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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

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2018 Impact Factor: 1.205

Front. Earth Sci.    2018, Vol. 12 Issue (4) : 765-778    https://doi.org/10.1007/s11707-018-0736-6
RESEARCH ARTICLE
Development of the DayCent-Photo model and integration of variable photosynthetic capacity
Jonathan R. STRAUBE1,2(), Maosi CHEN1, William J. PARTON1,2, Shinichi ASSO1,2, Yan-An LIU3,4,5,1(), Dennis S. OJIMA2, Wei GAO1,6,5
1. USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80521, USA
2. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA
3. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
4. School of Geographic Sciences, East China Normal University, Shanghai 200241, China
5. ECNU-CSU Joint Research Institute for New Energy and the Environment, Shanghai 200062, China
6. Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523, USA
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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·m2, AIC= 2099) versus a version with a single Amax parameter (NEE RMSE= 0.74 gC·m2, 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     
Corresponding Author(s): Jonathan R. STRAUBE,Yan-An LIU   
Just Accepted Date: 12 September 2018   Online First Date: 26 October 2018    Issue Date: 20 November 2018
 Cite this article:   
Jonathan R. STRAUBE,Maosi CHEN,William J. PARTON, et al. Development of the DayCent-Photo model and integration of variable photosynthetic capacity[J]. Front. Earth Sci., 2018, 12(4): 765-778.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0736-6
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I4/765
Fig.1  The DayCent model with the photosynthetic modifications. Maintenance respiration is based on the PS2Mrsp parameter.
Fig.2  The optimized seasonal values of Amax at Niwot Ridge, CPER, Harvard Forest, and Konza sites.
Parameter Description Range Starting value
+PS2Mrsp Fraction of GPP applied to maintenance respiration 0.3 to 0.65 0.46
*Amax Maximum net CO2 assimilation rate 0 to 34 4.9 (gC·m2)
*AmaxFrac Ave. daily max photosynthesis as a fraction of Amax fixed 0.76
AmaxScalar1 Scalar value of Amax at GrowthDays1 fixed 0
AmaxScalar2 Scalar value of Amax at GrowthDays2 0.8 to 1.6 1.22
AmaxScalar3 Scalar value of Amax at GrowthDays3 0.7 to 1.5 1.03
AmaxScalar4 Scalar value of Amax at GrowthDays4 0.3 to 0.8 0.6
*Attenuation Canopy Par extinction coefficient fixed 0.5
*BaseFolRespFrac Foliar respiration as a fraction of Amax 0.05 to 0.3 0.06
*CFracLeaf Fraction of carbon applied to leaf growth 0.2 to 1 0.52
#DVPDExp Exponent of VPD-photosynthesis relationship fixed −0.35
#DVPDSlope Slope of VPD-photosynthesis relationship fixed 1.55 (kPa1)
GrowthDays1 First day of growth to apply AmaxScalar1 scalar fixed 1 (day)
GrowthDays2 Number of days after the start of growth to apply AmaxScalar2 scalar 20 to 120 42 (days)
GrowthDays3 Number of days after the start of growth to apply AmaxScalar3 scalar 121 to 180 136 (days)
GrowthDays4 Number of days after the start of growth to apply AmaxScalar4 scalar 181 to 220 209 (days)
*HalfSatPAR Half saturation point of PPFD-photosynthesis relationship 4 to 12 8.3 (mol·m2·d1)
*LeafCSPWT Carbon content of needles on a per-area basis fixed 270
*PsntMin Minimum temperature for photosynthesis −8 to 8 −3.5 (°C)
*PsntOpt Optimum temperature for photosynthesis 5 to 30 18.9 (°C)
Tab.1  Photosynthetic parameters, starting values and ranges. Parameters marked with an (*) are taken from Moore et al. (2008). Values marked with a (#) are from Sarah Davis, personal communication. Estimates for PS2Mrsp are taken from literature values for maintenance respiration (Ryan and Waring, 1992). Fixed parameters do not change during the optimization process. Parameters are considered to have no units unless labeled otherwise
Parameter Single-Amax values DayCent-Photo values
PS2Mrsp 0.50 (0.48±0.03) 0.44 (0.45±0.02)
Amax 3.63 (3.83±0.34) 3.76 (3.79±0.22)
#AmaxFrac 0.76 0.76
#AmaxScalar1 1.0 0.0
AmaxScalar2 1.0 1.27 (1.27±0.04)
AmaxScalar3 1.0 1.17 (1.16±0.04)
AmaxScalar4 1.0 0.38 (0.4±0.02)
#Attenuation 0.5 0.5
BaseFolRespFrac 0.100 (0.112±0.021) 0.055 (0.095±0.027)
CFracLeaf 0.56 (0.58±0.04) 0.59 (0.62±0.03)
#DVPDExp −0.35 −0.35
#DVPDSlope 1.55 1.55
#GrowthDays1 1 1
GrowthDays2 1 44 (44±0.48)
GrowthDays3 1 135 (135±1.22)
GrowthDays4 1 211 (210±2.00)
HalfSatPAR 4.02 (4.21±0.19) 4.25 (4.25±0.14)
#LeafCSPWT 270.0 270.0
PsntMin −1.51 (−1.69±0.54) −6.67 (−6.23±0.60)
PsntOpt 15.68 (15.85±0.28) 11.67 (11.68±0.14)
Tab.2  Parameters marked with (#) are fixed and do not vary during optimization. The numbers beside the best parameter set are parameterizations from the runs that were statistically similar (P = 0.05) in terms of likelihood. With the first number being the mean and the number after (±) being the standard deviation of the mean
DayCent Model Single-Amax DayCent-Photo
Log-likelihood (Ltotal) ?1850.0 ?1029.5
NEE RMSE/(gC·m?2) 0.74 0.63
NEE R2 0.57 0.69
GPP RMSE/(gC·m?2) 1.02 0.88
GPP R2 0.78 0.84
Number of parameters (K) 12 20
AIC 3723.9 2099.0
Tab.3  Model comparison for DayCent model. Single-Amax is the photosynthetic version of DayCent with a single Amax value. DayCent-Photo is the photosynthetic model with seasonal variation of Amax. Root Mean Square Error (RMSE) is a measure of the amount of variance not explained by the model, lower is better. Akaike Information Criterion (AIC) equation is 2K– 2ln(L), where ln(L) = Ltotal. A lower AIC indicates the model that has better support from the data
Fig.3  Model comparison of GPP and NEE for the optimization period from 1999?2005. The data is averaged into 52 weekly periods. Note there is no GPP data for the Base DayCent model because the Base DayCent model where DayCent-Photo is developed from does not have the specialized module for GPP calculation.
Fig.4  Model versus data comparisons for the optimization period 1999?2005 and validation period 2006?2008. GPP data is not available for 2008. Positive NEE number denotes release of carbon from the ecosystem to the atmosphere. The large number of data points above one-to-one line for the GPP optimization period is a result of an unexplained large drop in summer time GPP from the observed data in the summer of 2004. If this data is removed, there is a much better correlation (R2 = 0.91) with the validation period GPP.
Fig.5  Comparison of model vs. observed daily GPP (a), RESP (b), and NEE (c) for 2005 at the Niwot Ridge site.
Fig.6  Comparison of Inter-annual NEE for Niwot Ridge for the DayCent with fixed (DayCent-Single) and variable (DayCent-Photo) vs. observed NEE.
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