iii. mapping critical levels for vegetation...horticultural crops, semi-natural vegetation, natural...

Updated: june 2015

III. MAPPING CRITICAL LEVELS FOR VEGETATION

Summer, 2010: Major revision to include new flux-based critical levels and response functions for ozone and re-structuring of text.

June, 2011: Minor text changes made March, 2014 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 2014.

June, 2015 Text updated for tomato, reflecting decisions made at the 28th

Task Force Meeting of the ICP Vegetation

DRAFT DOCUMENT (June, 2015)

Updated : June 2015

Please refer to this document as: CLRTAP, 2015. 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 www.icpmapping.org .”

Updated: june 2015

Page III - 3 Chapter III – Mapping Critical Levels for Vegetatio n

TABLE OF CONTENT

III. MAPPING CRITICAL LEVELS FOR VEGETATION............. ...................................... 1

III.1 GENERAL REMARKS AND OBJECTIVES .................... .................................... 7

III.2 CRITICAL LEVELS FOR SO 2, NOX AND NH3 ..................................................... 8

III.2.1 SO2 ................................................................................................................... 8

III.2.2 NOx ................................................................................................................... 8

III.2.3 NH3 ................................................................................................................... 9

III.3 CRITICAL LEVELS FOR O 3 .............................................................................. 12

III.3.1 Overview of critical levels, flux modelling and their recommended uses .......... 12

III.3.1.1 Critical levels for ozone and their uses ................................................................... 13

III.3.1.2 Modelling risk of damage to generic receptors within large-scale Integraterd Assessment Models ................................................................................................ 14

III.3.1.3 Species-specific flux models ................................................................................... 14

III.4 PROCEDURES FOR CALCULATING OZONE CRITICAL LEVELS AN D THEIR EXCEEDANCE (ALL RECEPTORS) ........................ ......................................... 17

III.4.1 Establishing critical levels ................................................................................ 17

III.4.2 Modelling the ozone concentration at the top of the canopy ............................ 17

III.4.3 Modelling stomatal flux .................................................................................... 20

III.4.4 Stages in calculating exceedance of a flux-based critical level ........................ 25

III.4.5 Calculation of AOT40 ...................................................................................... 25

III.4.6 Stages in calculating exceedance of an AOT40-based critical level ................ 26

III.4.7 Calculation of AOT30VPD ................................................................................. 27

III.4.8 Stages in calculating exceedance of an AOT30VPD-based critical level ............ 27

III.5 CRITICAL LEVELS OF OZONE AND RISK ASSESSMENT METHOD S FOR AGRICULTURAL AND HORTICULTURAL CROPS .............. ........................... 28

III.5.1 Ozone sensitivity of agricultural and horticultural crops ................................... 28

III.5.2 Stomatal flux-based methods .......................................................................... 29

III.5.2.1 Scientific basis and robustness of flux-based methods and critical levels.............. 29

III.5.2.2 Flux-based critical levels and response functions for crops.................................... 30

III.5.2.3 Method for calculating ozone flux for crops using species-specific flux models ..... 33

III.5.2.4 Calculation of PODY and exceedance of the flux-based critical levels for crops .... 36

III.5.2.5 Regional parameterisations of species-specifc flux models for crops .................... 39

III.5.2.6 Estimation of risk of damage using a generic crop flux model (for integrated assessment modelling within GAINS) ..................................................................... 39

III.5.3 AOT40-based methods ................................................................................... 43

III.5.3.1 Scientific basis of AOT40-based methods and critical levels ................................. 43

III.5.3.2 AOT40-based critical levels and response functions .............................................. 44

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Chapter III – Mapping Critical Levels for Vegetatio n Page III - 4

III.5.3.3 Method for calculating exceedance of the AOT40-based critical levels for crops .. 45

III.5.4 VPD-modified AOT30 method.......................................................................... 47

III.5.4.1 Scientific basis of the critical level for visible leaf injury on crops ........................... 47

III.5.4.2 The AOT30VPD critical level for visible leaf injury on crops ...................................... 47

III.6 CRITICAL LEVELS OF OZONE AND RISK ASSESSMENT METHOD S FOR FOREST TREES ................................................................................................ 48

III.6.1 Ozone sensitivity of forest trees ....................................................................... 48

III.6.2 Flux-based methods ........................................................................................ 49

III.6.2.1 Scientific basis and robustness of flux-based critical levels for forest trees ........... 49

III.6.2.2 Stomatal flux-based critical levels and response-functions for deciduous and evergreen trees ....................................................................................................... 50

III.6.2.3 Method for calculating ozone flux for forest trees using species-specificflux models52

III.6.2.4 Calculation of PODY and exceedance of flux-based critical levels for forest trees . 54

III.6.2.5 Regional parameterisations for species-specific flux models for forest trees ......... 55

III.6.2.6 Estimation of risk of damage for a generic forest trees (for integrated assessment modelling) ................................................................................................................ 56

III.6.3 AOT40-based critical levels for forest trees ..................................................... 58

III.6.3.1 Scientific basis of AOT40-based methods and critical levels .................................. 58

III.6.3.2 AOT40-based critical levels ..................................................................................... 60

III.6.3.3 Calculating exceedance of the AOT40-based critical level for forest trees ............ 61

III.7 CRITICAL LEVELS OF OZONE FOR (SEMI-)NATURAL VEGETAT ION .......... 61

III.7.1 Ozone sensitivity of (semi-)natural vegetation .................................................. 61

III.7.2 Stomatal flux-based methods for (semi-)natural vegetation ............................. 62

III.7.2.1 Scientific background and robustness of flux-based critical levels ......................... 62

III.7.2.2 Flux-based critical levels for (semi-)natural vegetation ........................................... 64

III.7.2.3 Calculating ozone flux for (semi-)natural vegetation using species-specific flux models ..................................................................................................................... 65

III.7.2.4 Calculation of PODY and exceedance of the flux-based critical levels for (semi-)natural vegetation ................................................................................................... 66

III.7.2.5 Regional parameterisations for flux models for (semi-)natural vegetation ............. 66

III.7.2.6 Estimation of risk of damage for a generic (semi-) natural vegetation (for integrated assessment modelling) ............................................................................................ 66

III.7.3 AOT40-based critical levels for (semi-)natural vegetation ................................ 67

III.7.3.1 Scientific background and critical levels .................................................................. 67

III.7.3.2 Calculating exceedances of the AOT40-based critical levels for (semi-) natural vegetation ................................................................................................................ 71

III.7.3.3 Mapping (semi-)natural vegetation communities at risk from exceedance of the critical level .............................................................................................................. 72

III.8 REFERENCES ................................................................................................... 73

III.9 ANNEXES FOR CHAPTER 3 ............................. ............................................... 84

III.9.1 Annex 1: Additional information for agricultural and horticultural crops ............ 84

III.9.1.1 Further details on flux model parameterisations included in Chapter III ................. 84

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III.9.1.2 Parameterisation of bread wheat and durum wheat for application in Mediterranean areas........................................................................................................................ 94

III.9.1.3 Flux models for additional crops suitable for regional risk assessment .................. 95

III.9.1.4 Additional published flux models for crops for local-scale application .................. 103

III.9.1.5 Additional flux-effect relationships and flux-based critical levels ........................ 103

III.9.1.6 References related to crops .................................................................................. 104

III.9.2 Annex 2: Additional information for forest trees ............................................. 111

III.9.2.1 Flux parameterisation for generic species of forest trees ..................................... 111

III.9.2.2 Species- and region-specific flux parameterisations for forest trees .................... 113

III.9.2.3 Additional published flux models for trees for local-scale application ................... 128

III.9.2.4 Additional flux-effect relationships and flux-based critical levels .......................... 128

III.9.2.5 References ............................................................................................................ 129

III.9.3 Annex 3 : Additional information for (semi-) natural vegetation ...................... 136

III.9.3.1 Flux model parameterisation ................................................................................. 136

III.9.3.2 Additional published flux models for (semi-) natural vegetation for local-scale application ............................................................................................................. 138

III.9.3.3 Additional flux-effect relationships and flux-based critical levels .......................... 138

III.9.3.4 References ............................................................................................................ 138

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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 O3, SO2, NO2 and NH3 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 SO2, NOx and NH3, 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 flux-based (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


III.2 CRITICAL LEVELS FOR SO 2, NOX AND NH3

III.2.1 SO2

The critical levels for SO2 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 SO2 under winter conditions.

Table III.1 : Critical levels for SO2 (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 Annual mean and Half-year mean (October-March)

(Semi-)natural 20 Annual mean and Half-year mean (October-March)

Agricultural crops 30 Annual mean and Half-year mean (October-March)

*The forest ecosystem includes the response of the understorey vegetation.

III.2.2 NOX

The critical levels for NOx are based on the sum of the NO and NO2 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 NOx were established in 1992 at the Egham workshop. The

background papers on NOx and NH3 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 2000. This further analysis incor-porated 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 3.2, which are identical to those of WHO (2000), should now be used.

Updated: june 2015


Table III.2 : Critical levels for NOx (NO and NO2 added, expressed as NO2 (µg m-3)).

Vegetation Type

Critical Level NO x

(expressed as NO 2) [µg m -3]

Time period

All 30 Annual mean

All 75 24-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 NOx 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 NH3

The fertilisation effect of NH3 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 out-competed) and increased susceptibility to abiotic (drought, frost) and biotic stresses. In the short-term there are also direct effects. As for NOx, for application for

mapping critical levels and their exceedance, it is strongly recommended that only the annual mean values of NH3 are used, as mapped and modelled values of this parameter have much greater reliability, and the long-term effects of NH3 are thought to be more significant than the short-term effects.

Table III.3 : Critical levels for NH3 (µg m-3).

Vegetation type

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)

1 Annual mean

Higher plants (including heathland, grassland and forest ground flora)

3* Annual mean

Provisional critical level

Higher plants 23 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


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 20th Task Force meeting of the ICP Vegetation (Dubna, Russian Federation, 5-8 March, 2007) and adopted at the 23rd meeting of the Task Force on Modelling and Mapping (Sofia, Bulgaria, 26-27 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 concen-trations. The workshop noted that the long-term critical levels could not be assumed to provide a protection for longer than 20-30 years;

5. To retain the monthly critical level (23 µg NH3 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 exposure-response 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


Table III.4 : Summary of ‘no-effect’ concentrations (NOECs) of the impact of long-term exposure to NH3 on species composition of lichens and bryophytes.

Location Vegetation type Lowest measured NH3 concentration

[µg m -3]

Estimated NOEC * [µg m -3]

Reference

SE Scotland, poultry farm

Epiphytic lichens 0.6 0.7 (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 NH3 network

Epiphytic lichens 0.1 1.0 (Leith et al., 2005, Sutton et al., 2008)

Switzerland Lichen population index

1.9 (modelled) 2.4 (Rihm et al., 2008)

SE Scotland, field NH3 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 0.5 1 (Pinho et al., 2008)

Italy, pig farm Epiphytic lichens 0.7 2.5 (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 NH3 on vegetation.

**In these cases NH3 concentration data were available for less than one year, which is why these results are categorised as “corroborative evidence”.

Updated : June 2015


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 “worst-case” 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 23rd 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 generic-species 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 28th 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.

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III.3.1.1 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), Fst (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 (PODY, formerly named AFstY), is calculated for the appropriate time-window as the sum over time of the differences between hourly mean values of Fst and Y nmol m-2 PLA s-1 for the periods when Fst exceeds Y. The stomatal flux-based critical level of ozone, CLef mmol m-2 PLA, is then the cumulative stomatal flux of ozone, PODY, above which direct adverse effects may occur according to present knowledge. Values of CLef 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 CLec 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 short-term 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 fVPD factor to get the VPD-modified ozone concentration ([O3]VPD). The [O3]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 AOT30VPD (VPD-modified concentration accumulated over a

Updated : June 2015


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.

III.3.1.2 MODELLING RISK OF DAMAGE TO GENERIC RECEPTORS WITHI N 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 sto-matal 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.3.1.3 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 species-specific 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, region-specific 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


Table III.5 : Critical levels for ozone.

(a) Flux -based critical levels (see Mills et al., 2011b)

Receptor Effect

(per cent reduction)

Parameter

Critical level

(mmol m -2 PLA)

Scientific basis in Section

Wheat Grain yield (5%) POD6 1 3.5.2.1

Wheat 1000 grain weight (5%)

POD6 2 3.5.2.1

Wheat Protein yield (5%) POD6 2 3.5.2.1

Potato Tuber yield (5%) POD6 5 3.5.2.1

Tomato Fruit yield (5%) POD6 3 3.5.2.1

Tomato Fruit quality (5%) POD6 4 3.5.2.1

Norway spruce Biomass (2%) POD1 8 3.6.2.1

Birch and beech Biomass (4%) POD1 4 3.6.2.1

Productive grasslands (clover) Biomass (10%) POD1 2 3.7.2.1

Conservation grasslands (clover) Biomass (10%) POD1 2 3.7.2.1

Conservation grasslands (Viola spp), provisional

Biomass (15%) POD1 6 3.7.2.1

(b) Concentration -based critical levels

Receptor Effect

Parameter

Critical level

(ppm h)


Agricultural crops Yield reduction AOT40 3 3.5.3.1

Horticultural crops Yield reduction AOT40 8 3.5.3.1

Forest trees Growth reduction AOT40 5 3.6.3.1

(Semi-)natural vegetation communities dominated by annuals

Growth reduction and/or seed production

AOT40 3 3.7.3.1

(Semi-)natural vegetation communities dominated by perennials

Growth reduction AOT40 5 3.7.3.1

(c) VPD-modified concentration -based critical level

Receptor Effect Parameter Critical level

(ppm h)


Vegetation (derived for clover species)

Visible injury on leaves

AOT30VPD 0.16 3.5.4.1

Updated : June 2015


Table III.6 : Terminology for critical levels of ozone.

Term Abbreviation

[Units] Explanation

Terms for flux-based critical levels

Projected leaf area PLA [m2]

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.

Stomatal flux of ozone Fst [nmol m-2 PLA s-1]

Instantaneous flux of ozone through the stomatal pores per unit projected leaf area (PLA). Fst can be defined for any part of the plant, or the whole leaf area of the plant, but for this manual, Fst refers specifically to the sunlit leaves at the top of the canopy. Fst is normally calculated from hourly mean values and is regarded here as the hourly mean flux of ozone into the stomata.

Stomatal flux of ozone above a flux threshold of Y nmol m-2 PLA s-1

FstY [nmol m-2 PLA s-1]

Instantaneous flux of ozone above a flux threshold of Y nmol m-2 s-1, through the stomatal pores per unit projected leaf area. FstY can be defined for any part of the plant, or the whole leaf area of the plant, but for this manual FstY refers specifically to the sunlit leaves at the top of the canopy. FstY is normally calculated from hourly mean values and is regarded here as the hourly mean flux of ozone through the stomata.

Phytotoxic ozone dose (expressed as the accumulated stomatal flux of ozone above a flux threshold of Y nmol m-2 PLA s-1)

PODY [mmol m-2 PLA]

Phytotoxic ozone dose (POD) is the accumulated flux above a flux threshold of Y nmol m2ـ s1ـ, accumulated over a stated time period during daylight hours1). Similar in mathematical concept to AOTX.

Flux-based critical level of ozone

CLef [mmol m-2 PLA]

Phytotoxic ozone dose above a flux threshold of Y nmol m-2 s-1 (PODY), over a stated time period during daylight hours, above which direct adverse effects may occur on sensitive vegetation according to present knowledge.

Terms for concentration-based critical levels

Concentration accumulated over a threshold ozone concentration of X ppb

AOTX [ppm h]

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.

Concentration-based critical level of ozone

CLec [ppm h]

AOTX over a stated time period, above which direct adverse effects on sensitive vegetation may occur according to present knowledge.

Concentration accumulated over a threshold ozone concentration of X ppb modified by vapour pressure deficit (VPD)

AOTXVPD

[ppm h]

The sum of the differences between the hourly mean ozone concentration (in ppb) modified by a vapour pressure deficit factor ([O3]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


III.4 PROCEDURES FOR CALCULATING OZONE CRITICAL LEV ELS AND THEIR EXCEEDANCE (ALL RECEPTORS)

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 O3 concentrations. All response functions used to derive critical levels are statistically significant at at least p < 0.05, and more

usually p < 0.01. 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 PODY. Critical levels have been derived as either the lowest AOTX or PODY that induces an effect that is significantly different from the effect at zero AOTX or PODy, 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 TO P 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

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.

Updated : June 2015


calculated for determining each of the indices used.

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 over-estimates of ozone concentrations and hence of the degree of exceedance of CLec and CLef. 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, Ftot.

Conversion of ozone concentration at measurement height to that at canopy-top height (z1) 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 sub-layer (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 O3 gradients can be used. The relationship between O3 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 gmax = 450 mmol O3 m

-2 PLA s-1. The total leaf surface area index (LAI) is set to 5 m2 PLA /m2, and the green LAI is set at 3 m2 PLA /m2,

assumed to give a canopy-scale phenology factor (fphen) of 0.6. The soil moisture factor (fSWP) 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, gmax to 270 mmol O3 m

-2 PLA s-1 and fSWP 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 (z0) are set to 0.7 and 0.1 of canopy height (z1), respectively.

Table III.7 shows the average relationship between O3 concentrations at selected heights, derived from runs of the EMEP module over May-July, and selecting the noontime factors as representative of daytime multipliers. O3 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 O3 concentration measured over land-use X (e.g. short grasslands) could be used to estimate the O3 concentration at a reference height, and then the gradient profile appropriate for desired land use Y

Updated: june 2015


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 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 O3 gradients above artificial (1m) crop, and short grasslands (0.1m). O3 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]

O3 concentration gradient

Crops (where z1=1m,

gmax= 450 mmol O3 m-2 PLA s-1)

Short Grasslands and Forest Trees (where z1=0.1m,

gmax=270 mmol O3 m-2 PLA s-1)

20 1.0 1.0

10 0.99 0.99

5 0.97 0.97

4 0.96 0.97

3 0.95 0.96

2 0.93 0.95

1 0.88 0.92

0.5 0.81* 0.89

0.2 - 0.83

0.1 - 0.74

* 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 zu, ozone concentration at a reference height of zR, and an estimate of z0, then we find concentration values appropriate to any height z1 near the surface (e.g. the top of the canopy for crops, ca1m) by making use of the constant-flux assumption and definition of aerodynamic resistance (neglecting the roughness sub-layer near the canopy top):

III.1)

),z(zR)C(z)C(z

)C(z)(zV1Ra

1RRRg

= * =

−flux Total

Where Vg(zR) is the deposition velocity (m s-1) at height zR, and Ra(zR,z1) is the aerodynamic resistance between the two heights (s m-1). Re-arranging the second two terms, we get:

(III.2)

][ *1 = )(zV),z(zR) C(z)C(z Rg1RaR1 −

In neutral stability, friction velocity (u*) and Ra are easily obtained. Once u* is found, then the wind speed at a height near the surface can be found by substituting zu with the required height, z in equation III.3.

Updated : June 2015


(III.3)

−=

0

ln

)zu(*

z

dzu

kuu

(III.4)

−−

=dz

dz

kuR R

zza Rln

*

1),(

where the von Kármán konstant, k = 0.4, and z can be any height within the so-called surface layer (typically lowest 10s of metres near the ground), e.g. 1m for crops.

The deposition velocity requires further information:

(III.5)

cbRaRg RR)z,zd(zR

)(zV+++ 0

1 =

Note: Ra in equation III.5 is the aerodynamic resistance from zR to d+z0 (the level where Ra becomes zero), not to z1 (). For ozone, Rb = 6.85/u*. The canopy resistance, Rc, is a function of temperature, radiation, relative humidity and soil water. If local meteorology allows an assessment of these, the formulation of Rc 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 O3 concentration measured over le.g. short grasslands could be used to estimate the O3 concentration at a reference height, and 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(zR, d+z0) over grassland using z0, d values appropriate to grass, through eqns (III.3), (III.4).

Calculate the deposition velocity, Vg(zR), over grass using eqn (III.5). Note that that Rb, Rc resistances should be calculated for grassland, not forest, in this case.

Calculate the O3 concentration at eg z1=20m using eqn (III.2).

(Where z1 > zR, the Ra(zR,z1) term will be negative, so that C(z1) will be higher than C(zR).)

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 DO3SE model (Tuovinen et al., 2009).

III.4.3 MODELLING STOMATAL FLUX

Stomatal flux is modelled using an algorithm incorporating effects of air temperature (ftemp), vapour pressure deficit of the air surrounding the leaves (fVPD), light (flight), soil water potential (fSWP) or plant available water content (fPAW), plant phenology (fphen) and ozone concentration (fozone) on the maximum stomatal conductance measured under optimal conditions (gmax). 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 DO3SE (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.6.2.3 and III.7.2.3 resepctively. The DO3SE model is available in downloadable form at http://sei-

Updated: june 2015


international.org/do3se. 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 (Fst) 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 the flag leaf (in the case of wheat) and the sunlit upper canopy leaves (in the case of other receptors). If c(z1) is the concentration of ozone at canopy top (height z1, unit: m), in nmol m-3, then Fst (nmol m-2 PLA s-1), is given by:

(III.6)

extsto

sto

cbst gg

grr

zcF++

= *1

*)( 1

The 1/(rb+rc) term represents the deposition rate to the leaf through resistances rb (quasi-laminar resistance) and rc (leaf surface resistance). The fraction of this ozone taken up by the stomata is given by gsto/(gsto+gext), where gsto is the stomatal conductance, and gext is the external leaf, or cuticular, resistance. As the leaf surface resistance, rc, is given by rc = 1/(gsto + gext), we can also write equation (III.6) as:

(III.7)

cb

cstost rr

rgzcF

+= **)( 1

A value for gext has been chosen to keep consistency with the EMEP deposition modules “big-leaf” external resistance, Rext = 2500/SAI, where SAI is the surface area index (green + senescent LAI). Assuming that SAI can be simply scaled:

(III.8)

gext = 1/2500 [m s-1]

In order to be used correctly in Equations (III.6) and (III.7), gsto 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 41000 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 rb 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 z1, u(z1):

(III.9)

]m [s )(

*150*3.1 1-

1

zuL

rb =

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 (gsto) 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)

gsto = gmax *[min(fphen, fO3)]* flight * max{fmin, (ftemp * fVPD * fSWP)}

or

(III.10b)

gsto = gmax *[min(fphen, fO3)]* flight *

max{fmin, (ftemp * fVPD * fPAW)}

Where gsto is the actual stomatal conductance (mmol O3 m-2 PLA s-1) and gmax is the species-specific maximum stomatal conductance (mmol O3 m

-2 PLA s-1). The parameters fphen, fO3, flight, ftemp, fVPD and fSWP or fPAW are all expressed in relative terms (i.e. they take values between 0 and 1 as a proportion of gmax).

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 fphen and fO3 is a “most limiting factor”

Updated : June 2015


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 fphen is always limiting and fO3 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 original formulation given in Emberson et al. (2000a).

To calculate the flux of ozone from gsto, equation (III.10c) should be used.

(III.10c)

Fst (O3) in nmol m-2 s-1 = (gsto/1000) * ozone concentration in ppb

gmax and f min

Receptor-specific values are provided in the relvant Sections for gmax and fmin based on analysis of published data. gmax values provided here are in mmol O3 m

-2 PLA s-1. Unless otherwise stated, these have been converted from gmax (water vapour) using a conversion factor of 0.663 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).

fphen

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. fphen 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 PODY where Astart and Aend 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 fphen_a and fphen_b denote the maximum fraction of gmax that gsto takes at the start and end of the accumulation period for ozone flux. fphen_c to fphen-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 fphen 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 Astart ≤ yd < (Astart + fphen_c)

(III.11a) fphen = (1 – fphen_a) * ((yd – Astart)/fphen_c) + fphen_a

when (Astart + fphen_c) ≤ yd ≤ (Aend – fphen_d)

(III.11b) fphen = 1

when (Aend – fphen_d) < yd ≤ Aend

(III.11c) fphen = (1 – fphen_b) * ((Aend – yd)/fphen_d) + fphen_b

where yd is the year day; Astart and Aend are the year days for the start and end of the ozone accumulation period respectively.

Method (b): based on temperature sum accumulation

when Astart ≤ tt < (Astart + fphen_e)

(III.12a)

( )tt−+

−−= )f(A

f

ff phen_estart

phen_e

phen_aphen

11

when (Astart + fphen_e) ≤ tt ≤ (Aend – fphen_f)

Updated: june 2015


(III.12b)

fphen = 1

when (Aend – fphen_d) < tt ≤ Aend

(III.12c)

( ))f(Af

ff phen_fend

phen_f

phen_bphen −−

−−= tt

11

where tt is the effective temperature sum in ˚C days using a base temperature of 0˚C and Astart and Aend 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 Astart will be equal to 0 ˚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.

flight

The function used to describe flight is given in Equation (3.13)

(III.13)

flight = 1 – EXP((–lighta)*PFD)

where PFD represents the photosynthetic photon flux density in units of µmol m-2 s-1.

ftemp

The function used to describe ftemp is given in Equation (III.14).

when Tmin < T < Tmax

(III.14a)

ftemp = max { fmin, [(T – Tmin) / (Topt – Tmin)] * [(Tmax – T) / (Tmax – Topt)]

bt}

when Tmin > T > Tmax

(III.14b)

ftemp = fmin

where T is the air temperature in °C, Tmin and Tmax are the minimum and maximum temperatures at which stomatal closure occurs to fmin, Topt is the optimum temperature and bt is defined as follows:

(III.15)

bt = (Tmax – Topt) / (Topt – Tmin)

fVPD 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 fVPD function (Equation (III.16)).

(III.16)

fVPD = min{1,max {fmin, ((1–fmin)*(VPDmin – VPD) / (VPDmin – VPDmax)) + fmin}}

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 fVPD function this would allow the stomata to re-open if there had been a limitation by fVPD 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

Updated : June 2015


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 ΣVPD ≥ ΣVPD_crit, then:

(III.17)

gsto_hour_n+1 ≤ gsto_hour_n

Where gsto_hour_n and gsto_hour_n+1 are the gsto values for hour n and hour n+1 respectively calculated according to equation (III.10a).

ΣVPD (kPa) should be calculated for each daylight hour until the dawn of the next day. Thus, if ΣVPD is larger than or equal to ΣVPDcrit , the gsto value calculated using equation (III.10a) is valid if it is smaller or equal to the gsto value of the preceding hour. If gsto according to equation (III.10a) is larger than gsto of the preceding hour, given that ΣVPD is larger than or equal to ΣVPDcrit, it is replaced by the gsto 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 fVPD 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.

fSWP

The function used to describe fSWP is given in Equation (III.18a).

(III.18a)

fSWP = min{1, {fmin, ((1–fmin)*(SWPmin–SWP) / (SWPmin – SWPmax

fPAW

For some receptors (see relevant sections for details), fPAW is used instead of fSWP. The function used to describe fPAW 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. PAWt is the threshold PAW, above which stomatal conductance is at a maximum, i.e

when PAWt ≤ PAW ≤ 100 %

(III.18b1) fPAW = 1

when PAW < PAWt

(III.18b2)

t

tPAW

1

PAW

PAWPAWf

−+=

fO3

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).

Updated: june 2015


III.4.4 STAGES IN CALCULATING EXCEEDANCE OF A FLUX- BASED CRITICAL LEVEL

To calculate the relevant PODY 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 (gsto) values for each hour within the accumulation period are calculated using the stomatal flux algorithm presented in Equations (3.6) to (3.10a) according to the receptor-specific 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, Fst expressed in nmol m-2 PLA s-1 (Equation (III.7)).

Step 5 : The value Y is subtracted from each hourly Fst value, and then multiplied by 3600 to obtain hourly FstY values in nmol O3 m

-2 PLA h-1.

Step 6: The sum of all hourly FstY values is calculated for the specified accumulated period. The resulting value is PODY in mmol m-2 PLA.

Step 7 : If the PODY value is larger than the flux-based critical level for ozone CLef, then there is exceedance of the critical level.

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.1.

Updated : June 2015


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time of day (h)

O3

conc

entr

atio

n (p

pb)

Contributes to AOT40

Does not contribute to AOT40

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 11th hour) + 35 (12th hour) + 30 (13th hour) + 47 (14th hour) + 51 (15th hour) + 55 (16th hour) + 52 (17th hour) + 51 (18th hour) + 45 (19th hour). Exceedance of 40 ppb in the 20th hour is not included

because it occurred after daylight had ended.

III.4.6 STAGES IN CALCULATING EXCEEDANCE OF AN AOT4 0-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).

Daylight hours

Updated: june 2015


III.4.7 CALCULATION OF AOT30 VPD

The AOTXVPD 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 [O3] is multiplied by an hourly fvpd factor, reflecting the influence of VPD on stomatal conductance, to get hourly, modified ozone concentrations [O3]VPD:

(III.19)

[O3]VPD = fvpd * [O3]

where:

fvpd = 1

if VPD < 1.1 kPa

fvpd = –1.1 * VPD + 2.2

if 1.1 kPa ≤ VPD ≤ 1.9 kPa

fvpd = 0.02

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 AOT 30VPD-BASED CRITICAL LEVEL

In the case of the short-term critical level for ozone injury AOT30VPD 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


III.5 CRITICAL LEVELS OF OZONE AND RISK ASSESSMENT METHODS FOR AGRICULTURAL AND HORTICULTURAL CROPS

III.5.1 OZONE SENSITIVITY OF AGRICULTURAL AND HORTI CULTURAL CROPS

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 O3 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). Dose-response 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.

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

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 1994. 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 flux-based critical levels for crops were established for wheat and potato at the Gothenburg workshop in 2002. The methodology has since been refined and

Updated: june 2015


new data sets have been added, with revisions to this part of the chapter being made in 2004 and 2007. The current (revised) flux-based critical levels for effects on the quanity and quality of yield were established at the 23rd Task Force

meeting of the ICP Vegetation in February 2010 following discussions at the Ispra workshop in November 2009. These are included here together with the AOT40- and AOT30VPD-based critical levels previously agreed.

III.5.2 STOMATAL FLUX-BASED METHODS

III.5.2.1 SCIENTIFIC BASIS AND ROBUSTNESS OF FLUX-BASED METHO DS AND CRITICAL LEVELS

The index PODY 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 PODY 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 PODY 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 POD6 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 (Fst) 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 peer-reviewed 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,

Updated : June 2015


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.

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.5.2.2 FLUX-BASED CRITICAL LEVELS AND RESPONSE FUNCTIONS F OR CROPS

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 flux-based critical level for 5% yield loss of a POD6 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 flux-based 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).

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.

Updated: june 2015


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

Protein yield Tuber yield Fruit yield (Fruit quality)

% reduction for critical level

5% 5% 5% 5% 5%

Critical level

(POD6, mmol m -2)

1 2 2 5 3 (4)

Countries involved in experiments

Belgium, Finland, Italy,

Sweden

Belgium, Finland, Sweden

Belgium, Finland, Sweden

Belgium, Finland,

Germany, Sweden

Italy, Spain

Number of data points

36 33 33 17 17

Number of cultivars 5 4 4 1 5 (sensitive cultivars only)

Data sources Pleijel et al., 2007

Piikki et al., 2008

Piikki et al., 2008

Pleijel et al., 2007

González-Fernández et

al., 2014

Time period 200 ͦͦC days before

anthesis to 700 ͦͦC days after anthesis

200 ͦͦC days before


200 ͦͦC days before


1130 ͦͦC days starting

at plant emergence

250 to 1500 ºC days starting

at tomato planting in the field (at 4th true

leaf stage)

Response function RY=1.00 - 0.038*POD6

RY=1.00 - 0.033*POD6

RY=1.01 - 0.025*POD6

RY=1.01 - 0.013*POD6

RY=1.00 - 0.027*POD6

(RY=1.01-0.013*POD6)

r2 0.84 0.71 0.63 0.76 0.60

(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


BE

FI

IT

SE

0 2 4 6 8

0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e yi

eld

y = 1.00 - 0.038 * POD6r2 = 0.84p < 0.001

POD6, mmol m-2

BE

FI

IT

SE

0 2 4 6 8

0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e yi

eld

y = 1.00 - 0.038 * POD6r2 = 0.84p < 0.001

POD6, mmol m-2

BE

FI

IT

SE

BEBE

FIFI

ITIT

SESE

0 2 4 6 8

0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e yi

eld

y = 1.00 - 0.038 * POD6r2 = 0.84p < 0.001

POD6, mmol m-2

0 2 4 6 8

0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e 10

00-g

rain

wei

ght

BE

FI

SE

y = 1.00 - 0.033 * POD6r2 = 0.71p < 0.001

POD6, mmol m-2

0 2 4 6 8

0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e 10

00-g

rain

wei

ght

BE

FI

SE

y = 1.00 - 0.033 * POD6r2 = 0.71p < 0.001

BEBE

FIFI

SESE

y = 1.00 - 0.033 * POD6r2 = 0.71p < 0.001

POD6, mmol m-2

0 2 4 6 8

0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e pr

otei

n yi

eld

y = 1.01 - 0.025 * POD6r2 = 0.63p < 0.001

POD6, mmol m-2

BE

FI

SE

0 2 4 6 8

0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e pr

otei

n yi

eld

y = 1.01 - 0.025 * POD6r2 = 0.63p < 0.001

POD6, mmol m-2

BE

FI

SE

0 2 4 6 8

0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e pr

otei

n yi

eld

y = 1.01 - 0.025 * POD6r2 = 0.63p < 0.001

POD6, mmol m-2

BEBE

FIFI

SESE

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., 2012.

Updated: june 2015


(a) (b)

POD6, mmol m -2

4 8 12 160 20

0

0.2

0.4

0.6

0.8

1.0

1.2

BEFIGESE

y = 1.01 – 0.013 * POD6 r2 = 0.76p < 0.001

PotatoR

ela

tive

yie

ldTomato

Figure III.3 :The relationship between the relative a) tuber yield of potato and POD6 for sunlit leaves based on data from four European Countries (BE: Belgium, FI: Finland, GE: Germany, SE:

Sweden) and b) tomato fruit yield and POD6 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., 2014.

III.5.2.3 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 POD6 for wheat, potato, and tomato applying the

stomatal flux algorithm are shown in Table 3.10. 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 (fPAW) instead of soil water potential (fSWP) 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


Table III.10 : Summary of the parameterisation for the stomatal flux algorithms for POD6 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, 2014.

Parameter Units Wheat Potato Tomato

gmax mmol O3 m-2 PLA s-1 500 750 3301

fmin fraction 0.01 0.01 0.06

SGS °C day 2502

EGS °C day 15002

fphen_a fraction 0.3 0.4 1

fphen_b fraction 0.7 0.2 0

fphen_c days 20

fphen_d days 50

fphen_e °C days 200 330 0

fphen_f* °C days 0 800 27702

fphen_g °C days 100

fphen_h °C days 525

fphen_i °C days 700

Light_a constant 0.0105 0.005 0.0125

Tmin °C 12 13 18

Topt °C 26 28 28

Tmax °C 40 39 37

VPDmax kPa 1.2 2.1 1

VPDmin kPa 3.2 3.5 4

ΣVPDcrit kPa 8 10

PAWt % 50

SWPmax MPa -0.5

SWPmin MPa -1.1

fozone POD0, mmol m-2 s-1 (wheat) 14

fozone AOT0, ppmh (potato) 40

fozone exponent 8 5

Height m 1 1 1

Leaf dimension m 0.02 0.04 0.03 (leaflet width) 1mean value, cultivar specific values can be used (González-Fernández et al., 2014). 2flux accumulation period in degree-days over a base temperature of 10 °C since the date of tomato planting in the field at the 4th true leaf stage, BBCH code of 14.

Updated: june 2015


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

fphen

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 23rd 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.

• when (fphen_f − fphen_e) ≤ tt ≤ (fphen_f + fphen_g) (III.7a_wheat) fphen = 1

• when (fphen_f + fphen_g) < tt ≤ (fphen_f + fphen_h)(III.7b_wheat)

( )phen_g

phen_gphen_h

phen_aphen f

ff

ff −

−−= tt1

• when (fphen_f + fphen_h) < tt ≤ fphen_i (III.7c_wheat)

( )phen_h

phen_hphen_i

phen_bphen_bphen f

ff

fff −

−−= tt

where tt is the effective temperature sum in ˚C days using a base temperature of 0˚C and Astart and Aend 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 Astart will be equal to 200 ˚C days before Amid-anthesis (-200 ˚C), Amid-anthesis to 0 ˚C days, Aend to 700 ˚C days after Amid-anthesis. The total temperature sum thus being 900 ˚C days.

fVPD

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).

fPAW

fPAW is used instead of fSWP for wheat, see Equation 3.10b. fO3

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 (fO3) on wheat and potato via the onset of early senescence. As such this function is used in association with the fphen function to estimate gsto. The fO3 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):

(3.20) fO3 = ((1+(POD0/14)8)-1)

where POD0 is accumulated from Astart

Potato

fphen

For potato, fphen is calculated using method (b) based on accumulation of thermal time.

fSWP

fSWP is used for potato fO3

Updated : June 2015


The ozone function for potato (based on Pleijel et al. (2002)):

(3.21)

fO3 = ((1+(AOT0/40)5)-1)

where AOT0 is accumulated from Astart

Tomato

fphen

Method (b), based on a fixed thermal-time based interval, is used to determine fphen for tomato.

III.5.2.4 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 species-specific information :

Step 1: Determine the accumulation period

The start and end of the PODY accumulation period are identified by Astart and Aend respectively. Four methods are suggested for estimating the timing of the PODY 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

PODY 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 3.11. 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).

Updated: june 2015


For winter wheat in Central Europe, growth can be assumed to restart at day 60 of the respective year according to the well-validated 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 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 mid-anthesis 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 web-pages. It should be noted that such data sources may use alternative growth-stage keys.

Table III.11 : Observed sowing dates for spring wheat in Europe1

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 10-30 Apr 20 Apr

Slovakia 10-30 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

Updated : June 2015


Region Range Default

Mediterranean Europe

Bulgaria -

Portugal 20 Jan-10 Mar 10 Feb

Spain 1-28 Feb 10 Feb

1 According to Broekhuizen (1969)

Potato

For potato, POD6 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 Astart for potato plant emergence. No phenological models are suggested for use to define Astart for this species.

Tomato

The timing of Astart (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 (Aend) based on degree-days over a base temperature of 10 °C since phenological stage 4th 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.4.4

Updated: june 2015


III.5.2.5 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.5.2.6 ESTIMATION OF RISK OF DAMAGE USING A GENERIC CROP FLUX MODEL (FOR INTEGRATED ASSESSMENT MODELLING WI THIN GAINS)

Note: This text was updated following the 27 th ICP Vegetation Task Force meeting, Paris, January, 2014 including a yield-res ponse relationship and critical level for use in scenario analysis and optimisation runs within the GAINS, and a Mediterranean-specific flux model parameterisation.

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.5.2.2-5) is used, including for Mediterranean areas, the Mediterranean-specific 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

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


limiting to flux. Even using the Mediterranean parameterisation, this approach may over-estimate 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 large-scale 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 fphen 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, fO3 is set to 1.The function for plant available water (fPAW) 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-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 POD3IAM (formerly described as POD3 gen) to indicate that this flux model is for use in large scale integrated assessment modelling, and that this is a different parameter to POD3 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

Updated: june 2015


Table III.12 : Parameterisation of POD3IAM, 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

gmax mmol O3 m-2 PLA s-1 500

430

fmin fraction 0.01 0.01

fphen 1 1

flight constant lighta = 0.0105 lighta = 0.0105

Tmin °C 12 12

Topt °C 26 28

Tmax °C 40 39

VPDmax kPa 1.2 3.2

VPDmin kPa 3.2 4.6

ΣVPDcrit kPa 8 8

fPAW 1 1

fozone 1 1

SAI m2 PLA m-3 5 5

Green LAI m2 PLA m-3 3.5 3.5

Height m 1 1

Leaf dimension m 0.02 0.02

1 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

Estimation of risk of crop yield loss using POD3IAM

The ICP Vegetation Task Force expressed some concern over the use of POD3IAM 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.

Updated : June 2015


Some uncertainty arises from this approach, but the Task Force agreed that

this was an acceptable level of uncertainty within large scale modelling.

Figure III.4: The relationship between relative yield and POD3IAM (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.

POD3IAM 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 POD3IAM of 8 mmol m -2

(accumulated over 90 days)

Updated: june 2015


III.5.3 AOT40-BASED METHODS

III.5.3.1 SCIENTIFIC BASIS OF AOT40-BASED METHODS AND CRITICA L 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 3.5, 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 2.3-4.4 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.5.2.4 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 (r2 = 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 16th Task Force meeting of the ICP Vegetation (2003) and the 19th 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., 2014.

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 ozone-sensitive cultivars (r2 = 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 4th true leaf

Updated : June 2015


stage (BBCH 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.5.3.2 AOT40-BASED CRITICAL LEVELS AND RESPONSE FUNCTIONS

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)

3 8


Belgium, Finland, Italy, Sweden

Spain, Italy

Number of data points 52 17

Number of cultivars 9 5 ozone-sensitive cultivars

Data sources Described in Mills et al., 2007a

Described in González-Fernández et al., 2014

Time period 3 months 3 months

Response function RY = 0.99 - 0.0161*AOT40 RY = 1.01 - 0.0069*AOT40

r2 0.89 0.63

P value < 0.001 < 0.001

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.

Updated: june 2015


Three month AOT40 (ppm h)

0 10 20 30 40

Rel

ativ

e yi

eld

0.0

0.2

0.4

0.6

0.8

1.0

1.2

AlbisDrabant Ralle EchoAbe Arthur Roland Satu Dragon

Regression95% CI

Figure III.5 : Wheat yield-response function used to derive the concentration-based critical levels for agricultural crops (r2 = 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 (r2 = 0.63, p<0.001) (González-Fernández et al., 2014). Dotted lines represent 95% confidence intervals.

III.5.3.3 METHOD FOR CALCULATING EXCEEDANCE OF THE AOT40-BASED CRITICAL LEVELS FOR CROPS


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

Updated : June 2015


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 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.

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


CCE

15 April to 15 July

Armenia, Austria, Azerbaijan, Belarus, Czech Republic, France1, Georgia, Germany, Hungary, Kazakhstan, Kyrgyzstan, Liechtenstein, Republic of Moldova, Poland, Romania, Russian Federation, Slovakia, Switzerland, Ukraine


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

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

Updated: june 2015


III.5.4 VPD-MODIFIED AOT30 METHOD

III.5.4.1 SCIENTIFIC BASIS OF THE CRITICAL LEVEL FOR VISIBLE LEAF INJURY ON CROPS

Note: This critical level was revised following new analysis conducted after the 17th 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 well-documented 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 (AOT30VPD) 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.5.4.2 THE AOT30VPD CRITICAL LEVEL FOR VISIBLE LEAF INJURY ON CROPS

The critical level for visible leaf injury is exceeded when the AOT30VPD 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

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

Updated : June 2015


the conclusion from the studies by Pihl Karlsson et al. (2003).

The AOT30VPD index accumulated during daylight hours, using an exposure period of 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 AOT30VPD 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.4.8.

y = 0.03x + 5.08 r2 = 0.602

0

10

20

30

40

50

0 0.5 1.0 1.5AOT30VPD (8 days, ppm h)

Vis

ible

inju

ry, %

Figure III.7 : The extent of visible injury (percentage ozone injured leaves) versus AOT30VPD. 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 ozone-sensitive 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


III.6.2 FLUX-BASED METHODS

III.6.2.1 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 23rd 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 pre-industrial 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 DO3SE 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 POD1 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


III.6.2.2 STOMATAL FLUX-BASED CRITICAL LEVELS AND RESPONSE-FUNCTIONS FOR DECIDUOUS AND EVERGREEN TREES

Note : The content of this section will be consider ed for revision at the 2015 Task Force meeting of the ICP Vegetation following new a nalysis conducted by Büker et al. (submitted).

At the 23rd 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 species-specific 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:

yearsyr biombiom

1

=

where biomyr 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. POD0 or POD1). 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. POD1 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 3.15). 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

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.

Updated: june 2015


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. 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) 4 8

Countries contributing data. Finland, Sweden and Switzerland

France, Sweden and Switzerland

Number of data points 38 (14 different experiments)

27 (8 different experiments)

Years of experiments 0.5-5 1-5

Data sources Uddling et al., 2004. Braun and Flückiger, 1995

Karlsson et al, 2004. Braun and Flückiger, 1995

Time period Growing season Growing season

Response function RY=1.00 -0.011*POD1 RY=1.00 -0.0024*POD1

r2 0.64 0.55

P value <0.001 <0.001

(a) (b)

y = 1.00 - 0.0024 * POD1

r² = 0.55p < 0.001

0.6

0.7

0.8

0.9

1.0

1.1

0 10 20 30 40

Re

lativ

e to

tal b

iom

ass

POD1, mmol m-2

Norway spruce

y = 1.00 - 0.011 * POD1r² = 0.64p < 0.001

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 20 40 60

Re

lativ

e to

tal b

iom

ass

POD1, mmol m-2

BirchBeech

Figure III.8 : The relationship between the relative total biomass and POD1 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


III.6.2.3 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.

Table III.16 : Flux model parameterisation for beech, birch and Norway spruce.

Parameter Units Beech and birch

Norway Spruce

Beech and birch

Norway spruce

Region CCE CCE NE NE

Land use Eunis class, area in km2

Deciduous broadleaved

forests

Coniferous forests


forests

Coniferous forests

gmax mmol O3 m

-2 projected leaf

area s-1 150 (132-300) 125 (87-140)

196 (180-211 range)

112 (111-118 range)

fmin (fraction) 0.13 0.16 0.1 0.1

SGS year day Latitude model ftemp Latitude model

Latitude model

EGS year day Latitude model ftemp Latitude model

Latitude model

fphen_a (fraction) 0 0 0 0

fphen_b (fraction) (1) (1) (1) (1)

fphen_c (fraction) 1 1 1 1

fphen_d (fraction) (1) (1) (1) (1)

fphen_e (fraction) 0.4 0 0 0

fphen_1 (days) 20 0 20 20

fphen_2 (days) (200) (200) (200) (200)

fphen_3 (days) (200) (200) (200) (200)

fphen_4 (days) 20 0 30 30

fphen_limA (days) (0) (0) (0) (0)

fphen_limB (days) (0) (0) (0) (0)

light_a 0.006 0.01 0.0042 0.006

Tmin oC 5 0 5 0

Topt oC 16 14 20 20

Tmax** oC 33 35 200** 200**

VPDmax kPa 1.0 0.5 0.5 0.8

VPDmin kPa 3.1 3.0 2.7 2.8

SWCmax (medium)* % volume 15 15

SWCmin (medium)* % volume 1 1

SWPmax MPa -0.05 -0.05

SWPmin MPa -1.25 -0.5

h m 25 20 20 20

L cm 7 0.8 5 0.8

Updated: june 2015


For forest trees, the following additional information is required for calculating stomatal flux using the method provided in section III.4.3 :

gmax

Use of the conversion factor of 0.663 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 gmax values contained here and used to calculate POD1 values in Figure III.8 was that previously used (0.613).

Tmax

The Tmax 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 Tmax value should be viewed as a forcing rather than descriptive parameter.

fozone

fozone is not included for forest trees, and should be set to 1 in equation (III.10).

fphen

For forest trees, a modified formulation for the fphen relationship is used (see below and Figure III.9). This method allows the use of a consistent formulation irrespective of whether there is a mid-season dip in fphen (as is required to model fphen 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 mid-season dip does not occur.

fphen = 0

when yd ≤ SGS

fphen = ((1-fphen_a)*((yd-SGS)/fphen1)+fphen_a)

when SGS < yd ≤ fphen1+SGS

fphen = fphen_b

when fphen1+SGS < yd ≤ fphen_limA

fphen = (1-fphen_c) * (((fphen2 + fphen_limA) - (fphen_limA + (yd-fphen_limA))) /fphen2) + fphen_c

when fphen_limA < yd < fphen_limA + fphen2

fphen = fphen_c

when fphen_limA + fphen2 ≤ yd ≤ fphen_limB - fphen3

fphen = (1-fphen_c) * ((yd-(fphen_limB - fphen3))/fphen3) + fphen_c

when fphen_limB - fphen3 < yd < fphen_limB

fphen = fphen_d

when fphen_limB ≤ yd ≤ EGS - fphen4

fphen = (1-fphen_e) * ((EGS-yd) / fphen4) + fphen_e

when EGS - fphen4 < yd < EGS

fphen = 0

when yd ≥ EGS

Updated : June 2015


0

0.2

0.4

0.6

0.8

1

1.2

0 50 100 150 200 250 300 350

Year day

f phe

n

fphen_limA

start of "soil w ater" limitationfphen_lim

B

end of "soil w ater" limitation

Fphen_a

Fphen_b

Fphen_c

Fphen_d

Fphen2 Fphen3

SGS EGS

Fphen_e

Fphen1 Fphen4

Figure III.9 : An illustration of the formulation of fphen for forest trees.

III.6.2.4 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 fphen will start to decrease from gmax. Between the onset of dormancy and leaf fall gsto 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).

Updated: june 2015


For Norway spruce in CCE the growth period is determined by air temperature defined according to the ftemp function (the growing season is assumed to occur when air temperatures fall within the Tmin and Tmax thresholds of the ftemp relationship). During such periods fphen 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.6.2.3.

The stomatal conductance (gsto) 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.6.2.5 REGIONAL PARAMETERISATIONS FOR SPECIES-SPECIFIC FLUX MODELS 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).

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.

Updated : June 2015


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.6.2.6 ESTIMATION OF RISK OF DAMAGE FOR A GENERIC FOREST TREES (FOR INTEGRATED ASSESSMENT MODELLING)

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 para-meterisations began after the Obergurgl workshop (November, 2005); the parameterisations presented in Table III.18 were approved by the 20th ICP Vegetation Task Force meeting (Dubna, Russian Federation, March, 2007) and were reviewed in 2014.

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, fphen is constructed within the year round growing season so as to allow for a reduction in gsto during the summer when soil water deficits are commonly high in Mediterranean areas. Since the influence of soil water status on gsto is to a certain extent incorporated within this parameter for this generic model, the fSWP function is set equal to 1. For the “Broadleaved deciduous” forests the fphen parameterisation is based on data describing the increase and reduction in gsto 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 (flight), temperature (ftemp) and

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.

Updated: june 2015


vapour pressure deficit (fVPD) 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 gsto is incorporated within the fphen 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 (POD1IAM). 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 km2

All forested areas Mediterranean evergreen forest species

gmax1 mmol O3 m

-2 projected leaf area (PLA) s-1

150 175


SGS year day Latitude model 1 (1 Jan) EGS year day Latitude model 365 (31 Dec) fphen_a (fraction) 0 1 fphen_b (fraction) (1) 1 fphen_c (fraction) 1 0.3 fphen_d (fraction) (1) 1 fphen_e (fraction) 0 1 fphen_1 (days) 15 (0) fphen_2 (days) (200) 130 fphen_3 (days) (200) 60 fphen_4 (days) 20 (0) fphen_limA

(days) = SGS 80 (21 Mar) fphen_limB

(days) = EGS 320 (16 Nov) light_a costant 0.006 0.009 Tmin

oC 0 2 Topt

oC 21 23 Tmax

oC 35 38 VPDmax

kPa 1.0 2.2 VPDmin

kPa 3.25 4.0 SWPmax

MPa fSWP=1 fSWP=1

SWPmin MPa fSWP=1 fSWP=1

Height m 20 8 Leaf dimension m 7 3.5

1 gmax. Use of the conversion factor of 0.663 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 gmax values contained within this Annex was that previously used (0.613).

Updated : June 2015


Since it remains difficult to represent the true effect of soil water limitations to gsto on individuals, for the “Deciduous” parameterisation it is assumed that soil moisture is not limiting gsto 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 fSWP = 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 O3 m-2 PLA s-1 is used in agreement with that used to derive the flux-based critical level.

III.6.3 AOT40-BASED CRITICAL LEVELS FOR FOREST TREE S

III.6.3.1 SCIENTIFIC BASIS OF AOT40-BASED METHODS AND CRITICA L LEVELS

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 r2 values (Table III.20).

Table III.19 : Sensitivity classes for the tree species based on effects of ozone on growth (Karlsson et al., 2003b).

Ozone-sensitive species

Moderately ozone-

sensitive species

Deciduous Coniferous Deciduous Coniferous

Fagus sylvatica

Betula pendula

Picea abies

Pinus sylvestris

Quercus petrea,

Quercus robur

Pinus halepensis

Using the sensitivity categories described above, AOT40 gave the highest r2 values of the AOTX indices tested (Figure III.10). However, the difference between the r2 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, CLec, 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


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 16th Task Force meeting of the ICP Vegetation and the 19th 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

r2 p for the slope p for the intercept slope

AOT20

Birch, beech 0.52 <0.01 0.70 - 0.357

Oak 0.57 <0.01 0.73 - 0.142

Norway spruce, Scots pine 0.73 <0.01 0.31 - 0.086

AOT30

Birch, beech 0.61 <0.01 0.63 - 0.494

Oak 0.61 <0.01 0.79 - 0.170


AOT40

Birch, beech 0.62 <0.01 0.31 - 0.732

Oak 0.65 <0.01 0.73 - 0.216


AOT50

Birch, beech 0.53 <0.01 0.05 - 1.033

Oak 0.62 <0.01 0.82 - 0.248


Updated : June 2015


III.6.3.2 AOT40-BASED CRITICAL LEVELS

The AOT40-based critical level for forest trees is presented in Table III.21, with the associated function presented in Figure III.10.

-30

-20

-10

0

10

0 10 20 30 40

Daylight AOT40 (ppm h)

% r

educ

tion

Birch, beech F.sylvatica, CH

B. pendula,Ostad, SE

B. pendula,Kuopio1, 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 Birch and beech species

Effect parameter Whole tree biomass

% reduction for critical level 5 %

Critical level (AOT40, ppm h) 5 ppm h

Countries involved in experiments Sweden, Finland, Switzerland

Number of data points 21

Data sources Described in karlsson et al., 2007

Time period Growing season

Response function Based on Annual reduction in total plant biomass

r2 0.62

P value <0.01

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.

Updated: june 2015


III.6.3.3 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 V EGETATION

III.7.1 OZONE SENSITIVITY OF (SEMI-)NATURAL VEGETAT ION

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 over-wintering 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 4000+ 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-

Although many species of semi-natural vegetation are sensitive to ozone, the least progress has been made with developing flux and flux-effect models for this type of vegetation. Any new developments since the 2014 revision will be added to Annex 3.

Updated : June 2015


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 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 clover-based critical levels may be applicable to such grassland.

III.7.2 STOMATAL FLUX-BASED METHODS FOR (SEMI-)NATU RAL VEGETATION

III.7.2.1 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 flux-based 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. POD0 or POD1). 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 23rd 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 early-season 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 3.22 and derived from functions in Figure 3.11 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

Updated: june 2015


species); the new flux-based critical level protects against a 10% reduction in biomass.

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 1990 – 2006, 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 km2 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 23rd 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 (gmax, 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


III.7.2.2 FLUX-BASED CRITICAL LEVELS FOR (SEMI-)NATURAL VEGETATION

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 response-functions 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*

Grasslands of high conservation

value (provisional)*

Representative species Trifolium spp. Trifolium spp. Viola spp.

Effect parameter Above-ground biomass

Above-ground biomass

Above-ground biomass

% reduction for critical level

10% 10% 15%

Critical level (POD 1, mmol m -2)

2 2 6


UK, Switzerland UK, Switzerland UK

Number of data points 9 9 10

Years of experiments 1993, 2000, 2002 1993, 2000, 2002 2003, 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

Hayes et al., 2006; Hayes et al., unpublished

Time period 3 months 3 months 3 months

Response function RY=0.97 – (0.035*POD1)

RY=0.97 – (0.035*POD1)

RY=0.98- (0.02*POD1)

r2 0.87 0.87 0.45

P value <0.001 <0.001 0.034

* may also be applicable to natural grassland ecosystems

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.

Updated: june 2015


(a) (b)

y = 0.97 -0.035 * POD1

r² = 0.87p < 0.001

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 4 8 12 16

Re

lativ

e bi

om

ass

POD1, mmol m-2

UK

CH

Clover

y = 0.98 - 0.020 * POD1r² = 0.45p = 0.034

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 2 4 6 8 10 12

Re

lativ

e bi

om

ass

POD1, mmol m-2

Violet

Figure III.11 : The relationship between the relative above-ground biomass and POD1 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.7.2.3 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 POD1 for representative (semi-)natural vegetation species.

Parameter Units Trifolium spp. Viola spp.

gmax1 mmol O3 m-2 projected

leaf area s-1 390 242


SGS year day 105 91

EGS year day 195 181

fphen_a fraction 1 1

fphen_b fraction 1 1

fphen_c fraction 1 1

fphen_d fraction 1 1

light_a constant 0.008 0.0072

Tmin °C 8 0.4

Topt °C 24 16

Tmax °C 39 32

VPDmax kPa 2.8 2.1

VPDmin kPa 4.5 3.5

SWPmin MPa -1.5 -

SWPmax MPa -0.49 -

LAImin m2 PLA m-2 0.5

LAImax m2 PLA m-2 4

Updated : June 2015


Parameter Units Trifolium spp. Viola spp.

Height m 0.2 0.15


1 gmax Use of the conversion factor of 0.663 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 gmax values contained here and used to calculate POD1 values in Figure 3.11 was that previously used (0.613).

III.7.2.4 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.7.2.3

Steps 4 – 7: Continue as described in Section III.4.4.

III.7.2.5 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.7.2.6 ESTIMATION OF RISK OF DAMAGE FOR A GENERIC (SEMI-) NATURAL VEGETATION (FOR INTEGRATED ASSESSMENT MODEL LING)

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.

Updated: june 2015


III.7.3 AOT40-BASED CRITICAL LEVELS FOR (SEMI-)NATU RAL VEGETATION

III.7.3.1 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.

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)

Growth reduction and/or seed production reduction in annual species

Perennials An AOT40 of 5 ppm h 6 months

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 exposure-response 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 3.25 summarises the key field chamber and field fumigation experiments which supported the original proposal of this critical level for (semi-) natural vegetation.

Updated : June 2015


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

Individual plants Solanum nigrum

Malva sylvestris

4.2

3.9

-23%; shoot mass

-54%; seed mass

Bergmann et al., 1996

Mesocosms of four species Trifolium repens 5.0 -13%; shoot mass Ashmore & Ainsworth, 1995

Mesocosms of seven species Festuca ovina

Leontodon hispidus

7.0

7.0

-32%; shoot mass

-22%; shoot mass

Ashmore et al., 1996

Ryegrass-clover sward Trifolium repens 5.0 -20%; shoot mass Nussbaum et al., 1995

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 2.5-4.5 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.

Updated: june 2015


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* Gillespie & Barnes, unpublished data

Veronica chamaedrys Poa pratensis Species biomass ratio 3.6 ppm h Bender et al. (2002)

Trifolium cherleri, T. striatum Briza maxima Flower production 2.2-2.7 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

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 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.

Updated : June 2015


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 Finland1 Dry grassland

(7 species) in open-top chambers

3 years

1.7 ppm h (C)

8.5 ppm h (T)

3 months fumigation in summer of 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

Wales2 Upland grassland

(7 species) in solardomes

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

Significant increase in community senescence detected in 10 ppm h treatment;

7% reduction in cumu-lative above-ground community biomass in 30 ppm h treatment

Significant increase in senescence for Festuca ovina and Potentilla erecta at 10 ppm h;

15% reduction in Anthoxanthum odoratum biomass within the community at 30 ppm h

Northern England3 Upland grassland

Species-rich (11 species), in open-top chambers

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’

16% reduction in total above-ground biomass

Significant reduction in biomass of Briza media and Phleum bertolonii

Southern England4 Calcareous grassland

(38 species) in open-top chambers

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

No significant effect on above-ground biomass

Significant change in community composition;

loss of Campanula rotundifolia

Switzerland5

60 year old alpine pasture

in field release system

5 years

8.4 ppm h (C)

34.0 ppm h (T)

ca. 6 months of fumigation each year

23% reduction in above-ground biomass

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


III.7.3.2 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:


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


III.7.3.3 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 http://mrw.wallonie.be/dgrne/sibw/Eunis/) 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 analysis1

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) 1Mills 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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III.9 ANNEXES FOR CHAPTER 3

III.9.1 ANNEX 1: ADDITIONAL INFORMATION FOR AGRICUL TURAL AND HORTICULTURAL CROPS

III.9.1.1 FURTHER DETAILS ON FLUX MODEL PARAMETERISATIONS INCLUDED IN CHAPTER III

III.9.1.1.1 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 gmax 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 gsto measurements made on cultivars grown either under field conditions or using field-grown plants in open top chambers in Europe were considered.

This Section provides the scientific justification for the flux parameterisations for wheat, potato and tomato provided in Table 3.10. Additional parameterisations have been added as described below. Revision history: August 2011 Minor edits March 2014 As agreed at 27th 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) April 2015 Parameterisation for tomato for local application in Mediterranean areas

(Section A1.4) was updated to match that in González-Fernández et al., 2014.

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 27th Task Force meeting of the ICP Vegetation, January, 2014.

Updated: june 2015


Measurements had to be made during those times of the day and year when gmax would be expected to occur and full details had to be given of the gas for which conductance measurements were made (e.g. H2O, CO2, O3) and the leaf surface area basis on which the measurements were given (e.g. total or projected). All gsto measurements were made on the flag leaf for wheat and for sunlit leaves of the upper canopy for potato using recognized gsto measurement apparatus. Tables A1.1 and A1.2 give details of the published data used for gmax derivation on adherence to these rigorous criteria. Figure A1.1 shows the mean, median and range of gmax values for each of the 14 and four different cultivars that provide the approximated gmax values of 500 and 750 mmol O3 m

-2 PLA s-1 for wheat and potato, respectively.

It should be noted that the wheat gmax value has been parameterised from data collected for spring and winter wheat cultivars. For potato additional gmax values from three USA grown cultivars are included in Figure A.1 for comparison (Stark, 1987), further substantiating the gmax value established for this crop type.

Potato, gmax

0

200

400

600

800

1000

g max

(m

mol

O3

m-2

s-1

, pro

ject

ed le

af a

rea)

Mar

is p

iper

Bin

tje

Sat

urna

Pro

min

ent

Bin

tje

Kar

dal

Rus

set B

urba

nk

Ken

nebe

c

Lem

hi R

usse

t

Cultivar

USA cultivars

Figure A1.1: Derivation of gmax for wheat and potato (see Tables A.1 and A.2 for details).

Updated : June 2015


Table A.1: Derivation of wheat gmax parameterisation. PLA = projected leaf area. The data used was first published in Pleijel et al., 2007 and updated in Grünhage et al., 2012.

Reference

gmax [mmol O 3

m−−−−2 s−−−−1 PLA]

gmax derivation Country Wheat type and cultivar Time of day Time of year gsto measuring

apparatus Gas / leaf area Growing conditions Leaf

Araus et al. (1989) 435

Value in Table. Cultivar and sowing time (average of 3) gsto used. Means of 5 to 7 replicates. gsto mmol CO2 m−2 s−1. Adaxial: 313, abaxial: 149

Spain Spring wheat,

Kolibri 9 to 13 hrs 14 March to 21 May

LI 1600 steady state porometer CO2 / PLA Field Flag

Araus et al. (1989)

376 Value in Table. Means and SE ± of 5 to 7 replicates. gsto mmol CO2 m−2 s−1. Adaxial: 267 ± 29, abaxial: 92 ± 16.

Spain Spring wheat, Astral

9 to 13 hrs 14 March to 21 May

LI 1600 steady state porometer

CO2 / PLA Field Flag

Araus et al. (1989) 366

Value in Table. Means and SE ± of 5 to 7 replicates. gsto mmol CO2 m−2 s−1. Adaxial: 251 ± 15, abaxial: 99 ± 22.

Spain Spring wheat, Boulmiche 9 to 13 hrs 14 March

to 21 May LI 1600 steady state porometer CO2 / PLA Field Flag

Ali et al. (1999) 660

From graph showing leaf conductance plotted against time in days. Maximum approximately 1 mol H2O m−2 s−1; ± 0.12. ± SE of 4 to 6 replicates.

Denmark Spring wheat,

Cadensa (Assumed mid-day) August IRGA LI-6200

H2O / * (Assume PLA as

use LAI)

Field Lysimeter Flag

Grüters et al. (1995) 525 Value in text. Maximum measured conductance (0.97 cm s−1 H2O

total leaf area after Jones (1983)). Germany Spring wheat,

Turbo 11 to 12 hrs 17 June to 7 August

LI 1600 steady state porometer

H2O / total leaf area Field Flag

Danielsson et al. (2003) 548

Value in text. "The maximum conductance value, 414 mmol H2O m−2 s−1, was taken as gmax for the Östad multiplicative model. The conductance values represent the flag leaf and are given per total leaf area".

Sweden Spring wheat,

Dragon 13 hrs 13 August 1996 (AA) Li-Cor 6200

H2O / total leaf area

Field OTC & AA Flag

Körmer et al. (1979) 492 Value given in table. 0.91 cm s−1 for H2O on a total leaf surface

area basis. Austria Durum wheat,

Janus - - Ventilated diffusion porometer

H2O / total leaf area Field Flag

Grünhage et al. (2012) 433 653 mmol H2O m−2 s−1 Germany Winter wheat,

Astron measured at 10 hrs

24 May to 14 June 2006 Li-Cor 6400 H2O / total leaf

area OTC (NF) Flag

Grünhage et al. (2012)

431 650 mmol H2O m−2 s−1 Germany Winter wheat, Pegassos

measured at 10 CET

24 May to 14 June 2006

Li-Cor 6400 H2O / total leaf area

OTC (NF) Flag

Grünhage et al. (2012) 556 839 mmol H2O m−2 s−1 (adaxial=524, abaxial=315) Germany Winter wheat,

Opus measured at 11 CET


Leaf porometer SC-1 H2O / PLA Field Flag


511 770 mmol H2O m−2 s−1 (adaxial=439, abaxial=331) Germany Winter wheat, Manager -

measured at 10 CET


Leaf porometer SC-1

H2O / PLA Field Flag


Carenius measured at 13 CET


Leaf porometer SC-1 H2O / PLA Field Flag


563 849 mmol H2O m−2 s−1 (adaxial=485, abaxial=364) Germany Winter wheat, Manager +

measured at 11:30 CET


Leaf porometer SC-1



Limes measured

at 11:30 CET 26 May to

02 June 2009 Leaf porometer

SC-1 H2O / PLA Field Flag


593 894 mmol H2O m−2 s−1 (adaxial=595, abaxial=299) Germany Winter wheat, Cubus

measured at 11:30 CET


Leaf porometer SC-1


Grünhage et al. (2012) 474 714.4 ± 42.1 mmol H2O m−2 s−1 France Winter wheat,

Soissons 11 to 16 CET 6 to 27 May 2009

PP systems CIRAS-2 H2O / PLA Field Flag

Grünhage et al. (2012) 492 741.6 ± 72.8 mmol H2O m−2 s−1 France Winter wheat,

Premio 11 to 16 CET 6 to 27 May 2009

PP systems CIRAS-2 H2O / PLA Field Flag

Mean : Median :

497 492 Range: 366 to 660 mmol O3 m−2 s−1

Updated: june 2015

Page III - 87 Page III - 87 Chapter III – Mapping Critical Levels for Vegetatio n

Table A1.2 : Derivation of potato gmax parameterisation. PLA = projected leaf area

Reference gmax

[mmol O3 m-2 s-1 PLA]

gmax derivation Country Potato

cultivar Time of day

Time of year

gsto 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 H2O / 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 co-efficient of variation typically ranged from 15 to 25%.

Netherlands

Bintje - June / July

Li-Cor 1600 steady state diffusion porometer

H2O / 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 co-efficient of variation typically ranged from 15 to 25%.

Netherlands

Saturna - June

Li-Cor 1600 steady state diffusion porometer

H2O / PLA Field Youngest

fully grown leaf

Marshall & Vos (1991)

643

Value given in Figure. gmax of 527 mmol H2O m-2 s-1 at intermediate N supply. Each point represents the mean of at least three leaves (usually four).

Netherlands

Prominent

- July LCA2 portable infra-red gas

analyser

H2O / assumed PLA

Field

Most recently expanded

measurable leaf

Pleijel et al. (2002) 836 Value given in Table. gmax of 1371 mmol m-2 s-1 for H2O per projected leaf area.

Germany Bintje 12 June Li-Cor 6200 H2O / PLA Field Fully

expanded in upper canopy

Danielsson (2003)

737 Value given in text. gmax of 604 mmol H2O m-2 s-1 per total leaf area.

Sweden Kardal 11 July Li-Cor 6200 H2O /

Total leaf area Field

Fully expanded in upper canopy

Mean

Median

738

743 Range: 643 to 836

Updated : June 2015


fmin

The data presented in Pleijel et al. (2002) and Danielsson et al. (2003) clearly show that for both species, fmin under field conditions frequently reaches values as low as 1% of gmax. Hence an fmin of 1% of gmax is used to parameterise the model for both species.

fphen

The data used to establish the fphen relationships for both wheat and potato are given in Figure A1.2 as °C days from gmax (in the case of wheat gmax is assumed to occur between growth stages "flag leaf fully unrolled, ligule just visible" and "mid-anthesis"; in the case of potato gmax is assumed to occur at the emergence of the first generation of fully developed leaves). Methods for estimating the timing of mid-anthesis are provided in Section III.5.2.4. whilst those for estimating fphen using the function illustrated in Figure A1 and the parameterisations given in Table III.10 are provided in Section III.5.2.3.

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)

-400 -200 0 200 400 600 800 1000 1200

Potato, fphen relationship

Thermal time from day of gmax, base temperature 0 °C

0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e g

Accumulation period

Updated: june 2015


Potato, ftemp relationship

Pleijel et al. (2002, OTC data)

Pleijel et al. (2002, AA data)

Temperature (°C)

0 10 20 30 40 500

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e g

Ku et al. (1977, W729R)

Dwelle et al. (1983, Russet Burbank)

flight

The data used to establish the flight relationship for both wheat and potato are shown in Figure A1.3.

Irradiance (µmol m-2 s-1 PPFD)

0 400 800 1200 1600 20000

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e g

Machado & Lagoa (1994)

Weber & Rennenberg (1996)

Gruters et al. (1995)

Bunce J.A. (2000)

Danielsson et al. (2003, OTC data)

Danielsson et al. (2003, AA data)

Wheat, flight relationship

Potato, flight relationship

Irradiance (µmol m-2 s-1 PPFD)

0 400 800 1200 1600 20000

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e g

Stark J.C. (1987, Kennebec)

Stark J.C. (1987, Russet Burbank)

Stark J.C. (1987, Lemhi Russet)



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, A66107-51)

Figure A1.3 : Derivation of flight for wheat and potato (see Pleijel et al., 2007 for further details)

ftemp

The data used to establish the ftemp relationship for both wheat and potato are shown in Figure A1.4.

Wheat, ftemp relationship

Temperature (°C)

0 10 20 30 40 500

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e g

Gruters et al. (1995)

Bunce J.A. (2000)

Danielsson et al. (2003, OTC data)

Danielsson et al. (2003, AA data)

Figure A1.4: Derivation of ftemp for wheat and potato (see Pleijel et al., 2007 for further details).

Updated : June 2015


fVPD and ΣΣΣΣVPDcrit

The data used to establish the fVPD 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 ΣVPDcrit for wheat and potato are given in Table III.10.

Potato, fVPD relationship

VPD (kPa)

0 1 2 3 4 50

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e g

Tuebner F. (1985)



Figure A1.5 : Derivation of fVPD for wheat and potato (see Pleijel et al., 2007 for further details).

fPAW for wheat

The shape of the fPAW function for wheat is shown in Figure A1.7.

Figure A1.7 : fPAW for wheat (for further details, see Grünhage et al., 2012).

fSWP for potato

The data used to establish the fSWP relationship for potato are given in Figure A1.8. It should be noted that the fSWP relationship for potato is derived from data that describe the response of potato gsto 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 pre-dawn leaf water potential is less than 0.1 to 0.2 MPa. As such, it may be necessary to revise this fSWP relationship so that potato gsto responds more sensitively to increased soil water stress.

Wheat, fVPD relationship

VPD (kPa)

0 1 2 3 4 50

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e g

Weber & Rennenberg (1996)

Tuebner F. (1985)

Bunce J.A. (2000)

Danielsson et al. (2003)

Updated: june 2015


Potato, fSWP relationship

Vos & Oyarzun (1987)

Basu et al. (1999)

SWP (MPa)

-2.0 -1.6 -1.2 -0.8 -0.4 00

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e g

Figure A1. 8: Derivation of fSWP for potato (see Pleijel et al., 2007 for further details).

fO3

The functions described for wheat and potato in Section III.5.2.3 should be used.

III.9.1.1.2 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., 2014.

The parameterisation for tomato was derived from gsto measurements made on seven cultivars grown in pots under open-top chambers conditions in southern Europe (Spain and Italy). Daily profiles of gsto were measured in different days from July to October, under varying environmental conditions. All gsto measurements were made in sunlit leaves of the upper canopy using standard gsto measurement systems. The gmax value for tomato was set as the average of the values above the 95th percentile of all the gsto measurements (Figure A1.9).

Figure A1.9 : Derivation of gmax for tomato

Updated : June 2015


fmin

The fmin value for tomato has been derived from the average of the values below the 5th percentile of all the gsto measurements.

fphen

The data used to establish the fphen function for tomato are presented in Figure A1.10. gmax was assumed to occur at a fixed number of days since the start of the growing season.

Figure A1.10 : fphen function for tomato

flight

The data used to establish the flight function for tomato are shown in Figure A1.11. The flight modifying function was adjusted by boundary line analysis

Figure A1.11 : Derivation of flight for tomato.

Updated: june 2015


ftemp

The data used to establish the ftemp function for tomato are shown in Figure A1.12. The ftemp modifying function was adjusted by boundary line analysis

Figure A1.12 : Derivation of ftemp for tomato.

fVPD

The data used to establish the fVPD function for tomato are shown in Figure A1.13. The fVPD modifying function was adjusted by boundary line analysis

Figure A1.13: Derivation of fVPD for tomato.

Updated : June 2015


fSWP

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., 2014. Setting ozone critical levels for protecting horticultural Mediterranean crops: Case study of tomato. Environmental Pollution 185, 178-187.

III.9.1.2 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. Gmax 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

gmax mmol O3 m-2 PLA s-1 430 410


Astart °C day -300 -300

Aend °C day 600 675

fphen_a fraction 0 0

fphen_b fraction 0 0

fphen_e °C day -300 -300

fphen_f °C day 0 0

fphen_g °C day 70 100

fphen_h °C day 0 0

fphen_i °C day 550 675

Light_a constant 0.0105 0.0105

Tmin °C 12 11

Topt °C 28 28

Tmax °C 39 45

VPDmax kPa 3.2 3.1

VPDmin kPa 4.6 4.9

Updated: june 2015


Parameter Units Bread wheat Durum wheat

ΣVPDcrit kPa 8 8

fPAW - -

fozone 1 1

SWCmax % 18.6 18

SWCmin % 4.7 4.1

Height m 0.75 0.75


III.9.1.3 FLUX MODELS FOR ADDITIONAL CROPS SUITABLE FOR REGIONAL RISK ASSESSMENT

The 27th 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.9.1.3.1 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 sub-tropical climate)

C. Reporting of gsto and information about measuring device used, leaf area reference (total vs. projected) and conditions under which gsto 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.

Updated : June 2015


Figure A1.12 Screening process and exclusion criteria for literature search. Example for poplar.

Derivation of gmax

The values for gmax 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 gsto was measured (H2O, CO2 or O3), 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 gsto 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 gsto values were deemed to represent gmax that were measured under non-limiting environmental conditions as clearly stated in the methods section of the respective paper. If gmax was reported for water vapour (gmax mmol H2O m

-2 PLA s-1), which was the case in the majority of papers, it was converted to gmax for O3 (gmax mmol O3 m

-2

PLA s-1) using the conversion factor of 0.663 (Grünhage et al., 2012) to account for the difference in the molecular diffusivity of water vapour to that of O3.

Values for gmax are provided in Table A1.4.

Table A1.4 : gmax derivation

Grapevine

(Vitis vinifera)

Maize

(Zea mays)

Soybean

(Glycine max)

Sunflower (Helianthus annuus)

Poplar

(Populus sp)

Median gmax

(mmol O3 m-2 PLA s-1)

229 326 706 386 392

standard deviation

(mmol O3 m-2 PLA s-1)

72 28 291 139 177

No. of studies 14 6 8 13 7

No. of data points 23 6 8 15 10

Updated: june 2015


III.9.1.3.2 PARAMETERISATION FOR GRAPEVINE (VITIS VINIFERA)

The parameterisation of the flux model for grapevine provided in Table A1.5 is considered robust. This is due to the use of both published data and gsto measurement datasets that have been kindly donated by a number of scientists who have worked with this species in the past.

Four studies (Petit et al., 2008; Tramontini et al., 2013; Vitali et al., 2013; Zsofi et al., 2009) provided new sets of gmax measurements for six varieties since the earlier study in 2006. Taking these new data points and the new diffusivity conversion factor into account, the new median gmax value for grapevine is 229 mmol O3 m-2 PLA s-1, with a standard deviation of 72 mmol O3 m

-2 PLA s-1 (Figure A1.13; Table A1.5). When separating red (n = 17; filled symbols in Fig. A1.13) from white (n = 6, empty symbols in Fig. A1.13) grapevine varieties, the median gmax value and standard deviation were 229 and 72 mmol O3 m-2 PLA s-1 respectively for red varieties (i.e. identical to entire dataset) and 205 and 77 mmol O3 m-2 PLA s-1 respectively for white varieties. In Table A1.5, the parameterisation provided is an average of all data availale for red and white varieties of grape.

New information for the derivation of fVPD was available from Vitali et al. (2013), which supported the existing fVPD paramaterisation. Tramontini et al. (2013) provided additional information for the parameterisation of fSWP, which also supported the existing fSWP paramaterisation. No additional information was available for the other environmental functions.

Table A1.5 : Parameterisation of the flux model for grapevine.

Parameter Units Grapevine

(Vitis vinifera)

Reference

SGS dd 105 (Jones et al., 2005)

EGS dd 270

Astart dd as SGS

Aend dd as EGS

LAI_max m2/m2 3.0 (Mascart et al., 1991); (Padro et al., 1994)

LAI_min m2/m2 0

Ls dd 20

Le dd 20

Albedo fraction 0.2 (Simpson et al., 2003)

Lm m 0.15 (Massman et al., 1994)

h m 1.7

root m 1.0 (Simpson et al., 2003)

gmax mmol O3 m-2 PLA s-1

229

(s.d.= 72)

(Correia et al., 1995) [305]; (Jacobs et al., 1996) [231]; (Massman et al., 1994) [348]; (Medrano et al., 2003) [308, 225]; (Naor and Wample, 1995) [229]; (Patakas et al., 2003) [146, 149, 139]; (Petit et al., 2008) [179]; (Schultz, 2003a) [248]; (Schultz, 2003b) [148, 166]; (Tramontini et al., 2013) [225, 106]; (Vitali et al., 2013) [232, 212]; (Winkel and Rambal, 1990) [292, 371, 239]; (Winkel and Rambal, 1993) [295, 213]; (Zsófi et al., 2009) [325]

Updated : June 2015


Parameter Units Grapevine

(Vitis vinifera)

Reference

fmin (fraction) 0.01 (Jacobs et al., 1996)

fphen_a (fraction) 0.2 (Correia et al., 1995); (Winkel and Rambal, 1993)

fphen_b (fraction) 0.2

fphen_c days 60

fphen_d days 45

lighta (constant) 0.0076 (Jacobs et al., 1996); (Lu et al., 2003); (Massman et al., 1994); (Schultz, 2003a); (Winkel and Rambal, 1990); (Winkel and Rambal, 1993)

Tmin oC 9 (Correia et al., 1995); (Flexas et al., 1999); (Jacobs et

al., 1996); (Massman et al., 1994); (Schultz, 2003a); (Schultz, 2003b) Topt

oC 30

Tmax oC 43

VPDmax kPa 1.6 (Correia et al., 1995); (During, 1987); (Jacobs et al., 1996); (Massman et al., 1994); (Medrano et al., 2003); (Schultz, 2003a), (Vitali et al., 2013) VPDmin kPa 6.2

SWPmax MPa -0.35 (Correia et al., 1995); (Quick et al., 1992); (Winkel and Rambal, 1993)

SWPmin MPa -1.2

0

100

200

300

400

500

0 5 10 15 20 25

gm

ax(m

mol

O3m

-2 P

LA s

-1)

Observations

Grapevine g max

median g max = 229 mmol O 3 m-2 PLA s -1

Fig. A1.13 : gmax derivation for grapevine. Filled symbols refer to red cultivars, empty symbols

refer to white cultivars. The black line represents the median value for the combined paramaterisation for both red and white culitvars.

Updated: june 2015


III.9.1.3.3 PARAMETERISATION FOR MAIZE (ZEA MAYS)

Using data from six studies, the median gmax value is 326 mmol O3 m-2 PLA s-1, with a

standard deviation of 28 mmol O3 m-2 PLA s-1 (Figure A1.14; Table A1.6).

No new data have been found to parameterise the phenological relationship so here we use the default parameterisation provided by Simpson et al. (2003). The flight parameterisation is based on seven studies, whereas ftemp, fVPD and fSWP are parameterised each based only on one or two studies. This lack of corroboration by different datasets reduces the certainty of the flux model for maize.

Table A1.6 Flux model parameterisation for maize.

Parameter Units Maize

(Zea mays)

Reference

SGS dd 130

FAO, AGL (2002)

http://www.fao.org/ag/agl/aglw/cropwater/maize.stm

EGS dd 250

Astart dd as SGS

Aend dd as EGS

LAI_max m2/m2 3.0

LAI_min m2/m2 0

Ls dd 40

Le dd 30

Albedo fraction 0.2

Lm m 0.1

h m 2.0 (Simpson et al., 2003)



326 (28) (Körner et al., 1979) [343]; (Ozier-Lafontaine et al., 1998) [326] ; (Sinclair et al., 1975) [386]; (Stigter and Lammers, 1974) [321]; (Tardieu et al., 1991) [312]; (Vitale et al., 2007) [312]

fmin (fraction) 0.05 (Bethenod and Tardieu, 1990); (Sanguineti et al., 1999)

fphen_a (fraction) 0.1 (after Simpson et al., 2003)

fphen_b (fraction) 1

fphen_c days 15

fphen_d days 45

lighta (constant) 0.0048 (Bethenod and Tardieu, 1990); (Guilioni et al., 2000); (Machado and Lagôa, 1994); (Olioso et al., 1995); (Ozier-Lafontaine et al., 1998); (Rochette et al., 1991); (Turner and Begg, 1973) ; (Vitale et al., 2007)

Tmin oC 2

(Rodriguez and Davies, 1982); (Turner and Begg, 1973) Topt oC 25

Tmax oC 48

VPDmax kPa 0 (Olioso et al., 1995)

VPDmin kPa 5.0

SWPmax MPa -0.12 (Davies et al., 1994); (Tardieu et al., 1992)

SWPmin MPa -0.8

Updated : June 2015


0

100

200

300

400

500

0 1 2 3 4 5 6 7

gm

ax(m

mol

O3

m-2

PLA

s-1

)

Observations

Maize gmax


Fig. A1.14 : gmax derivation for maize. The black line represents the median value.

III.9.1.3.4 PARAMETERISATION FOR SOYBEAN (GLYCINE MAX)

Most published studies for soybean have been conducted outside Europe, most commonly in North America (e.g. from the SoyFACE experiment in Illinois). Only two field studies from Europe (Bou Jaudé et al., 2008; Taconet et al., 1995) were deemed suitable for the derivation of gmax. Hence, the gmax parameterisation for soybean is based on two European and six North American datasets. A median gmax of 706 mmol O3 m

-2 PLA s-1 and a fairly large standard deviation of 291 mmol O3 m-2 PLA s-1 (Figure A1.15, Table A1.7) were derived, based on eight data points.

Data for the parameterisation of environmental functions was patchy, but various peer-reviewed studies - together with an Italian primary dataset (Rana, pers. comm.) – enabled the derivation of a parameterisation for fLight, fTemp, fVPD and fPhen.

Table A1.7 Flux model parameterisation for soybean.

Parameter Units Soybean

(Glycine max L.)

Reference

SGS dd 160 (Bou Jaudé et al., 2008); (Taconet et al., 1995)

EGS dd 270

Astart dd as SGS

Aend dd as EGS

LAI_max m2/m2 6 (Olioso et al., 1995); (Taconet et al., 1995)

LAI_min m2/m2 0

Ls dd 40

Le dd 30


Lm m 0.1 (Taconet et al., 1995)

Updated: june 2015


Parameter Units Soybean

(Glycine max L.)

Reference

h m 0.65



706

(s.d. = 291)

(Bernacchi et al., 2006) [484]; (Betzelberger et al., 2012) [716]; (Bou Jaudé et al., 2008) [1064]; (Gilbert et al., 2011) [1061]; (Gillespie et al., 2012) [696]; (Taconet et al., 1995) [381]; (Ward and Bunce, 1986) [431]; (Wilson and Bunce, 1997) [1041]

fmin (fraction) 0.06 (Rana, pers. comm.)

fphen_a (fraction) 0.4 (Bernacchi et al., 2006); (Bou Jaudé et al., 2008); (Gillespie et al., 2012)

fphen_b (fraction) 0.4

fphen_c days 50

fphen_d days 45

lighta (constant) 0.0035 (Gillespie et al., 2012), (Rana, pers. comm.)

Tmin oC 17 (Bernacchi et al., 2006); (Bunce, 1998), (Rana,

pers. comm.) Topt

oC 28

Tmax oC 38

VPDmax kPa 2.5 (Gilbert et al., 2011); (Gillespie et al., 2012), (Rana, pers. comm.)

VPDmin kPa 5.3

SWPmax MPa -1.5 (Rana, pers. comm.)

SWPmin MPa -4.0

0

200

400

600

800

1000

1200

1 2 3 4 5 6 7 8

gm

ax(m

mol

O3 m

-2 P

LA s

-1)

Observations

Soybean g max


Fig. A1.15 : gmax derivation for soybean. The black line represents the median value.

Updated : June 2015


III.9.1.3.5 PARAMETERISATION FOR SUNFLOWER (HELIANTHUS ANNUUS)

The flux model that was established for sunflower is reasonably robust with the exception of ftemp, which could not be parameterised yet due to a lack of data on the relationship between gsto and either air or leaf temperature. To overcome this problem, the fTemp parameterisation for maize is provided as a substitute.

A median gmax of 386 mmol O3 m-2 PLA s-1 and a standard deviation of 136 mmol O3 m

-2 PLA s-1 were derived, based on 15 data points from 13 studies (Table A1.8, Figure A1.16). Considering the fairly large number of peer-reviewed studies that contribute to the DO3SE parameterisation for sunflower, this parameterisation is considered to be fairly robust.

Table A1.8 Flux model parameterisation for sunflower.

Parameter Units Sunflower

(Helianthus annuus)

Reference

SGS dd 150 Putnam et al. (2013): Alternative Field Crops Manual

http://www.hort.purdue.edu/newcrop/afcm/sunflower.html

EGS dd 250

Astart dd as SGS

Aend dd as EGS

LAI_max m2/m2 5 (Sims et al., 1999)

LAI_min m2/m2 0

Ls dd 30

Le dd 0


Lm m 0.25 Putnam et al. (2013): Alternative Field Crops Manual

http://www.hort.purdue.edu/newcrop/afcm/sunflower.html h m 2

root m 1

gmax mmol O3 m-2 PLA s-

1

386

(s.d. = 139)

(Connor and Jones, 1985) [353]; (Csajbók et al., 2008) [226]; (Hirasawa et al., 1995) [514]; (Körner et al., 1979) [419; 386; 386]; (Rivelli et al., 2002) [586]; (Quick et al., 1992) [381]; (Schurr et al., 1992) [431]; (Steduto et al., 2000) [732]; (Turner et al., 1984) [351]; (Turner et al., 1985) [404]; (Ward and Bunce, 1986) [424]; (Wample and Thornton, 1984) [254]; (Wookey et al., 1991) [166]

fmin (fraction) 0.05 (Hirasawa et al., 1995)

fphen_a (fraction) 0.6 (Angadi and Entz, 2002); (Connor and Jones, 1985); (Wookey et al., 1991) fphen_b (fraction) 0.4

fphen_c days 34

fphen_d days 34

lighta (constant) 0.002 (Turner, 1970); (Fay and Knapp, 1996)

Tmin oC 2 Maize parameterisation used as default

Topt oC 25

Tmax oC 48

VPDmax kPa 1.2 (Teubner, 1985); (Turner et al., 1984); (Ward and Bunce, 1986) VPDmin kPa 4.0

SWPmax MPa -0.25 (Fambrini et al., 1994); (Gollan et al., 1986); (Hirasawa et al., 1995); (Quick et al., 1992); (Sadras et al., 1993); (Zhang and Davies, 1989) SWPmin MPa -1.65

Updated: june 2015


0

100

200

300

400

500

600

700

800

0 5 10 15g

max

(mm

ol O

3m

-2P

LA s

-1)

Observations

Sunflower g max


Fig. A1.16 : gmax derivation for sunflower. The black line represents the median value.

III.9.1.4 ADDITIONAL PUBLISHED FLUX MODELS FOR CROPS FOR LOCAL-SCALE APPLICATION

The papers listed below have been published in the peer review literature and contain flux parameterisations. They have not been approved by the ICP Vegatation Task Force for use on a European scale, but are included here as a useful reference for those wishing to conduct a local-scale risk assessment in the Mediterranean region.

Bean (Phaseolus vulgaris)

G. Gerosa, Riccardo Marzuoli, Micol Rossini, Cinzia Panigada, Michele Meroni, Roberto Colombo, Franco Faoro and Marcello Iriti, 2009b. A flux-based assessment of the effects of ozone on foliar injury, photosynthesis, and yield of bean (Phaseolus vulgaris L. cv. Borlotto Nano Lingua di Fuoco) in open-top chambers. Environmental Pollution 157, 1727-1736

Barley ( Hordeum vulgare)

Gerosa, G., Marzuoli, R., Cieslik, S., & Ballarin-Denti, A., 2004. Stomatal ozone fluxes over a barley field in Italy.“Effective exposure” as a possible link between exposure-and flux-based approaches. Atmospheric Environment, 38(15), 2421-2432.

III.9.1.5 ADDITIONAL FLUX-EFFECT RELATIONSHIPS AND FLUX-BASED CRITICAL LEVELS

No new information is currently available that has been approved for inclusion by the Task Force of the ICP Vegetation.

Updated : June 2015


III.9.1.6 REFERENCES RELATED TO CROPS

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Updated: june 2015


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Emberson, L.D., Simpson, D., Tuovinen, J.-P., Ashmore, M.R., Cambridge, H.M., 2000b. Towards a model of ozone deposition and stomatal uptake over Europe, Norwegian Meteorological Institute, Oslo, EMEP MSC-W Note 6/2000, 57p.

Emberson, L.D., Simpson, D., Tuovinen, J.-P., Ashmore, M.R., Cambridge, H.M., 2001. Modelling and Mapping ozone deposition in Europe, Water Air Soil Poll., 130, 577-582.

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Flexas, J., Escalona, J.M., Medrano, H., 1999. Water stress induces different levels of photosynthesis and electron transport rate regulation in grapevines. Plant Cell Environ. 22, 39–48.

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Gilbert, M.E., Holbrook, N.M., Zwieniecki, M.A., Sadok, W., Sinclair, T.R., 2011. Field confirmation of genetic variation in soybean transpiration response to vapor pressure deficit and photosynthetic compensation. Field Crops Res. 124, 85–92.

Gillespie, K.M., Xu, F., Richter, K.T., Mcgrath, J.M., Markelz, R.J.C., Ort, D.R., Leakey, A.D.B., Ainsworth, E.A., 2012. Greater antioxidant and respiratory metabolism in field-grown soybean exposed to elevated O3 under both ambient and elevated CO2: Antioxidant response to elevated O3 and CO2. Plant Cell Environ. 35, 169–184.

Updated : June 2015


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Updated: june 2015


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Updated : June 2015


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Updated: june 2015


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Taconet, O., Olioso, A., Ben Mehrez, M., Brisson, N., 1995. Seasonal estimation of evaporation and stomatal conductance over a soybean field using surface IR temperatures. Agric. For. Meteorol. 73, 321–337.

Tardieu, F., Katerji, N., Bethenod, O., Zhang, J., Davies, W.J., 1991. Maize stomatal conductance in the field: its relationship with soil and plant water potentials, mechanical constraints and ABA concentration in the xylem sap. Plant Cell Environ. 14, 121–126.

Tardieu, F., Zhang, J., Davies, W.J., 1992. What information is conveyed by an ABA signal from maize roots in drying field soil? Plant Cell Environ. 15, 185–191.

Teubner, F., 1985. Messung der Photosynthese und Transpiration an Weizen, Kartoffel und Sonnenblume. Diplomarbeit University Bayreuth, West Germany.

Tramontini, S., van Leeuwen, C., Domec, J.C., Destrac-Irvine, A., Basteau, C., Vitali, M., Mosbach-Schulz, O., Lovisolo, C., 2013. Impact of soil texture and water availability on the hydraulic control of plant and grape-berry development. Plant Soil 368, 215–230.

Tricker, P.J., Pecchiari, M., Bunn, S.M., Vaccari, F.P., Peressotti, A., Miglietta, F., Taylor, G., 2009. Water use of a bioenergy plantation increases in a future high CO2 world. Biomass Bioenergy 33, 200–208.

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Turner, N.C., Begg, J.E., 1973. Stomatal Behavior and Water Status of Maize, Sorghum, and Tobacco under Field Conditions. I. At High Soil Water Potential. Plant Physiol. 51, 31–36.

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Vitale, L., Di Tommasi, P., Arena, C., Fierro, A., Virzo De Santo, A., Magliulo, V., 2007. Effects of water stress on gas exchange of field grown Zea mays L. in Southern Italy: An analysis at canopy and leaf level. Acta Physiol. Plant. 29, 317–326.

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Updated : June 2015


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, 253–264.

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Updated: june 2015


III.9.2 ANNEX 2: ADDITIONAL INFORMATION FOR FOREST TREES

III.9.2.1 FLUX PARAMETERISATION FOR GENERIC SPECIES OF FOREST TREES

Table A2.1 (also included as Table III.18 in Section III.3.4.8) contains the parameterisations for a generic deciduous tree species and a generic broadleaved evergreen species suitable for Mediterranean areas. Additonal details are provided here on the sources of data for the parameterisation of the models. Use of the conversion factor of 0.663 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 gmax values contained within this Annex was that previously used (0.613).

Table A2.1: Parameterisation for generic deciduous tree and broadleaved evergreen tree flux models (POD1IAM). The data sources are provided in the footnotes at the end of the table, denoted

by (x).

Parameter Units Deciduous species Broadleaved evergr een species

Land use EUNIS class, area in km2

All forested areas Mediterranean evergreen forest species

gmax mmol O3 m-2

projected leaf area (PLA) s-1

150 (1 175 (2

fmin (3 fraction 0.1 0.02

SGS year day Latitude model 1 (1 Jan)

EGS year day Latitude model 365 (31 Dec)

fphen_a (fraction) 0 1

fphen_b (fraction) (1) 1

fphen_c (fraction) 1 0.3

fphen_d (fraction) (1) 1

fphen_e (fraction) 0 1

fphen_1 (days) 15 (0)

fphen_2 (days) (200) 130

fphen_3 (days) (200) 60

fphen_4 (days) 20 (0)

fphen_limA (days) = SGS 80 (21 Mar)

fphen_limB (days) = EGS 320 (16 Nov)

light_a (4 constant 0.006 0.009

Tmin (5 oC 0 2

Topt (5 oC 21 23

Tmax (5 oC 35 38

VPDmax (6 kPa 1.0 2.2

VPDmin (6 kPa 3.25 4.0

SWPmax (7 MPa fSWP=1 fSWP=1

SWPmin (7 MPa fSWP=1 fSWP=1

LAImin m2 PLA m-2 0 5

LAImax m2 PLA m-2 4 5

LAIs m2 PLA m-2 15 -

LAIe m2 PLA m-2 30 -

Height m 20 8

Leaf dimension m 7 3.5

Updated : June 2015


(1 Median value, range 100 – 180 mmol O3 m-2 PLA s-1, based on data for mature beech

trees from the following papers (gmax value in mmol O3 m-2 PLA s-1 given in square brackets

after reference): Raftoyannis & Radoglou (2002) [156]; cf. Körner et al. (1979) [100; 140]; Matyssek et al. (2004) [132]; Nunn et al. (2005) [147]; Keel et al. (2007) [180]; Schaub et al. (2005) [137] and Aranda et al. (2000) [180].

(2 Median value, range 70-365 mmol O3 m-2 s-1, based on data for mature Holm oak from

Vitale et al. (2005) [68]; Castell et al. (1994) [177]; Damesin et al. (1998) [171]; Mediavilla & Escudero (2003) [122]; Corcuera et al. (2005) [134]; Gratani et al. (2000) [159]; Ogaya & Peñuelas (2003) [67]; Rhizopoulos & Mitrakos (1990) [250]; Manes et al. (1997) [366]; Filho et al. (1998) [225]; Tognetti et al. (1998) [195]; Sala & Tenhunen (1994) [165]; Elvira et al. (2005) [183] and Infante et al. (1999) [323]; N.B. gmax value in mmol O3 m

-2 PLA s-1 given in square brackets after reference.

(3 fmin for “Deciduous” forests based on data from Tognetti et al. (1995); Matyssek et al. (2004); Nunn et al. (2005); Braun et al. (2010) and for “Mediterranean evergreen” forests based on data from Tognetti et al. (1998) and Infante et al. (1999).

(4 flight for “Deciduous” forests based on data from Nunn et al. (2005) mature beech data that defines a more sensitive response to irradiance within which other data (Kutsch et al., 2001; Eamus & Murray, 1991; Braun pers. comm.) are incorporated. flight for “Mediterranean evergreen” forests based on data for Holm oak (Quercus ilex) (Sala & Tenhunen, 1994) and Quercus coccifera (Tenhunen et al., 1985).

(5 ftemp for “Deciduous” forests based on data 2 datasets describing the gsto relationships with temperature for sun leaves of a beech canopy (Nunn et al., 2005; Novak, pers. comm.). To incorporate the more northern parts of Europe, a combination of birch and beech is used to define a Tmin threshold of 0oC; the birch data upon which this value is based are from Larcher (1969) whilst sap flow beech data (Braun, pers. comm.) support a Tmin of 3 °C. ftemp for “Mediterranean evergreen” forests is based on data from Tenhunen et al., 1987; Vitale et al., 2005; Manes et al., 1997; Corcuera et al., 2005; Ogaya & Peñuelas, 2003).

(6 fVPD for “Deciduous” forests is based on data from Kutsch et al., 2001; Nunn et al., 2005; Aranda et al., 2000; Keel et al., 2007; Novak, pers. comm.. fVPD for “Mediterranean evergreen” forests is based on Sala & Tenhunen, 1994; Tognetti et al., 1998; Vitale et al., 2005; Manes et al., 1997; Elvira et al., 2005.

(7 The value 1 would capture most sensitive ecosystem in a grid cell but not give information on variation due to this parameter within a grid cell.

Updated: june 2015


III.9.2.2 SPECIES- AND REGION-SPECIFIC FLUX PARAMETERISATIONS FOR FOREST TREES

III.9.2.2.1 SELECTION OF REPRESENTATIVE SPECIES BY CLIMATIC REGION AND SOURCES OF UNCERTAINTY

The methods presented in this section were developed by the forest sub-group (established at the LRTAP Convention Critical Level meeting held in Obergurgl, 2006) and adopted at the Task Force meeting of ICP Forests, Cyprus, 25 – 28 May, 2008.

The parameterisation for “generic” “broadleaved deciduous” and “broadleaved Mediterranean evergreen” forest tree species (see Section III.6.2.6) were defined first for an assessment of the degree of risk of damage to forest trees across Europe. Since it is well recognised that flux is strongly affected by a range of factors that determine both seasonal and diurnal flux patterns, an obvious improvement in the flux-based method was incorporation of these factors through the definition of parameterisations for a number of different species that are specific to climatic regions. The species-specific parameterisation defined here was an attempt to achieve this by refining the estimation of flux through identification of the most important influencing factors and developing methods to incorporate these in the risk assessment in relation to key species and climate regions.

The chosen species (listed in Table A2.2) have been selected for their known sensitivity to ozone, representation of a range of plant functional types, and ecological and economic importance within four distinct European climate regions. Parameterisations have, where necessary, been defined for different ecotypes of key species (e.g. Norway spruce and beech) to allow for climate and genotype influences on stomatal ozone flux. These should improve on stomatal flux estimates by incorporating ecotype responses to variations in rather extreme climatic conditions, for example the ability of Norway spruce to have a greater stomatal activity at lower temperatures commonly experienced by trees growing in Northern compared to central Europe or the ability of Mediterranean beech to retain stomatal conductance at far higher VPDs than trees of the same species found in Atlantic central Europe. However, the certainty with which these finer aspects of the parameterisation are made depend on the availability and reliability of the data from which parameterisations are derived; this should always be kept in mind when applying the species-specific parameterisations.

This section provides the scientific justification for the flux parameterisations for beech, birch and Norway spruce provided in Table 3.16. It also includes parameterisations for other representative species and regions. These were not included in the derivation of flux-based critical levels for forest trees as there was insufficient effect data available at the time. Should any new parameterisations for additional trees or regional parameterisations be approved by ICP Vegetation Task Force meetings following the summer, 2010 revision, they will be added to the end of this section.

Revision history

March 2014 As agreed at 27th Task Force meeting of the ICP Vegetation, Paris, 2014, the following was added:

Parameterisation for Poplar for regional application (Section A2.3)

Updated : June 2015


Table A2.2 : Representative forest species by European region.

European region Coniferous Deciduous Mediterranean broadleaved Evergreen

Northern Europe Norway Spruce Birch -


Scots pine Beech & temperate

Oak -


Norway spruce Beech -

Mediterranean Coastal/Continental

location Aleppo pine Beech Holm oak

The species selected incorporate a range of forest tree functional types (i.e. coniferous, deciduous, Mediterranean broadleaved and needleleaf) to try to capture the variety of seasonal physiological activity that exists across Europe. This provides an estimate of the influence of phenology, one of the key drivers of stomatal ozone flux, on risk. However, there are uncertainties associated with the estimation of key growth periods (e.g. start of physiological activity and onset of dormancy in conifers, initiation of leaf flush and onset of leaf fall in deciduous species etc...) for the “real” species. In the methods to define the parameters SGS and EGS, and how the seasonal LAI and fphen profiles sit in relation to these, many assumptions have had to be made using limited data. These profiles provide improvements in modelling the seasonal variation of flux but are dependent upon data providing reliable descriptions of phenological characteristics in relation to species, ecotypes and prevailing environmental conditions (perhaps model importantly temperature and soil moisture status). The provision of new datasets in the future will aid evaluation of the seasonality of flux and inform the continued development of appropriate modelling methods. The influence that soil water status has on gsto is arguably the most important environmental driver of stomatal ozone flux (especially during hot, dry years); as such, the development of modelling methods to incorporate this environmental variable into flux estimates must be a priority for future work.

The leaf level gsto is calculated for daylight hours according to equation III.10a (Section III.4.3) using parameterisations and methods described in Section III.6.2.3. However, it is important to be aware that significant night-time transpiration (and hence PODy) has been observed in many tree species (e.g. Körner, 1994; Matyssek et al., 1993; Musselman & Minnick, 2000; Grulke et al., 2004); the possibility of limiting the flight term by fmin (defined for forest trees as the lowest gsto normally found in the field; after Körner, 1994) was considered by the forest-sub group. The lack of observational data describing night-time gsto for the species across the climate regions coupled with the implications of allowing night-time gsto (which might significantly alter PODy patterns across Europe e.g. the spatial pattern of flux may alter disproportionately with latitude in relation to both length of night-time period and growing season) combine to prevent any current change in the formulation. However, this issue is one that should be addressed in future revisions of the species-specific method once more data is available.

Updated: june 2015


Finally, and perhaps most importantly, it is necessary to emphasise the limitations of the use of the species-specific stomatal ozone flux (PODy) estimates. These estimates should be viewed as providing an indication of risk relative stomatal flux between and within species and climate regions; not the actual PODy across Europe. This is exemplified by the suggestion within the species-specific parameterisations to model and compare both upper canopy leaf- and whole canopy- stomatal fluxes for risk assessment. This clearly indicates that additional work is needed both to develop modelling methods (in this case, rather specifically, up-scaling methods) as well as to interrogate resulting flux maps to interpret the predicted spatial variation in risk. For example, further work should be targeted towards evaluation of these relative estimates of risk; perhaps one important aspect of this would be to compare high and low ozone years as well as climatically extreme years (i.e. hot, dry years with cool wet years). This would help to determine the sensitivity of the flux estimate to plant functional type and ecotype as well as prevailing climatic conditions providing an indication of the robustness of the index. In addition, flux maps should be compared with field based evidence of effects to ensure that damage is occurring in those regions identified through modelling and mapping as being at risk. Further improvements would be to aim at an improved understanding of the variability of the detoxification threshold (Y) with leaf morphology, phenology and environmental conditions.

III.9.2.2.2 SPECIES-SPECIFIC MODEL FORMULATION AND PARAMETERISATION

III.9.2.2.2.1 RELATIONSHIP BETWEEN LEAF AREA INDEX (LAI), START OF GROWING SEASON (SGS) AND END OF GROWING SEASON ( EGS)

SGS and EGS

A key driver of stomatal ozone flux is seasonality (i.e. the timing of the physiologically active growth period); this will primarily depend on geographical location but will also be influenced by species. The EMEP latitude model was developed to identify the timing of the growing season of the “generic” deciduous species (Section III.6.2.4). This model gave good agreement with observed phenological data for a range of deciduous species (e.g. birch, beech and oak; see also Figure A2.1); with measurements of carbon flux from CarboEurope (http://www.carboeurope.org/) which were used to identify the initiation and cessation of physiological activity; and was also able to describe the onset of forest green-up and dormancy as determined from European remotely sensed data (Zhang et al. 2004). Thus, the EMEP latitude model was chosen to estimate the timing of the physiologically active growth period for the “real” species.

Updated : June 2015


50

100

150

200

250

300

350

35 40 45 50 55 60 65 70

Latitude

Yea

r D

ay

Figure A2.1: Comparison of observational phenological data with the EMEP latitude model. The black lines show the SGS and EGS determined from the EMEP latitude model. The green and

orange lines show the onset of green-up and dormancy described by remotely sensed data for the year 2001 (Zhang et al. 2004) and the vertical red lines show the variation in observed SGS and

EGS dates for sites at specific latitudes for a number of different years.

The physiologically active growth period is defined using the terms SGS and EGS. SGS is defined as the date of leaf unfolding (deciduous & broadleaved evergreen species); or the start of leaf/needle physiological activity (coniferous and evergreen species). For the species-specific flux models, SGS is estimated by the EMEP latitude model with the exception of temperate conifers south of ~55oN, where SGS is defined by prevailing environmental conditions (using the ftemp function), and for Mediterranean trees where a year round growth period is assumed. EGS is defined as the onset of dormancy; the EMEP latitude model is used to identify EGS again with the exception of temperate conifers south of ~55oN where EGS is defined by prevailing environmental conditions (using the ftemp function) and for Mediterranean trees where a year round growth period is assumed. The methods used to derive SGS and EGS for different species by forest type are included in the tables of regional paramterisations.

III.9.2.2.2.2 FPHEN

Coniferous and deciduous forest species

The minimum length of these respective periods (see Tables A1.3 to A1.6) has been used in the parameterisation to ensure that periods when forests are potentially experiencing higher ozone uptake are incorporated in the risk assessment.

For the beginning of the growing season the increase in gsto to gmax will begin on the year day defined as SGS. The time to reach gmax is defined by fphen_e. For the end of the growing season, the decrease in gsto from gmax is defined by fphen_f and assumed to occur on the year day defined as EGS. For forests species the fphen parameterisation is based on data describing the increase and reduction in gsto with the onset and end of physiological activity (coniferous species) and the green growth period (deciduous species) respectively. A modified formulation for the fphen relationship is used that allows for a consistent formulation irrespective of whether there is a mid-season dip in fphen (see Section III.6.2.3, as is required to model fphen for some Mediterranean species in the absence of methods to simulate the effect of mid-season water stress on stomatal conductance). For non-Mediterranean species parameterisations, the values in brackets for the phenology function in the tables represent “dummy” values to be used in areas where this mid-season dip does not occur.

Updated: june 2015


Mediterranean evergreen forest species

For Mediterranean forests, the fphen value would ideally be set equal to 1 and the reduction in gsto that is frequently observed during the summer period would be estimated as a function of soil water status. However, if tested methods to determine soil water content for the “real” species and conditions are not available, it is recommended to default to the fphen relationship used previously for Mediterranean evergreen “generic” species (see Table 3.18, and notes for Table A2.6). This function acts as a surrogate for soil water stress and may also incorporate phenological limitations to gsto that have been suggested to occur during the summer. For example, a depletion in gsto for Holm Oak was found during summer even under apparent non-drought stress conditions (Alonso et al., 2008) suggesting both the fphen and fSWP terms may be required to estimate gsto. The fphen function (in the absence of information describing soil water or phenological influence on gsto) now includes the parameters fphen_lim

A and fphen_limB representing the start and end of the

assumed soil water limiting period. For explanation see Table III.16 and associated text, Section I.6.2.3.

Figure A2.2 describes the variation in both leaf area index (LAI) and fphen for the Mediterranean broadleaved evergreen forest species (i.e. Holm oak). Mediterranean forest species are assumed to have a year round growth period within which particular phenologically relevant periods occur that help to define the seasonal LAI and fphen profiles. LAI will be strongly influenced by the start and end of the leaf flush period (initiation of which is defined as LAIs and LAIe in the formulations referred to in Table A2.6).

Figure A2.2: Seasonal variation in LAI and fphen assumed for Mediterranean evergreen broadleaved forest species (see Table A2.6 for further details of parameterisations and

formulations).

Updated : June 2015


III.9.2.2.2.3 F FUNCTION PARAMETERISATIONS

fmin, flight, ftemp, fVPD and fSWP functions are provided in Tables A2.3 to A2.6 for the species- and region-sepcific flux models. The formulations relating to these functions are the same as those described for beech and birch, and Norway spruce in Table III.18, Section III.6.2.6. For “real” species we define values for the fSWP function to emphasise that stomatal ozone flux should be estimated on consideration of the soil water status; the fSWP limits represent the maximum soil water status (SWCmax or SWPmax) below which gsto will start to decline and the minimum soil water status (SWCmin or SWPmin) at which gsto will reach fmin; with SWCtexture (soil water content for a particular soil texture class indicated) or SWP (soil water potential) values provided. The calculation of soil water status should use these parameters as appropriate for the selected method.

If an appropriate method for the estimation of fSWP is unavailable, it is recommended to assume that soil moisture is not limiting gsto (i.e. fSWP = 1); hence stomatal ozone flux is assumed unaffected by water stress. However, it should be recognised that this is likely to overestimate the risk determined from stomatal ozone flux where water stress conditions do occur.

III.9.2.2.2.4 TABLES OF REGIONAL FLUX MODEL PARAMETERISATIONS FOR TREES

Table A2.3 : Northern European “real” species parameterisation for Norway spruce and Silver birch. The parameters for the “generic” broadleaved deciduous tree species are also given for

reference.

Parameter Units Norway spruce parameterisation

Silver birch parameterisation

Generic Broadleaved Deciduous

species


Coniferous forests

Deciduous broadleavedforests

All forested areas

gmax mmol O3 m

-2 projected leaf area

s-1

112 (111-118 range)

196 (180-211 range)

150

fmin (fraction) 0.1 0.1 0.1

SGS year day Latitude model Latitude model Latitude model

EGS year day Latitude model Latitude model Latitude model

fphen_a (fraction) 0 0 0

fphen_b (fraction) (1) (1) (1)

fphen_c (fraction) 1 1 1

fphen_d (fraction) (1) (1) (1)

fphen_e (fraction) 0 0 0

fphen_1 (days) 20 20 15

fphen_2 (days) (200) (200) (200)

fphen_3 (days) (200) (200) (200)

fphen_4 (days) 30 30 20

fphen_limA (days) (0) (0) (0)

fphen_limB (days) (0) (0) (0)

light_a 0.006 0.0042 0.006

Tmin oC 0 5 0

Updated: june 2015



Silver birch parameterisation


species

Topt oC 20 20 21

Tmax* oC 200** 200** 35

VPDmax kPa 0.8 0.5 1.0

VPDmin kPa 2.8 2.7 3.25

SWCmax

(medium)** % volume 15 15 fSWP=1

SWCmin (medium)* % volume 1 1 fSWP=1

Y 1 1 1

h m 20 20 20

L cm 0.8 5 7

* The Tmax value is set at 200oC to simulate the weak response to high temperatures of Norway spruce trees growing under Northern European conditions (the stomatal response is instead mediated by high VPDs). Hence, this Tmax value should be viewed as a forcing rather than descriptive parameter. ** SWC is calculated as the soil water content available for transpiration, i.e. the actual SWC minus the SWC at the wilting point.

Data sources:

gmax: Norway spruce median value 112, range (111-118) mmol O3 m-2 PLA s-1, based on data for

mature Norway spruce trees from Zimmerman et al. (1988) [112]; Sellin (2001) [119]; Hansson et al. (in prep.) [111]

Silver birch median value 196, range (180-211) mmol O3 m-2 PLA s-1, based on data for mature silver birch trees from Uddling et al. (2005a) [180]; Sellin et al. (2005) [211]

fphen : Norway spruce: Expert opinion

Scots pine: Expert opinion

fmin : Norway spruce : Hansson et al. (in prep.)

Silver birch: Uddling et al. (2005a)

f light : Norway spruce: Karlsson et al. (2000); Hansson et al. (in prep.)


f temp : Norway spruce: Karlsson et al. (2000); Hansson et al. (in prep.); Jarvis (1980); Lagergren & Lindroth (2002)


fVPD: Norways spruce: Hansson et al. (in prep.), Zimmermann et al. (1988)


fSWP: Norway spruce: Sellin (1997)


Updated : June 2015


Table A2.4 : Atlantic Central European “real” species parameterisation for Scots pine, temperate oak and beech. The parameters for the “generic” broadleaved deciduous tree species are also

given for reference.

Parameter Units Scots pine parameterisation

Temperate Oak parameterisation

Beech parameterisation*



Coniferous forests


forests


forests


forests

gmax mmol O3 m

-2 projected

leaf area s-1

180 (171-188 range)

230 (177-325 range) 150 (100-180) 150 (100-

180)

fmin (fraction) 0.1 0.06 0.1 0.1

SGS year day ftemp Latitude model Latitude model Latitude model

EGS year day ftemp Latitude model Latitude model Latitude model

fphen_a (fraction) 0.8 0 0 0

fphen_b (fraction) (1) (1) (1) (1)

fphen_c (fraction) 1 1 1 1

fphen_d (fraction) (1) (1) (1) (1)

fphen_e (fraction) 0.8 0 0 0

fphen_1 (days) 40 20 15 15

fphen_2 (days) (200) (200) (200) (200)

fphen_3 (days) (200) (200) (200) (200)

fphen_4 (days) 40 30 20 20

fphen_limA (days) (0) (0) (0) (0)

fphen_limB (days) (0) (0) (0) (0)

light_a 0.006 0.003 0.006 0.006

Tmin oC 0 0* 0 0

Topt oC 20 20* 21 21

Tmax oC 36 35* 35 35

VPDmax kPa 0.6 1.0* 1.0 1.0

VPDmin kPa 2.8 3.25* 3.25 3.25

SWPmax MPa -0.7 -0.5 -0.8 -0.8

SWPmin MPa -1.5 -1.2 -1.5 -1.5

Y 1 1 1 1

h m 20 20 20 20

L cm 0.8 5 7 7

* „generic“ deciduous parameterisation used as surrogate.

Updated: june 2015


Data sources

gmax: temperate oak median value 230, range (177-325) mmol O3 m-2 PLA s-1, based on data for

mature temperate oak trees: Q. petraea from Breda et al. (1995) [228]; Epron & Dreyer (1993) [177]; Breda et al. (1993a) [233]; Breda et al. (1993b) [275]; Q. robur from Epron & Dreyer (1993) [198]; Breda et al. (1993b) [325]; Dolman & Van den Burg (1988) [264]

Scots pine median value 175, range (171-188) mmol O3 m-2 PLA s-1, based on data for mature Scots pine trees from Whitehead et al. (1984) [188]; Beadle et al.(1985) [175]; Sturm et al. (1998) [171]

fphen : temperate oak: Breda et al. (1993b); Breda et al. (1995)

Scots pine: Körner et al. (1995)

fmin : temperate oak : Breda et al. (1993b)

Scots pine Körner et al. (1995)

f light : tenperate oak: Breda et al. (1995); Dolman & Van den Burg (1988)

Scots pine: Beadle et al.(1985); Sturm et al. (1998); Ng (1979)

f temp : tenperate oak: „generic“ deciduous parameterisation used as surrogate

Scots pine: Beadle et al.(1985); Sturm et al. (1998); Ng (1979)

fVPD: temperate oak:: Dolman & Van den Burg (1988)

Scots pine: Beadle et al.(1985); Sturm et al. (1998); Ng (1979); Whitehead et al. (1984)

fSWP: temperate oak: Epron & Dreyer (1993); Breda et al (1993a); Breda et al. (1993b); Vivin et al. (1993)

Scots pine: Sturm et al. (1998); Ng (1979)

Updated : June 2015


Table A2.5 : Continental Central European “real” species parameterisation for Norway spruce and beech. The parameters for the “generic” broadleaved deciduous tree species are also given for

reference.


Beech parameterisation



Coniferous forests Deciduous broadleaved

forests


forests

gmax mmol O3 m

-2 pro-jected leaf area s-1

125 (87-140) 150 (132-300) 150 (100-180)1

fmin (fraction) 0.16 0.13 0.1

SGS year day ftemp Latitude model Latitude model

EGS year day ftemp Latitude model Latitude model

fphen_a (fraction) 0 0 0

fphen_b (fraction) (1) (1) (1)

fphen_c (fraction) 1 1 1

fphen_d (fraction) (1) (1) (1)

fphen_e (fraction) 0 0.4 0

fphen_1

(days) 0 20 15

fphen_2 (days) (200) (200) (200)

fphen_3 (days) (200) (200) (200)

fphen_4 (days) 0 20 20

fphen_limA (days) (0) (0) (0)

fphen_limB (days) (0) (0) (0)

light_a 0.01 0.006 0.006

Tmin oC 0 5 0

Topt oC 14 16 21

Tmax oC 35 33 35

VPDmax kPa 0.5 1.0 1.0

VPDmin kPa 3.0 3.1 3.25

SWPmax ** MPa -0.05 -0.05 fSWP=1

SWPmin MPa -0.5 -1.25 fSWP=1

Y 1 1 1

h m 20 25 20

L cm 0.8 7 7

Updated: june 2015


Data sources

gmax: beech median value 150, range 132 – 300 mmol O3 m-2 PLA s-1, based on data for mature

beech trees from Nunn et al. (2005) [147]; Matyssek et al. (2004) [132]; Keel et al. (2007) [180]; Kutsch et al. (2001) [300]; Freeman (1998) cf. Medlyn et al. (2001) [180]; cf. Körner et al. (1979) [150]; Schaub (pers. comm.) [137]

Norway spruce median value 125, range (87-140) mmol O3 m-2 PLA s-1, based on data for

mature Norway spruce trees from cf. Körner et al. (1979) [87]; Dixon et al. (1995) [121]; Emberson et al. (2000) [130]; Zweifel et al. (2000, 2001, 2002) [140]

N.B. gmax value in mmol O3 m-2 PLA s-1 given in square brackets after reference.

fphen : beech: Braun et al. (in prep)

Norway spruce: Expert opinion

fmin : beech and Norway spruce Körner (1994)

f light : beech: „generic“ deciduous parameterisation used as surrogate

Norway spruce: Theone et al. (1991); Körner et al. (1995); Zweifel et al. (2000, 2001, 2002)

f temp : beech: Braun et al. (in prep)

Norway spruce: Zweifel et al. (2000, 2001, 2002); Braun et al. (in prep.)

fVPD: beech: Braun et al. (in prep)

Norway spruce: Zweifel et al. (2000, 2001, 2002); Braun et al. (in prep.)

fSWP: beech: Braun et al. (in prep)

Norway spruce: Zweifel et al. (2000, 2001, 2002)

Updated : June 2015


Table A2.6: Mediterranean Europe Species-specific parameterization for Holm oak and Aleppo pine. The parameters for the “generic“ Mediterranean evergreen tree species are also given for

reference. Please note the additional parameterization for LAI, explained in the footnote below the table. Footnotes are denoted by (x.

Parameter Units Holm Oak, Continental /Coastal (1

Aleppo Pine Beech


Generic Broadleaved Evergreen

Mediterranean

Mixed oak and pine forest (3


Mediterranean broadleaved evergreen

Mediterranean needle leaf evergreen

Deciduous Mediterranean broadleaved


Mediterranean broadleaved evergreen

Mixed deciduous

and coniferous woodland

gmax mmol O3 m

-2 projected leaf

area s-1 180 (134-365) 215 145 (100-183) 150 (100-180) 175 (70-365)

204

fmin (fraction) 0.02 0.15 0.02 0.1 0.02 0.02

SGS year day 1 (1 Jan) 1 (1 Jan) Latitude Model Latitude Model 1 (1 Jan) 1

EGS year day 365 (31 Dec) 365 (31 Dec) Latitude Model LatitudeModel 365 (31 Dec) 365

fphen_a (fraction) 1 1 0 0 1 (3

fphen_b (fraction) 1 1 - (1) - (1) 1 (3

fphen_c (fraction) 0.3 0. 1 1 0.3 (3

fphen_d (fraction) 1 1 - (1) - (1) 1 (3

fphen_e (fraction) 1 1 0 0 1 (3

fphen_1 (days) (0) (0) 15 15 (0) (3

fphen_2 (days) 130 130 (200) (200) 130 (3

fphen_3 (days) 60 60 (200) (200) 60 (3

fphen_4 (days) (0) (0) 20 20 (0) (3

fphen_lim (days) 80 (21 Mar) 80 (21 Mar) - (0) - (0) 80 (21 Mar) (3

fphen_lim (days) 320 (16 Nov) 320 (16 Nov) - (0) - (0) 320 (16 Nov) (3

light_a 0.012 0.013 0.006 0.006 0.009 0.02

Tmin oC 1 10 4 0 2 2

Topt oC 23 27 21 21 23 20

Tmax oC 39 38 37 35 38 38

VPDmax kPa 2.2 1 1 1.0 2.2 1.65

VPDmin kPa 4.0 3.2 4.0 3.25 4.0 4.31

SWPmax MPa fSWP= -1.0 fSWP= -1.0 fSWP= -2.0 fSWP=1 fSWP=1 fSWP=1

SWPmin MPa fSWP= -4.5 fSWP= -2.5 fSWP= -3.8 fSWP=1 fSWP=1 fSWP=1

LAImin (2 3 1 0 n.a.

LAImax (2 5 2 5 4.76

LAIs (2 100 100 -

LAIe (2 166 166 -

h m 15 10 20 20 10 25

L cm 5.5 0.8 7 7 3.5 5

Updated: june 2015


1) For now only a single ecotype for Holm Oak is defined (i.e. no differentiation has been made between species growing in coastal and continental climates). This may be revisited in the future as further datasets become available.

2) Mediterranean evergreen forest species are assumed to have a year round growth period within which particular phenologically relevant periods occur that help to define the seasonal LAI and fphen profiles. LAI will be strongly influenced by the start and end of the leaf flush period (initiation of which is defined as LAIs and LAIe in the formulations referred to in Table A2.6). Thus, a LAI function has been derived for Holm Oak based on data for that species from Ferretti & Bussotti (2007) as follows:

when dd ≤ LAIs

LAI = 0.35*((LAIs-dd)/LAIs)+LAImin

when LAIs < dd ≤ (366 – LAIe)

LAI = (LAImax-LAImin)*(dd-LAIs)/LAIs+LAImin

when dd > (366 – LAIe)

LAI = (LAImax-(LAImin+0.35))*((366-dd)/LAIe)+(LAImin+0.35)

3) Please refer to Fares et al., 2013 for further details

Data sources for the parameterisation

gmax: Holm oak: median value 180, range (165-336) mmol O3 m-2s-1, based on data for mature

holm oak from Rhizopoulos & Mitrakos (1990) [250]; Manes et al. (1997) [366]; Filho et al. (1998) [225]; Tognetti et al. (1998) [195]; Sala & Tenhunen (1994) [165]; Alonso et al. (2007) [183, 191]; Infante et al. (1999) [323]; Castell et al. (1994) [177]; Damesin et al. (1998) [171]; Mediavilla & Escudero (2003) [122]; Corcuera et al. (2005) [134]; Gratani et al. (2000) [159]; Bussotti & Ferretti (2007) [166, 188, 156]

Aleppo pine median value 215, range (-) mmol O3 m-2 PLA s-1, based on data for mature

Aleppo pine from Elvira et al. (2007) [215]

beech: median value 145, range (100-183) mmol O3 m-2 PLA s-1, based on data for mature

beech from Raftoyannis & Radoglou (2002) [156]; cf. Körner et al. (1979) [100; 140]; Nunn et al. (2005) [147]; Matyssek et al. (2004) [132]; Aranda et al. (2000) [183]

N.B. gmax value in mmol O3 m-2 PLA s-1 given in square brackets after reference.

fphen : Holm oak: “generic“ Mediterranean evergreen parameterisation used as surrogate if information describing soil water status and influence on gsto is unavailable (see note 2)

Aleppo pine: Elvira et al. (2007)

Beech: “generic“ deciduous parameterisation used as surrogate

fmin : Holm oak: Bussotti & Ferretti (2007); Alonso et al. (2007)



Updated : June 2015


f light : Holm oak: Bussotti & Ferretti (2007); Alonso et al. (2007)



f temp : Holm oak: Tenhunen et al. (1987); Vitale et al. (2005); Manes et al. (1997); Corcuera et al. (2005); Ogaya & Peñuelas (2003); Bussotti & Ferretti (2007); Elvira et al. (2005)


Beech : Damesin & Rambal (1998); Rico et al (1996)

fVPD: Holm oak : Sala & Tenhunen (1994); Tognetti et al. (1998); Vitale et al. (2005); Manes et al. (1997); Elvira et al. (2005); Bussotti & Ferretti (2007); Alonso et al. (2007)

Aleppo pine: Elvira et al. (2007),

Beech : Grassi & Magnani (2005); Aranda et al (2000); Damesin & Rambal (1998); Rico et al (1996)

fSWP: Holm oak: Epron & Dreyer (1990); Acherer & Rambal (1992); Sala & Tenhunen (1992); Castell et al. (1994); Tognetti et al. (1998); Pesoli et al. (2003); Elvira et al. (2005); Rhizopoulos & Mitrakos (1990); Alonso et al. (2007)

Aleppo pine: Picon et al. (1996), Baquedano and Castillo (2007) (LWPmax -1.5); Schiller & Cohen (1998) (SWPmax -1.5); Filella and Peñuelas (2003) LWPmin -3Mpa; Borghetti et al. (1998) LWPmax -1MPa/ LWPmin -2 MPa; Schiller G, Cohen Y (1995) LWPmin -2.9 MPa. Therefore SWPmax = -1; SWPmin= -2.5 Mpa are used.

Beech: Aranda et al. (2000); Damesin & Rambal (1998); Rico et al. (1996); Mediavilla & Escudero (2003); Grassi & Magnani (2005)

LAI : Holm oak : ICP Level II site data (Ferretti; pers. comm.). Other references: Damesin et al. (1998) (LAI: 2.9-6); Reichstein et al. (2002) (LAI: 2.9-3.5); Baquedano (2003) (LAI: 2-4.5).

Aleppo pine: References: López- Serrano et al. (2000) (LAI: 0.3-1.7); Raz-Yaseef et al. (2006) (LAI: 1.1-1.5); Baquedano (2003) (LAI: 1.5-4). Borghetti et al. (1998) LAI=1.3. Therefore LAI values of 1 – 2 are used.

Beech: ICP Level II site data (Ferretti; pers. comm.) Also: Aranda et al. (2005).

Mixed oak and pine forest

All parameterisations are from Fares et al., 2013.

Updated: june 2015


III.9.2.2.2.5 FLUX MODEL PARAMETERISATION FOR POPLAR

Note : This parameterisation was added in 2014.

The gmax parameterisation for poplar is fairly robust with a median gmax of 392 mmol O3 m-2

PLA s-1 and a large standard deviation of 177 mmol O3 m-2 PLA s-1 (Figure A2.2; Table

A2.7), based on ten data points from seven studies. The reason for the large variation in gmax might be attributable to the use of different hybrid cultivars in these seven studies. However, the use of a median gmax value secures a good representation of all used hybrid cultivars.

A comprehensive poplar parameterisation for the DO3SE model was published by Marzuoli et al. (2009), which was largely used here (additional information was available for fLight) as parameterisation of the environmental parameter functions fLight, fTemp and fVPD. All other required parameters were taken from the CCE beech parameterisation as published in the LRTAP Convention (2010), since it was believed that central European beech would be a justifiable surrogate for poplar.

Table A2.7 : Flux model parameterisation for Poplar

Parameter Units Poplar

(Populus sp)

Reference

Regions for application

Northern Europe; Atlantic Central

Europe; Continental

Central Europe;

Land use Eunis class, area

in km2

Deciduous broadleafved

SGS dd Latitude model As for CCE beech (LRTAP Convention, 2010)

EGS dd Latitude model

LAI_max m2/m2 5 As for CCE beech (LRTAP Convention, 2010)

LAI_min m2/m2 0

Lm m 0.07 As for CCE beech (LRTAP Convention, 2010)

h m 20


392

(s.d. = 177)

(Ceulemans et al., 1989) [313]; (Manzanera and Martínez-Chacón, 2007) [160]; (Marzuoli et al., 2009) [575]; (Oliver et al., 2012) [343]; (Tricker et al., 2009) [228]; (Voltas et al., 2006) [631, 520, 631, 440]; (Zhang et al., 1999) [226]

fmin (fraction) 0.13 As for CCE beech (LRTAP Convention, 2010)

fphen_a (fraction) 0 As for CCE beech (LRTAP Convention, 2010)

fphen_b (fraction) (1)

fphen_c (fraction) 1

fphen_d (fraction) (1)

fphen_e (fraction) 0.4

fphen_1 days 20

fphen_2 days 20

Updated : June 2015


Parameter Units Poplar

(Populus sp)

Reference

lighta (constant) 0.007 (Ceulemans et al., 1989); (Manzanera and Martínez-Chacón, 2007); (Marzuoli et al., 2009)

Tmin oC 11 (Marzuoli et al., 2009)

Topt oC 27

Tmax oC 36

VPDmax kPa 2.7 (Marzuoli et al., 2009)

VPDmin kPa 3.7

SWPmax MPa -0.05 As for CCE beech (LRTAP Convention, 2010)

SWPmin MPa -1.25

0

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8 9 10

gm

ax(m

mol

O3 m

-2 P

LA s

-1)

Observations

Poplar g max


Fig. A2.2 gmax derivation for poplar. The black line represents the median value.

III.9.2.3 ADDITIONAL PUBLISHED FLUX MODELS FOR TREES FOR LOCAL-SCALE APPLICATION




Updated: june 2015


III.9.2.5 REFERENCES

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Updated: june 2015


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Updated : June 2015


III.9.3 ANNEX 3 : ADDITIONAL INFORMATION FOR (SEMI- ) NATURAL VEGETATION

III.9.3.1 FLUX MODEL PARAMETERISATION

III.9.3.1.1 CLOVER SPECIES

Stomatal conductance data for Trifolium repens from Ch-Liebefeld, UK-Bangor and UK-Newcastle (published in González-Fernández et al., 2008. was used for model parameterisation. Plots of stomatal conductance against temperature, light and VPD showed that there was good agreement between the three datasets, justifying the use of a combined stomatal conductance model for all sites. Gmax (for water) was calculated as the 95th centile of all datapoints and multiplied by the molecular diffusivity ratio from water vapour to ozone (0.613 was used) to give the value provided in Table III.23. To derive parameterisations for flight, ftemp and fVPD, the x-axis data was subdivided into segments, for each segment the 90th centile for relative stomatal conductance was calculated. The physiologically relevant curve was then fitted to the 90th centiles (therefore, on the figures there are datapoints outside of the boundary line). The figures for flight, ftemp and fVPD are shown in Figure A3.1 with the datapoints from Ch-Liebefeld, UK-Bangor and UK-Newcastle separately identified.

Figure A3.1: derivation of paramterisation of flight, ftemp and fVPD for clover.

This Section provides the scientific justification for the flux parameterisations for clover and Viola species provided in Table III.23 (Section III.7.2.3).

Revision history

March 2014 Section A3.2 added

Updated: june 2015


III.9.3.1.2 VIOLA SPECIES

Note: The ICP Vegetation Task Force acknowledges the reduced certainty associated with this parameterisation

Stomata conductance data were available for Viola riviniana and Viola lutea (both from UK-Bangor). However, the Viola spp. stomatal conductance data was over an insufficient range of climatic conditions to robustly parameterise a stomatal flux model. Plots of stomatal conductance against temperature, light and VPD showed that there was good agreement for Viola spp. with an existing model at UK-Bangor for Ranunculus acris. Thus, to derive parameterisations for flight, ftemp and fVPD additional data from Ranunculus acris was used. The x-axis data was subdivided into segments, for each segment the 90th centile for relative stomatal conductance was calculated. The physiologically relevant curve was then fitted to the 90th centile datapoints (therefore, on the figures there are datapoints outside of the boundary line). gmax (for water) was calculated as the 95th centile of all Viola datapoints and multiplied by the molecular diffusivity ratio (0.613 was used) from water vapour to ozone to give the value provided in Table 3.23. The figures for flight, ftemp and fVPD are shown in Figure A3.2, with the datapoints from Viola spp. and R. acris separately identified.

Figure A3.2 : The derivation of flight, ftemp and fVPD using data from Viola spp., and Ranunculus acris.

Updated : June 2015


III.9.3.2 ADDITIONAL PUBLISHED FLUX MODELS FOR (SEMI-) NATURAL VEGETATION FOR LOCAL-SCALE APPLICAT ION

The following paper provides a flux model for the ubiqitous grass species, Dactylis glomerata, including a modification for drier climates :

Hayes, F., Wagg, S., Mills, G., Wilkinson, S., Davies, W., 2012. Ozone effects in a drier climate: Implications for stomatal fluxes of reduced stomatal sensitivity to soil drying in a typical grassland species. Global Change Biology, 18, 948 – 959



III.9.3.4 REFERENCES

González-Fernández I, Bass D, Muntifering R, Mills G, Barnes J (2008) Impacts of ozone pollution on productivity and forage quality of grass/clover swards. Atmospheric Environment 42:8755-8769.

iii. mapping critical levels for vegetation...horticultural crops, semi-natural vegetation, natural...

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