1 / 13

RHESSys in grasslands

RHESSys in grasslands. Motivation information / data model / uncertainty relationships in environmental modelling Grasslands National Park Earlier work (CENTURY) Problems encountered using RHESSys Interim solutions. Scott W. Mitchell, University of Toronto.

gjude
Download Presentation

RHESSys in grasslands

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. RHESSys in grasslands • Motivation • information / data model / uncertainty relationships in environmental modelling • Grasslands National Park • Earlier work (CENTURY) • Problems encountered using RHESSys • Interim solutions Scott W. Mitchell, University of Toronto

  2. Grasslands National ParkVal Marie, SK (49°N, 107°W) Archaeology Visitor loads / services Local residents Fire Grazing Wildlife Native / Invasive Climate Change

  3. Grass Productivity • Current status - inventory, diversity, native versus introduced, carbon budget • Effects of grazing • Fuel load - standing dead • Potential response to climate change • Feedbacks between biogeochemistry and biogeography

  4. First experiment - CENTURY • What can a non-spatial, monthly time step provide ? • Uncertainty in ANPP • UNCERTAINTY in climate change scenarios

  5. RHESSys - why ? • Daily, spatial (implicit) • Attractive data model (worldfile hierarchy, snapshots) • Links with GRASS (GIS) • Active “local” development • Use of BGC - some reports of prior use (BUT: untested, questions re: applicability of submodels, computer stability issues)

  6. What was missing ? (Round 1) • Grass morphology (no woody bits) • Standing dead • Seed bank ? • Differentiating C3 & C4 photosynthesis • Parameterization • Numerical sanity ?!

  7. How did it do ? • “That doesn’t look semi-arid !” • Very high productivity, driven by sunlight, not precipitation

  8. Where is the water ? Unsaturated Zone Zsat Moisture Saturated Zone

  9. Solution (aka workaround) • moisture control on photosynthesis: stomatal control • Farquhar model control through conductance term • conductance from Jarvis multiplicative model • modify leaf water potential multiplier

  10. Phenology • “fixed” phenology model not good for semi-arid grasslands, especially leaf-on • phenology data relatively rare, let alone models - main source of help White et al. (1997) using degree days + precipitation • implemented minimum degree days for earliest possible leaf allocation, then adjusted daily rate of carbon allocation according to soil moisture

  11. Summary • Modifications: • C4 photosynthesis (update psn from BGC) • “shallower” moisture response (kludge) • phenology model • Outstanding issues: • more work needed on hydrology; probably need another layer, probably need to stop using TOPMODEL (get more data!) • test and improve phenology • verify C4 predictions

More Related