Modelling annual pasture dynamics: Application to stomatal ozone deposition

Atmospheric Environment 44 (2010) 2507e2517

Contents lists available at ScienceDirect

Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Modelling annual pasture dynamics: Application to stomatal ozone deposition Ignacio González-Fernández*, Victoria Bermejo, Susana Elvira, Javier Sanz, Benjamín S. Gimeno, Rocío Alonso Ecotoxicology of Air Pollution, CIEMAT, Avda. Complutense 22 (Ed. 70), 28040 Madrid, Spain

a r t i c l e i n f o

a b s t r a c t

Article history: Received 30 November 2009 Received in revised form 15 April 2010 Accepted 19 April 2010

Modelling ozone (O3) deposition for impact risk assessment is still poorly developed for herbaceous vegetation, particularly for Mediterranean annual pastures. High inter-annual climatic variability in the Mediterranean area makes it difficult to develop models characterizing gas exchange behaviour and air pollutant absorption suitable for risk assessment. This paper presents a new model to estimate stomatal conductance (gs) of Trifolium subterraneum, a characteristic species of dehesa pastures. The MEDPAS (MEDiterranean PAStures) model couples 3 modules estimating soil water content (SWC), vegetation growth and gs. The gs module is a reparameterized version of the stomatal component of the EMEP DO3SE O3 deposition model. The MEDPAS model was applied to two contrasting years representing typical dry and humid springs respectively and with different O3 exposures. The MEDPAS model reproduced realistically the gs seasonal and inter-annual variations observed in the field. SWC was identified as the major driver of differences across years. Despite the higher O3 exposure in the dry year, meteorological conditions favoured 2.1 times higher gs and 56 day longer growing season in the humid year compared to the dry year. This resulted in higher ozone fluxes absorbed by T. subterraneum in the humid year. High inter-family variability was found in gas exchange rates, therefore limiting the relevance of single species O3 deposition flux modelling for dehesa pastures. Stomatal conductance dynamics at the canopy level need to be considered for more accurate O3 flux modelling for present and future climate scenarios in the Mediterranean area. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Dehesa Soil water content Pasture growth Stomatal conductance Stomatal ozone flux

1. Introduction Over recent decades, air pollution abatement policies have resulted in a reduction of peak ozone (O3) concentrations across Western Europe (Jonson et al., 2006). However, background levels of O3 pollution have increased globally and could continue rising in the Northern hemisphere if current emission trends persist (Dentener et al., 2006). Ozone is a pollutant of major concern due to its wide distribution and its negative effects on human health, materials and vegetation. Current ambient levels in Europe and North America are high enough to cause effects on growth, reproductive development or competitive ability on a number of herbaceous species (Davison and Barnes, 1998; Gimeno et al., 2004a,b; Bassin et al., 2007). A range of O3-induced changes in composition, diversity and productivity of natural and semi-natural

* Corresponding author. Tel.: þ34 91 346 6556; fax: þ34 91 346 6121. E-mail addresses: [email protected] (I. González-Fernández), victoria. [email protected] (V. Bermejo), [email protected] (S. Elvira), javier.sanz@ ciemat.es (J. Sanz), [email protected] (B.S. Gimeno), rocio.alonso@ ciemat.es (R. Alonso). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.04.033

plant communities have been identified (Davison and Barnes, 1998; Bassin et al., 2007). In Europe, O3 critical levels have been proposed for the protection of crops, forests and semi-natural vegetation under the framework of the Convention on Long-Range Transboundary Air Pollution (CLRTAP; UNECE, 2009). The semi-natural vegetation term includes an enormous amount of very diverse plant community types with complex interactions among species and environmental factors, increasing the difficulties for defining O3 critical levels (Bassin et al., 2007). Ozone critical levels and target values established for the protection of vegetation in the European legislation (Directive 2008/50/EC) are based on the cumulative O3 atmospheric concentrations over 40 ppb (nmol mol
1) during daylight hours (AOT40). However, there is substantial experimental evidence that plant responses are more closely related to the O3 dose absorbed through the stomata than to O3 exposure. As a consequence, considerable attention has focused on the development of fluxbased critical levels and models needed to estimate stomatal O3 flux. Particular efforts have been made on the parameterization of the stomatal component of the DO3SE model (Emberson et al., 2000; Simpson et al., 2007), incorporated into the EMEP (European Monitoring and Evaluation Programme) model, used to estimate O3

2508

I. González-Fernández et al. / Atmospheric Environment 44 (2010) 2507e2517

deposition across Europe within the CLRTAP framework (Ashmore et al., 2007). Dehesas are savannah-like cleared woodlands representing one of the most characteristic landscapes of South-Western Spain and Portugal. Their productivity covers agricultural, timber and extensive livestock exploitation. These pastures present a remarkably high plant species richness and they are included among the protected areas in the Nature 2000 Network (EU Directive 92/43/EEC). Dehesa pastures are dominated by annual species, whose floristic composition and dynamics vary largely in space and time depending on environmental factors (topography, meteorological and edaphic conditions), grazing management and tree coverage (Ortega et al., 1997; Peco et al., 1998). Among annual Mediterranean species, legumes are important components in the nitrogen cycling and represent a valuable nutritive source for livestock in dehesa pastures. Although the Mediterranean region is frequently exposed to ambient O3 levels reported as phytotoxic for annual species (Gimeno et al., 2004a), the number of studies focusing on O3 effects on dehesa pastures is still scarce. Less than 20% of the species characteristic of these communities have been screened for O3 sensitivity in terms of visible injury or biomass loss (Bermejo et al., 2003; Gimeno et al., 2004a), and just a few have been used to study O3 impacts on other traits such as reproductive ability or forage quality (Gimeno et al., 2004b; Sanz et al., 2005, 2007). In particular, little information is available about the ecophysiology and gas exchange behaviour of dehesa pastures for O3 risk assessment based on stomatal fluxes. Previous field measurements of gas exchange rates have revealed a great inter-specific variation in key parameters of the DO3SE stomatal component in a dehesa pasture in central Spain (Alonso et al., 2007). Furthermore, plant dynamics vary greatly from year to year in the Mediterranean area, influenced by the characteristic high inter-annual variability in meteorological conditions (Peñuelas et al., 2004; Montaldo et al., 2008). Modelling approaches to calculate O3 fluxes for risk assessment need to cover the variability associated with annual pasture dynamics. In the present paper, field-based gas exchange measurements in naturally growing dehesa pastures are analyzed with the following objectives: (1) characterize the factors controlling stomatal conductance, plant growth, leaf area index (LAI) dynamics and length of the growing season of dehesa pastures; (2) explore the variability in the maximum stomatal conductance value at leaf level in annual Mediterranean species, a key parameter of the DO3SE model; and (3) derive a model to describe annual pasture dynamics

using a simple, low input approach by means of coupling the DO3SE with two additional modules describing soil water balance and vegetation growth and to use the derived model, hereafter referred as MEDPAS model, for calculating O3 stomatal fluxes for risk assessment in annual dehesa pastures. 2. Materials and methods 2.1. Study site and environmental conditions The study site was located in a dehesa ecosystem in Miraflores de la Sierra (40 48.40 N, 3 48.40 W; 1059 m.a.s.l) in central Spain. This dehesa is a Quercus ilex subsp. ballota (Desf.) open woodland devoted to extensive cattle feeding. The climate is continental Mediterranean, characterized by relatively cold and humid winters followed by warm dry summers. The characteristic high interannual weather variability makes unpredictable the start and length of the summer drought. Mean annual rainfall (1961e1990) was 720 mm with a very irregular distribution throughout the year. Hourly O3 concentration and meteorological variables were collected from the closest air quality monitoring station of the Air Quality Control Network, operated by the Comunidad Autónoma de Madrid, located at Colmenar Viejo, a suburban area 17 km from the study site, both places having a similar slope orientation and elevation. The 2004/05 growing season (hereafter referred as dry year) was dryer, warmer and sunnier and with higher O3 concentrations than the 2006/07 season (humid year) (Table 1). 2.2. Gas exchange measurements Stomatal conductance to water vapour at leaf level (gs) was measured using a LICOR-6400 infrared gas analysis system (LiCor Inc., Lincoln, NE, USA). Fully expanded leaves were selected for gas exchange measurements performed under prevailing environmental conditions. Photosynthetic active radiation (PAR), relative humidity (RH) and temperature (T) were recorded simultaneously with gs. Field work started when plants begun to grow actively after the winter and continued until leaf senescence, yielding a database of 336 and 435 individual leaf measurements performed during 6 and 10 days spanning 4 and 10 weeks during the dry and humid years respectively, representing the most active growing period of this community in each year. The gas exchange field measurements of the dry year were described in Alonso et al. (2007).

Table 1 Meteorological conditions during the dry (2004/2005) and the humid (2006/2007) hydrological year. Values represent daylight averages  SD of photosynthetic active radiation (PAR) and daily averages  SD of temperature (T) and vapour pressure deficit (VPD). P, accumulated precipitation; AOT40, accumulated ozone concentrations over 40 ppb during daylight hours. Year

Season

PAR (mmol m
2 s
1)

T ( C)

VPD (kPa)

P (mm)

Dry

Autumn (October 1steDecember 21st) Winter (December 22ndeMarch 21st) Spring (March 22ndeJune 21st) Summer June 22ndeSeptember 21st)

553  291

10.1  4.8

0.46  0.5

164

1321

652  339

5.3  4.8

0.44  0.3

46

2800

901  506

16.2  6.7

1.1  0.9

80

14 791

957  482

23.2  5.3

2.1  1.0

18

18 000

524  308

11.0  5.0

0.4  0.4

353

505

583  327

6.5  3.6

0.32  0.3

22

732

816  488

13  5.4

0.7  0.5

246

5617

950  488

21.5  4.7

1.8  0.9

14

4298

Humid

Autumn (October 1steDecember 21st) Winter (December 22ndeMarch 21st) Spring (March 22ndeJune 21st) Summer (June 22ndeSeptember 21st)

AOT40 (ppb.h)

I. González-Fernández et al. / Atmospheric Environment 44 (2010) 2507e2517

In the humid year, the field work spanned from April 20th to June 27th. Gas exchange measurements were performed between 9 and 18 h, while the pasture was receiving direct sunlight. Cuvette temperatures were in the range 17e35  C, while vapour pressure deficit (VPD) and PAR ranged 0.5e4.4 kPa and 100e2300 mmol m
2 s
1 respectively. Between 1 and 13 plants per species were measured each day, although not all the species were measured every sampling day. In total, gs was determined for 6 different families (Cistaceae, Compositae, Geraniaceae, Gramineae, Leguminosae and Plantaginaceae) and 15 genera. Only Bromus hordeaceus and Trifolium subterraneum were extensively measured both years. Stomatal conductance values are expressed on a projected leaf area (PLA) basis. Leaf area was determined using Win FOLIA software (Régent Instruments Inc., Canada).

pollutants absorption of Mediterranean annual pastures. The MEDPAS model couples three modules estimating SWC, growth and gs respectively in order to calculate the O3 deposition flux (Fig. 1). SWC is calculated by means of a soil water balance. Modelled SWC is used as an input for the growth module, which predicts vegetation growth and biomass. Biomass values are used to compute leaf area index (LAI) and canopy height (h), in turn, inputs of the SWC module. Also, the growth module serves to establish the length of the growing season. SWC and biomass are computed on a daily basis throughout the hydrological year from October 1st to September 30th. Calculated SWC is used to model hourly gs values using the multiplicative approach included in the EMEP DO3SE deposition module. Finally stomatal O3 fluxes are calculated for the whole growing season using the methodology described in the CLRTAP Mapping Manual (UNECE, 2009). T. subterraneum was used as a representative dehesa annual species for estimating stomatal O3 fluxes. This species was selected due to its reported O3 sensitivity (Gimeno et al., 2004a,b; Sanz et al., 2005). In the humid year, legumes represented up to 35% of the total dry weight biomass of the community.

2.3. Soil moisture and plant biomass Soil water content (SWC) was measured with quasi-timedomain reflectometry technique (TDR) (TRIMEGM, IMKO GmbH, Germany) during gas exchange determination. Four to seven measurements at 0e15 cm depth were performed on 10 sampling days covering the most active growing period, at five independent plots representing the study area. Plant biomass was determined in the humid year throughout the spring growing season. Between three and five disks of 0.05 m2 were placed randomly over the pasture each sampling day and all the individuals within the area were collected. Plant material was dried in the oven at 65  C until constant weight, classified and weighted to determine dry biomass per family.

2.4.1. Soil water content module The SWC module calculates daily SWC (q) at two soil layers 0e15 and 15e30 cm deep, q0e15 cm and q15e30 cm respectively. The SWC module consists of a two-layered bucket model following Nouvellon et al. (2000). Infiltrated rainfall is distributed down the soil profile filling the two layers successively from the top to the bottom. A soil water balance is computed for each soil layer independently (see Appendix 1) following the scheme described in Allen et al. (1998). The two layers were selected to account for the soil depth affected by evapotranspiration (ETc), set down to 15 cm as recommended by Allen et al. (1998), and the soil layer where only transpiration losses (Tc) occur, which is equivalent to the lower end of

2.4. Model description A new MEDPAS (MEDiterranean PAStures) model has been developed to describe growth, gas exchange behaviour and air

SWC MODULE

15 30 cm

ET0

GROWTH MODULE B (i)

(i 1)

(

0 15 cm

,

15 30 cm

) (i 1)

Input variables: (hourly and daily) SR, PAR, T, Tmax, Tmin, RH, RHmax, RHmin, VPD, P, u, pressure, [O3], latitude, elevation

O3 FLUX

HOURLY TIME SCALE SPECIES LEVEL

gs (i) MODULE

GROWING PERIOD

DAILY TIME SCALE COMMUNITY LEVEL

LAI g, h (i)

ETc

2509

EMEP DO3SE DEPOSITION MODULE

Fig. 1. MEDPAS model diagram. Soil water content (SWC) and pasture growth modules were used at a daily time scale, and the species-specific stomatal conductance (gs) module at an hourly time scale. Variables coupling the modules are depicted by arrows. Growth and gs modules compute values for day ‘i’ using the SWC at the end of day ‘i-1’. ET0, reference evapotranspiration; ETc, actual evapotranspiration; q15e30 cm, soil water content at 15e30 cm deep; LAIg, green leaf area index; h, canopy height; ðq0
15cm q15
30cm Þ average soil water content at 0e15 cm and 15e30 cm deep. SR, solar radiation; PAR, photosynthetic active radiation; T, temperature; Tmax, Tmin, daily maximum and minimum temperature; RH, relative humidy; RHmax, RHmin, daily maximum and minimum relative humidity; VPD, vapour pressure deficit; P, precipitation; u, wind speed; [O3], ozone concentration.

2510

I. González-Fernández et al. / Atmospheric Environment 44 (2010) 2507e2517

the rooting zone. The rooting depth was set at 30 cm for annual pasture communities (Moreno et al., 2005). A number of simplifications were made from the water balance presented in Allen et al. (1998). Irrigation (Ii) was disregarded since natural pastures are not irrigated. The water table was considered to be deep enough (more than 1 m) for not contributing through capillary rise to the water balance of the upper soil layers (Allen et al., 1998). Runoff in flat areas like the dehesa of this study was considered insignificant. Daily q values were computed from October 1st, start of the hydrological year, until September 30th of the following year. Initial q was set to represent a dry soil, where no further ETc occurs. 2.4.2. Pasture growth module The pasture growth module is a modified version of the vegetation dynamic model described by Montaldo et al. (2005, 2008). The growth module computes daily changes of biomass (B) based on rates of carbon gain (photosynthesis, Pg) and carbon loss (respiration and senescence) (Appendix 1). Two biomass components were simulated: green biomass (Bg) and standing dead aerial biomass (Bd). The sum of these two components constitutes the total biomass production (Bt). B was related to LAI and h through relationships taken from Montaldo et al. (2008) and Arora and Boer (2005) respectively (Appendix 1). Some of the parameters used in this module were selected to reduce the residuals between observed and modelled values (Appendix 1). Two algorithms specifically describing the phenology of annual species in dehesa pastures were included in the growth module. These algorithms estimate the start and end of the growing season. To determine the start of the growing period, a sub-model presented by Arora and Boer (2005) was used. There, leafing is established when the average carbon gain of a given LAI is positive over seven days (Appendix 1). For establishing the end of the growing season, a second algorithm was added simulating plant flowering and senescence. Flowering start is calculated based on a double threshold fulfilment of temperature sum (1880  C day) and photoperiod (13.5 h) according to Iannucci et al. (2008). The threshold values used in this study were based on field observations. Temperature sum values were accumulated from the start of the hydrological year (October 1st). A negative leaf carbon balance after flowering during 15 consecutive days triggered the start of the senescence process (Appendix 1). This specific lag was selected according to Montaldo et al. (2008). Senescence was reflected by an increase in the death rate of green leaves (da) and the litter production (ka) rate (Appendix 1). Two outputs were used to couple the growth module with the SWC and gs modules (Fig. 1): (1) daily values of LAIg and h were used for calculating ETc of the SWC module; (2) the length of the growing period, from seed emergence to plant senescence, was introduced in the DO3SE deposition module to adjust the period for accumulating O3 stomatal flux to the growing season length. 2.4.3. Stomatal conductance module Stomatal conductance was estimated using the multiplicative approach proposed by Jarvis (1976) and modified by Emberson et al. (2000) following the EMEP DO3SE deposition model (Appendix 1). Stomatal conductance values for T. subterraneum (n ¼ 159) were collected in 16 sampling days throughout 10 weeks representing the most active growing period of this species. In the stomatal conductance model, gmax was calculated as the average of selected values above the 90th percentile of the entire dataset using a minimum n ¼ 3. Values increasing the coefficient of variation of gmax by more than 20% were considered as outliers and were

excluded of gmax calculation. Functions flight, fphen, fT, fVPD and fSWC were defined using the boundary line technique (Schmidt et al., 2000; Gonzalez-Fernandez et al., 2008) adjusted to the 99th percentiles of the data cloud. The parameterization already published for T. subterraneum (Alonso et al., 2007), based only in measurements made during the dry spring (2005) and referred hereafter as the ‘dry model’, was compared with the MEDPAS model. The MEDPAS model includes a gs module reparameterized to fit both dry and humid year data and a new SWC function (fSWC) to describe the effects of soil moisture availability on gs. 2.5. Stomatal O3 flux calculation Accumulated stomatal O3 fluxes (AFst) at the leaf level were calculated following the Mapping Manual (UNECE, 2009) (see Appendix). The value for the difference in diffusivity between O3 and H2O vapour in air ðDO3 =H2 O ¼ 0:663Þ was taken from Massman (1998). Hourly O3 concentrations measured at 2 m height were corrected to yield concentrations at canopy height following the neutral stability profile method (UNECE, 2009) considering canopy height values modelled by the growth module and an average dry deposition velocity measured in a grassland in central Spain (De Miguel and Bilbao, 1999). Flux values for T. subterraneum were derived using the two gs parameterizations described above. Fluxes were accumulated for the whole growing season, defined by the phenology function. In the MEDPAS model the start and end of the growing season were obtained from the growth module as described in the previous section. 2.6. Statistical analysis Soil water content, Bt and gs modelled values in the dry and humid years were compared with field measurements (only humid year field data available for Bt). Simple regression analyses were used to quantify the relationship between measured and modelled data. The goodness of fit for each model was tested with the R2 coefficient and the root mean squared error (RMSE) while model bias was tested using the mean bias (MB) between observed and modelled values. Maximum gs values collected in the field per family and year were subjected to one-way ANOVA and Tukey HSD post-hoc analysis in order to explore the variability found in the field. Stomatal conductance values were transformed using the Box-Cox transformation when normality and homoscedasticity were not met. If transformed variables did not meet ANOVA assumptions, then ManneWhitney U-test was used instead. Differences in the weather conditions (T, PAR and VPD) at the time of the maximum gs measurements between years were compared using MANOVA. Statistical significance was considered for p-values <0.05. Regression analysis was used to fit the boundary lines when reparameterizations of the existing gs multiplicative model were needed to fit the data presented in this study. All statistical analyses were performed using Statistica 6.0 (StatSoft Inc., Tulsa, USA). 3. Results 3.1. Inter-specific and inter-annual variability of gs values Inter-family and inter-specific variability of gs was analyzed using the maximum gs recorded in the field. Maximum gs values showed a great inter-family variability ranging from 0.745 through to 1.77 mol H2O m
2 s
1 (Fig. 2a). Significant differences were detected (p < 0.001, df ¼ 38) with Geraniaceae showing the highest value and Cistaceae and Gramineae the lowest. Differences on gs

I. González-Fernández et al. / Atmospheric Environment 44 (2010) 2507e2517

2.0

Maximum gs (mol m-2 s-1)

1.8

a

2511

b

c

1.6 b

1.4

b

b

1.2 1.0 a a

0.8 0.6 0.4 0.2

eu

s

m

rd e ho B.

er ra n bt T.

su

ac

eu

ea in

ce

e

ae G ra m

ae is C

gi ta Pl

an

ta

ce

ae in

na

os

ta m gu Le

om C

G er an

ia

po

si

ce

ae

e

0.0

Fig. 2. (a) Inter-families variability found on maximum stomatal conductance (mol H2O m
2 s
1) measured in a dehesa pasture in a humid year. (b) Inter-annual variability found comparing maximum stomatal conductance of two species recorded in a dry (black bars) and a humid year (white bars): Bromus hordeaceus and Trifolium subterraneum. Bars represent the mean  SE.

among species of the same genus were explored comparing three Trifolium species: Trifolium glomeratum, Trifolium striatum and T. subterraneum, measured in the humid year. A significant variation (p < 0.001, df ¼ 11) of nearly 5-fold was detected between the highest maximum gs (T. subterraneum, 1.18 mol m
2 s
1) and the lowest value (T. glomeratum, 0.25 mol m
2 s
1) (data not shown). T. subterraneum, selected to calculate stomatal O3 deposition, showed a maximum gs value close to the mean value across plant families, 1.19 mol m
2 s
1 (Fig. 2a). The inter-annual variability of maximum gs values recorded in the field was studied comparing dry and humid year data for T. subterraneum and B. hordeaceus, representative species of legumes and grasses respectively (Fig. 2b). Both species showed significantly higher values in the humid compared to the drier year (p < 0.001, df ¼ 11 for T. subterraneum; p < 0.005, df ¼ 8 for B. hordeaceus). Lower maximum gs of T. subterraneum in the dry year could only be related with lower SWC, averaging 0.06 and 0.18 m3 m
3 in the dry and humid years respectively. Significant differences were found in VPD (p < 0.01, df ¼ 11) but values were not limiting gs in any year when the maximum gs was observed. The lower B. hordeaceus maximum gs recorded in the dry year could be related to the lower (p < 0.01, df ¼ 8) T recorded (averaging 16  C) compared to the humid year (averaging 22  C) and lower soil moisture, averaging 0.05 and 0.19 m3 m
3 in dry and humid years respectively. 3.2. Soil water content module The model simulated efficiently the evolution of soil moisture according to the prevailing meteorological conditions, showing an increase of SWC after the first autumn rains and a rapid soil drying at the end of the spring when the drought period started (Fig. 3a, b). Increased precipitation in the humid spring allowed a SWC above the wilting point for a longer period during that growing season compared with the dry year (Fig. 3b). The average of modelled values of SWC ðq0
15 cm ; q15
30 cm Þ was best related with TDR field observations. RMSE values pointed to similar differences between measured and modelled values and positive MB indicated a trend to underestimate measurements in both years by 34% on average (Table 2). The SWC module was able

to explain 82 and 77% of the variability of measured values in the dry (n ¼ 4) and humid (n ¼ 6) years respectively and a 66% over both years. 3.3. Pasture growth module Simulated biomass dynamics throughout the dry and humid growing seasons followed the prevailing hydro-meteorological conditions (Fig. 3a,c). Growth started after the first important autumn rains which eliminated the limitation imposed by low q on photosynthesis during the summer. The growth module established the leaf onset on October 24th (DOY 297) in 2004 and October 21st (DOY 294) in 2006. During autumn and winter months, growth was mainly limited by low T and solar radiation (data not shown), though slightly sunnier conditions during this period allowed a greater growth in the dry year (Table 1; Fig. 3c). Early drought conditions in the dry year resulted in an early senescence due to soil water shortage. The modelled end date of the growing season was set on May 14th (DOY 134) in the dry year. In the humid year, mid-spring rains allowed the pasture to grow further until July 9th (DOY 190) (Fig. 3c). Inter-annual variability in the amount and distribution of precipitation in spring induced differences up to 56 days in the modelled length of the growing period between a dry and a humid year, variation that matched field observations. The growth model was able to explain 65% of the variability of measured biomass values (n ¼ 7) with RMSE of 98 g dry matter (DM) m
2 and a trend to underestimate actual values by 31% (Table 2). 3.4. Stomatal conductance modelling MEDPAS gs module is a reparameterized version of an existing model for T. subterraneum published in Alonso et al. (2007). Interestingly, fT, fPAR and fVPD fitted the gs data measured in the humid year. Only gmax, fphen and fmin required revision to fit the new dataset (Figs. 2b and 4; Table 3). A new fSWC was also added. The new gmax matched the higher gs values measured in the humid year. Phenology (i.e. growing season) was taken from the MEDPAS growth module in order to reflect the inter-annual variation of

2512

I. González-Fernández et al. / Atmospheric Environment 44 (2010) 2507e2517

a

b

c

Fig. 3. Meteorology (a), soil water content (SWC) (b) and pasture biomass (Bt) dynamics (c) during the dry and humid growing seasons. Temperature is represented in (a) by the solid line and precipitation by vertical bars. Solid and dashed lines in (b) depict modelled SWC (mm) for the 0e15 cm and 15e30 cm depth soil layers respectively. Solid lines in (C) represent modelled Bt (g DM m
2). Thick lines in (C) represent the estimated length of the growing season. Field measurements of SWC at 0e15 cm depth and Bt are shown as dots (mean  SE) in (b) and (c) respectively.

growing season length observed. In Fig. 4d relative gs values observed in the humid year were over 0.6 even after the end of the growing season based on the ‘dry model’ parameterization. SWC was identified as a major driver of gs. Thus a new fSWC function was added to specifically describe the effects of soil moisture on gs (Table 3, Fig. 4e). The parameterization of the MEDPAS model outperformed the previous, year-specific, parameterization, showing higher R2 values

Table 2 Goodness of fit between observed and modelled soil water content (SWC), pasture growth and stomatal conductance (gs). DM, dry matter. Model

RMSE 2005

MEDPAS SWC module MEDPAS pasture growth module gs module ‘Dry model’ MEDPAS model

R2

MB 2007

5.1 mm 5.9 mm 98 g DM m
2

2005

2007

2.9 mm 4.9 mm

2005 2007 0.82

89 g DM m
2

y ¼ 0.1 x þ 0.27; P ¼ 0.12; R2 ¼ 0.03; RMSE ¼ 1.21 y ¼ 0.83 x þ 0.23; P < 0.001; R2 ¼ 0.64; RMSE ¼ 0.27

0.77 0.65

and lower RMSE values (Table 2). The ‘dry model’ resulted in very low R2 and high RMSE when data for both dry and humid years were considered. Both parameterizations tended to overestimate low values. Of particular importance for O3 flux calculations was the fact that the observed inter-annual variability in maximum gs and length of the growing season were covered by the MEDPAS model. On the contrary, the ‘dry model’ would need a different year-specific parameterization to fit the data of the humid year. Using year-specific parameterizations to estimate gs yielded an overall R2 ¼ 0.54 and RMSE ¼ 0.28 (data not shown) while MEDPAS improved this R2 by 10% using one single parameterization of the gs module. 3.5. Accumulated stomatal O3 fluxes Accumulated O3 fluxes absorbed by T. subterraneum depended on climatic conditions, O3 concentrations and the parameterization chosen for gs estimation. Despite the higher O3 exposure in the dry year growing season (AOT40 18912 ppb h, October 1st to June 21st), the MEDPAS model predicted 61% higher accumulated fluxes in the humid year (AOT40 6854 ppb h, October 1st to June 21st) in response to the higher gs and the longer growing season (Fig. 5).

I. González-Fernández et al. / Atmospheric Environment 44 (2010) 2507e2517

1.2

c

1.0

1.0

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0.0

0.0 5

10

15

20

25

30

35

0

500

1000 1500 2000

0

1

-2 -1

T (ºC)

2

3

4

VPD (kPa)

PAR ( mol m s )

1.2

1.2

e

d Relative gH2O

Relative gH2O

b

a

1.0

1.0

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0.0

0.0

-100

-50

0

50

100

150

200

0.00

0.05

0.10

0.15

Relative gH2O

Relative gH2O

1.2

2513

0.20

SWC (m3 m-3)

DOY

Fig. 4. Modifying functions of the gs multiplicative model for Trifolium subterraneum. Data measured throughout the dry and humid growing seasons is represented by open and closed dots respectively. Functions used both in the ‘dry model’ and MEDPAS model parameterizations are depicted by solid lines (a, b and c). In (d) the solid line represents the phenology function (fphen) used by the ‘dry model’ based on data collected in the dry year and the dashed line represents the phenology function that would fit data collected in the humid year. In (e), the dashed line represents the fSWC included in the MEDPAS parameterization. DOY, day of the year.

Table 3 Trifolium subterraneum stomatal conductance (gs) model parameterizations. The ‘Dry model’ parameterization is that published in Alonso et al. (2007). Dry model

MEDPAS model

gmax fmin

0.550 0.04

1.18 0.02

fT

Tmin Topt Tmax

8 22 33

8 22 33

fPAR

a

0.013

0.013

fVPD

VPDmax VPDmin

2.2 4.3

2.2 4.3

fphen

SGS EGS a b c d

0 150 0.2 110 32 0

From growth module

qWP qopt

e e e

0.03 0.188 4.18

fSWC

e

e e e e

gmax, maximum gs (mol H2O m
2 s
1); fmin, minimum gs; Tmin, Topt, Tmax, minimum, optimum and maximum air temperatures ( C) for fT function; VPDmax and VPDmin, maximum and minimum VPD values (kPa) for fVPD; SGS, start of growing season; EGS, end of growing season; a, minimum fphen; b, number of days from SGS for fphen to reach its maximum; c, number of days during the decline of fphen to again reach its minimum; d, number of days with fphen at its maximum; qWP and qopt, minimum and maximum soil water contents (m3 m
3) for fSWC; e, slope of the decline of fSWC from qopt to qWP.

Interestingly, daily mean ozone fluxes were similar in both years, with AFst averages from March 15th until the end of the growing season of 0.46 and 0.47 mmol m
2 in the dry and humid years respectively. Both parameterizations showed that AFst values mainly increased from March 15th (DOY 74) until the end of the growing season in both years. AFst values calculated under dry year conditions using the MEDPAS model resembled those calculated using the ‘dry model’ without the need for adjustments in response to inter-annual variability of the prevailing meteorological conditions. However, big differences in AFst were found when values modelled under humid year conditions were compared. AFst estimates using the ‘dry model’ were just 30% of those modelled using MEDPAS. 4. Discussion 4.1. MEDPAS model evaluation The MEDPAS model reproduced realistically the seasonal and inter-annual variations of plant behaviour observed in the field during two contrasting years. The springs of 2005 and 2007 can be considered as representative of dry and humid springs respectively. The MEDPAS model reflected this variability, showing lower SWC and growth and shorter growing season length in the dry compared to the humid year. Moreover, the variables modelled in this study laid within the range of published values for semi-arid pastures under Mediterranean conditions; MEDPAS maximum modelled biomass of 160 and 300 g DM m
2 in the dry and humid years respectively, laid within the range of published values typically

2514

I. González-Fernández et al. / Atmospheric Environment 44 (2010) 2507e2517

Fig. 5. Accumulated stomatal ozone flux (AFst) (mmol m
2) calculated using MEDPAS (solid line) and ‘dry model’ (dashed line) parameterizations.

found in annual grasslands (Xu and Baldocchi, 2004; Aires et al., 2008; Montaldo et al., 2008). The three modules coupled by MEDPAS are based on well validated, widely used models. Independent validations of each module have been carried out in Allen et al. (2005a,b) and Montaldo et al. (2005) for SWC and pasture growth modelling respectively. The multiplicative model for gs has been widely used and validated in the context of flux-based ozone risk assessment for several vegetation types (Ashmore et al., 2007; Karlsson et al., 2007; Pleijel et al., 2007; UNECE, 2009). Albeit relatively simple, the multiplicative approach yields similar results compared with other models using net photosynthesis rates to simulate gs of crop and forest species (Misson et al., 2004; Büker et al., 2007). In our study, MEDPAS tended to underestimate SWC and Bt. The underestimation of biomass production could be partly explained by the underestimation of topsoil SWC. SWC estimations could be improved by a better characterization of soil properties, leading to a better estimation of ETc processes. Furthermore, plants could be extracting water from deeper soil layers, stressing the importance of rooting depth in soil water balance models. In this study rooting depth was considered to be 30 cm following Moreno et al. (2005) although higher values up to 80 cm have been reported for annual grasslands of dehesa ecosystems (Moreno et al., 2005). Similar results have been described for other grassland communities where SWC from the topsoil was not indicative of total plant available soil water, stressing the importance of rooting depth as a factor strongly influencing plant responses to soil water stress (Jäggi et al., 2005). Despite some underestimations, the MEDPAS model reflected the patterns of soil rewetting and drying and the drought onset according with the rain events and ETc conditions in both years. Differences in model performance observed between years (see Table 2) might be related to changes in plant dynamics induced by water stress, including modified allocation of photoassimilates or acclimation responses that affect both plant and root growth (Chaves et al., 2002). Soil moisture availability was the most important factor controlling the length of the growing period. The different rain and SWC pattern determined a difference up to 56 days in the length of the growing season comparing the dry and humid years. Many species of the Mediterranean region adapt the length of the growing season according to rainfall distribution and soil moisture availability (Chaves et al., 2002; Peñuelas et al., 2004; Xu and Baldocchi, 2004; Montaldo et al., 2008). The boundaries of the growing season were efficiently described by the model, reflecting the inter-annual variability of the arrival of the drought season. Soil moisture availability also determined gs dynamics. Maximum gs

values of two representative annual species found in this community (T. subterraneum and B. hordeaceus) were 53% lower in the dry than in the humid year, associated with lower SWC values. The performance of the MEDPAS gs module was within the range of previous studies modelling gs for different species using the multiplicative approach (Misson et al., 2004; Büker et al., 2007). Compared with previous approaches modelling gs of annual T. subterraneum, MEDPAS outperformed the proposed ‘dry model’ parameterization. The combination of the SWC, growth and gs modules in the MEDPAS model resulted in a single model parameterization that can be used under the varying climatic conditions common in Mediterranean environments. In order to model gs of annual species following the previous methodology, a year-specific combination of fphen and maximum measured gs would be needed as surrogates for SWC limiting effects on gas exchange rates and growing period length under varying environmental conditions, resulting in a simpler but unsuitable approach. Both gs parameterizations tested resulted in overestimations of low gas exchange measurements. The overestimation of low gs values obtained with the multiplicative model has been related to the reduced sensitivity of stomata to VPD during the late afternoon (Elvira et al., 2007), suggesting that some diurnal changes in stomatal function may result from metabolism processes with a circadian rhythm (Chaves et al., 2002) or from a reduction of plant water potential over the course of the day (Pleijel et al., 2007). Besides the inter-annual variability found at the species level, a whole range of maximum gs values were found comparing families growing within the same pasture in the dry year (Alonso et al., 2007) and in the humid year (Fig. 2). Inter-families variability was similar to the inter-annual variability found on just one species. In spite of the high variability found in gas exchange, gs and O3 stomatal fluxes were only estimated for one species, in agreement with other studies modelling O3 deposition to grasslands (Ashmore et al., 2007). This constitutes one of the main limitations of the present study, related to the current approach to establish O3 risk assessment based on a single species level. Pasture dynamics and species composition are modified by the rainfall pattern, soil properties, grazing management, nitrogen fertilization and other environmental variables (Chaves et al., 2002; Ashmore et al., 2007; Montaldo et al., 2008; Peco et al., 2006), which affect the rates of growth, gas exchange and ozone stomatal fluxes at the community level. Therefore, in order to apply MEDPAS at wider scales, information on soil characteristics such as SWC at field capacity and wilting point, as well as rooting depths and canopy gas exchange measurements should be available. Validation across sites and sensitivity analysis to these parameters at the

I. González-Fernández et al. / Atmospheric Environment 44 (2010) 2507e2517

canopy level should be addressed in a further development of MEDPAS. 4.2. Ozone stomatal flux calculations for dehesa pasture communities Efforts have been made to develop a generic modelling approach to simulate seasonal time courses of stomatal O3 fluxes to grasslands in Europe using a perennial grass, Lolium perenne, as a model species (Ashmore et al., 2007). Though this well validated model can be appropriate for perennial grasslands, it could not be applied to dehesa pastures since these communities avoid the summer drought through the seed stage. Therefore, no ozone stomatal flux is accumulated by annual pastures during the summer, when high O3 exposures occur. On the contrary, early spring O3 doses can be extremely relevant in terms of O3 sensitivity for dehesa annual species, since spring is the period when annual plants accumulate reserves for flowering and seed production (Aers et al., 1991). Indeed, deleterious effects on biomass production and forage quality as well as flower and seed production have been reported after short-term O3 exposures of T. subterraneum during the vegetative period (Gimeno et al., 2004a; Sanz et al., 2005). As a result of higher gs and a longer growing season, the MEDPAS model yielded higher AFst at the leaf level in the humid year compared with the dry year, despite the lower AOT40 value recorded in the humid year. In both years, AOT40 spring values were well above the critical level of 3000 ppb h to protect vegetation, set in the European legislation (Directive 2008/50/EC), and were also higher than reported phytotoxic levels for T. subterraneum (Gimeno et al., 2004a; Sanz et al., 2005). Ozone impacts under such high exposures would be expected but, whether lower flux values caused by soil water shortage during the dry year would result in reduced O3-induced effects is something that needs further investigation. Stable carbon isotope ratios have indicated that, under natural field conditions, lower gs may not protect plants from O3 stress in some species (Jäggi et al., 2005). There is a need to clarify the possible interactive effects of O3 and other abiotic stresses common in the Mediterranean basin such as drought. The MEDPAS model has been used to calculate absorbed O3 fluxes at the leaf level. From an O3 risk assessment perspective, some limitations have been identified with the use of the leaf level scale. It has been recognized that flux modelling scale should be related with the nature of the impacts to be assessed (Ashmore et al., 2007). In this regard, the development of canopy stomatal gas exchange measurement techniques seem to be the most appropriate approach for future gas exchange modelling in dehesa pastures. Given the high biodiversity of dehesa pastures and the variability of gas exchange rates found across families, up-scaling to modelling O3 deposition fluxes at canopy level needs further development. Moreover, O3-induced responses at the community level require further investigation.

2515

The influence of climate on SWC, pasture growth and gs dynamics at the canopy level need to be considered for more accurate O3 flux modelling for present and future climate scenarios in the Mediterranean area. Acknowledgements The authors thank JM Gil for his help in the field work. This study was funded by an agreement between the Spanish Ministry of Environment (MARM) and CIEMAT on ‘Critical loads and levels’, the Spanish projects Consolider-Ingenio Montes (CSD2008-00040) and EDEN (CGL2009-13188-C03-02) and the Comunidad de Madrid project AgriSost (S2009AGR-1630). Appendix. A.1 SWC module Soil water balance of the two soil layers considered by the SWC module (Eq. (1A)):

qi ð0
15cmÞ ¼ qi
1 ð0
15cmÞþðPi
Inti Þ
ETci
DPi ð15cmÞ qi ð15
30cmÞ ¼ qi
1 ð15
30cmÞþDPi ð15cmÞ
Tci
DPi ð30cmÞ




(1A) Where qi and qi
1 are the soil water content at the end of days i and i
1 respectively in each soil layer. Soil water balance depends on the amount of water gained or lost by the soil layer at day i due to precipitation (Pi), canopy interception loss (Inti), evapotranspiration (ETci), plant transpiration (Tci) and deep percolation (DPi). All the units are given in mm. Interception loss (Inti) by the pasture canopy (Eq. (2A)) was calculated from LAI values obtained from the growth module (see Appendix A.2) following White et al. (2000).

Inti ¼ 0:0225$ð2$LAIi Þ$Pi

(2A)

ETc is calculated as the sum of direct soil evaporation and plant transpiration following the standard FAO Penman-Monteith methodology described in Allen et al. (1998). Some parameters of this methodology were modified to meet field observations of SWC (Table 1A). Plants extract water (Tci) from both soil layers considered. The partitioning of plant transpiration between the two layers was based on the work by Allen et al. (2005a) for shallow rooted plants (less than 50 cm rooting depth), and it was proportional to the amount of available water in each layer. When the infiltration input fills the water holding capacity, the water is lost from the surface by DPi (15 cm) in benefit of the 15e30 cm soil layer. DPi (15 cm) is calculated as the excess of infiltrated water when q0e15 cm is at field capacity. Appendix. A.2 Pasture growth module

5. Conclusions The MEDPAS model, coupling the SWC, growth and gs modules, was able to reflect the inter-annual variability observed in annual pasture dynamics induced by the amount and distribution of rainfall. Soil water availability was identified as a strong controller of plant dynamics determining the length of the growing period, plant growth and plant stomatal conductance. In this sense, the MEDPAS model can be used to estimate stomatal O3 fluxes in dehesa annual pastures. However, some limitations were identified related to the up-scaling from leaf to canopy gas exchange and from one species to community level O3 deposition. Validation of MEDPAS model results across sites is still needed.

The equations for the estimation of pasture biomass production (Montaldo et al., 2005, 2008) were (Eq. (3A)):

Table 1A Parameters modified from Allen et al. (1998) included in the SWC module. Parameter Soil water content at field capacity Soil water content at wilting point Minimum Kc value for dry bare soil Readily extractable water by evaporation

Source

qFC (m3 m
3) qWP (m3 m
3) Kc min REW (mm)

0.18 0.09 0 9

Field Field A2005b A2005b

Sources: A2005b, Allen et al., 2005b; Field means measured or calibrated in the field.

2516

I. González-Fernández et al. / Atmospheric Environment 44 (2010) 2507e2517

Bg ¼ aa $Pg
Rg
Sg Bd ¼ S g
L a


(3A)

Where Pg is the rate of carbon gain, aa is the partitioning coefficient to the leaves, Rg is the respiration rate of leaves, Sg is the senescence rate of leaves, and La is the litter fall rate, as explained in Montaldo et al. (2005). Stress functions used to estimate Pg (Montaldo et al., 2005, 2008) depending on T and VPD were adjusted to field observations of assimilation at the leaf level, while the parameterization of the soil moisture stress function was adopted from the SWC module (Table 2A). Biomass was related to leaf area index (LAI) and plant height (h) by means of the following equations (Arora and Boer, 2005; Montaldo et al., 2008) (Eq. 4A to 7A):

LAIg ¼ cg $Bg

(4A)

LAId ¼ cd $Bd

(5A)

LAIt ¼ LAIg þ LAId

(6A)

h ¼

ch $CL0:5

(7A)

Where LAIg, LAId and LAIt are, respectively, the green, dead and total leaf area index; cg and cd are the specific leaf area of the green and dead biomass respectively (0.0105 and 0.010 m2 g
1 DM) taken from Nouvellon et al. (2000); ch is the conversion factor to calculate pasture height (0.5 m kg
1 C); CL is the amount of green biomass (kg C m
2) calculated by multiplying g DM by a factor of 0.46  10
3 kg C g
1 DM (Larcher, 2003). Phenology of annual species in dehesa pastures was described using two algorithms to establish the start of the growing season and the start of senescence respectively. The algorithm used to determine seed germination (start of the growing season) (Arora and Boer, 2005) was (Eq. (8A)): nþ6 P i¼n



Pg ðLAI0 Þ$aa
Rg i  7

>0 0

Start leafing Seed dormancy

(8A)

Initial LAI (LAI0) is calculated as a function of available nonstructural carbohydrate reserves, which in annual species are contained inside the seeds. Carbohydrate reserves for initial growth are estimated from the soil seed pool, assuming that the proportion of species in the seed bank is the same as the proportion of adult plants in the field. LAI0 is calculated based on the 20% of the leaf biomass (Bg0) that the reserves (seeds) could produce according to the root:shoot ratio (Arora and Boer, 2005) (Eq. 9A):

LAI0 ¼ 0:2$cg $Bg0 Bg0 ¼ 0:75$Bseeds


(9A)

The mean number of seeds ready to germinate in the soil of central Iberian dehesas has been estimated in 105 seeds m
2 (Ortega et al., 1997). Seed weights (SWf) of the most representative species per family found in the field published by Sánchez et al. (2002) were used to calculate the seed biomass (Eq. 10A):

X   SWf $abundancef Bseeds gm
2 ¼ 105 $

(10A)

f

where “abundance” is the proportion of biomass of a specific family (f) found in the biomass samples collected in the field in 2007. The partitioning of the seed biomass between roots and leaves was equivalent to 0.32, the average root:shoot ratio found in common annual species of dehesa ecosystems (Gimeno et al., 2004a). The algorithm to determine the start of senescence (end of the growing season) was (Eq. (11A)):

9
> Thermal time > 1880  C day > Flowering > = Photoperiod > 13:5 h Senescence start nþ14   P   > > > <0 Pg LAIg $aa
Rg i ; P

i¼n

(11A) Thermal time values are accumulated from the start of the hydrological year (October 1st) over a base temperature of 0  C day. The end of the growing season was established when LAIg < 0.1 m2 m
2. Appendix. A.3 Stomatal conductance module

Table 2A Parameters modified from Montaldo et al. (2005) included in the pasture growth module. Parameter

Source

PAR extinction coefficient Temperature coefficient in respiration Death rate green leaves

da (d

Rate litter production

ka (d
1)

Radiation use efficiency coefficients

a0 a1 a2

Limiting soil moisture Soil moisture at senescence Minimum temperature Lower limit of the optimum temperature Higher limit of the optimum temperature Maximum temperature Limiting VPD Maximum VPD

ke Q10

0.58 2

N2000 N2000 M2005 N2000

T min (K) Tlim low (K)

0.0064 0.14 (senescence) 0.001 0.23 (senescence) 0.0143 0.0842 0.146 0.11 0.09 273.15 288.15

SWC model Field Field Field

Tlim

299.15

Field


1

)

qlim (m3 m
3) qsen (m3 m
3) 0

high

(K)

T0 max (K) 308.15 25 VPDlim (hPa) VPD0 max (hPa) 45

Field M2008 From field assimilation rates

Field Field Field

Sources: M2005, Montaldo et al., 2005; M2008, Montaldo et al., 2008; N2000, Nouvellon et al., 2000. Field means measured or calibrated in the field.

The stomatal conductance multiplicative model employed in the gs module was (Eq. (12A)):

gs ¼ gmax $flight $fphen $maxffmin ; ðfT $fVPD $fSWC Þg

(12A)

Where gmax is a constant representing the species-specific maximum stomatal conductance (mol H2O m
2 s
1). The remaining relative functions, ranging from 0 to 1, describe the factors modifying gmax: PAR (flight), plant phenology (fphen), temperature (fT), VPD (fVPD) and SWC (fSWC). fmin is the minimum gs that occurs during daylight hours expressed as relative to the maximum. The parameterization of the multiplicative model is presented in Table 3. Appendix. A.4 Stomatal O3 flux calculation Accumulated stomatal O3 fluxes were calculated following the Mapping Manual (UNECE, 2009):

AFst ¼

X

cO3 $gs $DO3 =H2 O $

 rc $3600$10
6 rb þ rc

(13A)

Where, AFst is the accumulated O3 flux (mmol m
2); CO3 is the hourly O3 concentrations (nmol m
3); gs is the stomatal conductance (m s
1) to water vapour; DO3 =H2 O represents the difference in

I. González-Fernández et al. / Atmospheric Environment 44 (2010) 2507e2517

diffusivity between O3 and H2O vapour in air; rc (s m
1) is the leaf surface resistance accounting for both non-stomatal and stomatal deposition of O3; and rb (s m
1) represents the quasi-laminar boundary layer resistance. AFst is accumulated during the whole growing season length. References Aers, R., Boot, R.G.A., van der Aart, P.J.M., 1991. The relation between above- and below-ground biomass allocation patterns and competitive ability. Oecologia 87, 551e559. Aires, L.M.I., Pio, C.A., Pereira, J.S., 2008. Carbon dioxide exchange above a Mediterranean C3/C4 grassland during two climatologically contrasting years. Global Change Biology 14, 539e555. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements: FAO Irrigation and Drainage Papers e 56. FAO e Food and Agriculture Organization of the United Nations, Rome. Available on line at. http://www.fao.org/docrep/X0490E/x0490e00.htm. Allen, R.G., Pereira, L.S., Smith, M., Raes, D., Wright, J.L., 2005a. FAO-56 dual crop coefficient method for estimating evapotranspiration from soil and application extensions. Journal of Irrigation and Drainage Engineering-Asce 131, 2e13. Allen, R.G., Pruitt, W.O., Raes, D., Smith, M., Pereira, L.S., 2005b. Estimating evaporation from bare soil and the crop coefficient for the initial period using common soils information. Journal of Irrigation and Drainage Engineering-Asce 131, 14e23. Alonso, R., Bermejo, V., Sanz, J., Valls, B., Elvira, S., Gimeno, B.S., 2007. Stomatal conductance of semi-natural Mediterranean grasslands: implications for the development of ozone critical levels. Environmental Pollution 146, 692e698. Arora, V.K., Boer, G.J., 2005. A parameterization of leaf phenology for the terrestrial ecosystem component of climate models. Global Change Biology 11, 39e59. Ashmore, M.R., Buker, P., Emberson, L.D., Terry, A.C., Toet, S., 2007. Modelling stomatal ozone flux and deposition to grassland communities across Europe. Environmental Pollution 146, 659e670. Bassin, S., Volk, M., Fuhrer, J., 2007. Factors affecting the ozone sensitivity of temperate European grasslands: an overview. Environmental Pollution 146, 678e691. Bermejo, V., Gimeno, B.S., Sanz, J., de la Torre, D., Gil, J.M., 2003. Assessment of the ozone sensitivity of 22 native plant species from Mediterranean annual pastures based on visible injury. Atmospheric Environment 37, 4667e4677. Büker, P., Emberson, L.D., Ashmore, M.R., Cambridge, H.M., Jacobs, C.M.J., Massman, W.J., Müller, J., Nikolov, N., Novak, K., Oksanen, E., Schaub, M., de la Torre, D., 2007. Comparison of different stomatal conductance algorithms for ozone flux modelling. Environmental Pollution 146, 726e735. Chaves, M.M., Pereira, J.S., Maroco, J., Rodrigues, M.L., Ricardo, C.P.P., Osório, M.L., Carvalho, I., Faria, T., Pinheiro, C., 2002. How plants cope with water stress in the field. Photosynthesis and Growth. Annals of Botany 89, 907e916. Davison, A.W., Barnes, J.D., 1998. Effects of ozone on wild plants. New Phytologist 139, 135e151. De Miguel, A., Bilbao, J., 1999. Ozone dry deposition and resistances onto green grassland in summer in central Spain. Journal of Atmospheric Chemistry 34, 321e338. Dentener, F., Stevenson, D., Ellingsen, K., van Noije, T., Schultz, M., Amann, M., Atherton, C., Bell, N., Bergmann, D., Bey, I., Bouwman, L., Butler, T., Cofala, J., Collins, B., Drevet, J., Doherty, R., Eickhout, B., Eskes, H., Fiore, A., Gauss, M., Hauglustaine, D., Horowitz, L., Isaksen, I.S.A., Josse, B., Lawrence, M., Krol, M., Lamarque, J.F., Montanaro, V., Muller, J.F., Peuch, V.H., Pitari, G., Pyle, J., Rast, S., Rodriguez, J., Sanderson, M., Savage, N.H., Shindell, D., Strahan, S., Szopa, S., Sudo, K., Van Dingenen, R., Wild, O., Zeng, G., 2006. The global atmospheric environment for the next generation. Environmental Science and Technology 40, 3586e3594. Elvira, S., Alonso, R., Gimeno, B.S., 2007. Simulation of stomatal conductance for Aleppo pine to estimate its ozone uptake. Environmental Pollution 146, 617e623. Emberson, L.D., Simpson, D., Tuovinen, J.-P., Ashmore, M.R., Cambridge, H.M., 2000. Towards a model of ozone deposition and stomatal uptake over Europe. EMEP/ MSCW 6/2000. Norwegian Meteorological Institute Research Note No. 42. Gimeno, B.S., Bermejo, V., Sanz, J., de la Torre, D., Elvira, S., 2004a. Growth response to ozone of annual species from Mediterranean pastures. Environmental Pollution 132, 297e306. Gimeno, B.S., Bermejo, V., Sanz, J., De la Torre, D., Gil, J.M., 2004b. Assessment of the effects of ozone exposure and plant competition on the reproductive ability of three therophytic clover species from Iberian pastures. Atmospheric Environment 38, 2295e2303. Gonzalez-Fernandez, I., Bass, D., Muntifering, R., Mills, G., Barnes, J.D., 2008. Impacts of ozone pollution on productivity and forage quality of grass/clover swards. Atmospheric Environment 42, 8755e8769.

2517

Iannucci, A., Terribile, M.R., Martiniello, P., 2008. Effects of temperature and photoperiod on flowering time of forage legumes in a Mediterranean environment. Field Crops Research 106, 156e162. Jäggi, M., Saurer, M., Volk, M., Fuhrer, J., 2005. Effects of elevated ozone on leaf delta C-13 and leaf conductance of plant species grown in semi-natural grassland with or without irrigation. Environmental Pollution 134, 209e216. Jarvis, P.G., 1976. Interpretation of variations in leaf water potential and stomatal conductance found in canopies in the field. Philosophical Transactions of the Royal Society of London Series B e Biological Sciences 273, 593e610. Jonson, J.E., Simpson, D., Fagerli, H., Solberg, S., 2006. Can we explain the trends in European ozone levels? Atmospheric Chemistry and Physics 6, 51e66. Karlsson, P.E., Braun, S., Broadmeadow, M., Elvira, S., Emberson, L., Gimeno, B.S., Le Thiec, D., Novak, K., Oksanen, E., Schaub, M., Uddling, J., Wilkinson, M., 2007. Risk assessments for forest trees: the performance of the ozone flux versus de AOT concepts. Environmental Pollution 146, 608e616. Larcher, W., 2003. Physiological Plant Ecology. Ecophysiology and Stress Physiology of Functional Groups. Springer, New York, 513 pp. Massman, W.J., 1998. A review of the molecular diffusivities of H2O, CO2, CH4, CO, O3, SO2, NH3, N2O, NO, and NO2 in air, O2 and N2 near STP. Atmospheric Environment 32, 1111e1127. Misson, L., Panek, J.A., Goldstein, A.H., 2004. A comparison of three approaches to modelling leaf gas exchange in annually drought-stressed ponderosa pine forests. Tree Physiology 24, 529e541. Montaldo, N., Rondena, R., Albertson, J.D., Mancini, M., 2005. Parsimonious modelling of vegetation dynamics for ecohydrologic studies of water-limited ecosystems. Water Resources Research 41, W10416. Montaldo, N., Albertson, J.D., Mancini, M., 2008. Vegetation dynamics and soil water balance in a water-limited Mediterranean ecosystem on Sardinia, Italy. Hydrology and Earth System Sciences 12, 1257e1271. Moreno, G., Obrador, J.J., Cubera, E., Dupraz, C., 2005. Fine root distribution in dehesas of central-western Spain. Plant and Soil 277, 153e162. Nouvellon, Y., Rambal, S., Lo Seen, D., Moran, M.S., Lhomme, J.P., Bégué, A., Chehbouni, A.G., Kerr, Y., 2000. Modelling of daily fluxes of water and carbon from shortgrass steppes. Agricultural and Forest Meteorology 100, 137e153. Ortega, M., Levassor, C., Peco, B., 1997. Seasonal dynamics of Mediterranean pasture seed banks along environmental gradients. Journal of Biogeography 24, 177e195. Peco, B., Espigares, T., Levassor, C., 1998. Trends and fluctuations in species abundance and richness in Mediterranean annual pastures. Applied Vegetation Science 1, 21e28. Peco, B., Sánchez, A.M., Azcárate, F.M., 2006. Abandonment in grazing systems: consequences for vegetation and soil. Agriculture, Ecosystems and Environment 113, 284e294. Peñuelas, J., Filella, I., Zhang, X.Y., Llorens, L., Ogaya, R., Lloret, F., Comas, P., Estiarte, M., Terradas, J., 2004. Complex spatiotemporal phenological shifts as a response to rainfall changes. New Phytologist 161, 837e846. Pleijel, H., Danielsson, H., Emberson, L., Ashmore, M.R., Mills, G., 2007. Ozone risk assessment for agricultural crops in Europe: further development of stomatal flux and flux-response relationships for European wheat and potato. Atmospheric Environment 41, 3022e3040. Sánchez, A.M., Azcárate, F.M., Arqueros, L., Peco, B., 2002. Volumen y dimensiones como predictores del peso de semilla de especies herbáceas del centro de la península Ibérica. Anales del Jardín Botánico de Madrid 59, 249e262. Sanz, J., Muntifering, R.B., Bermejo, V., Gimeno, B.S., Elvira, S., 2005. Ozone and increased nitrogen supply effects on the yield and nutritive quality of Trifolium subterraneum. Atmospheric Environment 39, 5899e5907. Sanz, J., Bermejo, V., Gimeno, B.S., Elvira, S., Alonso, R., 2007. Ozone sensitivity of the Mediterranean terophyte Trifolium striatum is modulated by soil nitrogen content. Atmospheric Environment 41, 8952e8962. Schmidt, U., Thöni, H., Kaupenjohann, M., 2000. Using a boundary line approach to analyze N2O flux data from agricultural soils. Nutrient Cycling in Agroecosystems 57, 119e129. Simpson, D., Emberson, L., Ashmore, M., Tuovinen, J., 2007. A comparison of two different approaches for mapping potential ozone damage to vegetation. A model study. Environmental Pollution 146, 715e725. UNECE, 2009. Manual on Methodologies and Criteria for Modelling and Mapping Critical Loads and Levels and Air Pollution Effects, Risks and Trends. In: Mapping Critical Levels for Vegetation. Convention on Long-Range Transboundary Air Pollution Available on line at. http://www.icpmapping.org (Chapter 3). White, M.A., Thornton, P.E., Running, S.W., Nemani, R.R., 2000. Parameterization and sensitivity analysis of the biome-BGC terrestrial ecosystem model: net primary production controls. Earth Interactions 4, 1e85. Xu, L.K., Baldocchi, D.D., 2004. Seasonal variation in carbon dioxide exchange over a Mediterranean annual grassland in California. Agricultural and Forest Meteorology 123, 79e96.