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RHESSys in grasslands Scott W. Mitchell, University of Toronto Motivation information / data model / uncertainty relationships in environmental modelling.

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Presentation on theme: "RHESSys in grasslands Scott W. Mitchell, University of Toronto Motivation information / data model / uncertainty relationships in environmental modelling."— Presentation transcript:

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

2 Grasslands National Park Val 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 C 3 & C 4 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 ? Z sat Unsaturated Zone Saturated Zone Moisture

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

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11 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

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13 Summary Modifications: –C 4 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 C 4 predictions


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