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Models and Their Adaptation

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Atmospheric Greenhouse Gases: The Hungarian Perspective

Abstract

This chapter describes computer simulation models developed for ­estimating greenhouse gas fluxes of different ecosystems. In general, each model is used for the simulation of specific parts of the complex systems; therefore, merging the results of various kind of models can give a better insight. In this chapter, we describe four models used for estimating biospheric fluxes of greenhouse gases in Hungary. The Biome-BGC and the DNDC models are process-based ecological models. The adapted version of Biome-BGC is capable of describing the carbon, nitrogen, and water fluxes of the Hungarian arable lands and grasslands. This model is used to estimate the net primary production (NPP) and the net biome production. DNDC is used to predict the soil fluxes of nitrous oxide and methane, and simulates the biogeochemical cycles of carbon and nitrogen occurring in agricultural soil. MOD17 is a simple lightweight model that is based on remote sensing and ancillary meteorological information, and provides gross primary production (GPP) and NPP data. The CASMOFOR model was developed to estimate how much carbon can be accumulated in afforestation projects.

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Notes

  1. 1.

    Multi-objective ­optimization is the process of simultaneously optimizing two or more conflicting objectives, which means that more reference data are considered during the calibration procedure.

  2. 2.

    Plant availabe water is the portion of the water field capacity that can be absorbed by plant.

  3. 3.

    Field capacity is the soil water content after the soil has been saturated (all pores filled with water) and allowed to drain freely for about 24–28 h.

  4. 4.

    Wilting point is the soil water content when plants have extracted all the water they can.

References

  • Bürger G (1997) On the disaggregation of climatological means and anomalies. Climate Res 8:183–194

    Article  Google Scholar 

  • Büttner G, Bíró M, Maucha M, Petrik O (2000) Land Cover mapping at scale 1:50.000 in Hungary: Lessons learnt from the European CORINE programme, 20th EARSeL Symposium, 14–16 June 2000. In: A decade of Trans-European Remote Sensing Cooperation, pp 25–31

    Google Scholar 

  • Farquhar GD, Caemmerer SV, Berry JA (1980) A biochemical-model of photosynthetic CO2 assimilation in leaves of C-3 species. Planta 149:78–90

    Article  Google Scholar 

  • Franks SW, Beven KJ, Gash JHC (1999) Multi-objective conditioning of a simple SVAT model. Hydrol Earth Syst Sci 3:477–489

    Article  Google Scholar 

  • Herrmann A, Kelm M, Kornher A, Taube F (2005) Performance of grassland under different ­cutting regimes as affected by sward composition, nitrogen input, soil conditions and weather – a simulation study. Eur J Agron 22:141–158

    Article  Google Scholar 

  • Hollinger DY, Richardson AD (2005) Uncertainty in eddy covariance measurements and its ­application to physiological models. Tree Physiol 25:873–885

    Google Scholar 

  • Horváth L, Asztalos M, Führer E, Mészáros R, Weidinger T (2005) Measurement of ammonia exchange over grassland in the Hungarian Great Plain. Agric For Meteorol 130:282–298

    Article  Google Scholar 

  • IPCC (2003) In: Penman J, Gytarsky M, Hiraishi T, Kruger D, Pipatti R, Buendia L, Miwa K, Ngara T, Tanabe K, Wagner F (eds) Good practice guidance for land use, land-use change and forestry. IPCC/IGES, Hayama, Japan

    Google Scholar 

  • IPCC (2006) In: Eggleston HS, Miwa K, Ngara T, Tanabe K (eds) 2006 IPCC guidelines for national greenhouse gas inventories. IGES, Hayama, Japan

    Google Scholar 

  • Jolly W, Nemani RR, Running SW (2005) A generalized, bioclimatic index to predict foliar ­phenology in response to climate. Global Change Biol 11:619–632

    Article  Google Scholar 

  • Kennedy M, O’Hagan A (2001) Bayesian calibration of computer models. J R Stat Soc 63B:425–463

    Article  Google Scholar 

  • Knyazikhin Y, Glassy J, Privette JL, Tian Y, Lotsch A, Zhang Y, Wang Y, Morisette JT, Votava P, Myneni RB, Nemani RR, Running SW (1999) MODIS Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Product (MOD15) algorithm theoretical basis document. http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf

  • Li CS (2000) Modeling trace gas emissions from agricultural ecosystems. Nutr Cycl Agroecosyst 58:259–276

    Article  Google Scholar 

  • Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller EJ (1953) Equation of state calculation by fast computing machines. Chem Phys 21:1087–1092

    Google Scholar 

  • Mo X, Beven K (2004) Multi-objective parameter conditioning of a three-source wheat canopy model. Agric For Meteorol 122:39–63

    Article  Google Scholar 

  • Monteith J (1972) Solar radiation and productivity in tropical ecosystems. J Appl Ecol 9:747–766

    Article  Google Scholar 

  • Monteith J (1977) Climate and efficiency of crop production in Britain. Phil Trans R Soc Lond Ser B 281:277–294

    Article  Google Scholar 

  • Mosegaard K, Tarantola A (1995) Monte Carlo sampling of solutions to inverse problems. J Geophys Res 100B:12431–12447

    Article  Google Scholar 

  • Nabuurs GJ, Garza-Caligaris JF, Kanninen M, Karjalainen T, Lapvetelainen T, Liski J, Masera O, Mohren GMJ, Pussinen A, Schelhaas MJ (2001) CO2FIX V2.0 – manual of a model for ­quantifying carbon sequestration in forest ecosystems and wood products. ALTERRA, Wageningen, The Netherlands

    Google Scholar 

  • New M, Lister D, Hulme M, Makin I (2002) A high-resolution data set of surface climate over global land areas. Climate Res 21:1–25

    Article  Google Scholar 

  • Reichstein M (2001) Drought effects on carbon and water exchange in three Mediterranean ­ecosystems. PhD Thesis, Universitat Bayreuth, Max-Planck-Institute for Biogeochemistry, Hans-Knoll-Strasse 10, 07745, Jena, Germany

    Google Scholar 

  • Running SW, Coughlan JC (1988) A general model of forest ecosystem processes for regional applications I. Hydrological balance, canopy gas exchange and primary production processes. Ecol Modell 42:125–154

    Article  Google Scholar 

  • Running SW, Gower ST (1991) A general model of forest ecosystem processes for regional ­applications II. Dynamic carbon allocation and nitrogen budgets. Tree Physiol 9:147–160

    Google Scholar 

  • Running SW, Nemani RR, Glassy JM, Thornton PE (1999) MODIS Daily Photosynthesis (PSN) and Annual Net Primary Production (NPP) product (MOD17) algorithm theoretical basis document. www.ntsg.umt.edu/modis/ATBD/ATBD_MOD17_v21.pdf

  • Somogyi Z (2000) Possibilities for carbon mitigation in the forestry sector in Hungary. Biotechnol Agron Soc Environ 4:296–299

    Google Scholar 

  • Somogyi Z (2008) Recent trends of tree growth in relation to climate change in Hungary. Acta Silvatica Lignaria Hungarica 4:17–27. http://aslh.nyme.hu/fileadmin/dokumentumok/fmk/acta_silvatica/cikkek/Vol04-2008/02_somogyi_p.pdf

  • Somogyi Z, Cienciala E, Mäkipää R, Muukkonen P, Lehtonen A, Weiss P (2007) Indirect ­methods of large-scale forest biomass estimation. Eur J For Res 126(2):197–207. doi:10.1007/s10342-006-0125-7

    Google Scholar 

  • Strahler A, Muchoney, D., Borak, J., Friedl, M., Gopal, S., Lambin, E., Moody, A., 1999. MODIS Land cover product Algorithm Theoretical Basis Document (ATBD). http://modis.gsfc.nasa.gov/data/atbd/atbd_mod12.pdf

  • Tardieu F, Simonneau T (1998) Variability among species of stomatal control under fluctuating soil water status and evaporative demand: modelling isohydric and anisohydric behaviours. J Exp Bot 49:419–432

    Article  Google Scholar 

  • Thornton PE, Hasenauer H, White MA (2000) Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: application over complex terrain in Austria. Agric For Meteorol 104:255–271

    Article  Google Scholar 

  • Trusilova K, Trembath J, Churkina G (2009). Parameter estimation and validation of the terrestrial ecosystem model BIOME-BGC using eddy-covariance flux measurements. Technical Reports – Max-Planck-Institut für Biogeochemie 16:4–14

    Google Scholar 

  • Tsubo M, Walker S (2005) Relationships between photosynthetically active radiation and ­clearness index at Bloemfontein, South Africa. Theor Appl Climatol 80:17–25

    Article  Google Scholar 

  • Van Oijen M, Rougier J, Smith R (2005) Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiol 25:915–927

    Google Scholar 

  • Verbeeck H, Samson R, Verdonck F, Lemeur R (2006) Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: a Monte Carlo analysis. Tree Physiol 26:807–817

    Google Scholar 

  • Wang YP, Trudinger CM, Enting IG (2009) A review of applications of model–data fusion to studies of terrestrial carbon fluxes at different scales. Agric For Meteorol 149:1829–1842

    Article  Google Scholar 

  • White MA, Thornton PE, Running SW, Nemani RR (2000) Parameterization and sensitivity analysis of the Biome-BGC terrestrial ecosystem model: net primary production controls. Earth Interact 4:1–85

    Article  Google Scholar 

  • Wu X, Luo Y, Weng E, White L, Ma Y, Zhou X (2009) Conditional inversion to estimate parameters from eddy-flux observations. J Plant Ecol 2:1–14

    Article  Google Scholar 

  • Zhao M, Heinsch FA, Nemani RR, Running SW (2005) Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens Environ 95:164–176. doi:10.1016/j.rse.2004.12.011

    Article  Google Scholar 

  • Zhao M, Running SW, Nemani RR (2006) Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses. J Geophys Res 111:G01002. doi:10.1029/2004JG000004

    Article  Google Scholar 

Download references

Acknowledgments

Biome-BGC version 4.1.1 was provided by the NTSG at the University of Montana. NTSG assumes no responsibility for the proper use of Biome-BGC by others. We are very grateful to Maosheng Zhao (NTSG) for providing us the official MOD17 ­product, the version 5.1 model parameters, and the GMAO meteorological database. The DNDC simulations were supported by NitroEurope Integrated Project of the European Commission’s 6th R&D Framework Programme, and by GVOP AKF 3.1.1.2004–05 0358/3.0 research project. The development of CASMOFOR was supported by various Hungarian projects of the National Scientific Research Found, the Ministry of Environment and Water, the Ministry of Agriculture and Rural Development, as well as by the CarboInvent project (Multi-Source Inventory Methods For Quantifying Carbon Stocks And Stock Changes In European Forests; Contract number EVK2-CT-2002-00157), see www.joanneum.at/CarboInvent.

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Appendix

Appendix

1.1 Equations and Variables Applied in the Accounting System of CASMOFOR to Model the Carbon Cycle of Forests and Forestry

Equations of CASMOFOR describing the carbon cycle are listed below by pool, and classified in various categories. Balance equations (BE) are ones that are used to calculate new values of the carbon pools at time t. FLUX IN refers to a process, i.e. a sink, by the yield of which the carbon content of the whole forestry system increases. Similarly, FLUX OUT refers to emissions by the amount of which the carbon content of the whole forestry system decreases. The emissions can come from carbon that was fixed by the FLUX IN processes, or from carbon that was already stored in the soil before the start of the simulation. Some carbon that flows through various pools eventually goes to the long-term SINK (in the soil), and remains part of the forestry system for the entire period of the simulation. Equations that are not classified into any of the above categories are used to calculate variables in the above equations, and are noted by “other.” By the forest and forestry system, all the pools and processes of the systems diagram in Fig. 9.1 are meant.

Variables related to carbon pools (in italics) and rates of fluxes are expressed as amount of carbon (tonnes of carbon – tC), and is calculated once for each calendar year of the simulation for each species and yield class.

In the equations below, index t refers to any year of the simulation, (t-1) to the previous year, whereas index (t − x) refers to year x year(s) before the current year. Index (i) means that the ­variable is a vector of values from year 1 to year x (the factor used in the formula varies with age), where x depends on the process.

To check that all carbon is accounted for, i.e. all incoming fluxes equal to all outgoing fluxes + changes of the carbon content of the system (including ­permanent sinks), an additional equation is tested to conserve mass:

$$\begin{array}{ll}\hbox{Total Flux IN}_{\rm t}&=\text{Total}({\text{POOLS}}_{t}-{\text{POOLS}}_{\text{t}-1})\\ &-\hbox{Total Flux OUT}_{t}-\hbox{Total Flux to SINK}_{\rm t}\end{array}$$

1.1.1 Aboveground Biomass

Type of equation

Equations, variables and parameters (all pools and processes in terms of tC, and calculated once for year t)

BE

AGWB t = AGWB t1 + CAIC − M − TH − FC

AGWB: Aboveground woody biomass

CAIC: current annual increment of aboveground woody biomass

M: mortality

TH: thinning

FC: final cuttings

Flux IN

CAIC = CAI * d * cf

CAI: current annual increment of tree volume; m−3

d: weight of oven-dry biomass/volume of fresh wood (at the stand level); tdm m−3

cf: carbon fraction of oven dry wood; tC tdm−1

Other

M = AGWB t1 * (ddm + dim)

ddm: density dependent mortality rate; species dependent; dimensionless

dim: density independent mortality rate; randomly generated; dimensionless

ddm + dim <= 0.4

Other

TH = AGWB t1 * thr

thr: species specific, depends on age and yield class; dimensionless

FC = AGWB t1 of stands of rotation age

(final cutting is supposed to take place at the beginning of the year, and is immediately followed by regeneration)

Rotation age: species specific, depends on yield class

1.1.2 Dead Wood

Type of equation

Equations, variables and parameter (all pools and processes in terms of tC ha−1)

BE

DW = DW t1 + DWI − EW

DW: dead wood

DWI: deadwood increment

EW: decomposition of (i.e., emission from) decomposable dead wood

Other

DWI = [M + TH * (1 − wpTH fpTH ) + FC * (1 − wpFC fpFC )] * (1ndf)

wpTH : wood product part of TH, dimensionless

fpTH : fuelwood part of TH, dimensionless

wpFC :wood product part of FC, dimentionless

fpFC: fuelwood part of FC; dimensionless

ndf: nondecomposable fraction; dimensionless

Flux OUT

EW = Sum i (DW ti /DTw i ) for i = 1 to x

DTw: time needed for deadwood to decompose, years

Flux to SINK

UWI = [M + TH * (1 − wpTH fpTH ) + FC * (1 − wpFC fpFC )] * ndf

UWI: Nondecomposable dead wood fraction

1.1.3 Living Leaves

Type of equation

Equations, variables, and parameters (all pools and processes in terms of tC ha−1)

BE

LL = LL t−1 + LI − DLI − DLI * ndf /(1 − ndf)

LL: amount of (living) leaves

LI: increment due to tree growth

DLI: decomposable leaf loss due to harvest and mortality at the end of year

Flux IN

LI = CAIC * increment ratio

increment ratio: leaf increment/aboveground woody biomass increment; dimensionless

other

DLI = [(LL t−1 + LI) * nll + (LL t−1 + LI) * (1 − nll ) * fdlleaves ] * (1 − ndf)

nll : ratio of nonliving/living biomass = DWI/AGWB t−1 ; dimensionless

fdlleaves : fraction of leaves dying and falling at the end of year; species specific (broadleaves: 1; conifers: <1); dimensionless

Flux to SINK

ULI = DLI * ndf /(1 − ndf)

ULI: Nondecomposable fraction; dimensionless

1.1.4 Dead Leaves

Type of equation

Equations, variables, and parameters (all pools and processes in terms of tC ha−1)

BE

DL = DL t−1 + DLI − EL

DL: dead decomposable leaves

DLI: amount of decomposable leaves that die in year

EL: decomposition of dead leaves

Flux OUT

EL = Sum i (DL t−i /DTL t ) for i = 1 to x

EL: Total emission due to decomposition of leaves

DTL: time needed to decompose; year

1.1.5 Living Roots (Belowground Biomass)

Type of equation

Equations, variables and parameters (all pools and processes in terms of tC ha−1)

BE

R = R t−1 + RI − DRI − DRI * ndf /(1 − ndf)

R: amount of roots

RI: increase of root biomass

DRI: amount of decomposable roots that die in year

Flux IN

RI = CAIC * rts

rts : root-to-shoot ratio

Other

DRI = [(R t−1 + RI) * nll + (LI t−1 + LRI) * (1 − nll ) * fdlroots ] * (1 − ndf)

fdlroots : Fraction of roots of trees (relative to root increment) that dies at the end of year

Flux to SINK

URI = DRI * ndf /(1 − ndf)

URI: Nondecomposable fraction; dimensionless

1.1.6 Dead Roots

Type of equation

Equations, variables, and parameters (all pools and processes in terms of tC ha−1)

BE

DR = DR t−1 + DRI – ER

ER: decomposition of (i.e., emission from) decomposable dead roots

Flux OUT

ER = DR t−i / DTR i

DTR: time needed to decompose; year

1.1.7 (Industrial) Wood Products

Type of equation

Equations, variables, and parameters (all pools and processes in terms of tC ha−1)

BE

WP = WP t−1 + WPI – WPU

WP: wood products

WPI: wood product increment

WPU: wood products becoming unused

Other

WPI = [FC * used part * (1 − fuelwood part ) + TH * used part (t) * (1 − fuelwood part (t)] * (1 − lost part)

used part : ratio of wood harvested that is used for wood products, dimensionless

fuelwoodpart : ratio of wood harvested that is used for fuelwood, dimensionless

lost part: ratio unused part of wood harvested, dimensionless

Other

WPU = WPI t−i

Maximum of i: mean life time of wood product (species-specific); years

Flux OUT

EUUWP = WPU t−1 * (1 − unburnt )* WTD

EUUWP: Emission from (decomposition of) unused and unburned wood product

unburnt: unburned fraction; dimensionless

WTD: (mean) time needed to decompose the wood products

1.1.8 Fuelwood

Type of equation

Equations, variables, and parameters (all pools and processes in terms of tC ha−1)

BE

FW = FW t−1 + FWI + LWP + UWPF − EFW

FW: fuelwood

FWI: fuelwood increment (from harvest)

LWP: loss in wood processing

UWPF: unused wood products becoming fuelwood

EFW: emission from firewood

Other

FWI = FC * used part * fuelwood part + TH * used part t * fuelwood part t

Other

LWP = WPI * lost part /(1 − lost part)

Other

UWPF = WPU t−1 * unburnt

Flux OUT

EFW = FW t−1 * (1 − unburn)

unburn: unburnable fraction; dimensionless

Flux to Sink

UFWI = FW t−1 * unburn

UFWI: unburnable fuelwood

1.1.9 Soil

Type of equation

Equations, variables, and parameters (all pools and processes in terms of tC ha−1)

Equations, variables, and parameters (all pools and processes in terms of tC ha−1)

BE

S = St−1 + FS CLO GAL

S: soil

FS: net flux to sink

CLO: loss of C from soil due to afforestation and regeneration operations

GAL: loss of C from soil due to afforesting grasslands

(Net flux to sink, which includes soil respiration, and loss due to afforesting grasslands are only calculated for 75 years after afforestations. After that time, net flux to sink is supposed to be zero, i.e. transfer of carbon from other compartments to soil is equal to soil respiration, and no more losses are supposed to take place due to converting grassland to forest.)

Other

CLO = A * asl

A: afforestation area by tree species and yield class; ha (to be provided by the user before running of the model)

asl: area specific carbon loss; to be specified by the user; tC ha-1

Other

GAL = A * glp * diff _CL_GL

glp: percent of area afforested on former grassland; to be specified by the user; percent

diff_CL_GL: time-dependent difference of carbon stock between cropland and grassland; tC ha−1

Flux to SINK

FS = ULI + URI + UWI + UFWI

1.2 Equations and Variables Applied to Model Carbon Economics in CASMOFOR

$$ \begin{array}{ll}Net\,annual\,forestry\,costs_{t}&=Total\,of\,all\,annual\,costs_{t}\\ &-total\,of\,all\,annual\,revenues_{t}\end{array} $$

where Total of all annual costs t is the sum of all costs, at current prices, (in either EUR or Hungarian Forints, HUF) of all forestry operation on all area of the afforestation project that are due in year t; and Total of all annual revenues t is the sum of all revenues, at current prices, of all forestry operation on all area of the afforestation project that are due in year t.

$$ Total\,net\,forestry\,costs_{t}=SUM_{i}\left(Net\,annual\,forestry\,cost{s}_{i}\right) $$

where Total net forestry costs t is the total costs from the beginning of the ­afforestation project, i.e. year 1, summed up to year t; and i = 1 to t.

$$ \begin{array}{ll}Total\,carbon\,in\,forestry\,system_{t}&=AGWB_{t}+L{L}_{t}+{R}_{t}+D{W}_{t}\\ &+D{L}_{t}+D{R}_{t}+W{P}_{t}+F{W}_{t}+{S}_{t}\end{array} $$

where Total carbon in forestry system t is the total amount of carbon accumulated in the afforestation project area up to year t in all carbon pools since year 1; all other symbols as in the table above.

$$ \begin{array}{ll}&\quad Total\,net\,specific\,costs_{t}\left(in\,EUR/tCO_{2}or\,HUF/tCO\right)\\ &=Total\,net\,forestry\,cost{s}_{t}/Total\,carbon\,in\,forestry\,system_{t}\end{array} $$

where Total net specific costs t is the total net forestry costs per tonne of CO2 ­sequestered by the afforestation system by year t.

Total net specific costs t are also calculated with only the sum of AGWB t + R t in the denominator to estimate total net specific costs of sequestering carbon in the tree biomass only.

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Somogyi, Z. et al. (2011). Models and Their Adaptation. In: Haszpra, L. (eds) Atmospheric Greenhouse Gases: The Hungarian Perspective. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9950-1_9

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