Geostatistical Analysis of Hydrologic Parameters
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Transcript of Geostatistical Analysis of Hydrologic Parameters
Geostatistical Analysis of Hydrologic Geostatistical Analysis of Hydrologic
ParametersParameters
Nishesh MehtaNishesh Mehta
Hydrology - CE394KHydrology - CE394K
2626thth April 07 April 07
Outline of the problemOutline of the problem
An interesting study to investigate geospatial
correlationship between hydrologic parameters.
Industrial Water Use Public Supply Water Use Irrigation Water Use Slope (% flatlands) Geologic Texture (% sand) Bedrock Permeability Climate (Precipitation-PET)
Data SourcesData Sources
Water Use data for the USWater Use data for the US
http://water.usgs.gov/watuse/data/2000/index.htmlhttp://water.usgs.gov/watuse/data/2000/index.html
contains: contains: Industrial Water UseIndustrial Water Use
Public Supply Water UsePublic Supply Water Use
Agricultural Water UseAgricultural Water Use Hydrologic landscape regions of the United States
http://water.usgs.gov/GIS/metadata/usgswrd/XML/hlrus.xmlhttp://water.usgs.gov/GIS/metadata/usgswrd/XML/hlrus.xml
Contains: Contains:
Parameters Description
PMPE Mean annual precipitation minus potential evapotranspiration
SAND as Percent sand in soil
SLOPE in percent rise
AQPERMNEW
Aquifer permeability class (1-7, lowest- highest)
How to do it?How to do it? Geostatistical AnalystGeostatistical Analyst
The semivariogram captures the spatial dependence between samples by plotting semivariance against separation distance
h= h=
0.5 * avg[ (value i –value j)2 ]0.5 * avg[ (value i –value j)2 ]
Semivariograms-Semivariograms-
SemivariogramSemivariogram
Preliminary ResultsPreliminary Results
Correlation lengths Correlation lengths calculated on county calculated on county basisbasis
Data used was raw Data used was raw (not treated to have a (not treated to have a normal distribution)normal distribution)
Semivariance Semivariance calculated between calculated between each set of counties each set of counties within the continental within the continental USUS
Variable Correlation Length
Public Supply Water Use1000 kms
Self Supplied Industrial Water Use 253 kms
Irrigation Water Use1400 kms
Slope (as % flatlands)375 kms
% Sand 1204 kms
Climate (Precipitation – PET) 3318 kms
Bedrock Permeability 470 kms
Surface generated using the semivariogramSurface generated using the semivariogram
Synthesis of AnalysisSynthesis of Analysis base unit of analysis - base unit of analysis -
Counties – 3077 Counties – 3077
HUCs – 2158 (grouped on similar hydrologic HUCs – 2158 (grouped on similar hydrologic properties)properties)
Spatial JoinSpatial Join – A tool that helps to associate and – A tool that helps to associate and interpolate values spatially. Ex- convert interpolate values spatially. Ex- convert parameter classified by county basis to HUC basisparameter classified by county basis to HUC basis
Random SamplingRandom Sampling – –
Basis of all statistical processesBasis of all statistical processes
Enables sampling out of a large number of Enables sampling out of a large number of points points
Randomization ToolRandomization Tool
CUAHSI test bed sites as pilot testCUAHSI test bed sites as pilot test Random samplingRandom sampling of of HUCsHUCs from the site from the site
comprised of HUC unitscomprised of HUC units Use any parameter from the attribute table Use any parameter from the attribute table
Sierra Nevada
ResultsResults
Scaling length Scaling length Industrial water for Industrial water for the entire Sierra the entire Sierra Nevada– Nevada– 110 kms110 kms
Scaling length Scaling length Industrial water for Industrial water for a random sample a random sample of Sierra Nevada- of Sierra Nevada- 110 kms110 kms
Statistical SignificanceStatistical Significance
Treatment of Data – to Treatment of Data – to attain normalityattain normality
logIndustrialWaterUse=loglogIndustrialWaterUse=log10(0.1+ 10(0.1+ IndustrialWaterUse)IndustrialWaterUse)
A quick fix method to A quick fix method to check resultscheck results
Moran’s indexMoran’s index - A test for - A test for spatial autocorrelation spatial autocorrelation PositivePositive spatial spatial autocorrelation indicates autocorrelation indicates spatial clustering spatial clustering
What to take back ?!What to take back ?!
The The coolcool randomizing randomizing tooltool ( ($$$$ in royalty) in royalty)
The The intellectual frameworkintellectual framework of how Geospatial of how Geospatial correlation may be computedcorrelation may be computed
ArcGISArcGIS has powerful has powerful geostatisticgeostatistic tools tools
Questions?Questions?