Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://doi.org/10.5194/gmd-12-4133-2019 below:

Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Adams, H. R., Barnard, H. R., and Loomis, A. K.: Topography alters tree growth–climate relationships in a semi-arid forested catchment, Ecosphere, 5, 1–16, 2014. a

Ali, A. A., Xu, C., Rogers, A., Fisher, R. A., Wullschleger, S. D., Massoud, E. C., Vrugt, J. A., Muss, J. D., McDowell, N. G., Fisher, J. B., Reich, P. B., and Wilson, C. J.: A global scale mechanistic model of photosynthetic capacity (LUNA V1.0), Geosci. Model Dev., 9, 587–606, https://doi.org/10.5194/gmd-9-587-2016, 2016. a

Archer, G., Saltelli, A., and Sobol,I.: Sensitivity measures, anova-like techniques and the use of bootstrap, J. Stat. Comput. Simu., 58, 99–120,1997. a

Arora, V. K., Boer, G. J., Friedlingstein, P., Eby, M., Jones, C. D., Christian, J. R., Bonan, G., Bopp, L., Brovkin, V., Cadule, P., Brovkin, V., Cadule, P., and Hajima, T.: Carbon–concentration and carbon–climate feedbacks in cmip5 earth system models, J. Climate, 26 5289–5314, 2013. a, b

Bastidas, L. A., Gupta, H. V., Sorooshian, S., Shuttleworth, W. J., and Yang, Z. L.: Sensitivity analysis of a land surface scheme using multicriteria methods, J. Geophys. Res.-Atmos., 104, 19481–19490, 1999. a

Benton, T. G. and Grant, A.: Elasticity analysis as an important tool in evolutionary and population ecology Trends Ecol. Evol., 14, 467–471, 1999. a

Bonan, G. B., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein, M., Lawrence, D. M., and Swenson, S. C.: Improving canopy processes in the community land model version 4 (CLM4) using global flux fields empirically inferred from fluxnet data, J. Geophys. Res.-Biogeo., 116, G02014, https://doi.org/10.1029/2010JG001593, 2011. a

Campolongo, F., Saltelli, A., Sørensen, T. M., and Tarantola, S.: Hitchhiker's guide to sensitivity analysis, in: Sensitivity analysis, IEEE Computer Society Press, 15–47, 2000. a

Chave, J., Réjou-Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B., Duque, A., Eid, T., Fearnside, P. M., Goodman, R. C., and Henry, M.: Improved allometric models to estimate the aboveground biomass of tropical trees, Glob. Change Biol., 20, 3177–3190, 2014. a

Christoffersen, B. O., Gloor, M., Fauset, S., Fyllas, N. M., Galbraith, D. R., Baker, T. R., Kruijt, B., Rowland, L., Fisher, R. A., Binks, O. J., Sevanto, S., Xu, C., Jansen, S., Choat, B., Mencuccini, M., McDowell, N. G., and Meir, P.: Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro), Geosci. Model Dev., 9, 4227–4255, https://doi.org/10.5194/gmd-9-4227-2016, 2016. a

Claussen, M., Mysak, L., Weaver, A., Crucifix, M., Fichefet, T., Loutre, M. F., Weber, S., Alcamo, J., Alexeev, V., Berger, A., and Calov, R.: Earth system models of intermediate complexity: closing the gap in the spectrum of climate system models, Clim. Dynam., 18, 579–586, 2002. a

Collalti, A., Thornton, P. E., Cescatti, A., Rita, A., Borghetti, M., Nole, A., Trotta, C., Ciais, P., and Matteucci, G.: The sensitivity of the forest carbon budget shifts across processes along with stand development and climate change, Ecol. Appl., 29, e01837, https://doi.org/10.1002/eap.1837, 2019. a, b, c

Collins, D. C. and Avissar, R.: An evaluation with the fourier amplitude sensitivity test (FAST) of which land-surface parameters are of greatest importance in atmospheric modeling, J. Climate, 7, 681–703, 1994. a

Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., and Totterdell, I. J.: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, 184–187, 2000. a, b

Crossley, J. F., Polcher, J., Cox, P. M., Gedney, N. and Planton, S.: Uncertainties linked to land-surface processes in climate change simulations, Clim. Dynam., 16, 949–961, 2000. a

Cukier, R., Fortuin, C., Shuler, K. E., Petschek, A., and Schaibly, J.: Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I theory, J. Chem. Phys., 59, 3873–3878, 1973. a

Da Rocha, H. R., Nobre, C. A., Bonatti, J. P., Wright, I. R., and Sellers, P. J.: A vegetation-atmosphere interaction study for amazonia deforestation using field data and a 'single column' model, Q. J. Roy. Meteor. Soc., 122, 567–594, 1996. a

Díaz, S., Kattge, J., Cornelissen, J. H., Wright, I. J., Lavorel, S., Dray, S., Reu, B., Kleyer, M., Wirth, C., Prentice, I. C., and Garnier, E.: The global spectrum of plant form and function, Nature, 529, 167–171, 2016. a

Dietze, M. C., Wolosin, M. S., and Clark, J. S.: Capturing diversity and interspecific variability in allometries: a hierarchical approach, Forest Ecol. Manage., 256, 1939–1948, 2008. a

Dietze, M. C., Serbin, S. P., Davidson, C., Desai, A. R., Feng, X., Kelly, R., Kooper, R., LeBauer, D., Mantooth, J., McHenry, K., and Wang, D.: A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes, J. Geophys. Res.-Biogeo., 119, 286–300, 2014. a, b, c, d, e, f, g, h, i, j, k, l, m

Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevliakova, E., Stouffer, R. J., Cooke, W., Dunne, K. A., Harrison, M. J., and Krasting, J. P.: GFDL's ESM2 global coupled climate-carbon earth system models. part i: Physical formulation and baseline simulation characteristics, J. Climate, 25, 6646–6665, 2012. a

Dupuy, J. M. and Chazdon, R. L.: Effects of vegetation cover on seedling and sapling dynamics in secondary tropical wet forests in costa rica, J. Trop. Ecol., 22, 65–76, 2006. a

Entekhabi, D. and Eagleson, P. S.: Land surface hydrology parameterization for atmospheric general circulation models including subgrid scale spatial variability, J. Climate, 2, 816–831, 1989. a

Falster, D. S., Duursma, R. A., and FitzJohn, R. G.: How functional traits influence plant growth and shade tolerance across the life cycle, P. Natl. Acad. Sci. USA, 115, E6789–E6798, 2018. a, b

Farquhar, G. D.: Models of integrated photosynthesis of cells and leaves, Philos. T. Roy. Soc. B, 323, 357–367, https://doi.org/10.1098/rstb.1989.0016, 1989. a

Farrior, C. E., Bohlman, S. A., Hubbell, S., and Pacala, S. W.: Dominance of the suppressed: Power-law size structure in tropical forests, Science, 351, 155–157, 2016. a

Feldpausch, T. R., Banin, L., Phillips, O. L., Baker, T. R., Lewis, S. L., Quesada, C. A., Affum-Baffoe, K., Arets, E. J. M. M., Berry, N. J., Bird, M., Brondizio, E. S., de Camargo, P., Chave, J., Djagbletey, G., Domingues, T. F., Drescher, M., Fearnside, P. M., França, M. B., Fyllas, N. M., Lopez-Gonzalez, G., Hladik, A., Higuchi, N., Hunter, M. O., Iida, Y., Salim, K. A., Kassim, A. R., Keller, M., Kemp, J., King, D. A., Lovett, J. C., Marimon, B. S., Marimon-Junior, B. H., Lenza, E., Marshall, A. R., Metcalfe, D. J., Mitchard, E. T. A., Moran, E. F., Nelson, B. W., Nilus, R., Nogueira, E. M., Palace, M., Patiño, S., Peh, K. S.-H., Raventos, M. T., Reitsma, J. M., Saiz, G., Schrodt, F., Sonké, B., Taedoumg, H. E., Tan, S., White, L., Wöll, H., and Lloyd, J.: Height-diameter allometry of tropical forest trees, Biogeosciences, 8, 1081–1106, https://doi.org/10.5194/bg-8-1081-2011, 2011. a

Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S., Cox, P., Huntingford, C., Meir, P., and Woodward, F. I.: Assessing uncertainties in a second-generation dynamic vegetation model caused by ecological scale limitations, New Phytol., 187, 666–681, 2010. a, b, c, d

Fisher, R. A., Muszala, S., Verteinstein, M., Lawrence, P., Xu, C., McDowell, N. G., Knox, R. G., Koven, C., Holm, J., Rogers, B. M., Spessa, A., Lawrence, D., and Bonan, G.: Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED), Geosci. Model Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, 2015. a, b, c, d, e, f, g, h

Fisher, R. A., Koven, C. D., Anderegg, W. R., Christoffersen, B. O., Dietze, M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence, P. J., and Lichstein, J. W.: Vegetation demographics in earth system models: A review of progress and priorities, Glob. Change Biol., 24, 35–54, 2018. a, b, c

Foley, J. A., Prentice, I. C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., and Haxeltine, A.: An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics, Global Biogeochem. Cy., 10, 603–628, 1996. a

Francos, A., Elorza, F. J., Bouraoui, F., Bidoglio, G., and Galbiati, L.: Sensitivity analysis of distributed environmental simulation models: understanding the model behaviour in hydrological studies at the catchment scale, Reliab. Eng. Syst. Safe., 79, 205–218, 2003. a

Friedlingstein, P., Cox, P., Betts, R., Bopp, L., von Bloh, W., Brovkin, V., Cadule, P., Doney, S., Eby, M., Fung, I., and Bala, G.: Climate-carbon cycle feedback analysis: Results from the C4MIP model intercomparison, J. Climate, 19, 3337–3353, 2006. a

Geromel, J. C.: Optimal linear filtering under parameter uncertainty, IEEE T. Signal Proces., 47, 168–175, 1999. a

Golaz, J. C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019. a

Groenendijk, M., Dolman, A. J., Van Der Molen, M. K., Leuning, R., Arneth, A., Delpierre, N., Gash, J. H. C., Lindroth, A., Richardson, A. D., Verbeeck, H., and Wohlfahrt, G.: Assessing parameter variability in a photosynthesis model within and between plant functional types using global Fluxnet eddy covariance data, Agr. Forest Meteorol., 151, 22–38, 2011. a

Gupta, H. V., Bastidas, L. A., Sorooshian, S., Shuttleworth, W. J., and Yang, Z. L.: Parameter estimation of a land surface scheme using multicriteria methods, J. Geophys. Res.-Atmos., 104, 19491–19503, 1999. a

Haaker, M., and Verheijen, P.:, Local and global sensitivity analysis for a reactor design with parameter uncertainty, Chem. Eng. Res. Des., 82, 591–598, 2004. a

Helton, J. C.: Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal, Reliab. Eng. Syst. Safe., 42, 327–367, 1993. a

Hickler, T., Smith, B., Sykes, M. T., Davis, M. B., Sugita, S., and Walker, K.: Using a generalized vegetation model to simulate vegetation dynamics in northeastern USA, Ecology, 85, 519–530, 2004. a, b

Holm, J. A., Knox, R. G., Zhu, Q., Fisher, R. A., Koven, C. D., Lima, A. J. N., Riley, W. J., Longo, M., Negron-Juarez, R. I., de Araujo, A. C., Manzi, A., Kueppers, L. M., Moorcroft, P. R., Higuchi, N., and Chambers, J. Q.: The Central Amazon forest sink under current and future atmospheric CO₂: Predictions from big-leaf and demographic vegetation models, J. Geophys. Res., in review, 2019. a

Hunter, M. O., Keller, M., Victoria, D., and Morton, D. C.: Tree height and tropical forest biomass estimation, Biogeosciences, 10, 8385–8399, https://doi.org/10.5194/bg-10-8385-2013, 2013. a, b

Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J. F., Large, W. G., Lawrence, D., Lindsay, K., and Lipscomb, W. H.: The community earth system model: a framework for collaborative research, B. Am. Meteorol. Soc., 94, 1339–1360, 2013. a

Johnson, D. J., Needham, J., Xu, C., Massoud, E. C., Davies, S. J., Anderson-Teixeira, K. J., Bunyavejchewin, S., Chambers, J. Q., Chang-Yang, C. H., Chiang, J. M., and Chuyong, G. B.: Climate sensitive size-dependent survival in tropical trees, Nature Ecology and Evolution, 2, 1436–1442, 2018. a

Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009. a

Kattge, J., Diaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., and Cornelissen, J. H. C.: TRY – a global database of plant traits, Glob. Change Biol., 17, 2905–2935, 2011. a

Kattge, J., Ogle, K., Bönisch, G., Díaz, S., Lavorel, S., Madin, J., Nadrowski, K., Nöllert, S., Sartor, K., and Wirth, C.: A generic structure for plant trait databases, Methods Ecol. Evol., 2, 202–213, 2011. a

Kioutsioukis, I., Tarantola, S., Saltelli, A., and Gatelli, D.: Uncertainty and global sensitivity analysis of road transport emission estimates, Atmos. Environ., 38, 6609–6620, 2004. a

Kitanidis, P. K.: Parameter uncertainty in estimation of spatial functions: Bayesian analysis, Water Resour. Res., 22, 499–507, 1986. a

Knyazikhin, Y., Glassy, J., Privette, J. L., Tian, Y., Lotsch, A., Zhang, Y., Wang, Y., Morisette, J. T., Votava, P., Myneni, R. B., Nemani, R. R., and Running, S. W.: MODIS leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by vegetation (FPAR) product (MOD15) algorithm theoretical basis document, Theoretical Basis Document, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, 1999. a

Krinner, G.,  Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005. a

Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., Lighty, L., Eastman, J. L., Doty, B., Dirmeyer, P., and Adams, J.: Land information system: An interoperable framework for high resolution land surface modeling, Environ. Modell. Softw., 21, 1402–1415, 2006. a

Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S. C., Lawrence, P. J., Zeng, X., Yang, Z. L., Levis, S., Sakaguchi, K., and Bonan, G. B.: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model, J. Adv. Model Earth Sy., 3, https://doi.org/10.1029/2011MS00045, 2012. a

LeBauer, D. S., Wang, D., Richter, K. T., Davidson, C. C., and Dietze, M. C.: Facilitating feedbacks between field measurements and ecosystem models, Ecol. Monogr., 83, 133–154, 2013. a, b, c, d, e, f

Leuning, R.: Temperature dependence of two parameters in a photosynthesis model, Plant Cell Environ., 25, 1205–1210, 2002. a, b

Lieberman, D., Lieberman, M., Hartshorn, G., and Peralta, R.: Growth rates and age-size relationships of tropical wet forest trees in costa rica, J. Trop. Ecol., 1, 97–109, 1985. a

Lu, Y. and Mohanty, S.: Sensitivity analysis of a complex, proposed geologic waste disposal system using the fourier amplitude sensitivity test method, Reliab. Eng. Syst. Safe., 72, 275–291, 2001. a

Masson, V., Champeaux, J. L., Chauvin, F., Meriguet, C., and Lacaze, R.: A global database of land surface parameters at 1-km resolution in meteorological and climate models, J. Climate, 16, 1261–1282, 2003. a, b

Massoud, E. C., Purdy, A. J., Christoffersen, B. O., Santiago, L. S., and Xu, C.: Bayesian inference of hydraulic properties in and around a white fir using a process-based ecohydrologic model, Environ. Modell. Softw., 115, 76–85, 2019a. a

Massoud, E., Xu, C., Fisher, R., Knox, R., Walker, A., Serbin, S., Christoffersen, B., Holm, J., Kueppers, L., Ricciuto, D., Wei, L., Johnson, D., Chambers, J., Koven, C., McDowell, N., and Vrugt, J.: Identification of key parameters controlling demographically structured vegetation dynamics in a Land Surface Model, 1.0, NGEE Tropics Data Collection, (dataset), https://doi.org/10.15486/ngt/1497413, 2019b. a

McDowell, N. G., Williams, A. P., Xu, C., Pockman, W. T., Dickman, L. T., Sevanto, S., Pangle, R., Limousin, J., Plaut, J., Mackay, D. S., and Ogee, J.: Multi-scale predictions of massive conifer mortality due to chronic temperature rise, Nat. Clim. Change, 6, 295–300, 2016. a

McDowell, N., Allen, C. D., Anderson–Teixeira, K., Brando, P., Brienen, R., Chambers, J., Christoffersen, B., Davies, S., Doughty, C., Duque, A., Espirito–Santo, F., Fisher, R., Fontes, C. G., Galbraith, D., Goodsman, D., Grossiord, C., Hartmann, H., Holm, J., Johnson, D. J., Kassim, A. R., Keller, M., Koven, C., Kueppers, L., Kumagai, T., Malhi, Y., McMahon, S. M., Mencuccini, M., Meir, P., Moorcroft, P., Muller–Landau, H. C., Phillips, O. L., Powell, T., Sierra, C. A., Sperry, J., Warren, J., Xu, C., and Xu, X.: Drivers and mechanisms of tree mortality in moist tropical forests, New Phytol., 219, 851–869, 2018. a

McRae, G. J., Tilden, J. W., and Seinfeld, J. H.: Global sensitivity analysis – a computational implementation of the Fourier Amplitude Sensitivity Test (FAST), Comput. Chem. Eng., 6, 15–25, 1982. a

Menberg, K., Heo, Y., and Choudhary, R.: Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information, Energy Buildings, 133, 433–445, 2016. a

Moorcroft, P. R., Hurtt, G., and Pacala, S. W.: A method for scaling vegetation dynamics: the ecosystem demography model (ED), Ecol. Monogr., 71, 557–586, 2001. a, b

Noilhan, J. and Planton, S.: A simple parameterization of land surface processes for meteorological models, Mon. Weather Rev., 117, 536–549, 1989. a

O'Hagan, A., and Leonard, T.: Bayes estimation subject to uncertainty about parameter constraints, Biometrika, 63, 201–203, 1976. a

Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z., Swenson, S. C., and Thornton, P. E.: Technical description of version 4.5 of the Community Land Model (CLM), NCAR Technical Note NCAR/TN-503+STR, 420 pp., https://doi.org/10.5065/D6RR1W7M, 2013. a

Pan, Y., McGuire, A. D., Melillo, J. M., Kicklighter, D. W., Sitch, S., and Prentice, I. C.: A biogeochemistry-based dynamic vegetation model and its application along a moisture gradient in the continental united states, J. Veg. Sci., 13, 369–382, 2002. a

Pappas, C., Fatichi, S., Leuzinger, S., Wolf, A., and Burlando, P.: Sensitivity analysis of a process-based ecosystem model: Pinpointing parameterization and structural issues, J. Geophys. Res.-Biogeo., 118, 505–528, https://doi.org/10.1002/jgrg.20035, 2013. a, b, c, d, e, f, g, h, i

Qian, T., Dai, A., Trenberth, K. E., and Oleson, K. W.: Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluations, J. Hydrometeorol., 7, 953–975, 2006. a, b

Razavi, S. and Gupta, H. V.: A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. theory, Water Resour. Res., 52, 423–439, 2016. a

Rogers, A.: The use and misuse of V_c,max in Earth System Models, Photosynth. Res., 119, 15–29, 2014. a

Rogers, A., Medlyn, B. E., Dukes, J. S., Bonan, G., Caemmerer, S., Dietze, M. C., Kattge, J., Leakey, A. D., Mercado, L. M., Niinemets, Ü., and Prentice, I. C.: A roadmap for improving the representation of photosynthesis in earth system models, New Phytol., 213, 22–42, 2017. a

Rosolem, R., Gupta, H. V., Shuttleworth, W. J., de Gonçalves, L. G. G., and Zeng, X.: Towards a comprehensive approach to parameter estimation in land surface parameterization schemes, Hydrol. Process., 27, 2075–2097, 2013. a

Ruppert, D., Wand, M. P., and Carroll, R. J.:Semiparametric regression, Cambridge university press, 2003. a

Saltelli, A., Tarantola, S., and Chan, K. S.: A quantitative model-independent method for global sensitivity analysis of model output, Technometrics, 41, 39–56, 1999. a

Saltelli, A., Chan, K., and Scott, E. M.: Sensitivity analysis, vol. 1, Wiley New York, 2000. a, b

Sargsyan, K., Safta, C., Najm, H. N., Debusschere, B. J., Ricciuto, D., and Thornton, P.: Dimensionality reduction for complex models via bayesian compressive sensing, Int. J. Uncertain. Quan., 4, 63–93, 2014. a, b, c, d

Sato, H., Itoh, A., and Kohyama, T.: SEIB–DGVM: A new dynamic global vegetation model using a spatially explicit individual-based approach, Ecol. Model., 200, 279–307, 2007. a

Scheiter, S., Langan, L., and Higgins, S. I.: Next-generation dynamic global vegetation models: learning from community ecology, New Phytol., 198, 957–969, 2013. a

Schwalm, C. R., Williams, C. A., Schaefer, K., Anderson, R., Arain, M. A., Baker, I., Barr, A., Black, T. A., Chen, G., Chen, J. M., and Ciais, P.: A model-data intercomparison of CO₂ exchange across North America: Results from the North American carbon program site synthesis, J. Geophys. Res.-Biogeo., 115, G00H05, https://doi.org/10.1029/2009JG001229, 2010. a

Sen, O. L., Bastidas, L. A., Shuttleworth, W. J., Yang, Z. L., Gupta, H. V., and Sorooshian, S.: Impact of field-calibrated vegetation parameters on gcm climate simulations, Q. J. Roy. Meteorol. Soc., 127, 1199–1223, 2001. a

Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., and Thonicke, K.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, 2003. a

Sitch, S., Huntingford, C., Gedney, N., Levy, P. E., Lomas, M., Piao, S. L., Betts, R., Ciais, P., Cox, P., Friedlingstein, P., and Jones, C. D.: Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five dynamic global vegetation models (DGVMs), Glob. Change Biol., 14, 2015–2039, 2008. a

Sobol', I.M.: On sensitivity estimation for nonlinear mathematical models, Matematicheskoe Modelirovanie, 2, 112–118, 1990. a, b, c

Song, X. M., Zhang, J. Y., Zhan, C. S., Xuan, Y. Q., Ye, M., and Xu, C. G.: Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications, J. Hydrol., 523, 739–757, 2015. a

Sudret, B.: Global sensitivity analysis using polynomial chaos expansions, Reliab. Eng. Syst. Safe., 93, 964–979, 2008. a, b

Thonicke, K., Venevsky, S., Sitch, S., and Cramer, W.: The role of fire disturbance for global vegetation dynamics: coupling fire into a dynamic global vegetation model, Global Ecol. Biogeogr., 10, 661–677, 2001. a

Vrugt, J., Wijk, M. V., Hopmans, J. W., and Šimunek, J.: One-, two-, and three-dimensional root water uptake functions for transient modeling, Water Resour. Res., 37, 2457–2470, 2001. a

Wahba, G.: Spline models for observational data, SIAM, ISBN: 978-0-89871-244-5, 161 pp., https://doi.org/10.1137/1.9781611970128, 1990. a

Wang, G., Gertner, G., Liu, X., and Anderson, A.: Uncertainty assessment of soil erodibility factor for revised universal soil loss equation, Catena, 46, 1–14, 2001. a

Wang, J., Li, X., Lu, L., and Fang, F.: Parameter sensitivity analysis of crop growth models based on the extended Fourier amplitude sensitivity test method, Environ. Modell. Softw., 48, 171–182, 2013. a, b, c, d, e, f

Waring, R., Landsberg, J., and Williams, M.: Net primary production of forests: a constant fraction of gross primary production?, Tree Physiol., 18, 129–134, 1998. a

Waring, R. H. and Running, S. W.:Forest ecosystems: analysis at multiple scales, Elsevier, 2010. a

Wood, E. F., Lettenmaier, D. P., and Zartarian, V. G.: A land-surface hydrology parameterization with subgrid variability for general circulation models, J. Geophys. Res.-Atmos., 97, 2717–2728, 1992. a

Worbes, M.: Annual growth rings, rainfall-dependent growth and long-term growth patterns of tropical trees from the caparo forest reserve in venezuela, J. Ecol., 87, 391–403, 1999. a

Xu, C. and Gertner, G. Z.: Extending a global sensitivity analysis technique to models with correlated parameters, Comput. Stat. Data An., 51, 5579–5590, 2007. a

Xu, C. and Gertner, G. Z.: Uncertainty and sensitivity analysis for models with correlated parameters, Reliab. Eng. Syst. Safe., 93, 1563–1573, 2008. a

Xu, C. and Gertner, G. Z.: Uncertainty analysis of transient population dynamics, Ecol. Model., 220, 283–293, 2009. a, b, c

Xu, C. and Gertner, G. Z.: Understanding and comparisons of different sampling approaches for the fourier amplitudes sensitivity test (FAST), Comput. Stat. Data An., 55, 184–198, 2011. a, b, c, d, e

Xu, C. and Gertner, G. Z.: Reliability of global sensitivity indices, J. Stat. Comput. Sim., 81, 1939–1969, 2011. a

Xu, C., Gertner, G. Z., and Scheller, R. M.: Uncertainties in the response of a forest landscape to global climatic change, Glob. Change Biol., 15, 116–131, 2009. a, b

Xu, C., Legros, M., Gould, F., and Lloyd, A. L.: Understanding uncertainties in model-based predictions of Aedes aegypti population dynamics, PLoS Neglect. Trop. D., 4, e830, https://doi.org/10.1371/journal.pntd.0000830, 2010.  a

Xu, C., McDowell, N. G., Sevanto, S., and Fisher, R. A.: Our limited ability to predict vegetation dynamics under water stress, New Phytol., 200, 298–300, 2013. a

Zaehle, S., Sitch, S., Smith, B., and Hatterman, F.: Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics, Global Biogeochem. Cy., 19, GB3020, https://doi.org/10.1029/2004GB002395, 2005. a

Zeng, X.: Global vegetation root distribution for land modeling, J. Hydrometeorol., 2, 525–530, 2001. a

Zhou, X., Lin, H., and Lin, H.: Global Sensitivity Analysis, in: Encyclopedia of GIS, 408–409, 2008. a

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.3