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Frontiers of Earth Science

, Volume 12, Issue 4, pp 765–778 | Cite as

Development of the DayCent-Photo model and integration of variable photosynthetic capacity

  • Jonathan R. StraubeEmail author
  • Maosi Chen
  • William J. Parton
  • Shinichi Asso
  • Yan-An LiuEmail author
  • Dennis S. Ojima
  • Wei Gao
Research Article
  • 22 Downloads

Abstract

We integrated a photosynthetic sub-model into the daily Century model (DayCent) to improve the estimations of seasonal changes in carbon fluxes at the Niwot Ridge LTER site and the Harvard forest LTER site (DayCent-Photo). The photosynthetic sub-model, adapted from the SIPNET/PnET family of models, includes solar radiation and vapor pressure deficit controls on production, as well as temperature and water stress terms. A key feature we added to the base photosynthetic equations is the addition of a variable maximum net photosynthetic rate (Amax). We optimized the parameters controlling photosynthesis using a variation of the Metropolis-Hastings algorithm along with data-assimilation techniques. The model was optimized and validated against observed net ecosystem exchange (NEE) and estimated gross primary production (GPP) and ecosystem respiration (RESP) values for AmeriFlux sites at Niwot Ridge and Harvard forest. The inclusion of a variable Amax rate greatly improved model performance (NEE RMSE = 0.63 gC·m–2, AIC = 2099) versus a version with a single Amax parameter (NEE RMSE = 0.74 gC·m–2, AIC = 3724). DayCent-Photo was able to capture the inter-annual and seasonal flux patterns for NEE, GPP, ecosystem respiration (RESP), and daily actual evapotranspiration (AET), but tended to overestimate yearly NEE uptake. The DayCent-Photo model has been successfully set up to simulate daily NEE, GPP, RESP, and AET for deciduous forest, conifer forests, and grassland systems in the US using AmeriFlux data sets and has recently been improved to include the impact of UV radiation surface litter decay (DayCent-UV). The simulated influence of a variable Amax rate suggests a need for further studies on the process controls affecting the seasonal photosynthetic rates. The results for all of the forest and grassland sites show that maximum Amax values occurs early during the growing period and taper off toward the end of the growing season.

Keywords

DayCent-Photo model seasonal maximum net photosynthetic rate net ecosystem exchange gross primary production UV radiation 

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Notes

Acknowledgements

This work is supported by the US Department of Agriculture (USDA) UV-B Monitoring and Research Program, Colorado State University, under USDA National Institute of Food and Agriculture Grant 2016-34263-25763.

References

  1. Aber J D, Federer C A (1992). A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems. Oecologia, 92(4): 463–474Google Scholar
  2. Baldocchi D D (2008). Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust J Bot, 56(1): 1–26Google Scholar
  3. Baldocchi D D (2003). Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob Change Biol, 9(4): 479–492Google Scholar
  4. Baldocchi D D, Hincks B B, Meyers T P (1988). Measuring biosphereatmosphere exchanges of biologically related gases with micrometeorological methods. Ecology, 69(5): 1331–1340Google Scholar
  5. Blanken P D, Monson R K, Burns S P, Turnipseed A A (2010). Data and Information for the US-NR1 Niwot Ridge Subalpine Forest AmeriFlux Site (LTER NWT1). AmeriFlux Management Project. Lawrence Berkeley National Laboratory, CaliforniaGoogle Scholar
  6. Bourdeau P F (1959). Seasonal variations of the photosynthetic efficiency of evergreen conifers. Ecology, 40(1): 63–67Google Scholar
  7. Braswell B H, Sacks W J, Linder E, Schimel D S (2005). Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations. Glob Change Biol, 11(2): 335–355Google Scholar
  8. Chen M, Parton W J, Adair E C, Asao S, Hartman M D, Gao W (2016). Simulation of the effects of photodecay on long-term litter decay using DayCent. Ecosphere, 7(12): e01631Google Scholar
  9. Chen M, Parton WJ, Del Grosso S J, Hartman M D, Day K A, Tucker C J, Derner J D, Knapp A K, Smith W K, Ojima D S, Gao W (2017). The signature of sea surface temperature anomalies on the dynamics of semiarid grassland productivity. Ecosphere, 8(12): e02069Google Scholar
  10. Del Grosso S J, Parton W J, Mosier A R, Hartman M D, Brenner J, Ojima D S, Schimel D S (2001). Simulated Interaction of Carbon Dynamics and Nitrogen Trace Gas Fluxes Using the DAYCENT Model. In: Shaffer M J, Ma L W, Hansen S, eds. Modeling Carbon and Nitrogen Dynamics for Soil Management. Boca Raton: CRC Press, 303–332Google Scholar
  11. Del Grosso S J, Parton W J, Mosier A R, Ojima D S, Kulmala A E, Phongpan S (2000). General model for N2O and N2 gas emissions from soils due to dentrification. Global Biogeochem Cycles, 14(4): 1045–1060Google Scholar
  12. Del Grosso S J, Parton W J, Stohlgren T J, Zheng D L, Bachelet D, Prince S, Hibbard K, Olson R (2008). Global potential net primary production predicted from vegetation class, precipitation, and temperature. Ecology, 89(8): 2117–2126Google Scholar
  13. Delbart N, Picard G, Le Toan T, Kergoat L, Quegan S, Woodward I, Dye D, Fedotova V (2008). Spring phenology in boreal Eurasia over a nearly century time scale. Glob Change Biol, 14(3): 603–614Google Scholar
  14. Drake J E, Raetz L M, Davis S C, Delucia E H (2010). Hydraulic limitation not declining nitrogen availability causes the age-related photosynthetic decline in loblolly pine (Pinus taeda L.). Plant Cell Environ, 33(10): 1756–1766Google Scholar
  15. Fisher R A (1932). Inverse probability and the use of likelihood. Math Proc Camb Philos Soc, 28(03): 257–261Google Scholar
  16. Frey S D, Lee J, Melillo J M, Six J (2013). The temperature response of soil microbial efficiency and its feedback to climate. Nat Clim Chang, 3(4): 395–398Google Scholar
  17. Gea-Izquierdo G, Mäkelä A, Margolis H, Bergeron Y, Black T A, Dunn A, Hadley J, Paw U K T, Falk M, Wharton S, Monson R, Hollinger D Y, Laurila T, Aurela M, McCaughey H, Bourque C, Vesala T, Berninger F (2010). Modeling acclimation of photosynthesis to temperature in evergreen conifer forests. New Phytol, 188(1): 175–186Google Scholar
  18. Granda E, Scoffoni C, Rubio-Casal A E, Sack L, Valladares F (2014). Leaf and stem physiological responses to summer and winter extremes of woody species across temperate ecosystems. Oikos, 123 (11): 1281–1290Google Scholar
  19. Guan M, Jin Z, Wang Q, Li Y, Zuo W (2014). Response of photosynthesis traits of dominant plant species to different light regimes in the secondary forest in the area of Qiandao Lake, Zhejiang, China. China Journal of Applied Ecology, 25: 1615–1622Google Scholar
  20. Hartman M D, Baron J S, Ewing H A, Weathers K C (2014). Combined global change effects on ecosystem processes in nine U.S. topographically complex areas. Biogeochemistry, 119(1–3): 85–108Google Scholar
  21. Helms J A (1965). Diurnal and seasonal patterns of net assimilation in Douglas-Fir, Pseudotsuga Menziesii (Mirb). Franco, as Influenced by Environment. Ecology, 46(5): 698–708Google Scholar
  22. Hilborn R, Mangel M (1997). The ecological detective: confronting models with data. Monogr Popul Biol, 28: 315Google Scholar
  23. Hurtt G C, Armstrong R (1996). A pelagic ecosystem model calibrated with BATS data. Deep Sea Res Part II Top Stud Oceanogr, 43(2–3): 653–683Google Scholar
  24. Huxman T E, Turnipseed A A, Sparks J P, Harley P C, Monson R K (2003). Temperature as a control over ecosystem CO2 fluxes in a high-elevation, subalpine forest. Oecologia, 134(4): 537–546Google Scholar
  25. Johnson J B, Omland K S (2004). Model selection in ecology and evolution. Trends Ecol Evol, 19(2): 101–108Google Scholar
  26. Kelly R H, Parton W J, Hartman M D, Stretch L K, Ojima D S, Schimel D S (2000). Intra-annual and interannual variability of ecosystem processes in shortgrass steppe. Journal of Geophysical Research: Atmospheres, 105(D15): 20093–20100Google Scholar
  27. Li Z, Li X, Rubert-Nason K F, Yang Q, Fu Q, Feng J, Shi S (2018). Photosynthetic acclimation of an evergreen broadleaved shrub (Ammopiptanthus mongolicus) to seasonal climate extremes on the Alxa Plateau, a cold desert ecosystem. Trees (Berl), 32(2): 603–614Google Scholar
  28. Linkosalo T, Häkkinen R, Terhivuo J, Tuomenvirta H, Hari P (2009). The time series of flowering and leaf bud burst of boreal trees (1846-2005) support the direct temperature observations of climatic warming. Agric Meteorol, 149(3–4): 453–461Google Scholar
  29. Luyssaert S, Ciais P, Piao S L, Schulze E D, Jung M, Zaehle S, Schelhaas M J, Reichstein M, Churkina G, Papale D, Abril G, Beer C, Grace J, Loustau D, Matteucci G, Magnani F, Nabuurs G J, Verbeeck H, Sulkava M, van der WERF G R, Janssens I A (2010). The European carbon balance. Part 3: forests. Glob Change Biol, 16 (5): 1429–1450Google Scholar
  30. Luyssaert S, Schulze E D, Börner A, Knohl A, Hessenmöller D, Law B E, Ciais P, Grace J (2008). Old-growth forests as global carbon sinks. Nature, 455(7210): 213–215Google Scholar
  31. Marshall J D, Rehfeldt G E, Monserud R A (2001). Family differences in height growth and photosynthetic traits in three conifers. Tree Physiol, 21(11): 727–734Google Scholar
  32. Martinez K A, Fridley J D (2018). Acclimation of leaf traits in seasonal light environments: Are non-native species more plastic? J Ecol, 20: 207–216Google Scholar
  33. Massman W J, Lee X (2002). Eddy covariance flux corrections and uncertainties in long-term studies of carbon and energy exchanges. Agric Meteorol, 113(1–4): 121–144Google Scholar
  34. McGarvey R C, Martin T A, White T L (2004). Integrating within-crown variation in net photosynthesis in loblolly and slash pine families. Tree Physiol, 24(11): 1209–1220Google Scholar
  35. Mohren G M J, van de Veen J R (1995). Forest growth in relation to site conditions. Application of the model forgro to the Solling spruce site. Ecol Modell, 83(1–2): 173–183Google Scholar
  36. Monson R K, Sparks J P, Rosenstiel T N, Scott-Denton L E, Huxman T E, Harley P C, Turnipseed A A, Burns S P, Backlund B, Hu J (2005). Climatic influences on net ecosystem CO2 exchange during the transition from wintertime carbon source to springtime carbon sink in a high-elevation, subalpine forest. Oecologia, 146(1): 130–147Google Scholar
  37. Monson R K, Turnipseed A A, Sparks J P, Harley P C, Scott-Denton L E, Sparks K, Huxman T E (2002). Carbon sequestration in a highelevation, subalpine forest. Glob Change Biol, 8(5): 459–478Google Scholar
  38. Moore D J P, Hu J, Sacks W J, Schimel D S, Monson R K (2008). Estimating transpiration and the sensitivity of carbon uptake to water availability in a subalpine forest using a simple ecosystem process model informed by measured net CO2 and H2O fluxes. Agric Meteorol, 148(10): 1467–1477Google Scholar
  39. Papale D, Valentini R (2003). A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Glob Change Biol, 9(4): 525–535Google Scholar
  40. Parton W J, Hanson P J, Swanston C, Torn M, Trumbore S E, Riley W, Kelly R (2010). ForCent model development and testing using the enriched background isotope study experiment. J Geophys Res, 115 (G4): G04001Google Scholar
  41. Parton WJ, Hartman M, Ojima D, Schimel D (1998). DAYCENT and its land surface submodel: description and testing. Global Planet Change, 19(1–4): 35–48Google Scholar
  42. Parton W J, Rasmussen P E (1994). Long-term effects of crop management in wheat/fallow: II. CENTURY model simulations. Soil Sci Soc Am J, 58(2): 530–536Google Scholar
  43. Parton W, Holland E A, Del Grosso S J, Hartman D, Martin M, Mosier A, Ojima D S, Schimel D S (2001). Generalized model for NOx and N2O emissions from soils. J Geophys Res, 106(D15): 17403–17419Google Scholar
  44. Paustian K, Parton W J, Persson J (1992). Modeling soil organic matter in organic-amended and nitrogen-fertilized long-term plots. Soil Sci Soc Am J, 56(2): 476–488Google Scholar
  45. Piao S, Ciais P, Friedlingstein P, Peylin P, Reichstein M, Luyssaert S, Margolis H, Fang J, Barr A, Chen A, Grelle A, Hollinger D Y, Laurila T, Lindroth A, Richardson A D, Vesala T (2008). Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451(7174): 49–52Google Scholar
  46. Rastetter E B, Aber J D, Peters D P C, Ojima D S, Burke I C (2003). Using mechanistic models to scale ecological processes across space and time. Bioscience, 53(1): 68Google Scholar
  47. Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grunwald T, Havrankova K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival J M, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakir D, Valentini R (2005). On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob Change Biol, 11(9): 1424–1439Google Scholar
  48. Richardson A D, Keenan T F, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013). Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric Meteorol, 169: 156–173Google Scholar
  49. Ryan M G, Waring R H (1992). Maintenance respiration and stand development in a young subalpine lodgepole pine forest. Ecology, 73: 2100–2108Google Scholar
  50. Sacks WJ, Schimel D S, Monson R K (2007). Coupling between carbon cycling and climate in a high-elevation, subalpine forest: a modeldata fusion analysis. Oecologia, 151(1): 54–68Google Scholar
  51. Sacks WJ, Schimel D S, Monson R K, Braswell B H (2006). Model-data synthesis of diurnal and seasonal CO2 fluxes at Niwot Ridge, Colorado. Glob Change Biol, 12(2): 240–259Google Scholar
  52. Savage K E, Parton WJ, Davidson E A, Trumbore S E, Frey S D (2013). Long-term changes in forest carbon under temperature and nitrogen amendments in a temperate northern hardwood forest. Glob Change Biol, 19(8): 2389–2400Google Scholar
  53. Schimel D (1995). Terrestrial ecosystems and the carbon cycle. Glob Change Biol, 1(1): 77–91Google Scholar
  54. Speckman H N, Frank J M, Bradford J B, Miles B L, Massman W J, Parton W J, Ryan M G (2015). Forest ecosystem respiration estimated from eddy covariance and chamber measurements under high turbulence and substantial tree mortality from bark beetles. Glob Change Biol, 21(2): 708–721Google Scholar
  55. Tang X, Wang X, Wang Z, Liu D, Jia M, Dong Z, Xie J, Ding Z, Wang H, Liu X (2013). Influence of vegetation phenology on modelling carbon fluxes in temperate deciduous forest by exclusive use of MODIS time-series data. Int J Remote Sens, 34(23): 8373–8392Google Scholar
  56. Tang X, Wang Z, Liu D, Song K, Jia M, Dong Z, Munger J W, Hollinger D Y, Bolstad P V, Goldstein A H, Desai A R, Dragoni D, Liu X (2012). Estimating the net ecosystem exchange for the major forests in the northern United States by integrating MODIS and AmeriFlux data. Agric Meteorol, 156: 75–84Google Scholar
  57. Turnipseed A A, Anderson D E, Blanken P D, Baugh WM, Monson R K (2003). Airflows and turbulent flux measurements in mountainous terrain. Part 1. Canopy and local effects. Agric Meteorol, 119(1–2): 1–21Google Scholar
  58. Turnipseed A A, Anderson D E, Burns S, Blanken P D, Monson R K (2004). Airflows and turbulent flux measurements in mountainous terrain: Part 2: Mesoscale effects. Agric Meteorol, 125(3–4): 187–205Google Scholar
  59. Turnipseed A A, Blanken P D, Anderson D E, Monson R K (2002). Energy budget above a high-elevation subalpine forest in complex topography. Agric Meteorol, 110(3): 177–201Google Scholar
  60. Urban O, Holub P, Klem K (2017). Seasonal courses of photosynthetic parameters in sun- and shade-acclimated spruce shoots. Beskydy, 10 (1–2): 49–56Google Scholar
  61. Wang Y P, Baldocchi D, Leuning R, Falge E, Vesala T (2007). Estimating parameters in a land-surface model by applying nonlinear inversion to eddy covariance flux measurements from eight FLUXNET sites. Glob Change Biol, 13(3): 652–670Google Scholar
  62. Wang Y P, Barrett D J (2003). Estimating regional terrestrial carbon fluxes for the Australian continent using a multiple-constraint approach I. Using remotely sensed data and ecological observations of net primary production. Tellus B Chem Phys Meterol, 55: 270–289Google Scholar
  63. Weiskittel A R, Maguire D, Garber S M, Kanaskie A (2006). Influence of Swiss needle cast on foliage age-class structure and vertical foliage distribution in Douglas-fir plantations in north coastal Oregon. Can J Res, 36(6): 1497–1508Google Scholar
  64. Zhang Y J, Holbrook N M, Cao K F (2014). Seasonal dynamics in photosynthesis of woody plants at the northern limit of Asian tropics: potential role of fog in maintaining tropical rainforests and agriculture in Southwest China. Tree Physiol, 34(10): 1069–1078Google Scholar
  65. Zhang Y J, Sack L, Cao K F, Wei X M, Li N (2017). Speed versus endurance tradeoff in plants: leaves with higher photosynthetic rates show stronger seasonal declines. Sci Rep, 7(1): 42085Google Scholar
  66. Ziello C, Estrella N, Kostova M, Koch E, Menzel A (2009). Influence of altitude on phenology of selected plant species in the Alpine region (1971-2000). Clim Res, 39: 227–234Google Scholar
  67. Zobitz J M, Moore D J P, Sacks W J, Monson R K, Bowling D R, Schimel D S (2008). Integration of process-based soil respiration models with whole-ecosystem CO2 measurements. Ecosystems (N Y), 11(2): 250–269Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jonathan R. Straube
    • 1
    • 2
    Email author
  • Maosi Chen
    • 1
  • William J. Parton
    • 1
    • 2
  • Shinichi Asso
    • 1
    • 2
  • Yan-An Liu
    • 3
    • 4
    • 5
    • 1
    Email author
  • Dennis S. Ojima
    • 2
  • Wei Gao
    • 1
    • 6
    • 5
  1. 1.USDA UV-B Monitoring and Research Program, Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsUSA
  2. 2.Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsUSA
  3. 3.Key Laboratory of Geographic Information Science (Ministry of Education)East China Normal UniversityShanghaiChina
  4. 4.School of Geographic SciencesEast China Normal UniversityShanghaiChina
  5. 5.ECNU-CSU Joint Research Institute for New Energy and the EnvironmentShanghaiChina
  6. 6.Department of Ecosystem Science and SustainabilityColorado State UniversityFort CollinsUSA

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