Use of BIOME-BGC to simulate water and carbon fluxes within Mediterranean macchia

IForest

Volumn 5, Issue APRIL, 2012, Pages 38-43

  Chiesi, Marta ^a   Chirici, Gherardo ^b   Corona, Piermaria ^c   Duce, Pierpaolo ^d   Salvati, Riccardo ^c   Donatella, Spano ^e   Vaccari, Francesco P ^a   Maselli, Fabio ^a  

^a INSTITUTE OF BIOMETEOROLOGY   (Italy)

^b EcoGeoFor Università del Molise   (Italy)

^c UNIVERSITY OF TUSCIA   (Italy)

^d CNR IBIMET   (Italy)

^e UNIVERSITY OF SASSARI   (Italy)

Author keywords

C Fix; E OBS dataset; Evapotranspiration; Gross primary production

Indexed keywords

EID: 84861747001     PISSN: None     EISSN: 19717458     Source Type: Journal    
DOI: 10.3832/ifor0605-009     Document Type: Article

References (31)

1. Alberti G, Inglima I, Arriga N, Piermatteo D, Pecchiari M, Zaldei A, Papale D, Peressotti A, Valentini R, Cotrufo MF, Magnani F, Miglietta F (2007). Cambiamenti nel regime pluviometrico in ecosistemi mediterranei: il progetto MIND. Forest@ 4 (4): 460-468.-doi: 10.3832/ efor0493-0040460
2. Baraldi R, Vaccari FP, Colom MR, Cotrufo MF, D'Acqui LP (2004). The Pianosa_LAB: an integrated research project to assess the carbon balance of Pianosa island. Journal of Mediterranean Ecology 5: 5-13. [online] URL: http://www.jmecology. com/pdf/2004/2.BARALDI_R.pdf
3. Blasi C (2005). Spazializzazione di variabili climatiche su base nazionale. Manuscript. Ministero dell'Ambiente e del Territorio, Roma, Italy.
4. Chiesi M, Maselli F, Moriondo M, Fibbi L, Bindi M, Running SW (2007). Application of BIOMEBGC to simulate Mediterranean forest processes. Ecological Modelling 206 (1-2): 179-190.-doi: 10.1016/j.ecolmodel.2007.03.032
5. Chiesi M, Moriondo M, Maselli F, Gardin L, Fibbi L, Bindi M, Running SW (2010). Simulation of Mediterranean forest carbon pools under expected environmental scenarios. Canadian Journal of Forest Research 40 (5): 850-860.-doi: 10.1139/X10-037
6. Chiesi M, Fibbi L, Genesio L, Gioli B, Magno R, Maselli F, Moriondo M, Vaccari F (2011). Integration of ground and satellite data to model Mediterranean forest processes. International Journal of Applied Earth Observation and Geoinformation 13 (3): 504-515.-doi: 10.1016/j.jag. 2010.10.006
7. Churkina G, Tenhunen J, Thornton P, Falge EM, Elbers JA, Erhard M, Grunwald T, Kowalski AS, Rannik U, Sprinz D (2003). Analyzing the ecosystem carbon dynamics of four European coniferous forests using a biogeochemistry model. Ecosystems 6 (2): 168-184.-doi: 10.1007/ s10021-002-0197-2
8. Colom MR, Vaccari FP, Scartazza A, Brugnoli E, Zerbi G, Sforzi S, Baraldi R, Cotrufo MF, D'Acqui L, Santi C, Vazzana C, Vivoli R, Spaccino L (2004). Pianosa island: structure, functioning and biodiversity of main ecosystems. Journal of Mediterranean Ecology 5 (1): 31-40. [online] URL: http://pages.unibas.ch/geo/mcr/Projects/PIANOSA/ History/Articles/pianosa_LAB_Colom. pdf
9. Corona P (2010). Integration of forest mapping and inventory to support forest management. iForest-Biogeosciences and Forestry 3 (1): 59-64.-doi: 10.3832/ifor0531-003
10. Cramer W, Kicklighter DW, Bondeau A, Iii BM, Churkina G, Nemry B, Ruimy A, Schloss AL, Intercomparison TP (1999). Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Global Change Biology 5 (S1): 1-15.-doi: 10.1046/j.1365-2486.1999.00009.x
11. D'Acqui LP, Santi CA, Maselli F (2007). Use of ecosystem information to improve soil organic carbon mapping in a Mediterranean island. Journal of Environment Quality 36: 262-271.-doi: 10.2134/jeq2005.0283
12. FAO (2010). Global forest resources assessment. Food and Agriculture Organization of the United Nations, Rome, Italy, pp. 375.
13. Haylock MR, Hofstra N, Klein Tank AM, Klok EJ, Jones PD, New M (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research 113 (D20).-doi: 10.1029/2008JD010201
14. INFC (2007). Le stime di superficie 2005-Prima parte. Inventario Nazionale delle Foreste e dei Serbatoi Forestali di Carbonio. MiPAF-Corpo Forestale dello Stato-Ispettorato Generale, CRA-ISAFA, Trento. [online] URL: http://www.infc. it
15. IPCC (2007). Climate change 2007: impacts, adaptation and vulnerability. IPCC Forth Assessment Report (Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE eds). Cambridge University Press, Cambridge, UK, pp. 976. [online] URL: http://www. ipcc.ch/
16. ISPRA (2010). La realizzazione in Italia del Progetto Corine Land Cover 2006. Istituto Superiore per la Protezione e la Ricerca Ambientale, Rome, Italy, pp. 50.
17. ISSS/ISRIC/FAO (1998). World reference base for soil resources. Acco Press, Leuven, Belgium, pp. 79.
18. Marras S, Pyles RD, Sirca C, Paw U KT, Snyder RL, Duce P, Spano D (2011). Evaluation of the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) model performance over Mediterranean maquis ecosystem. Agricultural and Forest Meteorology 151: 730-745.
19. Maselli F, Papale D, Puletti N, Chirici G, Corona P (2009). Combining remote sensing and ancillary data to monitor the gross productivity of water-limited forest ecosystems. Remote Sensing of Environment 113 (3): 657-667.-doi: 10.1016/ j.rse.2008.11.008
20. Maselli F, Pasqui M, Chirici G, Chiesi M, Fibbi L, Salvati R, Corona P (2012). Evaluation of a 1-km daily meteorological dataset for modelling vegetation production in Italy. Climate Research (under revision).
21. Matsushita B, Tamura M (2002). Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia. Remote Sensing of Environment 81 (1): 58-66.-doi: 10.1016/S0034-4257(01)00331-5 Mollicone D, Matteucci G, Köble R, Masci A,
22. Chiesi M, Smits PC (2003). A model-based approach for the estimation of carbon sinks in European forests. In: "Fluxes of carbon, water and energy in European forests" (Valentini R ed). Springer-Verlag, Berlin, Germany, pp. 297.
23. Papale D, Reichstein M, Aubinet M, Canfora E, Bernhofer C, Kutsch W, Longdoz B, Rambal S, Valentini R, Vesala T, Yakir D (2006). Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3 (4): 571-583.-doi: 10.5194/ bg-3-571-2006
24. Running SW, Hunt ER (1993). Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: "Scaling physiological processes: leaf to globe" (Ehleringer JR, Field CB eds). Academic Press, San Diego, USA, pp. 141-158. [online] URL: http://secure.ntsg.umt.edu/ publications/1993/RH93/Running_CH8_Scaling PhysProcesses.pdf
25. Schulze E-D, Kelliher FM, Korner C, Lloyd J, Leuning R (1994). Relationships among maximum stomatal conductance, ecosystem surface conductance, carbon assimilation rata and plant nitrogen nutrition: a global ecology scaling exercise. Annual Reviews of Ecological Systems 25: 629-662.
26. Thornton PE, Hasenauer H, White MA (2000). Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: an application over complex terrain in Austria. Agricultural and Forest Meteorology 104 (4): 255-271.-doi: 10.1016/S0168-1923(00)00170-2
27. Vaccari FP, Lugato E, Gioli B, D'Acqui L, Genesio L, Toscano P, Matese A, Miglietta F (2012). Land use change and soil organic carbon dynamics in Mediterranean agro-ecosystems: The case study of Pianosa Island. Geoderma 175-176: 29-36.
28. Veroustraete F, Sabbe H, Eerens H (2002). Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data. Remote Sensing of Environment 83 (3): 376-399.-doi: 10.1016/S0034-4257(02)00043-3
29. rd edn). Academic Press, San Diego, USA.
30. White MA, Thornton PE, Running SW, Nemani RR (2000). Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: net primary production controls. Earth Interactions 4 (3): 1-85.-doi: 10.1175/1087-3562 (2000)004<0003:PASAOT>2.0.CO;2
31. Yang F, Ichii K, White MA, Hashimoto H, Michaelis AR, Votava P, Zhu AX, Huete A, Running SW, Nemani RR (2007). Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach. Remote Sensing of Environment 110 (1): 109-122.-doi: 10.1016/j.rse.2007.02.016