Geostatistical Analysis of Hydrologic Parameters Nishesh Mehta Hydrology - CE394K 26th April 07

Outline 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 Sources Water Use data for the US http://water.usgs.gov/watuse/data/2000/index.html contains: Industrial Water Use Public Supply Water Use Agricultural Water Use Hydrologic landscape regions of the United States http://water.usgs.gov/GIS/metadata/usgswrd/XML/hlrus.xml 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? Semivariograms- Geostatistical Analyst The semivariogram captures the spatial dependence between samples by plotting semivariance against separation distance h= 0.5 * avg[ (value i –value j)2 ]

Semivariogram

Preliminary Results Correlation lengths calculated on county basis Data used was raw (not treated to have a normal distribution) Semivariance calculated between each set of counties within the continental US Variable Correlation Length Public Supply Water Use 1000 kms Self Supplied Industrial Water Use 253 kms Irrigation Water Use 1400 kms Slope (as % flatlands) 375 kms % Sand 1204 kms Climate (Precipitation – PET) 3318 kms Bedrock Permeability 470 kms

Surface generated using the semivariogram

Synthesis of Analysis base unit of analysis - Counties – 3077 HUCs – 2158 (grouped on similar hydrologic properties) Spatial Join – A tool that helps to associate and interpolate values spatially. Ex- convert parameter classified by county basis to HUC basis Random Sampling – Basis of all statistical processes Enables sampling out of a large number of points

Randomization Tool

CUAHSI test bed sites as pilot test Random sampling of HUCs from the site comprised of HUC units Use any parameter from the attribute table Sierra Nevada

Results Scaling length Industrial water for the entire Sierra Nevada– 110 kms Scaling length Industrial water for a random sample of Sierra Nevada- 110 kms

Statistical Significance Treatment of Data – to attain normality logIndustrialWaterUse=log10(0.1+ IndustrialWaterUse) A quick fix method to check results Moran’s index - A test for spatial autocorrelation Positive spatial autocorrelation indicates spatial clustering

What to take back ?! The cool randomizing tool ($$ in royalty) The intellectual framework of how Geospatial correlation may be computed ArcGIS has powerful geostatistic tools

Questions?