3 Mapping Critical Levels for Vegetation
3.1 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 2).
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. 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.
(1999; Fuhrer and Achermann, 1999), Gothenburg (2002; Karlsson, Selldén and Pleijel,
2003), Obergurgl (2005; Wieser and Tausz,
2006) and Edinburgh (2006; UNECE, 2007).
For SO2, NOx and NH3, recommendations are
made for concentration-based critical levels
(Note: please see Chapter 5 for information on
critical loads for sulphur and nitrogen). For
ozone, separate cumulative concentrationbased (previously described as level I) and
cumulative stomatal flux-based (previously
described as level II) critical levels are described which use different scientific bases of
risk assessment, as explained in Section 3.3.
Since the earlier version of this manual was
published (UNECE, 1996), much progress has
been made with the critical levels for ozone and
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 (Section 3.6).
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
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3 Mapping Critical Levels for Vegetation
3.2 Critical levels for SO2, NOx, NH3
and O3
3.2.1 SO2
The critical levels for SO2 that were established
in Egham in 1992 (Ashmore and Wilson, 1993)
are still valid (Table 3.1). 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 halfyear mean concentration is greater than the
critical level; this is because of the greater
impact of SO2 under winter conditions.
Table 3.1: Critical levels for SO2 (µg m-3) by vegetation category
Vegetation
Type
Critical
Level SO2
-3
[µg m ]
Time
period
Cyanobacterial
lichens
10
Annual mean
Forest
ecosystems*
20
Annual mean and
Half-year mean
(Semi-) natural
20
(October-March)
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.
of response varies from a fertilizer effect 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 first 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
WHO in 2000. This further analysis incorporated a formal statistical model to identify
concentrations to protect 95% of species at a
95% confidence level. In this re-analysis,
growth stimulation was also considered as a
potentially adverse ecological effect. Furthermore, a critical level based on 24h mean concentrations was considered to be more effective
than one based on 4h mean concentrations as
included in the earlier version of the Mapping
Manual (UNECE, 1996). Since the WHO guidelines were largely based on analysis extending
the background information presented at the
Egham workshop, the critical levels in Table
3.2, which are identical to those of WHO
(2000), should now be used.
Table 3.2: Critical levels for NOx (NO and NO2
added, expressed as NO2 (µg m-3))
Critical
Vegetation
Level NOx
Type
(expressed as NO2)
Time
period
[µg m-3]
3.2.2 NOx
All
30
Annual mean
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,
although some evidence indicates that at low
concentrations typical of ambient conditions,
NO is more phytotoxic than NO2. Since the type
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
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3 Mapping Critical Levels for Vegetation
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.
3.2.3 NH3
The fertilisation effect of NH3 can in the longer
term lead to a variety of adverse effects, including growth stimulation 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 3.3: Critical levels for NH3 (µg m-3)
Critical
Time
Level NH3 period
-3
Vegetation
Type
[µg m ]
Lichens and bryophytes
(including ecosystems where
lichens and bryophytes are a
key part of ecosystem
integrity)
Higher plants
(including heathland, grassland and forest ground flora)
1
Annual
mean
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.)
The critical levels in Table 3.3 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 3.3 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 3.3 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 longterm critical level values are based on observation of actual species changes from
both field surveys and long-term exposure
experiments, where effects were related to
measured ammonia concentrations. The
workshop noted that the long-term critical
levels could not be assumed to provide a
protection for longer than 20-30 years;
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3 Mapping Critical Levels for Vegetation
5. To retain the monthly critical level
(23 μg m-3) for higher plants only as a provisional value in order to deal with the possibility of high peak emissions during periods of manure application (e.g. in spring);
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 3.4.
The proceedings of the UNECE Workshop on
Ammonia (Edinburgh, December 2006) was
published in 2009 by Springer: Sutton M.A.,
Baker S., Reis S. (ed.), Atmospheric Ammonia:
Table 3.4: 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]
SE Scotland,
poultry farm
Epiphytic lichens
0.6
Devon,
SW England
Epiphytic lichens
diversity (twig)
0.8 (modelled)
1.6
(Wolseley, et al.,
2006)
0.1
1.0
(Leith, et al., 2005,
Sutton, et al., 2008)
1.9 (modelled)
2.4
(Rihm, et al., 2008)
0.5
<4
(Sheppard, et al.,
2008)
United Kingdom,
national NH3
Epiphytic lichens
network
Switzerland
Lichen population
index
SE Scotland, field Lichens and
NH3 experiment, bryophytes –
Whim bog
damage and death
Estimated
NOEC *
[µg m-3]
Reference
0.7 (on twigs) (Pitcairn, et al., 2004,
1.8 (on trunks) Sutton, 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”.
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3 Mapping Critical Levels for Vegetation
3.2.4 O3
3.2.4.1
Terminology for ozone parameters
and critical levels
Three cumulative exposure approaches are
used to define critical levels for ozone: stomatal
fluxes, ozone concentrations and vapourpressure 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 a
defined time period. In each case, a specific
threshold is used and only ozone concentrations, VPD-modified ozone concentrations or
instantaneous stomatal fluxes above that
threshold are summed. When the sum of the
values above the threshold exceeds the critical
value defined in the relevant table for each
approach and vegetation type, then the critical
level for that approach and vegetation type has
been exceeded.
The earlier 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 AOTX-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
November 2002 that ozone flux-effect models
were sufficiently robust for the derivation of fluxbased critical levels, and such critical levels
should be included in this revised Manual for
wheat, potato and provisionally for beech. An
additional simplified flux-based risk assessment
method for use in large-scale and integrated
assessment modelling was discussed at the
Obergurgl Workshop (November, 2005) and
after further revision (approved at appropriate
Task Force Meetings) is included here in Section 3.6. This additional method does not involve exceedance of critical levels; it assumes
that increasing flux is equivalent to increasing
risk.
To accommodate these different approaches,
the terminology and symbols used in describing
critical levels of ozone have been revised and
are described in Table 3.5 and the critical levels
are provided in Table 3.6. Table 3.7 provides
guidance on the selection of critical levels
and which methods to use. The scientific
bases of these critical levels are provided in
Section 3.3 and methods for calculating exceedance are in Sections 3.4 and 3.5. The
three approaches can be summarised as follows:
Stomatal flux-based critical levels (Clef) for
ozone take into account the varying influences of temperature, water vapour pressure deficit (VPD), light (irradiance), soil
water potential (SWP), 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. This is an important new development in the derivation of critical levels because, for example, for a given ozone concentration, the stomatal flux in warm, humid conditions with moist soil can be much
greater than that in hot, dry conditions with
dry soil because the stomatal pores will be
more widely open. Concentration-based
critical levels do not differentiate between
such climatic conditions and would not indicate the increased risk of damage in
warm, humid conditions. The hourly mean
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 stomatal flux of ozone above a flux
threshold of Y (AFstY), is calculated for the
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3 Mapping Critical Levels for Vegetation
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, AFstY, above which
direct adverse effects may occur according
to present knowledge. Values of CLef have
been identified for some crops (wheat and
potato) and provisionally for sensitive forest trees (represented by birch and beech).
This approach cannot yet be applied to
(semi-) natural vegetation.
Concentration-based critical levels (CLec)
for ozone use the concentration at the top
of the canopy accumulated over a threshold concentration for the appropriate timewindow 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.
It should be noted that consideration was given
at the Gothenburg Workshop (November 2002)
to a fourth approach, the Maximum Permissible
Ozone Concentration (MPOC) (Krause et al.,
2003). The MPOC approach could possibly be
adopted as an additional concentration-based
index for mapping potential risk in national
evaluations, but should not be used for mapping purposes at the European scale. This
approach was considered as potentially useful
in the context of forest trees, but not for (semi-)
natural vegetation and crops. Economic losses
attributable to ozone pollution could not be
estimated on the basis of this approach. Further
validation and development of the methodology
may be possible under the framework of ICP
Forests. A brief description of the MPOC approach is provided in Section 3.3.3.3.
VPD-modified concentration-based critical
levels take 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 VPDmodified 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
threshold ozone concentration of 30 ppb)
has been adopted for this index; this index
is only used to define the short-term critical
level for the development of visible injury
on crops.
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3 Mapping Critical Levels for Vegetation
Table 3.5: Terminology for critical levels of ozone
Abbreviation
[Units]
Term
Explanation
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.
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.
Concentration accumulated
over a threshold ozone
concentration of X ppb
modified by vapour
pressure deficit (VPD)
Terms for concentration-based critical levels
PLA
[m2]
Projected leaf area
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
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
Fst
plant, but for this manual, Fst refers specifically to the
[nmol m-2 PLA s-1] 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 through the
stomata.
Stomatal flux of ozone
above a flux threshold of Y
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
FstY
[nmol m-2 PLA s-1] 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.
Accumulated stomatal flux
of ozone above a flux
threshold of Y nmol m-2
PLA s-1
Flux-based critical level of
ozone
AFstY
[nmol m-2 PLA]
Accumulated flux above a flux threshold of Y
nmol m‫ـ‬2 s‫ـ‬1, accumulated over a stated time period
during daylight hours. Similar in mathematical concept
to AOTX.
CLef
[nmol m-2 PLA]
Accumulated flux above a flux threshold of Y nmol m-2
s-1 (AFstY), over a stated time period during daylight
hours, above which direct adverse effects may occur on
sensitive vegetation according to present knowledge.
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3 Mapping Critical Levels for Vegetation
Table 3.6: Critical levels for ozone.
Approach
Crops
(Semi-) natural vegetation
Birch and
beech
Provisionally
AFst1.6 of
4 mmol m-2
PLA
Wheat: An AFst6 of
1 mmol m-2 PLA
CLef
Stomatal fluxbased critical
level
Potato: An AFst6 of
5 mmol m-2 PLA
Time
period
Wheat: Either 970˚C days,
starting 270˚C days before
mid-anthesis (flowering) or
55 days starting 15 days
before mid-anthesis
Forest trees
Not available
One growing
season
Potato: Either 1130˚C days
starting at plant emergence or
70 days starting at plant
emergence
Growth
reduction
Effect Yield reduction
Agricultural crops:
An AOT40 of 3 ppm h
For communities dominated
by annuals:
An AOT40 of 3 ppm h
CLec
Horticultural crops:
An AOT40 of 6 ppm h
ConcentrationAgricultural crops:
based critical
3 months
level
Time
period
Horticultural crops:
3.5 months
Yield reduction for both
Effect agricultural and horticultural
crops
CLec An AOT30VPD of 0.16 ppm h
VPD-modified
concentration- Time
Preceding 8 days
based critical period
level
Effect Visible injury to leaves
For communities dominated
by perennials:
An AOT40 of 5 ppm h
An AOT40 of
5 ppm h
For communities dominated
by annuals: 3 months (or
Growing
growing season, if shorter)
season
For communities dominated
by perennials: 6 months
Growth reduction in
perennial species and growth Growth
reduction and/or seed
reduction
production in annual species
Not available
Not available
Note: The methods for calculating each critical level are described in Sections 3.4 and 3.5.
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3 Mapping Critical Levels for Vegetation
Guidance on choice of methods for
assessing impacts of ozone on
vegetation
potato for the assessment of local risks presents greater uncertainties and use of local
relationships is preferred if they are available.
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”. As described in the chapter, all
indicators for ozone impacts are based on the
accumulation of ozone (either as concentration
or stomatal flux) above a predetermined
threshold over a specified time period. The
scientific support for the critical levels of ozone
is described in Section 3.3. Methods for calculating exceedance of flux-based and concentration-based critical levels are provided in Sections 3.4 and 3.5 respectively. Estimation of risk
of damage at the regional/European scale using
flux-based methods for generic tree and generic
crop species is described in Section 3.6.
Application of the flux-based methodology for
local risk assessments (b) Forest trees
Work is ongoing to define additional parameterisations for local risk assessments and to
inform emission control options performed
within integrated assessment modelling. These
parameterisations are referred to as “real”
species parameterisations and are intended to
represent a number of key European tree
species (including Scots pine, Norway spruce,
beech, oak, birch, poplar, Holm oak and Aleppo
pine) parameterised as necessary to represent
different ecotypes induced by climate variability
across Europe. The formulations used in the
application of these “real” species parameterisations will include an assessment of the influence of soil water deficits on gsto and hence
stomatal ozone flux. As such, fluxes estimated
using this approach would incorporate one of
the key drivers of variability in risk to ozone
damage. Finalised “real” species formulations
and parameterisations are envisaged to be
available in spring 2008.
3.2.4.2
A summary of the suitability and availability of
each method for typical applications is provided
in Table 3.7.
Application of the flux-based methodology for
local risk assessments (a) Agricultural crops
The flux-based response relationships for crops
in this chapter (Section 3.3.1.2) were based on
experiments from four countries in the case of
wheat (Belgium, Sweden, Finland, Italy) and
four countries in the case of potato (Belgium,
Germany, Sweden, Finland). Five cultivars
were included for wheat, four spring wheat
types and one winter wheat. The very widely
grown cultivar Bintje was used for potato.
Consequently, the data sets included, which
met a number of quality criteria, represent only
a sample of wheat and potato grown in Europe,
geographically and genetically. Nevertheless,
the data used for the flux-response relationships presented in this manual represent a
substantial range of geographical variation.
Since the data from different countries conformed to common relationships for wheat and
potato, these relationships should be applicable
for general purposes. If available, local response data may be more representative for
assessing effects in a particular part of Europe.
The use of the general functions for wheat and
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Table 3.7: Guidance on choice of risk assessment methods for crops, (semi-) natural vegetation and forest trees.
Purpose
Detailed flux-based risk
assessment, including
economic impact
Flux-based integrated Concentration-based risk
assessment and other assessment
large-scale modelling
Crops
Use AFst6- response
functions for wheat and
potato or exceedance of
AFst6-based critical levels
(provided for wheat and
potato). See Section
3.3.1.2.
Use generic crop flux
model.
Use exceedance of AOT40 (or
AOT30)- based critical levels for
Note: no critical levels yield reduction, provided for
are provided; increasing agricultural crops (wheat) and
flux indicates increas- horticultural crops (tomato). See
ing risk of damage. See Section 3.3.1.3.
A short-term critical level to
Section 3.6.1.
protect against ozone injury (based
on VPD-modified AOT30) is also
included. See Section 3.3.1.4.
(Semi-)
No methods currently
natural
available
vegetation
No methods currently
available
Use exceedance of AOT40- based
critical levels (provided for communities dominated by annuals and
communities dominated by perennials). See Section 3.3.2.
Forest
trees
Use generic deciduous
tree and/or evergreen
Mediterranean tree flux
model.
Note: no critical levels
are provided; increasing
flux indicates increasing risk of damage. See
Section 3.6.2.
Use exceedance of AOT40 (or
AOT30) -based critical levels for
growth reduction (provided for
combined beech and birch). See
Section 3.3.3.2.
Use exceedance of provisional AFst4 - based
critical levels for beech
and birch.
Note: economic impact
assessment is not recommended. See Section
3.3.3.1.
Note: Section numbers quoted provide an explanation of the scientific basis of each method/critical level
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3.3 Scientific bases of the critical levels
for ozone
3.3.1 Crops
3.3.1.1
Crop sensitivity to ozone
Table 3.8 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 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 the
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.
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 3.8: The range of sensitivity of agricultural and horticultural crops to ozone
Sensitive
Moderately
sensitive
Cotton, Lettuce, Pulses, Soybean, Potato, Rapeseed,
Salad Onion, Tomato, Turnip,
Sugarbeet, Tobacco
Watermelon, Wheat
Moderately
resistant
Insensitive
Broccoli, Grape,
Maize, Rice
Barley, Fruit (plum &
strawberry)
Note: see Mills et al., 2007a for response functions and definition of sensitivities
3.3.1.2
Stomatal flux-based critical levels
for yield reduction in wheat and
potato
At the workshop in Obergurgl, November, 2005,
it was concluded that for the time being, it is
only possible to derive flux-based ozone critical
levels for the crops of wheat and potato. Ozone
fluxes through the stomata of leaves found at
the top of the canopy are calculated using a
multiplicative algorithm based on the methodology described by Emberson et al. (2000b). The
stomatal flux algorithm used within the EMEP
ozone deposition module is described in Section 3.4.4.
The index AFstY 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 in wheat
and potato, it has been observed that the best
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3 Mapping Critical Levels for Vegetation
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 AFstY were
obtained using Y = 6 nmol m-2 s-1 for both wheat
and potato. In effect, this means that ozone
exposure started to contribute to AFstY at an
ozone concentration at the top of the crop
canopy of approximately 22 ppb for wheat and
at approximately 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 AFst6 for wheat and potato, respectively.
The threshold of 6 provides the highest r2 value
for the relationship between yield reduction and
AFstY for all of the thresholds tested for both
crops. For example, for wheat the r2 values
were 0.51, 0.74, 0.83 and 0.77 for Y= 0, 3, 6
and 9 respectively, whilst for potato, the r2
values were 0.6, 0.72, 0.76 and 0.75 for Y= 0,
3, 6 and 9 respectively.
Based on the combination of data from a number of open-top chamber experiments with fieldgrown 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. In the case of wheat, thirteen
experiments with field-grown wheat exposed to
different ozone levels in open-top chambers
from four different countries (Belgium, Finland,
Italy and Sweden), representing five cultivars
(Minaret, Dragon, Drabant, Satu and Duilio)
were used, and for potato seven experiments
from four different countries (Belgium, Denmark, Finland, and Sweden) were included,
representing one commonly grown cultivar
(Bintje) (Pleijel et al., 2003).
In order to derive response relationships which
are consistent with the ozone deposition module of the EMEP model (Emberson et al.,
2000b), the parameterisations of the conductance model presented in Pleijel et al. (2002,
2003) and Danielsson et al. (2003) were revised to achieve a full compatibility with the
EMEP model calibration. The resulting algorithms and their parameterisation are presented
in Sections 3.4.4 and 3.4.5. This revision resulted in stronger correlations between relative
yield and AFstY for both wheat (a change in r2
from 0.77 to 0.83) and potato (a change in r2
from 0.64 to 0.76), compared to the relationships presented in Pleijel et al. (2003). For
further explanation, please see Pleijel et al.
(2007).
The response relationship for wheat:
(3.1)
RYwheat = 1.00 − (0.048 * AFst 6 )
is presented in Figure 3.1.
The response relationship for potato:
(3.2)
RYpotato = 1.01 − (0.013 * AFst 6 )
is presented in Figure 3.2.
In line with earlier concepts used for crop
critical levels, 5% yield reduction was used as
the loss criterion for the identification of
stomatal flux-based critical levels (UNECE,
1996). For wheat the suggested stomatal fluxbased critical level for 5% yield loss of an AFst6
of 1 mmol m-2 was statistically significant according to the confidence limits of the yield
response regressions, which is an important
criterion when using a yield loss level such as
5% (Pleijel, 1996). For potato, the value was
rounded upwards from 4.6 mmol O3 m-2 (representing 5% yield loss) to 5 mmol O3 m-2.
As such, the flux-based critical level, CLef of
ozone for wheat is an AFst6 of 1 mmol O3 per
unit projected flag leaf area, accumulated
during an effective temperature sum period
starting 270˚C days before anthesis (flowering)
and ending 700˚C days after anthesis. The total
period is 970˚C days (base temperature 0˚C).
On average, the 970˚C days corresponded to
54 days in the experiments used to calibrate the
function.
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3 Mapping Critical Levels for Vegetation
1 .2
W heat
1 .0
Relative yield
0 .8
0 .6
0 .4
BE
0 .2
FI
y = 1 .0 0 – 0 .0 4 8 * A F S T 6
r 2 = 0 .8 3
p < 0 .0 0 1
0
1
0
2
3
IT
SE
4
5
6
A F s t 6 , m m o l m -2
Note: Dotted lines represent 95% confidence intervals.
Figure 3.1: Relationship between the relative yield of wheat and AFst6 for the wheat flag leaf based on five wheat
cultivars from four European countries using effective temperature sum to describe phenology.
1.2
Potato
Relative yield
1.0
0.8
0.6
0.4
0.2
0
BE
FI
GE
SE
y = 1.01 – 0.013 * AFST6
r2 = 0.76
p < 0.001
0
4
8
12
16
20
AFst6, mmol m-2
Note: Dotted lines represent 95% confidence intervals.
Figure 3.2: The relationship between the relative yield of potato and the AFst6 for sunlit leaves based on data
from four European countries and using effective temperature sum to describe phenology. Dotted
lines represent 95% confidence intervals.
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3 Mapping Critical Levels for Vegetation
The flux-based critical level, CLef, of ozone for
potato is an AFst6 of 5 mmol O3 per m2 projected, sunlit leaf area 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; this value
has been rounded up to 70 days for Table 3.6.
3.3.1.3
AOTX-based critical levels for crop
yield reduction
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 opentop chamber experiments conducted in Europe
and the USA (Figure 3.3). Newly published 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 the start of anthesis (see Section 3.5.2.2 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, 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 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
A concentration-based critical level has been
derived for horticultural crops that are growing
with adequate nutrient and water supply. An
AOT40 of 6.02 ppm h is equivalent to a 5%
reduction in fruit yield for tomato, and has been
derived from a dose-response function (Figure
3.4) developed from a comprehensive dataset
including 14 cultivars (r2 = 0.48, p<0.001). This
value has been rounded down to 6 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., 2003).
Tomato is considered suitable for the derivation
of the critical level since it is classified as an
ozone-sensitive crop (Table 3.8). 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 3.5 months, starting from the
emergence of the crop (see Section 3.5.3.2 for
guidance). For further explanation, please see
Mills et al. (2007a).
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).
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3 Mapping Critical Levels for Vegetation
1.2
Albis
Drabant
Ralle
Echo
Abe
Arthur
Roland
Satu
Dragon
1.0
Relative yield
0.8
0.6
0.4
Regression
95% CI
0.2
0.0
0
10
20
30
40
Three month AOT40 (ppm h)
Dotted lines represent 95% confidence intervals.
Figure 3.3: Wheat yield-response function used to derive the concentration-based critical levels for agricultural
crops (r2 = 0.89) (Fuhrer et al., 1997, Gelang et al., 2000)
1,4
1,2
Relative Yield
1,0
0,8
Fireball
Baladey
Tiny Tim
Nikita
Murrieta
Claudia
Piedmont
FM 785
E 6203
UC 204c
Ailsa Craig
Moneymaker
UCE 82 L
Hybrid 31
0,6
0,4
0,2
0,0
0
10
20
30
40
50
Fourmonth
monthAOT40
AOT40 (ppm
(ppm h)
3.5
Dotted lines represent 95% confidence intervals.
Figure 3.4: Tomato yield-response function used to derive the concentration-based critical levels for horticultural
crops (r2 = 0.48, p<0.001) (Hassan et al., 1999; McLean & Schneider, 1976; Reinert et al., 1997;
Temple et al., 1985; Temple, 1990; Bermejo, 2002; Calvo, 2003).
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3 Mapping Critical Levels for Vegetation
3.3.1.4
VPD-modified AOT30 – based
critical level for visible 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); participants were
asked by email if they objected to the changes
made and no objections were raised.
Acute visible ozone injury, resulting from shortterm ozone exposure, represents the most
direct evidence of the harmfulness of elevated
ozone concentrations. The aim of this shortterm 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).
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.
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).
The critical level for visible 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% ( ± 3.5% according to the
99% confidence limits of the regression shown
in Figure 3.5) of the leaves on sensitive plants,
such as subterranean clover. The 10% level for
visible ozone injury was chosen based on the
conclusion from the studies by Pihl Karlsson et
al. (2003).
The 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 3.5.
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.
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
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3 Mapping Critical Levels for Vegetation
Visible injury, %
50
40
30
20
10
0
y = 0.03x + 5.08
r2 = 0.602
0
0.5
1.0
AOT30VPD (8 days, ppm h)
1.5
Figure 3.5: 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).
3.3.2 (Semi-) natural vegetation
The critical levels for (semi-) natural vegetation
are applicable to all sensitive semi-natural
vegetation and natural vegetation, described
here collectively as (semi-) natural vegetation
(forest trees and woodlands are not included
here as (semi-) natural vegetation). Two concentration-based critical levels were agreed at
the Obergurgl (2005) workshop; flux-based
methods are not currently sufficiently advanced
for this vegetation type for inclusion in this
manual.
Critical level for effects on communities of
(semi-) natural vegetation dominated by annuals: The criteria for adverse effects on (semi-)
natural vegetation communities dominated by
annuals are effects on growth and seed production for annual species. This critical level is
based on statistically significant effects or
growth reductions of greater than 10% on
sensitive taxa of grassland and field margin
communities. The value of 3 ppm h is sufficient
to protect the most sensitive annuals. In contrast to crops and tree species, only limited
experimental data are available for a small
proportion of the vast range of species found
across Europe. This means that analysis of
exposure-response data for individual species
to derive a critical level value is more difficult.
Instead, the recommended critical level is
based on data from a limited number of sensitive species. The value of 3 ppm h was originally proposed at the Kuopio workshop
(Kärenlampi & Skärby, 1996) and confirmed at
the Gerzensee workshop (Fuhrer & Achermann, 1999) and subsequently at the Obergurgl
workshop (Wieser and Tausz, 2006). At the
time of the Kuopio workshop, no exposureresponse studies were available for derivation
of the critical level for (semi-) natural vegetation, based on a 10% response. Instead, data
from field-based experiments with control and
ozone treatments were used to identify studies
showing significant effects at relatively low
ozone exposures. Table 3.9 summarises the
key field chamber and field fumigation experiments which supported the original proposal of
this critical level for (semi-) natural vegetation.
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3 Mapping Critical Levels for Vegetation
Table 3.9: 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
Individual plants
Mesocosms of four species
Mesocosms of seven species
Ryegrass-clover sward
Most sensitive
species
AOT40
(ppm h)
Response
Reference
Solanum nigrum
4.2
Malva sylvestris
3.9
-23%; shoot mass Bergmann et al.,
-54%; seed mass 1996
Trifolium repens
5.0
-13%; shoot mass
Festuca ovina
7.0
Leontodon hispidus
7.0
-32%; shoot mass Ashmore et al.,
-22%; shoot mass 1996
Trifolium repens
5.0
-20%; shoot mass
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 3.9 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 EUFP5 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 twospecies 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.
Ashmore &
Ainsworth, 1995
Nussbaum et al.,
1995
species, are summarised in Table 3.10. 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
more recent 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.
The key experiments from the BIOSTRESS
programme, which support the proposed critical
level for communities dominated by annual
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3 Mapping Critical Levels for Vegetation
Table 3.10: 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
Reference
AOT40
Trifolium pratense Poa pratensis
Biomass (-10%)
4.4 ppm h*
Gillespie & Barnes,
unpublished data
Veronica
chamaedrys
Species biomass ratio
3.6 ppm h
Bender et al. (2002)
Poa pratensis
Trifolium cherleri,
Briza maxima Flower production
T. striatum
Trifolium cherleri
Briza maxima Seed output
2.2-2.7 ppm h
2.4 ppm h
Gimeno et al. (2003a);
Gimeno et al. (2004).
Gimeno et al. (2004)
* estimated from exposure-response functions
Critical level for effects on communities dominated by perennial species: A new 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 3.11 summarises the key findings of the studies which were
used to establish the new critical level.
Guidance on the time-window for calculating
AOT40, for mapping (semi-) natural communities at risk of ozone and determining the ozone
concentration at the canopy height is provided
in Section 3.5.5.
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3 Mapping Critical Levels for Vegetation
Table 3.11: Key findings of the studies which were 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 opentop 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 cumulative 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
18 months
Northern England
3 ppm h (C).
Upland grassland
Significant reduction in
10 ppm h (T)
16% reduction in total
biomass of Briza media
Species-rich
6 months exposure during
above-ground biomass
and Phleum bertolonii
(11 species), in open- ‘summer’ to 50 ppb versus
top chambers
30 ppb reducing to 35 ppb
versus 20 ppb over ‘winter’
3
3 years
2.6 ppm h (C)
Southern England4
Calcareous grassland 10.5, 13.3, 18.2 ppm h (T) No significant effect on
above-ground biomass
(38 species) in open- Exposure for periods of
3-5
months
each
year
at
top chambers
three levels of ozone, effects
observed at lowest exposure
Switzerland5
60 year old alpine
pasture
in field release
system
Significant change in
community composition;
loss of Campanula
rotundifolia
5 years
8.4 ppm h (C)
23% reduction in
Small reduction in
34.0 ppm h (T)
above-ground biomass proportion of legumes
ca. 6 months of fumigation
each year
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.
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3 Mapping Critical Levels for Vegetation
3.3.3 Forest trees
3.3.3.1
Provisional flux-based critical
levels
Please note: This section is currently under
review. Since this section was first written
and agreed, the provisional parameteristions provided for beech and birch have
been further developed and form the basis
of the generic species parameterisations
(Section 3.6.2). During 2008, parameterisations for specific forest tree species selected to represent climate regions across
Europe have been added as Annex 1.
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 future ozone critical
levels for forest trees. AFstY is considere to be
much more physiologically relevant than AOTX,
however, more time and data are needed
before AFstY-response relationships for trees
could be considered sufficiently robust for
establishing a stomatal flux-based critical level
of ozone for specific forest trees; calculation of
exceedance of a provisional flux-based critical
level for beech and birch is described in Section
3.4.6. Although model parameterisation and
flux-response relationships are available for
other species (Karlsson et al., 2003b), provisional parameterisation of the multiplicative
model is given only for beech and birch, and it
is intended that these species are used as
indicators of risk to sensitive receptors. The
choice of beech and birch also ensures continuity with the concentration-based critical level
described below.
As for crop species, ozone flux is estimated on
a projected leaf area basis for sunlit upper
canopy leaves. Ozone flux should be accumulated for an exposure window as defined in
Section 3.4.6, either using the default values
given, locally derived information or phenological models. Ozone concentrations used for the
calculation of stomatal ozone flux are those at
the top of the canopy, as discussed in Section 3.4.6.1. As is the case for both wheat and
potato, a flux threshold, Y, should be applied.
For beech and birch, a value of 1.6 nmol m-2
PLA s-1 is proposed for Y, based on the statistical analysis of flux-response relationships for a
variety of sensitive deciduous tree species
(Karlsson et al., 2003b, 2004). This same
analysis concluded that for Y=1.6 nmol m-2 s-1,
a growing season cumulative stomatal flux
(AFst1.6) of 4 mmol m-2 was statistically significant (99% confidence) and represented a 5%
reduction in biomass under experimental conditions. This indicative value represents the
provisional flux-based critical level, above which
there is a risk of a negative impact of ozone on
growth for the most sensitive tree species. The
dataset, on which this analysis is based, is
identical to that from which the AOT40-based
critical level was derived, and represents a
combination of data for both birch and beech.
For further information, please see Karlsson et
al. (2004).
It should be emphasised that this provisional
critical level should be viewed as a first step in
the derivation of new critical levels which could
be used to make quantitative assessments of
the ozone impacts on mature forest trees since
the value currently selected is based on a
number of assumptions (these are discussed in
more detail in Karlsson et al., 2003b, 2004). In
the future it is envisaged that additional analysis
and reworking of existing experimental data and
improvement of stomatal conductance models
for forest trees should provide better estimates
of both cumulative stomatal flux and associated
flux thresholds and critical levels to provide
more robust estimates of damage for forest
trees.
3.3.3.2
AOTX-based critical levels for
forest trees
The AOTX approach is retained as the recommended method for calculating critical levels for
forest trees until the stomatal flux approach
becomes sufficiently refined. However, the
methodology and critical levels have been
substantially revised since the earlier version of
this Manual was published (UNECE, 1996).
The experimental database that was presented
at the UNECE Workshop in Gothenburg 2002
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
has been re-analysed 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 3.12) 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 and there
are no significant intercepts (Table 3.12). The
disadvantage was that there are insufficient
data available for the moderately sensitive
coniferous category for adequate statistical
analysis. This may change in the future, if more
datasets become available.
(beech and birch, Figure 3.6). The 5% growth
reduction was clearly significant as judged by
the 99% confidence intervals in Figure 3.6. 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).
Table 3.12: Sensitivity classes for the tree species
based on effects of ozone on growth
Ozone-sensitive species
Moderately ozonesensitive 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 3.6). 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 3.13).
Based on the analysis described in Table 3.13,
the concentration-based critical level of ozone
for forest trees, CLec, has been 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
3.6). This value of 5 ppm h is associated with a
5% growth reduction per growing season for the
deciduous sensitive tree species category
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3 Mapping Critical Levels for Vegetation
Table 3.13: 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
Ozone index/plant category
Linear regression
r2
p for the slope
p for the intercept
slope
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
Birch, beech
0.61
<0.01
0.63
- 0.494
Oak
0.61
<0.01
0.79
- 0.170
Norway spruce, Scots pine
0.76
<0.01
0.61
- 0.110
Birch, beech
0.62
<0.01
0.31
- 0.732
Oak
0.65
<0.01
0.73
- 0.216
Norway spruce, Scots pine
0.79
<0.01
0.86
- 0.154
Birch, beech
0.53
<0.01
0.05
- 1.033
Oak
0.62
<0.01
0.82
- 0.248
Norway spruce, Scots pine
0.76
<0.01
0.16
- 0.188
AOT20
AOT30
AOT40
AOT50
% reduction
10
Birch, beech
F.sylvatica, CH
B. pendula,
Ostad, SE
0
B. pendula,
Kuopio1, FI
B. pendula,
Kuopio2, FI
B. pendula,
Kuopio3, FI
-10
-20
-30
0
10
20
30
40
Daylight AOT40 (ppm h)
Figure 3.6: 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).
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3 Mapping Critical Levels for Vegetation
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 timewindows 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 concentrationbased 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.
3.3.3.3
are seasonal (April to September) 24h mean
ozone concentrations in the range 25-74 ppb.
As is the case with the AOT40 approach to
mapping areas in which sensitive receptors are
at risk from ozone pollution, the MPOC approach does not account for environmental
limitations (soil moisture deficit, leaf-air vapour
pressure deficit) to physiologically effective
ozone exposure. The methodology is based on
a review of 22 peer-review publications and 50
individual datasets, including a range of different exposure facilities. Although the datasets on
which the concept are based cover a range of
species and locations across Europe, species
common in southern Europe and the Mediterranean region are under-represented. The methodology has also only been validated for sites in
Germany, while datasets published after 1999
have not been included in the analysis to date.
It is thus suggested that the approach could
possibly be adopted as an additional concentration-based index for mapping potential risk in
national evaluations, but that economic losses
attributable to ozone pollution could not be
estimated on the basis of this approach. Further
validation and development of the methodology
may be possible under the framework of ICP
Forests.
MPOC approach to risk
assessment for forest trees
Note: This method should not be used for
mapping at the European scale or for economic impact assessment.
An additional approach is provisionally available
for application in national-level evaluations,
using an alternative concentration-based indicator of potential risk. The Maximum Permissible
Ozone Concentration approach (MPOC: see
Grünhage et al., 2001; Krause et al., 2003 for
detailed descriptions of the methodology) is a
largely qualitative indicator, relating a range of
significant response effects (including growth
and physiology) to the concentration of ozone
above the canopy over a variety of timeframes.
This method should not be used for mapping at
the European scale. Using the data for longer
term effects identified with the MPOC approach,
it is possible to indicate that the upper and
lower range for effects reported in the literature
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3 Mapping Critical Levels for Vegetation
3.4 Calculating exceedance of stomatal
flux-based critical levels for ozone
3.4.1 Stages in calculating AFstY and CLef
exceedance
To calculate the relevant AFstY to sunlit leaves
and exceedance of the stomatal flux-based
critical level, the following steps have to be
taken:
1. Hourly ozone concentrations at the top of
the canopy are determined (see Section
3.4.2 for conversion of concentrations considering ozone concentration gradients
above the canopy).
2. The time period to consider is determined as
described in Section 3.4.3. The hourly
stomatal conductance (gsto) values for the
relevant periods, for sunlit leaves of the receptor plant are identified using the algorithm presented in Equation (3.12).
3. Every hourly stomatal conductance thus
identified is multiplied by the corresponding
hourly ozone concentration at the top of the
canopy, resulting in hourly stomatal fluxes of
ozone, Fst expressed in nmol m-2 PLA s-1
(Equation (3.9)).
4. 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 h1
.
5. The sum of all hourly FstY values is calculated for the specified accumulated period.
The resulting value is AFstY in units mmol m2
PLA.
6. If the AFstY value is larger than the fluxbased critical level for ozone CLef, there is
exceedance of the critical level.
3.4.2 Ozone concentrations at the canopy
height
Note: Sources of ozone concentration estimates and their spatial interpolation are considered in Chapter 2 of this manual.
All ozone indices described in this chapter are
based on ozone concentrations at the top of the
canopy. For crops and other low vegetation,
canopy-top concentrations may be significantly
lower than those at conventional measurement
heights of 2-5m above the ground, and hence
use of measured data directly or after spatial
interpolation may lead to significant overestimates of ozone concentrations and hence of
the degree of exceedance of CLec and CLef. In
contrast, for forests, measured data may underestimate ozone concentration at the top of
the canopy. The difference between measurement height and canopy height is a function of
several factors, including wind speed and other
meteorological factors, canopy height 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 (ground level 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
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3 Mapping Critical Levels for Vegetation
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. Thus, the upperboundary of the quasi-laminar layer (d+z0), is
simply 0.8z1, where z1 is canopy height.
Table 3.14 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 3.14, 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.
Table 3.14: 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.
O3 concentration gradient
Height
[m]
Crops (where z1=1m, gmax= 450 mmol
O3 m-2 PLA s-1)
Short Grasslands
(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.
For forests, ozone concentrations must often be
derived from measurements made over grassy
areas or other land-cover types. In principal, 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 could be applied. How-
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3 Mapping Critical Levels for Vegetation
ever, 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 3.14 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 3.14 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.
Method (b): Use of neutral stability profiles
If we have wind speed, u (m s-1) at reference
height zR, and an estimate of z0, then we find
concentration values appropriate to a height z1
(e.g. 1m) by making use of the constant-flux
assumption and definition of aerodynamic
resistance:
Ra ( zR ,z1 ) =
⎛z −d ⎞
1
⎟
ln ⎜⎜ R
ku * ⎝ z1 − d ⎟⎠
where the von Kármán konstant, k = 0.4
The deposition velocity requires further information:
(3.7)
Vg(zR ) =
1
Ra(zR ,z1 ) + Rb + Rc
Note: Ra is here the aerodynamic resistance
from zR to z0 (the level where Ra becomes
zero), not to z1 (which is any height fairly near
the ground, for example the height of the top of
the canopy). 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).
3.4.3 Accumulation period
(3.3)
Total flux = Vg(zR ) * C(zR ) =
C(zR ) − C(z1 )
Ra(zR ,z1 )
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). Rearranging the second two terms, we get:
(3.4)
C(z1 ) = C(zR ) [1 − Ra(zR ,z1 ) *Vg(zR )]
In neutral stability, friction velocity (u*) and Ra
are easily obtained:
(3.5)
u* =
(3.6)
u ( z )k
⎛z−d ⎞
⎟⎟
ln⎜⎜
⎝ z0 ⎠
It is important that the cumulative period over
which the AFstY value is calculated is consistent
with the period when the relevant crop or forest
species is actively growing and most sensitive
to the absorbed ozone dose. Thus, receptor
specific time periods are defined in the appropriate sections below. For flux-based critical
levels this is achieved by identifying, within the
growing season, the start (Astart) and end (Aend)
of the accumulation period by receptor type.
The following sources of information can be
considered for determining the accumulation
period:a) Information from local or national agricultural or forest experts
b) Phenological models. These predict the
start and end of the growing season,
typically defined as a function of climatic
factors such as cumulative temperature
and water availability.
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3 Mapping Critical Levels for Vegetation
3.4.4 The stomatal flux algorithm
The estimation of stomatal flux of ozone (Fst) is
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 potato and trees).
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:
(3.8)
Fst = c (z1 ) *
1
gsto
*
rb + rc gsto + gext
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 (3.8) as:
level rb term (McNaughton & van der Hank,
1995) is suggested, making use of the crosswind leaf dimension L (unit: m) and the wind
speed at height z1, u(z1):
(3.11)
rb = 1.3 * 150 *
L
u ( z1 )
[s m -1 ]
Where the factor 1.3 accounts for the differences in diffusivity between heat and ozone.
Potential values of L for wheat, potato and
beech/birch are 0.02m, 0.04m and 0.05m
respectively.
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:
(3.12)
gsto = gmax *[min(fphen, fO3)]* flight * max{fmin, (ftemp *
fVPD * fSWP)}
(3.9)
Fst = c (z1 ) * gsto *
rc
rb + rc
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:
(3.10)
gext = 1/2500
[m s-1]
In order to be used correctly in Equations (3.8)
and (3.9), gsto from equation (3.12) 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-
Where gsto is the actual stomatal conductance
(mmol O3 m-2 PLA s-1) and gmax is the speciesspecific maximum stomatal conductance (mmol
O3 m-2 PLA s-1). The parameters fphen, fO3, flight,
ftemp, fVPD and fSWP 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 vapour pressure
deficit (VPD) and soil water potential (SWP)) on
stomatal conductance to be estimated. Figure
3.7 shows the derivation of these functions and
sources of data for parameterisation are provided in Table 3.17 and Table 3.18 (at the end
of the chapter). The part of Equation (3.12)
related to fphen and fO3 is a most limiting factor
approach. 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
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
stomatal conductance in the afternoon hours,
an additional ΣVPD algorithm is included
(Equation (3.18)). This may result in a stronger
limitation of stomatal conductance than that
suggested by Equation (3.12) which represents
the original formulation given in Emberson et al.
(2000b). Receptor specific parameterisations
are provided for wheat and potato in Table 3.16
(see Section 3.4.5.3) and provisionally for
birch/beech in Table 3.19 (see Section 3.4.6.2).
always limiting and fO3 then does not come into
operation. The original parameterisations given
in Emberson et al. (2000b) have been revised
recently based on data collected from the
literature and from ozone exposure experiments
conducted in Sweden for wheat (Danielsson et
al., 2003) and from a number of sites across
Europe for potato (Pleijel et al., 2003).
To account for the effect by transpiration on leaf
water potential, which may lead to a limitation of
Figure 3.7: Parameterisation of wheat and potato stomatal conductance models
The range of gmax values for different wheat and potato cultivars grown in Europe, and additional data for potato
cultivars grown in the USA. The horizontal line represents the chosen gmax values (450 mmol m-2 s-1 for wheat,
(average of observations: 455 mmol m-2 s-1); 750 mmol m-2 s-1 for potato (average of observations 738 mmol m-2
s-1)). gmax values are expressed on a projected leaf area basis.
gmax
200
Lemhi Russet
Kennebec
Kardal
Maris piper
Cultivar
Russet Burbank
Cultivar
USA cultivars
0
Durum wheat,
cv. Janus
Dragon
Turbo
Cadensa
Astral
Boulmiche
0
400
Bintje
200
600
Prominent
400
800
Saturna
600
1000
Bintje
gmax (mmol O3 m-2 s-1, projected leaf area)
Potato, gmax
800
Kolibri
gmax (mmol O3 m-2 s-1, projected leaf area)
Wheat, gmax
1000
fphen
Potato, fphen relationship
1.2
1.2
1.0
1.0
0.8
0.8
Relative g
Relative g
Wheat, fphen relationship
0.6
0.4
0.2
0.6
0.4
0.2
0
-400
Accumulation period
-200
0
200
400
600
800
1000
Thermal time from day of gmax, base temperature 0 °C
0
-400 -200
Accumulation period
0
200
400
600
800 1000 1200
Thermal time from day of gmax, base temperature 0 °C
Figure 3.7 (cont.):
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3 Mapping Critical Levels for Vegetation
flight
Potato, flight relationship
1.2
1.0
1.0
0.8
0.8
Relative g
Relative g
Wheat, flight relationship
1.2
0.6
0.4
0.6
0.4
0.2
0.2
0
0
0
400
800
1200
1600
0
2000
400
800
1200
1600
2000
Irradiance (μmol m-2 s-1 PPFD)
Irradiance (μmol m-2 s-1 PPFD)
Vos & Oyarzun (1987)
Machado & Lagoa (1994)
Weber & Rennenberg (1996)
Bunce J.A. (2000)
Gruters et al. (1995)
Danielsson et al. (2003, AA data)
Danielsson et al. (2003, OTC data)
Ku et al. (1977)
Dwelle et al. (1983, Russet Burbank)
Stark J.C. (1987, Kennebec)
Stark J.C. (1987, Russet Burbank)
Dwelle et al. (1983, Lemhi Russet)
Dwelle et al. (1983, A6948-4)
Stark J.C. (1987, Lemhi Russet)
Pleijel et al. (2002, OTC data)
Dwelle et al. (1983, A66107-51)
Pleijel et al. (2002, AA data)
ftemp
Potato, ftemp relationship
1.2
1.0
1.0
0.8
0.8
Relative g
Relative g
Wheat, ftemp relationship
1.2
0.6
0.4
0.6
0.4
0.2
0.2
0
0
0
10
20
30
40
0
50
10
20
Temperature (°C)
Gruters et al. (1995)
Bunce J.A. (2000)
30
40
50
Temperature (°C)
Danielsson et al. (2003, OTC data)
Danielsson et al. (2003, AA data)
Ku et al. (1977, W729R)
Dwelle et al. (1983, Russet Burbank)
Pleijel et al. (2002, OTC data)
Pleijel et al. (2002, AA data)
fVPD
Potato, fVPD relationship
1.2
1.0
1.0
0.8
0.8
Relative g
Relative g
Wheat, fVPD relationship
1.2
0.6
0.4
0.2
0.6
0.4
0.2
0
0
0
1
2
3
4
5
0
1
VPD (kPa)
Weber & Rennenberg (1996)
Tuebner F. (1985)
2
3
4
5
VPD (kPa)
Bunce J.A. (2000)
Danielsson et al. (2003)
Tuebner F. (1985)
Pleijel et al. (2002, OTC data)
Pleijel et al. (2002, AA data)
Figure 3.7 (cont.):
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
Page III - 30
3 Mapping Critical Levels for Vegetation
fSWP
Potato, fSWP relationship
1.2
1.0
1.0
0.8
0.8
Relative g
Relative g
Wheat, fSWP relationship
1.2
0.6
0.4
0.6
0.4
0.2
0.2
0
0
-2.0
-1.6
-1.2
-0.8
-0.4
0
-2.0
-1.6
Gollan et al. (1986)
Emberson L. (1997)
Morgan J.A. (1994)
-1.2
-0.8
-0.4
0
SWP (MPa)
SWP (MPa)
Ali et al. (1999)
Zhang et al. (1998)
Vos & Oyarzun (1987)
Basu et al. (1999)
Danielsson et al. (2003)
Figure 3.7 (cont.):
gmax and fmin
Method (a): based on a fixed time interval
Receptor-specific values are provided for gmax
and fmin based on analysis of published data
(see Sections 3.4.5 and 3.4.6 for details).
when Astart ≤ yd < (Astart + fphen_c)
(3.13a)
fphen = (1 – fphen_a) * ((yd – Astart)/fphen_c) + fphen_a
fphen
The phenology function can be based on either
a fixed number of days or effective temperature
sum accumulation and has the same shape for
both approaches. However, use of the effective
temperature sum is generally accepted to
describe plant development more accurately
than using a fixed time period since it allows for
the influence of temperature on growth. fphen is
calculated according to Equations (3.13a, b, c)
when using a fixed number of days and (3.14a,
b, c) when using effective temperature sum
accumulation. Each pair of equations gives fphen
in relation to the accumulation period for AFstY
where Astart and Aend are the start and end of the
accumulation period respectively. The parameters fphen_a and fphen_b denote the maximum
fraction of gmax that gsto takes at the start and
end of the integration period for ozone flux. The
start and end of the integration period are
expressed as either number of days before
(fphen_c) and after (fphen_d) anthesis and tuber
initiation in wheat and potato (for equations
(3.13a, b, c) or the temperature sum before
(fphen_e) and after (fphen_f) anthesis and tuber
initiation in wheat and potato (for equations
(3.14a, b, c).
when (Astart + fphen_c) ≤ yd ≤ (Aend – fphen_d)
(3.13b)
fphen = 1
when (Aend – fphen_d) < yd ≤ Aend
(3.13c)
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 thermal time accumulation
when Astart ≤ tt < (Astart + fphen_e)
(3.14a)
⎛ 1 − fphen_a ⎞
⎟((Astart + fphen_e) − tt )
fphen = 1 − ⎜⎜
⎟
⎝ fphen_e ⎠
when (Astart + fphen_e) ≤ tt ≤ (Aend – fphen_f)
(3.14b)
fphen = 1
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
Page III - 31
3 Mapping Critical Levels for Vegetation
when (Aend – fphen_d) < tt ≤ Aend
fVPD and ΣVPD routine
(3.14c)
The VPD 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
vapor 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.
⎛ 1 − fphen_b ⎞
⎟(tt − (Aend − fphen_f) )
fphen = 1 − ⎜⎜
⎟
f
phen_f
⎝
⎠
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.
flight
The function used to describe flight is given in
Equation (3.15)
(3.15)
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 (3.16).
when Tmin < T < Tmax
(3.16a)
ftemp = max { fmin, [(T – Tmin) / (Topt – Tmin)] *
[(Tmax – T) / (Tmax – Topt)]bt}
when Tmin > T > Tmax
(3.16b)
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:
(3.17)
bt = (Tmax – Topt) / (Topt – Tmin)
The data used to establish the fVPD relationship
for both wheat and potato are shown in Figure
3.7. The functions used to describe fVPD are
given in Equation (3.18).
(3.18)
fVPD = min{1, max{ fmin, ((1–fmin)*(VPDmin – VPD) /
(VPDmin – VPDmax)) + fmin}}
Secondly, there is another effect on stomata by
water relations which can be modelled using
VPD. 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. This results in a
reduction of the plant water potential during the
course of the day and prevents stomata reopening 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
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
function (∑VPD) which is calculated in the
following manner:
If ΣVPD ≥ ΣVPD_crit, then:
(3.19)
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 (3.12).
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 (3.12) is valid if it is smaller or equal to the
gsto value of the preceding hour. If gsto according
to equation (3.12) 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.
one-month period and only comes into operation if it has a stronger senescence-promoting
effect than normal senescence. The functions
are given in Equations (3.21) and (3.22).
The ozone function for spring wheat (based on
Danielsson et al. (2003) but recalculated for
PLA):
(3.21)
fO3 = ((1+(AFst0/11.5)10)-1)
where AFst0 is accumulated from Astart
The ozone function for potato (based on Pleijel
et al. (2002)):
(3.22)
fO3 = ((1+(AOT0/40)5)-1)
where AOT0 is accumulated from Astart
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 (3.20).
(3.20)
fSWP = min{ 1, max{fmin, ((1–fmin)*(SWPmin–SWP) /
(SWPmin – SWPmax)) + fmin }}
f O3
The flux-effect models developed by Pleijel et
al. (2002) and Danielsson et al. (2003) include
a function to allow for the influence of ozone
concentrations on stomatal conductance (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
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
3.4.5 Calculation of AFstY and exceedance
of CLef for agricultural crops
CLef
An AFst6 of 1 mmol m-2 PLA
Either 970˚C days, starting
Time 200˚C days before midWheat
period anthesis or 55 days starting 15
days before mid-anthesis
Effect Yield reduction
CLef
Potato
An AFst6 of 5 mmol m-2 PLA
Either 1130˚C days, starting at
Time
plant emergence or 70 days
period
starting at plant emergence
Effect Yield reduction
3.4.5.1
Ozone concentration at the top of
the wheat and potato canopy
The ozone concentration at the top of the
canopy can be calculated using the methods
described in Section 3.4.2. For wheat and
potato the default height of the top of the canopy is 1.0m.
3.4.5.2
Estimating the time period for
ozone flux accumulation for wheat
and potato
Section 3.4.3 described two methods to estimate the influence of phenology on stomatal
conductance (i.e. fphen) based on a fixed time
interval and based on thermal time accumulation. This section describes, for each of these
methods, the procedures to estimate the AFstY
accumulation period for both wheat and potato.
The start and end of the accumulation period
are identified by Astart and Aend respectively.
Application of the fixed time interval method
requires that Astart and Aend be defined in terms
of day of the year whilst application of the
thermal time method requires that Astart and Aend
be defined in terms of °C days above a base
temperature of 0°C; in this case Astart will always
equal 0°C days though it is necessary to determine the year day for Astart.
The four methods that are suggested for estimating the timing of the AFstY accumulation
period listed in order of desirability are: 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).
Wheat
It is necessary to identify the timing of midanthesis (defined as growth stage 65 according
to Zadoks et al., 1974) for both spring and
winter wheat. 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 3.15. 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
3cm) (Hodges & 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 (it
should be noted that these calculations ignore
any effects of photoperiod).
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
(3.23). However, it should be recognised that
this method is less preferable to the use of the
effective temperature sum models described
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
above since latitude is not directly related to
temperature and this method will not distinguish
between spring and winter wheat growth patterns.
(3.23)
Equation (3.23) is based on data collected by
the ICP Vegetation (Mills and Ball, 1998, Mills
et al., 2007) from ten sites across Europe
(ranging in latitude from Finland to Slovenia)
describing the date of anthesis of commercial
winter wheat. Applying equation (3.23) 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).
Table 3.15: Observed sowing dates for spring wheat
in Europe1
Range
Default
Finland
1-30 May
30 May
Norway
1-20 May
20 May
Sweden
1-20 Apr
20 Apr
Northern Europe Denmark
Range
Default
-
Mediterranean Europe Bulgaria
Portugal
Mid-anthesis = 2.57 * latitude + 40
Region
Region
20 Jan-10 Mar 10 Feb
Spain
1-28 Feb
10 Feb
Italy
-
(not
grown)
(soft and durum wheat)
1
According to Broekhuizen (1969)
Potato
For potato, 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.
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
Romania
-
Hungary
-
Germany
10 Mar-10 Apr 01 Apr
Atlantic Central Europe UK
The Netherlands
France
20 Feb-20 Mar 10 Mar
1-30 Mar
15 Mar
1 Mar-10 Apr 20 Mar
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
Page III - 35
3 Mapping Critical Levels for Vegetation
3.4.5.3
Parameterisation of stomatal flux
models for wheat and potato
The original parameterisations given in Emberson et al. (2000b) have been revised based on
data collected from more recently published
literature and from ozone exposure experiments
conducted in Sweden for wheat (Danielsson et
al., 2003) and from a number of sites across
Europe for potato (Pleijel et al., 2003). Those
recommended for use in calculating AFstY using
the stomatal flux algorithm described in Section
3.4.4 are shown in Table 3.16.
Table 3.16: Summary of the parameterisation for the
stomatal flux algorithms for wheat flag
leaves and potato upper-canopy sunlit
leaves.
Parameter
Units
Wheat
Potato
(Triticum (Solanum
aestivum) tuberosum)
tained from gsto measurements made on cultivars grown either under field conditions or using
field-grown plants in open top chambers in
Europe were considered. 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.
Table 3.17 and Table 3.18 give details of the
published data used for gmax derivation on
adherence to these rigorous criteria. Figure 3.7
shows the mean, median and range of gmax
values for each of the six and four different
cultivars that provide the approximated gmax
values of 450 and 750 mmol O3 m-2 PLA s-1 for
wheat and potato, respectively.
gmax
mmol O3
m-2 PLA s-1
450
750
fmin
(fraction)
0.01
0.01
fphen_a
(fraction)
0.8
0.4
fphen_b
(fraction)
0.2
0.2
fphen_c
Days
15
20
fphen_d
Days
40
50
fphen_e
°C days
270
330
fphen_f
°C days
700
800
lighta
(constant)
0.0105
0.005
Tmin
°C
12
13
Topt
°C
26
28
Tmax
°C
40
39
VPDmax
kPa
1.2
2.1
VPDmin
kPa
3.2
3.5
fmin
ΣVPDcrit
kPa
8
10
SWPmax
Mpa
-0.3
-0.5
SWPmin
MPa
-1.1
-1.1
The data presented in Pleijel et al. (2003) 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.
The gmax values for wheat and potato have
been derived from published data conforming to
a strict set of criteria so as to be deemed acceptable for use in establishing this key parameter of the flux algorithm. Only data ob-
It should be noted that the wheat gmax value
has been parameterised from data collected for
spring wheat cultivars. Comparisons with data
for winter wheat presented by Bunce (2000)
would suggest that the gmax for spring and
winter wheat are likely to be similar.
Bunce (2000) gives a gmax value of 464 mmol
O3 m-2 s-1 on a projected leaf area basis for
winter wheat. These measurements were
conducted in the USA but nevertheless are
useful in indicating a similarity in the gmax of
both wheat types. Similarly, for potato additional
gmax values from three USA grown cultivars are
included in Figure 3.7 for comparison (Stark,
1987) further substantiating the gmax value
established for this crop type.
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
Page III - 36
3 Mapping Critical Levels for Vegetation
fphen
f O3
The data used to establish the fphen relationships for both wheat and potato are given in
Figure 3.1 as °C days from gmax (where gmax is
assumed to occur at mid-anthesis for wheat
and at the emergence of the first generation of
fully developed leaves in potato). Methods are
also provided in Section 3.4.4 to estimate fphen
according to fixed time periods using the
parameterisation given in Table 3.16. These
data are not shown in Figure 3.7.
The functions described for wheat and potato in
Section 3.4.4 should be used.
Once parameterisation of the model is complete
for the species being studied, AFstY and CLef
exceedance can be calculated following the
stages described in Section 3.4.1.
flight
The data used to establish the flight relationship
for both wheat and potato are shown in Figure
3.7.
ftemp
The data used to establish the ftemp relationship
for both wheat and potato are shown in Figure
3.7.
fVPD and ΣVPDcrit
The data used to establish the fVPD relationship
for both wheat and potato are shown in Figure
3.7.
Values of ΣVPDcrit for wheat and potato are
given in Table 3.16.
fSWP
The data used to establish the fSWP relationship
for both wheat and potato are given in Figure
3.7. 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.
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
Page III - 37
[mmol O3
m-2 s-1
PLA]
610 (±
73)
444
345
336
485
455
507
455
455
Reference
Ali et al.
(1999)
Araus et al.
(1989)
Araus et al.
(1989)
Araus et al.
(1989)
Gruters et al.
(1995)
Körner et al.
(1979)
Danielsson et al.
(2003)
Mean:
Median:
gmax
Country
Wheat type
and cultivar
Time of
day
Time of
year
Range: 336 to 610
From graph showing leaf conductance plotted
Spring wheat, (Assumed
against time in days. Maximum approximately 1 Denmark
August
Cadensa
mid-day)
-2 -1
mol H2O m s ; ± 0.12. ± SE of 4 to 6 replicates.
Value in Table. Cultivar and sowing time
Spring wheat,
14 March
(average of 3) gsto used. Means of 5 to 7
Spain
9 to 13 hrs
Kolibri
to 21 May
replicates. gsto mmol CO2 m-2 s-1. Adaxial: 313,
abaxial: 149.
Value in Table. Means and SE ± of 5 to 7
Spring wheat,
14 March
replicates. gsto mmol CO2 m-2 s-1. Adaxial: 267 ±
Spain
9 to 13 hrs
Astral
to 21 May
29, abaxial: 92 ± 16.
Value in Table. Means and SE ± of 5 to 7
Spring wheat,
14 March
replicates. gs mmol CO2 m-2 s-1. Adaxial: 251 ±
Spain
9 to 13 hrs
Boulmiche
to 21 May
15, abaxial: 99 ± 22.
Value in text. Maximum measured conductance
Spring wheat,
17 June to
Germany
(0.97 cm s-1 H2O total leaf area after Jones
11 to 12 hrs
Turbo
7 August
(1983)).
-1
Value given in table. 0.91 cm s for H2O on a
Durum wheat,
Austria
total leaf surface area basis.
Janus
Value in text. "The maximum conductance value,
414 mmol H2O m-2 s-1, was taken as gmax for the
Spring wheat,
13 Aug,
Sweden
13 hrs
Östad multiplicative model. The conductance
Dragon
1996 (AA)
values represent the flag leaf and are given per
total leaf area".
gmax derivation
Table 3.17: Derivation of wheat (Triticum aestivum) gmax parameterisation. PLA = projected leaf area
Li-Cor 6200
Ventilated diffusion
porometer
LI 1600 steady state
porometer
LI 1600 steady state
porometer
H2O / total
leaf area
H2O / total
leaf area
H2O / total
leaf area
CO2 / PLA
CO2 / PLA
CO2 / PLA
LI 1600 steady state
porometer
LI 1600 steady state
porometer
Field
Lysimeter
H2O/ 8
(Assume PLA
as use LAI)
IRGA LI-6200
Field
OTC and AA
Field
Field
Field
Field
Field
Growing
conditions
Gas / leaf
area
gsto measuring
apparatus
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Leaf
3 Mapping Critical Levels for Vegetation
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
Page III - 38
gmax
800
665
750
643
836
737
738
743
Vos & Groenwald
(1989)
Vos & Groenwald
(1989)
Marshall & Vos
(1991)
Pleijel et al. (2002)
Danielsson
(2003)
Mean
Median
[mmol O3
m-2 s-1 PLA]
Jeffries
(1994)
Reference
12
Value given in Table. gmax of 1371 mmol
m-2 s-1 for H2O per projected leaf area.
Range: 643 to 836
Value given in text. gmax of 604 mmol H2O
Sweden
m-2 s-1 per total leaf area.
Kardal
11
-
Value given in Figure. gmax of 527 mmol
H2O m-2 s-1 at intermediate N supply.
Netherlan Prominen
Each point represents the mean of at least
ds
t
three leaves (usually four).
Bintje
-
Value given in Figure. Maximum value of
15 mm s-1. Replicates approx. 20, the co- Netherlan
Saturna
ds
efficient of variation typically ranged
from 15 to 25%.
Germany
-
Bintje
Value given in Figure. Maximum value of
13.3 mm s-1 Replicates approx. 20, the co- Netherlan
ds
efficient of variation typically ranged
from 15 to 25%.
8 to
16 hrs
July
June
July
June
June /
July
June
H2O / PLA
Li-Cor 1600
steady state
diffusion
porometer
Li-Cor 6200
Li-Cor 6200
H2O /
Total leaf area
H2O / PLA
H2O /
assumed PLA
H2O / PLA
Li-Cor 1600
steady state
diffusion
porometer
LCA2 portable
infra-red gas
analyser
Assumed H2O /
assumed PLA
Diffusion
porometer
Field
Field
Field
Field
Field
Field
Potato Time Time of gsto measuring
Growing
Gas / leaf area
apparatus
cultivar of day
year
conditions
Maris
piper
Country
Value given in Figure. Maximum value of
16 mm s-1. Error bar represents SE of the Scotland
difference between two means (n=48).
gmax derivation
Table 3.18: Derivation of potato (Solanum tuberosum) gmax parameterisation. PLA = projected leaf area
Fully
expanded in
upper canopy
Fully
expanded in
upper canopy
Most recently
expanded
measurable
leaf
Youngest
fully grown
leaf
Youngest
fully grown
leaf
Fully
expanded in
upper canopy
Leaf
3 Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
3.4.6 Calculation of AFstY and exceedance
of CLef for forest trees
Please note: This section is currently under
review. Since this section was first written
and agreed in 2004, the provisional parameteristions provided for beech and birch have
been further developed and form the basis
of the generic species parameterisations
(see Section 3.6.2).
CLef
Beech
and
Time
birch period
Effect
Provisionally an AFst1.6 of 4
mmol m-2 PLA
One growing season
Growth reduction
Note: The recommended method for forest trees is AOTXbased critical levels; a provisional AFstY method is
provided for guidance only.
As proposed at the Gothenburg workshop
(Karlsson et al., 2003a), a default methodology
for a flux-based risk assessment for forest trees
is presented, as an alternative to concentrationbased AOT40 exposure assessments. Concentration-based assessment methods do not take
climatic limitation (soil moisture deficit, water
vapour pressure deficit) of exposure into account and AOTX-based approaches may, for
example, overestimate environmental risk in
southern Europe where summer moisture
deficits are commonplace. However, at the time
of writing this manual, it was considered that
the uncertainties associated with using a fluxbased assessment for forest trees for all of
Europe were too large for the method to be fully
recommended for use (these are discussed in
detail in Karlsson et al., 2003b, 2004). It should
also be emphasised that the methodology
presented here should not be used for economic assessments of damage or yield loss,
but should be used solely for mapping areas at
potential risk from ozone and as an alternative
to the recommended AOT40 approach.
3.4.6.1
Ozone concentration at the top of
the forest canopy
The ozone concentration at the top of the
canopy can be calculated using the methods
described in Section 3.4.2. For beech and birch,
the default height is 20m.
3.4.6.2
Parameterisation of the stomatal
flux model for beech and birch
The calculation of stomatal conductance (gsto)
for sunlit leaves in the upper canopy uses the
same multiplicative model as described in
Equation (3.12) for wheat and potato and
described in Section 3.4.4, with the exceptions
that there is no fO3 function and the ΣVPD
routine is not applied to estimate fVPD (see
later). The parameterisation of this model is
similar to that described in Emberson et al.
(2000a), but has been improved as information
has become available from recently published
literature (as described in Karlsson et al.,
2003b, 2004). The most significant change has
been to the ftemp parameterisation that now
allows gsto to occur at temperatures down to
−5°C (previously the Tmin value had been 13°C).
The model parameterisation described here
was used for the derivation of the forest tree
flux-based critical level AFst1.6 of 4 mmol m-2
PLA as described in Karlsson et al. (2003b,
2004).
The accumulation period for AFstY for forest
trees currently lasts for the entire growing
season since no information is available to
define a particular phenologically sensitive
period. As such, Astart and Aend will be equal to
the start (onset of bud-burst) and the end (end
of leaf senescence period) of the growing
season respectively. The default value for Astart
is year day 90 and for Aend is year day 270.
Currently, no data are available to define fphen
according to effective temperature sum models
so only the fixed time interval fphen methods
described in Section 3.4.3 are applied. Where
local parameterisations of the model are used,
it is recommended that parallel assessments
are also made and reported using the default
values given in Table 3.19, thus maintaining
compatibility between individual national assessments.
Insufficient information is available to define fO3
for forest trees and thus it is recommended that
fO3 is set to 1.0 in Equation (3.12).
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3 Mapping Critical Levels for Vegetation
The ΣVPD routine described for wheat and
potato that accounts for lack of re-opening of
stomata in the afternoon, cannot be applied to
forest trees with confidence as insufficient data
exists to complete the parameterisation. It is
thus recommended that this routine is omitted
from the fVPD procedure described in Section
3.4.4.
AFstY and CLef exceedance should be calculated following the stages described in Section
3.4.1.
Table 3.19: Default parameters for beech for use in
estimating ozone flux for forest tree
species.
Parameter
Value
gmax
134 mmol O3 m-2 projected
leaf area s-1
fmin
0.13 (fraction)
fphen_a
0.3 (fraction)
fphen_b
0.3 (fraction)
fphen_c
50 (days)
fphen_d
50 (days)
fphen_e
no parameterisation available,
use fixed time period method
fphen_f
no parameterisation available,
use fixed time period method
light_a
0.006
Tmin
-5oC
Topt
22oC
Tmax
35oC
VPDmax
0.93 kPa
VPDmin
3.4 kPa
ΣVPDcrit
No parameterisation available,
omit ΣVPD routine
SWPmax
-0.05 MPa
SWPmin
-1.5 MPa
Note: values have been modified since original publication
of Emberson et al. (2000a).
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3 Mapping Critical Levels for Vegetation
3.5 Calculating exceedance of concentration-based critical levels for
ozone
3.5.1 Stages in calculating AOTX and CLec
exceedance
The calculation of AOTX for comparison with
the CLec value for a given vegetation type or
plant species is based on hourly mean values
of ozone at the top of the canopy (see Section
3.4.2 for estimating the canopy height ozone
concentration from the measurement height
ozone concentration). For all daylight hours the
difference between the hourly mean concentration and X ppb is calculated; then the sum of all
hourly values with a positive value (i.e. with
hourly mean ozone concentrations above X
ppb) is calculated for the growth period of the
receptor. This calculation is illustrated in Figure
3.8.
It is recommended that AOTX values for comparison with CLec 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 stages are required
for calculation of AOTX and exceedance of
CLec:
1. Identify the relevant growth periods for
the receptor for accumulation of the exposure index.
2. Obtain high quality ozone data for this
growth period for the five most recent
years.
3. Adjust the ozone data from measurement height to canopy height using an
appropriate model or the algorithm in
this manual.
4. Calculate the AOTX index for each of
the five years.
Obtain the mean of the five values of AOTX and
compare with CLec for the receptor.
Contributes to AOT40
140
Does not contribute to AOT40
O3 concentration (ppb)
120
Daylight hours
100
80
60
40
20
0
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)
Figure 3.8: 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.
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3 Mapping Critical Levels for Vegetation
3.5.2 Calculation of AOTX and CLec exceedance for agricultural crops
Agricultural
crops
CLef
An AOT40 of 3 ppm h
Time
period
3 months
Effect
Yield reduction
These critical levels are only applicable when
nutrient supply and soil moisture are not limiting, the latter because of sufficient precipitation
or irrigation.
3.5.2.1
Ozone concentrations at the
canopy height for agricultural crops
In all AOTX calculations, the ozone concentration at the canopy height is used and calculated
as described in Section 3.4.2. The default
canopy height is 1m for agricultural crops.
3.5.2.2
Accumulation period for
agricultural crops
The timing of the three month accumulation
period for agricultural crops should reflect the
period of active growth of wheat and be centred
on the timing of anthesis. A survey of the development of winter wheat conducted at
13 sites in Europe by ICP Vegetation participants in 1997 and 1998, revealed that anthesis
can occur as early as 2 May in Spain and as
late as 3 July in Finland (Mills and Ball, 1998,
Mills et al., 2007a). Thus, a risk assessment for
ozone impacts on crops would benefit from the
use of a moving time interval to reflect the later
growing seasons in northern Europe. For guidance, default time periods have been provided
for five geographical regions as indicated in
Table 3.20.
Once the time period has been established,
AOT40 and CLec exceedance can be calculated following the stages described in Section
3.5.1.
Table 3.20: Regional classification of countries for default time periods for calculation of AOTX for agricultural
crops
Region
Three month time period Possible default countries
Eastern
Mediterranean
1 March to 31 May
Albania, Bosnia and Herzogovina, Bulgaria, Croatia,
Cyprus, Greece, FYR Macedonia, Malta, Slovenia,
Turkey, Yugoslavia
Western
Mediterranean
1 April to 30 June
Italy, Portugal, Spain
Continental
Central Europe
15 April to 15 July
Armenia, Austria, Azerbaijan, Belarus, Czech Republic, France1, Georgia, Germany, Hungary, Kazakhstan, Krygyzstan, Liechtenstein, Moldova,
Poland, Romania, Russian Federation, Slovakia,
Switzerland, Ukraine
Atlantic Central
Europe
1 May to 31 July
Northern Europe
1 June to 31 August
1
Belgium, Ireland, Luxembourg, Netherlands, United
Kingdom
Denmark, Estonia, Faero Islands, Finland, Iceland,
Latvia, Lithuania, Norway, Sweden
As an average between Western Mediterranean and Atlantic Central Europe
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3 Mapping Critical Levels for Vegetation
3.5.3 Calculation of AOTX and CLec exceedance for horticultural crops
An AOT40 of 6 ppm h
CLef
Horticultural Time
crops
period
Effect
3,5 months
Yield reduction
These critical levels are only applicable when
nutrient supply and soil moisture are not limiting, the latter because of sufficient precipitation
or irrigation.
3.5.3.1
Ozone concentrations at the
canopy height for horticultural
crops
In all AOTX calculations, the ozone concentration at the canopy height is used and calculated
as described in Section 3.4.2. The default
canopy height is 1m for horticultural crops.
3.5.3.2
3.5.4 Calculation of AOTXVPD and
exceedance of the short-term critical
level for ozone injury
Accumulation period for
horticultural crops
Since horticultural crops are repeatedly sown
over several months in many Mediterranean
regions, it is recommended that locallyappropriate 3.5 month periods are selected
between March and August for eastern Mediterranean areas, and March and October for
western Mediterranean areas, although other
time periods could be used depending on local
crops and horticultural practices. Whereas it is
most appropriate to recommend usage of this
critical level for Mediterranean regions, it may
also be appropriate to apply the critical level to
other parts of Europe since several of the
cultivars used to derive the critical level are
grown in other regions of Europe (Figure 3.4).
For such countries, appropriate 3.5 month
periods should be selected within the period
April to September.
Once the time period has been established,
AOT40 and CLec exceedance can be calculated following the stages described in Section
3.5.1.
CLec
Agricultural
Time
and horticulperiod
tural crops
Effect
A VPD-modified
AOT30 of 0.16 ppm h
Previous 8 days
Visible injury to
leaves
This critical level represents the risk of development of visible injury on sensitive agricultural
and horticultural crop species. 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.
The AOTXVPD index is calculated by using
hourly values of the ozone concentration,
expressed in ppb at the height of the top of the
canopy (see Section 3.4.2). 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:
(3.24)
[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).
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3 Mapping Critical Levels for Vegetation
In the case of the short-term critical level
for ozone injury X, in AOTXVPD, is 30 ppb. The
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 CLec 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.
3.5.5 Calculation of AOTX and CLec exceedance for (semi-) natural vegetation
3.5.5.1
Ozone concentrations at canopy
height
(Semi-) natural vegetation dominated by:
Critical level
Annuals
3 months
Growth reduction and/or seed
production reduction in annual
An AOT40 of 3 ppm h (or growing
season, if shorter) species
Perennials
An AOT40 of 5 ppm h 6 months
The AOT40 value should be calculated as the
concentration at canopy height, using the
information provided in Section 3.4.2. The
transfer functions to make this calculation,
based on deposition models, depend on a
number of factors which may vary systematically between EUNIS categories (described in
Section 3.5.5.3). 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 3.4.2 for short
grasslands are used as a default.
3.5.5.2
Time period
Time windows for calculating
AOT40 for ( 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
Effect
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
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 3.21).
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.
Once the time period has been established,
AOT40 and CLec exceedance can be calculated following the stages described in Section
3.5.1.
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3 Mapping Critical Levels for Vegetation
Table 3.21: Default timing for the start and end of ozone exposure windows for (semi-) natural vegetation.
(Note: regional classifications of countries are suggested in Table 3.20.)
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
mid-April
mid-July
mid-October
Northern Europe
* For mountain areas where the altitude is above 1500 m, use a start date of 1 April, with end dates of 30 June for annualdominated communities and 30 September for perennial-dominated communities.
3.5.5.3
Mapping ( semi-) natural
vegetation communities at risk
from exceedance of the critical
level
For more detailed mapping based on communities which are more likely to be sensitive to
ozone, the classification of the European Nature Information System, EUNIS (refer to
http://mrw.wallonie.be/dgrne/sibw/Eunis/) may
be used for the preliminary 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 3.22 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 new 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 3.22. For individual
countries, national databases may provide
better quality data on the distribution of the
communities.
Table 3.22: 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
Commu- Species level
nity level sensitivity
evidence 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)
1
Mills et al. (2007b).
Note: (Yes) represents a habitat classification for which
species show a positive response in above-ground biomass
but for which there is evidence that this might be associated with changes in shoot/root partitioning.
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3 Mapping Critical Levels for Vegetation
3.5.6 Calculation of AOTX and CLec
exceedance for forest trees
Forest
trees
CLec
Once the time period has been established,
AOT40 and CLec exceedance can be calculated following the stages described in Section
3.5.1.
An AOT40 of 5 ppm h
Time period Growing season
Effect
3.5.6.1
Growth reduction
Ozone concentrations at canopy
height.
It is important that the calculation of AOT40 is
based on ozone concentrations at the top of the
canopy as described in Section 3.4.2. The
default canopy height for forest trees is 20m.
3.5.6.2
Time-window for calculating ozone
exposure for forest trees
For the purposes of this manual, 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.
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3 Mapping Critical Levels for Vegetation
3.6 Estimation of risk of damage within
Integrated Assessment Modelling
At the Obergurgl workshop (2005), participants
in both the crop and forest tree working groups
recommended a flux-based risk assessment
method for use only in large-scale modelling,
including integrated assessment modelling.
This method represents a general, robust fluxbased risk assessment approach for a generic
crop, a generic deciduous tree and a generic
Mediterranean evergreen tree. It is intended to
provide estimates of the potential effective
phytotoxic cumulative stomatal ozone uptake
and hence should be viewed as an indicator of
the degree of risk for crop loss or negative
impacts on trees with a stronger biological basis
than AOT40. This method is not yet available
for a risk assessment for (semi-) natural vegetation. Furthermore, it should not be used for
yield/biomass loss or economic assessments or
to define exceedance of a cumulative flux below
which damage should not be expected to occur.
Hence, no critical level is defined. For such
assessments a complete model should be used
(Section 3.4) wherever data is available.
The rationales for this approach are: 1) to
ensure that the variability likely to occur with
regionally modelled parameters will not lead to
a misrepresentation of ozone risk given the
sensitivity of the flux-based critical level method
to co-variation in pollutant concentrations and
meteorology, 2) the uncertainty in modelling
associated with the ozone uptake rate threshold
of the flux-based critical level, 3) the variability
and uncertainty in identifying the exact timing of
the period of integration for phenology and
ozone, 4) the uncertainty associated with a
critical level, 5) for crops, the rather short,
biologically determined time-windows suggested for the application of the flux-based
critical levels are difficult to identify using largescale modelling, since they will vary considerably according to climate, year-to-year variation
in meteorology and agricultural practices locally
and across Europe.
3.6.1 Estimation of risk of damage to a
generic crop
The parameterisation for this generic flux model
is summarized in Table 3.23. It is suggested to
use a time window of three months symmetrically around the estimated date for anthesis in
wheat as identified by data collection within the
ICP Vegetation:
Mid-anthesis = 2.57 * latitude + 40
For these calculations it would also be assumed
that fphen for the leaves at the top of the canopy
are equal to 1. The function for ozone, fO3 is set
to one, although it was necessary for the derivation of the more mechanistic relationships
described by eq. (3.1) and (3.2). Also the function for soil water potential fSWP is set to one
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.
The functions for photosynthetically active
radiation (PAR), temperature, vapour pressure
deficit (VPD) and VPDsum are parameterized
as for wheat according to Table 3.16 and associated equations of this chapter.
Furthermore, due to difficulties to estimate the
ozone flux using Y = 6 nmol m-2 s-1 in IAM,
arising from the strong increase in the uncertainty in modelled AFst with increasing Y, AFst3
will be used. Since several other parts of the
parameterization are also changed as compared to the full flux model, the exposure index
used will be denoted AFst3gen to indicate the
generic character of the method for IAM.
For this method for IAM, the height (h) of the
crop canopy is assumed to be 1 m, the green
leaf area index (LAI) 3.5 m2 PLA m-2 and the
surface area index (SAI = green leaf area index
+ senescent leaf area index) 5.0 m2 PLA m-2
(PLA = projected leaf area). The characteristic
leaf dimension (L) is set to 0.02 m.
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3 Mapping Critical Levels for Vegetation
Table 3.23: Parameterisation for the flux-based
simplified method for IAM to estimate
relative risk for crop loss due to ozone.
Functions
Equations Parameter values
and
constants
gmax
450 mmol m-2 PLA s-1
fmin
0.01
fphen
1
flight
Eq. (3.15) Lighta = 0.0105
ftemp
Eq. (3.16) Tmin = 12 ºC
and (3.17) Topt = 26 ºC
Tmax = 40 ºC
fvpd
Eq. (3.18) VPDmax = 1.2 kPa
VPDmin = 3.2 kPa
ΣVPD
routine
Eq. (3.19) ΣVPDcrit = 8 kPa
fSWP
1
fO3
1
Y
3 nmol m-2 PLA s-1
SAI
5 m2 PLA m-2
Green LAI
3.5 m2 PLA m-2
h
1m
L
0.02 m
3.6.2 Estimation of risk of damage to a
generic deciduous tree and a generic
Mediterranean evergreen tree
Two generic parameterisations were felt necessary to capture, albeit only broadly, the diversity
that exists in European forests. A generic
“Deciduous” and “Mediterranean evergreen”
parameterisation were selected to account for
the variation in phenology and climate that are
considered to be important drivers of stomatal
ozone uptake in trees. Discussions to establish
these forest parameterisations began after the
Obergurgl workshop; the parameterisations
presented in Table 3.24 were approved by the
20th ICP Vegetation Task Force Meeting
(Dubna, Russian Federation, March, 2007).
For the “Mediterranean evergreen” forests, a
year round growing season is assumed. For the
“Deciduous” forests, 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 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.
This latitude model agreed well with ground
observations from the Mediterranean (Mediavilla & Escudero, 2003; Aranda et al., 2005;
Damesin & Rambal, 1995; Grassi & Magnani,
2005), Continental Central Europe (Defila &
Clot 2005; Deckmyn, pers. comm.; Arora &
Boer, 2005), Atlantic Central Europe (Broadmeadow, pers comm.; Duchemin et al., 1999)
and Northern Europe (Aurela et al., 2001;
Karlsson, pers. comm.) and remotely sensed
observations for the whole of Europe (Zhang et
al., 2004).
For the “Mediterranean 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 “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.
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
The functions for photosynthetically active
radiation (flight), temperature (ftemp) and vapour
pressure deficit (fVPD) are parameterised as
described in Table 3.24 and equations provided
in relevant sections of this Mapping Manual. For
the “Mediterranean 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.
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 IAM.
Values for Leaf Area Index (LAI, in m2 m-2),
height (h, in m), cross wind leaf dimension (L, in
cm) are provided and based on the experience
of the forest group. The stomatal flux threshold
(Y) value of 1.6 mmol O3 m-2 PLA s-1 is used as
agreed at the ozone critical levels Obergurgl
workshop.
Table 3.24: Parameterisation for the flux-based simplified method for IAM to estimate relative risk for adverse
ozone effects on forests.
Parameter
Units
Deciduous species Evergreen species for the Mediterranean area
Land use
Eunis class, area in km2
-2
All forested areas
gmax
mmol O3 m projected
leaf area (PLA) s-1
150
fmin
(fraction)
0.1
(1
Mediterranean evergreen forest species
(2
175
(3
(3
0.02
SGS
(4
year day
Latitude model
1 (1 Jan)
EGS
(4
year day
Latitude model
365 (31 Dec)
fphen_lim1
(4
year day
= SGS
80 (21 Mar)
fphen_lim2
(4
year day
= EGS
320 (16 Nov)
fphen_a
(4
(fraction)
0.0
1.0
fphen_b
(4
(fraction)
0.0
1.0
fphen_c
(4
(fraction)
1.0
0.3
fphen_d
(4
(fraction)
0.0
1.0
fphen_e
(4
days
15
130
fphen_f
(4
days
20
60
(5
(co-efficient)
0.006
Tmin
o
06
Topt
o
21
(6
23
(6
Tmax
o
C
35
(6
38
(6
VPDmax
kPa
1.0
VPDmin
kPa
3.25
SWPmax
MPa
fSWP=1
(8
fSWP=1
(8
SWPmin
MPa
fSWP=1
(8
fSWP=1
(8
C
C
0.009
(5
light_a
26
(7
7
2.2
(7
4.0
(7
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
Parameter
Units
Deciduous species Evergreen species for the Mediterranean area
2
Land use
Eunis class, area in km
All forested areas
Mediterranean evergreen forest species
Y
nmol m-2 PLA s-1
1.6
1.6
LAImin
2
-2
m m
0
5
LAImax
2
-2
m m
4
5
LAIs
m2 m-2
15
-
2
-2
LAIe
m m
30
-
h
m
20
8
L
m
7
3.5
1
Median value, range 100 – 180 mmol O3 m-2 PLA s-1, based on data for mature beech trees from
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 Arunda et al. (2000)
[180]; N.B. gmax value in mmol O3 m-2 PLA s-1 given in square brackets after reference.
2
Median value, range 70-365 mmol O3 m-2s-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 pers. comm. and for “Mediterranean evergreen” forests based on data from Tognetti
et al. (1998) and Infante et al. (1999).
4
It should be noted that the structure of the fphen function has changed here from that provided in
Sections 3.4.4 and 3.6.2 to allow for the “Mediterranean evergreen” fphen profile. The new fphen functions fphen_lim1 and fphen_lim2 described here allow for the decrease in gsto that is commonly found in
Mediterranean forests during the summer period which is driven by soil moisture limitation and
phenology. In the following formulations SGS is the start of growing season, EGS is the end of growing season and dd is the year day.
fphen = 0
when SGS ≥ dd ≥ EGS
fphen = fphen_a
when SGS < dd < fphen_lim1
fphen = fphen_b + (fphen_c - fphen_b) * (dd – fphen_lim1) / fphen_e
when fphen_lim1 ≤ dd < (fphen_lim1 + fphen_e)
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
fphen = fphen_c
when (fphen_lim1 + fphen_e) ≤ dd < (fphen_lim2 - fphen_f)
fphen = (fphen_d + (fphen_c - fphen_d) * (fphen_lim2 – dd) / fphen_f
when (fphen_lim2 - fphen_f) ≤ dd < fphen_lim2
fphen = fphen_d
when fphen_lim2 ≤ dd ≤ EGS
5
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).
6
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).
7
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
8
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.
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
Application of these “real” species parameterisations is intended to inform the European
scale Integrated Assessment Modelling (IAM)
conducted using the “generic” forest parameterisations. Figure 3.9 provides an overview of
how the “real” species parameterisation may be
used within the LRTAP Convention ozone risk
assessment modelling and mapping work.
EMEP and the Centre for Integrated Assessment Modelling (CIAM) at IIASA are largely
responsible for implementing the “generic” style
risk assessments for use in the European scale
IAM. The “real” species parameterisation is
notionally to be performed at the national level
using nationally specific modelling and mapping
tools (e.g. national emissions data, photooxidant models, land-cover data etc…). This
distinction is exemplary rather than definitive
since EMEP may also perform the modelling
and mapping using the “real” species parameterisation.
3.7 Estimation of risk of damage at the
European region or national scale
for “real” species
The methods described above to perform fluxbased risk assessments for generic crops
(Section 3.6.1) and two generic forest trees
(“Deciduous” and “Mediterranean evergreen”,
Section 3.6.2) were defined for use in integrated assessment and large-scale modelling.
Since the establishment of these “generic”
parameterisations, the forest sub-group (established at the LRTAP Convention Critical Level
meeting held in Obergurgl, 2006) has directed
efforts to defining methods to assess the
stomatal ozone flux for representative “real”
species. These methods have been defined to
represent species in particular European regions (using regions defined in Table 3.20,
Section 3.5.5.2) and to allow the modelling of
stomatal ozone flux and hence ozone risk to be
more specific to local species and climatically
determined forest tree physiology. To date,
“real” species parameterisations have not been
developed for crops species for application to
European regions; it is envisaged that such a
development may occur in the future.
Note: Because this is a rapidly developing
area of research, the “real” species parameterisations are presented in Annex I. This
text will be updated regularly as the research develops further.
EMEP European emissions data
National emissions
EMEP photo-oxidant model
National photo-
data
monitoring
Nationally derived O3 concentrations
European O3 concentrations
EMEP
“Generic” parameterisation
AFstY: IAM wheat
AFstY: Deciduous & Mediterranean evergreen Forests
AOT40: semi-natural
National O3
oxidant model
DO3SE deposition model
“Real” parameterisation
AFstY: wheat, potato
AFst(Y): Scots pine, Norway spruce,
beech, oak, birch, holm oak, Aleppo pine
AOT40 semi-natural
EMEP Flux mapping:
Identification of areas of higher flux
EMEP
or National Flux mapping:
Identification of areas of higher flux
Comparison between EMEP and National Mapping
Integrated Assessment Modelling (IAM)
EMEP / CIAM at IIASA
Revision of the 1999 Gothenburg protocol
Optimising emission controls with
“Generic” parameterisation
“What if” scenario analysis (with
“Generic” or “Real” parameterisation
National strategies for
emission control or mitigation
Revision of the 1999 Gothenburg protocol
Figure 3.9: An overview of the use of “real” species parameterisation within the LRTAP Convention modelling
and mapping of ozone impacts on forest trees.
Mapping Manual 2004 • Chapter III Mapping Critical Levels for Vegetation
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3 Mapping Critical Levels for Vegetation
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Annex I
“Real” species parameterisations for application at European region or
national scale.
Note: Please see Section 3.7 for an explanation of the application of this approach to assessing
risk of damage from ozone pollution.
Annex 1.1 Crops
These methods will be included here as soon as they become available.
Annex 1.2 Forest Trees
The methods presented below have been 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.
A1.2.1 Selection of representative “Real” species by climatic region
Representative “real” species were defined by the forest sub-group on 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, “real” species occur 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 “real” species selected to represent each region are given in Table
A1.1.
Table A1.1: Representative “Real” species by European region.
European region
Coniferous
Deciduous
Mediterranean
broadleaf 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
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A1.2.2 “Real” species model formulation and parameterisation.
Leaf Level (Leaf AFstY)
The leaf level AFstY model is described fully in Section 3.4.4). The existing formulation of the stomatal
conductance (gsto) component of this model (see eq. (3.12) and [1]) is maintained for the modelling of
leaf level AFstY for “real” forest trees species.
[1]
gsto = gmax * [min(fphen, fO3)] * flight * max {fmin, (ftemp * fVPD * fSWP)}
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). fmin is the lowest value normally found in the
field. The parameters fphen, fO3, fmin, flight, ftemp, fVPD and fSWP are all expressed in relative terms (i.e. they
take values between 0 and 1) as a proportion of gmax. Insufficient information is available to define fO3
for forest trees that is subsequently here set to 1.0.
Calculations of “Real” species leaf AFstY would use a constant threshold value Y of 1.6 mmol O3 m-2 s1
(Karlsson et al. 2007) since no new datasets are available for “Real” species that would warrant any
change in this value.
It is important to be aware that significant night-time transpiration (and hence AFst) 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 “Real” species across the climate regions coupled
with the implications of allowing night-time gsto (which might significantly alter AFst 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 “Real” species
method.
Canopy Level stomatal ozone flux (Canopy AFst)
AFst based risk assessments for forest trees might be improved by estimating whole-canopy rather
than leaf-level stomatal flux. This is due to the fact that whole tree sensitivity to ozone may not be
adequately represented through the calculation of uptake by only the sunlit leaves of the upper canopy; rather the uptake of sun and shade leaves of the entire canopy may have to be considered. As
such, here we define provisional methods to estimate canopy stomatal flux (canopy AFst). Since
these methods are only provisional they are only recommended to be used to provide comparisons
with leaf level AFst estimates to give an indication of whether the geographical distribution of ozone
risk to forest trees may be altered when using canopy vs. leaf level fluxes. For comparative purposes,
leaf level stomatal ozone fluxes should be estimated without the use of the threshold (Y) since there is
evidence of that detoxification capacity may change with age dependant changes in leaf/needle morphology (e.g. Wieser et al. 2002).
However, measurement data suggest that a more appropriate division of leaf and needle populations
within a canopy would be made according to leaf/needle morphology (i.e. sun and shade
leaves/needles). This is supported by data which shows shade needles to have significantly lower gmax
(ca. 70%) compared to sun needles. Based on data from Schulze et al. (1977) a simple 60:40
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(sun:shade) split is suggested, applicable all year round, for provisional modelling of canopy AFst. It is
noted that these proportions are likely dependent on tree type, stand density and other factors, and
efforts should be targeted in the future to refining these canopy fractions as new data become
available.
The incorporation of leaf/needle morphology in estimates of canopy stomatal flux would improve
methods used to scale up from the leaf to the canopy for both coniferous and deciduous forests. A
simple method described below assumes that sun and shade leaves/needles are evenly distributed
over the tree canopy.
[2]
gmax_shade = 0.6 * gmax_sun
Canopy gsto = 0.6 * gmax_sun + 0.4 * gmax_shade
which can be simplified, assuming a 60:40 (sun:shade) split to :[3]
gmax_canopy = 0.84 * gmax_sun
The estimation of forest tree canopy stomatal ozone flux would be made using [3] in combination with
existing up-scaling methods in the DO3SE dry deposition model. These methods use standard
algorithms to define light extinction with canopy depth to estimate the fraction of- and irradiance tosunlit and shaded leaves within the canopy (e.g. Emberson et al. 2000). Resulting flight values for both
sunlit and shaded fractions are then scaled to the canopy level using an estimate of LAI; hence
definition of this parameter is crucial in canopy level stomatal ozone flux estimates. Values for LAI for
each “Real” species are given by climate region in the parameterisation table provided in the following
sections. Further details of the up-scaling methods used can be found in Simpson et al. (2003).
In the future methods that define the LAI fractions of sun and shade leaves/needles with canopy depth
can be combined with estimates of sunlit and shaded fractions of the canopy to refine these up-scaling
methods. However, the increased complexity of these methods would wrrant larger datasets with
which to parameterise the fractional distribution of leaf/needle morphologies within the canopy and
hence will not be considered here.
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 3.6.2). This model gave good agreement with observed phenological data
for a range of deciduous species (e.g. birch, beech and oak; see also Figure A1); 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).
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350
300
Year Day
250
200
150
100
50
35
40
45
50
55
60
65
70
Latitude
Fig A1: 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 EMEP latitude model for the estimation of SGS and EGS is given below.
SGS occurs at year day 105 at latitude 50oN. SGS will alter by 1.5 day per degree latitude earlier on moving
south and later on moving north.
EGS occurs at year day 297 at latitude 50oN. EGS will alter by 2 days per degree latitude earlier on moving
north and later on moving south.
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.
Temperature based phenology models also agreed reasonably well with observational data but
showed no obvious advantage over the simpler latitude model. Concern over uncertainties in the
EMEP night-time temperature data were raised in relation to achieving reliable phenological modelling
based on 24 hour temperatures. Additionally, the phenological models incorporating temperature were
not well enough validated across the whole of Europe for a long enough time series (i.e. for different
years) to be certain that they would provide a more realistic basis for estimating the between year
variation in SGS that may be due to variability in climatic conditions. As such, the EMEP latitude
model will be used to estimate the timing of the physiologically active growth period for the “real”
species.
It is useful to define the physiologically active growth period using the terms SGS and EGS, which
have been used previously in descriptions of stomatal ozone flux modelling (Section 3.6.2). 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 “real” species, 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
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~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. Table A1.2 summarises the
methods used to derive SGS and EGS for different real species by forest type.
Table A1.2: Methods to derive SGS and EGS for different species by European region.
Climate region / species
SGS
EGS
Birch
EMEP latitude model
EMEP latitude model
Norway spruce
EMEP latitude model
EMEP latitude model
Beech
EMEP latitude model
EMEP latitude model
Temperate Oak
EMEP latitude model
EMEP latitude model
ftemp
ftemp
ftemp
ftemp
EMEP latitude model
EMEP latitude model
Holm Oak
Year round growth period
Year round growth period
Aleppo Pine
Year round growth period
Year round growth period
EMEP latitude model
EMEP latitude model
Northern Europe
Atlantic Central Europe
Scots pine
Continental Central Europe
Norway spruce
Beech
Mediterranean Europe
Beech
Relationship between LAI, fphen and SGS & EGS
LAI
Coniferous forest species
Given the variability in LAI of coniferous forests which will be a function of both needle flushing and
needle shedding, the latter continually occurring over much of the year, a constant, year round, LAI is
assumed for this forest type. The large latitude range (> 15o) of the Northern European region means
that trees are exposed to rather extreme environmental conditions which may limit their structural
growth. To accommodate this, the height (h) and LAI of trees occurring north of 60oN is decreased by
a reduction factor (lat_factor) which is a function of latitude [4]
[4]
lat_factor = max ( 0.3, (1.0-0.05*[lat-60.0])
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3 Mapping Critical Levels for Vegetation
Maximum LAI values are defined for all “real” species by European region, as given in Tables A1.3 to
A1.6; overcoming, at least to some extent, issues related to within species variability in LAI which is
known to occur with geographical location. The assumption regarding LAI (and h) will be revisited in
future years as more data become available.
Deciduous forest species
The LAI for deciduous forests follows the same profile used for the “generic” deciduous parameterisation; the “real” species-specific parameterisations are provided in Tables A1.3 to A1.6. Once again, for
Northern European trees the latitude reduction factor [4] is applied for trees growing north of 60oN.
Mediterranean evergreen forest species
Data for Holm oak from Ferretti & Bussotti (2007) suggest that for Mediterranean evergreen broadleaf
forests, LAI is not constant throughout the growing season but increases significantly during the period
of spring-time leaf flushing (Figure A1.2). These data are used to define the LAI for this forest group
using the parameterisation given in the notes to Table A1.6.
LAI relationships
6
5
LAI (m2/m2)
4
3
Leaf flushing
2
LAI variable
LAI Generic / real
Observed LAI at Cala Viola in 2005
Observed budburst
Observed end of leaf flushing
LAI at TOS1
LAI at LAZ1
1
0
0
50
100
150
200
250
300
350
Year day
Figure A1.2: Annual variation in LAI of Holm oak (Mediterranean evergreen broadleaf forest) growing in Italy;
data from Ferretti & Bussotti (2007).
fphen
Coniferous and deciduous forest species
For the “Coniferous” and “Deciduous” 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. 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.
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3 Mapping Critical Levels for Vegetation
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 notes for Table A1.6). 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. In conclusion it is recognised that further work is needed to
improve methods to understand the influence of drought on gsto and hence O3 uptake; for example,
ideally, the onset of soil water stress would determine the timing and extent of leaf flushing avoiding
inconsistencies that currently exist between assumed fixed periods of stress and leaf flushing.
Figure A1.3 describes the variation in both LAI and fphen for the Mediterranean broadleaf 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 A1.6);
similarly fphen (in the absence of information describing soil water or phenological influence on gsto) will
be dependent upon the start and end of the assumed soil water limiting period (defined by the fphen_limA
and fphen_limB parameters given in Table A1.6).
6
f phen_limA
start of "soil w ater" limitation
5
f phen_limB
end of "soil w ater" limitation
end of flush
"LAIe"
3
0.7
0.5
2
fphen
LAI
1.1
0.9
4
0.3
1
0.1
start of leaf flush
"LAIs"
0
0
100
200
Yead day
fphen
Fig A1.3:
1.3
300
-0.1
400
LAI
Seasonal variation in LAI and fphen assumed for Mediterranean evergreen broadleaf forest species
(see Table A1.6 for further details of parameterisations and formulations)
f function parameterisations
fmin, flight, ftemp, fVPD and fSWP functions are provided in Tables A1.3 to A1.6 for the “real” species. The
formulations relating to these functions are the same as those used previously for real species as
described in section 3.4.4. 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
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3 Mapping Critical Levels for Vegetation
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.
Values for Leaf Area Index (LAI, in m2 m-2), height (h, in m), cross wind leaf dimension (L, in cm) are
provided and based on the experience of the forest group are provided in Tables A1.3 to A1.6. In
these tables the corresponding “generic” species parameterisation used to define risk at the European
scale for IAM is provided for comparison.
A1.2.3 Uncertainty
It is important to emphasise the benefits and limitations of this “local” or “real” species parameterisation within the context of the flux-based risk assessment approach for ozone. It is generally accepted
that flux provides a better indicator of risk than the previous concentration-based approaches (e.g.
AOT40). The problem is perhaps less in the theoretical acceptance of flux as an indicator of risk, but
rather the practical application of the flux-based approach; in short, the degree of certainty with which
we feel we can describe the spatial and temporal variation in flux that exists between different species
and species ecotypes across Europe. The “generic” based parameterisation that was defined previously (see Section 3.6) established methods to estimate flux-based risk for representative “deciduous”
and “Mediterranean evergreen” forest tree species. These methods gave a rather limited suggestion
as to the variation in flux that may occur due forest type and climate across the region. 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 local or “real” parameterisations for a number of different species. The “real”
species parameterisation defined here has attempted 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 “real” species 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 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 “real” species parameterisations.
The species selected incorporate a range of forest tree functional types (i.e. coniferous, deciduous,
Mediterranean broadleaf 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 should provide
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3 Mapping Critical Levels for Vegetation
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.
Finally, and perhaps most importantly, it is necessary to emphasise the limitations of the use of the
“real” species stomatal ozone flux (AFst) 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
AFst across Europe. This is exemplified by the suggestion within the “real” species 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. A mention should also be
made to support continued efforts to define flux-response relationships, perhaps in the first instance
these should aim at an improved understanding of the variability of the detoxification threshold (Y) with
leaf morphology, phenology and environmental conditions. Ultimately, the goal would be to develop
flux-response relationships that could be used to move towards defining absolute rather than relative
risk for forest trees.
A1.2.4 Acknowledgements
The material for this Annex was prepared by the forest sub-group established at the Obergurgl workshop. The editorial group of ICP Vegetation finalized the text.
A1.2.5 References
General References
Emberson, L.D., Simpson, D., Tuovinen, J.-P., Ashmore, M.R., Cambridge, H.M., 2000a. Towards a
model of ozone deposition and stomatal uptake over Europe. Norwegian Meteorological Institute, Oslo. EMEP MSC-W Note 6/2000, 57p.
Grulke, N.E., Alonso, R., Nguyen, T., Cascio, C., Dobrowolski, W. (2004) Stomata open at night:
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Karlsson, P.E. Braun, S., Broadmeadow, M., Elvira, S., Emberson, L., Gimeno, B.S., Le Thiec, D.,
Novak, K., Oksanen, E., Schaub, M., Uddling, J., Wilkinson, M. (2007) Risk assessments for
forest trees: The performance of the ozone flux versus the AOT concepts. Env. Poll. : 146 608616
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Körner, C. (1994) Leaf Diffusive conductances in the major vegetation types of the globe. In: Schulze,
E.D., Caldwell, MM (eds) 1994. Ecophysiology of photosynthesis. Ecological studies 100.
Springer, Berlin Heidelberg New York, pp 463-490
Matyssek, R., Günthardt-Goerg, M.S., Landolt, W., Keller, T. (1993): Whole-plant growth and leaf
formation in ozonated hybrid poplar (Populus x euramericana). Environmental Pollution 81, 207212
Musselman, R.C. and T.J. Minnick. 2000. Nocturnal stomatal conductance and ambient air quality
standards for ozone. Atmos. Environ. 34:719–733.
Schulze, E.-D., Fuchs, M. and Fuchs, M.I. (1977) Spatial distribution of photosynthetic capacity and
performance in a mountain spruce forest of northern Germany. III. The ecological significance of
the evergreen habit. Oecologia 30:239-248.
Simpson, D., Fagerli, H., Jonson, J.E., Tsyro, S., Wind, P. (2003). Transboundary Acidification, Eutrophication and Ground Level Ozone in Europe. Part I - Unified EMEP Model Description. Norwegian Meteorological Institute, Oslo. <http://www.emep.int/publ/common_publications.html>,
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Wieser, G., Tegischer, K., Tausz, M., Häberle, K.-H., Grams, T.E.E., Matyssek, R. (2002) Age effects
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avoidance to defense. Tree physiology 22: 583-590
Zhang, X., Friedl, M.A, Schaaf, C.B., Strahler, A.H. (2004). Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Change Biology 10: 1133-1145.
Parameterisation References
Northern Europe
Norway spruce (Picea abies)
Hansson, M., , Höglund, H-O.,, Karlsson, P.E. (in preparation) Stomatal conductance, shoot water
potentials and meteorology at two different canopy levels in a Norway spruce (Picea abies (L.)
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Jarvis, P.G. 1980. Stomatal response to water stress in conifers. In Adaptations of plants to water and
high temperature stress. Eds N.C. Turner and P.J. Kramer. pp. 105-122. Springer Verlag.
Karlsson, P.E., H. Pleijel, G. Pihl Karlsson, E.L. Medin, L. Skärby. 2000. Simulations of stomatal
conductance and ozone uptake to Norway spruce saplings in open-top chambers. Environmental Pollution 109, 443-451.
Lagergren & Lindroth, 2002. Transpiration response to soil moisture in pine and spruce trees in Sweden. Agric. For. Meteorol. 112, 67-85.
Nihlgårdh, B. 1972. Plant biomass, primary production and distribution of chemical elements in a
beech and a planted spruce forest in south Sweden. Oikos 23, 69-81
Sellin, A., 1997. Variation in shoot water status of Picea abies (L.) Karst. trees with different life histories. Forest Ecology and Management. 97(1), 53-62.
Sellin, A., 2001. Morphological and stomatal responses of Norway spruce foliage to irradiance within a
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Schulze, E.D. (2000) The carbon and nitrogen cycle of forest ecosystems. Ecological Studies 142 3 13, 2000
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Silver birch (Betula pendula)
Uddling, J., Hall, M,., Wallin, G., Karlsson, P.E. (2005a) Measuring and modelling stomatal conductance and photosynthesis in mature birch in Sweden. Agricultural and Forest Meteorology 132,
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Beadle, C.L., Neilson, R.E., Talbot, H. and Jarvis, P.G. (1985) Stomatal conductance and photosynthesis in a mature scots pine forest. I. Diurnal, seasonal and spatial variation in shoots. J. of
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Ng, P. (1979) response of stoamta to environmnetal variables in Pinus sylvestris L. PhD Thesis.
University of Edinburgh
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controls on leaf- and stand-level water conductance in a Scots pine plantation. Annales des Sciences Forestieres 55 (1-2): 237-253
Whitehead, D., Jarvis, P.G., and Waring, R.H. (1984) Stomatal conductance, transpiration, and resistance to water uptake in a Pinus sylvestris spacing experiment. Can. J. For. Res. 14: 692-700
Temperate Oak (Quercus petraea & robur)
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Breda, N., Cochard, H., Dreyer, E. and Granier, A. (1993a) Water transfer in a mature oak stand
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Breda, N., Cochard, H., Dreyer, E., Granier, A. (1993b) Field comparison of transpiration, stomatal
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Dolman, A.J. and van den Burg, G.J. (1988) Stomatal behaviour in an oak canopy. Agricultural and
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Vivin, P., Aussenac, G. and Levy, G. (1993) Differences in drought resistance among 3 deciduous oak
species grown in large boxes. Annales des Sciences Forestieres 50(3): 221-233
Beech:
See Generic Deciduous parameterisation (Section 3.6.2)
Continental Central Europe
Norway spruce (Picea abies)
Breuer, L., Eckhardt, K., Frede, H.-G. (2003) Plant parameter values for models in temperate climates.
Ecological Modelling 169: 237-293
Körner, CH., Perterer, J., Altrichter, CH., Meusburger, A., Slovik, S., Zoschg, M. (1995) Ein einfaches
empirisches Modell zur Berechnung der jahrlichen Schadgasaufnahme von Fichten- und Kiefernadeln. Allg. Forst- und Jagzeitung 165: 1-9.
Körner, C. (1994) Leaf Diffusive conductances in the major vegetation types of the globe. In: Schulze,
E.D., Caldwell, MM (eds) 1994. Ecophysiology of photosynthesis. Ecological studies 100.
Springer, Berlin Heidelberg New York, pp 463-490
Körner, Ch., Scheel, J.A. and Bauer, H. (1979) Maximum leaf diffusive conductance in vascular plants.
Photosynthetica 13 (1): 45-82.
Dixon, M., Le Thiec, D., Garrec, J.P. (1995) The growth and gas exchange responses of soil-planted
Norway spruce (Picea abies (L.) Karst.) and red oak (Quercus rubra L.) exposed to elevated
CO2 and to naturally occurring drought. New Phytologist 129: 265-273
Emberson, L.D., Wieser, G., Ashmore, M.R. (2000) Modelling of stomatal conductance and ozone flux
of Norway spruce: comparison with field data. Environmental Pollution 109: 393-402
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Beech (Fagus sylvatica)
Braun, S., Leuzinger, S., Schindler, C., Flückiger, W. (in prep): Use of sapflow measurements to
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flows in a mixed Mediterranean woodland: a scaling exercise. Ann. Sci. For. 55: 173-190.
Gratani, L. (1993). Response to micro-climate of morphological leaf attributes, photosynthetic and
water relations of evergreen sclerophyllous shrub species. Photosynthetica 29: 573-582.
Infante, J.M., Damesin, C., Rambal, S., Fernandez-Ales, R., (1999). Modelling leaf gas exchange in
Holm oak trees in Southern Spain. Agricultural and Forest Meteorology 95: 203-223.
Manes, F., Seufert, G., Vitale, M. (1997). Ecophysiological studies of Mediterranean plant species at
the Castelporziano Estate. Atmospheric Environment 31 (SI): 51-60.
Mediavilla, S. and Escudero, A.E. (2003). Stomatal responses to drought at a Mediterranean site: a
comparative study of co-occurring woody species differing in leaf longevity. Tree Physiology 23:
987–996.
Ogaya R., Peñuelas J. (2003). Comparative seasonal gas exchange and chlorophyll fluoresecence of
two dominant woody species in a Holm Oak Forest. Flora 198: 132-141.
Rhizopoulou, S. and Mitrakos, K. (1990). Water relations of evergreen sclerophylls I. Seasonal
changes in the water relations of eleven species from the same environment. Annals of Botany
65: 171-178.
Sala, A. and Tenhunen, J.D. (1994). Site-specific water relations and stomatal response of Quercus
ilex in a Mediterranean watershed. Tree Physiology 14: 601-617.
Tenhunen, J.D., Pearcy, R.W., Lange, O.L. (1987). Diurnal variations in leaf conductance and gas
exchange in natural environments. In Zeiger E, Farquhar GD, Cowan I. (eds) Stomatal Function. Stanford, California.
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3 Mapping Critical Levels for Vegetation
Tognetti, R., Longobucco, A., Miglietta, F., Raschi, A. (1998). Transpiration and stomatal behaviour of
Quercus ilex plants during the summer in a Mediterranean carbon dioxide spring. Plant, Cell
and Environment 21: 613-622.
Vitale M., Gerosa G., Ballarin-Denti A., Manes F. (2005). Ozone uptake by an evergreen Mediterranean forest (Quercus ilex L.) in Italy – Part II: flux modelling. Upscaling leaf to canopy ozone uptake by a process-based model. Atmospheric Environment 39: 3267-3278.
Aleppo Pine (Pinus halepensis Mill.)
Elvira, S., Alonso, R., Gimeno, B.S. (2007) Simulation of stomatal conductance for Aleppo pine to
estimate its ozone uptake. Env. Poll. 146: 617-623
Picon, C., Guehl, J.M., Ferhi, A. (1996) Leaf gas exchange and carbon isotope composition responses
to drought in a drought-avoiding (Pinus pinaster) and a drought-tolerant (Quercus petraea)
species under present and elevated CO2 concentrations. Plant, Cell and Environment 19: 182190
Beech (Fagus sylvatica)
Aranda, I., Gil, L., Pardos, A.J. (2000). Water relations and gas exchange in Fagus sylvatica L. and
Quercus petraea (Mattuschka) Liebl. In a mixed stand at their southern limit of distribution in
Europe. Trees 14: 344-352.
Damesin C., Rambal S., Joffre R. (1998). Co-occurrence of trees with different leaf habit: a functional
approach on Mediterranean oaks. Acta Oecologica 19: 195-204.
Grassi G., Magnani F. (2005) Stomatal, mesophyll condcutance and biochemical limitations to photosynthesis as affected by drought and leaf ontogeny in ash and oak trees. Plant, Cell and Environment 28: 834-849.
Körner, Ch., Scheel, J.A. and Bauer, H. (1979) Maximum leaf diffusive conductance in vascular plants.
Photosynthetica 13 (1): 45-82.
Matyssek, R., Wieser, G., Nunn, A. J., Kozovits, A. R., Reiter, I. M., Heerdt, C., Winkler, J. B.,
Baumgarten, M., Häberle, K.-H., Grams, T. E. E., Werner, H., Fabian, P., Havranek, W. M.
(2004): Comparison between AOT40 and ozone uptake in forest trees of different species, age
and site conditions. Atmospheric Environment 38: 2271-2281.
Mediavilla, S. and Escudero, A.E. (2003). Stomatal responses to drought at a Mediterranean site: a
comparative study of co-occurring woody species differing in leaf longevity. Tree Physiology 23:
987–996.
Nunn, A., Kozovits, A. R., Reiter, I. M., Heerdt, C., Leuchner, M., Lütz, C., Liu, X., Löw, Winkler, J. B.,
Grams, T. E. E., Häberle, K.-H., Werner, H., Fabian, P., Rennenberg, H., Matyssek, R. (2005).
Comparison of ozone uptake and sensitivity between a phytotron study with young beech and a
field experiment with adult beech (Fagus sylvatica). Environmental Pollution 137: 494-506.
Raftoyannis, Y. and Radoglou, K. (2002). Physiological responses of beech and sessile oak in a
natural mixed stand during a dry summer. Annals of Botany 89: 723-730.
Rico M., Gallego H.A., Moreno G., Santa Regina I. (1996). Stomatal response of Quecus pyrenaica
Willd to environmental factors in two sites differing in their annual rainfall (Sierra de Gata, Spain.
Annales des Sciences Forestières 53: 221-234.
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3 Mapping Critical Levels for Vegetation
Table A1.3: Northern European “real” species parameterisation for Norway spruce and Silver birch. The parameters for the “generic” deciduous tree species are also given for reference.
Parameter
Land use
Units
Eunis class,
area in km2
Norway spruce
parameterisation
Silver birch
parameterisation
Generic Deciduous
species
Coniferous forests
Deciduous broadleaf
forests
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
0.0
fphen_b
(fraction)
0
0
0.0
fphen_c
(fraction)
1
1
1.0
fphen_d
(fraction)
0
0
0.0
fphen_e
(days)
20
20
15
fphen_f
(days)
30
30
20
0.006
0.0042
0.006
light_a
Tmin
oC
0
5
0
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
1.6
1.6
1.6
LAImin
-
0
0
LAImax
6.5
3
4
LAIs
-
15
15
LAIe
-
30
30
h, m
20
20
20
L, cm
0.8
5
7
Y
* SWC is calculated as the soil water content available for transpiration, i.e. the actual SWC minus the
SWC at the wilting point.
** 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
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3 Mapping Critical Levels for Vegetation
by high VPDs). Hence, this Tmax value should be viewed as a forcing rather than descriptive parameter.
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)
flight: Norway spruce: Karlsson et al. (2000); Hansson et al. (in prep.)
Silver birch: Uddling et al. (2005a)
ftemp: Norway spruce: Karlsson et al. (2000); Hansson et al. (in prep.); Jarvis (1980); Lagergren &
Lindroth (2002)
Silver birch: Uddling et al. (2005a)
fVPD: Norways spruce: Hansson et al. (in prep.), Zimmermann et al. (1988)
Silver birch: Uddling et al. (2005a)
fSWP: Norway spruce: Sellin (1997)
Silver birch: Uddling et al. (2005a)
LAI: Norway spruce: Schulze (2000); Lagergren & Lindroth (2002); Slovik et al (1995); Nihlgårdh
(1972)
Silver birch: Wang et al. (1995)
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3 Mapping Critical Levels for Vegetation
Table A1.4: Atlantic Central European “real” species parameterisation for Scots pine, temperate oak and beech.
The parameters for the “generic” deciduous tree species are also given for reference.
Parameter
Land use
Units
Eunis class, area in
km2
Scots pine
parameterisation
Temperate Oak
parameterisation
Beech
parameterisation*
Generic
Deciduous
Coniferous forests
Deciduous broadleaf
forests
Deciduous broadleaf forests
Deciduous
broadleaf 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
0.0
fphen_b
(fraction)
0.8
0
0.0
0.0
fphen_c
(fraction)
1
1
1.0
1.0
fphen_d
(fraction)
0.8
0
0.0
0.0
fphen_e
(days)
40
20
15
15
fphen_f
(days)
40
30
20
20
0.006
0.003
0.006
0.006
light_a
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
1.6
1.6
1.6
1.6
LAImin
4.5
0
0
0
LAImax
4.5
4
4
4
LAIs
-
20
15
15
LAIe
-
30
30
30
h, m
20
20
20
20
L, cm
0.8
5
7
7
* „generic“ deciduous parameterisation used as surrogate
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]
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3 Mapping Critical Levels for Vegetation
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)
flight: tenperate oak: Breda et al. (1995); Dolman & Van den Burg (1988)
Scots pine: Beadle et al.(1985); Sturm et al. (1998); Ng (1979)
ftemp: 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)
LAI: temperate oak:
Scots pine
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3 Mapping Critical Levels for Vegetation
Table A1.5: Continental Central European “real” species parameterisation for Norway spruce and beech. The
parameters for the “generic” deciduous tree species are also given for reference.
Units
Norway spruce
parameterisation
Beech
parameterisation
Generic
Deciduous
Land use
Eunis class, area in km2
Coniferous forests
Deciduous broadleaf
forests
Deciduous broadleaf forests
gmax
mmol O3 m-2 projected
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)
1
0.0
0.0
fphen_b
(fraction)
1
0.0
0.0
fphen_c
(fraction)
1
1.0
1.0
fphen_d
(fraction)
1
0.4
0.0
fphen_e
(days)
-
20
15
fphen_f
(days)
-
20
20
0.01
0.006
0.006
Parameter
light_a
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
1.6
1.6
1.6
LAImin
-
0
0
LAImax
12
5
4
LAIs
-
15
15
LAIe
-
20
30
h, m
20
25
20
L, cm
0.8
7
7
Y
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]
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3 Mapping Critical Levels for Vegetation
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)
flight: beech: „generic“ deciduous parameterisation used as surrogate
Norway spruce: Theone et al. (1991); Körner et al. (1995); Zweifel et al. (2000, 2001, 2002)
ftemp: 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)
LAI: beech: Bugman (1994) cf. Zierl (2000); Brassel & Brändli (1999)
Norway spruce: Breuer et al. (2003); Schaub (Pers. Comm.); Braun (Pers. Comm.)
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3 Mapping Critical Levels for Vegetation
Table A1.6: Mediterranean Europe “real“ species parameterisation for Holm oak and Aleppo pine. The
parameters for the “generic“ Mediterranean evergreen tree species are also given for reference.
Units
Holm Oak,
Continental/Coastal 1
parameterisation
Eunis class,
area in km2
gmax
Generic
Evergreen
Mediterranean
Aleppo Pine
parameterisation
Beech
parameterisation
Generic
Deciduous
Mediterranean broadleaf evergreen
Mediterranean
needle leaf evergreen
Deciduous Mediterranean broadleaf
Deciduous
broadleaf
Mediterranean
broadleaf
evergreen
mmol O3 m-2
projected leaf
area s-1
180 (134-365)
215
145 (100-183)
150 (100-180)
175 (70-365)
fmin
(fraction)
0.02
0.15
0.02
0.1
0.02
SGS 3
year day
100 (9 April)
100 (9 April)
Latitude Model
Latitude
Model
1 (1 Jan)
EGS 3
year day
-
-
Latitude Model
LatitudeModel
365 (31 Dec)
fphen_limA
(fraction)
80 (21 Mar)
80 (21 Mar)
-
-
80 (21 Mar)
fphen_limB
(fraction)
320 (16 Nov)
320 (16 Nov)
-
-
320 (16 Nov)
fphen_a 2
(fraction)
1.0
1.0
0
0.0
1.0
fphen_b 2
(fraction)
1.0
1.0
0
0.0
1.0
fphen_c 2
(fraction)
0.3
0.1
1
1.0
0.1
fphen_d 2
(fraction)
1.0
1.0
0
0.0
1.0
fphen_e 2
(days)
130
130
15
15
130
fphen_f 2
(days)
60
60
20
20
60
0.012
0.013
0.006
0.006
0.009
Parameter
Land use
light_a
Tmin
oC
1
10
4
0
2
Topt
oC
23
27
21
21
23
Tmax
oC
39
38
37
35
38
VPDmax
kPa
2.2
1
1
1.0
2.2
VPDmin
kPa
4.0
3.2
4.0
3.25
4.0
SWPmax
MPa
fSWP= -1.0
fSWP= -0.5
fSWP= -2.0
fSWP=1
fSWP=1
SWPmin
MPa
fSWP= -4.5
fSWP= -1.0
fSWP= -3.8
fSWP=1
fSWP=1
Y
1.6
1.6
1.6
1.6
1.6
LAImin3
3.5
5
0
0
5
LAImax3
5
5
5
4
5
LAIs3
100
-
15
15
-
LAIe3
166
-
30
30
-
h, m
15
10
20
20
8
L, cm
5.5
0.8
7
7
3.5
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.
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3 Mapping Critical Levels for Vegetation
2
It should be noted that the fphen function should ideally be set equal to 1; however, this would only be
possible once an alternative method to incorporate the effects of summer drought on gsto has been
developed. Until that time, a provisional fphen function that acts as a surrogate for soil water stress, as
used for “generic“ Mediterranean forest species, is provided here and recommended for use where
soil water status influence on gsto is unknown. The fphen parameters for this function are described
below assuming a year round growing season.
fphen = fphen_a
when dd < fphen_limA
fphen = fphen_b + (fphen_c - fphen_b) * (dd – fphen_limA) / fphen_e when fphen_limA ≤ dd < (fphen_limA + fphen_e)
fphen = fphen_c
when (fphen_limA + fphen_e) ≤ dd < (fphen_limB - fphen_f)
fphen = (fphen_d + (fphen_c - fphen_d) * (fphen_limB – dd) / fphen_f when (fphen_limB - fphen_f) ≤ dd < fphen_limB
fphen = fphen_d
when fphen_limB ≤ dd
3
The structure of the LAI function for Holm Oak is based on data for that species from Ferretti &
Bussotti (2007) as follows:
LAI = 0.35*((LAIs-dd)/LAIs)+LAImin
when dd ≤ LAIs
LAI = (LAImax-LAImin)*(dd-LAIs)/LAIs+LAImin
when LAIs < dd ≤ (366 – LAIe)
LAI = (LAImax-(LAImin+0.35))*((366-dd)/LAIe)+(LAImin+0.35) when dd > (366 – LAIe)
N.B. Aleppo pine and beech follow the original LAI function described in Section 3.6.2.
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.
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3 Mapping Critical Levels for Vegetation
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)
Aleppo pine: Elvira et al. (2007)
Beech: “generic“ deciduous parameterisation used as surrogate
flight: Holm oak: Bussotti & Ferretti (2007); Alonso et al. (2007)
Aleppo pine: Elvira et al. (2007)
Beech: “generic“ deciduous parameterisation used as surrogate
ftemp: 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)
Aleppo pine: Elvira et al. (2007)
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)
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.)
Aleppo pine: Gimeno (Pers Comm.)
Beech: ICP Level II site data (Ferretti; pers. comm.)
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