Linking Dynamic Temporal Processes And Spatial Domains

1. Linking Dynamic Temporal Processes And Spatial Domains East Asian Basins February 2001 Casey McLaughlin University of Kansas, USA

2. Extracting global effects from data on local scales • Field studies are inherently site-specific. • Can we obtain global understanding from mosaics of local processes ? • Cluster analysis can organize habitats by function.

3. Managing Granularity of Models & Data • How much resolution is necessary? • Averaging to coarser scales is easy, but what about sub-pixel characterization? • Do components scale linearly?

4. 4,000 km2 25km2 Linking dynamic mosaics of local biogeochemical flux budgets to large scale regional and ....

5. Coastal Development The Yellow River Example: Historic records provide a context of long-term sediment fluxes and geomorphic development within which to evaluate short-term, high resolution remote sensing images and the acceleration of anthropogenic effects.

6. The Typology Approach to Globalization of Function • Develop global database at a scale (30’) appropriate to the parent data and global models • Include sub-grid-scale parameterization: statistics on spatio-temporal variability, alternative time slices • Use similarity analysis to extrapolate function measures and to test for effectiveness of proxy variables (clustering – LoiczView) • Encourage community collaboration to develop local-regional higher resolution analogs, extensions, and tests (eg East Asian Basins Workshop)

7. The LOICZ domain: Grid Cells Coastal (30’, shoreline defined),Terrestrial (~1o inland),Oceanic I (~1o seaward, or shelf) The CoML domain:Oceanic I, Oceanic II, and Oceanic III (all the rest)

8. Global Cell Structure

9. 0.5 Degree Cells

10. Effective spatial resolution can be enhanced by inclusion of statistics or summaries from higher resolution data sets Coastal cells can be populated with complexity statistics derived from GIS analysis of digital shorelines – length, tortuosity, number of islands, land area, etc. Coastal and oceanic cells contain 2’ bathymetry statistics – mean, s.d., range, areas within selected depth classes, etc. Land cells are similarly treated based on one-km DEMs

11. Mechanistic details useful for simulating the past, but can they predict the future? • Often hard to parameterize or constrain with limited data sets • Limited dimensionality • Time, Depth or Time x Depth • Difficult to invert Complex Process Models

12. An interactive WWW database link permits selection of variables by type, by geographic region, and by cell type for viewing, downloading and augmentation, clustering and visualization.

13. Geospatial Clustering (LOICZVIEW) is a Tool for: • User-friendly, robust cluster analysis of georeferenced data • Visualization of results, with comparison features and GIS-compatibility • Nested and cross-scale applications (using both internal and external dataset characteristics) • Community building and linking of distributed databases • Developing the power of the internet for long-range collaboration on major, spatially distributed issues

14. What is this thing called LoiczView ? Developed by B. A. Maxwell http://www. palantir . swarthmore . edu /~ maxwell / loicz / 1. A program for similarity analysis of high dimensionality (= lots of - variables) data sets using k - means clustering techniques (conceptual analog = PCA and dendrogram techniques). 2. Clusters are determined on the basis of the data vectors in n - dimensional space. 3. Operator has control of data inputs, cell classes for analysis number of clusters, and distance measure. 4. Designed to be robust with sub-optimal data sets, scale - independent. 5. Has built - in Geo-spatial and similarity visualization capabilities. 6. Going into final beta - test phase.

15. Clustering of means and standard deviations permits assessment of habitat and variability. Sea surface temperature, precipitation, and runoff were clustered into 5 classes using a k-means clustering algorithm Cluster of Intra-Annual Std Deviations Cluster of Annualized Values High Runoff Med Runoff, High SST Med Precip, Low Runoff Low SST, Low Runoff Low Precip, Low Runoff Low Precip, Low SST, Low Runoff High Runoff Low Precip, Med SST, Low Runoff Med Precip, Low SST, Low Runoff High SST, Low Runoff

16. Critical aspects of temporal variability – seasonal and interannual – can be captured by climatology statistics Total annual precipitation (CRU, 1961-1990) Mean Std. Dev • Areas with similar average totals show major differences in seasonality. • Max, Min, Median and Range statistics can be similarly used. • Other statistics can provide interannual variability indices. • The example also illustrates the power of latitude as a proxy variable. Low....HighNo Data

17. Inland effects: continent-scale impacts on the local CZ Classed river basin flow/cell Classed runoff/cell Local effects vs. coastal projection of continental forcing: most of the world CZ is locally controlled!

18. Budget Types Expert typology “Calibration” of clustering by expert judgment Alternative 1 Alternative 2

19. Simplification and Aggregation Across Spatial Domains • Can we achieve reliable predictions for variables of interest? • Can these simplified relations be generalized or are they site/domain specific?

20. Balancing Objectives: Scientific Enlightenment and Predictive Accuracy Ie. Identify proxies for comparisons Acknowledgements & Apologies:

21. Contributers: University of Kansas, Lawrence, KS Casey J. McLaughlin (CJM@UKANS.EDU) Dr. Robert Buddemeier Jeremy Bartley Moss Landing Laboratories, Monterey Bay. CA Dr. Richard Zimmerman.