Data-driven diagnostics of terrestrial carbon dynamics over North America

Abstract

a b s t r a c tThe 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 FluxnetCanada 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 * Corresponding author. Tel.: +1 603 862 1873; fax: +1 603 862 0188. This article is a U.S. government work, and is not subject to copyright in the United States.J. Xiao et al. / Agricultural and Forest Meteorology 197 (2014) 142-157 143 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.

Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements

“…1), a global network that measures the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere (Baldocchi et al, 2001). The FLUXNET data (Baldocchi et al, 2001;Baldocchi and Ryu, 2011) have been widely used to understand the evapotranspiration processes and trend (Jung et al, 2010;Fang et al, 2015), develop AET and ecosystem models (Sun et al, 2011b; et al, 2016), and map continental-scale ecosystem productivity (Xiao et al, 2014;Zhang et al, 2016).…”

Grassland and Cropland Net Ecosystem Production of the U.S. Great Plains: Regression Tree Model Development and Comparative Analysis

“…Cubist was used because of its use of generalized rules and widespread successful applications in the remote sensing field [5,10,11,14,18,[42][43][44][45][46][47][48][49][50][51]. Each regression tree model consisted of the dependent variable to be estimated and the set of independent variables.…”

“…Although a flux tower measures only its surrounding vicinity, numerous studies have shown that these recordings can be used in regional or land cover specific synthesis [3,4] or up-scaled to much greater levels through analyses of geospatial data relationships using computer modeling [9,13,18]. Of particular interest is the use of data mining regression tree algorithms for carbon flux mapping [5,10,11,13,14,18]. An acute capability to address nonlinear relationships, deal with complex high order interactions, easily interpret results, and utilize both categorical and continuous variables, lends data mining regression tree [10], which contrasts with process-based modeling approaches that are frequently heavily driven by precipitation, a difficult variable to map accurately, particularly in rural regions where weather stations are sparse.…”

“…Throughout modern history, scientists have been developing models to formulate generalized algorithms to expand geographically and temporally sparse field observations to a much larger landscape means [2][3][4] or to make up-scaled maps of the measured field characteristics [5]. Understanding, mapping, and quantifying regional carbon flux magnitudes and variability of terrestrial non-point carbon sinks (removal of atmospheric carbon) and sources (emission of carbon to the atmosphere) is important in the promotion of ecosystem sustainability.…”

“…Remote sensing data [5,10,11], light use efficiency modeling [9,12], data mining techniques [5,10,11,13,14], process-base modeling [15,16], and greenhouse gas inventories [17] have enhanced regional understanding and monitoring capabilities. Mapping efforts are sometimes at coarse resolutions, long time step intervals, have large (continental or global) study areas which may miss local detail, and can have highly automated or general gap filling strategies for temporary flux tower instrument failures.…”