During the period between 2000 and 2010 there was eight NUTS 3 regions in which, because of storm damages, more than 1,100 m3 of trees per 100 ha of forest were cut. All of them are located in Germany and in Austria and only one is located directly in the Alps (Figure 6, Table 1). Considering common time span for all selected regions (from 2002 to 2008) it shows that because of storm damages in recent years (2007 and 2008) almost 65 % of all volume which has been recorded in timber harvest records was cut. The available data set illustrated that there were two extremely stormy periods (2000 and 2007/08) with high volumes of storm-damaged trees in harvest records, but the period of available data in this analysis is too short to predict if the storm damages in the forests of selected countries are increasing. Also reviewed literature on historical storm damages in EU show that some of the authors conclude that the forest damages because of storms are increasing [5, 6, 7] and there has been also various studies of the historical wind climate which conclude that the increasing is not significant but there are significant fluctuations between decades [8] (Dorland et al., 1999).

**Figure 6.** NUTS 3 regions with maximum volumes of cut trees [m3/100 ha of forest] because of storm damage be‐ tween years 2000 and 201

#### **3. Contributing factors**

It is clear that forest storm damages are triggered by many factors. This study tried to analyze some of the most contributing factors under commonly recognized forest and climatic conditions, aiming at provoking actions and plans to reduce storm risks and formulating strategies to adapt alpine space mountain forests to changing climate in the foreseeable future.

#### **3.1. Tree height**

Among other factors, tree height is one of the most sensitive factors contributing to the damage probability. Both statistical models [9,10] and mechanistic models [11] concluded the same results that damage probability goes up along with increasing tree height. Schmidt et al (2010) simulated the change pattern of damage probability for Norway spruce with varying tree heights (see Figure 7). The model [13] was run by varying only tree height and DBH to maintain the fixed Height-DBH-ratio as 80 cm/cm, while keeping other predictors constant. This means the "standard" Norway spruce is assumed to be exposed to the same topographic exposition. The Numbers at the right side indicates 5 different locations in Baden-Württemberg, which stands for 5 different wind field characteristics. As a consequence, the overall trend of increasing damage probability with tree height going up is common in various geographical locations.

**Figure 7.** Impacts of tree height on storm damage probability (Retrieved from Schmidt et al.2010)

#### **3.2. Tree species**

on historical storm damages in EU show that some of the authors conclude that the forest damages because of storms are increasing [5, 6, 7] and there has been also various studies of the historical wind climate which conclude that the increasing is not significant but there are

**Figure 6.** NUTS 3 regions with maximum volumes of cut trees [m3/100 ha of forest] because of storm damage be‐

It is clear that forest storm damages are triggered by many factors. This study tried to analyze some of the most contributing factors under commonly recognized forest and climatic conditions, aiming at provoking actions and plans to reduce storm risks and formulating strategies to adapt alpine space mountain forests to changing climate in the foreseeable future.

Among other factors, tree height is one of the most sensitive factors contributing to the damage probability. Both statistical models [9,10] and mechanistic models [11] concluded the same

tween years 2000 and 201

**3.1. Tree height**

**3. Contributing factors**

significant fluctuations between decades [8] (Dorland et al., 1999).

94 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

In general, conifers are more vulnerable than deciduous trees in winter storm, whilst conifers might be less vulnerable than deciduous trees in summer thunder storms. The historical storm data is also in favor of this rule (see Figure 8). It has shown that there is a significant positive correlation (p= 0, Pδ = 0.381) between the volume of damage trees and the percentage of coniferous forest in NUTS 3 regions. The higher the percentage of coniferous forest is, the larger the volume of damaged trees is. In contrary with the increasing of area with broad leaved forests (p= 0, Pδ = - 0.381), which are apparently much more resistant to the damages produced by storms, the amount of damaged wood decreases. Some other authors [12,13] also came to the similar results who also discuss that coniferous species are considered more vulnerable to wind damage than broadleaved species, with Norway spruce (Picea abies (L.) Karst) being regarded as particularly vulnerable.

**Figure 8.** Percent of coniferous forest in 2006 (EEA,2006)and the NUTS 3 regions with maximum volumes of cut trees [m3/100 ha of forest] because of storm damage between years 2000 and 2010.

In terms of species' susceptibility to storms, some statistical studies showed that spruce and silver fir are the most and least vulnerable conifers, and poplar and oak for the broadleaves, respectively [2]. According to a robust statistical model developed by Schmidt et al [13], the predicted damage probability under *ceteris paribus* condition indicated that the ranking from highest to lowest damage probability is: Norway spruce, silver fir/douglas fir, scots pine/larch, beech/oak, and then other broadleaves. When the interaction between height and species are taken into consideration at the same time, the two species groups, e.g. Silver fir/ Douglas fir and Scots pine/larches showed higher damage probability than Norway spruce at lower tree heights, but lower damage probability at higher tree height [13]. Many other experts have also studied on the resistance of different species to windthrows and obtained somehow contra‐ dictory results [14,15,16,17]. This on the other hand reflects the fact that trees' vulnerability to windthrows depends on various factors, including, but are not limited to, stands structure, forest management, site condition, soil condition, and meteorological differences and so on. Nevertheless, even-green conifers are in general more sensitive to windthrows than deciduous trees, especially during winter storms.

#### **3.3. Stand structure**

forests (p= 0, Pδ = - 0.381), which are apparently much more resistant to the damages produced by storms, the amount of damaged wood decreases. Some other authors [12,13] also came to the similar results who also discuss that coniferous species are considered more vulnerable to wind damage than broadleaved species, with Norway spruce (Picea abies (L.) Karst) being

**Figure 8.** Percent of coniferous forest in 2006 (EEA,2006)and the NUTS 3 regions with maximum volumes of cut trees

[m3/100 ha of forest] because of storm damage between years 2000 and 2010.

regarded as particularly vulnerable.

96 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

According the report of European Commission, stand structure has no clear straightforward correlation with storm damage [2]. Although it is commonly expected that the interpenetration of the crowns, complex social structure of trees with stands and vertical stratification of rooting system could enhance the ability of stands to resist against storm, there is no clear conclusion on the effect of stand mixture on stability of stands [2]. Whether a stand with mixed tree species can have positive influence on stand stability is also controversial. Slodicak 1995 [18]and Schütz et al.2006 [16] found out species mixture has positive effects, while others did not draw such conclusion on the beneficial impacts of mixture [19,20].

#### **3.4. Soil condition**

Different soil conditions have largely differing impacts on storm damages. For example, Silin et al 2000 argued that forest soils that are deeply frozen in winter in North Europe are less vulnerable to windthrows, while moist soils with water saturation are more sensitive to wind [10]. Soil types and profiles determine its ability to resist wind damage too. For instance, Bock et al. [21] found out large damaged beech stands on superficial calcareous soils in Lorraine in Northeast France during Lothar 1999. As a matter of fact, forest stands that have deep rooting system and better root anchorage on frozen, not-saturated soils can reduce significantly the level of damage.

#### **3.5. Topography**

Schmidt et al 2010 developed a sound statistical model to explore the influence of local topographic exposure to air flow on storm damage probability at individual tree level based on the National Forest Inventory 2002 data, which was the best database to record the Lothar 1999 damages. The importance of each topographical exposure (TOPEX) index at various directions depends largely on major wind direction and aspects and exposure of forest stands to wind direction. For example, the Lothar crossed the state of Baden-Württemberg mainly from southwest to northeast. Therefore, the model [13] that was developed only using the Lothar damage data also show higher coefficient of the TOPEX index with this same direction as Lothar's track than other TOPEX index. This is to say, forest stands locating on the top of northwest to southeast running ridges would have higher damage probability than other topographical locations, given the same tree-related information and the same wind field variables.

#### **3.6. Wind speed**

Wind speed is one of the most dominating meteorological factors that is positively correlated to the potential storm damage magnitude. The levels of storm damages was classified into several categories based on the maximal gust wind speed: no appreciated damage, moderate levels of damage, high levels of damage and severe levels of damage which is defined by the percentage of damaged woods in the national growing stock [2]. On the other hand, the time duration of storm over a certain area also plays a key role in the total damage. Equally important is that the local natural and climatic condition can influence considerably the levels of damage, since trees can acclimate themselves to windy characteristics by adapting their rooting system to improve the root anchorage to resist against the windthrows [22]. As a consequence, more severe damages could possibly be found in areas where strong winds are common climate phenomenon than those areas with quite few strong winds, when these areas are exposed to the same storm event. High wind speed penetrate more into the inland countries in alpine space, not only the coastal countries, like Lothar, the gust wind speed peak reached up to 51 m/s and 64 m/m in Baden-Württemberg and in Switzerland, respectively1 . This might put eastern alpine space member states under highly potential storm damage risks in the future.

#### **4. Storm damage under changing climate**

Firstly, the storm intensity is increasing. Leckebush et al. [23] developed an index to predict the storm severity for the past decades (1960-2000) and for the future under 2 climate change scenarios (SRES Scenarios A1B and A2). Both estimates showed the trend of increasing storm intensity. Not only had the projection indicated this tendency, but also the meas‐ ured data. For example, in Switzerland, the measurement of wind gust speeds has also increased strongly since the beginning of records in 1933 [24]. Another tendency is that the hurricanes originated from Atlantic oceans tend to travel far further into the mainland of Europe with wider path. Although the 1999 lothar did not induced serious damage to southern and southeastern regions of alpine mountain, the alpine ridge might not be able to serve as a shield in the near future. This is to say, central Europe will be possibly exposed to larger storm damages as is different from the past. Regarding the return period of

1 http://en.wikipedia.org/wiki/Cyclone\_Lothar

devastating storms, Della-Marta and Pinto [25] also stated that it will be reduced as a consequence of increasing storm intensity.

to wind direction. For example, the Lothar crossed the state of Baden-Württemberg mainly from southwest to northeast. Therefore, the model [13] that was developed only using the Lothar damage data also show higher coefficient of the TOPEX index with this same direction as Lothar's track than other TOPEX index. This is to say, forest stands locating on the top of northwest to southeast running ridges would have higher damage probability than other topographical locations, given the same tree-related information and the same wind field

98 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

Wind speed is one of the most dominating meteorological factors that is positively correlated to the potential storm damage magnitude. The levels of storm damages was classified into several categories based on the maximal gust wind speed: no appreciated damage, moderate levels of damage, high levels of damage and severe levels of damage which is defined by the percentage of damaged woods in the national growing stock [2]. On the other hand, the time duration of storm over a certain area also plays a key role in the total damage. Equally important is that the local natural and climatic condition can influence considerably the levels of damage, since trees can acclimate themselves to windy characteristics by adapting their rooting system to improve the root anchorage to resist against the windthrows [22]. As a consequence, more severe damages could possibly be found in areas where strong winds are common climate phenomenon than those areas with quite few strong winds, when these areas are exposed to the same storm event. High wind speed penetrate more into the inland countries in alpine space, not only the coastal countries, like Lothar, the gust wind speed peak reached

up to 51 m/s and 64 m/m in Baden-Württemberg and in Switzerland, respectively1

**4. Storm damage under changing climate**

1 http://en.wikipedia.org/wiki/Cyclone\_Lothar

put eastern alpine space member states under highly potential storm damage risks in the

Firstly, the storm intensity is increasing. Leckebush et al. [23] developed an index to predict the storm severity for the past decades (1960-2000) and for the future under 2 climate change scenarios (SRES Scenarios A1B and A2). Both estimates showed the trend of increasing storm intensity. Not only had the projection indicated this tendency, but also the meas‐ ured data. For example, in Switzerland, the measurement of wind gust speeds has also increased strongly since the beginning of records in 1933 [24]. Another tendency is that the hurricanes originated from Atlantic oceans tend to travel far further into the mainland of Europe with wider path. Although the 1999 lothar did not induced serious damage to southern and southeastern regions of alpine mountain, the alpine ridge might not be able to serve as a shield in the near future. This is to say, central Europe will be possibly exposed to larger storm damages as is different from the past. Regarding the return period of

. This might

variables.

future.

**3.6. Wind speed**

Secondly, climatic characteristic is also a crucial factor leading to the storm damage variations. Temperature and precipitation are the most influential climatic factors. As explained in the previous section, unfrozen soil imposes potentially higher risk to forest stands during windthrow events. With the increasing temperature due to global warm‐ ing, the unfrozen period in winter especially in northern Alpine space at high latitude will likely to be extended in the future, which makes the forests even less resistant to wind‐ throws. Heavy rains are normally accompanied to big storm events either in the preced‐ ing days of or during hurricanes. This evidence was found in many unforgettable storm events in Europe, like Vivian & Wiebke 1990, Lothar 1999. Under the tendency of climate change, precipitation in winter is expected to be increasing, which result in moister soil. As a consequence, the water-logged soil will further weaken the ability of forest soil to stand against storm damage. Therefore, the damage could be expected to be more serious in winter in the future than it is now.

However, it is also important to point out that a meteorologically strong storm event does not necessarily induce catastrophic forest damages. It is highly dependent on the actual forest conditions, e.g. the growing stock at the time of storm. The most devastating ever storm Lothar has triggered enormous damages in Europe, which was partly due to the well forest growth and increment in standing volume in the past decades before the storm event. It can be foreseen that the damage magnitude would be the same or higher if the forests continue to grow with less cuts, leading the net increase of growing stock. Otherwise, the serious damage could be mitigated if appropriate forest management regimes are to be implemented. On the other hand, trees can acclimate to windy condition by strengthen‐ ing their anchorage [24], so a storm might cause more serious damages in an area where high wind speed is rare than those areas with high wind speed as a common phenom‐ ena. Taking into consideration the different resistance capacity of different species to storm damage, silviculture strategies is also being adapted in many countries. For example, the damage inventory showed that 85% of damaged woods by Lothar in the German state of Baden-Württemberg were conifer species. After Lothar, more storm proof deciduous tress or mixed stands are planned for the regeneration in order to prevent storm damages. The goal of the share of species composition will be half deciduous and half conifer, with the aim to minimize the potential loss by future strong storm events.

Forest storm damage changes over time can be projected using different forest growth model and climate change scenarios. Schelhaas et al. [4] made a great effort to estimate the future storm damages for the whole EU27 plus Norway and Switzerland by multiplying the predicted growing stock by the observed percentage of annual storm damage between 1950 and 2010, as well as the percentage of maximum damage during 1950-2010. Two forest management scenarios were applied, e.g. business as usual and high demand. The estimation was made under the assumption of no changes in wind climate and no changes in vulnerability due to changes in age class distribution. The results showed that the annual average damage will increase from 17.5 Mil.m3 in 2000 to 31.0 Mil.m3 or to 24.1 Mil.m3 by 2050 under these two management scenarios, respectively. This is equal to 78% and 38% increase of annual damage by 2050 [4]. Even though these estimates were made for the whole Europe forests, the Alpine space forests as a core component of the European forests are expected to experience the similar damage changes.

In accordance with the trend of higher temperature, more precipitation, more intensive storm activities, the storm damage in forests can be expected to be substantially increasing by the end of this century. Gardinar et al [2] stated that it is expected to be at least double under these climatic trends together with even-growing and ageing forest standing volumes. However, projections are always accompanied by uncertainties. Various forest growth models produce varying results. In General, under warmer climate in the future particularly in the north Alpine regions, higher temperate provides good conditions for trees to grow faster, which might aggravate the damage levels under the same annual harvest amounts. The forest harvest practice can be altered by forest policies and planning or even policies beyond forest sectors. For example, if more agricultural land is need for some reasons, wooded land might be cleared, or if bio-energy is becoming more demanding, requiring more woods for power generation, then the annual harvest will go up sharply, leading to slow net increment in growing stock. This in turn reduces the potential storm damage risk. In other words, apart from the impact of natural climatic changes on storm damages, anthropogenic interventions is also of great influence on future storm damages.

Drawing on the Lothar model (Schmidt et al. 2010), two exemplary storm risk maps are made for the second case study region in the context of MANFRED project, which is a joint trans‐ national area consisting of Ravensburg (Baden-Württemberg, Germany), Allgäu (Bavaria, Germany) and Vorallberg (Austria). The risk maps illustrate the storm damage (either stem broken or uprooted) probability at single tree level. The color shifting from green to red indicates the damage probability increases. In order to compare the impact of two climatic characteristics, e.g. wind speed, the risk maps were made for pre-defined model trees, e.g. Norway spruce with height 40 meters, DBH 53 cm. The upper map (Figure 9) shows the potential risk of the model Norway spruce when it is exposed to high wind speed at each specific real topographic features. The lower map (Figure 10) deliver the same message under moderate wind speed scenario at the same geographical location. Figure 9 tells us that Norway spruce would be under very high risk to be damaged if a future storm event with similar wind flow characteristics as Lothar would hit this case study region.

When storm damages to other sectors are also taken into account, global and regional climatic models showed the consistent trend of increasing damages. For the perspective of insurance industry, a joint study by Swiss Re and the Swiss Federal Institute of Technology (ETH) Zürich found out that winter storms in Europe will cause a 16%-68% increase in average annual loss over the period 1975-2085 in constant currency depending on models applied [26]. In monetary term, the annual insured loss from winter storms would be 3.5 billion Euros in 2085. The effect of climate change will also result in greatly differing storm damages in various countries. For instance, Germany is expected to suffer insured loss almost three times the corresponding European value [28].

**Figure 9.** Storm damage probability under high speed scenario

management scenarios, respectively. This is equal to 78% and 38% increase of annual damage by 2050 [4]. Even though these estimates were made for the whole Europe forests, the Alpine space forests as a core component of the European forests are expected to experience the similar

100 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

In accordance with the trend of higher temperature, more precipitation, more intensive storm activities, the storm damage in forests can be expected to be substantially increasing by the end of this century. Gardinar et al [2] stated that it is expected to be at least double under these climatic trends together with even-growing and ageing forest standing volumes. However, projections are always accompanied by uncertainties. Various forest growth models produce varying results. In General, under warmer climate in the future particularly in the north Alpine regions, higher temperate provides good conditions for trees to grow faster, which might aggravate the damage levels under the same annual harvest amounts. The forest harvest practice can be altered by forest policies and planning or even policies beyond forest sectors. For example, if more agricultural land is need for some reasons, wooded land might be cleared, or if bio-energy is becoming more demanding, requiring more woods for power generation, then the annual harvest will go up sharply, leading to slow net increment in growing stock. This in turn reduces the potential storm damage risk. In other words, apart from the impact of natural climatic changes on storm damages, anthropogenic interventions is also of great

Drawing on the Lothar model (Schmidt et al. 2010), two exemplary storm risk maps are made for the second case study region in the context of MANFRED project, which is a joint trans‐ national area consisting of Ravensburg (Baden-Württemberg, Germany), Allgäu (Bavaria, Germany) and Vorallberg (Austria). The risk maps illustrate the storm damage (either stem broken or uprooted) probability at single tree level. The color shifting from green to red indicates the damage probability increases. In order to compare the impact of two climatic characteristics, e.g. wind speed, the risk maps were made for pre-defined model trees, e.g. Norway spruce with height 40 meters, DBH 53 cm. The upper map (Figure 9) shows the potential risk of the model Norway spruce when it is exposed to high wind speed at each specific real topographic features. The lower map (Figure 10) deliver the same message under moderate wind speed scenario at the same geographical location. Figure 9 tells us that Norway spruce would be under very high risk to be damaged if a future storm event with similar wind

When storm damages to other sectors are also taken into account, global and regional climatic models showed the consistent trend of increasing damages. For the perspective of insurance industry, a joint study by Swiss Re and the Swiss Federal Institute of Technology (ETH) Zürich found out that winter storms in Europe will cause a 16%-68% increase in average annual loss over the period 1975-2085 in constant currency depending on models applied [26]. In monetary term, the annual insured loss from winter storms would be 3.5 billion Euros in 2085. The effect of climate change will also result in greatly differing storm damages in various countries. For instance, Germany is expected to suffer insured loss almost three times the corresponding

damage changes.

influence on future storm damages.

European value [28].

flow characteristics as Lothar would hit this case study region.

**Figure 10.** Storm damage probability under moderate wind speed scenario

### **5. Management strategies and policy recommendations to cope with storm damages**

Over the period 1950 – 2000 storms were responsible for 53 % of total damages in European forests [9]. Because storms have a high potential to cause severe economic and ecological consequences in forests[15] it is important to minimize the damages as much as possible. In one of previous chapters it is discussed that many factors (climate, wind speed, topography, soil condition, forest structure) are important triggers for storm damages in forests. On most of those factors humans have no direct influence, but with appropriate silvicultural and forest management decisions it is possible for humans to control the structure of forest.

To minimize the forest damages produced by storms it is important to discuss two questions, first how to improve the resistance of forests to strong winds (PREVENTION) and second how to proceed if severe storm damage in forest happens (POST-STORM MANAGE STRATEGIES).

Review of most important actions to prevent storm damages in forests:

**Figure 10.** Storm damage probability under moderate wind speed scenario

102 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

Despite appropriate use and implementation of reviewed preventive measures, it is not always possible to avoid storm damages in the forests. In cases of disasters it is therefore necessary to ensure rapid and coordinated sanitation of the damaged forests. Successful post-storm management requires participation of different disciplines from federal authorities to forestry actors. From this point of view good preparations on those events are essential and to achieve this regional or country risk management systems should be established. Improvements in the following areas should be done [32]:

Until time of writing of this text it is already possible to find some guides which aims to help both public institutes and wood-chain professionals to take decisions, prepare their future actions and improve cooperation in the event of hurricanes [33,34]. In the future all EU countries should establish effective post-storm management systems

#### **Author details**

Bin You1\* and Mitja Skudnik2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Forest Research Institute of Baden-Württemberg, FVA, Freiburg, Germany

2 Slovenian Forestry Institute, Slovenia

#### **References**

[5] EuroGeographicsAdministrative or Statistical units ((2010). Eurostat.

this regional or country risk management systems should be established. Improvements in the

Until time of writing of this text it is already possible to find some guides which aims to help both public institutes and wood-chain professionals to take decisions, prepare their future actions and improve cooperation in the event of hurricanes [33,34]. In the future all EU

[1] Eurostat(2009). Forestry statistics.2009 edition.Puplications Office of the European

[2] Gardiner, B, Blennow, K, Carnus, J. M, et al. (2010). Destructive Storms in European Forests: Past and Forthcoming Impacts, Final report to European Commission-DG

[3] Schelhaas, M. J. (2008a). Impacts of natural disturbances on the development of Euro‐ pean forest resources: application of model approaches from tree and stand levels to large-scale scenarios. DissertationesForestales 56, Alterra Scientific Contributions 23.

[4] Schelhaas, M, Hengeveld, J, Moriondo, G, Reinds, M, Kundzewicz, G. -J, Maat, Z. W, & Bindi, H. t. M. (2010). Assessing risk and adaptation options to fires and wind‐ storms in European forestry. Mitigation and Adaptation Strategies for Global

following areas should be done [32]: **•** Optimal management of the crisis

**•** Implementation of logistics

**Author details**

**References**

Bin You1\* and Mitja Skudnik2

2 Slovenian Forestry Institute, Slovenia

Union, Luxembourg.

Change, , 15, 681-701.

Enviroment

**•** Organization of the labour force and equipment **•** Selection of appropriate techniques and methods

104 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

**•** Determination of the financial requirements

\*Address all correspondence to<EMAIL_ADDRESS>
countries should establish effective post-storm management systems

1 Forest Research Institute of Baden-Württemberg, FVA, Freiburg, Germany

[32] Mason, W. L. Are irregular stands more windfirm? Forestry ((2002).

[20] Kohnle, U, & Gauckler, S. (2003). Vulnerability of forests to storm damage in a forest district of southwestern Germany situated in the periphery of the 1999 Storm (Loth‐ ar). In: Ruck B., Kottmeier C., Mattheck C., Quine C., Wilhelm G. Eds, Proceedings of the International Conference Wind Effects on Trees. University of Karlsruhe. Germa‐ ny September Laboratory for Buildingand Environmental Aerodynamics, Institut for

[21] Slodicák(1995). Thinning regime in stands of Norway spruce subjected to snow and wind damage . In Wind and Trees . M.P. Couts and J. Grace (eds). Cambridge Uni‐

[22] Colin, F, Brunet, Y, Vinckler, I, & Dhote, J. F. (2008). Résistance aux vents forts des peuplementsforestiers, etnotamment des mélanges d.'espèces. Revue forestièrefrang‐

[23] Colin, F, Vinkler, I, Rou-nivert, P, Renaud, J, Hervé, P, Bock, J-C, & Piton, J. B., (2009). Facteurs de risques de chablisdans les peuplementsforestiers : les lecnstirées des tem‐ pêtes de 1999. In: Birot Y., Landmann G., Bonhême I. eds., La forêt face aux tempêtes.

[24] Bock, J, Vinkler, I, Duplat, P, Renaud, J, Badeau, P, & Dupouey, V. J.-L., (2005). Stabi‐ lité au vent des hêtraies : les enseignements de la tempête de 1999. Revue forestière‐

[25] Nicoll, B. C, Gardiner, B. A, & Peace, A. J. (2008). Improvements in anchorage pro‐ vided by the acclimation of forest trees to wind stress. Forestry , 81, 389-39.

[26] Leckebusch, G. C, Renggli, D, & Ulbrich, U. and application of an objective storm se‐ verity measure for the Northeast Atlantic region. MeteorologischeZeitschrift , 17,

[27] Usbeck, T, Wohlgemuth, T, Pfister, C, Volz, R, Beniston, M, & Dobbertin, M. (2010b). Wind speed measurements and forest damage in Canton Zurich (Central Europe)

[28] Della-marta, P. M, & Pinto, J. G. (2009). Statistical uncertainty of changes in winter storms over the North Atlantic and Europe in an ensemble of transient climate simu‐

[29] Swiss Re(2006). Swiss Re Focus Report: the effects of climate change: Storm damages

[30] Intermühle, M, Raetz, P, & Volz, R. LOTHAR UrsächlicheZusammenhänge und Risi‐ koentwicklung. Synthese des Teilprogramms 6. In: Umwelt-Materialien ((2005). Bern:

[31] Dvorák, L, Bachmann, P, & Mandallaz, D. Sturmschäden in ungleichförmigenBestän‐ den | Storm damage in irregular stands. SchweizerischeZeitschrift fur Forstwesen

from 1891 to winter 2007. International Journal of Climatology , 30, 347-358.

lations. Geophysical Research Letters 36: L14703.

BundesamtfürUmwelt, Wald und Landschaft. 145

Hydromechanics, Karlsruhe 151-155., 16-18.

106 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

versity Press, Cambridge

Editions Quae, , 177-228.

francise, , 57(2), 143-158.

in Europe on the rise

575-587.

((2001).

ise, LV (2) 191-205.

## **Ozone Fluxes to a Larch Forest Ecosystem at the Timberline in the Italian Alps**

Giacomo Gerosa, Angelo Finco, Antonio Negri, Riccardo Marzuoli and Gerhard Wieser

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56282

#### **1. Introduction**

Ozone is a phytotoxical pollutant causing negative effects on vegetation at biochemical, physiological, individual and ecosystem level [1]. Alpine forests, could experience high level of ozone concentrations, in particular in the southern part of the Alps. The reason of this phenomenaisexplainedmoreindetailinthechapter9,aswellas theozoneeffectsonvegetation.

At the moment, from a regulative point of view, AOT40 (Accumulated Ozone over a Threshold of 40ppb) is the only instrument to evaluate the ozone hazard, even if its scientific soundness is widely discussed [2,3 ]. In fact, the AOT40 is an exposure index which does not take into account the physiology of the vegetation which is exposed to that ozone concentration. Since the greatest damages to vegetation are produced by the ozone entering through the plant stomata and plants can regulate stomatal opening as a response to the environmental param‐ eters, a new approach based on stomatal ozone fluxes as been proposed by UN/ECE. In fact, the magnitude of the negative effects of ozone on vegetation is related to the real amount of this pollutant taken up through stomata [4], i.e. the dose or stomatal flux, and high environ‐ mental ozone concentrations in the air do not necessarily lead to high ozone doses, representing only a potential risk (more correctly an hazard).

The effective ozone dose, based on the flux of ozone into the leaves through the stomatal pores, represents the most appropriate approach for setting future ozone critical levels for forest trees. However, uncertainties in the development and application of flux-based approaches to setting critical levels for forest trees are at present too large to justify their application as a standard risk assessment method at a European scale [5].

The scientific community is hence moving toward an evaluation of the ozone risk based on stomatal ozone fluxes. This can be realized by means of measures or by models. Measurements

© 2013 Gerosa et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

allow to estimate only local ozone risk but they are necessary to parameterize and validate the models, which, once all the input data are available, allow to estimate ozone risk at regional, national or continental level.

Measurements of ozone fluxes are hence the first fundamental step for a proper evaluation of the ozone risk. The most used technique for the measure of ozone fluxes is called eddy covariance. This technique is based on the atmospheric turbulence and it requires particular instrumentation, which must be able to measure at least ten times per second the three wind components, the air temperature and the ozone and other gases concentrations. This kind of instrumentation must be mounted above the studied ecosystem. Additional meteorological measurements are useful for a better comprehension of the exchange process.

**Figure 1.** a) Geographical location of the Case Study 4: Valle Camonica, Northern Italy. The red dot indicates the posi‐ tion of the eddy covariance tower.; b) the studied larch forest, the photo was taken from a nearby refuge; c) local orography of the measurement site, the centre of the figure corresponds to tower localization

The eddy covariance technique allow to measure the total ozone deposition to the whole ecosystem, without distinguishing between how much ozone enters through plant stomata (the most harmful pathway) and how much is destroyed on the plant surfaces or on the terrain. In order to estimate the ozone stomatal fluxes, water is used as a tracer. In fact, it is assumed that ozone can enter into plant stomata when they are open during carbon uptake and plant transpiration: for this reason concomitant water fluxes measurements allow for the partition the total ozone fluxes between the stomatal and the non-stomatal component [6].

Once the stomatal flux is known it is possible to calculate the ozone dose (which is simply the sum of the stomatal ozone fluxes) and the Phytotoxical Ozone Dose (POD1, which is the cumulated dose over the threshold of 1 nmol O3 m-2 s-1) [4]. The POD1 have been introduced in the UN/ECE scientific community because it takes into account the internal capability of the vegetation to detoxify part of the ozone entering through the stomata. Moreover, many experiments have showed that the POD1 is better correlated with the biomass reduction than the simple ozone dose, allowing thus to estimate the harmful effects of ozone on vegetation.

In this chapter an example of micrometeorological measurements of ozone fluxes over a high elevation larch forest, performed within the framework of the MANFRED project, are pre‐ sented and discussed. The aims of this field campaign were to quantify the ozone deposition to a forest ecosystem at the timberline in the southern Alps (the more exposed to ozone) and to assess the actual ozone taken up by trees through stomata; a further aim was to gather data for the development of a stomatal uptake model to be employed for the simulation of the ozone deposition to timberline forests in future climatic and ozone pollution scenarios.

Till now ozone uptake by larch at treeline has only been investigated at the twig level [7], so the Eddy Covariance measuring technique has been chosen to get information at whole ecosystem level.

#### **2. Methodology**

allow to estimate only local ozone risk but they are necessary to parameterize and validate the models, which, once all the input data are available, allow to estimate ozone risk at regional,

Measurements of ozone fluxes are hence the first fundamental step for a proper evaluation of the ozone risk. The most used technique for the measure of ozone fluxes is called eddy covariance. This technique is based on the atmospheric turbulence and it requires particular instrumentation, which must be able to measure at least ten times per second the three wind components, the air temperature and the ozone and other gases concentrations. This kind of instrumentation must be mounted above the studied ecosystem. Additional meteorological

(a)

(b) (c)

orography of the measurement site, the centre of the figure corresponds to tower localization

**Figure 1.** a) Geographical location of the Case Study 4: Valle Camonica, Northern Italy. The red dot indicates the posi‐ tion of the eddy covariance tower.; b) the studied larch forest, the photo was taken from a nearby refuge; c) local

The eddy covariance technique allow to measure the total ozone deposition to the whole ecosystem, without distinguishing between how much ozone enters through plant stomata (the most harmful pathway) and how much is destroyed on the plant surfaces or on the terrain. In order to estimate the ozone stomatal fluxes, water is used as a tracer. In fact, it is assumed that ozone can enter into plant stomata when they are open during carbon uptake and plant

measurements are useful for a better comprehension of the exchange process.

national or continental level.

110 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

#### **2.1. Measurements**

A micrometeorological tower was run for two years in Paspardo, Valle Camonica, in the Brescia district in Italy (46°2'40"N, 10°23'04") (Figure 1a). Measurements interested only the summer season of the two years: July-October in 2010 and June-September in 2011. The studied ecosystem was a 10 ha larch forest (*Larix decidua*, Mill) with a mixed grass understorey used as a cattle pasture at 1750 m a.s.l.. Trees were between 100 and 350 years old and their average height was about 26 m. The trees coverage of the population had a LAI equal to 0.9 and a SAI equal to 0.7, while the understorey grass and shrubs had a LAI equal to 1.3 and a SAI equal to 0.2. The ecosystem lays on a westward gentle slope, with an inclination ranging between 3° and 13° (Figure 1b and Figure 1c).

The selected micrometeorological technique was the eddy covariance which requires fast response instrumentation for wind, temperature, water and ozone. For this sake an ultrasonic three axial anemometer-thermometer (USA-1, Metek, D) was installed at 29 m, on the top of the tower, just above the canopy. At the same height an open path krypton-light hygrometer (KH2O, Campbell, USA) was set. Ozone concentrations were sampled from air drawn near the sonic anemometer with two instruments: a fast response one (COFA, Ecometrics, I) and a standard UV photometer (1308, SIR, E) used as a reference since COFA uses cumarine targets whose sensitivity decays in 5 days and have to be changed. At four different levels of the tower additional thermo-hygrometrical (HD9000, Deltaohm,I), radiation (LPAR01, Deltaohm, I) were installed in order to obtain vertical profiles of each parameter. Moreover three reflec‐ tometers (CS616, Campbell, USA) and three thermopiles (SHFP, Hukseflux, NL) were deepen into the ground to measure soil water content and soil heat fluxes. In order to assess the energy balance closure a net-radiometer (NR-LITE, Kipp&Zonen, NL) as well as a pyranometer (LI 200 SZ, LI-COR, USA) were installed on the top of the tower. Finally a rain gauge (mod. 52202,Young, USA) was set in a little nearby clearance and a leaf wetness sensor (mod.237, Campbell, USA) was used too. Fast sensor data were sampled 20 times per second and collected by a personal computer through a customized program, saving data in a new file every half an hour. Slow sensor data were sampled by a datalogger (CR10x and AM416, Campbell, USA) every 15 seconds and the 30 minutes averages of each parameter were stored.

**Figure 2.** a) The flux tower in Paspardo; b) photo of the instrumentation installed on the top of the tower: the ultrasonic anemometer, the fast hygrometer and the inlet of the fast ozone analyzer; c) box at the bottom of the tower, inside the box there was the data acquisition system and the ozone reference analyzer

#### **2.2. Data processing**

Vertical fluxes of sensible (H) and latent (LE) heat and ozone (FO3) were calculated from the 30 minutes data files of the fast instrumentation as the covariance between the vertical component of the wind and the corresponding scalar quantity, following the eddy covariance theory [8]. In particular these fluxes are obtained with the following equations:

Ozone Fluxes to a Larch Forest Ecosystem at the Timberline in the Italian Alps http://dx.doi.org/10.5772/56282 113

$$H = \rho c\_p \overline{w'T'} \tag{1}$$

$$LE = \lambda \rho \,\,\overline{w'q'}\tag{2}$$

$$FO\_3 = \overline{w'O\_3}^\cdot \tag{3}$$

where *ρ* is the air density, *c p* is the specific heat at constant pressure, *w* is the vertical component of the wind intensity, *T* is the air temperature, *λ* is the latent heat of vaporization, *q* is the specific humidity and *O 3* is ozone concentration; primed variables mean the fluctuations around their 30 minutes averages which are represented as overscript bars [7].

additional thermo-hygrometrical (HD9000, Deltaohm,I), radiation (LPAR01, Deltaohm, I) were installed in order to obtain vertical profiles of each parameter. Moreover three reflec‐ tometers (CS616, Campbell, USA) and three thermopiles (SHFP, Hukseflux, NL) were deepen into the ground to measure soil water content and soil heat fluxes. In order to assess the energy balance closure a net-radiometer (NR-LITE, Kipp&Zonen, NL) as well as a pyranometer (LI 200 SZ, LI-COR, USA) were installed on the top of the tower. Finally a rain gauge (mod. 52202,Young, USA) was set in a little nearby clearance and a leaf wetness sensor (mod.237, Campbell, USA) was used too. Fast sensor data were sampled 20 times per second and collected by a personal computer through a customized program, saving data in a new file every half an hour. Slow sensor data were sampled by a datalogger (CR10x and AM416, Campbell, USA)

(b)

)

(c)

**Figure 2.** a) The flux tower in Paspardo; b) photo of the instrumentation installed on the top of the tower: the ultrasonic anemometer, the fast hygrometer and the inlet of the fast ozone analyzer; c) box at the bottom of the tower,

Vertical fluxes of sensible (H) and latent (LE) heat and ozone (FO3) were calculated from the 30 minutes data files of the fast instrumentation as the covariance between the vertical component of the wind and the corresponding scalar quantity, following the eddy covariance

theory [8]. In particular these fluxes are obtained with the following equations:

inside the box there was the data acquisition system and the ozone reference analyzer

every 15 seconds and the 30 minutes averages of each parameter were stored.

112 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

(a)

**2.2. Data processing**

The covariances in the above mentioned equations are usually obtained from the rotated covariance matrix, following the methodology proposed by McMillen [9]; the rotations of the covariance matrix remove small tilts of the sonic anemometer from the verticality. In this case, since the forest was located on a gentle slope an *ad hoc* rotation procedure was developed.

The coordinate rotations align the "tower" vertical axis to the perpendicular of the wind streamlines. In this way the mean w component is zeroed and the advective flux component is removed. The angle of the rotation varies in agreement to the slope in the upwind direction (Figure 1c).

Once the total ozone fluxes have been calculated, a flux partition procedure has been employed in order to separate the two main deposition pathways: stomatal uptake by leaves and nonstomatal ozone disruption by both chemical sinks and ozone removal from non-living surfaces.

The partition procedure is based on an electrical analogy which is typical of the SVAT (Soil Vegetation Atmosphere Transfer) models where the flux corresponds to an electrical current flowing through a resistances network. Every resistance describes a part of the whole depo‐ sition process whose driving force is represented by the ozone concentration differences between the measuring height *z* and the substomatal leaf cavity at the standard height *d+z 0* (d 2/3 of canopy height, z01/10 of canopy height, for further details see, for instance, [2], where ozone concentration is assumed to be zero [10].

The total resistance, which is obtained directly from measurements as the ratio between the ozone concentration at the measuring height and the total ozone fluxes, is equal to three series resistances:

$$R\_{tot}(\mathbf{z}) = R\_a(d + z\_{0\prime}, \mathbf{z}) + R\_b + R\_c \tag{4}$$

where Ra is the aerodynamic resistance that ozone faces during the turbulent transport from the height z to the height d+z0 (momentum sink), Rb is the resistance faced by ozone while crossing the thin layer of still air surrounding leaves (diffusive transport) and Rc is the integrated resistance of the exchanging surface (leaves, stems, soil).

Ra was calculated following the formulation proposed by Dyer [11] while Rb was calculated using Hicks et al. [12] equation. Rc is finally obtained as a residual from equation (Eq. 4) all the others variables being known.

Rc is hence considered equivalent to two parallel resistances:

$$R\_c^{-1} = R\_{ST}^{-1} + R\_{NS}^{-1} \tag{5}$$

where RST is the stomatal resistance and RNS is the non-stomatal resistance.

RST could then be deduced from the Penman-Monteith equation [13] which describes the water loss process between a wet surface and the atmosphere. This equation is based on the energy balance closure at the evaporating surface so, when the net incoming energy (net radiation) as well as all the other energy losses (soil, sensible and latent heat fluxes) are known -as in our case-, RST is the only unknown term which can be derived by the inversion of the equation.

Finally the stomatal flux is obtained, using the Ohm's:

$$F\_{ST} = \frac{R\_c}{\left(R\_a + R\_b + R\_c\right)R\_{ST}} \mathcal{C}\_m \tag{6}$$

where Cm is the ozone concentration at the measuring height.

The stomatal dose, D, is simply given by the integral of FST over the measuring period (from ta to tb).

$$D = \int\_{t\_a}^{t\_b} F\_{ST}(t)dt$$

The UNE/ECE POD1 is the dose which exceeds the instantaneous threshold of 1 nmol m-2 s-1.

$$POD\_1 = \bigwedge\_{t\_a}^{t\_b} F\_{ST}(t) - 1 \bigsqcup dt \quad \forall F\_{ST} \rangle 1 \ nmod O\_3 m^{-2} s^{-1} \tag{8}$$

For comparison purposes, the currently set AOT40 ozone exposure index was calculated too, as it follows:

$$AOT40 \underset{\underset{\begin{subarray}{c}\{O\_{3}\}\_{i}>40\forall pbb\\hat{a}\neq 50\forall/\ m^{2}\end{subarray}}{\sum}} \qquad \text{([O\_{3}]\_{i}-40)} \cdot \Delta t$$

#### **2.3. Estimation of the ozone uptake by Larch trees**

crossing the thin layer of still air surrounding leaves (diffusive transport) and Rc is the

Ra was calculated following the formulation proposed by Dyer [11] while Rb was calculated using Hicks et al. [12] equation. Rc is finally obtained as a residual from equation (Eq. 4) all the

*R R RR* = + + (6)

*D F t dt* = ò (7)

2 1

(9)

*POD F t dt F nmolO m s* - - = - "ñ é ù ò ë û (8)

111 *RR R c ST NS*

( ) *c ST m a b c ST R F C*

The stomatal dose, D, is simply given by the integral of FST over the measuring period (from

( )

The UNE/ECE POD1 is the dose which exceeds the instantaneous threshold of 1 nmol m-2 s-1.

For comparison purposes, the currently set AOT40 ozone exposure index was calculated too,

= å - ×D

3

*i*

*ST t*

*b*

*t*

*a*

1 3 () 1 1

3 2

*AOT O t* > >

50 / 40 : ([ ] 40)

[ ] 40

*O ppb RadGlob W m*

*i*

*ST ST*

RST could then be deduced from the Penman-Monteith equation [13] which describes the water loss process between a wet surface and the atmosphere. This equation is based on the energy balance closure at the evaporating surface so, when the net incoming energy (net radiation) as well as all the other energy losses (soil, sensible and latent heat fluxes) are known -as in our case-, RST is the only unknown term which can be derived by the inversion of the equation.

where RST is the stomatal resistance and RNS is the non-stomatal resistance.

integrated resistance of the exchanging surface (leaves, stems, soil).

Rc is hence considered equivalent to two parallel resistances:

114 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

Finally the stomatal flux is obtained, using the Ohm's:

where Cm is the ozone concentration at the measuring height.

*b*

*t*

*a*

*t*

others variables being known.

ta to tb).

as it follows:

The dose calculated in the previous paragraph takes into account the ozone fluxes entering all the stomata of the whole ecosystem, i.e. the stomata of grass and trees. In order to estimate the uptake of the larch needles only, a two layers resistive model was developed (Figure 2). Differently from the SVAT model of the previous paragraph, this model is a prognostic model which tries to predict the stomatal ozone flux from meteorological and atmospheric turbulence data. This model simulates separately the stomatal behaviour of the trees (1st layer) and of the understorey grass (2nd layer) as well as the ozone removal by chemical reactions with terpenes in the trunk space and the ozone deposition to the underlying soil and the external non transpiring surfaces (cuticles, branches, stems). The stomatal processes were modelled using the Jarvisian approach adopted also by UN/ECE *Manual on the Methodologies and Criteria for Modelling and Mapping Critical Loads and Levels and Air Pollution Effects, Risks and Trends*, hereafter simply called Mapping Manual [5]. A maximum stomatal conductance of 125 mmol m-2 s-1 was chosen for larch, following the findings of Sandford and Jarvis [14] and Wieser et al.[15], while the other parameterizations were taken from the generic continental conifers in the UN/ECE Mapping Manual, since for larch nothing else was available. The second layer was parameterized as the generic grass in UN/ECE Mapping Manual, which prescribes a maximum stomatal conductance of 270 mmol m-2 s-1.

**Figure 3.** Deposition scheme of the prognostic multi-layer model: going from the top to the bottom the first resist‐ ance is the aerodynamic one (Ra). Each vegetation layer (i.e. larch one and grass one) is composed by a sub-laminar resistance (Rb) followed by two parallel resistances: a stomatal one and a non stomatal one. The larch and the grass layer are connected passing by an in-canopy resistance. In parallel to this one there is also the chemical resistance (Rchem). Below the lower layer (the grass one) there is the soil resistance (Rsoil).

#### **3. Results**

The meteorological conditions of the two measuring periods are reported in Table 1. Com‐ paring the common trimester (July-September) of the two years (2010 and 2011), it is evident that 2011 summer was hotter and drier than 2010. The average temperature ranged between 9.4 °C and 15.4 °C in 2010 and between 13.6 °C and 18.1°C in 2011. In the same period the rain received by the forest ecosystem was 806 mm in 2010 and 431 mm in 2011.

**Table 1.** Meteorological and soil measurements; Taverage is the monthly average of the air temperature, TMax is the monthly maximum value of the daily averages of air temperature, TMin is the monthly minimum value of the daily averages of air temperature, Rainfalls are the cumulated rainfalls in the month, RHaverage is the monthly average of the relative humidity, RHmin is the minimum of daily averages of the relative humidity, PAR Max is the monthly maximum of the daily averages of PAR, SWC average is the monthly average of soil water content. Air temperature, air relative humidity and PAR measurements are referred to top canopy measurements.

Most of the energy received by the ecosystem as solar radiation (Rn) was dissipated backward to the atmosphere as sensible heat (H) which reached on average a daily maximum value around 300 W m-2 at 3.00 PM local time (GMT +2) (Figure 3). A minor part of the energy was employed to drive the evapo-transpirative processes (LE) with a daily maximum of 150 W m-2 and an almost constant minimum of 20 W m-2 in the dark hours. The residual energy, a very little part with respect to the other two components, heated the ground (G) with a maximum average daily value of 12 W m-2 in the late afternoon. The heat stored in the soil during the daylight hours was completely returned to the ecosystem.

Figure 4 shows a detailed example of the energy fluxes (Figure 4a) as well as of the ozone concentrations and fluxes (Figure 4.b) measured at the top of the tower. Each point in Figure 4 is a semi-hourly averaged sample from 36000 rapid measurements collected 20 times per second.

**3. Results**

PAR Max

second.

[μmol m-2 s-1]

The meteorological conditions of the two measuring periods are reported in Table 1. Com‐ paring the common trimester (July-September) of the two years (2010 and 2011), it is evident that 2011 summer was hotter and drier than 2010. The average temperature ranged between 9.4 °C and 15.4 °C in 2010 and between 13.6 °C and 18.1°C in 2011. In the same period the rain

> **October 2010**

T Average [°C] 15.4 13.3 9.4 6.2 12.7 13.8 18.1 13.6

T Max [°C] 20.3 18.2 12.8 10.9 19.5 19.2 23.7 16.5 T Min [°C] 11.0 8.2 3.6 0.7 8.7 9.4 12.02 3.9 Rainfalls [mm] 89 291 426 55 90 67 156 208

RH Average [%] 70.4 77.0 78.9 85.7 79.5 74.2 72.9 74.2

RH Min [%] 43.9 38.5 43.5 66.7 57.7 49.4 47.1 51.8

SWC Average [%] 23 28 29 30 31 23 25 26

**Table 1.** Meteorological and soil measurements; Taverage is the monthly average of the air temperature, TMax is the monthly maximum value of the daily averages of air temperature, TMin is the monthly minimum value of the daily averages of air temperature, Rainfalls are the cumulated rainfalls in the month, RHaverage is the monthly average of the relative humidity, RHmin is the minimum of daily averages of the relative humidity, PAR Max is the monthly maximum of the daily averages of PAR, SWC average is the monthly average of soil water content. Air temperature, air relative

Most of the energy received by the ecosystem as solar radiation (Rn) was dissipated backward to the atmosphere as sensible heat (H) which reached on average a daily maximum value around 300 W m-2 at 3.00 PM local time (GMT +2) (Figure 3). A minor part of the energy was employed to drive the evapo-transpirative processes (LE) with a daily maximum of 150 W m-2 and an almost constant minimum of 20 W m-2 in the dark hours. The residual energy, a very little part with respect to the other two components, heated the ground (G) with a maximum average daily value of 12 W m-2 in the late afternoon. The heat stored in the soil

Figure 4 shows a detailed example of the energy fluxes (Figure 4a) as well as of the ozone concentrations and fluxes (Figure 4.b) measured at the top of the tower. Each point in Figure 4 is a semi-hourly averaged sample from 36000 rapid measurements collected 20 times per

637 596 469 304 638 616 529 403

**June 2011 July**

**2011**

**August 2011**

**September 2011**

received by the forest ecosystem was 806 mm in 2010 and 431 mm in 2011.

**September 2010**

**Unit July 2010 August**

**2010**

116 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

humidity and PAR measurements are referred to top canopy measurements.

during the daylight hours was completely returned to the ecosystem.

**Figure 4.** Energy fluxes at the Paspardo flux tower between July and September 2010. Mean daily courses of Net radi‐ ation (Rn), sensible heat flux (H), latent heat (water) flux (LE), soil heat flux (G). The scale of G curve has been en‐ hanced five times for a better reading.

**Figure 5.** Example of semi-hourly measurements of a) sensible (H) and latent heat (LE) fluxes, and b) ozone concentra‐ tions (right axis) and total ozone fluxes (left axis) in a selected month (September 2010) at the Paspardo flux tower.

The ozone fluxes showed in Figure 4b are the net ozone amount received by the ecosystem from the atmosphere. In the convention adopted here a positive ozone flux value means a flux directed from atmosphere toward the ecosystem, while a negative value means the opposite direction. The studied ecosystem behaved as a net ozone sink with very rare episodes of ozone effluxes in the night-time. Despite ozone concentrations varied during the presented month ranging from 23 to 71 ppb the behaviour of the total ozone fluxes was almost the same. This fact highlights that the ozone deposition is not only driven by the atmospheric chemistry but also by the atmospheric turbulence and the plant physiology, which in turn responds to the soil water content.

**Figure 6.** Ozone flux partition a) in 2010 and b) in 2011.

The importance of the plant physiology is further confirmed by the results of the flux partition (Figure 5), where it is evident that a significant part of the ozone deposition was removed by the stomatal activity. This process accounted for a maximum flux of about 10 nmol m-2 s-1 which was nearly 50 % of the ozone deposition in the afternoon of both years, even if this daily stomatal peak was located in the late afternoon in 2010 and earlier in 2011. During the night a residual stomatal uptake around 2 nmol m-2 s-1 was observed, revealing an incomplete stomatal closure as already reported by Matyssek and Innes [16].

On average the daily peak of the total ozone deposition (30 nmol m-2 s-1 in 2010 and 27 nmol m-2 s-1 in 2011) was measured at noon when turbulence is usually high. At the same time, the non-stomatal deposition, i.e. the ozone disruption on the external non-living surfaces, was maximum as can be inferred by the difference between the two curves in Figure 5 which is just the non-stomatal deposition.

The diurnal behaviour of stomatal and total fluxes, of course, showed a clear dependence on the seasons as it can be observed in Figure 6, which presents as an example the average daily course of every month in the 2010 measuring period. The total flux (Figure 6b) increased until August, when it reached its maximum values, and then decreased until October when it became four times lower. On the contrary, the stomatal flux maximum was observed in July and, in the following months, it decreased gradually reaching values three times lower in October.

The stomatal fraction of the total ozone deposition, during the central hours of the day, was 40%, 24%, 23% and 37% respectively in July, August, September and October. The nonstomatal deposition hence was always the main ozone sink for this measuring site.

Ozone concentrations (Figure 6a) reached their maximum values in August and after that slowly decreased. It is worth noticing the reversed bell shape of the ozone concentration daily courses which is typical of high elevation sites [17]. The concentration minimum occurred when the total ozone deposition was maximum. This fact highlights the sink role of the forest in the ozone removal from the atmosphere, a role which decreased as the end of the growing season was approaching.

Ozone Fluxes to a Larch Forest Ecosystem at the Timberline in the Italian Alps http://dx.doi.org/10.5772/56282 119

**Figure 7.** Mean daily courses of ozone concentration (a) and ozone fluxes (b), both total and stomatal fluxes, regis‐ tered in the four months July-October 2010 at the Paspardo flux tower.

The importance of the plant physiology is further confirmed by the results of the flux partition (Figure 5), where it is evident that a significant part of the ozone deposition was removed by the stomatal activity. This process accounted for a maximum flux of about 10 nmol m-2 s-1 which was nearly 50 % of the ozone deposition in the afternoon of both years, even if this daily stomatal peak was located in the late afternoon in 2010 and earlier in 2011. During the night a residual stomatal uptake around 2 nmol m-2 s-1 was observed, revealing an incomplete stomatal

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00

FST FTOT

**[nmol m -2 s-1]**

> 0.00 4.00 8.00 12.00 16.00 20.00 0.00 **Time (GMT +2)**

On average the daily peak of the total ozone deposition (30 nmol m-2 s-1 in 2010 and 27 nmol m-2 s-1 in 2011) was measured at noon when turbulence is usually high. At the same time, the non-stomatal deposition, i.e. the ozone disruption on the external non-living surfaces, was maximum as can be inferred by the difference between the two curves in Figure 5 which is just

The diurnal behaviour of stomatal and total fluxes, of course, showed a clear dependence on the seasons as it can be observed in Figure 6, which presents as an example the average daily course of every month in the 2010 measuring period. The total flux (Figure 6b) increased until August, when it reached its maximum values, and then decreased until October when it became four times lower. On the contrary, the stomatal flux maximum was observed in July and, in the following months, it decreased gradually reaching values three times lower in

The stomatal fraction of the total ozone deposition, during the central hours of the day, was 40%, 24%, 23% and 37% respectively in July, August, September and October. The non-

Ozone concentrations (Figure 6a) reached their maximum values in August and after that slowly decreased. It is worth noticing the reversed bell shape of the ozone concentration daily courses which is typical of high elevation sites [17]. The concentration minimum occurred when the total ozone deposition was maximum. This fact highlights the sink role of the forest in the ozone removal from the atmosphere, a role which decreased as the end of the growing

stomatal deposition hence was always the main ozone sink for this measuring site.

closure as already reported by Matyssek and Innes [16].

(a) (b)

**Figure 6.** Ozone flux partition a) in 2010 and b) in 2011.

0.00 4.00 8.00 12.00 16.00 20.00 0.00 **Time (GMT +2)**

118 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

the non-stomatal deposition.

season was approaching.

October.

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00

FST FTOT

**[nmol m -2 s-1]**

**Figure 8.** Model sub-partition of stomatal flux into a larch component and a grass component (2010 data)

In order to assess the ozone risk for the larch only, it was necessary to separate the stomatal flux taken up by the whole ecosystem into a stomatal component for the larch forest and a stomatal component for the understorey grass. The results of the sub-partition modelling exercise on 2010 data are showed in Figure 7. The model results were in satisfactory agreement with the measured fluxes, taking into account the correction by Moelder et al. [18] for the measurements taken in the roughness sub-layer, as in our case.

In the central hours of the day, the ozone fraction taken up by the larch needles through stomata was around 70% of the bulk stomatal flux calculated for the whole ecosystem. The remaining part (around 30%) was due to the understorey grass uptake. On average, the larch stomatal flux was about 25% of the total ozone deposition to the whole ecosystem.

Taking into account the uptake of both larch and grass, their overall stomatal uptake was on average 32% of the total flux, thus confirming the results of the flux partition obtained from the measurements. It is worth noticing that most of the ozone was destroyed by non-stomatal processes (68%) which include deposition on soil, leaf cuticles and stems.

Using the outputs of this model in order to estimate the phytotoxical ozone dose received by the larch trees during the whole measuring period, a POD1 value of 17.9 mmol m-2 was found, which is more than twice the critical flux level set by UN/ECE to protect Norway spruce, the species which was chosen as representative for all conifers.

By applying the flux-effect relationship proposed by UN/ECE for Norway spruce, a biomass reduction around 4 % can be predicted.

This reduction should be intended as the missing plant growth in the measuring period. Even if this value seems low, it is not negligible considering a pluriennial time scale. In fact it could be responsible of a slowing of the forest development as well as a minor plant capability to cope with other biotic and abiotic stressors.

It is important to highlight that the UN/ECE flux effect relationship had been developed on epigean biomass data only. However some studies showed a more significant effect of ozone uptake on root development [19, 20]. On a long time scale this effect could lead to a general impairment of the plant capability to stabilize mountain slopes and to prevent hydro-geolog‐ ical instability.

In any case it should be important to remark that the quantification of the effects is based on Norway spruce, the only species for which data are currently available in literature. The effects on larch could be lower or even worse but this is the best that could be done with the present knowledge. Ozone will obviously affect even the understorey vegetation and the other ecosystem components. So the estimated negative effects are likely higher than those predicted for larch only.

From a regulative point of view, the larch forest experienced an ozone exposure of 5.1 ppm h in the measuring period. This value was calculated as AOT40, the exposure index currently in use, following the EU and Italian legislation. Using the relationship between epigean biomass reduction and AOT40 for Norway spruce [5], in this case a biomass reduction of 0.8 % can be expected, a value that is 5 times lower than the corresponding biomass reduction estimated with the POD1 approach. This great difference underlines once more the criticism addressed toward AOT40, which does not take into account the real interaction between ozone and the plant, but only mimes it by the exclusion of the night-time measurements.

In this case, although this situation is typical of the mountain sites and particularly of the most elevated ones, the relatively low AOT40 value is the consequence of the ozone concentrations minima experienced in the midday hours of the daily concentration profile (Figure 6a). On the contrary the highest concentrations were always measured at night, when the AOT40 index is not calculated. It is worth noticing that the highest stomatal fluxes are experienced just when the ozone concentrations are lower, thus causing this risk assessment discrepancy. For this reason, besides the fact that the plant physiological processes are taken into account, the fluxbased approach is considered more scientifically sound and advanced.

with the measured fluxes, taking into account the correction by Moelder et al. [18] for the

In the central hours of the day, the ozone fraction taken up by the larch needles through stomata was around 70% of the bulk stomatal flux calculated for the whole ecosystem. The remaining part (around 30%) was due to the understorey grass uptake. On average, the larch stomatal

Taking into account the uptake of both larch and grass, their overall stomatal uptake was on average 32% of the total flux, thus confirming the results of the flux partition obtained from the measurements. It is worth noticing that most of the ozone was destroyed by non-stomatal

Using the outputs of this model in order to estimate the phytotoxical ozone dose received by the larch trees during the whole measuring period, a POD1 value of 17.9 mmol m-2 was found, which is more than twice the critical flux level set by UN/ECE to protect Norway spruce, the

By applying the flux-effect relationship proposed by UN/ECE for Norway spruce, a biomass

This reduction should be intended as the missing plant growth in the measuring period. Even if this value seems low, it is not negligible considering a pluriennial time scale. In fact it could be responsible of a slowing of the forest development as well as a minor plant capability to

It is important to highlight that the UN/ECE flux effect relationship had been developed on epigean biomass data only. However some studies showed a more significant effect of ozone uptake on root development [19, 20]. On a long time scale this effect could lead to a general impairment of the plant capability to stabilize mountain slopes and to prevent hydro-geolog‐

In any case it should be important to remark that the quantification of the effects is based on Norway spruce, the only species for which data are currently available in literature. The effects on larch could be lower or even worse but this is the best that could be done with the present knowledge. Ozone will obviously affect even the understorey vegetation and the other ecosystem components. So the estimated negative effects are likely higher than those predicted

From a regulative point of view, the larch forest experienced an ozone exposure of 5.1 ppm h in the measuring period. This value was calculated as AOT40, the exposure index currently in use, following the EU and Italian legislation. Using the relationship between epigean biomass reduction and AOT40 for Norway spruce [5], in this case a biomass reduction of 0.8 % can be expected, a value that is 5 times lower than the corresponding biomass reduction estimated with the POD1 approach. This great difference underlines once more the criticism addressed toward AOT40, which does not take into account the real interaction between ozone and the

plant, but only mimes it by the exclusion of the night-time measurements.

measurements taken in the roughness sub-layer, as in our case.

120 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

flux was about 25% of the total ozone deposition to the whole ecosystem.

processes (68%) which include deposition on soil, leaf cuticles and stems.

species which was chosen as representative for all conifers.

reduction around 4 % can be predicted.

cope with other biotic and abiotic stressors.

ical instability.

for larch only.

**Figure 9.** Estimated reduction of biomass production in Larch due to the ozone negative effects. See text for explana‐ tion.

The flux-based approach allows further considerations on the forest ecosystem services and in particular on the forest capability to remove the air pollutants, an aspect which is difficult to quantify, and it is not often treated.

In order to assess this aspect it necessary to consider also that part of ozone deposition which is called non-stomatal flux. This process is responsible for the removal of 38 kg O3 ha-1(Figure 9), which are destroyed without harming the vegetation, while the total amount of ozone removed by the forest ecosystem is equal to 53 kilograms of ozone per hectare in three months, an amount that is remarkable. In fact, considering an air volume with a basis of 1 ha and a thickness of three meters, containing ozone at a concentration of 90 ppb (i.e. nearly 180 μg m-3, the attention threshold for the Italian regulation) the amount of ozone is only 5 grams.

This significant ecosystem service has a counterpart of forest growth reduction of about 4%.

**Figure 10.** Evolution of stomatal and total fluxes during the measuring period in 2010.

#### **4. Conclusions**

The MANFRED project allowed to run a micrometeorological tower to measure directly ozone fluxes over a larch forest ecosystem for the first time in the Alpine region, with the eddy covariance technique.

Results showed that ozone removed by the forest was significant (53 kg ha-1), but the quantity absorbed by leaves was the minority (between 23 to 40%), the most being non-stomatal.

A double layer (4-sinks) SVAT model was developed to study the sub-partition of the stomatal fluxes between the larch trees and the understorey grass, as well as to give an insight to the non-stomatal processes. The model is now suitable for simulations of ozone uptake in climate change scenarios.

Larch needles uptake 25% of the total ozone flux, around 70% of the bulk stomatal flux. The remaining 30% of the bulk stomatal flux was absorbed by the understorey grass.

Even taking into account the detoxifying capability of the plants (instantaneous flux threshold) the phytotoxically active ozone dose (POD1) appears above the critical level for fluxes provisionally set by the UN/ECE to 8 mmol m-2 for Norway spruce, the species chosen as a reference for conifers. At such ozone flux levels, the calculated POD1 for larch suggests a possible trees growth reduction of 4 – 5% in a growing season. This missing growth is counterbalanced by the offer of an ecosystem service, that is the removal of 53 kg ha-1 of ozone from the atmosphere.

#### **Acknowledgements**

This publication was funded by the Catholic University's program for promotion and divulgation of scientific research. The authors thank the Adamello Park and the mountain community of valle Camonica, themunicipality of Paspardo and in particular dr G.B. Sangalli, dr A. Ducoli and dr D. Orsignola for their support in realizing this field campaign. The authors would like also to thank Stefano Oliveri and Luca Francesco Garibaldo for their important help during the installation. A sincere thank to Michela Scalvenzi for her support in 2011.

#### **Author details**

**38** *kg O3 / ha*

110 *mmol m-2*

110

31 *mmol m-2*

**15** *kg O3 / ha*

**53** *kg O3 / ha*

**Ozone fluxes [mmol m-2]**

**4. Conclusions**

covariance technique.

change scenarios.

from the atmosphere.

**Acknowledgements**

11/07/2010 11/08/2010 11/09/2010 11/10/2010

122 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

**Figure 10.** Evolution of stomatal and total fluxes during the measuring period in 2010.

POD1 (Larch + Grass) Ftot cumulated

Non-stomatal

Stomatal

The MANFRED project allowed to run a micrometeorological tower to measure directly ozone fluxes over a larch forest ecosystem for the first time in the Alpine region, with the eddy

Results showed that ozone removed by the forest was significant (53 kg ha-1), but the quantity absorbed by leaves was the minority (between 23 to 40%), the most being non-stomatal.

A double layer (4-sinks) SVAT model was developed to study the sub-partition of the stomatal fluxes between the larch trees and the understorey grass, as well as to give an insight to the non-stomatal processes. The model is now suitable for simulations of ozone uptake in climate

Larch needles uptake 25% of the total ozone flux, around 70% of the bulk stomatal flux. The

Even taking into account the detoxifying capability of the plants (instantaneous flux threshold) the phytotoxically active ozone dose (POD1) appears above the critical level for fluxes provisionally set by the UN/ECE to 8 mmol m-2 for Norway spruce, the species chosen as a reference for conifers. At such ozone flux levels, the calculated POD1 for larch suggests a possible trees growth reduction of 4 – 5% in a growing season. This missing growth is counterbalanced by the offer of an ecosystem service, that is the removal of 53 kg ha-1 of ozone

This publication was funded by the Catholic University's program for promotion and divulgation of scientific research. The authors thank the Adamello Park and the mountain

remaining 30% of the bulk stomatal flux was absorbed by the understorey grass.

Giacomo Gerosa1 , Angelo Finco1 , Antonio Negri1 , Riccardo Marzuoli1 and Gerhard Wieser2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Mathemathics and physics department, Catholic University of the Sacred Heart, Italy

2 Department of Alpine Timberline Ecophysiology, Federal Research and Training Centre for Forests, Natural Hazards and Landscape, Innsbruck, Austria

#### **References**

**Assessing Present and Future Ozone Hazards to Natural Forests in the Alpine Area — Comparison of a Wide Scale Mapping Technique with Local Passive Sampler Measurements**

Angelo Finco, Stefano Oliveri, Giacomo Gerosa, Wilfried Winiwarter, Johann Züger and Ernst Gebetsroither

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56280

#### **1. Introduction**

[7] Wieser, G, & Havranek, W. M. Environmental control of ozone uptake in Larix de‐ cidua Mill.: a comparison between different altitudes. Tree Physiology (1995). , 15,

[8] Gerosa, G, Marzuoli, R, Cieslik, S, & Ballarin-denti, A. Micrometeorological determi‐ nation of time-integrated stomatal ozone fluxes over wheat: a case study in Northern

[9] Mcmillen, R. T. An eddy correlation technique with extended applicability to non‐

[10] Laisk, A, Kull, O, & Moldau, H. Ozone concentration in leaf intercellular air spaces is

[11] Dyer, A. J. A review of flux-profile relationships. Boundary-Layer Meteorology

[12] Hicks, B. B, Baldocchi, D. D, Meyers, T. P, Hosker, R. P, & Matt, D. R. A Preliminary multiple resistance routine for deriving dry deposition velocities from measured

[13] Monteith, J. L. Evaporation and surface temperature. Quarterly Journal of the Royal

[14] Sandford, A. P, & Jarvis, P. G. Stomatal responses to humidity in selected conifers.

[15] Wieser, G, Häsler, R, Goetz, B, Koch, W, & Havranek, W. M. Role of climate, crown position, tree age and altitude in calculated ozone flux into needles of Piceaabies and

[16] Matyssek, R, & Innes, J. L. Ozone- A risk factor for trees and forests in Europe? Water

[17] Loibl, W, Winiwarter, W, Kopcsa, A, Zuger, J, & Baumann, R. Estimating the spatial distribution of ozone concentrations in complex terrain using a function of elevation and day time and Kriging techniques. Atmospheric Environment(1994). , 28(16),

[18] Moelder, M, Grelle, A, Lindroth, A, & Halldin, S. Flux-profile relationships over a boreal forestroughness sublayer corrections, Agriculture andForest Meteorology,

[19] Grulke, N. E, Andersen, C. P, Fenn, M. E, & Miller, P. R. Ozone exposure and nitro‐ gen deposition lowers root biomass of ponderosa pine in the San Bernardino Moun‐

[20] Mccrady, J. K, & Andersen, C. P. The effect of ozone on below-ground carbon alloca‐

Pinuscembra: a synthesis. Environmental Pollution (2000). , 109, 415-422.

simple terrain. Boundary-layer Meteorology (1988). , 43, 231-245.

close to zero. Plant Physiology (1989). , 90, 1163-1167.

quantities. Water, Air and Soil Pollution (1987). , 36, 311-330.

253-258.

(1974). , 7, 363-372.

2557-2566.

(1999).

Italy. Atmospheric Environment (2004).

124 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

Meteorological Society (1981). , 107, 1-27.

Air and Soil Pollution (1999). , 116, 199-226.

tains, California, Environmental Pollution (1998).

tion in wheat, Environmental Pollution (2000). , 107(3), 465-472.

Tree Physiology (1986). , 2, 89-103.

#### **1.1. Ozone and vegetation**

Ozone is considered as one of the most phytotoxic pollutant for vegetation [1]. Ozone pene‐ trates the leaves through the stomata and it dissolves in the water film enveloping the cellular walls of the substomatal cavity. The reaction of ozone and water releases oxidative radicals like hydrogen peroxide, superoxide and similar compounds, which are known as ROS (Reactive Oxygen Species). The ROS are responsible for oxidative stress causing biological injuries. The injuries begin with the membranes disruption through the peroxidation of the membrane lipids, and go on with the oxidation of the reduced groups of biomolecules, particularly enzymes with a subsequent change of the biochemical pathways. Once ROS enter the chloroplasts, the chlorophylls of the photosystem reaction centers are damaged leading to a general reduction of the plant growth and productivity [2, 3, 4].

The microscopic effects can appear as visible injuries at leaf level, such as chlorosis, bronzing and necrosis, as increased transparency at crown level, as a reduced root growth, and as a general increased susceptivity to both biotical and abiotical stresses [5]. Nevertheless in many cases ozone damages may not show any visible symptoms but only physiological changes which can however lead to a productivity decrease [6,7].

© 2013 Finco et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

At ecosystem level this stressor can cause slow structural changes to the plant community composition, favoring more resistant species[8]; in fact, different level of plant sensitivity to ozone were observed. Functional changes at ecosystem level can affect ecosystem services reducing carbon storage capability, increasing water loss and reducing the forest capability of stabilizing mountain slopes.

No direct ozone sources, both anthropogenic and natural, exist, ozone in fact is a secondary pollutant which is produced by photochemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOC). These reactions are burst by UV radiation and hence the ozone production is obviously greater in summer and at higher elevation, provided not limiting air concentrations of the precursors (i.e. NOx and VOC). NOx mostly come from high temperature combustions both from vehicles and energy production plants, while VOC can be emitted also by vegetation itself (isoprene and terpenes) as well as by the same anthropo‐ genic sources of NOx when the combustion is not perfectly balanced. In mountain regions the breeze cycle generates pollution transport from valley bottom or planes, where typically precursor sources are located, to higher elevation forested areas. As a consequence mountain forests can experience higher ozone concentrations than the valley floor. In the southern side of the Alps these processes are enhanced because of the higher solar radiation and temperature, the higher elevation and the great number of precursor sources in the Po valley; furthermore the enclosed morphological configuration of the Po basin can contribute to trap the ozone precursors and to push them towards the mountain tops.

#### **1.2. Ozone hazard assessment an EU legislation**

Ozone hazard was widely studied during last decades. The United Nations Economic Commission for Europe (UN/ECE) gave birth in 1979 to the Convention on Long-range Transboundary Air Pollution (LRTAP). The Convention is composed by more 50 states from Europe, Asia and North-America and several protocols were signed to identify specific measures to limit their emissions of pollutants and gradually reduce pollution levels and also long-range transboundary air pollution. In particular, the Gothenburg protocol (1999) fixed the critical levels of ozone for vegetation with target values for the following years. A following EU directive (2002/03) acknowledged the Gothenburg protocol, which already explained how to assess the ozone hazard, introducing the AOT40 (Accumulated Ozone Exposure over a threshold of 40 ppb) index. The AOT40 is defined as:

$$AOT40 \quad \coloneqq \sum\_{\substack{\{O\_3\}\_i > 40 \le b \\ \text{RadGolo} > 50 \le t/m^2}} \{ [O\_3]\_i - 40 \} \cdot \Delta t \tag{1}$$

that is the AOT40 is calculated as the sum of the concentrations of ozone (when they are above 40 ppb) minus the threshold of 40 ppb multiplied by the measuring time which is usually one hour. This sum is calculated only during daylight hours in order simulate the stomatal behavior of vegetation. Even though this index reflects the concept of exposure, the choice of calculating it only during daylight hours was an attempt to mime the concept of dose, that is the amount of ozone taken up by plants through stomata, whose opening is regulated by sun light (but also by other environmental factors).

The critical levels set by EU for vegetation is 18000 μg m-3 h over a period of six months, from 1st April to 30th September. This critical level can be expressed also in terms of ppb h, and its value is 9000 ppb h. UN/ECE, after the signing of the Gothenburg protocol, enhanced and promoted studies about ozone effects on vegetation and suggested a new critical level for forest (5000 ppb h during the growing season).

At ecosystem level this stressor can cause slow structural changes to the plant community composition, favoring more resistant species[8]; in fact, different level of plant sensitivity to ozone were observed. Functional changes at ecosystem level can affect ecosystem services reducing carbon storage capability, increasing water loss and reducing the forest capability of

No direct ozone sources, both anthropogenic and natural, exist, ozone in fact is a secondary pollutant which is produced by photochemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOC). These reactions are burst by UV radiation and hence the ozone production is obviously greater in summer and at higher elevation, provided not limiting air concentrations of the precursors (i.e. NOx and VOC). NOx mostly come from high temperature combustions both from vehicles and energy production plants, while VOC can be emitted also by vegetation itself (isoprene and terpenes) as well as by the same anthropo‐ genic sources of NOx when the combustion is not perfectly balanced. In mountain regions the breeze cycle generates pollution transport from valley bottom or planes, where typically precursor sources are located, to higher elevation forested areas. As a consequence mountain forests can experience higher ozone concentrations than the valley floor. In the southern side of the Alps these processes are enhanced because of the higher solar radiation and temperature, the higher elevation and the great number of precursor sources in the Po valley; furthermore the enclosed morphological configuration of the Po basin can contribute to trap the ozone

Ozone hazard was widely studied during last decades. The United Nations Economic Commission for Europe (UN/ECE) gave birth in 1979 to the Convention on Long-range Transboundary Air Pollution (LRTAP). The Convention is composed by more 50 states from Europe, Asia and North-America and several protocols were signed to identify specific measures to limit their emissions of pollutants and gradually reduce pollution levels and also long-range transboundary air pollution. In particular, the Gothenburg protocol (1999) fixed the critical levels of ozone for vegetation with target values for the following years. A following EU directive (2002/03) acknowledged the Gothenburg protocol, which already explained how to assess the ozone hazard, introducing the AOT40 (Accumulated Ozone Exposure over a

3 2

*AOT O t* > >

50 / 40 : ([ ] 40)

[ ] 40

*O ppb RadGlob W m*

*i*

3

= å - ×D

that is the AOT40 is calculated as the sum of the concentrations of ozone (when they are above 40 ppb) minus the threshold of 40 ppb multiplied by the measuring time which is usually one hour. This sum is calculated only during daylight hours in order simulate the stomatal behavior of vegetation. Even though this index reflects the concept of exposure, the choice of calculating it only during daylight hours was an attempt to mime the concept of dose, that is the amount of ozone taken up by plants through stomata, whose opening is regulated by sun

*i*

(1)

stabilizing mountain slopes.

precursors and to push them towards the mountain tops.

126 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

**1.2. Ozone hazard assessment an EU legislation**

threshold of 40 ppb) index. The AOT40 is defined as:

light (but also by other environmental factors).

The choice of this new critical level was based on some experiments where seedlings of beech and birch (two sensitive species) where exposed to different levels of ozone concentrations, and hence different levels of AOT40 were measured; after the growing season a biomass reduction of the plants exposed to ozone was observed and plotted versus the corresponding values of the AOT40 (Figure 1). The critical level of 5000 ppb h corresponds to a potential biomass reduction of 5% (intercept of the two dashed lines). Knowing the AOT40 to which an ecosystem was exposed (for example 20.000 ppb h, that is equal to 20 ppm h), the potential biomass reduction can be estimated following the blue arrow first and then the red one, which leads to a 16% potential biomass reduction.

**Figure 1.** Modified from [9].The relationship between percentage reduction in biomass and AOT40, on an annual ba‐ sis, for the deciduous, sensitive tree species category, represented by beech and birch. The blue and red line are used to estimate the biomass reduction as a function of the AOT40 value.

The EU directive gives recommendations to the member state about how to measure ozone and how to calculate AOT40, but, since the monitoring networks are mostly dedicated to estimate the risk for the population rather than vegetation, ozone data in mountain regions are not so often available, leading to a not proper evaluation of the ozone hazard for forests. Three main options are available to avoid this lack: mobile laboratory, passive samplers and modeling predictions.

Mobile laboratories are vans which are equipped with a small meteorological station and automatic instrumentation to measure continuously the pollutant concentrations (Figure 2). However, due to their high electric power consumption, mobile labs are seldom suitable for monitoring in forest remote areas.

**Figure 2.** The mobile laboratory employed in the MANFRED project

Passive samplers, on the contrary, do not require electricity since they are tubes which are filled with a substance which reacts to ozone (Figure 3). Passive samplers must be exposed to ambient air, typically for one week, and then collected and sent to chemical lab to obtain the average ozone concentration of the exposure period. Passive samplers are a valuable option for measurements in remote area even tough the time resolution of these devices is their main disadvantage.

Assessing Present and Future Ozone Hazards to Natural Forests in the Alpine Area — Comparison of a Wide Scale Mapping Technique with Local Passive Sampler Measurements http://dx.doi.org/10.5772/56280 129

**Figure 3.** Ozone passive samplers

Mobile laboratories are vans which are equipped with a small meteorological station and automatic instrumentation to measure continuously the pollutant concentrations (Figure 2). However, due to their high electric power consumption, mobile labs are seldom suitable for

monitoring in forest remote areas.

128 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

**Figure 2.** The mobile laboratory employed in the MANFRED project

disadvantage.

Passive samplers, on the contrary, do not require electricity since they are tubes which are filled with a substance which reacts to ozone (Figure 3). Passive samplers must be exposed to ambient air, typically for one week, and then collected and sent to chemical lab to obtain the average ozone concentration of the exposure period. Passive samplers are a valuable option for measurements in remote area even tough the time resolution of these devices is their main Ozone concentrations modeling is another valuable option which allows to map ozone hazard at wide scale, in some way avoiding the technical, economical and logistical constraints involved in the use of the instrumentation. However the goodness of the modeling outputs is strictly correlated to the quality of the input data supplied. This could be an important limitation as well as the fact that the model predictions should be validated with direct measurements in the studied area.

In this chapter the modeling of ozone hazard and the related AOT40 maps at Alpine scale will be showed and compared with direct measurements performed at a finer scale in a selected case study area. Finally an attempt of estimating the future ozone hazard in the Alpine area will be showed too.

#### **2. The present ozone risk in the Alps and a case study in depth analysis**

#### **2.1. Mapping AOT40 at Alpine scale level**

#### *2.1.1. Model description*

During the years 1990 - 1994 the Foresight & Policy Development Department of AIT devel‐ oped a model to generate hourly ozone concentration maps using ozone monitoring data, considering the influence of complex terrain on diurnal ozone variation within mountain areas. The model was designed and tested within the Geographic Information System (GIS) ARC/ INFO. Validity tests showed that the spatio-temporal dynamics of ground level ozone concentrations were calculated with high accuracy and spatial resolution. In 1994 the model was rewritten and included into the ozone monitoring network visualization interface at the Federal Environment Agency Austria (www.ubavie.gv.at).

The idea of the interpolation approach is to make use of the well known elevation dependence of ozone to calculate the overall spatial trend and to include monitoring data to consider seasonal and local influences. The approach needs a digital elevation model of the study area and a monitoring network.

The model is divided into several steps:

The relative elevation is the vertical distance between the valley bottom and any location within the mountains. It is calculate out of a digital elevation model (DEM) where the valley bottom was assumed as the lowest absolute elevation within a distance of 5km around every DEM - grid point [10, 11].

The diurnal variation of ozone concentration depending on daytime and elevation is described by an analytical function. It was approximated by analyzing scatter plots of hourly ozone concentrations against relative elevation of the monitoring sites [10, 11].

Eq. 2 shows the basic function to model standard ozone concentrations:

$$\log O\_{\rm std}(h\_{rel}, t) = (a\_1 + a\_2 \cdot e^{-(t - a\_3)^2 a\_4}) \cdot \ln \left( \frac{b\_5 \cdot h\_{rel}}{100} + b\_1 + b\_2 \cdot e^{-(t - b\_3)^2 b\_4} \right) - \frac{c\_1}{1 - (t - c\_2)} \cdot \frac{h\_{rel}}{c\_3} \tag{2}$$

with

considering the influence of complex terrain on diurnal ozone variation within mountain areas. The model was designed and tested within the Geographic Information System (GIS) ARC/ INFO. Validity tests showed that the spatio-temporal dynamics of ground level ozone concentrations were calculated with high accuracy and spatial resolution. In 1994 the model was rewritten and included into the ozone monitoring network visualization interface at the

The idea of the interpolation approach is to make use of the well known elevation dependence of ozone to calculate the overall spatial trend and to include monitoring data to consider seasonal and local influences. The approach needs a digital elevation model of the study area

**1.** The core of the interpolation model is based on a function that reflects the dependence of

**2.** The function generates daytime-specific standard ozone/elevation curves showing the

**3.** Hourly ozone concentration measurements are used to include seasonal and day-specific influence to the average ozone concentration by fitting the standard ozone/elevation

**4.** The day-specific curves and a digital elevation model are used to generate day-specific

**5.** The deviations of the observed from the calculated ozone concentrations reflect the local influences at the monitoring stations. The interpolation of those deviations leads to a

**6.** The combination of the (hourly) day-specific ozone concentration surface with the hourly deviation surface leads to actual daytime- and day-specific ozone distribution maps of the

The relative elevation is the vertical distance between the valley bottom and any location within the mountains. It is calculate out of a digital elevation model (DEM) where the valley bottom was assumed as the lowest absolute elevation within a distance of 5km around every

The diurnal variation of ozone concentration depending on daytime and elevation is described by an analytical function. It was approximated by analyzing scatter plots of hourly ozone

> 2 2 3 4 3 4 ( ) 5 ( ) 1

*ta a rel tb b rel*

( ,) ( ) ln 100 1( )

*b h c h O h t a ae b be*

2 3

*tc c*

è ø - - (2)

concentrations against relative elevation of the monitoring sites [10, 11].

Eq. 2 shows the basic function to model standard ozone concentrations:

1 2 1 2

ozone concentration on relative elevation and daytime in mountain regions.

increase of ozone concentration with increasing elevation.

ozone concentration surfaces per daytime.

study area considering the local influences.

Federal Environment Agency Austria (www.ubavie.gv.at).

130 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

and a monitoring network.

curves.

deviation surface.

DEM - grid point [10, 11].

*std rel*

The model is divided into several steps:

Ostd = standard-ozone-concentration,

hrel = relative height, t = time of day,

ax, bx, cx = parameters of the function

This function generates a 2-dimensional trend surface of ozone concentration reflecting the dependencies of ozone on elevation and daytime (Figure 4). Slicing the surface parallel to the time-(x)-axis one gets the diurnal variation for any elevation level, slicing the surface parallel to the elevation-(y)-axis one gets ozone/elevation curves for any time of day. These standard ozone/elevation curves show the increase of ozone concentration depending on increasing elevation at a specific time of the day and are the basis to generate daytime-specific ozone concentration.

**Figure 4.** Trend surface of the standard ozone/daytime/elevation function

#### *2.1.2. Ozone measurement data*

The Ozone monitoring data have been taken from AirBase1 . This is a public air quality database containing hourly air monitoring information for 35 countries throughout Europe. In total there are more than 2500 surface ozone measurement stations available. Approximately 330 AirBase ozone measurement sites can be found within the Greater Alpine Region, the study Area of the MANFRED project. The data availability for the countries surrounding the Alps (A, CH, D, F, I, FL, SLO) is shown in Figure 5.

1 http://acm.eionet.europa.eu/databases/airbase/

**Figure 5.** Number of measurement stations and amount of valid data for countries surrounding the Alps (left) and the Geographical coverage in 2007 (right)

#### *2.1.3. Results for the Greater Alpine Region (GAR)*

Ozone interpolation was done from 2003 to 2007 with 1 hour time and 1 km spatial resolution resulting in 43824 maps with 1018x668 grid points and app. 400 GB of data. In addition AOT40 maps with 1km spatial resolution were calculated for the years 2003 to 2007, which can be seen in Figure 6.

Figure 6 shows that the interannual variability of the AOT40 values is significant (2003 was an extreme year). The 5 yr mean (Figure 6, lower right map) gives an overview about the spatial distribution of the AOT40 values. In general a higher AOT40 value can be expected in the south of the alpine main ridge.

#### **2.2. Measurements at local scale with passive samplers: The Valle Camonica case study**

Valle Camonica was chosen as a case study area for ozone since this pollutant is a well known problem for the Lombardy region where it is located [12]. Even tough Valle Camonica is one of the biggest forested valley in Lombardy, few information on ozone hazard for forest were available for this valley and some ozone-like foliar symptoms were observed on beech leaves.

In summer 2010 a six month field campaign was performed with passive samplers located in 11 sites throughout all the valley: 10 of them were placed in remote forest areas while the remaining one was placed in Darfo, near the only automatic monitoring station available in Valle Camonica (Regional environmental agency, ARPA). The sites elevation ranged between 300 m and 1800 m a.s.l. spatially covering all the forest areas of the valley.

The employed passive samplers were polypropylene tubes with a glass fiber filter dipped in a solution of DPE (1,2-di(4-pyridyl)-ethylene) in acetic acid at the closed end (Passam ag, CH). The ozone diffusing in the tubes reacts stoichiometrically with DPE forming an ozonide which undergoes cleavage and yields an aldehyde. The amount of aldehyde is then determined spectrophotometrically in lab by the MBTH (3-methyl-2-benzothiazolinone hydraze ethylene) Assessing Present and Future Ozone Hazards to Natural Forests in the Alpine Area — Comparison of a Wide Scale Mapping Technique with Local Passive Sampler Measurements http://dx.doi.org/10.5772/56280 133

**Figure 5.** Number of measurement stations and amount of valid data for countries surrounding the Alps (left) and the

Ozone interpolation was done from 2003 to 2007 with 1 hour time and 1 km spatial resolution resulting in 43824 maps with 1018x668 grid points and app. 400 GB of data. In addition AOT40 maps with 1km spatial resolution were calculated for the years 2003 to 2007, which can be seen

Figure 6 shows that the interannual variability of the AOT40 values is significant (2003 was an extreme year). The 5 yr mean (Figure 6, lower right map) gives an overview about the spatial distribution of the AOT40 values. In general a higher AOT40 value can be expected in the south

**2.2. Measurements at local scale with passive samplers: The Valle Camonica case study**

Valle Camonica was chosen as a case study area for ozone since this pollutant is a well known problem for the Lombardy region where it is located [12]. Even tough Valle Camonica is one of the biggest forested valley in Lombardy, few information on ozone hazard for forest were available for this valley and some ozone-like foliar symptoms were observed on beech leaves.

In summer 2010 a six month field campaign was performed with passive samplers located in 11 sites throughout all the valley: 10 of them were placed in remote forest areas while the remaining one was placed in Darfo, near the only automatic monitoring station available in Valle Camonica (Regional environmental agency, ARPA). The sites elevation ranged between

The employed passive samplers were polypropylene tubes with a glass fiber filter dipped in a solution of DPE (1,2-di(4-pyridyl)-ethylene) in acetic acid at the closed end (Passam ag, CH). The ozone diffusing in the tubes reacts stoichiometrically with DPE forming an ozonide which undergoes cleavage and yields an aldehyde. The amount of aldehyde is then determined spectrophotometrically in lab by the MBTH (3-methyl-2-benzothiazolinone hydraze ethylene)

300 m and 1800 m a.s.l. spatially covering all the forest areas of the valley.

Geographical coverage in 2007 (right)

in Figure 6.

of the alpine main ridge.

*2.1.3. Results for the Greater Alpine Region (GAR)*

132 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

**Figure 6.** AOT40 maps for 2003 to 2007 and 5 yr mean, derived from interpolated 1-hourly ozone maps

method at 442 nm, giving directly the mass of the captured ozone. The ozone concentration in the air (μg m-3) is calculated using the Fick's law, knowing the diffusion coefficient of ozone in the air and the tube geometry.

Two polypropylene tubes were exposed for one week in each site, inside a protective shelter preventing the advective ozone capture due to the wind (Figure 3). The first exposure began at 6th April 2010 and the last one (the 26th one) ended at 6th October 2010.

In order to calculate the AOT40, the collected ozone air concentrations were converted into volumetric ratios (ppb) taking into account the elevation of each site, following the method‐ ology described in [13]. For this purpose temperature and pressure data from meteorological stations in the valley were used.

In order to calculate the AOT40 it is necessary to estimate hourly ozone concentrations starting from the weekly averages, the only data available from passive sampler measurements. For this sake the Loibl function (Eq. 2) was used. This function models the ozone concentration hour by hour given the relative height of the site, i.e. the difference between the elevation of the site and the elevation of the lowest point within a range of 5 km. The Loibl function has been shifted so that its average was set equal to the passive sampler value (weekly average) and it was also assumed that the daily course of all the days of the exposure period was the same. Further details on this methodology can be found in [14]. The Loibl function capability of predicting the ozone daily course in our case study has been tested by setting up 8 intensive monitoring campaigns in different sites of the valley with a mobile lab equipped with continuous analyzers.

AOT40 was then calculated for each measurement site (Eq. 1) using the hourly ozone concen‐ tration obtained from the shifted and replicated Loibl function for all the days of each exposure week. Weekly values of AOT40 were mapped on the Valle Camonica domain using a geostat‐ istical technique known as ordinary kriging [10, 11], which requires a model of the spatial data variability estimated from the semivariogram plots.

Further details on this mapping technique can be found in [14]. Then the 26 weekly AOT40 maps were summed to obtain the ozone hazard map for forests in Valle Camonica (6th April – 6th October).

#### Passive sampler vs monitoring station

**Figure 7.** Passive sampler measurements compared with a nearby continuous analyzer

The performance of the passive sampler was very good as it can be verified from Figure 7 where the passive sampler measurements were compared with the calibrated continuous analyzer ones (R2 = 0.96, slope= 0.91).

**Figure 8.** Ozone concentration measurements of all the sites in the measuring period

In order to calculate the AOT40 it is necessary to estimate hourly ozone concentrations starting from the weekly averages, the only data available from passive sampler measurements. For this sake the Loibl function (Eq. 2) was used. This function models the ozone concentration hour by hour given the relative height of the site, i.e. the difference between the elevation of the site and the elevation of the lowest point within a range of 5 km. The Loibl function has been shifted so that its average was set equal to the passive sampler value (weekly average) and it was also assumed that the daily course of all the days of the exposure period was the same. Further details on this methodology can be found in [14]. The Loibl function capability of predicting the ozone daily course in our case study has been tested by setting up 8 intensive monitoring campaigns in different sites of the valley with a mobile lab equipped with

AOT40 was then calculated for each measurement site (Eq. 1) using the hourly ozone concen‐ tration obtained from the shifted and replicated Loibl function for all the days of each exposure week. Weekly values of AOT40 were mapped on the Valle Camonica domain using a geostat‐ istical technique known as ordinary kriging [10, 11], which requires a model of the spatial data

Further details on this mapping technique can be found in [14]. Then the 26 weekly AOT40 maps were summed to obtain the ozone hazard map for forests in Valle Camonica (6th April –

Passive sampler vs monitoring station

0 10 20 30 40 50 60 70 **monitoring station [ppb]**

y = **0.9154** x + 0.4974

 **= 0.9613**

**R2**

continuous analyzers.

6th October).

variability estimated from the semivariogram plots.

134 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

0

**Figure 7.** Passive sampler measurements compared with a nearby continuous analyzer

10

20

30

40

**passive sampler [ppb]**

50

60

70

Figure 8 reports all the measurements of all the sites in the whole measuring period. There is a general increase of the ozone concentrations with increasing elevation. It is also evident a clear variability between one week and the other, which highlights the influence of local meteorology on the ozone production. Finally ozone levels show a seasonal trend which follows the intensity of the solar radiation, with a slight increase until the end of June, a maximum in July and a decrease from August. September values appears anomalous with respect to this general trend and they are likely due to ozone intrusion from the upper troposphere after the autumnal repositioning of the tropopause. In fact these anomalous values were measured mainly in high elevation sites.

Figure 9 shows monthly average maps of ozone concentrations in the studied domain. It can be clearly seen that ozone concentration is low in valley floor and it increases regularly with the elevation reaching the maximum values in the Adamello massif, at the eastern edge of the valley. High concentrations were observed also in the southern part of the valley, namely in the Borno upland and at the Montecampione relief, both above at an elevation of 1000 m a.s.l.. In June and in July a general increase of the ozone background level was measured with a marked increase in south-eastern part of the domain. In August average concentrations lowered in most of the valley with the exception of Borno and Montecampione areas.

The final map assessing the ozone exposure for forests, i.e. the ozone hazard expressed as AOT40, is showed in Figure 10. With the only exception of the valley floor nearby Malonno,

**Figure 9.** Monthly average maps of ozone concentrations in the Case Study 4

Assessing Present and Future Ozone Hazards to Natural Forests in the Alpine Area — Comparison of a Wide Scale Mapping Technique with Local Passive Sampler Measurements http://dx.doi.org/10.5772/56280 137

**Figure 10.** AOT40 map obtained from passive samplers measurements.

**Figure 9.** Monthly average maps of ozone concentrations in the Case Study 4

136 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

all the valley is above the EU critical level for forest (9000 ppb h), with exceedances up to 6 times the critical level in the most elevated areas. If the UN/ECE critical level is considered the situation is even worse. This map confirm a potential ozone risk for vegetation in this area.

For a deeper analysis of the risk for the forested area in Valle Camonica refer to the chapter about this case study area.

#### **2.3. Comparison with the wider scale estimation (the case of Valle Camonica)**

To compare interpolation results, as described in chapter 2.1, with the measurements (chapter 2.2) AIT started to run the ozone interpolation model for Valle Camonica on a very local scale. UNICATT provided ozone data for the period April, 1st 2010 to September, 30th 2010 from 19 measurement stations in the vicinity of Valle Camonica. In a first step we tried to use the interpolation routine in the same way as we did for the entire alpine region. With that we faced some troubles, mainly depending on the low number of stations. Originally the interpolation routine was developed to work with numerous stations (more than 300 for the Greater Alpine Area) and an acceptable station density. Especially the fact that a maximum of two stations at higher altitudes were available was unsatisfactory. As a result of this the algorithm was unable to fit the dependency curve in the top layer and therefore the whole interpolation did not work correctly. We removed the whole day specific fitting part from our routines and did the interpolation just based on the standard elevation/daytime ozone dependency curve. With these modifications the model worked satisfactorily. The following Figure 11 shows the location of measurement stations and AOT40 values from 2010 calculated with the modified interpolation method using 19 local ozone measurement stations. Additionally a comparison of different methods to estimate AOT40 values was done. Because the continuous measure‐ ment of ozone in forest areas is difficult often passive samplers are used. The disadvantage of this method is the coarse time resolution, which does not allow calculating AOT40 values directly from measurements. As we found a reasonable correlation between monthly mean ozone and AOT40 values we tested some variants of calculation. The top row in Figure 12 shows results for AOT40 values derived from interpolated 1-hourly ozone maps, calculated with monthly mean/AOT40 correlation function derived from 2010 Valle Camonica data and a function derived from 5-year data of all measurement stations used for the interpolation for the Greater Alpine Region.

**Figure 11.** Location of passive samplers and ozone measurement stations (left) and AOT40 map for 2010 derived from 1-hourly Ozone maps produced by local interpolation for Valle Camonica (right)

#### **3. AOT40 estimations in the Alpine area for the future**

As for the future no hourly based estimations of Ozone concentration are available, we investigated the possibility to calculate the AOT40 values out of monthly means. For all years 2003 to 2007 the 1-hourly maps were aggregated to monthly means and the values at the locations of measurement stations were extracted. As shown in Figure 13 this estimations show a significant correlation between the monthly means and AOT40 values. So we used this approximation for the calculation of the future AOT40 values according to different ozone concentration scenarios.

Assessing Present and Future Ozone Hazards to Natural Forests in the Alpine Area — Comparison of a Wide Scale Mapping Technique with Local Passive Sampler Measurements http://dx.doi.org/10.5772/56280 139

higher altitudes were available was unsatisfactory. As a result of this the algorithm was unable to fit the dependency curve in the top layer and therefore the whole interpolation did not work correctly. We removed the whole day specific fitting part from our routines and did the interpolation just based on the standard elevation/daytime ozone dependency curve. With these modifications the model worked satisfactorily. The following Figure 11 shows the location of measurement stations and AOT40 values from 2010 calculated with the modified interpolation method using 19 local ozone measurement stations. Additionally a comparison of different methods to estimate AOT40 values was done. Because the continuous measure‐ ment of ozone in forest areas is difficult often passive samplers are used. The disadvantage of this method is the coarse time resolution, which does not allow calculating AOT40 values directly from measurements. As we found a reasonable correlation between monthly mean ozone and AOT40 values we tested some variants of calculation. The top row in Figure 12 shows results for AOT40 values derived from interpolated 1-hourly ozone maps, calculated with monthly mean/AOT40 correlation function derived from 2010 Valle Camonica data and a function derived from 5-year data of all measurement stations used for the interpolation for

138 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

**Figure 11.** Location of passive samplers and ozone measurement stations (left) and AOT40 map for 2010 derived

As for the future no hourly based estimations of Ozone concentration are available, we investigated the possibility to calculate the AOT40 values out of monthly means. For all years 2003 to 2007 the 1-hourly maps were aggregated to monthly means and the values at the locations of measurement stations were extracted. As shown in Figure 13 this estimations show a significant correlation between the monthly means and AOT40 values. So we used this approximation for the calculation of the future AOT40 values according to different ozone

from 1-hourly Ozone maps produced by local interpolation for Valle Camonica (right)

**3. AOT40 estimations in the Alpine area for the future**

the Greater Alpine Region.

concentration scenarios.

**Figure 12.** AOT40 maps for Valle Camonica 2010 (top row) derived from 1-hourly Ozone maps (left), correlation monthly mean/AOT40 using 2010 Valle Camonica data (center), using 2003-2007 measurement data from the alpine area (right) and differences compared to AOT40

**Figure 13.** Correlation between monthly mean ozone and AOT40 values for all available stations in the GAR

Two scenarios2 , originally developed to research future climate conditions and providing ozone concentrations globally until the final decade of this century, were used to estimate the ozone hazard to forests. While application towards air pollution exceeds the scope of climate scenarios, at least some guidance on possible future situations may be derived. Scenarios chosen are "RCP2.6", developed as a low climate forcing scenario that minimizes anthropo‐ genic climate impacts, and the "business as usual" scenario "RCP8.5" which extends current trends. They represent very different views of how the world may look in 2100.

The climate change influence on ozone concentrations from the first to the last decade of the 21st century has been obtained from the World Climate Research Program[3 ] (Figure 14) [15]. This data represent 10-year means of monthly mean ozone concentrations on a global scale with a spatial resolution of 5°. Within MANFRED this data were used to estimate changes of the background concentrations towards the end of this century, upon which we added the fine resolution data as obtained by the interpolation method.

**Figure 14.** Estimation of future monthly mean ozone concentration – April 10-year means for scenario RCP2.6 (top row) and scenario RCP8.5 (bottom row) for decades 2010/2019, 2050/2059 and 2090/2099 (from left to right)

The change signals found in this dataset (Figure 15 second row) were added to the 5-year means (2003-2007) of monthly mean ozone concentration derived from the 1-hourly interpo‐ lated ozone concentration maps (Figure 15 first row). For future scenarios AOT40 maps have been estimated by using the dependencies between monthly mean ozone concentrations and monthly parts of the AOT40 (Figure 15third row). Possible changes of AOT40 values towards the end of the century are shown in the fourth row of Figure 15.

2 The Representative Concentration Pathway or RCP scenarios describe future emission situations at different climate forcing (2.6 W/m² and 8.5 W/m²) in the year 2100, but encompass air pollutant emissions also.

3 (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model datasetis made available publicly by the modelling groups, following the RCP scenarios. This data represent monthly mean ozone concentrations on a global scale with a spatial resolution of 5°. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) collects and archives this data, and the WCRP's Working Group on Coupled Modelling (WGCM) organized the model data analysis activity. The WCRP CMIP3 multi-model dataset is supported by the Office of Science, U.S. Department of Energy.

Assessing Present and Future Ozone Hazards to Natural Forests in the Alpine Area — Comparison of a Wide Scale Mapping Technique with Local Passive Sampler Measurements http://dx.doi.org/10.5772/56280 141

scenarios, at least some guidance on possible future situations may be derived. Scenarios chosen are "RCP2.6", developed as a low climate forcing scenario that minimizes anthropo‐ genic climate impacts, and the "business as usual" scenario "RCP8.5" which extends current

The climate change influence on ozone concentrations from the first to the last decade of the

This data represent 10-year means of monthly mean ozone concentrations on a global scale with a spatial resolution of 5°. Within MANFRED this data were used to estimate changes of the background concentrations towards the end of this century, upon which we added the fine

**Figure 14.** Estimation of future monthly mean ozone concentration – April 10-year means for scenario RCP2.6 (top row) and scenario RCP8.5 (bottom row) for decades 2010/2019, 2050/2059 and 2090/2099 (from left to right)

The change signals found in this dataset (Figure 15 second row) were added to the 5-year means (2003-2007) of monthly mean ozone concentration derived from the 1-hourly interpo‐ lated ozone concentration maps (Figure 15 first row). For future scenarios AOT40 maps have been estimated by using the dependencies between monthly mean ozone concentrations and monthly parts of the AOT40 (Figure 15third row). Possible changes of AOT40 values towards

2 The Representative Concentration Pathway or RCP scenarios describe future emission situations at different climate

3 (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model datasetis made available publicly by the modelling groups, following the RCP scenarios. This data represent monthly mean ozone concentrations on a global scale with a spatial resolution of 5°. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) collects and archives this data, and the WCRP's Working Group on Coupled Modelling (WGCM) organized the model data analysis activity. The WCRP CMIP3 multi-model dataset is supported by the Office of Science, U.S. Department of Energy.

the end of the century are shown in the fourth row of Figure 15.

forcing (2.6 W/m² and 8.5 W/m²) in the year 2100, but encompass air pollutant emissions also.

] (Figure 14) [15].

trends. They represent very different views of how the world may look in 2100.

21st century has been obtained from the World Climate Research Program[3

resolution data as obtained by the interpolation method.

140 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

**Figure 15.** Monthly mean ozone concentrations for April, potential changes in the future, expected values towards the end of 21st century and differences to the period 2003-2007 following scenario RCP2.6 (left) and RCP8.5 (right).

The two RCP scenarios has lead to two significantly different forecasts for ozone risk assess‐ ment, mostly due to the characteristics of the scenarios. In fact, The RCP8.5 (the results on the right in Figure 15) can be understood as a scenario that comprises economic growth as well as increased energy demand including associated emission trends. Some technological improve‐ ments on abatement technologies are overcompensated by growth [16]. At least in mountain‐ ous regions this scenario reflects an increase compared to current levels of ozone accumulation which result in an increased potential ozone risk for forest. There is an overall increase in a large part of the Alpine region but in particular in the southeast of the Swiss and in the central and west part of Austria. The situation for the RCP2.6 Scenario [17] is in plain contrast. As cobenefits to the reduction in energy consumption needed to achieve the low climate forcing, also pollution is reduced drastically leading to ozone levels clearly below the current adverse conditions. The strong reduction of the pollution levels might lead also the AOT40 to values which might be under current critical level of the AOT40 index.