It is worth noticing that AOT40 represents only a potential risk [9] and that this index does not take into account the plant physiology, so even in a positive situation like RCP2.6, the real situation on field could be different, in fact other climatic results of the project highlighted an increase in summer drought and in autumn and winter precipitation. The combined effect of these two results lead to a decrease of the ozone uptake during the summer, as a consequence of a reduced stomatal activity but also to a possible increase of the ozone uptake in winter. This period is usually not considered in the evaluation of the ozone risk assessment, mainly because the vegetation activity and the ozone concentrations are low, but taking also into account the increase in temperature of all climatic scenarios, the ozone risk during the winter months can significantly increase. Furthermore it should be remarked that the two chosen scenarios represent in one case a very good situation (strong reduction of pollution) and in the other a balanced situation (economic growth taking care of the atmospheric emissions) and no pejorative scenario was considered.

#### **4. Conclusions**

The ozone risk assessment was evaluated for the first time at the whole Alps scale, realizing AOT40 maps for 5 consecutive years (2003-2007) and averaging them as suggested in [9]. A great interannual variability was observed and a potential higher ozone risk was identified for the southern part of the Alps.

The experimental field campaign run in Valle Camonica and the following analyses lead to develop local scale maps of the ozone risk which were in a substantial good agreement with the one obtained from monitoring stations only. Some differences were observed, especially at high elevations, but this was mostly due to the denser grid of passive sampler measuring point in the valley.

This intercomparison allowed to develop a semi-empirical relationship between monthly AOT40 values and the monthly average concentration. This relationship was used to estimate future AOT40 values, in fact the monthly average concentrations are the only output of chemical composition models. In this way it was possible assess the ozone risk in the period 2090/2099: in the more optimistic scenario, with strong pollution reduction, a substantial decrease of the AOT40 values was observed, which might result lower than the critical level; in the other scenario, where economic growth is balanced by more attention to the emissions, a significant increase of the AOT40 in the central Alps is forecast.

#### **Acknowledgements**

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

It is worth noticing that AOT40 represents only a potential risk [9] and that this index does not take into account the plant physiology, so even in a positive situation like RCP2.6, the real situation on field could be different, in fact other climatic results of the project highlighted an increase in summer drought and in autumn and winter precipitation. The combined effect of these two results lead to a decrease of the ozone uptake during the summer, as a consequence of a reduced stomatal activity but also to a possible increase of the ozone uptake in winter. This period is usually not considered in the evaluation of the ozone risk assessment, mainly because the vegetation activity and the ozone concentrations are low, but taking also into account the increase in temperature of all climatic scenarios, the ozone risk during the winter months can significantly increase. Furthermore it should be remarked that the two chosen scenarios represent in one case a very good situation (strong reduction of pollution) and in the other a balanced situation (economic growth taking care of the atmospheric emissions) and no

The ozone risk assessment was evaluated for the first time at the whole Alps scale, realizing AOT40 maps for 5 consecutive years (2003-2007) and averaging them as suggested in [9]. A great interannual variability was observed and a potential higher ozone risk was identified for

The experimental field campaign run in Valle Camonica and the following analyses lead to develop local scale maps of the ozone risk which were in a substantial good agreement with the one obtained from monitoring stations only. Some differences were observed, especially at high elevations, but this was mostly due to the denser grid of passive sampler measuring

This intercomparison allowed to develop a semi-empirical relationship between monthly AOT40 values and the monthly average concentration. This relationship was used to estimate future AOT40 values, in fact the monthly average concentrations are the only output of chemical composition models. In this way it was possible assess the ozone risk in the period 2090/2099: in the more optimistic scenario, with strong pollution reduction, a substantial decrease of the AOT40 values was observed, which might result lower than the critical level;

which might be under current critical level of the AOT40 index.

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

pejorative scenario was considered.

the southern part of the Alps.

**4. Conclusions**

point in the valley.

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 and in particular dr G.B. Sangalli, dr A. Ducoli and for their support in realizing this field campaign. A sincere thank to dr E. Soncina and Federico Fausti for their support in 2010.

### **Author details**

Angelo Finco1 , Stefano Oliveri1 , Giacomo Gerosa1 , Wilfried Winiwarter2 , Johann Züger2 and Ernst Gebetsroither2

1 Catholic University of the Sacred Heart, Mathemathics and Physics Department, Catholic University of the Sacred Heart, Brescia, Italy

2 Austrian Institute of Technology GmbH, Vienna, Austria

#### **References**

[16] Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakice‐ novic, N., Rafaj, P.,. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change 2011;109, 33–57.

[5] Bermejo, V., Gimeno, B. S., Sanz, M. J., De La Torre, D., and Gil, J. M.: Assessment of the ozone sensitivity of 22 native plant species from Mediterranean annual pastures

[6] King, J. S., Kubiske, M. E., Pregitzer, K. S., Hendrey, G. R., Mc Donald, E. P., Giardi‐ na, C. P., Quinn, V. S., and Karnosky, D. F.: Tropospheric O3 compromises net pri‐ mary production in young stands of trembling aspen, paper birch and sugar maple

in response to elevated atmospheric CO2. New Phytologist2005; 168, 623–636.

United States using a biogeochemistry model, Tellus B 2004; 56, 230–248.

trees. Environmental pollution 2001; 115, 373-393.

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ronmental Science and Pollution Research1996; 3(4) 213-217.

GIS ReseachII.Taylor& Francis Ltd. London. 1997; 667 - 678.

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Chemistry and Physics 2011 11, 11267-11292.

[7] Felzer, B., Kicklighter, D., Melillo, J., Wang, C., Zhuang, Q., and Prinn, R.: Effects of ozone on net primary production and carbon sequestration in the conterminous

[8] Kolb T.E., Matyssek R.. Limitations and perspectives about scaling ozone impacts in

[9] UN/ECE, Mapping Manual Revision, 2004. UNECE convention on long-range trans‐ boundary air pollution. Manual on the Methodologies and Criteria for Modelling and Mapping Critical Loads and Levels and Air Pollution Effects, Risks and Trends.

[10] Loibl, W., Smidt, St.. Potential Ozone Risk for Selected Tree Species in Austria, Envi‐

[11] Loibl, W., Modelling Tropospheric Ozone Distribution considering the spatio-tempo‐ ral dependencies within complex terrain; in J. Kraak, M. Molenaar (eds.) Advances in

[12] Gerosa, G., Ballarin Denti, A..Regional scale risk assessment of ozone and forests. In: Karnosky D.F., Percy K.E:, Chappelka A.H., Simpson C., Pikkarainen J. (Eds). "Air Pollution, Global Change and Forests in the New Millennium", Elsevier Ltd., 2003

[13] Gerosa,G.,Finco A.,Marzuoli R.,Ferretti M. and GottardiniE.. Errors in ozone risk as‐ sessment using standard conditions for converting ozone concentrations obtained by passive samplers in mountain region. Journal of Environmental Monitoring 2012;14,

[14] Gerosa, G., Ferretti, M., Bussotti, F. and Rocchini, D.. Estimates of ozone AOT40 from passive sampling in forest sites in South-Western Europe. Environmental Pollution

[15] Cionni, I., Eyring, V., Lamarque, J. F., Randel, W. J., Stevenson, D. S., Wu, F., Bodek‐ er, G. E., Shepherd, T. G., Shindell, D. T., and Waugh, D. W.. Ozone database in sup‐ port of CMIP5 simulations: results and corresponding radiative forcing, Atmospheric

based on visible injury, Atmospheric Environment 2003; 37, 4667–4677.

[17] Vuuren, D.P., Stehfest, E., Elzen, M.G.J., Kram, T., Vliet, J., Deetman, S., Isaac, M., Klein Goldewijk, K., Hof, A., Mendoza Beltran, A., Oostenrijk, R., Ruijven, B.,. RCP2.6: exploring the possibility to keep global mean temperature increase below 2°C. Climatic Change 2011; 109, 95–116.

## **A New webGIS Platform Dedicated to Forest Extreme Events in the Alps: Aims and Functionalities**

Stefano Oliveri, Marco Pregnolato and Giacomo Gerosa

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56269

#### **1. Introduction**

Many recent studies [1-6] show that, in the next future, climate change could represent a relevant threat for the state of health of Alpine forests. As a consequence, Alpine communities could be constrained to face an increase of both the frequency and the severity of damaging events affecting their forests (fires, pests attacks, windthrows, heavy snows). Although not all extreme damage occurrences are a direct consequence of extreme meteorological events, many of them have been linked to climatic phenomena. In some cases the relation is absolutely immediate: the storms Vivian (1990) and Lothar (1999), e.g., produced huge windthrows damages to the Alpine forests mainly in Germany, Switzerland and France with millions cubic meters of timber affected. The well-known heat wave registered during summer 2003 pro‐ duced, following the statistics, peaks in the frequency of forest fires both in the same season and in the next winter. Well the same, the extremely hot summer of 2003 has been correlated to several pests outbreaks [7,8] and several studies are conjecturing the possible influences of climate change on forest pathology. Hypothesis have been made about effects on the plant chemical defense mechanisms and on pests range expansions and diapauses, showing both a direct influence of temperatures and droughts periods on the biological cycle of the agents and an impact over the physiology of weakened host trees. Moving from these considerations, when MANFRED project was conceived partners recognized the need for a tool aimed at enabling Alpine communities to share their knowledge on past extreme events, in order to facilitate the diffusion and the sharing of practices which can be applied to prevent those extreme events, mitigate their effects and recover after extreme damaging occurrences. Whilst the greatest part of MANFRED activities have been intended to model and predict the future, the activities related to the development of the webGIS platform implied to look at the past,

© 2013 Oliveri 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.

with the aim to derive the lessons learnt from a set of past extreme occurrences and use them as a tool to better manage the future.

#### **2. Materials and methods**

From an operative point of view, the work has been developed through the following steps:

**Figure 1.** The project home page, with the button allowing the access to the webGIS platform

#### **2.1. Definition of extreme events**

The concept of extreme occurrence has to do with one's perception, familiarity and personal sensitivity. As a consequence, the definition of extreme event is associated with an intrinsic level of subjectivity. Yet, even sticking to the mere technical point of view, the interpretation of extreme stress event in a forest environment may dramatically depend on the different technical backgrounds and experience of the individuals giving the meaning. Foresters, landscape ecologist, conservationists, disaster and risk managers, statisticians and meteorol‐ ogists would probably provide several keys, also referring to different forest-related benefits and roles. In fact, according to a multi-criteria and cross-sector approach, there could be more than one effective way to define an extreme event. Moreover, lack of numeracies and high heterogeneity of available data between the different Alpine regions, make it difficult the adoption of quantitative methods aimed at a standard identification of extreme occurrences. As a consequence, to warrant the selection of extraordinary events, a set of differentiated methods and approaches were applied based on the information and data available at the different areas of the Alpine arch. This kind of approach has been considered consistent with the aims of the activities carried out in the framework of MANFRED project, whose goal did not consist in a scientific definition of extreme event. It was considered much more suitable and useful to the aim of the project to describe an event as extreme mainly following the characteristic of being a non ordinary occurrence, the management of which requires going beyond present and local technical or theoretical know-how, to acquire new knowledge and to develop and implement ad hoc strategies.

#### **2.2. Data on fires**

with the aim to derive the lessons learnt from a set of past extreme occurrences and use them

From an operative point of view, the work has been developed through the following steps:

**•** identification of a set of relevant occurrences, which have been investigated in depth using

**•** publication of results on a webGIS platform (available at the Internet address www.man‐ fredproject.eu/webgis) [9], giving open access to the whole set of data gathered and allowing

**•** census of the events, with a special focus on extreme occurrences;

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

to perform, on those data, both geographic and thematic queries.

**Figure 1.** The project home page, with the button allowing the access to the webGIS platform

The concept of extreme occurrence has to do with one's perception, familiarity and personal sensitivity. As a consequence, the definition of extreme event is associated with an intrinsic level of subjectivity. Yet, even sticking to the mere technical point of view, the interpretation of extreme stress event in a forest environment may dramatically depend on the different

as a tool to better manage the future.

**2. Materials and methods**

a case histories approach;

**2.1. Definition of extreme events**

A dedicated survey allowed the identification of a reference set of data on forest fires, available at Alpine scale. Based on those information, a pan-alpine database of fires has been generated, also in cooperation with another Alpine Space Programme project: ALPFFIRS (www.alpf‐ firs.eu). In the database, each fire is described through a broad set of attributes, listed in Table 1. The database contains information on about 82.000 forest fires, with a differentiated level of thematic contents based on regional availability of data. More than 26.000 occurrences are referred to the decade 2000 – 2009, the time span for which each Alpine region was able to provide information on fires. With specific regard to the identification of extreme events, it has to be highlighted that Total Burnt Area is the only quantitative attribute recorded, without significant lacks of numeracy, in all the fire dataset available at Alpine level. As a consequence, it was used as reference attribute for extreme events selection at Alpine scale. The 99th percentile of the values of the parameter was used for extreme events identification and 105 ha resulted as Alpine threshold for extreme occurrences selection. This kind of approach has a relevant drawback: extreme fires should be better identified in connection with an integrated evaluation of a broad set of variables (dealing, e.g., with territorial and environmental conditions, fire propagation and overall impacts). On the other hand, it allows to make comparisons between different Alpine regions, monitor the spatial and temporal evolution of extreme occurrences and make quantitative evaluations regarding ongoing trends. The analysis at Alpine scale were integrated with a set of regional deepening. It is well known, in fact, that forest fires have distinctive features depending on local conditions. A fire perceived as an exceptional occur‐ rence in one region could fall into a normal regime of events in a different place. Therefore, the different co-operating Alpine regions were asked to make local selections of extreme fire occurrences, based on the whole set of data (both temporal and thematic) available and by adopting locally suitable methodologies.

**Table 1.** Attributes of the pan-alpine database of fires

#### **2.3. Data on biotic and other abiotic factors**

Whilst data on fires are basically comparable between the different areas of the Alps (at least for the core set of attributes gathered in the pan-alpine database), information on pathogens, windthrows and heavy snows are characterized by high heterogeneity in data availability between the different Alpine regions. In fact:

Given the high unhomogeneities in the data available at the different Alpine regions, data on biotic and abiotic factors have been introduced in distinct regional sub-sections. They are not conceived to build a comprehensive Alpine picture of the investigated phenomena and do not allow comparisons between the different regions. Moreover, whilst for fires the provided data allowed the definition of an overall Alpine threshold which was estimated with the aim to identify extreme events, information available on biotic and abiotic factors showed not to be suitable for a quantitative selection of extreme events. Besides the heterogeneity of the data, it must be highlighted that information on biotic and abiotic factors are often acquired on yearly basis (e.g. overall loggings or damages due to a certain disturbance), thus making difficult or impossible to link the values to one or more specific occurrences. With the aim to identify the most relevant occurrences a questionnaire has been produced, that partner spread, in their reference regions, to make a list of memory sifted events that, in the last decades, local population, authorities or practitioners perceived as "non-ordinary" or extreme. The ques‐ tionnaire (Figure 2) asks for a basic set of information: area interested by the event, year or time-span in which the event occurred, stress category (biotic or abiotic), damage factor, main forest cover interested, overall damage (expressed as overall area or damaged volume), eventual further documentation and comments.

**Figure 2.** Structure of the questionnaire used to make a census of "non-ordinary" biotic and abiotic events occurred in the last decades in the Alps

In spite of the relevant drawback of such an approach, consisting in the high subjectivity in the process of signaling the events, it must be pointed out that it allowed to carry out the first geo-referenced census available at Alpine level related to 'non-ordinary' or extreme biotic and abiotic occurrences that affected forests in the Alps.

#### **3. Structure and contents of the platform**

**Attribute Notes Domain**

November-March: winter

DTM

Of the ignition point. Derived from the "Map of Natural Vegetation of Europe"

Whilst data on fires are basically comparable between the different areas of the Alps (at least for the core set of attributes gathered in the pan-alpine database), information on pathogens, windthrows and heavy snows are characterized by high heterogeneity in data availability

**•** in other regions data are available only in paper format, which makes their acquisition and

**•** even in areas with information available, the categories of data acquired are highly hetero‐ geneous both in terms of thematic contents (e.g. some regions monitor loggings and other regions monitor damages) and spatial/temporal resolution of the monitoring systems.

use difficult in the framework of a project with limited temporal duration;

1. natural 2. doubt 3. anthropogenic

1. 0 – 500m 2. 500 – 1.000m 3. 1.000 – 1.500m 4. "/> 1.500m

The legend has been derived from the "Map of Natural Vegetation of Europe" report and structured in three different levels

Date Of the signaling Season April-October: summer

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

Of the ignition point. UTM WGS84 Y coordinate

Of the ignition point NUTS3

Altitude Of the ignition point

Aspect Of the ignition point. Derived from

Year

X coordinate

Cause

NUTS2

Total Burnt Area Forest Burnt Area Non Forest Burnt Area

Vegetation Unit

Data Owner Mail of the Data Owner Data Provider Mail of the Data Provider

**Table 1.** Attributes of the pan-alpine database of fires

**2.3. Data on biotic and other abiotic factors**

between the different Alpine regions. In fact:

**•** some areas do not run dedicated monitoring systems at all;

From a technological point of view, the platform has been developed using J2EE (Java enterprise edition) technology. It runs on an Application Server Tomcat 6.0 and uses a db server PostgreSQL 8.4 and PostGIS 1.5

The webGIS platform consists of three distinct sections (Figure 3) dedicated to:

Each section contains:

The webGIS platform consists of three distinct sections (Figure 3) dedicated to:

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

**•** fires;

**•** biotic factors;

**•** abiotic factors.

**Figure 3.** The main sections of the webGIS platform

It must be pointed out that all the reports produced are based on reference schemes developed for the specific purposes of the project.

#### **3.1. Fires section**

It allows to work on three reference dataset:

When the Alpine option is activated from the Reference dataset box, the platform allows to query and map the data of the pan-alpine database of fires (decade 2000 – 2009) at three aggregation levels: NUTS2 (Region), NUTS3 (District) or it is possible to visualize the distri‐ bution of the ignition points of the single fires (Figure 4). Data can be queried based on many of the attributes of the database (Date, Cause, Class of altitude, Aspect, Class of extension) and it is possible to map the results of the queries both based on the overall number of fires or on the overall Burnt Area. In the resulting maps, grey areas evidence the territories for which a specific kind of information is not available. Besides working on the overall set of occurrences, the platform is also conceived to perform queries and produce maps specifically referred to extreme occurrences. The Identify button can be used to obtain, from the maps, analytical information on the results of the queries and the detail of both the Data Owner and the Data Provider. The Legend tool optimizes the visualization of data on the map, based on each query performed. When single fire events are visualized, the legend classes are pre-defined and intended to distinguish ordinary from "extreme" (>105 ha) Alpine occurrences. The Download area hosts a dedicated report summarizing the main results of the analysis carried out on the pan-alpine database. Specifically: state of the art on data availability, descriptive statistics of the overall dataset and of extreme occurrences selection, results of a fire selectivity analysis carried out with the aim to evidence ongoing trends in fire patterns at Alpine scale and results of a deepening performed to identify the Fire Danger Indexes better performing in predicting extreme fire occurrences in the Alps.

When the Regional option is selected in the Reference dataset box, the platform forces to choose (Zone box) a Region. Once the Region has been selected, the system allows to visualize and query (based on the same attribute categories of the previous section) the occurrences that the different regional partners have considered as extreme, using the whole set of data (both from a temporal and thematic perspective) locally available on fires (Figure 5). The Download area hosts the reports that regional partners have produced to comment the results of the analysis on local extreme occurrences and the set of data introduced in the webGIS platform.

**Figure 4.** Fire section of the webGIS platform: querying and mapping environment of the Alpine sub-section

**Figure 5.** Fire section of the webGIS platform: querying and mapping environment of the Regional sub-sections

Last area of the fire section is the one dedicated to Case histories. When this option is activated, the platform opens an Alpine map, with the spatial distribution of the fire case histories investigated in the project. The Identify button can be used to obtain basic information on each case history and to download the reports on them carried out by the partners (Figure 6).

The following figures (Figure 7 and Figure 8) provide general schematizations of the Alpine and regional fire sections. For each of them, they give an indication of the main querying options and functionalities.

**Figure 6.** Fire section of the webGIS platform: querying and mapping environment of the Case histories sub-section

**Figure 5.** Fire section of the webGIS platform: querying and mapping environment of the Regional sub-sections

**Figure 4.** Fire section of the webGIS platform: querying and mapping environment of the Alpine sub-section

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

**Figure 7.** General schematization of the Alpine fire section, with its main querying options and functionalities

**Figure 8.** General schematization of the regional fire sections, with their main querying options and functionalities

#### **3.2. Biotic and abiotic factors section**

These sections have the same structure and can be considered comparable from an architec‐ tural point of view. They respectively contain data and information on pathogens, windthrows and heavy snows.

They work on four reference set of data:

**Figure 7.** General schematization of the Alpine fire section, with its main querying options and functionalities

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

**Figure 8.** General schematization of the regional fire sections, with their main querying options and functionalities

The Loggings sub-section is conceived to visualize, for the regions with available data on loggings at the level of forest parcel, the distribution of the parcels that, on a yearly basis, have been characterized by "non-ordinary" logging levels (Figure 9). For the selection process a dedicated procedure has been applied which was developed by partners in the framework of the project. The procedure integrates yearly data on loggings at parcel level and information on the area of the parcels. It has been intended to minimize the influence of parcels extension in the identification of outlying logging occurrences and to make the different regional distributions of logging data in fact comparable, by means of a normalization process. Besides allowing to show the distribution of outlying parcels, the platform gives the possibility to perform queries at District level, based on the date of loggings and, when available, on the specification of the damaging agent.

The Download Area hosts the reports produced by regional partners to summarize and comment the data introduced in the platform.

The following figure (Figure 10) provides a general schematization of the sub-sections dedicated to loggings both in the biotic and abiotic factors sections.

The Damages sub-section allows to map and query data on any Indicator (e.g. loggings, damaged area, number of signaling), provided by the partners regions, useful to describe the spatial and temporal (yearly basis) evolution of the "pressure" of biotic and abiotic agents on local forests. The platform is conceived to query and map the data, at NUTS2 (Region) or NUTS3 (District) level, based on a broad set of parameters: time-span, various levels of thematic information on the damaging agent (e.g., for pathogens, Category, Order, Family, Species) and querying Key parameter (Figure 11).

Like in the previous case, a dedicated area can be accessed to download regional reports commenting and summarizing the data hosted by the platform.

The Case Histories sub-section is intended to allow the user to visualize, on an Alpine map, the distribution of case histories investigated in the framework of the project. Through the Identify button, the user can obtain basic information on each case history and download the dedicated report produced on the specific occurrence.

**Figure 9.** Sections dedicated to Biotic factors and Abiotic factors of the webGIS platform: querying and mapping envi‐ ronment of the Loggings sub-section

**Figure 10.** General schematization of the sub-sections on logging both in the section dedicated to biotic and abiotic factors. Main querying options and functionalities are evidenced

A New webGIS Platform Dedicated to Forest Extreme Events in the Alps: Aims and Functionalities http://dx.doi.org/10.5772/56269 159

**Figure 11.** Sections dedicated to Biotic factors and Abiotic factors of the webGIS platform: querying and mapping en‐ vironment of the Damages sub-section

The following figure (Figure 12) provides a general schematization of damages area, both in the biotic and abiotic factors sections.

**Figure 12.** General schematization of the sub-sections on damages both in the section dedicated to biotic and abiotic factors. Main querying options and functionalities are evidenced

**Figure 10.** General schematization of the sub-sections on logging both in the section dedicated to biotic and abiotic

**Figure 9.** Sections dedicated to Biotic factors and Abiotic factors of the webGIS platform: querying and mapping envi‐

factors. Main querying options and functionalities are evidenced

ronment of the Loggings sub-section

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

The Memory Sifted Events sub-section has been conceived to allow the user to query the database generated through the spread of the questionnaire used to make a census of the events perceived as "non-ordinary" or extreme. The sub-section gives access to a page conceived to allow the query (not the mapping) of the database, based on District, Damage factor and Year of occurrence of the events. The system lists the occurrences meeting the querying rules, with the specification of the whole set of details introduced in the questionnaire (Figure 13).

**Figure 13.** Sections dedicated to Biotic factors and Abiotic factors of the webGIS platform: querying environment of the Memory Sifted Events sub-section

#### **4. Conclusions**

Building a webGIS within the MANFRED project had different goals. Obviously, to contain and to make easily accessible the vast dataset gathered during the three years of the project among several partners, giving body to the effort of collecting a common knowledge base, provided by the several research centers and institutions of all the involved countries. The webGIS provides, in the form of an interactive reference book, the useful key to the consultation and interpretation of those data. It also gives an overall picture of the situation regarding forest damages monitoring procedures on the Alpine scale. Both the database and the webGIS are intended to be open-ended activities, to be further improvable, and to be run steadily as a web resource. The possibility to locate and retrieve information about extreme events occurred in the recent past can be particularly meaningful for local administrators and involved practi‐ tioners, who could find themselves in the necessity to deal with new similar events due to fire, snow, storm or pests within an Alpine forest environment. To this purpose, contact details of the main referents for the case histories have been made available in the webGIS. We remark here the importance to confront each others on our experiences from the recent past and on the strategies we developed to manage them, both assimilating the good practices and learning from errors. We believe, in fact, that the most effective way to face the uncertainties related to climate change is to have at disposal the widest and most various range of knowledge, enriched by the contribute of everyone between the two ends of the Alps and beyond. This research made the contacts between several experts and professionals from the six countries that have been involved. These actions have hopefully created a stable network of people and of shared knowledge: a wealth in resources that we wish to be durable and to be able to bear fruit in the preservation and the future management of our forests and our communities.

With specific regard to the data gathered and the investigations carried out, it is important to remark that:

Based on the work done and taking the difficulties experienced in collecting and integrating the different set of data available at Alpine level as a starting point for mentioning some relevant future challenges, we can underline that it would be strategic, with the aim to build an integrated picture on the state of health of forests in the Alps, to improve the existing monitoring tools and steer the acquisition of data towards a higher degree of homogeneity between the different Alpine regions. Moreover, the opportunity to run permanent monitoring systems on the Alpine forests should be recognized and, hopefully, the platform generated in the framework of MANFRED project should be further integrated and updated in the future.

#### **Acknowledgements**

The Memory Sifted Events sub-section has been conceived to allow the user to query the database generated through the spread of the questionnaire used to make a census of the events perceived as "non-ordinary" or extreme. The sub-section gives access to a page conceived to allow the query (not the mapping) of the database, based on District, Damage factor and Year of occurrence of the events. The system lists the occurrences meeting the querying rules, with the specification of the whole set of details introduced in the questionnaire (Figure 13).

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

**Figure 13.** Sections dedicated to Biotic factors and Abiotic factors of the webGIS platform: querying environment of

Building a webGIS within the MANFRED project had different goals. Obviously, to contain and to make easily accessible the vast dataset gathered during the three years of the project among several partners, giving body to the effort of collecting a common knowledge base, provided by the several research centers and institutions of all the involved countries. The webGIS provides, in the form of an interactive reference book, the useful key to the consultation and interpretation of those data. It also gives an overall picture of the situation regarding forest damages monitoring procedures on the Alpine scale. Both the database and the webGIS are intended to be open-ended activities, to be further improvable, and to be run steadily as a web resource. The possibility to locate and retrieve information about extreme events occurred in the recent past can be particularly meaningful for local administrators and involved practi‐

the Memory Sifted Events sub-section

**4. Conclusions**

This work was conducted under the European Project MANFRED, funded by the European Regional Development fund of the Alpine Space Program, reference number 15-2-3-D (MANFRED).

A large set of partners co-operated in the activities: ERSAF. Ente Regionale per i Servizi all'Agricoltura e alle Foreste; FVA. Forstliche Versuchs - und Forschungsanstalt BW; Slovenian Forest Service; Slovenian Forest Institute; Catholic University of Brescia; Italian Ministry of the Environment, Land and Sea; University of Camerino; CEMAGREF. Institut de recherche pour l'ingénierie de l'agriculture et de l'environnement; IPLA. Istituto per le Piante da Legno e l'Ambiente; Regione Autonoma Valle d'Aosta; LWF. Bavarian State Institute of Forestry; WSL. Swiss Federal Institute for Forest, Snow and Landscape Research; BOKU. University of Natural Resources and Life Sciences; BFW. Research and Training Centre for Forests, Natural Hazards and Landscape.

External contributions were provided by the University of Torino and by Regione Autonoma Friuli Venezia Giulia.

The research and its publication have been funded (partially or fully) by the Catholic Univer‐ sity of the Sacred Heart, in the framework of its programs aimed at the promotion and the dissemination of scientific research.

#### **Author details**

Stefano Oliveri1\*, Marco Pregnolato2 and Giacomo Gerosa2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Ecometrics srl., Italy

2 Catholic University of Brescia, Mathematics and Physics Department, Italy

#### **References**

[5] Battisti, A, Stastny, M, Netherer, S, Robinet, C, Schopf, A, Roques, A, & Larsson, S. Expansion of Geographic Range in the Pine Processionary Moth Caused by Increased WinterTemperatures. Ecological Applications (2005). , 15(6), 2084-2096.

Forest Service; Slovenian Forest Institute; Catholic University of Brescia; Italian Ministry of the Environment, Land and Sea; University of Camerino; CEMAGREF. Institut de recherche pour l'ingénierie de l'agriculture et de l'environnement; IPLA. Istituto per le Piante da Legno e l'Ambiente; Regione Autonoma Valle d'Aosta; LWF. Bavarian State Institute of Forestry; WSL. Swiss Federal Institute for Forest, Snow and Landscape Research; BOKU. University of Natural Resources and Life Sciences; BFW. Research and Training Centre for Forests, Natural Hazards

External contributions were provided by the University of Torino and by Regione Autonoma

The research and its publication have been funded (partially or fully) by the Catholic Univer‐ sity of the Sacred Heart, in the framework of its programs aimed at the promotion and the

and Giacomo Gerosa2

[1] Faccoli, M, & Battisti, A. in press) Climate change and forest pests: models from the

[2] Marini, L, Ayres, M. P, Battisti, A, & Faccoli, M. Climate affects severity and altitudi‐ nal distribution of outbreaks in an eruptive bark beetle. Climatic Change (2012).

[3] Lindner, M, Maroschek, M, Netherer, S, et al. Climate change impacts, adaptive ca‐ pacity, and vulnerability of European forest ecosystems. For Ecol Manag (2010). , 259,

[4] Boisvenue, C, & Running, S. W. Impacts of climate change on natural forest produc‐ tivity-evidence since the middle of the 20th century. Glob Change Biol. (2006). , 12,

and Landscape.

Friuli Venezia Giulia.

**Author details**

1 Ecometrics srl., Italy

**References**

dissemination of scientific research.

Stefano Oliveri1\*, Marco Pregnolato2

DOIs10584-012-0463-z.

698-709.

1-21.

\*Address all correspondence to<EMAIL_ADDRESS>
162 Management Strategies to Adapt Alpine Space Forests to Climate Change Risks

Alps- Canadian Journal of Forest Research

2 Catholic University of Brescia, Mathematics and Physics Department, Italy

## **Drought in Alpine Areas Under Changing Climate Conditions**

Ernst Gebetsroither, Johann Züger and Wolfgang Loibl

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56277

#### **1. Introduction**

Forest ecosystem sensitivity to climate change is a result of direct impacts from climate (e.g. changes of temperature and precipitation) and indirect impacts from several biotic (e.g. pests) and abiotic factors (CO2 and Ozone concentration) influenced by climate change [12]. Within this chapter we will concentrate on possible changes for drought hazards due to climate change scenarios. Drought generally has a negative impact on ecosystem productivity and increases mortality. Species adapted to cold and wet condi‐ tions with low reproduction rates and limited mobility seem to be most affected. It was found that beech, and surprisingly the broadleaved Mediterranean forests are highly sen‐ sitive to drought [1]. The drought of 2003 was in some areas, especially in Germany and France, the strongest drought during the last 50 years. The analysis showed that some time lag effect can occur, thus e.g. for beech the growth reduction was stronger in the following year 2004 after the drought event of 2003. Besides the strong impact of ex‐ treme years as 2003 experts assume that in the long run, a change in the frequency of hot and dry years could affect tree species composition and diversity more than one sin‐ gle event [6]. The hydrological cycle at the local scale might superimpose the influences from climate change on a broader scale but extreme events such as droughts cause growth reductions across many site conditions [4]. For the drought analysis within the MANFRED project and because of the information gap1 on local site conditions for the whole study area we concentrated our analysis on the predicted input changes from pre‐ cipitation and temperature.

<sup>1</sup> like information about the soil, groundwater, surface runoff etc.

© 2013 Gebetsroither 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.

#### **2. Method**

#### **2.1. Regional climate modelling**

Global Circulation Models (GCMs) simulate large scale features of global atmospheric and ocean circulation, based on physical principles. A relevant disadvantage is the coarse hori‐ zontal resolution of app. 100-120km in mid-latitudes. This resolution is inadequate to reflect the orographic influences and effects of land cover and soil on atmospheric and hydrological processes at regional scales (Figure 1). Therefore a Regional Climate model (RCM) is embedded into a global model to calculate the atmospheric and hydrological processes on finer scales down to 10x10 km (Figure 2). Along borders the RCM is supplied with data from the GCM, inside the model domain the RCM develops its own dynamics. While the GCM simulates the response of the climate system to global driving forces (greenhouse gas, solar activity, volcanoes …) the RCM simulates additionally the impact of regional factors like terrain, soil and land cover.

**Figure 1.** Temperature values in a N-S cross section through the Alps as calculated by GCMs (red triangles) and as ex‐ pected due to elevation (black squares) ( AIT, figure based on ECHAM4 results and a temperature-elevation gradient based on observation data)

Another important point is to distinguish between weather prediction models and climate models. A weather prediction model starts from actual state of the atmosphere and calculates a few days into the future, but after 10-14 days the model is no longer correlated to the real state of the atmosphere. On the other hand the simulation time in climate models may be up to centuries. Therefore climate models do not describe actual weather, but a potential realisa‐ tion of weather conditions. Only in long term statistics like mean values or variability a climate model should be similar to real weather behaviour. Weather prediction like: "It`s raining on April, 24th 2045" is not possible, but statistical information similar to: "The probability of precipitation in June in the 2040s is higher than today" can be derived.

**Figure 2.** RCM domain (Δx= 10 – 50 km) embedded into a GCM (Δx= 100 – 300 km), Wegener Center Univ.Graz)

#### **2.2. Three steps to regional climate scenarios**

**2. Method**

and land cover.

based on observation data)

**2.1. Regional climate modelling**

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

Global Circulation Models (GCMs) simulate large scale features of global atmospheric and ocean circulation, based on physical principles. A relevant disadvantage is the coarse hori‐ zontal resolution of app. 100-120km in mid-latitudes. This resolution is inadequate to reflect the orographic influences and effects of land cover and soil on atmospheric and hydrological processes at regional scales (Figure 1). Therefore a Regional Climate model (RCM) is embedded into a global model to calculate the atmospheric and hydrological processes on finer scales down to 10x10 km (Figure 2). Along borders the RCM is supplied with data from the GCM, inside the model domain the RCM develops its own dynamics. While the GCM simulates the response of the climate system to global driving forces (greenhouse gas, solar activity, volcanoes …) the RCM simulates additionally the impact of regional factors like terrain, soil

**Figure 1.** Temperature values in a N-S cross section through the Alps as calculated by GCMs (red triangles) and as ex‐ pected due to elevation (black squares) ( AIT, figure based on ECHAM4 results and a temperature-elevation gradient

Another important point is to distinguish between weather prediction models and climate models. A weather prediction model starts from actual state of the atmosphere and calculates a few days into the future, but after 10-14 days the model is no longer correlated to the real state of the atmosphere. On the other hand the simulation time in climate models may be up to centuries. Therefore climate models do not describe actual weather, but a potential realisa‐ tion of weather conditions. Only in long term statistics like mean values or variability a climate model should be similar to real weather behaviour. Weather prediction like: "It`s raining on April, 24th 2045" is not possible, but statistical information similar to: "The probability of

precipitation in June in the 2040s is higher than today" can be derived.

Regional climate is influenced by global atmospheric conditions and the global climate processes. Hence, for calculations of regional climate scenarios it is necessary to use a stepwise approach, consisting of three main steps:

**Step 1:** Selection of a particular emission scenario for consideration of atmospheric changes.

**Step 2:** Selection of a global climate model to calculate the scenarios for global climate processes.

**Step 3:** Selection of an appropriate regional climate model and calculation of the regional climate scenarios.

#### *2.2.1. Step 1: Selection of a particular emission scenario for consideration of atmospheric changes*

The Intergovernmental Panel on Climate Change (IPCC) provides a wide range of Green House Gas scenarios based on assumptions of the future development of technologies and society (Figure 3).

**Figure 3.** CO2 concentrations in the atmosphere as predicted by different IPCC scenarios (IPCC 3rd Assessment Report – Climate Change, 20012)

The selection of an appropriate scenario is the first important step towards the estimation of climate change signals. The A1 storyline for example describes a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and assumes rapid introduction of new and more efficient technologies. Major underlying themes are convergence among regions, capacity building, and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income. The A1 scenario family can be divided into three branches describing alternative directions of technological change in the energy system. They are distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all sources (A1B)3 . Out of this the scenario A1B was selected because it represents a moderate increase of Green House Gases (GHG) and is located in the centre of all covered assump‐ tions (Figure 4). The predicted temperature boost for different emission scenarios addition‐ ally depends on the used GCM and covers a range from app. 1°C to more than 5°C. The estimated global temperature increase for the scenario A1B until the end of the century is in the range of +1.5°C to +2.5°C, which underlines its conservative position as it does not tend to any extreme at the upper or lower bounds.

**Figure 4.** Increase of the global temperature according to different IPCC scenarios4, (IPCC, 2001, 3rd Assessment re‐ port)

<sup>2</sup> http://www.grida.no/publications/other/ipcc%5Ftar/?src=/climate/ipcc\_tar/wg1/figspm-5.htm , accessed 15.10.2012 3 http://www.ipcc.ch/pdf/special-reports/spm/sres-en.pdf , IPCC SPECIAL REPORT, EMISSIONS SCENARIOS, 2000 4 http://www.grida.no/publications/other/ipcc%5Ftar/?src=/climate/ipcc\_tar/wg1/fig9-14.htm, accessed 15.10.2012

**Figure 5.** Increase of the global temperature according to the IPCC scenario A1B predicted by different GCMs. The arrows mark the temperature timeline given by the ECHAM5 model (dashed yellow line), [15]

#### *2.2.2. Step 2: Selection of a global climate model to calculate the scenarios for global climate processes*

The 2nd step in the approach is the choice of the GCM providing the driving data. In our case results from the German ECHAM5/MPI-OM ([17], [18]) model have been used. The tempera‐ ture increase towards the end of the century predicted by the ECHAM5 model tends to be at the lower end compared to other models (black arrows in Figure 5). Further information on the ECHAM5 model can be obtained from http://www.mpimet.mpg.de/en/wissenschaft/ modelle.html5 .

#### *2.2.3. Step 3: Selection of an appropriate regional climate model*

The selection of an appropriate scenario is the first important step towards the estimation of climate change signals. The A1 storyline for example describes a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and assumes rapid introduction of new and more efficient technologies. Major underlying themes are convergence among regions, capacity building, and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income. The A1 scenario family can be divided into three branches describing alternative directions of technological change in the energy system. They are distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all

increase of Green House Gases (GHG) and is located in the centre of all covered assump‐ tions (Figure 4). The predicted temperature boost for different emission scenarios addition‐ ally depends on the used GCM and covers a range from app. 1°C to more than 5°C. The estimated global temperature increase for the scenario A1B until the end of the century is in the range of +1.5°C to +2.5°C, which underlines its conservative position as it does not tend

**Figure 4.** Increase of the global temperature according to different IPCC scenarios4, (IPCC, 2001, 3rd Assessment re‐

2 http://www.grida.no/publications/other/ipcc%5Ftar/?src=/climate/ipcc\_tar/wg1/figspm-5.htm , accessed 15.10.2012 3 http://www.ipcc.ch/pdf/special-reports/spm/sres-en.pdf , IPCC SPECIAL REPORT, EMISSIONS SCENARIOS, 2000 4 http://www.grida.no/publications/other/ipcc%5Ftar/?src=/climate/ipcc\_tar/wg1/fig9-14.htm, accessed 15.10.2012

. Out of this the scenario A1B was selected because it represents a moderate

sources (A1B)3

port)

to any extreme at the upper or lower bounds.

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

The 3rd step includes the appropriate choice of the RCM providing high resolution data needed for regional analyses. In our case the German "Consortial Runs" [8] were chosen. They were carried out with the RCM Cosmo-CLM6 . The Grid on which these model runs are based consists of 257x271 cells with a resolution of 0.165° which corresponds to app. 18x18km in mid-latitudes (Figure 6). The time range of these simulations starts in 1961 and covers 140 years up to 2100. Many atmospheric parameters (like temperature, precipitation, snow, wind components, radiation, cloud cover, etc.) are available on a daily or even hourly basis from the German Climate Data Center (DKRZ) via the Climate and Environmental Retrieval and Archive (CERA7 ).

<sup>5</sup> http://www.mpimet.mpg.de/en/science/models/echam/echam5.html

**Figure 6.** Model domain for Cosmo-CLM Consortial Runs (257x271 cells, resolution: 0,165°), [8]

#### **2.3. Drought hazard calculation with regional climate input data**

As there is no single drought parameter which can summarize the impact of climate change on drought for forests, it has been decided to calculate several parameters to provide as much information for forest practitioners as possible. For the current study results of the IPCC climate scenario A1B8 downscaled to a spatial resolution of 1x1km and daily time resolution have been applied. Temperature as well as precipitation data for every day have been analysed within the time period from 1961 to 2100. As the scenario A1B is rather moderate concerning emissions and temperature increase (cf. Figure 4&5) the results according to climate change impacts on drought presented within the MANFRED project can be seen, despite of the uncertainty of the climate change scenarios, as a quite probable development.

<sup>6</sup> http://www.clm-community.eu/, accessed 3.9.2012

<sup>7</sup> http://cera-www.dkrz.de,accessed3.9.2012

<sup>8</sup> http://www.ipcc.ch/ipccreports/tar/wg1/029.htm (accessed 3.9.2012) and Figure 3&4

All calculations have been performed for the whole study area of the MANFRED project with a horizontal resolution of 1x1km. Climate data where statistically downscaled from the original 18x18km simulation results and investigated for 10 case study regions. The downscaling exercise has been conducted by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL)9 (see therefore the particular chapter in this book).

The following list shows the main parameters, which have been calculated as 30-year means:

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

The Figures 7 and 8 show that precipitation in general is reduced in the whole area at any time. For regions south of the Alps a stronger reduction is estimated and the decrease after 2050 is more significant than till 2050.

To calculate the number of dry days we have used a value used a threshold of 1mm precipi‐ tation per day as threshold for a dry day. This was done because 1mm of rainfall usually will not reach the soil in forested areas because it is intercepted in the canopy. Furthermore, climate models tend to overestimate the precipitation and values of zero precipitation occur almost never. The calculation of the number of dry days can be performed with different thresholds, but this will not significantly change the results as we focus on the change signals13 and not the absolute values of dry days (Figures 9 and 10).

**Figure 6.** Model domain for Cosmo-CLM Consortial Runs (257x271 cells, resolution: 0,165°), [8]

As there is no single drought parameter which can summarize the impact of climate change on drought for forests, it has been decided to calculate several parameters to provide as much information for forest practitioners as possible. For the current study results of the IPCC climate

applied. Temperature as well as precipitation data for every day have been analysed within the time period from 1961 to 2100. As the scenario A1B is rather moderate concerning emissions and temperature increase (cf. Figure 4&5) the results according to climate change impacts on drought presented within the MANFRED project can be seen, despite of the uncertainty of the

downscaled to a spatial resolution of 1x1km and daily time resolution have been

**2.3. Drought hazard calculation with regional climate input data**

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

climate change scenarios, as a quite probable development.

8 http://www.ipcc.ch/ipccreports/tar/wg1/029.htm (accessed 3.9.2012) and Figure 3&4

6 http://www.clm-community.eu/, accessed 3.9.2012 7 http://cera-www.dkrz.de,accessed3.9.2012

scenario A1B8

<sup>9</sup> http://www.wsl.ch/

<sup>10</sup> vegetation period (April to September), annual and seasonal results

<sup>11</sup> a dry day is a day with less than 1mm precipitation

<sup>12</sup> heat waves are: 3 consecutive days with Temp. > 30°C & until mean max. Temp. < 30 °C & not max. Temp. of one day < 25°C

**Figure 7.** Relative change of total precipitation between 1971/2000 and 2021/2050 (30 yr mean) (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and spatial analy‐ sis: AIT)

**Figure 8.** Relative change of total precipitation between 1971/2000 and 2071/2100 (Source: ECHAM5/CLM A1B Con‐ sortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and spatial analysis: AIT)

<sup>13</sup> the relative differences not the absolute values

**Figure 9.** Change of number of dry days during vegetation period in 2041/2070 compared to 1971/2000 (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and spatial analy‐ sis: AIT)

**Figure 10.** Change of number of dry days during vegetation period in 2071/2100 compared to 1971/2000 (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and spatial analy‐ sis: AIT)

13 the relative differences not the absolute values

sis: AIT)

**Figure 7.** Relative change of total precipitation between 1971/2000 and 2021/2050 (30 yr mean) (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and spatial analy‐

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

**Figure 8.** Relative change of total precipitation between 1971/2000 and 2071/2100 (Source: ECHAM5/CLM A1B Con‐

sortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and spatial analysis: AIT)

The amount of total precipitation will be reduced in the future, and the number of dry days and also the maximum length of dry episodes will increase. But also the distribution of rainfall during the year will change which may be assumed to have a stronger impact on vegetation (Figure 11).

**Figure 11.** Mean annual cycle of daily precipitation for different periods (30 yr means). (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and analysis AIT)

The 30y mean of daily precipitation behaviour for the entire GAR shows a shift from main precipitation during the period from May to August to a maximum in April and November. Major reductions may occur from May to end of September so that during summer the drought hazard will increase significantly and also be strengthened due to the temperature increase in this period of the year, as predicted for the future.

#### **3.1. Development of heat waves**

As additional parameter we calculated a Heat Wave Index (HWI) according to [7] and [9], which is defined as at least 3 consecutive days with max. temperature above 30 °C and each further day with max. temperature above 25 °C as long as the mean of the max. temperatures over the whole period is not below 30°C. Usually, temperature decreases with higher altitudes, which means that a threshold of 30°C will never be reached in these regions. Nevertheless, some "heat waves" will also occur at higher elevations. To be able to calculate a HWI in a mountainous region, all temperature values had to be normalized to sea level. To achieve this, an average temperature gradient of 0.7°C for each 100m of altitude was assumed. Thus, from a Digital Elevation Model (DEM) of the Greater Alpine Region with a spatial resolution of 1km a theoretical temperature at sea level was calculated. These normalized temperature values were used to determine the HWI for the vegetation period from April to September (Figure 12 and 13). As shown in Figure 14 the maximum length of heat waves increases over the entire GAR, with significant stronger rise south of the Alps.

The amount of total precipitation will be reduced in the future, and the number of dry days and also the maximum length of dry episodes will increase. But also the distribution of rainfall during the year will change which may be assumed to have a stronger impact on vegetation

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

**Figure 11.** Mean annual cycle of daily precipitation for different periods (30 yr means). (Source: ECHAM5/CLM A1B

The 30y mean of daily precipitation behaviour for the entire GAR shows a shift from main precipitation during the period from May to August to a maximum in April and November. Major reductions may occur from May to end of September so that during summer the drought hazard will increase significantly and also be strengthened due to the temperature increase in

As additional parameter we calculated a Heat Wave Index (HWI) according to [7] and [9], which is defined as at least 3 consecutive days with max. temperature above 30 °C and each further day with max. temperature above 25 °C as long as the mean of the max. temperatures over the whole period is not below 30°C. Usually, temperature decreases with higher altitudes, which means that a threshold of 30°C will never be reached in these regions. Nevertheless, some "heat waves" will also occur at higher elevations. To be able to calculate a HWI in a mountainous region, all temperature values had to be normalized to sea level. To achieve this, an average temperature gradient of 0.7°C for each 100m of altitude was assumed. Thus, from a Digital Elevation Model (DEM) of the Greater Alpine Region with a spatial resolution of 1km a theoretical temperature at sea level was calculated. These normalized temperature values were used to determine the HWI for the vegetation period from April to September (Figure

Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and analysis AIT)

this period of the year, as predicted for the future.

**3.1. Development of heat waves**

(Figure 11).

**Figure 12.** 30-year mean of maximum heat wave length during vergetation period vegetation season 1971/2000 – temperature normalized to sea level. (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km down‐ scaling: WSL. Data compilation and analysis: AIT)

**Figure 13.** 30-year mean of maximum heat wave length during vergetation period vegetation season 2071/2100 – temperature normalized to sea level. (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km down‐ scaling: WSL. Data compilation and analysis: AIT)

**Figure 14.** Change of maximum heat wave length during vergetation period for 2071/2100 compared to 1971/2000 – temperature normalized to seal level (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km down‐ scaling: WSL. Data compilation and analysis: AIT)

#### **4. Presentation of major results for the case study regions**

This section discusses exemplarily major results for five of the ten case study regions. The different location of the regions can be seen in Figure 15. Location and extent of the regions have been provided by the project partners of MANFRED. The criteria for taking the five samples out of the ten was to select one from each country providing sample regions as well as covering the climate characteristics from north to south and from west to east.

All results represent statistical means of the entire regions of interest. Larger regions – overall the Italian region I1 (Lombardy) – show a wider range of climate characteristics as the area reaches from low elevated areas with Mediterranean influence to high Alpine areas. The differences in the area mean and area max values give some hint regarding the range of the values.

The following table of diagrams (Figure 16) presents the annual variation of precipitation for the selected case study regions. They all show declining precipitation during the summer months. But from north to south the changes somehow deviate. In the northern regions – influenced by Atlantic climate ("Nordstaulagen") – the case study regions D1, A1 may expect a growing peak of spring precipitation and from May to September a high decrease during the future decades. The regions, which are influenced by the Mediterranean Sea (I1 and S1) will face a decline of summer precipitation and the evolution of two distinct peaks: a smaller spring

**Figure 15.** Location of the ten case study regions explored in MANFRED

**Figure 14.** Change of maximum heat wave length during vergetation period for 2071/2100 compared to 1971/2000 – temperature normalized to seal level (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km down‐

This section discusses exemplarily major results for five of the ten case study regions. The different location of the regions can be seen in Figure 15. Location and extent of the regions have been provided by the project partners of MANFRED. The criteria for taking the five samples out of the ten was to select one from each country providing sample regions as well

All results represent statistical means of the entire regions of interest. Larger regions – overall the Italian region I1 (Lombardy) – show a wider range of climate characteristics as the area reaches from low elevated areas with Mediterranean influence to high Alpine areas. The differences in the area mean and area max values give some hint regarding the range of the

The following table of diagrams (Figure 16) presents the annual variation of precipitation for the selected case study regions. They all show declining precipitation during the summer months. But from north to south the changes somehow deviate. In the northern regions – influenced by Atlantic climate ("Nordstaulagen") – the case study regions D1, A1 may expect a growing peak of spring precipitation and from May to September a high decrease during the future decades. The regions, which are influenced by the Mediterranean Sea (I1 and S1) will face a decline of summer precipitation and the evolution of two distinct peaks: a smaller spring

**4. Presentation of major results for the case study regions**

as covering the climate characteristics from north to south and from west to east.

scaling: WSL. Data compilation and analysis: AIT)

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

values.

peak and a high peak in November. The Swiss region (C1) seems to be affected by both influences – the precipitation decline in summer is still high, two precipitation peaks occur – a higher spring peak which is characteristic for the northern regions and a smaller autumn peak which is typical for Mediterranean influence. In all areas there is a significant secondary sink in October.

The diagrams in Figure 17 show the evolution of the total precipitation during the growing season for the selected case study regions: All regions show a moderate decline of the average and the maximum rainfall sum. The German (D1) as well as the Slovenian (S1) case study region show moderate precipitation ranges between 600 and 800 mm which declines in Germany to 600 for max values and 550 mm for average values, in Slovenia the max values remain almost the same, the average values decline from almost 700 mm to 350 mm. All other case study regions show an absolute decline of about 100 - 200 mm of the max values in the high alpine areas and again an absolute decline of about 50 - 100 mm of the average numbers referring more to the low elevated areas. For the lower elevated areas like the Italian region I1 the decline may turn out to be severe as the precipitation sum during the vegetation period is between 500 and 700mm and the temperature is going to be higher which results in higher evapotranspiration.

While in Figure 17 the total precipitation during the growing season is shown, Figure 18 depicts the seasonal precipitation sums during different 30 year periods. In all regions a significant decrease of the summer precipitation is obvious: the total rainfall during summer declines from 400 to 300 mm in the northern regions, from app. 400 to 250 mm in the Slovenian region (S1) and from 250 to 150 mm in the Italian areas with Mediterranean influences (I1).

**Figure 16.** Mean yearly distribution of precipitation from 1971/2000 to 2071/2100 for case study regions D1, A1, C1, I1 and S1 (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compila‐ tion and analysis: AIT)

The following table of diagrams (fig. 19) indicates the evolution of the number of dry days during the vegetation season for the selected case study regions. All regions show a distinct increase of the average and the maximum number of dry days: the less alpine German region (D1) starting from 69 (average) / 73 (max) dry days may expect an extra of about 20 dry days

**Figure 17.** Mean total precipitation during the vegetation period from 1971-2000 to 2071/2100 for case study re‐ gions D1, A1, C1, I1 and S1 (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and analysis: AIT)

till 2100, the Swiss region (C1) starts from 48 (average) / 55 (max) dry days and will face an increase of app. 15 dry days till 2100. The Vorarlberg Region (A1) with alpine climate and some moderate influence of Lake Constance starts with 80 (average) / 90 (max) dry days and may expect another 20 dry days till 2100. In the small Slovenian region (S1) the number of dry days will increase from app. 120 by 10 to 20 days till 2100. The large Italian region (I1) shows the widest range because of the Alpine and the Mediterranean influences with hottest tempera‐

The following table of diagrams (fig. 19) indicates the evolution of the number of dry days during the vegetation season for the selected case study regions. All regions show a distinct increase of the average and the maximum number of dry days: the less alpine German region (D1) starting from 69 (average) / 73 (max) dry days may expect an extra of about 20 dry days

**Figure 16.** Mean yearly distribution of precipitation from 1971/2000 to 2071/2100 for case study regions D1, A1, C1, I1 and S1 (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compila‐

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

tion and analysis: AIT)

**Figure 18.** Mean seasonal total precipitation sums from 1971-2000 to 2071/2100 for case study regions D1, A1, C1, I1 and S1 (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and analysis: AIT)

tures in the Po Valley: 110 (average) and 135 (max.) dry days with an expected increase of 15 dry days.

The following table of diagrams (fig. 20) shows the evolution of the seasonal 30 year mean number of drought periods greater than 10 days for the selected case study regions. In general the number of more than 10-day dry periods for current climate accounts to 1-2 events per

**Figure 19.** Mean number of dry days during vegetation season from 1971/2000 to 2071/2100 for case study regions D1, A1, C1, I1 and S1 (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and analysis: AIT)

season and is expected to grow by app. 1 event during summer months till the end of the century. All regions show a significant increase of such drought periods during summer. The northern regions (A1 and D1) show a rise up to nearly 200 %, the Slovenian region up to 90% and the Italian regions – due to higher occurrences at present – app. 40 %. At most locations there may be some reduction in spring, but overall changes during the other seasons except summer are not very significant.

tures in the Po Valley: 110 (average) and 135 (max.) dry days with an expected increase of 15

**Figure 18.** Mean seasonal total precipitation sums from 1971-2000 to 2071/2100 for case study regions D1, A1, C1, I1 and S1 (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation

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

The following table of diagrams (fig. 20) shows the evolution of the seasonal 30 year mean number of drought periods greater than 10 days for the selected case study regions. In general the number of more than 10-day dry periods for current climate accounts to 1-2 events per

dry days.

and analysis: AIT)

**Figure 20.** Relative change of mean number of dry periods greater than 10 days compared to period 1971/2000 for case study regions D1, A1, C1, I1 and S1 (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km downscaling: WSL. Data compilation and analysis: AIT)

#### **5. General result discussion**

The Figures above let expect on the one hand a decrease of total precipitation within the vegetation period during the coming decades till 2100 and on the other hand a substantial increase of dry day periods – both aspects may lead to increasing drought stress in the particular forest areas in the greater alpine area.

**•** The tendency to significant lower rainfall in summer can be found in all areas. The decline of precipitation south of the Alpine main ridge (Northern Italy, Slovenia) is much stronger due to the Mediterranean influence. The applied climate scenario assumes a decline of summer precipitation up to 50%. This surely will affect forests in southern regions and may lead to extended drought stress and fire hazard.

**•** A similar picture gives the comparison of the number of dry days in the future with the 30 year mean for 1971/2000 (Figure 20). The number of dry days north of the alpine main ridge is 1/3 less than south of it. The increase of dry days is estimated to achieve 15 to 20 additional days, which is quite severe in the southern regions with numbers above 120 dry days for current climate. The number of dry day episodes as well as the length of the periods is expected to increase in all regions by 2 or 3 events per length class for the shorter episodes. The long periods (20 to 30 days) occur only in the southern regions and are also expected to increase.

Thus the combination of precipitation decrease, increase of the length of dry day periods as well as the increase of temperature may result in severe drought stress effects on the forest areas.

Investigations of forest growth and dendroecology compiled from tree ring chronologies have shown that differences between tree species to cope drought events in wide range occur. Over a longer period after an intense drought event14 Norway spruce tend to be more effected as common beech and Scots pine. Pedunculate and sessile oak species showed very little growth losses in the investigations by Beck [1].

In general the estimation of the local risks for forest and forest management needs local forest experts and stakeholders which are able to balance the different demands on forests to sustain in the future. The presented results of the single case study regions of the project MANFRED can be used as input for these analyses by local experts. The MANFRED project provides much more information than discussed within this book chapter. Detailed data have been provided to the stakeholder of the different case study regions and all MANFRED project partners.

#### **6. Uncertainty and spatial variability of regional climate projections**

Uncertainty in regional climate projections can be roughly divided into four components: (1) Natural variability15, (2) uncertainty in external forcing (mainly anthropogenic forcing like greenhouse gas emissions and land use change)16, (3) uncertainty due to imperfect simulation of the climate system (model uncertainty)17, and (4) downscaling uncertainty. Prein et al. ([15, 16]) analysed the relative contributions of natural variability, emission scenarios and models to total uncertainty over Europe. The uncertainty components for *air temperature* and *precipi‐*

**Figure 20.** Relative change of mean number of dry periods greater than 10 days compared to period 1971/2000 for case study regions D1, A1, C1, I1 and S1 (Source: ECHAM5/CLM A1B Consortial run, 18x18km resolution, 1x1km

The Figures above let expect on the one hand a decrease of total precipitation within the vegetation period during the coming decades till 2100 and on the other hand a substantial increase of dry day periods – both aspects may lead to increasing drought stress in the

**•** The tendency to significant lower rainfall in summer can be found in all areas. The decline of precipitation south of the Alpine main ridge (Northern Italy, Slovenia) is much stronger due to the Mediterranean influence. The applied climate scenario assumes a decline of

downscaling: WSL. Data compilation and analysis: AIT)

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

particular forest areas in the greater alpine area.

**5. General result discussion**

<sup>14</sup> e.g. the drought of 2003 in middle Europe

<sup>15</sup> The term natural variability refers to deterministic and random fluctuations in the climate system, that occur on various spatial and temporal scales,[3].

<sup>16</sup> GHG emissions depend on many factors including growth of population and economy, energy prices and energy source selection, cultural and social interactions, technological development and land use, the latter serving either as GHG sink or as GHG emission accelerator. Since it is impossible to strictly predict the future development of these factors, the pathway of future emissions remains uncertain. The IPCC Special Report on Emission Scenarios (SRES) [14] provides different storylines of how the world might develop and the associated trajectory of future GHG emissions.

*tation* over Europe due to internal variability, model formulation, and emission scenario assumptions until the mid and the end of the 21st century are regarded in Prein's study, based on 23 different GCMs driving 84 simulations of the CMIP318 ensemble.

They found that uncertainty due to the formulation of the climate models describing atmos‐ pheric processes is largest for temperature as well as precipitation regime changes. The following Figure 21 presents a comparison of simulation results for seasonal change of temperature and precipitation in the south western part of the greater alpine Region (GAR) – the region, where most of our case study regions are located.

Changes of precipitation amount are calculated relative with respect to 1961-1990. The black lines indicate the median changes, [5].

The cross in the diagrams marks the median of precipitation change (horizontal) and temper‐ ature change (vertical): The temperature change medians of the four seasons oscillate between 1.2 and 2 °C while the seasonal precipitation change medians shows rates of 0 to 7% of the particular precipitation total. The changes of all seasonal temperature means show the widest range between +0.5°C and 3°C during summer. The ECHAM5-driven simulation marked with green dots show always the smallest temperature gain, while the HADCM3-based19 results marked with blue and purple dots show constantly the highest increase. For precipitation the single models show high deviations during the seasons, ranging from -20 to +20 %. ECHAMbased models show declines in spring and summer, and (little) precipitation increase in autumn and winter, while the HADCM3-scenarios show no clear trend indicating some increase as well as some decrease during the seasons. These results give an idea of the uncertainty range which can be expected also for the model output applied in MANFRED.

But uncertainty shall not be mixed up with spatial variability unless the variability itself shows uncertainty: A rough exploration of the spatial variability – and the uncertainty - has been conducted in the project reclip:century carried out by AIT ([13], http://reclip.ait.ac.at/ reclip\_century).

Besides uncertainty, there are also distinct location related differences between the regions, which are effects of terrain and position within the global atmospheric pressure and humidity motion patterns. Thus the simulations show a clear influence of the Alpine main ridge on the spatial distribution of climate change signals, with e.g. precipitation increase north of the Alps in spring, summer and autumn, and some decline in the southern and western parts. The temperature change pattern show rather an east-west- than a north-south-gradient. For Austria regional change trends for temperature and precipitation have been extracted from three regional climate simulation runs (based on 2 GHG scenarios and 3 RCM/GCM-combinations20)

<sup>17</sup> Uncertainties due to climate models arise from incomplete understanding and simplified formulation of climate processes in the models [19]. In order to quantify these uncertainties, ensembles of different or modified climate models are used.

<sup>18</sup> Coupled Model Intercomparison Project Phase III, http://cmip-pcmdi.llnl.gov/, accessed 08.04.2013

<sup>19 &</sup>quot;HadCM3 stands for the Hadley Centre Coupled Model version 3. It was developed in 1999 and was the first unified model climate configuration not to require flux adjustments (artificial adjustments applied to climate model simulations to prevent them drifting into unrealistic climate states)".http://www.metoffice.gov.uk/research/modelling-systems/ unified-model/climate-models/hadcm3, accessed 5 September 2012

*tation* over Europe due to internal variability, model formulation, and emission scenario assumptions until the mid and the end of the 21st century are regarded in Prein's study, based

They found that uncertainty due to the formulation of the climate models describing atmos‐ pheric processes is largest for temperature as well as precipitation regime changes. The following Figure 21 presents a comparison of simulation results for seasonal change of temperature and precipitation in the south western part of the greater alpine Region (GAR) –

Changes of precipitation amount are calculated relative with respect to 1961-1990. The black

The cross in the diagrams marks the median of precipitation change (horizontal) and temper‐ ature change (vertical): The temperature change medians of the four seasons oscillate between 1.2 and 2 °C while the seasonal precipitation change medians shows rates of 0 to 7% of the particular precipitation total. The changes of all seasonal temperature means show the widest range between +0.5°C and 3°C during summer. The ECHAM5-driven simulation marked with green dots show always the smallest temperature gain, while the HADCM3-based19 results marked with blue and purple dots show constantly the highest increase. For precipitation the single models show high deviations during the seasons, ranging from -20 to +20 %. ECHAMbased models show declines in spring and summer, and (little) precipitation increase in autumn and winter, while the HADCM3-scenarios show no clear trend indicating some increase as well as some decrease during the seasons. These results give an idea of the uncertainty range which can be expected also for the model output applied in MANFRED. But uncertainty shall not be mixed up with spatial variability unless the variability itself shows uncertainty: A rough exploration of the spatial variability – and the uncertainty - has been conducted in the project reclip:century carried out by AIT ([13], http://reclip.ait.ac.at/

Besides uncertainty, there are also distinct location related differences between the regions, which are effects of terrain and position within the global atmospheric pressure and humidity motion patterns. Thus the simulations show a clear influence of the Alpine main ridge on the spatial distribution of climate change signals, with e.g. precipitation increase north of the Alps in spring, summer and autumn, and some decline in the southern and western parts. The temperature change pattern show rather an east-west- than a north-south-gradient. For Austria regional change trends for temperature and precipitation have been extracted from three regional climate simulation runs (based on 2 GHG scenarios and 3 RCM/GCM-combinations20)

17 Uncertainties due to climate models arise from incomplete understanding and simplified formulation of climate processes in the models [19]. In order to quantify these uncertainties, ensembles of different or modified climate models

19 "HadCM3 stands for the Hadley Centre Coupled Model version 3. It was developed in 1999 and was the first unified model climate configuration not to require flux adjustments (artificial adjustments applied to climate model simulations to prevent them drifting into unrealistic climate states)".http://www.metoffice.gov.uk/research/modelling-systems/

18 Coupled Model Intercomparison Project Phase III, http://cmip-pcmdi.llnl.gov/, accessed 08.04.2013

unified-model/climate-models/hadcm3, accessed 5 September 2012

on 23 different GCMs driving 84 simulations of the CMIP318 ensemble.

the region, where most of our case study regions are located.

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

lines indicate the median changes, [5].

reclip\_century).

are used.

**Figure 21.** Scatter plots for the changes in 2-m air temperature and precipitation amount between 2021/2050 and 1961/1990 for GAR-South-west for 22 ENSEMBLES simulations (http://www.ensembles-eu.org) [10, 11].

<sup>20</sup> The observed seasonal changes till 2050 as explored in these three reclip:century simulations are integrated in the diagrams in Fig. 21 and marked by names: AIT-CCLM is based on a A1B/HADCM3/CCLM combination, WEGC-CCLM is based on an A1B/ECHAM5/CCLM combination and ZAMG-CCLM is based on a B1/ECHAM5/CCLM model combination.

for the four seasons and related to Austrian climate regions. The range of climate change signals within a region refers to uncertainty, the range of climate change signals between the regions refers to local effects of spatial variability21.

Such general location based variability/uncertainty in climate signal change can be expected for the entire Greater Alpine Region.

#### **7. Further research and conclusion**

The drought analysis within the MANFRED project where concentrated on the analysis of predicted input changes from precipitation and temperature. The local effects of changes in precipitation, drought length and frequencies or heat waves have to be estimated with local experts including local site conditions. The results show different manifestations of the assumed climate change scenario (A1B) within different areas, especially north or south of the alpine main ridge. It has to be noticed that the analysed climate change scenario is not an extreme scenario, thus the real changes in temperature and precipitation might lead to more severe hazards for forests in the future (see Figure 4 and 5). Above we briefly mentioned the uncertainties of climate change scenarios, which especially referring to precipitation are not neglectable, thus reducing this uncertainty should be one important research task for the future. Beyond this more ample spatial referenced information about site conditions as soil texture, surface runoff, evapotranspiration etc. would be very important to develop a closer understanding of the local hydrological conditions for the whole analysed Greater Alpine Region. A general increase in winter precipitation (mainly in northern and central Europe) and

<sup>21</sup> This Tabble below referes to results for Austria and it is for the year 2050, not further

decreases in summer precipitation (mainly in central and southern Europe) can be found in different climate scenario simulation. Analysis of extreme events (as drought periods, length and frequencies) have more drastic consequences on tree growth than gradual changes in mean climate conditions [12]. In the future more advanced extreme event analyses have to be developed corresponding with reduced uncertainty in climate change scenarios.

#### **Author details**

for the four seasons and related to Austrian climate regions. The range of climate change signals within a region refers to uncertainty, the range of climate change signals between the regions

temperature: +1.6 to +2.2 °C, prec.: +8 to +13% increase more in E less increase in the S and W

temperature: +1.0 to +2.5 °C, prec.: little loss, scenarios disagree! more distinct in the S

temperature: +1.7 to +2.3 °C, prec.: little loss,

21 This Tabble below referes to results for Austria and it is for the year 2050, not further

temperature: +1.0 to +1.2 °C. prec.: constant to light decreases,

W, S: higher increase, N: divergent more distinct in S, SE and eastern Alps

Such general location based variability/uncertainty in climate signal change can be expected

The drought analysis within the MANFRED project where concentrated on the analysis of predicted input changes from precipitation and temperature. The local effects of changes in precipitation, drought length and frequencies or heat waves have to be estimated with local experts including local site conditions. The results show different manifestations of the assumed climate change scenario (A1B) within different areas, especially north or south of the alpine main ridge. It has to be noticed that the analysed climate change scenario is not an extreme scenario, thus the real changes in temperature and precipitation might lead to more severe hazards for forests in the future (see Figure 4 and 5). Above we briefly mentioned the uncertainties of climate change scenarios, which especially referring to precipitation are not neglectable, thus reducing this uncertainty should be one important research task for the future. Beyond this more ample spatial referenced information about site conditions as soil texture, surface runoff, evapotranspiration etc. would be very important to develop a closer understanding of the local hydrological conditions for the whole analysed Greater Alpine Region. A general increase in winter precipitation (mainly in northern and central Europe) and

refers to local effects of spatial variability21.

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

more distinct in the E

for the entire Greater Alpine Region.

**7. Further research and conclusion**

**Winter:**

**Spring:**

**Summer:**

**Autumn:**

Ernst Gebetsroither\* , Johann Züger and Wolfgang Loibl

\*Address all correspondence to<EMAIL_ADDRESS>
AIT Austrian Institute of Technology GmbH, Foresight & Policy Development Department, Vienna, Austria

#### **References**

[16] Prein, A. F, Gobiet, A, & Truhetz, H. Analysis of uncertainty in large scale climate change projections over Europe Meteorol. Z., doi:(2011). , 20, 383-395.

[6] Hemery, G. E. Forest management and silvicultural responses to predicted climate change impacts on valuable broadleaved species. Short-Term Scientific Mission re‐ port for Working Group 1, COST Action E42, (2007). refs. www.ForestryHori‐

[7] Hollweg, H, Böhm, D, Fast, U, Hennemuth, I, Keuler, B, Keup-thiel, K, Kysely, E, Kalvova, J, & Kveton, J. V. Heat Waves in the South Moravian Region during the Pe‐

[8] Keuler, K, Lautenschlager, M, Wunram, C, Keup-thiel, E, Schubert, M, Will, A, Rock‐ el, B, & Boehm, U. Climate Simulation with CLM, Scenario A1B run Data Stream 2:

[9] Kysely, J. Mortality and displaced mortality during heat waves in the Czech Repub‐

[10] Lautenschlager, M, Legutke, S, Radtke, K, Rockel, B, Schubert, M, Will, A, Woldt, M, & Wunram, C. Ensemble Simulations over Europe with the Regional Climate Model CLM forced with IPCC AR4 Global Scenarios. Model and Data group, Max Planck Institute for Meteorology, Hamburg, (2008). Technical Report 1619-2257(3), 150. [11] Lautenschlager, M, Keuler, K, Wunram, C, Keup-thiel, E, Schubert, M, Will, A, Rock‐ el, B, & Boehm, U. Climate Simulation with CLM, Climate of the 20th Century run Data Stream 2: European region MPI-M/MaD. World Data Center for Climate., (2009). doi:DOI:10.1594/WDCC/CLM\_C20\_1\_D2.http://dx.doi.org/DOI:10.1594/

[12] Lindner, M, Garcia-gonzalo, J, & Kolström, M. (2008). Impacts Of Climate Change On European Forests And Options For Adaptation." European Forest, 2008; http:// www.metla.eu/tapahtumat/2009/JFNW2009/Lindner.pdf.doi:DOI:10.1594/WDCC/ CLM\_A1B\_1\_D2.http://dx.doi.org/DOI:10.1594/WDCC/CLM\_A1B\_1\_D2, accessed 28

[13] Loibl, W, Züger, J, & Köstl, M. eclip:century 1. Report Part C: Climate Scenarios: Comparative Analysis, AIT, Vienna, 2011; (http://reclip.ait.ac.at/reclip\_centu‐

[14] Nakicenovic, N, Alcamo, J, Davis, G, De Vries, B, Fenhann, J, Gaffin, S, Gregory, K, Grübler, A, Jung, T. Y, & Kram, T. La Rovere EL, Michaelis L, Mori S, Morita T, Pep‐ per W, Pitcher H, Price L, Raihi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z. IPCC Special Report on Emissions Scenarios. Cambridge University Press: Cambridge, United Kingdom and New York., 2000; http://www.ipcc.ch/pdf/special-reports/spm/sres-en.pdfac‐

[15] Prein, A. Uncertainties in the driving data of regional climate models in the Alpine region. Wegener Center Scientific Report Wegener Center Verlag: Graz, Austria,

European region MPI-M/MaD. World Data Center for Climate, (2009). (1)

riod 1961-1995. Studia geoph. Et geod., (2000). , 44, 57-72.

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## **Eco-Engineering and Protection Forests Against Rockfalls and Snow Avalanches**

Frederic Berger, Luuk Dorren, Karl Kleemayr, Bernhard Maier, Spela Planinsek, Christophe Bigot, Franck Bourrier, Oliver Jancke, David Toe and Gillian Cerbu

Additional information is available at the end of the chapter

http :/ /dx .doi.org/10.5772/56275

**1. Introduction**

The primary function considered to be of economic importance for forest stands has always been wood production . Today, society is aware of the necessity of forests' provision of additional functions: tourism, outdoor activities, fauna and flora protection, biodiversity, etc. 1n mountain areas, forests can also serve a protective function against natura! hazards.

Many forests in the Alps cover steep to very steep slopes (gradients of 35 - 70 degrees) and so have an important protective function against natura! hazards, such as: rockfall, snow avalanches, shallow landslides and erosion . The primary function of protection forests is to protect people or assets against the impacts of natura! hazards. In order to provide this function" the first 'products' of these forests are standing trees. Trees act as obstacles to mass movement hazards and/or to the propagation downslope of these hazards (within the MANFRED project we have focused on rockfall and snow avalanche risks). 1n the European Alps, mountain forests and the protection they provide have a long and distinguished history. This function has been recognized for centuries, as evident from logging bans declared from ~ 1350 onwards. However, in the last few decades, forest management has shifted its focus from timber management to multiple uses and forest ecosystem management. During this transition, there has been an increasing awareness of the need to manage the multiple functions of mountain forests aside from production and protection .

This protective function is also clearly identified in the first paragraph of the Mountain Forest Protocol of the Alpine Convention of 1996: "mountain forests ... provide the most effective, the

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Klaus Dolschak, Robert Jandl and Thomas Ledermann Klaus Dolschak, Robert Jandl and Thomas Ledermann

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56268 10.5772/56268

#### **1. Introduction**

The impact of climate change on forests can be assessed with simulation models. The obvious advantage is the possibility of running a large number of case studies, without the need for field trials for each specific case. Especially in the context of climate change effects simulation models have become a standard tool. The alternative approach of evaluating the forest growth at sites with site conditions that may resemble the future conditions of the target site has numerous weaknesses. Therefore, models are deemed superior for the interpretation of the consequences of climate change. Simulation models are representative for the state-of-knowledge. Upon their development they are validated against data of existing forests, *i.e.* either data from national forest inventories or long-term forest productivity experiments [1–4].

The available models vary widely with respect to the model structure and the embedded modules. *Productivity models* are often conceived as a practical tool where forest practitioners find the opportunity to directly define specific silvicultural treatments such as thinning interventions, and the model provides information on the consequence of these actions on the temporal trend of stem growth. An important incentive for the development of this model type was that formerly used yield tables proved to be less reliable for several reasons. Yield tables were often derived from a rather limited number of field experiments and neither their species composition nor their management is reconcilable with present concepts and requirements of forestry [5, 6].

*Succession models* place their emphasis on the regeneration success of different tree species under future climate conditions. The focus is on the identification of site conditions, with the potential of changing the tree species combination. The strong part of these models is

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 Dolschak 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.

©2012 Dolschak et al., licensee InTech. This is an open access chapter distributed under the terms of the

the description of the competition between the different tree species within a forest and the success of trees for natural regeneration [7–10].

*Biogeochemical models* such as Biome-BGC are large scale models that model primary productivity [11]. The simulation results mostly apply for large regions and are skillfully broken down for smaller regions. Although the models are generally not conceived for the description of silvicultural treatments, there are efforts underway to introduce options of forest management [12].

We chose a productivity model, because we wanted to properly depict management options that are expressed in terms of generally understandable silvicultural interventions. In productivity models the emphasis is typically on the effect of silvicultural interventions, but not necessarily on the provision of ecosystem services such as carbon sequestration.

The sequestration of carbon in forest soils is often not integrated in the productivity models. Instead, the carbon dynamics are calculated separately. A notable exception is a Swiss study were the impact of storm damage on the soil carbon pool had been evaluated [13]. The effect of tree species on the forest soil carbon pool has been shown [14–17]. Therefore, we are convinced that a full assessment of forest management options needs to comprise both the carbon pools in the total biomass and the soil.

Our objective was the linkage of a soil productivity model with a soil carbon model in order to emphasize the implication of forest management on the soil carbon pool.

#### **2. Sites and methods**

#### **2.1. Site**

The chosen site for our simulation exercise in the Ossiacher Tauern. It is a montane forest area on silicatic bedrock. Although the site would be naturally dominated by deciduous trees, it is currently ideal for growing Norway spruce (*Picea abies* (L.) Karst.) [18].

#### **2.2. The forest productivity model CALDIS**

CALDIS is derived from the celtic term "Caldis Vâtis" translating to "forest prognosticator". The core of CALDIS is a module simulating the basal area increment of individual trees of the dominating tree species in Austria, *i.e.* Norway spruce, fir, larch, three pines, European beech, oak, and other deciduous species. In recognition of the scarcity of georeferenced single trees the model was intentionally developed as distance-independent model. The data for model development were taken from the Austrian National Forest Inventory. The model handles competition between trees with the basal area as the parameter. The basal area increment of individual trees; BAI, was derived from ?tree size?, ?competition? and ?site properties?. The by far most important parameter for the basal area increment is 'tree size", represented by stem diameter and size of the canopy. The competitive situation of a single tree is captured as the sum of the basal area of all trees with a larger diameter than the sample tree. Site factors such as topography, elevation, aspect, depth of the organic soil layer, and soil type contribute a small, yet significant, improvement of the model. Besides the diameter and height growth module, the model contains a mortality module and a regeneration module. The model has been used with the acronym "PrognAus" [6]. The productivity model has been amended

10.5772/56268

with several additional modules. In order to capture the long-term development of a forest, an ingrowth module was added describing the recruitment of naturally regenerating trees. "Recruitment" is the estimation of the number of trees exceeding a predefined threshold value of height and diameter. The recruitment of trees was estimated from the appearance of new small trees in the fixed plots of the Austrian National Forest Inventory that were observed in consecutive inventories. A model component estimates the probability of the successful regeneration for 13 tree species. An important site factor for most tree species was soil moisture. Several additional site parameters such as exposition and elevation are have a significant influence. The module describes the regional situation of Austria particularly well, but may be less valid in other countries, because the impact of site factors on the natural regeneration is derived from the data set and therefore the amplitude of Austrian forests is well described [19]. A climate-sensitive part of the model was developed in order to make the productivity programme available for long-term scenarios. Additional climatic parameters used were the annual temperature sum (sum of daily mean temperatures above 3 ◦C), the annual precipitation, the temperature sum and the precipitation during the growing season. In order to capture the impact of the past, the precipitation during the growing season of the previous year and the average of temperature sum and precipitation in the past 30 years are used as parameters. Dryness is derived from a transformation of the monthly precipitation and temperature. The response of different tree species to climate is embedded in the model. The basal area increment of European beech responds strongly to increases in the temperature sum, and is greatly reduced at lower precipitation rates. Oak responsds similarly to the temperature sum, and is less affected by declining precipitation rates. At lower temperature sums, the basal area increment of Norway spruce is higher than the increment of beech and oak. Nevertheless, the increment rate remains high, even under warmer conditions. The dependence on the precipitation shows that spruce can tolerate dry conditions rather poorly [20].

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

success of trees for natural regeneration [7–10].

carbon pools in the total biomass and the soil.

**2.2. The forest productivity model CALDIS**

forest management [12].

**2. Sites and methods**

**2.1. Site**

the description of the competition between the different tree species within a forest and the

*Biogeochemical models* such as Biome-BGC are large scale models that model primary productivity [11]. The simulation results mostly apply for large regions and are skillfully broken down for smaller regions. Although the models are generally not conceived for the description of silvicultural treatments, there are efforts underway to introduce options of

We chose a productivity model, because we wanted to properly depict management options that are expressed in terms of generally understandable silvicultural interventions. In productivity models the emphasis is typically on the effect of silvicultural interventions, but not necessarily on the provision of ecosystem services such as carbon sequestration.

The sequestration of carbon in forest soils is often not integrated in the productivity models. Instead, the carbon dynamics are calculated separately. A notable exception is a Swiss study were the impact of storm damage on the soil carbon pool had been evaluated [13]. The effect of tree species on the forest soil carbon pool has been shown [14–17]. Therefore, we are convinced that a full assessment of forest management options needs to comprise both the

Our objective was the linkage of a soil productivity model with a soil carbon model in order

The chosen site for our simulation exercise in the Ossiacher Tauern. It is a montane forest area on silicatic bedrock. Although the site would be naturally dominated by deciduous

CALDIS is derived from the celtic term "Caldis Vâtis" translating to "forest prognosticator". The core of CALDIS is a module simulating the basal area increment of individual trees of the dominating tree species in Austria, *i.e.* Norway spruce, fir, larch, three pines, European beech, oak, and other deciduous species. In recognition of the scarcity of georeferenced single trees the model was intentionally developed as distance-independent model. The data for model development were taken from the Austrian National Forest Inventory. The model handles competition between trees with the basal area as the parameter. The basal area increment of individual trees; BAI, was derived from ?tree size?, ?competition? and ?site properties?. The by far most important parameter for the basal area increment is 'tree size", represented by stem diameter and size of the canopy. The competitive situation of a single tree is captured as the sum of the basal area of all trees with a larger diameter than the sample tree. Site factors such as topography, elevation, aspect, depth of the organic soil layer, and soil type contribute a small, yet significant, improvement of the model. Besides the diameter and height growth module, the model contains a mortality module and a regeneration module. The model has been used with the acronym "PrognAus" [6]. The productivity model has been amended

trees, it is currently ideal for growing Norway spruce (*Picea abies* (L.) Karst.) [18].

to emphasize the implication of forest management on the soil carbon pool.

An important element of forest development is the disturbance regime. Prominent factors in the Alpine Space are insect attacks, storm damages, and possibly fire [21–23]. It has been shown that the pressure is not solely caused by climate change effects, but also by forest management effects, that potentially can be modified by means of strategies for adaptive forest management [24, 25].

The influence of storm events on forests was derived from field evidence for tree mortality due to disturbance, and wind speed data from the Zentralanstalt für Meteorologie und Geodynamik. A challenge was linking forest damage to a particular storm event, because the data of the Austrian Forest Inventory allow to assign a damage event to a period of a few years whereas the wind-speed is recorded with a resolution of 2 seconds. The pragmatic assumption was that the strongest storm in the observation period of the Forest Inventory was responsible for the observed forest damage. It was shown that Norway spruce is particularly vulnerable to storms during the dormant season, whereas the fully developed canopy of beech forests makes them more vulnerable during the growing season. The storm module represents the strong influence of tree size parameters. Larger trees and trees with a high height/diameter ratio are more vulnerable to storm damages [26, 27].

The future pressure from insects is difficult to predict for a particular site in a certain time span, although clear indicators for the potential danger are available [22, 28, 29].

**Figure 1.** Yasso07 is a simulation model for the decomposition of soil organic matter. The input parameters are the annual climate, represented by the mean annual temperature, the difference of temperatures between the coldest and the warmest month, and the annual precipitation, the total carbon input to the soil, and its chemical quality.

The annual output of CALDIS comprises stem volume, tree height, and stem diameter of the individual trees of stand. The disturbance regime is focused on storm damages, because the storm-inflicted damages are well described in measurable parameters. Disturbances by an inadequately high density of the deer population and by pests and pathogens are not incorporated in the simulation model and need to be taken into account in the interpretation of the simulation results.

The CALDIS model was fed by climate data from A1B and B1 scenarios. The A1B scenario foresees a stronger warming trend, the B1 scenario is more optimistic with respect to controlling the warming trend [30].

#### **2.3. The soil carbon simulator Yasso07**

Yasso is an intentionally simple soil carbon simulation model. Its general concept is shown in Figure 1: The user supplies air temperature and precipitation as climate parameters, the quantity of the above- and belowground influx of carbon to the soil, and information on the chemical quality of the incoming carbon. The model can deal with different temporal resolutions of the input information. The core of Yasso is a decomposition model of soil organic material [31, 32]. In our simulation Yasso was run 10 times for each data set in order to account for the uncertainty about the parameter values. These repetitions do not account for the variability in the user-provided input data.

As output Yasso gives a time series of the total soil C pool, which is divided into carbon in woody matter, non-woody matter and the acid-, water-, ethanol and insoluble fractions. It can be understood that carbon in woody material describes the coarse woody debris and the forest floor material and the sum of the remaining fractions are carbon in the mineral soil. Yasso does not distinguish between soil horizons and does not explicitly state a soil depth. Operationally, a soil depth of 1 *m* can be assumed, although the reference to the "total soil C pool" is more appropriate, because all incoming carbon is processed by Yasso07.

### **3. Building a bridge between CALDIS and Yasso07**

#### **3.1. Data and folders**

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

**Yasso07**

**Figure 1.** Yasso07 is a simulation model for the decomposition of soil organic matter. The input parameters are the annual climate, represented by the mean annual temperature, the difference of temperatures between the coldest and the warmest

The annual output of CALDIS comprises stem volume, tree height, and stem diameter of the individual trees of stand. The disturbance regime is focused on storm damages, because the storm-inflicted damages are well described in measurable parameters. Disturbances by an inadequately high density of the deer population and by pests and pathogens are not incorporated in the simulation model and need to be taken into account in the interpretation

The CALDIS model was fed by climate data from A1B and B1 scenarios. The A1B scenario foresees a stronger warming trend, the B1 scenario is more optimistic with respect to

Yasso is an intentionally simple soil carbon simulation model. Its general concept is shown in Figure 1: The user supplies air temperature and precipitation as climate parameters, the quantity of the above- and belowground influx of carbon to the soil, and information on the chemical quality of the incoming carbon. The model can deal with different temporal resolutions of the input information. The core of Yasso is a decomposition model of soil organic material [31, 32]. In our simulation Yasso was run 10 times for each data set in order to account for the uncertainty about the parameter values. These repetitions do not account

As output Yasso gives a time series of the total soil C pool, which is divided into carbon in woody matter, non-woody matter and the acid-, water-, ethanol and insoluble fractions. It can be understood that carbon in woody material describes the coarse woody debris and the forest floor material and the sum of the remaining fractions are carbon in the mineral soil. Yasso does not distinguish between soil horizons and does not explicitly state a soil depth. Operationally, a soil depth of 1 *m* can be assumed, although the reference to the "total soil C

pool" is more appropriate, because all incoming carbon is processed by Yasso07.

month, and the annual precipitation, the total carbon input to the soil, and its chemical quality.

of the simulation results.

controlling the warming trend [30].

**2.3. The soil carbon simulator Yasso07**

for the variability in the user-provided input data.

**Climate litterfall chemical quality**

∆

The CALDIS forest management model produces output in annual time steps. Folders, named corresponding to the year of the simulation, are located in one major CALDIS output folder. Each of these folders contains yet another set of subfolders which, at last, comprise the data: Content of folder [sim] describes the standing biomass, folder [mort] contains data about trees discarding from the simulation due to natural mortality events, and folders [VN] and [EN] comprise tree data about harvesting removals. Folder [verb] contains information about remaining trees, after a disturbance event or harvest has taken place.

Every line in an output file represents one tree of a sample plot. At the time this work was conducted, the forest management model CALDIS was still under development. Forty-two comma-separated output parameters described each tree. Depending on the number of model runs and the timeframe of the simulation the output can become quite extensive and may therefore present considerable data-handling challenges. A strategy for accessing the data generated by CALDIS and for skimming the data in order to assemble the parameters for the Yasso07 input file is shown in figure 1.

In this approach advanced options of the SAS® 9.2 datastep were used to access and process the data generated by CALDIS [33, 34]. If more than one model run was conducted, the program which is presented here is able to access all model runs at once.

#### *3.1.1. The import of CALDIS output data into a SAS®9.2 dataset*

First, a list is created, storing all data-paths and filenames of CALDIS output files in a SAS®9.2 dataset. The SAS Command [pipe] enables the SAS application to access MS-DOS/UNIX commands [35]. The output of the command is accessed by SAS again, and written to a dataset. In this case, it is the common MS-DOS® command [dir] which displays the content of a defined folder. Three sub-options are used, in order to prevent the input of non-relevant information.

filename DIR\_FILE pipe "dir /b/s d:\XYZ\YOUR\_CALDIS\_OUTPUT\\*.cds";

Thereby a dataset which lists all CALDIS output files inside a stated folder is created. Its only parameter is the full data-path plus the filename stored as a character string. To avoid truncation of data path descriptions, the length of the variable is set to a maximum of 256 characters.

```
data FILE_LIST;
length data_path $256;
infile DIR_FILE length=reclaeng;
input data_path $ varying256. reclaeng ;
run;
```
In the next step, this list is used to import the data files into one major data record, consisting of the output of all model scenarios and runs. This is achieved by running a macro loop from the first observation of the generated list to the last. Each cycle of the loop the character string parameter at the current observation is stored as a global macro variable. Then this string is used in a data step behind an [infile] statement. A temporary data record is created which contains data of the current CALDIS output file. The global variable, in which location and filename are defined, is also stored as parameter making it possible to identify the origin of the data. If this is the first iteration of the loop, a new dataset is created by storing the temporary one. For all following iterations the temporary dataset is appended on this new dataset. After this event the next run of the loop starts. After the last iteration of the loop the data import is complete. This way all observations of all CALDIS output files are gathered in one dataset.

```
%macro data_import;
    %do o=1 %to &Num_Of_Obs;
        data _NULL_; set FILE_LIST;
            if _n_ = &o; call symput('path_to_file',data_path);
        run;
        data temp;
            infile "&path_to_file"
            DLM=',';
            INPUT all input parameters;
            data_path_key ="&path_to_file";
        run;
        %if &o ne 1 %then %do;
            proc append base=full_dataset data=temp; run;
        %end;
        %else %do;
            data full_dataset; set temp; run;
        %end;
    %end;
%mend data_import;
```
#### *3.1.2. The break-up of the data location into key parameters*

The labeling of the storage location provides information about the comprised data itself. In this next step, the path to the output data is used to create key parameters, which can be used as filter criteria at a given point of time. This is achieved by 'breaking down' the variable [data\_path] into single 'words'. Every element of the location is stored as an own parameter. In our case numerous CALDIS runs of different base conditions were conducted. It seems reasonable to store the output with consistent labeling. The name of the subfolder, which comprises the output of one single run, was formatted containing information about CALDIS model setting details. An example is *P*\_12\_*S*\_3\_*C*\_2\_*R*\_30 describing:

• Plot 12

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

in one dataset.

%macro data\_import;

run;

run;

%end; %else %do;

%end;

%end; %mend data\_import;

data temp;

%do o=1 %to &Num\_Of\_Obs;

DLM=',';

%if &o ne 1 %then %do;

*3.1.2. The break-up of the data location into key parameters*

data \_NULL\_; set FILE\_LIST;

infile "&path\_to\_file"

INPUT all input parameters; data\_path\_key ="&path\_to\_file";

data full\_dataset; set temp; run;