郑循华 中国科学院大气物理研究所, 北京 碳水循环与地气交换研讨会, 耶鲁大学 - 南京信息工程大学大气环境中心,2014 年 6 月 3 日, 南京

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1 地气碳氮气体交换及面向生态系统优化管理的挑战 Land-Atmosphere Exchanges of C- and N-Gases and Challenges for Best Management of Ecosystems 郑循华 中国科学院大气物理研究所, 北京 碳水循环与地气交换研讨会, 耶鲁大学 - 南京信息工程大学大气环境中心,2014 年 6 月 3 日, 南京

2 Research Focuses and Key Questions Focuses: To understand the processes of terrestrial C and N cycling; To quantify the sources and sinks of C-and N-gases gases. Key questions on the sources/sinks: Where? When? Why? Proper answers are essential for How? - understanding biogeochemical processes of C & N; Climate change impacts - promoting ESM development; Management impacts - making proper decisions of Mitigation of negative ecosystem management. effects

3 Research Approach -N modules ESM -- Land C- CH4MOD Agro-C DNDC IAP-N Model development Process understandings Model simulation Techniques & experiments Inventories Mitigation strategies & BMPs GIS RS Other data Techniques Environments Management Climate changes

4 Technique/Method: GFSC-SMOS SMOS H 2 H 2 O H 2 DOC/H 2 CO2/H2O DOC CO 2 DOC CO 2 H 2 O DOC CO 2 NO 3 - NO 2 - NO N 2 O N 2 DOC CO 2 NO - 2 /SO4 2- NO N 2 O N /H S/S DOC/H 2 NO CO2/H2O NH 4 + NO2- CO 2 + H 2 CH 4 + H 2 O CH 3 COO - + H + CH 4 + CO 2 DOC H 2 + H 2 O + CO 2 Anae erobic soil core

5 Technique/Method: GFSC-SMOSSMOS SMOS GFSC (Wang et al., 2011, EST; Liao et al., 2013, Chemosphere)

6 Technique/Method: GFSC-SMOSSMOS Dynamical measurements of denitrification products and substrates using GFSC-SMOSSMOS Gas emission rates: N 2, N 2 O, NO, CH 4, CO 2 Soil substrate cons.: NH 4+, NO 3-, NO 2-, DOC (Liao et al., 2013, PhD thesis of IAP-CAS, UCAS)

7 Technique/Method: GFSC-SMOSSMOS GFSC-SMOS SMOS measurements Fractions of denitrif fication gases Initial DOC:nitrate-N ratio To promote deep insights into nitrogen and/or carbon transformation processes; To provide key parameters for process models. (Wang et al., 2013, Plant Soil)

8 Technique/Method: N 2 -CO 2 for GC Identified the problem of the N 2 method for analyzing N 2 O by GCs Invented its alternative N 2 -CO 2 method Extended application of the N 2 -CO 2 method (> 80% GCs in China or > 50 lab/stations) Problem N 2 Bias Technical solution (Zheng et al., 2008, Plant Soil; Wang et al., 2010, AAS) Ar-CH 4 N 2 -CO 2

9 Experiments: Field Network Studies Measurements of terrestrial gases exchanges Previous sites (before 2012) Current sites ( ) 2015) GC-ECD method for N 2 O at the current sites (N 2 -CO 2 ) Forest Wetland Cropland Grassland

10 Technique/Method: AMEG Automatic monitor for exchanges of trace gases (CH 4, N 2 O, NO, NEE-CO 2 ) between an ecosystem and the atmosphere (AMEG) F = H ρ 0 T 0 / T) P/P 0 dc t /dt For positive fluxes Time (min) CO 2 浓度 (ppmv) For negative fluxes Time (min) (Wang et al., 2012, AFM; Wang et al., 2013, BG)

11 Technique/Method: AMEG AMEG (chamber) fluxes versus eddy covariance fluxes EC fluxes of N 2 O (y, g C m -2 d -1 ) Cotton: N 2 O AMEG fluxes of N 2 O (µg N m -2 h -1 ) AMEG fluxes of N 2 O: % lower EC fluxes of NEE (y, g C m -2 d -1 ) Cotton: NEE Comparable Wheat: NEE Comparable AMEG fluxes of NEE (y, g C m -2 d -1 ) (Wang et al., 2012, AFM; Wang et al., 2013, BG)

12 Process Understandings: N2O, NO

13 Process Understandings: N 2 O, NO Site-scale trade-off effects between N-derived gas emissions ( N 2 O or NO) and NUE Seasonal N 2 O Annual N 2 O Annual NO (Yan et al., 2013, AGEE; adapted from Liu et al., 2012, BG)

14 Process Understandings: N 2 O, NO Holes-in-the-pipe model (Firestone & Davidson, 1989) NO N 2 O NO NO N 2 O NO N 2 O N 2 O Log (N 2 O+NO) = α Log (NH 4+ +NO 3- ) + β (Davidson & Verchot, 2000) (R 2 < 0.40) NH 4 + Nitrification N sources NO - 2 / N NO Denitrification (R 2 doubled) Log (N 2 O+NO) = A Log (NH 4+ +NO 3- ) + B Log (M) E a / (R T) (Yan et al., 2013, AGEE)

15 Process Understandings: N 2 O, NO Dual Michaelis-Menten Menten and Arrhenius kinetics well account for the trade-off effects α k 1 k 2 k 3 E a F N2O (n = 104, R 2 = 0.60, P < 0.01) F NO (n = 104, R 2 = 0.54, P < 0.01) (Yan et al., 2013, AGEE)

16 Process Understandings: N 2 O Effects of grazing in temperate semi-arid steppe Soil moisture Gross min. * * Microbe N * * * * (Wolf # & Zheng et al., 2010, Nature)

17 Process Understandings: CH 4 Effects of grazing in temperate semi-arid steppe Grazed CH 4 uptake flu ux Ungrazed CH 4 sink (kg C ha -1 y r-1 ) (Chen et al., 2010, JGR; Chen et al., 2011, GCB)

18 Model Design/Test: Agro-C Tool for simulating terrestrial NEE & SOC NPP P = = PM n i= 1 n i= 1 ( GPP ( GPP PAR i i RA i ) ( RG i + RM i )) i i i = β + PAR i f ( Ti ) f ( W i ) f ( CO 2 ) N uptake = Min ( NA i, ND i ) dc dt i, j = k j F ( T s i F ( Clay ) ) F ( W F ( ph ) C i ) i, j RH i = dc dt s, i + dc dt l, i + dc dt r, i (Huang et al., 2009, AFM)

19 Model Design/Test: Agro-C Validation of plant growth, NEE & SOC dynamics Modelled (Mg Chā 1 ) NPP validation Rice Wheat Maize Soybean Rapeseed Cotton 13 y = 0.98 x (R 2 = 0.79, n =856) Observed (Mg C ha -1 ) NEE (g C m -2 d -1 ) Cropland NEE validation Single crop (rice) field in the Sanjiang Plain 黑龙江三江平原单季稻 modeled observed 12/03 03/04 06/04 09/04 01/05 04/05 07/05 10/05 02/06 Modelled (g kḡ 1 ) SOC content validation Xinhua Taizhou Hanshou Nanxian Danyang Xuzhou Tongshan Fenqiu Shenyang y = 0.83x (R 2 = 0.96, n =232) Observed (g kg -1 ) NEE (gc m -2 d -1 ) Wheat-maize in the Northern China Plain 山东禹城冬小麦 / 玉米轮作 Winter Maize Winter -20 wheat wheat 09/02 12/02 03/03 06/03 09/03 12/03 03/04 06/04 08/04 月 / 年 (Huang et al., 2009, AFM)

20 Model Design/Test: CH 4 MOD Tool for simulating managed/natural wetland CH 4 CH 4 产生 production dp dc R dc = α F Eh SI TI [ β + γ dt dt dt C = k V I S I ( f W ) R 3 CH 4 排放 emission 土壤 Soil Eh Eh 1 _ C O 2 E = ( 1 P ) ( P E ) E Eh p o x b l W = F bl (1 ) W bl i 1 _ CO max = Eh i 1 θ ( Eh i 1 Eh c 大气 CO 2 & CO N effects 2 升高效应 f 2 = e 0.02 N P ln ( c / c 0 ) ) OM ] Water table (Huang et al., 2004, JGR; Li et al., 2010, EM; Xie et al., 2010, AAS; Li et al., 2012, BG)

21 Model Design/Test: CH 4 MOD Test of simulation for rice-field CH 4 Ambient CO 2 Modeled (y, kg C ha -1 ) Ambient Elevated (Huang et al., 2004, JGR; Xie et al., 2010, AAS)

22 Model Design/Test: CH 4 MOD Test of simulation for natural wetland CH Michigan, USA Scheuchzeria palustris 美国密西根 :S. palustris Observed Modeled Saskatchewan, Canada 加拿大萨斯喀彻温 : Mixture with various species 美国密西根 :C. oligosperma CH4 flux(mg m -2 h -1 ) CH4 flux(mg m -2 h -1 ) 05/94 06/94 07/94 08/94 09/94 10/94 Michigan, USA Carex oligosperma 月 / 年 三江 Sanjiang, : 小叶章 China Deyeuxia angustifolia Observed Modeled 若尔盖 : 乌拉苔 三江 : 毛果苔 若尔盖 : 木里苔 Carex meyeriana CH4 flux (mg m -2 h -1 ) 04/03 06/03 08/03 10/03 12/03 02/04 04/04 06/04 08/04 10/04 12/04 02/05 04/05 06/05 08/05 10/05 月 / 年 CH4 flux (mg m -2 h -1 ) 01/01 02/01 03/01 04/01 05/01 06/01 07/01 08/01 09/01 10/01 11/01 12/01 01/00 03/00 06/00 09/00 12/00 03/01 06/01 09/01 12/01 03/02 06/02 09/02 12/02 03/03 Roergai, China: Carex muliensis Sanjiang, China Carex lasiocarpa Roergai, China: 月 / 年 (Li et al., 2010, EM; Li et al., 2012, BG; Li et al., 2014, in preparation)

23 Agricultural section of China annually emitted 0.62 Pg CO 2 -eq ( ) 2005) Source Model Application: GHG Inventories Livestock CH 4 Cropland N 2 O Rice CH 4 0 Sink Rice CH 4 Cropland N 2 O Cropland SOC Cropland SOC Livestock CH 4 (Lin et al., 2011, Chinese J Environ. Sci. (in Chinese); Sun & Huang, 2012, EP; Yu et al., 2012, GPC; N 2 O provided by Yao Huang)

24 Model Application: Agro-C cropland SOC and its responses to management and climate change SOC between Annual SOC (Tg C yr -1 ) 2051 (Yu et al., 2012, GPC; Yu et al., 2013, AGEE)

25 Model Application: CH4MOD Global change effects on rice CH 4 National total (Tg CH4 yr -1 ) Rice (Tg CH 4 yr - 1 ) Rice + Wetland Northeast fraction Wetland Northeast China (Zhang et al., 2011, GCB; Huang et al., 2010, GCB)

26 Model Application: CH4MOD Methane emission from global wetlands (CH4MOD vs. TEM) Annual flooding (assumed): 173 Tg yr -1 Seasonal flooding: 98 Tg yr -1 TEM CH4MOD grids (Li et al., 2014, in preparation) Area: 585 M km 2

27 Principles of multi-goal management CO 2 CH 4 NH 3 CO 2 VOCs Productivity NO HONO N 2 O NO N 2 OC NH + 4 NO - Min. Nitr. 2 Denitr. DOC NO - 3 & ON Leaching or runoff Aquatic Leaching Leaching Hydrological

28 Measurement ability for the processes Processes that an be precisely me easured at Site or Catchment ecosystem scale scale

29 Current ability of process models 3-D models at catchment scale 1-D models at site or ecosystem scale

30 Designing 3-D models (Challenge 1) which are essential, but currently missing tools 3-D model is required: to fuse data of different spatial and/or temporal scales; to integrate understandings of the complex processes within a catchment; and, to explore strategies of multi-goal ecosystem management under changing environment & climate. 3-D model is expected: (from Y. Li, personal commun.) to be capable of simulating horizontal and vertical movement and biogeochemical transformation of C and N within a catchment.

31 DHSVM (Wigmosta et al., 2001) DNDC (Li et al., 2007, 2012) 3-D catchment-ecosystem ecosystem model: may be developed by coupling 1-D process-based biogeochemical models (e.g. DNDC) into 3-D distributed hydrological models (e.g. DHSVM)

32 3-D model validation (Challenge 2) which is lacking of complete datasets Few datasets can be found, which require: validated parameters of soil, vegetation, hydrology; validated driving data to facilitate model running; simultaneously measured data in a catchment on: dynamical gas fluxes (CO 2, CH 4, N 2 O, NO, NH 3, ) for ecosystems; slope or stream fluxes (runoff, PN, SOC, DOC, IN, DON, ); and, other variable dynamics (e.g. soil NH 4+, NO 3-, DOC, ).

33 Best management (Challenge 3) is limited by lacking of strategy-developing tools Technical feasibility Resource availability Other constrains Management scenarios under changing climate 3-D process-based biogeochemical model Assessment index ( I ) Screening Marginal benefit Economic constrains Selected scenario for BMP BMP scenario: under given constrains the scenario with the largest I value is the best (Adapted from Cui et al., 2014, BG)

34 Best management (Challenge 3) is limited by lacking of strategy-developing tools Function to determine the I value of a given scenario A price-based Productivities (e.g. crop yield) CO N leaching proxy 2+CH 4 +N 2 O GHG O 3 -layer depletion matter I n = i= 1 a i P i b NEGE c NH 3 d NL e NO - f N O 2 ODM Protocol-based parameters a $ ton -1 C a $ ton -1 C E.g. referredto market prices of wheat and maize For a double-crop regime b 7.00 $ ton -1 CO 2 eq Referredto market price of carbon trade c 5.02 $ kg -1 N Birch et al d 1.92 $ kg -1 N van Grinsven et al. 2010; Dodds et al. 2009; Compton et al e $ kg -1 N Birch et al. 2010, Compton et al f 1.33 $ kg -1 N Compton et al (Cui et al., 2014, BG)

35 Ongoing attempts for catchments JCM (Jinjing Catchment Model) DHSVM (a hydrological model) + (coupling) WNMM (an ecosystem model) (from Yong. Li, personal commun.)

36 Ongoing attempts for catchments Observed JCM Catchment runoff Tea plantation plots: N 2 O fluxes Paddy plots: CH 4 fluxes JCM validation Forest plots: N 2 O fluxes Jinjing catchment (from Yong. Li, personal commun.)

37 Ongoing attempts for catchments Catchment simulation test with JCM Input data Output data NO 3 - Con. 河道分级 mg N kg -1 d.s. < > 8.1 N 2 O flux g N ha h -1 < >.230 (from Yong. Li, personal commun.)

38 Zoige catchment (seasonal frost, alpine) Ongoing attempts for catchments Comprehensive observation to validate 3-D catchment-ecosystem ecosystem model(s) Yanting catchment Jinjing catchment Yangtze watershed

39 To improve and couple 3-D catchment -ecosystem models of critical zone with AGCM Future Studies To understand the feedbacks between exchanges of C- and N-gases and global changes so as to support port proper management.

40 Thanks for your attention! LAPC / IAP-CAS 40