TOPO-DRIVEN
HYDROLOGY
Using LiDAR,
“WATER”, and
TOPMODEL
INTRODUCTIONS
 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
OVERVIEW
 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
KENTUCKY’S APPROACH
TO RISKMAP
Better Data,
Strong
Partnerships,
and Better
Answers
BETTER DATA
 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
L I DA R
AC Q U I S IT I ON
STRONG PARTNERSHIPS
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
BETTER ANSWERS
 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?
WHAT IS “WATER”?
“WATER”,
TOPMODEL,
and the VSA
Concept
THE “WATER” SOFTWARE
 Developed by USGS in
conjunction with
KDOW
 The
Water
Availability
Tool for
Environmental
Resources
 A “User-Friendly
Decision Support
System”
THE “WATER” SOFTWARE
 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
SCRE E N SH OT S
OF WATE R
TOPMODEL
 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
THE VARIABLE SOURCE AREA CONCEPT
Infiltration-Excess:
 Developed by Horton (1933)
 “Typical” method
Figure from Wolock, 1993
THE VARIABLE SOURCE AREA CONCEPT
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
Figure from Wolock, 1993
ILLUSTRATION
OF THE
VARIABLE
SOURCE AREA
CONCEPT
A d a p te d f r o m
B u l l e t i n 16 4 ,
Loganathan, et
al (1989)
SUBSURFACE FLOW
 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.
TOPOGRAPHICALLY “DRIVEN”
 Topographic Wetness Index (TWI)
𝑢𝑝𝑠𝑙𝑜𝑝𝑒 𝑐𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑛𝑔 𝑎𝑟𝑒𝑎
𝑇𝑊𝐼 = ln
tan(𝑠𝑙𝑜𝑝𝑒)
 High values of TWI = High potential for saturation
 Low values of TWI = Low potential for saturation
SEMI-DISTRIBUTED
TWI Histogram
 Typically, 30 bins
 Mean
 Fraction of total
watershed area
Groups areas that
are hydrologically
similar
Figures
from USGS
OUR STUDY
Objectives
and
Methods
THE CHALLENGE
 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
OBJECTIVES
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
STUDY AREA
L ev i s a Fo r k
Wa te r sh e d
7 Gages – from
0 . 8 to 5 6
square miles in
drainage area
Levisa Fork
TEST 1: LIDAR-BASED TWI
1. Check T WI-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 necessary
 Substitute daily precipitation values
 In TOPMODEL R
 Select events
 Design storm
 Historic storms
 Check Q’s versus gage values
RESULTS & CONCLUSIONS
What we
discovered
LIDAR-BASED TWI
 Graph of T WI-results (% error vs drainage area)
HOURLY PRECIPITATION - BASELINE RUN
 Baseline Runs (i.e. WATER with daily precipitation input)
BASELINE RUN
STATUS OF HOURLY TEST
1. Clear indications that hourly data will lead to improvement
2. Computation of precipitation time series:
a)
b)
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)
Processing is underway for NexRAD data
i.
ii.
iii.
iv.
Completed for Grapevine Creek (6 square miles)
Begun for Johns Creek (56 square miles)
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)
NexRAD provides insight into the use of continuous simulation for
estimation of peakflow
CONCLUSIONS
1. LiDAR-based T WI 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)
b)
% Error
Cost
REFERENCES/CREDITS
 Thanks to Jeremy Newsom, Mike Grif fin, 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