[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.
[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.
[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.
Agricultural and Forest Meteorology 07/2014; 197:142–157. · 3.89 Impact Factor
[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.
Agricultural and Forest Meteorology 06/2014; 191:33-50. · 3.89 Impact Factor
[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: 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: 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.
Agricultural and Forest Meteorology 12/2013; 182-183(SI):111-127. · 3.89 Impact Factor
[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.
[Show abstract][Hide abstract] ABSTRACT: Simultaneous biometric measurements of aboveground net primary production (ANPP) and eddy-covariance measurements of gross primary production (GPP) were made at 18 forest stands with 80 site-years of data across Canada – to assess the fraction of photosynthesis that is used to produce plant tissues and the consistency of carbon allocation patterns across forest ecosystems. The stands included boreal and temperate forests and spanned very young to mature stand ages. Across all sites, ANPP averaged 298 ± 138 g C m−2 yr−1 (mean ± 1 s.d.), with the highest values for temperate white pine plantations (307–630 g C m−2 yr−1) and harvested Douglas-fir stands (219–459 g C m−2 yr−1), and the lowest values for boreal harvested jack pine stands (97–185 g C m−2 yr−1). ANPP more than doubled from newly established (≤12 years) to young (13–25 years) stands, then stabilized in young to mature (≥51 years) stands but with diverging trends among species. Inter-site variations in ANPP and GPP were closely related to site characteristics, in particular, to leaf area index, which explained 66% of the variation in ANPP and 80% of the variation in GPP, and absorbed photosynthetically active radiation, which explained 80% of the variation in ANPP and 82% of the variation in GPP. Both ANPP and GPP were also positively correlated with mean annual air temperature, mean annual precipitation, and total soil nitrogen in the upper 10 cm of the mineral soil. ANPP was strongly, positively correlated with GPP and the ANPP/GPP ratio was relatively constant (0.29 ± 0.06), with no consistent differences among species or age classes. The results support the use of a constant ANPP/GPP ratio as a reasonable assumption in models of forest productivity for boreal and northern temperate forests. A similar conclusion is reached for the NPP/GPP ratio when published values of belowground NPP are considered.
Agricultural and Forest Meteorology 06/2013; s 174–175:54–64. · 3.89 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Even though dissolved organic carbon (DOC) is the most active carbon (C)
cycling that takes place in soil organic carbon (SOC) pools, it is
missing from the global C budget. Fluxes in DOC are critical to aquatic
ecosystem inputs and contribute to C balances of terrestrial ecosystems.
Only a few ecosystem models have attempted to integrate DOC dynamics
into terrestrial C cycling. This study introduces a new process-based
model, TRIPLEX-DOC that is capable of estimating DOC dynamics in forest
soils by incorporating both ecological drivers and biogeochemical
processes. TRIPLEX-DOC was developed from Forest-DNDC, a biogeochemical
model simulating C and nitrogen (N) dynamics, coupled with a new DOC
process module that predicts metabolic transformations,
sorption/desorption, and DOC leaching in forest soils. The model was
validated against field observations of DOC concentrations and fluxes at
white pine forest stands located in southern Ontario, Canada. The model
was able to simulate seasonal dynamics of DOC concentrations and the
magnitudes observed within different soil layers, as well as DOC
leaching in the age-sequence of these forests. Additionally, TRIPLEX-DOC
estimated the effect of forest harvesting on DOC leaching, with a
significant increase following harvesting, illustrating that change in
land use is of critical importance in regulating DOC leaching in
temperate forests as an important source of C input to aquatic
Geoscientific Model Development Discussions 06/2013; 6(2):3473-3508.
[Show abstract][Hide abstract] ABSTRACT: The energy balance at most surface-atmosphere flux research sites remains unclosed. The mechanisms underlying the discrepancy between measured energy inputs and outputs across the global FLUXNET tower network are still under debate. Recent reviews have identified exchange processes and turbulent motions at large spatial and temporal scales in heterogeneous landscapes as the primary cause of the lack of energy balance closure at some intensively-researched sites, while unmeasured storage terms cannot be ruled out as a dominant contributor to lack of energy balance closure at many other sites. We analyzed energy balance closure across 173 ecosystems in the FLUXNET database and explored the relationship between energy balance closure and landscape heterogeneity using MODIS products and GLOBEstat elevation data. Energy balance closure per research site ( C EB,s ) averaged 0.84 ± 0.20, with best average closures in evergreen broadleaf forests and savannas (0.91-0.94) and worst average closures in crops, deciduous broadleaf forests, mixed forests and wetlands (0.70-0.78). Half-hourly or hourly energy balance closure on a percent basis increased with friction velocity ( u * ) and was highest on average under near-neutral atmospheric conditions. C EB,s was significantly related to mean precipitation, gross primary productivity and landscape-level enhanced vegetation index (EVI) from MODIS, and the variability in elevation, MODIS plant functional type, and MODIS EVI. A linear model including landscape-level variability in both EVI and elevation, mean precipitation, and an interaction term between EVI variability and precipitation had the lowest Akaike's information criterion value. C EB,s in landscapes with uniform plant functional type approached 0.9 and C EB,s in landscapes with uniform EVI approached 1. These results suggest that landscape-level heterogeneity in vegetation and topography cannot be ignored as a contributor to incomplete energy balance closure at the flux network level, although net radiation measurements, biological energy assimilation, unmeasured storage terms, and the importance of good practice including site selection when making flux measurements should not be discounted. Our results suggest that future research should focus on the quantitative mechanistic relationships between energy balance closure and landscape-scale heterogeneity, and the consequences of mesoscale circulations for surface-atmosphere exchange measurements.
Agricultural and Forest Meteorology 04/2013; 171-172:137-152. · 3.89 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The eddy-covariance method often underestimates fluxes under stable, low-wind conditions at night when turbulence is not well developed. The most common approach to resolve the problem of nighttime flux underestimation is to identify and remove the deficit periods using friction-velocity (u*) threshold filters (u*(Th)). This study modifies an accepted method for u*(Th) evaluation by incorporating change-point-detection techniques. The original and modified methods are evaluated at 38 sites as part of the North American Carbon Program (NACP) site-level synthesis. At most sites, the modified method produced u*(Th) estimates that were higher and less variable than the original method. It also provided an objective method to identify sites that lacked a u*(Th) response. The modified u*(Th) estimates were robust and comparable among years. Inter-annual u*(Th) differences were small, so that a single u*(Th) value was warranted at most sites. No variation in the u*(Th) was observed by time of day (dusk versus mid or late night), however, a few sites showed significant u*(Th) variation with time of year. Among-site variation in the u*(Th) was strongly related to canopy height and the mean annual nighttime u*. The modified u*(Th) estimates excluded a high fraction of nighttime data - 61% on average. However, the negative impact of the high exclusion rate on annual net ecosystem production (NEP) was small compared to the larger impact of underestimating the u*(Th). Compared to the original method, the higher u*(Th) estimates from the modified method caused a mean 8% reduction in annual NEP across all site-years, and a mean 7% increase in total ecosystem respiration (R-e). The modified method also reduced the u*(Th)-related uncertainties in annual NEP and R-e by more than 50%. These results support the use of u*(Th) filters as a pragmatic solution to a complex problem. (C) 2012 A.G. Barr. Published by Elsevier B.V. All rights reserved.
Agricultural and Forest Meteorology 04/2013; 171:31-45. · 3.89 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 20002006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 145 g C m-2 yr-1 during the spring transition period and +75 +/- 130 g C m-2 yr-1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately and therefore will misrepresent the seasonality and interannual variability of key biosphereatmosphere feedbacks and interactions in coupled global climate models.
Global Change Biology 02/2013; 18:566-584. · 8.22 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: There is a continued need for models to improve consistency and agreement with observations [Friedlingstein et al., 2006], both overall and under more frequent extreme climatic events related to global environmental change such as drought [Trenberth et al., 2007]. Past validation studies of terrestrial biosphere models have focused only on few models and sites, typically in close proximity and primarily in forested biomes [e.g., Amthor et al., 2001; Delpierre et al., 2009; Grant et al., 2005; Hanson et al., 2004; Granier et al., 2007; Ichii et al., 2009; Ito, 2008; Siqueira et al., 2006; Zhou et al., 2008]. Furthermore, assessing model‐data agreement relative to drought requires, in addition to high‐quality observedCO2 exchange data, a reliable drought metric as well as a natural experiment across sites and drought conditions.
Journal of Geophysical Research Atmospheres 02/2013; 115. · 3.44 Impact Factor