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
contains: Industrial Water Use
Public Supply Water Use
Agricultural Water Use
Hydrologic landscape regions of the United States
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)

4. 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 ]

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
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

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
Random sampling of HUCs from the site
comprised of HUC units
Use any parameter from the attribute table
Sierra Nevada

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?