Development and optimization of an Agro-BGC ecosystem model for C4 perennial grasses

Extrapolating simulations of bioenergy crop agro-ecosystems beyond data-rich sites requires biophysically accurate ecosystem models and careful estimation of model parameters not available in the literature. To increase biophysical accuracy we added C₄ perennial grass functionality and agricultural practices to the Biome-BGC (BioGeochemical Cycles) ecosystem model. This new model, Agro-BGC, includes enzyme-driven C₄ photosynthesis, individual live and dead leaf, stem, and root carbon and nitrogen pools, separate senescence and litter fall processes, fruit growth, optional annual seeding, flood irrigation, a growing degree day phenology with a killing frost option, and a disturbance handler that simulates nitrogen fertilization, harvest, fire, and incremental irrigation. To obtain spatially generalizable vegetation parameters we used a numerical method to optimize five unavailable parameters for Panicum virgatum (switchgrass) using biomass yield data from three sites: Mead, Nebraska, Rockspring, Pennsylvania, and Mandan, North Dakota. We then verified simulated switchgrass yields at three independent sites in Illinois (IL). Agro-BGC is more accurate than Biome-BGC in representing the physiology and dynamics of C₄ grass and management practices associated with agro-ecosystems. The simulated two-year average mature yields with single-site Rockspring optimization have Root Mean Square Errors (RMSE) of 70, 152, and 162 and biases of 43, −87, 156 g carbon m⁻² for Shabbona, Urbana, and Simpson IL, respectively. The simulated annual yields in June, August, October, December, and February have RMSEs of 114, 390, and 185 and biases of −19, −258, and 147 g carbon m⁻² for Shabbona, Urbana, and Simpson IL, respectively. These RMSE and bias values are all within the largest 90% confidence interval around respective IL site measurements. Twenty-four of twenty-six simulated annual yields with Rockspring optimization are within 95% confidence intervals of Illinois site measurements during the mature fourth and fifth years of growth. Ten of eleven simulated two-year average mature yields with Rockspring optimization are within 65% confidence intervals of Illinois site measurements and the eleventh is within the 95% confidence interval. Rockspring optimized Agro-BGC achieves accuracies comparable to those of two previously published models: Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) and Integrated Farm System Model (IFSM). Agro-BGC suffers from static vegetation parameters that can change seasonally and as plants age. Using mature plant data for optimization mitigates this deficiency. Our results suggest that a multi-site optimization scheme using mature plant data from more sites would be adequate for generating spatially generalizable vegetation parameters for simulating mature bioenergy crop agro-ecosystems with Agro-BGC.     

Multiple sources of predictive uncertainty in modeled estimates of net ecosystem CO2 exchange

Net ecosystem CO₂ exchange (NEE) is typically measured directly by eddy covariance towers or is estimated by ecosystem process models, yet comparisons between the data obtained by these two methods can show poor correspondence. There are three potential explanations for this discrepancy. First, estimates of NEE as measured by the eddy-covariance technique are laden with uncertainty and can potentially provide a poor baseline for models to be tested against. Second, there could be fundamental problems in model structure that prevent an accurate simulation of NEE. Third, ecosystem process models are dependent on ecophysiological parameter sets derived from field measurements in which a single parameter for a given species can vary considerably. The latter problem suggests that with such broad variation among multiple inputs, any ecosystem modeling scheme must account for the possibility that many combinations of apparently feasible parameter values might not allow the model to emulate the observed NEE dynamics of a terrestrial ecosystem, as well as the possibility that there may be many parameter sets within a particular model structure that can successfully reproduce the observed data. We examined the extent to which these three issues influence estimates of NEE in a widely used ecosystem process model, Biome-BGC, by adapting the generalized likelihood uncertainty estimation (GLUE) methodology. This procedure involved 400,000 model runs, each with randomly generated parameter values from a uniform distribution based on published parameter ranges, resulting in estimates of NEE that were compared to daily NEE data from young and mature Ponderosa pine stands at Metolius, Oregon. Of the 400,000 simulations run with different parameter sets for each age class (800,000 total), over 99% of the simulations underestimated the magnitude of net ecosystem CO₂ exchange, with only 4.07% and 0.045% of all simulations providing satisfactory simulations of the field data for the young and mature stands, even when uncertainties in eddy-covariance measurements are accounted for. Results indicate fundamental shortcomings in the ability of this model to produce realistic carbon flux data over the course of forest development, and we suspect that much of the mismatch derives from an inability to realistically model ecosystem respiration. However, difficulties in estimating historic climate data are also a cause for model-data mismatch, particularly in a highly ecotonal region such as central Oregon. This latter difficulty may be less prevalent in other ecosystems, but it nonetheless highlights a challenge in trying to develop a dynamic representation of the terrestrial biosphere.     

Analyzing the carbon dynamics of central European forests: comparison of Biome-BGC simulations with measurements

Biogeochemical models are often used for making projections of future carbon dynamics under scenarios of global change. The aim of this study was to assess the accuracy of the process-based biogeochemical model Biome-BGC for application in central European forests from the lowlands to upper treeline as a pre-requisite for environmental impact assessments. We analyzed model behavior along an altitudinal gradient across the alpine treeline, which provided insights on the sensitivity of simulated average carbon pools to changes in environmental factors. A second set of tests included medium-term (30 years) simulations of carbon fluxes, and a third set of tests focused on daily carbon and water fluxes. Model results were compared to aboveground biomass measurements, leaf area index recordings as well as net ecosystem exchange (NEE) and actual evapotranspiration (AET) measurements. The simulated medium-term forest growth agreed well with measured data. Also daily NEE fluxes were simulated adequately in most cases. Problems were detected when simulating ecosystems close to the upper timberline (overestimation of measured growth and pool sizes), and when simulating daily AET fluxes (overestimation of measured fluxes). The results showed that future applications of Biome-BGC could benefit much from an improvement of model algorithms (e.g., the Q₁₀ model for respiration) as well as from a detailed analysis of the ecological significance of crucial parameters (e.g., the canopy water interception coefficient).     

Long-term simulation of the effect of climate changes on the growth of main Central-European forest tree species

The simulation of forest production until 2100 under different environmental scenarios and current management practices was performed using a process-based model BIOME-BGC previously parameterized for the main Central-European tree species, and adapted to include forest management practices. Three climatic scenarios (HadCM3, NCAR-PCM, CSIRO) used were taken from the IPCC database created within the 3^rd Assessment Report. They were combined with a scenario of CO₂ concentration development and a scenario of N deposition. The control scenario considered no changes of climatic characteristics, CO₂ concentration and N deposition.     

遥感数据结合Biome-BGC模型估算黄淮海地区生态系统生产力

植被净生态系统生产力(NEP)和净第一性生产力(NPP)作为表征植被活动的关键变量,在全球变化研究及区域生态环境评价中起着很重要的作用。Biome-BGC是一个模拟生态系统植被和土壤中的能量、水、碳、氮的流动和存储的生物地球化学循环模型。论文利用2004年时间序列MODIS LAI遥感产品和气象数据,对黄淮海地区的NEP和NPP进行了模拟估算,由于Biome-BGC模型没有农作物生理生态参数,农作物模拟通过修改草地生理生态参数,并在增加施肥、灌溉和收割代码基础上实现。结果表明,2004年黄淮海地区NEP、NPP呈现南部大于北部的空间分布特征;不同植被类型平均NEP和NPP大小顺序分别为:混交林>落叶阔叶林>常绿针叶林>农作物>灌木>草地、混交林>农作物>落叶阔叶林>常绿针叶林>灌木>草地;与观测数据、MODIS NPP产品和统计数据进行对比,表明Biome-BGC模型可较好用于区域植被生产力的模拟,农作物模拟结果与统计数据的决定系数达到0.612 3,且模拟得到的黄淮海地区农作物NPP比MODIS NPP产品更接近统计值。     

Modeling Carbon and Water Budgets in the Lushi Basin with Biome-BGC

1 INTRODUCTIONEvapotranspiration (ET) is the key process controlling theexchange of energy and hydrologic flux for vegetatedsurface. Terrestrial net primary productivity (NPP)represents the carbon available for plant allocation toleaves, stems, roots, defensive compounds, reproductionand is the basic measure of biological productivity. Treegrowth, forage available for grazing, food productivity,and atmospheric CO2 levels are all strongly controlled byNPP (White et al., 2000). The exch…     

A hierarchical analysis of terrestrial ecosystem model Biome-BGC: Equilibrium analysis and model calibration

The increasing complexity of ecosystem models represents a major difficulty in tuning model parameters and analyzing simulated results. To address this problem, this study develops a hierarchical scheme that simplifies the Biome-BGC model into three functionally cascaded tiers and analyzes them sequentially. The first-tier model focuses on leaf-level ecophysiological processes; it simulates evapotranspiration and photosynthesis with prescribed leaf area index (LAI). The restriction on LAI is then lifted in the following two model tiers, which analyze how carbon and nitrogen is cycled at the whole-plant level (the second tier) and in all litter/soil pools (the third tier) to dynamically support the prescribed canopy. In particular, this study analyzes the steady state of these two model tiers by a set of equilibrium equations that are derived from Biome-BGC algorithms and are based on the principle of mass balance. Instead of spinning-up the model for thousands of climate years, these equations are able to estimate carbon/nitrogen stocks and fluxes of the target (steady-state) ecosystem directly from the results obtained by the first-tier model. The model hierarchy is examined with model experiments at four AmeriFlux sites. The results indicate that the proposed scheme can effectively calibrate Biome-BGC to simulate observed fluxes of evapotranspiration and photosynthesis; and the carbon/nitrogen stocks estimated by the equilibrium analysis approach are highly consistent with the results of model simulations. Therefore, the scheme developed in this study may serve as a practical guide to calibrate/analyze Biome-BGC; it also provides an efficient way to solve the problem of model spin-up, especially for applications over large regions. The same methodology may help analyze other similar ecosystem models as well.     

Modeling Carbon and Water Budgets in the Lushi Basin with Biome-BGC

Abstract

In this article, annual evapotranspiration (ET) and net primary productivity (NPP) of four types of vegetation were estimated for the Lushi basin, a subbasin of the Yellow River in China. These four vegetation types include: deciduous broadleaf forest, evergreen needle leaf forest, dwarf shrub and grass. Biome-BGC—a biogeochemical process model was used to calculate annual ET and NPP for each vegetation type in the study area from 1954 to 2000. Daily microclimate data of 47 years monitored by Lushi meteorological station was extrapolated to cover the basin using MT-CLIM, a mountain microclimate simulator. The output files of MT-CLIM were used to feed Biome-BGC. We used average ecophysiological values of each type of vegetation supplied by Numerical Terradynamic Simulation Group (NTSG) in the University of Montana as input ecophysiological constants file. The estimates of daily NPP in early July and annual ET on these four biome groups were compared respectively with field measurements and other studies. Daily gross primary production (GPP) of evergreen needle leaf forest measurements were very close to the output of Biome-BGC, but measurements of broadleaf forest and dwarf shrub were much smaller than the simulation result. Simulated annual ET and NPP had a significant correlation with precipitation, indicating precipitation is the major environmental factor affecting ET and NPP in the study area. Precipitation also is the key climatic factor for the interannual ET and NPP variations.