On the Ecology of Mountainous Forests in a Changing Climate: A ...

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54 Chapter 3 3.2.3 Environment submodel: The abiotic forest environment The submodels for plant dynamics and soil organic matter turnover are based on abiotic input variables, such as the annual sum of degree-days, which could be calculated best from very detailed weather records, e.g. hourly temperature measurements. However, within an ecosystem model that calculates forest succession over many centuries, such a resolution is hardly feasible. Thus, there arises the need to sacrifice the precision of detailed weather data to allow for general and simple calculations of the abiotic conditions. Monthly weather data capture some of the basic features of the annual weather cycle, and they mediate between the annual time step of the other submodels and more detailed approaches. Thus, monthly temperature and precipitation data seem to be a good compromise and will be used in FORCLIM. ForClim-E: Abiotic environment submodel WEATHER GENERATION Climatic parameters T y,m,l BIOCLIMATIC VARIABLES PET m,y,l DD m,y,l PET y,l uWiT uDD uDrStr ForClim-P: Plants P y,m,l WD m,y,l SM y,m,l uAET ForClim-S: Soil Fig. 3.4: Structure of the submodel of the abiotic environment (FORCLIM-E). The identifiers are explained in the text and in Fischlin et al. (1994). The environment submodel is divided in two parts (Fig. 3.4): (1) The generation of monthly weather data from the long-term statistical distributions, and (2) the translation of monthly weather data into bioclimatic variables, i.e. environmental scalars that influence
The forest model FORCLIM 55 the establishment and growth of trees and the decomposition of soil organic matter (cf. Prentice & Helmisaari 1991, Prentice et al. 1993). GENERATION OF WEATHER DATA The monthly means of temperature (T m,y,l ) and precipitation (P m,y,l ) are sampled stochastically from their respective long-term statistics (Fig. 3.4). It is assumed that both variables are distributed normally around their long-term means. This assumption is met better for temperature than for precipitation (Fliri 1974). In principle, a different statistical distribution (such as the γ distribution, Bonan et al. 1990) could be fitted on a site-by-site basis to the precipitation data, and sampling from such a distribution would provide more realistic precipitation data for the current climate. However, there is no evidence that the assumption of normality of monthly precipitation sums falls short relative to the sensitivity of forest gap models; moreover, all the parameters of more complicated distributions probably change with a changing climate, and it is more difficult to generate consistent scenarios for the parameters of complicated distributions than for those of the simple normal distribution. Thus, the assumption of normality is used for sampling both temperature and precipitation data in FORCLIM-E. Warm, sunny summer months tend to be dry, whereas cool, cloudy ones often are wet. In other words, the temperature and precipitation data are cross-correlated. It may be important to take this phenomenon into account when modelling the water balance of the soil: For example, if the evaporative demand in a given month is large due to high temperatures, then the soil water content will be reduced to a larger extent if there is little rainfall at the same time; this effect is not negligible even if the correlation is moderate (|r| < 0.64, cf. Appendix III), which was used as an argument against modelling the cross-correlation (Kräuchi & Kienast 1993). Thus the cross-correlation between monthly temperature and precipitation data is modelled explicitly in FORCLIM (Fig. 3.4). BIOCLIMATIC VARIABLES Winter minimum temperature (uWiT) Based on the global data set by Müller (1982), Prentice et al. (1992) showed that there is a good correlation between the absolute minimum temperature and the average temper-
Page 1: Diss. ETH No. 10638 On the Ecology
Page 4 and 5: ii Table of contents A BSTRACT.....
Page 6 and 7: iv APPENDIX .......................
Page 8 and 9: vi Harald BUGMANN, 1994: On the eco
Page 10 and 11: viii Harald BUGMANN, 1994: Aspekte
Page 13 and 14: 1 1 . Introduction 1.1 Climatic cha
Page 15 and 16: Introduction 3 1.2 Methods for the
Page 17 and 18: Introduction 5 in a changing climat
Page 19 and 20: Introduction 7 Their integrative ca
Page 21 and 22: Introduction 9 (1984) provides a mo
Page 23 and 24: Introduction 11 The main advantage
Page 25 and 26: 13 2 . Analysis of existing forest
Page 27 and 28: Analysis of existing forest gap mod
Page 39 and 40: The forest model FORCLIM 45 carbon
Page 41 and 42: The forest model FORCLIM 47 TREE GR
Page 43 and 44: The forest model FORCLIM 49 gBFlag
Page 45 and 46: The forest model FORCLIM 51 Disturb
Page 47: The forest model FORCLIM 53 decay o
Page 51 and 52: The forest model FORCLIM 57 3.3 Mod
Page 53 and 54: The forest model FORCLIM 59 Light a
Page 55 and 56: The forest model FORCLIM 61 Overall
Page 57 and 58: The forest model FORCLIM 63 D (cm)
Page 59 and 60: The forest model FORCLIM 65 a) b) c
Page 61 and 62: The forest model FORCLIM 67 Growth
Page 63 and 64: The forest model FORCLIM 69 Stress-
Page 65 and 66: The forest model FORCLIM 71 Tab. 3.
Page 67 and 68: The forest model FORCLIM 73 3.3.2 F
Page 69 and 70: The forest model FORCLIM 75 where k
Page 71 and 72: The forest model FORCLIM 77 NITROGE
Page 73 and 74: The forest model FORCLIM 79 peratur
Page 75 and 76: The forest model FORCLIM 81 of degr
Page 77 and 78: The forest model FORCLIM 83 microcl
Page 79 and 80: The forest model FORCLIM 85 Tab. 3.
Page 81 and 82: The forest model FORCLIM 87 3.4.3 F
Page 83 and 84: The forest model FORCLIM 89 All the
Page 85 and 86: The forest model FORCLIM 91 The mas
Page 87 and 88: The forest model FORCLIM 93 FORCLIM
Page 89 and 90: Behaviour of FORCLIM along a transe
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Behaviour of FORCLIM along a transe
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Parameter sensitivity & model valid
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153 6 . Model applications Climatic
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Model applications 155 The simulate
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Model applications 157 6.2 Possible
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Model applications 159 simulation s
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Model applications 161 Simulation e
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Model applications 163 At the site
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Model applications 165 Bever Biomas
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Model applications 167 tainty inher
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Model applications 169 scenario cho
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Discussion 171 Finally, the analysi
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Discussion 173 bitrarily chosen par
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Discussion 175 comparably small imp
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Discussion 177 end of the 21st cent
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Conclusions 179 Ecological factors
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Conclusions 181 species composition
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References 183 Begon, M., Harper, J
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References 185 Burger, H., 1951. Ho
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References 187 Faber, P.J., 1991. A
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References 189 Huntley, B. & Birks,
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References 191 Leemans, R. & Prenti
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References 193 Olson, J.S., 1963. E
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References 195 Rudloff, W., 1981. W
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References 197 Smith, T.M., Leemans
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References 199 Whittaker, R.H., 195
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Appendix 201 II. Derivation of para
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Appendix 203 Tab. A-4: Tree species
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Appendix 205 Tab. A-7: Values for m
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Appendix 207 The minimum winter tem
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Appendix 209 Tab. A-11: Shade toler
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Appendix 211 Tab. A-14: Climatic pa
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Appendix 213 IV. Source code of the
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Appendix 215 FROM SimBase IMPORT De
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Appendix 217 END; (* IF *) prevDay
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Appendix 219 PROCEDURE InitializeFW
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Appendix 221 InstallSeparator( fMen
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Appendix 223 FROM FCPFileIO IMPORT
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Appendix 225 modelSp^.p.kHm := sens
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Appendix 227 DeclMV( meanLitt[leafS
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Appendix 229 Swiss Federal Institut
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Appendix 231 (*********************
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Appendix 233 BEGIN WITH sp^ DO d :=
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Appendix 235 Definition module FCPB
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Appendix 237 Purpose Simulation mod
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Appendix 239 END Immobilization; PR
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Appendix 241 CONST lem = 3; VAR ef:
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Appendix 243 Purpose Provides the b
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Appendix 245 Example of a text file
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Appendix 247 Tab. A-15: Lower end o
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Appendix 249 Tab. A-17: Percentage
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Appendix 251 Tab. A-19: Significant
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Appendix 253 VI. Derivation of para
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Appendix 255 Tab. A-21: Species-spe
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Appendix 257 Tab. A-22 (continued)
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