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Assessing Productivity and Carbon Sequestration Capacity of Eucalyptus Globulus Plantations Using the Process Model Forest-DNDC: Calibration and Validation

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Assessing Productivity and Carbon Sequestration Capacity of Eucalyptus Globulus Plantations Using the Process Model Forest-DNDC: Calibration and Validation Ecological Modelling 192 (2006) 83–94 Assessing productivity and carbon sequestration capacity of Eucalyptus globulus plantations using the process model Forest-DNDC: Calibration and validation P. Miehle a,∗, S.J. Livesley a, P.M. Feikema b,C.Lic, S.K. Arndt a a Greenhouse Technical Unit, School of Forest and Ecosystem Science, University of Melbourne, Water St., Creswick, Australia b School of Forest and Ecosystem Science, University of Melbourne, Heidelberg, Australia, c Complex Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, NH, USA Received 24 December 2004; received in revised form 5 June 2005; accepted 18 July 2005 Available online 19 September 2005 Abstract The tree growth sub-module (PnET) of the mechanistic model Forest-DNDC was calibrated and validated for plantation grown Eucalyptus globulus. Forest-DNDC describes the biogeochemical cycles of C and N and can assist in estimating soil-borne greenhouse gas fluxes. For validation of the forest growth sub-module, data from commercial forest plantations in south-eastern Australia was used. Growth predictions agreed well with growth measurements taken at age 6 years from 28 permanent sample plots, with an average prediction error of −1.62tCha−1 (−3.19%). Differences between predicted and measured aboveground C stocks ranged between −23.5 and 12.6tCha−1, which amounted to a relative root mean square error in prediction of 17.9%. Correlation between modelled and measured C in standing biomass was good (r2 = 0.73), with a Nash–Sutcliffe coefficient of model efficiency, ME = 0.65. The results obtained from the validation test reveal that Forest-DNDC can predict growth of E. globulus to a high level of precision across a broad range of climatic conditions and soil types. Forest-DNDC performed satisfactorily in comparison to other growth and yield models that have already been calibrated for E. globulus (e.g. BIOMASS, 3-PG, PROMOD or CABALA). In contrast to these growth and yield models, Forest-DNDC can additionally estimate total greenhouse gas budgets. The slightly lower precision of Forest-DNDC in comparison with specific management models, such as CABALA, are compensated for by the simple input requirements and application to regional situations. © 2005 Elsevier B.V. All rights reserved. Keywords: Forest-DNDC; Prediction error; Eucalyptus globulus; Plantation; Productivity; Model validation; Carbon sequestration; Afforestation 1. Introduction There is general consensus that the increasing con- ∗ Corresponding author. Tel.: +61 3 5321 4154; fax: +61 3 5321 4277. centration of greenhouse gases (e.g. CO2,CH4,N2O, E-mail address: [email protected] (P. Miehle). O3) have led to changes in the earth’s climate and 0304-3800/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2005.07.021 84 P. Miehle et al. / Ecological Modelling 192 (2006) 83–94 a warming of the earth’s surface. Furthermore, there est growth model PnET (Aber et al., 1996) as a sub- is agreement that human activities such as fossil fuel module. PnET has not been validated for E. globulus, combustion, land-use change and agricultural practices but parameterisations for a variety of forest types exist have contributed substantially to the rise in atmospheric (Aber and Federer, 1992; Aber et al., 1996). greenhouse gas concentrations (IPCC, 1997). Forestry, The importance of E. globulus as a plantation tree and afforestation in particular, is regarded as an impor- species in Australia, Brazil, Portugal, Spain and China, tant contributor to the offset of greenhouse gas emis- has increased rapidly over the past decade (Reed and sions. Projects that increase the area of plantations have Tome, 1998; Wood et al., 2001; Xu et al., 2001; Parsons been suggested for inclusion under the clean develop- et al., 2004). Because of its fast growth, E. globulus has ment mechanism (CDM) as defined in Article 12 of the become important to the pulp and paper industries of Kyoto Protocol (Van Vliet et al., 2003). However, sig- many countries. In Australia, the hardwood resource nificant uncertainties in the reliability of carbon pool increased by 354,500 ha between 1995 and 2000 with and flux measurements make it difficult to determine E. globulus comprising 62% (Wood et al., 2001). By the the (net) carbon benefits of afforestation or forestry end of 2000, a total of 311,344 ha had been planted with management practices As a result, further investment E. globulus, mainly in Western Australia, South Aus- in, and development of, the plantation industry is threat- tralia and Victoria. The area of hardwood plantations ened (Van Vliet et al., 2003). continues to expand across southern Australia, and E. There is a growing need for computer simulation globulus is the dominant hardwood species planted models that can assist in the estimation of carbon bud- (Wood et al., 2001; Parsons et al., 2004). In the state of gets, net ecosystem exchange or trace gas emissions Victoria, a total of 99,506 ha, or 83% of all hardwood (Mosier, 1998; Landsberg, 2003; Van Vliet et al., 2003; plantations were planted with E. globulus by the end Battaglia et al., 2004). Preferably, such models would of 2000. require only basic and readily obtainable input param- The aim of this study was the calibration and vali- eters whilst producing reliable predictions across a dation of the PnET tree growth sub-module of Forest- wide range of climatic and edaphic conditions. There DNDC for plantation grown Eucalyptus globulus. The are a number of process-based, forest growth mod- Forest-DNDC model was calibrated using measured els described in the literature that have been vali- biomass growth and relevant soil data from 20 perma- dated for Eucalyptus globulus; the species investigated nent sample plots (PSPs) in commercial forest planta- in this study. Amongst them are BIOMASS (Linder tions in south-eastern Australia. The parameterisation et al., 1985; Hingston and Galbraith, 1998; Hingston et was based almost entirely on published data or the al., 1998), 3-PG (Landsberg and Waring, 1997; Sands results of direct measurements. Validation of model and Landsberg, 2002), PROMOD (Battaglia and Sands, performance involved a comparison of simulated and 1997) and CABALA (Battaglia et al., 2004). These measured data from a further 28 PSPs in 6-year-old four forest productivity models could be used for car- plantations established in 1997 that were not used for bon accounting purposes. CABALA (from CArbon calibration, and a rigorous statistical analysis of the BALAnce), a further development of PROMOD, and simulation results. the most detailed of the four models, is particularly designed to model the carbon balance of even-aged, 2. Methods homogeneous stands. However, none of these growth and yield models is capable of modelling a total green- 2.1. Model description house gas budget, including soil borne emissions of NO, N2OorCH4. Carbon accounting is only one aspect The Forest-DNDC model is a process-based model of evaluating the net benefit of land use change. for predicting forest growth and production, soil car- The Forest-DNDC model (Li et al., 2000; Stange et bon and nitrogen dynamics, carbon sequestration and al., 2000) is primarily designed for the prediction of soil-borne trace gas emissions in upland and wet- forest production, soil carbon sequestration and trace land forested ecosystems. It integrates photosynthe- gas emissions (NO, N2O, CH4,NH3) in upland and sis and evapotranspiration-nitrification-denitrification wetland forest ecosystems. Forest-DNDC uses the for- and decomposition (PnET-N-DNDC), a forest biogeo- P. Miehle et al. / Ecological Modelling 192 (2006) 83–94 85 chemical model (Li et al., 2000; Stange et al., 2000), Further details on model development (Li et al., 1992a, with a matrix of approaches handling hydrological con- 2000), validation (Smith et al., 1997, 2002; Stange et ditions. al., 2000) and regional and local scale applications (Li The PnET-N-DNDC model consists of two compo- et al., 1992b, 1994; Butterbach-Bahl et al., 2001, 2004; nents. The first component is driven by environmental Brown et al., 2002), are available from the cited publi- controls, in particular climate, soil properties, vege- cations. tation type and anthropogenic activities. In this first component, sub-modules of soil climate, forest growth 2.2. Data and decomposition predict biomass accumulation in roots, aboveground woody tissue and foliage as well 2.2.1. Soils, growth and silvicultural management as water content, temperature, pH, redox potential and Soil information and plantation growth data, as substrate concentration profiles in the soil. The second well as information on relevant management operations component utilises the modelled soil environmental (e.g. fertiliser applications), were collected to develop factors as inputs to drive nitrification, denitrification a climate and soil database for farm forestry research and fermentation sub-modules and predicts NO, N2O, (Feikema et al., 2003b; Feikema et al., 2003a). The CH4 and NH3 fluxes. study involved a network of 302 permanent sample The simulation of forest growth has been adapted plots in 93 plantations across south-eastern Australia, from the PnET model (Aber and Federer, 1992; Aber predominantly in Victoria. The PSPs are located in et al., 1995; Aber et al., 1996). In PnET, foliar N plantations of E. globulus or E. nitens that were estab- concentration is used to determine photosynthesis lished between 1997 and 2001 on land previously used (Amax). Solar radiation, temperature and vapour pres- for grazing or agricultural production. sure deficit, together with other mean daily climate Soil profile descriptions were made to represent the variables, drive the calculation of realised Amax at the range of soil types observed. Adjacent to each PSP, top of the canopy. Radiation and specific leaf weight, a soil pit was dug to a maximum depth of 4 m, or to and therefore also Amax, decline with canopy depth.
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