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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
2
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
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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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