1. New Variables, Gage Data, and WREG REGIONAL ANALYSIS IN THE LEVISA FORK AND TUG FORK BASINS

2.  Carey Johnson, KY Division of Water  State CTP Lead  Has led Kentucky through MapMod for all 120 counties in the Commonwealth  Davis Murphy, URS  Water Resources Engineer INTRODUCTIONS

3. Watershed- based analysis, and establishing a greater understanding KENTUCKY’S APPROACH TO RISKMAP

4.  Part of the Risk MAP Vision  Credible data—reliable, accurate, watershed-based KENTUCKY AND RISK MAP

5.  Asking the right questions:  What’s being done throughout the nation?  What tools are available?  What can we do now? TAKE ADVANTAGE

6. Review, Goals, Methods, Results, and Closing Thoughts OUR STUDY

7.  Review regression analyses throughout the country  Nationwide there are over 150 explanatory variables tested  From Drainage area to the Rotundity Ratio  Test new variables  Update gage peak flow estimates PEAK FLOW STUDIES THROUGHOUT THE NATION

8.  Top 5 tested variables:  drainage area 1  mean annual precipitation  main-channel slope  main-channel length  forested area  Top 5 final variables for the 1%:  drainage area 1  main-channel slope  mean basin elevation  mean annual precipitation  main-channel length  OLS, GLS regression and in a few cases RoI regression  Regionalization technique is commonly used 1 Includes total drainage area, and contributing drainage area.

9.  Linear Regression technique  Normally looks like: Q 100 = 71.4TDA 0.907 S 0.632  Typically, variables are log transformed  Takes the form: Y i = a i + b i X 1 + c i X 2 + … + z n,i X n + e i  e i = Y - Y i  Where you minimize the Σ (e i ) 2 ORDINARY LEAST SQUARES

10.  Much like OLS except it accounts for…  Differences in the variance of stream flows from site-to-site  Error in peak flow estimates should be the same at all gages  Cross-correlation of gage data  Violates independence assumptions  Uncertainty in the weighted skewness estimator (B17-B) GENERALIZED LEAST SQUARES

11.  Custom equation  3 versions  Geographic (GRoI)  Predictor Variable (PRoI)  Hybrid (HRoI) REGION OF INFLUENCE

12. KENTUCKY’S REGRESSION EQUATIONS

13.  Report published in 2003  Equations use TDA (all regions) and Main-channel slope (2/7 regions)  27 explanatory variables tested  Many variables estimated from USGS’s 7.5-minute topo quads and 1:250,000-scale DEM  Top 4 variables: total drainage area, main-channel slope, main-channel sinuosity, and basin-shape factor  OLS and GLS regression techniques used KENTUCKY’S REGRESSION EQUATIONS

14.  WREG  Released Jan. 2010  OLS, GLS, and RoI *  PeakFQ  Analyze many gages at once  * WREG will perform RoI for demonstration purposes only TOOLS

15.  Explanatory Variables  Indentify and compute new hydrologic variables  In Progress… Re-compute explanatory variables using better data  Main-channel slope  Mean basin elevation  Evaluate updated explanatory variables for significance  Gage Frequency Analysis  Update B17-B analyses with new gage data  Determine if updated peak flow analyses provide statistically meaningful effects GOALS OF ANALYSIS

16.  Data  Watstore database of basin characteristics  NHD Plus – create basin polygons, compute other variables  Gage peak flows – update B17-B analyses  Software  ArcGIS & ArcHydro – variables  PeakFQ – Recurrence interval flows (100-yr, etc.)  Excel – OLS METHODS

17. WATSTORE BASIN CHARACTERISTICS

18. STUDY AREA

19.  Preferred basin orientation  Borrowed from HMR-52 (PMP)  Suggests average basin orientation affects extreme rainfall events  Procedure well defined and accepted  Time of Concentration  Kirpich’s formula  Average basin slope  Main-channel length (longest flowpath) NEW EXPLANATORY VARIABLES

20.  Estimate the average orientation angle at each basin  Determine the preferred basin orientation from HMR- 52  Calculate the difference PREFERRED BASIN ORIENTATION

21. DA vs 1%-AEPAdjusted DA vs 1%-AEP PREFERRED BASIN ORIENTATION

22. Tc (min) vs 1%-AEP Cross-correlation: Tc vs DA TIME OF CONCENTRATION

23. 2003 FLOWS VS TODAY 1%-AEP

24. 2003 FLOWS VS TODAY  2003 flows (cfs)  MEAN = 13670  MEDIAN = 9510  Today’s flows (cfs)  MEAN = 14370  MEDIAN = 9512 ΔMEAN = 702 (0.01437) Δ MEDIAN = 2 (9.1x10 -5 )

25.  RECAP  Summarized nation’s regression studies  Tested new variables for significance  No effect to very small improvement to OLS regression  Reviewed preliminary results of new gage frequency analyses  Very little change in the overall pool of flow data  POSSIBLE FUTURE ANALYSIS  GLS regression on new flows with and without ABO  Explore RoI  Incorporate pending changes to B17-B (EMA, historic data) CLOSING THOUGHTS

26.  LESSONS LEARNED  Unique opportunity  Consider cross-correlation of variables  Regional changes in peak flow signify need to update  Urbanization  Recent floods of record CLOSING THOUGHTS

27. QUESTIONS?