ISSN 1016-3158
MITTEILUNGEN
der Eidgenössischen Forschungsanstalt für Wald, Schnee und Landschaft
Band 69 1994 Heft 2
Norbert Kräuchi
Modelling Forest Succession as Influenced
by a Changing Enviromnent
Organisation der Eidgenössischen Forschungsanstalt für Wald, Schnee und Landschaft, WSUFNP
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Stellvertretender Direktor Stab
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Forstschutz und Immissionen Forstliche Hydrologie Bereich Landschaft Zoologie
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Wetter, Lawinen, Schneedecke, Lawinenwarnung Schneemechanik, Lawinenmechanik, Lawinenverbau Schneedecke und Vegetation/Wald
Physik von Schnee und Eis Bereich Wissenschaftliche Dienste Landesforstinventar (LFI)
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Prof. Rodolphe Schlaepfer Dr. Gerhard Eichenberger
Christina Balass Dr. Hans Peter Bucher Ulrike Bleistein vakant
Dr. Walter Keller Albert Böll
Dr. Walter Schönenberger vakant
Dr. Peter Blaser Dr. Rudolf Häsler Dr. Jürg Bucher Dr. Hans Martin Keller PD Dr. Otto Wildi PD Dr. Peter Duelli Dr. Walter Ammann
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Inhaltsverzeichnis Band 69
1 Köhl, Michael: Statistisches Design für das zweite Schweizerische Landesforst- inventar: Ein Folgeinventurkonzept unter Verwendung von Luftbildern und
Seite
terrestrischen Aufnahmen . . . 3 2 Kräuchi, Norbert: Modelling Forest Succession as Influenced by a Changing
Environment . . . 143
MITTEILUNGEN
der Eidgenössischen Forschungsanstalt für Wald, Schnee und Landschaft
Herausgeber
Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft
Birmensdorf
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Teufen, Kommissionsverlag F. Flück-Wirth
ISSN 1016-3158
MITTEILUNGEN
der Eidgenössischen Forschungsanstalt für Wald, Schnee und Landschaft
Band 69 1994 Heft 2
Norbert Kräuchi
Modelling Forest Succession as Influenced by a Changing Environment
Herausgeber
Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft
Birmensdorf
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Professor Rodolphe Schlaepfer, Direktor WSL
Herausgeberkommission WSL: Dr. Simon Egli , Konrad Häne, Dr. Bruno Jans, Dr. Walter Keller, Dr. Alois Kempf, Dr. Nino Kulm, Dr. Ruth Landolt, Dr. Christoph Scheidegger, Dr. Ulrike Bleistein Diese Arbeit wurde 1994 von der Eidgenössischen Technischen Hochschule in Zürich als Disserta- tion Nr. 10479 angenommen.
Die Dissertation wurde am 14. Juni 1994 zur Veröffentlichung in der Reihe Mitteilungen der Eidgenössischen Forschungsanstalt für Wald, Schnee und Landschaft freigegeben.
Zitierung: Mitt. Eidgenöss. Forsch.ans!. Wald Schnee Landsch.
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©Eidgenössische Forschungsanstalt für Wald , Schnee und Landschaft, Birmensdorf, 1994 Druck: Trüb-Sauerländer AG, Buchs AG
Kräuchi, Norbert, 1994:
Modelling Forest Succession as lnfluenced by a Changing Environment.
Mitteilungen der Eidgenössischen Forschungsanstalt für Wald, Schnee und Landschaft 69, 2: 143-271.
Summary, Zusammenfassung, Resume, Riassunto, lll.
ISSN 1016-3158 ISBN 3-905620-39-1
DK 630*182.2: 57.087:: 519.876.5
FDK 182.2: 111.83: 181.31: (494): (430): (043)
Abstract
Modelling Forest Succession as Influenced by a Changing Environment
FORSUM, a forest succession model of the JABOWA/FORET-type has been developed to explore the relationships between long-term forest succession and biogenic ( e.g., brows- ing) and anthropogenic factors. FORSUM combines long-term (decades to centuries) and short-term (minutes to hours) processes. lt calculates soil water movement and root water uptake by the plants by calculating the water flow through a one-dimensional, non homogeneous soil profile on a daily basis. Different management options are implemen- ted in the model. Model testing included plausibility tests ( e.g., modelling efficiency, mod- el performance) and sensitivity analyses of factors intrinsic or extrinsic to species and stands, respectively.
Assuming the validation criterions tobe sufficient FORSUM was applied to different for- est ecosystems in Switzerland and Germany to evaluate its use as a 'predictive' tool forfor- est succession dynamics or climate impact risk assessment.
Keywords: climate change, JABOWA/FO RET, forest succession, risk assessment, simula- tion, IPCC
Modellierung von Waldsukzession unter dem Einfluß sich ändernder Umweltfaktoren FORSUM, ein Waldsukzessionsmodell vom Typ JABOWA/FORET, wurde entwickelt, um die langfristige Waldentwicklung in Abhängigkeit von biogenen (z.B. Wildverbiß) und anthropogenen Faktoren darzustellen. Das Modell vereint langfristige (Jahrzehnte bis Jahrhunderte) sowie kurzfristige (Minuten bis Stunden) Prozesse. FO RSUM berücksich- tigt das Sickerungsverhalten des Wassers im Boden und die Wasseraufnahme der Pflan- zen über die Wurzeln, indem die Wasserbewegung in einem inhomogenen, eindimensio- nalen Bodenprofil berechnet wird. Die Evaluierung der Modellgüte beinhaltete Plausibi- litäts- und Sensitivitätsanalysen von inneren und äußeren Parametern in bezug auf den Bestand oder die Baumart.
Unter der Annahme, daß die gewählten Validierungskriterien ausreichend sind, wurde FORSUM auf verschiedenen Standorten in der Schweiz und in Deutschland angewandt, um dessen Wert als Werkzeug für die Voraussage der Waldsukzessionsdynamik oder zur Risikoabschätzung von Klimafolgen zu evaluieren.
Keywords: Klimaveränderung, JABOWA/FORET, Waldsukzession, Risikoabschätzung, Simulation, IPCC
Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69, 1994, 2 145
Table ofContents
Abstract . . . . 145
List of figures . . . 149
List of tables . . . 150
List of symbols. . . 151
Glossary . . . 151
Preface . . . . 152
lntroduction . . . 153
1.1 Our future- a challenge . . . 153
1.2 The simulation and modelling concept . . . 154
1.3 Forest models. . . 158
2 State of Knowledge . . . 159
2.1 Introduction. . . 159
2.2 Gap models . . . 159
2.3 Goals and structure ofthe project . . . 161
3 Basics of the Model Approach . . . 164
3.1 Aboutsoils... . . . 164
3.2 About climate change . . . . 166
4 FORSUM, aForest Dynamics Model . . . 168
4.1 General overview . . . 168
4.2 Model structure and initialisation . . . 171
4.3 Model parameters . . . 180
4.4 Modelling soil water characteristics . . . 180
4.5 Growth model. . . 191
4.5.1 Competition and biomass allocation. . . 194
4.5.2 Browsing . . . 196
4.5.3 Senescence and regeneration . . . 197
4.6 Managementmodel. . . 198
5 Study Sites. . . 201
5.1 Solling. . . 201
5.1.1 Site description . . . 201
5.1.2 Definition of input data . . . 201
5.2 Derborence . . . 203
5.2.1 Site description . . . 203
5.2.2 Definition of input data . . . 203
5.3 Zürichberg. . . 207
5.3.1 Site description . . . 207
5.3.2 Definition of input data . . . 207
6 Plausibility Testsand Sensitivity Analysis . . . 208
6.1 Plausibility tests ofthe soil submodel . . . 208
6.2 Model convergence . . . 212
6.3 Sensitivity analysis . . . 213
Mitt. Eidgenöss. Forsch.ans!. Wald Schnee Landsch. 69, 1994, 2 147
6.3.1 Effects of browsing on species composition. . . 214
6.3.2 Model sensitivity to degree day parameter values of Fagus sylvatica and Picea abies . . . . 215
7 Applications . . . 219
7.1 Scenario information . . . 219
7.2 Application ofFORSUM to the Solling spruce site . . . 220
7.3 Application ofFORSUM to the Derborence virgin forest. . . 225
7.4 Application ofFORSUM to the Zürichberg beech site. . . 229
8 Discussion. . . 238
9 Conclusions . . . . 242
10 Summary Modelling Forest Succession as lnfluenced by a Changing Environment 244 Zusammenfassung Modellierung von Waldsukzession unter dem Einfluß sich ändernder Umweltfaktoren. . . 246
Resume Mode!isation de Ja succession forestiere en fonction des modifications des conditions environnementales . . . . 248
Riassunto Modellizzazione di successioni forestali in funzione de! cambiamento di fattori ambientali. 250 11 References 252 12 Appendix 148 I Parameter !ist ofthe FORest SUccession Model FORSUM. . . 259
II Input-file 'stand inp', providing initial forest composition . . . 265
III Input-file 'soil inp', providing soil physical data. . . 266
IV Outputfiles . . . 270
Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69, 1994, 2
List offigures
1 Monthly atmospheric C02 concentrations between 1958 and 1990 measured
on Mauna Loa (Hawaii) . . . 153
2 Modelling steps . . . 155
3 Metamorphosis of a forest. . . 156
4 Markov transition diagram . . . 157
5 Geographiedistribution of some gap models derived from JABOWA/ FORET models. . 160
6 Moisture retention curve as characterised by the soil characteristics . . . 165
7 Gap principle. . . 169
8 Ecogram of Quercus petraea exhibiting potential and optimal range of the species . . . 170
9 Factors influencing tree growth on a single gap. . . 170
10 Flowchart ofFO RSUM . . . 179
11 Maximum diameter increment at breast height . . . 181
12 One-dimensional, non homogeneous soil profile . . . 187
13 Temperature/precipitation correlation for Chur (1931-1960) on a monthly basis . . . 189
14 Temperature/precipitation correlation for Bern (1901-1988) on a seasonal basis . . . 189
15 Principle of precipitation simulator . . . 190
16 Degree day response function used to modify annual tree growth. . . 193
17 Functional relationship between above- and belowground biomass . . . 196
18 N umber of trees by diameter classes represented on semi-logarithmic axes. . . 199
19 Soil water release curves for the Solling spruce site . . . 202
20 Stern distribution by diameter classes in the virgin forest of Derborence . . . 204
21 Species composition on plots 2 and 3 in the virgin forest of Derborence between 1955 and 1981 . . . 205
22 Species composition on plot 5 in the virgin forest of Derborence between 1955 and 1981 . 206 23 Simulated changes in the soil water content used to assess the error in the water balance . 209 24 Comparison of measured versus simulated soil water pressure heads at 3 different depths at the Höri site . . . 210
25 Throughfall and interception in the year 1980 (Solling) as simulated by FORSUM. . . 211
26 'Business-as-usual' scenario on the Derborence site (model convergence, 120 plots). . . . 212
27 'Business-as-usual' scenario on the Derborence site (model convergence, 300 plots). . . . 213
28 Estimates of global mean temperature changes for the IPCC 1992 scenarios (IS92a-e) assuming the "best estimate" climate sensitivity. . . 221
29 Simulated forest succession on the Solling site; spruce plantation, no thinning. . . 221
30 Simulated forest succession on the Solling site; natural succession with periodical spruce plantation and thinnings. . . 222
31 Simulation of real vegetation for the Solling site. . . 223
32 IPCC 'business-as-usual' scenario for the Solling site. . . 224
33 Succession of forest tree species on the Solling site (IPCC 'business-as-usual') . . . 225
34 Simulated forest succession at Derborence, assuming today's climatic pattem. . . 226
35 Simulated forest succession at Derborence (IPCC 'business-as-usual') . . . 227
36 Succession of forest tree species at Derborence (IPCC 'business-as-usual'). . . 228
37 Simulated forest succession at Derborence (IPCC scenario D) . . . 229
38 Simulation of forest succession on the Zürichberg; natural succession based on today's climatic pattem . . . 230
39 Development of total number of stems/ha (natural succession; Zürichberg) . . . 231
40 Development of total basal area (natural succession; Zürichberg) . . . 231
41 Simulation offorest succession on the Zürichberg; natural succession based on the IPCC 'business-as-usual' scenario . . . 232
42 Succession of forest species on the Zürichberg site (IPCC 'business-as-usual'). . . 233
Mitt. Eidgenöss. Forsch.ans\. Wald Schnee Landsch. 69, 1994, 2 149
43 Simulation of forest succession on the Zürichberg; natural succession based on the IPCC 'business-as-usual' temperature scenario combined with reduced
precipitation (-15%)... . . . . 235
44 Simulation offorest succession on the Zürichberg; natural succession based on the IPCC 'business-as-usual' temperature scenario combined with reduced precipitation (-15%) and roe deer browsing . . . . 236
45 Simulation of forest succession on the Zürichberg; natural succession based on the IPCC scenario D . . . 237
List oftables Hypothetical transition matrix for vegetation succession . . . . 157
2 Classes of soil water availability . . . . 165
3 Inputfile with run-control parameters . . . . 173
4 Input and output files used in FORSUM and CURAN . . . 178
5 Species growth parameters as used in FORSUM and FORECE. . . . 182
6 Model parameters (degree day requirements and growth parameters) . . . 183
7 Model parameters (species specific ecological parameters). . . . 184
8 Model parameters (competition and help parameters). . . . 185
9 Model parameters (phytosociological requirements). . . . 186
10 Interception matrix used in FORSUM. . . 191
11 Biomass equations for deciduous trees. . . 195
12 Biomass equations for coniferous trees . . . 195
13 Tendency of main species being browsed . . . . 196
14 Plantation selection matrix . . . 200
15 Soil characteristics of the Solling site . . . 201
16 Climatic input parameters for the Solling site. . . 202
17 Soil characteristics of the Derborence site. . . 204
18 Climatic input parameters for the Derborence site . . . 204
19 Soil characteristics of the Zürichberg site . . . 207
20 Climatic input parameters for the Zürichberg site. . . 207
21 Soil submodel performance analysis . . . . 211
22 Effects of browsing on Fagus sylvatica, Picea abies, Abi es alba, Betula pendu/a and Pinus sylvestris looking at the number of stems/ha. . . . 214
23 Effects ofbrowsing on Fagus sylvatica, Picea abies; Abies alba, Betu/a pendula and Pinus sylvestris looking at the basal area/ha . . . 215
24 Effects of the model sensitivity to the degree day minimum parameter values of Fagus sylvatica looking at the number of stems/ha . . . 216
25 Effects of the model sensitivity to the degree day minimum parameter values of Fagus sylvatica looking at the basal area/ha . . . 216
26 Effects of the model sensitivity to the degree day maximum parameter values of Picea abies looking at the number of stems/ha . . . 217
27 Effects of the model sensitivity to the degree day maximum parameter values of Picea abies looking at the basal area/ha . . . 218
28 IPCC climate scenarios. . . 220
29 Averaged forest composition (120 plots) after 1000 years for the IPCC 'business-as-usual' scenario. . . 234
150 Mitt. Eidgenöss. Forsch.ans\. Wald Schnee Landsch. 69.1994, 2
a a(h) D E h h50 I K ks L /c(z) Lr n p PET pH p.p.m.
er
es
Se
T
List ofsymbols
Empirical constant determining the shape of the hydraulic function Dimensionless water stress function
Drainage (L) Evaporation (L) Pressure head (L)
Crop and soil specific parameter in the stress response function Interception (L)
Hydraulic conductivity (LT-1)
Saturated hydraulic conductivit y (LT-1) Rooting depth (L)
Depth dependent root distribution function Lateral surface and subsurface runoff (L)
Empirical constant determining the shape of the hydraulic function Precipitation (L)
Potential evapotranspiration
The negative logarithm of the hydrogen ion concentration in moles/l Parts per million mg/l
Residual volumetric water content Saturated volumetric water content Reduced water content
Temperature Time
Glossary
The following section briefly summarises some essential definitions often used in the modelling con- text:
• A continuous system is one for which the state variables change continuously with respect to time
• A discrete system is one for which the state variables change instantaneously at separated points in time. A discrete model is not always used to model a discrete system and vice versa
• A dynamic simulation model represents a system that evolves over time
• A model is a simplified representation of a system, which can be expressed in symbolic or mathe- matical form (BROWN and RoTHERY 1993)
• Potential natural vegetation (PNV) of a site is the vegetation that would finally develop (terminal community) at a certain time (e.g., today) if all human influence on the site and its immediate sur- roundings would stop at once and the terminal stage be reached at once (TüXEN 1956; BRZEZIECKI et al. 1993)
• Simulation is an attempt to describe and predict the behaviour of a system without using the system itself
• A static simulation model is a representation of a system at a particular time, or one that may be used to represent a system in which time simply plays no role
• Validation is testing a model against independent data sets to sec how weil it predicts
• Verification is testing a model with the data on which the model was based to eliminate lapses in programming logic, flaws in algorithms and bias in computations (BRUCE et af. 1987)
Mitt. Eidgenöss. Forsch.ans!. Wald Schnee Landsch. 69, 1994. 2 151
Preface
Tue present study is the result of research undertaken at (1) the Swiss Federal Institute of Technology (ETH), Chair of Forest Sciences and Chair of Soil Physics in Zürich, (2) the Swiss Federal Institute for Forest, Snow and Landscape Research in Birmensdorf and (3) the Department of Environmental Sciences at the University of Charlottesville, Virginia (USA). Partial funding was provided through an interdisciplinary, environmental re- search program ofthe Swiss Federal Institute ofTechnology, known as ''WaBoLu" ("Was- ser, Boden, Luft", meaning Water, Soil, Air), and through the Global Systems Analysis Program (Academic Enhancement Program Grant) from the University of Virginia.
I would like to express my gratitude to all those people who provided me with the op- portunity and the means to conduct this research. First and foremost I would like to thank Prof. Rodolphe Schlaepfer who Jet me undertake this research task and for accepting the responsibility of accompanying the thesis as main supervisor and examiner. Secondly, I would like to thank Prof. Dr. Hannes Flühler for awakening my interests in soil-related matters and last but not least Dr. Felix IGenast, whose ecosystem modelling knowledge was the basis of this project. Furthermore, my thanks go to Prof. Dr. Werner Stumm for having set up the "WaBoLu" project with its interdisciplinary scope.
I am also very grateful to Prof. Dr. Bank Shugart and his group for providing an oppor- tunity to learn the skills of ecological modelling in a great personal and scenic environ- ment.
I am very grateful to Mrs Rosmarie Louis for her editing and language revision.
There are also many scientists throughout the world whose feedback at numerous workshops had a big influence on this project.
I would finally like to express my gratitude to Heidi Henck for her support and valuable advice in many instances.
152 Mitt. Eidgenöss. Forsch.ans!. Wald Schnee Landsch. 69, 1994, 2
1 Introduction
1.1 Our future - a challenge
We are living in a changing environment and one of the changes occurring due to human activities is the increase of greenhouse gases in the atmosphere. Since 1958 concentrations of C02 at the Mauna Loa observatory in Hawaii (Fig. 1) have increased from 312 to 352 ppm (SIEGENTHALER et al. 1988; KEELING et al. 1989), and global mean surface air tempe- rature has increased over the past 100 years by 0.3-0.6 °C (HouGHTON et al. 1990). Data ta- ken from ice cores in the Antarctic and Greenland show a strong correlation between tem- perature and atmospheric concentration of carbon dioxide and methane (LORIUS 1989).
According to BRUCK (1990) it can be stated with about 99% probability confidence that current temperatures represent a real warming trend rather than a chance fluctuation over the past 30-year period (1951-1980). However SLINGO (1990) states: "The fact that 14 dif- ferent models give 14 different answers for cloud feedback, shows that we are far from the goal of accurate predictions of future climate change ( ... ) it will be several years be- fore reliable predictions of global and regional climate change are available from the mod- els."
General circulation models indicate a temperature increase of 3 to 4 °C due to a C02 doubling (HouGHTON et al. 1990). A warming of 3 °C would confront natural systems with a warmer world than has been experienced in the past 100'000 years (SCHNEIDER and LoN- DER 1984). A temperature increase by 4 °C would make the earth its warmest since the Eocene, 40 million years ago (WEBB 1992). The warming would be very fast compared to
i
360 350 ]; 340
§ 330
1
3200IWVV
8
3108
3001960 1965 1970 1975
year
1980 1985 1990
Fig. 1. Monthly atmospheric C02 concentrations betwecn 1958 and 1990 measured on Mauna Loa (Hawaii). Data from KEELING and WHORF (1991).
Milt. Eidgenöss. Forsch.ans!. Wald Schnee Landsch. 69, 1994, 2 153
recent warmings (15-40 times faster, GLEICK et al. 1990). Iftemperature changes as fast as predicted animals and plants need to adapt within the life span of individual trees instead of over several generations of trees. However, it is not known how species change their ecophysiological characteristics under higher C02 concentrations and thus might colonise unknown niches (BLUM 1991). Geographical shifts in environmental conditions result in shifts in genetic composition within a species related to the dispersal capacity of the indi- viduals (HENGEVELD 1990) andin changed inter-specific competitive ability. When cli- mate warms, species might (1) shift to high er altitudes if the environmental factors remain suitable for them or (2) persist in lower zones due to 'extreme' site conditions (azonal or 'permanent communities'; BRAUN-BLANQUET 1964). Generally, a short rise in altitude corresponds to a major shift in latitude. Tue 3 °C cooling of 500 m in elevation equals roughly 250 km in latitude (MACARTHUR 1972).
To predict possible impacts of environmental long-term changes on forest ecosystems we must use simulation models. Simulation models are tools that allow scenario analyses of complex and interacting natural phenomena which could hardly be performed other- wise. Tue most obvious factors determining characteristics and spatial distribution offor- est ecosystems, beside land use practices, are (1) the amount and variability of insolation (warmth) and (2) the amount and variability ofwater available to the plant. Beyond these climatological factors ecological communities are dependent upon site factors such as type, texture and composition of soils, slope, elevation, exposition and composition of flora and fauna (e.g., roe deer).
1.2 The simulation and modelling concept
Change is one of the most fundamental characteristics of ecosystems. As individuals, eco- systems change through the various stages of their life history. These changes can be as- sessed by long-time ecological research studies. Yet it is hardly possible, without adequate tools, to predict potential behaviour of those ecosystems und er the influence of a changing environment. Simulation models represent such a tool. The verb "to simulate" is a deriva- tion of the Latin word "simulare" ("to imitate"). Simulation can therefore be defined as the attempt to describe and predict the behaviour of a system without using the system it- self. An aircraft simulator for example imitates the conditions that might be encountered in some situations during a flight - but without taking off.
Model building can be viewed as a sequential structuring of ideas in relation to the working of a system. lt starts with an initial isolation of one or two simple components of the system under investigation and is followed by the study of their interrelationships.
Once the significance of these relationships has been specified, further attributes can be built into the model until it achieves a level of explanatory power which is sufficient within the given boundaries. The key issue of model development is the fact that for each system there is only one correct model - the system itself.
To derive a model at a given scale, one must either (1) parameterize laws established at a lower microscale in order to predict the key variables at the required scale, (2) disaggre- gate models confirmed at a higher scale in order to produce more detailed predictions at the required scale, or (3) attempt to establish new laws at the required scale and confirm them by observations at this scale (DOOGE 1992). Figure 2 illustrates the steps needed to 154 Mitt. Eidgenöss. Forsch.ans!. Wald Schnee Landsch. 69.1994. 2
Fig. 2. Modelling steps.
Problem Formulation
Word Model
Mathematical Model
Computer Simulation Model
Calibration
Verification
Validation
Pictorial Model
Publication ?
Scenario Analysis
solve a scientific problem with a simulation model. The model discussed in this report has been developed according to this scheme. The only difference was the fact that part of the mathematical model already existed.
To scale up from the leaf to the ecosystem level we must understand how information is transferred from fine scales to broad scales and vice versa. We must learn how to aggregate and simplify, retaining the essential information. A good model does not attempt to re- produce every detail of the biological system; the system itself suffices for that purpose as the most detailed model of itself (LEVIN 1992). The objective of a model should be to ask how much detail can be ignored without producing results contradictory to real observa- tions. Further one must be aware that there exists no single "correct" scale to describe populations or ecosystems (ALLEN and STARR 1982; MEENTENMEYER and Box 1987).
Mitt. Eidgenöss. Forsch.ans!. Wald Schnee Landsch. 69. 1994. 2 155
There are two mathematical simulation modelling approaches (LAW and KELTON
1991). A general purpose programming language such as FORTRAN, Modula or C, is an event-scheduling approach, since the times of future events are explicitly coded into the model and are scheduled to occur in the simulated future. The so-called process approach, however, views the simulation in terms of the individual entities involved, and the code written describes the "experience" of a "typical" entity as it "flows" through the system (special purpose simulation software needed).
As each ecosystem can be described at different scales every forest stand can be ex- pressed as a model with different levels of complexity. These levels of complexity are dis- played in Figure 3. Tue problem modellers are confronted with, is to minimise the infor- mation loss between level L1 and level L3.
Forest stand (Ll)
Diagram (L2)
Formula (L3) f (x) = k · e -a · x
Fortran Statement (L4) Dens (1)
=
K · EXP [-ALPHA· DBH (I)]Computerbits (LS) 1001101100010011001011100001001101
Fig. 3. Metamorphosis of a forest (Ll-LS: levels of complexity).
Forest succession can be considered as a tree-by-tree replacement process. According to KEMENY and SNELL (1960) who define a Markov chain as a statistic process in which transitions among various "states" occur with characteristic probabilities that depend on the current state only and not on any previous state of the system, succession might be de- scribed by a markovian transition matrix. Markov models share obvious relationships with other quantitative approaches used in plant ecology. They are constructed by deter- mining the probability of vegetation on a prescribed (usually small) area belonging to some other vegetation type after a given time interval. The idea of Markov models is illustrated in Table 1 andin Figure 4.
156 Mitt. Eidgenöss. Forsch.ans!. Wald Schnee Landsch. 69, 1994, 2
Tab. 1. Hypothetical transition matrix for vegetation succession (i.e., the probability that shrub veg- etation changes between t0 and t1 into woodland vegetation equals 30% ).
Future state t1
Present state t0 SJ S2 S3
Post-fire vegetation SI 0.1 0.8 0.1
Shrub vegetation S2 0.1 0.6 0.3
Woodland vegetation S3 0.4 0.1 0.5
;::-1
0.8 0.1
0.2
Fig. 4. Markov transition diagram.
According to JEFFERS (1988) Markov models require the following informations:
• a classification that separates successional stages in time or space into definable categories;
• data to determine the transfer probabilities or rates at which states change from one category of this classification to another over time;
• data describing the initial conditions at some particular time, usually following a well- documented perturbation.
SHUGART (1990) states that it is essential for these models to have a scheme for clas- sifying the vegetation into identifiable categories.
Another type of models often used in simulation studies are regression models. For every data set, a unique best fit empirical relationship exists. Mathematical expressions are precise and abstract, they transfer information in a logical way, and they act as an un- equivocal medium of communication (JEFFERS 1988). Their disadvantage lies in the apparent complexity of the symbolic logic - at least to a non-mathematician.
Simulation models having at least some random input components are called stochastic models. A model is called deterministic, when it does not contain any probabilistic (ran- dom) components. Tue output is determined once the set of input quantities and rela- tionships in the model have been specified.
Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69, 1994, 2 157
1.3 Forest models
Growth models, originally based upon graphical descriptions and interpretations, are used in forestry since the beginning of this century. During the last decades more and more mathematical growth models have been developed and applied. Due to the simultaneous development of microelectronics and sophisticated statistical methods, modelling and simulation became an established section of forest mensuration and forest ecology. A main goal of a forest modeller is to understand better how trees and forests grow and tobe able to understand and predict the responses ofphysiological processes to environmental changes. The intended use of the model to be developped determines time and spatial scale and therefore the type ofthe appropriate model. The modelling approach is different whether we want to describe the Iong time ecological processes of a coniferous forest in the Prealps, or whether we are interested in short- time assimilation responses of a single beech due to increased atmospheric carbon dioxide concentrations. We can differentiate between two categories: "top down" and "bottom up"-models. " Bottom up " models are usually very detailed physiological models whereas "top down " -models provide a simpli- fied representation of physiological processes based on some key variables. LANDSBERG (1986) suggests that the ideal modelling approach would be to link a detailed physiologi- cal process model with a more simplified " top-down"-model in the same system.
Forest simulation models can be classified differently, depending on the point of view.
MuNRO (1974) for example distinguished between distance dependent and independent models. SHUGART (1980, 1984) classified the models offorest dynamics using the assump- tions, structure, and parameters of the models as criteria for categorisation. A review of for- est dynamics models can be found in MUNRO (1974) and a compilation of examples from ecology in SHUGART (1984). Tree models (SHUGART and WEST 1980) take the individual tree as the basic unit of a forest simulator. Tue degree of complexity ranges from simple tab- ulation of the probabilities of a tree being replaced by an individual of an another kind to extremely detailed models that include 3-dimensional geometry of different species at dif- ferent sizes. Forest models consider the forest as the focal point of the simulation model.
Forestry yield tables constitute a highly de pendent subset of these forest models. They are tabular representations of volume per unit area and other stand characteristics of even- aged forest stands by age classes, site index classes, species, and density. These yield tables are widely used in the forest community being a useful tool for prediction of stand growth.
Nevertheless the Iimitations of the yield tables are obvious. During the last decade the bio- logical weakness of such models was shown in connection with "forest decline" , which is usually used to describe a decrease of tbe vitality in the forest ecosystem which can lead in some cases to the death ofthe stands (SCHLAEPFER 1992). In addition, yield tables Jack any sensitivity to forest management practices. Gap models dynamically simulate particular attributes of each individual tree on a prescribed spatial unit of comparatively small size.
Tue spatial unit is usually a gap in the forest canopy or a sample quadrate. A closer view of gap models is presented in the following 'State of Knowledge' chapter.
158 Mitt. Eidgenöss. Forsch .ans!. Wald Schnee Landsch. 69, 1994, 2
2 State of Knowledge
Science never proves anything, it makes guesses and goes by them as long as they work weil.
STEINBECK 2.1 Introduction
According to R1zzo and WIKEN (1992) responses of ecosystems to climatic change can be assessed through development of classification models of climate and ecosystems, or through process models.
One type of process model is the gap model, which simulates the succession by calculat- ing the interrelationships among vegetation in a gap within a forest stand. The gap models have been widely used and were applied to different forest ecosystems all over the world.
Detailed statistics about the JABOWA/FORET models are given in BOTKIN (1993). A map of the geographical distribution of some gap models is shown in Figure 5.
Classification models are based on generalised climate statistics, correlating the distri- bution of major vegetation types with biologically important features of the climate ( e.g., Holdrigde Life Zones; HOLDRIDGE 1947). This system provides a means for relating the distribution of natural vegetation associations to climate indices of average annual precip- itation, biotemperature, and a potential evapotranspiration ratio. This approach is useful in identifying potential shifts in vegetation rather than changes in species composition.
KAUPPI and PoscH (1985) used values of growing degree days (GDD) to delimit ecologi- cal boundaries by critical GDD-isolines. These models assume that the changes occur in- stantaneously and that ecosystem processes remain unchanged. The equilibrium-based approaches assume a time scale sufficient for plant migration and eventual equilibrium of vegetation to the new changed climate pattern. Change in vegetation response through time and the processes responsible for the predicted vegetation changes are not considered.
Ecosystems are balanced with a structure that may depend on a few critical species.
TRESHOW (1970) indicats that the response of vegetation may be slow, but once natural balances are sufficiently disrupted, subsequent alterations may occur much more rapidly because of irreversible alterations of essential system function or species interactions. Indi- vidual-plant succession models provide a theoretical framework to integrate individual- level phenomena with systems-level responses. Such growth models derive community dynamics from explicit formulation of life-history characteristics (SHUGART et al. 1988).
2.2 Gap models
Tue main principles of gap models are thoroughly explained in chapter 4. The following section only summarises some key features of selected gap models. Tue modelling ap- proach used in developing gap models is a top-down approach. The first forest simulator based on the gap ideology was the JABOWA (JAnak BOtkin WA!is) model (BOTKIN et al.
1972). Establishment, growth and death of individual trees were simulated on 10 by 10 m squares with 13 species competing for light. A site subroutine provided information about
Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69.1994, 2 159
Fig. 5. Geographie distribution of some gap models derived from JABOWA/FORET models.
1. JABOWA (BOTKIN et al. 1972); Northern Hardwood Forests. 2. LINKAGES (PASTOR and POST 1986); Temperate-Boreal Transition. 3. FORET (SHUGART and WEST 1977, SHUGART 1984); Southern Appalachian Deciduous Forests. 4. FORECE (KIENAST 1987); Central European Forests. 5. FOR- SUM (KRÄUCHI 1993); Central European Forests. 6. ForClim (FISCHLIN and Bua MANN 1993); Central European Forests. 7. SJABO (OJA 1983); Estonian Conifer Forest. 8. KIABRAM (SHUGART et al.
1980); Australian Subtropical Rain Forest. 9. BRIND (SHUGART and NOBLE 1981); Australian Euca- lyptus Forest. 10. OUTENIQUA (VAN DAALEN and SHUGART 1989); African Temperate Rain Forest.
11. FORICO (DOYLE 1981); Puerto Rican Montane Rain Forest. 12. CLIMACS (DALE and HEM- STROM 1984); Pacific Northwest Coniferous Forests. 13. SILVA (KERCHER and AxELROD 1984); Mix- ed Conifer Forests. 14. ZEUG (SMITH and URBAN 1988); Temperate Deciduous Forests. 15. LOKI (BONAN 1989); North American Boreal Forest. 16. FORSKA (LEEMANS and PRENTICE 1987); Scan- dinavian Forest.
soil characteristics and calculated the number of growing degree days. SHUGARTand WEST (1977) changed the JABOWA model to assess the impact of the chestnut blight on Appa- lachian deciduous forests. Their FO RET (FO Rest of East Tennessee) model uses circular plots of one twelfth of a hectare, considers 33 tree species and calculates the growing de- gree days randomly. Competition of individual trees for light and for water is an enhance- ment of the FORENA (SOLOMON 1986) model over FORET. Monthly precipitation en- ters a soil column of predefined depth and soil moisture capacity and leaves the column ei- ther as runoff or as evapotranspiration. Dry days are defined as the number of days during the growing season with soil moisture below the wilting point. In PASTOR and POST (1985) developed a linked forest productivity model (LINKAGES). Their model simulates the carbon nitrogen cycle of the forest ecosystem as constrained by climate and geology. Tue hydrological part of this model - a simple black box approach- is also used in FORECE (KIENAST 1987). Compared to other gap models FORECE has been improved by imple- menting various plant ecological parameters such as light, winter cold and spring frost sen-
160 Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69, 1994, 2
sitivity. FORECE was the first gap model parameterized for Central European forests.
Tue ZEUG model (SMITH and URBAN 1988) differs from other gap models. lt is imple- mented on a grid or transect of model plots. The grid cells can be made interactive to the extent that trees on a given cell can shade or be shaded by trees on neighbouring cells. This modelling approach is mainly useful to evaluate tree establishment as influenced by differ- ent light attenuation on the forest floor due to different sun angles. FORSKA (LEEMANS and PRENTICE 1987) was developed for Scandinavian forests. lt differs from other gap mod- els in that the maximum diameter specification is not required for the functional rela- tionship between height and diameter. lt further uses vertically distributed tree crowns.
Most other models assume that the leaves are concentrated at the top of the trees. There are also attempts to include ground cover such as Calluna, Vaccinium spp. or Calamagro- stis spp. into gap models (KELLOMÄKI and VÄISÄNEN 1991 ). FORCAT (WALDROP et al.
1986) a single tree model of stand development following clearcutting was developed as a management tool. Tue regeneration process is driven by seed weight, and simulation starts with mature stands. KEANE et al. (1989) simulate the effect of different fire regimes on tree composition, stand structure and fuel loading in forests of the inland portion of the north- western United States. An important feature of this model (FIRESUM) is the regenera- tion algorithm which accounts for additional stochastic elements contributing to seedling establishment: cone crop size, seed dissemination, seed germination and seed lost to birds and animals are linked to weather and soil conditions.
There is a tendency of increasing the information needed to simulate forest succession with gap models by adding sophisticated subroutines to the original models. Hence, there exist also attempts to remove sets of input parameters which proved tobe of minor im- portance. The FORCLIM model (FISCHLIN and BUGMANN 1993) is such a descendant of the FORECE (KIENAST 1987) model. Common to all descendants ofthe JABOWA/FO- RET model is a strong positive feedback between light competition and growth (big trees have an advantage that is progressively amplified through time). Abrupt local changes fol- lowing the death of !arge trees, slight differences in initial conditions andin the pattern of establishment and mortality simulated for a particular plot are amplified into !arge differ- ences in the trajectory of succession on that plot. Thus gap models should not be excepted to predict the long-term behaviour of a small forest gap, although the average of many simulations may predict the behaviour of a !arge area (SHUGART and PRENTICE 1992).
2.3 Goals and structure ofthe project
Mathematical modelling of natural phenomena has a long tradition in physics and chemis- try, a shorter history in the biological sciences and has recently (20-30 years ago) become a major component in forestry related research, especially in the United States of Ameri- ca, in Canada and Scandinavia. However, countries with traditional, near-natural forest management still lack good modelling approaches which take into consideration the coun- try-specific silvicultural aspects. 111is is probably due to a negative attitude towards a mathematical description of biological systems and due to the size of the forested area investigated. In small countries like Switzerland, with a high degree of expertise many decisions can be made with expert knowledge alone. Many decisions made in those forests are nevertheless based on pure models, static yield tables.
Mit!. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69, 1994, 2 161
As listed in chapter 2.1, gap models have been successfully applied to numerous forest ecosystems all over the world, and a lot of system-specific factors have been addressed. Yet many important ecosystem processes have only been implemented rudimentarily or have even been neglected. Especially boundary conditions such as water availability, pest inter- actions, interception, soil characteristics are taken into account inappropriately. Manage- ment options have only rarely been implemented in gap models. Most management- oriented models are based on a deterministic modelling approach (e.g., KIMMINS 1993,
LEMM 1991). The models Jack furthermore an adequate description of the regeneration processes ( e.g., seedlings are added 137 cm tall and 1 cm diameter ).
Water influences the distribution and growth of forest vegetation and acts as a solvent for transporting nutrients to the roots. The soil water content influences soil aeration, soil temperature, soil microbiology, the microbial activity, and soil erosion. Many factors in- fluence the amount of water available in a soil that is readily available to the tree. For example, this amount is influenced by amount and frequency of precipitation, runoff, stor- age and leaching and the demand from the plants. All these different pools change more or less rapidly in the course oftime. lt is therefore very important to gain sufficient knowl- edge about the current water status at any point in time. Soils also play an important role in ecosystem development and may be a factor limiting climate-induced migration of spe- cies. Recently published succession models of the JABOWA/FORET-type compute soil processes based on black box approaches. Simulation runs with these models often fail to describe the hydrological processes in different soils - which are essential for plant growth - appropriately. Generally soil water status is only calculated on a monthly basis.
These models lack a differentiation between a) precipitation occurring during a phase of saturation and b) during a phase of drought. The calculations of the soil moisture status is often based on black box approaches.
Part of this project was therefore the realisation of a model that combines both, the short-term hydrological aspects and the long-term successional aspects. The FORest SUccession Model FORSUM presented in this paper was developed to explore the rela- tionships between long-term forest succession and biogenic and anthropogenic factors such as soil moisture, management practices or climatic changes. To this end the main goals for this model can be summarised as follows:
1 Consideration of the hydrological cycle
2 Implementation offorest management routines
3 Performance of risk analyses for climate change and other environmental changes 4 Sensitivity analysis of the driving factors of forest succession
The analysis of the potential impacts of different factors, especially the climatological ones, such as precipitation and temperature, on the successional characteristics of forest development as well as on biodiversity aspects became a major issue during this project.
According to the aim of the project to develop a modelling tool to assess forest succes- sion in Switzerland and central Europe, an evaluation of existing succession models led to the conclusion that the development of a completely new model was not necessary, since existing ecosystem models for central Europe have been tested successfully (KIENAST and
KUHN 1989). The model FORECE of K!ENAST (1987) was considered tobe a valid basis for the present model improvements.
162 Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69, 1994, 2
In a first step the existing model was thoroughly analysed. This was done partly parallel to investigations of BUG MANN (1994), who concentrated his work on a thorough review of the model in a systems analysis sense. Several subroutines were considered tobe either in excess ( e.g„ air pollution) or not sophisticated enough ( e.g„ water balance) to fit the goals of the project. In a second step tree parameters were either adapted to the Central Euro- pean environment or deleted in the case of minor relevance to the observed system. In a third step the algorithms of the new model parts were evaluatecl ancl the source code was consequently updated. Model development was considerecl as a sequential structuring of ideas consiclering the entity of functional characteristics of the forest as a working system.
The different steps in model clevelopment ancl use are discussed in chapter 1. The plausi- bility test (verification and validation) of the different parts of the model (i.e., hyclrologic part) was followecl by sensitivity analyses of factors intrinsic or extrinsic to the species and the stand, respectively. The model was then applied to different forest ecosystems in Switzerland and Germany to evaluate its use as a 'predictive' tool for forest succession clynamics or climate impact risk assessment. The simulation runs were based on the IPCC- climate change scenarios. Aclditionally to these scenarios complementary sirnulation runs were carried out to evaluate cornbined effects on temperature increase and precipitation decrease.
Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69. 1994. 2 163
3 Basics of the Model Approach
3.1 About soils
Within a geographical region, the availability of soil water for plant uptake depends upon the water storing properties of the soils. A soil consists of three phases. A solid, a liquid and a gaseous phase. Soil parameters such as pore size distribution and aggregate size distri- bution are mainly determined by the structure and the composition of the solid phase. On the other band these parameters determine the water conductivity and the water content in acertain soil depth. About 50% ofthe total volume consists ofmaterial derived from the parent rocks. This material is irregularly penetrated by either water or air-filled pores. Tue main function of soil as a medium for plant growth is to provide mechanical support for the plant and living space for other organisms and, in addition, to cover major plant require- ments concerning nutrients, water, and partially air.
To describe quantitatively the water exchange of an ecosystem with the atmosphere, the underground and other system components and ecosystems, respectively, the follow- ing water balance equation is often used:
P - 1 - Lp - E - T - D
=
LiS (1)(P = precipitation; 1 = interception; Lp = lateral surface and subsurface runoff; E = soil evaporation; T = transpiration; D = drainage; LiS = change in storage, respectively)
(2)
(8 = water content by volume; z1,0 = vertical coordinate at the bottom (0), and at the top (1) of the soil profile; t =time)
Precipitation and lateral flow are the only input variables of the system. The water cy- cle starts with the precipitation event. Upon arriving on the canopy the precipitation is di- vided in throughfall, interception, stemflow and canopy drip. As it passes the canopy, qual- itative as well as quantitative properties of the rainfall change. Tue following factors in- fluence the spatial variability and the percentage of the water input to the soil: Vegetation cover, precipitation intensity, duration of the precipitation event, wind, and temperature.
A considerable amount of rainfall is used to wet the above-ground vegetation and evapo- rates after the rain ceases. This part is called interception. The remaining water reaches the ground either as throughfall, canopy drip, or stemflow. Depending on the soil water status the rainfall infiltrates the soil or accumulates on the soil surface and under certain circum- stances contributes to surface runoff. Infiltrating water increases the amount of water stor- ed in the soil pores and provides a soil water reservoir from which the plant roots extract water. Tue water retention curve (pF-curve ), the features of the rooting system, and the
164 Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69, 1994, 2
[Mpa]
1.5 0.5
"1
·.::::
i:: 0.1
i5
0.0.03 ';;;
'§
s
0.01 cn0.003
microporcs
- - - . wilting point
mcsoporcs
---· ficld capacity extractable water
macroporcs
1
•
QOOl ,
0.0002 gravitational
10 20 30 40 50 60
Soil watcr contcnt (g/IOOg ovcn-dry soil) Fig. 6. Moisture retention curve as characterised by the soil characteristics.
water conductivity determine to what extent water is available for plants. The pF-curve may also be used to estimate how fast the water moves downward or laterally (Fig. 6).
These physical properties vary usually from horizon to horizon. The strength of the forces holding the water depends on the pore size and increases with decreasing pore diameter (Tab. 2). The water potential in a water-filled pore is inversely related to pore diameter and can be calculated as follows:
lfl pore water = -0 · 3/ d
(3)
where water potential is in MPa and d in µm (WILD 1988).
Adhesion of water molecules to a solid surface (London-van der Waals forces) is responsible for the water binding in the solid matrix.
Tab. 2. Classes of soil water availability according to RICHARD et al. (1981 ).
dass water potential water availabilily for uptake pore dass diameter
1 'P :2:-0.008 MPa easy to extract 3*103 µm > 0:2:36 µm
2 -0.069 MPa o; 'P <-0.008 MPa good to extract 36µm>0:2:4µm 3 1.5 MPa o; 'P o;-0.069 MPa hard to extract 4µm>0:2:0.2 µm
4 'P < -1.50 MPa not available 0<0.2µm
Mitt. Eidgenöss. Forsch.ans\. Wald Schnee Landsch. 69, 1994. 2 165
The theory of movement of liquid water is based on Darcy's Law which states that the quantity of water passing a unit cross section of soil is proportional to the difference in hy- draulic head:
(4)
where q is the rate of discharge per unit area, Q is the rate of discharge ofwater through a cross section area A, which is taken normal to the direction of flow; k is the hydraulic con- ductivity; and 1'.H is the hydraulic head difference measured at two positions separated by a distance L along a straight line parallel to the direction of flow.
The field capacity defines the amount of water that is held in the soil against the action of gravity. Soil water is only available for plants if the root xylem water potential ( and the leaf water potential) can be lowered below the soil water potential. Studies have shown that plants can withdraw water from pores wider than about 2 µm, corresponding to a wa- ter potential of -1.5 MPa (permanent wilting point). Once the water of these pores has been exhausted, there is none left tobe transported to the leafs and the plant will wilt and die, unless the soil is recharged with water (FITIER and HAY 1987). Tree roots do not grow at water potentials below about-0.7 MPa (DAY and MAC GJLLIVRAY 1975; LARSON 1980) but with the aid of mycorrhizal fungi, whose mycelia are one-hundredth of the diameter of tree roots (DUDDRJDGE et al. 1980) trees may extract water from the soil until water po- tential reaches -1.5 to -2.0 MPa.
In the soil-plant-atmosphere system water potentials are usually negative. Water flows towards regions with more negative values. "Lower" and "higher" water potential indi- cate more and less negative values, respectively.
3.2 About climate change
Climate change can be defined as the long-term fluctuations in temperature, precipitation, wind and all other aspects ofthe Earth's climate. These fluctuations are often related with the term greenhouse effect which is used to describe the roles of water vapour, carbon di- oxide, methane, and man-made chlorofluorocarbons and other trace gases in keeping the Earth's surface warmer than it would be otherwise. These radiatively active gases are re- latively transparent to incoming short-wave radiation, but are relatively opaque to out- going long-wave radiation. Tue latter which would otherwise escape to space, is trapped by these gases within the lower levels ofthe atmosphere. Without the greenhouse effect tem- perature on earth would be -15 °C.
International research efforts, mainly based on mathematical models, support the as- sumption that increasing greenhouse gases might lead to a global climate change. Tue mod- els used are able to simulate global climate very well but still fail to describe the climate on a regional scale due to the low spatial resolution. Attempts to assess local climatic
· changes by downscaling from global scale to regional scale are described by several authors (GYALISTRAS et al. 1993; GIORGI and MEARNS 1991 ). An important deficiency of the current coupled ocean-atmosphere general circulation models is that opposing factors
166 Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69.1994. 2
such as increasing concentration of sulphate aerosols and stratospheric ozone depletion will still have tobe incorporated.
When air is enriched with carbon dioxide, the group of C3 plants will respond with sig- nificantly higher photosynthetic fixation of carbon (KRUPA and KICKERT 1989). The inter- active effects of other environmental factors ( e.g., temperature) with respiratory re- sponses to C02 concentration is largely unknown. TI1e temperature changes expectecl would be very fast compared to recent warming and might cause shifts in the vertical and spatial distribution of trees and migrant pests. Geographically localised species, species with limited genetic diversity and specialised species are the groups most likely to be affected by a climate change. Tue synergy between climate change ancl habitat destruction threatens many more species than either factor alone. Increasing temperatures in the Arctic regions will cause increased soil microbial activity ancl turnover rates of soil organic matter, resulting in a release of C02 to the atmosphere ancl further increasing the greenhouse effect (HENDRICKSON 1985). For further information about climate change, its detection, its impacts ancl research needs see KRÄUCHI (1993).
Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69, 1994, 2 167
4 FORSUM, a Forest Dynamics Model
4.1 General overview
FORSUM (FORest SUccession Model) has been developed to simulate forest dynamics and forest structure und er the influence of a eh anging environment. This general overview is abrief description of the model for users which are not interested in the code as explain- ed later in this chapter. Tue simulation model FORSUM belongs to the family of gap mod- els, and represents a JABOWA-FORET type model. This model type was originally de- signed to simulate gap phase dynamics in mixed coniferous-deciduous forests (Hubbard Brook) of the north-eastern United States (BOTKIN et al. 1972).
FORSUM is based on approaches of BüTKIN et al. (1972), SHUGART (1984) and KIEN- AST (1987), and has been modified (FORSUM-s specific developments, changes and enhancements are indicated with ®) by implementing various subroutines to calculate the hydraulic cycle®, deer browsing®, biomass production by sortiments®, and management operations®. Growth, death and establishment of all trees (31 species) is simulated on multiple circular forest plots. Tue plot size of 1/12 ha corresponds to the size of a gap resulting from removal of an average dominant tree (SHUGART and WEST 1979). The term gap is used in the sense of an elementary (representative) area for a stand.
By aggregating these plots a forest development on a regional scale may be obtained.
This approach is supported by many plant succession studies showing that forest ecosys- tems can be described as a statistical population of plots with different successional stages (BRAY 1956). Figure 7 displays the gap principle as used in gap models. Stand development of each forest plot is considered a markovian process with a transformation matrix contain- ing stochastic and deterministic components. Each species is characterised by a particular pattern of deterministic growth, stochastic regeneration and death, and response to a local physical environment with interspecific interactions resulting from the effect of each species on the environment of all other species. Forest succession is driven by extrinsic and intrinsic variables of the species or the stand, respectively (SoLOMON 1986). Each model run starts with a randomly selected cohort of seedlings in a gap to simulate tree establish- ment. Unfavourable environmental factors and site conditions control the exclusion of species from the seed pool. Growth of each individual tree is simulated by decreasing the maximum potential growth rate at its respective age by factors that are less than optimum.
Death of individual trees is determined with a mortality function that assumes that only 1 % of all trees reach the maximum physiological age. Trees are also killed, if their annual diameter increment is below an user-defined threshold value. Tue individual species' da- ta® (Tab. 5-9) for light and soil moisture requirements, maximum age, etc., were derived from MITSCHERLICH (1978), ELLENBERG (1986), LEIBUNDGUT (1984), WICKI (1985), and ANONYMOUS (1993).
Tue parameterization® of the potential and optimal range of species occurrence has been accomplished in FORSUM using phytosociological vegetation descriptions. Degree
168 Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69, 1994. 2
14 12 ulü
.... 8
0 j :
2a 14
ulO 12 .... 8
§ 6 4
0
2b
0 200 400 600 800 1000 1200 0 200 400 600 800 1000 1200
14 12 .... 8
0 j :
Stcms/ha
c 14
"' 1"
10
8
ll) 6
§ 4
0
2Stcms/ha
d
200 400 600 800 1000 1200 200 400 600 800 1000 1200
14 12
B
10 s 0:
2Stems/ha e
200 400 600 800 1000 1200 Stems/ha
Stcms/ha
= deciduous trees ...,. conifcrous trecs
Fig. 7. Gap principlc: frcquency distribution showing number of simulated trees in a specific year in a subalpine forest. 111e width of the diameter classes is 8 cm. The principle of the gap model is to aver- age the results of single gaps ( a. b, c, d) to obtain forest succession on a regional scale ( e ). ( a, b, c, d:
single gaps, e: average of 100 gaps). Adapted from KRÄUCHI (1992).
days have been calculated on the basis of chorological maps of MEUSEL et al. (1965, 1978, 1992). Figure 8 exhibits an ecogram for Quercus petraea. Figure 9 presents the factors in- fluencing tree growth on a single gap.
According to previous sensitivity analyses incoming sunlight is the major driving vari- able of the model. Daily rainfall and monthly temperature patterns are calculated by stochastically varying the values around mean values® (cf. Fig. 15).
Tue following site data are provided by the user:
• Latitude and longitude for sun angle corrections.
• Days ofthe year the growing season begins and ends (first and last killing frost).
• Monthly mean temperatures and precipitation and their standard deviation.
• Number of days with more than 1 mm rainfall for each month and the corresponding standard deviation.®
• Soil characteristics for each horizon including pH, saturated water conductivity, soil water retention curve and rooting depth.®
Mitt. Eidgenöss. Forsch.anst. Wald Schnee Landsch. 69, 1994, 2 169
