Online Electronic Resources (OER): Evaluating the productivity of four main tree species in Germany under climate change with static reduced models

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1 Online Electronic Resources (OER): Evaluating the productivity of four main tree species in Germany under climate change with static reduced models Martin GUTSCH, Petra LASCH-BORN, Felicitas SUCKOW, Christopher P.O. REYER* Potsdam Institute for Climate Impact Research Telegrafenberg A Potsdam, Germany Phone: Fax: gutsch@pik-potsdam.de lasch@pik-potsdam.de suckow@pik-potsdam.de *reyer@pik-potsdam.de

2 Text OER 1: Forest growth model 4C The process-based forest growth model 4C has been developed to simulate the growth, water, and carbon budget of trees and soils under the current and projected climate and to analyze the long-term growth behaviour of forest stands with different tree species. It was validated on a wide range of forest sites (Bugmann et al. 1997, Lasch et al. 2002, Lasch et al. 2005, Reyer et al. 2010, Reyer et al. 2014). It describes processes based on eco-physiological experiments, long term studies of stand development, and physiological modelling (Haxeltine and Prentice 1996) at the tree and stand level. Establishment, growth, and mortality are explicitly modelled on individual patches for which homogeneity is assumed. The model simulates tree species composition, forest structure, leaf area index as well as ecosystem carbon and water balances. The length of the growing season is provided by a species-specific phenological model describing the interaction of growth prohibitors and inhibitors developed by Schaber and Badeck (2003). The cohort structure of simulated tree growth allows a detailed analysis from the stand to the single-tree level. For simulating water, carbon, and nitrogen balances in the forest soil, a multilayered soil model is implemented in 4C. Water balances are calculated using a bucket model approach (Glugla 1969, Koitzsch 1977). The soil column including the litter layer is divided into different layers with optional thickness according to the horizons of the soil profile. Each layer is considered homogeneous concerning its physical and chemical parameters. Water content and soil temperature of each soil layer are estimated as functions of the soil parameters, air temperature, stand precipitation and evapotranspiration, and control the decomposition and mineralisation of organic matter (Grote and Suckow 1998). The potential evapotranspiration for simulating actual evapotranspiration is calculated with the approach of Turc (1961) combined with the modificated Ivanov-equation (Wendling and Müller 1984) as described in (DVWK 1996). Although 4C allows the simulation of increasing CO 2 concentrations, it was not considered in this study due to the general uncertainty about its effect on tree growth.

3 OER Fig. 1 Change of climate factors depending on the climate scenario for all 2342 climate stations for (A) X T = temperature, B) X P = precipitation, C) X D = drought index, D) X R = radiation). Black horizontal line represents the average of 2342 climate stations for Boxes show the lower quartile (25%), the median (50%) and the upper quartile (75%). The whiskers represent the 1.5 fold interquartile range.

4 OER Fig. 2 Forest eco-regions in Germany (after Wolff 2002) with the mean elevation derived from the elevation of the climate stations.

5 OER Fig. 3 Distribution of the residuals of the SRM against fitted Y NPP, SRM values for Scots pine (left) and Box-plots of the residuals for the soil type groups (right).

6 OER Fig. 4 Scatter plot with Y NPP,4C and Y NPP,SRM of the model validation group with the regression line between both NPP values for Scots Pine.

7 OER Fig. 5 Simulated NPP with 4C (grey crosses) for Scots pine on rich soils with low water availability and sensitivity of the SRM (black line) to changes in the parameter value for temperature (A), radiation (B) and the drought index (C) with other climate factors (X T =8.9 C, X R =1082 Jcm -2, X D =3.9 days) kept fixed.

8 OER Fig. 6 Relationship between the mean elevation of a forest eco-region and the NPP range (left) and NPP change (right) of Scots pine. The NPP change is the difference between the NPP values of the 0 K climate scenario and the 3 K climate scenario for the period. The range is the difference between the maximum and the minimum NPP within the 50 realizations of the 3 K climate scenario for the period.

9 OER Table 1 Statistical equations used in the analysis. y i = simulated with 4C Y NPP,4C, ŷ i = simulated with SRM Y NPP,SRM, ỹ = arithmetic mean of simulated Y NPP,4C, n = number of cases, k = number of independent variables. Statistics Equation Root mean square error 1 RMSE = n n ( y i yˆ i ) i= 1 2 Relative root mean square error Absolute bias Relative bias Adjusted coefficient of determination RMSE RMSE % = ~ 100 y 1 BIAS = n n ( y i yˆ i ) i= 1 BIAS BIAS % = ~ 100 y R ( y yˆ ) i i ( y y ) n 2 2 i= 1 = 1 n 2 i= 1 i 2 R adj = 1 2 ( 1 R )( n 1) n k 1

10 OER Table 2 Regression coefficients for the species-specific SRMs and their standard error (s.e.). Asterisk indicates significance at p<0.01 level. beech oak pine spruce Intercept X W α estimate s.e. estimate s.e. estimate s.e. estimate s.e * * * * * * * * X CN * * * * X T 0.314* * * * X R 0.905* * * * X D 1.432* * * * X D² * * * * X W X CN * * * * 0.002

11 OER Table 3 Minimum, maximum and median of simulated average annual NPP (Y NPP,SRM ) in t C ha -1 year -1 separated by species, soil type and climate scenario. Values are given over all forest eco-regions for site species value 0K 0.5K 1.0K 1.5K 2.0K 2.5K 3.0K PL beech min PL beech median PL beech max PL oak min PL oak median PL oak max PL pine min PL pine median PL pine max PL spruce min PL spruce median PL spruce max PH beech min PH beech median PH beech max PH oak min PH oak median PH oak max PH pine min PH pine median PH pine max PH spruce min PH spruce median PH spruce max RL beech min RL beech median RL beech max RL oak min RL oak median RL oak max RL pine min RL pine median RL pine max RL spruce min RL spruce median RL spruce max RH beech min RH beech median RH beech max RH oak min RH oak median RH oak max RH pine min RH pine median RH pine max RH spruce min RH spruce median RH spruce max

12 References: Bugmann H, Grote R, Lasch P, Lindner M, Suckow F (1997). A new forest gap model to study the effects of environmental change on forest structure and functioning. Impacts of Global Change of Tree Physiology and Forest Ecosystem. In: Proceedings of the International Conference on Impacts of Global Change on Tree Physiology and Forest Ecosystems, held November 1996, Wageningen. Mohren GMJ, Kramer K and Sabate S. (eds). Dordrecht, Kluwer Academic Publisher, DVWK (1996) Ermittlung der Verdunstung von Land- und Wasserflächen. Bonn, Wirtschafts- und Verlagsgesellschaft Gas und Wasser mbh Bonn. Glugla G (1969). Berechnungsverfahren zur Ermittlung des aktuellen Wassergehaltes und Gravitationswasserabflusses im Boden. Albrecht-Thaer-Archiv 13, Grote R, Suckow F (1998) Integrating dynamic morphological properties into forest growth modelling: I. Effects on water balance and gas exchange. For Ecol Manage 112: doi: /S (98) Haxeltine A, Prentice IC (1996) BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. Global Biogeochem Cycles 10: doi: /96GB02344 Koitzsch R (1977) Schätzung der Bodenfeuchte aus meteorologischen Daten, Boden- und Pflanzenparametern mit einem Mehrschichtmodell. Z. f. Meteor. 27 (5), Lasch P, Lindner M, Erhard M, Suckow F, Wenzel A (2002b) Regional impact assessment on forest structure and functions under climate change the Brandenburg case study. For Ecol Manage 162: doi: /S (02) Lasch P, Badeck FW, Suckow F, Lindner M, Mohr P (2005) Model-based analysis of management alternatives at stand and regional level in Brandenburg (Germany). For Ecol Manage 207: Reyer C, Lasch P, Sterck FJ, Mohren GMJ (2010) Inter-specific competition in mixed forests of Douglas-fir (Pseudotsuga menziesii) and Common beech (Fagus sylvatica) under a drier and warmer climate a model-based analysis. Ann For Sci 67:805 Reyer C, Lasch-Born P, Suckow F, Gutsch M, Murawski A, Pilz T (2014) Projections of regional changes in forest net primary productivity for different tree species in Europe driven by climate change and carbon dioxide. Ann For Sci 71: DOI /s Schaber J, Badeck F-W (2003) Physiology-based phenology models for forest tree species in Germany. Int J Biometeorol 47: doi: /s Turc L (1961) Évaluation des besoins en eau d irrigation, évapotranspiration potentielle. Ann. Agron. Paris 12: Wendling U, Müller J (1984) Entwicklung eines Verfahrens zur rechnerischen Abschätzung der Verdunstung im Winter. Z. Meteorol. 34:82-85.