1. Using LiDAR, “WATER”, and TOPMODEL TOPO-DRIVEN HYDROLOGY

2.  Carey Johnson, KY Division of Water  State CTP Program Manager  Has led Kentucky through MapMod for all 120 counties in the Commonwealth  Trevor Timberlake, URS  Water Resources Engineer  Hydrology/Hydraulics Modeling, Project Management  FEMA, Dams, Transportation INTRODUCTIONS

3.  Kentucky’s Approach to RiskMAP  What is “WATER”?  The WATER Software  TOPMODEL  The Variable Source Area Concept  Our Study  Objectives  Methods  Results & Conclusions  Results of Analysis  Conclusions  Direction for Moving Forward OVERVIEW

4. Better Data, Strong Partnerships, and Better Answers KENTUCKY’S APPROACH TO RISKMAP

5.  Better Input = Better Output  Part of the RiskMAP Vision  “High-quality elevation data form the foundation for increasing the quality of the flood maps…” – FEMA’s RiskMAP Report to Congress (3/15/11)  Acquisition of LiDAR  Acquired for portions of Kentucky  Has been a catalyst for new Statewide LiDAR acquisition BETTER DATA

6. LIDAR ACQUISITION

7.  Find“win-win” relationships  USGS and KDOW  Development of WATER tool, funding from KDOW  Gage Data  NWS and KDOW  KDOW’s contribution to the Ohio River Community Model  Receipt of NexRAD data for entire OHRFC area STRONG PARTNERSHIPS

8.  Credibility  The Floodplain Managers, City Engineers, etc.  The Public  Should regression equations be the default answer?  What about ungaged, unmodeled areas?  What about a software package:  …developed by a partnership between KDOW and USGS  …utilizing NWS data ...incorporating a model “driven” by topography  …tested in an area for which we have LiDAR data  …that can yield “model-based” results  …efficiently? BETTER ANSWERS

9. “WATER”, TOPMODEL, and the VSA Concept WHAT IS “WATER”?

10.  Developed by USGS in conjunction with KDOW  The Water Availability Tool for Environmental Resources  A “User-Friendly Decision Support System” THE “WATER” SOFTWARE

11.  Originally Intended for Water Budget Modeling  “A Water-Budge Modeling Approach for Managing Water-Supply Resources”  Based on daily inputs of precipitation, evapotranspiration, withdrawals, and other data; used to estimate shortages  Phase 1  Initial Software Development  Calibrated for Non-Karst Areas  Includes basin delineation tool  Uses TOPMODEL for simulation  Phase 2 (under review)  Calibration updated to include Non-Karst Areas  Construction of Estimated Flow-Duration and Load-Duration Curves  Has seen widespread application in USGS  Water availability estimation in KY and AL  Load-Duration Curves for TMDLs  Flood Assessments in IN THE “WATER” SOFTWARE

12. SCREENSHOTS OF WATER

13.  Developed by Keith Beven and Mike Kirkby in 1979  Has been used in more than 30 countries worldwide  Simulates the Variable Source Area concept  Topographically-driven  Semi-distributed  Note: There are many “implementations” of TOPMODEL TOPMODEL

14.  Infiltration-Excess:  Developed by Horton (1933)  “Typical” method THE VARIABLE SOURCE AREA CONCEPT Figure from Wolock, 1993

15.  Variable Source Area  Initial concepts developed by Hursh  John D. Hewlitt coined the phrase  Early career: Mountainous watersheds in the southern Appalachians  Struggled with developing model that incorporated VSA concept THE VARIABLE SOURCE AREA CONCEPT Figure from Wolock, 1993

16. ILLUSTRATION OF THE VARIABLE SOURCE AREA CONCEPT Adapted from Bulletin 164, Loganathan, et al (1989)

17.  3 types of flow are computed  Direct Flow  Return Flow  Subsurface Flow  Soil parameters that are typically used  Hydraulic Conductivity  Available Water Capacity  Transmissivity ... and others. SUBSURFACE FLOW

18. TOPOGRAPHICALLY “DRIVEN”

19.  TWI Histogram  Typically, 30 bins  Mean  Fraction of total watershed area  Groups areas that are hydrologically similar SEMI-DISTRIBUTED Figures from USGS

20. Objectives and Methods OUR STUDY

21.  How does the WATER software need to be modified to fit FEMA needs?  Need: Computation of peak discharges  Annual peaks  Recurrence interval events (1%-AEP, aka 100-year)  Need: Efficient methodology  Placement of pour points  Delineation of watersheds  Need: “Better” answers  Better than regression equations?  As good as other rainfall-runoff models?  How does LiDAR affect the answer?  Paper by Bhaskar indicated need for using more resolute elevation data THE CHALLENGE

22. Two Tests 1.LiDAR and Topographic Wetness Index (TWI)  WATER results (baseline)  LiDAR re-sampled at same resolution as WATER DEM (30’ x 30’ DEM)  LiDAR at “native” resolution (5’ x 5’ DEM) 2.Daily vs Hourly Timestep  Mimic WATER results using TOPMODEL  Replace precipitation time series with hour-based storm event OBJECTIVES

23. STUDY AREA Levisa Fork Watershed 7 Gages – from 0.8 to 56 square miles in drainage area Levisa Fork

24. TEST 1: LIDAR-BASED TWI 1.Check TWI-grid creation by comparison with WATER results  Same elevation source (30-foot DEM, NED-based)  Substitute DEM file in the WATER database and run 2.Re-sample LiDAR DEM to same resolution as original DEM  5’ x 5’ DEM to 30’ x 30’ DEM  Better estimate of elevation values (averaging 36 cells into one)  Substitute DEM file in the WATER database and run 3.Use LiDAR DEM at native resolution  5’ x 5’ DEM  Substitute DEM file in the WATER database and run

25. TEST 2: HOURLY PRECIPITATION  Create baseline run  Run TOPMODEL (R Version) using same daily precipitation and evapotranspiration values as WATER  Compare results and calibrate as necessary  Substitute daily precipitation values  In TOPMODEL R  Select events  Design storm  Historic storms  Check Q’s versus gage values

26. What we discovered RESULTS & CONCLUSIONS

27.  Graph of TWI-results (% error vs drainage area) LIDAR-BASED TWI

28. HOURLY PRECIPITATION - BASELINE RUN  Baseline Runs (i.e. WATER with daily precipitation input)

29. BASELINE RUN

30. 1.Clear indications that hourly data will lead to improvement 2.Computation of precipitation time series: a)Hourly precipitation time series have been created for each gage using NOAA Atlas 14 data for precipitation depths, and NRCS Type II 24-hour rainfall distribution (for the 10- and 100-year events) b)Processing is underway for NexRAD data i.Completed for Grapevine Creek (6 square miles) ii.Begun for Johns Creek (56 square miles) iii.Process the storm for each annual peak flood in the USGS gage record that is within the period of record of the NexRAD data (1997=2010) iv.NexRAD provides insight into the use of continuous simulation for estimation of peakflow STATUS OF HOURLY TEST

31. 1.LiDAR-based TWI grids did not have a significant effect, however:  Other parameters should be re-calibrated when resolution changes  The effect of the # of histogram bins was not tested  At least one other study in the region has indicated better resolution = better result 2.We anticipate improvement of peak flow estimation with the implementation of hourly data 3.Collaboration with USGS could lead to further development of WATER, and an efficient, accurate means of estimating recurrence interval peak flows – for ungaged locations. 4.Any improvement in discharge estimation will have to be weighed against two factors: a)% Error b)Cost CONCLUSIONS

32. REFERENCES/CREDITS  Thanks to Jeremy Newsom, Mike Griffin, and Pete Cinotto, USGS KY Science Center!  Thanks to Tom Adams, NWS Ohio River Forecast Center  Sources:  Williamson, et al. The Water Availability Tool for Environmental Resources (WATER). Phase I. US Geological Survey Scientific Investigations Report 2009-5248, 34 p.  Wolock, David M. Simulating the Variable-Source-Area Concept of Streamflow Generation with the Watershed Model TOPMODEL. US Geological Survey Water-Resources Investigations Report 93-4124, 39 p.  Loganathan, GV, et al. Variable Source Area Concept for Identifying Critical Runoff-Generating Areas in a Watershed: Bulletin 164. Virginia Polytechnical Institute and State College, May 1989, 125 p. Online at http://vwrrc.vt.edu/pdfs/bulletins/bulletin164.pdf