Articles | Volume 10, issue 9
https://doi.org/10.5194/gmd-10-3277-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-3277-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
05 Sep 2017
Development and technical paper |
| 05 Sep 2017
SUPECA kinetics for scaling redox reactions in networks of mixed substrates and consumers and an example application to aerobic soil respiration
Jin-Yun Tang
CORRESPONDING AUTHOR
Earth and Environmental Sciences Area, Lawrence Berkeley National
Laboratory, Berkeley, CA, 94720, USA
William J. Riley
Earth and Environmental Sciences Area, Lawrence Berkeley National
Laboratory, Berkeley, CA, 94720, USA
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Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
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Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
Robinson I. Negrón-Juárez, Jennifer A. Holm, Boris Faybishenko, Daniel Magnabosco-Marra, Rosie A. Fisher, Jacquelyn K. Shuman, Alessandro C. de Araujo, William J. Riley, and Jeffrey Q. Chambers
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The temporal variability in the Landsat satellite near-infrared (NIR) band captured the dynamics of forest regrowth after disturbances in Central Amazon. This variability was represented by the dynamics of forest regrowth after disturbances were properly represented by the ELM-FATES model (Functionally Assembled Terrestrial Ecosystem Simulator (FATES) in the Energy Exascale Earth System Model (E3SM) Land Model (ELM)).
Kuang-Yu Chang, William J. Riley, Patrick M. Crill, Robert F. Grant, and Scott R. Saleska
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Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
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It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Cited articles
Achat, D. L., Augusto, L., Gallet-Budynek, A., and Loustau, D.: Future challenges in coupled C-N-P cycle models for terrestrial ecosystems under global change: a review, Biogeochem., 131, 173–202, https://doi.org/10.1007/s10533-016-0274-9, 2016.
Aksnes, D. L. and Egge, J. K.: A theoretical-model for nutrient-uptake in phytoplankton, Mar. Ecol. Prog. Ser., 70, 65–72, 1991.
Allison, S. D.: A trait-based approach for modelling microbial litter decomposition, Ecol. Lett., 15, 1058–1070, 2012.
Armstrong, R. A.: Nutrient uptake rate as a function of cell size and surface transporter density: A Michaelis-like approximation to the model of Pasciak and Gavis, Deep-Sea Res. Pt. I, 55, 1311–1317, 2008.
Arora, V. K., Boer, G. J., Friedlingstein, P., Eby, M., Jones, C. D., Christian, J. R., Bonan, G., Bopp, L., Brovkin, V., Cadule, P., Hajima, T., Ilyina, T., Lindsay, K., Tjiputra, J. F., and Wu, T.: Carbon-Concentration and Carbon-Climate Feedbacks in CMIP5 Earth System Models, J. Climate, 26, 5289–5314, 2013.
Batjes, N. H.: Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks, Geoderma, 269, 61–68, 2016.
Berg, H. C. and Purcell, E. M.: Physics of Chemoreception, Biophys. J., 20, 193–219, 1977.
Blanke, J. H., Lindeskog, M., Lindstrom, J., and Lehsten, V.: Effect of climate data on simulated carbon and nitrogen balances for Europe, J. Geophys. Res.-Biogeo., 121, 1352–1371, 2016.
Bonachela, J. A., Raghib, M., and Levin, S. A.: Dynamic model of flexible phytoplankton nutrient uptake, P. Natl. Acad. Sci. USA, 108, 20633–20638, 2011.
Borden, R. C. and Bedient, P. B.: Transport of dissolved hydrocarbons influenced by oxygen-limited biodegradation .1. Theoretical development, Water Resour. Res., 22, 1973–1982, 1986.
Borghans, J. A. M., DeBoer, R. J., and Segel, L. A.: Extending the quasi-steady state approximation by changing variables, B. Math. Biol., 58, 43–63, 1996.
Bouskill, N. J., Tang, J. Y., Riley, W. J., and Brodie, E. L.: Trait-based representation of biological nitr fication: model development testing, and predicted community composition, Front. Microbiol., 3, https://doi.org/10.3389/fmicb.2012.00364, 2012.
Bouskill, N. J., Riley, W. J., and Tang, J. Y.: Meta-analysis of high-latitude nitrogen-addition and warming studies implies ecological mechanisms overlooked by land models, Biogeosciences, 11, 6969–6983, https://doi.org/10.5194/bg-11-6969-2014, 2014.
Brandt, B. W., van Leeuwen, I. M. M., and Kooijman, S. A. L. M.: A general model for multiple substrate biodegradation. Application to co-metabolism of structurally non-analogous compounds, Water Res., 37, 4843–4854, 2003.
Bratbak, G. and Dundas, I.: Bacterial dry-matter content and biomass estimations, Appl. Environ. Microb., 48, 755–757, 1984.
Briggs, G. E. and Haldane, J. B. S.: A note on the kinetics of enzyme action, Biochem. J., 19, 338–339, 1925.
Button, D. K.: Kinetics of nutrient-limited transport and microbial-growth, Microbiol. Rev., 49, 270–297, 1985.
Chellaboina, V., Bhat, S. P., Haddad, W. M., and Bernstein, D. S.: Modeling and analysis of mass-action kinetics, nonnegativity, realizability, reducibility, and semistability, IEEE Contr. Syst. Mag., 29, 60–78, 2009.
Ciais, P., Gasser, T., Paris, J. D., Caldeira, K., Raupach, M. R., Canadell, J. G., Patwardhan, A., Friedlingstein, P., Piao, S. L., and Gitz, V.: Attributing the increase in atmospheric CO2 to emitters and absorbers, Nat. Clim. Change, 3, 926–930, 2013.
Coleman, K. and Jenkinson, D. S.: RothC-26.3 – A model for the turnover of carbon in soil: model description and windows users guide: November 1999 issue, Lawes Agricultural Trust, Harpenden, UK, 1999.
Dwivedi, D., Riley, W. J., Torn, M. S., Spycher, N., Maggi, F., and Tang, J. Y.: Mineral properties, microbes, transport, and plant-input profiles control vertical distribution and age of soil carbon stocks, Soil Biol. Biochem., 107, 244–259, 2017.
English, B. P., Min, W., van Oijen, A. M., Lee, K. T., Luo, G. B., Sun, H. Y., Cherayil, B. J., Kou, S. C., and Xie, S. N.: Ever-fluctuating single enzyme molecules: Michaelis-Menten equation revisited (vol 2, pg 87, 2006), Nat. Chem. Biol., 2, 168–168, 2006.
Feynman, R. P., Leighton, R. B., and Sands, M.: The Feynman lectures on physics: Vol. I., Addison-Wesley Publishing Company, Inc., Reading, Massachusetts, 1963.
Follows, M. J., Dutkiewicz, S., Grant, S., and Chisholm, S. W.: Emergent biogeography of microbial communities in a model ocean, Science, 315, 1843–1846, https://doi.org/10.1126/science.1138544, 2007.
Franzluebbers, A. J.: Microbial activity in response to water-filled pore space of variably eroded southern Piedmont soils, Appl. Soil Ecol., 11, 91–101, 1999.
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A., Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 Climate Projections due to carbon cycle feedbacks, J. Climate, 27, 511–526, 2014.
Grant, R. F.: A Technique for estimating denitrification rates at different soil temperatures, water Contents, and nitrate concentrations, Soil Sci., 152, 41–52, 1991.
Grant, R. F.: A review of the canadian ecosystem Model-Ecosys, in: Modeling carbon and nitrogen dynamics for soil management, CRC Press, Boca, Raton, 173–264, 2001.
Grant, R. F.: Modelling changes in nitrogen cycling to sustain increases in forest productivity under elevated atmospheric CO2 and contrasting site conditions, Biogeosciences, 10, 7703–7721, https://doi.org/10.5194/bg-10-7703-2013, 2013.
Grant, R. F. and Rochette, P.: Soil microbial respiration at different water potentials and temperatures – theory and mathematical-modeling, Soil Sci. Soc. Am. J., 58, 1681–1690, 1994.
Grant, R. F., Juma, N. G., and Mcgill, W. B.: Simulation of Carbon and Nitrogen Transformations in Soil – Mineralization, Soil Biol. Biochem., 25, 1317–1329, https://doi.org/10.1016/0038-0717(93)90046-E, 1993.
Griffin, D. M.: A Theoretical study relating concentration and diffusion of oxygen to biology of organisms in soil, New Phytol., 67, 561–577, 1968.
Gross, D., Shortle, J. F., Thompson, J. M., and Harris, C. M.: Fundamentals of queueing theory, Wiley series in probability and statistics, ISBN: 978-1-118-21164-9, 2011.
Habgood, K. and Arel, I.: A condensation-based application of Cramer's rule for solving large-scale linear systems, J. Discrete Algorithms, 10, 98–109, https://doi.org/10.1016/.j.jda.2011.06.007, 2012.
He, Y. J., Trumbore, S. E., Torn, M. S., Harden, J. W., Vaughn, L. J. S., Allison, S. D., and Randerson, J. T.: Radiocarbon constraints imply reduced carbon uptake by soils during the 21st century, Science, 353, 1419–1424, 2016.
Holling, C. S.: Some characteristics of simple types of predation and parasitism, Can. Entomol., 91, 385–398, https://doi.org/10.4039/Ent91385-7, 1959.
Holling, C. S.: The functional response of invertebrate predators to prey density, Mem. Entomol. Soc. Can., 48, 1–86, 1966.
Kausch, M. F. and Pallud, C. E.: Modeling the impact of soil aggregate size on selenium immobilization, Biogeosciences, 10, 1323–1336, https://doi.org/10.5194/bg-10-1323-2013, 2013.
Keiluweit, M., Nico, P. S., Kleber, M., and Fendorf, S.: Are oxygen limitations under recognized regulators of organic carbon turnover in upland soils?, Biogeochem., 127, 157–171, https://doi.org/10.1007/s10533-015-0180-6, 2016.
Kolditz, O., Ratke, R., Diersch, H. J. G., and Zielke, W.: Coupled groundwater flow and transport .1. Verification of variable density flow and transport models, Adv. Water Resour., 21, 27–46, 1998.
Kooijman, S.: Dynamic energy budget theory for metabolic organisation, Cambridge University Press, Cambridge, 2010.
Kooijman, S. A. L. M.: The Synthesizing Unit as model for the stoichiometric fusion and branching of metabolic fluxes, Biophys. Chem., 73, 179–188, 1998.
Koven, C. D., Riley, W. J., Subin, Z. M., Tang, J. Y., Torn, M. S., Collins, W. D., Bonan, G. B., Lawrence, D. M., and Swenson, S. C.: The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4, Biogeosciences, 10, 7109–7131, https://doi.org/10.5194/bg-10-7109-2013, 2013.
Le Roux, X., Bouskill, N. J., Niboyet, A., Barthes, L., Dijkstra, P., Field, C. B., Hungate, B. A., Lerondelle, C., Pommier, T., Tang, J. Y., Terada, A., Tourna, M., and Poly, F.: Predicting the responses of soil nitrite-oxidizers to multi-factorial global change: A trait-based approach, Front. Microbiol., 7, 628, https://doi.org/10.3389/fmicb.2016.00628, 2016.
Litchman, E. and Klausmeier, C. A.: Trait-based community ecology of phytoplankton, Annu. Rev. Ecol. Evol. S., 39, 615–639, 2008.
Luo, Z., Wang, E., Zheng, H., Baldock, J. A., Sun, O. J., and Shao, Q.: Convergent modelling of past soil organic carbon stocks but divergent projections, Biogeosciences, 12, 4373–4383, https://doi.org/10.5194/bg-12-4373-2015, 2015.
Maggi, F. and Riley, W. J.: Transient competitive complexation in biological kinetic isotope fractionation explains nonsteady isotopic effects: Theory and application to denitrification in soils, J. Geophys. Res.-Biogeo., 114, G04012, https://doi.org/10.1029/2008jg000878, 2009.
Manzoni, S., Moyano, F., Katterer, T., and Schimel, J.: Modeling coupled enzymatic and solute transport controls on decomposition in drying soils, Soil Biol. Biochem., 95, 275–287, 2016.
Mao, X., Prommer, H., Barry, D. A., Langevin, C. D., Panteleit, B., and Li, L.: Three-dimensional model for multi-component reactive transport with variable density groundwater flow, Environ. Modell. Softw., 21, 615–628, 2006.
Melillo, J. M., Aber, J. D., Linkins, A. E., Ricca, A., Fry, B., and Nadelhoffer, K. J.: Carbon and Nitrogen Dynamics Along the Decay Continuum – Plant Litter to Soil Organic-Matter, Plant Soil, 115, 189–198, https://doi.org/10.1007/Bf02202587, 1989.
Merico, A., Bruggeman, J., and Wirtz, K.: A trait-based approach for downscaling complexity in plankton ecosystem models, Ecol. Model., 220, 3001–3010, https://doi.org/10.1016/j.ecolmodel.2009.05.005, 2009.
Michaelis, L. and Menten, M. L.: The kenetics of the inversion effect, Biochem. Z., 49, 333–369, 1913.
Monod, J.: The growth of bacterial cultures, Annu. Rev. Microbiol., 3, 371–394, 1949.
Murdoch, W. W.: Functional response of predators, J. Appl. Ecol., 10, 335–342, 1973.
Niu, S. L., Classen, A. T., Dukes, J. S., Kardol, P., Liu, L. L., Luo, Y. Q., Rustad, L., Sun, J., Tang, J. W., Templer, P. H., Thomas, R. Q., Tian, D. S., Vicca, S., Wang, Y. P., Xia, J. Y., and Zaehle, S.: Global patterns and substrate-based mechanisms of the terrestrial nitrogen cycle, Ecol. Lett., 19, 697–709, https://doi.org/10.1111/ele.12591, 2016.
Oleson, K. W., Lawrence, D. W., Bonan, G. B., Brewniak, B., Huang, M., Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C., Thornton, P. E., Bozbiyik, A., Fisher, R., Kluzek, E., Lamarque, J. F., Lawrence, P. J., Leung, L. R., Muszala, S., Ricciuto, D. M., Sacks, W., Tang, J. Y., and Yang, Z. L.: Technical description of version 4.5 of the Community Land Model (CLM). Ncar TechRep., Note NCAR/TN-503+ STR. National Center for Atmospheric Research, Boulder, CO, 422 pp., https://doi.org/10.5065/D6RR1W7M, 2013.
Parton, W. and Rasmussen, P.: Long-term effects of crop management in wheat-fallow: II. CCENTURY model simulations, Soil Sci. Soc. Am. J., 58, 530–536, 1994.
Pedersen, M. G., Bersani, A. M., and Bersani, E.: Quasi steady-state approximations in complex intracellular signal transduction networks – a word of caution, J. Math. Chem., 43, 1318–1344, 2008.
Pyun, C. W.: Steady-state and equilibrium approximations in chemical kinetics, J. Chem. Educ., 48, 194, https://doi.org/10.102/ed048p194, 1971.
Qian, Y., Yan, H. P., Hou, Z. S., Johannesson, G., Klein, S., Lucas, D., Neale, R., Rasch, P., Swiler, L., Tannahill, J., Wang, H. L., Wang, M. H., and Zhao, C.: Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5, J. Adv. Model Earth Sy., 7, 382–411, 2015.
Renault, P. and Stengel, P.: Modeling oxygen diffusion in aggregated Soils .1. Anaerobiosis inside the aggregates, Soil Sci. Soc. Am. J., 58, 1017–1023, 1994.
Resat, H., Bailey, V., McCue, L. A., and Konopka, A.: Modeling microbial dynamics in heterogeneous environments: growth on soil carbon sources, Microb. Ecol., 63, 883–897, https://doi.org/10.1007/s00248-011-9965-x, 2012.
Reuveni, S., Urbakh, M., and Klafter, J.: Role of substrate unbinding in Michaelis-Menten enzymatic reactions, P. Natl. Acad. Sci. USA, 111, 4391–4396, 2014.
Riley, W. J., Subin, Z. M., Lawrence, D. M., Swenson, S. C., Torn, M. S., Meng, L., Mahowald, N. M., and Hess, P.: Barriers to predicting changes in global terrestrial methane fluxes: analyses using CLM4Me, a methane biogeochemistry model integrated in CESM, Biogeosciences, 8, 1925–1953, https://doi.org/10.5194/bg-8-1925-2011, 2011.
Riley, W. J., Maggi, F., Kleber, M., Torn, M. S., Tang, J. Y., Dwivedi, D., and Guerry, N.: Long residence times of rapidly decomposable soil organic matter: application of a multi-phase, multi-component, and vertically resolved model (BAMS1) to soil carbon dynamics, Geosci. Model Dev., 7, 1335–1355, https://doi.org/10.5194/gmd-7-1335-2014, 2014.
Schimel, J. P. and Bennett, J.: Nitrogen mineralization: Challenges of a changing paradigm, Ecology, 85, 591–602, 2004.
Schnell, S. and Maini, P. K.: Enzyme kinetics at high enzyme concentration, B Math. Biol., 62, 483–499, 2000.
Schnell, S. and Mendoza, C.: Enzyme kinetics of multiple alternative substrates, J. Math. Chem., 27, 155–170, 2000.
Shankar, R.: Principles of quantum mechanics, second edition, Springer, ISBN 978-1-4757-0578-2, 1994.
Shao, P., Zeng, X. B., Sakaguchi, K., Monson, R. K., and Zeng, X. D.: Terrestrial carbon cycle: climate relations in eight CMIP5 earth system models, J. Climate, 26, 8744–8764, 2013.
Shi, M., Fisher, J. B., Brzostek, E. R., and Phillips, R. P.: Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model, Glob. Change Biol., 22, 1299–1314, https://doi.org/10.1111/gcb.13131, 2016.
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S., and Janssens, I.: Sensitivity of decomposition rates of soil organic matter with respect to simultaneous changes in temperature and moisture, J. Adv. Model Earth Sy., 7, 335–356, https://doi.org/10.1002/2014ms000358, 2015.
Smith, O. L.: Analytical Model of the Decomposition of Soil Organic-Matter, Soil Biol. Biochem., 11, 585–606, https://doi.org/10.1016/0038-0717(79)90027-0, 1979.
Sols, A. and Marco, R.: Concentrations of metabolites and binding sites. Implications in metabolic regulation, in: Current Topics in Cellular Regulation, Vol. 2, edited by: Horecker, B. and Stadtman, E., New York, Academic Press, 227–273, 1970.
Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E., and Pacala, S. W.: Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2, Nat. Clim. Change, 4, 1099–1102, 2014.
Tang, J., Zhuang, Q., Shannon, R. D., and White, J. R.: Quantifying wetland methane emissions with process-based models of different complexities, Biogeosciences, 7, 3817–3837, https://doi.org/10.5194/bg-7-3817-2010, 2010.
Tang, J. Y.: On the relationships between the Michaelis–Menten kinetics, reverse Michaelis–Menten kinetics, equilibrium chemistry approximation kinetics, and quadratic kinetics, Geosci. Model Dev., 8, 3823–3835, https://doi.org/10.5194/gmd-8-3823-2015, 2015.
Tang, J. Y. and Zhuang, Q. L.: Equifinality in parameterization of process-based biogeochemistry models: A significant uncertainty source to the estimation of regional carbon dynamics, J. Geophys. Res.-Biogeo., 113, G04010, https://doi.org/10.1029/2008JG000757, 2008.
Tang, J. Y. and Zhuang, Q. L.: A global sensitivity analysis and Bayesian inference framework for improving the parameter estimation and prediction of a process-based Terrestrial Ecosystem Model, J. Geophys. Res.-Atmos., 114, D15303, https://doi.org/10.1029/2009JD011724, 2009.
Tang, J. Y. and Riley, W. J.: A total quasi-steady-state formulation of substrate uptake kinetics in complex networks and an example application to microbial litter decomposition, Biogeosciences, 10, 8329–8351, https://doi.org/10.5194/bg-10-8329-2013, 2013a.
Tang, J. Y. and Riley, W. J.: A new top boundary condition for modeling surface diffusive exchange of a generic volatile tracer: theoretical analysis and application to soil evaporation, Hydrol. Earth Syst. Sci., 17, 873–893, https://doi.org/10.5194/hess-17-873-2013, 2013b.
Tang, J. Y., Tang, J., and Wang, Y.: Analytical investigation on 3D non-Boussinesq mountain wave drag for wind profiles with vertical variations, Appl. Math. Mech.-Engl., 28, 317–325, 2007.
Tang, J. Y. and Riley, W. J.: Weaker soil carbon-climate feedbacks resulting from microbial and abiotic interactions, Nat. Clim. Change, 5, 56–60, 2015.
Tang, J. Y. and Riley, W. J.: Technical Note: A generic law-of-the-minimum flux limiter for simulating substrate limitation in biogeochemical models, Biogeosciences, 13, 723–735, https://doi.org/10.5194/bg-13-723-2016, 2016.
Tang, J. Y., Riley, W. J., Koven, C. D., and Subin, Z. M.: CLM4-BeTR, a generic biogeochemical transport and reaction module for CLM4: model development, evaluation, and application, Geosci. Model Dev., 6, 127–140, https://doi.org/10.5194/gmd-6-127-2013, 2013.
Tilman, D.: Resource competition and community structure, Princeton University Press, Princeton, New Jersey, 1982.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013
Tokunaga, T. K.: Hydraulic properties of adsorbed water films in unsaturated porous media, Water Resour. Res., 45, W06415, https://doi.org/10.1029/2009WR007734, 2009.
Van Slyke, D. D. and Cullen, G. E.: The mode of action of urease and of enzymes in general, J. Biol. Chem., 19, 141–180, 1914.
van Werkhoven, K., Wagener, T., Reed, P., and Tang, Y.: Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models, Adv. Water Resour., 32, 1154–1169, 2009.
Vitousek, P.: Nutrient cycling and nutrient use efficiency, Am. Nat., 119, 553–572, 1982.
Vitousek, P. M., Porder, S., Houlton, B. Z., and Chadwick, O. A.: Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen-phosphorus interactions, Ecol. Appl., 20, 5–15, 2010.
Wang, Y. P., Leuning, R., Cleugh, H. A., and 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., 7, 495–510, 2001.
Wieder, W. R., Bonan, G. B., and Allison, S. D.: Global soil carbon projections are improved by modelling microbial processes, Nat. Clim. Change, 3, 909–912, 2013.
Wieder, W. R., Grandy, A. S., Kallenbach, C. M., and Bonan, G. B.: Integrating microbial physiology and physio-chemical principles in soils with the MIcrobial-MIneral Carbon Stabilization (MIMICS) model, Biogeosciences, 11, 3899–3917, https://doi.org/10.5194/bg-11-3899-2014, 2014.
Wieder, W. R., Cleveland, C. C., Lawrence, D. M., and Bonan, G. B.: Effects of model structural uncertainty on carbon cycle projections: biological nitrogen fixation as a case study, Environ. Res. Lett., 10, 044016, https://doi.org/10.1088/1748-9326/10/4/044016, 2015a.
Wieder, W. R., Allison, S. D., Davidson, E. A., Georgiou, K., Hararuk, O., He, Y. J., Hopkins, F., Luo, Y. Q., Smith, M. J., Sulman, B., Todd-Brown, K., Wang, Y. P., Xia, J. Y., and Xu, X. F.: Explicitly representing soil microbial processes in Earth system models, Global Biogeochem. Cy., 29, 1782–1800, 2015b.
Wieder, W. R., Cleveland, C. C., Smith, W. K., and Todd-Brown, K.: Future productivity and carbon storage limited by terrestrial nutrient availability, Nat. Geosci., 8, 441–444, 2015c.
Williams, M., Schwarz, P. A., Law, B. E., Irvine, J., and Kurpius, M. R.: An improved analysis of forest carbon dynamics using data assimilation, Glob. Change Biol., 11, 89–105, 2005.
Williams, P. J.: Validity of application of simple kinetic analysis to heterogeneous microbial populations, Limnol. Oceanogr., 18, 159–164, 1973.
Yang, X. F., Richmond, M. C., Scheibe, T. D., Perkins, W. A., and Resat, H.: Flow partitioning in fully saturated soil aggregates, Transport. Porous. Med., 103, 295–314, 2014.
Yeh, G. T., Burgos, W. D., and Zachara, J. M.: Modeling and measuring biogeochemical reactions: system consistency, data needs, and rate formulations, Adv. Environ. Res., 5, 219–237, 2001.
Zhu, Q. and Riley, W. J.: Improved modelling of soil nitrogen losses, Nat. Clim. Change, 5, 705–706, 2015.
Zhu, Q., Riley, W. J., Tang, J., and Koven, C. D.: Multiple soil nutrient competition between plants, microbes, and mineral surfaces: model development, parameterization, and example applications in several tropical forests, Biogeosciences, 13, 341–363, https://doi.org/10.5194/bg-13-341-2016, 2016a.
Zhu, Q., Iversen, C. M., Riley, W. J., Slette, I. J., and Vander Stel, H. M.: Root traits explain observed tundra vegetation nitrogen uptake patterns: Implications for trait-based land models, J. Geophys. Res.-Biogeo., 121, 3101–3112, https://doi.org/10.1002/2016JG003554, 2016b.
Zhu, Q., Riley, W. J., and Tang, J. Y.: A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions, Ecol. Appl., 27, 875–886, 2017.
Short summary
We proposed the SUPECA kinetics to scale from single biogeochemical reactions to a network of mixed substrates and consumers. The framework for the first time represents single-substrate reactions, two-substrate reactions, and mineral surface sorption reactions in a scaling consistent manner. This new theory is theoretically solid and outperforms existing theories, particularly for substrate-limiting systems. The test with aerobic soil respiration showed its strengths for pragmatic application.
We proposed the SUPECA kinetics to scale from single biogeochemical reactions to a network of...
