Simulation research on the effects of nitrogen input on carbon cycle process in a coastal wetland: review and prospects

CHEN Yawen; HAN Guangxuan; CAI Yanjiang

doi:10.11833/j.issn.2095-0756.20210118

Volume 38 Issue 5

Oct. 2021

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CHEN Yawen, HAN Guangxuan, CAI Yanjiang. Simulation research on the effects of nitrogen input on carbon cycle process in a coastal wetland: review and prospects[J]. Journal of Zhejiang A&F University, 2021, 38(5): 883-895. doi: 10.11833/j.issn.2095-0756.20210118

Citation:

CHEN Yawen, HAN Guangxuan, CAI Yanjiang. Simulation research on the effects of nitrogen input on carbon cycle process in a coastal wetland: review and prospects[J]. Journal of Zhejiang A&F University, 2021, 38(5): 883-895. doi: 10.11833/j.issn.2095-0756.20210118

Simulation research on the effects of nitrogen input on carbon cycle process in a coastal wetland: review and prospects

doi: 10.11833/j.issn.2095-0756.20210118

CHEN Yawen^{1, 2
,},
HAN Guangxuan^{1
,
,},
CAI Yanjiang³

1.
Key Laboratory of Coastal Environmental Processes and Ecological Restoration, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, Shandong, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
College of Environment and Resources, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China

Received Date: 2021-01-18
Rev Recd Date: 2021-05-28

Available Online: 2021-10-12

Publish Date: 2021-10-20

Abstract

As one of the key processes controlling global carbon storage, carbon cycle in coastal wetland is a process significantly affected by nitrogen input due to the inshore water eutrophication. Given the fact that nitrogen input affects the carbon cycle of coastal wetland in a rather complex way, carbon cycle model has been selected as an effective method to clarify the process with the ultimate purpose to evaluate the carbon storage function of coastal wetland under the global climate change. With an review conducted of the migration and transformation of carbon components in coastal wetlands at different interfaces of atmosphere, vegetation, water and soil and a summary made of the regulations of the response of carbon cycle on nitrogen, it was found that carbon storage and flux is affected by multiple factors. Also, on this basis, carbon cycle models with carbon, nitrogen and water related modules were introduced along with the efforts made to promote their adaptability to wetlands and their application on wetlands, which shall provide reference for the employment of the model in the description of the carbon exchange in coastal wetland under the influence of nitrogen input. Finally, it was concluded that, to further develop the model, closer attention should be focused on the tidal process with nitrogen input and the promotion of model simulation accuracy. [Ch, 1 fig. 1 tab. 126 ref.]
- coastal wetland,
- nitrogen input,
- carbon cycle,
- model analysis

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References

[1]	MCLEOD E, CHMURA G L, BOUILLON S, et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO₂ [J]. Front Ecol Environ, 2011, 9(10): 552 − 560.
[2]	KIRWAN M L, GUNTENSPERGEN G R. Feedbacks between inundation, root production, and shoot growth in a rapidly submerging brackish marsh [J]. J Ecol, 2012, 100(3): 764 − 770.
[3]	KIRWAN M, TEMMERMAN S, SKEEHAN E, et al. Overestimation of marsh vulnerability to sea level rise [J]. Nat Clim Change, 2016, 6(3): 253 − 260.
[4]	KROEGER K D, CROOKS S, MOSEMAN-VALTIERRA S, et al. Restoring tides to reduce methane emissions in impounded wetlands: a new and potent Blue Carbon climate change intervention [J]. Sci Rep, 2017, 7(1): 11914. doi: 10.1038/s41598-017-12138-4.
[5]	GALLOWAY J N, TOWNSEND A R, ERISMAN J W, et al. Transformation of the nitrogen cycle: recent trends, questions, and potential solutions [J]. Science, 2008, 320(5878): 889 − 892.
[6]	CHMURA G L, ANISFELD S C, CAHOON D R, et al. Global carbon sequestration in tidal, saline wetland soils [J]. Global Biogeochem Cycles, 2003, 17(4): 1111. doi: 10.1029/2002gb001917.
[7]	LI Juanyong, HAN Guangxuan, ZHAO Mingliang, et al. Nitrogen input weakens the control of inundation frequency on soil organic carbon loss in a tidal salt marsh [J]. Estuarine Coastal Shelf Sci, 2020, 243: 106878. doi: 10.1016/j.ecss.2020.106878.
[8]	HOBBIE S E, NADELHOFFER K J, HÖGBERG P. A synthesis: the role of nutrients as constraints on carbon balances in boreal and arctic regions [J]. Plant Soil, 2002, 242(1): 163 − 170.
[9]	TAO Baoxian, SONG Changchun, GUO Yuedong. Short-term effects of nitrogen additions and increased temperature on wetland soil respiration, Sanjiang Plain, China [J]. Wetlands, 2013, 33(4): 727 − 736.
[10]	LIU Jun, WU Nana, WANG Hui, et al. Nitrogen addition affects chemical compositions of plant tissues, litter and soil organic matter [J]. Ecology, 2016, 97(7): 1796 − 1806.
[11]	SONG Bing, SUN Jian, ZHOU Qingping, et al. Initial shifts in nitrogen impact on ecosystem carbon fluxes in an alpine meadow: patterns and causes [J]. Biogeosciences, 2017, 14(17): 3947 − 3956.
[12]	WANG Jing, GAO Yingzhi, ZHANG Yunhai, et al. Asymmetry in above- and belowground productivity responses to N addition in a semi-arid temperate steppe [J]. Global Change Biol, 2019, 25(9): 2958 − 2969.
[13]	QU Wendi, HAN Guangxuan, ELLER F, et al. Nitrogen input in different chemical forms and levels stimulates soil organic carbon decomposition in a coastal wetland [J]. Catena, 2020, 194: 104672. doi: 10.1016/j.catena.2020.104672.
[14]	TRUMBORE S. Carbon respired by terrestrial ecosystems-recent progress and challenges [J]. Global Change Biol, 2006, 12(2): 141 − 153.
[15]	LUO Yiqi, KEENAN T F, SMITH M. Predictability of the terrestrial carbon cycle [J]. Global Change Biol, 2015, 21(5): 1737 − 1751.
[16]	CAO Lei, SONG Jinming, LI Xuegang, et al. Research progresses in carbon budget and carbon cycle of the coastal salt marshes in China [J]. Acta Ecol Sin, 2013, 33(17): 5141 − 5152.
[17]	MITSCH W J, GOSSELINK J G. Wetlands[M]. 4th ed. Hoboken: John Wiley & Sons, Inc., 2007.
[18]	WANG Dongqi, CHEN Zhenlou, XU Shiyuan. Methane emission from Yangtze estuarine wetland, China [J]. J Geophys Res, 2009, 114: G02011. doi: 10.1029/2008JG000857.
[19]	HAN Guangxuan. Effect of tidal action and drying-wetting cycles on carbon exchange in a salt marsh: progress and prospects [J]. Acta Ecol Sin, 2017, 37(24): 8170 − 8178.
[20]	TONG Chuan, E Yan, LIAO Ji, et al. Carbon dioxide emission from tidal marshes in the Min River Estuary [J]. Acta Sci Circumstantiae, 2011, 31(12): 2830 − 2840.
[21]	WANG Lingling, SUN Zhigao, MOU Xiaojie, et al. A preliminary study on carbon dioxide, methane and nitrous oxide fluxes from intertidal flat wetlands of the Yellow River estuary [J]. Acta Pratacult Sin, 2011, 20(3): 51 − 61.
[22]	HE Wenjun, HAN Guangxuan, SONG Weimin, et al. Effects of tidal action on methane emissions over a salt marsh in the Yellow River Delta, China [J]. Acta Ecol Sin, 2019, 39(17): 6238 − 6246.
[23]	POFFENBARGER H J, NEEDELMAN B A, MEGONIGAL J P. Salinity influence on methane emissions from tidal marshes [J]. Wetlands, 2011, 31(5): 831 − 842.
[24]	CHOI Y, WANG Yang. Dynamics of carbon sequestration in a coastal wetland using radiocarbon measurements [J]. Global Biogeochem Cycles, 2004, 18: GB4016. doi: 10.1029/2004GB002261.
[25]	KAYRANLI B, SCHOLZ M, MUSTAFA A, et al. Carbon storage and fluxes within freshwater wetlands: a critical review [J]. Wetlands, 2010, 30(1): 111 − 124.
[26]	THORNTON S F, MCMANUS J. Application of organic carbon and nitrogen stable isotope and C/N ratios as source indicators of organic matter provenance in estuarine systems: evidence from the Tay Estuary, Scotland [J]. Estuarine Coastal Shelf Sci, 1994, 38(3): 219 − 233.
[27]	VERNBERG F J. Salt-marsh processes: a review [J]. Environ Toxicol Chem, 1993, 12(12): 2167 − 2195.
[28]	SYED K H, FLANAGAN L B, CARLSON P J, et al. Environmental control of net ecosystem CO₂ exchange in a treed, moderately rich fen in northern Alberta [J]. Agric For Meteorol, 2006, 140(1): 97 − 114.
[29]	EXBRAYAT J F, PITMAN A J, ZHANG Q, et al. Examining soil carbon uncertainty in a global model: response of microbial decomposition to temperature, moisture and nutrient limitation [J]. Biogeosciences, 2013, 10(11): 7095 − 7108.
[30]	BRAGAZZA L, BUTTLER A, HABERMACHER J, et al. High nitrogen deposition alters the decomposition of bog plant litter and reduces carbon accumulation [J]. Global Change Biol, 2012, 18(3): 1163 − 1172.
[31]	YUAN Zhiyou, CHEN H Y H. A global analysis of fine root production as affected by soil nitrogen and phosphorus [J]. Proc Biol Sci, 2012, 279(1743): 3796 − 3802.
[32]	YUAN Zhiyou, LI Linghao, HAN Xingguo, et al. Nitrogen response efficiency increased monotonically with decreasing soil resource availability: a case study from a semiarid grassland in northern China [J]. Oecologia, 2006, 148(4): 564 − 572.
[33]	BOBBINK R, HICKS K, GALLOWAY J, et al. Global assessment of nitrogen deposition effects on terrestrial plant diversity: a synthesis [J]. Ecol Appl, 2010, 20(1): 30 − 59.
[34]	KOOTTATEP T, POLPRASERT C. Role of plant uptake on nitrogen removal in constructed wetlands located in the tropics [J]. Water Sci Technol, 1997, 36(12): 1 − 8.
[35]	SUN Yuan, WANG Cuiting, CHEN H Y H, et al. Responses of C∶N stoichiometry in plants, soil, and microorganisms to nitrogen addition [J]. Plant Soil, 2020, 456(1/2): 277 − 287.
[36]	MOE S J, STELZER R S, FORMAN M R, et al. Recent advances in ecological stoichiometry: insights for population and community ecology [J]. Oikos, 2005, 109(1): 29 − 39.
[37]	HOUGHTON R. Terrestrial carbon sinks-uncertain explanations [J]. Biologist, 2002, 49: 155 − 60.
[38]	LIU Deyan, SONG Changchun. Responses of marsh wetland plant Calamagrostis angustifolia to exogenous nitrogen input [J]. Chin J Appl Ecol, 2008, 19(12): 2599 − 2604.
[39]	PENG Qin, DONG Yunshe, QI Yuchun. Influence of external nitrogen input on key processes of carbon cycle in terrestrial ecosystem [J]. Adv Earth Sci, 2008, 23(8): 874 − 883.
[40]	NAKAJI T, TAKENAGA S, KUROHA M, et al. Photosynthetic response of Pinus densiflora seedlings to high nitrogen load [J]. Environ Sci, 2002, 9: 269 − 282.
[41]	ZHANG Lixin, LI Shengxiu. Effects of nitrogen and potassium on photosynthetic characteristics in summer maize leaves under long-term water stress [J]. Plant Nutr Fert Sci, 2009, 15(1): 82 − 90.
[42]	HUANGFU Chaohe, LI Huiyan, CHEN Xinwei, et al. Response of an invasive plant, Flaveria bidentis, to nitrogen addition: a test of form-preference uptake [J]. Biol Invasions, 2016, 18(11): 3365 − 3380.
[43]	LU Xiankai, VITOUSEK P M, MAO Qinggong, et al. Plant acclimation to long-term high nitrogen deposition in an N-rich tropical forest [J]. Proc Natl Acad Sci, 2018, 115(20): 5187 − 5192.
[44]	BREITBURG D, LEVIN L A, OSCHLIES A, et al. Declining oxygen in the global ocean and coastal waters [J]. Science, 2018, 359(6371): eaam7240. doi: 10.1126/science.aam7240.
[45]	ZHANG Yaohong, XU Xianju, LI Yang, et al. Effects of Spartina alterniflora invasion and exogenous nitrogen on soil nitrogen mineralization in the coastal salt marshes [J]. Ecol Eng, 2016, 87: 281 − 287.
[46]	LUO Min, HUANG Jiafang, ZHU Wenfeng, et al. Impacts of increasing salinity and inundation on rates and pathways of organic carbon mineralization in tidal wetlands: a review [J]. Hydrobiologia, 2019, 827(1): 31 − 49.
[47]	MIN K, KANG H, LEE D. Effects of ammonium and nitrate additions on carbon mineralization in wetland soils [J]. Soil Biol Biochem, 2011, 43(12): 2461 − 2469.
[48]	EISENLORD S D, FREEDMAN Z, ZAK D R, et al. Microbial mechanisms mediating increased soil C storage under elevated atmospheric N deposition [J]. Appl Environ Microbiol, 2013, 79(4): 1191 − 1199.
[49]	LINDA T A, FREY S D, STHULTZ C M, et al. Changes in litter quality caused by simulated nitrogen deposition reinforce the N-induced suppression of litter decay [J]. Ecosphere, 2015, 6(10): 205. doi: 10.1890/ES15-00262.1.
[50]	PESCHEL A R, ZAK D R, CLINE L C, et al. Elk, sagebrush, and saprotrophs: indirect top-down control on microbial community composition and function [J]. Ecology, 2015, 96(9): 2383 − 2393.
[51]	KUZYAKOV Y, FRIEDEL J K, STAHR K. Review of mechanisms and quantification of priming effects [J]. Soil Biol Biochem, 2000, 32(11): 1485 − 1498.
[52]	CHENG Shulan, FANG Huajun, XU Meng, et al. Regulation of plant-soil-microbe interactions to soil organic carbon in natural ecosystems under elevated nitrogen deposition: a review [J]. Acta Ecol Sin, 2018, 38(23): 8285 − 8295.
[53]	TAO Baoxian, LIU Chenyang, ZHANG Baohua, et al. Effects of inorganic and organic nitrogen additions on CO₂ emissions in the coastal wetlands of the Yellow River Delta, China [J]. Atmos Environ, 2018, 185: 159 − 167.
[54]	HASSELQUIST N J, METCALFE D B, HÖGBERG P. Contrasting effects of low and high nitrogen additions on soil CO₂ flux components and ectomycorrhizal fungal sporocarp production in a boreal forest [J]. Global Change Biol, 2012, 18(12): 3596 − 3605.
[55]	XU Yehong, FAN Jianling, DING Weixin, et al. Characterization of organic carbon in decomposing litter exposed to nitrogen and sulfur additions: links to microbial community composition and activity [J]. Geoderma, 2017, 286: 116 − 124.
[56]	JIAN Siyang, LI Jianwei, CHEN Ji, et al. Soil extracellular enzyme activities, soil carbon and nitrogen storage under nitrogen fertilization: a meta-analysis [J]. Soil Biol Biochem, 2016, 101: 32 − 43.
[57]	YUAN Xia, QIN Wenkuan, XU Hao, et al. Sensitivity of soil carbon dynamics to nitrogen and phosphorus enrichment in an alpine meadow [J]. Soil Biol Biochem, 2020, 150: 107984. doi: 10.1016/j.soilbio.2020.107984.
[58]	BAUER J E, CAI Weijun, RAYMOND P A, et al. The changing carbon cycle of the coastal ocean [J]. Nature, 2013, 504(7478): 61 − 70.
[59]	BIANCHI T S. The role of terrestrially derived organic carbon in the coastal ocean: a changing paradigm and the priming effect [J]. Proc Natl Acad Sci, 2011, 108(49): 19473. doi: 10.1073/pnas.1017982108.
[60]	MAJIDZADEH H, UZUN H, RUECKER A, et al. Extreme flooding mobilized dissolved organic matter from coastal forested wetlands [J]. Biogeochemistry, 2017, 136(3): 293 − 309.
[61]	HERRMANN M, NAJJAR R G, KEMP W M, et al. Net ecosystem production and organic carbon balance of U. S. East Coast estuaries: a synthesis approach [J]. Global Biogeochem Cycles, 2015, 29(1): 96 − 111.
[62]	NEUBAUER S C, ANDERSON I C. Transport of dissolved inorganic carbon from a tidal freshwater marsh to the York River estuary [J]. Limnol Oceanogr, 2003, 48(1): 299 − 307.
[63]	WANG Z A, KROEGER K D, GANJU N K, et al. Intertidal salt marshes as an important source of inorganic carbon to the coastal ocean [J]. Limnol Oceanogr, 2016, 61(5): 1916 − 1931.
[64]	CZAPLA K M, ANDERSON I C, CURRIN C A. Net ecosystem carbon balance in a north Carolina, USA, salt marsh [J]. J Geophys Res Biogeosci, 2020, 125(10): e2019JG005509. doi: 10.1029/2019JG005509.
[65]	MORRIS J T, SHAFFER G P, NYMAN J A. Brinson review: perspectives on the influence of nutrients on the sustainability of coastal wetlands [J]. Wetlands, 2013, 33(6): 975 − 988.
[66]	DAVIS J, CURRIN C, MORRIS J T. Impacts of fertilization and tidal inundation on elevation change in microtidal, low relief salt marshes [J]. Estuaries Coasts, 2017, 40(6): 1677 − 1687.
[67]	GRAHAM S A, MENDELSSOHN I A. Coastal wetland stability maintained through counterbalancing accretionary responses to chronic nutrient enrichment [J]. Ecology, 2014, 95(12): 3271 − 3283.
[68]	MENÉNDEZ M C, DELGADO A L, BERASATEGUI A A, et al. Seasonal and tidal dynamics of water temperature, salinity, chlorophyll-a, suspended particulate matter, particulate organic matter, and zooplankton abundance in a shallow, mixed estuary (Bahía Blanca, Argentina) [J]. J Coastal Res, 2015, 32(5): 1051 − 1061.
[69]	BLOESCH J. Mechanisms, measurement and importance of sediment resuspension in lakes [J]. Marine Freshwater Res, 1995, 46(1): 295 − 304.
[70]	ZHANG Yafei, LIANG Jie, ZENG Guangming, et al. How climate change and eutrophication interact with microplastic pollution and sediment resuspension in shallow lakes: a review [J]. Sci Total Environ, 2020, 705: 135979. doi: 10.1016/j.scitotenv.2019.135979.
[71]	LI Changsheng. Biogeochemical concepts and methodologies: development of the DNDC model [J]. Quaternary Sci, 2001, 21(2): 89 − 99.
[72]	XU Wen, LUO X S, PAN Yuepeng, et al. Quantifying atmospheric nitrogen deposition through a nationwide monitoring network across China [J]. Atmos Chem Phys Discuss, 2015, 15: 18365 − 18405.
[73]	ZHANG Zhao, XIN Xiaoping. Research progress of biogeochemistry model DNDC in carbon dynamic modeling [J]. Acta Agrestia Sin, 2017, 25(3): 445 − 452.
[74]	CHENG S J, HESS P G, WIEDER W R, et al. Decadal fates and impacts of nitrogen additions on temperate forest carbon storage: a data-model comparison [J]. Biogeosciences, 2019, 16(13): 2771 − 2793.
[75]	WANG Yingping, LAW R M, PAK B. A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere [J]. Biogeosciences, 2010, 7(7): 2261 − 2282.
[76]	ZAEHLE S, FRIEND A D, FRIEDLINGSTEIN P, et al. Carbon and nitrogen cycle dynamics in the O-CN land surface model: 2. Role of the nitrogen cycle in the historical terrestrial carbon balance [J]. Global Biogeochem Cycles, 2010, 24: GB1006. doi: 10.1029/2009GB003522.
[77]	YUAN Wenping, CAI Wenwen, LIU Dan, et al. Satellite-based vegetation production models of terrestrial ecosystem: an overview [J]. Adv Earth Sci, 2014, 29(5): 541 − 550.
[78]	MONTEITH J L. Solar radiation and productivity in tropical ecosystems [J]. J Appl Ecol, 1972, 9: 747 − 766.
[79]	POTTER C. The carbon budget of California [J]. Environ Sci Policy, 2010, 13(5): 373 − 383.
[80]	VETTER M, CHURKINA G, JUNG M, et al. Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly in Europe using seven models [J]. Biogeosci Discuss, 2008, 4: 1201 − 1240.
[81]	BALDOCCHI D. Measuring fluxes of trace gases and energy between ecosystems and the atmosphere-the state and future of the eddy covariance method [J]. Global Change Biol, 2014, 20(12): 3600 − 3609.
[82]	WANG Xiaoguo, ZHU Bo, LI Changsheng, et al. Dissecting soil CO₂ fluxes from a subtropical forest in China by integrating field measurements with a modeling approach [J]. Geoderma, 2011, 161(1/2): 88 − 94.
[83]	BOHN T J, PODEST E, SCHROEDER R, et al. Modeling the large-scale effects of surface moisture heterogeneity on wetland carbon fluxes in the West Siberian Lowland [J]. Biogeosciences, 2013, 10(10): 6559 − 6576.
[84]	FRIEDLINGSTEIN P, ANDREW R M, ROGELJ J, et al. Persistent growth of CO₂ emissions and implications for reaching climate targets [J]. Nat Geosci, 2014, 7(10): 709 − 715.
[85]	RUNNING S W, COUGHLAN J C. A general model of forest ecosystem processes for regional applications Ⅰ. hydrologic balance, canopy gas exchange and primary production processes [J]. Ecol Modelling, 1988, 42(2): 125 − 154.
[86]	BOND-LAMBERTY B, GOWER S T, AHL D E. Improved simulation of poorly drained forests using Biome-BGC [J]. Tree Physiol, 2007, 27(5): 703 − 715.
[87]	ZENG Huiqing, LIU Qijing, FENG Zongwei, et al. GPP and NPP study of Pinus elliottii forest in red soil hilly region based on BIOME-BGC model [J]. Acta Ecol Sin, 2008, 28(11): 5314 − 5321.
[88]	MA Zeqing, LIU Qijing, WANG Huimin, et al. Productivity observation and simulation of pine forest (Pinus elliottii) in subtropical constructed wetland [J]. Sci Sin Terrae, 2008, 38(8): 1005 − 1015.
[89]	LUO Zhongkui, SUN Osbert Jianxin, WANG Enli, et al. Modeling productivity in mangrove forests as impacted by effective soil water availability and its sensitivity to climate change using Biome-BGC [J]. Ecosystems, 2010, 13(7): 949 − 965.
[90]	PARTON W J, STEWART J W B, COLE C V. Dynamics of C, N, P and S in grassland soils: a model [J]. Biogeochemistry, 1988, 5(1): 109 − 131.
[91]	CHIMNER R A, COOPER D J, PARTON W J. Modeling carbon accumulation in Rocky Mountain fens [J]. Wetlands, 2002, 22(1): 100 − 110.
[92]	LI Changsheng, FROLKING S, FROLKING T A. A model of nitrous oxide evolution from soil driven by rainfall events: 1. model structure and sensitivity [J]. J Geophys Res Atmos, 1992, 97(D9): 9759 − 9776.
[93]	ZHANG Yu, LI Changsheng, TRETTIN C C, et al. An integrated model of soil, hydrology, and vegetation for carbon dynamics in wetland ecosystems [J]. Global Biogeochem Cycles, 2002, 16(4): 1061. doi: 10.1029/2001GB001838.
[94]	TIAN Zhan. NIU Yilong, SUN Laixiang, et al. China’s rice field greenhouse gas emission under climate change based on DNDC model simulation [J]. Chin J Appl Ecol, 2015, 26(3): 793 − 799.
[95]	GENG Xuemeng, YANG Meng, GRACE J, et al. Simulating methane emissions from the littoral zone of a reservoir by wetland DNDC Model [J]. J Resour Ecol, 2016, 7(4): 281 − 290.
[96]	CUI Jianbo, LI Changsheng, TRETTIN C. Analyzing the ecosystem carbon and hydrologic characteristics of forested wetland using a biogeochemical process model [J]. Global Change Biol, 2005, 11(2): 278 − 289.
[97]	DENG Jia, LI Changsheng, FROLKING S. Modeling impacts of changes in temperature and water table on C gas fluxes in an Alaskan peatland [J]. J Geophys Res Biogeosci, 2015, 120(7): 1279 − 1295.
[98]	JI Jinjun. A climate-vegetation interaction model: simulating physical and biological processes at the surface [J]. J Biogeogr, 1995, 22(2/3): 445 − 451.
[99]	JI Jinjun, HUANG Mei, LI Kerang. Prediction of carbon exchanges between China terrestrial ecosystem and atmosphere in 21st century [J]. Sci China Ser D Earth Sci, 2008, 51(6): 885 − 898.
[100]	SHI Xueli, ZHANG Fang, ZHOU Wenyan, et al. Impacts of CG-LTDR land cover dataset updates on the ground temperature simulation with BCCAVIM 1.0 [J]. J Geo-Inf Sci, 2015, 17(11): 1294 − 1303.
[101]	WENG Ensheng, LUO Yiqi. Soil hydrological properties regulate grassland ecosystem responses to multifactor global change: a modeling analysis [J]. J Geophys Res Biogeosci, 2008, 113: G03003. doi: 10.1029/2007JG000539.
[102]	LUO Yiqi, WHITE L W, CANADELL J G, et al. Sustainability of terrestrial carbon sequestration: a case study in Duke Forest with inversion approach [J]. Global Biogeochem Cycles, 2003, 17(1): 1021. doi: 10.1029/2002GB001923.
[103]	WHITE L W, LUO Yiqi. Modeling and inversion of net ecological exchange data using an Ito stochastic differential equation approach [J]. Appl Math Comput, 2008, 196(2): 686 − 704.
[104]	HUANG Yuanyuan, JIANG Jiang, MA Shuang, et al. Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: constrained forecast with data assimilation [J]. J Geophys Res Biogeosci, 2017, 122(8): 2046 − 2063.
[105]	WU Y, BLODAU C. PEATBOG: a biogeochemical model for analyzing coupled carbon and nitrogen dynamics in northern peatlands [J]. Geosci Model Dev, 2013, 6(4): 1173 − 1207.
[106]	WU Y, BLODAU C, MOORE T R, et al. Effects of experimental nitrogen deposition on peatland carbon pools and fluxes: a modelling analysis [J]. Biogeosciences, 2015, 12(1): 79 − 101.
[107]	HAN Guangxuan, CHU Xiaojing, XING Qinghui, et al. Effects of episodic flooding on the net ecosystem CO₂ exchange of a supratidal wetland in the Yellow River Delta [J]. J Geophys Res Biogeosci, 2015, 120(8): 1506 − 1520.
[108]	KANG Xiaoming, LI Yong, WANG Jinzhi, et al. Precipitation and temperature regulate the carbon allocation process in alpine wetlands: quantitative simulation [J]. J Soils Sediments, 2020, 20(9): 3300 − 3315.
[109]	CHENG S J, HESS P G, WIEDER W R, et al. Decadal impacts of nitrogen additions on temperate forest carbon sinks: a data-model comparison [J]. Biogeosci Discuss, 2018, doi: 10.5194/bg-2018-505.
[110]	JANSSENS I A, DIELEMAN W, LUYSSAERT S, et al. Reduction of forest soil respiration in response to nitrogen deposition [J]. Nat Geosci, 2010, 3(5): 315 − 322.
[111]	FREY S D, OLLINGER S, NADELHOFFER K, et al. Chronic nitrogen additions suppress decomposition and sequester soil carbon in temperate forests [J]. Biogeochemistry, 2014, 121(2): 305 − 316.
[112]	SULMAN B N, BRZOSTEK E R, MEDICI C, et al. Feedbacks between plant N demand and rhizosphere priming depend on type of mycorrhizal association [J]. Ecol Lett, 2017, 20(8): 1043 − 1053.
[113]	WIEDER W R, BONAN G B, ALLISON S D. Global soil carbon projections are improved by modelling microbial processes [J]. Nat Clim Change, 2013, 3(10): 909 − 912.
[114]	ZHU Qing, RILEY W J, TANG Jinyun. A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions [J]. Ecol Appl, 2017, 27(3): 875 − 886.
[115]	BINGHAM A H, COTRUFO M F. Organic nitrogen storage in mineral soil: implications for policy and management [J]. Sci Total Environ, 2016, 551/552: 116 − 126.
[116]	KEENAN T F, BAKER I, BARR A, et al. Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO₂ exchange [J]. Global Change Biol, 2012, 18(6): 1971 − 1987.
[117]	de KAUWE M G, MEDLYN B E, ZAEHLE S, et al. Forest water use and water use efficiency at elevated CO₂: a model-data intercomparison at two contrasting temperate forest FACE sites [J]. Global Change Biol, 2013, 19(6): 1759 − 1779.
[118]	LUO Yiqi, RANDERSON J T, ABRAMOWITZ G, et al. A framework for benchmarking land models [J]. Biogeosciences, 2012, 9(10): 3857 − 3874.
[119]	RANDERSON J T, HOFFMAN F M, THORNTON P E, et al. Systematic assessment of terrestrial biogeochemistry in coupled climate-carbon models [J]. Global Change Biol, 2009, 15(10): 2462 − 2484.
[120]	SMITH M J, PURVES D W, VANDERWEL M C, et al. The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties [J]. Biogeosciences, 2013, 10(1): 583 − 606.
[121]	HARARUK O, XIA Jianyang, LUO Yiqi. Evaluation and improvement of a global land model against soil carbon data using a Bayesian Markov chain Monte Carlo method [J]. J Geophys Res Biogeosci, 2014, 119(3): 403 − 417.
[122]	WARD N D, MEGONIGAL J P, BOND-LAMBERTY B, et al. Representing the function and sensitivity of coastal interfaces in Earth system models [J]. Nat Commun, 2020, 11(1): 2458. doi: 10.1038/s41467-020-16236-2.
[123]	PRENTICE I C, LIANG X, MEDLYN B E, et al. Reliable, robust and realistic: the three R’s of next-generation land-surface modelling [J]. Atmos Chem Phys, 2015, 15(10): 5987 − 6005.
[124]	SULMAN B N, MOORE J A M, ABRAMOFF R, et al. Multiple models and experiments underscore large uncertainty in soil carbon dynamics [J]. Biogeochemistry, 2018, 141(2): 109 − 123.
[125]	TANG Guoping, ZHENG Jianqiu, XU Xiaofeng, et al. Biogeochemical modeling of CO₂ and CH₄ production in anoxic arctic soil microcosms [J]. Biogeosciences, 2016, 13(17): 5021 − 5041.
[126]	ADAM L J, MOZDZER T J, SHEPARD K A, et al. Tidal marsh plant responses to elevated CO₂, nitrogen fertilization, and sea level rise [J]. Global Change Biol, 2013, 19(5): 1495 − 1503.

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沈阳化工大学材料科学与工程学院沈阳 110142

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滨海湿地是土壤碳(C)的大型储存库，能够以高于陆地森林生态系统10~100倍的速率持续固定大气二氧化碳(CO₂)^[1]。尽管全球滨海湿地仅占地球海洋表面的2%，但碳储量却相当可观。据估计，全球滨海湿地每年碳储量约116 Tg，占海洋储碳总量的50%以上^[2]，这些碳储量和碳通量统称为滨海湿地“蓝碳”。滨海湿地的碳汇速率主要取决于其垂直沉积物的代谢速率和土壤碳汇密度^[3]，在海平面上升的影响下，这些蓝碳生态系统的土壤不会达到碳饱和^[1]，是显著区别于内陆生态系统的重要特征。只要土壤的增加量继续与海平面上升保持同步，沉积物的吸收速率就可以保持在一定水平，因此滨海湿地碳汇的寿命相比内陆碳汇的潜力更大，长时期内减缓气候变化能力也更强。目前，滨海湿地作为温室气体排放抵消工程的目标，受到了广泛的关注。然而，定量评估预测全球变化影响滨海湿地生态系统碳汇功能的研究较少，还需进一步分析滨海湿地对全球气候变化与人类干扰的响应机制，为滨海湿地的保护、管理及利用提供参考方向，以达到增加未来碳储存的目的^{[1, 4]}。近代以来，人类活动向生态系统输入了大量的活性氮^[5]，大气氮富集量已超过工业革命前的2倍^[6]。过剩的含氮化合物经由淋洗、径流以及挥发等途径进入江河湖海和大气中，使沿海近岸水体富营养化^[7]。在潮汐作用下，富氮水体经由海水入侵进入滨海湿地，对滨海湿地生态系统的碳循环过程产生重要影响^[8-10]。已有多项研究证实：氮输入对陆地碳循环过程的影响结果并不一致，由生态系统类型、覆被种类和其他多种环境因素共同决定^[11-13]。如何在不同的环境背景下，评估和预测生态系统碳循环过程对氮输入的响应，目前未有定论。基于滨海湿地碳汇功能的重大生态价值，本研究系统阐述了氮输入对滨海湿地生态系统碳循环过程的影响，总结了几种常见的碳循环模型在湿地生态系统应用中的主要研究进展。一方面梳理过去相关研究，提高对氮输入下滨海湿地碳动态变化及碳循环模型领域发展态势的认识；另一方面总结碳循环模型在滨海湿地的应用和不足，为模型改进提供参考方向，有利于更精确地预测滨海湿地碳汇功能的未来发展。

2. 氮输入对滨海湿地碳循环过程的影响机制

2.1. 氮输入影响植被碳库的机制

氮输入通过影响植被-土壤产生和消耗CO₂的过程来改变其排放通量，所得结果或促进、或抑制或不显著^{[12, 30]}。研究发现：在较大浓度范围内，氮输入均会促进植物生长^[31]，提高地上净初级生产力，显著增加植物组织的碳氮含量，从而增加凋落物的产量，并有助于土壤有机质(SOM)的积累。但同时氮添加会增强环境过滤作用，降低植物的氮素利用效率^[32]，减少微生物量碳和植物组织、土壤和微生物量的碳氮比，甚至威胁陆地植物的群落多样性^[33]。在氮限制的滨海湿地生态系统中，湿地植物对氮输入的响应与其所积累的氮比例有关，然而该比例同时具有确定性和随机性过程^[34]，因此得到的结果并不唯一。此外，这些反应还取决于氮的输入速率，以及不同氮化合物在生态系统中的持续时间^[35]。

在植物生理方面，氮输入会改变植物的物候规律，叶片化学计量中的碳氮比是控制该因素的主要表现形式，可以为评估地上和地下系统如何响应全球变化提供有用的策略^[36]。植物叶片氮含量与植物的光合作用紧密相关，氮输入会增加叶片氮含量^[37]，使与其相关的叶绿体和光合二氧化碳固定酶大量合成，同时加快光反应和暗反应速率，最终使植物提前进入成熟期和衰老期^[38]。氮输入还会提高植物组织中的氮浓度，使植物呼吸作用显著增强^[39]，但对植物净光合速率与植物光合速率的影响基本一致，随植被类型的变化可能得出不同的结果^[40-41]。不同的氮供应形态和浓度也会对植物光合能力产生不同的影响，如添加铵态氮比硝态氮能够更多地刺激植物光合作用^[42]。然而植物自身存在适应环境的机制，可以通过增加蒸腾作用来维持营养平衡，从而适应长期的氮沉降^[43]。因此，在探究氮输入对碳循环影响机制的过程中，还应考虑植物、微生物等对维持环境稳态的适应性所表现的体征。

2.2. 氮输入影响土壤碳库的机制

输入生态系统的活性氮是影响滨海湿地土壤有机碳(SOC)损失的关键因素^[44-45]。受氮输入水平、氮形态、初始土壤性质和其他环境参数差异的影响，氮输入可能导致SOC损失前后不一致^[46-47]。氮输入对土壤有机质的影响分为直接和间接2个作用，土壤微生物在其中充当重要的媒介。直接作用表现在改变土壤氮素的可利用性，引起土壤微生物群落的变化，最终影响有机质的产量^[48]。间接作用则是先提高植被的生产力，使得凋落物性质发生变化^[49]，基质的可利用性发生改变^[50]，最终通过影响土壤微生物间接影响有机质的累积。根据养分挖掘理论与化学计量学理论，氮有效性对SOC存在“激发效应”(priming effect)^[51]，即增加凋落物的输入会促进土壤有机质的分解，当养分氮处于低有效性时，由养分挖掘理论主导，此时微生物生物量处于k生存策略的正激发，生物能量来源于SOM的分解；当养分氮处于高有效性时，由化学计量学理论主导，微生物生物量处于r生存策略的正激发，主要利用新添加的底物进行生长繁殖^[52]。

滨海湿地属于氮限制的生态系统，多项研究证明：氮输入会加速滨海湿地SOC分解并刺激CO₂排放^{[9, 53]}，然而该结果受到多个因素的共同作用，例如生态系统类型、覆被种类、水位高度、温度等。低氮输入水平会刺激SOC分解，该促进作用可能与土壤养分、植被、土壤碳储量和土壤环境的氮相关变化有关^[13]。而在高氮输入水平下，SOC分解则会受到抑制^[54-55]，使土壤碳的有效性降低^[56]。长期和短期的氮输入研究结果也存在差异，两者均会促进土壤养分的整体增加，从而潜在地加速SOM分解。但与短期相比，长期氮输入的生态系统中，总SOC或矿物有机碳(MAOC)的含量可能不会发生变化，而是改变SOC的化学成分，如减少烃基氧等不稳定官能团的百分比，这可能是由于不稳定有机质的分解以及根沉积物输入增强引起的^[57]。

2.3. 氮输入影响水体碳化合物的机制

滨海生态系统内部和系统间的碳循环是全球碳循环和碳收支的主要组成部分^[58]。据统计，每年有超过19 Pg的悬浮物在滨海系统间进行交换，其中包括0.3 Pg DOC和0.2 Pg POC^[59]。大多数陆源性POC随地表径流散布到海洋中，而存在于近地表土壤水及地下水中的可溶性有机质(DOM)，则通过海洋与陆地间的水文连通进行交换，这种交换也称为滨海湿地的碳横向输出。滨海湿地在碳的横向输出过程中起关键作用，在强降雨和冲刷作用下，滨海湿地流失DOM的量达到森林地下有机碳年产量的1/3^[60]，并为河口提供约35%的总有机碳(TOC)^[61]。横向的碳通量是湿地净生态系统碳平衡收支(NECB)中的重要术语^[62-63]，也是NECB中属于湿地碳损失过程中具有较高重要性和不确定性的组成部分。碳横向输出包括地下水流通和潮汐交换2种途径，虽然地下水DIC浓度高于潮汐水体，但潮汐水体交换速率大于地下水排水速率，使得潮汐作用下碳输出速率大于地下水碳输出速率。最终地下水横向DIC和DOC通量均比潮汐横向通量低1个数量级，因此对整体横向碳输出而言，潮汐的输出占主导地位^[64]。

当湿地被潮汐淹没时，呼吸作用产生的大部分CO₂以DIC的形式溶解，与总初级生产力(GPP)产生或从植物生物量中浸出的DOC同时进行横向输出。当含氮化合物增加了地上生物量(AGB)，并且潮汐水中含有丰富的沉积物时，氮输入会增加该地区外源碳的积累^[65]和海拔高度^[66]。在氮输入的内陆湿地中，由于垂直排放而损失的碳和横向碳通量能够由外来碳沉积物的增加所抵消^[67]，因此氮输入对系统内部NECB的影响很小。但是，在滨海湿地的边缘，由于垂直排放和横向输出，碳输入量不足以抵消碳的损失，氮输入将大幅增加滨海系统边缘碳的净损失。因此，如果仅在湿地内部进行相同的研究，就将大大低估氮输入对湿地碳积累的负面影响。氮输入通过增加代谢率来增加横向DIC和DOC的出口，这表明增加对滨海湿地生态系统的氮输入将增加碳向相邻水域的横向输出。同时，在水生环境中除了水平运输外，表层沉积物的垂直再悬浮现象也普遍存在^[68]。当电流和波浪引起的底部切应力超过底部沉积物的侵蚀阈值时，就会发生沉积物的潜在迁移^[69]。在重悬过程中，重悬的物质包括有机碳及其通过细菌和原生动物产生的降解产物，可能加重水体富营养化^[70]。

3. 氮输入影响生态系统碳循环的建模方法与分类

陆地生态系统模型的建立主要基于量(abundance)、群(coupling)、流(flow)、场(field)的概念，分别表示数量、有机整体、交换流动及各种作用力^[71]。在模型中，陆地碳循环通常被概念化为一组库或池，如叶、木、根和土壤，通过生态过程以不同的形式如气态、溶解态、固态进行碳的储存和交换^[72]。早期的碳循环模型通常以不连续的时间段(如天或年)对系统状态进行更新，并且仅更新单独某一池的碳库，而其他模型则基于具有可变时间间隔的一般或偏微分方程组来实现更新规律。生物地球化学模型对物质循环和元素运动轨迹的追踪表现出较高的吻合度和实用性，真正实现了对整个生态系统过程的模拟。为了实现对生态系统的所有过程参量更全面的模拟，生物地球化学模型一般以小时、日或月为步长，对SOC、植物生物量、土壤含水量、CO₂浓度等状态变量进行积分，得到长时间尺度的模拟结果。同时该模型还可结合大面积高频采样数据，实现对生态系统空间上的多指标模拟，从而增大模拟的尺度范围，通过积分最终得到区域尺度上的生态系统状态的估计^[73]。

为了准确捕获氮对地球系统模型中陆地碳汇的影响，需要评估模型对氮限制和生态系统氮输入的响应。一方面可以通过评估固定量的氮输入对植物-土壤碳库中碳储量变化的影响，另一方面也可追踪氮输入生态系统后，通过氮损失途径减少的氮量及轨迹^[74]。目前，氮循环已被整合到多个全球陆地模型中，各模型按照不同的方式表示碳氮耦合过程^[75-76]。例如，当土壤中氮的有效性无法满足植物生长所需氮含量时，这些模型均可模拟氮限制下植物的生长过程，但植物生长对氮需求的表现会有所不同^[75-76]。同时，按照模型的建立方法和建立过程，碳循环模型可分为经验、参数和过程模型3个大类。经验模型是严格由数据确定，基于经验推导出的统计关系，包括气候和遥感相关模型^[77]。它的局限性在于不涉及潜在机制的知识，仅在与其相关的数据范围内最准确。参数模型利用光合有效辐射及与其相关的调控因素，实现植被生产力的估算，因此也称为光能利用效率模型^[78]，目前与遥感结合的应用较为广泛^[79]。过程模型则是通过生态生理过程以机械方式模拟生态系统的功能，通常需要考虑各个过程的机制以及多个因素的共同作用，能够运用于长时间、大尺度的模拟^[80]，包括简单过程模型、过程耦合模型和遥感-过程耦合模型。此外，模型中是否含有随机成分，例如某些参数的值随时间或个体是否变化，决定了相同初始条件和时间段对应的结果是否相同，因此也分为确定性模型和随机性模型^[15]。

4. 氮输入影响滨海湿地碳循环的模型应用和局限

4.1. 氮输入影响滨海湿地碳循环的模型应用

碳循环有非常丰富的建模历史，目前已经提出了各种具有不同复杂程度和侧重方向的模型^[81-82]。利用模型了解和确定生态系统的主要特征和机制，并对人类活动改变生态系统中碳循环的方式(如化石燃料燃烧、施肥和开发等)进行评估，再借助于真实的观测数据验证模型的拟合度^[83]。代表大气与陆地生物圈之间碳交换的模型包括多个过程和机制，其复杂性在过去几十年中不断增加，主要表现在模型过程中的细节被不断增加和完善。由于各模型所代表的过程、应用范围以及运算方式各有不同，因此不同模型之间难以比较，只能以模型输出数值的准确度来评估模型的性能^[82]，而不能直接在模型中实现概念和数学的评价及比较。

滨海湿地碳循环过程主要由生物和非生物因素共同驱动，常见为基于点位的测量。而在较大空间尺度的研究中，则通常使用替代技术(如涡度协方差技术等)对长时间、大尺度的数据进行补充^[84]。然而全球数据存在高度异质性，包括采样时间、试验持续时间和植被类型变化等，这些都进一步增加了滨海湿地碳交换估算的不确定性。由于滨海湿地生态系统的复杂性，经验和参数模型相比过程模型所受限制更多，因为过程模型还可通过增加相应模块建立子模型，达到对特定生态系统进行拟合的目的。同时，为了研究氮输入对滨海湿地生态系统的影响，碳循环模型中除了包含各碳库之间的相互交换，还应该包括水文、碳氮耦合过程的模块。目前在研究过程模型的领域中，满足以上要求且应用较为广泛的有DNDC(过程模型)、PEATBOG(过程模型)、TECO(过程模型)、Biome-BGC(过程模型)、AVIM(过程耦合模型)、TEM(遥感-过程耦合模型)和CENTURY(过程模型)等几种模型，或可应用于氮输入影响滨海湿地生态系统碳循环过程的模拟(表1)。

模型名称	模型类型	模型概述	建立年份	时间步长	适用范围	应用案例
Biome-BGC^[85]	过程模型(生物地球化学模型)	模拟生态系统中植被、凋落物、土壤碳、氮、水的储量和通量，模拟木本植物、C₃/C₄草本植物的碳、氮、水的循环过程与交互影响	1988	1 d	常绿/落叶、针叶/阔叶林、C₃/C₄草本植物和灌木林，点位、区域和全球尺度	增加了地下水、苔藓植被、土壤营养物质分解、土壤水分压力指数等作用机理的描述。应用于加拿大森林湿地、红壤丘陵区湿地、千烟洲人工湿地、中国南海湿地红树林等湿地生态系统净初级生产力(NPP)、生物量和土壤碳积累的模拟研究^[86-89]

CENTURY^[90]	过程模型(生物地球化学模型)	基于土壤的结构功能，模拟碳、氮和磷的生物地球化学循环过程，同时结合气温、降水量等气候驱动因子，模拟生态系统生产力	1988	30 d	森林、草原生态系统	通过调整厌氧参数，用于泥炭湿地碳动态模拟^[91]

DNDC^[92]	过程模型(生物地球化学模型)	增加了苔藓及草本植物的生长参数，开发了地下水位动态变化、厌氧条件下土壤生物地球化学过程等算法^[93]	1992	1 d	森林、农田、草地、湿地生态系统，点位和区域尺度	最初建立用于描述农业生态系统，现可应用于水稻田、湿地、泥炭地等生态系统的碳氮循环研究^[94-97]

AVIM^[98]	过程耦合模型(大气-植被耦合模型)	陆面物理-植被生理生态的有机耦合，包含植被-土壤-大气间热量和水分的交换以及植物光合-呼吸等CO₂的交换，实现了大气和包括根圈在内的植物圈之间的动态相互作用	1995	1 h	森林、草地、农田、冰川、湿地、湖泊等生态系统	添加土壤碳氮动态模块^[99]，再与其他模块相结合，已应用于研究湿地覆被类型对模拟结果的影响^[100]

TECO^[101]	过程模型(生物地球化学模型)	具有与目前大多数生物地球化学模型相似的碳池结构和参数。经过改进和完善，可用于模拟陆地生态系统中的碳、氮和水文循环	2008	冠层光合作用和土壤水分动态子模型：1 h；植物生长和土壤碳转移子模型：1 d	陆地生态系统	调节植物和生态系统对CO₂升高、变暖和降水变化的交互响应的关键过程，已应用于Duke森林应对CO₂升高的固碳过程的若干研究中^[102-103]以及SPRUCE泥炭地的碳动力和土壤动力学的研究中^[104]

PEATBOG^[105]	过程模型(生物地球化学模型)	强调了土壤固、水、气相与植被之间的物质流动，土壤组分的高空间分辨率，对碳、氮通量的化学计量控制，以及对植被和土壤中碳、氮反应活性的持续概念化	2013	1 d	泥炭地碳氮耦合循环的模拟	已应用于研究长期施氮对泥炭沼泽碳循环的影响分析，并模拟预测了未来80 a间各碳组分的动态趋势，以确定氮肥的潜在影响和影响模型行为的主要因素^[106]

Table 1. Development and application in wetland of carbon cycle models

4.2. 碳循环模型应用的局限性

滨海湿地显著区别于其他湿地的特征是周期性的潮汐作用，整个湿地在淹水-暴露中形成干湿交替的生境，土壤环境改变，最终影响湿地碳交换过程^{[19, 107]}。同时，潮汐作用也使得富营养化的近岸水体将大量活性氮输入系统中。与单个影响因素的作用相比，2个过程的共同作用可能对湿地碳变化的影响结果有所不同。然而，已有碳循环模型对周期性潮汐水文过程的关注不足。碳循环机制取决于当前研究对碳循环过程认识的程度，一些机制性的问题依然利用经验模型解决，例如微生物分解碳速率、植物-土壤碳分配等。且碳循环模型简化了各碳库之间的交互关系，模拟相对静态的过程时效果较好，但无法解释和表达动态的过程，例如不同氮素种类或浓度对优势物种、微生物的选择等。碳动态预测要求考虑土壤、水文和植被等之间的相互作用，却很少有同时存在这些作用的且适用于湿地生态系统的综合型碳模型。虽然一些模型(如DNDC和BIOME-BGC模型)在改进过程中增加了描述湿地生态系统机理的模块^{[86, 108]}，然而，湿地生态系统碳循环过程较为复杂，兼有草本和木本植物，包括沼生植物、湿生植物和水生植物等，化学组分也存在较大差异，形成多种不同类型的湿地，改进后的模型适应性仍有待论证。例如，改良的Biome-BGC模型仍然无法模拟长期遭受洪水侵袭的真正湿地，因为它不会追踪有机土壤形成、地下水位变化、土壤氧化还原电位或厌氧过程^[86]。

评估氮输入对碳储量的影响，可以通过测量固定量的氮输入下植物-土壤碳库的变化，还可追踪氮进入系统后通过氮损失途径减少的氮量及轨迹^[109]。因此，氮与碳循环之间的相互作用还需进一步完善。例如在氮输入过程中，土壤碳储量的增加并不等同于植物凋落物产量的增加，还与分解速率的降低相关^[110-111]。这种增加可以通过植物-土壤-微生物反馈的变化来解释，即激发效应是增加还是减少生态系统碳储量，取决于加速分解造成的土壤碳损失，以及与氮矿化增加促进的植被碳吸收之间的平衡^[112]。此外，先前的建模研究证明微生物能够改善土壤碳储量预测^[113]，精确地估算植物和微生物生物量及其对无机氮的酶亲和力，可以更好地捕获生态系统中氮的轨迹^[114]。部分模型具有隐含的微生物过程，这些过程构成了氮进入土壤有机质的主要途径^[115]，但模型没有明确微生物对氮的吸收和转化过程，在植物、土壤和氮素损失途径中的氮分配方面尚有不足。

目前，在提高模型预测能力方面研究依然进展缓慢，对初始条件差异的极端敏感性，以及对系统状态描述的不完备，从根本上限制了未来预测的精确度。建模领域已经采用了多种不同的方法来改善陆地碳模型，但仍未显著减少模型预测之间的差异^[109]。常见的方法是将更多的已知过程纳入影响碳循环的过程，以使模型尽可能逼真。但是，包含的过程越多，模型就越复杂且难以处理。其他方法如模型比较，虽然可以有效揭示模型预测之间差异的程度^[116-117]，但通常对于其起源只提供了有限的解释。但迄今为止，基准分析能提供针对标准数据集的模型性能评估^[118]，仅限于短时间内发生的过程(例如数天至数年)^[119]。数据同化可将简单模型或模型组件直接约束于观测值^[120]，作为1种集成多源空间数据，它能够高效利用多种数据，但不适用于系统性的复杂模型^[121]。

5. 问题与展望

滨海湿地生态系统处于陆地与海洋之间，是能量和物质加工和转化的热点，也是生产力、碳储存/分解的热点。近岸水体富营养化引起的氮输入，将改变滨海湿地植物-土壤-大气碳分配的碳循环关键过程和碳汇功能，然而这些影响存在不确定性。相比于其他的生态系统，滨海湿地植被、土壤、水体之间的交互作用更为复杂，碳交换包括垂直和横向2个过程，因此碳循环建模需要考虑更多的因素，模拟过程中仍存在以下问题，有待进一步发展和完善：①需加强对潮汐水文过程模块的开发。虽然当前尚无将湿地水文、生物地球化学和植被相结合的综合方法，但存在有用的“构建模块”，从一组选定的源模型中组合这些关键因素可能是编译此类通用模型的可行且有效的方法^[122]。②碳氮耦合机制有待进一步完善。改进植物和土壤中氮素命运的模型显示：利用植物-土壤-微生物反馈过程，能够更准确地反映土壤碳对氮输入的响应^[112]。同时，增加参数或生态过程可能会增加模型预测的不确定性，为限制这种增加的不确定性，可以使用基于过程的、更加稳健且有代表性的数据集来设计和评估新的模型表现^[123-124]。③提高模型模拟精度。较小规模的试验可在处理和响应变量评估中提供更高的精度^[125]，而较大规模的试验，诸如升温、淹水、氮输入等处理对整个生态系统规模的影响试验，在提供了更高真实性^[126]的同时，也增加了模拟精度的复杂性。理想状态是将响应归因于一个整体变化的驱动因素，但试验处理与混杂的环境驱动因素(例如土壤水分、盐度、pH和氧化还原状态)可以同时控制生态系统过程，并调节全球变化驱动因素的影响。在这些不确定性下模拟不同尺度的变化，需要加强不同环境条件下，对应系统中植被、土壤等参数的研究，实现标准化测定，减少参数本地化带来的模拟误差。可尝试将多模型过程进行耦合，提高模型模拟的准确性，形成更完善的环境反馈机制。引入多时相、多传感器的遥感数据及其产品，也是在区域乃至全球尺度进行碳循环模拟的主要方向之一。同时还可以利用数据同化的方法，尽可能减少和控制数据本身及模型模拟过程中所产生的误差，提高模型的可信度和准确性。

Reference (126)

[1]	MCLEOD E, CHMURA G L, BOUILLON S, et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO₂ [J]. Front Ecol Environ, 2011, 9(10): 552 − 560.
[2]	KIRWAN M L, GUNTENSPERGEN G R. Feedbacks between inundation, root production, and shoot growth in a rapidly submerging brackish marsh [J]. J Ecol, 2012, 100(3): 764 − 770.
[3]	KIRWAN M, TEMMERMAN S, SKEEHAN E, et al. Overestimation of marsh vulnerability to sea level rise [J]. Nat Clim Change, 2016, 6(3): 253 − 260.
[4]	KROEGER K D, CROOKS S, MOSEMAN-VALTIERRA S, et al. Restoring tides to reduce methane emissions in impounded wetlands: a new and potent Blue Carbon climate change intervention [J]. Sci Rep, 2017, 7(1): 11914. doi: 10.1038/s41598-017-12138-4.
[5]	GALLOWAY J N, TOWNSEND A R, ERISMAN J W, et al. Transformation of the nitrogen cycle: recent trends, questions, and potential solutions [J]. Science, 2008, 320(5878): 889 − 892.
[6]	CHMURA G L, ANISFELD S C, CAHOON D R, et al. Global carbon sequestration in tidal, saline wetland soils [J]. Global Biogeochem Cycles, 2003, 17(4): 1111. doi: 10.1029/2002gb001917.
[7]	LI Juanyong, HAN Guangxuan, ZHAO Mingliang, et al. Nitrogen input weakens the control of inundation frequency on soil organic carbon loss in a tidal salt marsh [J]. Estuarine Coastal Shelf Sci, 2020, 243: 106878. doi: 10.1016/j.ecss.2020.106878.
[8]	HOBBIE S E, NADELHOFFER K J, HÖGBERG P. A synthesis: the role of nutrients as constraints on carbon balances in boreal and arctic regions [J]. Plant Soil, 2002, 242(1): 163 − 170.
[9]	TAO Baoxian, SONG Changchun, GUO Yuedong. Short-term effects of nitrogen additions and increased temperature on wetland soil respiration, Sanjiang Plain, China [J]. Wetlands, 2013, 33(4): 727 − 736.
[10]	LIU Jun, WU Nana, WANG Hui, et al. Nitrogen addition affects chemical compositions of plant tissues, litter and soil organic matter [J]. Ecology, 2016, 97(7): 1796 − 1806.
[11]	SONG Bing, SUN Jian, ZHOU Qingping, et al. Initial shifts in nitrogen impact on ecosystem carbon fluxes in an alpine meadow: patterns and causes [J]. Biogeosciences, 2017, 14(17): 3947 − 3956.
[12]	WANG Jing, GAO Yingzhi, ZHANG Yunhai, et al. Asymmetry in above- and belowground productivity responses to N addition in a semi-arid temperate steppe [J]. Global Change Biol, 2019, 25(9): 2958 − 2969.
[13]	QU Wendi, HAN Guangxuan, ELLER F, et al. Nitrogen input in different chemical forms and levels stimulates soil organic carbon decomposition in a coastal wetland [J]. Catena, 2020, 194: 104672. doi: 10.1016/j.catena.2020.104672.
[14]	TRUMBORE S. Carbon respired by terrestrial ecosystems-recent progress and challenges [J]. Global Change Biol, 2006, 12(2): 141 − 153.
[15]	LUO Yiqi, KEENAN T F, SMITH M. Predictability of the terrestrial carbon cycle [J]. Global Change Biol, 2015, 21(5): 1737 − 1751.
[16]	曹磊, 宋金明, 李学刚, 等. 中国滨海盐沼湿地碳收支与碳循环过程研究进展[J]. 生态学报, 2013, 33(17): 5141 − 5152.	CAO Lei, SONG Jinming, LI Xuegang, et al. Research progresses in carbon budget and carbon cycle of the coastal salt marshes in China [J]. Acta Ecol Sin, 2013, 33(17): 5141 − 5152.
[17]	MITSCH W J, GOSSELINK J G. Wetlands[M]. 4th ed. Hoboken: John Wiley & Sons, Inc., 2007.
[18]	WANG Dongqi, CHEN Zhenlou, XU Shiyuan. Methane emission from Yangtze estuarine wetland, China [J]. J Geophys Res, 2009, 114: G02011. doi: 10.1029/2008JG000857.
[19]	韩广轩. 潮汐作用和干湿交替对盐沼湿地碳交换的影响机制研究进展[J]. 生态学报, 2017, 37(24): 8170 − 8178.	HAN Guangxuan. Effect of tidal action and drying-wetting cycles on carbon exchange in a salt marsh: progress and prospects [J]. Acta Ecol Sin, 2017, 37(24): 8170 − 8178.
[20]	仝川, 鄂焱, 廖稷, 等. 闽江河口潮汐沼泽湿地CO₂排放通量特征[J]. 环境科学学报, 2011, 31(12): 2830 − 2840.	TONG Chuan, E Yan, LIAO Ji, et al. Carbon dioxide emission from tidal marshes in the Min River Estuary [J]. Acta Sci Circumstantiae, 2011, 31(12): 2830 − 2840.
[21]	王玲玲, 孙志高, 牟晓杰, 等. 黄河口滨岸潮滩湿地CO₂、CH₄和N₂O通量特征初步研究[J]. 草业学报, 2011, 20(3): 51 − 61.	WANG Lingling, SUN Zhigao, MOU Xiaojie, et al. A preliminary study on carbon dioxide, methane and nitrous oxide fluxes from intertidal flat wetlands of the Yellow River estuary [J]. Acta Pratacult Sin, 2011, 20(3): 51 − 61.
[22]	贺文君, 韩广轩, 宋维民, 等. 潮汐作用对黄河三角洲盐沼湿地甲烷排放的影响[J]. 生态学报, 2019, 39(17): 6238 − 6246.	HE Wenjun, HAN Guangxuan, SONG Weimin, et al. Effects of tidal action on methane emissions over a salt marsh in the Yellow River Delta, China [J]. Acta Ecol Sin, 2019, 39(17): 6238 − 6246.
[23]	POFFENBARGER H J, NEEDELMAN B A, MEGONIGAL J P. Salinity influence on methane emissions from tidal marshes [J]. Wetlands, 2011, 31(5): 831 − 842.
[24]	CHOI Y, WANG Yang. Dynamics of carbon sequestration in a coastal wetland using radiocarbon measurements [J]. Global Biogeochem Cycles, 2004, 18: GB4016. doi: 10.1029/2004GB002261.
[25]	KAYRANLI B, SCHOLZ M, MUSTAFA A, et al. Carbon storage and fluxes within freshwater wetlands: a critical review [J]. Wetlands, 2010, 30(1): 111 − 124.
[26]	THORNTON S F, MCMANUS J. Application of organic carbon and nitrogen stable isotope and C/N ratios as source indicators of organic matter provenance in estuarine systems: evidence from the Tay Estuary, Scotland [J]. Estuarine Coastal Shelf Sci, 1994, 38(3): 219 − 233.
[27]	VERNBERG F J. Salt-marsh processes: a review [J]. Environ Toxicol Chem, 1993, 12(12): 2167 − 2195.
[28]	SYED K H, FLANAGAN L B, CARLSON P J, et al. Environmental control of net ecosystem CO₂ exchange in a treed, moderately rich fen in northern Alberta [J]. Agric For Meteorol, 2006, 140(1): 97 − 114.
[29]	EXBRAYAT J F, PITMAN A J, ZHANG Q, et al. Examining soil carbon uncertainty in a global model: response of microbial decomposition to temperature, moisture and nutrient limitation [J]. Biogeosciences, 2013, 10(11): 7095 − 7108.
[30]	BRAGAZZA L, BUTTLER A, HABERMACHER J, et al. High nitrogen deposition alters the decomposition of bog plant litter and reduces carbon accumulation [J]. Global Change Biol, 2012, 18(3): 1163 − 1172.
[31]	YUAN Zhiyou, CHEN H Y H. A global analysis of fine root production as affected by soil nitrogen and phosphorus [J]. Proc Biol Sci, 2012, 279(1743): 3796 − 3802.
[32]	YUAN Zhiyou, LI Linghao, HAN Xingguo, et al. Nitrogen response efficiency increased monotonically with decreasing soil resource availability: a case study from a semiarid grassland in northern China [J]. Oecologia, 2006, 148(4): 564 − 572.
[33]	BOBBINK R, HICKS K, GALLOWAY J, et al. Global assessment of nitrogen deposition effects on terrestrial plant diversity: a synthesis [J]. Ecol Appl, 2010, 20(1): 30 − 59.
[34]	KOOTTATEP T, POLPRASERT C. Role of plant uptake on nitrogen removal in constructed wetlands located in the tropics [J]. Water Sci Technol, 1997, 36(12): 1 − 8.
[35]	SUN Yuan, WANG Cuiting, CHEN H Y H, et al. Responses of C∶N stoichiometry in plants, soil, and microorganisms to nitrogen addition [J]. Plant Soil, 2020, 456(1/2): 277 − 287.
[36]	MOE S J, STELZER R S, FORMAN M R, et al. Recent advances in ecological stoichiometry: insights for population and community ecology [J]. Oikos, 2005, 109(1): 29 − 39.
[37]	HOUGHTON R. Terrestrial carbon sinks-uncertain explanations [J]. Biologist, 2002, 49: 155 − 60.
[38]	刘德燕, 宋长春. 湿地植物小叶章对外源氮输入的响应[J]. 应用生态学报, 2008, 19(12): 2599 − 2604.	LIU Deyan, SONG Changchun. Responses of marsh wetland plant Calamagrostis angustifolia to exogenous nitrogen input [J]. Chin J Appl Ecol, 2008, 19(12): 2599 − 2604.
[39]	彭琴, 董云社, 齐玉春. 氮输入对陆地生态系统碳循环关键过程的影响[J]. 地球科学进展, 2008, 23(8): 874 − 883.	PENG Qin, DONG Yunshe, QI Yuchun. Influence of external nitrogen input on key processes of carbon cycle in terrestrial ecosystem [J]. Adv Earth Sci, 2008, 23(8): 874 − 883.
[40]	NAKAJI T, TAKENAGA S, KUROHA M, et al. Photosynthetic response of Pinus densiflora seedlings to high nitrogen load [J]. Environ Sci, 2002, 9: 269 − 282.
[41]	张立新, 李生秀. 长期水分胁迫下氮、钾对夏玉米叶片光合特性的影响[J]. 植物营养与肥料学报, 2009, 15(1): 82 − 90.	ZHANG Lixin, LI Shengxiu. Effects of nitrogen and potassium on photosynthetic characteristics in summer maize leaves under long-term water stress [J]. Plant Nutr Fert Sci, 2009, 15(1): 82 − 90.
[42]	HUANGFU Chaohe, LI Huiyan, CHEN Xinwei, et al. Response of an invasive plant, Flaveria bidentis, to nitrogen addition: a test of form-preference uptake [J]. Biol Invasions, 2016, 18(11): 3365 − 3380.
[43]	LU Xiankai, VITOUSEK P M, MAO Qinggong, et al. Plant acclimation to long-term high nitrogen deposition in an N-rich tropical forest [J]. Proc Natl Acad Sci, 2018, 115(20): 5187 − 5192.
[44]	BREITBURG D, LEVIN L A, OSCHLIES A, et al. Declining oxygen in the global ocean and coastal waters [J]. Science, 2018, 359(6371): eaam7240. doi: 10.1126/science.aam7240.
[45]	ZHANG Yaohong, XU Xianju, LI Yang, et al. Effects of Spartina alterniflora invasion and exogenous nitrogen on soil nitrogen mineralization in the coastal salt marshes [J]. Ecol Eng, 2016, 87: 281 − 287.
[46]	LUO Min, HUANG Jiafang, ZHU Wenfeng, et al. Impacts of increasing salinity and inundation on rates and pathways of organic carbon mineralization in tidal wetlands: a review [J]. Hydrobiologia, 2019, 827(1): 31 − 49.
[47]	MIN K, KANG H, LEE D. Effects of ammonium and nitrate additions on carbon mineralization in wetland soils [J]. Soil Biol Biochem, 2011, 43(12): 2461 − 2469.
[48]	EISENLORD S D, FREEDMAN Z, ZAK D R, et al. Microbial mechanisms mediating increased soil C storage under elevated atmospheric N deposition [J]. Appl Environ Microbiol, 2013, 79(4): 1191 − 1199.
[49]	LINDA T A, FREY S D, STHULTZ C M, et al. Changes in litter quality caused by simulated nitrogen deposition reinforce the N-induced suppression of litter decay [J]. Ecosphere, 2015, 6(10): 205. doi: 10.1890/ES15-00262.1.
[50]	PESCHEL A R, ZAK D R, CLINE L C, et al. Elk, sagebrush, and saprotrophs: indirect top-down control on microbial community composition and function [J]. Ecology, 2015, 96(9): 2383 − 2393.
[51]	KUZYAKOV Y, FRIEDEL J K, STAHR K. Review of mechanisms and quantification of priming effects [J]. Soil Biol Biochem, 2000, 32(11): 1485 − 1498.
[52]	程淑兰, 方华军, 徐梦, 等. 氮沉降增加情景下植物-土壤-微生物交互对自然生态系统土壤有机碳的调控研究进展[J]. 生态学报, 2018, 38(23): 8285 − 8295.	CHENG Shulan, FANG Huajun, XU Meng, et al. Regulation of plant-soil-microbe interactions to soil organic carbon in natural ecosystems under elevated nitrogen deposition: a review [J]. Acta Ecol Sin, 2018, 38(23): 8285 − 8295.
[53]	TAO Baoxian, LIU Chenyang, ZHANG Baohua, et al. Effects of inorganic and organic nitrogen additions on CO₂ emissions in the coastal wetlands of the Yellow River Delta, China [J]. Atmos Environ, 2018, 185: 159 − 167.
[54]	HASSELQUIST N J, METCALFE D B, HÖGBERG P. Contrasting effects of low and high nitrogen additions on soil CO₂ flux components and ectomycorrhizal fungal sporocarp production in a boreal forest [J]. Global Change Biol, 2012, 18(12): 3596 − 3605.
[55]	XU Yehong, FAN Jianling, DING Weixin, et al. Characterization of organic carbon in decomposing litter exposed to nitrogen and sulfur additions: links to microbial community composition and activity [J]. Geoderma, 2017, 286: 116 − 124.
[56]	JIAN Siyang, LI Jianwei, CHEN Ji, et al. Soil extracellular enzyme activities, soil carbon and nitrogen storage under nitrogen fertilization: a meta-analysis [J]. Soil Biol Biochem, 2016, 101: 32 − 43.
[57]	YUAN Xia, QIN Wenkuan, XU Hao, et al. Sensitivity of soil carbon dynamics to nitrogen and phosphorus enrichment in an alpine meadow [J]. Soil Biol Biochem, 2020, 150: 107984. doi: 10.1016/j.soilbio.2020.107984.
[58]	BAUER J E, CAI Weijun, RAYMOND P A, et al. The changing carbon cycle of the coastal ocean [J]. Nature, 2013, 504(7478): 61 − 70.
[59]	BIANCHI T S. The role of terrestrially derived organic carbon in the coastal ocean: a changing paradigm and the priming effect [J]. Proc Natl Acad Sci, 2011, 108(49): 19473. doi: 10.1073/pnas.1017982108.
[60]	MAJIDZADEH H, UZUN H, RUECKER A, et al. Extreme flooding mobilized dissolved organic matter from coastal forested wetlands [J]. Biogeochemistry, 2017, 136(3): 293 − 309.
[61]	HERRMANN M, NAJJAR R G, KEMP W M, et al. Net ecosystem production and organic carbon balance of U. S. East Coast estuaries: a synthesis approach [J]. Global Biogeochem Cycles, 2015, 29(1): 96 − 111.
[62]	NEUBAUER S C, ANDERSON I C. Transport of dissolved inorganic carbon from a tidal freshwater marsh to the York River estuary [J]. Limnol Oceanogr, 2003, 48(1): 299 − 307.
[63]	WANG Z A, KROEGER K D, GANJU N K, et al. Intertidal salt marshes as an important source of inorganic carbon to the coastal ocean [J]. Limnol Oceanogr, 2016, 61(5): 1916 − 1931.
[64]	CZAPLA K M, ANDERSON I C, CURRIN C A. Net ecosystem carbon balance in a north Carolina, USA, salt marsh [J]. J Geophys Res Biogeosci, 2020, 125(10): e2019JG005509. doi: 10.1029/2019JG005509.
[65]	MORRIS J T, SHAFFER G P, NYMAN J A. Brinson review: perspectives on the influence of nutrients on the sustainability of coastal wetlands [J]. Wetlands, 2013, 33(6): 975 − 988.
[66]	DAVIS J, CURRIN C, MORRIS J T. Impacts of fertilization and tidal inundation on elevation change in microtidal, low relief salt marshes [J]. Estuaries Coasts, 2017, 40(6): 1677 − 1687.
[67]	GRAHAM S A, MENDELSSOHN I A. Coastal wetland stability maintained through counterbalancing accretionary responses to chronic nutrient enrichment [J]. Ecology, 2014, 95(12): 3271 − 3283.
[68]	MENÉNDEZ M C, DELGADO A L, BERASATEGUI A A, et al. Seasonal and tidal dynamics of water temperature, salinity, chlorophyll-a, suspended particulate matter, particulate organic matter, and zooplankton abundance in a shallow, mixed estuary (Bahía Blanca, Argentina) [J]. J Coastal Res, 2015, 32(5): 1051 − 1061.
[69]	BLOESCH J. Mechanisms, measurement and importance of sediment resuspension in lakes [J]. Marine Freshwater Res, 1995, 46(1): 295 − 304.
[70]	ZHANG Yafei, LIANG Jie, ZENG Guangming, et al. How climate change and eutrophication interact with microplastic pollution and sediment resuspension in shallow lakes: a review [J]. Sci Total Environ, 2020, 705: 135979. doi: 10.1016/j.scitotenv.2019.135979.
[71]	李长生. 生物地球化学的概念与方法:DNDC模型的发展[J]. 第四纪研究, 2001, 21(2): 89 − 99.	LI Changsheng. Biogeochemical concepts and methodologies: development of the DNDC model [J]. Quaternary Sci, 2001, 21(2): 89 − 99.
[72]	XU Wen, LUO X S, PAN Yuepeng, et al. Quantifying atmospheric nitrogen deposition through a nationwide monitoring network across China [J]. Atmos Chem Phys Discuss, 2015, 15: 18365 − 18405.
[73]	张钊, 辛晓平. 生物地球化学模型DNDC的研究进展与碳动态模拟应用[J]. 草地学报, 2017, 25(3): 445 − 452.	ZHANG Zhao, XIN Xiaoping. Research progress of biogeochemistry model DNDC in carbon dynamic modeling [J]. Acta Agrestia Sin, 2017, 25(3): 445 − 452.
[74]	CHENG S J, HESS P G, WIEDER W R, et al. Decadal fates and impacts of nitrogen additions on temperate forest carbon storage: a data-model comparison [J]. Biogeosciences, 2019, 16(13): 2771 − 2793.
[75]	WANG Yingping, LAW R M, PAK B. A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere [J]. Biogeosciences, 2010, 7(7): 2261 − 2282.
[76]	ZAEHLE S, FRIEND A D, FRIEDLINGSTEIN P, et al. Carbon and nitrogen cycle dynamics in the O-CN land surface model: 2. Role of the nitrogen cycle in the historical terrestrial carbon balance [J]. Global Biogeochem Cycles, 2010, 24: GB1006. doi: 10.1029/2009GB003522.
[77]	袁文平, 蔡文文, 刘丹, 等. 陆地生态系统植被生产力遥感模型研究进展[J]. 地球科学进展, 2014, 29(5): 541 − 550.	YUAN Wenping, CAI Wenwen, LIU Dan, et al. Satellite-based vegetation production models of terrestrial ecosystem: an overview [J]. Adv Earth Sci, 2014, 29(5): 541 − 550.
[78]	MONTEITH J L. Solar radiation and productivity in tropical ecosystems [J]. J Appl Ecol, 1972, 9: 747 − 766.
[79]	POTTER C. The carbon budget of California [J]. Environ Sci Policy, 2010, 13(5): 373 − 383.
[80]	VETTER M, CHURKINA G, JUNG M, et al. Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly in Europe using seven models [J]. Biogeosci Discuss, 2008, 4: 1201 − 1240.
[81]	BALDOCCHI D. Measuring fluxes of trace gases and energy between ecosystems and the atmosphere-the state and future of the eddy covariance method [J]. Global Change Biol, 2014, 20(12): 3600 − 3609.
[82]	WANG Xiaoguo, ZHU Bo, LI Changsheng, et al. Dissecting soil CO₂ fluxes from a subtropical forest in China by integrating field measurements with a modeling approach [J]. Geoderma, 2011, 161(1/2): 88 − 94.
[83]	BOHN T J, PODEST E, SCHROEDER R, et al. Modeling the large-scale effects of surface moisture heterogeneity on wetland carbon fluxes in the West Siberian Lowland [J]. Biogeosciences, 2013, 10(10): 6559 − 6576.
[84]	FRIEDLINGSTEIN P, ANDREW R M, ROGELJ J, et al. Persistent growth of CO₂ emissions and implications for reaching climate targets [J]. Nat Geosci, 2014, 7(10): 709 − 715.
[85]	RUNNING S W, COUGHLAN J C. A general model of forest ecosystem processes for regional applications Ⅰ. hydrologic balance, canopy gas exchange and primary production processes [J]. Ecol Modelling, 1988, 42(2): 125 − 154.
[86]	BOND-LAMBERTY B, GOWER S T, AHL D E. Improved simulation of poorly drained forests using Biome-BGC [J]. Tree Physiol, 2007, 27(5): 703 − 715.
[87]	曾慧卿, 刘琪璟, 冯宗炜, 等. 基于BIOME-BGC模型的红壤丘陵区湿地松(Pinus elliottii)人工林GPP和NPP[J]. 生态学报, 2008, 28(11): 5314 − 5321.	ZENG Huiqing, LIU Qijing, FENG Zongwei, et al. GPP and NPP study of Pinus elliottii forest in red soil hilly region based on BIOME-BGC model [J]. Acta Ecol Sin, 2008, 28(11): 5314 − 5321.
[88]	马泽清, 刘琪璟, 王辉民, 等. 中亚热带人工湿地松林(Pinus elliottii)生产力观测与模拟[J]. 中国科学: 地球科学, 2008, 38(8): 1005 − 1015.	MA Zeqing, LIU Qijing, WANG Huimin, et al. Productivity observation and simulation of pine forest (Pinus elliottii) in subtropical constructed wetland [J]. Sci Sin Terrae, 2008, 38(8): 1005 − 1015.
[89]	LUO Zhongkui, SUN Osbert Jianxin, WANG Enli, et al. Modeling productivity in mangrove forests as impacted by effective soil water availability and its sensitivity to climate change using Biome-BGC [J]. Ecosystems, 2010, 13(7): 949 − 965.
[90]	PARTON W J, STEWART J W B, COLE C V. Dynamics of C, N, P and S in grassland soils: a model [J]. Biogeochemistry, 1988, 5(1): 109 − 131.
[91]	CHIMNER R A, COOPER D J, PARTON W J. Modeling carbon accumulation in Rocky Mountain fens [J]. Wetlands, 2002, 22(1): 100 − 110.
[92]	LI Changsheng, FROLKING S, FROLKING T A. A model of nitrous oxide evolution from soil driven by rainfall events: 1. model structure and sensitivity [J]. J Geophys Res Atmos, 1992, 97(D9): 9759 − 9776.
[93]	ZHANG Yu, LI Changsheng, TRETTIN C C, et al. An integrated model of soil, hydrology, and vegetation for carbon dynamics in wetland ecosystems [J]. Global Biogeochem Cycles, 2002, 16(4): 1061. doi: 10.1029/2001GB001838.
[94]	田展, 牛逸龙, 孙来祥, 等. 基于DNDC模型模拟气候变化影响下的中国水稻田温室气体排放[J]. 应用生态学报, 2015, 26(3): 793 − 799.	TIAN Zhan. NIU Yilong, SUN Laixiang, et al. China’s rice field greenhouse gas emission under climate change based on DNDC model simulation [J]. Chin J Appl Ecol, 2015, 26(3): 793 − 799.
[95]	GENG Xuemeng, YANG Meng, GRACE J, et al. Simulating methane emissions from the littoral zone of a reservoir by wetland DNDC Model [J]. J Resour Ecol, 2016, 7(4): 281 − 290.
[96]	CUI Jianbo, LI Changsheng, TRETTIN C. Analyzing the ecosystem carbon and hydrologic characteristics of forested wetland using a biogeochemical process model [J]. Global Change Biol, 2005, 11(2): 278 − 289.
[97]	DENG Jia, LI Changsheng, FROLKING S. Modeling impacts of changes in temperature and water table on C gas fluxes in an Alaskan peatland [J]. J Geophys Res Biogeosci, 2015, 120(7): 1279 − 1295.
[98]	JI Jinjun. A climate-vegetation interaction model: simulating physical and biological processes at the surface [J]. J Biogeogr, 1995, 22(2/3): 445 − 451.
[99]	JI Jinjun, HUANG Mei, LI Kerang. Prediction of carbon exchanges between China terrestrial ecosystem and atmosphere in 21st century [J]. Sci China Ser D Earth Sci, 2008, 51(6): 885 − 898.
[100]	史学丽, 张芳, 周文艳, 等. CG-LTDR地表覆盖数据对BCC-AVIM1.0陆面温度模拟的影响研究[J]. 地球信息科学学报, 2015, 17(11): 1294 − 1303.	SHI Xueli, ZHANG Fang, ZHOU Wenyan, et al. Impacts of CG-LTDR land cover dataset updates on the ground temperature simulation with BCCAVIM 1.0 [J]. J Geo-Inf Sci, 2015, 17(11): 1294 − 1303.
[101]	WENG Ensheng, LUO Yiqi. Soil hydrological properties regulate grassland ecosystem responses to multifactor global change: a modeling analysis [J]. J Geophys Res Biogeosci, 2008, 113: G03003. doi: 10.1029/2007JG000539.
[102]	LUO Yiqi, WHITE L W, CANADELL J G, et al. Sustainability of terrestrial carbon sequestration: a case study in Duke Forest with inversion approach [J]. Global Biogeochem Cycles, 2003, 17(1): 1021. doi: 10.1029/2002GB001923.
[103]	WHITE L W, LUO Yiqi. Modeling and inversion of net ecological exchange data using an Ito stochastic differential equation approach [J]. Appl Math Comput, 2008, 196(2): 686 − 704.
[104]	HUANG Yuanyuan, JIANG Jiang, MA Shuang, et al. Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: constrained forecast with data assimilation [J]. J Geophys Res Biogeosci, 2017, 122(8): 2046 − 2063.
[105]	WU Y, BLODAU C. PEATBOG: a biogeochemical model for analyzing coupled carbon and nitrogen dynamics in northern peatlands [J]. Geosci Model Dev, 2013, 6(4): 1173 − 1207.
[106]	WU Y, BLODAU C, MOORE T R, et al. Effects of experimental nitrogen deposition on peatland carbon pools and fluxes: a modelling analysis [J]. Biogeosciences, 2015, 12(1): 79 − 101.
[107]	HAN Guangxuan, CHU Xiaojing, XING Qinghui, et al. Effects of episodic flooding on the net ecosystem CO₂ exchange of a supratidal wetland in the Yellow River Delta [J]. J Geophys Res Biogeosci, 2015, 120(8): 1506 − 1520.
[108]	KANG Xiaoming, LI Yong, WANG Jinzhi, et al. Precipitation and temperature regulate the carbon allocation process in alpine wetlands: quantitative simulation [J]. J Soils Sediments, 2020, 20(9): 3300 − 3315.
[109]	CHENG S J, HESS P G, WIEDER W R, et al. Decadal impacts of nitrogen additions on temperate forest carbon sinks: a data-model comparison [J]. Biogeosci Discuss, 2018, doi: 10.5194/bg-2018-505.
[110]	JANSSENS I A, DIELEMAN W, LUYSSAERT S, et al. Reduction of forest soil respiration in response to nitrogen deposition [J]. Nat Geosci, 2010, 3(5): 315 − 322.
[111]	FREY S D, OLLINGER S, NADELHOFFER K, et al. Chronic nitrogen additions suppress decomposition and sequester soil carbon in temperate forests [J]. Biogeochemistry, 2014, 121(2): 305 − 316.
[112]	SULMAN B N, BRZOSTEK E R, MEDICI C, et al. Feedbacks between plant N demand and rhizosphere priming depend on type of mycorrhizal association [J]. Ecol Lett, 2017, 20(8): 1043 − 1053.
[113]	WIEDER W R, BONAN G B, ALLISON S D. Global soil carbon projections are improved by modelling microbial processes [J]. Nat Clim Change, 2013, 3(10): 909 − 912.
[114]	ZHU Qing, RILEY W J, TANG Jinyun. A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions [J]. Ecol Appl, 2017, 27(3): 875 − 886.
[115]	BINGHAM A H, COTRUFO M F. Organic nitrogen storage in mineral soil: implications for policy and management [J]. Sci Total Environ, 2016, 551/552: 116 − 126.
[116]	KEENAN T F, BAKER I, BARR A, et al. Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO₂ exchange [J]. Global Change Biol, 2012, 18(6): 1971 − 1987.
[117]	de KAUWE M G, MEDLYN B E, ZAEHLE S, et al. Forest water use and water use efficiency at elevated CO₂: a model-data intercomparison at two contrasting temperate forest FACE sites [J]. Global Change Biol, 2013, 19(6): 1759 − 1779.
[118]	LUO Yiqi, RANDERSON J T, ABRAMOWITZ G, et al. A framework for benchmarking land models [J]. Biogeosciences, 2012, 9(10): 3857 − 3874.
[119]	RANDERSON J T, HOFFMAN F M, THORNTON P E, et al. Systematic assessment of terrestrial biogeochemistry in coupled climate-carbon models [J]. Global Change Biol, 2009, 15(10): 2462 − 2484.
[120]	SMITH M J, PURVES D W, VANDERWEL M C, et al. The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties [J]. Biogeosciences, 2013, 10(1): 583 − 606.
[121]	HARARUK O, XIA Jianyang, LUO Yiqi. Evaluation and improvement of a global land model against soil carbon data using a Bayesian Markov chain Monte Carlo method [J]. J Geophys Res Biogeosci, 2014, 119(3): 403 − 417.
[122]	WARD N D, MEGONIGAL J P, BOND-LAMBERTY B, et al. Representing the function and sensitivity of coastal interfaces in Earth system models [J]. Nat Commun, 2020, 11(1): 2458. doi: 10.1038/s41467-020-16236-2.
[123]	PRENTICE I C, LIANG X, MEDLYN B E, et al. Reliable, robust and realistic: the three R’s of next-generation land-surface modelling [J]. Atmos Chem Phys, 2015, 15(10): 5987 − 6005.
[124]	SULMAN B N, MOORE J A M, ABRAMOFF R, et al. Multiple models and experiments underscore large uncertainty in soil carbon dynamics [J]. Biogeochemistry, 2018, 141(2): 109 − 123.
[125]	TANG Guoping, ZHENG Jianqiu, XU Xiaofeng, et al. Biogeochemical modeling of CO₂ and CH₄ production in anoxic arctic soil microcosms [J]. Biogeosciences, 2016, 13(17): 5021 − 5041.
[126]	ADAM L J, MOZDZER T J, SHEPARD K A, et al. Tidal marsh plant responses to elevated CO₂, nitrogen fertilization, and sea level rise [J]. Global Change Biol, 2013, 19(5): 1495 − 1503.