Stoy Lab
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/14931
In the Stoy Lab, we study the role of vegetation in the climate system. To do so we measure and model the exchange of water, heat, and trace gases like carbon dioxide and methane between the terrestrial surface and the atmosphere. Recent efforts seek to understand feedbacks between land management and precipitation processes.
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Item Linking flux network measurements to continental scale simulations: Ecosystem gas exchange capacity along a European transect under non-water-stressed conditions(2007-01) Owen, Katherine E.; Tenhunen, John; Reichstein, Markus; Wang, Quan; Falge, Eva; Geyer, Ralf; Xiao, Xiangming; Stoy, Paul C.; Ammann, Christof; Arain, M. Altaf; Aubinet, Marc; Aurela, Mika; Bernhofer, Christian; Chojnicki, Bogdan H.; Granier, Andre; Gruenwald, Thomas; Hadley, Julian; Heinesch, Bernard; Hollinger, David Y.; Knohl, Alexander; Kutsch, Werner L.; Lohila, Annalea; Meyers, Tilden P.; Moors, Eddy J.; Moureaux, Christine; Pilegaard, Kim; Saigusa, Nobuko; Verma, Shashi B.; Vesala, Timo; Vogel, ChrisThis paper examines long‐term eddy covariance data from 18 European and 17 North American and Asian forest, wetland, tundra, grassland, and cropland sites under non‐water‐stressed conditions with an empirical rectangular hyperbolic light response model and a single layer two light‐class carboxylase‐based model. Relationships according to ecosystem functional type are demonstrated between empirical and physiological parameters, suggesting linkages between easily estimated parameters and those with greater potential for process interpretation. Relatively sparse documentation of leaf area index dynamics at flux tower sites is found to be a major difficulty in model inversion and flux interpretation. Therefore, a simplification of the physiological model is carried out for a subset of European network sites with extensive ancillary data. The results from these selected sites are used to derive a new parameter and means for comparing empirical and physiologically based methods across all sites, regardless of ancillary data. The results from the European analysis are then compared with results from the other Northern Hemisphere sites and similar relationships for the simplified process‐based parameter were found to hold for European, North American, and Asian temperate and boreal climate zones. This parameter is useful for bridging between flux network observations and continental scale spatial simulations of vegetation/atmosphere carbon dioxide exchange.Item Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes(2007-04) Yuan, Wenping; Liu, Shuguang; Zhou, Guangsheng; Zhou, Guoyi; Tieszen, Larry L.; Baldocchi, Dennis D.; Bernhofer, Christian; Gholz, Henry; Goldstein, Allen H.; Goulden, Michael L.; Hollinger, David Y.; Hu, Yueming; Law, Beverly E.; Stoy, Paul C.; Vesala, Timo; Wofsy, Steven C.The quantitative simulation of gross primary production (GPP) at various spatial and temporal scales has been a major challenge in quantifying the global carbon cycle. We developed a light use efficiency (LUE) daily GPP model from eddy covariance (EC) measurements. The model, called EC-LUE, is driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux (used to calculate moisture stress). The EC-LUE model relies on two assumptions: First, that the fraction of absorbed PAR (fPAR) is a linear function of NDVI; Second, that the realized light use efficiency, calculated from a biome-independent invariant potential LUE, is controlled by air temperature or soil moisture, whichever is most limiting. The EC-LUE model was calibrated and validated using 24,349 daily GPP estimates derived from 28 eddy covariance flux towers from the AmeriFlux and EuroFlux networks, covering a variety of forests, grasslands and savannas. The model explained 85% and 77% of the observed variations of daily GPP for all the calibration and validation sites, respectively. A comparison with GPP calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) indicated that the EC-LUE model predicted GPP that better matched tower data across these sites. The realized LUE was predominantly controlled by moisture conditions throughout the growing season, and controlled by temperature only at the beginning and end of the growing season. The EC-LUE model is an alternative approach that makes it possible to map daily GPP over large areas because (1) the potential LUE is invariant across various land cover types and (2) all driving forces of the model can be derived from remote sensing data or existing climate observation networks.Item Interannual variability of ecosystem carbon exchange: From observation to prediction(2017-09) Niu, Shuli; Zheng, Fu; Yiqi, Luo; Stoy, Paul C.; Keenan, Trevor F.; Poulter, Benjamin; Zhang, Leiming; Piao, Shilong; Zhou, Xuhui; Zheng, Han; Han, Jiayin; Wang, Qiufeng; Yu, GuiruiAim Terrestrial ecosystems have sequestered, on average, the equivalent of 30% of anthropogenic carbon (C) emissions during the past decades, but annual sequestration varies from year to year. For effective C management, it is imperative to develop a predictive understanding of the interannual variability (IAV) of terrestrial net ecosystem C exchange (NEE). Location Global terrestrial ecosystems. Methods We conducted a comprehensive review to examine the IAV of NEE at global, regional and ecosystem scales. Then we outlined a conceptual framework for understanding how anomalies in climate factors impact ecological processes of C cycling and thus influence the IAV of NEE through biogeochemical regulation. Results The phenomenon of IAV in land NEE has been ubiquitously observed at global, regional and ecosystem scales. Global IAV is often attributable to either tropical or semi‐arid regions, or to some combination thereof, which is still under debate. Previous studies focus on identifying climate factors as driving forces of IAV, whereas biological mechanisms underlying the IAV of ecosystem NEE are less clear. We found that climate anomalies affect the IAV of NEE primarily through their differential impacts on ecosystem C uptake and respiration. Moreover, recent studies suggest that the carbon uptake period makes less contribution than the carbon uptake amplitude to IAV in NEE. Although land models incorporate most processes underlying IAV, their efficacy to predict the IAV in NEE remains low. Main conclusions To improve our ability to predict future IAV of the terrestrial C cycle, we have to understand biological mechanisms through which anomalies in climate factors cause the IAV of NEE. Future research needs to pay more attention not only to the differential effects of climate anomalies on photosynthesis and respiration but also to the relative importance of the C uptake period and amplitude in causing the IAV of NEE. Ultimately, we need multiple independent approaches, such as benchmark analysis, data assimilation and time‐series statistics, to integrate data, modelling frameworks and theory to improve our ability to predict future IAV in the terrestrial C cycle.Item Land management and land-cover change have impacts of similar magnitude on surface temperature(2014-04) Luyssaert, Sebastiaan; Jammet, Mathilde; Stoy, Paul C.; Estel, Stephan; Pongratz, Julia; Ceschia, Eric; Churkina, Galina; Don, A.; Erb, K.; Ferlicoq, M.; Gielen, Bert; Grünwald, Thomas; Houghton, Richard A.; Klumpp, K.; Knohl, A.; Kolb, T.; Kuemmerle, T.; Laurila, T.; Lohila, A.; Loustau, Denis; Meyfroidt, P.; Moors, Eddy J.; Novick, Kimberly A.; Otto, Juliane; Pilegaard, Kim; Pio, C. A.; Rambal, Serge; Rebmann, C.; Ryder, J.; Suyker, Andrew E.; Varlagin, Andrej B.; Wattenbach, M.; Dolman, A. J.Anthropogenic changes to land cover (LCC) remain common, but continuing land scarcity promotes the widespread intensification of land management changes (LMC) to better satisfy societal demand for food, fibre, fuel and shelter1. The biophysical effects of LCC on surface climate are largely understood2,3,4,5, particularly for the boreal6 and tropical zones7, but fewer studies have investigated the biophysical consequences of LMC; that is, anthropogenic modification without a change in land cover type. Harmonized analysis of ground measurements and remote sensing observations of both LCC and LMC revealed that, in the temperate zone, potential surface cooling from increased albedo is typically offset by warming from decreased sensible heat fluxes, with the net effect being a warming of the surface. Temperature changes from LMC and LCC were of the same magnitude, and averaged 2 K at the vegetation surface and were estimated at 1.7 K in the planetary boundary layer. Given the spatial extent of land management (42–58% of the land surface) this calls for increasing the efforts to integrate land management in Earth System Science to better take into account the human impact on the climate8.Item Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration(2010-02) Hollinger, David Y.; Ollinger, S. V.; Richardson, Andrew D.; Meyers, T. P.; Dail, D. B.; Martin, M. E.; Scott, N. A.; Arkebauer, T. J.; Baldocchi, Dennis D.; Clark, K. L.; Curtis, P. S.; Desai, Ankur R.; Dragoni, Danilo; Goulden, Michael L.; Gu, Lianhong; Katul, Gabriel G.; Pallardy, S. G.; Paw U, Kyaw Tha; Schmid, H. P.; Stoy, Paul C.; Suyker, Andrew E.; Verma, Shashi B.Vegetation albedo is a critical component of the Earth's climate system, yet efforts to evaluate and improve albedo parameterizations in climate models have lagged relative to other aspects of model development. Here, we calculated growing season albedos for deciduous and evergreen forests, crops, and grasslands based on over 40 site‐years of data from the AmeriFlux network and compared them with estimates presently used in the land surface formulations of a variety of climate models. Generally, the albedo estimates used in land surface models agreed well with this data compilation. However, a variety of models using fixed seasonal estimates of albedo overestimated the growing season albedo of northerly evergreen trees. In contrast, climate models that rely on a common two‐stream albedo submodel provided accurate predictions of boreal needle‐leaf evergreen albedo but overestimated grassland albedos. Inverse analysis showed that parameters of the two‐stream model were highly correlated. Consistent with recent observations based on remotely sensed albedo, the AmeriFlux dataset demonstrated a tight linear relationship between canopy albedo and foliage nitrogen concentration (for forest vegetation: albedo=0.01+0.071%N, r2=0.91; forests, grassland, and maize: albedo=0.02+0.067%N, r2=0.80). However, this relationship saturated at the higher nitrogen concentrations displayed by soybean foliage. We developed similar relationships between a foliar parameter used in the two‐stream albedo model and foliage nitrogen concentration. These nitrogen‐based relationships can serve as the basis for a new approach to land surface albedo modeling that simplifies albedo estimation while providing a link to other important ecosystem processes.Item Separation of Net Ecosystem Exchange into Assimilation and Respiration Using a Light Response Curve Approach: Critical Issues and Global Evaluation(2010-01) Lasslop, Gitta; Reichstein, Markus; Papale, Dario; Richardson, Andrew D.; Arneth, Almut; Barr, Alan G.; Stoy, Paul C.; Wohlfahrt, GeorgThe measured net ecosystem exchange (NEE) of CO2 between the ecosystem and the atmosphere reflects the balance between gross CO2 assimilation [gross primary production (GPP)] and ecosystem respiration (Reco). For understanding the mechanistic responses of ecosystem processes to environmental change it is important to separate these two flux components. Two approaches are conventionally used: (1) respiration measurements made at night are extrapolated to the daytime or (2) light–response curves are fit to daytime NEE measurements and respiration is estimated from the intercept of the ordinate, which avoids the use of potentially problematic nighttime data. We demonstrate that this approach is subject to biases if the effect of vapor pressure deficit (VPD) modifying the light response is not included. We introduce an algorithm for NEE partitioning that uses a hyperbolic light response curve fit to daytime NEE, modified to account for the temperature sensitivity of respiration and the VPD limitation of photosynthesis. Including the VPD dependency strongly improved the model's ability to reproduce the asymmetric diurnal cycle during periods with high VPD, and enhances the reliability of Reco estimates given that the reduction of GPP by VPD may be otherwise incorrectly attributed to higher Reco. Results from this improved algorithm are compared against estimates based on the conventional nighttime approach. The comparison demonstrates that the uncertainty arising from systematic errors dominates the overall uncertainty of annual sums (median absolute deviation of GPP: 47 g C m−2 yr−1), while errors arising from the random error (median absolute deviation: ∼2 g C m−2 yr−1) are negligible. Despite site‐specific differences between the methods, overall patterns remain robust, adding confidence to statistical studies based on the FLUXNET database. In particular, we show that the strong correlation between GPP and Reco is not spurious but holds true when quasi‐independent, i.e. daytime and nighttime based estimates are compared.Item Productivity, Respiration, and Light-Response Parameters of World Grassland and Agroecosystems Derived From Flux-Tower Measurements(2010-01) Gilmanov, Tagir G.; Aires, Luis M. I.; Barcza, Zoltan; Baron, Vern S.; Belelli, Luca; Beringer, Jason; Billesbach, David; Bonal, Damien; Bradford, James A.; Ceschia, Eric; Cook, D.; Corradi, Chiara A. R.; Frank, Albert B.; Gianelle, Damiano; Gimeno, Cristina; Gruenwald, Thomas; Guo, Haiqiang; Hanan, Niall; Haszpra, Laszlo; Heilman, J.; Jacobs, Adrie F. G.; Jones, Mike B.; Johnson, Douglas A.; Kiely, Gerard K.; Li, Shenggong; Magliulo, Vincenzo; Moors, Eddy; Nagy, Zoltan; Nasyrov, M.; Owensby, Clenton E.; Pintér, Krisztina; Pio, Casimiro; Reichstein, Markus; Sanz-Sanchez, Maria José; Scott, Russell L.; Soussana, Jean-Francois; Stoy, Paul C.; Svejcar, T.; Tuba, Zoltán; Zhou, GuangshengGrasslands and agroecosystems occupy one-third of the terrestrial area, but their contribution to the global carbon cycle remains uncertain. We used a set of 316 site-years of CO2 exchange measurements to quantify gross primary productivity, respiration, and light-response parameters of grasslands, shrublands/savanna, wetlands, and cropland ecosystems worldwide. We analyzed data from 72 global flux-tower sites partitioned into gross photosynthesis and ecosystem respiration with the use of the light-response method (Gilmanov, T. G., D. A. Johnson, and N. Z. Saliendra. 2003. Growing season CO2 fluxes in a sagebrush-steppe ecosystem in Idaho: Bowen ratio/energy balance measurements and modeling. Basic and Applied Ecology 4:167–183) from the RANGEFLUX and WORLDGRASSAGRIFLUX data sets supplemented by 46 sites from the FLUXNET La Thuile data set partitioned with the use of the temperature-response method (Reichstein, M., E. Falge, D. Baldocchi, D. Papale, R. Valentini, M. Aubinet, P. Berbigier, C. Bernhofer, N. Buchmann, M. Falk, T. Gilmanov, A. Granier, T. Grünwald, K. Havránková, D. Janous, A. Knohl, T. Laurela, A. Lohila, D. Loustau, G. Matteucci, T. Meyers, F. Miglietta, J. M. Ourcival, D. Perrin, J. Pumpanen, S. Rambal, E. Rotenberg, M. Sanz, J. Tenhunen, G. Seufert, F. Vaccari, T. Vesala, and D. Yakir. 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology 11:1424–1439). Maximum values of the quantum yield (α=75 mmol · mol−1), photosynthetic capacity (Amax=3.4 mg CO2 · m−2 · s−1), gross photosynthesis (Pg,max=116 g CO2 · m−2 · d−1), and ecological light-use efficiency (εecol=59 mmol · mol−1) of managed grasslands and high-production croplands exceeded those of most forest ecosystems, indicating the potential of nonforest ecosystems for uptake of atmospheric CO2. Maximum values of gross primary production (8 600 g CO2 · m−2 · yr−1), total ecosystem respiration (7 900 g CO2 · m−2 · yr−1), and net CO2 exchange (2 400 g CO2 · m−2 · yr−1) were observed for intensively managed grasslands and high-yield crops, and are comparable to or higher than those for forest ecosystems, excluding some tropical forests. On average, 80% of the nonforest sites were apparent sinks for atmospheric CO2, with mean net uptake of 700 g CO2 · m−2 · yr−1 for intensive grasslands and 933 g CO2 · m−2 · d−1 for croplands. However, part of these apparent sinks is accumulated in crops and forage, which are carbon pools that are harvested, transported, and decomposed off site. Therefore, although agricultural fields may be predominantly sinks for atmospheric CO2, this does not imply that they are necessarily increasing their carbon stock.Item Upscaling as ecological information transfer: A simple framework with application to arctic ecosystem carbon exchange(2009-06) Stoy, Paul C.; Williams, Mathew; Prieto-Blanco, Ana; Huntley, Brian; Baxter, Robert; Lewis, PhilipTransferring ecological information across scale often involves spatial aggregation, which alters information content and may bias estimates if the scaling process is nonlinear. Here, a potential solution, the preservation of the information content of fine-scale measurements, is highlighted using modeled net ecosystem exchange (NEE) of an Arctic tundra landscape as an example. The variance of aggregated normalized difference vegetation index (NDVI), measured from an airborne platform, decreased linearly with log(scale), resulting in a linear relationship between log(scale) and the scale-wise modeled NEE estimate. Preserving three units of information, the mean, variance and skewness of fine-scale NDVI observations, resulted in upscaled NEE estimates that deviated less than 4% from the fine-scale estimate. Preserving only the mean and variance resulted in nearly 23% NEE bias, and preserving only the mean resulted in larger error and a change in sign from CO2 sink to source. Compressing NDVI maps by 70–75% using wavelet thresholding with the Haar and Coiflet basis functions resulted in 13% NEE bias across the study domain. Applying unique scale-dependent transfer functions between NDVI and leaf area index (LAI) decreased, but did not remove, bias in modeled flux in a smaller expanse using handheld NDVI observations. Quantifying the parameters of statistical distributions to preserve ecological information reduces bias when upscaling and makes possible spatial data assimilation to further reduce errors in estimates of ecological processes across scale.Item Artificial drainage and associated carbon fluxes (CO2/CH4) in tundra ecosystems(2009-11) Merbold, L.; Kutsch, Werner L.; Kolle, O.; Zimov, S. A.; Corradi, C.; Stoy, Paul C.; Schulze, E.-D.Ecosystem flux measurements using the eddy covariance (EC) technique were undertaken in 4 subsequent years during summer for a total of 562 days in an arctic wet tundra ecosystem, located near Cherskii, Far‐Eastern Federal District, Russia. Methane (CH4) emissions were measured using permanent chambers. The experimental field is characterized by late thawing of permafrost soils in June and periodic spring floods. A stagnant water table below the grass canopy is fed by melting of the active layer of permafrost and by flood water. Following 3 years of EC measurements, the site was drained by building a 3 m wide drainage channel surrounding the EC tower to examine possible future effects of global change on the tundra tussock ecosystem. Cumulative summertime net carbon fluxes before experimental alteration were estimated to be about +15 g C m−2 (i.e. an ecosystem C loss) and +8 g C m−2 after draining the study site. When taking CH4 as another important greenhouse gas into account and considering the global warming potential (GWP) of CH4 vs. CO2, the ecosystem had a positive GWP during all summers. However CH4 emissions after drainage decreased significantly and therefore the carbon related greenhouse gas flux was much smaller than beforehand (475 ± 253 g C‐CO2‐e m−2 before drainage in 2003 vs. 23 ± 26 g C‐CO2‐e m−2 after drainage in 2005).Item Nocturnal Evapotranspiration in Eddy-Covariance Records from Three Co-Located Ecosystems in the Southeastern U.S.: Implications for Annual Fluxes(2009-09) Novick, Kimberly A.; Oren, Ram; Stoy, Paul C.; Siqueira, Mario B. S.; Katul, Gabriel G.Nocturnal evapotranspiration (ETN) is often assumed to be negligible in terrestrial ecosystems, reflecting the common assumption that plant stomata close at night to prevent water loss from transpiration. However, recent evidence across a wide range of species and climate conditions suggests that significant transpiration occurs at night, frustrating efforts to estimate total annual evapotranspiration (ET) from conventional methods such as the eddy-covariance technique. Here, the magnitude and variability of ETN is explored in multiple years of eddy-covariance measurements from three adjacent ecosystems in the Southeastern U.S.: an old grass field, a planted pine forest, and a late-successional hardwood forest. After removing unreliable data points collected during periods of insufficient turbulence, observed ETN averaged 8–9% of mean daytime evapotranspiration (ETD). ETN was driven primarily by wind speed and vapor pressure deficit and, in the two forested ecosystems, a qualitative analysis suggests a significant contribution from nocturnal transpiration. To gapfill missing data, we investigated several methodologies, including process-based multiple non-linear regression, relationships between daytime and nighttime ET fluxes, marginal distribution sampling, and multiple imputation. The utility of the gapfilling procedures was assessed by comparing simulated fluxes to reliably measured fluxes using randomly generated gaps in the data records, and by examining annual sums of ET from the different gapfilling techniques. The choice of gapfilling methodology had a significant impact on estimates of annual ecosystem water use and, in the most extreme cases, altered the annual estimate of ET by over 100 mm year−1, or ca. 15%. While no single gapfiling methodology appeared superior for treating data from all three sites, marginal distribution sampling generally performed well, producing flux estimates with a site average bias error of <10%, and a mean absolute error close to the random measurement error of the dataset (12.2 and 9.8 W m−2, respectively).