不同气象条件下Biome-BGC模型碳通量模拟精度评价
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摘要
建立在植物生理生态机理上的过程模型Biome-BGC,能够较好地模拟陆地生态系统的行为和特征并被广泛采用。现有基于Biome-BGC的研究多侧重于模型季节和年尺度模拟精度的评价和应用,对不同气象条件下模型模拟精度的评价则较少。本研究利用全球长期通量观测网络Fluxnet验证了4个代表性针叶林站点——Lavarone (意大利)、Renon (意大利)、Loobos (荷兰)、Fyodorovskoye (俄罗斯),模拟了2011—2013年日总初级生产力(gross primary productivity,GPP),并着重分析该模型在上述研究区域不同气象条件下日GPP的模拟精度。结果表明:模拟值在整体趋势上较通量验证值偏小(4%～25%);有降水的条件下,Biome-BGC模型较无降水的条件下具有更好的模拟效果(相关系数rno rain为0.843～0.936和r_(rain)为0.887～0.952,P<0.01),并且典型雨天的模拟精度高于典型晴天(r_(sun)为0.830～0.915和r_(rain)为0.887～0.952,P<0.01)。本文结果加深了Biome-BGC模型在不同气象条件对GPP模拟的不确定性理解,明确了不同气象条件下模拟碳通量的适用性,为进一步提高模型模拟精度提供了参考。
The biome-biogeochemical cycles(Biome-BGC) model based on plant physiological and ecological processes has been widely applied to describe the behaviors and characteristics of terrestrial ecosystems.Most previous studies focused on the model evaluation or the model application at seasonal and annual scales,while daily scale performance of the model under different meteorological conditions has been less studied.In this study,flux data of the four needle-leaf fluxnet sites,Lavarone(Italy),Renon(Italy),Loobos(Holland),Fyodorovskoye(Russia) were used to evaluate the performance of the Biome-BGC model on daily gross primary productivity(GPP) in 2011—2013,and the accuracy of carbon flux simulation in those areas under different meteorological conditions were analyzed.The results show that the simulated daily GPP is generally smaller(varied from 4% to25%) than the flux measurements;the Biome-BGC model in the rainy days has better performance than that without precipitation(rno rain=0.843～0.936 and r_(rain)=0.887～0.952,P<0.01),and the simulation accuracy of typical rainy days was higher than that of typical sunny days(r_(sun)=0.830～0.915 and r_(rain)=0.887～0.952,P<0.01).The obtained results are helpful to understand the uncertainty of Biome-BGC model simulation under different meteorological conditions and to clarify the applicability of simulated carbon flux under different meteorological conditions,and provide a reference for further improving the accuracy of model simulations.
The biome-biogeochemical cycles(Biome-BGC) model based on plant physiological and ecological processes has been widely applied to describe the behaviors and characteristics of terrestrial ecosystems.Most previous studies focused on the model evaluation or the model application at seasonal and annual scales,while daily scale performance of the model under different meteorological conditions has been less studied.In this study,flux data of the four needle-leaf fluxnet sites,Lavarone(Italy),Renon(Italy),Loobos(Holland),Fyodorovskoye(Russia) were used to evaluate the performance of the Biome-BGC model on daily gross primary productivity(GPP) in 2011—2013,and the accuracy of carbon flux simulation in those areas under different meteorological conditions were analyzed.The results show that the simulated daily GPP is generally smaller(varied from 4% to25%) than the flux measurements;the Biome-BGC model in the rainy days has better performance than that without precipitation(rno rain=0.843～0.936 and r_(rain)=0.887～0.952,P<0.01),and the simulation accuracy of typical rainy days was higher than that of typical sunny days(r_(sun)=0.830～0.915 and r_(rain)=0.887～0.952,P<0.01).The obtained results are helpful to understand the uncertainty of Biome-BGC model simulation under different meteorological conditions and to clarify the applicability of simulated carbon flux under different meteorological conditions,and provide a reference for further improving the accuracy of model simulations.
引文
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[17]MAO F,LI P,ZHOU G,et al.Development of the BI-OME-BGC model for the simulation of managed Moso bamboo forest ecosystems[J].Journal of Environmental Management,2016,172:29-39.
[18]THORNTON P E,LAW B E,GHOLZ H L,et al.Modeling and measuring the effects of disturbance history an climate on carbon and water budgets in evergreen needleleaf forests[J].Agricultural and Forest Meteorology,2002,113(1-4):185-222.
[19]HIDY D,BARCZA Z,HASZPRA L,et al.Development of the Biome-BGC model for simulation of managed herbaceous ecosystems[J].Ecological Modelling,2015,226(1729):99-119.
[20]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.
[21]胡波，孙睿，陈永俊，等.遥感数据结合Biome-BGC模型估算黄淮海地区生态系统生产力[J].自然资源学报，2011,26(12):2061-2071.
[22]RUNNING S W.A general model of forest ecosystem processes for regional applications I.Hydrologic balance,canopy gas exchange and primary production processes[J].Ecological Modelling,1988,42(2):125-154.
[23]WHITE M A,THORNTON P E,RUNNING S W,et al.Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model:Net primary production controls[J].Earth Interactions,2000,4(3):1-84.
[24]LASSLOP G,REICHSTEIN M,PAPALE D,et al.Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach:Critical issues and global evaluation[J].Globle Change Biology,2010,16(1):187-208.
[25]PIAO S,WANG X,CIAIS P,et al.Changes in satellitederived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006[J].Global Change Biology,2011,17(10):3228-3239.
[26]HOUBORG R,CESCATTI A,MIGLIAVACCA M.Constraining model simulations of GPP using satellite retrieved leaf chlorophyll[C].2012 IEEE International Geoscience and Remote Sensing Symposium,Germany:IEEE,2012:22-27.
[27]FIELD C.Allocating leaf nitrogen for the maximization of carbon gain:Leaf age as a control on the allocation program[J].Oecologia,1983,56(2/3):341-347.
[28]HIREMATH A J.Photosynthetic nutrient-use efficiency in three fast-growing tropical trees with differing leaf longevities[J].Tree Physiology,2000,20(14):937.
[29]CHEN Z H,ZHA T,XIN J,et al.Leaf nitrogen is closely coupled to phenophases in a desert shrub ecosystem in China[J].Journal of Arid Environments,2015,122:124-131.
[30]GOEDHART C M,PATAKI D E,BILLINGS S A.Seasonal variations in plant nitrogen relations and photosynthesis along a grassland to shrubland gradient in Owens Valley,California[J].Plant&Soil,2010,327(1/2):213-223.
[31]WRIGHT I J,REICH P B,MARK W,et al.The worldwide leaf economics spectrum[J].Nature,2004,428(6985):821.
[32]叶子飘.光合作用光响应模型的比较[J].植物生态学报,2008,32(6):1356-1361.
[33]闫敏，李增元，田昕，等.黑河上游植被总初级生产力遥感估算及其对气候变化的响应[J].植物生态学报，2016,40(1):1-12.
(1)https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=9
(2)https://www.co2.earth/annual-co2
[2]陶波，葛全胜，李克让，等.陆地生态系统碳循环研究进展[J].地理研究，2001,20(5):564-575.
[3]何若雪，孙平安，于爽，等.岩溶区地表河不同时期高分辨率监测碳通量变化特征研究--以漓江流域为例[J].西南大学学报（自然科学版），2018,40(3):150-158.
[4]COOPS N C,HILKER T,HALL F G,et al.Estimation of light-use efficiency of terrestrial ecosystems from space:Astatus report[J].Bioscience,2010,60(10):788-797.
[5]COLLATZ G J,BALL J T,GRIVET C,et al.Physiological and environmental regulation of stomatal conductance,photosynthesis and transpiration:A model that includes a laminar boundary layer[J].Agricultural and Forest Meteorology,1991,54(2-4):107-136.
[6]VALENTINI R,MATTEUCCI U G,DOLMAN A J,et al.Respiration as the main determinant of carbon balance in European forests[J].Nature,2000,404(6780):861-865.
[7]于贵瑞，王秋凤，朱先进.区域尺度陆地生态系统碳收支评估方法及其不确定性[J].地理科学进展，2011,30(1):103-113.
[8]MASSMAN W J,LEE X.Eddy covariance flux corrections and uncertainties in long-term studies of carbon and energy exchanges[J].Agricultural and Forest Meteorology,2002,113(1):121-144.
[9]GRACE J,MALHI Y,LLOYD J,et al.The use of eddy covariance to infer the net carbon dioxide uptake of Brazilian rain forest[J].Global Change Biology,2010,2(3):209-217.
[10]WU C,MUNGER J W,NIU Z,et al.Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest[J].Remote Sensing Environment,2010,114(12):2925-2939.
[11]PARTON W J,SCURLOCK J M O,OJIMA D S,et al.Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide[J].Global Biogeochemical Cycles,1993,7(4):785-809.
[12]MCGUIRE A D,MELILLO J M,JOYCE L A,et al.Interactions between carbon and nitrogen dynamics in estimating net primary productivity for potential vegetation in North America[J].Global Biogeochemical Cycles,1992,6(2):101-124.
[13]LIU J,CHEN J M,CIHLAR J,et al.A process-based boreal ecosystem productivity simulator using remote sensing inputs[J].Remote Sensing Environment,1997,62(2):158-175.
[14]HALL F G,HUEMMRICH K F,GOETZ S J,et al.Satellite remote sensing of surface energy balance:Success,failures,and unresolved issues in FIFE[J].Journal of Geophysical Research:Atmospheres(1984-2012).1992,1992,97(D17):19061-19089.
[15]RUNNING S W,JR E R H.Generalization of a forest ecosystem process model for other biomes,BIOME-BGC,and an application for global-scale models[J].Scaling Physiological Processes,1993:141-158.
[16]CHIESI M,MASELLI F,MORIONDO M,et al.Application of BIOME-BGC to simulate Mediterranean forest processes[J].Ecological Modelling,2007,206(1/2):179-190.
[17]MAO F,LI P,ZHOU G,et al.Development of the BI-OME-BGC model for the simulation of managed Moso bamboo forest ecosystems[J].Journal of Environmental Management,2016,172:29-39.
[18]THORNTON P E,LAW B E,GHOLZ H L,et al.Modeling and measuring the effects of disturbance history an climate on carbon and water budgets in evergreen needleleaf forests[J].Agricultural and Forest Meteorology,2002,113(1-4):185-222.
[19]HIDY D,BARCZA Z,HASZPRA L,et al.Development of the Biome-BGC model for simulation of managed herbaceous ecosystems[J].Ecological Modelling,2015,226(1729):99-119.
[20]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.
[21]胡波，孙睿，陈永俊，等.遥感数据结合Biome-BGC模型估算黄淮海地区生态系统生产力[J].自然资源学报，2011,26(12):2061-2071.
[22]RUNNING S W.A general model of forest ecosystem processes for regional applications I.Hydrologic balance,canopy gas exchange and primary production processes[J].Ecological Modelling,1988,42(2):125-154.
[23]WHITE M A,THORNTON P E,RUNNING S W,et al.Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model:Net primary production controls[J].Earth Interactions,2000,4(3):1-84.
[24]LASSLOP G,REICHSTEIN M,PAPALE D,et al.Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach:Critical issues and global evaluation[J].Globle Change Biology,2010,16(1):187-208.
[25]PIAO S,WANG X,CIAIS P,et al.Changes in satellitederived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006[J].Global Change Biology,2011,17(10):3228-3239.
[26]HOUBORG R,CESCATTI A,MIGLIAVACCA M.Constraining model simulations of GPP using satellite retrieved leaf chlorophyll[C].2012 IEEE International Geoscience and Remote Sensing Symposium,Germany:IEEE,2012:22-27.
[27]FIELD C.Allocating leaf nitrogen for the maximization of carbon gain:Leaf age as a control on the allocation program[J].Oecologia,1983,56(2/3):341-347.
[28]HIREMATH A J.Photosynthetic nutrient-use efficiency in three fast-growing tropical trees with differing leaf longevities[J].Tree Physiology,2000,20(14):937.
[29]CHEN Z H,ZHA T,XIN J,et al.Leaf nitrogen is closely coupled to phenophases in a desert shrub ecosystem in China[J].Journal of Arid Environments,2015,122:124-131.
[30]GOEDHART C M,PATAKI D E,BILLINGS S A.Seasonal variations in plant nitrogen relations and photosynthesis along a grassland to shrubland gradient in Owens Valley,California[J].Plant&Soil,2010,327(1/2):213-223.
[31]WRIGHT I J,REICH P B,MARK W,et al.The worldwide leaf economics spectrum[J].Nature,2004,428(6985):821.
[32]叶子飘.光合作用光响应模型的比较[J].植物生态学报,2008,32(6):1356-1361.
[33]闫敏，李增元，田昕，等.黑河上游植被总初级生产力遥感估算及其对气候变化的响应[J].植物生态学报，2016,40(1):1-12.
(1)https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=9
(2)https://www.co2.earth/annual-co2