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UNIVERSITY OF GOTHENBURG
Department of Economy and Society, Human Geography &
Department of Earth Sciences
Geovetarcentrum/Earth Science Centre
Present and future
soil erosion in the
Kungsbackaån watershed
Karl Adler
ISSN 1400-3821
Mailing address
Geovetarcentrum
S 405 30 Göteborg
Address
Geovetarcentrum
Guldhedsgatan 5A
B919
Bachelor of Science thesis
Göteborg 2016
Telephone
031-786 19 56
Telefax
031-786 19 86
Geovetarcentrum
Göteborg University
S-405 30 Göteborg
SWEDEN
Abstract
When it comes to soil erosion prediction for present and future climates Sweden is seldom
represented. Sweden have been represented on a European scale for present climate but soil
erosion studies on particular locations in Sweden are scarce coupled with high resolution data.
The purpose of this study was thus to model soil erosion for the present and future (year 2070)
with two climate scenarios in the Kungsbackaån watershed in the south west of Sweden. The
revised universal soil loss equation (RUSLE) was used within a GIS environment coupled
with high resolution elevation data of 2 meters. Soil erosion is, globally, an immense problem
destroying more than 10 million hectares of cropland annually. Soil erosion is mostly affected
by precipitation and precipitation is set to increase in magnitude in northern Europe.
Publically available data was gathered on rainfall, soil, elevation and vegetation to be
implemented within the ArcGIS 10.3.1 suite. Results show that present and future estimated
mean annual soil loss in Kungsbackaån watershed will be within bounds of very low soil loss
of 0 – 0,5 t/ha/y but with isolated locations of higher estimated mean annual soil loss up to
>50 t/ha/y. When compared to a study on present estimated mean annual soil loss by the
European Soil Data Centre (ESDAC) the method was deemed credible. High resolution
elevation data gives rise to detailed topography and thus detailed locations of soil erosion can
be modeled compared to coarser resolutions. There are however problems with the method
when coupled with high resolution elevation data which makes the very high values of
estimated mean annual soil loss questionable depending on locations. These locations can be
streams or on bare bedrock and thus finer processes of removing non-erodible areas need to
be implemented.
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Sammanfattning
När det kommer till jorderosions estimeringar för nutida och framtida klimat är Sverige sällan
representerat. Sverige har blivit representerat på en Europeisk skala tidigare på nutida
jorderosion men studier på specifika platser i Sverige tillsammans med högupplöst data är
svårt att finna. Syftet med den här studien var således att modellera jorderosion för nutid och
framtid (år 2070) med två klimat scenarier i Kungsbackaåns avrinningsområde i syd västra
Sverige. The revised universal soil loss equation (RUSLE) användes inom ett GIS
tillsammans med högupplöst höjddata på 2 meter. Jorderosion är globalt sett ett enormt
problem där mer än 10 miljoner hektar av jordbruks mark förstörs årligen. Jorderosion
påverkas huvudsakligen av nederbörd och nederbörd förväntas öka i magnitud i norra Europa.
Offentligt tillgänglig data på nederbörd, jord, höjd och vegetation inhämtades och
implementerades inom ArcGIS 10.3.1. Resultaten visar att nutida och framtida beräknad
medeljordförlust per år är inom ramar för väldigt låg jordförlust på 0 – 0,5 t/he/å men med
isolerade platser med hög beräknad medeljordförlust per år upp till >50 t/he/å. Efter
jämförelser med en studie på nutida estimerad medeljordförlust av European Soil Data Centre
(ESDAC) så kan metoden anses som legitim. Högupplöst data ger upphov till en detaljerad
topografi som i sin tur kan modellera detaljerade platser med jordförlust som annars hade
filtrerats ut av grövre upplösningar. Det finns dock problem med metoden när det används
högupplöst data i den mån att platser med väldigt hög beräknad medeljordförlust per år som
återfinns på tveksamma platser såsom vattendrag och bar sten. Detta syftar på att detaljerat
arbete i att ta bort platser där ingen erosion kan ske krävs för att säkerhetsställa
representativiteten.
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Preface
This study is my Bachelor thesis in Geography conducted at the department of Earth Sciences
at the University of Gothenburg. Since the first lectures on soils I was amazed upon the
complexity and the importance of the soil beneath our feet. Coupled with a love towards
computers the mix of soil and GIS was set.
I would like to firstly thank my supervisor Dr. Fredrik Lindberg for all the great tips and help
with this thesis. Secondly I would like to thank Associate Professor Mats Olvmo for
answering my rambling questions on soils and the like. Lastly I would like to thank Professor
Sofia Thorsson for the wonderful help and suggestions with this thesis.
Apart from the academic help I would like to thank my parents and my better half Ebba.
Without your support and enthusiasm this feat would never have been realized.
Karl Adler
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1
Introduction ......................................................................................................................... 6
2
Aim and specific objectives ................................................................................................ 7
3
Background ......................................................................................................................... 8
3.1
Soil erosion .................................................................................................................. 8
3.1.1
Soil erosion in Sweden ......................................................................................... 9
3.2
Soil erosion models ................................................................................................... 10
3.3
Climate change and representative concentration pathways ..................................... 11
4
Study area.......................................................................................................................... 13
5
Methodology ..................................................................................................................... 15
5.1
RUSLE factor methodology ...................................................................................... 15
5.2
Data description ......................................................................................................... 17
5.2.1
Rainfall erosivity factor data .............................................................................. 17
5.2.2
Soil erodibility factor data .................................................................................. 17
5.2.3
Slope length and slope steepness factor data ..................................................... 18
5.2.4
Cover management factor data ........................................................................... 18
5.2.5
Other data ........................................................................................................... 18
5.3
6
7
Implementation in GIS .............................................................................................. 19
5.3.1
Implementing the soil erodibility factor ............................................................. 19
5.3.2
Implementing the rainfall erosivity factor .......................................................... 21
5.3.3
Implementing the slope length and slope steepness factor................................. 21
5.3.4
Implementing the cover management factor ...................................................... 21
5.3.5
Calculation of the estimated mean annual soil loss............................................ 21
Results ............................................................................................................................... 23
6.1
The rainfall erosivity factor for Kungsbackaån watershed ........................................ 24
6.2
The soil erodibility factor for Kungsbackaån watershed ........................................... 25
6.3
The slope length and slope steepness factor for Kungsbackaån watershed .............. 26
6.4
The cover management factor for Kungsbackaån watershed .................................... 27
6.5
The estimated mean annual soil loss for Kungsbackaån watershed .......................... 28
Discussion and discussion of methodology ...................................................................... 34
7.1
Discussion of the results ............................................................................................ 34
7.2
Discussion of methodology and data quality ............................................................. 35
8
Conclusion ........................................................................................................................ 38
9
References ......................................................................................................................... 39
5
1 Introduction
Globally soil erosion is an enormous problem which attributes to an estimate loss of 10
million hectares of cropland annually (Mullan, Favis-Mortlock & Fealy, 2011). The IPCC
have declared that present climate change from the year 1951 to 2010 is extremely likely
attributed to anthropogenic actions (IPCC, 2014). IPCC names several future implications
such as health problems related to heat strokes, water shortages due to higher temperatures
and sea level rise to name a few (Kovats et al, 2014). But with climate change soil erosion is
globally expected to increase in extent, frequency and magnitude (Mullan et al, 2011). With
an expecting increase in total world population regions suffering from soil erosion that are
dependent on domestic food production such as Asia, Africa and South America will be put at
a great stress with a changing climate but regions perceived as safe such as in the northern
hemisphere are not by chance exempted from the problem of soil erosion (Boardman & FavisMortlock, 1993). In a future where locally sourced food becomes more important for climate
change mitigation and food security soil erosion becomes a potential risk. This potential risk
should be investigated in order to be able to implement policies for combating soil erosion if
so needed.
Several studies on future soil erosion by climate change has been made in locations from
Asia, Africa and Europe in varying scales from agricultural fields to entire continents with
different soil erosion models such as the revised universal soil loss equation (RUSLE) (e.g.
Panagos et al, 2015b). There are however gaps in studies on smaller scales covering Sweden
where there is a lack of studies on future soil erosion by climate change. Sweden often only
get represented by European scale made estimations on soil erosion in a present climate such
as in the work by the European Soil Data Centre (ESDAC) (ibid.).
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2 Aim and specific objectives
The overall aim of the study is to see how soil erosion will change in a future climate in a
watershed in Sweden using RUSLE in a GIS environment. This with easily available methods
from peer reviewed articles coupled with publically available data on present and future
precipitation, elevation, soil and vegetation.
The specific objectives of the study are as follow:

How much is soil erosion will there be in the Kungsbackaån watershed in the present
and the year of 2070?

Which factor as in topography, vegetation, soil or precipitation is dominant for how
soil erosion is expressed in Kungsbackaån watershed in the present and the year of
2070?

Is it possible to make a credible soil erosion estimation based on publically available
data using the RUSLE model in a GIS environment?
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3 Background
3.1 Soil erosion
The most important erosion agent when it comes to soil erosion is the influence by water and
more specifically rain often called as a whole – water erosion. The major components of
water erosion are sheet erosion and rainsplash erosion. As the infiltration capacity in the soil
is exceeded, that is that the soil cannot take up more water, runoff is formed often as a thin
sheet of water moving down slope namely sheet flow (De Blij, Muller, Burt & Mason, 2013,
p. 436). This sheet flow, if more water is added to the system, will begin to dislodge and
transport soil particles thus eroding the soil. By this process rills can form which are small
channels of accumulated runoff that are not permanent and if even more water is added larger
more permanent rills namely gullies can form (De Blij et al, 2013, p. 445-456). This erosion
by runoff is called sheet erosion and is governed by rainfall intensity, soil type and the
gradient of the slope so that accumulation and deposition of soil particles can occur. The other
major factor in water erosion is by rainsplash erosion. Rainsplash erosion occurs as raindrops
impact the soil thus dislodging and displacing soil particles by the sheer kinetic force of the
raindrop. Rainsplash erosion like sheet erosion thrives when an articulated slope gradient is
present as dislodged soil particles can get deposited further down the slope (De Blij et al,
2013, p. 445).
Rainsplash erosion and sheet erosion work together interchangeably and form inter-rill and
rill erosion. Inter-rill erosion is the combination of rainsplash erosion and sheet erosion for the
transportation of soil particles. Rill erosion is the effect of inter-rill erosion as the runoff gets
concentrated in the rill which then erode the soil as soil particles get dislodged and transported
in the rill as water flows within it, the results presented by He, Sun, Gong, Cai & Jia (2016)
show that with an increased slope gradient there is an increase in formation and magnitude of
rill erosion up to a point when the slope is so steep that rill erosion stabilizes.
There are factors that can prevent rill and inter-rill erosion. One of these factors are the
presence of vegetation in the form of canopy cover such as leaves and root systems. Canopy
cover result in intercept on which when vegetation protects the soil from rainsplash erosion
(De Blij et al, 2013, p. 435). Roots are however equally important as they bind the soil
making it more stable where more shallow and dense root systems such as those from grasses
are most effective at prohibiting soil erosion (Gyssels, Poesen, Bochet & Li, 2005). Another
important factor for soil resistance are soil aggregates. Soil aggregates are soil particles
bonded together by a bonding agent such as organic matter or mycorrhiza fungi which in turn
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gives the soil structural strength to resists dislodgment of soil particles due to the high
cohesion of soil particles that the bonding agent provides (Bryan, 2000).
3.1.1 Soil erosion in Sweden
Sweden does not suffer from any serious soil erosion problems in the present compared to
developing countries in Asia or Africa for example. There are however isolated locations
where soil erosion from precipitation are noticeable such as particular river valleys in the
north and around particular lakes such as Siljan in Dalarna county (OECD, 2008).
The European Soil Data Centre (ESDAC) conducted research on a European scale where
Sweden is represented in regards to present day conditions for soil erosion (Panagos et al,
2015b). Figure 1 show the estimated mean annual soil loss with raster cells of 25 meters in
resolution. It can be seen that the dominant estimated mean annual soil loss in Sweden
situates within the boundaries of 0 – 0,5 t/ha/y with higher values in the northern Sweden. In
the work by Panagos et al (2015b) a mean for the whole country of Sweden is presented with
the value of 0,41 t/ha/y which is within boundaries of very low estimated mean annual soil
loss. This can be compared to countries in the south of Europe such as Spain where the
estimated mean annual soil loss is 3,94 t/ha/y which is much higher than Sweden (Ibid.).
Figure 1: Map showing the estimated mean annual soil loss in Europe in a present day climate. Source: Panagos
et al (2015b).
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3.2 Soil erosion models
There exist several soil erosion models such as the process-based model like WEPP (The
water erosion prediction project) and the more statistical RUSLE (Revised universal soil loss
equation). WEPP was for example created by the USDA and uses inputs to calculate for
example plant growth, soil consolidation and erosion mechanics to model soil erosion
dynamically (Nearing et al, 2005). As there exists several soil erosion models each model
utilizes information and knowledge on soil erosion in similar or different ways (e.g. Nunes,
Vieira, Seixas, Goncalves & Carvalhais, 2005; Zhang, Hernandez, Anson, Nearing, Wei,
Stone & Heilman, 2012).
However, one of the oldest and most widely used soil erosion model is RUSLE which is the
successor to the Universal Soil Loss Equation (USLE). USLE was created in 1978 by the
USDA with a goal to model water erosion in order to be able to implement efficient soil
conservation practices in United States (Renard, Yoder, Lightle & Dabney, 2011). The data
which the model is created upon is based on statistical relationships gathered from over
10,000 plot-years of data collected over 70 years of erosion data with standardized plots in
slope amount and dimension (ibid.). In 1985 however it was decided that the USLE model
were to be updated to accommodate new technology and thus development began on the
RUSLE (McCool, Foster, Renard, Yoder & Weesies, 1995). The main structure of the
RUSLE equation is:
𝐴 = 𝑅 × 𝐾 × 𝐿𝑆 × 𝐶 × 𝑃
(1)
where A is the estimated mean annual soil loss in ton/hectares per year (t/ha/y), R is the
rainfall erosivity factor, K is the soil erodibility factor, LS is the slope length and steepness
factor, C is the cover management factor and P is the soil conservation factor (ibid.).
Each factor in RUSLE represents a part to estimate the soil erosion caused by rill and inter-rill
erosion. The rainfall erosivity factor represents the climatic potential for water erosion and
there are equations and versions of the RUSLE estimating thaw cycles in soils and snowmelt
as a part of the rainfall erosivity factor (Wischmeier & Smith, 1978). The soil erodibility
factor represents the potential soil erosion due to soil characteristics which are for example
soil structure class, primary particle fraction, soil particles sizes, chemical buildup or the
permeability of the soil depending on what equation is used and what kind of soil data is
10
available for use (Ibid.). The slope length and steepness factor is linked to three main
phenomena where the slope length is the determining factor for rill and inter-rill erosion and
the steepness factor is linked to the magnitude of the potential runoff as a flat topography will
not produce pronounced sheet flows (Ibid.). The cover management factor is defined as the
ratio of soil loss from land cropped under specific conditions to the corresponding loss from
clean-tilled, continuous fallow (Ibid.). The cover management factor can be determined by
vegetation canopy cover by measurements of characteristics of vegetation from crops, shrubs
or trees but the cover management factor can also imply management practices such as
applied plant residue or mulch (Ibid.). The soil conservation practice factor encompasses
erosion reducing practices by humans that are not attributed by naturally occurring vegetation
such as terrace farming, minimizing and management of runoff, planting dates to minimize
soil erosion and sustainable tillage practices to name a few (Ibid.).
RUSLE is mainly a computer program but several articles have implemented it into a GIS
environment where multiple different iterations exist to calculate each factor in RUSLE. This
is one of the main strengths of RUSLE as it is very flexible and relatively simple in a GIS
environment. An example of this notion is the article by Panagos et al (2015a) where the
rainfall erosivity factor is calculated by using several variables such as maximum rainfall
during a 30-minute event and rainfall volume during a time period to name a few. Contra to
this there also exist methods where fewer variables are implemented such as in the article by
Shi, Cai, Ding, Li, Wang & Sun (2002) where only total annual precipitation and total
monthly precipitation is needed to calculate the rainfall erosivity factor.
3.3 Climate change and representative concentration pathways
The climate of the future is not clear due to how factors such as socio-economics, technology,
land use, and emissions of greenhouse gases will change and unfold (van Vuuren et al,
2011a). A climate change scenario represents a specific possible future climate with for
example high amounts of green technology contra a scenario with low amount of green
technology.
The dominant climate change scenarios are the RCP (representative concentration pathways)
family of climate change scenarios. There exist mainly four RCP scenarios which are the
RCP2.6, 4.5, 6 and 8.5. The two latter numbers indicate the radiative forcing target level for
the year 2100 given a specific timeline, where the radiative forcing is the net change in the
energy balance of the earth system due to some forcing agent expressed in watt per square
meters (W/m2) (Myhre et al, 2013; van Vuuren et al, 2011a). These radiative forcers can be
11
anthropogenic or natural which can be greenhouse gas emissions or volcanic eruptions
respectively (Myhre et al, 2013).
The RCP2.6 trajectory signifies immediate anthropogenic intervention with strong climate
change mitigation (van Vuuren et al, 2011b). The RCP4.5 trajectory signifies stabilization of
greenhouse gas emissions which as the RCP2.6 is also a scenario containing anthropogenic
climate change mitigation but as prolific (Thomson et al, 2011). The RCP6 trajectory is
similar to RCP4.5 but where climate change mitigation policies and technology
implementations are not as strong (van Vuuren et al, 2011a). The RCP8.5 trajectory signifies
what is called as the “business as usual” trajectory with an increase in population, slow socioeconomic development and slow innovation/implementation of technology (Riahi et al, 2011).
All four RCP trajectories can be seen in fig 2 in regards to air temperature increase.
Figure 2: Graph showing the four RCP trajectories from the year 1850 to 2300 in regards to temperature change
in degrees Celsius (Collins et al, 2013).
It is projected that there will be a mean annual increase of precipitation in northern and central
Europe but a decrease in southern Europe (Christensen et al, 2013). The increase of
precipitation is projected to increase in northern and central Europe due to warmer winter
months resulting in more precipitation as rainfall which could result in higher amounts of soil
erosion (ibid.).
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4 Study area
The study area is the Kungsbackaån watershed which is situated in the lower southwestern
part of Sweden south of Gothenburg as can be seen in figure 3. The Kungsbackaån watershed
situates mainly in the Kungsbacka municipality.
Figure 3: Map showing the geographic location and extent of the Kungsbackaån watershed with elevation
represented in meters relative to the sea level.
The watershed is heterogeneous in the sense of land cover with agricultural areas, urban areas
and forests where the two biggest urban areas are Kungsbacka and Lindome (Kungsbacka
kommun, 2015). The total area of the watershed is 301km2 which makes it one of the smaller
main watersheds in Sweden.
The area of Kungsbackaån watershed was chosen due to the good availability of data on the
watershed as in soil characteristics. There is also an advantage in the sense of heterogeneity as
in the case of soils, topography, vegetation and land cover. The size of the watershed is
favorable as high resolution elevation data is very processing heavy which limits the choice
when using a personal computer.
In the lower western part one can find most of the urban areas present in the watershed. In the
same area there are multiple locations with agricultural practices. The northern eastern part is
13
more dominated by forests and less so of urban and agricultural areas.
The Kungsbackaån watershed has a wide variety of soils. The most dominant soil types
present in the watershed are thin layers of soils overlying what is classified as bedrock.
Secondly soils with an abundance of clay and silts are very prevalent in the study area. The
least prevalent soils in the watershed are those of larger particle grain sizes such as sandy soils
and moraines.
The watershed has a heterogeneous topography with flatter areas in the south western part and
higher elevations coupled with sharp contrasts in elevation differences in the north western
part of the watershed as seen in figure 3. The watershed can be classified as typical Swedish
west coast in terms of geomorphology. A landscape of rifts is present as there are steep slopes
in the form of bare rock cliffs. Slopes present in the watershed can thus be very abrupt and
sharp at some locations. This can be seen throughout of the watershed but mostly in the south
western part of the watershed.
The mean annual precipitation for the watershed is 953mm which is based upon the years
spanning from 1961 to 1990 (SMHI, 2015). The mean annual precipitation for the watershed
differs from this baseline value between years and was 22,7% higher in the year of 2014 as an
example (ibid.).
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5 Methodology
In this section firstly the equations used to calculate each factor in RUSLE will be presented.
Second the data needed to calculate each factor will be described. Lastly the process of
implementation of RUSLE in ArcGIS will be presented.
5.1 RUSLE factor methodology
The rainfall erosivity factor (R) was calculated using the method by Prasannkumar, Shiny,
Geetha & Vijith (2011). The method was chosen as the calculation only requires data on
monthly and annual precipitation which becomes well suited for future prediction where daily
rainfall erosivity data and storm intensity is hard to come by. R is expressed as:
12
𝑅 = ∑ 1.735 ×
(2)
𝑇2
(1.5𝑙𝑜𝑔 𝑖 −0.08188)
𝑇
10
𝑖=1
where Ti is the total amount of precipitation a given month and T is the total annual
precipitation.
The method by Kouli, Soupios & Vallianatos (2009) was used to calculate the soil erodibility
factor (K). The method is suitable where there is limited data on exact particle size
distribution and organic soil content in the sample data (Ibid.). K is expressed as:
2
𝑙𝑜𝑔𝐷𝑔 + 1.659
𝐾 = 0.0034 + 0.0405 × exp [−0.5 (
) ]
0.7101
(3)
𝑑𝑖 + 𝑑𝑖−1
𝐷𝑔 = exp [∑ 𝑓𝑖 ln (
)]
2
(4)
where Dg is the estimated geometric mean particle size in mm. In equation 3 di is the
maximum diameter (mm) and di-1 is the minimum diameter of that particular particle size
class such as clay, silt, sand and gravel. fi is the mass fraction corresponding to the particle
size class.
15
The slope length and slope steepness factor (LS) was calculated with the method used by
Demirci & Karaburun (2012). The method is well suited to be implemented in a GIS
framework due to three variables in the calculation depend on information derived from a
DEM (Digital elevation model). LS is expressed as:
𝐿𝑆 = (𝐹𝑙𝑜𝑤𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛 ×
𝐶𝑒𝑙𝑙𝑠𝑖𝑧𝑒 0.4
sin 𝑠𝑙𝑜𝑝𝑒 1.3
) ×(
)
22.12
0.0896
(5)
where FlowAccumulation is the raster layer containing information about flow accumulation
which is the total amount of cells flowing in to that cell, Cellsize is the resolution of the
elevation data and slope is the slope amount in radians.
To calculate the cover management factor (C) the method used by Jianping, Tao, Rahimy, Fu,
Haiying & Jiahua (2012) was chosen which uses NDVI (Normalized difference vegetation
index). According to Durigon, Carvalho, Antunes, Oliveira & Fernandes (2014) one can
estimate the cover management factor accurately and fast with NDVI using remotely sensed
images which removes time consuming field surveys. NDVI is a measurement of the red and
NIR (Near-infrared) spectrum ranging from -1 to 1. High positive values indicate high
photosynthetic activity which can be translated to abundant or healthy vegetation while lower
negative values indicate the opposite (Ibid.). The calculation derives the NDVI value to a
corresponding land cover type such as a forest or bushes to name a few (Jianping et al, 2012).
C is expressed as:
𝐶 = 0.227 × exp(−7.337 × 𝑁𝐷𝑉𝐼)
(6)
where NDVI is the data derived from a remotely sensed image as a raster grid layer.
The soil conservation factor (P) is not deemed critical to the RUSLE equation as many studies
such Kouli et al (2008) and Demirci & Karaburun (2011) leave it at 1 thus omitting it from
the analysis. The omitting occurs if there are no data available on soil conservation practices.
The factor value for soil management in this study is assumed to be null as no information on
soil conservation is available or gathered. Thus the value for the soil management factor was
set as 1.
16
5.2 Data description
5.2.1 Rainfall erosivity factor data
In order to calculate a future rainfall erosivity statistically downscaled precipitation datasets
of the HadGEM2-ES, MIROC5 and MPI-ESM models were chosen. The precipitation
scenarios from the respective models were acquired from the website worldclim.org which is
a free climate data service. The scenarios RCP4.5 and RCP8.5 for the year 2070 (The average
for 2061-2080) were chosen in order to be able to compare results from a relatively mild and
realistic future climate change scenario and a worst case one. RCP2.5 was deemed to
unrealistic as a goal for 2070 and RCP6 was not deemed not to different from RCP4.5. The
precipitation scenario data comes as a raster format with a resolution of 30 arc seconds (1km
at the equator) in the GCS WGS 1984 reference system. Each dataset contains 12 raster layers
where each one represents a month in the year.
The HadGEM2-ES model is a climate model developed by the Met Office Hadley Centre in
the UK to be used in the Coupled Model Intercomparison Project (CMIP5) (Collins et al,
2011). The MIROC5 model is a climate model created jointly by Center for Climate System
Research (CCSR), National Institute for Environmental Studies at the University of Tokyo
(NIES) and the Japan Agency for Marine-Earth science and Technology for usage in CMIP5
(Watanbe et al, 2010). The MPI-ESM is a climate earth-system model as the HadGEM2-ES
and was created by the Max-Planck Institute in Switzerland for usage in CMIP5 (Giorgetta et
al, 2013).
Present day precipitation data was acquired from worldclim.org. The present day precipitation
dataset is for the period 1950-2000 which is made out of interpolation from observed weather
station data (Hijmans, Cameron, Parra, Jones & Jarvis, 2005). The dataset is from several
sources such as The global climatic network dataset (GHCN) and more regional ones such as
Nordklim for Scandinavian countries to name a few (Ibid.).
The choice of choosing this current dataset for precipitation stems in the argument of having
homogenous data from the same source, reference system and resolution in order to have a
more seamless comparison.
5.2.2 Soil erodibility factor data
Data on soil was provided by the Swedish Geological Survey (SGU) with soil samples
containing information on their characteristics in order to calculate the soil erodibility factor.
The soil sample point data is provided as a vector format in the SWEREFF99TM reference
system as points where each point represents a soil sample, sampling dates span over five
17
decades and at this present moment this soil sample data is not a publically available product
but will be released as one in the near future according to Gustav Sohlenius (personal
communication, 14 april 2016). Each sample point contains information on particle size
classes and their total mass percent fraction, sample depth and, if it was sampled, chemical
data such as calcium and organic matter to name a few (Ibid).
A Soil map from SGU was downloaded from the Geodata Extraction Tool in order to link
these soil sample points to a specific soil type. The soil map is in a vector format as polygons
which contains information about the geographical shapes and extent of soil types and comes
in the SWEREFF99TM reference system. The soil data in the Kungsbackaån watershed was
gathered by field surveys, aerial photography, economic and topographical maps with a mean
error in position of 50-75 meters (Sveriges Geologiska Undersökning, 2014).
5.2.3 Slope length and slope steepness factor data
Elevation data from Lantmäteriet acquired from the Geodata Extraction Tool was used to
compute the slope steepness and the slope length factor. The data is in a ASCII grid raster
format with a 2-meter resolution derived from laser data as a triangulated irregular network
(TIN) in the SWEREF99 reference system with a mean height error of 0,05 meters
(Lantmäteriet, 2015).
5.2.4 Cover management factor data
Cover management data was gathered from the SACCESS (Nationella satellitdatabasen) from
Lantmäteriet. The data chosen was a remotely sensed raster image from the OLI TIRS sensor
on the Landsat 8 satellite from the U.S Geological Survey, the date of acquisition is 2015-0821 with a cloud coverage of 0 in the GCS WGS 1984 reference system. The OLI TIRS sensor
captures the RGB and NIR (Near infrared) spectral band with a resolution of 100 meters
which is resampled to a resolution of 30 meters (U.S. Geological Survey, 2015).
5.2.5 Other data
Vector data was gathered from Lantmäteriet using their Geodata Extraction Tool for land
cover classification and types to be able to remove non-erodible surfaces like urban areas and
waterbodies. The land cover classification in the dataset are derived from scanning and
digitalization of analog maps, information gathering from municipalities or regions and
databases from multiple government institutions (Lantmäteriet, 2013).
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5.3 Implementation in GIS
RUSLE was implemented in the ArcGIS ArcMap 10.3.1 version where each factor of the
RUSLE was calculated separately. Each factor was later multiplied together as equation 1
shows to obtain an estimated mean annual soil loss as can be seen in figure 4. A total of 3
estimations of estimated mean annual soil loss were made with one as present precipitation,
precipitation in the year 2070 with Rcp4.5 and precipitation in the year 2070 with Rcp8.5. All
the tools mentioned in this section refer to geoprocessing tools available in the ArcGIS 10.3.1
suite unless stated otherwise.
Figure 4: Processing scheme for the RUSLE factors within a GIS.
5.3.1 Implementing the soil erodibility factor
The soil erodibility factor was calculated for each soil type of the soil map polygon layer by
using equation 4 on each soil sample point and then applying that value to the corresponding
overlapping soil type in order to use equation 3. The ISO 14688-1:2002 soil particle size
classification system was used for the maximum and minimum soil particle size for gravel,
sand, silt and clay. For example, if a soil sample point was overlapping a glacial sandy soil
that soil type across the study area would inherit the mean geometric particle size value from
the soil sample point overlapping it.
If there were multiple soil sample points on the same soil type the average of the soil sample
points mean geometric particle size was used for the soil type. Soil types that did not have an
overlapping soil sample point in the study area got their mean geometric particle size from the
19
corresponding soil type outside the study area where a soil sample point is overlapping the
corresponding soil type. For example, there did not exist a soil sample point on any peat
mosses in the study area and therefore a soil sample point overlapping a peat moss outside the
study area lay basis for the mean geometric particle size for the peat moss soils inside the
study area. If, however there was no soil sample point present inside or outside the study area
within the 6km radius an estimation of the mean geometric particle size was made as in the
instance of post glacial fine sand and sandy ice river sediment. This could be made as
according to the ISO 14688-1:2002 soil classification system with post glacial fine sand due
to fine sand situating in the lower end of the sand class and above the silt class. The post
glacial fine sand thus got the value between the calculated geometric mean particle size of
ordinary sand and coarse silt which are the larger particle size class above and smaller particle
size class below respectively. With sandy ice river sediment, the geometric mean particle size
of post glacial sand applied as both contain sand. Bedrock is according to the soil map the
dominant soil type dominant in the watershed. From observations with aerial photography
bare bedrock without any soil cover is not the norm in the watershed and thus the bedrock soil
type is perceived as having a thin soil layer thus overlapping soil sample points on bedrock
lay basis for this soil type.
The first priority was to gather mean geometric particle sizes from soil sample points within
the study area multiple or singular. If a soil type was lacking an overlapping soil sample point
the closest soil sample point to the study area was chosen. The maximum distance for
gathering a mean geometric particle size was with a 6km radius around the study area and
beyond that extrapolation of a mean geometric particle size was deemed unfit.
If a soil sample point was conflicting with the soil type it was overlapping or vice versa it was
omitted from the analysis. For example, if a soil sample point was overlapping a soil
containing gravel according to the soil map and the soil sample point contained no gravel the
soil sample point in question was omitted.
After each soil type in the study area got its corresponding mean geometric particle size
calculated the whole soil type layer was then converted to a raster with a resolution of 2
meters. The raster calculator tool was then applied to this now raster soil type layer with
equation 3 in order to calculate the soil erodibility factor for each soil type present in the
watershed.
20
5.3.2 Implementing the rainfall erosivity factor
The rainfall erosivity factor was calculated first by adding all the raster layers of precipitation
for each month together in the raster calculator tool in order to obtain a single raster layer
containing total annual precipitation. This was done for the current precipitation dataset and
the two future scenario datasets. Each month for the three models were added together and
divided in order to acquire the mean precipitation from the three models for each month, the
same was done with the annual precipitation. Equation 2 was then implemented in the raster
calculator tool for each dataset with its annual and monthly precipitation to obtain the rainfall
erosivity value for the current and the two future scenarios.
5.3.3 Implementing the slope length and slope steepness factor
The slope length and slope steepness factor was firstly processed by creating a slope raster of
the raster DEM with the slope tool set to degrees. The slope raster was then converted to
radians by multiplying 0,01745 to each slope cell using the raster calculator tool as to be able
to compute the sinus of the slope as ArcGIS requires values in radians for the sinus operation
present in equation 5.
The fill tool was used on the DEM to remove extreme or odd height values in order to smooth
out the DEM surface. Later the flow direction tool was applied on this smoothed DEM to
calculate a raster containing flow directions. Then the flow accumulation tool was used with
the flow direction raster as input to create a raster containing information on flow
accumulation. Lastly the calculated slope and flow accumulation raster layers were used in
equation 5 with the raster calculator tool to obtain the slope length and slope steepness factor.
5.3.4 Implementing the cover management factor
The cover management factor was first calculated by importing the thermal radiation
spectrum raster image of the area and using the NDVI tool in image analysis to acquire an
image containing NDVI ranging from -1 to 1. The NDVI raster was then calculated with
equation 6 in the raster calculator tool to acquire the cover management factor.
5.3.5 Calculation of the estimated mean annual soil loss
Last all non-erodible surfaces present in the data such as urban areas and waterbodies were
removed with the clip raster tool in order to minimize unrealistic flow accumulation values.
Stream polyline data acquired from Lantmäteriet was not included in the removal of nonerodible surfaces as the lines representing the streams was deemed too generalized in shape
and did not coincide with the real stream well enough as to be included in the analysis.
21
All the four factors were then implemented in the raster calculator tool as equation 1 states to
acquire the estimated mean annual soil loss for the present year (1950-2000), the year of 2070
with RCP4.5 and RCP8.5. The estimated mean annual soil loss was classified with the system
presented in Panagos et al (2015b) which is the research on European soil erosion done by the
ESDAC (European Soil Data Centre) on conditions present in the year 2010 with RUSLE.
This choice of classification was done in order to be able to validate and compare the output
of this study.
22
6 Results
In this section maps will firstly be shown on the whole watershed with the four calculated
factors within RUSLE except the soil conservation factor. Three maps containing the
estimated mean annual soil loss for each precipitation scenario will later be presented with a
complementary table on the total area coverage in percent for each soil loss class. Lastly two
detailed maps will be shown to see how the estimated mean annual soil loss will look up close
with RCP8.5. These two maps will show small features not visible on the watershed scale
maps on the estimated mean annual soil erosion The two locations were chosen as the areas
were deemed interesting when considering the variety of estimated mean annual soil loss
values. The choice of RCP8.5 stems in showing the maximum possible estimated mean
annual soil loss.
23
6.1 The rainfall erosivity factor for Kungsbackaån watershed
Figure 5 show the calculated rainfall erodibility factor for the present climate (1950-2000),
the year 2070 with RCP4.5 and RCP8.5. For the all the precipitation scenarios the highest
value can be found in the northeastern part of the Kungsbackaån watershed where higher
elevations are found. The lower values can be seen near the coast to the southeast where the
elevation is lower. For the present climate the maximum rainfall erodibility factor value is 337
with a mean of 299. For the year of 2070 with RCP4.5 the maximum value is 390 with a mean
of 344 which is an increase of 52 and 44 respectively compared to the present climate. For the
year of 2070 with RCP8.5 the maximum rainfall erodibility factor value is 407 with a mean of
359 which is an increase of 69 and 59 respectively compared to the present climate.
Figure 5: Map showing the calculated rainfall erosivity factor for present day precipitation (1950-2000), the year 2070 with
RCP4.5 and the year 2070 with RCP8.5. © Lantmäteriet & Worldclim.org.
24
6.2 The soil erodibility factor for Kungsbackaån watershed
Figure 6 show the calculated soil erodibility factor the Kungsbackaån watershed. The higher
soil erodibility factor values are representative of soils where the mean geometric particle size
is small for example in soils with abundance of clay and silt. The maximum value in the
Kungsbackaån watershed is 0,04. The higher values are therefore found near waterbodies and
streams where these kind of soil are more present that in turn is located in the lower parts
elevation-wise and especially in the south west part of the watershed. The lower soil
erodibility factor values are therefore linked to soils where larger geometric mean particle
sizes are more prevalent such as coarse soils overall like moraines where the minimum value
in the watershed is 0,003. The lower values are more prevalent in the north eastern part of the
watershed on locations with higher elevations.
Figure 6: Map showing the calculated soil erodibility factor in the kungsbackaån watershed with a
complementary soil map. © Lantmäteriet & SGU.
25
6.3 The slope length and slope steepness factor for Kungsbackaån watershed
Figure 7 show the calculated slope length and slope steepness factor for the Kungsbackaån
watershed with an image filter applied for better representation in the regional scale with a
stretch of standard deviations with the default at 2,5 standard deviations. The higher values
can be particularly found near streams and such where there is high flow accumulation and
topography with steep slopes. The lower values are found where in areas where there is low
flow accumulation and flat topography. The maximum value of 1716 is an extreme outlier in
the context as the distribution of the slope length and slope steepness factor is highly skewed
towards the higher values. The mean slope length and slope steepness factor value for the
Kungsbackaån watershed is 1,7 with a standard deviation of 6.
Figure 7: Map showing the calculated slope length and steepness factor for the kungsbackaån watershed. ©
Lantmäteriet.
26
6.4 The cover management factor for Kungsbackaån watershed
Figure 8 show the calculated cover management factor in the Kungsbackaån watershed where
the stretch is in percent clip set to 0,5 on maximum and minimum. The higher values are
situated where there is sparse vegetation such as over the shallow soils over the bedrock and
even on surfaces such as highroads which can be seen in south western part. The lower values
can be found mostly in the south west and in areas with high amount of vegetation such as
agricultural areas seen in the middle of the watershed. The lower values clearly dominate the
watershed as a whole due to the large swaths of forests present in the watershed. The
maximum cover management factor value is 0,4 and the minimum is 0,002. The mean cover
management value is 0,02 with a standard deviation of 0,01.
Figure 8: Map showing the calculated cover management factor in the Kungsbackaån watershed. © Lantmäteriet.
27
6.5 The estimated mean annual soil loss for Kungsbackaån watershed
Figure 9 show the calculated estimated mean soil loss for the year 1950 – 2000. Here it can be
seen that the soil loss class of 0 – 0,5 t/ha/y is highly dominant in the region covering 96,2%
of the watershed. The soil loss classes associated with high estimated mean annual soil loss
can be seen close to streams such as in the western part of the Kungsbackaån watershed. High
values can also be seen at locations where there is distinct change and slope steepness in the
topography as in the center of the Kungsbackaån watershed. The estimated mean annual soil
loss for the Kungsbackaån watershed as a whole for the year 1950 – 2000 is 0,11 t/ha/y with a
standard deviation of 1,25 t/ha/y. The maximum estimated mean annual soil loss is 1456
t/ha/y.
Figure 9: Map showing the estimated mean annual soil loss for the present day conditions of precipitation (1950-
2000). © Lantmäteriet, SGU, Worldclim.org & USGS.
28
Figure 10 show the calculated estimated mean annual soil loss for the year 2070 with RCP4.5.
Here it can be seen that the geographic locations of the soil classes remain the same as
compared to the present climate (Figure 9). The class of 0 – 0,5 t/ha/y cover 95,4% of the
watershed which is a decline of 0,8% compared to the present climate. The high values are
still to be found near streams and steep topography with low values permeating throughout
the watershed. The estimated mean annual soil loss for the watershed is 0,13 t/ha/y with a
standard deviation of 1,43 t/ha/y which is an increase from the present climate with 0,02 and
0,18 t/ha/y respectively. The maximum estimated mean annual soil loss is 1645 t/ha/y which
is an increase of 189 t/ha/y compared to the maximum value from the present climate.
Figure 10: Map showing the estimated mean annual soil loss in the year 2070 with RCP4.5. © Lantmäteriet,
SGU, Worldclim.org & USGS.
29
Figure 11 show the calculated estimated mean annual soil loss for the year 2070 with RCP8.5.
Here it can be seen that the geographic locations of the soil loss classes remain at large the
same as compared to the present climate and RCP4.5 (Figure 9, 10). The class of 0 – 0,5
t/ha/y cover 95,2% of the watershed which is a decline from the present climate by 1% and
0,2% from RCP4.5. The estimated mean annual soil loss for the watershed is 0,135 with a
standard deviation of 1,49 t/ha/y which is an increase of 0,005 t/ha/y and 0,06 t/ha/y
compared to RCP4.5. The maximum estimated mean annual soil loss is 1717 /t/ha/y which is
an increase of 72 t/ha/y compared to RCP4.5.
Figure 11: Map showing the estimated mean annual soil loss in the year of 2070 with RCP8.5. © Lantmäteriet,
SGU, Worldclim.org & USGS.
30
Table 1 show the calculated estimated mean annual soil loss in total area coverage in percent
for each soil loss class for all three precipitation scenarios. Here one can see that the soil loss
class of 0 – 0,5 is declining with increased precipitation. With increased precipitation all other
soil loss classes are increasing in total area coverage apart from the highest soil loss class of
>50 t/ha/y. No drastic changes in extent are present however with increase precipitation as the
0 – 0,5 class still is dominating the watershed in all the scenarios.
Table 1: Table showing the total area coverage in percent for each class of estimated mean annual soil loss for
all the precipitation scenarios.
Soil loss t/ha/y
0 - 0,5
0,5 - 1
1-2
2-5
5 - 10
10 - 20
20 - 50
>50
Present (1950-2000)
96,2
2,4
1,0
0,4
0,08
0,03
0,01
0,004
Total area in percent
2070 with RCP4.5
2070 with RCP8.5
95,4
2,8
1,2
0,5
0,09
0,03
0,02
0,005
31
95,2
2,9
1,2
0,5
0,10
0,03
0,02
0,005
100
Figure 12 show the calculated estimated mean annual soil loss in the year of 2070 with
RCP8.5 in a more detailed extent within Kungsbackaån watershed without an applied visual
filter. One can see multiple accounts of rilling occurring throughout the extent where the
corresponding estimated mean annual soil class of 2 – 5, 5 -10 and 10 – 20 t/ha/y dominates.
The very high values of estimated mean annual soil loss can be seen within locations of high
flow accumulations such as within streams and locations of flow accumulation such between
ridges.
Figure 12: Map showing a detailed extent of the estimated mean annual soil loss in the year 2070 with RCP8.5. ©
Lantmäteriet, SGU & USGS.
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Figure 13 show the calculated estimated mean annual soil loss in the year of 2070 with
RCP8.5 in a more detailed extent within Kungsbackaån watershed without an applied visual
filter. Here one can see a flatter topography compared to figure 12 where there is less
pronounced rilling. The very high values of estimated mean annual soil loss are located within
the streams going from top right of the extent to the bottom left of the extent.
Figure 13: Map showing a detailed extent of the estimated mean annual soil loss in the year 2070 with RCP8.5. ©
Lantmäteriet, SGU & USGS.
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7 Discussion and discussion of methodology
7.1 Discussion of the results
As the results show the estimated mean annual soil loss is low in the Kungsbackaån watershed
for the present (0,11 t/ha/y) and future climate (0,13 t/ha/y with RCP4.5 and 0,135 t/ha/y with
RCP8.5). No drastic geographic changes in the case of extent and shape of the estimated mean
annual soil loss are present between the precipitation regimes. There is however a shift from
the lower to the higher estimated mean annual soil loss classes as the precipitation increases in
magnitude. The mean and maximum soil loss is estimated to increase with increased
precipitation but in small amounts as precipitation is not set to drastically increase. According
to the soil loss classification the estimated mean annual soil loss in general within the
Kungsbackaån watershed will stay within bounds of very low amounts of estimated mean
annual soil loss despite the increase in mean annual soil loss. The method utilized in this study
and the results produced can therefore be acknowledged as successful when comparisons are
made with Panagos et al (2015b). In Panagos et al (2015b) the estimated mean annual soil loss
of 0 – 0,5 t/ha/y is the dominant class in the west coast of Sweden for the present climate and
the result for the present climate presented in this study in general show the same result with
the estimated mean annual soil loss of 0,11 t/ha/y.
It should be noted that the estimated mean annual soil loss in Sweden as a whole presented by
Panagos et al (2015b) does not ring true in the Kungsbackaån watershed. The estimated mean
annual mean soil loss presented in Panagos et al (2015b) for the whole of Sweden is at 0,41
t/h/y which is considerably higher than the estimated 0,11 t/ha/y for the Kungsbackaån
watershed. This can be due to the fact that there are other locations in Sweden that are
increasing the estimated mean annual soil loss such as in the north. By this one can argue that
the Kungsbackaån watershed is one of the lesser affected locations by soil erosion in Sweden.
There are particular locations where there is high estimated mean annual soil loss that are
several in numbers across the watershed. These high values are attributed to locations of high
flow accumulation or steep slopes. Panagos et al (2015b) utilizes data in lower resolutions
such as the elevation data having a resolution of 25 meters compared to the 2 meters used in
this study. The use of 2-meter elevation data however brings a great range of values compared
to Panagos et al (2015b) which can be attributed to the more detailed topography achieved in
this study. This means that high resolution data can better model potential locations with high
estimated mean annual soil loss that would otherwise be filtered out and not represented by
low resolution data. Higher resolution data can therefore potentially shed light on where to
34
implement soil erosion mitigation efforts if needed in a very detailed manner. These high
values of soil loss are however hard to validate with other studies as equally high resolution
data is not widely used at this present time. As an example of this Panagos et al (2015b)
argues that elevation data of 25 meters is extremely high.
There are factors that are attributing more than others to the estimated increase in soil loss as
can be seen in the results. One can argue that the slope length and slope steepness factor play
the dominant role when speaking of maximum values in the watershed due to the wide
topographic shapes present in the watershed. Slope length and slope steepness factor values in
other research can have, for an example, maximum values of 120 as in the article by Zhang et
al (2013) which tells us that the maximum values from this study are extraordinary. When
speaking generally the slope length and slope steepness factor is quite constant throughout the
watershed. This makes it not as important when speaking of watershed scale soil loss.
The cover management factor and the soil erodibility factor are arguably the most important
factors when speaking in general terms for the whole watershed. They greatly reduce the
estimated mean annual soil loss with the very low values associated with them. The reduction
is greatly part due to the watershed being covered well by dense vegetation such as forests
and that coarser soils dominate the watershed as a whole. There are however locations in the
cover management factor with very high values far from the mean that are attributed to urban
areas like asphalted roads.
The rainfall erosivity factor is moderately important in the watershed but not as substantial as
when compared to other research. Other locations can yield far greater rainfall erosivity factor
values as in the study by Jiang (2013) on a watershed in Uganda. The study by Jiang (2013)
uses the same iteration of the equation to calculate the rainfall erosivity factor, with values
ranging from 2800 to 800 which dwarfs the rainfall erosivity factor values for the
Kungsbackaån watershed. Furthermore, the future climate rainfall erosivity factor presented
in this study does not increase as to drastically change the estimated mean annual soil loss.
This notion puts the rainfall erosivity factor in perspective in the Kungsbackaån watershed as
not being the most important factor for soil erosion.
7.2 Discussion of methodology and data quality
As in the case of data quality there are gaps that are in need to be addressed in order to fully
appreciate the results presented in this study. High values associated with the slope length and
slope steepness are, as an example, seen near or in streams which indicates that better and
more detailed removal of waterbodies and other non-erodible areas need to be made. The
35
same case is true for the cover management factor with the NDVI values as areas such as
roads produce high values which should be excluded as they are non-erodible. The
complementary data used for this removal process is therefore in too coarse when coupled
with high resolution elevation data and NDVI data. This problem could be corrected by
manual digitization of streams, roads and bare rock to name a few non-erodible surfaces. This
however brings forth a new problem in the sense of time consuming manual digitization and
particularly in the case of doing soil loss modelling on the scale of a watershed. Caution
should be taken with the extraordinarily high values in the slope length and slope steepness
factor and cover management factor that end up in the estimated mean annual soil loss.
Especially if these high values are linked to a questionable location such as within streams or
on bare rock to name a few. Careful attention should be taken with validation that could be
either be realized as explained earlier with detailed digitization work of aerial photography or
in-situ validation of land cover or topography.
The NDVI data used for the study was from one date in the summer. The summer vegetation
has more canopy cover and thus the estimated mean annual soil loss could be higher because
of the sparse amount of vegetation in the winter months. The NDVI is also a measurement of
healthy productive vegetation which in turn does not encompass organic residues covering the
ground as an example (Sotiropoulou et al, 2011).
One important aspect is also the rainfall erosivity factor where snowfall and freeze/thaw
cycles are not included in the analysis. However, since more days with rainfall and less days
with snowfall in the future are to be expected as presented by (Kovats et al, 2014) there is
uncertainty in the future predictions of estimated mean annual soil loss. For the rainfall
erosivity factor a total of 3 models were used and more credible values could be gathered if
more models were implemented to the calculation of the rainfall erosivity factor.
The soil erodibility factor values have flaws in the sense of the estimation of mean geometric
particle sizes as in the case of the thin layer of soil on top of the bedrock, post glacial fine
sand and sandy ice river sediment. The assumption that the bedrock type throughout of the
watershed has a thin layer of soil on top is a generalization where manual digitization of aerial
photography could remove bare rock from the analysis and in-situ validation of soil types if
for example the thin layer of soil is not a moraine.
The soil data as in the case of the soil map and the soil sample point data is also subject to
question. The soil map has a mean location error of 50-75 meters which brings up questions
about the soil types geographical extent and how realistic they are. The soil sample data is
36
gathered from a period of over 5 decades where different soil classification system has been
used within that times span as well as uncertainties in the methodology for deriving soil
particle size distribution. From this the generalized aspect of the soil erodibility factor is
needed to be taken in to account where in-situ field surveys and, as stated above, detailed
digitization of aerial photography could make it more representative.
Another problem related to the temporal resolution of the NDVI data is that, according to this
analysis, the vegetation and general land covers are perceived to be constant throughout
present day until the year 2070. This can be a problem as future land covers can change as
forests are cut down and replanted as well as the possibility of agricultural expansion as
examples.
The last uncertainty is about the RUSLE model itself which is about that RUSLE does not
incorporate sediment deposition and gully erosion (Fistikoglu & Hermancioglu, 2002). For
example, RUSLE only models the dislocation and transportation of soil but not the
deposition. It is very likely that the estimated mean annual soil loss for each 2-meter cell is
lower due to potential deposition of soil in that very same 2-meter cell.
37
8 Conclusion
The results presented in the study show that soil erosion is in general a small problem in the
Kungsbackaån watershed – present and as well in the future. The estimated mean annual soil
loss will increase in the future as precipitation increases but it will stay within bounds of low
soil loss. There are however, in the present and future isolated locations with high amounts of
estimated soil loss. These locations with high estimated mean annual soil loss can bring new
light on otherwise unseen soil erosion due to coarser resolutions but should be taken with
caution if coincided with locations that are non-erodible such as streams and bare rock. The
dominant factor for estimated mean annual soil loss in the watershed is the slope length and
slope steepness factor when considering maximum values. However, in general terms the
cover management and soil erodibility factor are the most important as they contribute to very
low amounts of estimated mean annual soil loss. Publically available data can produce a high
resolution credible estimation of mean annual soil loss when compared to a study on present
day estimated mean annual soil loss. However, inconsistencies exist in the methodology and
data which indicates that the results presented in this study should be taken as a rule of thumb
rather than absolute values of mean annual soil loss.
Future research is needed to validate the estimated mean annual soil loss preferably with the
use of other soil erosion models. Furthermore, knowledge is needed on the interaction on
future freeze/thaw cycles on soil erosion as well as the coupling between land cover changes
and soil erosion. More societal research should be conducted on how climate change might
change the perception and action to produce more agricultural products in Sweden coupled
with the potential soil erosion that it might cause.
38
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