linking dynamic temporal processes and spatial domains

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

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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. Field studies are inherently site-specific. - PowerPoint PPT Presentation

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Page 1: Linking Dynamic Temporal Processes And Spatial Domains

Linking Dynamic Temporal Processes And

Spatial Domains

East Asian Basins

February 2001

Casey McLaughlin

University of Kansas, USA

Page 2: Linking Dynamic Temporal Processes And Spatial Domains

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.

Page 3: Linking Dynamic Temporal Processes And Spatial Domains

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?

Page 4: Linking Dynamic Temporal Processes And Spatial Domains

Linking dynamic mosaics of local biogeochemical flux budgets to large scale regional and ....

25km2

4,000 km2

Page 5: Linking Dynamic Temporal Processes And Spatial Domains

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

Page 6: Linking Dynamic Temporal Processes And Spatial Domains

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)

Page 7: Linking Dynamic Temporal Processes And Spatial Domains

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)

Page 8: Linking Dynamic Temporal Processes And Spatial Domains

Global Cell Structure

Page 9: Linking Dynamic Temporal Processes And Spatial Domains

0.5 Degree Cells

Page 10: Linking Dynamic Temporal Processes And Spatial Domains

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

Page 11: Linking Dynamic Temporal Processes And Spatial Domains

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?

Page 12: Linking Dynamic Temporal Processes And Spatial Domains

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.

Page 13: Linking Dynamic Temporal Processes And Spatial Domains

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

Page 14: Linking Dynamic Temporal Processes And Spatial Domains

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.

Page 15: Linking Dynamic Temporal Processes And Spatial Domains

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

Page 16: Linking Dynamic Temporal Processes And Spatial Domains

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.

Page 17: Linking Dynamic Temporal Processes And Spatial Domains

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!

Page 18: Linking Dynamic Temporal Processes And Spatial Domains

Expert typology

Alternative 2Alternative 1

“Calibration”

of clustering by expert judgment

Budget Types

Page 19: Linking Dynamic Temporal Processes And Spatial Domains

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?

Page 20: Linking Dynamic Temporal Processes And Spatial Domains

Acknowledgements & Apologies:

Balancing Objectives:Balancing Objectives:

Scientific Enlightenmentand

Predictive AccuracyIe. Identify proxies for

comparisons

Scientific Enlightenmentand

Predictive AccuracyIe. Identify proxies for

comparisons

Page 21: Linking Dynamic Temporal Processes And Spatial Domains

University of Kansas, Lawrence, KS

Casey J. McLaughlin ([email protected])Dr. Robert BuddemeierJeremy Bartley

Moss Landing Laboratories, Monterey Bay. CA

Dr. Richard Zimmerman.

Contributers: