A Method for Prompt Streamflow and Flood Inundation Estimation Over Large Extents at a High Spatial Resolution

Transcription

1 A Method for Prompt Streamflow and Flood Inundation Estimation Over Large Extents at a High Spatial Resolution Michael L. Follum, Alan D. Snow, Ahmad A. Tavakoly, and Mark D. Wahl USACE-ERDC-CHL Vicksburg, MS AWRA Specialty Conference Sacramento, CA July 12, 2016 Funded by: A2AD, MC, AF

2 Overview of Presentation Motivation Methods / Results Numerical Models and Data Requirements Flow Simulation Flood Inundation Deployment CONUS (Continental U.S.) Deployment OCONUS Future Work Conclusions GitHub Links

3 USACE ERDC CHL: Three Focus Areas Navigation Flood and Coastal Storm Damage Reduction Military Hydrology CHL deploys/develops physical and numerical models to answer questions in these areas

4 Flood Warning for the Military (Humanitarian) Example: Typhoon Koppu (Cat IV) threat to Philippines in October According to Republic of the Philippines, National Disaster Risk Reduction and Management Council: - 24,000 evacuated due to warnings from Philippine Atmospheric, Geophysical and Astronomical Services Administration - 48 fatalities, 83 injured, 4 missing - Damage in excess of 11 billion pesos (~$235 million) - Marines initiated a request for forecast flow and flood inundation data to determine if III Marine Expeditionary Force might be called to assist (see Wahl et al., 2016). BBC Washington Post

5 BLUF Presented to NATO Crisis Management and Disaster Response Center of Expertise in Sofia, Bulgaria

6 Methods: Globally Available Data Climate / Land Surface Models (LSMs) ECMWF, LIS, GLDAS, JULES, etc. Stream Hydrography Data HydroSHEDS or Derived from DEM Elevation Data HydroSHEDS (~90 m) SRTM (~30 m) (Bamler, 1999) Land Cover Data AVHRR Imagery (~1 km) (Hansen et al., 2000) Derived from LandSat (~30 m)

7 AutoRAPID Framework Follum et al., In Review, JAWRA BUILDING STRONG

8 RAPID Example of Mississippi River Basin 10-year simulation period Over 1.2 million stream reaches 45 minute simulation using 24 processors Continental scale : 200,000 river reaches (Tavakoly 2014) 1.2 Million river reaches (Tavakoly et al., In Review)

9 AutoRAPID Example of June 2008 Flood Event 230,000 km 2 area 69 million cross-sections ~12 minutes to simulate flood depth raster. ~15 minutes to simulation flood inundation polygon Follum et al. submitted to JAWRA

10 OCONUS Stream Hydrography Data Attributes Required for Each Reach: Stream location and slope Drainage area Connectivity between reaches Data Required: Elevation Data (we used SRTM 30 m) Tool Required: ESRI ArcGIS Tool currently being tested for inclusion into ArcToolbox / ArcHydro What Can Improve Hydrography Data: Any info on stream, dam, and lake locations in GIS format

11 OCONUS Application: Sava River Basin Zagreb, Croatia Davor, Croatia Sremska Mitrovica, Serbia Martin Brod, Bosnia and Herzegovina

12 OCONUS Application: Sava River Basin

13 OCONUS Application: Sava River Basin

14 Operational AutoRAPID models 17

15 Future Work Forecast / Hindcast Runoff from Land Surface Models (LSMs) Format: NetCDF Timestep: hourly to daily Resolution: 1 to 55 km Stream Hydrography Data (stream locations, slopes, drainage area) Format: Shapefile Elevation and Land Cover Maps Format: Raster RAPID River Routing Future Improvements? Flow and Inundation Format: 1.) Preissmann CSV, NetCDF 4-point Timestep: 2.) MESH hourly - Meselhe, to daily Resolution: Sotiropolous, variable and Holly 3.) SPRINT Flow 1.) Muskingum-Cunge AutoRoute Flood Inundation Inundation Format: 1.) HAND Raster / Shapefile Timestep: As Needed Resolution: 3 to 30 m Computation Server CKAN Server Tethys Streamflow Prediction Tool (Web Application GUI) GeoServer

16 Conclusions Speed Setup, Deployment, Post-Processing, Visualization Able to provide an initial estimate of flows and flood inundation. Accuracy Performs well where you would expect a Muskingum and Mannings approach to perform well (e.g. medium to high topography, channelized flow). Performs poorly in flat terrain and where backwater is the main cause of flooding. Flood map accuracy dependent on terrain data. Future Work Improved hydraulic routing and flood models Better accounting of dam and reservoir operations Improved runoff estimates from LSMs (e.g. snow runoff)

17 RAPID GitHub Links ArcGIS Tools to Develop Stream Network Tethys / Streamflow Prediction Tool AutoRoutePy AutoRoute To be released

18 Dataset References Bamler, R., The SRTM mission: A world-wide 30 m resolution DEM from SAR interferometry in 11 days, Photogrammetric week. Berlin, Germany: Wichmann Verlag, pp Fry, J.A., G. Xian, S. Jin, J.A. Dewitz, C.G. Homer, Y. Limin, C.A. Barnews, N.D. Helold, J.D. Wickham, Completion of the 2006 national land cover database for the conterminous United States. Photogrammetric Engineering and Remote Sensing, 77(9): Gesch, D., M. Oimoen, S. Greenlee, C. Nelson, M. Steuck, D. Tyler, The national elevation dataset. Photogrammetric engineering and remote sensing, 68(1): Hansen, M., DeFries, R., Townshend, J., Sohlberg, R., Global land cover classification at 1 km resolution using a decision tree classifier, Intern. J. Rem. Sensing, 21: Horizon Systems Corporation, National Hydrography Dataset Plus: Documentation. Available online at accessed December McKay, L., T. Bondelid, A. Rea, C. Johnston, R. Moore, T. Deward, NHDPlus version 2: user guide. Horizon Systems, Corp., available online at ftp://ftp.horizonsystems.com/nhdplus/nhdplusv21/documentation/nhdplusv2_user_guide. pdf.

19 Model References AutoRAPID Follum, M.L., A.A. Tavakoly, J.D. Niemann, A.D. Snow, In Review. AutoRAPID: A Model for Prompt Streamflow Estimation and Flood Inundation Mapping Over Regional to Continental Extents. Wahl, M., M.L. Follum, A.D. Snow, A.A. Tavakoly, Developing Hydrologic Awareness. The Military Engineer (700): RAPID Tavakoly, et al., In Review. Continental-Scale River Flow Modeling: River Routing for the Mississippi River Basin Using High-Resolution River Data from the NHDPlus Dataset. David, C.H., D.R. Maidment, G-Y. Niu, Z-L. Yang, F. Habets, V. Eijkhout, River network routing on the NHDPlus dataset. Journal of Hydrometeorology, 12(5): Tethys Snow, A.D. S.D. Christensen, N.R. Swain, E.J. Nelson, D.P. Ames, N.L. Jones, N. Noman, C.H. David, F. Pappenberger, in review. A cloud-based high-resolution national-scale hydrologic forecast system downscaled from a global ensemble land surface model. Journal of American Water Resources. Snow, A.D., A New Global Forecasting Model to Produce High-Resolution Stream Forecasts. Master of Science Thesis, Brigham Young University, Provo, UT, 63 pp.

20 Questions