2. Distributed Hydrologic Modeling-- Jodi Eshelman Analysis of the Number of Rain Gages Required to Calibrate Radar Rainfall for the Illinois River Basin REU sponsor: Dr. Baxter Vieux Dr. Fekadu Moreda Gary Brickley Jodi Eshleman

3. Distributed Hydrologic Modeling-- Jodi Eshelman Introduction Radar rainfall estimates are an important supplement to rain gage accumulations for modeling river basins. Radar estimates can be biased or in error and must be corrected. Questions: 1.How many rain gages are necessary to correct the radar? 2.What degree of accuracy can be achieved? 3.How do different correction methods compare?

4. Distributed Hydrologic Modeling-- Jodi Eshelman Background Are 10 gages adequate to calibrate the radar for the Illinois River Basin? Radar error in estimating rainfall –overshoot cloud tops –Z/R relationship transforms reflectance to rain rate Correction of radar by applying some correction based on rain gage accumulation Correcting radar estimates provides more accurate spatial estimates of rainfall for river basin simulation.

5. Distributed Hydrologic Modeling-- Jodi Eshelman WSR-88D or NEXRAD Weather Surveillance Radar-1988 Doppler Prototyped in Norman at NSSL Scans Every 5 or 6 minutes during precipitation 150+ installed in US and abroad 0.5° 1.5° 2.5°

6. Distributed Hydrologic Modeling-- Jodi Eshelman Location of Gages

7. Distributed Hydrologic Modeling-- Jodi Eshelman Presentation Outline Test 4 different correction factors –Mean field bias –Probability density function –<1mm –Weighted Gage density study Size and time progression analysis

8. Distributed Hydrologic Modeling-- Jodi Eshelman Correction Factor Comparison Adjustment to rain gage mean Average difference after correction Simulated discharge volume

9. Distributed Hydrologic Modeling-- Jodi Eshelman PDF is closest to Mesonet

10. Distributed Hydrologic Modeling-- Jodi Eshelman Volume Comparison

11. Distributed Hydrologic Modeling-- Jodi Eshelman Presentation Outline Test 4 different correction factors –Mean field bias –Probability density function –<1mm –Weighted Gage density study Size and time progression analysis

12. Distributed Hydrologic Modeling-- Jodi Eshelman Gage Density Statistically estimating the mean with prescribed accuracy Where: n=number of gages required s 2 =Variance d=Allowable margin of error (5-30% mean)  =% Confidence (60-90%)

13. Distributed Hydrologic Modeling-- Jodi Eshelman Calibration Comparison

14. Distributed Hydrologic Modeling-- Jodi Eshelman Standard Error Approach

15. Distributed Hydrologic Modeling-- Jodi Eshelman Presentation Outline Test 4 different correction factors –Mean field bias –Probability density function –<1mm –Weighted Gage density study Size and time progression analysis

16. Distributed Hydrologic Modeling-- Jodi Eshelman Mean Total Accumulation

17. Distributed Hydrologic Modeling-- Jodi Eshelman Time Progression

18. Distributed Hydrologic Modeling-- Jodi Eshelman Conclusions PDF correction factor is most effective –Mean adjustment is closer to Mesonet –Average difference is less than MFB Weighted PDF – –Weighting gages close to the basin improve discharge volume simulations Gage density study –10 gages are sufficient for 30% of the mean and 90% confidence –Due to large variance, smallest storms are negligible –little consideration for flooding