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SID 4 Annual/Interim Project Report for Period 04/07-03/08

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ACCESS TO INFORMATIONThe information collected on this form will be stored electronically and will be required mainly for research monitoring purposes. However, the contents may be used for the purpose of notifying other bodies or the general public of progress on the project. Defra may also disclose the information to any outside organisation acting as an agent authorised by Defra to process research reports on its behalf. Defra may be required to release information, including personal data and commercial information, on request under the Environmental Information Regulations or the Freedom of Information Act 2000. However, Defra will not permit any unwarranted breach of confidentiality or act in contravention of its obligations under the Data Protection Act 1998. Defra or its appointed agents may use the name, address or other details on your form to contact you in connection with occasional customer research aimed at improving the processes through which Defra works with its contractors.

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Project details

1. Defra Project code AW0601PP

2. Project titleDevelopment and application of methods for modelling and mapping ozone deposition and stomatal flux in Europe

3. Defra Project Manager Dr. Soheila Amin-Hanjani

4. Name and address of contractor

Stockholm Environment InstituteUniversity of YorkHeslingtonYork Postcode YO10 5DD

5. Contractor’s Project Manager Dr. Lisa Emberson

6. Project: start date................. 01/01/2007

end date.................. 31/12/2009

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Scientific objectives7. Please list the scientific objectives as set out in the contract. If necessary these can be expressed in

an abbreviated form. Indicate where amendments have been agreed with the Defra Project Manager, giving the date of amendment.

The overall aim of this programme of research is to enhance the capability of the DO 3SE model to provide evidence to support development of U.K. and European policy on control of O3 concentrations. The work programme is divided into 4 major Work Packages; the major aim, and specific objectives, of each Work Package are provided below. To date, it has not been necessary to amend any of these objectives.

Work Package 1: To develop the DO3SE model for application to semi-natural communities 1.1 To develop a generic version of the DO3SE model for grassland communities, comprising different component fractions

of up to three functional groups (grasses, forbs and legumes).1.2 To parameterise specific versions of this model for productive grass/clover swards and for U.K. grassland communities

of high conservation status.1.3 To develop methods to derive relationships between modelled O3 flux and yield, species composition and ecosystem

function in U.K. grassland communities.1.4 To apply the flux and flux-response models developed in 1.1-1.3 to assess O3 impacts on grassland communities at

U.K. and European scales.

Work Package 2: To develop and evaluate DO3SE estimates of seasonal variation in O3 deposition and flux. 2.1 To identify and parameterise suitable phenological and SMD models for incorporation into the DO 3SE model for

European and U.K. species and species groups. 2.2 To evaluate and compare the phenological and SMD models using a variety of data sets (site-specific and remotely

sensed) and comparisons with other models for key species, species groups and locations across the U.K. and Europe.

2.3 To assess the influence of phenology and SMD on modelled total O3 deposition and stomatal flux under variable meteorological and O3 concentration conditions across the U.K. and Europe.

Work Package 3: To develop a user interface for the DO3SE model3.1 To develop a user interface of the F-coded DO3SE model, that will provide the scientific effects and policy communities

with the capability to perform their own (local-scale) flux based risk assessments. 3.2 To ensure a wider and more accurate application of the DO3SE model and to increase the number of model evaluations

performed.3.3 To increase the application of the DO3SE model to national and pan-European policy assessments for O3

Work Package 4: To develop a detoxification module for the DO3SE model4.1 To further develop the DO3SE model for wheat by incorporating a module that predicts rates of variable detoxification 4.2 To assess the implications a variable flux threshold will have on the modelling of ozone fluxes to wheat using the

DO3SE model

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Summary of Progress8. Please summarise, in layperson’s terms, scientific progress since the last

report/start of the project and how this relates to the objectives. Please provide information on actual results where possible rather than merely a description of activities.Work Package 1: Development of DO3SE model for application to semi-natural communities

The milestones for this work Package up until March 2008 are summarised below. The work that has been performed for this work package, including a brief rationale for the research and the resulting achievement of these milestones is described thereafter.

M 7 Development of flux model for grassland communities of different species or functional groups (Mar 2008)M 8 Parameterisation of grassland flux model for generic application and for productive and species rich grasslands

(Mar 2008)

Currently, the risk assessment method suggested for use by the UNECE CLRTAP for semi-natural communities is still based on AOT40 (i.e. a concentration based risk assessment index). Since flux methods have now been developed and accepted by the UNECE CLRTAP for crops and forest species, this incongruity for semi-natural species is likely to result in systematic errors related to the spatial and temporal differences in flux and concentration based risk assessments for semi-natural communities. As such, the main aim of this work package is to develop flux based methods for semi-natural communities. This involves developing a generic framework for semi-natural communities that can be modified according to specific community parameterisations to provide community specific risk assessments. In this first instance we will modify the generic framework for M3 and M5 communities since these are particularly important in the UK context. Work has also been underway to collate data with which to evaluate the grassland flux model; CEH Edinburgh has performed this work and a brief summary of the measurement variables and their associated data capture is provided. N.B. The work described here complements research that is being conducted in the UNECE International Cooperative Programme on Vegetation contract (AQ0810). In contract AQ0810 the generic three component model has been parameterised specifically for productive grassland communities (i.e. grasslands dominated by Trifolium/Lolium mixtures) and also modified to allow re-analysis of experimental data for the derivation of flux-response relationships for these productive grassland communities.

Development of the flux model for grassland communities of different species or functional groups (M7)The whole canopy grassland flux model has been developed as a multi-layer model (Fig. 1). This is necessary to allow the variation in LAI fractions between component species (i.e. grasses, legumes and forbs) and subsequent variation in the exposure of these components to within canopy irradiance (net radiation) and ozone concentration to be incorporated in the assessment of ozone flux to component species of the canopy.

Fig. 1 Schematic of the multi-layer model framework accounting for vertical profiles of LAI, net radiation and ozone concentration.

Fig. 1 clearly indicates the importance of being able to estimate the LAI for individual canopy components; this has to be estimated both with time (e.g. estimate the evolution of LAI for components over the course of the community growing season) and with spatial distribution (i.e. to understand how prevailing climatic conditions may impact on the evolution of LAI across geographical regions). It is well known that LAI development is strongly related to temperature (which plays a significant role in determining the onset of the growth period and rate of growth for individual species) and soil moisture deficit (which is a key environmental variable capable of limiting plant growth and hence the continued development and maintenance of LAI). In view of this we have developed a simple method to simulate LAI based on temperature and soil moisture deficit; this simple model is based on results from a grassland growth model (Ashmore et al. 2007) which was run using EMEP data for a number of locations

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across Europe.

This model provides estimates of the grass (represented by ryegrass, Lolium perenne) fraction of the semi-natural community. As such, for the generic model, the grassland fraction represents the benchmark for the simulation of phenology and the LAI seasonal cycle of the other component species (e.g. forbs and legumes). As such, for each specific community it is crucial to identify the dominant grass, forb and legume species present and the relative variation in these species in terms of LAI (relative to the grass LAI component); this is discussed in more detail in the parameterisation section below.

Once the total and fractional LAI for each community component and canopy layer has been identified it is then possible to incorporate the influence of variation in ozone concentration and irradiance with canopy depth on the estimate of ozone flux. The resulting multi-layer model has been developed so as to provide estimates of both the total ozone deposition or flux term, as well as the stomatal deposition term to the component species group fractions of the canopy. The former provides estimates useful for ozone mass balance calculations as well as a means of evaluation against more readily available datasets measuring flux above the canopy. The latter can be used to provide relative estimates of ozone dose to grasses, legumes and forbs present within the community mix for the derivation of risk assessment methods.

The ozone concentration within a canopy will vary as a function of ozone loss to the canopy (i.e. uptake via the stomates and to the external plant parts) and ozone replacement from ambient air concentrations above the canopy. Limited data have been collected showing how ozone concentrations vary with canopy depth in semi-natural communities. Jaeggi et al. (2006) showed a typical vertical distribution of the ozone concentration in a Central European productive grassland (Fig. 2a). The minimum ozone concentration occurs at the bottom of the grassland canopy and increases exponentially to the canopy top (which was 1 m). Since the cumulative LAI increases exponentially from the top of a grassland canopy to the bottom of that canopy (Fig. 2b), the relationship between ozone concentration and LAI is close to linearity (Fig. 2c).

Fig. 2 Vertical profiles of the relative ozone concentration (a) and the Leaf Area Index (LAI, b) of a productive Central European grassland during growing season 2003, as well as the relationship between relative ozone concentration and LAI of that grassland (c). After Jaeggi et al. (2006).

The penetration of irradiance through the canopy is known to be a function of LAI and solar elevation (which is a function of time of day (in relation to solar noon), calendar day and latitude). Solar elevation is calculated according to standard principles of solar geometry as described in Jones (1992) and Campbell and Norman (1998). Light penetration is also dependant upon the angle of leaf inclination (of which there is some difference for grass (more vertically aligned leaves) and legume of forbs (leaves tend towards a horizontal alignment)). To model light penetration into the canopy it is therefore necessary to estimate the average leaf angle of inclination in each incremental accumulation of LAI, this is done based on the relative fractions of the component species mix of each canopy layer.

Application of the canopy extinction models requires the fraction of the PAR received at the top of the canopy that is direct and diffuse to be determined. The formulations of Weiss & Norman (1985) can then be used to estimate the potential diffuse (pPAR diff) and direct (pPARdir) irradiance that are necessary to estimate irradiance penetration and quality into the canopy. The potential total PAR (pPARtotal) is then simply the sum of the diffuse and direct components. The actual PAR dir and PARdiff can then simply be calculated by multiplying the respective direct and diffuse fractions with the actual total PAR (PAR total). Estimations of the diffuse and direct fractions are necessary to calculate the PAR incident on the sunlit and shaded portions of the canopy. These have to be estimated for each increment in accumulated LAI. The algorithms described below estimate the irradiance penetration into incremental LAI sections of the canopy (fLAI). The sunlit (fLAIsun) and shaded (fLAIshade) leaf area portions of the canopy are calculated as follows for the combined clover, grass and forb fractions: -

fLAI sun = [1 - exp (-γ* fLAI / sin) ] * 2 sin

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where γ represents the average angle of inclination of the leaf, this is defined according to the relative fractions of grass, forb and legume in each fLAI.

fLAI shade = fLAI - fLAI sun

fPARsun and fPARshade, which are dependant on the mean angle between leaves and the sun, is calculated according to the LAI fraction of the canopy being assessed using the canopy radiation transfer model of Zhang et al. (2001). The fLAI leaf stomatal conductance determined by irradiance is calculated for sunlit (fflightsun) and shaded (fflightshade) leaves for grass, legumes and forbs using the respective flight parameterisations (e.g. see Table 2 where the flighta parameter is equivalent to the constant in the equations below) and weighted according to the fraction of sunlit and shaded leaf area of that LAI canopy fraction.

fflightsungrass = [1 - exp (-grass*fPARsun)]fflightsunlegume = [1 - exp (-legume*fPARsun)]fflightsunforb = [1 - exp (-forb*fPARsun)]

fflightshadegrass = [1 - exp (-grass* fPARshade)]fflightshadelegume = [1 - exp (-legume* fPARshade)]fflightshadeforb = [1 - exp (-forb* fPARshade)]

fflight grass = fflightsungrass * fLAIsun / fLAI + fFlightshadegrass * fLAIshade / fLAIfflight legume = fflightsunlegume * fLAIsun / fLAI + fFlightshadelegume * fLAIshade / fLAIfflight forb = fflightsunforb * fLAIsun / fLAI + fFlightshadeforb * fLAIshade / fLAI

The above calculations standardise the total LAI to 1.

The average leaf stomatal conductance (gs) of the grass, legume and forb fractions of the different layers of the canopy ( fLAI) canopy is estimated using the multiplicative stomatal algorithm (e.g. Emberson et al. 2001). Ideally we would be able to differentiate variable parameterisation for f light, ftemp and fVPD for the grass, legume and forb fractions, these therefore would need to be applied to each layer in turn and weighted according to the LAI fraction (performed through the use of the fflight terms of the species components.

gs grass = fflight grass * fphen * (max{gmin grass, ftemp grass*fVPD grass})gs legume = fflight legume* fphen * (max{gmin legume, ftemp legume*fVPD legume})gs forb = fflight forb* fphen * (max{gmin forb, ftemp forb*fVPD forb})

This then gives the relative fractional gs of the species group components of each canopy layer which can be summed to provide an estimate of the entire canopy gs (Gsto). Here it is assumed that irradiance is the only variable that alters with canopy depth, i.e. the temperature and VPD profiles within the canopy remain constant.

The estimation of the canopy stomatal ozone flux (Fst) is performed according to the methods described in the UNECE Mapping Manual (2004). This requires knowledge of the canopy boundary layer resistance (R b) to ozone flux (which is determined according to the methods used in the existing DO3SE model (e.g. Ashmore et al. 2007)). The use of the multi-layer model allows the amount of ozone lost to each canopy layer ( fLAI) to be estimated as a function of the stomatal ozone flux, boundary layer and cuticular resistance of that particular layer. As ozone is lost with height through the canopy this ozone loss also informs the ozone available for stomatal ozone uptake at the lower fLAI. As such, the model is able to predict within canopy ozone concentrations (and hence provides a means of part evaluation of the model against within canopy ozone concentration data as well as providing an estimate of differential ozone uptake to different layers of the canopy which are populated by different species groups’ components). To this end, stomatal ozone flux for each species component and layer is estimated as :-

Fst grass = (gs grass + gext) * (gs grass /(gs grass + gext )) * fLAIgrass * O3 fLAI

Fst legume= (gs legume+ gext) * (gs legume/(gs legume+ gext )) * fLAIlegume * O3 fLAI Fst forb = (gs forb+ gext) * (gs forb/(gs forb + gext )) * fLAIforb * O3 fLAI

As for the estimation of Gsto, whole canopy Fst can be estimated by simply summing the individual fluxes to each species component within the different layers. This method also allows derivation of the stomatal ozone flux to the species group component fractions of the total canopy which can be used in derivation of flux-response relationships for these component species.

Parameterisation of grassland flux model for generic application and for productive and species rich grasslands (M8)For application of the grassland flux model it is useful to define a default parameterisation of the multiplicative gs model for grass, legumes and forbs. The maximum stomatal conductance (gmax) for these different plant functional groups of productive (here: grasses and legumes) as well as non-productive grasslands (here: grasses, legumes and forbs) is a key parameter. The g max for grasses and legumes was derived from Lolium perenne and Trifolium repens data respectively. This work was carried out as part of the Defra contract AQ0810, UNECE International Cooperative Programme on Vegetation and the results can be found in the annual report 2007/08 of this contract to Defra. The parameterisation of gmax for forbs was performed in this Defra project and was based on five secondary datasets of various forbs that are consistent in MG3 and MG5 vegetation communities grown in Switzerland, Wales and New Zealand (Table 1). Based on these datasets, a gmax of 300 mmol O3 m-2 PLA s-1 was derived. A derivation of gmax based on UK datasets only would have resulted in a gmax of 260 mmol O3 m-2 PLA s-1, but due to the fact that the

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this UK sub-dataset is limited in size, we here define gmax based on all datasets. As further datasets become available, we would ideally restrict the parameterisation of this value to UK (or European) data only.

Table 1 Derivation of gmax based on secondary datasets of forb species constant in MG3 and MG5 vegetation communities.

Reference Location Species gmax

(mmol O3 m-2 PLA s-1)Bungener et al., 1999 Switzerland Plantago lanceolata 262

Knautia arvensis 268Stirling et al., 1997 Wales Plantago lanceolata 239

Bellis perennis 283Nussbaum and Fuhrer, 2000 Switzerland Plantago lanceolata 274

Knautia arvensis 305Jaeggi et al., 2005 Switzerland Plantago lanceolata 549Clark et al., 1998 New Zealand Plantago lanceolata 213

average gmax (S.D.) of all species 299 (105)average gmax (S.D.) of species used in UK experiments only 261 (31)

Table 2 provides a summary of the full parameterisation for the DO3SE grassland model for forbs. This parameterisation is based on boundary line analyses of two primary datasets comprising of measurements of gs on Plantago lanceolata (Germany) and Geranium sylvaticum (U.K.) and associated environmental parameters. The corresponding tables for the grass and legume fractions can be found in the annual report 2007/08 of Defra contract AQ0810, UNECE International Cooperative Programme on Vegetation.

Table 2 Summary of parameterisation for DO3SE grassland model for forbs constant in MG3 and MG5 vegetation communities.

Parameter Value UnitLeaf dimensions (m) 0.03 mgmax 300 (mmol O3 m-2 PLA s-1) gmin 0.01 relative gSGS (start growing season) 90 Julian dayEGS (end growing seasons) 320 Julian dayfphen_a 0.1 relative gfphen_b 70 Julian dayfphen_c 29 Julian daylight_a 0.017 -T_min 10 °CT_opt 24 °CT_max 37 °Cbt 0.93 -VPD_max 2.3 kPaVPD_min 4 kPaSWP_max -0.05 MPaSWP_min -1.5 MPa

As stated above, the credibility of the model is heavily dependant upon the reliability of the estimate of the seasonal simulation of the grassland LAI and the estimation of the legume and forb LAI relative to this grass LAI. This model provides estimates of the grass (represented by ryegrass, Lolium perenne) fraction of the semi-natural community. This grassland fraction then represents the benchmark for the simulation of phenology and the LAI seasonal cycle of the other component species (e.g. forbs and legumes). As such, for each specific community, it is crucial to identify the dominant grass, forb and legume species present and the relative variation in these species in terms of phenology (timing and duration of the growth period), LAI (relative to the LAI for the grass component; both in terms of total LAI but also in relation to how this varies with canopy depth (e.g. see Fig. 1)) and response to key environmental variables such as SMD (i.e. are species more or less tolerant to water stress than Lolium, in which case they may be able to maintain LAI for longer under drought conditions) and light (in which case they may be found lower of higher in the canopy relative to the grass species).

To start to investigate ways of brining such information into the seasonal estimation of grassland flux modelling, we have made an inventory of the dominant species and their key environmentally based characteristics for the MG3 and MG5 vegetation communities. Table 3 gives an overview of the dominating grasses, legumes and forbs with high frequency of these vegetation communities, as well as the flowering time and Ellenberg indicator values for light, temperature, nitrogen and moisture for these species (Hill et al., 1999). This table also indicates (grey shaded cells) those species that are present at the Defra-funded (AQ3510) Keenley ozone field fumigation [KOFF] site, and for which additional information on key environmental and plant physiological parameters will be recorded in 2008 to inform the grassland flux model.

The listed flowering time is a good indicator of the phenology and will help to identify the change in the component species canopy fraction of productive grasslands over the course of a growing season. For instance, the early flowering species Anthoxanthum odoratum, Luzula campestris, Lotus corniculatus and Ranunculus bulbosus will dominate the grassland canopy at the beginning of the growing period and will therefore form the main ozone sink at that time, whereas species with a flowering period until late summer/early autumn, such as Dactylis glomerata, Lolium perenne, Trifolium repens, Plantago lanceolata and Geranium sylvaticum will possibly form the largest ozone sink at the end of the growing period due to their dominance at that time

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of year.

The Ellenberg indicator values used here may also help to provide species-specific information that can be used to modify the generic gs parameterisations according to i) whether a plant is adapted to shade conditions or likes direct sunlight (L) and therefore may indicate what position a specific species will have within a grassland canopy with important effects on its ozone uptake potential, ii) whether a plant prefers cooler/colder or warmer/hotter climates which impacts its geographical distribution and possibly its gs dependence on temperature, iii) whether a plant prefers infertile or fertile sites which might give an indication of growth rates and physiological activity (plants adapted to infertile sites will have less growth increments than plants adapted to fertile sites) and hence ozone uptake potentials, and iv) whether plants are adapted to dry or wet habitats with obvious effects on their gs behaviour in relation to soil moisture stress.

Table 3 Dominating grasses, legumes and forbs with high frequency in UK plant communities MG 3 and MG 5, flowering times and Ellenberg indicator values (1 to 9/12) recommended for use in the UK (Hill et al., 1999) describing the ecological characteristics of these species with regard to light (L), temperature (T), nitrogen supply (N) and moisture (F). Ranges of indicator values: L = 1: Plant in deep shade, L= 9: Plant in full light; T = 1: plant adapted to very low temperatures, T = 9: plant adapted to very high temperatures; N = 1: Indicator of extremely infertile sites, N = 9: Indicator of extremely rich situations; F = 1: Indicator of extreme dryness, F = 12: Submerged plant. x = broad amplitude. Asterisks denote geophytes (i.e. plants with early onset of physiological activity, hence early achievement of gmax). Shaded cells indicate species present at the Defra-funded (AQ3510) Keenley ozone field fumigation [KOFF] site in Northumberland.

MG3 MG5

Dominating grasses with high frequency

Ellenberg indicator values

(L, T, N, F)

Flowering time

Dominating grasses with high frequency

Ellenberg indicator values

(L, T, N, F)

Flowering time

Anthoxanthum odoratum 7, x, 3, 6 4 – 7 Festuca rubra 8, x, 5, 5 6 – 8Dactylis glomerata 7, x, 6, 5 6 – 9 Cynosurus cristatus 7, 5, 4, 5 6 – 8Poa trivialis 7, x, 6, 6 6 – 7 Holcus lanatus 7, 6, 5, 6 5 – 8Festuca rubra 8, x, 5, 5 6 – 8 Dactylis glomerata 7, x, 6, 5 6 – 9Agrostis capillaris 6, ?, 4, 5 6 – 8 Agrostis capillaris 6, ?, 4, 5 6 – 8Holcus lanatus 7, 6, 5, 6 5 – 8 Anthoxanthum odoratum 7, x, 3, 6 4 – 7Cynosurus cristatus 7, 5, 4, 5 6 – 8 Lolium perenne 8, 6, 6, 5 5 – 9

Trisetum flavescens 7, x, 4, 4 5 – 6Luzula campestris 7, x, 2, 4 4 – 5

Dominating legumes with high frequency

Dominating legumes with high frequency

(Trifolium repens) 7, x, 6, 5 4 – 10 Lotus corniculatus 7, x, 2, 4 4 – 9Trifolium repens 7, x, 6, 5 4 – 10

Dominating forbs with high frequency

Dominating forbs with high frequency

Geranium sylvaticum 6, 4, 5, 5 6 – 9 Plantago lanceolata 7, x, 4, 5 5 – 9Conopodium majus * 6, 4, 5, 5 5 – 7 Achillea millefolium 7, x, 4, 5 6 – 9Sanguisorba officinalis 7, 5, 5, 7 6 – 9 Ranunculus bulbosus * 7, 6, 4, 4 3 – 6Rhinanthus minor 7, 5, 4, 5 5 – 9

This community-specific based parameterisation will be further supplemented by field data collected in the UK at sites where these MG3 and MG5 community species are present; this information can help to further describe community structure and gs

parameters; these data will be collected under the ‘Ozone Umbrella’ Defra contract (AQ3510) from two sources:- (i) a long-term mesocosm experiment (approaching the 5th consecutive year of study) underway in the open-top chambers at Newcastle University’s field station, employing a 15-year-old MG3 community subject to contrasting management regimes, relating to nutrient input and use of hay rattle to restrict the growth of productive grasses, and (ii) an open-air fumigation experiment established during 2007 at Keenley Fell at a rural site (c 800 m. a.s.l) in the North Pennines, at which an upland species-rich grassland community is being exposed to a range of controlled ozone climates. It had originally been planned that one season of data collection would have been carried out under contract AQ3510 in 2007. However, delays in establishing the site, and obtaining reliable ozone fumigation, have meant that this was not possible. Hence, a more intensive campaign of field work is planned for the summer of 2008.

Collection of data with which to evaluate the grassland flux modelTo aid development and provide a dataset for evaluation of this grassland flux model, micrometeorological measurements have been made adjacent to the open-air fumigation experiment established at Keenly in the south Tyne Valley on an upland species-rich grassland community. The funding provided within this research programme has allowed the measurements of carbon dioxide and water vapour (in addition to the ozone flux measurements made by CEH Edinburgh supported by the Ozone umbrella contract) to be made to ensure that a full validation of both canopy stomatal and non-stomatal fluxes will be possible.

The parameters being measured are summarised in Table 4 along with the data capture achieved for the period 13/6/07 to 20/12/07. An open-path CO2/H2O sensor was installed to measure CO2 and water-vapour fluxes but this instrument developed a

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fault; the data are currently being reanalysed to recover all the useful data. A new closed-path instrument has been purchased and will be installed during April 2008, as this will operate in all weather conditions (rather than just dry) it will also provide useful data on the carbon balance at the site as well as stomatal conductance estimates.

grassland model for the specific MG5 and MG3 communities with a view to the ultimate application of the model at the UK scale for risk assessment.

Work Package 2: Development and evaluation of DO3SE to estimate seasonal variation in O3

deposition and flux

The milestones for this work Package up until March 2008 are summarised below. The work that has been performed for this work package, including a brief rationale for the research and the resulting achievement of these milestones is described

Development of DO3SE phenology and SMD models for key species and species groups (Oct 2007)Evaluation of DO3SE phenology and SMD models for key species and species groups (Mar 2008)Submission of peer reviewed paper describing the development and evaluation of the DO3SE SMD module (Mar

Phenology (i.e. the length and timing of the vegetative growth period) and soil moisture deficit (SMD) are two key drivers of the seasonal profile of stomatal O3 flux and total O3 deposition. Phenology determines the time window when effective exposure to O3

can occur, as well as the developmental cycles of leaf area index (LAI), height, and leaf/needle age, all of which are important in determining total and stomatal deposition. SMD is a measure of the soil water status in relation to that soil water that is available for plant use. Once a critical SMD is passed, water is no longer available to the plant and physiological activity will be shut down. A gradually increasing SMD limits O3 uptake as plants close stomata during periods of drought in order to conserve water loss. Since SMD accumulates over time it may affect plants for several weeks and hence be an important driver of seasonal flux, especially in hot dry climates. Phenology and SMD both exhibit strong gradients across Europe, since both parameters are strongly dependant upon the prevailing environmental conditions (namely, temperature and precipitation).

Earlier versions of the DO3SE model had significant limitations in modules used to determine phenology and the development of soil water stress. The research under this work package has focussed on improving these methods to capture the variation in seasonal flux that occurs across Europe. The regional application of these methods, and the necessity to ensure periods when O 3

risk might be high are not missed, has resulted in the targeting of methods that are simple yet robust in relation to European application. Below we describe progress in achievement of the milestones for i) phenology and ii) SMD in turn. We have focussed this work on forest tree species since this work has supported the derivation of the CLRTAP “real” species parameterisation required for the forthcoming revision of the UNECE Mapping Manual (2004) in April 2008. At the last ICP Vegetation meeting in Oulu, Finland, plans were discussed to define “real” crop species; once these plans are more clearly defined by the European O 3

effects community, efforts will be targeted towards defining crop phenology for appropriate species and locations across Europe.

Development and Evaluation of DO3SE phenology models for key species and species groups (M4 & M5)A literature search has identified the following methods commonly used to identify onset of physiological activity, leaf flush and dormancy for forest tree species. There are a number of different methods that could be used:-

This represents the existing DO3SE model method to estimate the start and end of the growing season for boreal/temperate deciduous trees across the whole Europe (with dates of year day 90 March 1st for the start of the growing season and year day 270 Oct 31st for the end of the growing season). Data collected from Zhang et al (2004) from the moderate-resolution imaging spectroradiometer (MODIS) in 2001 describes vegetation phenology across Europe (a study domain

N latitude) by cover type in terms of variation with latitude and land surface temperature. Figure 3 below shows the dates for both greenup onset and growing season length estimated from MODIS data for natural vegetation (providing the figure annotation) and forests.

Dates for greenup onset, dormancy onset and growing season length estimated from MODIS data for natural (providing the figure annotation) and forests from Zhang et al (2004).

Analysis of these data suggest that the use of a “fixed date” for the start of growing season would give an error of up to 10 days too late and 65 days to early for low and high latitudes respectively; for the end of the growing season “fixed date” phenology would give an error of up to 60 days too early and 15 days too late for low and high latitudes respectively. As such, it is clear that the use of a fixed date model is not appropriate for regional scale application of the DO3SE model.

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ii. Latitude model. This method is similar to the model currently suggested for crops in the UNECE Mapping Manual (UNECE, 2004) although the crop model assumes a fixed duration of the growing season (unless the thermal time model for wheat is applied in relation to the timing of anthesis). Generally the rate of change in green up onset is greater than in dormancy onset for most land cover types. For the purposes of establishing a latitude model, SGS is defined as the date of leaf unfolding (deciduous & broadleaved evergreen species); and the start of leaf/needle physiological activity (coniferous species). EGS is defined as the onset of dormancy. The DO3SE latitude model developed here has been constructed so as to give the “best fit” with the remotely sensed data described in Zhang et al. (2004); see Fig 3. The DO3SE latitude model established for the estimation of SGS and EGS is provided below and shown in Figure 4:-

SGS occurs at year day 105 at latitude 50oN. SGS will alter by 1.5 days 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.

Fig. 4 shows the SGS and EGS determined from the DO3SE latitude model (black lines). The green and orange lines show the onset of green-up and dormancy described by remotely sensed data (Zhang et al. 2004) and the vertical red lines show the variation in observed SGS and EGS dates for sites at specific latitudes.

50

100

150

200

250

300

350

35 40 45 50 55 60 65 70

Latitude

Year

Day

This latitude model agrees well with ground observations of SGS from the Mediterranean (Mediavilla & Escudero, 2003; Aranda et al., 2005; Damesin & Rambal, 1995; Grassi & Magnani, 2005), Continental Central Europe (Defila, 1991.; Deckmyn, pers. comm.; cf. Aurora & Boer, 2005), Atlantic Central Europe (Broadmeadow, pers comm.; Duchemin et al., 1999) and Scandinavia (Aurela et al., 2001; Karlsson, pers. comm.) and remotely sensed observations for the whole of Europe (Zhang et al., 2004). This model agrees well with ground observations of leaf senescence for Scandinavia (Aurela et al., 2001; Karlsson pers. comm.), Continental Central Europe (Deckmyn, pers. comm.; Defila, 1991), Atlantic Central Europe (Duchemin et al., 1999) and the Mediterranean (Mediavilla & Escudero, 2003; Aranda et al., 2005; Damesin & Rambal, 1995; Grassi & Magnani, 2005). Leaf discolouration is assumed to occur 20 days prior to dormancy and is assumed to be the point at which f phen will start to decrease from gmax.

The DO3SE latitude model is suggested for use to estimate SGS and EGS for all “real” forest tree species with the exception of all conifers south of ~55oN where SGS and EGS are defined according to prevailing environmental conditions (i.e. the DO 3SE models ftemp function is assumed to act as a surrogate for the timing of the start of physiological activity). Similarly, for Holm Oak (Mediterranean evergreen forest species) data recently made available from an “Ozone flux project” (Bussotti & Ferretti, 2007) project conducted in Italy is used to define leaf flush (see Fig 5). As shown in Fig 5, for all tree species the seasonal LAI is profile is fitted to the timing and duration of the physiologically active growth period (for evergreen species) or timing of leaf flush (for deciduous species).

Fig. 5 Data from the “Ozone flux project” (Bussotti & Ferretti, 2007) showing annual variation in LAI.

iii. Temperature index threshold Various threshold models are available for the estimation of SGS. For example, Braun (Pers Comm.) has suggested the use of the following temperature model that is used to relate annual mean T oC (Tavg) to different phenological stages for beech, these models have been evaluated against observations in Switzerland. The advantage of using

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this model, rather than the latitude model would be to incorporate the effect of local climate on the timing and length of the growing season.

budbreak = 147 – 3.202 * Tavg

time of leaf discolouration = 242 + 4.37 * TavgLeaf fall = 300 year day

The results of applying this model with temperature data provided by EMEP for the year 2000 are shown in Fig 6.

Fig. 6 Estimates of SGS and EGS using a “temperature index threshold” method to estimate phenology based on Swiss data for Beech (Braun, Pers. Comm.)

These temperature based phenology models also fit the remotely sensed data of Zhang et al. (2004) reasonably well, although it is clear that the use of a fixed EGS (leaf fall date) does not capture variability across Europe. As such, these data showed no obvious advantage over the simpler latitude model. In addition, uncertainties in the EMEP night-time temperature data were felt to be too large for temperature related phenological modelling reliant on 24 hour temperature. It was also felt that the phenological models incorporating temperature were not well enough validated across the whole of Europe for a long enough time series of data to be sure that they would provide a more realistic basis for estimating between year variation in SGS that may be due to variability in climatic conditions.

iii. Effective temperature sum. For trees, the initiation of growing season or the onset of greenness has commonly been modelled using a cumulative thermal time summation technique known as degree day sum or growing degree days (GDD). The degree day sum is defined as the sum of positive differences between daily mean air (or soil) temperature and some threshold temperature (usually 0oC or 5oC) following a predetermined date (usually January 1st in the Northern hemisphere). When the degree day sum exceeds a critical value leaf onset is predicted to occur.

These models are widely used and parameterisations have been developed for individual species (e.g. beech) and species groups (e.g. temperate deciduous trees, boreal deciduous trees). However, investigation of the literature shows the wide range in parameterisation, even for the same species, of these models. Table 5 below provides an indication in the variability in the parameterisation of these models. Further, models tend to be evaluated against limited spatial scale data (e.g. national data) and extensive time series data (e.g approx. 30 years ToC series). As such, these models have not currently been recommended for use in the European scale application of the DO3SE model.

Table 5 Boreal / Temperate Broadleaved forest Thermal time models

Dynamic Global Vegetation Models

SGS/EGS Cover type Species T/TT model Reference

TVM (uses BIOME phenology models)

SGS Boreal deciduous - ETS threshold base 5oC 350 ddg

Leemans & van den Born, 1994; cf. Prentice et al., 1992

TVM (uses BIOME phenology models)

SGS Temperate deciduous trees

- ETS threshold base 5oC 1200 ddg

Leemans & van den Born, 1994; cf. Prentice et al., 1992

PnET-DAY GDD to start foliar production

Broad leaved deciduous

Site-specific parameters, Deciduous

ETS threshold base 0oC 100 ddg

Aber, J.D. (1996); Aber et al. (1995)

PnET-DAY GDD to complete foliar production

Broad leaved deciduous

Site-specific parameters, Deciduous

ETS threshold base 0oC 900 ddg

Aber, J.D. (1996); Aber et al. (1995)

LPJ (Lund-potsdam-Jena) modelΔ

GDD to complete foliar production

Temperate /boreal broadleaved summergreen


Sitch et al. (2003)

BIOME3 Start of leaf growth

Temperate/boreal summer green


Haxeltine & Prentice (1996)

Fig. 7 shows the results of estimating SGS using the Haxeltine & Prentice (1996) ETS model (ETS threshold base 5 oC 200 ddg) with the 2000 EMEP ToC data. There is far more variation in SGS with latitude, this is reflected in the map in Fig. 7 where it is clear that key climatic effects on SGS (e.g. the cooler temperatures experienced over the Alps) would be captured suing this type pf modelling approach. However, it was felt that the uncertainties in the parameterisation of this method outweigh the

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potential advantages and more evaluation would need to be conducted at the European scale before this method could be applied within EMEP model runs.

Fig. 7 Estimates of SGS using the BIOME3 ETS model of Haxeltine & Prentice (1996) where ETS is modelled using a threshold base 5oC 200 ddg) using the 2000 EMEP ToC data.

iv. Chilling time and effective temperature sum. Most dormant trees will not break bud in warm temperatures except after exposure to cool temperatures, in the order of 10oC or less. Vegetation is sometimes assumed to respond to increased duration of previous chilling by decreasing the requirements of temperature forcing to initiate spring greenup. In addition to breaking dormancy, chilling temperatures have also been shown to accelerate bud growth from the state of quiescence (i.e. when dormancy is broken) to the state of burst, i.e. the more chilling temperatures are received; the less forcing temperatures are subsequently needed to reach budburst (Chuine, 2000). There are a number of different methods that use chilling time and effective temperature sum, perhaps the most common are:-

i) the sequential model (proposed by Sarvas, 1974); Constant ddg sum to budburst but the start date varies depending on when the constant chilling requirement is met.

ii) the parallel chilling models (proposed by Landsberg, 1974)Assumes there is a variable heat sum to budburst from 1st Jan which depends upon the chilling duration over the winter.

Zhang et al. (2004) using MODIS data found that the onset of forest greenup at continental scales was effectively described using thermal time chilling models. However, since these models are only relevant for regions with sufficient seasonality and hence were only applied in Europe between latitudes of 40-67oN. The results showed that 83% and 94% of the variation in thermal time required for greenup onset could be explained using a 0oC and 5oC base temperature threshold respectively, thus limiting the advantage that the application of these models may bring to modelling at the European scale. In addition, the requirement of this modelling to use ToC data of a previous year (i.e. the previous year’s winter chill period) does not make this type of modelling readily useable within the EMEP application of the DO3SE model. For these reasons it is currently felt that the additional effort to use chilling time and ETS methods is not sufficiently well proven to warrant inclusion on the EMEP DO3SE model application.

In addition, data from the CarboEurope flux measurement campaign has been collated and evaluated against a number of different phenological models (e.g. Fig. 8); the simple latitude model consistently performs better than any of the other modelling approaches.

Fig. 8 An example of the relationship between estimates of SGS (black: latittude model; blue : temperature threshold model ; green and red: ETS models with variable parameterisations) and CO2 flux data for an evergreen needleleaf forest located at a latitude of 67.4oN.

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In summary, the work described above to identify and evaluate appropriate phenological models for application within the modelling performed by EMEP at the European scale has identified the latitude model as being the most robust method currently available and fit for purpose. Work will continue in the coming year to investigate the possibility of further refining this latitude model to provide estimates of particular species SGS and EGS relative to the “core” estimate produced by the latitude model. This will have potentially important applications for the derivation of real species parameterisations for forest tree flux modelling.

Development of DO3SE SMD models for key species and species groups (M4)The existing DO3SE SMD module has now been updated to incorporate and assessment of AEt using the Penman-Monteith energy balance method; this method is used in conjunction with the box model to estimate soil water status as a function of precipitation, evaporation and transpiration. A simple mass balance calculation is carried out over a finite depth of soil (determined by the root depth). Water inputs into the soil are limited to that from precipitation. Water emissions from the soil are limited to transpiration through the stomata which is driven by the atmospheric water status of the surrounding air and limited by the plants ability to access water held in the soil and canopy stomatal conductance to water vapour (G stoH2O). Capillary movement of water from the soil below the box is ignored. Soil surface evaporation is also ignored. Run off and percolation are ignored until the soil reaches its field capacity; all water in excess of the field capacity is assumed to run off or percolate into the substrate. A certain percentage of the precipitation is assumed to enter directly into the soil (30%) whilst the remainder (i.e. 70% of precipitation) is assumed to be intercepted by the leaves of the plant, this precipitation fraction only reaches the soil if the 70% of the daily precipitation exceeds the daily evaporation from a wetted surface.

Daily precipitation (P), and transpiration (AEt) are calculated as follows, all calculations are made in mm day -1 and hence use resistance terms in s/mm:-

P =

Hourly actual evapotranspiration (AEt, mm per day) is calculated using Penman-Monteith equation :-

AEt =

where is the slope of the vapour pressure curve, Rn is net radiation, G is soil heat flux; 3600 allows for the conversion from seconds to day (60*60*60); ρa is the mean air density; Cρ is the specific heat of air; VPD (kPa) is vapour pressure deficit; Ra is atmospheric resistance between the canopy and VPD measurement height (equal to zero if VPD measured lower than the canopy); λ is the latent heat of vaporisation and γ is the Psychrometric constant; R sto(H2O) is the canopy stomatal resistance to water vapour (estimated from the DO3SE model).

The use of the DO3SE model to estimate the Rsto(H2O) and Ra values provides an internally consistent method with which to calculate soil water variables since values are modelled using the same methods that are used to estimate ozone flux (i.e. vegetation-specific LAI, phenology and sensitivity to environmental conditions). It also reduces the need to rely on empirical data derived for particular species under particular environmental and soil conditions.

In order to estimate Rsto(H2O) and hence AEt, it is necessary to estimate the influence of soil water status on canopy stomatal conductance. A simple method to achieve this requires knowledge of soil type and associated soil water holding characteristics. Currently, the DO3SE soil water module is parameterised for three soil types, coarse, medium and fine. For each soil type a soil water release curve has been constructed so that the plant relevant soil water (measured in MPa) can be related to water loss from the soil expressed in m (over a defined root depth) or m 3/m3 (expressed on a volumetric basis). Key soil characteristics of these soils are provided in Table 6.

Table 6 Soil characteristics used to derive DO3SE coarse, medium and fine soil water release curves

Soil texture Field capacity (FC) Permanent wilting point (PWP)

Available soil water

Bulk density

Curve constants

MPa m3/m3 MPa m3/m3 % vol g/cm3 a bCoarse -0.02 0.107 -1.5 0.016 9.09 1.6 -4 -2.3Medium -0.03 0.193 -1.5 0.059 13.37 1.3 -5.5 -3.3Fine -0.04 0.339 -1.5 0.174 16.6 1.1 -7 -5.4

The amount of soil available to the plant throughout the rooting depth (available soil water, ASW) is estimated assuming the roots are capable of extracting water from the soil from the level of Field Capacity (FC) to the point where the soil water content has reduced to the PWP which is assumed equal to the minimum threshold for stomatal conductance (SWP_min) and is calculated as described in equation [1] :-

PWP = pb * ((SWP_min / (a*10-2)) * 1000) 1/b [1]

where PWP is the permanent wilting point (m3/m3); pb is the bulk soil density in (g/cm3), SWP_ min (MPa) is the threshold soil water potential at which gs is equal to gmin, a and b are constants for the soil water release curves required to convert MPa (water

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potential) to m3/m3 (volumetric water content) for fine, medium and coarse textured soils.

At the start of the year when SMD calculations are initialized it is assumed that the soil is at field capacity and hence ASW is at a maximum (referred to as ASW*). Daily estimations of the amount of soil water held in the root zone of the soil are made according to mass balance formulations based on precipitation, interception loss (water evaporated from the wet surface of the vegetation, during and following precipitation) and transpiration (moisture transferred from the soil to the atmosphere through the root-stem-leaf system of the vegetation).

The amount of water lost from the soil (as AEt) is controlled by the effect of the soil water on g sto according to the relationship given in Fig. 9. This relationship is intended to incorporate a range of resistances to soil-plant-atmosphere water loss. Primarily, it is assumed that over the root zone holding capacity of the soil, soil water is readily able to leave the soil system up to a value of approx. –0.3 MPa past which point soil water is ever more tightly held.

Fig. 9 fsoil relationships used to mimic the ever-decreasing ease with which soil water can be extracted from the soil shown against both soil water potential (MPa).

To provide the internal consistency of this method, the fsoil value is then used within the estimation of gsto. This means that as the soil dries, the stomates shut thus limiting the amount of water lost from the soil system as well as providing an internally consistent assessment of stomatal ozone uptake.

A common alternative method of assessing the influence of soil water on gs is to use variations on the Richards equation; there are several models that use this approach (e.g. SWAP, GLOBAL, HYDRUS-ET). The Richards equation, based on the hydraulic conductivity of a user-defined set of layers (e.g. soil-plant-air) allows soil water content (SWC) to be calculated over time. But transpiration is calculated in advance based, on a pre-defined relationship between transpiration and average SWC. These models cannot be used to analyse the dependence of transpiration on the availability of soil water. A differing approach, used by DO3SE, is to use an empirically defined response to available soil water to modify stomatal conductance is dependent on soil characteristics, as described above. This approach has been used successfully in several studies (e.g. Olioso et al., 1996; Novák et al., 2005) and is similar to the approach used in the Met. Office MOSES land surface model. Within MOSES a critical soil moisture content is defined (Vcrit), below which stomates will begin to close and gs will decline. The best correlation with observed data within MOSES was seen when Vcrit was set to 5%. By comparison the critical soil moisture levels defined by Novák et al., were 20-25% and are 10-15% in DO3SE. The observed range of Vcrit values in the field was reported to be 5-25% by the Met. Office (Jogireddy et al., 2006), emphasising the importance of local soil properties to estimating plant transpiration.

Evaluation of DO3SE SMD models for key species and species groups (M5)We have identified data sets from across Europe with which to evaluate the soil moisture model, details of these datasets are given in Table 7. Example results of these model comparisons are presented below.

Table 7 Location and site parameters of data sets used for SMD evaluation in DO3SE

Site Tree species Root depth Soil typeAsa, Sweden Norway Spruce 0.4m CoarseForellenbach, Germany Beech 1.2m CoarseDavos, Switzlerland Norway spruce 0.8m MediumHortenkopf, Germany Beech 1.2m Coarse

i. Sweden, Asa datasetThe species cover type at Åsa is Norway spruce. The soil type is glacial till, which is somewhere between a medium and coarse soil. It is assumed that most of the roots are within the first 30cm of the soil surface. Soil water potential was measured for two years (1995 and 2000) using gypsum blocks and comparisons between measured and modelled soil water potentials are shown in Fig. 10.

Fig. 10 Modelled and observed soil water potential (SWP) at Åsa, Sweden in 1995 and 2000.

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During the year 2000 the soil remains saturated with soil water potentials remaining above –0.1 MPa; the model is able to simulate this lack of soil water stress within the system. In contrast, 1995 observations show significant decreases in soil water potential, the model is able to mimic the seasonal profile of this soil water depletion reasonably well with a correlation coefficient of 0.90).

ii) Germany, Forellenbach datasetSpecies cover at Forellenbach is dominated by beech. Root depth is given from measurements at 1.2 m. Soil texture is sandy loam. Soil water contents are based on measurements made at 5 different depths within the root zone. Data are available for two years, 2000 and 2005. There is a good fit between the available soil water (ASW) of the observations and the model (Fig. 11). This fit is confirmed by the strong correlation between observed and modelled ASW values (correlation coefficient = 0.87) shown in Fig. 12. However, It is hard to tell from the observed data exactly what the soil water content in mm is at field capacity, the highest value reaches 250mm at year day 220 in year 2002, however, this is after a sustained period of precipitation and hence may be evidence of a waterlogged soil. For all other times of both years ASW does not go above 150 mm and it is likely that this is the true field capacity of the soil (as mimicked by the selection of the soil water release curve used in the modelling).

Fig. 11 Modelled and observed plant available soil water (ASW) at Forrelenbach, Germany.

Fig. 12 Scatter plot of observed and modelled ASW at Forellenbach in 2003.

Fig. 12 also shows that the model tends to underestimate soil water deficits close to field capacity (which may be related to the field capacity characterisation of the soil water release curve), at mid-range soil water deficits the model predicts faster drying than is actually observed which results in the model reaching the point where the soil is almost emptied of the plant available soil water earlier than is occurs in reality. This again may be due to the characterisation of the soil water release curve, but this time in relation to the permanent wilting point, the data suggest that the assumed plant available water of the model is close to that in reality though it due to the measurements ending prematurely this statement cannot be made with absolute certainty since the observations do not show the soil water recharge.

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iv) Germany, Hortenkopf/TrierSpecies cover for the Trier dataset is beech with an assumed rooting depth of 1.2m. Data on soil type are unavailable and the closest match of modelled and observed soil moisture is achieved using coarse soil parameters. As with previous examples, there is some discrepancy between modelled and observed soil at field capacity, however there is a very good estimate of the soil water profile when water stress occurs.

Data from Hortenkopf are available for the period 1998-2003 and Fig. 13 shows data from an example year (2001), here there is a good agreement of modelled and observed data. A scatter plot also shows the relationship between observed and modelled ASW values for all years. There is a good correlation between observed and modelled ASW (correlation coefficient = 0.72), however there is a tendency for the DO3SE model to underestimate ASW when soil water contents are high (again probably sue to a discrepancy between actual and assumed field capacities); in addition, the model assumes that ASW extends to 20 mm ASW, however, the observed data shows that the plants are actually only able to access water to values of 60 mm ASW. Again, this would indicate that the model soil characterisation curves are not fully appropriate for the soil texture at the site.

Fig. 13 Modelled and observed plant available soil water (ASW) at Hortenkopf, Germany in 2001 and a scatter plot of observed against modelled ASW for all available data (1999-2003) at Hortenkopf, Germany

In summary, the DO3SE models soil water deficit module, using generic characterisations of soil parameters, is capable of providing a reasonable estimate of soil water content. There are often differences in field capacity and permanent wilting points under site-specific conditions. However, when estimating ozone flux over large areas it could be argued that it is more important to be able to provide a reliable indication of the relative status of soil water conditions. It is evident that within a 50 x 50 km grid square large variations will occur in precipitation, cloud cover (and hence radiation) and soil texture. As such, the application of a method which is capable of describing the key elements of soil water over the course of a year may be more important than ensuring the absolute values of soil water deficits are predicted well at individual sites. As such, we argue that the model evaluation presented here provides evidence towards establishing this simple and internally consistent soil moisture modelling method as suitable for application with the EMEP model for European flux estimates. Currently, work is expanding model evaluation to include data recorded in arid areas (i.e. Mediterranean holm oak) as well as defining parameters for key crop and grass species.

This work will be presented at the 40th US Air Pollution Workshop held in Raleigh, North Carolina. We will use the presentation of this work as a first peer review of the methods developed and use feedback from this conference to modify the paper currently being developed for journal submission.

Summary of progress of WP 2

In summary, two out of three milestones of WP2, i.e. the development of the DO3SE phenology and SMD models for key species and species groups and the evaluation of these models for key species and species groups have been fully achieved. The outstanding milestone, i.e. the submission of a peer reviewed paper describing the development and evaluation of the DO 3SE SMD module has been slightly delayed, because it was decided to first present the methods developed within this work package at the 40th US Air Pollution Workshop held in Raleigh, North Carolina, which was believed to be a very good opportunity to get constructive feedback from this highly experienced scientific community; this feedback will be used to finalise the peer reviewed paper for submission in summer 2008.

Work Package 3: Developing a user interface for the DO3SE model

The milestones for this work Package up until March 2008 are summarised below. The work that has been performed for this work package, including a brief rationale for the research and the resulting achievement of these milestones is described thereafter.

M 3 Identification of appropriate user interface software (Jan 2007)M 9 A working tested user interface of the DO3SE F-coded model (Mar 2008)

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Identification of appropriate user interface software (M3)The DO3SE interface has been developed using the Python programming language and the wxWidgets graphical user interface toolkit. This software was chosen for its cross platform compatibility, allowing it to run on almost all Microsoft Windows and Unix-based operating systems.

The Fortran version of the model has been written with a modular structure, meaning individual components, such as soil moisture, can be modified by SEI staff without altering the overall model. The interface uses the Fortran model to process the data, meaning changes to parts of the model also apply to the interface. All data input and parameter setting are carried out via the DO3SE interface, requiring no knowledge of Fortran programming by the user.

A working tested user interface of the DO3SE F-coded (M9)The DO3SE interface includes all calculations carried out by the current version of the DO 3SE model, including fSWP modifications detailed in WP2. Fig. 14 shows an overview of the interface model. Hourly, meteorological and ozone concentration data must be entered by the user, whilst parameters defining Rsto, fSWP, flight, ftemp, fVPD and fphen can be defined by the user via interface windows (see below) or taken from default values.

Fig. 14 Overview of the DO3SE model structure used by the DO3SE user interface.

The current DO3SE soil water module incorporates the Penman-Moneith equation (see WP2). This requires net shortwave solar radiation to estimate plant transpiration. However, net shortwave radiation is rarely measured at experimental sites; total radiation or Photosynthetically Active Radiation (PAR) are more commonly recorded. To ensure the DO3SE model interface is as widely accessible as possible, the interface allows either net radiation (Rn), total radiation (R ) or PAR to be entered as an input and incorporates a module, using standard FAO conversions, that calculates the Rn from either R or PAR. Both site and vegetation parameters can be defined by the user on separate tabs in the DO3SE interface. The site parameter tab allows the site location and elevation, soil texture and heights of canopy and data measurement to be added. All parameters for fphen, fvpd, femp and flight can be defined in the vegetation parameters tab. Once defined, all parameters can be saved for later use.

Once all site and vegetation parameters have been defined. Data files are input using comma delineated files (.csv) and the interface allows users to construct custom input fields to match their own data. The interface also allows lines to be trimmed from the top of input files; this has the advantage of allowing the column headers to be retained without interfering with data processing in Fortran. Once the model run has been completed the interface results screen lets the user select the DO 3SE output relevant to their research before saving these as a .csv file (Fig. 15). These results can then be opened in Excel for further work. Future versions of the DO3SE interface will show graphical representations of all output data and allow comparisons of several output files.

Fig. 15 Results screen of the DO3SE interface

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Outputs from the user interface are well within expected ranges, which are summarised in Table 8. DO3SE outputs for Ra and Fst from the Hortenkopf 2001dataset (a beech forest with a gmax of 150 growing on a coarse soil) are shown in Figure 16.

Table 8 Expected ranges of DO3SE interface output. Range of Rsto is defined by gsto, shown here in the range gmax to fmin for beech trees. Fst is shown for a range of possible atmospheric O3 concentrations. Bulk stomatal conductance and Fst is shown for an assumed LAI of 5.

Ra Rb gsto Rsto LAI Gsto Fst

nmol m2 s-1sm-1 sm-1 sm-1 m-1 sm-1

O3 concentration30ppb 50ppb 70ppb

2-25 2-30 0.13-150 100000-273 5 0.78-750 0.003-4.271 0.006-7.120

0.009-9.966

Fig. 16 Example output from the DO3SE user interface for Raand Fst.

In summary, the interfaced DO3SE model has been constructed and initial tests have shown the model to work and provide output for key variables in the expected ranges. We have already been in contact with colleagues in Germany, Finland and Sweden who are interested in helping with further detailed testing and development of this interfaced model. As this development continues we will modify the interface DO3SE model and associated manual for dissemination amongst the wider scientific community next year.

Summary of progress of WP 3 In summary, the two milestones of WP3, i.e. the identification of appropriate user interface software and the finalisation of a working and tested user interface of the DO3SE F-coded model have been fully achieved.

Work Package 4: Development of detoxification module for DO3SE model

The milestones for this work Package up until March 2008 are summarised below. The work that has been performed for this

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work package, including a brief rationale for the research and the resulting achievement of these milestones is described thereafter.

M 1 Comparison of modelled O3 detoxification rates using SODA with empirically-derived flux thresholds for wheat (March 2007)

M 2 Development of framework of Rmes module to predict rates of detoxification for wheat (March 2007)M 10 Incorporation of Rmes module into DO3SE model to assess temporal variation of detoxification rates (March 2008)

Comparison of modelled O3 detoxification rates using SODA with empirically-derived flux thresholds for wheat (M1)Vegetation responses to ozone are governed by the effective flux (EF) of the pollutant (and/or its reactive products) reaching the mesophyll plasmalemma (the key biological target for ozone). As a consequence, at any given point in time ( t), the effective flux into plant foliage represents the balance between ozone flux through the stomata (F) and the capacity exhibited by plant tissues to neutralize the incoming gas and/or its reactive dissolution products (i.e. defensive capability, D)1. The manner in which this is achieved, the importance of repair mechanisms and the metabolic costs associated (i.e. the fuelling of detoxification and repair processes), remain far from fully understood. However, there is growing evidence that the physical and chemical barrier provided by the walls of foliar mesophyll and palisade cells plays a key role in the interception of environmentally-relevant fluxes of ozone.

The available literature suggest that one component in particular - cell wall-localised ascorbate (ASC; Vitamin C – a powerful antioxidant) - plays a vital role in shielding the mesophyll plasmalemma from ozone-induced oxidative degradation and thus constitutes a major driver of environmentally-induced shifts in mesophyll resistance (Rmes), and the existing mechanistic model SODA (Plőchl et al., 2000) constitutes the platform for this work package. This model simulates ozone detoxification in the leaf apoplast via reaction with apoplast ASC (ASCapo) and diffusional limitations on entry into the leaf.

Using measured data for wheat (Triticum aestivum cv. Hanno) shown in Table 9, collected during previous collaborative DEFRA contracts (EPG 1/3/173 and EPG/1/3/193) to parameterise SODA over a range of stomatal conductances and ozone concencentrations, it was possible to compute the ozone flux impinging on the outer face of the plasmalemma of leaf mesophyll cells over a range of environmentally-relevant ozone fluxes. The predicted ozone flux detoxification capacity peeled from the resulting exponential relationship yielded values in the range of 5-8 nmol mol -1, which is consistent with equivalent empirically-derived flux thresholds derived by regression analysis of dose-response relationships for wheat (Pleijel et al., 2000; Gelang et al., 2000).

Table 9. Data for wheat (Triticum aestivum cv. Hanno) collected during the course of the execution of Defra contracts (EPG 1/3/173 and EPG/1/3/193) employed for the paramaterisation of the SODA model

Parameter Value ReferenceCell wall thickness 0.23 MeasuredMesophyll cell surface area 2.56 Measured

Chloroplast volume 0.0042 Measured

Cell wall tortuosity factor 0.3 Nobel (1991)

O3-ASC reaction rate constant 4.8 x 107 M-1 s-1 Kanofsky & Sima (1995)

ASC:O3 reaction stoichiometry 2:1 Van der Vliet et al. (1995)

Total leaf ascorbate conc. [mM] 0.06-1.07 mM Measured

Apoplast pH 4.6-4.9 Measured

Stomatal conductance to water vapour (gs, mmol m-2 s-1)

Measured

Ozone concentration at canopy height [ppb]

Measured

Development of framework of Rmes module to predict rates of detoxification for wheat (M2)

SODA was reformulated to facilitate the computation of mesophyll conductance (Gmes; mmol m-2 s-1) from Rmes (s m-1) based on simulated diffusion-reaction kinetics pursuant upon uptake of ozone into the leaf interior: Gmes = (900*(8.314*(T+273)))/(Rmes*106)

1 Hence , cumulative effective ozone flux = §T0 [F(t) – D(t)]dt

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Table 10 shows Rmes and Gmes values over a range of measured apoplast ASC concentrations computed using input data from measurement campaigns conducted within the framework of Defra contracts EPG 1/3/173 and EPG/1/3/193 during open-top chamber experiments at Newcastle University on wheat (Triticum aestivum cv. Hanno) and experiments conducted at a field site near Madrid, Spain (on Triticum durum Desf. Cv. Camacho), collected during a collaborative EU FP5 Marie Curie-funded Programme co-ordinated by the Newcastle team (HPMT-CT-00219-03). These experiments yielded a database comprising ≈ 130 computable points for which relevant microclimatic data were measured in parallel.

Table 10. SODA-computed Rmes and Gmes values over a range of measured apoplast ASC concentrations

Parameter Range of values UnitsApoplast ASC 0.06 -1.07 mM

Ozone flux to plasmalemma 0.04 nmol m-2 s-1

O3 reacted with apoplast ascorbate 11.9 - 81.9 %

Mesophyll resistance to ozone 309 - 601 s m-1

Mesophyll conductance to ozone 0.003 – 0.007 mmol m-2 s-1

Computed Gmes (Fig. 10) appeared to increase over the course of the day, attaining a maximum early in the afternoon. This was followed by a sharp decline in Gmes in the late afternoon. Based on the 95 th percentile, Gmes (max) was 0.0069 mmol m-2 s-1 and Gmes (min) was 0.0035 mmol m-2 s-1.

Figure 10. Computed changes in Gmes over the diel cycle, based on data gathered during experimentation on wheat in the U.K. and Spain.

A boundary line approach similar to that adopted by Emberson et al. (2000) for the derivation of the DO3SE model was employed in a bid to derive an algorithm facilitating the dynamic modelling of changes in G mes driven by key environmental and intrinsic factors. Although the dataset is not extensive, it proved possible to derive boundary line equations for the influence of time of day, ozone concentrations, irradiance, temperature and VPD on Gmes. No data were available to compute the influence of phenology or soil moisture deficit on Gmes.

Incorporation of Rmes module into DO3SE model to assess temporal variation of detoxification rates (M10)Utilising data from an extensive open-top chamber study conducted in the Newcastle-based open-top chambers in 2006 examining the response of a range of commercial winter wheat varieties to ozone, and facilitating the derivation of flux-response measurements via a season-long measurement campaign, it has been possible to compare flux-response relationship derivations using no flux threshold (AFst0), the empirically-driven flux threshold yielding the best fit to yield for the dataset (AF st6) or the derived detoxification algorithm (Gmes). This approach indicates at least as strong relationship using the derived detoxification algorithm to simulate dynamic changes in detoxification driven by environmental variables, as is delivered by the current empirical approach (see Fig. 11).

Figure 11. Comparison of empirically-derived flux-response relationships (left graph) versus the use of a derived detoxification algorithm (right graph). Flux-response data derived from OTC experiments conducted at Newcastle University on five ‘ozone sensitive’ cultivars of winter wheat.

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Summary of progress of WP 4

In summary, two out of three milestones of WP4, i.e. the comparison of modelled O 3 detoxification rates using SODA with empirically-derived flux thresholds for wheat and the development of a framework of an Rmes module to predict rates of detoxification for wheat have been fully achieved. The outstanding milestone, i.e. the incorporation of the R mes module into the DO3SE model to assess the temporal variation of detoxification rates, will be carried out within the next six months of this project. It is expected that this outstanding milestone will be fully achieved soon, because a first comparison of empirically-derived flux-response relationships with the use of a detoxification algorithm derived in this contract indicates that the detoxification algorithm might well be able to simulate dynamic changes in detoxification driven by environmental variables.

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Amendments to project9. Are the current scientific objectives appropriate for the remainder of the project?..................YES NO

If NO, explain the reasons for any change giving the financial, staff and time implications.Contractors cannot alter scientific objectives without the agreement of the Defra Project Manager.

Progress in relation to targets10. (a) List the agreed milestones for the year/period under report as set out in the contract or any agreed

contract variation.It is the responsibility of the contractor to check fully that all milestones have been met and to provide a detailed explanation when they have not been achieved.

MilestoneTarget date

Milestones met

Number Title In full On time

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http://www.oekodata.com/icpmapping/html/manual.html

M7 Development of flux model for grassland communities of different species or functional groups

Mar 2008 Yes Yes

M8 Parameterisation of grassland flux model for generic application and for productive and species rich grasslands

Mar 2008 Yes Yes

M4 Development of DO3SE phenology and SMD models for key species and species groups

Oct 2007 Yes Yes

M5 Evaluation of DO3SE phenology and SMD models for key species and species groups

Mar 2008 Yes Yes

M6 Submission of peer reviewed paper describing the development and evaluation of the DO3SE SMD module

Mar 2008 No No

M3 Identification of appropriate user interface software

Jan 2007 Yes Yes

M9 A working tested user interface of the DO3SE F-coded model

Mar 2008 Yes Yes

M1 Comparison of modelled O3 detoxification rates using SODA with empirically-derived flux thresholds for wheat

Jan 2007 Yes Yes

M2 Development of framework of Rmes module to predict rates of detoxification for wheat

Jan 2007 Yes Yes

M10 Incorporation of Rmes module into DO3SE model to assess temporal variation of

Mar 2008 No No

(b) Do the remaining milestones look realistic?....................................................................YES NO If you have answered NO, please provide an explanation.

Publications and other outputs11. (a) Please give details of any outputs, e.g. published papers/presentations, meetings attended during this

reporting period.

Atmospheric Brown Cloud reportEmberson, L.D. & Agrawal M. (2008). The impacts of the ground level ozone component of ABC on Agriculture. UNEP Publication (in press)This report provides details of the flux based risk assessment methods (developed from the DO3SE model) that have been developed in Europe. This paper focuses on the potential for this method to be applied in Asia to estimate impacts of ozone on agricultural crop yields.

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JAMSTEC Seminar: Dr Lisa Emberson gave a seminar at Frontier Research station, JAMSTEC, in Yokohama, Japan in March 2008 entitled “Modelling and Mapping ozone deposition for risk assessment in Europe”. This trip to Japan was in Dr Emberson’s capacity as a scientific advisor to a FACE project running in Jiandu, China. In particular she advised on the development of ozone deposition models within an existing photo-oxidant (CHASER and WRF models) to perform risk assessments across China and Japan.

(b) Have opportunities for exploiting Intellectual Property arising out of this work been identified?............................................................YES NO If YES, please give details.

(c) Has any other action been taken to initiate Knowledge Transfer?..................................YES NO If YES, please give details.

Future work12. Please comment briefly on any new scientific opportunities which may arise from the project.

Application of the DO3SE model for risk assessment in South and South East Asia. Could include work to relate emissions to impacts, understanding key emission sectors, both in relation to spatial and temporal distribution and future scenario modelling (could make use of an energy planning tool called LEAP that has been developed in part by SEI)

Application of the DO3SE model to understand the risk from ozone under climate change and elevated CO2 conditions

Application of the DO3SE model to understand the risk to key potential bio fuel species (e.g. poplar, willow) from ground level ozone pollution across Europe

Development of the DO3SE model and new flux-response relationships using data from Free Air Concentration Enrichment (FACE) experiments. Such work would be possible due to the cultivation of links with colleagues in the States (SoyFACE) and Asia (Jiangdu FACE site in China, investigating response of rice and wheat to ozone).

Declaration13. I declare that the information I have given is correct to the best of my knowledge and belief.

Name Lisa Emberson Date 8th April 2008

Position held Senior Research Fellow

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