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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.

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RHESSys in grasslands

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  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

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