 F1.50; the Morocco
Where;
R = rainfall erosivity factor (MJ/ha.mm/h)
MAR = mean annual rainfall (mm)
F = Founier index
P = Mean Annual Precipitation (mm)
24
TABLE 3. Relationships between the Mean Annual Rainfall (MAR) and Rainfall
Erosivity (R) Given by Kassam et al., 1992
MAR (mm)
R
MAR (mm)
R
170
140
913
307
212
146
998
335
256
153
1089
369
302
161
1189
409
350
170
1298
459
400
179
1419
522
453
189
1557
602
508
200
1711
708
566
213
1892
856
628
227
2108
1054
692
243
2376
1188
761
261
2729
1364
835
282
2878
1439
Table 3 shows that for a single value of MAR, there is corresponding value of R
factor. The table was used to choose the right equation for the study area.
In this study the F values were calculated for both Köyliö Yttila and Pöytyä Yläne
stations and the values are as follows;

KÖYLIÖ YTTILÄ rain station F = 68.27

PÖYTYÄ YLÄNE rain station F = 70.96
After testing the equations, some of the resulting R factors were found to be close
to the values given by (Kassam, 1992) and as shown in table 4. Other models
gave too high R values and others gave too low values and hence they were not
considered for calculation of the R factor. As a result, the following equations
25
(table 5) that gave results that were within the range of the values given for by
Kassan, 1992 were used to determine the R factor for Eurajoki Catchment
TABLE 4. Selected R factor Models
R and P or F Relationship
References
R = P x 0.5
Roose in Morgan and Davidson
(1991)
Rfactor = 79 + 0.363MAR
Singh et al., 1981
R1 = 0.6120 F1.56; Sicily- R = (R1 + R2)/2
Italy
Freimund (1994)
R2 = 0.264 F1.50; the
Morocco
From the above equation, R-factor was calculated on table 5
TABLE 5. Calculation of R-factor
Stations
F
P
MAR
Köyliö
68.27 590.88 624.06
Yttila
Pöytyä
70.96 602.87 621.29
Yläne
Mean R value of the two stations
4.1.2
R = P x 0.5
295.44
301.435
R = 0.6120 F1.56
Sicily-Italy
Average
R = 0.264 F1.50
Morocco
305.53
296.85
299.28
R = 79 +
0.363MAR
304.53
315.12
307.03
303.15
K Factors
The soil erodibility factor was calculated using the soil properties obtained from
Finnish Environmental Institute library and information center by using equation
5; given by (Wischmeier and Smith 1978). This equation was settled upon due to
availability of data on soil structure, organic matter and permeability. The
equation reads as shown below:
26
K = 2.1x10-6 x M1.14 (12-OM) + 0.025 (S-3) + 0.0325 (P-2)
(5)
Where;
K = soil erodibility factor in t.h/MJ.mm
M = (Percentage very fine sand + Percentage silt) × (100 – Percentage
clay)
OM = Percentage of organic matter
S = Code according to the soil structure (very fine granular = 1, fine
granular = 2, coarse granular = 3, lattice or massive = 4), and
P = Code according to the permeability/drainage class (fast = 1, fast to
moderately fast = 2, moderately fast= 3, moderately fast to slow = 4, slow
= 5, very slow = 6)
From figure 10 to below, the study area do not have very fine sand; as result the
computation of the M was done using sand and gravel, silt and clay contents.
27
FIGURE 10. Distribution of soil types on the entire study area (Kronvang 2005)
To apply equation 5, soil textural classes were estimated using the textural
triangle (figure 11) for the purpose of determining S and P values (Suresh 2000,
Arora 2003).
According to the textural classification system, the percentage of sand (size 0.05
to 2.0mm), silt (0.005 to 0.05mm), and clay (size less than 0.005mm) are plotted
along the three sides of an equilateral triangle. The equilateral triangle is divided
in to 10 zones; each zone indicates a type of soil. The soil can be classified by
determining the zone in which it lies.
As it mentioned above, the value of K factor is depend on the percentage of clay,
sand and silt available in the study area. The soil samples (clay, sand and silt)
obtained for this study were classified according to this textural classification
system. A correction factor was used because the study area contained particles
larger than 2.0mm size (>sand), moraine, bedrock and water. If a soil contains
particles larger than 2.00mm size, a correction is required in which the sum of the
percentage of sand, silt and clay is increased to 100%. For example, the soil in
which their particle size is less than or equal to 2.00mm of Eurajoki catchment
28
contains 12% sand, 4% silt and 30% clay (chapter one). So, the actual percentage
of sand, silt and clay of the study area is 46%. Therefore, each of this percentage
would be multiplied by the correction factor of 100/46, and the correction
percentage would become 26.09% sand, 8.70% silt and 65.22% clay. The point X
falls in to the zone labeled clay as shown on figure 11. Therefore, clay is the
majority of soil in the study area.
X
FIGURE 11. Eurajoki catchment soil textural classes (Suresh 2000, Arora 2003)
Since the soil class is clay and the organic matter of the study area is 11%, the
codes P and S value is 3 according to permeability and soil structure classes were
obtained from the soter database. On average, the approximate value for the soil
erodibility (K) factor estimated in the study plots was 0.04 t h/MJ.mm.
29
4.1.3
LS Factor
The LS factor (topographic factor) accounts for the effect of topography on
erosion in RUSLE. The slope length factor (L) represents the effect of slope
length on erosion, and the slope steepness factor (S) reflects the influence of slope
gradient on erosion. For this study L is the flow length and S is slope steepness
which is given by meter and percent respectively.
Basically, the LS factor can be estimated through field measurement or from a
digital elevation model (DEM).
With the incorporation of Digital Elevation
Models (DEM) into GIS, the slope gradient (S) and slope length (L) may be
determined accurately and combined to form a single factor known as the
topographic factor LS. The precision with which it can be estimated depends on
the resolution of the digital elevation model (DEM). The equation used to
determine this parameter was that recommended by (Morgan and Davidson,
1991) given in Equation 6
LS =√
(0.065 + 0.45s + 0.0065s2)
(6)
Where:
L = slope length in m
S = percent slope
From ArcGIS 10, figure 12 was developed by inputting the DEM data (data from
National Land Survey of Finland and PaITuli-paikkatietopalvelu – CSC) into
ArcGIS that used to determine the mean values of L and S of the study area. The
procedure to determine the slope gradient is; Spatial Analyst  Surface Analysis
 Slope in degree or percent. In the same fashion, the slope length was calculated
as; Spatial Analyst  Hydrology  Flow Length. After that figure 12 was
produced for the study area and from its layer properties, the mean, standard
30
deviation, minimum and maximum values slope length (L) and slope gradient (S)
were generated from the histogram that produce by ArcGIS.
FIGURE 12. 25mX25m DEM of Eurajoki area
The combined topographic (LS) factor was computed rather than the individual
slope length and slope angle factors. The inputs for the computation include the
slope in percent and the slope length.
4.1.4
Cover (C) Factor
By the help of “Raster Calculator” tool of the “Spatial Analyst” extension of
“ArcGIS” software package, the C-factor was calculated from NDVI, a spectral
ratio between near infrared and red reflectance, extracted from satellite image
31
(van der Knijff et al., 1999, 2000). In this study, due to the absence of satellite
image and other necessary data, the C value obtained from the literature (table 7).
4.1.5
Support Practice (P) Factor
Usually, the P factor is determined from experimental data like satellite images,
aerial photos and some field observations. Those data help to recognize the
erosion control measures applied on catchment area. Like C factor, the value of P
is also obtained from the literature (table 6) due to the absence of necessary
information.
FIGURE 13. Land use map in the Eurajoki catchment (Kronvang 2005)
As you can see from the figure 13 (Kronvang 2005), the major land cover of the
study area Pine and Spruce trees, and other part of the study area is open land and
fields. It is clear that the soil erosion from the fields and open area quiet much
higher than that of land which covered by forest like spruce and pine.
32
TABLE 6. Land use and C and P Factors
Land Use
C factor
Natural Vegetation/Forest
0.001
Agriculture/Crop
0.128
Grass
0.003
Urban
0.030
Average
0.31
Source: Soil and Water Conservation Society 2003
P factor
1.00
0.92
1.00
1.00
0.96
33
5
RESULTS AND DISCUSSION
Basically, the erosion values obtained through RUSLE is depend upon the above
six parameters of RUSLE and their values can vary considerably due to
varying weather conditions. The result of RUSLE parameters and average
annual soil loss are presented under this chapter as follows.
5.1 Rainfall and runoff erosivity R-factor
The distribution of the average annual rainfall of the study area for 10 years
period is different from station to station. This shows that the value of R factor
also vary according to rainfall distribution. As it calculated on table 5 above, the
average value of R factor for the entire catchment found to be 303.15
MJ.mm./ha.h.yr.
5.2 Soil erodibility K Factor
Usually, soil erodibility factor is obtained from erodibility index map which
derived from soil map of the area by the help of ArcGIS. But, due to the absence
of erodibility index map, the soil erodibility factor was calculated by using
equation 5 and textural triangle (figure 11). So, the final value of K factor is 0.04 t
h/MJ.mm.
34
5.3 Slope Length and Steepness LS Factor
As it mentioned in the chapter 4 and figure 12, the slope angle and slope length
(overland flow length) were generated using ARCGIS 10. So, the mean slope
length of the study area is 0.36m with standard deviation of 13.32 and the mean
slope is 18.27 in percent with standard deviation of 28.45. The LS factor was
calculated by using equation 4 and found to be 1.34. For both cases (flow length
and slope gradient), the standard deviation is quite high. This shows that there are
places where the slope is zero and high slope. The above result is the average of
the entire study area
5.4 C and P Factors
As it mentioned above (chapter 4), the value of C and P factors were obtained
from literatures depend on land use and land cover of the study area. The study
area includes farmland (including grazing) and savanna, settled area, forest, etc
(Fig. 12). Therefore, the average value of C and P factors found to be 0.31 and
0.96 (table 7) respectively.
5.5 Annual Average Soil Loss
Rainfall erosivity, soil erodibility, slope length and steepness, cover management,
and support practice factors were calculated as it shown above. The RUSLE
calculated the annual average soil loss (for the basin) from Eq. (1) using the six
factors and it is estimated as A= 5 Mg per ha per yr (5 mega gram per ha per yr)
which is equal to 5 ton per ha per yr.
35
The final result of this study was compared to results from different countries
(European Communities, Institute for Environment and Sustainability, 1995-2010
database and case study report) and concluded that the overall results of this study
is in an acceptable range.
Generally, the estimated value of soil loss in the RUSLE model highly depends on
LS factor next to R factor. This implies that the DEM information, which is
directly transformed to L and S factors, and rainfall data are crucial in calculating
soil loss.
36
6
CONCLUSIONS
In this study, the importance of the use of revised universal soil loss equation
(RUSLE) is well recognized, in which the R factor plays the most important role.
The R factor which is the mean annual sum of individual storm erosion index
values (EI30) depends on the value of the total kinetic energy of the storm and the
I30 value, the maximum 30 min rainfall intensity. Since energy of the storm and
30 min rainfall intensity of the study area are not available, an alternate procedure
was applied to compute the R factor that could make the RUSLE less effective
unless proper procedures are needed.
For reliable estimation of R value, three different modeling approaches were
applied by using the relevant data from ten rain gauge stations in which two of
them are located in the study area. In these approaches, the combination of
regression models between annual rainfall and elevation, monthly and mean
annual rainfall and Fournier index were used.
Generally, this study provides an approach for the evaluation of soil erosion loss
in Eurajoki watershed based on a combination of RUSLE and GIS. This is an
effective way to map and predict the soil erosion loss of certain area. However, an
error in a factors value will produce an equivalent percentage error in the soil loss
estimation. These errors are mainly due to inaccuracy components in each data
and the limitations in the methods used to derive the component factor values.
The accuracy of the predicted soil loss can be improved, if each parameter is
better estimated. For example, an R value can be better produced by using direct
storm energy and 30 min rainfall intensity. The LS factor can be improved by a
better generated DEM, maximum downhill slope and infinite flow direction. The
C factor can be improved by better estimation of the fractional vegetation cover.
To assess the accuracy of the produced maps, validation with independent data is
required. This can be obtained from field measurements, surveys, or high
resolution image.
35
REFERENCES
Books and Articles
















Arnoldous, H.M.J. 1980. An approximation of the rainfall factor in the
USLE in assessment of Erosion. England: Wiley Chichester.
Arora K. 2003. Soil Mechanics and Foundation Engineering, 6th Edition,
Standard Publishers Distributors, New Delhi.
Edwards, K. 1987. Runoff and soil loss studies in New South Wales: Soil
Conservation Service of NSW. Technical Handbook No. 10,. Sydney.
Hudson N.W. 1981. Soil Conservation. Batsford.
Hudad B. 2010. Multi-temporal Satellite Image Analysis for Assessing
Land Degradation. Addis Ababa University. Ethiopia.
Hyvärinen, V. 2003. Hydrological Yearbook 1996–2000. The Finnish
Environment 599. Finnish Environment Institute, Helsinki.
Kassam A.H., Velthuizen H.T, Mitchell A.J.B, Fischer G.W. and. Shah
M.M. 1992. Agro-Ecological Land Resources Assessment for Agricultural
Development Planning.
Kronvang B., Larsen, S.E., Jensen, J.P, Andersen, H.E. and Granlund, K.
Catchment report: Eurajoki, Finland. Trend Analysis, Retention and
Source Apportionment, EUROHARP report 13-2005. Norway: Oslo.
Mbugua W. 2009. Using GIS techniques to determine RUSLE‟S „R‟ and
„LS‟ factors.
Millward, A. A. & Mersey, J. E. 1999. Adapting the RUSLE to model soil
erosion potential in a mountainous tropical watershed. Catena.
Mitasova, H. 1996. GIS Tools for Erosion/Deposition Modelling and
Multidimensional Visualization. Part III: Process-based Erosion
Simulation, Geographic Modelling and Systems Laboratory. University of
Illinois: Illinois.
Morgan, R. P. C. and Davidson, D. A., 1991, Soil Erosion and
Conservation, Longman Group, U.K
Pyykkönen, S., Grönroos, J., Rankinen, K., Laitinen, P., Karhu, E. and
Granlund, K. 2004. Cultivation measures in 2000–2003 and their effects to
the nutrient runoff to the waters in the farms committed to the AgriEnvironmental Programme. The Finnish Environment 711
Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K. & Yoder, D.
C. 1997. Predicting Soil Erosion by Water: A Guide to Conservation
Planning with the Revised Soil Loss Equation (RUSLE). U.S. Dept. of
Agriculture, Agric. Handbook No. 703. USA.
Ritchie J. C, Walling, D. E. Peters J. 2003. Application of geographic
information systems and remote sensing for quantifying patterns of
erosion and water quality.
Simms A.D, Woodroffe C.D, Jone B.G. 2003. Application of RUSLE for
erosion management in a coastal catchment, southern NSW.
36











Singh Gurmel, Ram Babu, Subash Chandra. 1981. Soil loss prediction
research in India.
Soil and Water Conservation Society. 2003. Estimating water erosion and
sediment yield with GIS, RUSLE, and SEDD. Journal of Soil and Water
Conservation.
Suresh R. 2000. Soil and Water Conservation Engineering. Standard
Publishing Distributors. India: New Delhi.
Tattari, S. and Rekolainen, S. 2006. Soil erosion in Finland. In: Boardman,
J. and Poesen, J. (eds.): Soil erosion in Europe. John Wiley & Sons Ltd.
Van der Knijff J.M, Jones R.J.A. Montanarella L. 2000. Soil erosion risk
assessment in Europe. Office for Official Publications of the European
Communities. Luxembourg.
Vrieling, A. 2007. Mapping erosion from space. Ph.D. Thesis.
Wageningen University: The Netherlands.
Wang G, Wente S, Gertner GZ, Anderson A. 2002b. Improvement in
mapping vegetation cover factor for the universal soil loss equation by
geostatistical methods with Landsat Thametic Mapper images.
International Journal of Remote Sensing 23: 3649–3667
Weesies A. K factor, soil erodibility
Wischmeier, W. H. & Smith, D. D. 1978. Predicting rainfall erosion losses
- a guide to conservation planning. U.S. Dept. of Agriculture.
.Yitayew, M., Pokrzywka, S.J., Renard, K.G. 1999. “Using GIS for
facilitating erosion estimation.” Journal of Applied Engineering in
Agriculture.
Yu, B. & Rosewell, C. J. 1996. An assessment of a daily rainfall erosivity
model for New South Wales.
Internet Resources





ENVIRONMENTAL GIS: Lab 10 - Modeling soil erosion. Visited Date
01.12.2010. Available on World Wide Web:
http://www.utexas.edu/depts/grg/hudson/grg360g/EGIS/labs_04/Lab9/lab
9_soil_erosion_05.htm
European Commission - Joint Research Centre. Institute for Environment
and Sustainability. Cited on 01.05.2011. Available on World Wide Web:
http://eusoils.jrc.ec.europa.eu/library/themes/erosion/
Lepistö. Finnish Environmental Center. Remote sensing and measuring
technology in coupling of process-based catchment and lake models. Cited
on 01.12.2010. Available on World Wide Web:
http://www.ymparisto.fi/default.asp?contentid=372977&lan=FI&clan=en
RUSLE. Online soil erosion assessment tool. Visited Date 01.12.2010.
Available on World Wide Web: RUSLE Online
Sharma, Partha Das. Fundamentals of our environmental pollutions.
Environmental problems and its control measures. 2009. Cited on
37


22.12.2010. Available on World Wide Web:
http://knol.google.com/k/fundamentals-of-our-environmental-pollutions#
Soil texture. Cited on 10.03.2011. Available on World Wide Web:
http://www.soilsensor.com/images/soiltriangle_large.jpg
Technical Guide to RUSLE use in Michigan, NRCS-USDA state office of
Michigan. Cited on 01.12.2010. Available on World Wide Web:
http://www.iwr.msu.edu/~ouyangda/rusle/k_factor.htm
APPENDIXES
A. Precipitation data in mm from Köyliö Yttilä rain station
Months
January
February
March
April
May
June
July
August
September
October
November
December
Total (p)
Year
2005
2006
2001
2002
2003
2004
26.5
46.0
31.0
47.7
26.5
25.8
109.3
74.2
112.1
83.9
38.6
31.3
652.9
46949.83
76.5
43.3
32.0
2.5
60.2
76.9
102.9
41.0
14.6
23.2
45.6
7.3
526.0
33448.5
37.8
10.6
6.5
22.2
100.1
39.7
86.6
64.1
6.7
63.0
35.8
71.5
544.6
35688.54
23.7
25.4
23.1
5.3
32.3
81.8
73.9
40.0
95.0
33.5
48.5
73.2
555.7
34422.03
67.4
15.9
6.6
12.9
32.5
42.8
48.3
129.7
39.9
43.8
88.5
25.1
553.4
39021.32
71.91
63.59
65.53
61.94
70.51
2007
2008
2009
2010
22.5
17.9
27.2
55.1
35.5
34.1
15.7
58.5
61.7
163.7
52.9
72.9
617.7
49411.71
55.4
7.2
27.1
32.9
29.1
53.8
103.2
26.5
67.9
48.4
45.2
74.7
571.4
34607.66
55.9
40.4
41.3
38.2
9.5
89.8
36.4
169.7
26.3
123.9
74.8
48.2
754.4
70160.42
22.3
19.0
28.8
9.8
31.1
55.7
82.3
50.4
38.1
49.3
55.1
46.8
488.7
24275.27
14.2
45.2
47.7
33.6
87.5
62.0
51.2
35.1
114.1
42.8
71.3
39.3
644.0
42481.46
79.99
60.57
93.00
49.67
65.97
∑
∑
∑( ∑
)
68.27
B. Precipitation data in mm from Pöytyä Yläne rain station
Month
2001
2002
2003
2004
27.9
84.1
47.8
35.4
January
36.4
51.0
10.0
35.6
February
24.1
35.9
7.1
29.6
March
59.5
2.8
19.3
15.7
April
22.1
25.9
99.2
25.5
May
23.8
88.6
34.0
89.6
June
75.7
89.2
26.6
70.0
July
74.2
22.0
73.9
57.5
August
144.1
10.3
5.7
87.8
September
79.4
26.3
56.0
26.8
October
23.3
37.3
40.1
55.1
November
24.4
7.8
79.1
102.0
December
614.9
481.2
498.8
630.6
Total (p)
Year
2005
2006
46722.83
30181.78
31006.46
42394.92
84.7
23.2
8.8
13.7
31.5
53.2
67.6
195.5
27.4
41.1
88.8
33.8
669.3
66057.81
75.98
62.72
62.16
67.23
98.70
2007
2008
2009
2010
31.4
21.1
27.8
50.9
35.7
42.5
11.9
41.9
41.8
155.7
70.9
92.8
624.4
49401.16
82.7
6.3
38.9
34.2
38.3
59.6
95.2
42.7
64.3
63.9
59.2
81.7
667.0
43864.44
77.9
57.6
43.6
41.2
5.6
107.0
38.9
152.9
41.1
132.6
83.9
53.6
835.9
78540.69
19.9
23.1
28.0
4.6
21.0
56.4
55.2
51.6
39.6
52.5
53.7
44.2
449.8
20228.08
15.5
33.4
38.8
40.5
67.7
71.6
23.1
32.6
106.6
30.0
55.4
41.6
556.8
32871.00
79.12
65.76
93.96
44.97
59.04
∑
∑
∑( ∑
)
70.96
C. Ten years precipitation data from ten different stations
Year/
Köyliö Pöytyä Kaarina
Laitila
Turku TLA Kokemäki
Lieto
Huittinen Oripää Jokioine
Stations Yttila Yläne Yltöinen Haukka
Peipohja Hyrköla Tammentaka Sallila
2001 652.9 614.9
654.0
659.4
785.8
633.6
682.8
630.7
673.2
652.5
2002
526 481.2
468.8
467.0
559.9
499.2
488.7
518.4
511.2
440.3
2003 544.6 498.8
544.3
512.2
585.7
553.0
450.1
552.5
589.4
584.8
2004 555.7 630.6
840.2
660.9
796.7
612.3
635.9
649.3
647.2
726.4
2005 553.4 669.3
744.9
651.8
739.1
614.3
662.2
615.5
638.5
649.9
2006 617.7 624.4
690.9
625.7
715.5
695.6
746.2
618.0
693.7
586.9
2007 571.4
667
696.6
715.3
720.1
626.7
728.3
602.9
653.2
642.5
2008 754.4 835.9
845.1
771.0
830.6
820.9
870.7
781.5
890.9
760.5
2009 488.7 449.8
532.6
488.3
623.2
438.2
536.3
470.0
498.5
496.2
2010
644 556.8
555.6
503.1
541.3
566.8
556.2
539.3
534.7
573.6
D. Additional information of rain gauge stations
Lpnn
Name
103
118
1007
1101
1104
1106
1111
1117
1201
1113
1130
KAARINA YLTÖINEN
TURKU ARTUKAINEN
LAITILA HAUKKA
TURKU TURUN LENTOASEMA
KOKEMÄKI PEIPOHJA
HYRKÖLÄ
LIETO TAMMENTAKA
HUITTINEN SALLILA
ORIPÄÄ TEINIKIVI
JOKIOINEN JOKIOISTEN
OBSERVATORIO
KÖYLIÖ YTTILÄ
PÖYTYÄ YLÄNE
Lat Lat_sec
Lon Lon_sec
Grlat
Grlon
Elstat
6023
6027
6049
6030
6116
12
16
59
55
15
2233
2210
2145
2216
2215
17
54
37
39
8
6705580
6714565
6758433
6720950
6805090
3254967
3234980
3215199
3240731
3245443
6
8
21
49
37
6034
6101
6054
6048
25.5
23.6
15
49.9
2226
2242
2242
2330
59.1
7.3
54
3.3
6726788
6775868
6762575
6749989
3250627
3267721
3267553
3309619
39
70
82
104
6106
6052
36
16.6
2222
2223
39 6786717 3250885
29.1 6760108 3249760
46
53
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