Variance-based sensitivity analysis of BIOME-BGC for gross and net primary production

Ecological Modelling

Volumn 292, Issue , 2014, Pages 26-36

  Raj, R ^a   Hamm, N A S ^a   van der Tol, C ^a   Stein, A ^a  

^a UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

BIOME BGC; Gross and net primary production; Process based simulator; Sensitivity analysis

Indexed keywords

BIOME-BGC; NET PRIMARY PRODUCTION; PROCESS-BASED; VARIANCE-BASED SENSITIVITY ANALYSIS;

SENSITIVITY ANALYSIS;

CALIBRATION; COMPUTER SIMULATION; CONIFEROUS FOREST; LEAF AREA INDEX; NET PRIMARY PRODUCTION; NONLINEARITY; SENSITIVITY ANALYSIS; SOIL DEPTH;

NETHERLANDS;

EID: 84907483744     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2014.08.012     Document Type: Article

References (49)

1. Bastin L., Cornford D., Jones R., Heuvelink G.B.M., Pebesma E., Stasch C., Nativi S., Mazzetti P., Williams M. Managing uncertainty in integrated environmental modelling: the UncertWeb framework. Environ. Model. Softw. 2013, 39:116-134.
2. Bond-Lamberty B., Gower S.T., Ahl D.E., Thornton P.E. Reimplementation of the BIOME-BGC model to simulate successional change. Tree Physiol. 2005, 25(4):413-424.
3. Churkina G., Running S.W. Contrasting climatic controls on the estimated productivity of global terrestrial biomes. Ecosystems 1998, 1(2):206-215.
4. Dekker S.C. Modelling and monitoring forest evapotranspiration. Behaviour, concepts and parameters 2000, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, The Netherlands, Ph.D. thesis.
5. Domec J.-C., Lachenbruch B., Pruyn M.L., Spicer R. Effects of age-related increases in sapwood area, leaf area, and xylem conductivity on height-related hydraulic costs in two contrasting coniferous species. Ann. For. Sci. 2012, 69(1):17-27.
6. Duyzer J.H., Dorsey J.R., Gallagher M.W., Pilegaard K., Walton S. Oxidized nitrogen and ozone interaction with forests. II: Multi-layer process-oriented modelling results and a sensitivity study for Douglas fir. Q. J. R. Meteorol. Soc. 2004, 130(600):1957-1971.
7. Edmonds R.L. Decomposition rates and nutrient dynamics in small-diameter woody litter in four forest ecosystems in Washington, U.S.A. Can. J. For. Res. 1987, 17(6):499-509.
8. 3 species. Planta 1980, 149(1):78-90.
9. Hamm N.A.S., Hall J.W., Anderson M.G. Variance-based sensitivity analysis of the probability of hydrologically induced slope instability. Comput. Geosci. 2006, 32(6):803-817.
10. Harmon M.E., Baker G.A., Spycher G., Greene S.E. Leaf-litter decomposition in the Picea/Tsuga forests of Olympic National Park, Washington, U.S.A. For. Ecol. Manag. 1990, 31:55-66.
11. Hessl A.E., Milesi C., White M.A., Peterson D.L., Keane R.E. Ecophysiological parameters for Pacific Northwest trees 2004, United States Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, Technical report.
12. Hobbie S.E., Oleksyn J., Eissenstat D.M., Reich P.B. Fine root decomposition rates do not mirror those of leaf litter among temperate tree species. Oecologia 2010, 162(2):505-513.
13. Homma T., Saltelli A. Importance measures in global sensitivity analysis of nonlinear models. Reliab. Eng. Syst. Safety 1996, 52(1):1-17.
14. Houborg R., Cescatti A., Migliavacca M. Constraining model simulations of GPP using satellite retrieved leaf chlorophyll. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2012, 6455-6458.
15. Houborg R., Cescatti A., Migliavacca M., Kustas W.P. Satellite retrievals of leaf chlorophyll and photosynthetic capacity for improved modeling of GPP. Agric. For. Meteorol. 2013, 177:10-23.
16. Jung M., Verstraete M., Gobron N., Reichstein M., Papale D., Bondeau A., Robustelli M., Pinty B. Diagnostic assessment of European gross primary production. Glob. Change Biol. 2008, 14(10):2349-2364.
17. Keyes M.R., Grier C.C. Above- and below-ground net production in 40-year-old Douglas-fir stands on low and high productivity sites. Can. J. For. Res. 1981, 11(3):599-605.
18. Klaassen W., Bosveld F., de Water E. Water storage and evaporation as constituents of rainfall interception. J. Hydrol. 1998, 212-213:36-50.
19. 2 balance of boreal, temperate, and tropical forests derived from a global database. Glob. Change Biol. 2007, 13(12):2509-2537.
20. Marcelis D. Satellite-Born Monitoring of Gross Primary Productivity: In a Temperate Coniferous Forest by using MODIS Data and the Photochemical Reflectance Index 2011, LAMBERT Academic Publishing, Germany.
21. Mustafa Y. Improving forest growth estimation: Bayesian networks for integrating satellite images and process-based forest growth models 2012, University of Twente, The Netherlands, Ph.D. thesis.
22. Nobel P.S. Physicochemical and Environmental Plant Physiology 2009, Academic Press, Canada. 4th ed.
23. Oakley J. Eliciting Gaussian process priors for complex computer codes. J. Ro. Stat. Soc. Ser. D 2002, 51(1):81-97.
24. Odongo V.O., Onyando J.O., Mutua B.M., van Oel P.R., Becht R. Sensitivity analysis and calibration of the modified universal soil loss equation (MUSLE) for the upper Malewa catchment, Kenya. Int. J. Sediment Res. 2013, 28(3):368-383.
25. Portillo-Estrada M., Korhonen J.F.J., Pihlatie M., Pumpanen J., Frumau A.K.F., Morillas L., Tosens T., Niinemets U. Inter- and intra-annual variations in canopy fine litterfall and carbon and nitrogen inputs to the forest floor in two European coniferous forests. Ann. For. Sci. 2013, 70(4):367-379.
26. Reich P.B., Ellsworth D.S., Walters M.B., Vose J.M., Gresham C., Volin J.C., Bowman W.D. Generality of leaf trait relationships: A test across six biomes. Ecology 1999, 80(6):1955-1969.
27. Reyer C., Lasch P., Mohren G.M.J., Sterck F.J. Inter-specific competition in mixed forests of Douglas-fir Pseudotsuga menziesii and common beech Fagus sylvatica under climate change: a model-based analysis. Ann. For. Sci. 2010, 67(8):805.
28. Ruimy A., Saugier B., Dedieu G. Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophys. Res. Atmos. 1994, 99(D3):5263-5283.
29. Running S.W., Hunt E.R. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. Scaling Physiological Processes: Leaf to Globe 1993, 141-158. Academic Press, Inc., New York. J.R. Ehleringer, C.B. Field (Eds.).
30. Running S.W., Nemani R.R., Heinsch F.A., Zhao M., Reeves M., Hashimoto H. A continuous satellite-derived measure of global terrestrial primary production. BioScience 2004, 54(6):547-560.
31. Sensitivity Analysis 2000, Wiley, Chichester. A. Saltelli, K. Chan, E.M. Scott (Eds.).
32. Saltelli A., Ratto M., Andres T., Campolongo F., Cariboni J., Gatelli D., Saisana M., Tarantola S. Global Sensitivity Analysis: The Primer 2008, John Wiley & Sons, Ltd., Chichester.
33. Simpson D., Butterbach-Bahl K., Fagerli H., Kesik M., Skiba U., Tang S. Deposition and emissions of reactive nitrogen over European forests: a modelling study. Atmos. Environ. 2006, 40(29):5712-5726.
34. Sobol' I.M. Sensitivity analysis for non-linear mathematical models. Math. Model. Comput. Exp. 1993, 1:407-414.
35. Steingrover E.G., Jans W.W.P. Physiology of forest-grown Douglas r trees: Effect of air pollution and drought 1994, Institute for Forestry and Nature Research, Wageningen, the Netherlands, Technical Report 94/3, IBN DLO.
36. Su Z., Timmermans W.J., van der Tol C., Dost R., Bianchi R., Gomez J.A., House A., Hajnsek I., Menenti M., Magliulo V., Esposito M., Haarbrink R., Bosveld F., Rothe R., Baltink H.K., Vekerdy Z., Sobrino J.A., Timmermans J., van Laake P., Salama S., van der Kwast H., Claassen E., Stolk A., Jia L., Moors E., Hartogensis O., Gillespie A. Eagle 2006- Multi-purpose, multi-angle and multi-sensor in-situ and airborne campaigns over grassland and forest. Hydrol. Earth Syst. Sci. 2009, 13(6):833-845.
37. Sutton M., Reis S. The Nitrogen Cycle and Its Influence on the European Greenhouse Gas Balance 2011, NERC/Centre for Ecology & Hydrology, Edinburgh, UK, Integrated Project funded under the 6th Framework Programme 2006-2011.
38. Tatarinov F.A., Cienciala E. Application of BIOME-BGC model to managed forests: 1. Sensitivity analysis. For. Ecol. Manag. 2006, 237:267-279.
39. Thornton P.E. Description of a numerical simulation model for predicting the dynamics of energy, water, carbon, and nitrogen in a terrestrial ecosystem 1998, University of Montana, Missoula, Ph.D. thesis.
40. Thornton P.E., Law B.E., Gholz H.L., Clark K.L., Falge E., Ellsworth D.S., Goldstein A.H., Monson R.K., Hollinger D., Falk M., Chen J., Sparks J.P. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agric. For. Meteorol. 2002, 113:185-222.
41. Thornton P.E., Rosenbloom N.A. Ecosystem model spin-up: estimating steady state conditions in a coupled terrestrial carbon and nitrogen cycle model. Ecol. Model. 2005, 189:25-48.
42. Tiktak A., Bouten W. Soil hydrological system characterization of the two ACIFORN stands using monitoring data and the soil hydrological model "SWIF". Lab. of Physical Geography and Soil Science 1990, University of Amsterdam, The Netherlands.
43. Tiktak A., Bouten W. Soil water dynamics and long-term water balances of a Douglas fir stand in the Netherlands. J. Hydrol. 1994, 156:265-283.
44. Trusilova K., Trembath J., Churkina G. Parameter estimation and validation of the terrestrial ecosystem model BIOME-BGC using eddy-covariance flux measurements 2009, Max Planck Institute for Biogeochemistry, Germany, Technical report.
45. van Wijk M.T., Dekker S.C., Bouten W., Bosveld F.C., Kohsiek W., Kramer K., Mohren G.M.J. Modeling daily gas exchange of a Douglas-fir forest: comparison of three stomatal conductance models with and without a soil water stress function. Tree Physiol. 2000, 20(2):115-122.
46. van Wijk M.T., Dekker S.C., Bouten W., Kohsiek W., Mohren G.M.J. Simulation of carbon and water budgets of a Douglas-fir forest. For. Ecol. Manag. 2001, 145(3):229-241.
47. Verma M., Friedl M.A., Richardson A.D., Kiely G., Cescatti A., Law B.E., Wohlfahrt G., Gielen B., Roupsard O., Moors E.J., Toscano P., Vaccari F.P., Gianelle D., Bohrer G., Varlagin A., Buchmann N., van Gorsel E., Montagnani L., Propastin P. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET la thuile dataset. Biogeosci. Discuss. 2013, 10(7):11627-11669.
48. Wang Y.P., Leuning R., Cleugh H.A., Coppin P.A. Parameter estimation in surface exchange models using nonlinear inversion: how many parameters can we estimate and which measurements are most useful?. Glob. Change Biol. 2001, 7(5):495-510.
49. White M., Thornton P., Running S., Nemani R. Parameterization and sensitivity snalysis of the BIOME-BGC terrestrial ecosystem model: net primary production controls. Earth Interact. 2000, 4(3):1-85.