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Virtual Experiment © Oregon State University Models as a communication tool for HJA scientists Kellie Vache and Jeff McDonnell Dept of Forest Engineering.

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Presentation on theme: "Virtual Experiment © Oregon State University Models as a communication tool for HJA scientists Kellie Vache and Jeff McDonnell Dept of Forest Engineering."— Presentation transcript:

1 Virtual Experiment © Oregon State University Models as a communication tool for HJA scientists Kellie Vache and Jeff McDonnell Dept of Forest Engineering HJA Science Hour

2 Virtual Experiment © Oregon State University Outline  A rationale for modeling in LTER 5  The STELLA concept  Going beyond STELLA (visualization, more complex models, other languages, etc  Conclusions

3 Virtual Experiment © Oregon State University A rationale for modeling in LTER 5

4 Virtual Experiment © Oregon State University LTER 5  How do land use, natural disturbances, and climate change affect three key sets of ecosystem services: carbon and nutrient dynamics, biodiversity, and hydrology?  o ur component areas: (1) climate, (2) hydrology, (3) disturbance, (4) ecophysiology, (5) carbon and nutrient dynamics, (6) biodiversity, and (7) stream-forest interactions.  A major goal will be to test predictive rules (i.e., hypotheses) regulating temporal behaviors.

5 Virtual Experiment © Oregon State University LTER 20-yr review 2002 Recommendation 4. Ecological research by LTER scientists involving multiple disciplines, dimensions and scales should be organized a priori by hypotheses and theory, and tested by predictive models across broader and broader phenomena.

6 Virtual Experiment © Oregon State University Why should we consider models?  We have a stated desire and need to integrate across the bio-geo-hydro interface  Our group discussions are often left rather open ended  We tend to get caught up in the details of our particular field or area of interest  We have difficulty with each other’s jargon and terminology

7 Virtual Experiment © Oregon State University Another issue We may all visit and look at the same spot on the landscape, but may view it very differently based on our disciplinary focus

8 Virtual Experiment © Oregon State University Sapflow! DOC, DON! Stream Routing! Soil depth! Lateral flow!!! Our dominant processes bias

9 Virtual Experiment © Oregon State University Sapflow DOC flushing Lateral flow Soil depth Stream routing Watershed Function

10 Virtual Experiment © Oregon State University Sapflow DOC flushing Lateral flow Soil depth Stream routing Watershed Function

11 Virtual Experiment © Oregon State University Representing content in biogeochem Representing content in geomorph Representing content in hydrology Representing content in plant phys General HJA system representation Biogeochem Content Represented Geomporh Content Represented Plant Content Represented

12 Virtual Experiment © Oregon State University A STELLA PRIMER

13 Virtual Experiment © Oregon State University What is STELLA?  A visual modeling environment Simulations developed through ‘dragging and dropping’ of a standard set of modeling components OR  A software tool designed to simulate dynamic systems. OR  A tool to develop and solve systems of ordinary differential equations

14 Virtual Experiment © Oregon State University  Density independent population growth Described mathematically as a differential equation: Can be solved for population through integration: A STELLA Example Where N = population size and r = birth rate Where N 0 = initial population size and N t = population at any time t Plugging into a spreadsheet

15 Virtual Experiment © Oregon State University RUN STELLA HERE

16 Virtual Experiment © Oregon State University A STELLA Rainfall/Runoff Model

17 Virtual Experiment © Oregon State University STELLA discussions thusfar

18 Virtual Experiment © Oregon State University Some initial results  Dirt plot simulations

19 Virtual Experiment © Oregon State University Going beyond STELLA

20 Virtual Experiment © Oregon State University Limitations of the STELLA Approach  As a model becomes more complex, the STELLA environment becomes quite clunky  Spatial distribution of boxes is difficult  Direct incorporation of GIS data not feasible  Limited output potential

21 Virtual Experiment © Oregon State University Grid-based hydrologic models (eg. DHSVM) move water laterally and vertically, but each grid cell is, in fact, cast as a 1 dimensional lumped model A complicated STELLA-like simulation Lettenmaier, 2002 Beyond STELLA – A brief example Individual STELLA–like boxes! Important processes could be explored as a STELLA model Subsequent allocation and solution of many boxes, potentially using a GIS based grid, might then occur in a more fully featured language

22 Virtual Experiment © Oregon State University A distributed model that began with STELLA explorations Data originated as 50 m gridded DEM Simple assumptions about soil depth, porosity, rainfall distribution, etc. DHSVM like routing structure Simple models of ET, drainage, etc. Map color represents depth to water table

23 Virtual Experiment © Oregon State University A distributed model that began with STELLA explorations Data originated as 50 m gridded DEM Simple assumptions about soil depth, porosity, rainfall distribution, etc. DHSVM like routing structure Simple models of ET, drainage, etc. Map color represents depth to water table

24 Virtual Experiment © Oregon State University Conclusions

25 Virtual Experiment © Oregon State University Conclusions  While we will likely each continue to use detailed, process-specific models, a “group model” could be something we consider within LTER 5  We all take ownership in its development and construction  We use it to identify and explore disciplinary interfaces  A modeler could then incorporate this ‘simple’ model into a more sophisticated modeling environment

26 Virtual Experiment © Oregon State University Hypotheses that might be tested with a model  McDonnell Group Focus: What are potential effects of rainfall distribution on the accumulation and movement of water in the system? How might spatially variable depth to bedrock effect water movement? Can the model simulate residence time? How does it compare to Kevin McGuire’s measurement based estimates? What does this say about the model? How does the model split old water vs. new water?  HJA Group Focus: What are the potential effects of stand age or species change on hydrologic response? How do paired catchment studies reflect the larger lookout creek basin? How might geomorphology interact with hydrology? What are the potential meso-scale effects of wildfire?

27 Virtual Experiment © Oregon State University


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