Using LiDAR, “WATER”, and TOPMODEL

TOPO-DRIVEN HYDROLOGY

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

Kentucky’s Approach to RiskMAPWhat 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

Better Data, Strong Partnerships, and Better Answers

KENTUCKY’S APPROACH TO

RISKMAP

Better Input = Better OutputPart 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

LIDAR ACQUISITION

Find“win-win” relationshipsUSGS and KDOW

Development of WATER tool, funding from KDOW

Gage DataNWS and KDOW

KDOW’s contribution to the Ohio River Community Model

Receipt of NexRAD data for entire OHRFC area

STRONG PARTNERSHIPS

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 …effi ciently?

BETTER ANSWERS

“WATER”, TOPMODEL, and the VSA Concept

WHAT IS “WATER”?

Developed by USGS in conjunction with KDOW

The Water Availability Tool for Environmental Resources

A “User-Friendly Decision Support System”

THE “WATER” SOFTWARE

Original ly 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

Phase I

Phase II

SCREENSHOTS OF WATER

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

Infiltration-Excess:Developed by Horton (1933)“Typical” method

THE VARIABLE SOURCE AREA CONCEPT

Figure from Wolock, 1993

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

ILLUSTRATION OF THE VARIABLE SOURCE AREA CONCEPTAdapted from Bul let in 164, Loganathan, et al (1989)

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

“Dunne” Overland Flow Total

Discharge

Topographic Wetness Index (TWI)

High values of TWI = High potential for saturation

Low values of TWI = Low potential for saturation

TOPOGRAPHICALLY “DRIVEN”

TWI HistogramTypically, 30 binsMean Fraction of total watershed area

Groups areas that are hydrologically similar

SEMI-DISTRIBUTED

Figures from USGS

Objectives and Methods

OUR STUDY

How does the WATER software need to be modified to fi t FEMA needs? Need: Computation of peak discharges

Annual peaks Recurrence interval events (1%-AEP, aka 100-year)

Need: Effi cient 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

Two Tests1. 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

STUDY AREALevisa Fork Watershed

7 Gages – from 0.8 to 56 square miles in drainage area

Levisa Fork

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

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 necessarySubstitute daily precipitation values

In TOPMODEL R Select events

Design storm Historic storms

Check Q’s versus gage values

What we discovered

RESULTS & CONCLUSIONS

Graph of TWI-results (% error vs drainage area)

LIDAR-BASED TWI

HOURLY PRECIPITATION - BASELINE RUN

Baseline Runs (i.e. WATER with daily precipitation input)

BASELINE RUN

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 datai. 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

1. LiDAR-based TWI grids did not have a signifi cant eff ect, 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 fl ow estimation with the implementation of hourly data

3. Collaboration with USGS could lead to further development of WATER, and an effi cient, accurate means of estimating recurrence interval peak fl ows – for ungaged locations.

4. Any improvement in discharge estimation will have to be weighed against two factors:

a) % Errorb) Cost

CONCLUSIONS

REFERENCES/CREDITS

Thanks to Jeremy Newsom, Mike Griffi n, 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

Questions?