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Geostatistics: Principles of spatial analysis

Geostatistics: Principles of spatial analysis. Anna M. Michalak Department of Civil and Environmental Engineering Department of Atmospheric, Oceanic and Space Sciences The University of Michigan. Key Points.

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Geostatistics: Principles of spatial analysis

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  1. Geostatistics: Principles of spatial analysis Anna M. Michalak Department of Civil and Environmental Engineering Department of Atmospheric, Oceanic and Space Sciences The University of Michigan

  2. Key Points • If the parameter(s) that you are modeling exhibits spatial (and/or temporal) autocorrelation, this feature must be taken into account to avoid biased solutions • Spatial (and/or temporal) autocorrelation can be used as a source of information in helping to constrain parameter distributions • The field of geostatistics provides a framework for addressing the above two issues

  3. Outline • Motivation for geostatistical tools • What is geostatistics? • Traditional applications • Application to OCO sampling design • Introduction to inverse modeling • Application to groundwater contamination • Application to CO2 flux estimation

  4. What is Geostatistics? • A short answer: • An interpolation and extrapolation toolkit • A more sophisticated answer: • All of the above for modeling spatial relationship of available data and building from such a model (e.g. kriging, stochastic simulation, …) • Formal definition • Analysis and prediction of spatial or temporal phenomena (e.g. pollutant concentrations, soil porosities, elevations, etc.)

  5. Spatial Correlation • Measurements in close proximity to each other generally exhibit less variability than measurements taken farther apart. • Assuming independence, spatially-correlated data may lead to: • Biased estimates of model parameters • Biased statistical testing of model parameters • Spatial correlation can be accounted for by using geostatistical techniques

  6. Parameter Bias Example map of an alpine basin Q: What is the mean snow depth in the watershed? snow depth measurements kriging estimate of mean snow depth (assumes spatial correlation) mean of snow depth measurements (assumes spatial independence)

  7. Example cont… 5% H0 Rejected H0 Rejected! H0 is TRUE 5% H0 Rejected H0Not Rejected 5% H0 rejected

  8. Variogram Model z(x) =m(x)+ e(x) • Used to describe spatial correlation 1 2 3 4

  9. Geostatistics in Practice • Main uses: • Data integration • Numerical models for prediction • Numerical assessment (model) of uncertainty

  10. Caveats Geostatistics is a set of decision-making tools

  11. Steps in Geostatistical Study • Exploratory Data Analysis (EDA) • Data cleaning • Consistency of data • Identification of populations • Spatial Continuity Analysis • Experimental • Analysis, interpretation • Quantitative • Estimation • Uncertainty assessment • Account for spatial correlation • Integrate hard and soft information • Simulation • Alternative images of the field • Reproduce field heterogeneity • Honor all available information

  12. Go to Matlab…

  13. OCO Satellite • Planned launch in September 2008 • Will provide global column-integrated CO2 measurements • 1ppm measurement accuracy at a 1000km scale.

  14. OCO Measurements • 1ppm measurement accuracy at a 1000km scale. • Processing all spectral radiances to XCO2 is computationally prohibitive. • Limit Sampling to optimal locations

  15. OCO Subsampling Strategy • Objective: • Determine optimal sampling locations as a function of time and space that allow for the interpolation of XCO2 at unsampled locations with estimation error within a set threshold • Recent work: • Define modeled XCO2 spatial variability using CASA-MATCH data (Olsen and Randerson 2004) subsampled at 1pm local time • Preliminary approach for identifying optimal sampling locations

  16. Sample Modeled XCO2 Data April July August October

  17. Optimal Sampling Locations • Optimal sampling locations = potential sampling locations that will achieve a set estimation error threshold at unsampled locations • Estimation error = estimation standard deviation at unsampled locations • Geostatistical interpolation tools: • Use spatial correlation as a basis of estimation • Provide best linear unbiased estimates • Quantify associated estimation error

  18. h5 h6 h1 h2 h4 4 1 2 Semivariance, γ(h) h3 3 6 5 Separation Distance, h Spatial correlation (Variogram model)

  19. Global Spatial Variability ½ variance Correlation Length

  20. Global Spatial Variability

  21. 5.5 degrees 2000 km Local Variability (2000 km radius)

  22. XCO2 Variance and Correlation Length - April Correlation length (km) Variance (ppm 2)

  23. h0 =? Vmax=1ppm Distance to Achieve 1ppm Uncertainty (h0) • h0 = max distance from the interpolation point to sample for 1ppm error • h0 depends on spatial variability near interpolation point • Interpolation at each grid point on a 5.5o by 5.5o global grid

  24. Maximum Sampling Interval h0 - April Maximum sampling interval (km)

  25. Regular Grid Sampling Uncertainty July April

  26. Optimal Sampling Locations and Associated Uncertainties July April

  27. Sampling Constraints • Aerosols • Clouds • Satellite track • Maximum (sub)sampling rate • Albedo • Measurement error • Temporal aggregation • Others?

  28. Conclusions from OCO Study • XCO2 exhibits strong spatial correlation • XCO2 covariance structure is variable in space and time • Uniform sampling will not achieve uniform/acceptable interpolation uncertainty • Geostatistical tools can be used to incorporate the variability in the XCO2 covariance structure into a subsampling protocol

  29. Inverse Modeling

  30. Inverse models • Geostatistical inverse modeling objective function: H = transport information s = unknown fluxes y = CO2 measurements R = model-data mismatch covariance Q = spatial/temporal covariance of flux deviations from trend X and  = model of the trend Deterministic component Stochastic component

  31. Bayesian Inference Applied to Inverse Modeling for Inferring Historical Forcing Likelihood of forcing given available measurements Posterior probability of historical forcing Prior information about forcing p(y) probabilityofmeasurements y : available observations (n×1) s: discretized historical forcing (m×1)

  32. Dover Air Force Base Case Study • Dover Air Force Base located in Delaware, U.S.A. • Unconfined aquifer underlain by two-layer aquitard • Aquitard cores used to infer PCE and TCE contamination history in aquifer • Solute transport controlled by diffusive process:

  33. TCE at Location PPC11 Measured TCE concentrationas a function of depth Time variation ofboundary condition

  34. TCE at Location PPC13 Measured TCE concentrationas a function of depth Time variation ofboundary condition

  35. Sources of Atmospheric CO2 Information North American Carbon Program

  36. -24 hours -48 hours -72 hours -96 hours -120 hours What Surface Fluxes to Atmospheric Samples See? 24 June 2000: Particle Trajectories Latitude Height Above Ground Level (km) Longitude Longitude Source: Arlyn Andrews, NOAA-GMD

  37. Large Regions Inversion TransCom 3 Sites & Basis Regions TransCom, Gurney et al. (2003)

  38. Study Goals • Estimate carbon fluxes at fine spatial resolution (3.75o x 5.0o) • Avoid use of prior flux estimates • Incorporate and quantify effect of available auxiliary data Questions: • What will be the effect on estimated fluxes and their uncertainties? • Is there sufficient information in the atmospheric measurements to “see” the relationship between auxiliary data and fluxes?

  39. Auxiliary Data and Carbon Flux Processes: Other: Spatial trends (sine latitude, absolute value latitude) Environmental parameters: (precipitation, %land use, Palmer drought index) Anthropogenic Flux: Fossil fuel combustion (GDP density, population) Oceanic Flux: Gas transfer (sea surface temperature, air temperature) Terrestrial Flux: Photosynthesis (FPAR, LAI, NDVI) Respiration (temperature) Image Source: NCAR

  40. Sample Auxiliary Data

  41. Global Inversion Setup • Monthly fluxes for 1997 to 2001 at 3.75o x 5.0o resolution (s) • Atmospheric data from NOAA/ESRL cooperative air sampling network (y) • TM3 gridscale basis functions (H) • Select subset of auxiliary variables (X) • Quantify spatial covariance (Q) • Perform inversion to obtain: • Influence of auxiliary variables on fluxes (β) • Flux best estimates (ŝ) • Estimates of uncertainty for s and β ^

  42. Final Set of Auxiliary Variables Combined physical understanding with results of VRT to choose final set of auxiliary variables: • GDP Density • Leaf Area Index (LAI) • Fraction of photosynthetically active radiation (FPAR) • Percent forest / shrub • Precipitation

  43. Building up the Best Estimate

  44. Location of 22 Transcom Regions

  45. Conclusions - Methodology • Geostatistical inverse modeling avoids the use of prior flux estimates • Covariance structure of flux residuals and model-data mismatch can be quantified using atmospheric data • Benefit of auxiliary data can be quantified • Fluxes and the influence of auxiliary data are estimated concurrently (w/ uncertainties) • Approaches maximizes the use of information while minimizing assumptions • Geostatistical inverse modeling not constrained by prior estimates • Provides independent validation of bottom-up estimates in well-constrained regions • Approach well suited to show inter-annual variability • Provides accurate measure of uncertainty

  46. Key Points • If the parameter(s) that you are modeling exhibits spatial (and/or temporal) autocorrelation, this feature must be taken into account to avoid biased solutions • Spatial (and/or temporal) autocorrelation can be used as a source of information in helping to constrain parameter distributions • The field of geostatistics provides a framework for addressing the above two issues

  47. Acknowledgments • Collaborators: • Pieter Tans, Adam Hirsch, Lori Bruhwiler, Kevin Schaefer, Wouter Peters, Andy Jacobson NOAA/CMDL • Alanood Alkhaled, Sharon Gourdji, Charles Humphriss, Meng Ying Li, Miranda Malkin, Kim Mueller, and Shahar Shlomi, UM • Bhaswar Sen and Charles Miller, JPL • Kevin Gurney, Purdue U. • Peter Kitanidis, Stanford U. • Funding sources: • Elizabeth C. Crosby Research Award • University Corporation for Atmospheric Research (UCAR) • National Oceanic and Atmospheric Administration (NOAA) • National Aeronautic and Space Administration (NASA) and Jet Propulsions Laboratory (JPL) • National Science Foundation (NSF) • Michigan Space Grant Consortium (MSGC) • Data providers: • NOAA / CMDL cooperative air sampling network • Seth Olsen (LANL) and Jim Randerson (UCI) • Christian Rödenbeck, MPIB • Kevin Schaefer, NOAA / ESRL • NOAA CDC NASA, EROS USGS, CEISIN, Global Precipitation Climatology Centre, UCAR

  48. QUESTIONS? Anna.Michalak@umich.edu http://www-personal.engin.umich.edu/~amichala/

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