1 / 15

Geostatistical Analysis of Hydrologic Parameters

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

kira
Download Presentation

Geostatistical Analysis of Hydrologic Parameters

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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)

  3. 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:

  4. Semivariograms- How to do it? 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 ]

  5. Semivariogram

  6. 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

  7. Surface generated using the semivariogram

  8. 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

  9. Randomization Tool

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

  11. 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

  12. 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

  13. 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

  14. Questions?

More Related