A process-based model designed for filling of large data gaps in tower-based measurements of net ecosystem productivity

In this paper we present a simple hybrid gap-filling model (GFM) designed with a minimum number of parameters necessary to capture the ecological processes important for filling medium-to-large gaps in Flux data. As the model is process-based, the model has potential to be used in filling large gaps exhibiting a broad range of micro-meteorological and site conditions. The GFM performance was evaluated using “Punch hole” and extrapolation experiments based on data collected in west-central New Brunswick. These experiments indicated that the GFM is able to provide acceptable results (r² > 0.80) when >500 data points are used in model parameterization. The GFM was shown to address daytime evolution of NEP reasonably well for a wide range of weather and site conditions. An analysis of residuals indicated that for the most part no obvious trends were evident; although a slight bias was detected in NEP with soil temperature. To explore the portability of the GFM across ecosystem types, a transcontinental validation was conducted using NEP and ancillary data from seven ecosystems along a north-south transect (i.e., temperature–moisture gradient) from northern Europe (Finland) to the Middle East (Israel). The GFM was shown to explain over 75% of the variability in NEP measured at most ecosystems, which strongly suggests that the GFM maybe successfully applied to forest ecosystems outside Canada.     

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.     

近50年贵州净生态系统生产力时空分布特征

利用大气植被相互作用模型AVIM2(Atmosphere-Vegetation Interaction Model 2)估算分析了时间长度为50年、空间分辨率为0.02°×0.02°的贵州净生态系统生产力(NEP),分析了其对气候变化的响应。结果表明,(1) AVIM2模型能够模拟出贵州森林净初级生产力(NPP)的变化,模拟偏差随着树龄的增大而不断减小,其模拟效果优于综合模型。(2)1961-2010年,贵州NEP(以C计)平均值为23.9 g·m-2·a-1,碳源区面积比例仅为5%,且植被覆盖类型为南部部分常绿阔叶林。NEP总量的变动范围为-7.0~11.5 Tg ·a-1,平均每年吸收碳4.87Tg,碳汇量占中国区域的3~7%。(3)贵州境内31%的区域固碳能力下降明显(P<0.05)且主要集中在植被类型为常绿针叶林及农作物的北部地区,还有7%的区域固碳能力升高明显(P<0.05)且位于南部部分常绿阔叶林地区。(4)贵州NEP与气温显著负相关(P<0.01),与降水量显著正相关(P<0.05),气温对NEP的影响大于降水。     

菌根类型对森林树木净初级生产力的影响

菌根是土壤真菌与植物根系形成的共生体,存在于绝大多数植物（90%）的根系和生境中。菌根共有7种类型,在生态系统的过程和功能方面都扮演着十分重要的角色。为了增强对菌根在森林生态系统中重要功能的理解,文章基于全球森林数据库,在全球尺度上研究了不同菌根类型对森林树木净初级生产力（NPP）的影响。结果表明,森林树木NPP随菌根类型的不同而不同,AM类型菌根森林的NPP[679.49 g.m-2.a-1（以C计）]要显著高于含ECM类型菌根的森林[479.00 g.m-2.a-1（以C计）];菌根类型的不同对森林树木地上和地下及其各组分NPP的影响和贡献也存在着显著的不同,AM类型菌根对地下NPP的贡献要高于ECM菌根,而ECM菌根对地上NPP的贡献则较大。菌根类型对地上、地下NPP组分的影响分析则表明,AM类型的菌根对树叶和细根NPP的贡献较大,而ECM类型菌根则对树木主干和枝NPP的贡献较大。可见,森林树木总体NPP及其各组分NPP都随着菌根类型的不同而存在显著的差异。     

Integrative trophic network assessments of a lentic ecosystem by key ecological approaches of water chemistry, trophic guilds, and ecosystem health assessments along with an ECOPATH model

The objective of this study was to describe the trophic structure and energy flow in a lentic ecosystem in South Korea. Physicochemical water conditions were evaluated along with the reservoir ecosystem health using a multimetric IBI model. Nutrient analyses of the reservoir showed a nutrient rich and hypereutrophic system. Guild analysis revealed that tolerant and omnivorous species dominated the ecosystem. Tolerant fish, as a proportion of the number of individuals, were associated (R² > 0.90, p < 0.01) with TN and TP, the key indicators of trophic state in lentic ecosystems. The mean Reservoir Ecosystem Health Assessment (REHA) score was 19.3 during the study, which was judged as in ‘fair to poor’ condition. A trophic analysis of the reservoir estimated by the ECOPATH model shows that most activity in terms of energy flow occurred in the lower part of the trophic web, where there was intensive use of primary producers as a food source. Consequently, of the 10 consumer groups, nine fell within trophic levels <2.8. Trophic levels (TL) estimated from the weighted average of prey trophic levels varied from 1.0 for phytoplankton, macrophytes, and detritus to 3.25 for the top predator, Pseudobagrus fulvidraco. Our integrated approach to trophic network analysis may provide a key tool for determining the effects of nutrient influx on energy flow pathways in lentic ecosystems.     

Uncertainty in future soil carbon trends at a central U.S. site under an ensemble of GCM scenario climates

This study uses DAYCENT model to investigate the sensitivity of soil organic carbon (SOC) at an intensely cultivated site in the U.S. Midwest under an ensemble of scenario climates predicted by IPCC models. The model ensemble includes three IPCC models (Canadian, French, German), three emission scenarios (B1, A1B, A2) and three time periods (late 20th, mid-21st, late 21st century). DAYCENT shows that SOC at the site would decline by 0.3-2.6 kg m⁻² (5-35%) depending on the models and scenarios from late 20th to mid-21st century despite a larger increase of future net primary production (NPP) than respiration. The future SOC decrease is mostly attributable to harvest loss. The wide spread in future SOC decline rates are in part because SOC decrease (by respiration) is directly proportional to SOC itself. Any uncertainty in absolute SOC in DAYCENT would translate directly into its trend, unlike other variables such as temperature whose trends are independent of their values themselves, contrasting the reliability of SOC trend with temperature change.     

Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison

Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among CBM-CFS3 and four process models (ecosys, CN-CLASS, Can-IBIS and 3PG) over a 2500 ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (NPP), net ecosystem productivity (NEP) and net biome productivity (NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients was applied during disturbances. After the disturbance period, divergence in modelled NBP among models was much smaller, and attributed mainly to differences in simulated NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual NPP and NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July-September (T_amax) in all process models (R² = 0.4-0.6), indicating that these correlations were robust. The negative correlation between T_amax and NEP was attributed to different processes in different models, which were tested by comparing CO₂ fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (T_a). The general agreement in sensitivity of annual NPP to T_amax among the process models led to the development of a generalized algorithm for weather effects on NPP of coastal temperate coniferous forests for use in inventory-based models such as CBM-CFS3: NPP′ = NPP − 57.1 (T_amax − 18.6), where NPP and NPP′ are the current and temperature-adjusted annual NPP estimates from the inventory-based model, 18.6 is the long-term mean daily maximum air temperature during July-September, and T_amax is the mean value for the current year. Our analysis indicated that the sensitivity of NPP to T_amax was nonlinear, so that this algorithm should not be extrapolated beyond the conditions of this study. However the process-based methodology to estimate weather effects on NPP and NEP developed in this study is widely applicable to other forest types and may be adopted for other inventory based forest carbon cycle models.     

On temperature and water limitation of net ecosystem productivity: Implementation in the C-Fix model

Monitoring, understanding and modelling carbon emission and fixation fluxes are key actions to guide climate change stakeholders in the application of mitigation strategies. In this study, we use the remote sensing model C-Fix at the local stand scale to improve the integration of algorithms for water and temperature limitation. These new algorithms are applied to estimate net ecosystem productivity in a fully water limited mode.     

Inter-annual variability of ecosystem production in boreal jack pine forests (1975-2004) estimated from tree-ring data using CBM-CFS3

We describe and apply a method of using tree-ring data and an ecosystem model to reconstruct past annual rates of ecosystem production. Annual data on merchantable wood volume increment and mortality obtained by dendrochronological stand reconstruction were used as input to the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to estimate net ecosystem production (NEP), net primary production (NPP), and heterotrophic respiration (Rh) annually from 1975 to 2004 at 10 boreal jack pine (Pinus banksiana Lamb.) stands in Saskatchewan and Manitoba, Canada. From 1975 (when sites aged 41-60 years) to 2004 (when they aged 70-89 years), all sites were moderate C sinks except during some warmer than average years where estimated Rh increased. Across all sites and years, estimated annual NEP averaged 57 g Cm⁻² yr⁻¹ (range −31 to 176 g Cm⁻² yr⁻¹), NPP 244 g Cm⁻² yr⁻¹ (147-376 g Cm⁻² yr⁻¹), and Rh 187 g Cm⁻² yr⁻¹ (124-270 g Cm⁻² yr⁻¹). Across all sites, NPP was related to stand age and density, which are proxies for successional changes in leaf area. Regionally, warm spring temperature increased NPP and defoliation by jack pine budworm 1 year previously reduced NPP. Our estimates of NPP, Rh, and NEP were plausible when compared to regional eddy covariance and carbon stock measurements. Inter-annual variability in ecosystem productivity contributes uncertainty to inventory-based assessments of regional forest C budgets that use yield curves predicting averaged growth over time. Our method could expand the spatial and temporal coverage of annual forest productivity estimates, providing additional data for the development of empirical models accounting for factors not presently considered by these models.     

PIXGRO: A model for simulating the ecosystem CO2 exchange and growth of spring barley

A model, PIXGRO, developed by coupling a canopy flux sub-model (PROXEL_NEE; PROcess-based piXEL Net Ecosystem CO₂ Exchange) to a vegetation structure submodel (CGRO), for simulating both net ecosystem CO₂ exchange (NEE) and growth of spring barley is described. PIXGRO is an extension of the stand-level CO₂ and H₂O-flux model PROXEL_NEE, that simulates the NEE on a process basis, but goes further to include the dry matter production, partitioning, and crop development for spring barley. Dry matter partitioned to the leaf was converted to leaf area index (LAI) using relationships for the specific leaf area (SLA). The canopy flux component, PROXEL_NEE was calibrated using information from the literature on C3 plants and was tested using CO₂ flux data from an eddy-covariance (EC) method in Finland with long-term observations. The growth component (CGRO) was calibrated using data from the literature on spring barley as well as data from the Finland site. It was then validated against field data from two sites in Germany and partly via the use of MODIS remotely sensed LAI from the Finland site.Both the diurnal and the seasonal patterns of gross CO₂ uptake were very well simulated (R² = 0.92). A slight seasonal bias may be attributed to leaf ageing. Crop growth was also well simulated; simulated dry matter agreed with field observed data from Germany (R² = 0.90). For LAI, the agreement between the simulated and observed was good (R² = 0.80), giving an indication that functions describing the conversion of fixed CO₂ to dry matter and the subsequent partitioning leaf dry matter and LAI simulation were robust and provided reliable estimates.The MODIS LAI at a resolution of 1000 m agreed poorly (R² = 0.45) with the PIXGRO simulated LAI and the observed LAI at the Finland site in 2001. We attributed this to the coarse resolution of the image and/or the small size of the barley field (about 17 ha or 0.25 km²) at the Finland site. By deriving a regression relation between the observed LAI and NDVI from a higher resolution MODIS (500 m resolution), the MODIS-recalculated LAI agreed better with the PIXGRO-simulated LAI (R² = 0.86).PIXGRO provides a prototype model bridging the disciplines of plant physiology, crop modeling and remote sensing, for use in a spatial context in evaluating carbon balances and plant growth at stand level, landscape, regional, and with some care, continental scales. Since almost 50% of the European land surface is covered by crops, such a model is needed for the dynamic estimation of LAI and NEE of croplands.     

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.