Variation of gross primary productivity dominated by leaf area index in significantly greening area

doi:10.1007/s11442-023-2151-5

Abstract

Abstract:

The leaf area index (LAI) shows a significant increasing trend from global to regional scales, which is known as greening. Greening will further enhance photosynthesis, but it is unclear whether the contribution of greening has exceeded the CO₂ fertilization effect and become the dominant factor in the gross primary productivity (GPP) variation. We took the Yangtze River Delta (YRD) of China, where cropland and natural vegetation are significantly greening, as an example. Based on the boreal ecosystem productivity simulator (BEPS) and Revised-EC-LUE models, the GPP in the YRD from 2001 to 2020 was simulated, and attribution analysis of the interannual variation in GPP was performed. In addition, the reliability of the GPP simulated by the dynamic global vegetation model (DGVM) in the area was further investigated. The research results showed that GPP in the YRD had three significant characteristics consistent with LAI: (1) GPP showed a significant increasing trend; (2) the multiyear mean and trend of natural vegetation GPP were higher than those of cropland GPP; and (3) cropland GPP showed double-high peak characteristics. The BEPS and Revised-EC-LUE models agreed that the effect of LAI variation (4.29 Tg C yr^-1 for BEPS and 2.73 Tg C yr^-1 for the Revised-EC-LUE model) determined the interannual variation in GPP, which was much higher than the CO₂ fertilization effect (2.29 Tg C yr^-1 for BEPS and 0.67 Tg C yr^-1 for the Revised-EC-LUE model). The GPP simulated by the 7 DGVMs showed a huge inconsistency with the GPP estimated by remote sensing models. The deviation of LAI simulated by DGVM might be a potential cause for this phenomenon. Our study highlights that in significant greening areas, LAI has dominated GPP variation, both spatially and temporally, and DGVM can correctly simulate GPP only if it accurately simulates LAI variation.

Key words: leaf area index, gross primary productivity, dynamic global vegetation model, Yangtze River Delta

CHEN Xin, CAI Anning, GUO Renjie, LIANG Chuanzhuang, LI Yingying. Variation of gross primary productivity dominated by leaf area index in significantly greening area[J].Journal of Geographical Sciences, 2023, 33(8): 1747-1764.

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References 54

[1]	Ainsworth E A, Rogers A, 2007. The response of photosynthesis and stomatal conductance to rising CO₂: Mechanisms and environmental interactions. Plant Cell and Environment, 30(3): 258-270. doi: 10.1111/j.1365-3040.2007.01641.x
[2]	Badgley G, Anderegg L D L, Berry J A et al., 2019. Terrestrial gross primary production: Using NIRV to scale from site to globe. Global Change Biology, 25(11): 3731-3740. doi: 10.1111/gcb.v25.11
[3]	Badgley G, Field C B, Berry J A, 2017. Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances, 3(3): e1602244. doi: 10.1126/sciadv.1602244
[4]	Bell B, Hersbach H, Simmons A et al., 2021. The ERA5 global reanalysis: Preliminary extension to 1950. Quarterly Journal of the Royal Meteorological Society, 147(741): 4186-4227. doi: 10.1002/qj.v147.741
[5]	Chen C, Park T, Wang X et al., 2019a. China and India lead in greening of the world through land-use management. Nature Sustainability, 2(2): 122-129. doi: 10.1038/s41893-019-0220-7
[6]	Chen J M, Ju W, Ciais P et al., 2019b. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nature Communications, 10(1): 4259. doi: 10.1038/s41467-019-12257-8
[7]	Chen J M, Liu J, Cihlar J et al., 1999. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecological Modelling, 124(2/3): 99-119.
[8]	Chen J M, Mo G, Pisek J et al., 2012. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Global Biogeochemical Cycles, 26(1): GB1019.
[9]	Chen T, Dolman H, Sun Z et al., 2022a. Land management explains the contrasting greening pattern across China-Russia border based on paired land use experiment approach. Journal of Geophysical Research: Biogeosciences, 127(6): e2021JG006659.
[10]	Chen T, Guo R, Yan Q et al., 2022b. Land management contributes significantly to observed vegetation browning in Syria during 2001-2018. Biogeosciences, 19(5): 1515-1525. doi: 10.5194/bg-19-1515-2022
[11]	Chen X, Chen T, Shu Y et al., 2021. A framework to assess the potential uncertainties of three FPAR products. Journal of Geophysical Research: Biogeosciences, 126(10): e2021JG006320.
[12]	Chu H, Luo X, Ouyang Z et al., 2021. Representativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites. Agricultural and Forest Meteorology, 301: 108350.
[13]	Delire C, Seferian R, Decharme B et al., 2020. The global land carbon cycle simulated with ISBA-CTRIP: Improvements over the last decade. Journal of Advances in Modeling Earth Systems, 12(9): e2019MS001886.
[14]	Ding Z, Peng J, Qiu S et al., 2020. Nearly half of global vegetated area experienced inconsistent vegetation growth in terms of greenness, cover, and productivity. Earths Future, 8(10): e2020EF001618.
[15]	Farquhar G D, von Caemmerer S, Berry J A, 1980. A biochemical model of photosynthetic CO₂ assimilation in leaves of C3 species. Planta, 149(1): 78-90. doi: 10.1007/BF00386231 pmid: 24306196
[16]	Feng X, Liu G, Chen J M et al., 2007. Net primary productivity of China’s terrestrial ecosystems from a process model driven by remote sensing. Journal of Environmental Management, 85(3): 563-573. pmid: 17234327
[17]	Fleischer K, Rammig A, De Kauwe M G et al., 2019. Amazon forest response to CO₂ fertilization dependent on plant phosphorus acquisition. Nature Geoscience, 12(9): 736. doi: 10.1038/s41561-019-0404-9
[18]	Friedlingstein P, O’Sullivan M, Jones M W et al., 2020. Global Carbon Budget 2020. Earth System Science Data, 12(4): 3269-3340. doi: 10.5194/essd-12-3269-2020
[19]	He L, Chen J M, Croft H et al., 2017. Nitrogen availability dampens the positive impacts of CO₂ fertilization on terrestrial ecosystem carbon and water cycles. Geophysical Research Letters, 44(22): 11590-11600. doi: 10.1002/grl.v44.22
[20]	Houghton R A, 2020. Terrestrial fluxes of carbon in GCP carbon budgets. Global Change Biology, 26(5): 3006-3014. doi: 10.1111/gcb.15050 pmid: 32100912
[21]	Jung M, Schwalm C, Migliavacca M et al., 2020. Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17(5): 1343-1365. doi: 10.5194/bg-17-1343-2020
[22]	Keenan T F, Williams C A, 2018. The terrestrial carbon sink. Annual Review of Environment and Resources, 43: 219-243. doi: 10.1146/energy.2018.43.issue-1
[23]	Lawrence D M, Fisher R A, Koven C D et al., 2019. The community land model Version 5: Description of new features, benchmarking, and impact of forcing uncertainty. Journal of Advances in Modeling Earth Systems, 11(12): 4245-4287. doi: 10.1029/2018MS001583
[24]	Leuning R, Kelliher F M, De Pury D G G et al., 1995. Leaf nitrogen, photosynthesis, conductance and transpiration: Scaling from leaves to canopies. Plant, Cell & Environment, 18(10): 1183-1200.
[25]	Li X, Xiao J, 2019. A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sensing, 11(5): 517. doi: 10.3390/rs11050517
[26]	Ma H, Liang S, 2022. Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model. Remote Sensing of Environment, 273: 112985. doi: 10.1016/j.rse.2022.112985
[27]	Medlyn B E, Zaehle S, De Kauwe M G et al., 2015. Using ecosystem experiments to improve vegetation models. Nature Climate Change, 5(6): 528-534. doi: 10.1038/nclimate2621
[28]	Meiyappan P, Jain A K, House J I, 2015. Increased influence of nitrogen limitation on CO₂ emissions from future land use and land use change. Global Biogeochemical Cycles, 29(9): 1524-1548. doi: 10.1002/gbc.v29.9
[29]	Mengistu A G, Tsidu G M, Koren G et al., 2021. Sun-induced fluorescence and near-infrared reflectance of vegetation track the seasonal dynamics of gross primary production over Africa. Biogeosciences, 18(9): 2843-2857. doi: 10.5194/bg-18-2843-2021
[30]	Murray-Tortarolo G, Poulter B, Vargas R et al., 2022. A process-model perspective on recent changes in the carbon cycle of North America. Journal of Geophysical Research: Biogeosciences, 127(9) e2022JG006904.
[31]	Pei Y, Dong J, Zhang Y et al., 2022. Evolution of light use efficiency models: Improvement, uncertainties, and implications. Agricultural and Forest Meteorology, 317: 108905. doi: 10.1016/j.agrformet.2022.108905
[32]	Piao S, Wang X, Wang K et al., 2020. Interannual variation of terrestrial carbon cycle: Issues and perspectives. Global Change Biology, 26(1): 300-318. doi: 10.1111/gcb.14884 pmid: 31670435
[33]	Restrepo-Coupe N, Levine N M, Christoffersen B O et al., 2017. Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison. Global Change Biology, 23(1): 191-208. doi: 10.1111/gcb.13442 pmid: 27436068
[34]	Seiler C, Melton J R, Arora V K et al., 2022. Are terrestrial biosphere models fit for simulating the global land carbon sink? Journal of Advances in Modeling Earth Systems, 14(5): e2021MS002946.
[35]	Sellar A A, Jones C G, Mulcahy J P et al., 2019. UKESM1: Description and evaluation of the UK earth system model. Journal of Advances in Modeling Earth Systems, 11(12): 4513-4558. doi: 10.1029/2019MS001739
[36]	Smith B, Warlind D, Arneth A et al., 2014. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences, 11(7): 2027-2054. doi: 10.5194/bg-11-2027-2014
[37]	Sprintsin M, Chen J M, Desai A et al., 2012. Evaluation of leaf-to-canopy upscaling methodologies against carbon flux data in North America. Journal of Geophysical Research: Biogeosciences, 117(G1): G01023.
[38]	Sun Z, Wang X, Zhang X et al., 2019. Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO₂ trends. Science of the Total Environment, 668: 696-713. doi: 10.1016/j.scitotenv.2019.03.025
[39]	von Caemmerer S, Farquhar G D, 1981. Some relationships between the biochemistry of photosynthesis and the gas exchange of leaves. Planta, 153(4): 376-387. doi: 10.1007/BF00384257 pmid: 24276943
[40]	Vuichard N, Messina P, Luyssaert S et al., 2019. Accounting for carbon and nitrogen interactions in the global terrestrial ecosystem model ORCHIDEE (trunk version, rev 4999): Multi-scale evaluation of gross primary production. Geoscientific Model Development, 12(11): 4751-4779. doi: 10.5194/gmd-12-4751-2019
[41]	Walker A P, De Kauwe M G, Bastos A et al., 2021. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO₂. New Phytologist, 229(5): 2413-2445. doi: 10.1111/nph.v229.5
[42]	Walker A P, Quaife T, van Bodegom P M et al., 2017. The impact of alternative trait-scaling hypotheses for the maximum photosynthetic carboxylation rate (V-cmax) on global gross primary production. New Phytologist, 215(4): 1370-1386. doi: 10.1111/nph.2017.215.issue-4
[43]	Wang S, Zhang Y, Ju W et al., 2020. Recent global decline of CO₂ fertilization effects on vegetation photosynthesis. Science, 370(6522): 1295. doi: 10.1126/science.abb7772
[44]	Wang Z, Liu S, Wang Y-P et al., 2021. Tighten the bolts and nuts on GPP estimations from sites to the globe: An assessment of remote sensing based LUE models and supporting data fields. Remote Sensing, 13(2): 168. doi: 10.3390/rs13020168
[45]	Yu Z, Ciais P, Piao S et al., 2022. Forest expansion dominates China’s land carbon sink since 1980. Nature Communications, 13(1): 5374. doi: 10.1038/s41467-022-32961-2
[46]	Yuan W, Cai W, Xia J et al., 2014. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the La Thuile database. Agricultural and Forest Meteorology, 192: 108-120.
[47]	Yuan W, Zheng Y, Piao S et al., 2019. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Science Advances, 5(8): eaax1396. doi: 10.1126/sciadv.aax1396
[48]	Yue X, Unger N, 2015. The Yale Interactive terrestrial Biosphere model version 1.0: Description, evaluation and implementation into NASA GISS ModelE2. Geoscientific Model Development, 8(8): 2399-2417. doi: 10.5194/gmd-8-2399-2015
[49]	Zhang Y, Joiner J, Alemohammad S H et al., 2018. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences, 15(19): 5779-5800. doi: 10.5194/bg-15-5779-2018
[50]	Zhao M, Running S W, 2010. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 329(5994): 940-943. doi: 10.1126/science.1192666 pmid: 20724633
[51]	Zheng Y, Shen R, Wang Y et al., 2020. Improved estimate of global gross primary production for reproducing its long-term variation, 1982-2017. Earth System Science Data, 12(4): 2725-2746. doi: 10.5194/essd-12-2725-2020
[52]	Zheng Y, Zhang L, Xiao J et al., 2018. Sources of uncertainty in gross primary productivity simulated by light use efficiency models: Model structure, parameters, input data, and spatial resolution. Agricultural and Forest Meteorology, 263: 242-257. doi: 10.1016/j.agrformet.2018.08.003
[53]	Zhou S, Chen T, Zeng N et al., 2022. The impact of cropland abandonment of post-Soviet countries on the terrestrial carbon cycle based on optimizing the cropland distribution map. Biology, 11(5): 620. doi: 10.3390/biology11050620
[54]	Zhu Z, Piao S, Myneni R B et al., 2016. Greening of the Earth and its drivers. Nature Climate Change, 6(8): 791-795. doi: 10.1038/NCLIMATE3004

	Study area		Cropland		Natural vegetation
	Average	Trend	Average	Trend	Average	Trend
CLM5.0	1.48×10³	4.71*	1.3×10³	4.32*	1.7×10³	5.25*
ISAM	1.25×10³	1.85*	1.09×10³	0.89	1.5×10³	3.6*
ISBA-CTRIP	1.79×10³	13.37*	1.69×10³	13.13	1.88×10³	12.74*
JULES-ES	1.87×10³	6.87*	1.61×10³	6.15	2.19×10³	7.68*
LPJ-GUESS	1.51×10³	11.05*	1.36×10³	13.61*	1.73×10³	8.49*
ORCHIDEEv3	2.76×10³	33.78*	2.85×10³	37.18*	2.74×10³	30.43*
SDGVM	1.32×10³	1.62	1.23×10³	1.04*	1.47×10³	2.73

	S0	S1	S2	S3	CO₂	Climate	LUCC
CLM5.0	0	1.91*	1.98*	1.64*	1.91	0.07	-0.35
ISAM	-0.19	1.57*	1.53*	0.64*	1.77*	-0.05	-0.88*
ISBA-CTRIP	-0.37	1.59	3.73	4.65*	1.97*	2.14	0.92*
JULES-ES	-0.38	1.72	1.84	2.39*	2.1*	0.12	0.54
LPJ-GUESS	-0.07	1.94*	2.03*	3.84*	2.01*	0.09	1.81*
ORCHIDEEv3	-0.19	3*	4.68*	11.75*	3.19*	1.68*	7.06*
SDGVM	-0.92*	0.23	2.27*	0.56	1.15*	2.04*	-1.7*

	Study area		Cropland		Natural vegetation
	Average	Trend	Average	Trend	Average	Trend
CLM5.0	2.76	7.92×10^-3*	2.02	4.4×10^-3	3.65	12.19×10^-3*
ISAM	1.62	-2.6×10^-3	1.09	-2.7×10^-3	2.3	-1.37×10^-3
ISBA-CTRIP	2.46	13.45×10^-3	2.39	14.47×10^-3	2.5	11.15×10^-3
JULES-ES	2.89	3.2×10^-3*	2.42	3.76×10^-3*	3.48	2.6×10^-3*
LPJ-GUESS	2.63	16.53×10^-3*	2.41	21.39×10^-3*	2.96	11.22×10^-3*
ORCHIDEEv3	3.49	36.34×10^-3*	3.63	43.04×10^-3*	3.45	29.52×10^-3*
SDGVM	3.1	-3.12×10^-3	2.36	-2.85×10^-3	4.1	-2.88×10^-3