Forest simulation models in Spain: main developments and challenges Marc Palahí & Carles Gracia

1. COST ACTION FP0603: Forest models for research and decision support in sustainable forest management Forest simulation models in Spain: main developments and challenges Marc Palahí & Carles Gracia 1st Workshop and Management Committee Meeting.Institute of Silviculture, BOKU.8-9 of May 2008Vienna, Austria

2. Main features of Spanish forests • Forest cover (total/share): • 15 mil. ha/ 30 % of land • 12 mil. of other forest lands • Growing stock, annual growth and cuts: • 675 mil. m3, 35 mil m3 y-1, 50% of the annual growth is cut • Main species: • P. halepensis, P. pinaster, P. sylvestris, P. nigra, P. pinea, Q. ilex, Q. suber. • Main non-wood products and services: • cork, mushrooms, pine kernels • soil protection, hunting, biodiversity, recreation • Main risks: • Forest fires • Effects of climate change (droughts, etc) • new problems: balance GPP/respiration (reserve carbohydrates-> dieback) • Management and silvicultural characteristics: • Plenty of unmanaged forests- Low profitability of timber • High value of some non-timber products and services • Complex forests: mixed and unevenaged • Specialised areas on plantations (North-west of Spain)

3. Forest modelling approaches and trends Empirical models • The trend has been towards individual tree-level modelling due to the type of forests and silvicultural systems. • Tree level models exist for the main coniferous trees and Q. suber. • Diameter distribution models for the main species in given areas to implement individual-tree models with stand-level data. • Recent research is concentrating in: • Modelling regeneration • Modelling site quality in uneven-aged and mixed forests • Modelling non-timber products and services • Modelling risk of forest fires • Developing forest management information systems based on models

4. Forest modelling approaches and trends Mechanistic models GOTILWA+ (Growth of Trees Is Limited by Water) (www.creaf.uab.es/gotilwa+/), is a process based model to simulate growth processes and how is influenced by climate, tree stand structure, management techniques, soil properties and climate change. The Gotilwa+ model simulates carbon and water fluxes GRACIA C.A., TELLO E., SABATÉ S. i BELLOT (1999). GOTILWA: An integrated model of water dynamics and forest growth. A: RODÀ F., RETANA J., GRACIA C. i BELLOT J. (eds.), Ecology ofMediterranean Evergreen Oak Forests. Ecological Estudies, 137: 163-179. K KRAMER*, I LEINONEN, HH BARTELINK, P BERBIGIER, M BORGHETTI, CH BERNHOFER, E CIENCIALA, AJ DOLMAN, O FROER, C GRACIA, A GRANIER, T GRÜNWALD, P HARI, W JANS, S KELLOMÄKI, D LOUSTAU, F MAGNANI, G MATTEUCCI, GMJ MOHREN, E MOORS, A NISSINEN, H PELTOLA, S SABATÉ, A SANCHEZ, M. SONTAG, R VALENTINI, T VESALA  2002. Evaluation of 6 process-based forest growth models based on eddy-covariance measurements of CO2 and H2O fluxes at 6 forest sites in Europe. Global Change Biology. 8:213-230.

5. Modelling non-timber products and services • Pine cones and seed production Calama, R., Montero, G. 2007. Cone and seed production from stone pine (Pinus pinea L.) stands in Central Range (Spain). Eur J. Forest Res. 126: 23–35. • Cork growth and yield, Sánchez-González, M., Calama, R., Cañellas, I., Montero, G. 2007. Variables influencing cork thickness in spanish cork oak forests: A modelling approach. Ann. For. Sci. 64 (2007) 301-312. • Mushroom production Bonet, J.A., Pukkala, T., Fischer, C.R., Palahi, M., Aragón, J.M., Colinas, C. 2008. Empirical models for predicting the production of wild mushrroms in Scots pine (Pinus sylvestris L.) forests in the Central Pyrenees. Ann. For. Sci. 65. • Scenic beauty Blasco, E., Rodrigéz-Veiga, P., González, J.R., Pukkala, T., Kolhemainene, O., Palahí, M. 2008. Predicting Scenic Beauty of forest stands in Catalonia (North-east Spain). Manuscript. • Water yield and trade-offs of water and forest Pablo Morales, Martint.Sykes, I.Colin Prentice, Pete Smith,Benjamin Smith, Harald Bugmann, Barbel Zierl, Pierre Friedlingstein,Nicolas Viovy,Santi Sabate, Anabel Sanchez, Eduard Pla,Carlos Gracia, Stephen Sitch, Almut Arneth and Jerome Ogee. 2005.Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes.Global Change Biology. 11:2211-2233.

6. Models for predicting risk of hazards • Fire probability: Gonzalez, J. R., Palahí, M., Trasobares, A., Pukkala, T. 2006 A fire probability model for forest stands in Catalonia. Annals of Forest Science 63: 169–176. • Fire damage: González, J. R.; Trasobares, A.; Palahí, M.; Pukkala, T.; 2007. Predicting tree survival in burned forests in Catalonia (North-East Spain) for strategic forest planning. Annals of Forest Science, 64: 733-742.

7. Simulators and information systems • Model archives • SIMANFOR (www.palencia.uva.es/simanfor) • Inventory • SiBosc (Forest information system for Catalonia) (http://www.creaf.uab.es/sibosc/index.htm) • Stand level simulators • GESMO, algonjg@lugo.usc.es • SILVES, delrio@inia.es • RODAL, (www.forecotech.com) • Forest and Regional level simulation-planning systems • MONTE, multi-objetive forest planning (www.forecotech.com) • ESCEN, regional scenarios simulator (www.forecotech.com) • Process based simulators • GOTILWA+ (http://www.creaf.uab.es/gotilwa+/index.htm)

8. <85 [86,100] [101,115] [116,131] [132,146] [147,161] [162,177] [178,192] [193,207] [208,223] [224,238] [239,253] [254,269] [270,284] [285,300] Research highlight LENGHT OF THE GROWTH PERIOD (days) 1960-1990

10. <85 [86,100] [101,115] [116,131] [132,146] [147,161] [162,177] [178,192] [193,207] [208,223] [224,238] [239,253] [254,269] [270,284] [285,300] LENGHT OF THE GROWTH PERIOD (days) A2_HadCM3 2020

11. <85 [86,100] [101,115] [116,131] [132,146] [147,161] [162,177] [178,192] [193,207] [208,223] [224,238] [239,253] [254,269] [270,284] [285,300] LENGHT OF THE GROWTH PERIOD (days) A2_HadCM3 2050

12. <85 [86,100] [101,115] [116,131] [132,146] [147,161] [162,177] [178,192] [193,207] [208,223] [224,238] [239,253] [254,269] [270,284] [285,300] LENGHT OF THE GROWTH PERIOD (days) A2_HadCM3 2080

13. Modeled changes in the length of the growth period

14. Future challenges • Defining needs for new variables in forest inventories/modelling plots. • To improve the understanding of the trade-offs between forest growth and water use • How to simulate mixed forests in process-based models: complexity of species interaction. • Hybridizing models to optimize the trade off between the management applications and process-based. • Modelling open forest areas, maquis, rangelands, etc. • Non-timber products and services • Modelling risk and forest regeneration and succession (after hazards) • Closing gaps between modelers-end users

15. Innovative references Bonet, J.A., Pukkala, T., Fischer, C.R., Palahi, M., Aragón, J.M., Colinas, C. 2008. Empirical models for predicting the production of wild mushrroms in Scots pine (Pinus sylvestris L.) forests in the Central Pyrenees. Ann. For. Sci. 65. Schröter et al. 2005. Ecosystem Service Supply and Vulnerability to Global Change in Europe. Science 310 (5752), 1333-1337. (Published online first 27 Oct. 2005;10.1126/science.1115233 Science Express). González, J. R.; Trasobares, A.; Palahí, M.; Pukkala, T.; 2007. Predicting tree survival in burned forests in Catalonia (North-East Spain) for strategic forest planning. Keenan, T., Garcia, R., Sabate, S., Gracia, C. 2007. PROCESS BASED FOREST MODELLING: A THOROUGH VALIDATION AND FUTURE PROSPECTS FOR MEDITERRANEAN FORESTS IN A CHANGING WORLD. Cuadernos de la SECF: 81-93. Calama, R., Mutke, S., Gordo, J, Montero, G. 2008.An empirical ecological-type model for predicting stone pine (Pinus pinea L.) cone production in the Northern Plateau (Spain). Forest Ecology and Management 255 (3/4): 660-673