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

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130 Chapter 5 of the monthly variables shows a reasonably constant pattern over the whole climatological space, then it is possible to provide the climatic input data required by forest gap models such as FORCLIM. The climatological data from the 12 sites presented in Appendix III were analysed for their annual cycles (Fig. 5.2). The monthly mean temperature can be predicted well from the annual mean temperature because the temperature amplitude, i.e. the difference between the temperature of the warmest and the coldest month, does not vary much among the climate stations. The monthly precipitation sum can be expressed adequately as a fraction of the annual precipitation sum. The standard deviations of the two variables are more difficult to predict, with better results for temperature than for precipitation. Specifically, the two sites on the southern slope of the Alps (Airolo and Locarno) had to be excluded from the analysis of the standard deviation of precipitation because they exhibit a pattern strongly different from the one at the stations on the northern slope of the Alps. mean temperature (°C) annual 10 6 2 Psil Ocar, Qpub, Csat Qrob/pet, (Fsil) Qrob/pet, (Fsil) Pexc, Psil Pexc, Pcem Fsil (Fsil), Qpub, Csat Fsil, (Qrob/pet) Fsil, (Aalb), (Pexc) Pexc Pexc -2 400 800 1200 1600 annual precipitation sum (mm) 2000 Fig. 5.1: Dominating tree species in a space spanned by the annual precipitation sum and the annual mean temperature according to Rehder (1965) and Ellenberg (1986). Key to species: Aalb – Abies alba; Csat – Castanea sativa; Fsil – Fagus silvatica; Ocar – Ostrya carpinifolia; Pcem – Pinus cembra; Pexc – Picea excelsa; Psil – Pinus silvestris; Qpet – Quercus petraea; Qpub – Quercus pubescens; Qrob – Quercus robur. The dash-spotted line close to the bottom of the graph indicates the approximate location of the alpine timberline.
Parameter sensitivity & model validation 131 deviation from annual mean T (°C) 10 5 0 -5 -10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 5 4 3 2 1 0 standard deviation (°C) fraction of annual precipitation 0.4 0.3 0.2 0.1 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 6 4 2 0 dev. of P (cm/month) std. Fig. 5.2: Analysis of the annual cycle of the long-term climatic parameters at the 12 study sites (Appendix III). Left: Average deviation of monthly mean temperatures from the annual mean temperature (open squares); average standard deviation of the monthly mean temperatures (black squares). Right: Average fraction of the annual precipitation sum falling in every month (open squares); average standard deviation of the monthly precipitation sums at 10 sites on the northern slope of the Alps (black squares, excluding Airolo and Locarno). The error bars in both graphs denote one standard deviation. There is a confounding factor inherent in the derivation of the monthly temperature data from the annual mean: The temperature amplitude increases slightly in drier climates, and this is the reason why the alpine timberline in Fig. 5.1 is found at lower annual mean temperatures as precipitation decreases. Linear regressions of the annual temperature amplitude against the annual precipitation sum from various subsets and the whole set of the 12 climate stations (Appendix III) generally yielded insignificant correlation coefficients, but the intercept was always close to 20 °C, and the slope varied between -0.002 and -0.0009. In spite of the insignificance of the regressions, the following approximation for the effect of the annual precipitation sum (P l ) on temperature amplitude (A l ) was used to reconstruct the annual cycle of monthly mean temperatures: A l ≈ 20 – 0.0014 · P l (5.1) Based on these considerations, the long-term mean monthly temperature (T m,l ) is calculated from the annual mean temperature (T l ) and the annual precipitation sum (P l ) according to Eq. 5.2: T m,l = T l + ∆T m · (1180 – P l)·0.0007 + |∆T m | |∆T m | (5.2)
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Diss. ETH No. 10638 On the Ecology
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ii Table of contents A BSTRACT.....
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iv APPENDIX .......................
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vi Harald BUGMANN, 1994: On the eco
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viii Harald BUGMANN, 1994: Aspekte
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1 1 . Introduction 1.1 Climatic cha
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Introduction 3 1.2 Methods for the
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Introduction 5 in a changing climat
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Introduction 7 Their integrative ca
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Introduction 9 (1984) provides a mo
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Introduction 11 The main advantage
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13 2 . Analysis of existing forest
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Analysis of existing forest gap mod
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The forest model FORCLIM 45 carbon
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The forest model FORCLIM 47 TREE GR
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The forest model FORCLIM 49 gBFlag
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The forest model FORCLIM 51 Disturb
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The forest model FORCLIM 53 decay o
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The forest model FORCLIM 55 the est
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The forest model FORCLIM 57 3.3 Mod
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The forest model FORCLIM 59 Light a
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The forest model FORCLIM 61 Overall
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The forest model FORCLIM 63 D (cm)
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The forest model FORCLIM 65 a) b) c
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The forest model FORCLIM 67 Growth
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The forest model FORCLIM 69 Stress-
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The forest model FORCLIM 71 Tab. 3.
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The forest model FORCLIM 73 3.3.2 F
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The forest model FORCLIM 75 where k
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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
Page 115 and 116: Parameter sensitivity & model valid
Page 123: Parameter sensitivity & model valid
Page 147 and 148: 153 6 . Model applications Climatic
Page 149 and 150: Model applications 155 The simulate
Page 151 and 152: Model applications 157 6.2 Possible
Page 153 and 154: Model applications 159 simulation s
Page 155 and 156: Model applications 161 Simulation e
Page 157 and 158: Model applications 163 At the site
Page 159 and 160: Model applications 165 Bever Biomas
Page 161 and 162: Model applications 167 tainty inher
Page 163 and 164: Model applications 169 scenario cho
Page 165 and 166: Discussion 171 Finally, the analysi
Page 167 and 168: Discussion 173 bitrarily chosen par
Page 169 and 170: Discussion 175 comparably small imp
Page 171 and 172: Discussion 177 end of the 21st cent
Page 173 and 174: 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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