A Cloud-Based Flood Warning System For Forecasting Impacts to Transportation Infrastructure Systems

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1 A Cloud-Based Flood Warning System For Forecasting Impacts to Transportation Infrastructure Systems Jon Goodall Associate Professor, Civil and Environmental Engineering Associate Director, Link Lab April 09, 2018

2 Research Motivation Predicting street scale flooding, especially for low relief terrains, is a challenge. For these regions, we need high resolution datasets and 2-D hydrodynamic models. Flood predictions are needed in near real-time in a form that aids decision makers. Chloe Riffle, 7, watches as she is surrounded by water on Sunday, Sept. 4, 2016 in the Ocean View section of Norfolk, Virginia. 4/23/2018 University of Virginia 2

3 Need for an Automated, Accurate Flood Prediction System Request + + Post Rainfall Forecast Data Response Send Alerts Access Current Flooded Locations Information 4/23/2018 University of Virginia 3

4 Objectives Objective 1: Build a flood warning system based on modern cyberinfrastructure using an automated workflow for obtaining the real-time rainfall forecast data. Objective 2: Execute the model using cloud computing resources to identify the flooded roadways and bridges location in short-time duration for warning and emergency management purposes. Objective 3: Generate online inundation map with the location of the flooded roadways and bridges with the ability to send automated warning messages. 4/23/2018 University of Virginia 4

5 Study Areas VDOT s Hampton Roads District (Rural Area) Norfolk Area (Urban Area) 4/23/2018 University of Virginia 5

6 Modeling Work - VDOT s Hampton Roads District Hampton Roads district, Virginia which is 11x10 3 km 2 of combined watersheds This Study area includes about 500 VDOT bridges and culverts 11 subwatersheds that surround the study area are incorporated as boundary conditions 4/23/2018 University of Virginia 6

7 Regional River Severe Storm Model (R 2 S 2 ) R 2 S 2 is a diverse, multi-function digital platform that offers various applications to VDOT s Districts and Residencies, regional Emergency Services, and Environmental Agencies. VDOT Bridge Data USGS 10m DEM Terrain Data USGS Soil Maps USGS Land Use NOAA Point Rainfall Data NOAA NEXRAD Data Time Series Transformation WMS 9.0 GIS Data Terrain (Tin) Rivers & Streams Watersheds Sub-Watersheds and Terrain Data HEC-HMS Sub-Watersheds Rainfall Data Combined Watershed, Bridges, & Rainfall Distribution SMS 11.0 TUFLOW 2-D Hydrodynamic 10 Second Time Step Two Weeks Time Series 495 Bridges Sub-Watersheds Hydrographs Rainfall Data USGS Stream Gage Data Output Data Post Processing Equivalent to 495 HEC-RAS Unsteady Models Interactive Bridge Flooding Predictive Flooding Atlas 4/23/2018 University of Virginia 7

8 Enhancing the Model Model main Component Streamflow line Shapfile Generated from the DEM Road Shapfile files that include the Road Crossing and Elevation with streamflow lines TUFLOW Model DEM Rater with 10m Resolution Manning s Coefficient Shapefile Based on the Landuse 4/23/2018 University of Virginia 8

9 DEM Raster Enhancement Progress High resolution LiDAR data with resolution from 0.76m to 1.52m is available in most part of the study area. 0.76m X 0.76m 1m X 1m 1.52m X 1.52m 0.76m X 0.76m 10m X 10m LiDAR 1.52m X 1.52m Integrate 1m and 2m DEM 10m DEM 4/23/2018 University of Virginia 9

10 DEM Comparison Cross Section 1m DEM 10m DEM 4/23/2018 University of Virginia 10

11 Streamline Old Version Flowline New Version Flowline 4/23/2018 University of Virginia 11

12 Infiltration Implementation Soil Type Imperviousness Very small percentage of area has a imperviousness ratio greater then zero.

13 Prepare Soil Moisture from SMAP to TUFLOW SAMP Soil Moisture (m3/m3)

14 Observation Data Preparation USGS Stage Gage Data There are 10 USGS station located in the study. However, we believe more stations are necessary to better calibrating and verifying the model due to the complex and flat topographic. Stage Depth Data Availability USGS Station ID start End /1/ /13/ /1/ /13/ /1/ /13/ /1/ /13/ /11/ /14/ /1/ /13/ /25/ /14/ /1/ /13/ /30/ /14/ /1/ /14/ /1/ /13/ /1/ /13/ /26/ /14/ /1/ /14/2017

15 Observation Data Preparation Observation Gridded Rainfall Data Preparation Three sources we explored to obtain the gridded rainfall data: TRMM data A code have been developed to pull and process the TRMM data for any given shapefile in the USA. HRRR archived data A code have been developed to pull and process the archived HRRR data from the University of Utah repository for any given shapefile in the USA. NEXRAD data However this is the best gridded rainfall data available but sometime the rainfall data was not recorded for critical storm events period. Also it is time consuming to order, download, and preprocess the data manual using NOAA Weather and Climate Tool (WCT). All the codes are available through the project Phase II GitHub repository.

16 Rainfall Forecast Data Automation and Preparation for the Flood Warning System Dataset Data Provider Relevant Data Product Spatial (km) Resolution Temporal (hrs) Forecast (hrs) Model Cycle HRRR NCEP Surface total precipitation /day RAP NCEP Surface total precipitation /day NDFD NWS Quantitative precipitation forecast /day NAM NCEP Surface total precipitation /day High-Resolution Rapid Refresh (HRRR) NOAA/NCEP operational weather prediction system Forecasts hourly surface total precipitation at a spatial resolution of 3-km Comprised of a numerical forecast model and initialized by an analysis/assimilation system HRRR is run every hour of the day and forecasts out 18 hours on an hour time-step. We automated HRRR data access using Python and OPeNDAP e&width=859&model=hrrr&title=hrrr

17 TUFLOW Model Boundary Input Historical Runs The HEC-HMS model was built to simulate the outlet flow rate of each sub-watershed to be applied as boundary input of the 2D model. Study Area HEC-HMS model Flood Forecasting Runs The National Water Model simulation will be used as boundary input for flood forecasting system.

18 Hurricane Nicole, 2016

19 Hurricane Nicole, 2016

20 Preliminary Calibration for Hurricane Nicole, 2016

21 Preliminary Calibration for Hurricane Nicole, 2016

22 Modeling Work Norfolk Area A 2D hydrodynamic model needs to be developed to predict inundated roads in Norfolk Region. The ODU storm surge model will provide the tide level boundary condition. ODU Storm Surge Model TUFLOW Overland Flood Model

23 Storm Surge and Urban Flood models Coupling Workflow Storm surge model interaction with the urban flood model

24 Model main Component Storm Surge Forces Impervious Ratio Precipitation Buildings Soil Type Manning s Coefficient Shapefile Flowline Shapfile TUFLOW Model Road Network Topography Mesh 1m DEM and bathymetry

25 Integrating High Resolution DEM 1m Lidar Integrated DEM VB Bathymetry Waterbody Bathymetry NOAA Bathymetry (

26 Imperviousness and Building Footprint Building Footprint for Hague Community Surface Imperviousness Building Footprint

27 Hurricane Irene, 2011 The storm surge simulation we have so far is from 26-Aug-2011 to 28-Aug We generated a hotstart file at the start time of the storm surge model by running the TUFLOW model with NOAA tidal level observation. Then, the hotstart file is feed into the model to let it run from wet condition. Hotstart File Storm Surge Simulation Period

28 Hurricane Irene, 2011 Maximum Water Depth (m)

29 Hurricane Irene, 2011 Modeling the Hague Community A. Using the current model domain. B. Create a small domain for the Hague community from the current model domain. C. Using the small domain along with the storm drainage system for the Hague community. A B C

30 Hurricane Irene, 2011 Modeling the Hague Community

31 Cloud-based Flood Warning System

32 Architecture of the Flood Warning System Through GCP Store Input/Output Storage Bucket GPU Windows OS Model components Store Ouput in Rational DB Request and Retrieve Rainfall Data NOAA NOMADS Start Model Run Manually Start If Rainfall Recorded Database Instance Access DB Monitor Rainfall Data Initiate Model Run Linux OS Trigger/Visualization Power User Send Alerts Via Send Alerts Via Regular User Access Current and Archived Flooded Locations Information Access Current Flooded Locations Information 4/23/2018 University of Virginia 32

33 Model Running Speed on the GCP GPU Instance Google Cloud has completely customizable instances with up to 8 GPUs, either 8 GPUs of NVIDIA Tesla K80 (each $0.45/hr) or 4 GPUs of NVIDIA P100 (each $1.46/hr) (the maximum number of GPU on the current TUFLOW license is 8 GPU) Currently, we are using 4 GPUs of NVIDIA Tesla K80, 8 Gb of memory, and 50 Gb of storage as the basic configuration with cost of $2.078/hr ($0.278/hr without any GPUs). P100 is roughly 2x K80 in performance. Grid Cell Resolution (m) Running Time (hrs) for 19 hours modeling CPUs RAM GB GB GB (+$0.009/hr) GB (+$0.06/hr) (+$0.2/hr) 65 GB (+$0.26/hr) 4/23/2018 University of Virginia 33

34 Model Running Speed on the GCP GPU Instance RUN TIME IN HOURS Focusing on 30m resolution grid cell size, the following chart shows the different in the running time using K80 vs P100 GPUs. 30m K80 vs P K80 P NO. GPU using 4 P100 GPUs, the 20m resolution grid cell size runs in 0.91 hrs 4/23/2018 University of Virginia 34

35 Website SQL Database The database interaction is done by using SQLAlchemy 4/23/2018 University of Virginia 35

36 Visualization Website 4/23/2018 University of Virginia 36

37 Thank You Jonathan L. Goodall

A Cloud-Based Flood Warning System for Forecasting Impacts to

A Cloud-Based Flood Warning System for Forecasting Impacts to This is a manuscript of an article that was accepted in 21 May 2018 for publication in the Journal of Environmental Modelling & Software. The final publication is available at https://doi.org/10.1016/j.envsoft.2018.05.007.

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