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?