Pathways Interns: Annika O Dea, Ian Conery, Andrea Albright

Transcription

1 1 REMOTE SENSING OF COASTAL MORPHODYNAMICS Kate Brodie Brittany Bruder, Nick Spore, Alex Renaud Pathways Interns: Annika O Dea, Ian Conery, Andrea Albright File Name

2 COASTAL MORPHODYNAMICS Human influences Figure 2, The Future of Nearshore Processes Research, Winter 215 (Shore & Beach, Vol. 83, No.1) Pertinent Research Questions: 1. How do wave runup & setup, and sediment transport processes during extreme events differ from moderate storms or during recovery? 2. How do feedbacks between hydrodynamics and morphology affect flooding, erosion, and recovery of coastal areas? 3. What are the feedbacks and interactions between coastal processes at short time-scales, such as storms, and long time-scales, such as sea-level rise? 2

3 COASTAL MORPHODYNAMICS Human influences Figure 2, The Future of Nearshore Processes Research, Winter 215 (Shore & Beach, Vol. 83, No.1) Pertinent Applications: 1. Enhanced understanding, monitoring, and planning capability for coastal storm impacts, resiliency, and evolution at a range of engineering relevant timescales for improved management of Federal Beach Project sites 2. Improved situational awareness to ensure safe and efficient maneuverability during Joint Forces entry operations in littoral regions 3

4 REMOTE SENSING TOOLS Just another tool to help solve observation challenges The good: Offer high-resolution (in space & time) synoptic measurement capabilities Can be safer, and less expensive (e.g. cameras) The bad: May be an indirect or proxy measurement Can be expensive (e.g. lidar) The ugly: Lots of data that requires filtering and QA/QC Requires precise geo-position information (structure-from-motion) (video or radar) Active sensors emit or transmit energy Passive BUILDING sensors STRONG only receive energy 4

5 REMOTE SENSING APPLICATIONS Basic R&D Lidar observations of hydrodynamics Wave breaking type and wave transformation through the surf Lidar observations of beach morphology evolution Applied Research Spatial variability in dune morphology evolution at short- and long-timescales Improving swash in CShore using lidar data Assessing video-based bathymetric inversion algorithms During storm erosion on developed coasts Technology Transition suas for Flood Risk Management suas for Joint Littoral Operations Video Imaging for Coastal Monitoring 5

6 BASIC R&D - FRF TERRESTRIAL LIDAR Surf-zone wave shapes improve basic physics of wave-driven sediment transport Dune Erosion during Storms collect critical data during storms Beach Morphological Evolution high-spatial (cmscale) & temporal resolution (hourly) 3D data sets Wave Runup & Swash Hydrodynamics data available in near-realtime for model evaluation Elevation (m, NAVD88) Wave time-series paros raw laser filtered (f<.5hz) and averaged (.5s) laser data 8:4:3 8:4:45 8:5: 8:5:15 8:5:3 8:5:45 8:6: 8:6:15

7 BASIC R&D CONTINUOUS DUNE LIDAR TOWER Simultaneous observations of Wave Runup, Swash Hydrodynamics, Morphology Change Inner surf zone wave height & spectra Mean water level Runup elevations Foreshore beach profile (hourly & wave by wave) & 3D morphology data available in near-realtime on CHL THREDDS for model evaluation File Name 7

8 BASIC RESEARCH LIDAR OBSERVATIONS OF WAVES Validated Technology: Lidar and pressure measurements agree well A C..5 < f <.25 Hz Hs infragravity Hs sea/swell.4<f<.5 Hz.5 g25 g3 g35 g45 2 g55 r =.98 g65 rmse =.5m Lidar Setup ( ) 1 Lidar (m) 1.5 B..4 < f <.5 Hz Water Level 1.5.5<f<.25 Hz r =.91 rmse =.3m.5 Gauge (m) Gauge (m).5 2 r =.87 rmse =.7m Gauge (m) Example time-series paros raw laser filtered (f<.5hz) and averaged (.5s) laser data 1 Elevation (m, NAVD88) (Brodie et al., 215) :4:3 8:4:45 8:5: 8:5:15 8:5:3 8:5:45 8:6: Time (HH:MM:SS) BUILDING STRONG Brodie, K.L., B. Raubenheimer, Steve Elgar, R. K. Slocum, and J. E. McNinch, (215): Lidar and Pressure 8 Measurements of Inner-Surfzone Waves and Setup. J. Atmos. Oceanic Technol., 32, :6:15

9 BASIC R&D SURFZONE WAVE SHAPES Why Wave Shapes? Waves change shape as they shoal and break across the surf-zone, through a shorebreak, and into the swash These changes in shape affect the direction of transport of sand underneath them If we want to be able to predict the daily movement of sandbars and changes to the beach we need to understand these physical processes better Skewed waves have steep crests, flat broad troughs Asymmetrical waves have steep front faces, and gently sloping backs 9

10 BASIC R&D - LIDAR OBSERVATIONS OF WAVE SHAPES 3D lidar enables quantification of wave breaking shapes in the field Developing automated analytics to extract wave properties Velodyne lidar mounted on CRAB Concatenating data in the alongcrest direction allows extraction of plunging wave features 1

11 BASIC R&D - 3D LIDAR ERROR PROPAGATION, BEACH MORPHOLOGY OBSERVATIONS Hourly observations of beach topography allow for observations at the same timescale as most numerical models

12 APPLIED RESEARCH - COASTAL DUNE EVOLUTION Dune Erosion during Storms Nearshore collaboration project to improve dune erosion during storms Evaluating numerical models Merged data from dune & pier lidars! 3 Named Tropical systems observed!! During-storm dune erosion observations Short-term Dune Recovery Hourly continuous topographic observations, 2m N & S of FRF pier: Oct 215 to Jan 216 (FY16); Sep 216 to Present (FY17) Pier-mounted Lidar Dune Profiles (subsampled) 8 Elevation (m, NAVD88) Fall Hurricane Season Winter/Spring Nor'Easter Season Long-term Dune Evolution Cross-shore Distance (m) Developed Dune Evolution Analyzing 3-year FRF morphology data set Testing relationships to forcing factors 12

13 APPLIED RESEARCH PIER LIDAR DUNE OBSERVATIONS Hourly continuous (during storm) topographic observations, 2m N & S of FRF pier: Oct 215 to Jan 216 (FY16); Sep 216 to Present (FY17) 3 Named Tropical systems observed Pier-mounted Lidar Dune Profiles (subsampled) Aug 216Nov Elevation Change (m) Elevation (m, NAVD88) Fall Hurricane Season Winter/Spring Nor'Easter Season File Name Cross-shore Distance (m)

14 APPLIED RESEARCH VIDEO OBSERVATIONS OF SURF ZONE MORPHODYNAMICS 6 cameras collecting data since 1987 Argus Coastal Imaging Tower Quantitative Morphodynamic Data: Wave runup position Surf-zone sandbar position Proxy for wave dissipation Long-shore and rip surface current speeds Bathymetry from wave speed measurements using linear theory wave speed in shallow water water depth 14

15 APPLIED RESEARCH EVALUATE DEPTH INVERSIONS Utilize FRF survey & altimeter data to evaluate performance of a video-based depth inversion algorithm (cbathy) during a wide range of conditions Most comprehensive evaluation of cbathy to-date:.3 < H s < 4.6 m -4.8 m < h < -1.5 m Peak in wave breaking Outer surf altimeter RMSE =.35 m, bias: -.1 m, R 2 = shoreline Mid surf altimeter Inner surf altimeter cbathy was consistent with long-term patterns of bathymetric change (r 2 =.64, RMSE =.26 m, bias = -.1 m), particularly when Hs < 1.2 m (r 2 =.83) and were not breaking over the bar

16 TRANSITION TECHNOLOGY DEVELOPING NEW PLATFORMS Traditional long-term monitoring tower Hotel (or house)-mounted cameras Temporary tower systems UAS-based solutions Fixed wing longer coastlines, less frequently Multi-rotor small areas, daily monitoring

17 COASTAL TOPO-BATHYMETRIC MAPPING Goal: provide nimble and efficient technology to monitor USACE coastal project sites frequently, providing rapid, pre-storm risk assessments and post-storm damage assessments & to improve situational awareness during littoral entry operations Solution: 1-2 km Solution: 1s km Small, multi-rotor with single camera Wide field-of-view camera on board fixed-wing or large multi-rotor platform 17

18 QUESTIONS? Alex Renaud Brittany Bruder Nick Spore Ian Conery Annika O Dea 18