Flanagan, Larry

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    Coupled eco-hydrology and biogeochemistry algorithms enable the simulation of water table depth effects on boreal peatland net CO2 exchange
    (European Geosciences Union, 2017) Mezbahuddin, Mohammad; Grant, Robert F.; Flanagan, Larry B.
    Water table depth (WTD) effects on net ecosystem CO2 exchange of boreal peatlands are largely mediated by hydrological effects on peat biogeochemistry and the ecophysiology of peatland vegetation. The lack of representation of these effects in carbon models currently limits our predictive capacity for changes in boreal peatland carbon deposits under potential future drier and warmer climates. We examined whether a process-level coupling of a prognostic WTD with (1) oxygen transport, which controls energy yields from microbial and root oxidation–reduction reactions, and (2) vascular and nonvascular plant water relations could explain mechanisms that control variations in net CO2 exchange of a boreal fen under contrasting WTD conditions, i.e., shallow vs. deep WTD. Such coupling of eco-hydrology and biogeochemistry algorithms in a process-based ecosystem model, ecosys, was tested against net ecosystem CO2 exchange measurements in a western Canadian boreal fen peatland over a period of drier-weather-driven gradual WTD drawdown. A May–October WTD drawdown of  ∼  0.25 m from 2004 to 2009 hastened oxygen transport to microbial and root surfaces, enabling greater microbial and root energy yields and peat and litter decomposition, which raised modeled ecosystem respiration (Re) by 0.26 µmol CO2 m−2 s−1 per 0.1 m of WTD drawdown. It also augmented nutrient mineralization, and hence root nutrient availability and uptake, which resulted in improved leaf nutrient (nitrogen) status that facilitated carboxylation and raised modeled vascular gross primary productivity (GPP) and plant growth. The increase in modeled vascular GPP exceeded declines in modeled nonvascular (moss) GPP due to greater shading from increased vascular plant growth and moss drying from near-surface peat desiccation, thereby causing a net increase in modeled growing season GPP by 0.39 µmol CO2 m−2 s−1 per 0.1 m of WTD drawdown. Similar increases in GPP and Re caused no significant WTD effects on modeled seasonal and interannual variations in net ecosystem productivity (NEP). These modeled trends were corroborated well by eddy covariance measured hourly net CO2 fluxes (modeled vs. measured: R2  ∼  0.8, slopes  ∼ 1 ± 0.1, intercepts  ∼ 0.05 µmol m−2 s−1), hourly measured automated chamber net CO2 fluxes (modeled vs. measured: R2  ∼ 0.7, slopes  ∼ 1 ± 0.1, intercepts  ∼ 0.4 µmol m−2 s−1), and other biometric and laboratory measurements. Modeled drainage as an analog for WTD drawdown induced by climate-change-driven drying showed that this boreal peatland would switch from a large carbon sink (NEP  ∼  160 g C m−2 yr−1) to carbon neutrality (NEP  ∼  10 g C m−2 yr−1) should the water table deepen by a further  ∼ 0.5 m. This decline in projected NEP indicated that a further WTD drawdown at this fen would eventually lead to a decline in GPP due to water limitation. Therefore, representing the effects of interactions among hydrology, biogeochemistry and plant physiological ecology on ecosystem carbon, water, and nutrient cycling in global carbon models would improve our predictive capacity for changes in boreal peatland carbon sequestration under changing climates.
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    A model-data intercomparison of CO2 exchange across North America: results from the North American Carbon Program site synthesis
    (American Geophysical Union, 2010) Schwalm, Christopher R.; Williams, Christopher A.; Schaefer, Kevin; Anderson, Ryan S.; Arain, M. Altaf; Baker, Ian T.; Barr, Alan G.; Black, T. Andrew; Chen, Guangsheng; Chen, Jing M.; Ciais, Philippe; Davis, Kenneth J.; Desai, Ankur R.; Dietze, Michael C.; Dragoni, Danilo; Fischer, Marc L.; Flanagan, Larry B.; Grant, Robert F.; Gu, Lianhong; Hollinger, David Y.; Izaurralde, R. Cesar; Kucharik, Christopher J.; Lafleur, Peter M.; Law, Beverly E.; Li, Longhui; Li, Zhengpeng; Liu, Shuguang; Lokupitiya, Erandathie; Luo, Yiqi; Ma, Siyan; Margolis, Hank; Matamala, Roser; McCaughey, Harry; Monson, Russell K.; Oechel, Walter C.; Peng, Changhui; Poulter, Benjamin; Price, David T.; Ricciuto, Daniel M.; Riley, William J.; Sahoo, Alok Kumar; Sprintsin, Michael; Sun, Jianfeng; Tian, Hanqin; Tonitto, Christina; Verbeeck, Hans; Verma, Shashi B.
    Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models’ ability to simulate the seasonal cycle of CO2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.
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    Characterizing the performance of ecosystem models across time scales: a spectral analysis of the North American Carbon Program site-level synthesis
    (American Geophysical Union, 2011) Dietze, Michael C.; Vargas, Rodrigo; Richardson, Andrew D.; Stoy, Paul C.; Barr, Alan G.; Anderson, Ryan S.; Arain, M. Altaf; Baker, Ian T.; Black, T. Andrew; Chen, Jing M.; Philippe, Ciais; Flanagan, Larry B.; Gough, Christopher M.; Grant, Robert F.; Hollinger, David Y.; Izaurralde, R. Cesar; Kucharik, Christopher J.; Lafleur, Peter M.; Liu, Shugang; Lokupitiya, Erandathie; Luo, Yiqi; Munger, J. William; Peng, Changhui; Poulter, Benjamin; Price, David T.; Ricciuto, Daniel M.; Riley, William J.; Sahoo, Alok Kumar; Schaefer, Kevin; Suyker, Andrew E.; Tian, Hanqin; Tonitto, Christina; Verbeeck, Hans; Verma, Shashi B.; Wang, Weifeng; Weng, Ensheng
    Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program’s site-level intercomparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20–120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2–20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate-scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model-by-band effect but also a non significant model-by-site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.
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    On the use of MODIS EVI to assess gross primary productivity of North American ecosystems
    (American Geophysical Union, 2006) Sims, Daniel A.; Rahman, Abdullah F.; Cordova, Vicente D.; El-Masri, Bassil Z.; Baldocchi, Dennis D.; Flanagan, Larry B.; Goldstein, Allen H.; Hollinger, David Y.; Misson, Laurent; Monson, Russell K.; Oechel, Walter C.; Schmid, Hans P.; Wofsy, Steven C.; Xu, Liukang
    Carbon flux models based on light use efficiency (LUE), such as the MOD17 algorithm, have proved difficult to parameterize because of uncertainties in the LUE term, which is usually estimated from meteorological variables available only at large spatial scales. In search of simpler models based entirely on remote-sensing data, we examined direct relationships between the enhanced vegetation index (EVI) and gross primary productivity (GPP) measured at nine eddy covariance flux tower sites across North America. When data from the winter period of inactive photosynthesis were excluded, the overall relationship between EVI and tower GPP was better than that between MOD17 GPP and tower GPP. However, the EVI/GPP relationships vary between sites. Correlations between EVI and GPP were generally greater for deciduous than for evergreen sites. However, this correlation declined substantially only for sites with the smallest seasonal variation in EVI, suggesting that this relationship can be used for all but the most evergreen sites. Within sites dominated by either evergreen or deciduous species, seasonal variation in EVI was best explained by the severity of summer drought. Our results demonstrate that EVI alone can provide estimates of GPP that are as good as, if not better than, current versions of the MOD17 algorithm for many sites during the active period of photosynthesis. Preliminary data suggest that inclusion of other remote-sensing products in addition to EVI, such as the MODIS land surface temperature (LST), may result in more robust models of carbon balance based entirely on remote-sensing data
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    Impact of hydrological variations on modeling of peatland CO2 fluxes: results from the North American Carbon Program site synthesis
    (American Geophysical Union, 2012) Sulman, Benjamin N.; Desai, Ankur R.; Schroeder, Nicole M.; Ricciuto, Daniel M.; Barr, Alan G.; Richardson, Andrew D.; Flanagan, Larry B.; Lafleur, Peter M.; Tian, Hanqin; Chen, Guangsheng; Grant, Robert F.; Poulter, Benjamin; Verbeeck, Hans; Ciais, Philippe; Ringeval, Bruno; Baker, Ian T.; Schaefer, Kevin; Luo, Yiqi; Wong, Ensheng
    Northern peatlands are likely to be important in future carbon cycle-climate feedbacks due to their large carbon pools and vulnerability to hydrological change. Use of non-peatland-specific models could lead to bias in modeling studies of peatland-rich regions. Here, seven ecosystem models were used to simulate CO2 fluxes at three wetland sites in Canada and the northern United States, including two nutrient-rich fens and one nutrient-poor, sphagnum-dominated bog, over periods between 1999 and 2007. Models consistently overestimated mean annual gross ecosystem production (GEP) and ecosystem respiration (ER) at all three sites. Monthly flux residuals (simulated – observed) were correlated with measured water table for GEP and ER at the two fen sites, but were not consistently correlated with water table at the bog site. Models that inhibited soil respiration under saturated conditions had less mean bias than models that did not. Modeled diurnal cycles agreed well with eddy covariance measurements at fen sites, but overestimated fluxes at the bog site. Eddy covariance GEP and ER at fens were higher during dry periods than during wet periods, while models predicted either the opposite relationship or no significant difference. At the bog site, eddy covariance GEP did not depend on water table, while simulated GEP was higher during wet periods. Carbon cycle modeling in peatland-rich regions could be improved by incorporating wetland-specific hydrology and by inhibiting GEP and ER under saturated conditions. Bogs and fens likely require distinct plant and soil parameterizations in ecosystem models due to differences in nutrients, peat properties, and plant communities.