Rapid Flood Mapping Using Inundation Libraries
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Rapid Flood Mapping Using Inundation Libraries Jude Kastens, Kevin Dobbs, James Halgren, Katherine Balster 2017 ASFPM Conference | May 3, 2017 5 mi Kansas River Valley between Manhattan and Topeka Email: [email protected] Terrain Processing: DEM (Digital Elevation Model) This DEM was created using LiDAR data. Shown is a portion of the river valley for Mud Creek in Jefferson County, Kansas. DEM (shown in shaded relief) 2 Terrain Processing: Flow Direction Each pixel is colored based on its flow direction. Navigating by flow direction, every pixel has a single exit path out of the image. Flow direction map (gradient direction approximation) 3 Terrain Processing: Stream Delineation The Mud Creek streamline is identified (shown in blue) using an appropriate flow accumulation threshold. “Synthetic Stream Network” 4 Terrain Processing: Floodplain Mapping The 10-m floodplain was computed for Mud Creek using the FLDPLN model. FLDPLN is a static, 2D hydrologic model that requires only DEM data as input. Using simple surface flow properties, FLDPLN identifies the depth-varying floodplain in reference to the input stream network (floodwater source). 10-m Floodplain (DTF Map) 5 Amazon River in Brazil (1700 km). 90-m SRTM South DEM data were used. America Amazon surface elevation drop in study area: 17 m 1 m per 100 km! 6 Example: Delaware River Basin above Perry Lake in northeast Kansas Example: Walnut River Basin in southeast Kansas Each colored stream segment has its own inundation library Augusta Merged library The FLDPLN (“Floodplain”) Model— There are two ways that point Q can be flooded by water originating from point P: Backfill Spillover Flooding Flooding water surface Q P d uphill flow downhill flow P (swelling) (overland flow) Q } d “Water seeks its own level” “Water flows downhill” Backfill Flooding—accounts for floodwater expansion due to swelling processes flow divide water surface dry flood depth dry dry ground surface PIXEL ON RIDGELINE flow FLOODWATER directions SOURCE PIXEL OVER HERE 10 Spillover Flooding—accounts for floodwater rerouting (alternative flow path development) flow divide water surface spillover flood depth flood depth ground surface PIXEL ON RIDGELINE flow FLOODWATER directions SOURCE PIXEL OVER HERE 11 PLAN VIEW illustrating backfill and spillover flooding SPILLOVER FLOODING R tributary flow flow channel divide divide flood or source P point Q P Q Q P watershed boundary Depth To Flood (DTF) Contour BACKFILL FLOODING Spillover flooding meets backfill Q Q flood P flooding P source point 12 Why Backfill Flooding Is Not Sufficient Here is what a DTF map looks like determined using only backfill Flow divides flooding. in the flow Note the erroneous direction map discontinuities. These are caused by ridgelines in the DEM. 13 Why Backfill Flooding Is Not Sufficient By backfill flooding using small flood depth increments, and allowing spillover flooding to occur on the floodplain boundary between iterations, the DTF discontinuity problem is mostly resolved. The 10-m steady state floodplain is shown, computed using the FLDPLN model and 0.5 m increments. 14 Longitudinal Floodplain Cross Section FLDPLN Model Overhead view Solution Profile flow Ground H2O Normal Water Floodwater Level Source Flood Stage 1 → DTF Contour 1 Flood Stage 2 → DTF Contour 2 Inflowing Flood Stage 3 → DTF Contour 3 Channels Flood Stage 4 → DTF Contour 4 Side-channel flow back into the main channel results in depth decay15 Seamless modeling with FLDPLN spillover taper (depth decay) backfill taper (lake effect) maximum value composite* *Works for depth grids. Multi-segment merged DTF maps require minimum combined value compositing. 16 Seamless modeling with FLDPLN zoom area Arghandab – 5m floodplain East trib – 5m floodplain West trib – 5m floodplain Combined – 5m floodplain Now let’s see some actual flood extent mapping… Flood Extent Estimation (Example 1) Flooding along the Osage River in Missouri gage July 2007 Flood Extent Estimation (Example 2) June 13, 2008 Cedar Flooding on the Rapids, IA Cedar River crested more than 11 ft above the historic record in Cedar Rapids, Iowa Example 3: 1938 Texas Flood Simulation adapted from Burnett (2008) Brady San Saba Menard 25 1938 Flood Depth Grid This flood depth grid was determined using Intermap elevation data. 26 Example 3 – Verification Intermap N FLDPLN floodwater surface estimates Oblique aerial photo* over San Saba, Texas, during a using different elevation datasets NED record flood that occurred in July 1938. High water marks collected by the USACE in 1938 were used to model this event. *Burnett, J. (2008). Flash Floods In Texas. College Station, TX: Texas A&M University Press Example 3 (continued) Intermap Oblique aerial photo* of San Saba during the 1938 flood (not necessarily at crest). Note the locations of the water tower & the courthouse (green dots). N NED N “Reports and pictures in the Dallas Morning News, The Saba News and Star, and the Wichita Falls Record News show that in the City of San Saba, flood waters from the river spread through a great part of the business district and around the courthouse and spread over more than one-third of the City.” -- excerpt from http://www.texashillcountry.com/ san-saba-texas/san-saba-texas.php 28 N *Burnett, J. (2008). Flash Floods In Texas. College Station, TX: Texas A&M University Press Example 4: Reconstructing the 1993 Missouri River Flood in Kansas* *KDEM request for 2011 floods 29 Rulo St. Joseph Atchison Leavenworth Sibley Kansas City 30 Rulo St. Joseph Atchison Leavenworth Sibley Kansas City 31 Rulo St. Joseph Atchison Leavenworth Sibley Kansas City 32 Rulo St. Joseph Atchison Leavenworth Sibley Kansas City 33 Example 5: Susquehanna River Tropical Storm Lee (Sep 2011) Susquehanna River Water Surface Elevations CKLN6 Gage heights represent 9/8/11 flood crest BNGN6 From existing hydraulic model VSTN6 500-yr 100-yr 50-yr 10-yr Filled DEM zoom area Flood Depth Grid estimate for September 2011 flood event Kansas Coverage (5-m LiDAR) 39 Conceptual Framework Data Prep Database / Server Implementation DEM input FLDPLN Model “SLIE Selectors” • NED (MATLAB) Observed (point) • LiDAR Gauge Data • InterMap HWM Library Ground Observer • SRTM SLIE Database • other Segmented Satellite (raster) Library of GFDS (low res) Inundation DFO (mod res) DEM Extents Other Conditioning (ex. NLD, NID) Modeled HEC-RAS GIS Server HAZUS Other Arc Hydro Tools & Custom Extent Client Stream Map / Depth Applications Segmentation Grid 40 41 Recent Research 42 Flood Boundary Points as Gage Proxies – Lower Kavango (Africa) Flood Boundary Points as Gage Proxies – Southeast Kansas Study Using HEC-RAS 2D 44 HEC-RAS 2D Breaklines / Depth Grids 1K cms HEC-RAS 4.8K cms HEC-RAS 45 HEC-RAS Water Surface Elevation 46 FLDPLN Inundation Library Extent 47 5m FLDPLN DTF 48 Flood Boundary Points as Gage Proxies – Southeast Kansas Implement sampling strategy for simulation analysis 5 points was smallest number examined 49 Flood Boundary Points as Gage Proxies – Southeast Kansas Using selected points: - Identify flood source pixel for each boundary point - Build DTF profile for stream pixels 50 Flood Boundary Points as Gage Proxies – Southeast Kansas ₋ Use DTF profile to create library-based flood extent ₋ Compare to HEC- RAS 2D simulated flood extent Compute F-Statistic: F = 100 * ( Aop/(Ao+Ap-Aop)) Ao: observed area of inundation (HEC- RAS 2D) Ap: predicted area of inundation (FLDPLN) Aop: area that is both observed and predicted as inundated 51 Agreement between simulated and predicted flood extents μ = 90.7 – 96.0 (for 4.8K) = 67.1 – 83.5 (for 1K) SBP = simulated boundary point 52 Other Applications for the FLDPLN Model 53 River valley boundary delineation – masking for identification of floodplain wetlands 54 DTF maps provide a useful guide when specifying cross sections for hydraulic modeling. 55 Identifying High-Impact Riparian Areas 56 Fourmile Creek, Morris County, KS Thanks for Listening… Any Questions? Email: [email protected] 57.