Linking Dynamic Temporal Processes And
Spatial Domains
East Asian Basins
February 2001
Casey McLaughlin
University of Kansas, USA
Extracting global effects from data on
local scales
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.
• Field studies are inherently site-specific.
• Can we obtain global understanding from mosaics of local processes ?
• Cluster analysis can organize habitats by function.
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?
Linking dynamic mosaics of local biogeochemical flux budgets to large scale regional and ....
25km2
4,000 km2
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.
Coastal Development
The Typology Approach to Globalization of Function
1. Develop global database at a scale (30’) appropriate to the parent data and global models
2. Include sub-grid-scale parameterization: statistics on spatio-temporal variability, alternative time slices
3. Use similarity analysis to extrapolate function measures and to test for effectiveness of proxy variables (clustering – LoiczView)
4. Encourage community collaboration to develop local-regional higher resolution analogs, extensions, and tests (eg East Asian Basins Workshop)
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)
Global Cell Structure
0.5 Degree Cells
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
Complex Process Models
• Often hard to parameterize or constrain with limited data sets
• Limited dimensionality– Time, Depth or Time x
Depth• Difficult to invert
• Often hard to parameterize or constrain with limited data sets
• Limited dimensionality– Time, Depth or Time x
Depth• Difficult to invert
• Mechanistic details useful for simulating the past, but can they predict the future?
• Mechanistic details useful for simulating the past, but can they predict the future?
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.
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
What is this thing called LoiczView?
Developed by B. A. Maxwellhttp://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 analysisnumber 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.
Cluster of Annualized Values Cluster of Intra-Annual Std Deviations
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
Low Precip, Low SST, Low RunoffHigh RunoffLow Precip, Med SST, Low RunoffMed Precip, Low SST, Low RunoffHigh SST, Low Runoff
High RunoffMed Runoff, High SSTMed Precip, Low RunoffLow SST, Low RunoffLow Precip, Low Runoff
Critical aspects of temporal variability – seasonal and interannual – can be captured by climatology statistics
Low....HighNo Data
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.
Inland effects: continent-scale impacts on the local CZ
Classed runoff/cell Classed river basin flow/cell
Local effects vs.
coastal projection of continental
forcing: most of the world CZ is
locallycontrolled!
Expert typology
Alternative 2Alternative 1
“Calibration”
of clustering by expert judgment
Budget Types
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?
Acknowledgements & Apologies:
Balancing Objectives:Balancing Objectives:
Scientific Enlightenmentand
Predictive AccuracyIe. Identify proxies for
comparisons
Scientific Enlightenmentand
Predictive AccuracyIe. Identify proxies for
comparisons
University of Kansas, Lawrence, KS
Casey J. McLaughlin ([email protected])Dr. Robert BuddemeierJeremy Bartley
Moss Landing Laboratories, Monterey Bay. CA
Dr. Richard Zimmerman.
Contributers:
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