Groundwater Vulnerability Assessment for Railroad Construction in Stockholm, Sweden, Using Spatial Multi Criteria Analysis

GROUNDWATER VULNERABILITY ASSESSMENT FOR RAILROAD CONSTRUCTION IN STOCKHOLM, SWEDEN, USING SPATIAL MULTI CRITERIA ANALYSIS

Irina Tsoy

March 2015

TRITA-LWR Degree Project ISSN 1651-064X LWR-EX-2015:06 Irina Tsoy LWR-EX-2015:06

© Irina Tsoy 2015 Degree project at Master level Division of Land and Water Resources Engineering Royal Institute of Technology (KTH) SE-100 44 STOCKHOLM, Sweden Reference should be written as: Tsoy, I (2015) "Groundwater Vulnerability Assessment for Railroad Construction in Stockholm, Sweden, using Spatial Multi Criteria Analysis" LWR- EX-2015:06

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

SUMMARY IN ENGLISH Due to high demands on transportation Stockholms Lokaltrafik (SL) is in the process of developing a new railroad in the area located North of Stockholm between Vallentuna municipality and Arlanda airport (country’s largest international airport). However, the processes of transportation construction and operations not only contaminate air, soil and nearby surface water bodies but also the saturated zone through the infiltrating water. This study was focused on the impact of the railroad on groundwater, because in Sweden groundwater resources are an important water supply for drinking, household, industry etc. The objective of the study was to develop a model for groundwater vulnerability assessment for transportation planning using Multi Criteria Analysis (MCA) and Geographic Information Systems (GIS). To handle this complex problem seven critical criteria for groundwater vulnerability were identified (such as hydraulic conductivity, infiltration, and effective porosity, depth to groundwater, land cover, topographic wetness index and slope); weighted considering its influence on the groundwater and accuracy of the data; and then combined to produce groundwater vulnerability map. GIS (ArcGIS) tool was utilized in the study in order to handle the spatial heterogeneity within the study area and the complexity of MCA. To evaluate the influence of each criteria on the output vulnerability map the single variable method was used. By comparing the values of theoretical weight (assigned values) and effective weight (the effect of the criterion on the resulting map) of each criterion the most influential criterion was detected. As a result of the study, a groundwater vulnerability map of the study area was produced; which indicates that sand and till deposits are highly vulnerable to contamination due to high infiltration capacity of coarse grain soil, high value of hydraulic conductivity and effective porosity. However, because of low infiltration and hydraulic conductivity together with low effective porosity, clay located on the low elevated areas received the lowest value of vulnerability.

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Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

SUMMARY IN SWEDISH På grund av ökad efterfråga på transport planerar Stockholms Lokaltrafik (SL) en ny järnväg i norra Stockholm mellan Vallentuna kommun och Arlanda flygplats (Sveriges största internationella flygplats). Väg- och järnvägskonstruktion och underhåll förorenar inte bara luft, mark och närliggande vattendrag utan även den mättade zonen genom infiltrerande vatten. Denna studie fokuserar på effekterna av järnvägen på grundvattnet, eftersom grundvatten är en viktig resurs för vattenförsörjning (hushåll, industri etc.) Syftet med denna studie var att utveckla en modell för sårbarhetsanalys av grundvatten för transportplanering genom Multi Criteria Analysis (MCA) och geografiska informationssystem (GIS). För att hantera detta komplexa problem har sju kriterier kritiska för grundvattnets sårbarhet identifierats (t.ex. hydraulisk konduktivitet, infiltration och effektiv porositet, djup till grundvatten, marktäcke, topografisk wetness index (TWI) och lutning); viktats med tanke på dess påverkan på grundvattnet och dess noggrannhet i data; och till sist kombinerats för att framställa en grundvattensårbarhetkarta. GIS (ArcGIS) verktyg användes i studien för att hantera rumslig heterogenitet inom det studerade området samt komplexiteten av MCA. För att utvärdera påverkan av varje kriterium på resulterad sårbarhetskarta tillämpades enda variabel metoden. Genom att jämföra värdena från den teoretiska viktningen (tilldelade värden) och den effektiva viktningen (beräknade värden) för varje kriterium kunde det mest inflytelserika kriteriet identifieras. Som ett resultat av studien, producerades en grundvattensårbarhetskarta över det studerade området vilket indikerade att sand och moränavlagringar är mycket känsliga för föroreningar på grund av hög infiltrationskapacitet i grovkornig jord, högt värde av hydraulisk konduktivitet och effektiv porositet. Men på grund av låg infiltration och hydraulisk konduktivitet tillsammans med låg effektiv porositet, erhöll lera som ligger på låga upphöjda områden det lägsta värdet av sårbarhet.

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Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

ACKNOWLEDGEMENT I would like to express my gratitude to Professor Bo Olofsson for the time he allotted in spite of his busy schedule to give me his valuable advice and to keep my work in the scope of the project. My special thanks to my thesis advisors Caroline Karlsson and Imran Ali Jamali for their patience in guiding me through the whole process of scientific research and for boosting up my confidence when I needed it. Not to mention the great ideas they shared with me, GIS troubleshooting and the numerous proof readings of my paper. I would also like to thank Erasmus Mundus scholarship programme which provided me with financial support during my Master’s study. Here I would also like to express my everlasting gratefulness to my aunt Lera Tsoy for encouraging me to study abroad in the first place and for all the support she gave me throughout the way. I thank my dear friend Mubarek Nesru for squeezing the all of the engineering courses to one term and patiently teaching me everything starting from simple fractions and up to hydraulics. And of course, my study wouldn’t be possible without the support of my parents and my dear sister, my friends and all the others who I haven’t yet thanked.

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Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

TABLE OF CONTENTS Summary in English iii Summary in Swedish v Acknowledgement vii Table of contents ix Abstract 1 Key words 1 1. Introduction 1 1.1. Objective 1 1.2. Theoretical Background 2 1.2.1. Transportation Infrastructure as a Major Source of Groundwater Contamination 2 1.2.2. Groundwater Vulnerability Assessment 2 1.2.3. Spatial Multi Criteria Analysis (SMCA) 3 1.2.4. State of the art 4 2. Materials and methods 5 2.1. Study Area 5 2.1. Identification of Criteria and Rating 6 2.1.1. Hydraulic Conductivity 6 2.1.2. Infiltration Capacity 8 2.1.3. Effective Porosity 9 2.1.4. Depth to Groundwater 10 2.1.5. Land Cover 10 2.1.6. TWI 11 2.1.7. Slope 12 2.2. Weighting 13 2.3. Sensitivity Analysis 14 2.3.1. Robustness Test 14 2.3.2. Single Variable Analysis 14 2.4. Evaluation of Proposed Alternatives 14 3. Results 15 3.1. Weighting 15 3.2. Vulnerability Map 16 3.3. Robustness Test 16 3.4. Single Variable Analysis 18 3.5. Alternatives 18 4. Discussion 19 4.1. Vulnerability Map 19 4.2. Robustness Test 21 4.3. Single Variable Analysis 21 4.4. Alternatives 21 4.5. Uncertainties 22 4.6. Future Perspectives 22 5. Conclusion 22 References 23

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Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

ABSTRACT The environmental impact of transportation construction, operation and maintenance is of critical importance. Therefore it is vital to carry out a groundwater vulnerability assessment for transportation projects at an early stage. The objective of the study was to apply a methodology for groundwater vulnerability assessment for transportation planning in order to identify and avoid areas susceptible to contamination. In this study a spatial multi criteria analysis (SMCA) was conducted where the impacts of railroads on groundwater were examined. Physical factors that influence the groundwater vulnerability such as infiltration capacity, hydraulic conductivity, effective porosity, slope, land cover, depth to groundwater and topographic wetness index (TWI) were considered in this study. Results from the study show that about 30% of the total area was prone to high and very high groundwater vulnerability. The final vulnerability map illustrated the highly vulnerable areas in the sand deposits and till and less vulnerable areas of rock outcrops and clay deposits. According to the outcome of the sensitivity analysis the methodology used in this study shows promising results and can be employed further with some improvements.

KEY WORDS

Groundwater Vulnerability, GIS, MCA, Railroad/Railway Construction, Stockholm

1. INTRODUCTION It is expected that the population of Stockholm will increase during the next 20 years. In order to meet the increasing population demands, infrastructure of the city needs to be updated accordingly. To improve accessibility, Stockholms Lokaltrafik plans to construct a railroad between Vallentuna municipality and Arlanda airport (country’s largest international airport) (Håkansson et al., 2012). Such a project may cause serious environmental consequences, which need to be considered beforehand. In this study the environmental impacts of the transportation projects on groundwater were assessed, as the groundwater resources are important for drinking water supply, household, industry etc. 1.1. Objective The objective of the study was to apply a methodology for groundwater vulnerability assessment for infrastructural projects using geographic information system (GIS) and multi criteria analysis (MCA). It was also amid to choose the most suitable corridor with least potential to groundwater contamination for a railroad project, out of three different corridors proposed by Stockholms Lokaltrafik. The study was focused not on locating suitable areas for the railroad site from the construction point of view, but rather on the impact of the construction, operation, and maintenance of the railroad on groundwater dynamics, which sometimes can have an opposite effect. Specific objectives were: 1. Assess the effects of an infrastructural project on groundwater; 2. Define the major factors responsible for groundwater vulnerability; 3. Identify the most suitable technique for assigning weights to factors; 4. Analyze the sensitivity of the model;

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5. Apply the model to select the most suitable railroad corridor from the groundwater vulnerability point of view. 1.2. Theoretical Background 1.2.1. Transportation Infrastructure as a Major Source of Groundwater Contamination Transportation construction and operations are sources of contamination to air, soil and nearby surface water bodies; as well as to the saturated zone by infiltrating water (Ramwell et al., 2001; Lee et al., 2002; Cunningham et al., 2008; Lundmark and Jansson, 2008; Yoon et al., 2009; Yuen et al., 2012; Zhang et al., 2012). Railroad operation is a significant source of pollutants such as PAH, Zn, Cd, Cu, Fe, Mn, Cr, V, Pb and herbicides (Burkhardt et al., 2008; Zhang et al., 2012). Heavy metals, PAH and other contaminants originate from fuel combustion emissions, usage of substances for rolling stock exploitation, wooden sleepers, material abrasion and oil leaks (Wiłkomirski et al., 2011). Once released, some of them are transferred to the atmosphere by wind and vehicle movement (Lorenzo et al., 2006; Ostendorf et al., 2006; Daley et al., 2009; Budai and Clement, 2011; Fay and Shi, 2012). During winter the pollutants are accumulated in the snow and during spring the snowmelt containing a high concentration of contaminants percolates to the unsaturated and saturated zone (Hautala et al., 1995; Earon, 2011). Adsorption and ion exchange reactions in the unsaturated zone can significantly influence the migration of pollutants and depend upon the type of geology and contaminant. For instance, clay due to large surface area and negatively charged particles has the ability to adsorb pollutants and normally to attenuate. Whereas sand and gravel have a little ability to react with contaminants because of small surface area, which allows the contaminants to easily pass down to the saturated zone (Freeze and Cherry, 1979; Hiscock, 2009). Previous studies showed increased concentration of PAH and heavy metals particularly in the junction and platform areas due to congested traffic movement (Wiłkomirski et al., 2011). Besides chemical effects, transportation infrastructure causes soil compaction followed by changes in the general groundwater level for distances between 5 and 10 m on each side of a road. Changes include drawdown or the rising of groundwater table, reduced hydraulic conductivity and rerouting of water (Kahklen and Moll, 1999). This might affect the groundwater dynamics and abstraction from the wells located nearby the road. 1.2.2. Groundwater Vulnerability Assessment Groundwater is a vital resource and contributes to about half of the domestic needs for water supply. Therefore, it is important to conduct groundwater studies during the planning stage of railroad construction to find a suitable location. Recently different methods have been used to carry out groundwater vulnerability assessment (Maxe and Johansson, 1998; Kim and Hamm, 1999; Focazio, 2002; Gontier and Olofsson, 2003; Antonakos and Lambrakis, 2007; You-Hailin et al., 2011; Shirazi et al., 2012; Pirnia, 2012; Majandang and Sarapirome, 2013). According to Hiscock (2009), vulnerability of groundwater from surface- derived pollution hinges on:  the nature of the overlying soil cover;  the presence and nature of overlying superficial deposits;

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

 the nature of the geological strata forming the aquifer;  the depth of the unsaturated zone or thickness of confining deposits. However, it is significant for successful application of any chosen technique to identify the most important criteria to be assessed depending on the objective. Economic potential plays an important role in vulnerability assessment (Rosen, 1994). For instance, in some cases, aquifers with higher value of hydraulic conductivity and effective porosity i.e. favorable for groundwater production might be given the higher index of vulnerability regardless of the aquifer type; whereas, the aquifers with lower production would be granted a lower index, even though the risk of the contaminants reaching groundwater level could be the same. It is difficult to take all these factors into account and meet all the needs and expectations during vulnerability assessment (Maxe and Johansson, 1998). 1.2.3. Spatial Multi Criteria Analysis (SMCA) To handle this complex problem spatial multi criteria analysis (SMCA) was used, where MCA techniques were applied in a GIS environment. MCA is the procedure of designing or choosing the best alternatives based on the goals and preferences of the decision maker. The basic strategy is to divide the decision problem into small, understandable parts; analyze each part; and integrate the parts in a logical manner to produce a meaningful solution (Malczewski, 1999). Because of its versatility, MCA is widely used by the researches in different fields, such as urban planning (Chen et al., 2012), land use planning (Arciniegas et al., 2013), choosing the most suitable location for roads (Tille and Dumont, 2003) and various types of installations (Jamali et al., 2013; Perpiña et al., 2013). It is also practical for finding the way for sustainable distribution of scarce natural resources (Buchholz et al., 2009; Afify, 2010), for prediction of environmental impacts, their location and magnitude. Malczewski (1999) defined the general procedure for MCA, where “six components are involved: 1. a goal or a set of goals the decision maker or group of decision makers (interested group) attempts to achieve; 2. the decision maker or group of decision makers involved in the decision-making process along with their preferences with respect to evaluation criteria; 3. a set of evaluation criteria on the basis of which the decision makers evaluate alternative courses of action; 4. the set of decision alternatives, that is, the decision or action variables; 5. the set of uncontrollable variables or states of nature (decision environment); 6. the set of outcomes or consequences associated with each alternative- evaluation criteria pair.” The preferences are usually processed in terms of weights given to each evaluation criteria. Due to the complexity of MCA and spatial heterogeneity within the study area the process of decision making can be cumbersome. GIS is known for its capability in spatial data manipulation, acquisition, storage and analysis. GIS is therefore essential to MCA process as in general the majority of decision problems are connected to land use and land cover. Thus, the GIS-based MCA is a very advantageous tool, where data

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processed by using GIS serve as an input to obtain information for making decision (Malczewski, 1999). 1.2.4. State of the art MCA has been used for transportation planning by numerous researchers (Tille and Dumont, 2003; Cantarella and Vitetta, 2006; Brucker and Macharis, 2011; Gühnemann et al., 2012), however the main focus of the MCA was cost efficiency and road safety; and little attention was paid to sustainable management of the environment. Later on Hayati et al. (2013) performed a MCA for forest road planning taking into account environmental issues, such as soil erosion, pollution of off-site water and loss of habitat (Hayati et al., 2013). One example of using GIS for assessment of groundwater vulnerability is a DRASTIC model (Allen et al., 1987) developed by American National Water Well Association. The model uses seven parameters to classify the vulnerability, or pollution potential, of an aquifer (i.e. Depth to Water, Recharge, Aquifer Media, Soil Media, Topography, Impact of Vadose zone, and Hydraulic Conductivity of the Aquifer). The parameters are weighted according to their relative importance to the pollution potential of the aquifer. Depth to water and impact of vadose zone media are considered as the most important parameters and given the weight of 5, whereas topography is less important and given the weight of 1 (Rosen, 1994). The model is used worldwide (Fritch et al., 2000; Panagopoulos et al., 2006; Rahman, 2008; Wen et al., 2009; Javadi et al., 2011), however, it is argued by Rosen (1994) if the method is suitable for Swedish conditions. In Sweden areas with fractured bedrock outcrops mixed with areas of thin layers of glacial clay are predominant (Olofsson, 2002), which allows fast transportation through the fractures to the groundwater. Rosen (1994) suggests that reorientation of parameters should be done considering Swedish geology, to include such factors as sorption, travel time, and dilution.

Fig.1. Location of the study area.

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

2. MATERIALS AND METHODS 2.1. Study Area The area of interest is located in the northern part of Stockholm, between Arlanda airport (Sigtuna municipality) and Vallentuna station and is approximately 235 km2 (Fig. 1). The area has humid continental climate, with average annual precipitation of 540 mm and average annual temperature of +5°C. Monthly average temperature varies throughout the year from +1 in January to +17°C in July. Average annual evapotranspiration rate is 400 mm. During the cold season precipitation takes the form of snow and the average first day of snow is November 15 with the annual average maximum snow depth of 40 cm (SMHI (Sveriges Meteorologiska och Hydrologiska Institut), 2013). The geology in the Stockholm region in general and in the study area in particular, mostly consists of crystalline pre-cambrian rocks of the Baltic Shield (Persson, 1998). Rock outcrops dominate in the elevated areas, and are in some parts covered with a thin layer of sandy till. The rocks in the study area are predominantly granite and granodiorite with some minor areas of quartz diorite and fieldspat quartz (Fig. 2). A valley crosses through the study area in the south-western direction; the bottom of the valley is covered with glacial clay, silt, and peat in the deepest parts. The long glacio-fluvial deposit (Stockholmsåsen) is located in the western part and consists of sand and gravel; and is an important source of groundwater. The hydrology of the area is represented by numerous watercourses, three relatively big lakes and many small lakes. There are also six springs, two of them flow out from the esker and four of them flow out from the small area formed with fine sand on the eastern part of the study area.

Fig. 2. Geology of the study area (Geological Survey of Sweden, 2012).

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Table 1 List of criteria with data layers

Factor layers Type and scale Source of Data Hydraulic Polygon, 1 : 50 K Soil map, (SGU*) conductivity Infiltration Polygon, 1 : 50 K Soil map, (SGU*)

Effective porosity Polygon, 1 : 50 K Soil map, (SGU*) Depth to Polygon, 1 : 50 K Soil map, (SGU*) groundwater Point data Well archive, (SGU*) Land cover data, Land cover Raster, 1 : 50 K (NLSS**) Topographic Raster, 1 : 50 K DEM, (NLSS**) wetness index Slope Raster, 1 : 50 K DEM, (NLSS**)

SGU* (Geological Survey of Sweden, 2012)

NLSS** (National Land Survey of Sweden, 2012)

2.1. Identification of Criteria and Rating MCA assumes a set of criteria to be defined and analyzed in order to solve a problem. Because of the complexity of the problem (groundwater vulnerability), many different factors or criteria were considered on the subject of the aim of the study and data availability. A final list of seven criteria was developed (Table 1) taking into account previous research in this field. The criteria include hydraulic conductivity, infiltration, effective porosity, depth to groundwater, land cover, topographic wetness index and slope. The effect of each factor was divided in classes and each class was given a rating according to its effect on the groundwater vulnerability:  Very high vulnerability 10;  High vulnerability 8;  Moderate vulnerability 5;  Low vulnerability 2;  Very low vulnerability 1. The concept of groundwater vulnerability is not absolute and is rather complex; it should therefore be tailored to the problem. For this study the following criteria were used and for each one a rating map was produced: 2.1.1. Hydraulic Conductivity Hydraulic conductivity has dimensions [L T-1] and is a measure of ease of movement of water through a porous material (Hiscock, 2009). In this study, hydraulic conductivity represents a horizontal flux. There is a wide range of values of hydraulic conductivity in nature, from 10 m/s for karstified limestone to 10-13 m/s for igneous and metamorphic rocks. Hydraulic conductivity depends on the grain size distribution, the structure of the soil matrix and type of fluid. Overall, the coarser the grain size or the more fractured the material is, the higher the value of hydraulic conductivity will be; and vice versa, clay and silt due to their fine grain size have very small value of hydraulic conductivity. Hydraulic

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

Table 2 Rating of the criteria Vulnerability Criteria Classes Rating level Rock Moderate 5 Gravel Very high 10 Sand High 8 Hydraulic Peat Low 2 conductivity Silt Low 2 Sandy till Moderate 5 Clay Very low 1 Rock Low 2 Gravel Very high 10 Sand High 8 Infiltration Peat Moderate 5 Silt Low 2 Sandy till High 8 Clay Very Low 1 Rock Very high 10 Gravel Very low 1 Sand Low 2 Effective Peat High 8 porosity Silt High 8 Sandy till Moderate 5 Clay Very High 10 Rock (5-10 m) Very low 1 Gravel (5 m) Low 2 Depth to Sand (2-5 m) Moderate 5 groundwater Peat (0.5 m) Very high 10 (m) Silt (1 m) High 8 Sandy till (1-2 m) High 8 Clay (1 m) High 8 Rock outcrops Very high 10 Wetlands Very low 1 Land cover Grassland Low 2

Forest Moderate 5 Urban Very high 10 0 – 3.6, 19.3 – 41.8 Very low 1 3.6 – 5.8, 15.6 – 19.3 Low 2 Topographic 5.8 – 7.7, 13.2 – 15.6 Moderate 5 wetness index 7.7 – 9.5, 11.4 – 13.2 High 8 9.5 – 11.4 Very high 10 0 - 2 Very high 10 2 - 6 High 8 Slope (%) 6 - 12 Moderate 5 12 - 18 Low 2 18 > Very low 1

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Fig. 3. Vulnerability rating of hydraulic conductivity. conductivity was considered directly proportional to the groundwater vulnerability in this study. This is based on the hypothesis that higher hydraulic conductivity will allow more transmission and distribution of pollutants. For this study the values of hydraulic conductivity were classified according to the typical range of hydraulic conductivity values of different soil types (Freeze and Cherry, 1979). The classification of hydraulic conductivity with given rating is presented in the Table 2. Fig. 3. represents spatial distribution of different hydraulic conductivity classes and assigned vulnerability rating. 2.1.2. Infiltration Capacity The process of infiltration is the entry of water into the soil which is available at the groundwater table, together with the associated flow away from the surface within the unsaturated zone (Freeze and Cherry, 1979). Soil infiltration capacity depends on the grain size distribution, hence well-sorted coarse-grained soil or fractured rock has better infiltration capacity than silt or clay. From the groundwater vulnerability point of view areas with high infiltration capacity are most undesirable, since the travel time for water due to the macro-pore flow is short, whereas the slow infiltration through fine grained material increases the possibilities for physical, chemical, and biological reactions, which attenuates the contaminants (Maxe and Johansson, 1998). The classification of infiltration capacity (Fig. 4) was based on the results of previous studies (Maxe and Johansson, 1998), where different soil types were assigned different infiltration capacity values and therefore different vulnerability rating (Table 2).

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

Fig. 4. Vulnerability rating of infiltration capacity.

2.1.3. Effective Porosity The concept of effective porosity is not the same as the total porosity, the total porosity relates to the storage capability of the material, whereas effective porosity is related to the transmissive capability of the material (Hiscock, 2009). In coarse-grained soils and fractured rocks with large pores and fractures, the capillary films, which surround the solid particles, occupy only a small proportion of the pore space; water is therefore not bound due to the capillary forces and can easily flow, therefore effective porosity is almost equal to the total porosity. In fine-

Fig. 5. Vulnerability rating of effective porosity.

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grained soils, capillary forces dominate and water is bound, as a result the value of effective porosity is much lower than the total porosity (Horton et al., 1988). During a rainfall event the first flash normally contains the highest concentration of contaminants (Lee et al., 2002). High value of effective porosity allows more water to percolate and by dilution to reduce the concentration of the pollutants. A low value of effective porosity may result in a fast transportation of polluted water to the recipient. For effective porosity (Fig. 5) the rating was motivated by the general values of effective porosity of different soil types (Hiscock, 2009), the rating is presented in Table 2. 2.1.4. Depth to Groundwater Depth to groundwater represents the thickness of the unsaturated zone from the surface to the water table (Allen et al., 1987). The factor characterizes the potential of unsaturated material to accumulate and degrade the contamination as the potential contaminants must travel this distance before reaching the groundwater, greater depth implies lower vulnerability. The information about depth to the groundwater was retrieved from the well archive but also through general assumptions that for instance in marshlands and bogs the groundwater level is shallow (almost at the ground surface) (Table 2). Figure 6 illustrates the spatial distribution of different vulnerability rating. 2.1.5. Land Cover Depending on the type of land cover the rate of degradation of the contaminants can vary greatly. High organic content i.e. high biological activity coupled with long travel time result in increased retention and degradation of pollutants. In the areas of bare soil or areas with a thin layer of overburden material during/after the rainfall event, almost all the contaminants presented in a runoff can travel to the groundwater due to short travel time and lack of organic barrier. Whereas, in the forests, where canopy and forest litter serve as a barrier for water percolation (Peng et al., 2004) the degradation of the contaminants is

Fig. 6. Vulnerability rating of depth to groundwater.

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

Fig. 7. Vulnerability rating of land cover. higher. Asphalt and rock outcrops prevent water from straightway percolation as well, so the groundwater vulnerability is low. Water discharge and high biological activity in wetlands also prevent the groundwater from contamination. The classification of land cover with respect to groundwater vulnerability (Fig. 7) was based on the results of previous studies (Bouwer, 2002) and presented in the Table 2. 2.1.6. TWI Topography highly affects spatial distribution of moisture and the groundwater flow often follows surface topography (Sørensen et al., 2005). In 1979 Beven proposed Topographic Wetness Index (TWI), taking into account runoff changes due to topography. The TWI can be calculated as: 훼 ln( ⁄tan 훽), (1)

Where a is the local upslope area draining through a certain point per unit contour length and tanβ is the local slope (Beven and Kirkby, 1979). Usually TWI is calculated using digital elevation model (DEM). High TWI values are considered as areas accumulating water, such as wetlands, marshes and watercourses. They are located in areas with low elevation where groundwater discharges to the surface water; therefore contaminants cannot be transported to the groundwater. Areas with low values of TWI represent highly elevated areas where the depth to the groundwater table is significant along with a small fraction of infiltration, which also reduces the groundwater vulnerability. Moreover groundwater levels, on high positions with lower TWI values, will be deeper which will limit the transport of pollutants to groundwater. Hence very high and very low TWI values give very low vulnerability. So the middle range of TWI values was considered as the most vulnerable to the groundwater contamination.

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Fig. 8. Vulnerability rating of TWI. In this study TWI was calculated using DEM of the study area. The results were classified by using Jenk’s natural breaks method (ArcGIS built-in tool), where the features are divided into classes whose boundaries are set where there are relatively big jumps in the data values (Table 2). The map of spatial distribution of TWI rating is presented on the Figure 8. 2.1.7. Slope Different areas have different infiltration capacity depending on the slope. The fraction of runoff is greater on steeper slope and the fraction of infiltration is smaller, hence contaminants are transported downslope with the runoff. This reduces the risk of contaminants being transferred

Fig. 9. Vulnerability rating of slope.

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

to the groundwater in areas with steeper topography and hence reduces susceptibility in such areas (Allen et al., 1987). DEM was used as an input to calculate slope values, which were reclassified according to the results of previous studies (Allen et al., 1987). Table 2 shows the rating given to the different slope values. Figure 9 illustrates the spatial distribution of different vulnerability classes depending on slope. 2.2. Weighting Groundwater vulnerability of the area cannot be assessed considering the impact of each criterion in isolation. Different factors when acting together can increase or decrease the impact of each other on groundwater vulnerability. And the effects of some factors are more significant, than the effects of others. Therefore it is necessary to take into account the integration of all the criteria (Shaban et al., 2001). A similar method was presented by Shaban et al. (2001) where during the weighting stage the interaction between different factors was taken into account. The relative importance of the factors was estimated depending on the number of connections between one particular factor and the others. As for example steep slope together with low hydraulic conductivity and absence of grass would reduce the infiltration capacity to a great extent. And at the same time high infiltration capacity, high hydraulic conductivity and high effective porosity would reduce the depth to the groundwater level. Weight of each criterion was calculated as the percentage, where 100% is the total weight of all the criteria. During the weighting stage the accuracy of the data and the importance of the criteria in terms of groundwater vulnerability were taken into account. Accuracy of the data was user defined in % depending on the quality of data (Gontier and Olofsson, 2003). To obtain the resulting groundwater vulnerability map each criterion was multiplied by its weight and accuracy percentage and finally combined:

(2) 푆 = ∑ 푤𝑖푥𝑖,

where S is the final map of the vulnerable areas (vulnerability index), wi is the weight of factor i (percentage) and xi is the rate of the factor i (Kourgialas and Karatzas, 2011). A list of constraints was also developed (Table 3) that consists of groundwater wells, risk objects (landfills and gravel pits), and also springs (all with protection zone with a radius of 100 meters). The resulting groundwater vulnerability index map is multiplied with the constraints map to form the final groundwater vulnerability map.

Table 3 List of constraints with data layers Constraint Layers Type and Scale Source of Data Groundwater wells Point data Well archive, (SGU*) Risk objects with Groundwater data, Point data, 1 : 50 K protection zone (SGU*) Springs with protection Groundwater data, Point data, 1 : 50 K zone (SGU*) SGU* (Geological Survey of Sweden, 2012)

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2.3. Sensitivity Analysis 2.3.1. Robustness Test Sensitivity analysis of the criteria weights was performed in order to test the model for deviations caused by slight changes. To do so, three sets of weight values were generated with the small changes to the values of criteria weights. Set 1 is the original set, sets 2 and 3 were produced by altering the weights on ±1% so the degree of influence of each criterion on the output was perturbed. To reduce the influence of hydraulic conductivity and infiltration and give more weight to slope the set 4 was generated. Four maps were produced and compared to define a degree of deviation. 2.3.2. Single Variable Analysis To evaluate the influence of each criteria on the output vulnerability map the single variable method was employed. By comparing the values of theoretical weight (assigned values) and effective weight (the effect of the criterion on the resulting map) of each criterion the most influential criterion was detected (Majandang and Sarapirome, 2013). The effective weight is calculated by the following equation:

푃푟∗푃푤 푊 = ( ) ∗ 100, (3) 푉

Where W is the effective weight of each variable, Pr is the rating value of each variable and Pw is the assigned weight of each variable, V is the vulnerability index. For application of the equation 3 in ArcGIS the following example with hydraulic conductivity can be used:

푀푎푝 표푓 푣푢푙푛푒푟푎푏𝑖푙𝑖푡푦 푟푎푡𝑖푛푔 표푓 ℎ푦푑푟푎푢푙𝑖푐 푐표푛푑푢푐푡𝑖푣𝑖푡푦 ∗ 0.19 푊 = ( ) ∗ 100, ℎ푐 푉푢푙푛푒푟푎푏𝑖푙𝑖푡푦 𝑖푛푑푒푥 푚푎푝

Where Whc is the effective weight of hydraulic conductivity and 0.19 is the theoretical weight of hydraulic conductivity. 2.4. Evaluation of Proposed Alternatives

After obtaining a final groundwater vulnerability map, a simple statistical analysis was carried out in order to select the most suitable alternative route among the three suggested by Stockholms lokaltrafik (Fig. 10). To do so ArcGIS tool (Zonal statistics) was employed. Following statistical indicators were used in the analysis (ESRI (Environmental Systems Research Institute), 2015):  MEAN — Calculates the average of all cells in the value raster that belong to the same zone as the output cell;  MAJORITY — Determines the value that occurs most often of all cells in the value raster that belong to the same zone as the output cell;  MAXIMUM — Determines the largest value of all cells in the value raster that belong to the same zone as the output cell;  MEDIAN — Determines the median value of all cells in the value raster that belong to the same zone as the output cell;

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

Fig. 10. Proposed alternatives for the railroad corridor (Håkansson et al., 2012).

 MINIMUM — Determines the smallest value of all cells in the value raster that belong to the same zone as the output cell;  MINORITY — Determines the value that occurs least often of all cells in the value raster that belong to the same zone as the output cell;  STD — Calculates the standard deviation of all cells in the value raster that belong to the same zone as the output cell.

3. RESULTS 3.1. Weighting Figure 11 illustrates the interaction among the criteria. In this study, the most important factor in terms of its effect on the resulting map was found to be infiltration with 6 connections and the least important factor was slope with only 2 connections. By taking into account the relative importance of different criteria and the accuracy of the data available the weight of each criterion was identified (Table 4). Hydraulic conductivity received the highest weight followed by infiltration and effective porosity. Whereas, slope and depth to groundwater were weighted the least.

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Fig. 11. Interaction among the criteria. 3.2. Vulnerability Map As a result of the weighted overlay the groundwater vulnerability index map (Fig. 12) of the study area was obtained using Eq. (2) and due to the tradeoff between the factors the value of vulnerability differs in the interval from 3.06 to 7.75. To produce the final groundwater vulnerability map (Fig. 13) the index map was reclassified in five classes of groundwater vulnerability levels (1 Very low, 2 Low, 3 Moderate, 4 High and 5 Very high). The map shows most of the areas to be of very low and low vulnerability interspersed with high and very high vulnerability areas. There are few areas showing moderate vulnerability. The majority of cells of high and very high value of vulnerability fall on the north-west. Area of moderate to high and very high groundwater vulnerability crosses through the map in the south-western direction, whereas, on the central part of the map areas of low vulnerability are intermixed with areas of moderate and high vulnerability. 3.3. Robustness Test Table 5 contains three new sets of weighting which slightly diverge from original Set 1. Results of the sensitivity analysis are shown in figure 14, where the amount of cells of each rating group of the final vulnerability map are compared with the amount of cells of the four different sets of weighting. The histogram shows that all of the cells in all four sets are

Table 4 Weighting of the criteria Number of Accuracy of Weight, Criteria connections data, % % Hydraulic 4 95 19 conductivity Infiltration 6 70 17 Effective porosity 5 70 16 Depth to 5 40 11 groundwater Land cover 3 80 13 Topographic 5 60 14 wetness index Slope 2 95 10

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

Fig. 12. Groundwater vulnerability index map. divided in five groups according to the rating. There is a minor deviation between the amounts of cells of different sets within the each rating group, except set 4 that deviates considerably. The majority of cells are the ones falling into the rating group 2 (32.7 – 35.0% of all the cells), the next big group of cells is the group of rating 1 (23.9 – 24.7% of all the cells), followed by the group of rating 4 (18.6 – 20.3% of all the cells), the group of rating 3 (11.9 – 13.4%) and finally the group of rating 5 with 9.7 – 10.3% of all the cells. The set 4 differs from the other sets, where the group of cells rating 2 is still the biggest group (almost 36% of all the cells) followed by the group

Fig. 13. Final groundwater vulnerability map, where white color represents the area of constraints.

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Table 5 Weighting for the robustness analysis Criteria Set 1, % Set 2, % Set 3, % Set 4, % Hydraulic 19 18 16 14 conductivity Infiltration 17 18 20 16 Effective porosity 16 17 15 15 Depth to 11 10 12 13 groundwater Land cover 13 14 12 12 Topographic 14 13 15 15 wetness index Slope 10 11 9 17

of rating 4 (high vulnerability, 20.8% of all the cells). The simple statistic analysis results indicate that there is a small difference between the values of statistical indicators. The closest values of standard deviation and mean value are found to be between the set 1 and set 2. The biggest difference is between the values of set 3 and set 4. 3.4. Single Variable Analysis The results of the single variable method are presented in table 6. During the theoretical weighting the highest weight was given to the hydraulic conductivity, due to its importance and relatively high accuracy of the data. However, the single variable analysis indicated that the most influencing factor was in fact effective porosity. Conversely, the slope was assigned the lowest weight during the theoretical weighting, but the analysis showed that the effective weight of the slope was higher. The variable showing a lower effective weight than the theoretical weight was land cover, with the difference of 3.9%. 3.5. Alternatives The groundwater vulnerability of the three railroad corridors proposed is shown in Figure 15. The northern corridor contained the majority of cells with a high (22.0% of all the cells) and very high (13.13%) vulnerability rating. There are also several high vulnerable rated areas within the southern corridor (18.44% of all the cells) but also areas of very high vulnerability

Fig. 14. Histogram of robustness analysis results.

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

Table 6 Single variable analysis results Theoretical Effective Criteria Min Max STD weight, % weight, % Hydraulic 19 11.92 3.11 42.7 7.94 conductivity Infiltration 17 14.62 2.77 38.2 9.6 Effective 16 23.34 2.16 47.48 9.23 porosity Depth to 11 14.33 1.71 28.76 6.55 groundwater Land cover 13 9.08 1.98 29.75 9.08

TWI 14 12.93 2.07 32.41 6.48

Slope 10 13.78 1.4 25.25 6.12 rating (10.45% cells of all the cells). The central corridor received 17.41% of cells with a high vulnerability rating and 10.42% cells of very high vulnerability rating (Figure 16). The simple statistics (Table 7) show that the highest mean value of vulnerability together with relatively low value of standard deviation belongs to the northern corridor, while the southern corridor has the lowest mean and highest SDV values.

4. DISCUSSION 4.1. Vulnerability Map As the result of the study, a groundwater vulnerability map of the study area was produced (Figure 13). The map indicates that the areas of high and very high vulnerability correlate with the location of sand and till deposits (Figure 2). Due to high infiltration capacity of coarse grain soil, high value of hydraulic conductivity and effective porosity of sand and till were rated high and very high which resulted in a high value of

Fig. 15. Groundwater vulnerability of the alternatives.

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Fig. 16. Statistics of the alternatives. vulnerability. Short travel time does not allow adsorption and ion exchange reactions to attenuate the contaminants and let them pass to the saturated zone. The areas covered with gravel and peat are also vulnerable to groundwater contamination even though peat has low value of hydraulic conductivity. This could be due to a relatively high water table in peat deposits and high vulnerability rating of effective porosity; thus result in the short travel time for contaminants to reach groundwater table and low dilution rate. Conversely, due to low infiltration and hydraulic conductivity together with low effective porosity the clay rich areas located in the center of the study area in the valley and in the south-western part of the map received the lowest value of vulnerability. This could be attributed to high adsorption capacity caused by large surface area and negatively charged particles and makes clay a good barrier to contaminants. Rock outcrops also serve as a barrier for contaminants because of their low infiltration capacity and low effective porosity. So we can conclude that the soil type does affects the groundwater vulnerability to a great extent; which can be also supported by the results of the full-scale groundwater vulnerability assessment conducted in Sweden (Maxe and Johansson, 1998; Gontier and Olofsson, 2003) as well as in Thailand (Majandang and Sarapirome, 2013). It is also important to remember that the evaluation of the influence of the criteria plays a crucial role in the MCA, as the result entirely depends on the weight given to each criterion. The methods used in this study provide valuable information on the groundwater vulnerability in terms of increased transportation demand. Since field measurements necessary for evaluation of groundwater conditions are expensive and time consuming, it is practical to utilize the data which is already available; in this regard GIS-based MCA could be a feasible solution.

Table 7 Statistics of the alternatives Corridor Area, km2 Min Max Mean SDV Majority Minority Median Northern 7.4 Very low Very high 2.74 1.33 Low Moderate Very low Central 12.8 Very low Very high 2.52 1.32 Low Very high Very low Sounthern 20.8 Very low Very high 2.50 1.36 Low Very high Very low

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

4.2. Robustness Test Robustness test was introduced in order to reflect stability of the model towards the slight changes. The results of the linear overlay of the factors using the four different sets of weighting show the division of all the cells in five main groups with very low, low, moderate, high and very high vulnerability (Figure 14). The results deviate slightly during the first three sets when small changes were made and the main pattern was the same: group of low vulnerability is the largest group of cells, followed by the group of very low vulnerability; the group of high vulnerability is the third major group and the last two groups are of moderate and very high vulnerability. The last fourth set is the one that deviates notably from the results of the previous three sets, but the changes made were also considerably bigger than those for other sets, so the result is expected. Thus, a slight difference between the results of the analysis confirms that the decision is robust. 4.3. Single Variable Analysis Results of the single variable analysis (Table 6) show that the most influential factor was effective porosity with the effective weight of 23.3%. The second important factor was infiltration, though the effective weight turned to be less than the theoretical weight; which can be explained by the fact that the majority of cells had low rating, thus high rated cells did not cover the substantial part of the map. On the contrary, slope was given weight of 10%, but the effective weight increased to 13.78% due to the distribution of high rated cells. 4.4. Alternatives Figure 15 shows potential locations of the railroad corridor. Simple statistical analysis of the alternatives can be practical as the corridor with low mean score coupled with low standard deviation is more favorable from the groundwater vulnerability point of view. In this study, the central corridor satisfies this condition (Table 7), but the differences of these values for all three corridors are relatively small and so each of them should be studied closely. The southern corridor has the lowest mean value (2.50) and the highest standard deviation value (1.36). Going in a north-western direction one third of its length passes through the area of till and peat with high groundwater vulnerability, and then second third crosses a relatively safe area of rock outcrops and clay, the last third terminates on top of the highly vulnerable glacio-fluvial deposit. More than a half of the northern corridor is located on the vulnerable till deposit. That results in a high mean score (2.74) and low standard deviation value (1.33), which is not favorable in terms of groundwater vulnerability. The high mean value implies that the areas of high and very high groundwater vulnerability predominate in the polygon, and that the low value of standard deviation stands for a low variation from the average value that is relatively large. As for the central corridor, the results show that the mean value (2.52) and the standard deviation (1.32) are low, meaning a low variation from the average. Besides a few areas of till, sand and gravel, three fourths of the corridor is located on rock outcrops and clay deposits which are less vulnerable; therefore the central corridor the most favorable in terms of the groundwater vulnerability.

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4.5. Uncertainties Errors can appear at any stage of the research process from conception to representation. During the analysis data were generalized, simplified, and smoothed to make the model simpler, since it is not always useful to work with too detailed information. Since no field measurements were conducted and no data on the stratigraphy or any hydraulic barriers were available, the main source of the uncertainties during the study was the major assumption that the soil map represents not only the uppermost layer of soil but also the soil media; with thickness limited by the depth of the water table. 4.6. Future Perspectives The study could be improved by providing reliable field measurement of the water table depth together with assessment of stratigraphy of the area. Well pumping tests could provide relatively accurate data on aquifer storage, boundary conditions, hydraulic conductivity and transmissivity. It would be also beneficial for the study to develop a groundwater flow model for prediction of speed and spatial distribution of contaminants in the study area. Due to diversity of the nature of the contaminants, the preferential pathways of different contaminants could be considered such as heavy metals, non-aquatic substances, etc. In order to be able to foresee the groundwater vulnerability in the future different climate change scenarios could be also taken into account.

5. CONCLUSION The study was conducted to develop a GIS-based model for groundwater vulnerability assessment during the infrastructural planning using a multi criteria analysis (MCA) approach. In this regard, a literature review was carried out to determine the impacts of transportation construction and operation on groundwater. The main factors responsible for the groundwater vulnerability were identified (such as hydraulic conductivity, infiltration, and effective porosity, depth to groundwater, land cover, topographic wetness index and slope). The effect of each factor was divided in five classes and each class was given a rating according to its effect on the groundwater. To obtain the resulting groundwater vulnerability map, each factor was multiplied by its weight (depending on its contribution) and accuracy percentage and finally combined. The model was tested for robustness and sensitivity using the single variable analysis. Another aim of the study was to determine which of the proposed railroad corridors was the most suitable in terms of groundwater vulnerability. For this the three proposed corridors were examined as possible locations for a railroad in an area north of Stockholm based on different factors and constraints. The final groundwater vulnerability map illustrated the highly vulnerable areas of the sand deposits and till and less vulnerable areas of rock outcrops and clay. According to the result, the central corridor was selected as the most suitable corridor for the railroad in terms of groundwater vulnerability, because of its location predominately on clay deposits and rock outcrops, which was supported by low mean value of groundwater vulnerability and low value of standard deviation. The sensitivity analysis showed that the model is robust and small changes in the parameters do not influence the output. However, it is important to remember that both the data for the analysis and the rating and weighting approach are subject to high uncertainties, it is therefore 22

Groundwater Vulnerability Assessment for Railroad Construction in Sweden using MCA

necessary to carry out further investigations on the site to verify the information in this study as well as an environmental and social impact assessment to ascertain that no serious impacts will result from the construction of the railroad on the central corridor. The use of MCA in a GIS environment is proven to be a useful tool for groundwater vulnerability assessment in terms of the complex problem solution as well as cost and time efficiency. The model can be used as a first hand analysis of available information and then complemented with field measurements.

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