Information about the carbon cycle potentially constrains the water cycle, and vice versa. Benefitting from the public liberation of the data necessary to drive carbon and water cycle models, a recent study by the authors of this blog post succesfully demonstrated the utility of multiple observation sets - such as provided by the TERN Auscover and OzFlux facilities - to constrain carbon and water fluxes and stores in a land surface model, and a resulting determination of the Australian terrestrial carbon budget. It exemplifies the joint use of data from observational facilities and infrastructure (e-MAST).
In observations include streamflow from 416 gauged catchments, measurements of evapotranspiration (ET) and net ecosystem production (NEP) from 12 eddy-flux sites, litterfall data, and data on carbon pools. Coupled carbon and water cycles were simulated using a modified version of the CABLE land surface scheme in the BIOS2 modelling environment, a fine spatial resolution (0.05 degrees) offline environment built on capability developed for the Australian Water Availability Project (King et al., 2009; Raupach. et al., 2009). BIOS2 includes: (1) a modification of the CABLE land surface scheme (Wang et al., 2011) coupled with CASAcnp (a biogeochemical model) and SLI (Soil-Litter-Iso, a soil hydrology model including liquid and vapour water fluxes and the effects of litter).; (2) infrastructure for the treatment of inputs (gridded vegetation cover, meteorological data and parameters) and outputs for optimum efficiency; (3) a weather generator for downscaling of meteorological data; and (4) model-data fusion capability.
Results emerging from the multiply-constrained model are as: (1) on the Australian continent, a predominantly semi-arid region, over half (0.64±0.05) of the water loss through ET occurs through soil evaporation and bypasses plants entirely; (2) mean Australian NPP is 2200±400 TgC/y, making the NPP/precipitation ratio about the same for Australia as the global land average; (3) annually cyclic (“grassy”) vegetation and persistent (“woody”) vegetation respectively account for 0.56±0.14 and 0.43±0.14 of NPP across Australia; (4) the average interannual variability of Australia’s NEP (±180 TgC/y) is larger than Australia’s total anthropogenic greenhouse gas emissions in 2011 (149 TgCeq/y).
Sunday, March 3, 2013
Gap filling techniques to use for OzFlux eddy covariance data by Natalia Restrepo-Coupe, Alfredo Huete and Kevin Davis
Our goal is to use the OzFlux network micrometeorological flux measurements of carbon dioxide exchange between different Australian ecosystems (biosphere) and the atmosphere, to better understand the physical meaning of satellite data generally associated to photosynthetic capacity. The Eddy Covariance Method (EC) used at all OzFlux sites, measures Net Ecosystem Exchange (NEE) on an hourly basis. NEE can be separated on its 2 main components, Ecosystem Respiration (Re) and assimilation (photosynthesis), the latter assumed to be equivalent to Gross Ecosystem Productivity (GEP). Therefore, GEP = NEE + Re. For detailed information about the EC method see Goulden et al. (1996).
Figure 1. Rectangular hyperbola fitted to 8-day worth of Gross Ecosystem Productivity (GEP) and Short Wave Incoming Radiation (SWin) data measured at Calperum-Chowilla flux tower data (OZflux). Photosynthetic Capacity (Pc), Light Use Efficiency (LUE) and GEP at saturation (GEPsat) are calculated, as shown. Special thanks to Prof D. Chittleborough, Prof W. Meyer, Dr. G. Whiteman and T. Luckbe
In order to harmonize and standardize the flux data we carry out the following process: (1) clean the data (e.g. remove data collected during rainfall) and remove existent outliers, (2) define a site specific turbulence threshold, as fluxes need to be corrected (removed) if measured during low turbulence periods, when the basic assumptions of the EC method are not satisfied, (3) NEE is partitioned in Re and GEP, were Re is derived from nighttime NEE (when there is no photosynthesis, GEP=0), and finally, (4) fit a rectangular hyperbola to the relationship between GEP and incoming solar radiation and using the fit to obtain different measures of ecosystem photosynthetic capacity (e.g. productivity at saturating light, GEP
sat) see Figure 1.
We compare different MODIS vegetation indices and reflectances to the flux derived data (Figure 2), to determine the product that best (similar seasonality and year-to-year variations) matches the insitu measures of ecosystem photosynthetic capacity, required by the proposed Australian terrestrial GPP near real-time monitoring system. For more information regarding MODIS vegetation indices, see Measuring Vegetation NDVI and EVI, http://earthobservatory.nasa.gov by Weier and Herring (2000)
Figure 2.Annual cycle (16-day mean) for Calperum-Chowilla flux station (a) tower measured Gross Ecosystem Productivity, GEP (black line) and Ecosystem Light Use Efficiency, LUE (red line). Special thanks to Prof D. Chittleborough, Prof W. Meyer, Dr. G. Whiteman and T. Luckbe. (b) MODIS Enhanced Vegetation Index, EVI (black line) and Normalized Vegetation Index, NDVI (red line)http://earthobservatory.nasa.gov by Weier and Herring (2000)
In order to harmonize and standardize the flux data we carry out the following process: (1) clean the data (e.g. remove data collected during rainfall) and remove existent outliers, (2) define a site specific turbulence threshold, as fluxes need to be corrected (removed) if measured during low turbulence periods, when the basic assumptions of the EC method are not satisfied, (3) NEE is partitioned in Re and GEP, were Re is derived from nighttime NEE (when there is no photosynthesis, GEP=0), and finally, (4) fit a rectangular hyperbola to the relationship between GEP and incoming solar radiation and using the fit to obtain different measures of ecosystem photosynthetic capacity (e.g. productivity at saturating light, GEP
sat) see Figure 1.
We compare different MODIS vegetation indices and reflectances to the flux derived data (Figure 2), to determine the product that best (similar seasonality and year-to-year variations) matches the insitu measures of ecosystem photosynthetic capacity, required by the proposed Australian terrestrial GPP near real-time monitoring system. For more information regarding MODIS vegetation indices, see Measuring Vegetation NDVI and EVI, http://earthobservatory.nasa.gov by Weier and Herring (2000)
Figure 2.Annual cycle (16-day mean) for Calperum-Chowilla flux station (a) tower measured Gross Ecosystem Productivity, GEP (black line) and Ecosystem Light Use Efficiency, LUE (red line). Special thanks to Prof D. Chittleborough, Prof W. Meyer, Dr. G. Whiteman and T. Luckbe. (b) MODIS Enhanced Vegetation Index, EVI (black line) and Normalized Vegetation Index, NDVI (red line)http://earthobservatory.nasa.gov by Weier and Herring (2000)
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