MAPPING CRITICAL LEVELS FOR VEGETATION

Transcription

1 III. MAPPING CRITICAL LEVELS FOR VEGETATION Summer, 2010: June, 2011: March, 2014 June, 2015 Major revision to include new flux-based critical levels and response functions for ozone and re-structuring of text. Minor text changes made Text updated to reflect publication of included functions, models etc.; revised method included for use in Integrated Assessment Modelling (Section III.5.2.6); Annexes updated with additional flux models and updated models for some receptors. Minor editorial revisions were made in July Text updated for tomato, reflecting decisions made at the 28th Task Force Meeting of the ICP Vegetation

2 Please refer to this document as: CLRTAP, Mapping Critical Levels for Vegetation, Chapter III of Manual on methodologies and criteria for modelling and mapping critical loads and levels and air pollution effects, risks and trends. UNECE Convention on Long-range Transboundary Air Pollution; accessed on [date of consultation] on Web at Updated : June 2015

3 Chapter III Mapping Critical Levels for Vegetation Page III - 3 TABLE OF CONTENT III. MAPPING CRITICAL LEVELS FOR VEGETATION... 1 III.1 GENERAL REMARKS AND OBJECTIVES... 7 III.2 CRITICAL LEVELS FOR SO 2, NO X AND NH III.2.1 SO III.2.2 NO x... 8 III.2.3 NH III.3 CRITICAL LEVELS FOR O III.3.1 Overview of critical levels, flux modelling and their recommended uses III Critical levels for ozone and their uses III Modelling risk of damage to generic receptors within large-scale Integraterd Assessment Models III Species-specific flux models III.4 PROCEDURES FOR CALCULATING OZONE CRITICAL LEVELS AND THEIR EXCEEDANCE (ALL RECEPTORS) III.4.1 Establishing critical levels III.4.2 Modelling the ozone concentration at the top of the canopy III.4.3 Modelling stomatal flux III.4.4 Stages in calculating exceedance of a flux-based critical level III.4.5 Calculation of AOT III.4.6 Stages in calculating exceedance of an AOT40-based critical level III.4.7 Calculation of AOT30 VPD III.4.8 Stages in calculating exceedance of an AOT30 VPD -based critical level III.5 CRITICAL LEVELS OF OZONE AND RISK ASSESSMENT METHODS FOR AGRICULTURAL AND HORTICULTURAL CROPS III.5.1 Ozone sensitivity of agricultural and horticultural crops III.5.2 Stomatal flux-based methods III Scientific basis and robustness of flux-based methods and critical levels III Flux-based critical levels and response functions for crops III Method for calculating ozone flux for crops using species-specific flux models III Calculation of POD Y and exceedance of the flux-based critical levels for crops III Regional parameterisations of species-specifc flux models for crops III Estimation of risk of damage using a generic crop flux model (for integrated assessment modelling within GAINS) III.5.3 AOT40-based methods III Scientific basis of AOT40-based methods and critical levels III AOT40-based critical levels and response functions Updated: June 2015

4 Page III - 4 Chapter III Mapping Critical Levels for Vegetation III Method for calculating exceedance of the AOT40-based critical levels for crops.. 43 III.5.4 VPD-modified AOT30 method III Scientific basis of the critical level for visible leaf injury on crops III The AOT30 VPD critical level for visible leaf injury on crops III.6 CRITICAL LEVELS OF OZONE AND RISK ASSESSMENT METHODS FOR FOREST TREES III.6.1 Ozone sensitivity of forest trees III.6.2 Flux-based methods III Scientific basis and robustness of flux-based critical levels for forest trees III III Stomatal flux-based critical levels and response-functions for deciduous and evergreen trees Method for calculating ozone flux for forest trees using species-specificflux models50 III Calculation of POD Y and exceedance of flux-based critical levels for forest trees. 52 III Regional parameterisations for species-specific flux models for forest trees III Estimation of risk of damage for a generic forest trees (for integrated assessment modelling) III.6.3 AOT40-based critical levels for forest trees III Scientific basis of AOT40-based methods and critical levels III AOT40-based critical levels III Calculating exceedance of the AOT40-based critical level for forest trees III.7 CRITICAL LEVELS OF OZONE FOR (SEMI-)NATURAL VEGETATION III.7.1 Ozone sensitivity of (semi-)natural vegetation III.7.2 Stomatal flux-based methods for (semi-)natural vegetation III Scientific background and robustness of flux-based critical levels III Flux-based critical levels for (semi-)natural vegetation III III Calculating ozone flux for (semi-)natural vegetation using species-specific flux models Calculation of POD Y and exceedance of the flux-based critical levels for (semi- )natural vegetation III Regional parameterisations for flux models for (semi-)natural vegetation III Estimation of risk of damage for a generic (semi-)natural vegetation (for integrated assessment modelling) III.7.3 AOT40-based critical levels for (semi-)natural vegetation III Scientific background and critical levels III III Calculating exceedances of the AOT40-based critical levels for (semi-) natural vegetation Mapping (semi-)natural vegetation communities at risk from exceedance of the critical level III.8 REFERENCES III.9 ANNEXES FOR CHAPTER III.9.1 Annex 1: Additional information for agricultural and horticultural crops III Further details on flux model parameterisations included in Chapter III Updated : June 2015

5 Chapter III Mapping Critical Levels for Vegetation Page III - 5 III Parameterisation of bread wheat and durum wheat for application in Mediterranean areas III Flux models for additional crops suitable for regional risk assessment III Additional published flux models for crops for local-scale application III Additional flux-effect relationships and flux-based critical levels III References related to crops III.9.2 Annex 2: Additional information for forest trees III Flux parameterisation for generic species of forest trees III Species- and region-specific flux parameterisations for forest trees III Additional published flux models for trees for local-scale application III Additional flux-effect relationships and flux-based critical levels III References III.9.3 Annex 3 : Additional information for (semi-) natural vegetation III Flux model parameterisation III Additional published flux models for (semi-) natural vegetation for local-scale application III Additional flux-effect relationships and flux-based critical levels III References Updated: June 2015

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7 Chapter III Mapping Critical Levels for Vegetation Page III - 7 III.1 GENERAL REMARKS AND OBJECTIVES The purpose of this chapter is to provide information on the critical levels for sensitive vegetation and how to calculate critical level exceedance. Methods for mapping pollutant concentrations, deposition and exceedance are provided elsewhere (Chapter II). Excessive exposure to atmospheric pollutants has harmful effects for a variety of vegetation. Critical levels are described in different ways for different pollutants, including mean concentrations, cumulative exposures and fluxes through plant stomata. The effects considered significant here vary between receptor and pollutant and include growth changes, yield losses, visible injury and reduced seed production and quality as well as impacts on biodiversity and ecosystem services. The receptors are generally divided into five major categories: agricultural crops, horticultural crops, semi-natural vegetation, natural vegetation and forest trees. However, for some pollutants, e.g. ozone, semi-natural vegetation and natural vegetation have been grouped together under the name (semi-)natural vegetation. Critical levels were defined in an earlier version of this manual (UNECE, 1996) as the atmospheric concentrations of pollutants in the atmosphere above which adverse effects on receptors, such as human beings, plants, ecosystems or materials, may occur according to present knowledge. For this revised chapter, the critical levels for vegetation are defined as the concentration, cumulative exposure or cumulative stomatal flux of atmospheric pollutants above which direct adverse effects on sensitive vegetation may occur according to present knowledge. Critical level exceedance maps show the difference between the critical level and the mapped, monitored or modelled air pollutant concentration, cumulative exposure or cumulative flux. The critical level values have been set, reviewed and revised for O 3, SO 2, NO 2 and NH 3 at a series of UNECE Workshops: Bad Harzburg (1988); Bad Harzburg (1989); Egham (1992; Ashmore and Wilson, 1993); Bern (1993; Fuhrer and Achermann, 1994); Kuopio (1996; Kärenlampi and Skärby, 1996), Gerzensee (1999; Fuhrer and Achermann, 1999), Gothenburg (2002; Karlsson, Selldén and Pleijel, 2003a), Obergurgl (2005; Wieser and Tausz, 2006), Edinburgh (2006; UNECE, 2007), Ispra (2009, UNECE 2010, Harmens et al., 2010) and associated Task Force meetings of the ICP Vegetation. For SO 2, NO x and NH 3, recommendations are made for concentration-based critical levels (Note: please see Chapter V for information on critical loads for sulphur and nitrogen). For ozone, cumulative concentration-based (previously described as level I) and cumulative stomatal fluxbased (previously described as level II) critical levels are described for crops, forest trees and (semi-)natural vegetation. Since the earlier version of this manual was published (UNECE, 1996), much progress has been made with the critical levels for ozone and Section III.3 of this chapter provides an in-depth description of the critical levels, their scientific bases and how to calculate exceedance. As part of this progress, it was agreed that the level I and level II terminology is no longer appropriate to describe critical levels for ozone and these terms are not used in this chapter. An additional simplified flux-based risk assessment method is also described for use in large-scale and integrated assessment modelling. Updated: June 2015

8 Page III - 8 Chapter III Mapping Critical Levels for Vegetation III.2 CRITICAL LEVELS FOR SO 2, NO X AND NH 3 III.2.1 SO2 The critical levels for SO 2 that were established in Egham in 1992 (Ashmore and Wilson, 1993) are still valid (Table III.1). There are critical levels for four categories of receptors for sensitive groups of lichens, for forest ecosystems, (semi-) natural vegetation and for agricultural crops. These critical levels have been adopted by WHO (2000). Exceedance of the critical level for (semi-) natural vegetation, forests, and, when appropriate, agricultural crops occurs when either the annual mean concentration or the winter half-year mean concentration is greater than the critical level; this is because of the greater impact of SO 2 under winter conditions. Table III.1 : Critical levels for SO 2 (µg m -3 ) by vegetation category. Vegetation Type Critical Level SO 2 [µg m -3 ] Time period Cyanobacterial lichens 10 Annual mean Forest ecosystems* 20 (Semi-)natural 20 Agricultural crops 30 Annual mean and Half-year mean (October-March) Annual mean and Half-year mean (October-March) Annual mean and Half-year mean (October-March) *The forest ecosystem includes the response of the understorey vegetation. III.2.2 NO X The critical levels for NO x are based on the sum of the NO and NO 2 concentrations because there is insufficient knowledge to establish separate critical levels for the two pollutants. Since the type of response varies from growth stimulation to toxicity depending on concentration, all effects were considered to be adverse. Growth stimulations were of greatest concern for (semi-)natural vegetation because of the likelihood of changes in interspecific competition. Separate critical levels were not set for classes of vegetation because of the lack of available information. However, the following ranking of sensitivity was established: (semi-)natural vegetation > forests > crops Critical levels for NO x were established in 1992 at the Egham workshop. The background papers on NO x and NH 3 presented at the Egham workshop (Ashmore & Wilson, 1993) were further developed as the basis of the Air Quality Guidelines for Europe, published by the WHO in This further analysis incorporated a formal statistical model to identify concentrations to protect 95% of species at a 95% confidence level. In this re-analysis, growth stimulation was also considered as a potentially adverse ecological effect. Furthermore, a critical level based on 24h mean concentrations was considered to be more effective than one based on 4h mean concentrations as included in the earlier version of the Mapping Manual (UNECE, 1996). Since the WHO guidelines were largely based on analysis extending the background information presented at the Egham workshop, the critical levels in Table III.2, which are identical to those of WHO (2000), should now be used. Updated : June 2015

9 Chapter III Mapping Critical Levels for Vegetation Page III - 9 Table III.2 : Critical levels for NO x (NO and NO 2 added, expressed as NO 2 (µg m -3 )). Vegetation Type Critical Level NO x (expressed as NO 2 ) [µg m -3 ] Time period All 30 Annual mean All hour mean For application for mapping critical levels and their exceedance, it is strongly recommended that only the annual mean values are used, as mapped and modelled values of this parameter have much greater reliability, and the long-term effects of NO x are thought to be more significant than the short-term effects. Some biochemical changes may occur at concentrations lower than the critical levels, but there is presently insufficient evidence to interpret such effects in terms of critical levels. III.2.3 NH 3 The fertilisation effect of NH 3 can in the longer term lead to a variety of adverse effects, including growth stimulation (which can alter species balance with some less sensitive species being potentially outcompeted) and increased susceptibility to abiotic (drought, frost) and biotic stresses. In the short-term there are also direct effects. As for NO x, for application for mapping critical levels and their exceedance, it is strongly recommended that only the annual mean values of NH 3 are used, as mapped and modelled values of this parameter have much greater reliability, and the long-term effects of NH 3 are thought to be more significant than the short-term effects. Vegetation type Table III.3 : Critical levels for NH 3 (µg m -3 ). Critical level NH 3 [µg m -3 ] Time period Lichens and bryophytes (including ecosystems where lichens and bryophytes are a key part of ecosystem integrity) Higher plants (including heathland, grassland and forest ground flora) Provisional critical level Higher plants * Annual mean Annual mean Monthly mean *An explicit uncertainty range of 2-4 μg m -3 was set for higher plants (including heathland, semi-natural grassland and forest ground flora). The uncertainty range is intended to be useful when applying the critical level in different assessment contexts (e.g. precautionary approach or balance of evidence.) Updated: June 2015

10 Page III - 10 Chapter III Mapping Critical Levels for Vegetation The critical levels in Table III.3 refer to ecosystems with the most sensitive lichens and bryophytes and higher plants. The aim of the critical levels defined is to protect the functioning of plants and plant communities. Lichens and bryophyte species were found to be more sensitive than higher plants, while the critical levels given in Table III.3 apply for native and forest species. Critical levels are not currently set for intensively managed agricultural grasslands (pastures) and arable crops, which are often sources rather than sinks of ammonia. The critical levels presented in Table III.3 were recommended for inclusion in this manual at a workshop, held in Edinburgh from 4-6 December, 2006: Atmospheric ammonia: Detecting emission changes and environmental impacts (UNECE, 2007). Their inclusion was subsequently approved at the 20 th Task Force meeting of the ICP Vegetation (Dubna, Russian Federation, 5-8 March, 2007) and adopted at the 23 rd meeting of the Task Force on Modelling and Mapping (Sofia, Bulgaria, April, 2007). The Edinburgh meeting (December, 2006) recommended the following: 1. A revision of the currently set values of the ammonia critical levels. The data reviewed show that the previous/existing critical level (CLe) values of 3300 μg m -3 (hourly), 270 μg m -3 (daily), 23 μg m -3 (monthly) and 8 μg m -3 (annual) were not sufficiently precautionary; 2. A new long-term CLe for lichens and bryophytes, including ecosystems where lichens and bryophytes are a key part of the ecosystem integrity, of 1 μg m -3 (annual mean); 3. A new long-term CLe for higher plants, including heathland, grassland and forest ground flora and their habitats, of 3 μg m -3, with an uncertainty range of 2-4 μg m -3 (annual mean); 4. The workshop noted that these new long-term critical level values are based on observation of actual species changes from both field surveys and long-term exposure experiments, where effects were related to measured ammonia concentrations. The workshop noted that the long-term critical levels could not be assumed to provide a protection for longer than years; 5. To retain the monthly critical level (23 µg NH 3 m -3 ) for higher plants only as a provisional value. This value is based on the assessment of adverse effects of short-term exposures as discussed at the UNECE workshop on Critical Levels held in 1992 in Egham, United Kingdom (Van der Eerden et al. 1993). The monthly critical level was estimated with the envelope method using exposureresponse data from mainly short-term fumigation experiments. Thus, it has not the same relevance as the long-term Critical Levels (annual averages of 1 and 3 µg NH3 m-3) derived from long-term field studies. The provisionally retained monthly value has to be considered as expert judgement to allow the assessment of short-term peak concentrations which can occur during periods of manure application (e.g. in spring). The proceedings of the UNECE Workshop on Ammonia (Edinburgh, December 2006) was published in Sutton et al., 2009 by Springer: Sutton M.A., Baker S., Reis S. (ed.), Atmospheric Ammonia: Detecting emission changes and environmental impacts. This book includes details of the evidence used to justify the change in critical levels. In summary, the key evidence, which was based on observations of changes in species composition change (a true ecological endpoint) in response to measured air concentrations of ammonia, is provided in Table III.4. Updated : June 2015

11 Chapter III Mapping Critical Levels for Vegetation Page III - 11 Table III.4 : Summary of no-effect concentrations (NOECs) of the impact of long-term exposure to NH 3 on species composition of lichens and bryophytes. Location Vegetation type Lowest measured NH 3 concentration [µg m -3 ] Estimated NOEC * [µg m -3 ] Reference SE Scotland, poultry farm Epiphytic lichens (on twigs) 1.8 (on trunks) (Pitcairn et al., 2004, Sutton et al., 2008) Devon, SW England Epiphytic lichens diversity (twig) 0.8 (modelled) 1.6 (Wolseley et al., 2006) United Kingdom, national NH 3 network Epiphytic lichens (Leith et al., 2005, Sutton et al., 2008) Switzerland Lichen population index 1.9 (modelled) 2.4 (Rihm et al., 2008) SE Scotland, field NH 3 experiment, Whim bog Lichens and bryophytes damage and death 0.5 < 4 (Sheppard et al., 2008) Corroborative evidence ** SW England Epiphytic lichens 1.5 ca. 2 (Leith et al., 2005) South Portugal Epiphytic lichens (Pinho et al., 2008) Italy, pig farm Epiphytic lichens (Frati et al., 2007) *NOECs were directly estimated from exposure/response curves or calculated with regression analysis. The data are from recent experimental studies, both field surveys and controlled field experiments on the impact of NH 3 on vegetation. **In these cases NH 3 concentration data were available for less than one year, which is why these results are categorised as corroborative evidence. Updated: June 2015

12 Page III - 12 Chapter III Mapping Critical Levels for Vegetation III.3 CRITICAL LEVELS FOR O 3 The earliest version of this manual (UNECE, 1996) included concentration-based critical levels that used AOTX (ozone concentrations accumulated over a threshold of X ppb) as the ozone parameter. However, several important limitations and uncertainties have been recognised for using AOTX. In particular, the real impacts of ozone depend on the amount of ozone reaching the sites of damage within the leaf, whereas AOT40-based critical levels only consider the ozone concentration at the top of the canopy. The Gerzensee Workshop in 1999 recognised the importance of developing an alternative critical level approach based on the flux of ozone from the exterior of the leaf through the stomatal pores to the sites of damage (stomatal flux). This approach required the development of mathematical models to estimate stomatal flux, primarily from knowledge of stomatal responses to environmental factors. It was agreed at the Gothenburg Workshop in 2002 that ozone flux-effect models were sufficiently robust for the derivation of flux-based critical levels, and such critical levels should be included in this Manual for wheat, potato and provisionally for beech and birch combined. An additional simplified flux-based worstcase risk assessment method for use in large-scale and integrated assessment modelling was discussed at the Obergurgl Workshop (2005) and after further revision (approved at appropriate Task Force meetings) is included here for generic crops and forest trees. A critical level has been derived for effects on a generic crop, but not for effects on trees. This simplified method does not take into account effects of soil moisture and thus provides a worst-case risk asssement. At the Ispra Workshop in 2009 and subsequent 23 rd Task Force meeting of the ICP Vegetation in 2010, the flux-based critical levels were reviewed, revised where needed, and added for new receptors. The revision of this chapter completed in the summer of 2010 incorporates all of these new/revised critical levels together with the AOTX-based critical levels and genericspecies flux modelling methods already agreed. The text was updated in 2014 to include reference to publications of included functions, models etc., a revised method for use in Integrated Assessment Modelling and updated Annexes including flux models for additional species and updated models for some receptors. Critical levels for tomato and associated text were updated in 2015 as agreed at the 28 th ICP Vegetation Task Force Meeting. The critical levels and risk assessment methods described for ozone in this chapter were prepared by leading European experts from available knowledge on impacts of ozone on vegetation, and thus represent the current state of knowledge. III.3.1 OVERVIEW OF CRITICAL LEVELS, FLUX MODELLING AND THEIR RECOMMENDED USES Section III.3 describes three methods for the critical levels for ozone: stomatal fluxes, ozone concentrations and vapour-pressure deficit (VPD)-modified ozone concentrations. Each approach uses the ozone concentration at the top of the canopy and incorporates the concept that the effects of ozone are cumulative and values are summed over specific threshold for a defined time period. In addition, two further methods. have been developed for estimating risk of damage without quantifying the risk: species-specific flux models (including those for which critical levels have not been derived) and generic-species flux models. These approaches and their associated uses are summarised as follows with their scientific bases and detailed methods provided later Updated : June 2015

13 Chapter III Mapping Critical Levels for Vegetation Page III - 13 III CRITICAL LEVELS FOR OZONE AND THEIR USES Table III.5 summarises the critical levels of ozone and provides a link to the sections describing their scientific basis and Table III.6 lists the terminology used. (1) Stomatal flux-based critical levels for effects of ozone on growth or yield. These take into account the varying influences of air temperature, water vapour pressure deficit (VPD) of the surrounding leaves, light (irradiance), soil water potential (SWP) or plant available water (PAW), ozone concentration and plant development (phenology) on the stomatal flux of ozone. They therefore provide an estimate of the critical amount of ozone entering through the stomata and reaching the sites of action inside the plant and are species-specific. The hourly mean instantaneous stomatal flux of ozone based on the projected leaf area (PLA), F st (in nmol m -2 PLA s -1 ), is accumulated over a stomatal flux threshold of Y nmol m -2 s -1. The accumulated Phytotoxic Ozone Dose (i.e. the accumulated stomatal flux) of ozone above a flux threshold of Y (POD Y, formerly named AF st Y), is calculated for the appropriate timewindow as the sum over time of the differences between hourly mean values of F st and Y nmol m -2 PLA s -1 for the periods when F st exceeds Y. The stomatal fluxbased critical level of ozone, CLe f mmol m -2 PLA, is then the cumulative stomatal flux of ozone, POD Y, above which direct adverse effects may occur according to present knowledge. Values of CLe f have been identified for crops (wheat, potato and tomato), forest trees (represented by birch and beech, and Norway spruce), and (semi- )natural vegetation (represented by Trifolium spp. (clover family) and provisionally Viola spp. (violet family). Uses: The flux-based critical levels and associated response functions are suitable for mapping and quantifying impacts at the local and regional scale, including effects on food security (crops), roundwood supply for the forest sector industry and loss of carbon storage capacity and other beneficial ecosystem services (forest trees), and impacts on the vitality of fodder-pasture and natural grassland species ((semi-) natural vegetation). Where appropriate, they could be used for assessing economic losses. (2) Concentration-based critical levels for effects of ozone on growth or yield These are based on the concentration at the top of the canopy accumulated over a threshold concentration for the appropriate time-window and thus do not take account of the stomatal influence on the amount of ozone entering the plant. This value is expressed in units of ppm h (μmol mol -1 h). The term AOTX (concentration accumulated over a threshold ozone concentration of X ppb) has been adopted for this index; in this manual X is either 30 or 40 ppb for AOT30 and AOT40 respectively. Values of CLe c are defined for agricultural and horticultural crops, forests and (semi-)natural vegetation. The AOTX-based critical levels have a weaker biological basis than the flux-based critical levels. Uses: The concentration-based critical levels are suitable for estimating the risk of damage where climatic data or suitable flux models are not available. Economic losses should not be estimated using AOTX-based critical levels and associated response functions. (3) VPD-modified concentration-based critical level for visible leaf injury This index is only used to define the shortterm critical level for the development of visible injury on crops. The method takes into account the modifying influence of VPD on the stomatal flux of ozone by multiplying the hourly mean ozone concentration at the top of the canopy by an f VPD factor to get the VPD-modified ozone concentration ([O 3 ] VPD ). The [O 3 ] VPD is accumulated over a threshold concentration during daylight hours over the appropriate time-window. This value is expressed in units of ppm h (μmol mol -1 h). The term AOT30 VPD (VPD-modified concentration accumulated over a threshold ozone concentration of 30 ppb) has been adopted for this index. Uses: This critical level can be used to indicate the likelihood of visible ozone injury on vegetation, and is especially useful for estimating effects on leafy vegetable crops where leaf injury reduces the quality and market value. Updated: June 2015

14 Page III - 14 Chapter III Mapping Critical Levels for Vegetation III MODELLING RISK OF DAMAGE TO GENERIC RECEPTORS WITHIN LARGE-SCALE INTEGRATERD ASSESSMENT MODELS This method incorporates a simplified flux model that is parameterised for a representative species and is specifically for application in large-scale modelling, including integrated assessment modelling. It is intended to provide estimates of the potential effective phytotoxic cumulative stomatal ozone uptake and hence should be viewed as an indicator of the degree of risk of negative impacts. A critical level has been defined for the generic crop model only. Uses: This simplified flux method is specifically designed for large scale modelling, including integrated assessment modelling. Maps generated using the generic species flux methods do not take into account the limiting effect of soil moisture on ozone flux and thus can be used to indicate the risk under worse-case conditions. III SPECIES-SPECIFIC FLUX MODELS Detailed flux models have been derived for a number of crop, forest tree and (semi-) natural vegetation species using the modelling approach described above for the flux-based critical levels. These speciesspecific flux models, as well as additional ones included in the Annexes for receptors for which a robust flux model is available but a flux-effect relationship is not currently available, are suitable for mapping risk of effects without quantification of the extent of damage. For several receptors, regionspecific flux parameterisations are included. Uses: The species-specific flux models can be used at any geographical scale and are particularly useful for application at the local scale to indicate the degree of risk to a specific species. Updated : June 2015

15 Chapter III Mapping Critical Levels for Vegetation Page III - 15 Table III.5 : Critical levels for ozone. Receptor (a) Flux-based critical levels (see Mills et al., 2011b) Effect (per cent reduction) Parameter Critical level (mmol m -2 PLA) Wheat Grain yield (5%) POD 6 1 Scientific basis in Section Wheat 1000 grain weight (5%) POD Wheat Protein yield (5%) POD Potato Tuber yield (5%) POD Tomato Fruit yield (5%) POD Tomato Fruit quality (5%) POD Norway spruce Biomass (2%) POD Birch and beech Biomass (4%) POD Productive grasslands (clover) Biomass (10%) POD Conservation grasslands (clover) Biomass (10%) POD Conservation grasslands (Viola spp), provisional Biomass (15%) POD 1 6 (b) Concentration-based critical levels Receptor Effect Parameter Critical level (ppm h) Scientific basis in Section Agricultural crops Yield reduction AOT Horticultural crops Yield reduction AOT Forest trees Growth reduction AOT (Semi-)natural vegetation communities dominated by annuals Growth reduction and/or seed production AOT (Semi-)natural vegetation Growth reduction AOT communities dominated by perennials (c) VPD-modified concentration-based critical level Receptor Effect Parameter Critical level Vegetation (derived for clover species) Visible injury on leaves (ppm h) Scientific basis in Section AOT30 VPD Updated: June 2015

16 Page III - 16 Chapter III Mapping Critical Levels for Vegetation Table III.6 : Terminology for critical levels of ozone. Term Abbreviation [Units] Explanation Terms for flux-based critical levels Projected leaf area Stomatal flux of ozone Stomatal flux of ozone above a flux threshold of Y nmol m -2 PLA s -1 Phytotoxic ozone dose (expressed as the accumulated stomatal flux of ozone above a flux threshold of Y nmol m -2 PLA s -1 ) Flux-based critical level of ozone PLA [m 2 ] F st [nmol m -2 PLA s -1 ] F sty [nmol m -2 PLA s -1 ] POD Y [mmol m -2 PLA] CLe f [mmol m -2 PLA] Terms for concentration-based critical levels Concentration accumulated over a threshold ozone concentration of X ppb Concentration-based critical level of ozone Concentration accumulated over a threshold ozone concentration of X ppb modified by vapour pressure deficit (VPD) AOTX [ppm h] CLe c [ppm h] AOTX VPD [ppm h] The projected leaf area is the total area of the sides of the leaves that are projected towards the sun. PLA is in contrast to the total leaf area, which considers both sides of the leaves. For horizontal leaves the total leaf area is simply 2*PLA. Instantaneous flux of ozone through the stomatal pores per unit projected leaf area (PLA). F st can be defined for any part of the plant, or the whole leaf area of the plant, but for this manual, F st refers specifically to the sunlit leaves at the top of the canopy. F st is normally calculated from hourly mean values and is regarded here as the hourly mean flux of ozone into the stomata. Instantaneous flux of ozone above a flux threshold of Y nmol m -2 s -1, through the stomatal pores per unit projected leaf area. F sty can be defined for any part of the plant, or the whole leaf area of the plant, but for this manual F sty refers specifically to the sunlit leaves at the top of the canopy. F sty is normally calculated from hourly mean values and is regarded here as the hourly mean flux of ozone through the stomata. Phytotoxic ozone dose (POD) is the accumulated flux above a flux threshold of Y nmol m 2 s 1, accumulated over a stated time period during daylight hours 1). Similar in mathematical concept to AOTX. Phytotoxic ozone dose above a flux threshold of Y nmol m -2 s -1 (POD Y), over a stated time period during daylight hours, above which direct adverse effects may occur on sensitive vegetation according to present knowledge. The sum of the differences between the hourly mean ozone concentration (in ppb) and X ppb when the concentration exceeds X ppb during daylight hours, accumulated over a stated time period. Units of ppb and ppm are parts per billion (nmol mol -1 ) and parts per million (µmol mol -1 ) respectively, calculated on a volume/volume basis. AOTX over a stated time period, above which direct adverse effects on sensitive vegetation may occur according to present knowledge. The sum of the differences between the hourly mean ozone concentration (in ppb) modified by a vapour pressure deficit factor ([O 3] VPD), and X ppb when the concentration exceeds X ppb during daylight hours, accumulated over a stated time period. 1) Global radiation > 50 W m -2 Updated : June 2015

17 Chapter III Mapping Critical Levels for Vegetation Page III - 17 III.4 PROCEDURES FOR CALCULATING OZONE CRITICAL LEVELS AND THEIR EXCEEDANCE (ALL RECEPTORS) In this Section, the common methods for calculating stomatal ozone flux, AOTX, and critical level exceedance are described. The application of these methods to specific receptors is described in Sections III.5, III.6 and III.7 for crops, forest trees and (semi-)natural vegetation respectively. III.4.1 ESTABLISHING CRITICAL LEVELS Dose response relationships have been established using experimental data from exposure systems such as open-top chambers that enable plants to be grown under naturally varying climatic conditions for one or more growing seasons. Under these experimental conditions, the ozone fumigation concentration is that measured at the top of the canopy. Data from several countries and years of experiments have been combined, wherever possible, for a single receptor. The approach described by Fuhrer (1994) for calculating relative yield or biomass has been used for each experimental dataset, i.e. yield was calculated relative to that in an atmosphere with charcoal filtered air that may be considered reprensentative of pre-industrial O 3 concentrations. All response functions used to derive critical levels are statistically significant at at least p < 0.05, and more usually p < The 95% confidence intervals are shown on figures to give an indication of the strength of the relationship and the range of significance of effect for a given AOTX or POD Y. Critical levels have been derived as either the lowest AOTX or POD Y that induces an effect that is significantly different from the effect at zero AOTX or POD y, or for a percentage effect which is of economic or ecological importance providing that such an effect is statistically significant from zero effect. The critical level was rounded up or down to the nearest whole number. In each case, the critical level was derived from the response function using the following equation: Critical level for an X% reduction in relative yield/biomass = (Intercept (1-(X/100))/ slope III.4.2 MODELLING THE OZONE CONCENTRATION AT THE TOP OF THE CANOPY Note: Sources of ozone concentration estimates and their spatial interpolation are considered in Chapter II of this manual. All ozone indices described in this chapter are based on ozone concentrations at the top of the canopy. Within exposure systems such as open-top chambers, where air flow is omnidirectional the exposure concentration measured at the top of the canopy reflects the ozone concentration at the upper boundary of the leaves. Under unenclosed field conditions, it was decided that the ozone concentration at the top of the canopy provides a reasonable estimate of the ozone concentration at the upper surface boundary of the laminar boundary layer near the flag leaf (in the case of wheat) and the sunlit upper canopy leaves (in the case of other receptors), if the roughness sub-layer near the canopy top is not taken into account in the deposition modelling approach. Thus, the ozone concentration at the top of the canopy should be calculated for determining each of the indices used. Updated: June 2015

18 Page III - 18 Chapter III Mapping Critical Levels for Vegetation For crops and other low vegetation, canopy-top ozone concentrations may be significantly lower than those at conventional measurement heights of 2-5m above the ground, and hence use of measured data directly or after spatial interpolation may lead to significant overestimates of ozone concentrations and hence of the degree of exceedance of CLe c and CLe f. In contrast, for forests, measured data at 2-5m above the ground may underestimate ozone concentration at the top of the canopy. The difference in ozone concentration between measurement height and canopy height is a function of several factors, including wind speed and other meteorological factors, canopy height/surface roughness and the total flux of ozone, F tot. Conversion of ozone concentration at measurement height to that at canopy-top height (z 1 ) can be best achieved with an appropriate deposition model. It should be noted, however, that the flux-gradient relationships these models depend on are not strictly valid within the roughness sublayer (up to 2-3 times canopy height), so even such detailed calculations can provide only approximate answers. The model chosen will depend upon the amount of meteorological data that is available. Two simple methods are included here which can be used to achieve the necessary conversion if (a) no meteorological data are available, or (b) some basic measurements are available. Method (a): Tabulated gradient If no meteorological data are available at all, then a simple tabulation of O 3 gradients can be used. The relationship between O 3 concentrations at a number of different heights has been estimated with the EMEP deposition module (Emberson et al., 2000a), using meteorology from about 30 sites across Europe. Data were produced for an arbitrary crop surface and for short grasslands. For the crop surface, the assumptions made here are that we have a 1m high crop with g max = 450 mmol O 3 m -2 PLA s -1. The total leaf surface area index (LAI) is set to 5 m 2 PLA /m 2, and the green LAI is set at 3 m 2 PLA /m 2, assumed to give a canopy-scale phenology factor (f phen ) of 0.6. The soil moisture factor (f SWP ) is set to 1.0. Constant values of these parameters are used throughout the year in order to avoid problems with trying to estimate growth-stage in different areas of Europe. The concentration gradients thus derived are most appropriate to a fully developed crop but will serve as a reasonable approximation for the whole growing season. Other stomatal conductance modifiers are allowed to vary according to the wheat-functions. For short grasslands, canopy height was set to 0.1 m, g max to 270 mmol O 3 m -2 PLA s -1 and f SWP set to 1.0. All other factors are as given for grasslands in Emberson et al. (2000b). For the micrometeorology, the displacement height (d) and roughness length (z 0 ) are set to 0.7 and 0.1 of canopy height (z 1 ), respectively. Table III.7 shows the average relationship between O 3 concentrations at selected heights, derived from runs of the EMEP module over May-July, and selecting the noontime factors as representative of daytime multipliers. O 3 concentrations are normalised by setting the 20m value to 1.0. To use Table III.7, ozone concentration measurements made above crops or grasslands may simply be extrapolated downwards to the canopy top for the respective vegetation. For example, with 30 ppb measured at 3m height (above ground level) in a crop field, the concentration at 1m would be 30.0 * (0.88/0.95) = 27.8 ppb. For short grasslands we would obtain 30.0 * (0.74/0.96) = 23.1 ppb at canopy height, 0.1m. Experiments have shown that the vertical gradients found above for crops also apply well to tall (0.5m) grasslands. Some judgement may then be required to choose values appropriate to different vegetation types. For forests, ozone concentrations must often be derived from measurements made over grassy areas or other land-cover types. In principle, the O 3 concentration measured over land-use X (e.g. short grasslands) could be used to estimate the O 3 concentration at a reference height, and then the gradient profile appropriate for desired land use Y could be applied. However, in order to keep this simple methodology manageable, and in view of the uncertainties inherent in making use of any profile near the canopy itself, it is Updated : June 2015

19 Chapter III Mapping Critical Levels for Vegetation Page III - 19 suggested that concentrations are estimated by extrapolating the profiles given in Table III.7 upwards to the canopy height for forests. As an example, if we measure 30 ppb at 3m above short grassland, the concentration at 20 m is estimated to be 30.0*(1.0/0.96) = 31.3 ppb. It should be noted that the profiles shown in Table III.7 are representative only, and that site-specific calculations would provide somewhat different numbers. However, without local meteorology and the use of a deposition model, the suggested procedure should give an acceptable level of accuracy for most purposes. Table III.7 : Representative O 3 gradients above artificial (1m) crop, and short grasslands (0.1m). O 3 concentrations are normalised by setting the 20m value to 1.0. These gradients are derived from noontime factors and are intended for daytime use only. Note: see comments in text for application to trees. Measurement height above the ground [m] Crops (where z 1=1m, g max= 450 mmol O 3 m -2 PLA s -1 ) O 3 concentration gradient Short Grasslands and Forest Trees (where z 1=0.1m, g max=270 mmol O 3 m -2 PLA s -1 ) * * 0.5m is below the displacement height of crops, but may be used for taller grasslands, see text. Method (b): Use of neutral stability profiles If we have wind speed, u (m s -1 ) at a height z u, ozone concentration at a reference height of z R, and an estimate of z 0, then we find concentration values appropriate to any height z 1 near the surface (e.g. the top of the canopy for crops, ca 1m) by making use of the constant-flux assumption and definition of aerodynamic resistance (neglecting the roughness sub-layer near the canopy top): (III.1) C(zR ) C(z1 ) Total flux = V g (zr )* C(zR ) = R (z,z ) Where V g (z R ) is the deposition velocity (m s - 1 ) at height z R, and R a (z R,z 1 ) is the aerodynamic resistance between the two heights (s m -1 ). Re-arranging the second two terms, we get: a R 1 (III.2) C(z ) = C(z ) [ 1 R (z,z )* V (z ) 1 R a R 1 In neutral stability, friction velocity (u*) and R a are easily obtained. Once u* is found, then the wind speed at a height near the surface can be found by substituting z u with the required height, z in equation III.3. (III.3) u(zu) k u* zu d ln z0 (III.4) 1 z R d Ra( z R, z) ln ku* z d g R ] Updated: June 2015

20 Page III - 20 Chapter III Mapping Critical Levels for Vegetation where the von Kármán konstant, k = 0.4, and z can be any height within the socalled surface layer (typically lowest 10s of metres near the ground), e.g. 1m for crops. The deposition velocity requires further information: (III.5) V (z g R 1 ) = R (z,zd z0 ) R a R b R Note: R a in equation III.5 is the aerodynamic resistance from z R to d+z 0 (the level where R a becomes zero), not to z 1 (). For ozone, R b = 6.85/u*. The canopy resistance, R c, is a function of temperature, radiation, relative humidity and soil water. If local meteorology allows an assessment of these, the formulation of R c may be directly estimated using a canopy-scale stomatal flux algorithm (see Emberson et al., 2000b). For forests, ozone concentrations must often be derived from measurements made over grassy areas or other land-cover types (Tuovinen et al., 2009). In principle, the O 3 concentration measured over le.g. short grasslands could be used to estimate the O 3 concentration at a reference height, and c then the gradient profile appropriate for e.g. forests could be applied. However, in order to keep this simple methodology manageable, and in view of the uncertainties inherent in making use of any profile near the canopy itself (Tuovinen et al., 2009, Tuovinen & Simpson, 2008), the following procedure is suggested (assuming observations over grassland are to be used to estimate fluxes over forests): Calculate u*, Ra(z R, d+z 0 ) over grassland using z 0, d values appropriate to grass, through eqns (III.3), (III.4). Calculate the deposition velocity, Vg(z R ), over grass using eqn (III.5). Note that that Rb, Rc resistances should be calculated for grassland, not forest, in this case. Calculate the O 3 concentration at eg z 1 =20m using eqn (III.2). (Where z 1 > z R, the Ra(z R,z 1 ) term will be negative, so that C(z 1 ) will be higher than C(z R ).) More advanced methods of dealing with profiles and use of measurements can be found in the model PLATIN (Appendix K in Grünhage and Haenel, 2008) and the DO 3 SE model (Tuovinen et al., 2009). III.4.3 MODELLING STOMATAL FLUX Stomatal flux is modelled using an algorithm incorporating effects of air temperature (f temp ), vapour pressure deficit of the air surrounding the leaves (f VPD ), light (f light ), soil water potential (f SWP ) or plant available water content (f PAW ), plant phenology (f phen ) and ozone concentration (f ozone ) on the maximum stomatal conductance measured under optimal conditions (g max ). Each parameter modifies the maximum stomatal conductance in a different way. For example, stomatal conductance gradually increases as temperature inceases reaching a peak and then gradually declines as temperature increases beyond the optimum, whilst stomatal conductance increases rapidly as light levels increase, reaching a maximum at relatively low light levels and maintaining at that maxiumum as light levels increase further. Ozone flux is calculated at the leaf level using the DO 3 SE (Deposition of Ozone for Stomatal Exchange) model, adapted from Emberson et al., 2000a. The formulae used to calculate ozone flux using this model are provided below with species-specific parameterisations provided for crops, forest trees and (semi-) natural vegetation in Sections III.5.2.3, III and III respectively. The DO 3 SE model is available in downloadable form at Data sources for the parameterisation of the flux algorithm are found in Annex 1, 2 and 3 for crops, forest trees and (semi-) natural vegetation respectively. The estimation of stomatal flux of ozone (F st ) should be calculated based on the assumption that the concentration of ozone at the top of the canopy represents a reasonable estimate of the concentration at the upper surface of the laminar layer near Updated : June 2015

21 Chapter III Mapping Critical Levels for Vegetation Page III - 21 the flag leaf (in the case of wheat) and the sunlit upper canopy leaves (in the case of other receptors). If c(z 1 ) is the concentration of ozone at canopy top (height z 1, unit: m), in nmol m -3, then F st (nmol m -2 PLA s -1 ), is given by: (III.6) F st 1 c( z 1 )* * r r g b c sto gsto g The 1/(r b +r c ) term represents the deposition rate to the leaf through resistances r b (quasi-laminar resistance) and r c (leaf surface resistance). The fraction of this ozone taken up by the stomata is given by g sto /(g sto +g ext ), where g sto is the stomatal conductance, and g ext is the external leaf, or cuticular, resistance. As the leaf surface resistance, r c, is given by r c = 1/(g sto + g ext ), we can also write equation (III.6) as: (III.7) F st rc c( z 1 )* gsto * r r b c A value for g ext has been chosen to keep consistency with the EMEP deposition modules big-leaf external resistance, R ext = 2500/SAI, where SAI is the surface area index (green + senescent LAI). Assuming that SAI can be simply scaled: (III.8) g ext = 1/2500 [m s -1 ] In order to be used correctly in Equations (III.6) and (III.7), g sto from equation (III.10a) has to be converted from units mmol m -2 s -1 to units m s -1. At normal temperatures and air pressure, the conversion is made by dividing the conductance value expressed in mmol m -2 s -1 by to give conductance in m s -1. Consistency of the quasi-laminar boundary layer is harder to achieve, so the use of a leaf-level r b term (McNaughton & van der Hank, 1995) is suggested, making use of the cross-wind leaf dimension L (unit: m) and the wind speed at height z 1, u(z 1 ): (III.9) r b 1.3*150* L u( ) z 1 ext [s m -1 ] Where the factor 1.3 accounts for the differences in diffusivity between heat and ozone. The core of the leaf ozone flux model is the stomatal conductance (g sto ) multiplicative algorithm which has been developed over the past few years as described in Emberson et al. (2000b) and incorporated within the EMEP ozone deposition module (Emberson et al. 2000a). The multiplicative algorithm has the following formulation: (III.10a) g sto = g max *[min(f phen, fo 3)]* f light * or (III.10b) max{f min, (f temp * f VPD * f SWP )} g sto = g max *[min(f phen, fo 3)]* f light * max{f min, (f temp * f VPD * f PAW )} Where g sto is the actual stomatal conductance (mmol O 3 m -2 PLA s -1 ) and g max is the species-specific maximum stomatal conductance (mmol O 3 m -2 PLA s -1 ). The parameters f phen, fo 3, f light, f temp, f VPD and f SWP or f PAW are all expressed in relative terms (i.e. they take values between 0 and 1 as a proportion of g max ). These parameters allow for the modifying influence of phenology and ozone, and four environmental variables (light (irradiance), temperature, atmospheric water vapour pressure deficit (VPD) and soil water potential (SWP) or plant available water content (PAW)) on stomatal conductance to be estimated. The part of Equation (III.10a) related to f phen and fo 3 is a most limiting factor approach in that either senescence due to normal ageing is limiting or the premature senescence induced by ozone is limiting. Early in the growing season and at low ozone exposure f phen is always limiting and fo 3 then does not come into operation. In the case of crops, to account for the effect by transpiration on leaf water potential, which may lead to a limitation of stomatal conductance in the afternoon hours, an additional ΣVPD algorithm is included (Equation (III.16). This may result in a stronger limitation of stomatal conductance than that suggested by Equation (III.10a) which represents the Updated: June 2015

22 Page III - 22 Chapter III Mapping Critical Levels for Vegetation original formulation given in Emberson et al. (2000a). To calculate the flux of ozone from g sto, equation (III.10c) should be used. (III.10c) F st (O 3 ) in nmol m -2 s -1 = (g sto /1000) * ozone concentration in ppb g max and f min Receptor-specific values are provided in the relvant Sections for g max and f min based on analysis of published data. g max values provided here are in mmol O 3 m -2 PLA s -1. Unless otherwise stated, these have been converted from g max (water vapour) using a conversion factor of to account for the difference in the molecular diffusivity of water vapour to that of ozone. Thus, this factor has been used to convert from the conductance of water vapour (as usually measured by, for example, a porometer) to the conductance of ozone, and has been updated in this version of Chapter 3 based on analysis included in Massmann (1998). f phen for forest trees (Section III.6.2.3) and (semi-)natural vegetation (III.7.2.3). Method (a): based on a fixed time interval when A start yd < (A start + f phen_c ) (III.11a) f phen = (1 f phen_a ) * ((yd A start )/f phen_c ) + f phen_a when (A start + f phen_c ) yd (A end f phen_d ) (III.11b) f phen = 1 when (A end f phen_d ) < yd A end (III.11c) f phen = (1 f phen_b ) * ((A end yd)/f phen_d ) + f phen_b where yd is the year day; A start and A end are the year days for the start and end of the ozone accumulation period respectively. f phen The phenology function can be based on either (a) a fixed number of days or (b) effective temperature sum accumulation and has the same shape for both approaches. For forest trees and (semi-) natural vegetation method (a) is used whilst for crops method (b) is the preferred option. f phen is calculated according to Equations (III.11a, b, c) when using a fixed number of days and (III.12 a, b, c) when using effective temperature sum accumulation. Each pair of equations gives fphen in relation to the accumulation period for POD Y where A start and A end are the start and end of the accumulation period respectively (also described as SGS (start of growing season) and EGS (end of growing season) respectively for some receptors such as forest trees). The parameters f phen_a and f phen_b denote the maximum fraction of g max that g sto takes at the start and end of the accumulation period for ozone flux. f phen_c to f phen-i are receptor-specific parameters describing the shape of the function within the accumulation period. Note: The functions described below are for crops, please see separate functions for Method (b): based on temperature sum accumulation when A start tt < (A start + f phen_e ) (III.12a) f phen 1 f phen_a 1 (Astart fphen_e) tt fphen_e when (A start + f phen_e ) tt (A end f phen_f ) (III.12b) f phen = 1 when (A end f phen_d ) < tt A end (III.12c) f phen 1 f 1 f phen_b phen_f tt (A end f phen_f where tt is the effective temperature sum in C days using a base temperature of 0 C and A start and A end are the effective temperature sums (above a base temperature of 0 C) at the start and end of the ozone accumulation period respectively. As such A start will be equal to ) Updated : June 2015

23 Chapter III Mapping Critical Levels for Vegetation Page III C days. Note: A base temperature of 0 C is currently recommended for all receptors but this may be revised shortly to be receptor specific. f light The function used to describe f light is given in Equation (III.13) (III.13) f light = 1 EXP(( light a )*PFD) where PFD represents the photosynthetic photon flux density in units of μmol m -2 s -1. f temp The function used to describe f temp is given in Equation (III.14). when T min < T < T max (III.14a) f temp = max { f min, [(T T min ) / (T opt T min )] * [(T max T) / (T max T opt )] bt } when T min > T > T max (III.14b) f temp = f min where T is the air temperature in C, T min and T max are the minimum and maximum temperatures at which stomatal closure occurs to f min, T opt is the optimum temperature and bt is defined as follows: (III.15) bt = (T max T opt ) / (T opt T min ) f VPD and ΣVPD routine The VPD (in kpa) of the air surrounding the leaves is used in two different ways. First, there is a more or less instantaneous effect of high VPD levels on stomata resulting in stomatal closure which reduces the high rate of transpiration water vapour flux rates out of the leaf under such conditions. Under dry and hot conditions such limitation of VPD may occur early during the day after the sunrise. This instantaneous response of the stomata to VPD is described by the f VPD function (Equation (III.16)). (III.16) f VPD = min{1,max {f min, ((1 f min )*(VPD min VPD) / (VPD min VPD max )) + f min }} Secondly, for crops there is another effect on stomata by water relations which can be modelled using VPD; such an effect, if it occurs, is not yet included for forest trees. During the afternoon, the air temperature typically decreases, which is normally, but not always, followed (if the absolute humidity of the air remains constant or increases) by declining VPD. According to the f VPD function this would allow the stomata to re-open if there had been a limitation by f VPD earlier during the day. Most commonly this does not happen. This is related to the fact that during the day the plant loses water through transpiration at a faster rate than it is replaced by root uptake, resulting in a reduction of the plant water potential during the course of the day and preventing stomatal re-opening in the afternoon. The plant water potential then recovers during the following night when the rate of transpiration is low. A simple way to model the extent of water loss by the plant is to use the sum of hourly VPD values during the daylight hours (as suggested by Uddling et al., 2004). If there is a large sum it is likely to be related to a larger amount of transpiration, and if the accumulated amount of transpiration during the course of the day (as represented by a VPD sum) exceeds a certain value, then stomatal re-opening in the afternoon does not occur. This is represented by the VPDsum function ( VPD) which is calculated in the following manner: If VPD VPD_crit, then: (III.17) g sto_hour_n+1 g sto_hour_n Where g sto_hour_n and g sto_hour_n+1 are the g sto values for hour n and hour n+1 respectively calculated according to equation (III.10a). VPD (kpa) should be calculated for each daylight hour until the dawn of the next day. Thus, if VPD is larger than or equal to VPD crit, the g sto value calculated using equation (III.10a) is valid if it is smaller or equal to the g sto value of the preceding hour. If g sto according to equation (III.10a) is larger than g sto of the preceding hour, given that VPD is larger than or equal to Updated: June 2015

24 Page III - 24 Chapter III Mapping Critical Levels for Vegetation VPD crit, it is replaced by the g sto of the preceding hour in the estimation of stomatal conductance. The VPD routine acts as a more mechanistically oriented replacement of the time of day function used in the Pleijel et al. (2002) and Danielsson et al. (2003) parameterisations. The instantaneous effect of VPD represented by f VPD is allowed to be in operation as a function to further reduce the stomatal conductance also after the VPD routine has started to limit stomatal conductance. f SWP The function used to describe f SWP is given in Equation (III.18a). (III.18a) f SWP = min{ 1, {f min, ((1 f min )*(SWP min SWP) / (SWP min SWP max )) + f min }} f PAW For some receptors (see relevant sections for details), f PAW is used instead of f SWP. The function used to describe f PAW is given in Equation (III.18b). Rootzone Plant Available Water (PAW) is the amount of water in the soil (%) which is available to the plants. At PAW = 100 % the soil is at field capacity, at PAW = 0 % the soil it at wilting point. PAW t is the threshold PAW, above which stomatal conductance is at a maximum, i.e when PAW t PAW 100 % (III.18b1) f PAW = 1 when PAW < PAW t (III.18b2) f f O3 PAW 1 PAW PAW PAW For crops, a function is included to allow for the influence of ozone on stomatal flux by promoting premature senescence (see Section III.5.2.3). t t III.4.4 STAGES IN CALCULATING EXCEEDANCE OF A FLUX-BASED CRITICAL LEVEL To calculate the relevant POD Y to sunlit leaves and exceedance of the stomatal flux-based critical level for a specific receptor, the following steps have to be undertaken, with receptor-specific information for each step provided in the relevant sub-chapters : Step 1 : The receptor-specific accumulation period is determined. Step 2 : Hourly ozone concentrations at the top of the canopy are determined for the accumulation period. Step 3 : The mean instantaneous stomatal conductance (g sto ) values for each hour within the accumulation period are calculated using the stomatal flux algorithm presented in Equations (III.6) to (III.10a) according to the receptorspecific parameterisations. Step 4 : Every hourly mean stomatal conductance thus calculated is multiplied by the corresponding hourly ozone concentration at the top of the canopy, resulting in hourly mean stomatal fluxes of ozone, F st expressed in nmol m -2 PLA s -1 (Equation (III.7)). Step 5 : The value Y is subtracted from each hourly F st value, and then multiplied by 3600 to obtain hourly F st Y values in nmol O 3 m -2 PLA h -1. Step 6: The sum of all hourly F st Y values is calculated for the specified accumulated period. The resulting value is POD Y in mmol m -2 PLA. Step 7 : If the POD Y value is larger than the flux-based critical level for ozone CLe f, then there is exceedance of the critical level. Updated : June 2015

25 O3 concentration (ppb) Chapter III Mapping Critical Levels for Vegetation Page III - 25 III.4.5 CALCULATION OF AOT40 AOT40 is the sum of the difference between the hourly mean ozone concentration at the top of the canopy and 40 ppb for all daylight hours within a specified time period (e.g. three months). This calculation is illustrated in Figure III Daylight hours Contributes to AOT40 Does not contribute to AOT Time of day (h) Figure III.1 : Calculation of ozone accumulated over a threshold of 40 ppb (AOT40) in ppb h for Balingen (6 May, 1992). The AOT40 for this day is 383 ppb h, calculated as 17 (exceedance of 40 ppb for 11 th hour) + 35 (12 th hour) + 30 (13 th hour) + 47 (14 th hour) + 51 (15 th hour) + 55 (16 th hour) + 52 (17 th hour) + 51 (18 th hour) + 45 (19 th hour). Exceedance of 40 ppb in the 20 th hour is not included because it occurred after daylight had ended. III.4.6 STAGES IN CALCULATING EXCEEDANCE OF AN AOT40-BASED CRITICAL LEVEL It is recommended that AOT40 values for comparison with the critical level should be calculated as the mean value over the most recent five years for which appropriate quality assured data are available. For local and national risk assessment, it may also be valuable to choose the year with the highest AOT40 from the five years. In summary, the following steps are required for calculation of AOTX and exceedance of the criticl level for a given year for a specific receptor : Step 1: The receptor-specific accumulation period is determined. Step 2 : Collate the hourly mean ozone concentrations for the measurement height and accumulation period. Step 3 : Adjust the ozone data from measurement height to canopy height using an appropriate model or the algorithm in this manual (see Section III.4.2). Step 4: Calculate the AOTX index by subtracting X from each hourly mean during daylight hours (when global radiation > 50 W m - 2 ) and then summing the resulting values (see example in Figure III.1). Updated: June 2015

26 Page III - 26 Chapter III Mapping Critical Levels for Vegetation III.4.7 CALCULATION OF AOT30 VPD The AOTX VPD index is used in the critical level for visble leaf injury and is calculated by using mean hourly values of the ozone concentration, expressed in ppb at the height of the top of the canopy (see Section III.4.2). If VPD values are not available, the AOT30 value can be taken as an indication of potential risk for visible injury without modifying the ozone concentrations. However, over large areas of Europe, VPD typically exerts a considerable restriction to stomatal conductance during the growing season in situations with elevated ozone concentrations. A default height of 1m is applicable to agricultural and horticultural crops. Each hourly ozone concentration [O 3 ] is multiplied by an hourly f vpd factor, reflecting the influence of VPD on stomatal conductance, to get hourly, modified ozone concentrations [O 3 ] VPD : (III.19) [O 3 ] VPD = f vpd * [O 3 ] where: f vpd = 1 if VPD < 1.1 kpa f vpd = 1.1 * VPD f vpd = 0.02 if 1.1 kpa VPD 1.9 kpa if VPD > 1.9 kpa The calculation of VPD is described in text books (e.g. Jones, 1992). III.4.8 STAGES IN CALCULATING EXCEEDANCE OF AN AOT30 VPD - BASED CRITICAL LEVEL In the case of the short-term critical level for ozone injury AOT30 VPD is obtained by first subtracting 30 ppb from each hourly [O3]VPD > 30 ppb, and then making a sum of the resulting values. Thus, [O3]VPD values 30 ppb do not contribute to AOT30VPD. AOT30VPD is calculated over eight day periods to identify the potential risk of ozone injury on sensitive crops and expressed in units of ppm h. Thus, if the eight day AOT30VPD exceeds the critical level for ozone injury, then injury is likely. It is important that the period over which the AOT30VPD value is calculated is consistent with the period when the relevant receptor is actively growing and absorbing ozone. The AOT30VPD value is calculated using running eight day periods throughout the season, during daylight hours. Updated : June 2015

27 Chapter III Mapping Critical Levels for Vegetation Page III - 27 III.5 CRITICAL LEVELS OF OZONE AND RISK ASSESSMENT METHODS FOR AGRICULTURAL AND HORTICULTURAL CROPS III.5.1 CROPS OZONE SENSITIVITY OF AGRICULTURAL AND HORTICULTURAL Table III.8 : The range of sensitivity of agricultural and horticultural crops to ozone. Sensitive Moderately sensitive Moderately resistant Insensitive Cotton, Lettuce, Pulses, Soybean, Salad Onion, Tomato, Turnip, Watermelon, Wheat Potato, Rapeseed, Sugarbeet, Tobacco Broccoli, Grape, Maize, Rice Barley, Fruit (Plum & Strawberry) Note : see Mills et al., 2007a for response functions and definition of sensitivities A pan-european assessment has shown that ambient ozone causes visible leaf injury on over 20 crop species including wheat, potato, bean and tomato (Hayes et al., 2007a, Mills et al., 2011a). Effects on yield are also predicted in sensitive species, with evidence of effects being found in field-based experiments in which ozone was filtered out of ambient air (Hayes et al., 2007a, Mills et al., 2011a). Table III.8 provides an indication of the relative sensitivity to ozone of a wide range of agricultural and horticultural crops. This table was derived from a comprehensive review of over 700 published papers on crop responses to ozone leading to the derivation of AOT40-based response functions for 19 crops (Mills et al., 2007a). Data were included only where ozone conditions were recorded as 7h, 8h or 24h means or AOT40, and exposure to ozone occurred for a whole growing season using field-based exposure systems. The yield data presented in published papers ranged from % of control treatment to t ha -1 and were all converted to the yield relative to that in the charcoal-filtered air treatment. Where the data were published as 7h or 8h means, data points were omitted from the analysis if the O 3 concentration exceeded 100 ppb (considered to be outside the normal range for Europe). Data were converted to AOT40 using a function derived from the ICP Vegetation ambient ozone database (Mills et al., 2007a). Doseresponse functions were derived for each crop using linear regression with data taken from experiments conducted without soil moisture limitations. The crops were ranked in sensitivity to ozone by determining the AOT40 associated with a 5% reduction in yield. Wheat, pulses, cotton and soybean were the most sensitive of the agricultural crops, with several horticultural crops such as tomato and lettuce being of comparable sensitivity. Crops such as potato and sugar beet that have green foliage throughout the summer months were classified as moderately sensitive to ozone. In contrast, important cereal crops such as maize and barley can be considered to be moderately resistant and insensitive to ozone respectively. Data analysis conducted in the early 1990s indicated that crops were responding to accumulated ozone exposure rather than the mean concentration. Following a provisional AOT40-based critical level established in Egham, 1992, the first confirmed AOT40-based critical level for ozone effects on agricultural crops was set at the Berne Workshop in Although new data has been added since then, this critical level has remained unchanged for agricultural crops. During the late 1990s and early 2000s, the flux-based methodology was developed. The first fluxbased critical levels for crops were established for wheat and potato at the Gothenburg workshop in The methodology has since been refined and new data sets have been added, with revisions to this part of the chapter being Updated: June 2015

28 Page III - 28 Chapter III Mapping Critical Levels for Vegetation made in 2004 and The current (revised) flux-based critical levels for effects on the quanity and quality of yield were established at the 23 rd Task Force meeting of the ICP Vegetation in February 2010 following discussions at the Ispra workshop in November These are included here together with the AOT40- and AOT30 VPD -based critical levels previously agreed. III.5.2 STOMATAL FLUX-BASED METHODS III SCIENTIFIC BASIS AND ROBUSTNESS OF FLUX-BASED METHODS AND CRITICAL LEVELS The index POD Y is used to quantify the flux of ozone through the stomata of the uppermost leaf level that is directly exposed to solar radiation and thus no calculation of light exclusion, caused by the filtering of light through the leaves of the canopy, is required. The sunlit leaf level has the largest gas exchange in terms of net photosynthesis (i.e. contributes most strongly to yield) and ozone flux, both because it receives most solar radiation and because it is least senescent. Thus, the ozone flux is expressed as the cumulative stomatal flux per unit sunlit leaf area in order to reflect the influence of ozone on the fraction of the leaf area which is most important for yield. In deriving relationships between the relative yield and the stomatal flux of ozone, it has been observed that the best correlations between effect and accumulated stomatal flux are obtained when using a stomatal flux threshold (Y) (Danielsson et al., 2003; Pleijel et al., 2002). The strongest relationships between yield effects and POD Y were obtained using Y = 6 nmol m -2 s -1 for wheat, potato (Pleijel et al., 2007) and tomato (González- Fernández et al., 2014). In effect, this means that ozone exposure started to contribute to POD Y at an ozone concentration at the top of the crop canopy of approximately 22 ppb for wheat and 14 ppb for potato if the stomatal conductance was at its maximum. In the case of lower conductance, which prevails in most situations, a higher ozone concentration than 22 ppb and 14 ppb is required to contribute to POD 6 for wheat and potato respectively. Based on the combination of data from a number of open-top chamber experiments with field-grown crops performed in several European countries, relationships between relative yield (RY) and stomatal ozone flux (F st ) have been derived using the principles introduced by Fuhrer (1994) to calculate relative yield. A relative yield of 1 represents the absence of ozone effects. The robustness in the understanding of ozone damage to crops in Europe has been substantiated by the compilation of the observed effects in ambient air presented in the ICP Vegetation Evidence Report. This showed ozone injury occurrence on 27 species of agricultural and horticultural crops in 12 countries, and beneficial effects on yield of growing crops in filtered air from which ozone is excluded (Hayes et al, 2007a, Mills et al, 2011a). There is also a coherent pattern of response in crops when combining experiments from different countries with different climatic conditions and for a range of varieties (Figures III.2 and III.3). A recent meta-analysis of results in 53 peerreviewed studies of ozone effects on wheat indicated that ozone concentrations between 31 and 59 ppb (average 43 ppb) were associated with a significant decrease in the grain yield (18%) and biomass (16%) relative to charcoal-filtered air treatments (Feng et al., 2008). Of the three horticultural crops for which flux-based response-functions were derived (bean, lettuce and tomato), the ICP Vegetation Task Force agreed that only the function for tomato was sufficiently robust for the derivation of critical levels. It should be noted, however, that tomato is the least sensitive of the three crops and the use of this critical level or function to quantify impacts on all horticultural crops may lead to an underestimation of the extent of damage. Updated : June 2015

29 Chapter III Mapping Critical Levels for Vegetation Page III - 29 The main uncertainties in the application of this method arise from the effects of soil moisture on ozone flux and the extrapolation from different exposure systems to field conditions outside of the experimental systems. Soil moisture, which has the potential to strongly limit stomatal conductance and thus ozone uptake, varies on a local scale which is hard to model, and in the experiments was typically kept at a level that did not induce any water stress. When mapping effects using the flux-based response-functions, uncertainty arises as to whether irrigation is in use to overcome water stress; such decisions are usually made at the farm-scale and are difficult to map effectively. These uncertainties are especially important in areas where rainfed crops in water limited environments are common (such as wheat fields in Mediterranean areas). Although the flux approach represents a way to quantify several of the important factors that modify ozone uptake that may differ between exposure systems and the field, the application of flux-effect relationships still depends on extrapolation from conditions in expsure systems to those in the field. III FLUX-BASED CRITICAL LEVELS AND RESPONSE FUNCTIONS FOR CROPS These critical levels and response- functions can be used to quantify the negative impact of ozone on security of food supplies at the local and regional scale. In line with earlier concepts used for crop critical levels (UNECE, 1996), 5% yield reduction was used as the loss criterion for the identification of stomatal flux-based critical levels. For example, for the grain yield of wheat the suggested stomatal fluxbased critical level for 5% yield loss of a POD 6 of 1 mmol m -2 was statistically significant according to the confidence limits of the yield response regressions (Figure III.2), which is an important criterion when using a yield loss level such as 5% (Pleijel, 1996). The functions used to derive the fluxbased critical levels for crops are provided in Figures III.2 and III.3 and summarised in Table III.9. Table III.10 also provides the flux-based critical levels for these crops. Note: The critical levels and response functions for wheat and potato have been derived from data from central and Northern Europe and may have added uncertainty when used to estimate ozone impacts in the Mediterranean area. Further information about the flux-based critical levels for crops can be found in Mills et al., 2011b, Grünhage et al., 2012 and González-Fernández et al., 2014). Updated: June 2015

30 Page III - 30 Chapter III Mapping Critical Levels for Vegetation Table III.9 : Flux-based critical levels and response functions for agricultural crops (published in Mills et al., 2011b, Grünhage et al., 2012 and González-Fernández et al., 2014). Crop Wheat Wheat Wheat Potato Tomato Yield parameter Grain yield 1000 grain weight % reduction for critical level Critical level (POD 6, mmol m -2 ) Countries involved in experiments Number of data points Protein yield Tuber yield Fruit yield (Fruit quality) 5% 5% 5% 5% 5% (4) Belgium, Finland, Italy, Sweden Belgium, Finland, Sweden Belgium, Finland, Sweden Belgium, Finland, Germany, Sweden Italy, Spain Number of cultivars (sensitive cultivars only) Data sources Time period Pleijel et al., C days before anthesis to 700 C days after anthesis Response function RY= *POD 6 Piikki et al., C days before anthesis to 700 C days after anthesis RY= *POD 6 Piikki et al., C days before anthesis to 700 C days after anthesis RY= *POD 6 Pleijel et al., C days starting at plant emergence RY= *POD 6 González- Fernández et al., to 1500 ºC days starting at tomato planting in the field (at 4 th true leaf stage) RY= *POD 6 (RY= *POD 6) r (0.65) P value P<0.001 P<0.001 P<0.001 P<0.001 P<0.001 (P<0.001) Updated : June 2015

31 Relative protein yield Relative yield Relative 1000-grain weight Chapter III Mapping Critical Levels for Vegetation Page III BE y = * POD6 FI r 2 = 0.84 IT p < SE y = * POD6 BE r 2 = 0.71 FI p < SE POD6, mmol m -2 POD6, mmol m y = * POD6 BE r 2 = 0.63 FI p < SE POD6, mmol m -2 Figure III.2 The relationship between the relative yield of wheat and stomatal ozone flux for the wheat flag leaf based on five wheat cultivars from three or four European Countries (BE: Belgium, FI: Finland, IT: Italy, SE: Sweden) using effective temperature sum to describe phenology: a) grain yield, b) 1000-grain weight, and c) protein yield. The dashed lines indicate the 95%-confidence intervals. These functions are published in Grünhage et al., Updated: June 2015

32 Relative yield Page III - 32 Chapter III Mapping Critical Levels for Vegetation (a) Potato (b) Tomato y = * POD 6 r 2 = 0.76 p < POD 6, mmol m -2 BE FI GE SE Figure III.3 :The relationship between the relative a) tuber yield of potato and POD 6 for sunlit leaves based on data from four European Countries (BE: Belgium, FI: Finland, GE: Germany, SE: Sweden) and b) tomato fruit yield and POD 6 for sunlit leaves based on data from Italy (IT) and Spain (SP). The dashed lines indicate the 95% confidence intervals. The function for potato is published in Pleijel et al., 2007 and that for tomato is in González-Fernández et al., III METHOD FOR CALCULATING OZONE FLUX FOR CROPS USING SPECIES-SPECIFIC FLUX MODELS The original parameterisations given in Emberson et al. (2000a) have been revised based on data collected from more recently published literature including from ozone exposure experiments conducted in Sweden for wheat (Danielsson et al., 2003) and a number of sites across Europe for potato (Pleijel et al., 2003), and from field measurements conducted in Germany (Grünhage et al., 2012), Sweden and France for wheat. The parameterisations recommended for use in calculating POD 6 for wheat, potato, and tomato applying the stomatal flux algorithm are shown in Table Details on the derivation of each parameter can be found in Annex 1. Species - specific notes to aid calculation of fluxes are found below the table. The parameterisation table includes the use of plant available water (f PAW ) instead of soil water potential (f SWP ) for wheat, as described below. As it is assumed that tomato is irrigated to maximize yield at all times, no parameterisations are included for the effect of PAW or SWP. Updated : June 2015

33 Chapter III Mapping Critical Levels for Vegetation Page III - 33 Table III.10 : Summary of the parameterisation for the stomatal flux algorithms for POD 6 for wheat flag leaves and the upper-canopy sunlit leaves of potato and tomato. Note: where a blank space occurs, this parameter is not needed. Explantatory notes are provided on the next page. Parameterisations for wheat and potato are published in Grünhage et al., 2012 and Pleijel et al., 2007, respectively. A different parameterization has been defined for wheat in Mediterranean areas in Annex 1, Table A1.3 (González-Fernández et al, 2013). The parameterization of tomato has been updated to reflect that in González-Fernández et al, Parameter Units Wheat Potato Tomato g max mmol O 3 m -2 PLA s f min fraction SGS C day EGS C day f phen_a fraction f phen_b fraction f phen_c days 20 f phen_d days 50 f phen_e C days f phen_f* C days f phen_g C days 100 f phen_h C days 525 f phen_i C days 700 Light_a constant T min C T opt C T max C VPD max kpa VPD min kpa ΣVPD crit kpa 8 10 PAW t % 50 SWP max MPa -0.5 SWP min MPa -1.1 f ozone POD 0, mmol m -2 s -1 (wheat) 14 f ozone AOT0, ppmh (potato) 40 f ozone exponent 8 5 Height m Leaf dimension m (leaflet width) 1 mean value, cultivar specific values can be used (González-Fernández et al., 2014). 2 flux accumulation period in degreedays over a base temperature of 10 C since the date of tomato planting in the field at the 4 th true leaf stage, BBCH code of 14. Updated: June 2015

34 Page III - 34 Chapter III Mapping Critical Levels for Vegetation For crops, the following additional information is required for calculating stomatal flux using the method provided in Section III.4.3 and the parameterization provided in Table III.10 : Wheat f phen For wheat, it is recommended that fphen is calculated using method (b) based on accumulation of thermal time. Following discussions of new data from Germany, France and Sweden at the 23 rd ICP Vegetation Task Force meeting in Tervuren, it was agreed that the indicated shape of the phenology function for wheat should be modified from that described in Equations (III.7a_wheat), (III.7b_wheat) and (III.8c_wheat) below. f f when (f phen_f f phen_e ) tt (f phen_f + f phen_g ) (III.7a_wheat) f phen = 1 when (f phen_f + f phen_g ) < tt (f phen_f + f phen_h )(III.7b_wheat) phen 1 f f phen_h phen_a f phen_g tt f phen_g when (f phen_f + f phen_h ) < tt f phen_i (III.7c_wheat) phen f phen_b f phen_b tt f f phen_i f phen_h phen_h where tt is the effective temperature sum in C days using a base temperature of 0 C and A start and A end are the effective temperature sums (above a base temperature of 0 C) at the start and end of the ozone accumulation period respectively. As such A start will be equal to 200 C days before A mid-anthesis (-200 C), A mid-anthesis to 0 C days, A end to 700 C days after A mid-anthesis. The total temperature sum thus being 900 C days. f VPD Under Mediterranean conditions the stomata of wheat remain open under drier humidities (higher VPDs) than indicated with the above parameterisations for fvpd. A full parameterization for wheat to be used in Mediterranean areas is presented in Annex 1, Table A1.3 (González-Fernández et al, 2013). f PAW f PAW is used instead of f SWP for wheat, see Equation III.10b. f O3 The flux-effect models developed by Pleijel et al. (2002) and Danielsson et al. (2003) include a function to allow for the influence of ozone concentrations on stomatal conductance (fo 3) on wheat and potato via the onset of early senescence. As such this function is used in association with the f phen function to estimate g sto. The fo 3 function typically operates over a one-month period and only comes into operation if it has a stronger senescence-promoting effect than normal senescence. The ozone function for spring wheat (based on Danielsson et al. (2003) but recalculated for PLA): (III.20) fo 3 = ((1+(POD 0 /14) 8 ) -1 ) where POD 0 is accumulated from A start Potato f phen For potato, f phen is calculated using method (b) based on accumulation of thermal time. f SWP f SWP is used for potato f O3 The ozone function for potato (based on Pleijel et al. (2002)): (III.21) fo3 = ((1+(AOT0/40) 5 ) -1 ) where AOT0 is accumulated from A start Tomato f phen Method (b), based on a fixed thermal-time based interval, is used to determine f phen for tomato. Updated : June 2015

35 Chapter III Mapping Critical Levels for Vegetation Page III - 35 III CALCULATION OF POD Y AND EXCEEDANCE OF THE FLUX- BASED CRITICAL LEVELS FOR CROPS Use the procedure outlined in Section III.4.4 together with the following speciesspecific information : Step 1: Determine the accumulation period The start and end of the POD Y accumulation period are identified by A start and A end respectively. Four methods are suggested for estimating the timing of the POD Y accumulation period, listed in order of desirability: i) the use of observational data describing actual growth stages; ii) the use of local agricultural statistics/information describing the timings of growth stages by region or country; iii) the use of phenological growth models in conjunction with daily meteorological data; iv) the use of fixed time periods (which may be moderated by climatic region or latitude) or growth stage intervals. Wheat POD Y is accumulated during an effective temperature sum period starting 200 C days before mid-anthesis (full flowering) and ending 700 C days after mid-anthesis. The total period is 900 C days (base temperature is 0 C). For wheat growing in Mediterranean areas the accumulation period is presented in Annex 1, Table A1.3. Thus, it is necessary to identify the timing of mid-anthesis (defined as growth stage 65 according to Tottman et al., 1979). (i) Estimating mid-anthesis using phenological models In the absence of observational or statistical information describing growth stages, mid-anthesis can be defined using phenological models if daily mean temperature data for the entire year are available. For spring wheat, mid-anthesis can be estimated using a temperature sum value of 1075 C days calculated from plant emergence. In the absence of local information plant emergence can be estimated from typical sowing dates given for spring wheat by climatic region in Table III.11. These can be used to estimate the timing of emergence by assuming that the temperature sum (above a base temperature of 0 C) required for emergence would be 70 C days (assuming an average sowing depth of 3 cm) (Hodges and Ritchie, 1991). For winter wheat, growth can be assumed to restart after the winter when temperature exceeds 0 C. Traditionally, the starting date for the accumulation of the effective temperature sum to mid-anthesis for winter wheat is the first date after 1 January when the temperature exceeds 0 C, or 1 January if the temperature exceeds 0 C on that date. Using this start point, mid-anthesis can be estimated using a temperature sum of 1075 C days after 1 January, with different values recommended for Mediterranean areas in Annex A1.2 (it should be noted that these calculations ignore any effects of photoperiod). For winter wheat in Central Europe, growth can be assumed to restart at day 60 of the respective year according to the wellvalidated agrometeorological model for estimation of actual evapotranspiration AMBAV (Agrarmeteorologisches Modell zur Berechnung der aktuellen Verdunstung) of the German Meteorological Service. Using this start point, mid-anthesis can be estimated using a temperature sum of 1024 C days. (ii) Estimating mid-anthesis using a latitude model In the absence of appropriate temperature data, the timing of mid-anthesis for both spring and winter wheat could be approximated as a function of latitude (degrees N) using Equation (III.22). However, it should be recognised that this method is less preferable to the use of the effective temperature sum models described above since latitude is not directly related to temperature and this method will not distinguish between spring and winter wheat growth patterns. (III.22) Mid-anthesis = 2.57 * latitude + 40 Equation (III.22) is based on data collected Updated: June 2015

36 Page III - 36 Chapter III Mapping Critical Levels for Vegetation by the ICP Vegetation (Mills and Ball, 1998, Mills et al., 2007a) from ten sites across Europe (ranging in latitude from Finland to Slovenia) describing the date of anthesis of commercial winter wheat. Applying equation (III.22) across the European wheat growing region would give midanthesis dates ranging from the end of April to mid-august at latitudes of 35 to 65 N respectively. These anthesis dates fall appropriately within recognised spring wheat growing seasons as described by Peterson (1965) and also from data for winter wheat supplied for Spain by Gimeno et al. (2003b). (iii) Estimating anthesis using local data on the timing of growth stages As an alternative to using a fixed number of days, the time period between the first spikelet of the inflorescence being visible and the hard dough stage (growth stages 51 to 87, Tottman et al., 1979) could be used based on those published in national farming magazines and on farming webpages. It should be noted that such data sources may use alternative growth-stage keys. Table III.11 : Observed sowing dates for spring wheat in Europe 1 Region Range Default Northern Europe Finland 1-30 May 30 May Norway 1-20 May 20 May Sweden 1-20 Apr 20 Apr Denmark 1 Mar-20 Apr 20 Mar Continental Central Europe Poland 1-20 Apr 10 Apr Czech Republic Apr 20 Apr Slovakia Apr 20 Apr Germany 10 Mar-10 Apr 01 Apr Atlantic Central Europe UK 20 Feb-20 Mar 10 Mar The Netherlands 1-30 Mar 15 Mar France 1 Mar-10 Apr 20 Mar Mediterranean Europe Bulgaria - Portugal 20 Jan-10 Mar 10 Feb Spain 1-28 Feb 10 Feb 1 According to Broekhuizen (1969) Updated : June 2015

37 Chapter III Mapping Critical Levels for Vegetation Page III - 37 Potato For potato, POD 6 is accumulated over 1130 C days starting at plant emergence (base temperature 0 C). On average, the 1130 C days corresponded to 66 days in the experiments used to calibrate the function. Thus, it is necessary to identify plant emergence, which normally takes place one week to ten days after sowing. Although the sowing date varies to a considerable extent across Europe, information from the EU-funded research programme CHIP, which investigated the effects of ozone and other stresses on potato, found that plant emergence was obtained on average on day of year 146, with a variation from day 135 at southern and most western European sites to day 162 in Finland. As such, in the absence of local information describing sowing dates it is suggested that year day 146 be used as a default to define A start for potato plant emergence. No phenological models are suggested for use to define A start for this species. Tomato The timing of A start (SGS) is more difficult to define for tomato because such horticultural crops are repeatedly sown over several months in many regions especially in the Mediterranean region. For local application, an SGS and EGS (A end ) based on degree-days over a base temperature of 10 C since phenological stage 4 th true leaf (BBCH code = 14), the growth stage at which tomato is usually planted in the field is suggested. Step 2: Determine the ozone concentration at the top of the canopy The ozone concentration at canopy height can be calculated using the methods described in Section III.4.2. For wheat, potato and tomato the default height of the canopy is 1 m. Steps 3 to 7: Continue as described in Section III.4. III REGIONAL PARAMETERISATIONS OF SPECIES-SPECIFC FLUX MODELS FOR CROPS A Mediterranean-specific parameterisation for wheat is included in Annex 1 of this chapter. Should additional regional parameterisations become available at a later date they will also be included in Annex 1. III ESTIMATION OF RISK OF DAMAGE USING A GENERIC CROP FLUX MODEL (FOR INTEGRATED ASSESSMENT MODELLING WITHIN GAINS) Note: This text was updated following the 27 th ICP Vegetation Task Force meeting, Paris, January, 2014 including a yield-response relationship and critical level for use in scenario analysis and optimisation runs within the GAINS, and a Mediterranean-specific flux model parameterisation. This simplified flux method is specifically designed for indicating the degree of risk of damage to crops within large scale modelling, including scenario analysis and optimisation runs within GAINS. Soil moisture effects on flux are included within the EMEP model as a soil moisture index (Simpson et al., 2012) whilst the effects of phenology and ozone on fluxes are omitted in this approach. A separate parameterisation is supplied for Mediterranean areas. Updated: June 2015

38 Page III - 38 Chapter III Mapping Critical Levels for Vegetation Important background information The simplified flux model, response function and associated critical level described in this section are for integrated assessment modelling at the European scale. They are provided for use in scenario analysis and optimisation runs within the GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies) model to provide an indication of potential effects on wheat yield under non-limiting water availability. The parameterisation for ozone exposure (stomatal flux), as calculated for the source-receptor matrix used in GAINS, should be based on two parameterisations for cereals: i) Northern and Central Europe; ii) Mediterranean areas (as defined in Table III.14). For local and national application, it is recommended that the full wheat flux model (Section III ) is used, including for Mediterranean areas, the Mediterraneanspecific parameterisation. The generic crop flux method included here is intended to be used to provide estimates of the potential effective phytotoxic cumulative stomatal ozone uptake and hence should be viewed as an indicator of the degree of risk for crop loss with a stronger biological basis than AOT40. The flux model described here is a simplified model for application in large-scale modelling, including integrated assessment modelling (IAM) and uses a lower threshold (Y=3 compared to Y=6 nmol m -2 s -1 for the full flux model) and a longer time interval for accumulation of flux. The parameterisation for this generic flux model is summarized in Table III.12, including a separate parameterisation for application in Mediterranean areas. As the modifying effect of soil moisture on stomatal conductance is not included in the parameterisation of the generic crop flux model then this method would indicate the risk of damage under a worst case scenario where soil moisture is not limiting to flux. Even using the Mediterranean parameterisation, this approach may overestimate risk of damage for non-irrigated crops in dry climates because of the lack of inclusion of a soil moisture parameterisation. When applied, the generic flux model is applied within the EMEP model, a soil moisture index suitable for large-scale modelling is included (Simpson et al., 2012). The ICP Vegetation Task Force recommends that any tables or maps produced using this generic crop approach should include the following text within the legend When generating data/maps using the generic crop flux parameterisation within the EMEP model, a soil moisture index suitable for largescale modelling has been included in the EMEP model to account for soil moisture effects on the calculated ozone flux. Calculation of the generic flux model for crops The generic flux model parameterisations provided in Table III.12 are based on the full flux model for wheat (See Section III.5.2.3). The following modifications have been made to simplify the flux model for application within large-scale regional flux models such as GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies)). Time interval for accumulation: It is suggested to use a time window of three months symmetrically around the estimated date for anthesis (flowering) in wheat as identified by data collection within the ICP Vegetation (Equation III.22). Phenology function: For these calculations it would also be assumed that f phen for the leaves at the top of the canopy is equal to 1, i.e. that there is no change in ozone flux related to growth stage. The function for ozone, fo 3 is set to 1.The function for plant available water (f PAW ) is set to 1 considering the large problems in estimating the exact extent of irrigation in different grid squares. Thus, the risk estimates assume that soil water potential does not influence ozone uptake. Two parameterisations for vapour pressure deficit (VPD) and temperature are provided in Table III.12 : One for use in Mediterranean areas derived from González- Updated : June 2015

39 Chapter III Mapping Critical Levels for Vegetation Page III - 39 Fernández et al. (2013), and one for use in the rest of Europe. The countries considered in Table III.14 to be Eastern Mediterranean (EM) and Western Mediterranean (WM) should all be considered as Mediterranean for IAM. Choice of Y. Due to difficulties in estimating the ozone flux using Y = 6 nmol m- 2 s- 1 in IAM arising from the strong increase in the uncertainty in modelled POD with increasing Y, Y= 3 nmol m- 2 s- 1 is to be used. Notation. Since several aspects of this parameterisation are changed compared to the full flux model, the exposure index used will be denoted POD 3 IAM (formerly described as POD 3 gen ) to indicate that this flux model is for use in large scale integrated assessment modelling, and that this is a different parameter to POD 3 calculated using the full flux model. The functions for light and temperature are parameterized as for wheat according to Table III.10 and associated equations of this chapter. As in Table III.10, and the height (h) of the crop canopy is assumed to be 1 m, and the characteristic leaf dimension (L) is set to 0.02 m Table III.12 : Parameterisation of POD 3 IAM, the generic flux model for crops for use in integrated assessment modelling. Reductions in ozone flux associated with dry soils such as those found in Mediterranean areas are accounted for in the EMEP model by applying a soil moisture index (Simpson et al., 2012). Parameter Units Central and northern Europe 1,2 Mediterranean Europe 1,3 g max mmol O 3 m -2 PLA s f min fraction f phen 1 1 f light constant light a = light a = T min C T opt C T max C VPD max kpa VPD min kpa ΣVPD crit kpa 8 8 f PAW 1 1 f ozone 1 1 SAI m 2 PLA m Green LAI m 2 PLA m Height m 1 1 Leaf dimension m Allocated countries are provided in Table III.14 2 Modified from the full flux model for wheat provided in Table III.10 as described above 3 Modified from the full flux model for wheat provided in González-Fernández et al. (2013) as described above Updated: June 2015

40 Page III - 40 Chapter III Mapping Critical Levels for Vegetation Estimation of risk of crop yield loss using POD 3 IAM The ICP Vegetation Task Force expressed some concern over the use of POD 3 IAM to estimate effects of ozone on crop yield. It was agreed, however, that the function could be used in large-scale modelling to estimate effects on yield crops. As described above it is recommended that all maps and tables should have the important note explaining this limitation included within the legend. The function shown in Figure III.4 was derived from the data described in Table III.9 and presented in Figure III.2 for the full flux model for wheat. To accomodate the need for IAM to use a longer time period than the thermal time based time window used for the full flux model, the response function provided is based on 90 days, centred on anthesis. It should be noted that this function was first derived for datasets based on 45 days; the values have been doubled to accomodate the longer 90 day time interval required. Some uncertainty arises from this approach, but the Task Force agreed that this was an acceptable level of uncertainty within large scale modelling. POD 3 IAM critical level for use within GAINS The response function provided in Figure 3.4 has been used to derive a critical level for use in scenario analysis and optimisation runs within the GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies) model. As already described, this critical was derived for crops assuming non-limiting water availability. The parameterisation for ozone exposure (stomatal flux), as calculated for the source-receptor matrix used in GAINS, should be based on two parameterisations for cereals: 1) Northern and Central Europe; 2) Mediterranean areas (Table III.12). Using the response function in Figure III.4, a critical level representing a 5% reduction in crop yield has been derived for use in GAINS as: A POD 3 IAM of 8 mmol m -2 (accumulated over 90 days) Figure III.4: The relationship between relative yield and POD 3 IAM (accumulated over 90 days) for a generic crop represented by wheat. Please see text for application of this relationship within GAINS. Dotted lines represent 95% confidence intervals. Updated : June 2015

41 Chapter III Mapping Critical Levels for Vegetation Page III - 41 III.5.3 AOT40-BASED METHODS III SCIENTIFIC BASIS OF AOT40-BASED METHODS AND CRITICAL LEVELS Agricultural crops The concentration-based critical level for agricultural crops has been derived from a linear relationship between AOT40 and relative yield for wheat, developed from the results of open-top chamber experiments conducted in Europe and the USA (Figure III.5, Table III.13). Newer data (Gelang et al. 2000) has been added to that derived by Fuhrer et al. (1997) and quoted in the earlier version of the Mapping Manual (UNECE, 1996). Thus, the critical level for wheat is based on a comprehensive dataset including 9 cultivars. The AOT40 corresponding to a 5% reduction in yield is 3.3 ppm h (95% Confidence Interval range ppm h). This value has been rounded down to 3 ppm h for the critical level. The critical level for agricultural crops is only applicable when nutrient supply and soil moisture are not limiting, the latter because of sufficient precipitation or irrigation (Fuhrer, 1995). The time period over which the AOT40 is calculated should be three months and the timing should reflect the period of active growth of wheat and be centred around anthesis (see Section III for guidance). For further explanation, please see Mills et al. (2007a). An optional additional AOT30-based critical level of ozone has also been derived for agricultural crops, based on a re-working of the wheat response-function data used by Fuhrer et al. (1997) using AOT30 as the dose parameter (r 2 = 0.90, data not presented). The value for this critical level is an AOT30 of 4 ppm h applied to the same time-windows as described for AOT40. Following discussions at the Gothenburg Workshop (2002), the 16 th Task Force meeting of the ICP Vegetation (2003) and the 19 th Task Force meeting of the ICP Modelling and Mapping (2003), it was concluded that AOT40 should continue to be used for the concentration-based critical level for agricultural crops, but that AOT30 could be used in integrated assessment modelling on the European scale if this considerably reduces uncertainty in the overall integrated assessment model. It is not recommended that exceedance of the concentration-based critical level for agricultural crops is converted into economic loss; it should only be used as an indication of ecological risk (Fuhrer, 1995). Horticultural crops Note: This text has been revised following decisions made at the 2015 Task Force meeting of the ICP Vegetation based on new evidence published in González- Fernández et al., A concentration-based critical level has been derived for horticultural crops that are growing with adequate nutrient and water supply. An AOT40 of 8.4 ppm h is equivalent to a 5% reduction in fruit yield for tomato, and has been derived from a dose-response function developed from a comprehensive dataset including 5 ozonesensitive cultivars (r 2 = 0.63, p < 0.001, Figure III.6, see also González-Fernández et al., 2014). This value has been rounded down to 8 ppm h for the critical level. Although statistical analysis has indicated that water melon may be more sensitive to ozone than tomato, the dataset for water melon is not sufficiently robust for use in the derivation of a critical level because the data is only for one cultivar (Mills et al., 2007a). Tomato is considered suitable for the derivation of the critical level since it is classified as an ozone-sensitive crop (Table III.8) and a suitably robust function is available. Other horticultural crops such as lettuce and bean are mosre sensitive but had a less robust response-function. The data used in the derivation of the critical level for horticultural crops is from experiments conducted in the USA (California and North Carolina), Germany and Spain. The time period for accumulation of AOT40 is three months, starting at the 4 th true leaf stage (BBCH Updated: June 2015

42 Page III - 42 Chapter III Mapping Critical Levels for Vegetation code = 14) which is normally the date of planting. It is not recommended that the exceedance of the concentration-based critical level for horticultural crops is converted into economic loss; it should only be used as an indication of ecological risk during the most sensitive environmental conditions (Fuhrer, 1995). III AOT40-BASED CRITICAL LEVELS AND RESPONSE FUNCTIONS The AOT40-based critical levels for agricultural and horticultural crops are suitable for estimating damage where climatic data or suitable flux models are not available. Economic losses should not be estimated using this method. The AOT40-based critical levels and the response functions from which they were derived are presented in Table III.13 and Figures III.5 and III.6. Table III.13 : AOT40-based critical levels and response functions for agricultural and horticultural crops. Category Agricultural Horticultural Representative crop Wheat Tomato Yield parameter Grain yield Fruit yield % reduction for critical level 5 % 5 % Critical level (AOT40, ppm h) Countries involved in experiments 3 8 Belgium, Finland, Italy, Sweden Number of data points Spain, Italy Number of cultivars 9 5 ozone-sensitive cultivars Data sources Described in Mills et al., 2007a Time period 3 months 3 months Described in González- Fernández et al., 2014 Response function RY = *AOT40 RY = *AOT40 r P value < < Updated : June 2015

43 Chapter III Mapping Critical Levels for Vegetation Page III Relative yield Albis Drabant Ralle Echo Abe Arthur Roland Satu Dragon Regression 95% CI Three month AOT40 (ppm h) Figure III.5 : Wheat yield-response function used to derive the concentration-based critical levels for agricultural crops (r 2 = 0.89) (data from Fuhrer et al., 1997 and Gelang et al., 2000, reproduced in Mills et al., 2007a). Dotted lines represent 95% confidence intervals. Figure III.6 : Tomato yield-response function for ozone-sensitive cultivars used to derive the concentration-based critical levels for horticultural crops (r 2 = 0.63, p<0.001) (González-Fernández et al., 2014). Dotted lines represent 95% confidence intervals. III METHOD FOR CALCULATING EXCEEDANCE OF THE AOT40- BASED CRITICAL LEVELS FOR CROPS Step 1: Determine the accumulation period Agricultural crops The timing of the three month accumulation period for agricultural crops should reflect the period of active growth of wheat and be centred on the timing of anthesis. A survey of the development of winter wheat conducted at 13 sites in Europe by ICP Vegetation participants in 1997 and 1998, revealed that anthesis can occur as early as 2 May in Spain and as late as 3 July in Finland (Mills and Ball, 1998, Mills et al., 2007a). Thus, a risk assessment for ozone impacts on crops would benefit from the use of a moving time interval to reflect the later growing seasons in northern Europe. For guidance, default time periods have Updated: June 2015

44 Page III - 44 Chapter III Mapping Critical Levels for Vegetation been provided for five geographical regions as indicated in Table III.14. Horticultural crops The timing of the start of the growing season is more difficult to define because horticultural crops are repeatedly sown over several months in many regions especially in the Mediterranean area. For local application within Mediterranean countries, appropriate 3 month periods should be selected between March and August for eastern Mediterranean areas, and March and October the Western Mediterranean areas. Since the cultivars used to derive the response function for tomato also grow in other parts of Europe, it is suggested that appropriate 3 month periods are selected between the period April to September for elsewhere in Europe Steps 2 and 3 : Determine the ozone concentration at the top of the canopy The ozone concentration at the canopy height can be calculated using the methods described in Section III.4.2. For agricultural crops the default height of the canopy is 1 m whilst for horticultural crops (represented by tomato) it is 2m. Step 4 : Continue as described in Section III.4.6 Table III.14 : Regional classification of countries for default time periods for calculation of AOTX for agricultural crops. See text for time periods for horticultural crops. Region Abbreviation Three month time period Possible default countries Eastern Mediterranean EM 1 March to 31 May Albania, Bosnia and Herzogovina, Bulgaria, Croatia, Cyprus, Greece, FYR Macedonia, Malta, Montenegro, Serbia, Slovenia, Turkey Western Mediterranean WM 1 April to 30 June Holy See, Italy, Monacco, Portugal, San Marino, Spain Continental Central Europe CCE 15 April to 15 July Armenia, Austria, Azerbaijan, Belarus, Czech Republic, France 1, Georgia, Germany, Hungary, Kazakhstan, Kyrgyzstan, Liechtenstein, Republic of Moldova, Poland, Romania, Russian Federation, Slovakia, Switzerland, Ukraine Atlantic Central Europe ACE 1 May to 31 July Belgium, Ireland, Luxembourg, Netherlands, United Kingdom Northern Europe NE 1 June to 31 August Denmark, Estonia, Finland, Iceland, Latvia, Lithuania, Norway, Sweden 1 As an average between Western Mediterranean and Atlantic Central Europe Updated : June 2015

45 Chapter III Mapping Critical Levels for Vegetation Page III - 45 III.5.4 VPD-MODIFIED AOT30 METHOD III SCIENTIFIC BASIS OF THE CRITICAL LEVEL FOR VISIBLE LEAF INJURY ON CROPS This critical level is used to show the potential frequency of injury inducing ozone episodes. It is particularly useful for those horticultural crops (e.g. lettuce, spinach) that are ozone-sensitive and have their economic value reduced by the blemishes that ozone causes on the leaves Note: This critical level was revised following new analysis conducted after the 17 th Task Force meeting of the ICP Vegetation (Kalamata, February 2004). Acute visible ozone injury, resulting from short-term ozone exposure, represents the most direct evidence of the harmful effects of elevated ozone concentrations. The aim of this short-term critical level is to reflect the risk for this type of injury. For some horticultural crops, such as spinach, lettuce, salad onion and chicory sold for their foliage, visible ozone injury can cause significant financial loss to farmers. In addition, visible ozone injury can be easily demonstrated and thus used to highlight the problem of phytotoxic ozone to a broad audience (Klumpp et al., 2002). It should be noted, however, that the critical level for injury was derived form data on injury on clover. It will be tested shortly for horticultural crops. The short-term critical level is based on results from experiments performed with subterranean clover (Trifolium subterraneum) which was used as the key bioindicator plant for a number of years within the ICP Vegetation (Benton et al., 1995). Data from three participating countries (Sweden, Belgium and Austria) were used to derive a common relationship between ozone exposure and the risk of visible injury (Pihl Karlsson et al., 2004). Since Trifolium subterraneum is welldocumented as a very ozone sensitive plant in terms of having visible symptoms after ozone exposure, the critical level for visible injury on crops is expected to protect other ozone sensitive plants from visible injury. It has been shown that AOT30 is the best AOTX exposure index to describe the risk for visible ozone injury in subterranean clover under low VPD, i.e. relatively humid conditions (Pihl Karlsson et al., 2003). However, it has also been demonstrated that in drier climates VPD is a very important modifier of ozone uptake through its limiting effect on stomatal conductance and thus of the risk that a certain ozone concentration would contribute to visible injury (Ribas & Peñuelas, 2003). A VPD modified AOT30 (AOT30 VPD ) approach adequately describes the relationship between ozone exposure and risk for visible injury in subterranean clover when grown in well-watered conditions (as in the ICP Vegetation experiments). III THE AOT30 VPD CRITICAL LEVEL FOR VISIBLE LEAF INJURY ON CROPS The critical level for visible leaf injury is exceeded when the AOT30 VPD during daylight hours over eight days exceeds 0.16 ppm h. This represents a significant risk of having visible ozone injury on at least 10% of leaves (± 3.5% according to the 99% confidence limits of the regression shown in Figure III.7) of the leaves on sensitive plants, such as subterranean clover. The 10% level for visible ozone injury was chosen based on the conclusion from the studies by Pihl Karlsson et al. (2003). The AOT30 VPD index accumulated during daylight hours, using an exposure period of Updated: June 2015

46 Visible injury, % Page III - 46 Chapter III Mapping Critical Levels for Vegetation eight days explained 60% of the variation of the observed extent of visible injury (% injured leaves) accumulated during daylight hours (p<0.001 for the slope and intercept). The relationship between visible injury and AOT30 VPD during eight days before observation of visible injury is shown in Figure III.7. It is not recommended that the exceedance of the concentration-based critical level for visible injury on agricultural and horticultural crops is converted into economic loss; it should only be used as an indication of risk of injury during the most sensitive environmental conditions (Fuhrer, 1995). The method for calculating exccedance of this critical level is described in Section III y = 0.03x r 2 = AOT30 VPD (8 days, ppm h) Figure III.7 : The extent of visible injury (percentage ozone injured leaves) versus AOT30 VPD. The accumulation period was eight days before observation of visible injury. The accumulation was made during daylight hours. Confidence limits for p = 0.99 are also presented (Pihl Karlsson et al., 2004). III.6 CRITICAL LEVELS OF OZONE AND RISK ASSESSMENT METHODS FOR FOREST TREES III.6.1 OZONE SENSITIVITY OF FOREST TREES Ozone causes negative effects on forest trees such as reduced photosynthesis, premature leaf shedding and growth reductions, and reduced resistance to environmental stresses. Some ozonesensitive forest tree species are present over large areas of Europe: birch, Scots pine and Norway spruce are particularly important in central and northern Europe; beech and deciduous oaks are frequent across several European regions, in particular in central and southern areas; Holm oak and Aleppo pine are frequent in Mediterranean Europe (see, for example, Karlsson et al., 2007). Negative effects of ambient ozone on forest trees are already occurring all over Europe. For example, visible injury has been detected in ICP Forests surveys (Ferretti et al., 2007a), reduced stem growth has been reported in Sweden (Karlsson et al., 2006), reduced stem (Braun et al., 1999) and shoot growth in Switzerland (Braun et al., 2007) and leaf loss occurs in Greece (Velissariou, pers. com.). Updated : June 2015

47 Chapter III Mapping Critical Levels for Vegetation Page III - 47 III.6.2 FLUX-BASED METHODS III SCIENTIFIC BASIS AND ROBUSTNESS OF FLUX-BASED CRITICAL LEVELS FOR FOREST TREES At the UNECE workshop in Gothenburg in November 2002 (Karlsson et al., 2003a) it was concluded that the effective ozone dose, based on the flux of ozone into the leaves through the stomatal pores, represents the most appropriate approach for setting ozone critical levels for forest trees. A provisional flux-based critical level was set from a combined response function for beech and birch. Further data analysis was presented at the workshop in Obergurgl (November, 2005), but it wasn't until the Ispra workshop (November, 2009) and follow-on discussions at the 23 rd Task Force meeting of the ICP Vegetation (February, 2010) that flux-based critical levels were agreed for forest trees (See Section III.6.2.2). In the interim, parameterisations specific to indivudal tree species (species-specific tree parameterisations) were derived for forest tree species that could be applied at the local/regional level to indicate the degree of risk of damage without specifying the extent of damage (see Section III.6.2.6). The critical levels for forest trees were set to values for which there was a > 95% confidence of finding a significant effect at the percentage loss chosen. For each species, data was from at least three independent sources and from experiments conducted in at least three countries (see Table III.16 for data sources). The effect parameter chosen was reduction in mean annual growth, using changes in whole tree biomass as an indication of growth. Several sources of confirming evidence exist. For example, Fares et al. (2013), showed a strong correlation between measured and modelled stomatal fluxes in a Mediterranean mixed pine and oak forest and epidemiological studies of deciduous tree growth in Switzerland suggested ozone flux as one of the causal agents of detected decreases in stem and shoot growth, with a critical level comparable to that derived above from exposure experiments (Braun et al., 2007, 2010). In addition, a recent meta-analysis of published data on tree responses indicated that an ambient ozone concentration of ca. 40 ppb is sufficient to reduce total tree biomass by 7% compared with preindustrial levels (Wittig et al., 2009). Consistency of results across countries provides further strength to the analysis (see, for example, Karlsson et al., 2007). Furthermore, data presented at the Ispra workshop showed that ozone fluxes calculated from sap flow measurements of mature trees growing in forest stands were in broad agreement with those from the DO 3 SE model (Braun et al. 2010). Overall, regression analysis of the dataset used to derive new flux-based critical levels showed that effects relationships were stronger for POD 1 than for AOT40. The main source of uncertainty lies in the application of critical levels derived from effects on trees of up to 10 years of age growing in an exposure facility, to mature trees growing within a forest stand. It is encouraging, however, that the critical level for birch and beech would have protected mature beech trees in Switzerland (Braun et al., 2010). Further uncertainties arise from application of the soil moisture deficit function, considered especially important for Mediterranean climates, and scaling up of biomass effects from young to mature trees. Updated: June 2015

48 Page III - 48 Chapter III Mapping Critical Levels for Vegetation III STOMATAL FLUX-BASED CRITICAL LEVELS AND RESPONSE- FUNCTIONS FOR DECIDUOUS AND EVERGREEN TREES These methods and critical levels can be used at the local and regional scale to assess the impacts of ozone on roundwood supply for the forest sector industry, loss of carbon storage capacity in the living biomass of trees and other beneficial ecosystem services provided by trees such as reducing soil erosion, avalanches and flooding. Note : The content of this section will be considered for revision at the 2015 Task Force meeting of the ICP Vegetation following new analysis conducted by Büker et al. (submitted). At the 23 rd Task Force meeting of the ICP Vegetation, it was agreed to replace the provisional flux-based critical level included the previous version of this chapter with new critical levels and updated parameterisations based on new analysis. New critical levels were agreed based on dose response functions using the speciesspecific tree flux parameterisations and effects data from nine sources (Table III.15, Figure III.8). Where effects were reported over more than one year, the mean flux was determined by dividing the total by the number of years of ozone exposure. Based on the exponential nature of the growth of young trees, the following procedure was applied for the correction of the biomass change in multiannual experiments: biom yr biom 1 years where biom yr is the corrected biomass, biom is the biomass in fractions of the control and years is the duration of the experiment in years. This analysis has shown that across Europe, the effects of ozone on young trees in experiments are best correlated with modelled ozone uptake by the leaves, i.e. the ozone flux (Karlsson et al., 2007). For trees, dose-response relationships are strongest when there is either no threshold or a small threshold above which flux is accumulated (i.e. POD 0 or POD 1 ). As reported in earlier versions of this manual, there is nevertheless strong biological support for the use of a threshold to represent the detoxification capacity of the tree. For this reason, expert judgement has been used to set Y to 1 for forest trees (i.e. POD 1 is to be used). Using data from ozone exposure experiments, new ozone flux-effect relationships were developed for the following key forest tree species: Norway spruce, beech and birch, oak species excluding Holm oak, Holm oak and Aleppo pine. Of these, the functions for Norway spruce and combined beech and birch were selected as being sufficiently robust for the derivation of critical levels due to their statistical strength and good representation of the data sets for Europe (Figure III.8, Table III.15). It should be noted, however, that there is insufficient data available yet to derive a critical level specific to trees in the Mediterranean area, and that the suggested critical levels may not be fully applicable in this area as they were not derived from experiments conducted in a Mediterranean climate. Critical levels have been derived for the cumulative ozone flux responsible for either a 2% (Norway spruce) or a 4% (beech/birch) reduction in annual growth rates (whole tree biomass) of young trees of up to 10 years of age, dependant on species (Table III.15). The age criterion is set to reflect the age of the trees used in the ozone exposure experiments contributing data to the response function. There is strong support for these values from epidemiological studies of mature trees in Switzerland and Sweden (Karlson et al., 2006, Braun et al., 2007, 2010), and several scientific papers indicate that mature trees are at least as equally sensitive to ozone as young trees, and in some cases are even more sensitive. Updated : June 2015

49 Relative total biomass Relative total biomass Chapter III Mapping Critical Levels for Vegetation Page III - 49 Although these critical levels are derived from data on biomass reduction connected with the roundwood supply to the forest sector industry, it is expected that there will be additional benefit for protection against reductions in carbon storage, soil erosion, avalanches, flood amelioration, and loss in tree biodiversity. Table III.15 : Flux-based critical levels and response-functions for forest trees. Species Beech and birch Norway spruce Parameter Whole tree biomass Whole tree biomass % reduction for critical level 4% (annual) 2% (annual) Critical level (POD 1, mmol m - 2 ) Countries contributing data. 4 8 Finland, Sweden and Switzerland Number of data points 38 (14 different experiments) Years of experiments Data sources Uddling et al., Braun and Flückiger, 1995 France, Sweden and Switzerland 27 (8 different experiments) Karlsson et al, Braun and Flückiger, 1995 Time period Growing season Growing season Response function RY= *POD 1 RY= *POD 1 r P value <0.001 <0.001 (a) Norway spruce Birch Beech (b) y = * POD 1 r² = 0.55 p < POD 1, mmol m y = * POD 1 r² = 0.64 p < POD 1, mmol m -2 Figure III.8 : The relationship between the relative total biomass and POD 1 for sunlit leaves of a) Norway spruce (Picea abies) based on data from France, Sweden and Switzerland, and b) birch (Betula pendula) and beech (Fagus sylvatica) based on data from Finland, Sweden and Switzerland. The dashed lines indicate the 95%-confidence intervals; note the different starting point of the Y-axis for Norway spruce. Updated: June 2015

50 Page III - 50 Chapter III Mapping Critical Levels for Vegetation III METHOD FOR CALCULATING OZONE FLUX FOR FOREST TREES USING SPECIES-SPECIFICFLUX MODELS The species-specific flux parameterisations used to derive the critical levels for forest trees are those provided in Table III.16. The papers used to derive these parameterisations plus further information on their appplication can be found in Annex 2. The countries identified as representing Continental Central Europe (CCE) and Northern Europe (NE) are shown in Table III.14. Parameter Table III.16 : Flux model parameterisation for beech, birch and Norway spruce. Units Beech and birch Norway Spruce Beech and birch Norway spruce Region CCE CCE NE NE Land use g max Eunis class, area in km 2 Deciduous broadleaved forests Coniferous forests mmol O 3 m -2 projected leaf 150 ( ) 125 (87-140) area s -1 Deciduous broadleaved forests 196 ( range) Coniferous forests 112 ( range) f min (fraction) SGS year day Latitude model f temp EGS year day Latitude model f temp Latitude model Latitude model f phen_a (fraction) Latitude model Latitude model f phen_b (fraction) (1) (1) (1) (1) f phen_c (fraction) f phen_d (fraction) (1) (1) (1) (1) f phen_e (fraction) f phen_1 (days) f phen_2 (days) (200) (200) (200) (200) f phen_3 (days) (200) (200) (200) (200) f phen_4 (days) f phen_lima (days) (0) (0) (0) (0) f phen_limb (days) (0) (0) (0) (0) light_a T min T opt T max ** o C o C o C ** 200** VPD max kpa VPD min kpa SWC max (medium) * % volume SWC min (medium) * % volume 1 1 SWPmax MPa SWPmin MPa h m L cm Updated : June 2015

51 Chapter III Mapping Critical Levels for Vegetation Page III - 51 For forest trees, the following additional information is required for calculating stomatal flux using the method provided in section III.4.3 : g max Use of the conversion factor of to account for the difference in the molecular diffusivity of water vapour to that of ozone is recommended following recent analysis (see Section III.4.3). However, the conversion factor used for the g max values contained here and used to calculate POD 1 values in Figure III.8 was that previously used (0.613). T max The T max value is set at 200 C to simulate the weak response to high temperatures of Norway spruce and birch trees growing under Northern European conditions (the stomatal response is instead mediated by high VPD values). Hence, the T max value should be viewed as a forcing rather than descriptive parameter. f phen = 0 when yd SGS f phen = ((1-f phen_a )*((yd-sgs)/f phen1 )+f phen_a ) when SGS < yd f phen1 +SGS f phen = f phen_b when f phen1 +SGS < yd f phen_lima f phen = (1-f phen_c ) * (((f phen2 + f phen_lima ) - (f phen_lima + (yd-f phen_lima ))) /f phen2 ) + f phen_c when f phen_lima < yd < f phen_lima + f phen2 f phen = f phen_c when f phen_lima + f phen2 yd f phen_limb - f phen3 f phen = (1-f phen_c ) * ((yd-(f phen_limb - f phen3 ))/f phen3 ) + f phen_c when f phen_limb - f phen3 < yd < f phen_limb f phen = f phen_d when f phen_limb yd EGS - f phen4 f ozone f ozone is not included for forest trees, and should be set to 1 in equation (III.10). f phen f phen = (1-f phen_e ) * ((EGS-yd) / f phen4 ) + f phen_e when EGS - f phen4 < yd < EGS f phen = 0 when yd EGS For forest trees, a modified formulation for the f phen relationship is used (see below and Figure III.9). This method allows the use of a consistent formulation irrespective of whether there is a midseason dip in f phen (as is required to model f phen for some Mediterranean species in the absence of methods to simulate the effect of mid-season water stress on stomatal conductance, see flux model for Mediterranean species in Annex 2). The values in brackets for the phenology function in Table III.16 represent dummy values to be used in areas where this midseason dip does not occur. Updated: June 2015

52 Page III - 52 Chapter III Mapping Critical Levels for Vegetation Fphen1 f phen_lim A start of "soil w ater" limitation Fphen2 f phen_lim B end of "soil w ater" limitation Fphen3 Fphen4 fphen Fphen_d Fphen_b Fphen_a Fphen_c Fphen_e Year day SGS EGS Figure III.9 : An illustration of the formulation of f phen for forest trees. III CALCULATION OF POD Y AND EXCEEDANCE OF FLUX-BASED CRITICAL LEVELS FOR FOREST TREES The parameterisations in Table III.16 are species- and region-specific. Should it be necessary to map exceedance of the critical levels at the European-scale then we recommend the following, in order of preference: (1) Varying the parameterisation according to climatic region (as indicated in Table III.16). Impacts, including economic, can be estimated using this method. (2) The use of the generic tree flux methods for deciduous trees and Mediterranean evergreen trees. Note: this method is for indication of risk of damage and impacts cannot be quantified. Follow the procedure outlined in Section III.4.4 using the following forest-tree specific recommen-dations: Step 1 : Define the start and end of the growing season according to the following latitude model For beech and birch, the start of the growing season (SGS), which is defined as the date of budburst/ leaf emergence, is estimated using a simple latitude model where SGS occurs at year day 105 at latitude 50 N, SGS will alter by 1.5 days per degree latitude earlier on moving south and later on moving north. The end of the growing season (EGS), which is defined as the onset of dormancy, is estimated as occurring at year day 297 at latitude 50 N, EGS will alter by 2 days per degree latitude earlier on moving north and later on moving south. Leaf discolouration is assumed to occur 20 days prior to dormancy and is assumed to be the point at which f phen will start to decrease from g max. Between the onset of dormancy and leaf fall g sto will be assumed to be zero. The effect of altitude on phenology is incorporated by assuming a later SGS and earlier EGS by 10 days for every 1000 m a.s.l. This latitude model agreed well with ground observations from the Mediterranean (Mediavilla & Escudero, 2003; Aranda et al., 2005; Damesin & Rambal, 1995; Grassi & Magnani, 2005), Continental Central Europe (Defila & Clot 2005; Deckmyn, pers. comm.; Arora & Boer, 2005), Atlantic Central Europe (Broadmeadow, pers comm.; Duchemin et al., 1999) and Northern Europe (Aurela et al., 2001; Karlsson, pers. comm.) and remotely sensed observations for the whole of Europe (Zhang et al., 2004). For Norway spruce in CCE the growth period is determined by air temperature defined according to the f temp function (the Updated : June 2015

53 Chapter III Mapping Critical Levels for Vegetation Page III - 53 growing season is assumed to occur when air temperatures fall within the T min and T max thresholds of the f temp relationship). During such periods f phen is equal to 1 such that there is no limitation on conductance associated with leaf developmental stage. This allows for the capture of intermittent physiological activity which is driven by rapidly fluctuating air temperatures that frequently occur at the start and end of the growing season in this region. Step 2 : The ozone concentration at the top of the forest canopy can be calculated using the methods described in Section III.4.2 using actual tree height or the default heights of 25m for beech and 20m for birch and Norway spruce. Step 3 : Calculate ozone flux using the parameterisations provided in Section III The stomatal conductance (g sto ) values for each hour within the accumulation period are calculated using the stomatal flux algorithm presented in equation (III.10a).according to the receptor-specific parameterisations. Steps 4 7 : Continue as described in Section III.4.4. III REGIONAL PARAMETERISATIONS FOR SPECIES-SPECIFIC FLUX MODELS FOR FOREST TREES Regional and species-specific flux models have been derived that can be used to determine the degree of potential risk of damage for Forest trees. Species-specific flux parameterisations have been defined for representative forest tree species after consideration of a number of factors, i.e., known sensitivity to ozone, importance of the species by region (e.g. economically, ecologically, geographical coverage) and forest type (i.e. to ensure both evergreen and deciduous forests were represented). In some instances, one species occurs in more than one climatic region. In such cases, the species parameterisation represents a particular species ecotype i.e. a form or variety of the species that possesses both inherited- and genotype- determined characteristics enabling it to succeed in a particular habitat. The species selected to represent each region are listed in Table III.17 with the parameterisations presented in Annex 2. Response functions exist for combined beech and birch, and Norway spruce (Section III.6.2.2). Response functions are not available yet for other species and regions, however, these flux models can be used to indicate the extent of potential damage (i.e increasing flux is aussumed to be associated with increasing potential damage). Updated: June 2015

54 Page III - 54 Chapter III Mapping Critical Levels for Vegetation Table III.17 : Representative species for which species- and region-specific flux parameterisations have been derived by European region (see Annex 2 for the parameterisations). European region Coniferous Broadleaved Deciduous Mediterranean broadleaved Evergreen Northern Europe Norway spruce Birch - Atlantic Central Europe Scots pine Beech & temperate Oak - Continental Central Europe Norway spruce Beech - Mediterranean Coastal/Continental location Aleppo pine Beech Holm oak III ESTIMATION OF RISK OF DAMAGE FOR A GENERIC FOREST TREES (FOR INTEGRATED ASSESSMENT MODELLING) This simplified flux method is specifically designed for indicating the degree of risk of damage to a generic deciduous tree and a generic evergreen Mediterranean tree within large scale modelling, including integrated assessment modelling. Note: At the time of the 2014 revision, no response function was available for a generic tree. Two generic parameterisations were felt necessary to capture, albeit only broadly, the diversity that exists in European forests. A generic Broadleaved deciduous (suitable for all forested areas) and Broadleaved evergreen species (suitable for Mediterranean areas) were selected to account for the variation in phenology and climate that are considered to be important drivers of stomatal ozone uptake in trees. Discussions to establish these forest parameterisations began after the Obergurgl workshop (November, 2005); the parameterisations presented in Table III.18 were approved by the 20 th ICP Vegetation Task Force meeting (Dubna, Russian Federation, March, 2007) and were reviewed in For the Broadleaved evergreen forests, a year round growing season is assumed. For the Broadleaved deciduous forests, the start of the growing season (SGS), which is defined as the date of budburst/ leaf emergence, is estimated using the simple latitude model described above for the full flux model (Section III.6.2.3). For the Broadleaved evergreen forests, f phen is constructed within the year round growing season so as to allow for a reduction in g sto during the summer when soil water deficits are commonly high in Mediterranean areas. Since the influence of soil water status on g sto is to a certain extent incorporated within this parameter for this generic model, the f SWP function is set equal to 1. For the Broadleaved deciduous forests the f phen parameterisation is based on data describing the increase and reduction in g sto with the onset and end of the green growth period respectively. The minimal length of these respective periods has been used in the parameterisation to ensure that periods when forests are potentially experiencing higher ozone uptake are incorporated in the risk assessment. The functions for photosynthetically active radiation (f light ), temperature (f temp ) and Updated : June 2015

55 Chapter III Mapping Critical Levels for Vegetation Page III - 55 vapour pressure deficit (f VPD ) are parameterised as described in Table III.18 and equations provided in relevant sections of this Mapping Manual. For the Broadleaved deciduous evergreen parameterisation the influence of soil water stress on g sto is incorporated within the f phen relationship since for these species such limitation is an extremely common phenomenon that determines seasonal gas exchange profiles. Table III.18 : Parameterisation for generic Broadleaved deciduous and Broadleaved evergreen tree flux models (POD 1 IAM). The sources of the parameterisations within this table are provided in Annex 2.1. Parameter Units Broadleaved Deciduous species Broadleaved Evergreen species (suitable for the Mediterranean area) Land use EUNIS class, area in km 2 All forested areas Mediterranean evergreen forest species 1 g max mmol O 3 m projected leaf area (PLA) s -1 f min fraction SGS year day Latitude model 1 (1 Jan) EGS year day Latitude model 365 (31 Dec) f phen_a (fraction) 0 1 f phen_b (fraction) (1) 1 f phen_c (fraction) f phen_d (fraction) (1) 1 f phen_e (fraction) 0 1 f phen_1 (days) 15 (0) f phen_2 (days) (200) 130 f phen_3 (days) (200) 60 f phen_4 (days) 20 (0) f phen_lima (days) = SGS 80 (21 Mar) f phen_limb (days) = EGS 320 (16 Nov) light_a costant T min o C 0 2 T opt o C T max o C VPD max kpa VPD min kpa SWP max MPa f SWP =1 f SWP =1 SWP min MPa f SWP =1 f SWP =1 Height m 20 8 Leaf dimension m g max. Use of the conversion factor of to account for the difference in the molecular diffusivity of water vapour to that of ozone is recommended following recent analysis (see Section 3.4.3). However, the conversion factor used for the g max values contained within this Annex was that previously used (0.613). Updated: June 2015

56 Page III - 56 Chapter III Mapping Critical Levels for Vegetation Since it remains difficult to represent the true effect of soil water limitations to g sto on individuals, for the Deciduous parameterisation it is assumed that soil moisture is not limiting g sto and hence stomatal ozone flux. For example, within an EMEP 50 x 50 km grid square some trees may be under drought stress, but at the same time others may not. Hence, the setting of f SWP = 1 allows for a potential flux to be estimated which is appropriate for broad regional scale Integrated Assessment Modelling. Howevever, it should be noted that the EMEP model includes a soil moisture index to take account of soil moisture impacts (Simpson et al., 2012). Values for, height (h, in m) and cross wind leaf dimension (L, in cm) are provided and based on expert knowledge from within the forest group. The stomatal flux threshold (Y) value of 1 mmol O 3 m -2 PLA s -1 is used in agreement with that used to derive the fluxbased critical level. III.6.3 AOT40-BASED CRITICAL LEVELS FOR FOREST TREES III SCIENTIFIC BASIS OF AOT40-BASED METHODS AND CRITICAL LEVELS Table III.19 : Sensitivity classes for the tree species based on effects of ozone on growth (Karlsson et al., 2003b). Ozone-sensitive species Moderately ozonesensitive species Deciduous Coniferous Deciduous Coniferous Fagus sylvatica Betula pendula Picea abies Pinus sylvestris Quercus petrea, Quercus robur Pinus halepensis The experimental database that was first presented at the UNECE Workshop in Gothenburg 2002 was re-analysed for the Obergurgl Workshop (2005) and expanded to include additional correlations with AOT20, AOT30, and AOT50 (Karlsson et al., 2003b). Furthermore, the tree species included in the analysis have been separated into four species categories (Table III.19) based on the sensitivity of growth responses to ozone. It should be emphasised that this categorisation is based on growth as a measure of effect, and that the relative sensitivity of a given species may differ when an alternative measure such as visible injury is used. As a result of this differentiation of species, linear regressions between exposure and response have the highest r 2 values (Table III.20). Using the sensitivity categories described above, AOT40 gave the highest r 2 values of the AOTX indices tested (Figure III.10). However, the difference between the r 2 values for AOT40 and AOT30 was small (0.62 and 0.61 respectively for the combined birch and beech dataset, Table III.20). Based on the analysis described in Table III.20, the concentration-based critical level of ozone for forest trees, CLe c, was reduced from an AOT40 value of 10 ppm h (Kärenlampi & Skärby, 1996) to 5 ppm h (range 1-9 ppm h, determined by the 99% confidence intervals), accumulated over one growing season (Figure III.10). This value of 5 ppm h is associated with a 5% growth reduction per growing season for the deciduous sensitive tree species category (beech and birch, Figure III.10). Updated : June 2015

57 Chapter III Mapping Critical Levels for Vegetation Page III - 57 The 5 % growth reduction was clearly significant as judged by the 99% confidence intervals in Figure III.10. This increase in the robustness of the dataset and the critical level represents a substantial improvement compared to the 10% growth reduction associated with the previous ozone critical level of an AOT40 of 10 ppm h (Kärenlampi & Skärby, 1996). Furthermore, it represents a continued use of sensitive, deciduous tree species to represent the most sensitive species under most sensitive conditions. As previously, it should be strongly emphasized that these values should not be used to quantify ozone impacts for forest trees under field conditions. Further information can be found in Karlsson et al. (2004). Observation of visible injury in young trees in ambient air at Lattecaldo, in southern Switzerland has shown that a reduction of the ozone critical level to 5 ppm h AOT40 would also protect the most sensitive species from visible injury (Van der Hayden et al., 2001, Novak et al., 2003). Furthermore, Baumgarten et al. (2000) detected visible injury on the leaves of mature beech trees in Bavaria well below 10 ppm h AOT40. An optional additional AOT30-based critical level of ozone has also been derived for forest trees based on the response function for birch and beech. The value for this critical level is an AOT30 of 9 ppm h applied to the same time-windows as described for AOT40. Following discussions at the Gothenburg Workshop, the 16 th Task Force meeting of the ICP Vegetation and the 19 th Task Force meeting of the ICP Modelling and Mapping, it was concluded that AOT40 should be used for the concentration-based critical level for forest trees, but that AOT30 could be used in integrated assessment modelling on the European scale if this considerably reduces uncertainty in the overall integrated assessment model. Table III.20 : Statistical data for regression analysis of the relationship between AOTX ozone exposure indices (in ppm h) and percentage reduction of total and above-ground biomass for different tree species categories (Karlsson et al., 2003b). Ozone index/plant category Linear regression r 2 p for the slope p for the intercept slope AOT20 Birch, beech 0.52 < Oak 0.57 < Norway spruce, Scots pine 0.73 < AOT30 Birch, beech 0.61 < Oak 0.61 < Norway spruce, Scots pine 0.76 < AOT40 Birch, beech 0.62 < Oak 0.65 < Norway spruce, Scots pine 0.79 < AOT50 Birch, beech 0.53 < Oak 0.62 < Norway spruce, Scots pine 0.76 < Updated: June 2015

58 % reduction Page III - 58 Chapter III Mapping Critical Levels for Vegetation III AOT40-BASED CRITICAL LEVELS The AOT40-based critical levels for forest trees are suitable for estimating damage were climatic data or suitable flux models are not available. Economic losses should not be estimated using this method. The AOT40-based critical level for forest trees is presented in Table III.21, with the associated function presented in Figure III Birch, beech Daylight AOT40 (ppm h) F.sylvatica, CH B. pendula, Ostad, SE B. pendula, Kuopio1, FI B. pendula, Kuopio2, FI B. pendula, Kuopio3, FI Figure III.10 : The relationship between percentage reduction in biomass and AOT40, on an annual basis, for the deciduous, sensitive tree species category, represented by beech and birch. The relationship was analysed by linear regression with 99 % confidence intervals. Explanations for the figure legends can be found in Karlsson et al. (2003b). Table III.21 : AOT40-based critical levels and response functions for forest trees. Category Deciduous trees Representative species Effect parameter % reduction for critical level 5 % Critical level (AOT40, ppm h) Countries involved in experiments Number of data points 21 Birch and beech species Whole tree biomass 5 ppm h Sweden, Finland, Switzerland Data sources Described in karlsson et al., 2007 Time period Response function r P value <0.01 Growing season Based on Annual reduction in total plant biomass Updated : June 2015

59 Chapter III Mapping Critical Levels for Vegetation Page III - 59 III CALCULATING EXCEEDANCE OF THE AOT40-BASED CRITICAL LEVEL FOR FOREST TREES The method described in Section III.4.6 should be followed incorporating the following recom-mendations specific for forest trees: Step 1: The default exposure window for the accumulation of AOT40 is suggested to be 1 April to 30 September for all deciduous and evergreen species in all regions throughout Europe. This time period does not take altitudinal variation into account and should be viewed as indicative only. It should be stressed that it should only be used where local information is not available. When developing local exposure windows, the following definitions should be used: Onset of growing season in deciduous species: the time at which flushing has initiated throughout the entire depth of crown. Cessation of growing season in deciduous species: the time at which the first indication of autumn colour change is apparent. Onset of growing season in evergreen species: when the night temperatures are above -4 C for 5 days: if they do not fall below -4 C, the exposure window is continuous. Cessation of growing season in evergreen species: when the night temperatures are below -4 C for 5 days: if they do not fall below -4 C, the exposure window is continuous. Steps 2 and 3: It is important that the calculation of AOT40 is based on ozone concentrations at the top of the canopy as described in Section III.4.2. The suggested default canopy height for forest trees is 20m. Step 4: Continue as described in Section III.4.6. III.7 CRITICAL LEVELS OF OZONE FOR (SEMI-)NATURAL VEGETATION Although many species of semi-natural vegetation are sensitive to ozone, the least progress has been made with developing flux and fluxeffect models for this type of vegetation. Any new developments since the 2014 revision will be added to Annex 3. III.7.1 OZONE SENSITIVITY OF (SEMI-)NATURAL VEGETATION Ozone negatively impacts on (semi-) vegetation by causing early die-back, reduced seed production, reduced growth and reduced ability to withstand other stresses such as drought and overwintering in sensitive species (see review by Bassin et al., 2007). However, this vegetation type is the most florally diverse of the receptor types considered - there are species of (semi-)natural vegetation in Europe making the generalisations needed for setting critical levels difficult. Although response functions and relative sensitivities have been derived for >80 species (Hayes et al., 2007b), at least 98 % of species remain untested. Discussions at the workshops and Task Force meetings have mainly focussed on establishing AOT40-based critical levels for grassland species and communities as until recently, very little information has been available for setting flux-based critical levels. In 2010, the first flux-based critical levels for (semi)- natural vegetation were established. Due to the complexity of grassland communities it was considered not possible at that time to apply multi-layer models on a Europe-wide basis that adequately represented the diversity of species present within grasslands, the associated differential absorption of ozone within the canopy, and the different management practices such Updated: June 2015

60 Page III - 60 Chapter III Mapping Critical Levels for Vegetation as frequency and nature of grazing, cutting and fertilizer regimes. Instead, efforts were focused on establishing critical levels for widespread indicator species of three permanent grassland types: (a) Productive grasslands that are intensively managed and grazed; (b) Grasslands of high conservation value with low management and little/low fertilizer input; and (c) Natural unmanaged ecosystems (excluding forests). Note: arable non-permanent grassland is not included here as a (semi-) natural community, however, the cloverbased critical levels may be applicable to such grassland. III.7.2 STOMATAL FLUX-BASED METHODS FOR (SEMI-)NATURAL VEGETATION III SCIENTIFIC BACKGROUND AND ROBUSTNESS OF FLUX-BASED CRITICAL LEVELS As for crops and forest trees, there is a strong biological basis for the use of fluxbased methodology for (semi-)natural vegetation; however, the complexity of these communities in the natural world adds an extra layer of complexity to flux modelling. As an initial step towards defining flux-based critical levels for this vegetation type, flux models and effects data for widespread representative species were established. The resulting response functions are from experiments in which the selected species was growing in competition with other grassland species, as would be occurring in the natural environment. For (semi-)natural vegetation, flux-based response relationships are strongest when there is either no threshold or a small threshold above which flux is accumulated (i.e. POD 0 or POD 1 ). As reported in earlier versions of the Modelling and Mapping Manual, there is strong support for the use of a threshold to represent the detoxification capacity of the species. For this reason, expert judgement has been used to set Y to 1 for (semi-)natural vegetation. Although several potential representative species were considered, for only one species, Trifolium repens (white clover), was there flux-effect data available from more than one country. Ozone exposure experiments have confirmed that Trifolium species are amongst the most sensitive to ozone, with reductions in biomass, forage quality and reproductive ability noted at ambient and near-ambient concentrations in many parts of Europe. Since these species are widespread in Europe and has an important role in ecosystems as a nitrogen-fixer, the response function was accepted by the 23 rd ICP Vegetation Task Force as suitable for use as indicative of effects on perennial grassland. Data for Viola species, although only from experiments from the UK, were from two seasons of experiments and for two species, and thus were considered suitable for a provisional critical level for earlyseason exposure of grasslands of high conservation value. Many other species, such as Campanula spp. (e.g. harebell) are ozone sensitive and could also be used as indicator species of relevance to areas of high conservation value as more data become available (updates can be found in Annex 3). The flux-based critical levels for (semi-) natural vegetation in Table III.22 and derived from functions in Figure III.11 are for the following types of grassland: Productive grasslands. These were considered important for calculating ozone deposition to perennial grasslands across Europe and provide an indication of effects on productivity and biogeochemical cycling. The representative species for productive grasslands are Trifolium spp (clover species); the new flux-based critical level protects against a 10% reduction in biomass. Updated : June 2015

61 Chapter III Mapping Critical Levels for Vegetation Page III - 61 Grasslands of high conservation value. Currently very few flux-effect relationships exist for this type of vegetation. The ICP Vegetation Task Force agreed in 2010 that the critical level for clover is also applicable to this vegetation type. For central and northern Europe, a provisional flux-based critical level for perennial grasslands was proposed for Viola spp. (violets) as a representative family that is widespread and sensitive to early-season ozone exposure. This provisional critical level will protect against a biomass reduction of 15% for this species. For Mediterranean climates, it was not yet possible to derive a specific critical level, but a flux model for a typical Trifolium species from the Dehesa grassland is included in Annex 3. This could be used in a similar way to the generic crop and forest tree critical levels to identify areas where this species is predicted to be at risk of damage in Mediterranean areas, with the extent of risk increasing with increasing flux. The ICP Vegetation Task Force believed that the suggested critical levels would be likely to protect against biodiversity loss, but was unable to confirm this yet on experimental grounds. Natural ecosystems: No flux-based critical level could be derived yet for these, but it is assumed that the critical level for clover would provide adequate protection. The ICP Vegetation Evidence Report showed that ambient ozone concentrations were sufficient to induce injury on 95 species of forbs and grasses in Europe over the period , indicating that this vegetation type is already responding to ozone (Hayes et al., 2007a). Biomonitoring experiments performed by the ICP Vegetation using an ozone sensitive variety of Trifolium repens (white clover) have indicated effects in 10 countries, with biomass reductions being correlated with EMEP modelled flux at the 50 x 50 km 2 grid (Mills et al., in press). This analysis showed that species of the genus Trifolium were the most commonly reported as exhibiting visble ozone injury in the field, with injury being detected in Sweden, Belgium, the Netherlands, UK, Austria, France, Poland, the Russian Federation, Switzerland, Slovenia, and Spain. It should be noted that long-term ozone exposure of a complex intact long-standing alpine meadow community has not responded to enhanced ozone suggesting that under such conditions there may be either a build up of genetic resistance within the population to the already high ambient ozone or that there is a natural buffering of response to environmental stress in this high altitude environment (Bassin et al., 2009). Since other experimental exposures have shown changes in other communities (reviewed by Bassin et al., 2007) and effects have been detected in ambient air by the ICP Vegetation, the 23 rd Task Force meeting of the ICP Vegetation considered it to be important to include critical levels for (semi-)natural vegetation within this Manual. Flux-based critical levels for (semi-) natural vegetation can be considered the most uncertain of those described in this chapter. This is mainly due to the complexity of these ecosystems, with uncertainty increasing from productive grasslands, to low input grasslands and being highest for natural ecosystems. The uncertainties at present associated with the suggested approach, include variability of the maximum stomatal conductance (g max, a species-specific measure for the potential maximum gas exchange and hence inflow of gaseous pollutants such as ozone), genotypic variability of individual species, diversity of communities, soil moisture modelling, competition and management effects. Updated: June 2015

62 Page III - 62 Chapter III Mapping Critical Levels for Vegetation III FLUX-BASED CRITICAL LEVELS FOR (SEMI-)NATURAL VEGETATION The flux-based critical levels for (semi-)natural vegetation can be used to assess impacts on the vitality of fodder-pasture quality and and on the vitaility of natural species. These critical levels may also protect against loss of biodiversity but this has not yet been confirmed experimentally. The flux-based critical levels for (semi-)natural vegetation are described in Table III.22 and associated functions are shown in Figure III.11. Note: the critical levels and responsefunctions were derived from experimental data from central and Atlantic central Europe and do not include data from the Mediterranean basin. Mediterranean-specific parameterisations for representative species are included in Annex 3. Table III.22 : Flux-based critical levels for (semi-)natural vegetation and the functions from which they were derived. Category Productive grasslands Grasslands of high conservation value* Representative species Trifolium spp. Trifolium spp. Viola spp. Effect parameter % reduction for critical level Critical level (POD 1, mmol m -2 ) Countries involved in experiments Above-ground biomass Above-ground biomass 10% 10% 15% UK, Switzerland UK, Switzerland UK Number of data points Grasslands of high conservation value (provisional)* Above-ground biomass Years of experiments 1993, 2000, , 2000, , 2006 Data sources Nussbaum et al., 1995; Gonzalez- Fernandez et al., 2008; Hayes et al., 2009 Nussbaum et al., 1995; Gonzalez- Fernandez et al., 2008; Hayes et al., 2009 Time period 3 months 3 months 3 months Response function RY=0.97 (0.035*POD 1 ) RY=0.97 (0.035*POD 1 ) r P value <0.001 < * may also be applicable to natural grassland ecosystems Hayes et al., 2006; Hayes et al., unpublished RY=0.98- (0.02*POD 1 ) Updated : June 2015

63 Relative biomass Relative biomass Chapter III Mapping Critical Levels for Vegetation Page III - 63 (a) (b) Clover Violet y = * POD 1 r² = 0.87 p < POD 1, mmol m -2 UK CH y = * POD 1 r² = 0.45 p = POD 1, mmol m -2 Figure III.11 : The relationship between the relative above-ground biomass and POD 1 for sunlit leaves for a) clover (Trifolium spp) and b) violet (Viola spp), based on data from the UK and Switzerland and the UK, respectively. The dashed lines indicate the 95%-confidence intervals. III CALCULATING OZONE FLUX FOR (SEMI-)NATURAL VEGETATION USING SPECIES-SPECIFIC FLUX MODELS The flux parameterisations used to derive the critical levels for (semi-)natural vegetation are provided in Table III.23. The papers and functions used to derive this parameterisation can be found in Annex 3. Table III.23 : Paramerisation for POD 1 for representative (semi-)natural vegetation species. Parameter Units Trifolium spp. Viola spp. g max 1 mmol O 3 m -2 projected leaf area s -1 f min fraction SGS year day EGS year day f phen_a fraction 1 1 f phen_b fraction 1 1 f phen_c fraction 1 1 f phen_d fraction 1 1 light_a constant T min C T opt C T max C VPD max kpa VPD min kpa SWP min MPa SWP max MPa LAI min m 2 PLA m LAI max m 2 PLA m -2 4 Updated: June 2015

64 Page III - 64 Chapter III Mapping Critical Levels for Vegetation Parameter Units Trifolium spp. Viola spp. Height m Leaf dimension m g max Use of the conversion factor of to account for the difference in the molecular diffusivity of water vapour to that of ozone is recommended following recent analysis. However, the conversion factor used for the g max values contained here and used to calculate POD 1 values in Figure 3.11 was that previously used (0.613). III CALCULATION OF POD Y AND EXCEEDANCE OF THE FLUX- BASED CRITICAL LEVELS FOR (SEMI-)NATURAL VEGETATION Follow the procedure outlined in Section III.4.4 using the following (semi-)natural vegetation specific recommendations : Step 1: The accumulation period is a three-month window when the species are actively growing. Example dates corresponding to the start and end of the growing season have been suggested Table III.23. Step 2: The canopy heights of the indicative species are suggested in Table III.23 and are based on foliage height rather than flower height (which may be a few cm higher than the leaf canopy). Step 3: Calculate ozone flux using the parameterisations provided in Section III Steps 4 7: Continue as described in Section III.4.4. III REGIONAL PARAMETERISATIONS FOR FLUX MODELS FOR (SEMI-)NATURAL VEGETATION Parameterisations for species representative of Mediterranean areas will be provided shortly in Annex 3. As and when other region-specific parameterisations become available they will also be added to the Annex. III ESTIMATION OF RISK OF DAMAGE FOR A GENERIC (SEMI-) NATURAL VEGETATION (FOR INTEGRATED ASSESSMENT MODELLING) At the time of the revision of this chapter (summer 2010), it was not possible to set a parameterisation for generic grassland. Should a generic grassland model become available it will be added to Annex 3 of this chapter. III.7.3 AOT40-BASED CRITICAL LEVELS FOR (SEMI-)NATURAL VEGETATION III SCIENTIFIC BACKGROUND AND CRITICAL LEVELS The critical levels for (semi-)natural vegetation (Table III.24) are applicable to all sensitive semi-natural vegetation and natural vegetation excluding forest trees and woodlands, described here collectively as (semi-)natural vegetation. Two AOT40-based critical levels were agreed at the Obergurgl (2005) workshop. Updated : June 2015

65 Chapter III Mapping Critical Levels for Vegetation Page III - 65 Table III.24 : Summary of AOT40-based critical levels for (semi-)natural vegetation. (Semi-) natural vegetation dominated by: Critical level Time period Effect Annuals An AOT40 of 3 ppm h 3 months (or growing season, if shorter) Perennials An AOT40 of 5 ppm h 6 months Growth reduction and/or seed production reduction in annual species Effects on total above-ground or below-ground biomass and/or on the cover of individual species and/or on accelerated senescence of dominant species Critical level for effects on communities of (semi-)natural vegetation dominated by annuals: The criteria for adverse effects on (semi-)natural vegetation communities dominated by annuals are effects on growth and seed production for annual species. This critical level is based on statistically significant effects or growth reductions of greater than 10% on sensitive taxa of grassland and field margin communities. The value of 3 ppm h is sufficient to protect the most sensitive annuals. In contrast to crops and tree species, only limited experimental data are available for a small proportion of the vast range of species found across Europe. This means that analysis of exposure-response data for individual species to derive a critical level value is more difficult. Instead, the recommended critical level is based on data from a limited number of sensitive species. The value of 3 ppm h was originally proposed at the Kuopio workshop (Kärenlampi & Skärby, 1996) and confirmed at the Gerzensee workshop (Fuhrer & Achermann, 1999) and subsequently at the Obergurgl workshop (Wieser and Tausz, 2006). At the time of the Kuopio workshop, no exposureresponse studies were available for derivation of the critical level for (semi- )natural vegetation, based on a 10% response. Instead, data from field-based experiments with control and ozone treatments were used to identify studies showing significant effects at relatively low ozone exposures. Table III.25 summarises the key field chamber and field fumigation experiments which supported the original proposal of this critical level for (semi-) natural vegetation. Table III.25 : Summary of key experiments supporting the critical level of 3 ppm h (now adopted for communities dominated by annuals), as proposed at the Kuopio workshop (Ashmore & Davison, 1996). Species or community Most sensitive species AOT40 (ppm h) Response Reference Solanum nigrum %; shoot mass Individual plants Malva sylvestris %; seed mass Mesocosms of four species Trifolium repens %; shoot mass Festuca ovina %; shoot mass Mesocosms of seven species Leontodon hispidus %; shoot mass Ryegrass-clover sward Trifolium repens %; shoot mass Bergmann et al., 1996 Ashmore & Ainsworth, 1995 Ashmore et al., 1996 Nussbaum et al., 1995 Updated: June 2015

66 Page III - 66 Chapter III Mapping Critical Levels for Vegetation Table III.26 : Summary of experiments from the BIOSTRESS programme which support the recommended critical level for communities dominated by annuals (reviewed by Fuhrer et al., 2003). Responsive species Competitor species Variable showing significant response Corresponding AOT40 Reference Trifolium pratense Poa pratensis Biomass (-10%) 4.4 ppm h* Veronica chamaedrys Trifolium cherleri, T. striatum Gillespie & Barnes, unpublished data Poa pratensis Species biomass ratio 3.6 ppm h Bender et al. (2002) Briza maxima Flower production ppm h Gimeno et al. (2003a); Gimeno et al. (2004). Trifolium cherleri Briza maxima Seed output 2.4 ppm h Gimeno et al. (2004) * Estimated from exposure-response functions A number of studies have clearly demonstrated that the effects of ozone in species mixtures may be greater than those on species grown alone or only subject to intraspecific competition. Therefore, the critical level needs to take into account the possibility of effects of interspecific competition in reducing the threshold for significant effects; indeed three of the four experiments listed in Table III.25 include such competitive effects. By the time of the Gothenburg workshop (2002), the most comprehensive study of ozone effects on species mixtures involving species which are representative of different communities across Europe, is the EU-FP5 BIOSTRESS (BIOdiversity in Herbaceous Semi-Natural Ecosystems Under STRESS by Global Change Components) programme. Results to date from the BIOSTRESS programme, including experiments with species from the Mediterranean dehesa community, indicate that exposures to ozone exceeding an AOT40 of around 3 ppm h may cause significant negative effects on annual and perennial plant species (see Fuhrer et al., 2003). The BIOSTRESS mesocosm experiments with two-species mixtures indicated that exposures during only 4-6 weeks early in the growing season may cause shifts in species balance. The effect of this early stress may last for the rest of the growing season. The key experiments from the BIOSTRESS programme, which support the proposed critical level for communities dominated by annual species, are summarised in Table III.26. Taken together, these studies support a critical level in the range ppm h, with a mean value of 3.3 ppm h. The value of the critical level for communities dominated by annuals is further supported by other published data, for instance, for wetland species by Power and Ashmore (2002) and Franzaring et al. (2000). The latter authors observed a significant reduction in the shoot:root ratio in Cirsium dissectum at 3.3 ppm h after 28 days of exposure. In individual plants from wild strawberry populations growing at high latitudes, Manninen et al. (2003) observed a significant biomass decline of >10% at 5 ppm h from June-August. Critical level for effects on communities dominated by perennial species: A critical level of an AOT40 of 5 ppm h over 6 months to prevent adverse effects in communities dominated by perennial species was recommended at the Obergurgl workshop (2005). Since this critical level is based on average AOT40 values in experiments with a duration of several years, mapping of exceedance of this critical level should be based on 5-year mean values of AOT40. This new critical level is based on five studies that provide important new experimental evidence for the effects of ozone on plant communities dominated by perennial species, in chamber and field fumigation studies. Four studies involved mesocosms established from seed, from plants taken from the field, or by transplanting communities from the field, while one study involved exposure to ozone in situ. Because of the longer growth period of these communities, the AOT40 should be calculated over a six-month Updated : June 2015

67 Chapter III Mapping Critical Levels for Vegetation Page III - 67 growth period. The response variables of perennial dominated communities include significant effects on total above-ground or below-ground biomass, on the cover of individual species and on accelerated senescence of dominant species. Table III.27 summarises the key findings of the studies which were used to establish the new critical level. Table III.27 : Key findings of the studies used to establish the new critical level. Location and community Duration and AOT40 in control (C) and lowest effect treatment (T) Biomass data Species response Southern Finland 1 Dry grassland (7 species) in opentop chambers Wales 2 Upland grassland (7 species) in solardomes Northern England 3 Upland grassland Species-rich (11 species), in open-top chambers Southern England 4 Calcareous grassland (38 species) in opentop chambers Switzerland 5 60 year old alpine pasture in field release system 3 years 1.7 ppm h (C) 8.5 ppm h (T) 3 months fumigation in summer of each year 2 years 0 ppm h (C) 10, 12, 30 ppm h (T) 3 months fumigation each summer presented as current or 2050 background with/without episodic peaks 18 months 3 ppm h (C). 10 ppm h (T) 6 months exposure during summer to 50 ppb versus 30 ppb reducing to 35 ppb versus 20 ppb over winter 3 years 2.6 ppm h (C) 10.5, 13.3, 18.2 ppm h (T) Exposure for periods of 3-5 months each year at three levels of ozone, effects observed at lowest exposure 5 years 8.4 ppm h (C) 34.0 ppm h (T) ca. 6 months of fumigation each year 40% and 34% reduction in above- and below- ground biomass, respectively; reduced N availability 64% reduction in biomass of Campanula rotundifolia; 61% reduction in biomass of Vicia cracca Significant increase in Significant increase in community senescence senescence for Festuca detected in 10 ppm h ovina and Potentilla treatment; erecta at 10 ppm h; 7% reduction in cumulative above-ground community biomass in 30 ppm h treatment 16% reduction in total above-ground biomass No significant effect on above-ground biomass 23% reduction in above-ground biomass 15% reduction in Anthoxanthum odoratum biomass within the community at 30 ppm h Significant reduction in biomass of Briza media and Phleum bertolonii Significant change in community composition; loss of Campanula rotundifolia Small reduction in proportion of legumes Sources of data: 1 Rämö et al.(2006); 2 Mills et al. (2006); 3 Barnes & Samuelsson, quoted in Bassin et al. (2007); 4 Thwaites et al. (2006); 5 Volk et al. (2006); Note: (C) indicates AOT40 exposure in control treatment; (T) indicates AOT40 exposure in ozone treatments. The AOT40 values above were calculated over a six-month period, even though the experimental period was in some cases shorter. For studies in which the fumigation period was less than six months, exposure outside the experimental period was added to both the control and treatment AOT40. Updated: June 2015

68 Page III - 68 Chapter III Mapping Critical Levels for Vegetation III CALCULATING EXCEEDANCES OF THE AOT40-BASED CRITICAL LEVELS FOR (SEMI-) NATURAL VEGETATION Follow the procedure outlined in Section III.4.6 using the following recommendations specific to (semi-) natural vegetation: Step 1: Determine the accumulation period Ideally, a variable time-window should be used in the mapping procedure to account for different growth periods of annuals and perennials in different regions of Europe. The AOT40 is calculated over the first three or six months of the growing season. The start of the growing season can be identified using: 1. Appropriate phenological models; 2. Information from local or national experts; and 3. The default table below (Table III.28). For a small number of species, the growing season may be less than three months in duration. In such cases, values of AOT40 should be calculated over the growing season, identified using appropriate local information. Table III.28 : Default timing for the start and end of ozone exposure windows for (semi-) natural vegetation. (Note: regional classifications of countries are suggested in Table III.14.) Region Start date End date (annual-dominated communities) End date (perennial-dominated communities) Eastern Mediterranean* 1 March 31 May 31 Aug Western Mediterranean* 1 March 31 May 31 Aug Continental Central Europe 1 April 30 June 30 Sept Atlantic Central Europe 1 April 30 June 30 Sept Northern Europe mid-april mid-july mid-october * For mountain areas where the altitude is above 1500 m, use a start date of 1 April, with end dates of 30 June for annual-dominated communities and 30 September for perennial-dominated communities. Steps 2 and 3: Determine the ozone concentration at the top of the canopy The AOT40 value should be calculated as the concentration at canopy height, using the information provided in Section III.4.2. The transfer functions to make this calculation, based on deposition models, depend on a number of factors which may vary systematically between EUNIS categories (see below). These include canopy height and leaf area index (both natural variation and effects of management) and environmental variables such as vapour pressure deficit and soil moisture deficit. If such information is not available, it is recommended that the conversion factors described in Section III.4.2 for short grasslands are used as a default. Steps 3 4: continue as indicated in III.4.6. Updated : June 2015

69 Chapter III Mapping Critical Levels for Vegetation Page III - 69 III MAPPING (SEMI-)NATURAL VEGETATION COMMUNITIES AT RISK FROM EXCEEDANCE OF THE CRITICAL LEVEL For this receptor, more detailed mapping information is provided here to ensure comparability of communities mapped. The classification of the European Nature Information System, EUNIS (refer to should be used for the identification of those grassland types across Europe for which the critical level is supported by experimental data. The list of sensitive EUNIS classes included previously in this chapter has been reviewed in the light of new experimental community data, and analysis of individual species sensitivity (Mills et al., 2007b). Table III.29 shows those communities for which species level data suggest a risk of adverse effects and those communities for which this potential risk is supported by experimental evidence of changes in plant community studies. The merged Corine Land Cover 2000 and SEI 2002 land cover map provides information on the spatial distribution of these communities across Europe, including information on dominance by annual and perennial species for each community. For European risk assessment, critical level exceedance should only be mapped for areas dominated by those EUNIS classes identified in Table III.29. For individual countries, national databases may provide better quality data on the distribution of the communities. Table III.29 : EUNIS categories for those communities for which potential risk is supported by experimental evidence of changes in plant community studies and/or effects on individual species found in those communities. EUNIS CATEGORY Community level evidence Species level sensitivity analysis 1 E1: Dry grasslands Yes Yes E2: Mesic grassland Yes Yes E3: Wet grasslands No Yes E4: Alpine grasslands No Yes E5: Woodland fringes No Yes E7.3: Dehesa No Yes F4: Heathland No (Yes) 1 Mills et al. (2007b). Note: (Yes) represents a habitat classification for which species show a positive response in above-ground biomass but for which there is evidence that this might be associated with changes in shoot/root partitioning. Updated: June 2015

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77 Chapter III Mapping Critical Levels for Vegetation Page III - 77 Nunn, A., Kozovits, A. R., Reiter, I. M., Heerdt, C., Leuchner, M., Lütz, C., Liu, X., Löw, Winkler, J. B., Grams, T. E. E., Häberle, K.-H., Werner, H., Fabian, P., Rennenberg, H. & Matyssek, R. (2005). Comparison of ozone uptake and sensitivity between a phytotron study with young beech and a field experiment with adult beech (Fagus sylvatica). Environmental Pollution 137, Nussbaum, S., Geissmann, M. & Fuhrer, J. (1995). Ozone exposure-response relationships for mixtures of perennial ryegrass and white clover depend on ozone exposure patterns. Atmospheric Environment 29(9), Ogaya R. & Peñuelas J. (2003). Comparative seasonal gas exchange and chlorophyll fluoresecence of two dominant woody species in a Holm Oak Forest. Flora 198, Peterson, R.F. (1965). Wheat: Botany, cultivation, and utilization. Leonard Hill Books, London. Piikki, K., De Temmerman, L. Ojanpera, K., Danielsson, H. & Pleijel, H. (2008). The grain quality of spring wheat (Triticum aestivum L.) in relation to elevated ozone uptake and carbon dioxide exposure. European Journal of Agronomy 28, Pihl Karlsson, G., Karlsson, P.E., Danielsson, H. & Pleijel, H. (2003). Clover as a tool for bioindication of phytotoxic ozone 5 years of experience form Southern Sweden consequences for the short-term Critical Level. Science of the Total Environment. 301/1-3, Pihl Karlsson, G., Soja, G., Vandermeiren, K., Karlsson, P.E. & Pleijel, H. (2004). Test of the short-term Critical Levels for acute ozone injury on plants improvements by ozone uptake modelling and the use of an effect threshold. Atmospheric Environment 38, Pinho P., Branquinho C., Cruz C., Tang S.Y., Dias T., Rosa A.P., Máguas C., Louçãoa M.A.M. & Sutton M.A. (2008). Assessment of critical levels of atmospherically ammonia for lichen diversity in cork-oak woodland, Portugal, in: Sutton M.A., Baker S., Reis S., (ed.), Atmospheric Ammonia - Detecting emission changes and environmental impacts. Springer, Berlin, in press. Pitcairn C.E.R., Leith I.D., Sheppard L.J., van Dijk N., Tang Y.S., Wolseley P.A., James P. & Sutton M.A. (2004). Field intercomparison of different bioindicator methods to assess the effects of atmospheric nitrogen deposition, in: Sutton M.A., Pitcairn C.E.R., Whitfield C.P. (Ed.), Bioindicator and biomonitoring methods for assessing the effects of atmospheric nitorgen on statutory nature conservation sites, JNCC Report 356, pp Pleijel, H. (1996). Statistical aspects of Critical Levels for ozone. In: Kärenlampi, L. & Skärby, L., (eds). (1996). Op. cit. Pleijel, H., Danielsson, H., Emberson, L., Ashmore, M., & Mills, G. (2007). Ozone risk assessment for agricultural crops in Europe: Further development of stomatal flux and flux response relationships for European wheat and potato. Atmospheric Environment 4, Pleijel, H., Danielsson, H., Ojanperä, K., De Temmerman, L., Högy, P. & Karlsson, P.E. (2003). Relationships between ozone exposure and yield loss in European wheat and potato A comparison of concentration based and flux based exposure indices. In: Karlsson, P.E., Selldén, G., & Pleijel, H., (eds). (2003a) Op. cit. Pleijel, H., Danielsson, H., Vandermeiren, K., Blum, C., Colls, J. & Ojanperä, K. (2002). Stomatal conductance and ozone exposure in relation to potato tuber yield results from the European CHIP programme. European Journal of Agronomy 17, Updated: June 2015

78 Page III - 78 Chapter III Mapping Critical Levels for Vegetation Power, S.A. & Ashmore, M.R. (2002). Responses of fen and fen meadow communities to ozone. New Phytologist 156, Raftoyannis, Y. & Radoglou, K. (2002). Physiological responses of beech and sessile oak in a natural mixed stand during a dry summer. Annals of Botany 89, Rämö, K., Kanerva, T., Nikula, S. Ojanperä, K. & Manninen, S. (2006). Influences of elevated ozone and carbon dioxide in growth responses of lowland hay meadow mesocosms. Environmental Pollution 144, Reinert, R.A., Eason, G. & Barton, J. (1997). Growth and fruiting of tomato as influenced by elevated carbon dioxide and ozone. New Phytologist 137, Rhizopoulou, S. & Mitrakos, K. (1990). Water relations of evergreen sclerophylls I. Seasonal changes in the water relations of eleven species from the same environment. Annals of Botany 65, Ribas, A. & Peñuelas, J. (2003). Biomonitoring of tropospheric ozone phytotoxicity in rural Catalonia. Atmospheric Environment 37, Rihm B., Urech M., Peter K. (2008). Mapping Ammonia Emissions and Concentrations for Switzerland Effects on Lichen Vegetation, in: Sutton M.A., Baker S., Reis S., (ed.), Atmospheric Ammonia - Detecting emission changes and environmental impacts. Springer, Berlin, in press Sala, A. & Tenhunen, J.D. (1994). Sitespecific water relations and stomatal response of Quercus ilex in a Mediterranean watershed. Tree Physiology 14, Sanz, M. J., Calvo, E., Gimeno, C., Martin, C. & Cámara, P. V. (1999). Engineering tomato against environmental stress TOMSTRESS (FAIR5-CT ).First annual report, Schaub, M., Zimmermann, N. & Kräuchi, N. (2005). Temporal and spatial variation of Vcmax within a Swiss beech canopy. Abstract in International Forestry Review 7, Sheppard L.J., Leith I.D., Crossley A., van Dijk N., Fowler D. & Sutton M.A. (2008). Long-term cumulative exposure exacerbates the effects of atmospheric ammonia on an ombrotrophic bog: Implications for Critical Levels, in: Sutton M.A., Baker S., Reis S., (Ed.), Atmospheric Ammonia - Detecting emission changes and environmental impacts. Springer, Berlin, in press Simpson, D., Benedictow, A., Berge, H., Bergström, R., Emberson, L.D., Fagerli, H., Flechard, C.R., Hayman, G.D., Gauss, M., Jonson, J.E., Jenkin, M.E., Nýıri, A., Richter, C., Semeena, V.S., Tsyro, S., Tuovinen, J.-P., Valdebenito, Á.. & Wind, P. (2012). The EMEP MSC-W chemical transport model technical description. Atmospheric Chemistry and Physics 12, Stark, J.C. (1987). Stomatal behaviour of potatoes under non-limiting soil water conditions. American Potato Journal 64, Sutton M.A., Wolseley P.A., Leith I.D., van Dijk N., Tang Y.S., James P.W., Theobald M.R. & Whitfield C.P. (2008). Estimation of the ammonia critical level for epiphytic lichens based on observations at farm, landscape and national scales, in: Sutton M.A., Baker S., Reis S., (Ed.), Atmospheric Ammonia - Detecting emission changes and environmental impacts. Springer, Berlin, in press Temple, P.J. (1990). Growth and yield responses of processing tomato (Lycopersicon esculentum) cultivars to ozone. Environmental and Experimental Botany 30, Temple, P.J., Surano, K.A., Mutters. R.G., Bingham, G.E. & Shinn, J.H. (1985). Air Pollution causes moderate damage to tomatoes. California Agriculture, Updated : June 2015

79 Chapter III Mapping Critical Levels for Vegetation Page III - 79 Tenhunen, J.D., Lange, O.L., Harley, P.C., Beyschlag, W. & Meyer, A. (1985). Limitations due to water stress on leaf net photosynthesis of Quercus coccifera in the Portuguese evergreen scrub. Oecologia 67, Tenhunen, J.D., Pearcy, R.W. & Lange, O.L. (1987). Diurnal variations in leaf conductance and gas exchange in natural environments. In Zeiger E, Farquhar GD, Cowan I. (Eds) Stomatal Function. Stanford, California. Thwaites, R.H., Ashmore, M.R., Morton, A.J. & Pakeman, R.J. (2006). The effects of tropospheric ozone on the species dynamics of calcareous grasslands. Environmental Pollution 144, Tognetti, R., Johnson, J.D. & Michelozzi, M. (1995). The response of European beech (Fagus sylvatica L.) seedlings from two Italian populations to drought recovery. Trees 9, Tognetti, R., Longobucco, A., Miglietta, F. & Raschi, A. (1998). Transpiration and stomatal behaviour of Quercus ilex plants during the summer in a Mediterranean carbon dioxide spring. Plant, Cell and Environment 21, Tottman D.R., Markpeace R.J. & Broad H. (1979) An explanation of the decimal code for the growth stages of cereals, with illustration. Ann. Appl. Biol 93, Tuebner, F. (1985). Messung der Photosynthese und Transpiration an Weizen, Kartoffel und Sonnenblume. Diplomarbeit University Bayreuth, West Germany. Tuovinen, J.-P. & Simpson, D. (2008). An aerodynamic correction for the European ozone risk assessment methodology, Atmospheric Environment, 42, Tuovinen, J.-P.; Emberson, L. & Simpson, D. (2009). Modelling ozone fluxes to forests for risk assessment: status and prospects, Annals of Forest Science, 66, 401 Uddling, J., Günthardt-Goerg, M.S., Matyssek, R., Oksanen, E., Pleijel, H., Selldén, G., Karlsson, P.E Biomass reduction of juvenile birch is more strongly related to stomatal uptake of ozone than to indices based on external exposure. Atmospheric Environment 38, Uddling, J., Pleijel, H. & Karlsson, P.E. (2004). Modelling leaf diffusive conductance in juvenile silver birch, Betula pendula. Trees 18, UNECE. (1988). Final Draft Report of the Critical Levels Workshop, Bad Harzburg, Germany, March Available at Federal Environmental Agency Berlin, c/o Dr. Heinz Gregor, Germany. UNECE. (1996). Manual on methodologies and criteria for mapping critical levels/loads and geographical areas where they are exceeded. Texte 71/96, Umweltbundesamt, Berlin, Germany. UNECE (2007). Report of workshop on atmospheric ammonia:detecting emission changes and environmental impacts. ECE/EB.AIR/WG.5/2007/3. UNECE (2010). Flux-based assessment of ozone effects for air pollution policy (technical report from the ozone workshop in Ispra, 9 12 November, 2009). ECE/EB.AIR/WG.1/2010/13. Van der Eerden L.J., Dueck T.A., Posthumus A.C., Tonneijk A.E.G., 1993: Assessment of critical levels for air pollutant effects on vegetation: some considerations and a case study on NH 3. In: Ashmore M.R., Wilson R.B. (eds.), 1993: Critical Levels of Air Pollutants for Europe. Report of the Expert Workshop on Critical Levels held in Egham (United Kingdom), March 1992, under the Convention on Long-range Transboundary Air Pollution (UNECE). Department of the Environment, London Updated: June 2015

80 Page III - 80 Chapter III Mapping Critical Levels for Vegetation Van der Hayden D., Skelly J., Innes J., Hug, C., Zhang J., Landolt W. & Bleuler P. (2001). Ozone exposure thresholds and foliar injury on forest plants in Switzerland. Environmental Pollution 111, Vitale M., Gerosa G., Ballarin-Denti A. & Manes F. (2005). Ozone uptake by an evergreen Mediterranean forest (Quercus ilex L.) in Italy Part II: flux modelling. Upscaling leaf to canopy ozone uptake by a process-based model. Atmospheric Environment 39, Volk, M., Bungener, P., Montani, M., Contat, F. & Fuhrer, J. (2006). Grassland yield declined by a quarter in five years of free-air ozone fumigation. Global Change Biology 12, Vos, J. & Groenwald, J. (1989) Characteristics of photosynthesis and conductance of potato canopies and the effects of cultivar and transient drought. Field Crops Research 20, Vos, J. & Oyarzun, P.J. (1987). Photosynthesis and stomatal conductance of potato leaves - effects of leaf age, irradiance, and leaf water potential. Photosynthesis Research 11, Weber, P. & Rennenberg, H. (1996). Dependency of Nitrogen dioxide (NO 2 ) fluxes to wheat (Triticum aestivum L.) leaves from NO 2 concentration, light intensity, temperature and relative humidity determined from controlled dynamic chamber experiments. Atmospheric Environment 30, Wieser, G., & Tausz, M. (Eds), (2006). Proceedings of the workshop: Critical levels of ozone: further applying and developing the flux-based concept, Obergurgl, November, Wittig, V.E., Ainsworth, E.A., Naidu, S.L., Karnosky, D.F. & Long, S.P. (2009). Quantifying the impact of current and future tropospheric ozone on tree biomass, growth, physiology and biochemistry: a quantitative metaanalysis. Global Change Biology 15, WHO. (2000). Air Quality Guidelines for Europe. WHO Regional Publications, No. 91, World Health Organisation, Copenhagen. Available at Wolseley P.A., James P.W., Theobald M.R. & Sutton M.A. (2006). Detecting changes in epiphytic lichen communities at sites affected by atmospheric ammonia from agricultural sources. Lichenologist 38, Zadoks, J.C., Chang, T.T. & Konzak, C.F. (1974). A decimal code for the growth stages of cereals. Weed Research 14, Zhang, X., Friedl, M.A, Schaaf, C.B. & Strahler, A.H. (2004). Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Change Biology 10, Zhang, J., Xiangzhen, S., Li, B., Su, B., Li, J. & Zhou, D. (1998). An improved water-use efficiency for winter wheat grown under reduced irrigation. Field Crops Research 59, Updated : June 2015

81 Chapter III Mapping Critical Levels for Vegetation Page III - 81 III.9 ANNEXES FOR CHAPTER 3 The annexes contains the scientific basis of the parameterisation of the ozone flux models, together with any new flux models and flux-effect relationships derived after the Summer 2010 revision of this chapter and approved by the ICP Vegetation Task Force for inclusion. The text was updated in the spring of 2014 in accordance with agreements made at the 27 th Task Force meeting of the ICP Vegetation, January, III.9.1 ANNEX 1: ADDITIONAL INFORMATION FOR AGRICULTURAL AND HORTICULTURAL CROPS This Section provides the scientific justification for the flux parameterisations for wheat, potato and tomato provided in Table Additional parameterisations have been added as described below. Revision history: August 2011 Minor edits March 2014 April 2015 As agreed at 27 th Task Force meeting of the ICP Vegetation, Paris, 2014, the following were added: - New parameterization for bread and durum wheat for Mediterranean areas (Section A1.2) - Parameterisations for regional application for grapevine, maize, soybean and sunflower (Section A1.3) - References for parameterisations for bean, barley and tomato for local application in Mediterranean areas (Section A1.4) Parameterisation for tomato for local application in Mediterranean areas (Section A1.4) was updated to match that in González- Fernández et al., III FURTHER DETAILS ON FLUX MODEL PARAMETERISATIONS INCLUDED IN CHAPTER III III WHEAT AND POTATO (AS INCLUDED IN SECTION III.5.2) Please note: New parameterisations for bread wheat and Durum wheat for application in Mediterranean areas are included in Section A1.2. The g max values for wheat and potato have been derived from published data conforming to a strict set of criteria for use in establishing this key parameter of the flux algorithm. Only data obtained from g sto measurements made on cultivars grown either under field conditions or using fieldgrown plants in open top chambers in Europe were considered. Measurements had to be made during those times of the day and year when g max would be expected to occur and full details had to be given of the gas for which conductance measurements were made (e.g. H 2 O, CO 2, Updated: June 2015

82 Maris piper Bintje Saturna Prominent Bintje Kardal Russet Burbank Kennebec Lemhi Russet g max (mmol O 3 m -2 s -1, projected leaf area) Page III - 82 Chapter III Mapping Critical Levels for Vegetation O 3 ) and the leaf surface area basis on which the measurements were given (e.g. total or projected). All g sto measurements were made on the flag leaf for wheat and for sunlit leaves of the upper canopy for potato using recognized g sto measurement apparatus. Tables A1.1 and A1.2 give details of the published data used for g max derivation on adherence to these rigorous criteria. Figure A1.1 shows the mean, median and range of g max values for each of the 14 and four different cultivars that provide the approximated g max values of 500 and 750 mmol O 3 m -2 PLA s -1 for wheat and potato, respectively. It should be noted that the wheat g max value has been parameterised from data collected for spring and winter wheat cultivars. For potato additional g max values from three USA grown cultivars are included in Figure A.1 for comparison (Stark, 1987), further substantiating the g max value established for this crop type Potato, g max USA cultivars Cultivar Figure A1.1: Derivation of g max for wheat and potato (see Tables A.1 and A.2 for details). Updated : June 2015

83 Table A.1: Derivation of wheat g max parameterisation. PLA = projected leaf area. The data used was first published in Pleijel et al., 2007 and updated in Grünhage et al., Reference Araus et al. (1989) Araus et al. (1989) Araus et al. (1989) Ali et al. (1999) Grüters et al. (1995) Danielsson et al. (2003) Körmer et al. (1979) Grünhage et al. (2012) Grünhage et al. (2012) Grünhage et al. (2012) Grünhage et al. (2012) Grünhage et al. (2012) Grünhage et al. (2012) Grünhage et al. (2012) Grünhage et al. (2012) Grünhage et al. (2012) Grünhage et al. (2012) Mean : Median : g max [mmol O 3 m 2 s 1 PLA] g max derivation Value in Table. Cultivar and sowing time (average of 3) g sto used. Means of 5 to 7 replicates. g sto mmol CO 2 m 2 s 1. Adaxial: 313, abaxial: 149 Value in Table. Means and SE ± of 5 to 7 replicates. g sto mmol CO 2 m 2 s 1. Adaxial: 267 ± 29, abaxial: 92 ± 16. Value in Table. Means and SE ± of 5 to 7 replicates. g sto mmol CO 2 m 2 s 1. Adaxial: 251 ± 15, abaxial: 99 ± 22. From graph showing leaf conductance plotted against time in days. Maximum approximately 1 mol H 2O m 2 s 1 ; ± ± SE of 4 to 6 replicates. Value in text. Maximum measured conductance (0.97 cm s 1 H 2O total leaf area after Jones (1983)). Value in text. "The maximum conductance value, 414 mmol H 2O m 2 s 1, was taken as g max for the Östad multiplicative model. The conductance values represent the flag leaf and are given per total leaf area". Value given in table cm s 1 for H 2O on a total leaf surface area basis. Country Spain Spain Spain Denmark Germany Sweden Austria mmol H 2O m 2 s 1 Germany mmol H 2O m 2 s 1 Germany mmol H 2O m 2 s 1 (adaxial=524, abaxial=315) Germany mmol H 2O m 2 s 1 (adaxial=439, abaxial=331) Germany mmol H 2O m 2 s 1 (adaxial=451, abaxial=278) Germany mmol H 2O m 2 s 1 (adaxial=485, abaxial=364) Germany mmol H 2O m 2 s 1 (adaxial=510, abaxial=256) Germany mmol H 2O m 2 s 1 (adaxial=595, abaxial=299) Germany mmol H 2O m 2 s 1 France mmol H 2O m 2 s 1 France Range: 366 to 660 mmol O 3 m 2 s 1 Wheat type and cultivar Spring wheat, Kolibri Spring wheat, Astral Spring wheat, Boulmiche Spring wheat, Cadensa Spring wheat, Turbo Spring wheat, Dragon Durum wheat, Janus Winter wheat, Astron Winter wheat, Pegassos Winter wheat, Opus Winter wheat, Manager - Winter wheat, Carenius Winter wheat, Manager + Winter wheat, Limes Winter wheat, Cubus Winter wheat, Soissons Winter wheat, Premio Time of day 9 to 13 hrs 9 to 13 hrs 9 to 13 hrs (Assumed mid-day) 11 to 12 hrs 13 hrs Time of year 14 March to 21 May 14 March to 21 May 14 March to 21 May August 17 June to 7 August 13 August 1996 (AA) - - measured at 10 hrs 24 May to 14 June 2006 measured 24 May to at 10 CET 14 June 2006 measured 26 May to at 11 CET 02 June 2009 measured 26 May to at 10 CET 02 June 2009 measured 26 May to at 13 CET 02 June 2009 measured 26 May to at 11:30 CET 02 June 2009 measured 26 May to at 11:30 CET 02 June 2009 measured 20 May to at 11:30 CET 02 June to 16 CET 6 to 27 May to 16 CET 6 to 27 May 2009 g sto measuring apparatus LI 1600 steady state porometer LI 1600 steady state porometer LI 1600 steady state porometer IRGA LI-6200 LI 1600 steady state porometer Li-Cor 6200 Ventilated diffusion porometer Li-Cor 6400 Li-Cor 6400 Leaf porometer SC-1 Leaf porometer SC-1 Leaf porometer SC-1 Leaf porometer SC-1 Leaf porometer SC-1 Leaf porometer SC-1 PP systems CIRAS-2 PP systems CIRAS-2 Gas / leaf area Growing conditions Leaf CO 2 / PLA Field Flag CO 2 / PLA Field Flag CO 2 / PLA Field Flag H 2O / * (Assume PLA as use LAI) H2O / total leaf area H2O / total leaf area H2O / total leaf area H2O / total leaf area H2O / total leaf area Field Lysimeter Field Field OTC & AA Field OTC (NF) OTC (NF) Flag Flag Flag Flag Flag Flag H2O / PLA Field Flag H2O / PLA Field Flag H2O / PLA Field Flag H2O / PLA Field Flag H2O / PLA Field Flag H2O / PLA Field Flag H2O / PLA Field Flag H2O / PLA Field Flag Chapter III Mapping Critical Levels for Vegetation Page III - 83

84 Page III - 84 Chapter III Mapping Critical Levels for Vegetation Table A1.2 : Derivation of potato g max parameterisation. PLA = projected leaf area Reference g max [mmol O 3 m -2 s -1 PLA] g max derivation Country Potato cultivar Time of day Time of year g sto measuring apparatus Gas / leaf area Growing conditions Leaf Jeffries (1994) 800 Value given in Figure. Maximum value of 16 mm s -1. Error bar represents SE of the difference between two means (n=48). Scotland Maris piper 8 to 16 hrs June Diffusion porometer Assumed H 2 O / assumed PLA Field Fully expanded in upper canopy Vos & Groenwald (1989) 665 Value given in Figure. Maximum value of 13.3 mm s -1 Replicates approx. 20, the coefficient of variation typically ranged from 15 to 25%. Netherlan ds Bintje - June / July Li-Cor 1600 steady state diffusion porometer H 2 O / PLA Field Youngest fully grown leaf Vos & Groenwald (1989) 750 Value given in Figure. Maximum value of 15 mm s -1. Replicates approx. 20, the coefficient of variation typically ranged from 15 to 25%. Netherlan ds Saturna - June Li-Cor 1600 steady state diffusion porometer H 2 O / PLA Field Youngest fully grown leaf Marshall & Vos (1991) 643 Value given in Figure. g max of 527 mmol H 2 O m -2 s -1 at intermediate N supply. Each point represents the mean of at least three leaves (usually four). Netherlan ds Prominen t - July LCA2 portable infra-red gas analyser H 2 O / assumed PLA Field Most recently expanded measurable leaf Pleijel et al. (2002) 836 Value given in Table. g max of 1371 mmol m -2 s -1 for H 2 O per projected leaf area. Germany Bintje 12 June Li-Cor 6200 H 2 O / PLA Field Fully expanded in upper canopy Danielsson (2003) 737 Value given in text. g max of 604 mmol H 2 O m -2 s -1 per total leaf area. Sweden Kardal 11 July Li-Cor 6200 H 2 O / Total leaf area Field Fully expanded in upper canopy Mean Median Range: 643 to 836

85 Relative g Chapter III Mapping Critical Levels for Vegetation Page III - 85 f min The data presented in Pleijel et al. (2002) and Danielsson et al. (2003) clearly show that for both species, f min under field conditions frequently reaches values as low as 1% of g max. Hence an f min of 1% of g max is used to parameterise the model for both species. f phen The data used to establish the f phen relationships for both wheat and potato are given in Figure A1.2 as C days from g max (in the case of wheat g max is assumed to occur between growth stages "flag leaf fully unrolled, ligule just visible" and "midanthesis"; in the case of potato g max is assumed to occur at the emergence of the first generation of fully developed leaves). Methods for estimating the timing of midanthesis are provided in Section III whilst those for estimating f phen using the function illustrated in Figure A1 and the parameterisations given in Table III.10 are provided in Section III Potato, f phen relationship Accumulation period Thermal time from day of g max, base temperature 0 C Figure A1.2 : fphen functions for (a) wheat and (b) potato. The potato function was published in Pleijel et al., 2007; the wheat function has since been revised, with new data from Grünhage et al. (2012) Updated: june 2015

86 Relative g Relative g Relative g Relative g Page III - 86 Chapter III Mapping ical Levels for Vegetation f light The data used to establish the f light relationship for both wheat and potato are shown in Figure A Wheat, f light relationship Irradiance (μmol m -2 s -1 PPFD) Machado & Lagoa (1994) Weber & Rennenberg (1996) Gruters et al. (1995) Potato, f light relationship Bunce J.A. (2000) Danielsson et al. (2003, OTC data) Danielsson et al. (2003, AA data) Vos & Oyarzun (1987) Ku et al. (1977) Dwelle et al. (1983, Russet Burbank) Dwelle et al. (1983, Lemhi Russet) Dwelle et al. (1983, A6948-4) Dwelle et al. (1983, A ) Irradiance (μmol m -2 s -1 PPFD) Stark J.C. (1987, Kennebec) Stark J.C. (1987, Russet Burbank) Stark J.C. (1987, Lemhi Russet) Pleijel et al. (2002, OTC data) Pleijel et al. (2002, AA data) Figure A1.3 : Derivation of f light for wheat and potato (see Pleijel et al., 2007 for further details) f temp The data used to establish the f temp relationship for both wheat and potato are shown in Figure A Wheat, f temp relationship Potato, f temp relationship Gruters et al. (1995) Bunce J.A. (2000) Temperature ( C) Danielsson et al. (2003, OTC data) Danielsson et al. (2003, AA data) Ku et al. (1977, W729R) Dwelle et al. (1983, Russet Burbank) Temperature ( C) Pleijel et al. (2002, OTC data) Pleijel et al. (2002, AA data) Figure A1.4 : Derivation of f temp for wheat and potato (see Pleijel et al., 2007 for further details).

87 Relative g Relative g Chapter III Mapping Critical Levels for Vegetation Page III - 87 f VPD and VPD crit The data used to establish the f VPD relationship for both wheat and potato are shown in Figure A1.5. Under Mediterranean conditions, an alternative parameterization for VPD is provided that has been derived from Figure A1.6. Values of VPD crit for wheat and potato are given in Table III Wheat, f VPD relationship 1.2 Potato, f VPD relationship Weber & Rennenberg (1996) Tuebner F. (1985) VPD (kpa) Bunce J.A. (2000) Danielsson et al. (2003) Tuebner F. (1985) Pleijel et al. (2002, OTC data) Pleijel et al. (2002, AA data) VPD (kpa) Figure A1.5 : Derivation of f VPD for wheat and potato (see Pleijel et al., 2007 for further details). f PAW for wheat The shape of the f PAW function for wheat is shown in Figure A1.7. Figure A1.7 : f PAW for wheat (for further details, see Grünhage et al., 2012). f SWP for potato The data used to establish the f SWP relationship for potato are given in Figure A1.8. It should be noted that the f SWP relationship for potato is derived from data that describe the response of potato g sto to leaf water potential rather than soil water potential. Vos and Oyarzun (1987) state that their results represent long-term effects of drought, caused by limiting supply of water rather than by high evaporative demand, and hence can be assumed to apply to situations where predawn leaf water potential is less than 0.1 to 0.2 MPa. As such, it may be necessary to revise this f SWP relationship so that potato g sto responds more sensitively to increased soil water stress. Updated: june 2015

88 Relative g Page III - 88 Chapter III Mapping ical Levels for Vegetation 1.2 Potato, f SWP relationship Vos & Oyarzun (1987) Basu et al. (1999) SWP (MPa) Figure A1. 8: Derivation of f SWP for potato (see Pleijel et al., 2007 for further details). fo3 The functions described for wheat and potato in Section III should be used. III TOMATO (AS DESCRIBED IN SECTION III.5.2) Note: The following parameterisation has been updated in 2015 to match that in González-Fernández et al., The parameterisation for tomato was derived from g sto measurements made on seven cultivars grown in pots under opentop chambers conditions in southern Europe (Spain and Italy). Daily profiles of g sto were measured in different days from July to October, under varying environmental conditions. All g sto measurements were made in sunlit leaves of the upper canopy using standard g sto measurement systems. The g max value for tomato was set as the average of the values above the 95 th percentile of all the g sto measurements (Figure A1.9). Figure A1.9: Derivation of g max for tomato

89 Chapter III Mapping Critical Levels for Vegetation Page III - 89 f min The f min value for tomato has been derived from the average of the values below the 5 th percentile of all the g sto measurements. f phen The data used to establish the f phen function for tomato are presented in Figure A1.10. g max was assumed to occur at a fixed number of days since the start of the growing season. Figure A1.10 : f phen function for tomato f light The data used to establish the f light function for tomato are shown in Figure A1.11. The f light modifying function was adjusted by boundary line analysis Figure A1.11 : Derivation of f light for tomato. Updated: june 2015

90 Page III - 90 Chapter III Mapping ical Levels for Vegetation f temp The data used to establish the f temp function for tomato are shown in Figure A1.12. The f temp modifying function was adjusted by boundary line analysis Figure A1.12 : Derivation of f temp for tomato. f VPD The data used to establish the f VPD function for tomato are shown in Figure A1.13. The f VPD modifying function was adjusted by boundary line analysis Figure A1.13: Derivation of f VPD for tomato.

91 Chapter III Mapping Critical Levels for Vegetation Page III - 91 f SWP No limiting function for soil water content was considered for tomato since constant irrigation is provided during the whole growing period. Reference for tomato flux parameterisation González-Fernández, I., Calvo, E., Gerosa, G., Bermejo, V., Marzuoli, R., Calatayud, V., Alonso, R., Setting ozone critical levels for protecting horticultural Mediterranean crops: Case study of tomato. Environmental Pollution 185, III PARAMETERISATION OF BREAD WHEAT AND DURUM WHEAT FOR APPLICATION IN MEDITERRANEAN AREAS Since the 2010 revision of Chapter III, new research has been published on the parameterisation of the flux model for wheat growing in Mediterranean climates. In this study, González-Fernández et al. (2013) compiled data from 25 years of phenology data from areas representative of Mediterranean with Atlantic climate influence, coastal Mediterranean and continental Mediterranean climates together with stomatal conductance measurements made over five years for winter bread wheat (3 cultivars) and durum wheat (2 cultivars) growing near Madrid. G max was derived from a literature review of wheat growing under Mediterranean conditions. For further details including boundary line plots for the component parameters please see González- Fernández et al. (2013). In Table A1.3, we provide the parameterisation for bread wheat and durum wheat for application in Mediterranean areas. To estimate the timing of mid-anthesis, starting at the first date after 1 January when the temperature exceeds 0 C, or 1 January if the temperature exceeds 0 C on that date, use a temperature sum of 1256ºC days for bread wheat and 1192 ºC days for durum wheat. Table A1.3 Parameterisation for bread and durum wheat for application in Mediterranean areas (from González-Fernández et al., 2013) Parameter Units Bread wheat Durum wheat g max mmol O 3 m -2 PLA s f min fraction Astart C day Aend C day f phen_a fraction 0 0 f phen_b fraction 0 0 f phen_e C day f phen_f C day 0 0 f phen_g C day f phen_h C day 0 0 f phen_i C day Light_a constant T min C T opt C T max C VPD max kpa Updated: june 2015

92 Page III - 92 Chapter III Mapping ical Levels for Vegetation Parameter Units Bread wheat Durum wheat VPD min kpa ΣVPD crit kpa 8 8 f PAW - - f ozone 1 1 SWCmax % SWCmin % Height m Leaf dimension m III FLUX MODELS FOR ADDITIONAL CROPS SUITABLE FOR REGIONAL RISK ASSESSMENT The 27 th Task Force meeting (Paris, 2014) agreed to the inclusion of new flux parameterisations for grapevine, maize, soybean and sunflower, based on newly available data. III METHOD OF DATA SELECTION The initial, underpinning literature search to identify the relevant peer-reviewed information required to establish the species-specific parameterisation was carried according to the following criteria : A. Experimental studies in field plots; if none found, then search for glasshouse and controlled environment growth chamber studies B. Experiments conducted in Europe; if none found, then also consider studies conducted in the USA proving that the climate was similar to that found in Europe (e.g. no studies performed under subtropical climate) C. Reporting of g sto and information about measuring device used, leaf area reference (total vs. projected) and conditions under which g sto was measured D. Study published after 2005, if species was already searched for in last preliminary parameterisation study carried out in 2006 Figure A1.12 shows a typical hierarchical search tree schematic explaining the search method using Poplar as an example.

93 Chapter III Mapping Critical Levels for Vegetation Page III - 93 Figure A1.12 Screening process and exclusion criteria for literature search. Example for poplar. Derivation of g max The values for g max were extracted from figures or tables in the peer-reviewed literature. Only those studies were included that clearly stated the type of instrument used, the gas for which g sto was measured (H 2 O, CO 2 or O 3 ), the reference area (projected or measured), the name of the cultivar (European were preferred over North American cultivars; no tropical cultivars were used), the age of the plant or leaf used for g sto measurements, the timing of the measurements and the climatic conditions during the measuring campaign (field experiments were preferred over glasshouse or chamber experiments). Data points referring to the total leaf area were recalculated to represent the projected leaf area as reference. Only those reported g sto values were deemed to represent g max that were measured under non-limiting environmental conditions as clearly stated in the methods section of the respective paper. If g max was reported for water vapour (g max mmol H 2 O m -2 PLA s -1 ), which was the case in the majority of papers, it was converted to g max for O 3 (g max mmol O 3 m -2 PLA s -1 ) using the conversion factor of (Grünhage et al., 2012) to account for the difference in the molecular diffusivity of water vapour to that of O 3. Values for g max are provided in Table A1.4. Table A1.4 : g max derivation Grapevine (Vitis vinifera) Maize (Zea mays) Soybean (Glycine max) Sunflower (Helianthus annuus) Poplar (Populus sp) Median g max (mmol O 3 m -2 PLA s -1 ) standard deviation (mmol O 3 m -2 PLA s -1 ) No. of studies No. of data points Updated: june 2015