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SID 4 Annual/Interim Project Report for Period 04/08-03/09

SID 4 (Rev. 3/06) Page 1 of 24

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. Paola Cassanelli

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


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

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 2009 are summarised below with those described in this report highlighted in bold; for details of earlier completed milestone see the annual report of 2008.

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) M13 Flux-response relationships developed and applied for grassland communities (Mar 2009)M16 Evaluation of grassland flux model against appropriate primary and secondary datasets (Mar 2009)

The aim of this work package is to develop flux based methods for semi-natural communities. This has involved developing a generic multi-layer flux modelling framework that can estimate ozone uptake both to an entire canopy as well as to specific plant components (e.g. grass, legume and forb fractions) within a canopy. This generic model has been developed to enable parameterisation for specific semi-natural communities based on community characteristics such as phenology, total and within canopy distribution of LAI to provide community specific risk assessments. The community-specific parameterisation has enabled the derivation of flux-response relationships for two component canopies (grass-legume), with initial application performed within the Defra funded ICP vegetation contract (AQ0810) using two experimental datasets (Bangor and Liebefeld). Within this contract, this application has been extended to


include a third grass-legume dataset (Newcastle) and a detailed analysis of all results to inform the future development of flux-response derivation for both 2- and 3-component canopies. Development of the 3-component canopy model has targeted MG3 and MG5 communities since these are particularly important in the UK context representing U.K. grassland communities of high conservation value. Work has also included the analysis of data with which to evaluate the grassland flux model.

Generic Multi-layer flux modelThe canopy grassland flux model has been developed as a multi-layer model (Fig 1). This is necessary to allow for 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.

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

The model formulation (M7) and parameterisation (M8) were described in detail in the 2008 annual report. Updates to this work in terms of parameterising the generic model for application to specific communities are presented here. The development of the multi-layer model has two potential applications; firstly to be used in deriving flux-response relationships from experimental fumigation datasets and secondly to provide a means of assessing both total O3 deposition and O3 uptake to specific canopy components. To this end the model must be parameterised for the semi-natural community of interest and this parameterisation must include an assessment of both the seasonal drivers of flux (e.g. the seasonal cycles of LAI and soil moisture deficit and their association with phenology) and the day-to-day drivers of flux (e.g. stomatal O3 uptake). To achieve this parameterisation, we have first identified fumigation experiments that have investigated semi-natural communities. Holders of these datasets have been contacted to ask if the dataset could be made available for use within this project; as such Table 1 indicates which datasets have (or will) be made available, and of these, provides details of data collected that is particularly relevant to multi-layer flux and flux-response modelling.

Table 1. Fumigation experimental datasets that have been made available for multi-layer grassland flux modelling. Details are provided of the community characteristics (i.e. grasses (G), legumes (L) and forbs (F)); the experimental design; canopy response parameter and canopy species for which measurement data of relevance to multi-layer modelling are available (e.g. gs, biomass, LAI).

Reference Semi-natural community characteristics

Experimental design Canopy response parameters Effects

Species

Grasslands consisting of two plant functional typesGonzález-Fernández et al., 2008 (Newcastle)

Productive meadow (G, L) Sown, pot, OTC, 4 O3 treatments, 1 season

Slight reduction of total biomass, clover fraction reduced

Lolium perenne, Trifolium repens

Hayes, 2007 (Bangor) Productive meadow (G, L) Mature (swards), pot, solardomes, 2 O3 treatments, 1 season

Reduction of total biomass, clover fraction reduced


Nussbaum et al., 1995 (Liebefeld, CH)

Productive meadow (G, L) Sown, pot, OTC, 4 O3 treatments, 1 season

No reduction of total biomass, clover fraction reduced


Grasslands consisting of three plant functional typesBarnes et al., pers. comm. (Newcastle)

Mesotrophic grassland (G, L, F)

Sown (mesocosms), pot (40 dm3), OTC, 2 O3 treatments, 5 seasons

Reduction of total biomass, reduction in fraction of Phleum bertolonii, Briza media and Lotus corniculatus, increase in fraction of Alopecurus pratensis

MG3b community comprising various grasses, forbs and legumes

Power et al., pers. comm. (Silwood Park)

Calcifugous grassland (G, L, F)

Sown, pot, OTC, 2 O3 treatments, 1 season

Reduction of total biomass, fractional biomass changes ambiguous depending on N treatment

Plantago lanceolata, Trifolium pratense, Agrostis capillaris, Rumex acetosella, Lotus cornciculatus, Festuca rubra

Rämö et al., 2006, Northern European lowland Sown (mesocosms), Reduction of total biomass, forb Agrostis capillaris, Anthoxanthum


2007 (Jokioinen, Finland)

hay meadow (G, L, F) pot, OTC, 2 O3 treatments, 3 seasons

fraction reduced odoratum; Trifolium medium, Vicia cracca; Fragaria vesca, Campanula rotundifolia, Ranunculus acris

Wedlich et al., pers. comm. (Keenley)

Mesotrophic grassland (G, L, F)

Mature, field, FACE, 4 O3 treatments, 1 season

Reduction of forb fraction Holcus lanatus, Trifolium repens, Rumex acetosa

Of the 7 datasets identified in Table 1, only 3 had been made fully available by the end of February 2009 (Hayes, 2007; Nussbaum et al 1995 and Gonzalez-Fernandez et al., 2008). Of these datasets, Hayes (2007) and Nussbaum et al. (1995) were analysed under the ICP vegetation contract (AQ0810) for derivation of 2-component species flux-response relationships. Within this contract the third dataset from Newcastle (Bass, 2006; González-Fernández et al., 2008) is investigated and the results from all 3 datasets compared in relation to the derivation of O3 flux indices to predict canopy response to O3.

Of the remaining 4 datasets, two were targeted for use in the development of the 3-component multi-layer model: the Keenley, Northumberland dataset (Wedlich et al., pers. comm.) that is being collected from ongoing FACE experiments on a mesotrophic grassland community (Defra contract AQ3510) and a second Newcastle dataset (Barnes et al., pers comm.) that involves a long-running OTC-based exposure of ten-year old mesocosms containing a traditional upland species-rich hay meadow community (MG3b:d Anthoxanthum-Geranium-Briza sub-community). For the Keenley site, LAI, gs and associated meteorological data collated under the Defra contract AQ3510 (‘Ozone umbrella’) were available from the end of 2008 onward, which enabled us to perform the parameterisation of the grassland flux model. However, the hourly meteorological and O 3 data required to perform model runs for Keenley had still not been made available at the end of February 2009 due to storms at Keenley during 2008. This resulted in intermittent failure of some meteorological sensors. These problems have significantly extended the time needed to process the data (e.g. by requiring interpolation of large data gaps, identification of O3 exposure periods etc.). The data should be made available in April 2009, which will allow application of the multi-layer model to estimate whole and component canopy flux at Keenley. The Newcastle OTC dataset will be made available soon, once the environmental dataset have been drawn together and missing PAR data has been obtained from a nearby meteorological station (Newcastle airport). In conclusion, complete datasets will be available to complete the modelling required in April 2009.

Two additional datasets have also been identified to complement the data from Keenley and Newcastle, these are: i) data from a Finnish OTC experiment using a Nordic dry grassland (Rämö et al., 2006, 2007) and ii) data from an OTC experiment performed at Silwood Park, Imperial College using a calcifugous grassland. These datasets were only very recently made available (in spring 2009) and hence work to parameterise the flux model for these site specific experimental conditions with a view to deriving flux-response relationships will be conducted in this final year of the project.

Parameterisation of the multi-layer canopy flux model. Parameterisation of the multi-layer canopy flux model can be divided between aspects important in determining the seasonal O 3 flux (e.g. driven by phenology, LAI and seasonal O3 profile) and the diurnal O3 flux (primarily driven by the stomatal O3 flux and hence largely dependant on the predictive capabilities of the gs model). Arguably, the most difficult aspect of the model to parameterise are the seasonal flux attributes, largely due to limitations and difficulties associated with monitoring changes in LAI for entire canopies as well as individual species/plant functional types (PFTs) within the canopy. Defining parameterisation that is capable of representing the main PFT canopy components is particularly challenging for forbs given the variability in phenology, LAI and g s related attributes within the forb species group. As such, it will perhaps be more useful to target particular forbs (e.g. those that are dominant within the canopy or known to be particularly sensitive to O3) for specific estimation of O3 uptake to aid understanding of individual- and canopy-responses to O3 exposure.

To this end, we have explored the use of vegetation indices to estimate attributes important for canopy flux modelling for individual canopy components. The Ellenberg indicator values have here been used to provide species-specific information that can be used to attribute canopy characteristics such as relative phenological development, position and frequency distribution of individual species within the canopy. Table 2 gives an example of how these vegetation indices have been used to define characteristics for the species found in the semi-natural grassland community at the Keenley, Northumberland site.

Table 2. Grasses, legumes and forbs with high frequency in Keenley (representing NVC classes MG3 and MG5) and their flowering times, height, contribution to fLAI total (<5% of total biomass = low, >5% of total biomass = high), phenological characteristic (start of growing season in March/April, April/May and June expressed as earl, middle and late, respectively), position in canopy (Bottom = <30 cm, Middle = 30 – 70 cm, Top = > 70 cm) and Ellenberg indicator values recommended for use in the UK (Hill et al., 1999), describing the ecological characteristics of these species with regard to light (L) and nitrogen supply (N). Ranges of indicator values: L = 1: Plant in deep shade, L= 9: Plant in full light; N = 1: Indicator of extremely infertile sites, N = 9: Indicator of extremely rich situations.

Dominating grasses with high frequency

NVC Class

Ellenberg indicator values

(L, N)

Flowering time

Max. height (cm)

fLAI total (contribution to total biomass)

Phenology LAI,position in

canopyAgrostis capillaris MG3/5 6, 4 6 – 8 70 High Late MiddleAnthoxanthum odoratum MG3/5 7, 3 4 – 7 50 Low Early MiddleDactylis glomerata MG3/5 7, 6 6 – 9 100 Low Late TopFestuca rubra MG3/5 8, 5 6 – 8 90 Low Late TopHolcus lanatus MG3/5 7, 5 5 – 8 100 High Middle TopLolium perenne MG5 8, 6 5 – 9 100 High Middle TopPoa trivialis MG3 7, 6 6 – 7 60 Low Late MiddleTrisetum flavescens MG5 7, 4 5 – 6 80 Low Middle Top


Dominating legumes with high frequency

Trifolium repens MG3/5 7, 6 4 – 10 20 Low Early BottomDominating forbs with high

frequencyPlantago lanceolata MG5 7, 4 5 – 9 60 Low Middle MiddleRanunculus spec. MG3/5 6/7, 4/7 3/5 – 7/9 50-100 Low Early-Middle Middle - TopRhinanthus minor MG3/5 7, 4 5 – 9 50 Low Middle MiddleRumex acetosa MG3/5 7, 6 5 – 8 100 Low Middle Top

The early flowering species Anthoxanthum odoratum will potentially dominate the grassland canopy at the beginning of the growing period and will therefore form the main O3 sink at that time, whereas species with a later flowering period such as Agrostis capillaris (tenuis) and Holcus lanatus will tend to form the largest O3 sink at the end of the growing period due to their dominance at that time of year. Details of the maximum height attained by these individual species would suggest that the grasses and some of the forbs (i.e. Rumex acetosa and Ranunculus spp.) would have greater exposure due to their presence in the upper parts of the canopy where the O3 concentrations will be higher. Finally, at this site, the grasses appear to dominate the canopy mix in terms of contribution to total biomass, which may suggest these species will have the higher fractional LAI values. This type of information will be compared with more specific measured data describing LAI and harvest biomass once available from the site.

Parameterisation of the stomatal component of the grassland flux model has been based on primary and secondary data from various sites across the UK and Europe. The parameterisation for legumes is based on Trifolium and the one for grasses on Lolium, whereas the parameterisation for forbs is currently derived from data representing seven species, namely Bellis perennis, Centaurea jacea, Fragaria vesca, Geranium sylvaticum, Knautia arvensis, Plantago lanceolata, Rumex obtusifolius) (Table 3). Again, it should be noted that these particular forbs differ quite substantially in their habit, which is possibly mirrored in the high range of gmax values.

Table 3. Parameterisation for the three plant functional type grassland DO3SE model, represented by Trifolium (legume) and Lolium (grass) and a variety of forbs (see above). Note the gmax values provided in this table are for ozone (mmol O3 m-2 PLA s-1).

Trifolium Trifolium reference Lolium Lolium reference Forbs Forbs referenceLm (leaf

dimension, m) 0.05 Büker, pers. communication 0.02 Büker, pers. communication 0.05 Büker, pers.

communication

gmax (mmol O3 m-2

PLA s-1) 360

Median of primary (p) and secondary data (s): Nussbaum et al. 1995 [365] (p); Nussbaum & Fuhrer, 2000 [355] (s)

295

Median of primary (p) and secondary (s) data:

Sheehy et al., 1975 [123] (s); Gay, 1986 [319] (s); Nijs et al., 1989 [286] (s); Ferris et al., 1996 [268] (s); Jones et

al., 1996 [304] (s); Nussbaum et al., 1995 [244] (p); Hayes, 2007 [366] (p); Coyle, 2006

[310] (p)

294

Median of primary (p) and secondary (s) data:

Stirling et al., 1997 [283, 239](s), Nussbaum &

Fuhrer (2000) [366,305,275,256](s),

Wedlich (pers. Comm.) [470](p), Samuelsson

(pers. comm.) [256](p), Manninen (pers. comm.)

[474,476](p)

fmin 0.02

Nussbaum et al., 1995 [0.05] (p); Hayes, 2007 [0.05] (p); Gonzáles-

Fernández et al., 2008 [0.02] (p)

0.02Nussbaum et al., 1995 [0.22] (p); Hayes, 2007 [0.02] (p);

Coyle, 2006 [0.1] (p)0.14

Wedlich (pers. Comm.) [0.11](p), Samuelsson

(pers. comm.) [0.04](p), Manninen (pers. comm.)

[0.16,0.17](p)

fphen 1 Estimated 1 Estimated 1 Estimatedflight_a 0.008 As for fmin plus ICP

Vegetation (pers. comm.)

As for flight_aAs for flight_aAs for flight_aAs for flight_a

0.007as for fmin

as for fmin

as for fmin

as for fmin

as for fmin

0.015as for fmin

as for fmin

as for fmin

as for fmin

as for fmin

T_min 10 10 10T_opt 27 25 25T_max 43 40 38

VPD_max 2.8 2.0 2.2

VPD_min 4.5 As for flight_a 4.0 as for fmin 3.6 as for fmin

SWP_max -0.49 Ashmore et al., 2007 -0.49 Ashmore et al., 2007 -0.49 Neilson, 1995; Jones et al., 1980

SWP_min -1.5 Ashmore et al., 2007 -1.5 Ashmore et al., 2007 -1.5 Neilson, 1995; Jones et al. , 1980

Root depth* (cm) 100 Thorup-Kristensen, 2004 100 Thorup-Kristensen, 2004 100 Thorup-Kristensen, 2004

Table 3 shows that in comparison to the grasses and legumes, the forbs show a large range of g max values (240 to 480 mmol O3 m-2 s-1) based on data for 7 different species. However, the response of gs to environmental conditions, signified by the f functions (i.e. relationship of gs with light, temperature and VPD), is similar across grasses, legumes and forbs. This would suggest that the main drivers of canopy flux will be phenology and associated distribution of species’ LAI within the canopy and g max. As such, parameterising these attributes for flux modelling for specific semi-natural communities should be prioritised in the future.

Flux-response derivation using the multi-layer canopy flux model


Two productive grassland datasets were previously investigated in the ICP vegetation contract (AQ0810); here a third dataset made available from Newcastle (Bass, 2006; González-Fernández et al., 2008) is investigated with the results from all three datasets compared in relation to the ability of a number of O3 indices (representing both flux and concentration based) to predict canopy response in the form of species composition. From this analysis we have identified key questions and issues related to application of the multi-layer flux model for derivation of productive and non-productive grassland flux responses.

Table 4 describes the key attributes of each of the datasets used in this analysis providing details of the experimental apparatus, number of harvest periods and number and magnitude of O3 treatments expressed as AOT40; mean total canopy biomass (in g of dry weight) at the end of each harvest period; maximum canopy clover fraction within all treatments and harvest periods; estimated mean LAI and the most important limitations of the dataset (i.e. lack of data describing LAI or within chamber VPD conditions, important for g s

modelling); and finally the most extreme canopy response, defined as the reduction in the canopy clover fraction in relation to that at the start of the harvest period.

Table 4. Details of key aspects of the productive grassland datasets used for flux-response derivation.

Dataset Exp. apparatus Mean total canopy biomass(dry weight, g)

Max. canopy clover fraction

Mean canopy LAI*

Dataset limitations

Maximum response (clover fraction reduction)

Liebefeld OTC, 4 harvests, 4 O3 treat.; max. AOT40 = 26.3

18.5 0.18 (0.04) 3.8 LAI 0.44

Newcastle OTC, 3 harvests, 4 O3 treat.; max. AOT40 = 16.4; 2 N treat.

9.4 0.56 (0.15) 1.6 LAI 0.60

Bangor Solardome; 2 harvests; 2 O3

treat. max AOT40 = 13.848.7 0.79 (0.09) 8.5 LAI, VPD 0.86

* Estimated from biomass dry weight (g); treat. = treatments; AOT given in ppm.hrs

Table 4 shows that the canopies and treatments used in the experimental fumigations are quite different – in comparative terms the Liebefeld canopy is a medium sized canopy with a small clover fraction exposed to a high O3 concentration; the Newcastle canopy is a small canopy with a medium clover fraction exposed to a medium O3 concentration. The Bangor canopy is the largest canopy with a high clover fraction exposed to the lowest O3 concentration; these various aspects should be considered in the interpretation of the flux-response relationships.

Results are presented that provide comparisons of the resulting flux- and AOT40-models ability to describe the relationship between the selected O3 characterization method and the change in clover canopy fraction (relative to that at the start of the individual growth period). Figure 2 shows the comparison of whole canopy flux and AOT40 with relative clover fraction by harvest period. In Table 4 it is important to note that the use of the threshold in the AOT40 calculation to some extent helps improve the R2 of the regression since all control values have an AOT40 of zero; there is the possibility that the use of a flux threshold may confer a similar advantage to the flux modelling but this has not been tested here. Table 5 provides details of the R 2 values for regression lines plotted for different flux variables and AOT40 relationships with change in relative clover canopy fraction of individual harvest periods. This response parameter is chosen as the literature suggests this to be the dominant response of productive grasslands to elevated ozone concentrations (see also Table 1).

Figure 2. Relationship between O3 characterization indices (a. whole canopy AFst0 and b. AOT40) and relative clover fraction by individual harvest period.

Productive grassland O3 flux response relationship

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25 30 35

Canopy AFst0 (mmol O3 per canopy)

Rel

ativ

e cl

over

frac

tion

Bangor

Liebefeld

Newcastle

Productive grassland O3 concentration response relationship

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10 12 14 16 18

AOT40 (ppb.hrs)

Rel

ativ

e cl

over

frac

tion

Bangor

Liebefeld

Newcastle

Table 5. R2 values of regression lines plotted for different flux variables and AOT40 relationships with change in relative clover fraction of individual harvest periods. All results rounded to 2 d.p.

Experiment Canopy mean AFst0 Whole canopy AFst0 Clover Leaf AFst0 AOT40Liebefeld 0.54 0.74 0.76 0.64Newcastle 0.23 0.29 0.50 0.46Bangor 0.52 0.74 0.57 0.91


Mean 0.43 0.59 0.61 0.67 On average, Table 5 suggests AOT40 is marginally better than the various flux indices at predicting canopy response. However, these tabulated results should be interpreted with caution as evident from analysis of the individual experimental results. Since the Bangor results are comprised of only 2 control and 2 O3 treatment values, fitting regressions to these data is not really appropriate; for example, the high R2 for the AOT40 Bangor data are largely a result of the AOT40 (and hence use of a threshold type O 3 characterization) control treatment being equal to zero. If these Bangor results were to be removed from the analysis the clover leaf AFst0 commands the highest R2 at 0.63 compared with an AOT40 of 0.55 (data not shown). This would suggest that flux estimates are marginally superior to AOT40 in determining canopy response to O3. The poor performance of the whole- and fractional-canopy fluxes may well be explained by the rapid reduction in O3 concentration with penetration into the canopy (meaning that the lower canopy layers receive low O 3

doses); this, coupled with the similarly low penetration of irradiance into the canopy, would suggest these lower canopy fractions are both unlikely to contribute much to the overall canopy NPP, and that the flux to the upper canopy layers will be more important in determining the canopy response to O3. As such, it could be argued that the reason why the clover canopy fraction is more likely to suffer damage from O3 is its proportionally higher LAI prevalence in the upper parts of the grassland canopies represented by the datasets used here, which is better reflected by flux indices that focus on estimating uptake to the upper canopy leaves.

This is not to say that the multi-layer flux model is without use, on the contrary, the use of such a model could vastly improve our understanding of which canopy components are most likely at risk from O3 due to their position and prevalence within the canopy. This finding is of particular relevance for 3-component canopy models, given the heterogeneity in the LAI distribution of forbs within a canopy. For such a model to be useful, detailed information describing the change in seasonal LAI and the fractional components and within canopy distribution is necessary. To this end, as stated previously, the model could best be applied in situations where a risk assessment for a particular canopy, or individual species within a canopy, is required.

The completion of this analysis has raised some important questions and issues that need to be resolved. Perhaps the most important of these is how to deal with the lack of LAI data in the experimental datasets. This is the key driver of many aspects of canopy flux, with the fractional distribution of different plant functional types within the canopy determining the O3 load that the species receive. Without this information, given the large difference in O3 and irradiance profiles within the canopy, misconceptions as to the O3 dose different plant species are receiving will be made. It is not clear how best to overcome this given the lack of LAI information in the datasets that currently exist; however, a clear conclusion from this work is that detailed measurements of LAI are an absolute requirement for experimental campaigns to understand the differential O3 flux to and within the canopy using a multi-layer modelling approach.

The most important factors identified through this analysis in relation to multi-layer canopy flux modelling are listed below:

o The total LAI and how this changes throughout the growth period. A key question is how realistic are assumed LAI values based on final harvest biomass expressed as gram dry weight.

o The fractional LAI of each species (canopy component type) and how this changes with height or LAI increment) within the canopy.

o The O3 concentration at the soil surface. In canopies with LAI values lower than ~4 this is unlikely to be zero (Jaeggi et al. 2006).

o The gmax values of the species (canopy components).o The predictive ability of the gs model.o The flux parameter that is most likely to provide an indication of the whole canopy response i.e. flux to the entire canopy,

flux to the sensitive canopy component (e.g. clover fraction), proportional flux to the different plant canopy fractions, flux to that portion of the canopy that is most likely to contribute to canopy biomass production. Here only AFst0 values are calculated; it is possible that the introduction of a flux threshold (Y) (e.g. Y > 0) could yield improvements in the regression statistics.

o The response parameter. Based on earlier studies (see Table 1) we have here focussed on the change in the clover fraction relative to that at the start of each individual harvest period since evidence suggests that clover biomass is lost whilst grass biomass may increase resulting in the maintenance of a constant whole canopy biomass. Is this the best measure of canopy competition dynamics in relation to O3 exposure?

In conclusion, it would seem that, in general, estimates of a variety of canopy flux variables, even with the data limitations described above, are able to provide an improved estimate of canopy response than AOT40. However, the relationships between canopy response and flux are not yet clear enough to be able to derive meaningful or robust flux-response relationships. Additionally, the difficulties associated with deriving LAI data from the experimental campaigns are equivalent to difficulties associated with estimating LAI profiles for productive and non-productive grassland across the UK and Europe that would be necessary for national or regional application of the model. As such, until improved methods of defining LAI become available (e.g. from remote sensing or model simulations) it may be that the multi-layer flux model is of value in understanding specific within-canopy processes rather than performing regional based risk assessments. The latter may be best represented using the simpler and, at least for some canopy types, more effective, flux methods based on the flux to a representative leaf of the upper canopy that could perhaps take into account the likely within-canopy species distribution in the uppermost layers of the canopy.

Evaluation of the multi-layer canopy flux modelThe datasets available for evaluating the model can be categorised as follows:- i) those that provide evaluation of the stomatal conductance component; ii) those that provide evaluation of the canopy characteristics (namely total LAI and LAI fractions and within-canopy O3 profile) and iii) those that provide evaluation of the total ozone flux to the entire canopy. This structured evaluation helps to identify the strengths and weaknesses of the multi-layer model and target areas which need further attention either in terms of model formulation development or model parameterisation.

To achieve this evaluation we have identified and collated the following datasets described in Table 6. These datasets come both from


primary and secondary data published in the literature, the latter helping to provide a broad assessment of the capabilities of the model in predictions of O3 flux both for the UK but also for other locations across Europe. This will be important if the modelling framework developed here is to be accepted for use in international policy at the European scale.

Table 6. Datasets available for evaluation of the multi-layer grassland flux model.

Reference Location Grassland type Measurement period

Measurements made (e.g Ftot, gsto, Et etc…)

Measurement method

Coyle, 2006 Easter Bush, U.K.

Productive grassland dominated by Lolium

March-October, 3 years (2001-2003)

Ftot, gsto (both for O3 and H2O), SWC, LAI

Eddy-Correlation

Coyle, pers. comm.

Keenley, U.K. Mesotrophic grassland, MG3

June-December (2007, 2008)

Ftot, gsto (both for O3 and H2O, the former also for CO2), SWC, LAI

Eddy-Correlation

De Miguel and Bilbao, 1999

Northwest Spain

Green grassland (height: 25-40 cm)

6 days in late July 1995 FO3, VdO3, RcO3 Gradient

Pederson et al., 1995

San Joaquin Valey, California, US

Grazed unirrigated annual grassland

4 weeks July to August in 1990 and 1991

Vd (for O3,CO2 and H2O), momentum sensible heat, LAI

Eddy-Correlation (with help of aircraft)

Pio et al., 2000 Portugal Permanent and temporary grassland with mainly Festuca, Trifolium and Plantago

November 1994 to October 1995

VdO3, momentum sensible heat, LAI

Eddy-Correlation

Sorimachi et al., 2003

Northern China

Short grass, green (September), senescent (November)

10 days in September and November 2001

FO3, VdO3, RcO3 Gradient

Here we present results of evaluation of the grassland multi-layer flux model with ozone deposition velocity (Vg), LAI and soil moisture deficit data collected over 3 years at Easter Bush in the U.K. (Figure 3).This dataset is selected for presentation here since it provides a good indication of the models capability to estimate total O3 deposition to the whole canopy (through comparisons of modelled and measured Vg) as well as assessing the models ability to estimate two of the key drivers of seasonal ozone flux (LAI and soil moisture deficit; the latter parameter modelled according to the methods developed in WP2).

Figure 3. Evaluation of the multi-layer flux model with measurement data from Easter Bush, U.K.. Evaluation of i) LAI, ii) Vg and iii) soil water variables for 3 years (2002 to 2004).


Figure 3 shows good agreement for 2 out of the 3 years of evaluation in relation to Vg values, since Vg is perhaps the best indicator of a models ability to assess O3 deposition these results are extremely encouraging and suggest the model is capable of providing good estimates of total O3 deposition to grasslands. The measured Vg clearly shows seasonal amplitudes, which appear to coincide with the perceived variation in LAI. For example, 2003 indicates no cuts and hence a continuous LAI profile (which the DO 3SE model is able to predict) whilst 2002 clearly shows 4 distinctive periods of LAI growth divided by grassland cuts again captured by the DO 3SE model. Only in 2004 does the modelling suggest 3 LAI peaks whilst only two are apparent form the Vg measurements. As such, when the seasonal variation in LAI is captured by the DO3SE model, the estimates of Vg are close to the range of the observed values. In summary, the model tended to under-predict Vg in 2002 by between 4-10 mm/s (largely due to high LAI values of up to 7 m 2/m2

SID 4 (Rev. 3/06) Page 10 of 24

compared with the defined model maximum of 4.5 m2/m2); capture the Vg profile and magnitude remarkably well in 2003 (with modelled and measured max. values coinciding at the height of summer at values of ~ 15 mm/s); and capture the magnitude but not the seasonal profile of Vg in 2004. This again highlights the importance of LAI in determining Vg. Limited LAI data were collected from measurements at Easter Bush (shown as red circles in the LAI figures); these measurements are relatively infrequent throughout the year with a minimum of 5 and maximum of 9 measurements in any one-year, the large variability in the maximum LAI values recorded in each year (which vary between 1.5 and 7 m2/m2) highlights the difficulties associated with trying to simulate both within and between year variability in LAI. Although the model does not appear to capture the measured variation in LAI the good agreement between measured and modelled Vg suggests that LAI is modelled reasonably accurately with the exception of 2004. Of the soil water variables, only 2003 shows any sign of a drying soil and this is again captured remarkably well by the model with the measured profile and magnitude of reductions in soil water content to a minimum of ~20 % vol. being closely mimicked by the model.

In summary: The multi-layer grassland flux model has been developed and parameterised for 2 and 3 PFT component semi-natural vegetation communities. The model has been used to derive canopy flux-response relationships which seem to perform as well if not better than AOT40 indices in explaining the relationship between O3 exposure and canopy response (the latter estimated as the change in canopy clover fraction). Application of this model has highlighted issues for further consideration with regards to continued derivation of flux-response relationships, mostly associated with our limited understanding of LAI profiles within the canopy and how to interpret fluxes to individual canopy components in terms of whole canopy response to O3. Finally, the model has been shown to be capable of capturing the important aspects of canopy O3 flux (namely deposition velocity) in evaluations against observations. Again, the importance of accurate simulations of seasonal LAI profiles would appear to be the main requirement for accurate modelling of O 3

deposition.

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


M 4 Development of DO3SE phenology and SMD models for key species and species groups (Oct 2007)M 5 Evaluation of DO3SE phenology and SMD models for key species and species groups (Mar 2008)M 6 Submission of peer reviewed paper describing the development and evaluation of the DO3SE SMD module

(Mar 2008)M 11 Incorporation of the DO3SE phenology and SMD models within the DO3SE version of the EMEP Photo-oxidant

model (Oct 2008)

Work has continued to further develop and validate the SMD module with a specific focus on forest trees; this vegetation group has been targeted since the current lack of SMD modelling is most problematic for forest trees due to the long growth periods and management practices associated with this vegetation type. This means they are most likely to suffer stomatal limiting drought conditions that would alter O3 flux and hence O3 risk. In addition, the establishment of the “real” species parameterisation has progressed flux modelling for this species group; these advances would be especially well complemented with an incorporation of the influence of drought on forest O3 flux for European and UK wide risk assessment.

Since our last annual reporting we have identified and validated the model against additional soil moisture datasets that have been made available over the past year. The validation now clearly shows that the model is able to capture the seasonal changes in soil water content remarkably well for a variety of tree species with different climatological habits (i.e. beech, Norway spruce and holm oak) across the latitudinal breadth of Europe. A brief summary of the SMD model and current state of the models evaluation is given below.

Validation of the DO3SE SMD model for key species and species groups (M4)The existing DO3SE SMD module has now been updated to incorporate and assessment of Actual Evapotranspiration (AEt) using the Penman-Monteith energy balance method; this method is used in conjunction with a soil water 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 whilst the remainder is assumed to be intercepted by the leaves of the plant. Only the fraction of this precipitation that exceeds free surface evaporation is allowed to reach the soil.

The use of this model requires knowledge of soil type and associated soil water holding characteristics. Currently, the model is parameterised for four soil types, sandy loam (coarse), silt loam (medium coarse), loam (medium) and clay loam (very fine). For each soil type a soil water release curve has been defined 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 m3/m3 (expressed on a volumetric basis). Key soil characteristics of these soils are provided in Table 1.

SID 4 (Rev. 3/06) Page 11 of 24

Table 7. Soil characteristics used to derive DO3SE coarse, medium and fine soil water release curves

FC, m3 m-3 Ψe MPa bSandy loam Coarse 0.16 -0.00091 3.31Silt loam Medium coarse 0.26 -0.00158 4.38Loam Medium 0.29 -0.00188 6.58Clay loam Fine 0.37 -0.00588 7

The amount of water lost from the soil (as AEt) is controlled by the effect of the soil water on g s according to the relationship given in Figure 4.

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, soil water is readily able to leave the soil system up to a critical value (ψmax) past which point soil water is held increasingly tightly until a second critical value (ψmin) is reached, when gs is restricted to fmin. Appropriate values for ψmax, ψmin and fmin

were determined using experimental data collated for the tree species: beech (Fagus sylvatica), oak (Quercus robur, Q. petraea), Scots pine (Pinus sylvestrus) Norway spruce (Picea abies) and Holm oak (Q. ilex). Because fSWP relationships for coniferous and deciduous trees are very similar, we use a generic fSWP relationship for northern and central Europe derived using combined data for both tree types. Both north/central European and Mediterranean relationships are standardised to incorporate 80% of experimental data points.

Figure 4. fswp relationships used to mimic the ever-decreasing ease with which soil water can be extracted from the soil for North/Central European and Mediterranean tree species, shown against both soil water potential (MPa).

To provide the internal consistency of this method, the fswp value is then used within the gs estimation that provides an assessment of the stomatal O3 flux for risk assessment modelling. 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.

Model evaluationEvaluation of the DO3SE soil moisture module has been performed against 7 forest datasets collected in Sweden (Asa), Switzerland (Davos), Germany (Hortenkopf, Forellenbach) and Spain (Miraflores, Prades) and 1 from the USA (Crestline\Strawberry Peak). The Crestline/Strawberry Peak dataset comprises soil water values from two sites and meteorological values from a weather station at a third site. Trees at these sites are oak species (Quercus) and have been included in this evaluation since it is assumed that the forests at this location are similar to broadleaf evergreen forests of the Mediterranean region, represented by Holm oak in the DO 3SE model. Datasets were selected according to the following criteria: i) they represent forest species for which “real species” DO 3SE model parameterisation exists; ii) they represent a range of different forest species functional types (e.g. conifers, deciduous and broadleaf evergreen species) and iii) are derived from locations covering the broad climatic regions of Europe (e.g. boreal, temperate and Mediterranean). Soil water data were collected using several metrics: soil water potential (ψ soil MPa), pre-dawn leaf water potential (ψpdleaf MPa), soil water content (SWC %) and available soil water (ASW mm). Measured and modelled soil water data were compared to obtain the root mean square error (RMSE) (Table 8).

Table 8. Location and site parameters of data sets used for SMD evaluation in DO3SE

Site name Country Altitude (m a.s.l.)

Species Soil texture Soil water metric

Measurement Period

Reference

Asa Sweden 285 Norway spruce Silt loam ψsoil (MPa) 1995, 2000 Karlsson et al. pers comm.

SID 4 (Rev. 3/06) Page 12 of 24

Davos Switzerland 1640 Norway spruce Silt loam ψsoil (MPa) 2004 Zweifel et al. pers comm.

Forellenbach Germany 825 beech Sandy loam ASW (mm) 2003 Baumgarten et al. (2000)

Hortenkopf Germany 606 beech and oak Sandy loam SWC (%) 1998-2003 Werner (pers. comm.)

Prades Spain 930 Holm oak Clay loam SWC (%) 2001-2003 Asensio et al. (2007)

Strawberry Peak/Crestline

USA 1800 Evergreen oak (Quercus spp.)

Loam SWC (%) 1995 Grulke (pers. comm.)

Miraflores de la Sierra

Spain 600 Holm oak Sandy loam ψpdleafl(MPa) 2004 Alonso et al. (2008)

Measurement height: OF = Open Field; C = within Canopy

Estimations of soil water status calculated by DO3SE are given for each site and year in Figure 5 and compared to observed data (shown in original metrics). Also given are precipitation data and fSWP values, which give an indication of the impact of soil water on stomatal conductance and O3 flux. DO3SE estimates of soil water status agree with observed data and give good predictions of the relative annual profile of soil water at a given site (summary of statistical comparisons of measured and modelled soil water is given in Table 9). In particular, the model gives good predictions of “wet” years, when little or no water stress to the tree occurs, and “dry” years, when there is substantial drying of the soil (Ψsoil<Ψmax) and when these periods occur within the season.

Comparison of multiple years at a single site illustrates the inter-year capability of the model to differentiate between drought and non-drought conditions. For example, at the Asa site the year 2000 may be considered a wet year and the absence of water stress is shown by the lack of soil water deficits in both the modelled and observed ψ soil values. In contrast, the period of 8 th August to 4th September in 1995 is a dry period in which ψsoil falls below ψmax. The start and end of this period is predicted by DO 3SE as a period of stomatal restriction. The upper axis on Figure 5 shows fSWP, reflecting the effect of water status on gs. Again this compares modelled values with fSWP calculated from observed soil moisture values. This gives an indication of the sensitivity of gs to soil drying below ψmax. The results from the Hortenkopf site in Germany for the year 2003 also show the capability of the model to estimate the seasonal soil water content but highlight the sensitivity of the translation of soil water content into an effect on stomatal conductance as shown by the divergence between the measured and modelled fSWP values. This is due to the sensitivity of differences of soil water potential at low levels (i.e. those close to Ψmin) of soil water content, i.e. very small changes in soil water content will translate into large changes in soil water potential so that the limitation to gs occurs over a very limited range of soil water content.

Figure 5. Measured (○) and DO3SE estimated (- - -) soil water for a) Asa in 1995 and 2000 in relation to P events (-), and b) Hortenkopf in 2003. fSWP values are shown on the top axis for each site and compared to fSWP values derived from direct measurements (○). The value below which stomata begin to close determined by Ψmax is shown on the figures by the dotted line.

a).

b).

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Table 9 Statistical agreement (root mean square error and normalised root mean square error) of measured and modelled soil water values.

Site Metric year RMSEAsa ψsoil 1995 0.236

2000 0.0233Davos ψsoil 2004 0.0115Forellenbach PAW 2003 52.4Hortenkopf %SWC 1998 2.50

1999 2.592000 3.552001 4.702002 4.332003 2.44

Miraflores %SWC 2004-5 0.439Prades %SWC 2001-3 3.56Crestline, Strawberry Peak

%SWC 1995 1.11,3.07

We have also performed a sensitivity analysis of this SMD module for the key drivers of soil moisture (g max, LAI, root depth and soil texture) and assessed the effect of changing these parameters within observed ranges on soil moisture and subsequent stomatal O 3

flux. This analysis has been performed for the Hortenkopf site in Germany for the year 2003 since this was a year with frequent measurements with which to compare the modelled predictions.

A key difficulty in this evaluation work has been the limited availability of datasets that describe both soil moisture and stomatal conductance under drought conditions (generally one or the other may be described well, but rarely both) and that also record the hourly data necessary to perform the model runs. Therefore, although we can clearly show that the model is able to capture seasonal variations in soil moisture content, we have struggled to find data to validate our translation of soil moisture into limitations on stomatal conductance. However, encouragingly, the limited data that we do have would suggest that the model is broadly capable of making this transition.

Table 10. Sensitivity analysis performed for key drivers of soil moisture (soil texture, g max, LAI and root depth) and influence on stomatal O3 flux to beech (Afst1.6) for the Hortenkopf site for 2003. Values in bold denote default DO3SE model parameter values.

Parameter Value fSWP<1 (days) AFst1.6 (mmol O3 PLA m-2)

Range in AFst1.6(mmol O3 PLA m-2)

Soil texture Coarse 74 14.63.6

Fine 48 18.2

gmax (mmol m-2 s-1) 100 21 10.5

5.9150 74 14.6180 87 16.4

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LAI 4 62 16.5

4.45 74 14.69 78 12.1

Root depth (m) 0.7 109 9.5

5.61.2 74 14.61.3 69 15.1

The results in Table 10 show that altering gmax within the observed range for beech produces the greatest variation (~ 6 mmol O3 PLA m-

2 s-1) in the potential O3 flux (AFst1.6) whilst soil texture (here varied between the two soil texture extremes) has the least effect with a range in AFst1.6 of only 3.6 mmol O3 PLA m-2 s-1.

The DO3SE SMD module is currently being incorporated into the EMEP model which will provide an estimation of the effect of SMD on O3 flux at a European scale. In this format the SMD module could be applied with standard “real” species parameters for tree species based on UNECE (2004) recommendations; the model would also be likely to use a uniform soil texture (e.g. medium or loam soil texture). This will overcome the uncertainties involved in estimating parameters at a large (50 km 2) scale and allow an assessment of the relative risk from O3 flux between areas and years with differing levels of water stress. At present this module is being modified for application within EMEP to lessen sensitivity to threshold effects associated with critical soil moisture values.

The incorporation of this SMD model into the EMEP model has been delayed due to constraints on the time of David Simpson at EMEP which have meant that he has been unable to spend the length of time needed to code the model into the EMEP deposition model and produce European maps of soil moisture deficit. As such we have decided to refrain from publication of the specific development and evaluation of the SMD module since we feel this would benefit greatly from incorporation of the EMEP work. However, we have published two other papers in the peer reviewed scientific literature that relate to this soil moisture modelling work; the first, by Tuovinen et al. (2009) describes some of the key aspects for consideration in relation to performing O 3 flux based risk assessments for forest trees; in this paper we highlight the need to incorporate soil water limitations to flux and the difficulties associated with such modelling. We have also published a paper with colleagues in Germany (Baumgarten et al. 2009) which describes application of the soil moisture modelling approach for sites across Bavaria in Germany and highlights the importance of incorporating soil moisture effects as the main driver of difference in seasonal O3 exposure characterized as either concentration or flux based indices. As such we have partly met the peer review paper submission milestones; we intend to finalise the detailed SMD development and evaluation paper during the summer of 2009.

During this reporting period a UNECE LRTAP forest sub-group, chaired by Dr. Lisa Emberson, has also been successful in reaching agreement on the parameterisation of “real” forest species at the LRTAP Convention Critical Level meeting of ICP Forests, Cyprus held on 25-28 May, 2008. These parameterisations have been defined to represent species found in particular European regions and to allow the modelling of stomatal O3 flux and hence O3 risk to be more specific to local species and climatically determined forest tree physiology. Application of these “real” species is intended to inform the European scale Integrated Assessment Modelling (IAM) conducted using the generic forest parameterisations. For the UK three “real” forest tree parameterisations have been defined for beech (largely based on the “generic” forest tree parameterisation and hence providing a comparison with IAM modelling of O 3 risk), Scots pine and temperate oak. To give an indication of how the use of these parameterisations may affect the inference of risk of different forest species to O3 across the UK we have performed some provisional model runs using EMEP O3 and meteorological data supplied on a 50 x 50 km grid for the UK. Figure 6 shows the results of these model runs, made using the off-line version of the DO 3SE model held at SEI, York.

Figure 6. Estimates of AFst1.6 for Scots pine, beech and oak for the U.K. made using the UNECE CLARTAP “real” forest species parameterisations for the UK for the year 1997.

SID 4 (Rev. 3/06) Page 15 of 24

These results would suggest that beech is actually the forest tree species exposed to the lowest O 3 dose with a range of AFst1.6 between ~8 to 23 mmol O3 m-2; oak receives the next highest O3 dose with Afst1.6 ranging from 12 to 32 mmol O3 m-2; this is largely driven by the higher gmax value of oak (230 mmol O3 m-2 PLA s-1 for oak compared to a value of 150 mmol O 3 m-2 PLA s-1 for beech). Finally, Scots pine would seem to have be exposed to the highest O3 dose with a range in AFst1.6 that is similar to oak but with larger areas across the UK and southern Ireland being covered by O3 fluxes above 22 mmol m-2; this is largely attributable to the evergreen phenological habit of this coniferous species and the modelling which assumes once the f temp minimum threshold for stomatal conductance of 0°C is exceeded, physiological activity and hence O3 flux will begin.

In summary: the DO3SE models soil water deficit module, using “real” species parameterisations and varied soil texture parameters, has been shown to be capable of providing good estimates of the seasonal variation in soil water content for a range of different tree species across the latitudinal breadth of Europe. However, there are uncertainties associated with translating these estimates of soil water content into estimates of soil water potential which represent the biologically relevant soil parameter and hence will determine the influence of water stress conditions on stomatal O3 flux. A sensitivity analysis has been performed that gives an indication of the range in O3 flux estimates (calculated as AFst1.6) based on observed ranges in key drivers of soil moisture (i.e. g max, LAI, root depth and soil texture); these indicate that gmax is the variable most likely to influence estimates of soil moisture. The work to incorporate this SMD module into the EMEP model has been delayed due to constraints on Dr. Dave Simpson’s time; Dr. Simpson has identified the summer of 2009 as a time when incorporation of the SMD module into the EMEP code should be possible. In the mean-time we have performed model runs using the recently established “real” species parameterisations identified for the U.K. climate (oak, beech and Scots pine) using EMEP O3 and meteorological data for 1997. These runs would indicate that the current “generic” parameterisation used in IAM modelling to set O3 emission reduction targets across Europe may not protect forest species in the U.K. since O 3 fluxes to both Scots pine and oak are higher than those to beech due to differences in gmax and phenology respectively. This work package has also seen the publication of two peer reviewed papers providing details of issues associated with O 3 flux modelling of forest trees in relation to soil moisture and application of the SMD method for forest trees in Bavaria. We are still working towards the publication of the SMD development and evaluation paper which has been put on hold until the SMD module is incorporated into the EMEP model.

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


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)M12 Establishment of the dissemination routes for the DO3SE model within the UNECE (Sep 2008)

A working tested version of the DO3SE interface now exists that includes all calculations carried out by the current version of the DO 3SE model, incorporating fSWP modifications detailed in WP2. Figure 7 provides 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. We have been in contact with colleagues in Germany, Finland and Sweden who are interested in using this interfaced model. During this coming year we will finalise the user manual that will be provided with the DO3SE interface model. We have agreed with ICP vegetation that the model can be made available from the ICP Vegetation website (http://icpvegetation.ceh.ac.uk/) which is maintained under a separate Defra contract (AQ0810). The user interface will be made available for download before the end of this year as agreed in the project milestones.

Figure 7. Overview of the DO3SE model structure used by the DO3SE user interface.

Work Package 4: Development of detoxification module for DO3SE model

SID 4 (Rev. 3/06) Page 16 of 24

http://icpvegetation.ceh.ac.uk/


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)M15 Assessment of implications of a variable flux threshold on modelled ozone flux and wheat yield predictions

(March 2009).

Vegetation responses to O3 are governed by the effective flux (EF) of the pollutant (and/or its reactive products) reaching the mesophyll plasmalemma (the key biological target for O3). As a consequence, at any given point in time (t), the effective flux into plant foliage represents the balance between O3 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 O3.

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); the existing mechanistic model SODA (Plőchl et al., 2000) constitutes the platform for this work package. Previously reported work used the SODA model to simulate O3 detoxification in the leaf apoplast via reaction with apoplast ASC (ASCapo) and diffusional limitations on entry into the leaf (Table 11). This was achieved through reformulation of SODA to facilitate the computation of mesophyll resistance, Rmes (s m-1) based on simulated diffusion-reaction kinetics pursuant upon uptake of O3 into the leaf interior. Rmes values were estimated for 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 time-consuming and labour-intensive measurement campaigns yielded a database comprising ≈ 130 computable points for which relevant microclimatic data were measured in parallel.

Table 11. SODA-computed Rmes values over a range of measured apoplast ASC concentrations

Parameter Range of values

Units

Apoplast 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

Incorporation of Rmes module into DO3SE model to assess temporal variation of detoxification rates (M10)A boundary line approach similar to that adopted by Emberson et al (2000) for the derivation of the DO3SE model was used to derive an algorithm facilitating the dynamic modelling of changes in fmes (the fractional reciprocal of Rmes) driven by key environmental and intrinsic factors. Boundary line equations for the influence of time of day (fmes_hr), O3 concentrations (fmes_O3), irradiance (fmes_PAR), temperature (fmes_temp) and VPD (fmes_VPD) on Gmes were estimated as shown in Figure 8. No data were available to compute the influence of phenology or soil moisture deficit on fmes.

The following formulations were used to estimate the variation in Gmes as a relative fraction between 0.5 and 1.0 (see also Figure 8):

fmes_hr = 0.0000021943*hr5 - 0.0001360058*hr4 + 0.0029003988*hr3 - 0.0238*hr2 + 0.0548*hr + 0.7775where hr is the hour of day. fmes_O3 = Max[0.5, (-0.000000008*O3

4 - 0.000003* O3 3+ 0.00008* O3

2- 0.0004* O3 + 0.9998)]

fmes_PAR = 0.0000001*PAR2 - 0.0002*PAR + 0.7049where PAR is in mol m-2 s-1.

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

SID 4 (Rev. 3/06) Page 17 of 24

fmes_temp = when Tmin < T < Tmax

fmes_temp = fmin when Tmin>T>Tmaxwhere T is in oCelcius, fmin = 0.5, Tmin =10, Topt = 22, T_max = 42 and bt = (Tmax-Topt) / (Topt-Tmin)

fmes_VPD =Max (fmin, Min(1, ((1-fmin)*((VPDmin-VPD)/(VPDmin-VPDmax))+fmin)))where VPD is in kPa, fmin = 0.5, VPDmax = 2.2 and VPDmin = 4.2

Figure 8. Results of boundary line analysis depicting the relationship between mesophyll resistance (given as a fraction between 0.5 and 1.0) and time of day and other environmental variables including O3 concentration (fmes_O3), irradiance (fmes_PAR), temperature (fmes_temp) and vapour pressure deficit (fmes_VPD).

fmes_hr

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

0 5 10 15 20

Time, hour

fmes

fmes_O3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

0 20 40 60 80 100

Ozone, ppb

fmes

fmes_PAR

0

0.2

0.4

0.6

0.8

1

0 500 1000 1500 2000 2500

PAR, mol m-2s-1

fmes

fmes_temp

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

0 10 20 30 40 50

ToC

fmes

fmes_VPD

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

0 1 2 3 4 5 6

VPD, kPa

fmes

These relationships are combined in the following formula to provide an estimate of absolute mesophyll conductance (gmes) by equating the maximum and minimum gmes fractional values (which vary between 1.0 and 0.5) with a maximum gmes (gmes_max) of 130 mmol O3 m-2 s-1 and minimum gmes (gmes_min) of 70 mmol O3 m-2 s-1 respectively.

gmes = (gmes_max - gmes_min) *(fmes_hr * fmes_O3 * fmes_PAR * fmes_temp * fmes_VPD) + gmes_min)

gmes is then converted into corresponding values for its reciprocal mesophyll resistance (rmes) in units of s/m by division by 41, 000. Figure 9 shows the variation in rmes relevant environmental variables for an “example” day and the resulting variation in r mes bearing in mind the SODA modelling suggested rmes could vary between ~300 and 600 s/m.

Figure 9. Diurnal variation in environmental parameters for an “example” day and subsequent computed variation in r mes following the methods developed to incorporate the influence of environmental variables and time of day on rmes.

Variation in environmental variables over a dirunal period

0

500

1000

1500

2000

2500

0 5 10 15 20

Hour of day

PAR

, m

ol m

-2 s

-1

0

10

20

30

40

50

60

70

Tem

p (o C

), VP

D*1

0 (k

Pa),

O3 (

ppb)

Corresponding variation in Rmes over a dirunal period

400410420430440450460470480490500

0 5 10 15 20

Hour of day

Rm

es, s

/m

This variation in rmes can be introduced into leaf level flux modelling calculations to estimate the effective flux (EF). This has been achieved according to the following formulations:

SID 4 (Rev. 3/06) Page 18 of 24

Fst =

Where gsto(mes) = and rc(mes) =

Following these formulations, it is possible to assess the effect of a variable rmes on both total Fst and EF based on assumptions for gsto

(here assumed to be equal to gmax, i.e. 450 mmol O3 m-2 s-1 equivalent to an rsto of 91 s/m), an rb of 50 s/m and a constant O3

concentration of 60 ppb. Figure 10 shows the effect on Fst and EF with variation in rmes between 0 and 800 s/m. The results from this figure should be considered in light of the suggested variation in rmes between 300 and 600 according to the SODA model simulations (indicated by vertical dashed lines). This modelling would suggest that the SODA computed r mes values could limit Fst by more than 10 nmol m-2 s-1; translating this into EFs, the modelling suggests that the detoxification thresholds would vary between zero and ~14 nmol O3 m-2 s-1 as rmes increases from 0 to 600 s/m.

Figure 10. The effect on stomatal O3 flux (Fst) and effective flux (EF) with variation in rmes between 0 and 800 s/m based on assumptions for gsto (here assumed to be equal to gmax, i.e. 450 mmol O3 m-2 s-1 equivalent to an rsto of 91 s/m), an rb of 50 s/m and a constant O3 concentration of 60 ppb.

Variation in wheat Fst with RmesFst at gmax, rb = 50 s/m, O3 at 60 ppb

0

2

4

6

8

10

12

14

16

18

0 200 400 600 800Rmes (s/m)

Fst (

nmol

m-2

s-1

)

Variation in effective Fst (EF) for wheat according to constant (AFst6) and Rmes (AFstRmes) detoxification at

gmax, rb = 50 s/m, O3 at 60 ppb

0

2

4

6

8

10

12

14

16

0 200 400 600 800Rmes (s/m)

EF (n

mol

m-2

s-1

)

AFst6AFstRmes

Assessment of implications of a variable flux threshold on modelled ozone flux and wheat yield predictions. To estimate the effect of the rmes formulation on the detoxification capability (D), model runs for a location in the UK have been performed both with (Fstrmes) and without (Fst0) incorporation of the rmes formulations whilst assuming a zero threshold (Y=0). The difference in Fst between these runs should indicate the value of D according to the rmes formulations i.e. D = Fst0 - Fstrmes.Figure 11 shows mean diurnal results for the month of June (assumed to be during the peak of the wheat growth period) for a particular location in the UK (close to Edinburgh, Scotland). The rmes varies between ~460 and 500 s/m showing a decrease in the late afternoon which would indicate this as the period when the detoxification capabilities would be lowest. Conversely, Fst (the total stomatal ozone flux) is highest during the period from noon until late afternoon, during which time the detoxification capability conferred by rmes can only cope with a small fraction (less than 40%) of the total stomatal ozone flux.

Figure 11 Mean diurnal variation in mesophyll resistance (rmes), total stomatal O3 flux (Fst) and the effective stomatal O3 flux assuming a detoxification capability conferred by rmes (Fst_mes) for the month of June for a location close to Edinburgh in Scotland.

Mean diurnal varaition in Rmes, Fst and Fst_mes for June

450

460

470

480

490

500

510

0 5 10 15 20 25

Tim of day (hr)

Rm

es (s

/m)

0

1

2

3

4

Fst;

Fst_

mes

, nm

ol m

-2

s-1

Rmes Fst_mes Fst

SID 4 (Rev. 3/06) Page 19 of 24

The influence of rmes on seasonal stomatal detoxification can be seen in Figure 12 which shows the variation in the “detoxified” O 3 flux (Fst) over the course of the year. By comparison, the current method assumes a “detoxification” rate of 6 nmol m -2 s-1; the modelling in Figure 12 shows the high variability in detoxification and suggests values can reach as high as ~ 7 nmol m -2 s-1. The influence on yield can be estimated from the accumulated fluxes; here we compare yield losses based on an AFstY where Y=6 (and as such a constant detoxification threshold of 6 nmol m-2 s-1 is assumed) with those based on an AFst_rmes where Fst is limited by a variable rmes. The resulting yield losses are 2 and 13% for AFst6 and AFstrmes respectively. The substantially higher yield losses obtained using the AFstrmes are likely due to using a flux-response relationship calibrated for a constant detoxification threshold.

Figure 12. Variation in the Fst detoxification over the course of the growing season according to rmes modelling.

Utilising data from an extensive open-top chamber study conducted in the Newcastle-based open-top chambers in 2006 which examined the response of a range of commercial winter wheat varieties to O 3, and which facilitated the derivation of flux-response measurements via a season-long measurement campaign, it has also been possible to compare flux-response relationship derivations using no flux threshold (AFst0), with 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 a 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 Figure 13).

Figure 13. Comparison of empirically-derived flux-response relationships versus the use of a derived detoxification algorithm. Flux-response data derived from OTC experiments conducted at Newcastle University on 5 ‘ozone sensitive’ cultivars of winter wheat

ReferencesAlonso, R., Elvira, S., Sanz, M.J., Gerosa, G., Emberson, L.D., Bermejo, V., Gimeno, B.S. (2008). Sensitivity analysis of a parameterization of the

stomatal component of the DO3SE model for Quercus ilex to estimate ozone fluxes. Environmental Pollution 155, 473-480. Asensio, D., Peñuelas, Ogaya, R., Llusià (2007). Seasonal soil and leaf CO2 exchange rates in a Mediterranean holm oak forest and their

responses to drought conditions. Atmospheric Environment 41, 2447-2455.Ashmore, M.R., Büker, P., Emberson., L.D., Terry, A.C., Toet, S. (2007). Modelling stomatal ozone flux and deposition to grassland communities

across Europe. Env. Poll. 146: 659-670Bass, D.J. (2006). The impact of tropospheric ozone on semi-natural vegetation. PhD Thesis, Newcastle University, UK. Blaser, R., Hammes Jr., R.C., Fontenot, J.P., Bryant, H.T., Polan, C.E., Wolf, D.D., McClaugherty, F.S., Klein, R.G., Moore, J.S. (1986). Forage–

animal management systems. Virginia Polytechnic Institute, Bulletin 86-7. Baumgarten, M., Werner, H., Häberle, K.H., Emberson, L.D., Fabian, P., Matyssek, R. (2000). Seasonal ozone response of mature beech trees

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(Fagus sylvatica) at high altitude in the Bavarian forest (Germany) in comparison with young beech grown in the field and in phytotrons. Environmental Pollution 109, 431-442.

Baumgarten, M., Huber, C., Büker, P., Emberson, L.D., Dietrich, H.-P., Nunn, A.J., Heerdt, C., Beudert, B., Matyssek, R. (2009). Are Bavarian Forests (southern Germany) at risk from ground-level ozone? Assessment using exposure and flux based ozone indices . Environmental Pollution, in press.

Coyle, M. (2006). The Gaseous Exchange of Ozone at Terrestrial Surfaces: Non-stomatal Deposition to Grassland. PhD thesis University of Edinburgh.

De Miguel, A., Bilbao J. (1999). Ozone dry deposition and resistances onto green grassland in summer in Central Spain. Journal of Atmospheric Chemistry 34, 321-338.

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.

Ferris, R., Nijs, I., Behaeghe, T. & I. Impens (1996): Elevated CO2 and temperature have different effects on leaf anatomy of perennial ryegrass in spring and summer. Annals of Botany 78, 489-497.

Gay, A. P. (1986): Variation in selection for leaf water conductance in relation to growth and stomatal dimensions in Lolium perenne L. Annals of Botany 57, 361-369.

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

Hayes, F. (2007). Ozone impacts on semi-natural upland communities. PhD thesis, University of York.Hill, M.O., Mountford, J.O., Roy, D.B., Bunce, R.G.H. (1999). ECOFACT 2a Technical Annex – Ellenberg’s indicator values for British plants.

ECOFACT report, ITE Huntingdon, U.K. Jaeggi, M., Ammann, C., Neftel A., Fuhrer, J. (2006). Environmental control of profiles of ozone concentration in a grassland canopy.

Atmospheric Environment 40 (28), 5496–5507. Jones, M.B., Leafe, E.L., Stiles, W. (1980). Water stress in field-grown perennial ryegrass. II. Its effect on leaf water status, stomatal resistance

and leaf morphology. Annales of Applied Biology 96, 103-110. Jones, M. B., Jongen, M. & T. Doyle (1996): Effects of elevated carbon dioxide concentrations on agricultural grassland production. Agricultural

and Forest Meteorology 79, 243-252.Neilson, R.P., 1995. A model for predicting continental-scale vegetation distribution and water balance. Ecological Applications 5, 362-385. Nijs, I., Impens, I. & T. Behaeghe (1989): Leaf and canopy responses of Lolium perenne to long-term elevated atmospheric carbon-dioxide

concentration. Planta 177, 312-320.Nussbaum, S. and Fuhrer, J. (2000). Difference in ozone uptake in grassland species between open-top chambers and ambient air.

Environmental Pollution 109, 463-471.Nussbaum, S., Geissmann, M., Fuhrer, J. (1995). Ozone exposure-response relationships for mixtures of perennial ryegrass and white clover

depend on ozone exposure patterns. Atmospheric Environment 29, 989–995. Pederson, J. R., Massman, W. J., Mahrt, L., Delany A., Oncley, S., Hartog den, G., Neumann, H. H., Mickle, R. E., Shaw, R. H., Paw K. T.,

Grantz, D. A., Macpherson, J. I., Desjardins, R., Schuepp, P. H., Pearson, R., and Arcado, T. E. ( 1995). California ozone experiment: methods, results and opportunities. Atmospheric Environment 29, 3115–3132.

Pio, C.A., Feliciano, M.S., Vermeulen, A.T., Sousa, E.C. (2000). Seasonal variability of ozone dry deposition under southern European climate conditions, in Portugal. Atmospheric Environment 34, 195-205.

Plőchl , M., Lyons, T., Ollerenshaw, J.H., Barnes J.D. (2000) Simulating ozone detoxification in the leaf apoplast. Planta 210, 454-467Rämö, K., Karnerva, T., Nikula, S., Ojanperä, K., Manninen, S. (2006). Influences of elevated ozone and carbon dioxide in growth responses of

lowland hay meadow mesocosms. Environmental Pollution 144 (1), 101–111. Rämö, K., Karnerva, T., Nikula, S., Ojanperä, K., Manninen, S. (2007). Growth onset, senescence, and reproductive development of meadow

species in mesocosms exposed to elevated O3 and CO2. Environmental Pollution 145, 850-860. Sheehy, J. E., Green, R. M. & M. J. Robson (1975): The influence of water stress on the photosynthesis of a simulated sward of perennial ryegrass. Annales Botanicae 39, 387-401.

Sorimachi, A., Sakamoto, K., Ishihara, H., Fukuyama, T., Utiyama, M., Liu, H., Wang, W., Tang, D., Dong, X., Quan, H. (2003). Measurements of sulphur dioxide and ozone dry deposition over short vegetation in northern China – a preliminary study. Atmospheric Environment 37, 3157–3166.

Stirling, C.M., Davey, P.A., Williams, T.G., Long, S.P. (1997). Acclimatisation of photosynthesis to elevated CO2 and temperature in five British native species of contrasting functional type. Global Change Biology 3, 237-246.

Thorup-Kristensen, K. (2004). Ny lovende efteragrøde. Økologisk Jordbrug 24, 10 (in Danish). UNECE (2004). Revised manual on methodologies and criteria for mapping critical levels/loads and geographical areas where they are

exceeded, Chapter 3: Mapping Critical Levels for Vegetation. pps. 53, Umweltbundesamt, Berlin, Germany. http://www.oekodata.com/icpmapping/html/manual.html

Tuovinen, J. P., Emberson, L., Simpson, D. (2009). Modelling ozone fluxes to forests for risk assessment: status and prospects. Annals of Forest Sciences, in press.

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

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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

M 6 Submission of peer reviewed paper describing the DO3SE SMD module

March 2008 Yes Yes

M 11 Incorporation of the DO3SE phenology and SMD models within the DO3SE version of the EMEP photo-oxidant model

October 2008 Partially Partially

M 12 Establishment of the dissemination routes for the DO3SE model within the UNECE

September 2008 Yes Yes

M 13 Flux-response relationships developed and applied for grassland communities

March 2009 Yes Yes

M 14 Evaluation of grassland flux model against appropriate primary and secondary dataset

March 2009 Yes Yes

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

March 2008 Yes Yes

M 15 Assessment of implications of a variable flux threshold on modelled ozone flux and

March 2009 Yes Yes

(b) Do the remaining milestones look realistic?....................................................................YES NO

If you have answered NO, please provide an explanation.

SID 4 (Rev. 3/06) Page 22 of 24

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

reporting period.Meetings

Visit to Tokyo University, Japan as a scientific advisor on Chinese FACE project: Dr Lisa Emberson visited with colleagues in Tokyo and Koriyama, Japan in March 2009 to provide scientific advice on the development and application of O3 flux models for rice and wheat which are being established based on data from the Chinese FACE fumigation project running in Jiangdu, 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.

Royal Society: Dr Lisa Emberson gave a talk on “Assessing changes to ozone flux on vegetation” at the Royal Society as evidence to the Societies investigation into “Ground level ozone in the 21st Century in May 2008. She also attended a meeting in Delhi, India in November 2008 organised by the Royal Society on the future of food production and gave a talk on “O3 as a threat to food security”.

ICP Vegetation: Dr. Patrick Büker gave a talk on the “Development and provisional application of a multilayer grassland flux model” at the annual Task Force Meeting of the ICP Vegetation in February 2009 in Braunschweig. The further developmental stages of the grassland flux model were discussed in detail on that meeting.

US Air Pollution Workshop: Dr. Tim Morrissey gave a talk on “Soil moisture deficit as a key driver in stomatal ozone flux” and Dr. Lisa Emberson gave a talk on “Ground level O3 as a threat to continued food security” both at the US Air Pollution Workshop in Raleigh, South Carolina in April’08.

CAPER Annual Meeting 2009: Dr. Patrick Büker gave a talk on the “Development and application of a multilayer grassland flux model” at the CAPER meeting in April 2009 in Manchester. Dr. Tim Morrissey also gave a talk at that meeting on “Modelling soil moisture to determine ozone flux to European forest trees”.

Reports:

Atmospheric Brown Cloud report. Emberson, L.D. & Agrawal M. (2008). The impacts of the ground level ozone component of ABC on Agriculture. UNEP Publication, http://www.rrcap.unep.org/abc/impact/index.cfmThis 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.

UNECE Mapping Manual Revision. A revision of the Mapping Manual with the inclusion of an annexe describing the establishment of “real” species parameterisations for forest trees (based on discussion within the UNECE Forest sub-group chaired by Dr. Lisa Emberson) was completed in early 2008 and accepted at the ICP Forests meeting in Cyprus during May 2008.

Peer-reviewed publications:

Juha-Pekka Tuovinen, Lisa Emberson, David Simpson (2009). Modelling ozone fluxes to forests for risk assessment: status and prospects. Annals of Forest Sciences, in press.

Manuela Baumgarten, Christian Huber, Patrick Büker, Lisa Emberson, Hans-Peter Dietrich, Angela J. Nunn, Christian Heerdt, Burkhard Beudert, Rainer Matyssek (2009). Are Bavarian Forests (southern Germany) at risk from ground-level ozone? Assessment using exposure and flux based ozone indices. Environmental Pollution, in press.

Rocío Alonso, Susana Elvira, María J. Sanz, Giacomo Gerosa, Lisa D. Emberson, Victoria Bermejo, Benjamín S. Gimeno (2008). Sensitivity analysis of a parameterization of the stomatal component of the DO3SE model for Quercus ilex to estimate ozone fluxes. Environmental Pollution 155, 473-480.

Van Dingenen., R. Dentener, F.J., Raes, F., Krol, M.C., Emberson, L.D., Cofala, J. (2009) The global impact of ozone on agricultural crop yields under current and future air quality legislation. Atmos. Env. 43: 604-618

Ellingsen, K., Gauss, M., van Dingenen, R., Dentener, F.J., Emberson, L., Fiore, A.M., Schultz, M.G., Stevenson, D.S., Ashmore, M.R., Atherton, C.S., Bergmann, D.J., Bey, I., Butler, T., Drevet, J., Eskes, H., Hauglustaine, D.A., Isaksen, I.S., Horowitz, L.W., Krol, M., Lamarque, J.F., Lawrence, M.G., van Noije, T., Pyle, J., Rast, S., Rodriguez, J., Savage, N., Strahan, S., Sudo, K., Szopa, S., Wild, O. (2008) Global ozone and air quality: a multi-model assessment of risks to human health and crops Atmospheric Chemistry and Physics Discussions, Vol. 8, pp 2163-2223, 6-2-2008.

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

SID 4 (Rev. 3/06) Page 23 of 24

http://www.sref.org/site/?op=author&id=43883


































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).

Development of soil water stress mediated DO3SE modelled flux-response relationships for soybean could be established by working with an OTC fumigation dataset held by Italian colleagues.

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

Name Lisa Emberson Date 9th April 2009

Position held Senior Research Fellow

SID 4 (Rev. 3/06) Page 24 of 24

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