Sustainability in Stormwater Management in a Changing Climate - A Case Study in Falkenberg, Sweden
Albin Noreen
Uppsats för avläggande av masterexamen i naturvetenskap 30 hp Institutionen för biologi och miljövetenskap Göteborgs universitet June 2015
Abstract Heavy rainfall may cause pluvial flooding, especially in urban areas with much impermeable surface. Because of this there is a need for stormwater management, which historically has been aimed at quick drainage. This creates high peaks in runoff volume during intensive rains and may cause flooding downstream. Urban stormwater may contain pollutants and nutrients, which are unwanted in the end recipient. Because of this new management practices has been implemented to mimic natural processes to even out the peaks and increase remediation of the pollutants. A common practice in Sweden is to construct stormwater ponds, and several ponds have been constructed in the Swedish city of Falkenberg. The aim of this study was to qualitatively assess these ponds in the context of national and international research and to provide an analysis of the frequency and intensity of heavy precipitation in Falkenberg. Stormwater ponds have been shown to be cost- effective, flexible and resource- and energy-efficient green stormwater solution for equalizing flow volumes. Stormwater ponds can provide other values as well such as social and recreational values for nearby residents as well as improvements in air- and water quality. However, there are negative aspects with ponds as they can work as barriers and that they demand relatively much space, which may lead to conflicting interests and difficulties to find the necessary space for construction. Another negative aspect is the risk of drowning, mainly for small children. The ponds in Falkenberg are well planned from a recreational point of view, thus they provide great values for nearby residents. The ponds seem to have a limited success in remediating contaminants and nutrients, which is problematic because of the recipient showing signs of eutrophication. The analysis of heavy precipitation in Falkenberg suggests that it is likely that the city receives less intense daily rains than other locations in the surrounding area. The intensity of rains varies significantly between compared stations and between distribution models used. In comparison to the Dahlström formula, which is used for calculating dimensioning volume, it is likely that the formula overestimates the intensities for Falkenberg. However, in comparison with the surrounding measuring stations the results are more varied and depend on duration and return period of the precipitation in question. The generic Dahlström formula is not valid for all locations investigated and may both over- and underestimate the true value. The temporal trends in precipitation intensity for southern Sweden is varying but there is evidence that the climate has become wetter during the last century, especially in the fall and winter, and that precipitation has become more intense. These trends are expected to continue in the future due to the changing climate. For Falkenberg a climate factor of 1.3, which is used today, is a good estimate of what current research has found for the increase in heavy precipitation until the year 2100. However, there is a risk that the changes will be larger than this factor.
Keywords – Stormwater ponds, qualitative assessment, heavy precipitation, extreme value analysis, climate change
b
Sammanfattning Kraftiga regn kan orsaka pluviala översvämningar, speciellt i urbana områden med mycket hårdgjorda ytor. Historiskt sett har dagvattenhanteringen i städer varit inriktad på att snabbt avleda vatten från oönskade områden. Snabb avledning från hårdgjorda ytor skapar kraftiga toppar i avledd volym under kraftiga regn, vilket kan överbelasta systemet och orsaka översvämningar nedströms. Dagvatten från urbana områden kan även innehålla höga halter av föroreningar och näringsämnen vilka kan orsaka skador på recipienten. Därför har nya typer av dagvattenhantering utvecklats för att efterlikna naturliga miljöer, för att på så sätt utjämna flödet och rena vattnet. En vanlig metod i Sverige är att anlägga dagvattendammar vilket även har skett i Falkenberg. Syftet med denna studie var att utvärdera dessa dammar kvalitativt, samt att analysera frekvensen och intensiteten av kraftig nederbörd i Falkenberg. Dagvattendammar är kostnadseffektiva, flexibla och resurs- och energisnåla jämfört med andra flödesutjämningsalternativ. Dammarna kan även tillföra sociala värden och rekreationsvärden för närboende, samt förbättra luft- och vattenkvalitén. Det finns även negativa aspekter med dagvattendammar då de kan fungera som barriärer och är relativt ytkrävande, vilket kan göra det svårt att anlägga nya dammar om den tillgängliga ytan är begränsad. Dammar medför även en drunkningsrisk för barn. De undersökta dammarna i Falkenberg hade alla höga värden för rekreation, då de var välplanerade för detta ändamål. Däremot påvisar de relativt dålig rening av näringsämnen vilket är problematiskt då recipienten Kattegatt är känslig för övergödning. Nederbördsanalysen visade att det är troligt att Falkenberg utsätts för mindre intensiva dagliga regn än närliggande mätstationer. Spridingen i regnintensitet mellan de olika stationerna och mellan de olika distibutionsmodellerna som användes var relativt stor. Det är troligt att Dahlströms formel, som används för dimensioneringsberäkningar, överskattar regnintensiteten för Falkenberg för 24-timmarsregn. I jämförelse mellan Dahlströms formel och andra närliggande stationsvärden är resultatet dock mer varierat, beroende på varaktighet och återkomsttid för nederbörden i fråga. Formeln kan både över- och underskatta regnintesiteter beroende på vilka stationsvärden den jämförs med. Varierande nederbördstrender sågs i södra Sverige under förra århundradet, men studier visar att klimatet har blivit något blötare och nederbörden har blivit något mer intensiv. Troligen kommer dessa trender att fortsätta in i detta århundrade och den klimatfaktor om 1.3 som används i Falkenberg stämmer väl in med den senaste forskningen för modelleringar fram till år 2100. Dock så finns det en risk att förändringen i intensitet blir större än 30 %, men resultaten är mycket osäkra.
c
Table of contents 1. INTRODUCTION ...... 1 2. BACKGROUND ...... 1 2.1. STORMWATER AND STORMWATER MANAGEMENT...... 1 2.1.1. Precipitation...... 1 2.1.2. Measuring precipitation ...... 2 2.1.3. Return periods ...... 2 2.1.4. Extreme precipitation in Sweden ...... 3 2.1.5. Stormwater management ...... 3 2.1.6. Consequences of pluvial flooding...... 5 2.1.7. The Dahlström formula...... 6 2.2. POLLUTION AND WATER QUALITY...... 7 2.3. CLIMATE CHANGE AND EXTREME PRECIPITATION ...... 8 2.3.1. IPCC reports ...... 8 2.3.2. Regional and local changes in extreme precipitation ...... 9 2.4. CASE STUDY AREA ...... 9 2.4.1. Falkenberg ...... 9 2.4.2. The ponds...... 10 2.4.3. The recipient ...... 16 3. AIM OF THE STUDY ...... 17 3.1. RESEARCH QUESTIONS ...... 17 4. METHODS ...... 18 4.1. SUSTAINABILITY ANALYSIS...... 18 4.2. APPLICATION OF PRECIPITATION DATA ...... 19 4.2.1. SMHI data ...... 19 4.2.2. Daily precipitation data ...... 21 4.2.3. Calculating return periods ...... 22 5. RESULTS...... 24 5.1. LITERATURE FINDINGS...... 24 5.1.1. Global warming ...... 24 5.1.2. Large-scale and local air-quality...... 25 5.1.3. Water quality...... 26 5.1.4. Energy and raw materials...... 29 5.1.5. Direct costs ...... 30 5.1.6. Well-being/perceived welfare and socio-economic aspects ...... 30 5.2. PRECIPITATION DATA...... 32 5.2.1. Data provided from SMHI...... 32 5.2.2. Falkenberg blended station data ...... 36 5.2.3. Grid data...... 38 5.2.4. Temporal trends...... 40 6. DISCUSSION...... 44 6.1. THE SUSTAINABILITY OF THE STORMWATER PONDS IN FALKENBERG ...... 44 6.1.1. Greenhouse gas emissions, air quality and energy demand...... 44 6.1.2. Water quality...... 44 6.1.3. Well being/perceived welfare and socio-economic aspects ...... 46 6.1.4. Additional aspects...... 46 d
6.2. PRECIPITATION DATA...... 47 6.2.1. Measurement errors...... 47 6.2.2. Difference between stations ...... 48 6.2.3. Comparison with the Dahlström formula ...... 49 6.2.4. Difference between distributions...... 49 6.2.5. Differences between grid and station data ...... 50 6.2.6. Temporal trends and climate change ...... 51 7. CONCLUSIONS ...... 54 ACKNOWLEDGEMENTS...... 55 REFERENCES ...... 56 APPENDIX A – RAIN INTENSITIES FOR 4 SMHI AUTOMATIC STATIONS...... I APPENDIX B – LOCATIONS OF STATIONS AND GRIDS ...... I 1. SMHI DATA...... I 2. FALKENBERG BLENDED STATION DATA ...... I 3. GRID DATA ...... II APPENDIX C – ADDITIONAL PLOTS FOR FALKENBERG BLENDED STATION DATA ...... I APPENDIX D – ADDITIONAL PLOTS FOR THE GRID DATA ...... I
e
1. Introduction The amount of rainfall and the number of days with heavy rainfall in Sweden are increasing, and are expected to continue to increase in the future (Nikulin, Kjellström, Hansson, Strandberg, & Ullerstig, 2010). This will increase the surface water runoff in Swedish cities, where measures have to be taken to avoid flooding, erosion and other consequences of intense rainfalls. In the city of Falkenberg in the south-west of Sweden, retention basins or stormwater ponds have been constructed to halt storm water during heavy rains, thus preventing stormwater from flooding unwanted places. There is a need to assess the sustainability of these stormwater management practises in the urban setting to be able to evaluate and improve their function. From a technical point of view but also from a social and ecological point of view as a natural part our public space and our common society.
Heavy rains and cloudbursts are usually very local phenomena, thus they are hard to predict and the uncertainty in what will happen in a future climate is great. There are indications that the frequency and magnitude of heavy rains and cloudbursts will increase, with how much depends on how cloudbursts and heavy rains are defined and which indicator is modelled (Olsson & Foster, 2013). In urban areas hard, impermeable surfaces are common and without proper management the storm water runoff may cause big problems. This is already a problem today that costs Swedish authorities, insurance companies and private persons millions of SEK (Swedish Kronor) every year (MSB, 2010a). Therefore there is a need to investigate the patterns in intensive rainfall in Falkenberg today, to be able to tell how much rain can fall and to predict how much rain will fall in the future.
This master thesis project is related to the research project Framtidens regn och översvämningar i Sverige - ett ramverk till stöd för klimatanpassning, which is a collaborative project between the University of Gothenburg, Karlstad University and SMHI (Swedish Meteorological and Hydrological Institute) funded by MSB (Swedish Civil Contingencies Agency) aiming at providing methods and knowledge to local authorities for working with pluvial floods.
2. Background
2.1. Stormwater and stormwater management
2.1.1. Precipitation Precipitation is a term to describe all kinds of liquid and solid water-particles that fall trough the atmosphere, which is a consequence of the water cycle where water evaporates to the atmosphere from the sea, lakes and land to finally precipitate back. The moisture in the atmosphere form clouds, which consists of small droplets or ice crystals. When the moist air is lifted it condenses and the droplets and crystals grow bigger and finally falls as precipitation. At Swedish latitudes the precipitation usually starts as snow on high altitude, which will melt to rain on the way down if the temperature in the lower atmosphere is high enough. Depending on the air temperature, humidity and transport patterns of the droplets, the precipitation can fall as rain, snow, sleet, freezing rain or hail (Svenskt Vatten, 2011a; SMHI, 2014a). There are three processes that typically create precipitation in Sweden.
1
Orographic lift is when air is forced upward because of the ground terrain. This is common on the sides of mountains and highlands where the side that lies in the dominant wind direction in the area usually receives more precipitation than the opposing side. This process is also seen in coastal areas during onshore winds where the surface air slows down over land because of the terrain and the faster air layer on higher altitudes gets forced upwards over the slower air layers. Convective precipitation is created when colder air lies over a warmer sea- or land surface. Air bubbles of warmer air will raise though the cooler layer, condensate into clouds and possibly create precipitation. This process is most common in the summer over land and in the autumn over the sea. The most common type of precipitation in Sweden is in connection with low-pressure systems and frontal activity where cold air from the arctic travels down the north Atlantic and collides with warmer air from the south. The cold air is denser than the warm and wedges in under the warmer air mass, lifting it up and precipitation is created in the front. Usually the precipitation connected to cold fronts is heavier than for warm fronts (Svenskt Vatten, 2011a; SMHI, 2014b). 2.1.2. Measuring precipitation Precipitation data may look very different depending on what kind of measurement station that is used. Modern rain gauges used in Sweden today are usually either weighting-type precipitation gauges or tipping-bucket gauges. The automatic stations used by SMHI to measure precipitation in Sweden today are Geonor weighing-type gauges with heating and anti-freeze added to melt snow and a thin layer of oil to prevent evaporation. These gauges have the volume resolution of 0.1 mm of precipitation and the time resolution of 15 minutes, when the data is stored in a server (Wern & German, 2009; Svenskt Vatten, 2011a). Many municipalities measuring precipitation use the tipping-bucket gauge where the precipitation is collected into a seesaw-like container, which tips over when full. The time resolution of the tipping bucket can be accurate down to the second of tipping and the volume resolution is dependent on the tipping volume (usually 0.1-0.5 mm). Both of these methods tend to somewhat underestimate the ‘real’ precipitation due to losses because of wind, evaporation, measurement errors and other factors. There may be other types of measurements and manual measurements used, especially in older time-series, which may have a limited reliability that has to be accounted for (Svenskt Vatten, 2011a).
The raw data from the stations can be recalculated to represent the intensity or volume during a certain timeframe. This type of rain is called a ‘block rain’ and is expressed as the average intensity of the rain during the selected timeframe. In everyday speech usually daily and monthly precipitation is used to describe the weather, but when planning for stormwater management usually the shorter, high intensity events are the most interesting, ranging down to timeframes as low as five or ten minutes (Svenskt Vatten, 2011a). 2.1.3. Return periods Precipitation extremes are usually expressed as a return period of a given volume or intensity. This is a statistical expression meaning that an event with a defined intensity and duration that has a return period of e.g. ten years will occur or be exceeded once every ten years, on average. However, this does not mean that such events do not happen more than once every ten years. For every year the probability of an event with the return period of ten years is 10% (Wern & German, 2009). The probabilities of events, with different return periods, occurring within different time-spans is given in table 1.
2
Table 1. General probabilities of events occurring within different time-spans, considering their return periods. From Wern & German (2009). Time-span [years] Return period 1 2 5 10 20 50 100 [years] 1 63% 87% 99% 100% 100% 100% 100% 2 39% 63% 92% 99% 100% 100% 100% 5 18% 33% 63% 86% 98% 100% 100% 10 10% 18% 39% 63% 86% 99% 100% 20 5% 10% 22% 39% 63% 92% 99% 50 2% 4% 10% 18% 33% 63% 86% 100 1% 2% 5% 10% 18% 39% 63%
The values for precipitation return periods are calculated statistically based on historical data series. A thorough explanation of the techniques used can be found in the methods section, 4.2.3. It is important to remember that the predicted values are only as good as the accuracy in the measurements in the best case, and that the data series are often shorter than the long return periods wanted. This means that the measurements have to be statistically extrapolated, which gives uncertainty to the predictions. A rule of thumb is that predictions should not be made on return periods longer than twice the length of the available data series, which makes it problematic to predict events with return periods of 50 and 100 years where there are few data series ranging that long (Persson, 2014). 2.1.4. Extreme precipitation in Sweden The most intensive rainfall over land in Sweden is usually connected to convective precipitation, which is most common in the summer. This has been confirmed by e.g. Hernebring (2006) and Wern & German (2009) looking at rain statistics for a number of measuring stations in Sweden, which showed the most intense precipitation events predominantly occurred during the summer and especially in July. In the months June – August there is a larger number of rains with a larger average volume of precipitation but with shorter average duration than other months. Heavy precipitation may occur during any time of the day but is most common in the afternoon and early evening.
The record for daily precipitation in Sweden is 276 mm in Fulufjället, Dalarna, during 24 hours in the 30th-31st of August 1997. The measurement was done in personal capacity but has qualified as reliable by SMHI. The record for an official SMHI station is 198 mm during 24 hours, which was in Fagerheden, Norrbotten, the 28th of July 1997 (SMHI, 2014c). For shorter durations, 52 mm in 15 minutes has been measured in Högsäter, Dalsland, the 18th of July 2000 and 130 mm in an hour in Tegelstrand, Bohuslän, the 10th of July 1973 (Wern & German, 2009). 2.1.5. Stormwater management Precipitation over land creates stormwater runoff. The runoff drains differently depending on the land use in the area. In a natural environment unaffected by human activity the draining is halted by uptake in plants and retention in small puddles, ponds and trenches. Depending on the ground conditions and water table a portion of the stormwater is infiltrated down to the groundwater and some is evaporated on the surface or through 3 plants (Stahre, 2004; Svenskt Vatten, 2011b). In an urban setting the natural water balance is changed because of the larger area of hard surfaces, such as impermeable pavements and roofs, and the lack of plants and infiltration surfaces. This speeds up the draining of the stormwater and increases the need for planning and managing of stormwater drainage. In a highly urbanised area as much as 80-90% of the annual precipitation will drain as surface runoff, instead of 30-50% in a natural environment (Stahre, 2004; Svenskt Vatten, 2011a; Svenskt Vatten, 2011b; MSB, 2013).
Stormwater management has historically been focused on draining runoff from built-up areas as quickly as possible. Before the 1950s this was usually done by combined sewer systems with stormwater, drainage water and wastewater running in the same pipes underground. This changed in the 1960s when systems for diversion of stormwater and drainage water were separated from the wastewater system to decrease the load in the combined system and to prevent wastewater from flooding basements and low lying areas during heavy rains. Because of this older parts of Swedish cities often have a combined system while newer parts have separated systems (Stahre, 2004). In recent years the focus in stormwater management in Sweden has moved from closed systems in pipes underground to more open systems above ground mimicking natural systems, which in general is considered to be more sustainable stormwater management. The sustainable open stormwater system consists of chains of subsystems on different scale devoted to retention of stormwater runoff in order to delay the drainage speed and thus flattening the peak in runoff in urban areas, increasing infiltration and decreasing the risk of flooding. The open system can provide other values, such as cleaning of pollutants in the stormwater and aesthetical or recreational values (Svenskt Vatten, 2011b). Open stormwater systems are usually divided into four categories by the industry, as seen in fig 1.
Fig 1. Simple categorisation of stormwater management practises. From Stahre (2004).
Local management on private land is the first category, which differs from the other categories because it is very much dependent on the ownership of the land. It is defined as measures taken on private land before the stormwater runoff is supplied to the public system. The other categories are all in public land and ownership. Retention close to the source means retention early upstream in the catchment area of the public stormwater
4 system, slow diversion is slow transportation of stormwater runoff from the upper parts of the system downstream and collected retention is larger facilities with large catchment areas (Stahre, 2004). Examples of different measures and which category they fit in is given in table 2.
Table 2. Examples of measures to manage stormwater by category. From Stahre (2004) and Svenskt Vatten (2011b). Category Measure Local management (private land) Green roofs Infiltration on lawns Permeable coatings Infiltration in fillings and macadam Percolation Ponds Collection of roof water Retention close to the source Permeable coatings (public land) Infiltration on lawns Infiltration in fillings and macadam Temporary impoundment on flooding surfaces Ditches/trenches Ponds Wetlands Slow diversion (public land) Ditches Canals Brooks, streams and trenches Collected retention (public land) Ponds and basins Wetlands
In this study the focus has been on ponds and basins on public land, which could fall under two of the categories depending on the size of the catchment area. In Sweden there are more than 1000 municipality-operated stormwater ponds, and the number has been increasing in recent years. However little has been done to evaluate the function and efficiency of most ponds (Falk, 2007). 2.1.6. Consequences of pluvial flooding The concept of flooding, or a flood, is defined by FLOODsite as “a temporary covering of land by water outside its normal confines”(FLOODsite, 2009). A flood is a collective term describing a number of different varieties of water covering land and can have different origin and appearance. In this report the focus is on pluvial flooding. Pluvial flooding is rain- related flooding caused by direct runoff over land, in contrast to fluvial flooding which is induced by rivers or streams overflowing their banks. What is important to remember is that floods are a part of the natural hydrological cycle, which many organisms and ecosystems are adapted to. The problems with floods occur when it affects parts of our society that are not sufficiently adapted to cope with the flood. Anthropogenic alterations of the natural environment such as establishment of impermeable surfaces worsen the situation, especially in urban areas (Nyberg, 2008).
5
Pluvial flooding is most often caused by short periods of intense precipitation and can cause a lot of problems. The huge amounts of water do not have the time to drain and the consequences are often flooded basements, buildings, roads and erosion damages as reported by media in Sweden (Nilsson, 2012). Damages and costs of pluvial flooding can be divided into two categories: tangible and intangible. Tangible damage means that the damage is specific and quantifiable, while intangible damage is more diffuse, and hard or impossible to quantify. The costs of these damages can be direct and indirect, thus making four different damage types as shown in table 3. (MSB, 2010a).
Table 3. Examples of tangible and intangible damages associated with direct and indirect costs. From MSB (2010a). Tangible Intangible Direct costs Physical damage to property: - Loss of human lives - Buildings - Health effects - Equipment - Loss of natural - Infrastructure environment Indirect costs - Production decline - Inconvenience - Traffic disturbance - Increased - Emergency service vulnerability costs
This means that the exact costs of flooding, or any natural disaster, is very hard to calculate. The costs of direct physical damage can usually be calculated using data from insurance companies and indirect tangible costs can, to some extent, be calculated from actual data. Because intangible costs are hard or impossible to measure, they might be discussed but are often left out of calculations (MSB, 2010a; MSB, 2013).
In Sweden there have been several cases of pluvial flooding events that caused direct damages for tens of millions of SEK. The most costly events was on the island of Orust the 2nd and 3rd of August 2002. The cloudbursts on the 2nd were the most intense when approximately 180-200 mm of rain fell during 12 hours, which was followed up by 40-90 mm on the 3rd. Combined, these events cost society approximately 123 million SEK according to insurance data. If compared, extreme rains and pluvial flooding have about the same, or even higher, costs for society as fluvial flooding but much lower costs than severe storms (MSB, 2013). However, this is very much depends on where the rain falls and the flooding occurs. The 2nd of July 2011 an intense cloudburst hit the Danish capital Copenhagen. The station in the cities botanical garden measured 135.4 mm during 24 hours, which is significantly less than e.g. the Orust event, but most of the precipitation fell during 2 hours and over a densely populated area. The damages caused by the massive amounts of rain were enormous; basements and buildings were flooded, roads and railroads had to close for days, hospitals were minutes from evacuation due to power failure and many critical societal IT-systems crashed. The costs for the damages of the event has been estimated to about 700 million €, which made it the costliest weather event in Europe 2011 (Vejen, 2011; MSB, 2013). 2.1.7. The Dahlström formula There have been many attempts to predict precipitation intensity and runoff volumes in Sweden. In 1979, Dahlström described a method called the Z-value, which was a regional 6 parameter to describe the convective precipitation pattern in Sweden. The method and the Z-value made it possible to calculate the dimensioning precipitation intensity for stormwater management purposes. The Z-value was based on the average precipitation during July and August combined with a spring month, usually May, with low convection intensity. The method was simple and shortly became widespread throughout Sweden (Dahlström, 2006; Hernebring, 2006). Studies of more recent precipitation measurements have shown that the Z-value is still relatively valid, but the intraregional variation is big enough to question the regional division (Hernebring, 2006). Because of this there have been many reports and discussions to come up with a formula to be applied for all of Sweden. The end result was the [new] Dahlström formula, first published in Dahlström (2010), which is expressed as:
1/3 0,98 iÅ ≈ 190Å ln(TR) / (TR ) + 2
-1 -1 Where iÅ = rain intensity [l s ha ] TR = duration of rain event [minutes] Å = return period [years]
This formula is widely used in Sweden for calculating dimensioning precipitation intensities for durations between 5 minutes up to 24 hours and different return-periods. It has been recommended by the industry to use if there are no established precipitation statistics for the intended area. It is also recommended to complement the formula with a ‘climate factor’ of 5-30% because of expected climate-change (Svenskt Vatten, 2011a). 2.2. Pollution and water quality Improving the water quality is a very important reason why stormwater ponds are built in the first place (Falk, 2007). In the year 2000 all members of the European Union adopted the water framework directive, with the goal of securing access to water of good quality. This includes all waters, surface water and groundwater, which shall achieve good status until a preset deadline. The overall target is to preserve good water quality waters and improve waters that do not live up to the standards. The goal has been that all inland- coastal- and groundwater should be classed as good status by this year, 2015. The status is measured for two main groups, ecological status and chemical status, where ecological status considers mainly the biodiversity of the flora and fauna and the chemical status considers pollutants, nutrients and other chemical substances that shall not exceed limitation values. Preliminary results show that the goal will not be met and a new time frame (until 2021) has been set (Svenskt Vatten, 2011b; European Comission, 2015; VISS, 2015a). The authorities have pinpointed 45 priority substances, of which 21 are identified as priority dangerous substances (2013/39/EU, 2013). For all the substances there are environmental quality standards set, of which the concentration must be below for the waters chemical status to be considered as good. The goal is to gradually reduce the concentration of the priority substances and to eliminate emissions of the priority dangerous substances by the year 2020 (Svenskt Vatten, 2011b; 2013/39/EU, 2013). The substances are a mix of different pesticides, flame-retardants, heavy metal compounds, solvents and polycyclic aromatic hydrocarbons (PAH). Many of the substances have been banned in Sweden for a long time, or have been considered otherwise irrelevant to monitor by the Swedish Environmental Protection Agency (Naturvårdsverket), however some of them may be relevant to monitor locally due to local industries or other local emitters (Naturvårdsverket, 2008).
7
In addition to the priority substances, urban stormwater may contain many other substances and pollutants in potentially harmful concentrations, depending on what activities exist in the catchment area. In stormwater ponds the cleaning of the water usually is focused on metal contaminants, particles, organic particles and nutrients. The spreading and transport of contaminants in stormwater ponds can be divided into four types of processes.
- Erosion of particles and deposition and resuspension of sediment. - Adsorption and desorption to particles of dissolved substances. - Diffuse transport of dissolved particles. - Oxygen depletion processes and anaerobic processes.
To have an efficient water treatment in the ponds these processes are favoured or counteracted in order to clean the water, which is let out to the recipient (Vikström, Gustafsson, German, & Svensson, 2004; Stahre, 2004). 2.3. Climate change and extreme precipitation
2.3.1. IPCC reports The climate of the earth is changing. Because of the huge amounts of greenhouse gases emitted by our society today the atmospheric composition is altered, which in turn changes the balance in the global radiation budget and increases the surface temperature. The climate system is very complex but the Intergovernmental Panel on Climate Change (IPCC) concludes in their recent fifth assessment report that it is certain that the global average temperature has increased since the 19th century, and that it is likely that heavy precipitation events over land have increased globally and in Europe since the 1950s (IPCC, 2013a). Precipitation in general is predicted to increase during the winter months (December-February) and decrease during the summer months (June-August) at the end of this century, according to regional climate models for northern Europe (IPCC, 2013b). Heavy daily precipitation, defined as the 95th percentile of daily precipitation, may increase with about 30% in the winter and 25% in the summer according to ‘business-as-usual’ climate scenarios. The predictions have a high confidence for an increase of extreme precipitation for all seasons in northern Europe, as well as an increase in other extremes such as high temperature and sea level (IPCC, 2014a). The predicted increase in extreme precipitation events may affect urban areas in many different ways. The biggest impacts will be on the water supply and wastewater systems, which are already vulnerable today and will be increasingly vulnerable in the future. Also green infrastructure in cities, terrestrial ecosystems and ecologic infrastructure will be increasingly vulnerable as well as our housing, transport and communication infrastructure, especially in the long term towards the end of this century. Human health and well-being and key economic sectors might also be affected. It is important to remember that the vulnerability of different systems is not just against the increase in extreme precipitation but a combination of changes in the climate, which increases the total vulnerability of urban areas and societies. Also the ecological, economical and social systems are interconnected and vulnerabilities in one part of the system will affect the other parts as well. The IPCC presses the need for adaption and planning to increase the resilience in urban areas against climate change, which can be an opportunity for sustainable development and provide values other than risk reduction (IPCC, 2014b).
8
2.3.2. Regional and local changes in extreme precipitation Because of climate change in Sweden there are expected general changes in spatial and temporal distribution of precipitation. Downscaling climate models to represent change in the intensity of rains with a short duration (<1 hour-1 day) on a regional and local level is problematic because of the temporal and spatial resolution of the models, which leads to high uncertainty in the predictions (Svenskt Vatten, 2011a; Olsson & Foster, 2013). Climate models predict an increase in mean annual precipitation in western Sweden of 5-25% in the years 2071-2100 compared to the baseline years 1971-2000, depending on what emission scenarios are used. The projections show similar increases in heavy precipitation events (Jacob, et al., 2014). For stormwater management purposes usually short intense precipitation events are of interest. The studies that have been made in Sweden usually have a spatial resolution of 50 km times 50 km, which gives an area of 2500 km2. Studies made with better resolution and downscaled data have been shown give varying results compared to larger scale models, showing that local changes may be both bigger and smaller than regional changes. General conclusions from the models are that events with 10 year return periods, which are commonly used for dimensioning, are going to increase with 10- 35% until the end of this century and the change is biggest for rains with a duration of less than one hour (see table 4). However there is a great deal of uncertainty connected with these predictions (Olsson & Foster, 2013).
Table 4. Predicted general changes of precipitation intensity. Events with the duration of less than 1 hour and daily precipitation with the return period of 10 years. Low, average and high value of predictions. From Olsson & Foster (2013). Duration Today’s climate ! 2050 Today’s climate ! 2100 Low Average High Low Average High ≤ 1 hour ±0% +10% +20% +15% +25% +35% 1 day ±0% +5% +15% +10% +20% +30% 2.4. Case study area
2.4.1. Falkenberg The town of Falkenberg is located on the south-western coast of Sweden (See fig 2). It is the seat of the Falkenberg municipality in Region Halland. Falkenberg municipality had a population of 41 008 inhabitants in 2010 and about half of the municipality’s population were living within the town of Falkenberg (20 035 inhabitants) (SCB, 2013) located at the mouth of the river Ätran (Bergfast, 2013).
9
Fig 2. Location of Falkenberg in Falkenberg municipality (left) and Falkenberg municipality in south-western Sweden (right). © OpenStreetMap contributors
Falkenberg has a warm-temperate or cold climate, with rainfall throughout the year and warm summers, which gives the Köppen climate classification of Cfb or Dfb according to different studies (Peel, Finlayson, & McMahon, 2007; Rubel & Kottek, 2010; Jylha, Tuomenvirta, Ruosteenoja, Niemi-Hugaerts, Keisu, & Karhu, 2010). The annual mean temperature, during the reference period 1961-1990, was 7.2 °C and the annual mean precipitation was 709 mm for the SMHI station in Falkenberg (nr 6252) (SMHI, 2014d). The average monthly values for the period are shown in table 5.
Table 5. Monthly average temperature (T) and precipitation (P) for the station in Falkenberg (station nr 6252) during the reference period 1961-1990. Temperature is given in °C and precipitation in mm. From SMHI (2014d). Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yr T -1.3 -1.4 1.1 5.2 10.9 14.7 16.2 15.8 12.3 8.7 4.0 0.6 7.2 P 57 35 45 39 44 54 68 80 76 75 71 65 709
2.4.2. The ponds In Falkenberg the municipally owned water, sewage and waste management company VIVAB (earlier FAVRAB) has constructed stormwater ponds and connected existing ponds to the stormwater system. Since the 1990s there have been many such connections and constructions, and today Falkenberg has stormwater ponds in many areas. Many of the ponds have been surveyed for biodiversity and evaluated for optimising function and maintenance, with focus on biodiversity in the ponds themselves. Following are descriptions from these surveys of the ponds that are included in this study.
Skogsvägsdammen and the ponds in Kristineslätt Skogsvägsdammen was constructed in 1993. The water depth varies between 0.25 to 0.9 m and it covers an area of 500 m2 during high floods. The slopes of the pond are approximately 1:5 and the sides are covered with shrubs, grasses and trees covering about half of the ponds exposure to sunlight. For the bottom and the sides of the pond there have been stones and rocks laid out. The shrubs and grasses on the south side of the pond are cleared yearly. The surroundings are mostly larger lawns and grassed areas and residential plots. Stormwater is mainly supplied to the pond in pipes. There has been measurements made to 10 assess the nutrient content and remediation of nutrients in the pond in august 1999. The measurements showed that the water had a very high content of both total phosphorus and total nitrogen in the inlet, with 53 μg/l and 1300 μg/l respectively. In the outlet the total phosphorus content was 11 μg/l, which was considered as low, and gives a remediation of 79% in the pond. The total nitrogen content in the outlet was 1400 μg/l, which was about 8% higher than in the inlet. During an earlier measurement, in April 1999, the phosphorus content was below detection limit. For nitrogen the concentrations were not presented but the remediation was negligible (Nolbrant, 2004). A picture of Skogsvägsdammen is shown in fig 3.
Fig 3. The pond Skogsvägsdammen as seen from the east looking west. Picture taken the 13th of March 2015, after a relatively dry period. © Albin Noreen
There have been two surveys of biodiversity in the pond Skogsvägsdammen, conducted in 1999 and 2004. There was a significant increase in biodiversity between the two surveys, both for flora and fauna. In 1999 the number of species of plants was very low, and the colonisation of new species was slow due to the rock-laid bottom and sides of the pond. However, in 2004 there was almost twice as many species of plants found and there had been a development of underwater vegetation throughout the entire pond. The animal life and number of species in the pond had also increased from 1999 to 2004, where more than twice as many species and individuals of invertebrates were found in 2004. However, the number of species and individuals were considered moderately low. There were no fish found in the pond, which was considered positive for the invertebrate population, neither were amphibians found. Mallards had been seen, although the pond was considered too small to serve as any major nesting place for birds. Notable was that the southern shore hosted a good environment for many species of herbs and that the red-listed diving beetle Rhantus notaticollis (which is not red-listed any more (Andrén, 2010)) was caught in the pond. The overall assessment of the pond in 2004 was that the pond had good conditions and potential value for biodiversity because the lack of fish and the well developed underwater vegetation and surroundings. There were recommendations made to continue
11 the clearing of shrubs and grasses and to dispose of the grass cuttings to avoid additional nutrition in the pond (Nolbrant, 2004).
Following the stormwater system downstream from Skogsvägsdammen is a number of more recently constructed stormwater ponds. These ponds were surveyed in 2009. The two largest ones (Kristineslätt 3), constructed in 2007, are connected to Skogsvägsdammen via a trench running through a grassed recreation area (see fig 4). They are located adjacent to each other and connected with a small passage. Their combined surface area is approximately 2500 m2 during low floods and the water depth varies between 0.5-1.5 m. Because of their recent construction the number of species found was low, both for vegetation and animals. There were no larger trees or shrubs on the shores granting 100% exposure to sunshine. There were no fish found in the ponds but frog eggs were found. Because of the ponds recent construction the number of species were expected to increase much as the vegetation establishes. Also because of the closeness and connection to other ponds, vegetation and animal life was expected to spread. Further downstream, west of the two large ponds, there is a smaller pond (Kristineslätt 1), which is connected to the others through a ditch (see fig 4). The pond, which was constructed in 2006, also has a water depth of 0.5-1.5 m and had a more developed biodiversity than Kristineslätt 3 with respect to species diversity for vegetation and animal life with a judged high number of species of vegetation and invertebrates. Frog eggs were found in this pond, as well as an unusual backswimmer bug. The surroundings of the pond are grasslands and recreation areas, football fields and residential areas. Further south there is another pond (Kristineslätt 2), slightly smaller but with similar characteristics as Krisineslätt 1. Both these ponds were assessed to have good or very good conditions to evolve a valuable wetland flora and fauna, because of the lack of fish in the ponds and the closeness and connection to other ponds (BioDivers, 2009).
12
Fig 4. The ponds Kristineslätt 1 (top left), Kristineslätt 3 (bottom left) and the trench (right) connecting the ponds with Skogsvägsdammen. Pictures taken the 22nd of January 2015. © Albin Noreen.
Lerhålan The pond Lerhålan is an old masonry construction pond, which was constructed sometime in the 1950s. The pond is quite large, about 5000 m2, and 2-3 m deep with amplitude of about 1 m in water depth. The sides of the pond are quite steep, approximately 1:3 and the bottom substrate consists mostly of clay and organic material. The pond is located between a residential area, as seen in fig 5, and a more industrial area. There is a larger road passing by as well as a walking and biking track going around the pond. Groves of trees and higher bushes are shadowing the ponds water, but approximately 50% of the sides are exposed to sunlight. Stormwater is supplied to the pond in pipes. The water quality and remediation was measured for nutrients in August 1999, and the findings were that the water had very high concentrations of total phosphorus, 62 μg/l and 39 μg/l in the inlet and outlet respectively, giving a remediation of about 37%. During an earlier measurement in April the remediation was only 8%. For total nitrogen the concentrations were high in August, 960 μg/l in the inlet and 720 μg/l in the outlet giving a remediation of about 25%. In the earlier test in April the remediation was only about 9% (Nolbrant, 2004).
13
Fig 5. Lerhålan as seen from the south facing north. Picture taken the 13th of March 2015. © Albin Noreen
In the survey 2004 there were only 22 wetland species of vegetation found despite the ponds relatively large size, which was about the same as in the survey in 1999 and considered as moderate. This was probably because the steep slopes, the relatively turbid water and possible abundance of fish in the pond. The number and the diversity of invertebrates were considered to be very low in both surveys, probably because of the fish population. Other animals found in the pond were toads, mallards and other common birds. Overall the pond was assessed as having low values and potential for biodiversity in wetland flora and fauna, because of the fish population. However the ponds recreational value was acknowledged because of its location and accessibility (Nolbrant, 2004).
Lyckebäcksdammen Lyckebäcksdammen is a stormwater pond constructed in 1995. It has a surface area of about 2000 m2 and a depth of 0.5-1.6 m, which gives an amplitude of about 1.1 m. Steepness of the slopes of the pond is approximately 1:4. The stormwater is supplied to the pond via pipes and the surrounding are is mixed with park areas, lawns, groves of trees, houses and industries in the proximity. Fig 6 shows a picture of the pond and surroundings. There are walking paths running alongside the pond, but a fence surrounds the pond itself. About 40% of the sides are free from trees and thus exposed to sunlight. The water quality tests in August 1999 showed very high concentrations of total phosphorus in the inlet and outlet, with concentrations of 83 μg/l and 48 μg/l respectively giving a remediation of 44%. In the measurements in April the remediation was lower at 13%. For total nitrogen the concentrations high to very high with 1300 μg/l in the inlet and 730 μg/l in the outlet meaning a remediation rate of 42%. Similarly to phosphorus the remediation of nitrogen was lower in the measurement in April, at 10% (Nolbrant, 2004).
14
Fig 6. Lyckebäcksdammen as seen through the dense vegetation on the eastern shore facing north-west. Picture taken the 13th of March 2015. © Albin Noreen
The diversity of wetland vegetation in the pond was considered to be moderately high during the survey in 2004, which was an increase from the low diversity in 1999. However the structural variation in the pond had decreased in 2004 compared to 1999 because of the dominance of typha reed. The water showed signs of eutrophication by displaying an abundance of algae. The diversity and number of individuals of invertebrates was moderately low, although it had increased since 1999. The main reason for this was the occurrence of fish in the pond, probably the common roach. There were many frog eggs found, a large population of the smooth newt, five different species of dragonflies and also the previously red-listed backswimmer bug Plea minutissima. Overall the assessment of the pond in 2004 was that the fish present in the pond had a negative impact in the populations of invertebrates and vegetation. The fish could also have a negative impact on the eutrophicaton in the pond because of the disturbance in the bottom sediment. However, the pond was acknowledged as having a high recreational and educational value for the nearby residents (Nolbrant, 2004).
Fajanshålan The pond Fajanshålan is, similarly to Lerhålan, an old masonry construction pond constructed in the 1950s. It has a surface area of about 15 000 m2 but the water depth is unknown. The slope of the sides of the pond is estimated to approximately 1:3 in general. The pond is located adjacent to the river Ätran within a residential area. Directly around the pond there is a forested area and private gardens and lawns, as seen in fig 7. Trees and larger shrubs, preventing exposure to sunlight for most of the shoreline, surround the pond. When tested for nutrients in 1999 the measurements showed low to moderate concentrations of total phosphorus in the August sample with 16 μg/l in the inlet and 12 μg/l in the outlet, giving a remediation in the pond of 25%. In April the remediation was lower, at 10%. For total nitrogen, the measured concentrations in August was 1400 μg/l in the inlet
15 and 1300 μg/l in the outlet, which was considered to be very high, giving a remediation of only 7%. In April the measured remediation was negligible (Nolbrant, 2004).
Fig 7. Fajanshålan as seen from the east facing west. Picture taken the 13th of March 2015. © Albin Noreen
There was a moderate number of species of vegetation found in the pond during both surveys in 1999 and 2004. For invertebrates the diversity was considered low and the number of individuals found was very low, probably because of the dense fish population in the pond. However there were many species of amphibians found, such as the smooth newt, toads and frogs. There was thought to be a large population of fish in the pond, including the common Swedish freshwater species northern pike and European perch and also the grass carp, which was introduced in the pond in 1988. There were not many birds spotted in the pond, despite its size and location, which was linked to the limited diversity in invertebrates and vegetation, however in the adjacent forest there has been sightings of the red-listed lesser spotted woodpecker. The overall assessment of the pond in 2004 was that it had a low value of biodiversity for invertebrates, birds and wetland vegetation. The diversity and number of amphibians were greater and the recreational value was acknowledged due to its location within a residential area (Nolbrant, 2004).
2.4.3. The recipient The final recipient for the stormwater from Falkenberg is the sea, Kattegatt. The environmental status of Kattegatt has been improved in recent years, where the outer parts and the open sea showing few signs of eutrophication and the general concentrations of toxic substances such as mercury and organotin compounds are stable or decreasing. However many species of fish, which have historically been commercially important such as eel and cod, have had their populations collapsed with no or very little recovery seen (Havsmiljöinstitutet & HaV, 2014). For the near coastal waters the picture is not as positive. The status for the benthic fauna is varying, and for shallow bays there have been no improvement detected, due to eutrophication (Moksnes, Albertsson, Elfwing, Hansen,
16
Nilson, & Rolff, 2014). The sea outside Falkenberg is divided into two (administrative) areas, which separates just of the coast of Falkenberg. Both areas are classified as having a moderate ecological status and neither achieves good chemical status (VISS, 2015a). The reason for the moderate ecological status is mainly insufficient status of the benthic fauna and an excess of nutrients, especially in the winter. Good chemical status is achieved if none of the priority substances exceeds environmental quality standards, a criterion that is not fulfilled and the main problem is mercury. Even though improvements are made, there is a risk of not fulfilling the goal of having good ecological and chemical status by 2021. The major sources of nutrients and other chemicals to the coastal waters have been estimated, showing industrial point sources as a small part while the major part is diffuse sources from urban and agricultural runoff, individual sewage, forestry and atmospheric deposition (VISS, 2015b).
3. Aim of the study The aim of the study is to provide a sustainability analysis for stormwater retention ponds in Falkenberg in a long–term sustainability perspective, including environmental, social and economical factors as well as providing an analysis of the current situation for heavy precipitation and predicted future changes in precipitation intensity patterns for Falkenberg. 3.1. Research questions
- What is the current knowledge about the sustainability of stormwater ponds and related stormwater management systems? - How does the pattern for intensive rains look in Falkenberg, is it comparable to general assumptions and how will it change in the future? - How sustainable are the ponds in Falkenberg in the context of international and national research?
17
4. Methods
4.1. Sustainability analysis To gain knowledge about the current scientific findings and research in the area of sustainable stormwater management, and stormwater ponds in particular, a literature search was conducted. The articles found in the search were reviewed and the relevant findings were summarised. To include the full spectrum of sustainability, the search was divided into several categories in an attempt to cover as much as possible of the environmental, social and economical aspects of development. The categories chosen were taken from the matrix decision support tool developed and applied by the Swedish Geotechnical Institute (SGI). The tool itself is focused on the planning process for land use change, but the impact categories can be used to evaluate processes as well. The categories take into account different geographical scales, local, regional and global, and account for consequences in different time horizons. The flexibility of an option, to adapt to changing conditions, is also included (Andersson-Sköld, Helgesson, Enell, Suer, & Bergman, 2011). All of the categories, which are listed in table 6, were not fully relevant to stormwater ponds and stormwater management but they were the starting point for the literature search and for defining searched keywords. Therefore some of the categories were left out of the results and mainly seen as a part of the discussion.
Table 6. Impact categories, with additional sub-categories. From Andersson-Sköld, Helgesson, Enell, Suer, & Bergman (2011). Main categories Possible sub-categories Global warming - Release of greenhouse gases - Land-use or land changes that contributes to, or reduces, global warming Large-scale air quality (excluding global - Euthrophication warming). Includes air-emissions that - Acidification contribute to: - Tropospheric ozone - Bio accumulative air emissions - Long-distance transport of particles Local air quality - Odour - Particulates - Toxic gases Water quality - Drinking water quality - Biodiversity - Ecosystems - Fisheries - Marine and limnological properties of high conservational value - Eutrophication through leakage
18
Soil quality - Pollution load - Biodiversity - Ecosystems - Impact on terrestrial object of high conservational value Land resources - Use of land - Housing Energy - Energy consumption Raw materials - Raw material acquisition Well being/perceived welfare Direct costs - Costs for possible impacts - Costs for measures Socio-economic aspects - Infrastructure - Cultural - Accessibility - Business activity - Jobs - Recreation Flexibility
The database chosen for the search was Web of Science™ Core Collection. It is a multidisciplinary database containing a range of peer-reviewed articles covering different sciences, including natural science, social science, arts and humanities, medicine and technical literature, which is why it was considered to suit well for this interdisciplinary search (Göteborgs Univeristetsbibliotek, 2013; Thomson Reuters, 2015). The literature search was done during February to March 2015. The dates of search were noted and are presented with the corresponding keywords in the results. Irrelevant searches and terms were left out of the study. The searched field was for topics and for all years available (1945- present). Commands used was ‘AND’, which limits the search to topics containing both words or phrases preceding and following the command, and asterisk (*), which represents any group of characters and make it possible to search for words with multiple endings (e.g. ‘pond*’ will give results for both ‘pond’ and ‘ponds’). The search was conducted without any additional limitations if not otherwise noted in the results. All search results were gone through and their abstract was read. If deemed relevant the full article was downloaded and thoroughly read and the results or findings summarised. Some articles were unavailable through the university library, and if so they were searched for elsewhere. If not found available they were not used. 4.2. Application of precipitation data
4.2.1. SMHI data The dimensioning of the stormwater systems in Falkenberg is based on the Dahlström formula (see part 2.1.7). To see how well this generic formula applies to the local conditions in Falkenberg the dimensions obtained from the formula were compared with actual measurement data from SMHIs automatic stations. The data provided was calculated intensity expressed as millimetres (mm) of rain for four stations. The durations were 15, 30, 45, 60, 360, 720 and 1440 minutes (24 hours) and the return periods were 1, 2, 5, 10, 20, 30, 50 and 100 years. The calculations were based on precipitation series for the four different 19 stations since 1995, except for the station Torup, which was installed in 2008. At least 13 years of data was used for each station, except Torup where an adjacent station was used to obtain a longer time series. The values were based on the maximum value per duration measured each year (no correctional factors added) and the distribution model used was the Gumbel distribution (see section 4.2.3.). The four stations are all located close to Falkenberg, following is a short description of each station.
Hallands Väderö The station has the station nr 6226 and is located approximately 50 km south of central Falkenberg on the island named Hallands Väderö, which is located about 3 km from Torekov on the Swedish mainland.
Torup The station has the station nr 6359 and is located approximately 36 km east of central Falkenberg in the town of Torup.
Nidingen The station has the station nr 7119 and is located approximately 57 km north-northeast of central Falkenberg on the island named Nidingen, which is located about 6 km off the coast.
Ullared The station has the station nr 7209 and is located approximately 30 km north-northwest of central Falkenberg, southeast of the town of Ullared.
Coordinates and a map showing the locations of the stations in relation to Falkenberg are provided in Appendix A.
The Dahlström formula expresses precipitation intensity in the unit l/s, ha (litres per second and hectare), which is a very common unit in stormwater management in Sweden. Hectare is an older unit for area and is equivalent to 10 000 m2. The data, however, was expressed as mm, which is common practice in everyday meteorology (Svenskt Vatten, 2011a). To be able to compare the data with the formula, the data had to be recalculated to the unit l/s, ha. According to Svenskt Vatten (2011a), 1 mm of precipitation is equivalent to 10 m3 of water per ha, which in turn is equivalent to 10 000 litres. This makes the formula for recalculating as such:
iÅ = (h * Vha ) / ts
-1 -1 Where iÅ = rain intensity [l s ha ] h = measured precipitation [mm] -1 -1 Vha = volume per mm per hectare [l mm ha ] ts = duration of rain event [s]
The measured precipitation was already given in mm and the duration was given in minutes, which was recalculated to seconds by multiplying by 60.
20
4.2.2. Daily precipitation data In addition to the calculated return periods provided by SMHI, data series of daily precipitation for Jonstorp, Morup and Falkenberg was used. The data series was provided from the European Climate Assessment and Dataset (ECA&D) and consisted of two types of datasets. The first type was daily precipitation for one measuring station in Falkenberg, blended with data from two adjacent stations when data was missing for the Falkenberg station. The stations coordinates and location in relation to Falkenberg can be found in Appendix B. The dataset stretched between the 1st of January 1961 and the 30th of June 2003, with some years and months missing. Because of the disruption of the data between 30th of November 1997 and 1st of December 1998 the measurements from the two years was combined, adding December 1998 to the data from 1997 to create a full year of measurements. Also the data for the six months in 2003 was left out of the calculations. This made the dataset consists of 40 full years of daily precipitation data (with the exception of March 1968). The precipitation was measured from 06:00 UTC in the morning, and 24 hours forward until the same time next morning. The used data and the sources are shown in table 7 (Klein Tank, et al., 2002).
Table 7. Sources for the blended station dataset. Time period Source station Station ID 19610101 – 19621231 Jonstorp 5251 19640101 – 19641231 Morup 6014 19650101 – 19680229 Jonstorp 5251 19680401 – 19971130 19981201 – 20021231 Falkenberg 5247
The second dataset consisted of grid data for daily precipitation. It was also provided from ECA&D and contained daily median precipitation for four grids covering Falkenberg and the surrounding area during the period of time from 1st of January 1950 to 31st of December 2014. The grids were named grid 1 through 4, where grid 1 was centred over the ocean thus not containing any data. The remaining grids, 2-4, contained usable data for every day during the 64 years. The coordinates and location of the four grids can be found in Appendix B. The spatial resolution of the grids was 0.25 x 0.25 degrees, which means that the area of each grid varies depending on where on earth it is located. Because the three grids used were located adjacent to each other, the difference of each grids area was relatively small. The length of a degree depends on the latitude, and for approximation the coordinates for each grids centre was used. The differences between the lengths of the degrees are shown in table 8. The daily precipitation data was measured, or calculated, from 06:00 UTC and 24 hours forward. For more information on how the datasets were treated, see Klein Tank, et. al. (2002) and Haylock, Hofstra, Klein Tank, Klok, Jones, & New (2008).
Table 8. Approximations of degree lengths in km for the grids latitudes (CSGNetwork, 2011). Latitude Length of lat. Degree Length of lon. Grids affected [km] Degree [km] 56.875 111.358 60.976 Grid 1, grid 3 57.125 111.362 60.568 Grid 2, grid 4
21
The grids have sides of 0.25 x 0.25 degrees, thus being squares in the degree projection. However, because of the changing lengths of a degree in km on the earth’s surface they are in fact rectangles. Their approximate areas are;
Grid 1 & 3: (0.25 * 111.358) * (0.25 * 60.976)
Grid 2 & 4: (0.25 * 111.362) * (0.25 * 60.568)
Which gives grid 1 and 3 an approximate area of ~424.39 km2 and grid 2 and 4 an approximate area of ~421.56 km2. 4.2.3. Calculating return periods To be able to compare the data from the daily precipitation series with the data provided by SMHI and Dahlströms formula, the return periods for the heavy precipitation events had to be calculated. Because of the series only containing the daily precipitation, the only duration available was 24 hours. Both the datasets for the grids and the dataset for the blended station data were treated in the same way. The maximum daily amount of precipitation per year was sorted out in Microsoft Excel, using the sorting function and the “MAX” function. New sets containing only the yearly maximum values were created. Because of the equidistant 24-hour measurements, the maximum values for the blended station data was multiplied with a correction factor of 1.14, as recommended by the industry (Svenskt Vatten, 2011a), however this was not done with the grid data. The data was transferred to the statistics program R, and the return periods were calculated using the package “extRemes”.
The data was fitted to two types of distributions in the return period calculations; Generalized Extreme Value (GEV) distribution and Gumbel distribution. Following is a short description of the concepts of the distributions, as described by Coles (2001).
The GEV distribution is in fact a combination of three distribution families known as the Gumbel, Fréchet and Weibull families. The function for the GEV distribution can be expressed as such:
G(z) = exp { - [1 + ξ ({z - μ} / σ)]-1/ξ}
Where G(z) = distribution of the variable z μ = location parameter, -∞ < μ < ∞ σ = scale parameter, σ > 0 ξ = shape parameter, -∞ < ξ < ∞
As ξ ! 0 the GEV distribution behaves as the Gumbel distribution, for which ξ = 0, giving the function for the Gumbel distribution as such:
G(z) = exp [ - exp ( - {(z – μ)/ σ})]
22
When fitted to a distribution the return level for a given return period can be estimated using the formulas:
-ξ zp = μ – σ / ξ [1 – ( - log{1 - p}) for ξ ≠ 0 zp = μ – σ log( - log{1 - p}) for ξ = 0
Where zp = return level associated with a return period 1/p = return period
The return period, 1/p, in this study is given in years because of the z being annual maximum values.
In the output from the program (package “extRemes”, function “fevd”) a negative log- likelihood value was given, which is a relative estimate of the goodness of fit for the distribution where a lower value indicates a better relative fit. For more information regarding the underlying statistics and the program used, see Coles (2001) and Gilleland (2015).
23
5. Results
5.1. Literature findings
5.1.1. Global warming The main focus of the search was articles concerning stormwater ponds, however some articles covering wetlands, basins and other stormwater management practices was included to broaden the picture. Searched keywords and hits are shown in table 9.
Table 9. Keywords, hits and relevant articles found linked to global warming. Date of search was 4th of February 2015. Keywords greenhouse gas* greenhouse gas* greenhouse gas* carbon sequest* AND stormwater AND stormwater AND stormwater AND stormwater pond* manage* basin* manage* Hits 1 19 1 12 Relevant 1 4 1 4 hits
Ponds and wetlands are sources of greenhouse gas emissions, such as emissions of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2). Greenhouse gas emissions from the pond are dependent on several factors. Methane emissions are dependent on the oxygen content, which also is linked to the concentration of nutrients and the type and coverage of vegetation in the dam where covered. Nutrient rich and anaerobic condition leads to larger emissions of methane. Emissions of nitrous oxide also require anaerobic conditions, and temperature and sediment types are factors that affect the release of all three greenhouse gases mentioned (Sims, Gajaraj, & Hu, 2013). During flooding events there may be occasional peaks in emissions of methane and nitrous oxide in constructed stormwater biofilters. There is a need for more research in the field to fully understand these processes (Grover, Cohan, Chan, Livesley, Beringer, & Daly, 2013).
Apart from wetlands and ponds being sources of greenhouse gas emissions they are also carbon sinks, mainly because of plants abilities to sequester carbon from the atmosphere. However, the sequestered carbon is subsequently re-released through microbial respiration if not buried through sedimentation. Emergent vegetation is a key factor in sequestering carbon from the atmosphere to the soil. Especially macrophytes are important which are favoured by a relatively shallow water table. Non-vegetated ponds show little or no carbon sequestration (Moore & Hunt, 2012). Carbon sequestration by plants and green infrastructure associated with stormwater management has been observed. Examples include green roofs (Li & Babcock JR, 2010), vegetated filter strips (Bouchard, Osmond, Winston, & Hunt, 2013) and improvements in soil quality through vegetation (Chen, Day, Wick, & McGuire, 2014).
The materials, construction work and maintenance needed in stormwater management create greenhouse gas emissions. Depending on energy intensity and energy sources during the construction and maintenance processes, these emissions may vary greatly (Kandudlu, Connor, & Hatton MacDonald, 2014). However, studies show that local management of
24 stormwater and “green infrastructure” solutions may be significantly less energy demanding and have lower emissions of greenhouse gases than centralized treatment and “gray infrastructure” solutions, which generally require more extraction and processing of raw materials as well as heavier construction work and energy demanding maintenance (Fagan, Reuter, & Langford, 2010; De Sousa, Montalto, & Spatari, 2012; Moore & Hunt, 2013). Compared to other green infrastructure, the construction and maintenance of stormwater ponds may yield relatively low greenhouse gas emissions. For example permeable pavements, green roofs, rainwater harvesting systems and sand filters require more raw materials and processing of materials, which causes emissions of greenhouse gases. Wet ponds and constructed wetlands have been shown to emit the least greenhouse gases during their life span compared to other stormwater management practices (Wang, Eckelman, & Zimmerman, 2013; Moore & Hunt, 2013). 5.1.2. Large-scale and local air-quality The scope of the search has been to find articles about what impacts stormwater ponds have on air quality, thus only articles relevant within that scope has been further read. Searched keywords and hits are shown in table 10.
Table 10. Keywords, hits and relevant articles found linked to large-scale and local air quality. Date of search was 24th of March 2015. Keywords air pollution AND stormwater urban air quality AND green manage* infrastructure Hits 16 23 Relevant hits 2 10
The ponds and basins may be a source of malodour from emissions of e.g. sulphur compounds, carbonyl compounds, ammonia and volatile organic compounds. The emissions may not be toxic in low concentrations but can affect quality of life for people living nearby or visiting the sites and lower the sites recreational value. Proper planning and management of the stormwater ponds can reduce the problem (Kabir, Kim, Ahn, Hong, & Chang, 2010).
Land based vegetation is not a direct effect of stormwater ponds, but usually connected to the area where the ponds are located and also a part of open stormwater solutions. It has been shown in many studies that green infrastructure in cities has a positive effect in reducing air pollution. Trees and other vegetation can reduce concentrations of NOx, sulphur oxides, tropospheric ozone and particles. This has been confirmed by several studies, both by looking at direct reduction of pollution levels (Roy, Byrne, & Pickering, 2012; Pugh, MacKenzie, Whyatt, & Hewitt, 2012; Baró, Chaparro, Gómez-Baggethun, Langemeyer, Nowak, & Terradas, 2014; Demuzere, et al., 2014; Berardi, GhaffarianHoseini, & GhaffarianHoseini, 2014) and indirect effects of decreased pollution such as health effects and indoor air quality (Hartig, Mitchell, de Vries, & Frumkin, 2014; Wang, Bakker, de Groot, & Wörtche, 2014). However, vegetation may also create local air pollution such as allergens and volatile organic compounds, which, according to one study, may even outweigh the benefits on air quality from the vegetation under certain circumstances (Wang, Bakker, de Groot, & Wörtche, 2014).
25
5.1.3. Water quality The scope of the literature search has been to find scientific articles relevant to water quality and the different contaminants usually found in stormwater. The emphasis has been on what kind of processes and measures that can be considered to increase the ponds effectiveness in improving water quality to the recipient. Searched keywords and hits are shown in table 11.
Table 11. Keywords, hits and relevant articles found linked to water quality. Date of search was 24th of March 2015. Keywords water quality eutrophicati* nutrient metal contaminant* AND AND remov* AND remov* AND removal AND stormwater stormwater stormwater stormwater stormwater pond* NOT pond* pond* pond* pond wastewater (review articles only) Hits 3 12 54 74 24 Relevant 1 9 10 15 4 hits
Nutrient removal In a data synthesis article comparing over 100 different sites for effectiveness in nitrogen removal, the results were varying. The general conclusion was that stormwater ponds and other stormwater management practices were effective in removing nitrogen, but the results varied from negative remediation (i.e. the pond being a source of nitrogen) to 100% removal. There were limited evidence to factors affecting the remediation of nitrogen but generally smaller, shallower ponds were more effective and a combination of different techniques was deemed favourable. The efficiency declined with the age of the pond, which could be because of poor maintenance. The study showed no evidence of removal efficiency varying with loading rate, but the authors acknowledged this as an important factor along with the hydraulic function of the pond. The average removal of total nitrogen in ponds the study was 40%, and in other studies and reviews the average removal had been between 20- 33% in ponds (Koch, Febria, Gevrey, Wainger, & Palmer, 2014). The hydraulic function and residence time of the water in the pond has been identified as key factors in most of the studies reviewed, for both nitrogen and phosphorus and also for other pollutants.
A common practice is to use vegetation for uptake of nutrients in the water. There are many studies comparing different species of plants for their efficiency and there is great variability in their effectiveness of removing nutrients. There are three main types of practices in the design of the water flow through the vegetation. Surface-flow is where the water flows above the sediment through the vegetation, subsurface-flows is when the water is flowing through the root system of the plants, and floating treatment wetlands is when vegetation is placed on a floating mat with their roots emerging through the mat in the water. Both surface-flow and subsurface-flow systems are widely used, and have been for a long time, while floating treatment wetlands are a relatively new practice. A stormwater detention pond usually has the vegetation growing in the sediment, making it a surface-flow system, but it might be connected to a subsurface-flow system in the inlet or outlet. The remediation
26 of nutrients for surface-flow systems is highly variable and studies suggest that nitrogen removal range from 50-90% and phosphorus from negative remediation to 50% (Lu, He, Graetz, Stoffella, & Yang, 2008; White, 2013). Subsurface-flows might be favourable when phosphorus is the nutrient of greatest concerns, and by tailoring design and maintenance the remediation rate may be over 90% (White, 2013). Floating treatment wetlands have the advantage that they are relatively easy to retrofit to an existing pond. There have been many studies of this type of solution carried out in recent years with varying results in remediation, generally showing an significant increase in remediation of phosphorus while the increase in remediation of nitrogen is smaller (Tanner & Headley, 2011; Winston, Hunt, Kennedy, Merriman, Chandler, & Brown, 2013; Wang, Sample, & Bell, 2014; Wang & Sample, 2014). However, some studies suggest a similar or better remediation of nitrogen than phosphorus (Winston, Hunt, Kennedy, Merriman, Chandler, & Brown, 2013; Chang, Xuan, Marimon, Islam, & Wanielista, 2013; Lynch, Fox, Owen JR, & Sample, 2014). The remediation rates for the floating treatment wetlands did not exceed 50% for any of the nutrients in any of the studies. The root surface area and concentration of fine suspended solids in the pond are important factors for efficient removal of nutrients, especially phosphorus, by floating treatment wetlands (Tanner & Headley, 2011).
The nutrients taken up by the vegetation is cycled from the water and sediments to the plants, and back again through sedimentation and decomposition of the dead organic matter. Nitrogen is emitted to the atmosphere as N2 and N2O through bacterial denitrification processes. However, there might be a need to harvest the vegetation for an efficient removal of nutrients from the system, and to prevent nutrient saturation in the vegetation. Floating treatment wetlands needs to be harvested to be efficient and are relatively easy to harvest every year, but for standing plants the harvest might cause more harm than good because of the disruption of the sediment and breaking of roots (White, 2013; Wang & Sample, 2014; Wang, Sample, & Bell, 2014). Dead and decaying plants and organic material that sediments on the bottom might lead to a higher concentration of nutrients in the leached groundwater from the pond, providing additional nutrient loads to the surrounding area (Ouyang, 2013).
Contaminant remediation Other than nutrients there are many contaminants present in stormwater, especially from urban runoff, industrial areas and roads. Usually the contaminants associated with stormwater are metals and heavy metals, particles (oxygen demanding or others), organic contaminants and polycyclic aromatic hydrocarbons (PAHs), different types of pesticides and other chemicals. Also, the microbial content may be important due to occurrence of potentially hazardous bacteria. Studies have shown that the concentrations of contaminants found in stormwater ponds may exceed health and safety regulations for metals, carcinogenic substances and microbes (Jang, Jain, Tolaymat, Dubey, Singh, & Townsend, 2010; Weinstein, Crawford, Garner, & Flemming, 2010; Karlsson, Viklander, Scholes, & Revitt, 2010).
Many metals can be present as dissolved ions in the water or attached to particles, which makes both water and sediment samples preferable. Remediation may include sedimentation of particles as well as phytoremediation in vegetation. Many studies have been made to test stormwater ponds abilities to remediate heavy metals, and the results are
27 very variable as seen in reviews and synthesis articles. A study of heavy metal concentrations in 37 stormwater ponds, of which 26 were wet ponds, in different settings in southern Denmark showed that the highest concentration of the measured metals was found in ponds within industrial areas and the lowest in rural areas. The retention of the metals varied greatly between the ponds, but some trends where seen. The retention declined drastically with pond age, and just after 1-2 years it became negative for copper (Cu), cadmium (Cd) and chromium (Cr). Retention was increased with larger pond size, especially in the interval from 150-250 m3. In wet ponds the general retention was between 0-35% for all metals except for Cr and Cd, which in general had a higher concentration in the outflow (Egemose, Sønderup, Grudinina, Hansen, & Flindt, 2015). A study by Fassman(2012) who compared remediation efficiency of metals in stormwater ponds from a database search also found that the results from the retention ponds varied a lot. For the 33 sites tested for total suspended solids (TSS) the median removal rate was 71.8% while the mean was -17.7%, for total zinc (Zn) 59.2% and 39.1%, for dissolved Zn 41.7% and 17.4%, for total Cu 39.2% and -69.2% and for dissolved Cu 33.3% and 26.7% for median and mean respectively. The big difference between median and mean values suggests that some ponds function very poorly, which affects the mean value.
Many studies suggest that sedimentation is the main process of removing metals from the water. The ponds flow regime, or residence time and dead volume, affect the efficiency of removing TSS and metals significantly (Hossain, Alam, Yonge, & Dutta, 2005). A comparison of removal efficiency of heavy metals in a constructed wetland in Ireland showed that the wetland had removed a considerable amount of the heavy metals and that most of the metals had accumulated in the sediment and only a negligible amount hade been taken up in the vegetation (Gill, Ring, Higgings, & Johnston, 2014). This has also been shown in other studies with floating treatment wetlands (Borne, Fassman, & Tanner, 2013; Borne, Fassman- Beck, & Tanner, 2014). Other studies have found a significant uptake in certain species of floating plants, which make them useful for phytoremediation or as inexpensive bio indicators for metal contamination in water. Most of the uptake in the plants occurs in the roots, which may make floating treatment wetlands favourable (Ladislas, El-Mufleh, Gérente, Chazarenc, Andrès, & Béchet, 2012; Ladislas, Gérente, Chazarenc, Brisson, & Andrès, 2013). Vegetation in any form in ponds may help particles sediment by trapping particles and providing organic material, as well as possibly contributing by phytoremediation and providing aesthetic value (Istenič, et al., 2012; Headly & Tanner, 2012; Borne, Fassman, & Tanner, 2013; Borne, Fassman-Beck, & Tanner, 2014).
For PAHs and other organic contaminants, sedimentation has been identified as a key factor in remediation. Because of the many different kinds of PAHs with different characteristics there might be a conflict between different types of remediation practices. In a study of a swale and pond system monitored for general water quality, PAHs and other organic pollutants, the system proved to be effective in treating pollutants related to oxygen demand and solids. But the highly dynamic and site-specific nature of sustainable open drainage systems makes generally valid conclusions hard (Roinas, Tsavdaris, Williams, & Mant, 2014). Also in removal of microbes sedimentation is a key factor. Studies have shown that stormwater ponds are inefficient for remediating faecal bacteria and bacteriophages, and may be sources of these types of microorganisms (Davies, Yousefi, & Bavor, 2003; Krometis, Dummey, Characklis, & Sobsey, 2009; Pettersson & Åström, 2010)
28
Certain weather conditions may affect the remediation in stormwater ponds. During storms and high inflows there is an increased ‘first flush’ of contaminants, especially if there has been a dry spell before. In a study in Växjö, Sweden worst water quality in the summer was measured after a storm, which was preceded with a 10-day dry spell (Semadeni-Davies, 2006). In another study, the microbial concentration was nearly two orders of magnitude larger after a storm event than the average value (Krometis, Dummey, Characklis, & Sobsey, 2009). However, studies suggest that well planned stormwater ponds are effective in remediating stormwater, even during storm events with heavy inflows (Pettersson & Åström, 2010), and one study showed that the remediation of heavy metals in could be even higher during storm events than the average remediation rate, although the reasons for this were not clear (Gill, Ring, Higgings, & Johnston, 2014). Seasonal differences may be apparent under Swedish conditions. In the study in Växjö there were high concentrations of salt in the water during winter, probably from salted roads. This led to a strong stratification of the water and low oxygen levels at the bottom. The study showed that the ponds performance during winter-spring was lower than in the summer, but not as bad as in previous studies where the pond had been suggested to be a net polluter during winter (Semadeni-Davies, 2006).
Contaminants bound in the sediment and vegetation usually has to be removed. For certain types of plants in floating treatment wetlands this can be done fairly easily without disturbing the sediment in the pond (Headly & Tanner, 2012; Ladislas, El-Mufleh, Gérente, Chazarenc, Andrès, & Béchet, 2012; Ladislas, Gérente, Chazarenc, Brisson, & Andrès, 2013). For contaminated sediment and standing vegetation the pond has to be dredged and the plants excavated because of the higher concentrations of contaminants in the plants roots (Istenič, et al., 2012). In a study in a stormwater pond in Gothenburg, Sweden, heavy metal concentrations in sediment and water were measured before and after an excavation of the sediment. The water samples after the excavation showed large differences and a high variation due to the increase in suspended solids in the water because of the disturbance of the sediment. Because of this the authors recommended that all of the water in the pond had to be accounted for and treated. The sediment excavated was contaminated enough to be classified as hazardous waste (Karlsson, German, & Viklander, 2010). 5.1.4. Energy and raw materials There were few studies found on web of science where energy and raw materials used in building and maintaining stormwater ponds management were assessed, and only one study was deemed relevant for the question as shown in table 12. However, additional relevant articles were found through the references in the found article.
Table 12. Keywords, hits and relevant articles found related to energy and raw material demand of stormwater ponds. Date of search was 24th of March 2015. Keywords life cycle assessment* AND stormwater manage* Hits 23 Relevant 1 hits
Construction and maintenance of stormwater management systems are energy and raw material demanding. According to one study that compared stormwater basins, or ponds, 29 with green roofs and permeable pavements the basins required the least amount of construction material and the least amount of maintenance. This suggests that construction and maintenance of a stormwater retention pond may be less energy demanding and require less raw materials than other green management options. The material and energy needed for construction and maintenance were the dominating contributors to the energy and raw material use. The other parts of the stormwater pond life cycle that was investigated in the study, transportation and installation, had an almost negligible contribution to the total. The lifetime of the stormwater management systems in the study was 25-40 years and the pond was deemed the least energy and material demanding regardless of life span chosen (Wang, Eckelman, & Zimmerman, 2013). Another study in a densely urbanized area (Bronx, New York) compared green infrastructure upstream, such as porous pavements, street-end bio retention facilities, infiltration surfaces, rain gardens and smaller underground cisterns, with end-of-pipe detention facilities. According to the study, green infrastructure closer to the source is less energy and raw material demanding than the end-of-pipe solution, much depending on the additional water, sewer and pipeline construction in the latter strategy. This suggests that decentralised solutions closer to the source, i.e. upstream in the catchment area, are more resource and energy effective than central solutions in one specific place (De Sousa, Montalto, & Spatari, 2012). 5.1.5. Direct costs The literature search for direct costs, conducted in the same way as for the other impact categories, found no relevant hits. Therefore the information about direct costs of stormwater ponds has been taken from other sources such as industry standards and reports.
The direct costs very much depend on the size of the site and the local conditions. A comparison of the costs of constructing five ponds in Stockholm estimates the costs at about 4 million SEK per hectare of water surface area, in year 2008 monetary value. However, the costs vary between about 1.1 million to 6 million SEK per hectare in the different sites and the Swedish standard value is set at 2.5 million SEK based on the same report (Andersson, Owenius, & Stråe, 2012; VISS, 2013). The report also states a standard value of 20 000 SEK as annual maintenance costs and a discounted yearly cost of about 250 000 SEK per hectare of water surface area based on a lifetime of 20 years and a discount rate of 5%, all in year 2008 monetary value. The maintenance cost does not include park management or cleaning of the pond, which may contribute significantly to the costs (Andersson, Owenius, & Stråe, 2012). According to Wang, Eckelman, & Zimmerman (2013) a bioretention basin is the most cost effective of the green stormwater management strategies covered in the study. However, the study is set in the U.S. and may not be applicable under Swedish conditions. 5.1.6. Well-being/perceived welfare and socio-economic aspects Well-being and perceived welfare is a subjective term, which is hard to quantify in a single query. Socio-economic aspects is a broad term spanning over infrastructural aspects, cultural aspect, accessibility, business activity, jobs created and recreation, which may also be considered to be linked to well-being and perceived welfare (Andersson-Sköld, Helgesson, Enell, Suer, & Bergman, 2011). The main focus literature search has been on the socio- economic aspects, while the more subjective terms of well-being and perceived welfare are covered in the discussion section of this report. Searched keywords and hits for the literature study are shown in table 13.
30
Table 13. Keywords, hits and relevant articles found related to socio-economic aspects and stormwater ponds. Date of search was 7th of April 2015. Keywords cultur* AND recreation* AND recreation* value* AND stormwater manage* stormwater pond* stormwater Hits 25 14 25 Relevant 2 2 5 hits
A well-managed and well-planned stormwater detention pond can provide great cultural and recreational values. Moore & Hunt (2012) uses recreational and educational values as means for evaluation of a ponds cultural service. Legal and physical accessibility as well as existing recreational infrastructure are subcategories used for assessing the ponds recreational value. Location in relation to educational centres, such as schools, history of educational use and educational infrastructure, such as signs and activity stations, are used for assessing the ponds educational value. Other articles have similar approaches to the cultural or social values of stormwater ponds or urban water in general. Lundy & Wade (2011) defines cultural services as non-material benefits such as spiritual, aesthetic and educational values and opportunities for recreation for humans, gained by ecosystems services, which may all be provided by stormwater ponds according to their study. They argue that green spaces contribute to public health due to the effect of recreation for decreasing obesity and mental illness, and that green spaces containing water is associated with higher preferences than environment without water. Residents living nearby stormwater ponds valued them not only for flood prevention, but also for their role in improving the landscape and attracting wildlife. The effect of improved water quality of the recipient on well-being and perceived welfare is also linked to recreation and other social values. In a study in Perth, Australia the reasons for visiting and committing to a nearby wetland were examined. Six main factors were found as important the wetlands; accessibility, ownership (symbolic or real), participation, comfort, security and action (ability to use space for either social or non-social reasons) (Syme, Fenton, & Coakes, 2001). What aspects of water management that were considered to be important for the well-being and perceived welfare of residents in Portland, Oregon was studied by Larson (2009). In the study the bio centric goals, i.e. goals connected to clean water and habitat protection, ranked higher than anthropocentric goals, i.e. recreation and flood control. Still both types of goals were ranked highly among the residents. However there was a widespread opposition against different types of measures for protecting and improving the water quality, such as raising taxes and introducing regulations. Contradictory, a study of residents and tourists willingness to pay for improved stormwater management leading to a better coastal environment in Hawaii showed that the measured willingness to pay exceeded investment costs in various stormwater management practices, including ponds and wetlands (Penn, Hu, Cox, & Kozloff, 2014). Also, in an article by Lee & Li (2009) the property prices in proximity to dry detention basins were studied. The result was varying between the two types of basins investigated. Uni-use basins intended for stormwater management only lowered the adjacent property prices, suggesting that the basins being an unwanted feature of the neighbourhood, while a multi-use basin with an adjacent park and recreational value had a positive impact on adjacent property prices, suggesting that the park design and recreational values of the site overcame the negative image of the detention basin. However
31 the basins in the study were dry basins without a permanent water surface. Waterways and ponds may work as barriers in an area, which has to be taken into consideration during planning and design of the sites. In a project in Malmö, Sweden, concerns were raised by the residents on being excluded from parts of the neighbourhood and restricted access to recreational sites because of the construction of ponds, wetlands and streams for stormwater. Also the residents had complains over the prolonged construction time, leading to noise and dust, and over undesired changes to the neighbourhood such as removed trees and changed appearance of the neighbourhood. Thus evaluation the social aspects of the project showed mixed results (Villarreal, Semadeni-Davies, & Bengtsson, 2004). 5.2. Precipitation data
5.2.1. Data provided from SMHI The data shows an increasing intensity with longer return periods. The four stations show a great variability among each other, where the station at Nidingen has the highest intensities for all durations and return periods, except for the return period of one year as well as for the 24-hour duration where the station at Hallands Väderö has a higher intensity. The variation between the stations is generally larger for events with longer return periods and shorter durations. The plots of rain intensity and return periods for all station and all durations provided are shown below in fig 8. The full data set is presented in Appendix A.
a) 35 b) 70 30 60 25 50 20 40 15 30 10 20 Rainfall [mm] Rainfall [mm] 5 10 0 0 0 20 40 60 80 100 0 20 40 60 80 100 Return period [years] Return period [years]
c) 80 d) 100
60 80 60 40 40
Rainfall [mm] 20 Rainfall [mm] 20 0 0 0 20 40 60 80 100 0 20 40 60 80 100 Return period [years] Return period [years]
32
e) 120 f) 140 100 120 80 100 80 60 60 40 40 Rainfall [mm] Rainfall [mm] 20 20 0 0 0 20 40 60 80 100 0 20 40 60 80 100 Return period [years] Return period [years]
g) 140 120 Fig 8. Rain intensity [mm] and return periods 100 [years] for all durations provided for the 80 stations Hallands Väderö (blue) Torup (red), 60 Nidingen (green) and Ullared (purple). The 40
Rainfall [mm] durations are a) 15 min, b) 30 min, c) 45 min, 20 d) 60 min, e) 6 hours, f) 12 hours and g) 24 0 hours. 0 20 40 60 80 100
Return period [years]
To be able to compare the provided data with the Dahlström formula, the data had to be recalculated to the unit l s-1 ha-1, as described in the methods section. Usually the Dahlström formula is expressed as intensity as a function of duration, or
1/3 0,98 iÅ(TR) = 190Å ln(TR) / (TR ) + 2
This means that the return period (Å) must be determined to describe the function. In Falkenberg the dimensioning intensity used is the event with a 10-year return period. The calculated intensity with a return period of 10 years for the measurement stations was plotted together with the Dahlström formula for an event with the same return period. The result is shown in table 14 and fig 9.
Table 14. Intensity per station and intensity calculated using Dahlströms formula for all durations provided. The return period is 10 years. Duration Intensity per station [l s-1 ha-1] Dahlström [min] Hallands Torup Nidingen Ullared formula väderö [l s-1 ha-1] 15 130.0 155.6 213.3 164.4 180.6 30 101.1 86.1 192.2 82.2 115.7 45 72.6 70.4 155.2 62.6 87.5 60 58.6 57.5 132.2 50.3 71.4 360 20.2 18.1 29.4 18.8 19.2 720 14.3 12.1 16.4 12.0 11.8 1440 9.5 7.4 9.2 7.5 7.5
33
250
200
150
100 Intensity [l /s, ha]
50
0 10 100 1000 Dura on log[min]
Dahlström formula Hallands Väderö Torup Nidingen Ullared
Fig 9. Plotted intensities for the measurement stations and the Dahlström formula for an event with a return period on 10 years. Note that the scale for duration is logarithmic.
It is apparent that there is a difference between the stations and the formula. For shorter durations, < 1 hour, the formula gives a higher value for intensity than what has been measured on three of the stations (Hallands Väderö, Torup and Ullared) but a much lower value than has been measured by the station Nidingen. For longer durations, 6-24 hours, the results are more varied. For the 6 hour duration (360 min), the value for Nidingen is still much higher than, and the value for Hallands Väderö is slightly above the Dahlström value. For the 12 hour duration (720 min), all of the station values are above the formula and for the 24 hour duration (1440 min) two of the stations are above (Hallands Väderö and Nidingen), one has the same value (Ullared) and one is slightly below (Torup) the Dahlström formula value.
To visualise the difference between the Dahlström formula and the station values the quota between the average station value and the Dahlström formula was calculated. The average values were calculated for all stations combined and for all stations excluding Nidingen, because of the big impact on the average value from that particular station. The average values of the station intensities were divided with the intensities calculated with the Dahlström formula. A quota higher than 1 means that the average station value was higher than the calculated intensity and a quota lower than 1 means that the average station value was lower than the calculated. The quotas were calculated for all durations and return periods provided, and plotted in one plot for each of the durations. The results are shown in fig 10.
34
a) 2 b) 2
1 1 Quota Quota
0 0 1 2 5 10 20 30 50 100 1 2 5 10 20 30 50 100 Return period [yr] Return period [yr]
c) 2 d) 2
1 1 Quota Quota
0 0 1 2 5 10 20 30 50 100 1 2 5 10 20 30 50 100 Return period [yr] Return period [yr]
e) 2 f) 2
1 1 Quota Quota
0 0 1 2 5 10 20 30 50 100 1 2 5 10 20 30 50 100 Return period [yr] Return period [yr]
g) 2 Fig 10. Quotas between station values of rain intensity and rain intensity calculated using the Dahlström formula. Average for all 1
Quota stations (✕) and for all stations excluding Nidingen (+). The durations are a) 15 min, b) 30 min, c) 45 min, d) 60 min, e) 6 hours, f) 12 0 hours and g) 24 hours. 1 2 5 10 20 30 50 100 Return period [yr]
Nidingen usually has significantly higher intensity values than the other stations, which is why the quota for the average with Nidingen included is higher for almost every duration and return period. The quota is predominantly lower than 1, especially if Nidingen is
35 excluded. However, for shorter return periods (≤ 10 years) and longer durations (> 1 hour) the quota is predominantly larger than 1, meaning that the measured intensity is larger than expected using the Dahlström formula. The quota varies between 0.65-1.20 for the total average and between 0.50-1.19 if Nidingen is excluded. For events with a return period of 10 years the quota is lower than 1 for the durations 15 min and 30 min including Nidingen, and additionally for 45 min, 60 min and 6 hours if Nidingen is excluded. For the additional durations the quota is higher than 1, topping at 1.16 for the total average and 1.09 if Nidingen is excluded, for the 12 hour and 24 hour durations respectively. 5.2.2. Falkenberg blended station data The data from the three stations close to Falkenberg were combined in one dataset, covering daily precipitation from 1961-2002 with a few exceptions (explained in section 4.2.2.). A total of 40 years of daily precipitation values was used. The monthly distribution of the maximum daily precipitation values is displayed in fig 11. The annual daily maximum precipitation values were fitted to the two distributions and the return periods were calculated. The results are shown in table 15 and table 16.
10 9 8 7 6 5 4 Nr. of days 3 2 1 0 January February March April May June July May June April March August
January July August September October February December November September October November December
Fig 11. Nr of maximum daily precipitation values per month for the Falkenberg blended station data, displayed as a bar chart (left) and a colour coded pie chart (right) with colours representing the different seasons; winter (blue colours), spring (red colours), summer (green colours) and fall (yellow colours).
Table 15. Location, scale and shape for the two distribution models with the standard error. Distribution Location Scale Shape GEV 32.05 (1.32) 6.77 (1.11) 0.28 (0.19) Gumbel 33.18 (1.30) 7.88 (1.02) N/A
36
Table 16. Negative log – likelihood value and return periods for precipitation (in mm) for the Falkenberg blended station data. Distribution Negative Precipitation in mm for different return periods [years] log- likelihood 2 5 10 20 30 50 100 value GEV 145.86 34.67 44.65 53.21 63.27 70.03 79.65 95.02 Gumbel 147.77 36.07 45.00 50.91 56.59 59.85 63.93 69.43
The month containing most days with a maximum daily precipitation is July with 9 of the 40 values. The three consecutive months July, August and September covers more than half (23) of the total number of daily maximum values. None of the days occurred in January, March or April.
Because of the lower negative log-likelihood value for the GEV than the Gumbel distribution the GEV model fit the observation data slightly better than the Gumbel distribution model. The values from the two distribution models are relatively similar for shorter return periods < 10 years. However, this changes for the longer return periods and the GEV value for a return period of 100 years is about 37% higher than the corresponding Gumbel value. Because of the shape of the GEV distribution is larger than the standard error it is reasonable to assume the GEV distribution to better represent the data than the Gumbel distribution. There is a great deal of uncertainty in the predictions for the longer return periods as the 95% confidence interval for the 100-year value stretches between 33.7-156.3 mm for the GEV, and between 59.2-79.7 mm for the Gumbel distribution. For shorter return periods, < 10 years, the confidence interval is narrower for the GEV than for the Gumbel distribution. See additional information in Appendix C.
To be able to compare the Falkenberg blended station data with the data provided by SMHI they were plotted together (see fig 12) and, as with the SMHI data, the data had to be recalculated to the unit l s-1 ha-1 to be compared to the Dahlström formula, as shown in table 17.
37
140
120
100 Hallands väderö 80 Torup 60 Nidingen Rainfall [mm] 40 Ullared Falkenberg GEV 20 Falkenberg Gumbel 0 0 20 40 60 80 100 Return period [years]
Fig 12. The provided SMHI station data and the calculated return periods for the Falkenberg blended station data plotted together. The duration is 24 hours and the unit for precipitation is mm.
Table 17. The intensity values from the Dahlström formula compared with the return periods for the Falkenberg blended station data. Return period [years] Falkenberg GEV Falkenberg Gumbel [l Dahlström formula [l [l s-1 ha-1] s-1 ha-1] s-1 ha-1] 100 11.00 8.04 13.79 50 9.22 7.40 11.36 30 8.11 6.93 9.89 20 7.32 6.55 8.90 10 6.16 5.89 7.47 5 5.17 5.21 6.34 2 4.01 4.17 5.20
The calculated return period values for the Gumbel distributed Falkenberg blended station data is clearly lower than the return period values obtained from the SMHI automatic stations, which are also Gumbel distributed. The Falkenberg return periods calculated with the GEV distribution follows a steeper slope than the Gumbel distribution and give a larger value than some of the SMHI provided values for longer return periods. For both of the distributions the intensity return period values are significantly smaller than the Dahlström formula, where the GEV values are about 80% of the Dahlström formula values for all return periods while the Gumbel values vary from 80 to 58% of the formula values. 5.2.3. Grid data The grid data consisted of 64 years of daily precipitation data per grid and was treated in the same way as the Falkenberg blended station data, and the results for the monthly distribution of the maximum daily precipitation are displayed in fig 13 and the fitting of the data to the two distributions and calculated return periods and negative log-likelihood values are shown in table 18, table 19 and fig 14.
38
45 40 35 30 25 20 15 Nr. of max. values 10 5 0 January February March April May June July May June April March August July August September January October February December November September October November December
Fig 13. Nr of maximum daily precipitation values per month for the three grids combined, displayed as a bar chart (left) and a colour coded pie chart (right) with colours representing the different seasons; winter (blue colours), spring (red colours), summer (green colours) and fall (yellow colours).
Table 18. Location, scale and shape for the three grids and the two distribution models with the standard error. Grid Distribution Location Scale Shape nr 2 GEV 9.92 (0.23) 1.64 (0.18) 0.15 (0.11) Gumbel 10.06 (0.23) 1.75 (0.18) N/A 3 GEV 10.10 (0.31) 2.16 (0.24) 0.02 (0.12) Gumbel 10.13 (0.29) 2.18 (0.22) N/A 4 GEV 9.81 (0.29) 1.98 (0.22) 0.08 (0.11) Gumbel 9.90 (0.27) 2.06 (0.21) N/A
Table 19. Negative log-likelihood values and return periods for precipitation (in mm) for the three grids and the two different distributions. Grid Distribution Negative Precipitation in mm for different return periods [years] nr log- 2 5 10 20 30 50 100 likelihood value 2 GEV 140.44 10.54 12.68 14.30 16.04 17.12 18.58 20.72 Gumbel 141.36 10.70 12.69 14.00 15.26 15.99 16.90 18.12 3 GEV 153.99 10.90 13.41 15.10 16.76 17.72 18.94 20.60 Gumbel 154.01 10.93 13.41 15.05 16.62 17.52 18.65 20.18 4 GEV 150.41 10.54 12.98 14.72 16.49 17.56 18.95 20.92 Gumbel 150.71 10.65 12.98 14.52 16.00 16.85 17.92 19.35
39
25 25
20 20
15 15
10 10 Rainfall [mm] Rainfall [mm]
5 5
0 0 0 20 40 60 80 100 0 20 40 60 80 100 Return period [years] Return period [years]
Fig 14. Plotted return periods of rainfall for the three grids, grid 2 (blue), grid 3 (red) and grid 4 (green), and the two distributions, GEV (left) and Gumbel (right).
July was the month containing the most daily maximum values with 40 of the 199 values. The second most common month was August with 35 values. March contained more values than September, as opposed to the station data where there were no maximum values in March at all.
As for the Falkenberg blended station data the GEV distribution fitted slightly better than the Gumbel distribution, as shown by the lower negative log-likelihood values. However, the shape was only larger than the standard error for the GEV distribution for grid 2. The GEV distribution gave a higher value for the longer return periods for all grids, while the difference between the distributions were small for shorter return periods < 10 years. The three grids had relatively similar return period patterns and the plots for the GEV distributed values were almost indistinguishable from one another, while the Gumbel distributed plots were somewhat more dispersed with grid 2 showing the lowest values and grid 4 showing the highest. 5.2.4. Temporal trends To see if any clear trends in precipitation intensity could be found throughout the time series, the yearly maximum values of the data were plotted for the Falkenberg blended station data and the grid data (fig 15). Linear trend lines were added for easier trend spotting.
40
Fig 15. Plotted maximum daily precipitation values per year for the Falkenberg blended station data (left) and the three grids (right) with added linear trend lines. For the gridded data the different grids are grid 2 (black), grid 3 (red) and grid 4 (blue). Note that the scales are different in the two plots.
The dataset for the Falkenberg blended station data showed an increasing trend during the 40-year time span. The linear trend line increases with 0.166 mm per year even thought the highest value is in the beginning of the dataset, however there are missing data point for two years (1963 and 1998). For the grid data there are longer time series and no missing data. The trends for all the grids were slightly decreasing with 0.0054, 0.0021 and 0.0028 mm per year for grid 2, 3 and 4 respectively. None of the trends are statistically significant.
Another way of investigating temporal trends and changes in heavy daily precipitation is to compare how heavy precipitation for a certain return period changes over time. The time series for the Falkenberg station and the Falkenberg blended station data are relatively short, while data from other station were also used. The chosen station were all in proximity of Falkenberg and contained long datasets; København – Meteorologisk Institut and København – Botansik Have in Copenhagen, Denmark and Växjö in Sweden. The data was on precipitation with a 10-year return period and calculated for datasets of 20 years at a time, starting from 1941 until 2010, creating six time-spans. The data was downloaded from ECA&D and the results are shown in fig 16 (Klein Tank, et al., 2002; ECA&D, 2014a).
41
60
50
40
30
20
10
0 1941-1960 1951-1970 1961-1980 1971-1990 1981-2000 1991-2010
Falkenberg Växjö København MI København BH
Fig 16. Precipitation with a 10-year return period calculated for every two decades between 1941 – 2010 for measurement stations in Falkenberg, Växjö and two in Copenhagen. Note that the 20-year periods are overlapping and that not all stations stretch over the entire period (Klein Tank, et al., 2002; ECA&D, 2014a).
For all longer series (Copenhagen and Växjö) there are somewhat decreasing trend, although not statistically significant. To broaden the spectrum another type of analysis was collected form the ECA&D, which showed the trends in extremely wet days from 1951-2014 during the summer months, June, July and August. The extremely wet days were defined as days with a daily precipitation exceeding the 99th percentile of the daily amounts of precipitation for every day included. The resulting map shows the trends in each station included and is shown in fig 17.
42
Fig 17. Map showing precipitation measurement stations and the trend in extremely wet days with precipitation >99th percentile of daily amounts for stations in south Sweden, Norway and Denmark (ECA&D, 2014b).
Many of the stations in Sweden show no significant trend or a slight increase in extremely wet days every decade. Very few stations show a decrease in extremely wet days and those who do are not located on the western coast of Sweden. Other trend maps show similar results for very wet days (days with precipitation >95th percentile of daily amounts), moderately wet days (> 75th percentile) and highest 24-hour precipitation amount with slight but significant increases per decade since 1951 for the majority of stations during the summer months (June, July and August) (ECA&D, 2014b).
43
6. Discussion
6.1. The sustainability of the stormwater ponds in Falkenberg
6.1.1. Greenhouse gas emissions, air quality and energy demand The ponds in this study have already been constructed, however there are plans for constructing more ponds and green solutions to stormwater management issues are high on the agenda for municipalities in Sweden (Svenskt Vatten, 2011b). This makes it relevant mentioning the construction phase of the ponds even though it does not apply to the investigated ponds in Falkenberg. There is evidence that the construction of green stormwater solutions are more energy and material effective than corresponding gray solutions, which would make those kinds of investments favourable from an energy, raw material and global warming perspective. Ponds may, in fact, be one of the least energy and raw material intensive of the green solutions suggested by looking at the entire life cycle (De Sousa, Montalto, & Spatari, 2012; Wang, Eckelman, & Zimmerman, 2013). This would suggest that green stormwater solutions, and especially ponds, have an advantage considering greenhouse gas emissions and energy demand in the construction phase.
Release of greenhouse gases from the ponds has not been measured in Falkenberg, but studies show that these types of emissions are connected with low oxygen or anaerobic conditions (Sims, Gajaraj, & Hu, 2013). Excess of nutrients and organic content may lead to such conditions. How large these emissions can be varies depending on local conditions and inflow, and it is not certain that study findings are relevant for stormwater ponds in Falkenberg. The processes need to be studied further to be able to make estimations without excessive measurements on site. Considering eutrophication, residence time and hydraulic function of the pond can prevent anaerobic conditions. On the other hand, emerging vegetation sequesters carbon, especially macrophytes. All of the ponds in Falkenberg are vegetated to some extent, which means that they are carbon sinks. Shallow ponds promote emerging macrophytes, and there are several species of macrophytes found in all ponds, where the smaller ponds Skogsvägsdammen and Lyckebäcksdammen hade a larger portion of their area covered with macrophytes while the larger and deeper Lerhålan and Fajanshålan had vegetation mainly close to the shores(Nolbrant, 2004). Vegetation helps reduce air pollutants such as NOx, sulphur oxides, ozone and particles but the effect from water plants in the ponds is probably very small, and benefits from adjacent trees is not related to stormwater management. However, stormwater ponds may contribute to planning green areas in urban settings. 6.1.2. Water quality The nutrient concentrations in the ponds were measured in 1999, and the results were that all ponds had some issues with eutrophication (Nolbrant, 2004). Because of the end recipient, Kattegatt, being classified as having a moderate ecological status largely due to eutrophication problems this is an important issue that perhaps should be more closely monitored. Nitrogen, which is the main nutrient of concern for Kattegatt, shows varying remediation success during the measurements in 1999 ranging from 42% in Lyckebäcksdammen to Skogsvägsdammen being a net nitrogen polluter by having 8% higher nitrogen concentrations in the outlet than the inlet. Symptomatic for the ponds is that the
44 measurements in April showed a lower remediation rate than the measurements in August, possibly related to the same mechanisms as Semadeni-Davies (2006) found in a stormwater pond in Växjö, suggesting that the ponds function in the winter-spring is worse than for the summer months. The eutrophication problem for the recipient Kattegatt is especially large during the winter, and mainly caused by excessive nitrogen (VISS, 2015b). Poor nitrogen remediation in the stormwater ponds will cause additional eutrophication problems, which has to be considered because of the insufficient ecological status of the recipient. Measures should be taken to reduce the nitrogen content in the outlet to the recipient, especially during winter. Generally in international literature the nutrient removal rates for nutrients are varying, and have been shown to decrease with the age of the pond. With proper management and careful planning to optimise hydraulic function and residence time, as well as favouring certain plants for phytoremediation, it should be possible to have a average remediation of both nitrogen and phosphorus of more than 50% (at least during the summer months), as seen in several studies (Svenskt Vatten, 2011b; White, 2013; Koch, Febria, Gevrey, Wainger, & Palmer, 2014).
Other contaminants such as metals and PAHs have not been measured in the ponds in Falkenberg. Depending on the surroundings of the ponds it can be assumed that Lyckebäcksdammen and Lerhålan ay contain more contaminants than the other ponds because of their location in proximity to industrial areas and larger roads. Even so, there may be high concentrations of toxic contaminants even in ponds in residential and rural areas (Egemose, Sønderup, Grudinina, Hansen, & Flindt, 2015). The sediment of stormwater ponds in Sweden have been shown to exceed health and safety guideline concentrations for mainly heavy metals (Karlsson, Viklander, Scholes, & Revitt, 2010; Karlsson, German, & Viklander, 2010) which may be of concern to the nearby residents, even though the contaminants usually are not bio available. For the recipient Kattegatt, the main concern is mercury (VISS, 2015b). Mercury levels in Kattegatt sediments are decreasing, mainly because of the general out-phasing of the metal in recent years rather than improved stormwater management, even though stormwater management surely can help improve the mercury levels as well. For most of the contaminants the main remediation process is sedimentation, which suggests that improving the sedimentation processes in the pond will improve the water quality in the outlet. Phytoremediation with certain types of species of vegetation can also improve the water quality but will probably serve more use as sediment traps improving sedimentation (Svenskt Vatten, 2011b; Gill, Ring, Higgings, & Johnston, 2014). To completely remove the contaminants the sediment and the vegetation must be harvested regularly, thus it helps if there is an easy way to empty the pond and remove the contaminated sediment as a management practice (Stahre, 2004; Svenskt Vatten, 2011b). If not removed there is a risk that disturbance of the sediment will release the contaminants or make them increasingly bio available. To be able to completely assess the issue of contaminants in the ponds there is a need for conducting sediment and water samples. In Sweden, many of the constructed stormwater ponds are not properly assessed and monitored once they are built because of time and financial constraints in the municipalities, which may lead to inferior functionality in improving the water quality (Falk, 2007). The ponds that have been assessed shows varying effectiveness in decreasing nutrients and contaminants in the runoff, which emphasise the need to evaluate the ponds function after construction and improve if needed (Andersson, Owenius, & Stråe, 2012).
45
6.1.3. Well being/perceived welfare and socio-economic aspects There is no doubt that water in an urban setting possesses cultural, recreational and educational values, which may be hard to quantify but are nonetheless important for the people living nearby. Moore & Hunt (2012) points out accessibility as one of the main aspects for a high recreational value. In places where changes in infrastructure can affect areas on public land or where the general public has had access in the past there might be a conflict of interest between accessibility versus functionality of the stormwater management system. However for the ponds in Falkenberg the accessibility is very good. There are roads and walking paths leading to the ponds, or even encircling the pond, as for the larger ones Fajanshålan and Lerhålan. The recreational areas surrounding the ponds are well planned with benches, playgrounds and other recreational infrastructure. The pond Lyckebäcksdammen is fenced in, however the surrounding area is a well-maintained and easily accessible park area. Poorly planned ponds and additional stormwater management infrastructure can be perceived as barriers, as seen in the study by Villarreal, Semadeni- Davies & Bengtsson (2004) in Malmö. However, the collected judgement is that all the ponds score high in accessibility, which makes the recreational value high as well. A pond may have an educational value if it has educational infrastructure such as information signs or activity stations. This is something that the ponds lack in Falkenberg. In the proximity of Lyckebäcksdammen there is an information sign explaining why the pond is built and its dimensions, but the sign is poorly maintained and the colours are fading. Additional educational infrastructure could increase the cultural and educational value of the stormwater ponds, as well as increasing the understanding of the importance of stormwater management for the residents.
A negative social aspect that may worry nearby residents is the risk for drowning, mainly for small children. The Swedish Civil Contingencies Agency (MSB) released a report in 2010 investigating drowning accidents for children that showed that 45 preschool children (0-6 years old) drowned between the years 1998 and 2007. Of these 8 children, or 17.8%, drowned in ponds (excluding private pools and garden ponds) (MSB, 2010b). To prevent children to be able to play in or near the ponds, some are fenced in. In Falkenberg Lyckebäcksdammen is the only of the investigated ponds that is surrounded by a fence. However the fence might give a false sense of security, when it is relatively easy to break it shown by the vandalism to the fence demanding maintenance (possibly done by the kids themselves). In a city like Falkenberg situated on the coast and with the river Ätran running through the city centre, perhaps it is better to educate children to respect the dangers of water from an early age. 6.1.4. Additional aspects Many of the sustainability aspects concerned in this study are interconnected. An example is that an excess of nutrients in the pond may have an affect on oxygen content, the recipient as well as an effect on the ponds recreational value. This also means that there may be conflicts of interest between different values of a pond. Many of these conflicts are briefly discussed in the guidelines for constructing and maintaining stormwater ponds in Varberg and Falkenberg, which are the guidelines adopted by the municipality for increasing the biodiversity in the ponds (Nolbrant, 2013). In general the conflicts stand between function and additional values, such as biodiversity, aesthetic and recreational values. Another conflict of interest may be space constraints. In a study of 9 stormwater ponds in Sweden by Falk (2007) the ponds were all constructed for the purpose of flow equalization and, to some 46 extent, water quality improvement. They were usually dimensioned based on rain with 2, 5 or 10 years return periods, or simply because of space constraints which was also a big factor affecting the design. This is an issue in Falkenberg as well, especially when trying to retrofit green stormwater solutions to existing areas. A stormwater pond is not very space efficient, as it demands a relatively large area that could have been used for housing or other exploitation.
The costs of constructing and maintaining a stormwater pond varies a lot, as seen in the report by Andersson, Owenius & Stråe (2012). However studies suggest that ponds are the most cost effective of the green stormwater solutions and that green solutions in general are cheaper than gray solutions (Wang, Eckelman, & Zimmerman, 2013). Mikael Bergenheim at VIVAB also confirms this. It can be assumed that a stormwater pond is more flexible than a gray stormwater solution, because a pond can be altered and have techniques added to it without having to dig up pavement pipes. A combination of green stormwater management practices, starting high upstream in the catchment area, is probably the most cost effective way of equalizing flow and remediating contaminants while increasing biodiversity and social well being in an urban environment. 6.2. Precipitation data
6.2.1. Measurement errors For the Falkenberg blended station data there was a correction coefficient added because of the equidistantly measured volumes (24 hours). The correction factor was 1.14, as recommended by Swedish sources (Svenskt Vatten, 2011a), which is similar to the factor recommended by the National Oceanic and Atmospheric Administration in the USA (factor 1.13) (NOAA, 2014). The factor is based on the number of multiples of measurement intervals that the data point is based on. The Falkenberg station and its blending stations were manual stations, reported daily. Thus it is assumed that they are only measured once per 24 hours, giving one multiple of the measurement interval for daily data. The SMHI automatic stations have a measurement interval of 15 min, creating 96 measurement intervals per day, which makes the correction factor negligible for daily values. The correction factor is a general estimation and may not be completely correct for the Falkenberg data. It is added to compensate for the fact that rains may have other temporal distribution than 06:00-06:00 UTC, which makes the daily data underestimate rains that stretch over the time of measurement i.e. a “sliding” measurement window is supposed to be 14% larger than a fixed one. Adding the correction factor may also lead to overestimation of rains with a temporal distribution within the measurement window, which may be of concern because of the predominance of intensive rains during the afternoon, especially valid for intensive convective rain during the summer months (Hernebring, 2006; Wern & German, 2009).
There are other measurement errors for precipitation measurement stations that are not accounted for in this study. The biggest one is probably losses because of the wind or the aerodynamic conditions at the measuring site. The automatic and manual measuring stations are equipped with different types of wind protection gear to minimise the impact of wind conditions on the measurements. In a report by Alexandersson (2003) the measurement error because of the wind is thoroughly gone through for all the SMHI stations by assigning them a “wind class” deciding the general loss of data for each class. For the
47 station Falkenberg the wind class is set to class 3, meaning a relatively well protected setting, giving a correction factor for rain at 4.5% and for snow at 12%. For the blending stations Jonstorp is classified as a class 2, meaning a well-protected setting, giving a correction factor of 3.5% for rain and 8.5% for snow. Morup is an older station and is unfortunately not classified. Because the majority of the heavy precipitation events occurred during the summer months, and very few during winter or early spring it can be assumed that the losses due to winds are approximately 3.5-4.5%. Alexandersson (2003) also investigates other sources of measurement errors related to the measuring stations. Evaporation and adhesion are the two largest besides wind. Evaporation was estimated to reduce monthly values by 1-2 mm depending on the month and the average temperature. This makes the error for daily values very small. Also the loss because of evaporation is biggest in the early spring when the evaporation protection in the station is not yet put into place, and there were very few maximum daily values in the spring for the Falkenberg blended station data. Adhesion error is when the rainwater sticks to the sides of the container during the measurement, and the size of the error is largely due to the accuracy of the observer. As for evaporation the general monthly error has been estimated to between 1-2 mm depending on the month, which makes the daily error very small. Other measurement errors include frost, dew and human errors made by the observer. These errors are likely to be very small or hard to account for. 6.2.2. Difference between stations There are big differences in the calculated return period values for the different stations in the study. In the data provided by SMHI the station Nidingen is distinguished from the others with high intensity values for all durations and return periods, except for the 1-year return period precipitation. The three other stations show less dispersion, at least for shorter durations < 6 hours, while the station Hallands Väderö closes in and catches up on Nidingen for durations of 12-24 hours. This can probably be explained by the locations of the measuring stations, where both Nidingen and Hallands Väderö are low-lying (< 10 m above sea level) stations located on islands off the coast whilst the Torup and Ullared stations are located inland and at a higher elevation (> 100 m above sea level) (ECA&D, 2014a). Both Nidingen and Hallands Väderö have significantly lower annual rainfall than the Ullared and Torup stations, which is apparent in the annual rainfall maps created by SMHI and ECA&D, where the coastal stations and the coastal area show an annual precipitation of around 800 mm, whilst inland stations show annual precipitations of > 1200 mm (SMHI, 2014e; ECA&D, 2014a). This suggests that even though the inland stations see more rain, the more intense rainstorms occur by the coast and that the annual rainfall is a bad indicator for rain intensity. It is well known that intense rain is generally a product of convective precipitation, while the high values for annual rainfall in the stations Ullared and Torup may be because of orographic lift in the west Swedish highlands (Svenskt Vatten, 2011a). The pattern of more intensive rain along parts the western coast can also be seen in published SMHI reports on intensive precipitation (Wern & German, 2009).
The data from the station in Falkenberg differs from the SMHI data in three major ways; it was blended with adjacent station data to create a longer time series, it covers a partly different time-span than the SMHI data and it covers only daily data while the SMHI data covers durations down to 15 min. For the latter reason a correctional factor was used for the Falkenberg blended station data but for the other differences no correctional measures were done before comparison. It is clear that the results from the Gumbel distribution of the 48
Falkenberg blended station data are lower than the SMHI station data. The difference in intensity return period values are around 14-16 mm for the Gumbel distribution versus the Ullared and Torup values for all return periods, and the difference to the stations Nidingen and Hallands Väderö are even larger at longer return periods, being about 58 mm for the 100-year rain. This suggests that Falkenberg receives less intensive daily precipitation than its neighbouring stations, about 20-30% less than Ullared and Torup and about 25-45% less than Nidingen and Hallands Väderö. However, more extensive comparisons of full datasets would be required to confirm this. The comparison here is between the mean values, but all of the other stations 100-year precipitation values lies above the 95% confidence interval for the Falkenberg blended station data with the Gumbel distribution. The 50- and 100-year return period values are associated with large uncertainties, especially for the SMHI values, because of their shorter time series (Persson, 2014). Adding correctional factors for wind error and other measurement errors, as discussed above, may increase the value for the Falkenberg blended station data but it is unlikely that measurement error accounts for more than a 5-10% decrease of the results. Also SMHI has compared automatic stations with manual stations where the automatic stations in measured about 5-10% less precipitation than manual stations (Alexandersson, 2003). 6.2.3. Comparison with the Dahlström formula The comparison is interesting because of the Dahlström formula being widely used as a basis for dimensioning throughout Sweden. In Falkenberg the Dahlström formula value with a return period of 10 years is used, with an added climate factor as will be discussed later. For the Falkenberg blended station data the values are consistently below values obtained from the Dahlström formula for both distribution models. This would suggest that the formula overestimates the ’true’ precipitation for Falkenberg, even if additional correction factors are added. However, the Falkenberg blended data only covers the duration of 24 hours. The data from SMHI covers durations down to 15 minutes, which makes it possible to make a more thorough comparison with the Dahlström formula. For shorter return periods and longer durations the formula seem to underestimate the precipitation intensity with up to 20%, suggesting that intense rains occur more often than anticipated with the formula. For the dimensioning 10-year rain the calculated values for durations of > 1 hour is underestimated compared to the measurements. There is a big difference between the stations and especially Nidingen differs from the others by displaying a much higher intensity for short durations than the Dahlström formula.
Underestimating rain intensities and volumes could lead to major problems due to insufficient stormwater infrastructure, leading to flood damages, erosion and other tangible and intangible consequences. The data from the stations nearby Falkenberg show that there is a risk of the underestimating intensities, which should be taken into account. Even if the dimensioning volumes are correct, there is always a risk of a more intense event happening. Careful consideration of what happens when precipitation volumes exceed dimensioning volumes should always be taken when planning for stormwater management, regardless if the Dahlström formula is used or not. 6.2.4. Difference between distributions For the time series of daily precipitation provided the return periods were calculated using two common distribution models; generalized extreme value distribution (GEV) and Gumbel distribution. For the Falkenberg blended station data the difference between the two
49 distribution models was apparent for return periods larger than 10 years, where the GEV distribution model gave a 37% higher value than the Gumbel distribution model for the 100- year return period value. For the grid data the difference between the distributions is less apparent, which is related to the narrower range of values for the maximum daily precipitation in the grids. Because the grid data displays the average value for the entire grid and is a areal precipitation estimation, the top notations are evened out over the entire area of the grid (as discussed later) and this gives a much lower value than the station data which measures point precipitation. For two of the grids, grid 3 and 4, the value of the “shape” parameter was lower than the standard error, meaning that the Gumbel distribution, which lacks the “shape” parameter, is applicable. For the grid data the GEV distribution gave higher return period precipitation than the Gumbel distribution for all return periods > 5 years, and the difference was 2-18% for the 100-year return value.
The GEV distribution has sometimes been considered to be superior when calculating return periods of heavy rains (Wern & German, 2009). However, the uncertainty in the predictions for rains with longer return periods, such as 50- or 100-year rains, are significantly larger when using the GEV compared to the Gumbel distribution. The 95% confidence interval ranges over 100 mm for the GEV distribution of the Falkenberg station data at the 100-year return period rain, while the Gumbel distributed data gives a confidence interval of about 20 mm. This would suggest that the Gumbel distribution gives a better, more certain value for the longer return periods. While this is true the Gumbel distribution predicts a much lower value, which may lead to underestimation of the intensity of heavy rains, which can be seen in the outliers (see appendix C and D). There are other types of distributions used for calculating return periods such as log Pearson type III (Hernebring, 2006) and GEV with constant theta (Wern & German, 2009), which may have advantages when the available data series are short. Also the Generalized Pareto distribution can be used, where the distribution is not based on maximum values (such as the daily maximum values used in this study) rather than values exceeding a predetermined threshold (Mannshardt-Shamseldin, Smith, Sain, Mearns, & Cooley, 2010). These different types of distribution may give different values for intensities, especially for the longer return periods. Great consideration must be taken when choosing distribution method for return value analysis for stormwater planning purposes. 6.2.5. Differences between grid and station data There were big differences in the return periods of precipitation between the Falkenberg blended station data and the Grid data. The grid data is consistently between 20-30% of the value for the station data and the difference increases with increasing return period length. A similar pattern has been shown in other studies and is reasonable because of the increasing uncertainty and smaller scale of very heavy rains. However, the difference between the grid data and the point station data in this study is much larger than in comparable studies, where other studies have found areal data with an resolution of ~400 km2 to represent at least 70-80% of the point data (Hernebring, 2008; Mannshardt- Shamseldin, Smith, Sain, Mearns, & Cooley, 2010; Svenskt Vatten, 2011a). There may be several explanations for this large deviation as discussed below.
Different time periods The Falkenberg blended station data and the grid data cover different time periods. The grid data covers the period 1950-2014, a period of 64 consecutive years with very little data 50 missing while the data for the blended station data for Falkenberg covers roughly the period 1961-2003 with more missing data, ending up with a time series of almost 40 years. This would suggest that the grid data is more accurate, but limiting the data for the grids to represent the same years as for the blended station data do not change the overall picture very much.
No correction factor added to the gridded data The Falkenberg station data was corrected with a factor 1.14 to account for the equidistantly measured volumes to be able to compare the series with the SMHI provided return period values. This was not done with the grid data because of the factor being adapted to station data and not areal data and thus is not directly transferrable to areal datasets. Thus the factor could account for about 14% of the difference.
Smoothing of extremes due to interpolation The grid dataset is built on the kriging interpolation method, fitted with a theoretical function as a ‘variogram’ deciding the absolute difference in precipitation between stations as a function of their distance. The precipitation for each grid is expressed as the median precipitation of the selected grid (Haylock, Hofstra, Klein Tank, Klok, Jones, & New, 2008). This can be problematic when handling extremes, because of their local character. How well the grid represents the point precipitation depends on how extensive the station network is in, or adjacent to the grid. For Falkenberg this varies over the time period depending on which stations are in use and how reliable their data is. The smoothing of the extreme values in the grids due to lower precipitation values in adjacent stations are probably the main cause of the low return period values for the grids. Other studies investigating the relationship between areal and point precipitation, e.g. Hernebring (2008), most often uses more stations for their measurements, thus having a better areal reduction factor.
Convective precipitation is the kind of precipitation that most often leads to extreme volumes. The convective precipitation may have a very local spatial distribution and thus it is hard to estimate using grid data(Svenskt Vatten, 2011a). This type of precipitation is most common during the summer and early fall, which coincides well with the period when the most of the daily maximum values were measured for both the blended station data and the grid data, although the trend is a little less clear for the grid data. This suggests that most of the extreme values are derived from local convective precipitation, especially for the station data, which are evened out in the grid data. 6.2.6. Temporal trends and climate change There were no statistically significant trends found for the yearly maximum daily precipitation amount, either for the Falkenberg blended station data or for the grid data. The yearly maximum daily precipitation amount may not be the best indicator for seeing trends in precipitation over time due to a limited amount of observations, thus other types of data from the ECA&D database was included in the analysis. The 10-year return period precipitation values for a number of relatively close stations with long data series were chosen to represent the trend in southwest Sweden, however there was no clear trend since the 1940s. This has been confirmed in other studies as well were no or varying trends have been seen in for precipitation intensity in Swedish datasets (Hernebring, 2006; Hernebring, 2008; Olsson & Foster, 2013).
51
The trend in the number of days exceeding a given threshold is another indicator that can be used for spotting changes in precipitation patterns for an area. By using the ECA&D map creator it was clear that the number of days with precipitation exceeding the 75th, 95th or 99th percentile of daily amounts had increased for many stations in south Sweden, especially in the summer months; June, July and August. The increase was between 0 - 0.2 days per decade, which would suggest that heavy precipitation has become more common in the summer, although it does not say anything about the intensity of the rain because the scale is relative. About half the stations showed no or non-significant increasing trends, but no stations in south Sweden showed any significant decreasing trend for all of the percentiles. Because of the season it is likely that the increase in heavy precipitation is because of convective precipitation. Studies at the University of Gothenburg shows a trend towards a wetter climate for northern Europe during the last century (1901-2000) especially in the fall and winter, while the summer months shows a slightly drier trend. Precipitation intensities show an increase for all seasons in northern Europe albeit with a larger significance for the fall and winter seasons (Chen, Walther, Moberg, Jones, Jacobeit, & Lister, 2015).
Apart from investigating current trends, there have been attempts to predict how a changing climate will change the pattern for heavy precipitation. For the end of this century the intensity of rains is probable to increase with higher precipitation intensities and increased extremes throughout Sweden (Chen, Achberger, Ou, Postgård, Walther, & Liao, 2015). As mentioned in the background Olsson & Foster (2013) have used 6 different climate models for predicting rain intensity for durations from 30 min up to 24 hours. All scenarios used showed an increase in intensity for all durations, which was between 7-35% for the period 2071-2100 from the reference period 1981-2010. The increase in intensity was visible even in the period 2011-2040, for most of the scenarios used. In an [as of 18th of May 2015] unpublished SMHI report by Persson (2014) the expected changes in rain intensity for Falkenberg was calculated using the same method and scenarios as Olsson & Foster. The period of interest was 2071-2100 and the reference period was 1981-2010. The results showed an increase for all scenarios, durations and return periods. In general the increase was larger for shorter durations and longer return periods, as well as the uncertainty in the results, as shown in table 20.
Table 20. Modelled increase in precipitation intensity for the period 2071-2100 compared to the reference period 1981-2010 for Falkenberg for precipitation with a return period of 10 years (Persson, 2014). Duration Average Range 30 min 30% 17 – 56% 1 hour 30% 18 – 43% 6 hours 27% 9 – 40% 12 hours 25% 6 – 48% 24 hours 23% 5 – 39%
Especially interesting is to look at the change for the precipitation with a 10-year return period because of it being used for dimensioning of the stormwater systems in Falkenberg. The average predicted increase ranges between 23-30% depending on duration. The Dahlström formula, which is used for dimensioning estimated a value higher than most of the station values for 10 year return periods and shorter durations. Only the station
52
Nidingen shows a higher value than the Dahlström formula, except for durations of 12-24 hours where the Dahlström formula tend to underestimate the intensity compared to the SMHI automatic station values. The calculated return period values for the Falkenberg blended station data is about 20% lower than the Dahlström formula for the 10-year return period value, for both distribution models. In Falkenberg the climate correction factor 1.3 is added to the Dahlström formula value for dimensioning rains, which is equivalent to an increase in intensity of 30%. Considering the relation between measured data versus the calculated Dahlström formula and the predicted climate change versus the used climate correction factor there are few scenarios suggesting an underestimation of future rain intensities in the stormwater management in Falkenberg. However, all estimations and calculations are connected with a great deal of uncertainty and precaution must be taken at all times.
53
7. Conclusions A stormwater retention pond is a cost-effective, flexible and resource- and energy-efficient green stormwater solution for equalizing flow volumes. If additional care is taken it can be effective in improving water quality as well. For Falkenberg, this issue is especially important for nutrients because of the moderate ecological status of the recipient, Kattegatt, and the poor remediation in the last measurements. The most important factors for improving remediation in the ponds are the hydraulic function and the residence time of the water to improve sedimentation. Proper maintenance and monitoring is needed to retain the ponds functions. There is a risk that the remediation of nutrients function poorly during winter conditions, which affects the recipient negatively and this is likely to contribute to the insufficient ecological and chemical classification of Kattegatt. Careful planning must be undertaken when implementing green stormwater solutions, especially if there are conflicts of interest present.
The recreational values of the investigated ponds are high. Simple measures can be taken to increase the educational values of the ponds, such as putting up information signs and improving additional educational infrastructure. Accessibility and security are important factors, which are sometimes in conflict.
The Dahlström formula seems to be overestimating precipitation intensities for shorter durations and longer return periods. For longer durations and shorter return periods there is a risk of underestimating the precipitation intensity if relying on the Dahlström formula. This also applies to the dimensioning 10 year return period value for durations > 1 hour. It is likely that Falkenberg receives less intensive daily rains than surrounding measuring stations based on 40 years of daily data from blended station values. Grid data from the ECA&D gives intensity values at about 20-30% of the station values. The very low scaling factor may partly be because of added correction factor for the station data, but probably mainly because of poor station density underlying the grid estimations. There is a big difference among the different stations, which means that calculated return period values is associated with large uncertainties. There is also a big difference between chosen distribution models and great consideration has to be taken when choosing distribution model and method for calculating return periods.
It is hard to see any clear temporal trends for the data used in this study, however other studies have shown that the climate in Sweden has become wetter during the last century and that precipitation intensities have increased. There are great uncertainties on how climate change will affect the rain intensities in Falkenberg. Heavy rains are very likely to increase, especially for short durations. For durations between 30 min to 24 hours the intensity is likely to increase by 23-30% until 2100. This means that the applied climate factor of 1.3 that is commonly used in stormwater management in Falkenberg is a good choice.
54
Acknowledgements First of all like to thank my supervisor Deliang Chen, Professor of Physical Meteorology and August Röhss Professor of Physical Geography directed towards Geoinformatics at the Department of Earth Sciences at the University of Gothenburg, for his advice and guidance during the work of this thesis. I would also like to thank Lars Nyberg, Associate Professor at the Centre for Climate and Safety at Karlstad University, for his commitment and involvement in my work. Further I would like to thank Tinghai Ou and David Rayner at the University of Gothenburg for their help providing and understanding precipitation data, Kristin Gustafsson at Karlstad University for her support related to this project and Roland Bengtsson and Mikael Bergenheim at VIVAB for providing information about stormwater management in Falkenberg. Special thanks goes to my fellow environmental science students for support and good company during this thesis project, and during all my five years of studies in Gothenburg. Lastly I would like to thank my opponent Lorenzo Minola and my examiner Bengt Gunnarsson as well as the course leader for the examination course Lennart Bornmalm.
I acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles- eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu), as well as the Swedish Meteorological and Hydrological Institute (SMHI), especially Gunn Persson, for providing precipitation data and the Swedish Civil Contingencies Agency (MSB) for initiating the project.
55
References 2013/39/EU. (2013). Europaparlamentets och Rådets Direktiv 2013/39/EU av den 12 augusti 2013 om ändring av direktiven 2000/60/EG och 2008/105/EG vad gäller prioriterade ämnen på vattenpolitikens område. Europeska unionens officiella tidning, 226, pp. 1-17. Alexandersson, H. (2003). Korrektion av nederbörd enlight enkel klimatologisk metodik. Norrköping: SMHI. Andersson, J., Owenius, S., & Stråe, D. (2012). NOS-dagvatten - Uppföljning av dagvattenanläggningar i fem Stockholmskommuner. Stockholm: Svenskt Vatten AB. Andersson-Sköld, Y., Helgesson, H., Enell, A., Suer, P., & Bergman, R. (2011). Matrisbaserat beslutsstödsverktyg för bedömning av miljö- och samhällsaspekter vid markanvändning. Linköping: SGI. Andrén, B. (2010). ArtDatabanken. Retrieved 21 April 2015 from ArtDatabanken: http://www.artfakta.se/Artfaktablad/Rhantus_Notaticollis_102332.pdf Baró, F., Chaparro, L., Gómez-Baggethun, E., Langemeyer, J., Nowak, D., & Terradas, J. (2014). Contribution of Ecosystem Services to Air Quality and Climate Change Mitigation Policies: The Case of Urban Forests in Barcelona, Spain. Ambio, 43 (4), pp. 466-479. Berardi, U., GhaffarianHoseini, A., & GhaffarianHoseini, A. (2014). State-of-the-art analysis of the environmental benefits of green roofs. Applied Energy, 115, pp. 411-428. Bergfast, H. (2013). Falkenberg. Retrieved 17 April 2015 from Om Falkenberg - Falkenbergs historia: http://www.falkenberg.se/1/kommun--politik/om- falkenberg/historia.html#.VTDMvJSsWv0 BioDivers. (2009). Bedömning av flora och fauna i åtta nyanlagda dagvattendammar i Falkenberg 2009. Falkenberg: BioDivers Naturvårdskonsult. Borne, K., Fassman, E., & Tanner, C. (2013). Floating treatment wetland retrofit to improve stormwater pond performance for suspended solids, copper and zinc. Ecological Engineering, 54, pp. 173-182. Borne, K., Fassman-Beck, E., & Tanner, C. (2014). Floating Treatment Wetland influences on the fate of metals in road runoff retention ponds. Water Research, 48, pp. 430-442. Bouchard, N., Osmond, D., Winston, R., & Hunt, W. (2013). The capacity of roadside vegetated filter strips and swales to sequester carbon. Ecological Engineering, 54, pp. 227- 232. Chang, N.-B., Xuan, Z., Marimon, Z., Islam, K., & Wanielista, M. (2013). Exploring hydrobiogeochemical processes of floating treatment wetlands in a subtropical stormwater wet detention pond. Ecological Engineering, 54, pp. 66-76. Chen, D., Achberger, C., Ou, T., Postgård, U., Walther, A., & Liao, Y. (2015). Projecting Future Local Precipitation and Its Extremes for Sweden. Geografiska Annaler: Series A, Physical Geography, 97 (1), pp. 25-39.
56
Chen, D., Walther, A., Moberg, A., Jones, P., Jacobeit, J., & Lister, D. (2015). European Trend Atlas of Extreme Temperature and Precipitation Records. Dordrecht, The Netherlands: Springer Science+Business Media. Chen, Y., Day, S., Wick, A., & McGuire, K. (2014). Influence of urban land development and subsequent soil rehabilitation on soil aggregates, carbon, and hydraulic conductivity. Science of The Total Environment, 494-495, pp. 329-336. Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. London, UK: Springer-Verlag. CSGNetwork. (2011). Length Of A Degree Of Latitude And Longitude Calculator. Retrieved 12 May 2015 from CSGNetwork.com: http://www.csgnetwork.com/degreelenllavcalc.html Dahlström, B. (2006). Regnintensitet i Sverige - En klimatologisk analys. Stockholm: Svenskt Vatten AB. Dahlström, B. (2010). Regnintensitet – en molnfysikalisk betraktelse. Stockholm: Svenskt Vatten AB. Davies, C., Yousefi, Z., & Bavor, H. (2003). Occurrence of coliphages in urban stormwaterand their fate in stormwater management systems. Letters in Applied Microbiology, 37 (4), pp. 299-303. De Sousa, M., Montalto, F., & Spatari, S. (2012). Using Life Cycle Assessment to Evaluate Green and Grey Combined Sewer Overflow Control Strategies. Journal of Industrial Ecology, 16 (6), pp. 901-913. Demuzere, M., Orru, K., Heidrich, O., Olazabal, E., Geneletti, D., Orru, H., et al. (2014). Mitigating and adapting to climate change: Multi-functional and multi-scale assessment of green urban infrastructure. Journal of Environmental Management, 146, pp. 107-115. ECA&D. (2014a). Return value maps. Retrieved 28 April 2015 from European Climate Assessment & Dataset: http://www.ecad.eu/utils/mapserver/returnvalues.php#bottom ECA&D. (2014b). Trend Maps. Retrieved 28 April 2015 from European Climate Assessment & Dataset: http://www.ecad.eu/utils/mapserver/trend.php#bottom Egemose, S., Sønderup, M., Grudinina, A., Hansen, A., & Flindt, M. (2015 йил 18-April). Heavy metal composition in stormwater and retention in ponds dependent on pond age, design and catchment type. Environmental Technology , 36 (8), p. 959.969. European Comission. (2015). Introduction to the new EU Water Framework Directive. Retrieved 25 March 2015 from European Comission: http://ec.europa.eu/environment/water/water-framework/info/intro_en.htm Fagan, J. E., Reuter, M. A., & Langford, K. E. (2010). Dynamic performance metrics to assess sustainability and cost effectiveness of integrated urban water systems. Resources, Conservation and Recycling, 54 (10), pp. 719-736. Falk, J. (2007). Erfarenheter av kommunala Dagvattendammar. Svenskt Vatten Utveckling. Stockholm: Svenskt Vatten AB. Fassman, E. (2012). Stormwater BMP treatment performance variability for sediment and heavy metals. Separation and Purification Technology, 84, pp. 95-103.
57
FLOODsite. (2009). Flood risk assessment and flood risk management. An introduction and guidance based on experiences and findings of FLOODsite (an EU-funded Integrated Project). Delft, The Netherlands: Deltares | Delft Hyraulics. Göteborgs Univeristetsbibliotek. (2013). Web of Science - Databaser. Retrieved 9 April 2015 from Göteborgs Universitetsbibliotek: http://www.ub.gu.se/sok/db/show.xml?id=12745854 Gill, L., Ring, P., Higgings, N., & Johnston, P. (2014). Accumulation of heavy metals in a constructed wetland treating road runoff. Ecological Engineering, 70, pp. 133-139. Gilleland, E. (2015). Package ‘extRemes’. CRAN. Grover, S., Cohan, A., Chan, H. S., Livesley, S., Beringer, J., & Daly, E. (2013). Occasional large emissions of nitrous oxide and methane observed in stormwater biofiltration systems. Science of The Total Environment, 465, pp. 64-71. Hartig, T., Mitchell, R., de Vries, S., & Frumkin, H. (2014). Nature and Health. Annual Review of Public Health, 35, pp. 207-228. Havsmiljöinstitutet, & HaV. (2014). Så Mår Havet 2013/2014. 4-5. Haylock, M., Hofstra, N., Klein Tank, A., Klok, E., Jones, P., & New, M. (2008). A European daily high-resolution gridded dataset of surface temperature and precipitation. Journal of Geophysical Research, 113, p. D20119. Headly, T., & Tanner, C. (2012). Constructed Wetlands With Floating Emergent Macrophytes: An Innovative Stormwater Treatment Technology. Critical Reviews in Environmental Science and Technology, 42 (21), pp. 2261-2310. Hernebring, C. (2006). 10års-regnets återkomst, förr och nu - regndata för dimensionering/kontroll-beräkning av VA-system i tätorter. Stockholm: Svenskt Vatten AB. Hernebring, C. (2008). När regnet kommer - Effektivare utnyttjande av kommunernas nederbördsinformation. Stockholm: Svenskt Vatten AB. Hossain, A., Alam, M., Yonge, D., & Dutta, P. (2005). Efficiency and Flow Regime of a Highway Stormwater Detention Pond in Washington, USA. Water, Air and Soil Pollution, 164 (1-4), pp. 79-89. IPCC. (2013a). Observations: Atmosphere and Surface. In D. L. Hartmann, A. M. Klein Tank, M. Rusticucci, L. V. Alexander, S. Brönnimann, Y. A.-R. Charabi, et al., T. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. Allen, J. Boschung, et al. (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 159-254). Cabridge, UK and New York, NY, USA: Cambridge University Press. IPCC. (2013b). Climate Phenomena and their Relevance for Future Regional Climate Change. In J. Christensen, K. Krishna Kumar, E. Aldrian, S.-I. An, I. Cavalcanti, M. de Castro, et al., T. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. Allen, J. Boschung, et al. (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1217-1308). Cambridge, UK and New York, NY, USA: Cambridge University Press. IPCC. (2014a). Europe. In R. Kovats, R. Valentini, L. Bouwer, E. Georgopoulou, D. Jacob, E. Martin, et al., V. Barros, C. Field, D. Dokken, M. Mastrandrea, K. Mach, T. Bilir, et al. (Eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. 58
Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1267-1326). Cambridge, UK and New York, NY, USA: Cambridge University Press. IPCC. (2014b). Urban areas. In A. Revi, D. Satterthwaite, F. Aragón-Durand, J. Corfee-Morlot, R. Kiunsi, M. Pelling, et al., V. Barros, C. Field, D. Dokken, M. Mastrandrea, K. Mach, T. Bilir, et al. (Eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 535-612). Cambridge, UK and New York, NY, USA: Cambridge University Press. Istenič, D., Arias, C., Vollertsen, J., Nielsen, A., Vium-Andersen, T., Hvitved-Jacobsen, T., et al. (2012). Improved urban stormwater treatment and pollutant removal pathways in amended wet detention ponds. Journal of Environmental Science and Health, Part A: Toxic/Hazardous Substances and Environmental Engineering, 47 (10), pp. 1466-1477. Jacob, D., Petersen, J., Eggert, B., Alias, A., Bøssing Christensen, O., Bouwer, L. M., et al. (2014). EURO-CORDEX: new high-resolution climate change projections for European impact research. Regional Environmental Change, 14 (2), pp. 563-578. Jang, Y.-C., Jain, P., Tolaymat, T., Dubey, B., Singh, S., & Townsend, T. (2010). Characterization of roadway stormwater system residuals for reuse and disposal options. Science of The Total Environment, 408 (8), pp. 1878-1887. Jylha, K., Tuomenvirta, H., Ruosteenoja, K., Niemi-Hugaerts, H., Keisu, K., & Karhu, J. A. (2010). Observed and Projected Future Shifts of Climatic Zones in Europe and Their Use to Visualize Climate Change Information. Weather, Climate and Society, 2 (2), pp. 148-167. Kabir, E., Kim, K.-H., Ahn, J.-W., Hong, O.-F., & Chang, Y.-S. (2010). Offensive odorants released from stormwater catch basins (SCB) in an urban area. Chemosphere, 81 (3), pp. 327- 338. Kandudlu, J., Connor, J., & Hatton MacDonald, D. (2014). Ecosystem scervices in urban water investment. Journal of Environmental Management, 145, pp. 43-53. Karlsson, K., German, J., & Viklander, M. (2010). Stormwater Pond Sediments: Temporal Trends in Heavy Metal Concentrations and Sediment Removal. Soil and Sediment Contamination: An International Journal, 19 (2), pp. 217-230. Karlsson, K., Viklander, M., Scholes, L., & Revitt, M. (2010). Heavy metal concentrations and toxicity in water and sediment from stormwater ponds and sedimentation tanks. Journal of Hazardous Materials, 178 (1-3), pp. 612-618. Klein Tank, A., Wijngaard, J., Können, G., Böhm, R., Demarée, G., Gocheva, A., et al. (2002). Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology, 22 (12), pp. 1441-1453. Koch, B., Febria, C., Gevrey, M., Wainger, L., & Palmer, M. (2014). Nitrogen Removal by Stormwater Management Structures: A Data Synthesis. Journal of the American Water Resources Association, 50 (6), pp. 1594-1607. Krometis, L.-A., Dummey, P., Characklis, G., & Sobsey, M. (2009). Impact of Microbial Partitioning on Wet Retention Pond Effectiveness. Journal of Environmental Engineering, 135 (9), pp. 758-767.
59
Ladislas, S., El-Mufleh, A., Gérente, C., Chazarenc, F., Andrès, Y., & Béchet, B. (2012). Potential of Aquatic Macrophytes as Bioindicators of Heavy Metal Pollution in Urban Stormwater Runoff. Water, Air and Soil Pollution, 223 (2), pp. 877-888. Ladislas, S., Gérente, C., Chazarenc, F., Brisson, J., & Andrès, Y. (2013). Performances of Two Macrophytes Species in Floating Treatment Wetlands for Cadmium, Nickel, and Zinc Removal from Urban Stormwater Runoff. Water, Air and Soil Pollution, 224 (2). Larson, K. (2009). Social Acceptability of Water Resource Management: A Conceptual Approach and Empirical Findings from Portland, Oregon. Journal of the American Water Resources Association, 45 (4), pp. 879-893. Lee, J. S., & Li, M.-H. (2009). The impact of detention basin design on residential property value: Case studies using GIS in the hedonic price modeling. Landscape and Urban Planning, 89 (1-2), pp. 7-16. Li, Y., & Babcock JR, R. (2010). Green roofs against pollution and climate change. A review. Agronomy for Sustainable Development, 34 (4), pp. 695-705. Lu, Q., He, Z., Graetz, D., Stoffella, P., & Yang, X. (2008). Phytoremediation to remove nutrients and improve eutrophic stormwaters using water lettuce (Pistia stratiotes L.). Environmental Science and Pollution Research International, 17, pp. 84-96. Lundy, L., & Wade, R. (2011). Integrating sciences to sustain urban ecosystem services. Progress in Physical Geography, 35 (5), pp. 653-669. Lynch, J., Fox, L., Owen JR, J., & Sample, D. (2014). Evaluation of commercial floating treatment wetland technologies for nutrient remediation of stormwater. Ecological Engineering, 75, pp. 61-69. Mannshardt-Shamseldin, E., Smith, R., Sain, S., Mearns, L., & Cooley, D. (2010). Downscaling Extremes: A comparison of extreme value distributions in point-source and gridded precipitation data. The Annuals of Applied Statistics, 4 (1), pp. 484-502. Moksnes, P.-O., Albertsson, J., Elfwing, T., Hansen, J., Nilson, J., & Rolff, C. (2014). Havsmiljöns tillstånd ur miljömålsperspektiv. Havet 2013/2014, 6-12. Moore, T., & Hunt, W. (2012). Ecosystem service provision by stormwater wetlands and ponds – A means for evaluation? Water Research, 46 (20), pp. 6811-6823. Moore, T., & Hunt, W. (2013). Predicting the carbon footprint of urban stormwater infrastructure. Ecological Engineering, 58, pp. 44-51. MSB. (2010a). Ekonomiska konsekvenser av kraftiga skyfall. Tre fallstudier. Karlstad: MSB. MSB. (2010b). STATISTIK OCH ANALYS Varför drunknar barn? En retrospektiv studie över barn som drunknat i Sverige 1998–2007. Karlstad: MSB. MSB. (2013). Pluviala översvämningar. Konsekvenser vid skyfall över tätorter. En kunskapsöversikt. Karlstad: MSB. Naturvårdsverket. (2008). Övervakning av prioriterade miljöfarliga ämnen listade i Ramdirektivet för vatten. Stockholm: Naturvårdsverket. Nikulin, G., Kjellström, E., Hansson, U., Strandberg, G., & Ullerstig, A. (2010). Evaluation and Future Projections of Temperature, Precipitation and Wind Extremes over Europe in an Ensemble of Regional Climate Simulations. Tellus, 63 (1), pp. 41-55.
60
Nilsson, P. (2012). Intensivt regn i Sverige 2009 - 2011 - En kartläggning över händelser av intensivt regn och dess konsekvenser. Karlstad University, Faculty of social- and life sciences. Karlstad: Karlstad University. NOAA. (2014). Precipitation-Frequency Atlas of the United States. Silver Spring, MA, USA: National Oceanic and Atmospheric Administration. Nolbrant, P. (2004). Flora- och faunautveckling i Falkenbergs dagvattendammar. BioDivers Naturvårdskonsult. Falkenberg: FAVRAB. Nolbrant, P. (2013). Förslag till riktlinjer för anläggning och skötsel av dagvattendammar i Varberg och Falkenberg - biologisk mångfald. Skene: BioDivers Naturvårdskonsult. Nyberg, L. (2008). Översvämningar och riskhantering - en forskningsöversikt. Karlstad University, Centre for Climate and Safety. Karlstad: Karlstad University. Olsson, J., & Foster, K. (2013). Extrem korttidsnederbörd i klimatprojektioner för Sverige. Norrköping: SMHI. Ouyang, Y. (2013). Effects of a constructed wetland and pond system upon shallow groundwater quality. Environmental Monitoring an Assessment, 185 (5), pp. 4245-4259. Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Koppen- Geiger climate classification. Hydrology and Earth System Sciences, 11, pp. 1633-1644. Penn, J., Hu, W., Cox, L., & Kozloff, L. (2014). Resident and tourist preferences for stormwater management strategies in Oahu, Hawaii. Ocean & Coastal Management, 98, pp. 79-85. Persson, G. (2014). Intensiv Nederbörd i Dagens och Framtida Klimat [draft]. SMHI. Pettersson, T., & Åström, J. (2010). Microbial Contaminants Removal in a Stormwater Pond. In S. Rauch, G. Morrison, & A. Monzon, Highway and Urban Environment (Vol. 17, pp. 177- 185). Dordrecht, Netherlands: Springer. Pugh, T., MacKenzie, R., Whyatt, D., & Hewitt, N. (2012). Effectiveness of Green Infrastructure for Improvement of Air Quality in Urban Street Canyons. Environmental Science and Technology, 46 (14), pp. 7692-7699. Roinas, G., Tsavdaris, A., Williams, J., & Mant, C. (2014). Fate and Behavior of Pollutants in a Vegetated Pond System for Road Runoff. CLEAN - Soil, Air, Water, 42 (2), pp. 169-177. Roy, S., Byrne, J., & Pickering, C. (2012). A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban Forestry & Urban Greening, 11 (4), pp. 351-363. Rubel, F., & Kottek, M. (2010). Observed and projected climate shifts 1901–2100 depicted by world maps of the Koppen-Geiger climate classification . Meteorologische Zeitschrift, 19 (2), pp. 135-141. SCB. (2013). Statistiska Centralbyrån - Statistics Sweden. Retrieved 17 April 2015 from Tätorter; arealer, befolkning: http://www.scb.se/MI0810/#c_li_335300 Semadeni-Davies, A. (2006). Winter performance of an urban stormwater pond in southern Sweden. Hydrological Processes, 20, pp. 165-182.
61
Sims, A., Gajaraj, S., & Hu, Z. (2013). Nutrient removal and greenhouse gas emissions in duckweed treatment ponds. Water Research, 47 (3), pp. 1390-1398. SMHI. (2014a). Nederbörd - Kunskapsbanken. (Swedish Meteorological and Hydrological Institute) Retrieved 5 March 2015 from SMHI: http://www.smhi.se/kunskapsbanken/meteorologi/nederbord-1.361 SMHI. (2014b). Regn - Kunskapsbanken. Retrieved 6 March 2015 from SMHI: http://www.smhi.se/kunskapsbanken/meteorologi/regn-1.648 SMHI. (2014c). Kunskapsbanken - Svenska nederbördsrekord. Retrieved 11 March 2015 from SMHI: http://www.smhi.se/kunskapsbanken/meteorologi/svenska-nederbordsrekord- 1.6660 SMHI. (2014d). Dataserier med normalvärden för perioden 1961-1990. Retrieved 17 April 2015 from SMHI: http://www.smhi.se/klimatdata/meteorologi/temperatur/dataserier-med- normalvarden-1.7354 SMHI. (2014e). Normal uppskattad årsnederbörd, medelvärde 1961 - 1991. Retrieved 15 May 2015 from SMHI: http://www.smhi.se/klimatdata/meteorologi/nederbord/normal- uppskattad-arsnederbord-medelvarde-1961-1990-1.6934 Stahre, P. (2004). En Långsiktigt Hållbar Dagvattenhantering. Bromma: Svenskt Vatten. Svenskt Vatten. (2011a). Nederbördsdata vid dimensionering och analys av avloppssystem. Stockholm: Svenskt Vatten AB. Svenskt Vatten. (2011b). Hållbar dag- och dränvattenhantering - Råd vid planering och utformning. Stockholm: Svenskt Vatten AB. Syme, G., Fenton, D., & Coakes, S. (2001). Lot size, garden satisfaction and local park and wetland visitation. Landscape and Urban Planning, 56 (3-4), pp. 161-170. Tanner, C., & Headley, T. (2011). Components of floating emergent macrophyte treatment wetlands influencing removal of stormwater pollutants. Ecological Engineering, 37 (3), pp. 474-486. Thomson Reuters. (2015). Subscribed Databases. Retrieved 9 April 2015 from Web of Science: http://apps.webofknowledge.com.ezproxy.ub.gu.se/select_databases.do?highlighted_tab=s elect_databases&product=UA&SID=Y2b9IL7L8KzXzJtDR5C&last_prod=WOS&cacheurl=no Wang, C.-Y., & Sample, D. (2014). Assessment of the nutrient removal effectiveness of floating treatment wetlands applied to urban retention ponds. Journal of Environmental Management, 137, pp. 23-35. Wang, C.-Y., Sample, D., & Bell, C. (2014). Vegetation effects on floating treatment wetland nutrient removal and harvesting strategies in urban stormwater ponds. Science of The Total Environment, 499, pp. 384-393. Wang, R., Eckelman, M., & Zimmerman, J. (2013). Consequential Environmental and Economic Life Cycle Assessment of Green and Gray Stormwater Infrastructures for Combined Sewer Systems. Environmental Science and Technology, 47 (19), pp. 11189-11198.
62
Wang, Y., Bakker, F., de Groot, R., & Wörtche, H. (2014). Effect of ecosystem services provided by urban green infrastructure on indoor environment: A literature review. Building and Environment, 77, pp. 88-100. Weinstein, J., Crawford, K., Garner, T., & Flemming, A. (2010). Screening-level ecological and human health risk assessment of polycyclic aromatic hydrocarbons in stormwater detention pond sediments of Coastal South Carolina, USA. Journal of Hazardous Materials, 178 (1-3), pp. 906-916. Vejen, F. (2011). Tropisk styrtregn over København den 2. juli 2011 Alle danske monsterregns moder …! Vejret - tidsskrift for vejr og klima, 128, pp. 1-11. Wern, L., & German, J. (2009). Korttidsnederbörd i Sverige 1995 - 2008. Norrköping: SMHI. White, S. (2013). Wetland Technologies for Nursery and Greenhouse Compliance with Nutrient Regulations. HortScience, 48 (9), pp. 1103-1108. Vikström, M., Gustafsson, L.-G., German, J., & Svensson, G. (2004). Dagvattendammars avskiljningsförmåga – påverkande faktorer och metodik för bedömning. VA-Forsk. Stockholm: Svenskt Vatten AB. Villarreal, E., Semadeni-Davies, A., & Bengtsson, L. (2004). Inner city stormwater control using a combination of best management practices. Ecological Engineering, 22 (4-5), pp. 279-298. Winston, R., Hunt, W., Kennedy, S., Merriman, L., Chandler, J., & Brown, D. (2013). Evaluation of floating treatment wetlands as retrofits to existing stormwater retention ponds. Ecological Engineering, 54, pp. 254-265. VISS. (2013). Schablonvärde, Investeringskostntad dagvattendamm. Retrieved 17 February 2015 from VISS, Vatteninformationssystem Sverige: http://www.viss.lansstyrelsen.se/Dialogs/MeasureTypeFieldStandardValuesDialog.aspx?mea sureTypeID=181&measureFieldDefinitionID=1&edit=false&timeStamp=1424164949712 VISS. (2015a). Karta. Retrieved 25 March 2015 from Vatteninformationssystem Sverige: http://www.viss.lansstyrelsen.se/MapPage.aspx VISS. (2015b). Hämta data. Retrieved 19 May 2015 from Vatteninformationssystem Sverige: http://www.viss.lansstyrelsen.se/Exports.aspx?pluginType=StaticFiles&pluginGuid=25D838E 3-245C-4C8D-BB52-1008FD95F5DE&Category=2
63
APPENDIX A – Rain intensities for 4 SMHI automatic stations Rain intensity in mm for different return periods and the durations 15 min, 30 min, 45 min, 60 min, 6 hours, 12 hours and 24 hours for the stations Hallands Väderö, Torup, Nidingen and Ullared. Provided by SMHI (Persson, 2014). The values for return periods of 50 and 100 years are very uncertain. Note that the unit is in mm.
Duration: 15 min Return period [years] Station: 1 2 5 10 20 30 50 100 Hallands Väderö 6.80 8.30 10.20 11.70 13.10 14.00 15.10 16.50 Torup 7.20 9.30 12.00 14.00 16.00 17.20 18.70 20.80 Nidingen 6.80 10.60 15.50 19.20 22.90 25.10 27.90 31.60 Ullared 7.30 9.60 12.60 14.80 17.10 18.40 20.10 22.30
Duration: 30 min Return period [years] Station: 1 2 5 10 20 30 50 100 Hallands Väderö 9.20 11.90 15.50 18.20 20.90 22.50 24.50 27.20 Torup 9.80 11.50 13.80 15.50 17.20 18.20 19.50 21.20 Nidingen 9.60 17.10 27.10 34.60 42.20 46.60 52.20 59.70 Ullared 9.60 11.20 13.30 14.80 16.40 17.30 18.50 20.00
Duration: 45 min Return period [years] Station: 1 2 5 10 20 30 50 100 Hallands Väderö 11.00 13.60 17.00 19.60 22.20 23.80 25.70 28.30 Torup 11.40 13.70 16.70 19.00 21.30 22.60 24.30 26.60 Nidingen 11.80 20.90 32.80 41.90 50.90 56.20 62.90 72.00 Ullared 11.10 12.80 15.10 16.90 18.70 19.70 21.00 22.70
Duration: 60 min Return period [years] Station: 1 2 5 10 20 30 50 100 Hallands Väderö 12.20 14.90 18.40 21.10 23.80 25.30 27.30 30.00 Torup 13.00 15.30 18.40 20.70 23.10 24.40 26.20 28.50 Nidingen 13.20 23.60 37.30 47.60 58.00 64.00 71.60 82.00 Ullared 12.60 14.30 16.50 18.10 19.80 20.80 22.00 23.70
Duration: 6 h Return period [years] Station: 1 2 5 10 20 30 50 100 Hallands Väderö 24.50 30.20 37.90 43.70 49.40 52.80 57.10 62.80 Torup 25.90 29.90 35.20 39.20 43.20 45.60 48.50 52.50 Nidingen 23.00 35.20 51.30 63.40 75.60 82.70 91.70 104.00 Ullared 25.40 30.00 36.10 40.70 45.40 48.10 51.50 56.10
i
Duration: 12 h Return period [years] Station: 1 2 5 10 20 30 50 100 Hallands Väderö 30.40 39.80 52.20 61.60 71.00 76.50 83.40 92.80 Torup 34.70 40.00 47.00 52.30 57.50 60.60 64.50 69.80 Nidingen 26.80 40.10 57.60 70.80 84.00 91.80 102.00 115.00 Ullared 34.20 39.50 46.60 51.90 57.30 60.40 64.40 69.70
Duration: 24 h Return period [years] Station: 1 2 5 10 20 30 50 100 Hallands Väderö 34.80 49.00 67.70 81.90 96.10 104.40 115.00 129.00 Torup 43.20 49.40 57.80 64.10 70.30 74.00 78.70 85.00 Nidingen 32.10 46.40 65.20 79.50 93.80 102.10 113.00 127.00 Ullared 44.60 50.80 59.00 65.20 71.40 75.00 79.60 85.80
ii
APPENDIX B – Locations of stations and grids
1. SMHI data The data provided from SMHI was calculated at four automatic weather stations. Their locations are shown in table B1 and fig B1.
Table B1. Coordinates in decimal degrees for the SMHI stations. Station name Longitude Latitude Hallands Väderö 12.550 56.450 Torup 13.062 56.949 Ullared 12.780 57.110 Nidingen 11.906 57.304
Fig B1. The locations of the SMHI stations in relation to Falkenberg in west Sweden. The stations are; Nidingen (green), Ullared (purple), Torup (red) and Hallands Väderö (blue). © Esri, DeLorme, FAO, USGS 2. Falkenberg blended station data The dataset for the Falkenberg blended station data were created from three automatic measurement stations. Their coordinates and locations are given in table B2 and fig B2.
i Table B2. Coordinates in decimal degrees for the stations used in the Falkenberg blended dataset. Station name Longitude Latitude Falkenberg 12.480 56.900 Jonstorp 12.550 56.930 Morup 12.390 56.980
Fig B2. Locations of the stations used in the Falkenberg blended dataset in relation to Falkenberg city. The stations are; Falkenberg (purple), Jonstorp (green) and Morup (blue). © OpenStreetMap contributors 3. Grid data The locations of the grids used are shown in table B3 and fig B3. Grid 1 is located over the sea and contained no precipitation values, thus it was not used in the analysis and therefore not displayed on the map.
Table B3. Coordinates in decimal degrees for the centre points of the grids. Grid name Longitude Latitude Grid 1 12. 375 56. 875 Grid 2 12. 375 57. 125 Grid 3 12. 625 56. 875 Grid 4 12. 625 57. 125
ii
Fig B3. Location of the grids for the precipitation data in relation to Falkenberg. The points represent the centre of the grid, and are presented as follows; grid 2 (purple), grid 3 (orange) and grid 4 (green). © OpenStreetMap contributors
iii
APPENDIX C – Additional plots for Falkenberg blended station data
Additional plots from the statistical fitting of the annual daily maximum values for the Falkenberg blended station data for the GEV distribution (fig C1) and the Gumbel distribution (fig C2). Four plots are displayed for each distribution; two quantile plots showing the goodness of fit and 95% confidence interval for each quantile, one density plot showing the empirical and modelled density of the distribution and one return level plot showing average return level value and 95% confidence interval (dashed lines). All plots created with R using the package “extRemes”. “fbsmax” is the name given to the maximum daily values dataset and “fevd” is the command for the fitting function in extRemes.
Fig C1. Plots for the GEV distribution.
i
Fig C2. Plots for the Gumbel distribution.
ii
APPENDIX D – Additional plots for the grid data
Additional plots from the statistical fitting of the annual daily maximum values for the three grids for the GEV distribution (fig D1, D2, D3 and D4) and the Gumbel distribution (fig D5, D6, D7 and D8). Four plots are displayed for each grid and distribution; two quantile plots showing the goodness of fit and 95% confidence interval for each quantile, one density plot showing the empirical and modelled density of the distribution and one return level plot showing average return level value and 95% confidence interval (dashed lines). All plots created with R using the package “extRemes”. “f2max”, “f3max” and “f4max” are the names given to the maximum daily values dataset and “fevd” is the command for the fitting function in extRemes.
Fig D1. Quantile plots for the GEV distribution and for the grids, grid 2 (left), grid 3 (middle) and grid 4 (right). Note the different scales.
Fig D2. Quantile plots for the GEV distribution with 95% confidence intervals for the grids, grid 2(left), grid 3(middle) and grid 4(right). Note the different scales.
i
Fig D3. Density plots for the grid distributions compared to the modelled GEV distribution, grid 1(left), grid 2(middle) and grid 3(right). Note the different scales.
Fig D4. Return level plots for the GEV distribution for the grids, grid 2(left), grid 3(middle) and grid 4(right). Dashed lines indicate 95% confidence interval. Note the different scales on the y-axis.
Fig D5. Quantile plots for the Gumbel distribution and for the grids, grid 2 (left), grid 3 (middle) and grid 4 (right). Note the different scales.
ii
Fig D6. Quantile plots for the Gumbel distribution with 95% confidence intervals for the grids, grid 2(left), grid 3(middle) and grid 4(right). Note the different scales.
Fig D7. Density plots for the grid distributions compared to the modelled Gumbel distribution, grid 1(left), grid 2(middle) and grid 3(right). Note the different scales.
Fig D8. Return level plots for the Gumbel distribution for the grids, grid 2(left), grid 3(middle) and grid 4(right). Dashed lines indicate 95% confidence interval. Note the different scales on the y-axis.
iii