The general
equation of the model
Our Gross Primary
Production (GPP) model design builds on the well founded and tested light use
efficiency approach and incorporates recent developments in our
understanding of the relationships between Carbon dioxide (CO2), Vapour Pressure Deficit, Soil Moisture and
Photosynthesis. More specifically we recognise that;
→ VPD appears more
predictable than previously thought.
→ SM appears to modify
the VPD stomata relationship differently between plant functional types.
→ Soil dryness also
reduces Vcmax and Jmax, compared to what would otherwise be predicted from
stomatal conductance alone.
The general equation behind our modelling
approach can be written as follows;
GPP
= fAPAR . IPAR . E . f(T, CO2, VPD, SM)
Where fAPAR is the fraction of absorbed
photosynthetically active radiation, IPAR is the incoming photosynthetically
active radiation (400-700nm), E (epsilon) is an estimate of light use
efficiency which is a function of Temperature (T), Carbon Dioxide (CO2)
Vapour Pressure Deficit (VPD) and Soil Moisture (SM).
Key
features of the modelling system
1.
Capable of reading in a range
of datasets from the TERN and Bureau of Meteorology and re-grid them as
required; a.k.a. data abstraction.
2.
Be usable stand-alone, embedded
in a workflow or ported to a new model core; a.k.a. modular in design.
3.
Facilitate point-to-point calibration
and validation against OzFlux tower data across a range of spatial and temporal
scales.
4.
Output any metadata elements in
a human readable format.
5.
Written in an open source
language with cross platform interoperability.
Architecture
Technology
The computational requirements of this
project are overshadowed by the need for usability and transparency- given our open
access and source approach. As such, we identified the two most widely used
open source platforms in our core user group: R-Project and Python. R was chosen by the development team
given it is considered more widely used for benchmarking within our local user
community, for example: PALS.
Data abstraction will be done using the
Geospatial Data Abstraction Library (GDAL).
In the R version, RGDAL
bindings and the raster
package will be used for low-level reading and writing of data and to
facilitate the high-level model functions required for the model and modelling
system functionality.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.This project is supported by the Australian National Data Service (ANDS). ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative.
For more information visit the ANDS website ands.org.au and Research Data Australia services.ands.org.au.
