4D Floodplain representation in hydrologic flood forecasting

Chandana Gangodagamage, Thomas Adams, Youssef Loukili, and Zhe Li

Floodplain boundaries for variable water levels

4D Floodplain representation is presented for variable water levels

4D metric: distance along the main stem, flow depth, lateral distance from river

center line with the fourth dimension, time/variable water levels

VL(X,D,t) VR(X,D,t)

Inundation boundary at 5 m water stage (t=t2)

Inundation boundary at 2 m water stage (t=t1)

Main stem

Data complexity (spatial and temporal) for mapping flood plains using remote

sensing data

Inconsistencies caused by different environmental conditions (Clouds, Aerosols, Water

Vapor, Ice and Snow), so as it can offer standardized imagery regardless of where or

when the data was captured

Computational capabilities (cloud computing..)

Mapping flood plain boundaries for variable water stages from satellite data

Flood plain width as a function of time at a given reach

Delineating Flood plain width from Landsat data using machine learning algorithms

Mapping floodplain boundaries using computational algorithms

Example using LiDAR data

Future Direction

Scope of Presentation

Data complexity(spatial and temporal)

SWIR bands can pass through water vapor and CO2 and other ~atmospheric substance

Data complexity(spatial and temporal)

Flood inundation boundaries for ASTER thermal data

Inconsistencies caused by different environmental conditions

Change detection in complex channel geometry Gangodagamage et at. (2007), Rowland and Gangodagamage et al. (2014)

LiDAR point cloud

Bare earth points

Vegetation returns

Flood inundation width

Broad Scale Landscape Classification

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Region-A Region-A Region-A

(a) (b) (c)

Mackenzie River, NWT Canada Pleasant River, MA, USA SF Eel, CA, USA

Objectives of Characterization different for different disciplines: Hydrology, ecology, Geomorphology, Remote Sensing, Numerical computations, and Machine Learning (need Powerful Toolboxes)

Topographic statistics

Creating Grids from Polygonal Ground Characteristics

Use synthetically generated mesh or working with real polygon data (NGEE)

Generate synthetic geometries .or real geometries

Polygonal topographic mesh

Grand Challenges: Computational capability (cloud computing..)

A developer in Google Earth Engine Trusted Tester

Grand Challenges: Computational capability

A developer in Google Earth Engine Trusted Tester

Data complexity (spatial and temporal) for mapping flood plains using remote

sensing data

Inconsistencies caused by different environmental conditions (Clouds, Aerosols, Water

Vapor, Ice and Snow), so as it can offer standardized imagery regardless of where or

when the data was captured

Computational capabilities (cloud computing..)

Mapping flood plain boundaries for variable water stages from satellite data

Flood plain width as a function of time at a given reach

Delineating Flood plain width from Landsat data using machine learning algorithms

Mapping floodplain boundaries using computational algorithms

Example using LiDAR data

Future Direction

Scope of Presentation

Pearl River flood plain extent

@2 ft

@4ft

@6ft

@8ft

@10ft

Flood inundation contours (normalized to water levels at main stem)

Data complexity (spatial and temporal) for mapping flood plains using remote

sensing data

Inconsistencies caused by different environmental conditions (Clouds, Aerosols, Water

Vapor, Ice and Snow), so as it can offer standardized imagery regardless of where or

when the data was captured

Computational capabilities (cloud computing..)

Mapping flood plain boundaries for variable water stages from satellite data

Flood plain width as a function of time at a given reach

Delineating Flood plain width from Landsat data using machine learning algorithms

Mapping floodplain boundaries using computational algorithms

Example using LiDAR data

Future Direction

Scope of Presentation

Example: Ohio River basin

Flood inundation boundary (on 04/25/2014)

0

100000

200000

5/6/2013 0:00 8/14/2013 0:0011/22/2013 0:00 3/2/2014 0:00 6/10/2014 0:00 9/18/2014 0:0012/27/2014 0:00 4/6/2015 0:00 7/15/2015 0:0010/23/2015 0:00

USGSsite 03378500

USGSsite 03377500

# DD parameter Description # Discharge, cubic feet per second # Gage height, feet

Flood inundation boundary (on 4/12/2015)

0

100000

200000

5/6/2013 0:00 8/14/2013 0:0011/22/2013 0:00 3/2/2014 0:00 6/10/2014 0:00 9/18/2014 0:0012/27/2014 0:00 4/6/2015 0:00 7/15/2015 0:0010/23/2015 0:00

USGSsite 03378500

USGSsite 03377500

# DD parameter Description # Discharge, cubic feet per second # Gage height, feet

Boundary delineation and vectorizations

0

100000

200000

5/6/2013 0:00 8/14/2013 0:0011/22/2013 0:00 3/2/2014 0:00 6/10/2014 0:00 9/18/2014 0:0012/27/2014 0:00 4/6/2015 0:00 7/15/2015 0:0010/23/2015 0:00

USGSsite 03378500

USGSsite 03377500

# Discharge, cubic feet per second # Gage height, feet

Skeleton for flood inundations derived from machine learning algorithm

0

100000

200000

5/6/2013 0:00 8/14/2013 0:0011/22/2013 0:00 3/2/2014 0:00 6/10/2014 0:00 9/18/2014 0:0012/27/2014 0:00 4/6/2015 0:00 7/15/2015 0:0010/23/2015 0:00

USGSsite 03378500

USGSsite 03377500

# Discharge, cubic feet per second # Gage height, feet

Similar flood event in April 2014

0

100000

200000

5/6/2013 0:00 8/14/2013 0:0011/22/2013 0:00 3/2/2014 0:00 6/10/2014 0:00 9/18/2014 0:0012/27/2014 0:00 4/6/2015 0:00 7/15/2015 0:0010/23/2015 0:00

USGSsite 03378500

USGSsite 03377500

# Discharge, cubic feet per second # Gage height, feet

Data complexity (spatial and temporal) for mapping flood plains using remote

sensing data

Inconsistencies caused by different environmental conditions (Clouds, Aerosols, Water

Vapor, Ice and Snow), so as it can offer standardized imagery regardless of where or

when the data was captured

Computational capabilities (cloud computing..)

Mapping flood plain boundaries for variable water stages from satellite data

Flood plain width as a function of time at a given reach

Delineating Flood plain width from Landsat data using machine learning algorithms

Mapping floodplain boundaries using computational algorithms

Example using LiDAR data

Future Direction

Scope of Presentation

HEC-RAS and RAS Mapper: Inundation depth

Interpolations between Cross section

Flood plain geometries are correlated with

channel longitudinal profiles

Normalized DEM: Elevation are recalculated using a flow direction algorithm from the stream network

Zero elevation at the stream network

Flow direction algorithm is used to identify the next pixels with lowest elevation from the river to map the flood inundations when river stage increases

Each pixel locations at the main stem can map the left and right inundation boundaries for varying flow depths

Inundation boundary at different water stages

Inundation boundary at 8 ft water stage

Inundation boundary at 6 ft water stage

Inundation boundary at 4 ft water stage

Inundation boundary at 2 ft water stage

Inundation boundary at different water stages

Inundation boundary at 2 m water stage

Inundation boundary at 5 m water stage

Future direction

Time series of Landsat, ASTER, and other high resolution imageries provide detailed information about flood inundation boundaries and water inundation flow paths for variable water stages at reach scale

LiDAR and other high resolution digital elevation model (DEM) data can be used to route the water from main stem to flood plain and can delineate flood inundation boundaries using computation algorithms

By fusing information on water inundation flow paths from visible/near IR satellite images and water routing details from digital elevation model data, we can map flood inundation boundaries for variable water stages compute from hydrologic models

Any questions?