UNCERTAINTY IN FLOOD INUNDATION...

UNCERTAINTY IN FLOOD UNCERTAINTY IN FLOOD INUNDATION MAPPINGINUNDATION MAPPING

Francisco Olivera, Ph.D., P.E.Texas A&M University

Venkatesh Merwade, Ph.D.Purdue University

Flood Inundation MappingFlood Inundation Mapping

1. Hydrologic Modeling – Q2. Hydraulic Modeling – WSE 3. Flood Inundation Mapping – Flood Map

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Model Parameters!

Hydrologic Modeling

PrecipitationStreamflow LU/LCSoil

Hydraulic Modeling Flood Inundation Mapping

GIS Topography

Q WSE

ObjectiveObjective

To investigate the effect of data, model parameters and GIS techniques on flood inundation maps.

To investigate how error or uncertainty propagates through the flood inundation train.

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Demonstration AreaDemonstration Area

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0 8 Kilometers0 8 Kilometers

Eno River

Strouds Creek

Orange Durham

Length: 6.5 km long.

Width: 10 m.

Valley: v-shaped.

Data and Models Data and Models

Hydrologic Model – USGS NFF regression equation to get Q. Hydraulic Model – HEC-RAS Project from the North Carolina Floodplain Mapping Program (NCFMP) to get WSE.ArcGIS for flood inundation mapping on DEM (USGS) and LIDAR (NCFMP).

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Estimation of QEstimation of Q100100

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Q100 = 745 DA0.625

Q100 in cfs and DA in sq. mi.DA < 41 sq. mi.

Standard error of prediction in the range of -34% to 57%.

NFF equation

For Strouds Creek, Q100 = 83.3 m3/s (range: 53.3 m3/s to 130.7 m3/s)

0

500

1000

1500

2000

2500

0 50 100 150 200 250 300 350 400

Drainage Area (km 2)

Q10

0(m

3 /s)

Regression estimate

Standard error - upper bound

Standard error - low er bound

Effect of QEffect of Q100100 Estimation on WSEEstimation on WSE

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54.3m

67.2m

90.2m

153

154

155

156

157

158

159

160

161

162

0 20 40 60 80 100 120 140

Station (m)

Elev

atio

n (m

)

(2)

(1)

(3) 1m

0.4m

6m LIDAR DEM

(1) NFF estimated flow of 83.3 m3/s

(2) 64% of 83.3 m3/s (lower bound)

(3) 156.5% of 83.3 m3/s(upper bound)

Variations in WSE at one of the cross-sections in HEC-RAS resulting from variations in Q.

Effects of ManningEffects of Manning’’s n on WSEs n on WSE

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(1) a mix of Manning’s n for channel (0.035 – 0.065) and over bank (0.08 – 0.15)(2) 0.035 for channel and 0.08 for over banks (lower bound)(3) 0.065 for channel and 0.15 for over banks (upper bound).

63.9m

67.2m

82.9m

153

154

155

156

157

158

159

160

161

162

0 20 40 60 80 100 120 140

Station (m)

Elev

atio

n (m

)

(2)

(1)

(3)

0.6m0.2

6m LIDAR DEM

Effect of TopographicEffect of Topographic Data on WSEData on WSE

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WSE in HEC-RAS using 30m USGS DEM and 6m LIDAR as topographic inputs

86.6m

67.2m

153

154

155

156

157

158

159

160

161

162

0 20 40 60 80 100 120 140

Station (m)

Elev

atio

n (m

)

6m LIDAR DEM30m USGS DEM

LIDAR

USGS

0.3m

Effect of Topographic Data on Effect of Topographic Data on the Flood Inundation Mapthe Flood Inundation Map

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LIDAR USGS

0 100 Meters0 2 Kilometers

2.48

0.66

7

0.161

6.49

6

1.777

3.407

4.78

5

Effect of Topographic Data on Effect of Topographic Data on Flood Inundation MappingFlood Inundation Mapping

Cross-section Station No.

Water Surface Extent (m)

30m USGS DEM 6m LIDAR DEM Difference

0.161 56.5 50.9 5.6

0.667 130.5 66.1 64.4

1.777 87.5 91.8 -4.3

2.480 162.9 146.7 16.2

3.407 65.5 78.3 -12.8

4.785 21.9 45.4 -23.5

6.496 54.6 35.0 19.6 11

LIDAR USGS

0 100 Meters0 2 Kilometers

2.48

0.66

7

0.161

6.49

6

1.777

3.407

4.78

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DEM v/s TINDEM v/s TIN

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Floodplain for both 1.25m and 1.75m WSE

1.25m floodplain

1.75m floodplain

A A A A

Section A-A Section A-A

1.75m1.25m

2.0m

1.0m

Monte Carlo SimulatorMonte Carlo Simulator

f

x

Models

Integrated Modeling andIntegrated Modeling and Uncertainty Analysis FrameworkUncertainty Analysis Framework

14Map-to-map application from UT, Austin

Input Parameters

Uncertainty Analysis

Systems with Data

Model RSA

Key Input FactorsData

spatial/tempor alModel

parameter/structu re

Random Sampling

Model parameters ( Mannings N)

Hydraulic structures

Latin Hypercube Sampling

(LHS) n Sa m pl es

System Behavior

Temporal dataPrecip., flow, etc.

FLDWAV/ RAM2/ RMA10

GLUE

Geospatial DataTerrain

Systems without DataMorris’s OAT

FAST

Evaluate selected distribution functions

Flood inundatio

n map

Uncertainty Analysis Framework

+

Probabilistic Flood Probabilistic Flood Inundation MapsInundation Maps

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River Floodplain

Uncertainty zone

SummarySummary

Uncertainty is associated with all inputs and processes involved in flood inundation mapping, which are not communicated in the current flood mapping practiceTechnology exists to produce probabilistic flood inundation maps, and its applicability should be explored

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Thank you.Thank you.

Contacts:Francisco Olivera – folivera@civil.tamu.eduVenkatesh Merwade – vmerwade@purdue.edu