[Show abstract][Hide abstract] ABSTRACT: In this study, the Carbon and Nitrogen coupled Canadian Land Surface Scheme (CN-CLASS) was used to investigate the impact of climate variability, seasonal weather effects, disturbance, and CO2 fertilization effects on the historical carbon (C) dynamics of an eastern Canadian boreal forest landscape (6275 ha) from 1928 to 2008. The model was parameterized with ecological, soil texture, forest inventory and historical disturbance data and driven by hourly meteorological data constructed from the historical climate records. Before performing the landscape-level simulation, model results were evaluated against site-level eddy covariance (EC) measurements. Landscape-level simulated C fluxes showed that the forest ecosystem was a small C sink in all of the years prior to cutting and insect defoliation in 1963, which resulted in the removal of 23849 Mg C from the forest landscape. As a consequence, the study area was a large C source in 1963 (net biome productivity, NBP = −537 g C m−2 yr−1). After that, the forest landscape was mainly a net annual C sink, with total ecosystem C stocks increasing from 14.8 to 16.0 kg C m−2 by 2008, during which total biomass increased from 3.1 to 4.2 kg C m−2. Analysis of landscape-level, age-detrended, simulated C fluxes for the undisturbed forest landscape from 1928 to 2002 indicated that summer temperature was the dominant control on C fluxes with higher temperature causing a much faster increase in landscape-level annual Re than that of GPP (i.e. 12.3 vs. 1.3 g C m−2 yr−1 °C−1, respectively). Scenario analysis suggested that forest disturbances had a less profound impact on landscape-level C fluxes and stocks compared to inter-annual climate variability in this landscape. Climate sensitivity analysis revealed that landscape-level simulated C fluxes and stocks were sensitive to the change of air temperature, while only dead organic matter (DOM) and soil organic matter (SOM) were sensitive to the change of precipitation. This study will help to explore the impact of future climate change scenarios and forest management on boreal forest landscapes.
Full-text · Article · Feb 2016 · Ecological Modelling
[Show abstract][Hide abstract] ABSTRACT: Accurate and reliable estimates of gross primary productivity (GPP) are required for monitoring the global carbon cycle at different spatial and temporal scales. Because GPP displays high spatial and temporal variation, remote sensing plays a major role in producing gridded estimates of GPP across spatiotemporal scales. In this context, understanding the strengths and weaknesses of remote sensing-based models of GPP and improving their performance is a key contemporary scientific activity. We used measurements from 157 research sites (~470 site-years) in the FLUXNET "La Thuile" data and compared the skills of 11 different remote sensing models in capturing intra- and inter-annual variations in daily GPP in seven different biomes. Results show that the models were able to capture significant intra-annual variation in GPP (Index of Agreement. = 0.4-0.80) in all biomes. However, the models' ability to track inter-annual variation in daily GPP was significantly weaker (IoA. <. 0.45). We examined whether the inclusion of different mechanisms that are missing in the models could improve their predictive power. The mechanisms included the effect of sub-daily variation in environmental variables on daily GPP, factoring-in differential rates of GPP conversion efficiency for direct and diffuse incident radiation, lagged effects of environmental variables, better representation of soil-moisture dynamics, and allowing spatial variation in model parameters. Our analyses suggest that the next generation remote sensing models need better representation of soil-moisture, but other mechanisms that have been found to influence GPP in site-level studies may not have significant bearing on model performance at continental and global scales. Understanding the relative controls of biotic vis-a-vis abiotic factors on GPP and accurately scaling up leaf level processes to the ecosystem scale are likely to be important for recognizing the limitations of remote sensing model and improving their formulation.
No preview · Article · Dec 2015 · Agricultural and Forest Meteorology
[Show abstract][Hide abstract] ABSTRACT: Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at 6 FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8-day GPP. Optimized maximum light use efficiency of shaded leaves (εmsh) was 2.63 to 4.59 times that of sunlit leaves (εmsu). Generally, the relationships of εmsh and εmsu with εmax were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems and it is more robust with regard to usual biases in input data than existing approaches which neglect the bi-modal within-canopy distribution of PAR.
Full-text · Article · Nov 2015 · Journal of Geophysical Research: Biogeosciences
[Show abstract][Hide abstract] ABSTRACT: Carbon, water and energy exchanges between forests and the atmosphere depend upon seasonal dynamics of both temperature and precipitation, which are influenced by low frequency climate oscillations such as: El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Eastern Pacific Oscillation (EPO) and the Pacific-North American (PNA). This study investigated the influence of climate oscillations on the local climate and carbon fluxes in a 75-year old temperate pine (Pinus strobus L.) forest, near Lake Erie in southern Ontario, Canada. Analyses indicated mean winter temperatures were correlated to NAO, AO and EPO, total winter precipitation was influenced by PNA and AO, while total snowfall was correlated with PNA and ENSO. These impacts influenced carbon dynamics of the forest during the winter and spring seasons. The EPO had a significant inverse correlation with winter and spring carbon fluxes, while the Pacific Decadal Oscillation (PDO) was significantly correlated with winter respiration. In 2012, an extreme warm event linked to climate oscillations raised temperatures and resulted in a large release of carbon from the forest due to higher ecosystem respiration. As low frequency climate oscillations are important drivers of extreme weather events, affecting their intensity, frequency and spatial patterns, they can cause large changes in carbon exchanges in forest ecosystems in the northeastern parts of North America.
[Show abstract][Hide abstract] ABSTRACT: This study analyzed age-related water use dynamics across three temperate conifer forest plantations (aged 11-, 39-, and 74-years old, as of 2013, henceforth referred to as TP02, TP74, and TP39, where the last two digits represent the year of planting) in southern Ontario, Canada from 2008 to 2013. Eddy covariance-measured mean evapotranspiration over the growing season (April-October) was 438 ± 19, 392 ± 19 and 323 ± 25 mm at TP39, TP74 and TP02 respectively. Daytime bulk surface conductance was highest and most variable at the TP39 site (8.5 ± 4.0 mm s−1), followed by the TP74 (7.0 ± 2.8 mm s−1) and TP02 (5.4 ± 2.5 mm s−1) sites. Evapotranspiration at all the forests was sensitive to air temperature and also tended to decrease with increasing atmospheric dryness. The youngest forest's evapotranspiration was most conservative, which led to an increase in water use efficiency throughout the study period, in particular during drought events. The oldest forest was the least restrictive in its water use during drought, which led to lower water use efficiency during such events as compared to the younger forests. The oldest forest was thinned in early 2012, where about 1/3 of trees were commercially harvested. No significant change in evapotranspiration or water use efficiency was observed at this site following thinning, however daytime bulk surface conductance declined. Our results suggested that changes in stand structure with forest ageing, such as reduction in stem density and increase in sapwood area, were responsible for differences in soil water demand during drought and non-drought periods, leading to differences in forest water use. Hence, forest age, due to its structural implications, is an important control on the stand-level water use efficiency and forests’ response to drought events. Our study suggested that younger forests may be best suited to maximize growth and carbon uptake efficiency under rising air temperatures and increasing precipitation variability as predicted by climate models for eastern North America. This article is protected by copyright. All rights reserved.
Full-text · Article · May 2015 · Hydrological Processes
[Show abstract][Hide abstract] ABSTRACT: The soil water stress factor (fw) and the maximum photosynthetic carboxylation rate at 25 °C (Vcmax) are two of the most important parameters for estimating evapotranspiration and carbon uptake of vegetation. Ecologically these two parameters have different temporal variations and thus their optimization in ecosystem models poses a challenge. To minimize the temporal scale effect, we propose a three-stage approach to optimize these two parameters using an ensemble Kalman filter (EnKF), based on observations of latent heat (LE) and gross primary productivity (GPP) fluxes at three flux tower sites in 2009. First, the EnKF is applied daily to obtain precursor estimates of Vcmax and fw. Then, Vcmax is optimized at different time scales, assuming fw is unchanged from the first step. The best temporal period is then determined by analyzing the coefficient of determination (R2) of GPP and LE between simulation and observation. Finally, the daily fw value is optimized for rain-free days corresponding to the Vcmax curve from the best temporal period. We found that the variations of optimized fw are largely explained by soil water content in the summer. In the spring, the optimized fw shows a smooth increase following the rise of soil temperature, indicating that fw may respond to the development of fine roots, which is related to the amount of accumulated heat in the soil. The optimized Vcmax generally follows a pattern of a rapid increase at the leaf expansion stage in the spring, small variation in summer, and an abrupt decrease at foliage senescence. With eddy covariance fluxes data, data assimilation with a EnKF can retrieve the seasonal variations of water uptake and photosynthetic parameters in an ecosystem model, and such gives clues on how to model forest responses to water stress.
Full-text · Article · Dec 2014 · Ecological Modelling
[Show abstract][Hide abstract] ABSTRACT: Models of gross primary production (GPP) based on remote sensing measurements are currently
parameterized with vegetation-specific parameter sets and therefore require accurate information on
the distribution of vegetation to drive them. Can this parameterization scheme be replaced with a
vegetation-invariant set of parameters that can maintain or increase model applicability by reducing
errors introduced from the uncertainty of land cover classification? Based on the measurements of ecosystem
carbon fluxes from 168 globally distributed sites in a range of vegetation types, we examined the
predictive capacity of seven light use efficiency (LUE) models. Two model experiments were conducted:
(i) a constant set of parameters for various vegetation types and (ii) vegetation-specific parameters. The
results showed no significant differences in model performance in simulating GPP while using both set of
parameters. These results indicate that a universal of set of parameters, which is independent of vegetation
cover type and characteristics can be adopted in prevalent LUE models. Availability of this well tested
and universal set of parameters would help to improve the accuracy and applicability of LUE models in
various biomes and geographic regions.
Full-text · Article · Nov 2014 · Ecological Modelling
[Show abstract][Hide abstract] ABSTRACT: Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magni-tude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects – NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH 4 Inter-comparison of Models Project) – we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (σ) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0 ± 9.2 kg C m −2), then gross primary pro-duction (GPP) (0.22 ± 0.50 kg C m −2 yr −1), ecosystem res-piration (Re) (0.23 ± 0.38 kg C m −2 yr −1), net primary pro-duction (NPP) (0.14 ± 0.33 kg C m −2 yr −1), autotrophic res-piration (Ra) (0.09 ± 0.20 kg C m −2 yr −1), heterotrophic res-piration (Rh) (0.14 ± 0.20 kg C m −2 yr −1), net ecosystem ex-change (NEE) (−0.01 ± 0.19 kg C m −2 yr −1), and CH 4 flux (2.52 ± 4.02 g CH 4 m −2 yr −1). There were no consistent spa-tial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong car-bon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arc-tic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assess-ment of carbon cycle uncertainties may be used as a base-line for the improvement of experimental and modeling ac-tivities, as well as a reference for future trajectories in car-bon cycling with climate change in the Alaskan Arctic and larger boreal region.
[Show abstract][Hide abstract] ABSTRACT: The exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of the sink remain uncertain. We use a data-driven approach to upscale eddy covariance flux observations from towers to the continental scale by integrating flux observations, meteorology, stand age, aboveground biomass, and a proxy for canopy nitrogen concentrations from AmeriFlux and Fluxnet-Canada Research Network as well as a variety of satellite data streams from the MODIS sensors. We then use the resulting gridded flux estimates from March 2000 to December 2012 to assess the magnitude, distribution, and interannual variability of carbon fluxes for the U.S. and Canada. The mean annual gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP) of the U.S. over the period 2001–2012 were 6.84, 5.31, and 1.10 Pg C yr−1, respectively; the mean annual GPP, ER, and NEP of Canada over the same 12-year period were 3.91, 3.26, and 0.60 Pg C yr−1, respectively. The mean nationwide annual NEP of natural ecosystems over the period 2001–2012 was 0.53 Pg C yr−1 for the U.S. and 0.49 Pg C yr−1 for the conterminous U.S. Our estimate of the carbon sink for the conterminous U.S. was almost identical with the estimate of the First State of the Carbon Cycle Report (SOCCR). The carbon fluxes exhibited relatively large interannual variability over the study period. The main sources of the interannual variability in carbon fluxes included drought and disturbance. The annual GPP and NEP were strongly related to annual evapotranspiration (ET) for both the U.S. and Canada, showing that the carbon and water cycles were closely coupled. Our gridded flux estimates provided an independent, alternative perspective on ecosystem carbon exchange over North America.
[Show abstract][Hide abstract] ABSTRACT: Evapotranspiration (E) in the Amazon connects forest function and regional climate via its role in precipitation recycling. However, the mechanisms regulating water supply to vegetation and its demand for water remain poorly understood, especially during periods of seasonal water deficits In this study, we address two main questions: First, how do mechanisms of water supply (indicated by rooting depth and groundwater) and vegetation water demand (indicated by stomatal conductance and intrinsic water use efﬁciency) control evapotranspiration (E) along broad gradients of climate and vegetation from equatorial Amazonia to Cerrado, and second, how do these inferred mechanisms of supply and demand compare to those employed by a suite of ecosystem models? We used a network of eddy covariance towers in Brazil coupled with ancillary measurements to address these questions. With respect to the magnitude and seasonality of E, models have much improved in equatorial tropical forests by eliminating most dry season water limitation, diverge in performance in transitional forests where seasonal water deﬁcits are greater, and mostly capture the observed seasonal depressions in E at Cerrado. However, many models depended universally on either deep roots or groundwater to mitigate dry season water deﬁcits, the relative importance of which we found does not vary as a simple function of climate or vegetation. In addition, canopy stomatal conductance (gs) regulates dry season vegetation demand for water at all except the wettest sites even as the seasonal cycle of E follows that of net radiation. In contrast, some models simulated no seasonality in gs, even while matching the observed seasonal cycle of E. We suggest that canopy dynamics mediated by leaf phenology may play a signiﬁcant role in such seasonality, a process poorly represented in models. Model bias in gs and E, in turn, was related to biases arising from the simulated light response (gross primary productivity, GPP) or the intrinsic water use efﬁciency of photosynthesis (iWUE). We identiﬁed deﬁciencies in models which would not otherwise be apparent based on a simple comparison of simulated and observed rates of E. While some deﬁciencies can be remedied by parameter tuning, in most models they highlight the need for continued process development of belowground hydrology and in particular, the biological processes of root dynamics and leaf phenology, which via their
controls on E, mediate vegetation-climate feedbacks in the tropics.
Full-text · Article · Jun 2014 · Agricultural and Forest Meteorology
[Show abstract][Hide abstract] ABSTRACT: As a key component of the carbon cycle, soil CO2 efflux (SCE) is being increasingly studied to improve our mechanistic understanding of this important carbon flux. Predicting ecosystem responses to climate change often depends on extrapolation of current relationships between ecosystem processes and their climatic drivers to conditions not yet experienced by the ecosystem. This raises the question to what extent these relationships remain unaltered beyond the current climatic window for which observations are available to constrain the relationships. Here, we evaluate whether current responses of SCE to fluctuations in soil temperature and soil water content can be used to predict SCE under altered rainfall patterns. Of the 58 experiments for which we gathered SCE data, 20 were discarded because either too few data were available, or inconsistencies precluded their incorporation in the analyses. The 38 remaining experiments were used to test the hypothesis that a model parameterized with data from the control plots (using soil temperature and water content as predictor variables) could adequately predict SCE measured in the manipulated treatment. Only for seven of these 38 experiments, this hypothesis was rejected. Importantly, these were the experiments with the most reliable datasets, i.e., those providing high-frequency measurements of SCE. Regression tree analysis demonstrated that our hypothesis could be rejected only for experiments with measurement intervals of less than 11 days, and was not rejected for any of the 24 experiments with larger measurement intervals. This highlights the importance of high-frequency measurements when studying effects of altered precipitation on SCE, probably because infrequent measurement schemes have insufficient capacity to detect shifts in the climate-dependencies of SCE. Hence, the most justified answer to the question whether current moisture responses of SCE can be extrapolated to predict SCE under altered precipitation regimes is ‘no’ – as based on the most reliable datasets available. We strongly recommend that future experiments focus more strongly on establishing response functions across a broader range of precipitation regimes and soil moisture conditions. Such experiments should make accurate measurements of water availability, should conduct high-frequency SCE measurements, and should consider both instantaneous responses and the potential legacy effects of climate extremes. This is important, because with the novel approach presented here, we demonstrated that at least for some ecosystems, current moisture responses could not be extrapolated to predict SCE under altered rainfall conditions.
[Show abstract][Hide abstract] ABSTRACT: A fundamental question connecting terrestrial ecology and global climate change is the sensitivity of key terrestrial biomes to climatic variability and change. The Amazon region is such a key biome: it contains unparalleled biological diversity, a globally significant store of organic carbon, and it is a potent engine driving global cycles of water and energy. The importance of understanding how land surface dynamics of the Amazon region respond to climatic variability and change is widely appreciated, but despite significant recent advances, large gaps in our understanding remain. Understanding of energy and carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Land surface/ecosystem models have become important tools for extrapolating local observations and understanding to much larger terrestrial regions. They are also valuable tools to test hypothesis on ecosystem functioning. Funded by NASA under the auspices of the LBA (the Large-Scale Biosphere–Atmosphere Experiment in Amazonia), the LBA Data Model Intercomparison Project (LBA-DMIP) uses a comprehensive data set from an observational network of flux towers across the Amazon, and an ecosystem modeling community engaged in ongoing studies using a suite of different land surface and terrestrial ecosystem models to understand Amazon forest function. Here an overview of this project is presented accompanied by a description of the measurement sites, data, models and protocol.
Full-text · Article · Dec 2013 · Agricultural and Forest Meteorology
[Show abstract][Hide abstract] ABSTRACT: Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model-data agreement, but usually do not identify the time and frequency patterns of model-data disagreement, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and timescales at which two time series, for example time series of models and measurements, are significantly different. We applied wavelet coherence to interpret the predictions of 20 ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured net ecosystem exchange (NEE) from 10 ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, the inclusion of foliar nitrogen (N), and the use of model-data fusion. Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual timescales in grassland, wetland and agricultural ecosystems. Models that calculated NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual timescales in grassland and wetland ecosystems, but models that calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) timescales at Howland Forest, Maine. The model that employed a model-data fusion approach often, but not always, resulted in improved fit to data, suggesting that improving model parameterization is important but not the only step for improving model performance. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual timescales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual timescales.
[Show abstract][Hide abstract] ABSTRACT: Weather effects on forest productivity are not normally represented in inventory-based models for carbon accounting. To represent these effects, a meta-analysis was conducted on modeling results of five process models (ecosys, CN-CLASS, Can-IBIS, InTEC and TRIPLEX) as applied to a 6275 ha boreal forest landscape in Eastern Canada. Process model results showed that higher air temperature (Ta) caused gains in CO2 uptake in spring, but losses in summer, both of which were corroborated by CO2 fluxes measured by eddy covariance (EC). Seasonal changes in simulated CO2 fluxes and resulting inter-annual variability in NEP corresponded to those derived from EC measurements. Simulated long-term changes in above-ground carbon (AGC) resulting from modeled NEP and disturbance responses were close to those estimated from inventory data. A meta-analysis of model results indicates a robust positive correlation between simulated annual NPP and mean maximum daily air temperature (Tamax) during May–June in four of the process models. We therefore, derived a function to impart climate sensitivity to inventory-based models of NPP: NPP′i = NPPi + 9.5 (Tamax −16.5) where NPPi and NPP′i; are the current and temperature-adjusted NPP, 16.5 is the long-term mean Tamax during May–June, and Tamax is that for the current year. The sensitivity of net CO2 exchange to Ta is nonlinear. Although, caution should be exercised while extrapolating this algorithm to regions beyond the conditions studied in this landscape, results of our study are scalable to other regions with a humid continental boreal climate dominated by black spruce. Collectively, such regions comprise one of the largest climatic zones in the 450 Mha North American boreal forest ecosystems.
No preview · Article · Jul 2013 · Ecological Modelling