hyper-resolution and large scale flood assessments using ... · hyper-resolution and large scale...
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Hyper-resolution and Large Scale
Flood Assessments using 2D GPU
Modeling
ASFPM Conference 2017
Javier FernandezComputational Hydraulics Group
University of Zaragoza, Spain
Asier Lacasta, PhDComputational Hydraulics Group
University of Zaragoza, Spain
Mario Morales, PhDComputational Hydraulics Group
University of Zaragoza, Spain
Pilar Garcia-Navarro, PhDComputational Hydraulics Group
University of Zaragoza, Spain
Reinaldo Garcia, PhDDirector or Model Development and Applications
Hydronia, LLC
High-resolution and Large Scale Flood and
Hydrologic Simulations in 2D
Take advantage of elevation data sets
Provide adequate resolution to capture irregular terrain, and urban
areas
Long rivers reaches with wide floodplains
Large watersheds
Millions of computation cells Long runtimes
High Res Elevation Model for Managua
Terrain data: high resolution LiDAR
Terrain roughness: Aerials, Remote Sensing
Rainfall: Radar precipitation estimates (NEXRAD, etc.)
Inundation extent: Aerials, Remote Sensing
2D Model Requirements for High-Resolution and Large Scale
Simulations
Capable of resolving complex terrain and urban areas
Integration of Hydrology and Hydraulics
o Spatially and temporally variable rainfall, evaporation and
infiltration
o 2D flood routing
o [Sediment transport]
Huge memory storage capacity
o 64-bit GUI + Model Engine
Computational competence
o Reasonable Runtimes
Square-cell Grid vs Triangular-cell Mesh
400,000 Triangles 2,200,000 Squares
5.5 times more cells ==> More RAM memory, Larger runtimes
Flexible Mesh
Parallel Computation Approach
Multiple Processors (CPU or GPU)
Multiple steps at a time
Multiple Core Processors
… if the Model is not programed to run in
parallel you will not experience
any speedup
Parallelization in multiple core processors is not
enough
Number of cores is limited: 2, 4,…, 16…
Open MP parallelization often does not scale
well:
More cores ≠> better performance
Graphic Processing Units: GPUs
GPU: Graphic Processing Units
GPU is present on video cards
GPU’s often have more than 2000 processors
GTX 780GTX Titan
BlackGTX1080
Tesla c2075
Tesla k20 Tesla k40 Tesla k80
CUDA cores 2,304 2,880 2,560 448 2,496 2,880 2 x 2,496
Memory 3 Gb 6 Gb 8 Gb 6 Gb 5 Gb 12 Gb 24 Gb
~Cost US$ 500 850 700 - 2,000 3,000 4,500
RiverFlow2D 2D Flood Simulation
Model Combined Hydrologic-Hydraulic model
Flexible non-structured mesh
Dry-wet bed algorithm
Explicit Finite-Volume numerical engine
Supercritical and subcritical regimes
Hydraulic structures in 2D mesh (Bridges, etc.)
Parallelized for multiple core CPUs
High Performance GPU: up to150X faster
Add on Modules
o Erosion/Deposition
o Mud and Debris Flows
o Water Quality
SMS GUI
Autodesk Infraworks™ Flood Simulation plug-in
Tasmania State-wide Flood Model
• A preliminary investigation Into the feasibility of a
State-wide Flood Model for Tasmania
• Proof of Concept
• Prepared for the Office of Security and
Emergency Management. The Department of
Premier and Cabinet, Tasmania. May 2016
Acknowledgements
• Ted Rigby, Rienco Consulting.
• Colin Mazengarb, Mineral Resources Tasmania
• Alan Zundel and Tom Moreland, Aquaveo.
RIENCOConsulting
• 68,401 km²
• 26,410 mi2
• ~350 watersheds
315 Km
350 Km
Tasmania State-wide Flood Model
Background
The Department has been considering developing a centralised and more
consistent flood modelling for the State
Investigations until recently had suggested that construction of a single state-
wide flood model was not practical
Model Construction
RiverFlow2D &
SMS
5m and 25m
LiDAR
Ocean: 500m cells
Land: 10-250m
cells
2-5m cells on area
of interest
7.680,561 cells
Mesh View in 3D
9-hour
Storm
Comparison with Measurements
OBSERVEDWater Elevations
(m)
Coarse mesh Water Elevations
(m)Error 1
(m)
Refined Mesh Water Elevations
(m) Error 2(m)
29.28 30.25 -0.97 29.27 0.01
25.51 25.96 -0.45 25.41 0.10
20.73 22.01 -1.28 20.54 0.19
19.12 20.47 -1.35 19.11 0.01
19.07 20.48 -1.41 18.91 0.16
18.69 18.88 -0.19 18.44 0.25
16.77 16.19 0.58 16.58 0.19
15.77 15.48 0.29 15.71 0.06
13.23 13.58 -0.35 13.07 0.16
13.02 12.97 0.05 12.89 0.13
11.62 11.48 0.14 11.57 0.05
10.54 9.98 0.56 10.47 0.07
10.20 9.71 0.49 10.04 0.16
8.37 8.14 0.23 8.19 0.18
8.03 7.58 0.45 7.91 0.12
6.85 6.51 0.34 6.61 0.24
5.79 5.40 0.39 5.73 0.06
5.75 5.10 0.65 5.64 0.11
4.33 4.44 -0.11 4.12 0.21
3.96 3.90 0.06 3.85 0.11
3.04 3.20 -0.16 3.00 0.04
3.00 3.20 -0.20 2.85 0.15
1.18 1.35 -0.17 1.16 0.02
Average Error 1 0.48 Average Error 2 0.06
Tasmania State-wide RiverFlow2D Model
Runtimes for 9-hour Rainfall Storm
3 days 18 hours
4.5 hours
Conclusions
Large-scale high-resolution model is not practical using uniform fixed grid
resolution (square grid)
A single state-wide flood model for Tasmania is feasible using SMS and
RiverFlow2D GPU
Preliminary results compare well with measurements
Runtimes of few hours vs days or weeks
Use of GPU models open the door for realistic large scale 2D simulations
Red River of the North
Thanks to:
Mike DeWeese
Jonathan Thornburg
Justin Palmer
Pedro Restrepo
Mark Ziemer
NOAA – NCRFC
Chanhassen, MN
Hydronia-NOAA CRADA
Cooperative Research & Development Agreement (CRADA)
Create a large scale two-dimensional flooding simulations using advanced
GPU technology with the RiverFlow2D model
420-mile reach including the city of Fargo
Red River of the North
Inundated fields, Red River of the North (April 2013)
Hydraulic Modeling Challenges
Low accuracy of 1D approach
Complex overland flow
Excessive computational times: 1D model requires 3-9 hours!
Ideally run times should be less than 2 hours
Elevation Data Sets
+ =
In-channel surveyed cross sections
LiDAR Merged Elevations
Test B: Refined mesh 590,000 cells
Test A: Uniform mesh 1,070,000 cells
420 miles
Test C: Refined mesh 1,265,712 cells
Inflow Locations Merged Elevations
Animation created by Asier Lacasta, UZ.
Comparison with field data: Flood of 2011
20 Measurement locations
USGS Gauges and HWMs
HWM locations
RiverFlow2D model vs. HWMs 2011
1,070,000 cell mesh
R² = 0.9986
240
250
260
270
280
290
300
240 250 260 270 280 290 300
Fiel
d D
ata
Max
imu
m W
SE(m
)
RiverFlow2D Maximum WSE (m)
Data (m) Model (m)Difference
(m)
260.85 261.76 0.91
292.44 293.55 1.11
272.12 271.62 -0.51
288.90 287.78 -1.12
265.46 264.90 -0.56
284.98 285.28 0.31
295.13 294.99 -0.14
263.56 264.03 0.47
275.00 275.39 0.40
273.16 273.04 -0.11
255.71 255.59 -0.12
278.77 278.31 -0.46
241.62 241.62 0.00
243.64 243.60 -0.04
257.63 257.58 -0.05
252.96 254.03 1.07
282.71 282.59 -0.12
274.79 274.86 0.07
264.64 263.91 -0.73
247.45 246.91 -0.54
Comparison of RiverFlow2D GPU Model with Aerial
Images
2011 Flood
Comparison of RiverFlow2D GPU Model with Aerial
Images
2011 Flood
Summary of RRN Performance Tests
RiverFlow2D90-day hydrograph
GPU Time GTXTitan Black
1,070,000 cells 2.2 h
590,000 cells 36 min
HEC-RAS 1D Run Time
30-dayhydrograph
3.1 h
Performance Comparison with other 2D
Models
UK Test 5: Runtimes
57X fasterthan RAS2D
13X faster than RAS2D
99X fasterthan SRH-2D
RiverFlow2D vs FLO-2D BasicUK Test 5
1953X faster than FLO-2D
RiverFlow2D vs MIKE 21Dam-break / 200k cells / 6-hour simulation
≠
FeatureSub-Grid
Macro-cellTriangular cells
Accounts for momentum transfer inside cell No Yes
Velocity detailsNo Yes
Simplified frontal wave travel Yes No
Accurately capture complex hydrodynamics
No Yes