Near Real­Time Flood Mapping Project Final Report, 15 August 2017

Chris Lenhardt, Brian Blanton, Lisa Stillwell, Ray Idaszak, UNC­RENCI Jon Duncan (now at Penn State), John Lovette, UNC­IE

This project was made possible through a NC Policy Collaboratory grant.

 

      

                                                                               

                                            

                                 

                                       

                                                  

                                                                                                              

                        

                                                            

                    

                                                              

                                           

     

                                                            

                                                                                                                                                         

                                                                          

                                                                            

                                                              

 

Research Summary

This project developed the capability to deliver near­real time flood maps for North Carolina using the State’s investment in high resolution elevation LIDAR (Light Detection and Ranging) data as the primary input. We brought together RENCI’s expertise in data science and cyberinfrastructure as well as RENCI’s physical cyberinfrastructure with the hydrologic expertise at the Institute for the Environment. We also leveraged existing collaborations with national­level hydrological modeling groups to advance the state­of­the­art in flood inundation mapping for North Carolina.

The FY2017 Collaboratory project achieved the following milestones, using the Neuse River basin as our demonstration area:

1. Engaged with staff from the North Carolina Department of Public Safety, Division of Emergency Management.

2. Processed high resolution North Carolina LIDAR tiles into Digital Elevation Models (DEM). The Neuse River DEM is shown in figure 3.

3. Computed streamflow lines needed to identify channel axes. 4. Computed the Height Above Nearest Drainage (HAND) raster product. 5. Processed NOAA National Water Model (NWM) output through the HAND product to

generate a flood inundation extents map, using NWM output from Hurricane Matthew (2016)

6. Hurricane Matthew NWM data archived at RENCI covering roughly a month of data from approximately two week prior to the event and two weeks post­event.

The computational steps have been implemented on RENCI’s high­performance computing cluster Hatteras, which makes it feasible to extend the Phase I work to all river basins in North Carolina and at the available spatial resolutions of the State’s LIDAR collection. To support other uses of the HAND analysis and derived products, we have developed extensive documentation of the technical steps involved.

Analytics Process

Overall, the process of developing the HAND raster1 files for any given basin is dependent on the resolution of the underlying DEM. HAND is used as the basis for developing inundation predictions. While the number of small tiles in a basin is fixed, since each tile represents an area of approximately one square mile, the file size of the tiles increases with increasing spatial resolution. The most intensive step in computing the HAND raster for a 50 ft DEM required 64 cpus to compute efficiently. We expect that, when processing higher resolution DEMS, we will need to use 512 or more cpus to compute the rasters in a reasonable amount

1 Raster data is a form of geospatial data that allows for quantitative comparisons and integration with other geospatial data. Raster data is simply an array of data points organized in columns and rows. Each point, or pixel, is assigned a value for the particular measurement, e.g. elevation. The data points are also located in a spot in place and time, i.e. the points a georeferenced grid. In comparison, image files are more difficult to use for analytical purposes since you cannot attribute a particular measurement value to a particular pixel. However, raster data may be used to create an image.

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of time. However, we note that the computation of a HAND raster for a basin at a specific 2spatial resolution is a one­time cost, since the inputs to this computation are static .

The process of developing HAND rasters for an North Carolina basin is composed of multiple steps. We overview them here, with details deferred to the complete technical documentation.

1. Aggregate individual elevation LIDAR tiles into larger basin­scale DEMs. The State of North Carolina’s Division of Emergency Management provided the project with access to its entire collection of LIDAR data and derived products, including the individual DEMs tiles that cover approximately 1x1 mile square areas. Figure 1 shows two tiles in the Neuse River basin (Pamlico County) for the same area, at the 50 and 10 foot resolutions. The resolution difference is clear, with very small scale features such as channels being well resolved as opposed to pixelating when zoomed­in as in Figure 2. We aggregate all of the tiles to the county level, and then to the river basin level, applying a 1 mile buffer around the basin boundary. This results in a DEM for the entire basin (Figure 3).

Figure 1. DEM tiles for the same location in Pamlico County, at 50 (left) and 10 (right) ft resolution. The color scale represents feet above NAVD88 . To see how the resolutions differs also refer to Figure 2. 3

2 In this context static means that the geography generally does not change very fast relative to the scale of the image and that the source data are only collected on a periodic basis. 3 NAVD88 is basically a set of height measurements derived in a consistent way from a reference point.

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Figure 2. Magnification further demonstrates the differences in resolution at 50 (left) and 10 (right) ft. resolution. The color scale represents feet above NAVD88.

Figure 3. DEM for Neuse River Basin constructed from all small tiles in the basin. The color scale represents feet above NAVD88.

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2. Compute the HAND raster. For each basin, we then use the TauDEM software package 4 to compute the Height Above Nearest Drainage raster. The components in the workflow are shown in Figure 4. Several external datasets are needed (such as the National Hydrography Dataset (NHD) flowline data), but once these one­time datasets are acquired and stored locally, the processing is relatively straightforward. Figure 5 shows the HAND raster for the 50ft Neuse River Basin.

Figure 4. Workflow diagram for processing basin DEMs into HAND products. Each colored box represents a functionality that has been scripted to work on RENCI’s Hatteras cluster. The primary input is the North Carolina Merged River Basin DEMs, and the final output is the HAND raster (-dd) on the same geospatial resolution and coverage as the DEM. The flow direction steps (orange and gray boxes) require the most computational resources.

4 TauDEM has been developed by RENCI collaborator, Dr. David Tarboton (Utah State University). See http://hydrology.usu.edu/taudem/taudem5/ for more information.

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Figure 5. Height Above Nearest Drainage (HAND) for the 50 ft Neuse River Basin DEM. The color scale represents feet above NAVD88. Dark blue is near 0 ft above the nearest drainage location, which is effectively the channel network itself.

3. Post­process the HAND rasters. In order to use the HAND rasters for computing inundation maps, additional information must be generated such as a corresponding file for the hydraulic properties of each stream reach in the stream network (at the resolution of the DEM). This step also computes the relationship between the discharge and river stage at a point that is needed to convert computed discharges to water surface elevation..

4. Compute inundation level rasters. The NWM computes discharge on the national­level stream network. This discharge must be converted to water level, using the information from step 3. Once converted, these levels are compared to the HAND raster to produce the inundation level maps. An example of this is shown for the middle section of the Neuse River in Figures 6 and a magnified inset in Figure 7.

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Figure 6. Inundation level (color surface) overlaid on the 50 ft Neuse River basin DEM (hill shaded). The inundation is computed from the 50 ft HAND raster and the National Water Model short-range forecast channel flows for 09 October, 2016 (Hurricane Matthew) at 0 UTC.

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Figure 7. Inundation for the same NWM forecast as in Figure 6, detail near Kinston.

Papers and Presentations

In addition to presenting to the Collaboratory Advisory Board and generating project reports, a paper abstract has been submitted to be presented at the 2017 Fall Meeting of the American Geophysical Union (AGU) [Acceptance pending].

Findings and Recommendations

Findings 1. Feasibility: It is feasible to develop very high resolution DEMS for each North Carolina

river basin, using the State’s extensive and unique LIDAR dataset as the primary input. The resulting DEMs could be used in many different contexts, beyond our

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demonstration of their use in computing flood inundation maps from hydrological models (such as NOAA’s NWM). Examples include: deriving topographic features, generating additional measurements such as slope and aspect, siting telecommunications infrastructure, or estimating landslide potential.

2. Resource requirements: The computational resources needed to process the LIDAR into DEMs and then into HAND rasters are substantial. [We note that the national scale HAND products, computed on a 10 meter resolution, are being developed at the National Center for Supercomputing Applications at the University of Illinois, as part of the NSF­funded Cyber­GIS project.] The workflow itself involves several complex steps, using a mixture of single­processor and multi­processor/parallel analyses. While effective, additional effort should be spent on refining and hardening the workflow to facilitate more routine and operational use.

Recommendations 1. Finish processing the LIDAR data, generating DEM data and HAND products for the

rest of North Carolina. 2. Develop methods for data validation and QA/QC (quality assessment/control) and

review data. 3. Make DEM and HAND data available for North Carolina. 4. Leverage this work to explore complementing North Carolina’s existing flood forecasting

and mapping capabilities. 5. In principle, the flood forecast workflow used in this project, could be engineered to

leverage cloud­computing resources, thus avoiding the explicit need for access to a computing cluster.

Potential impact on management decisions and policies The finalized high resolution products could be used to map flood extent and duration, which is a major scientific advance that could help save lives and property damage. Specifically:

1. High resolution flood inundation products, over the full period of a flood, can provide information on the depth and duration of inundation at any site. This can have implications for general flood risk.

2. Similar data (same resolution, but not as spatially extensive as North Carolina) are being used to outreach with public safety officials and first responders to limit loss of life and property. Deciding which streets to evacuate, where to send first responders, and how to get people out of harm’s way is critical. These first steps we’ve taken aren’t sufficient yet for this type of application, but there is hope that continued development could provide tangible benefits to the people of North Carolina.

These results have broader implications beyond physical hydrology and economic impacts of flood.

3. Extent of inundation of a certain area can have dramatic impacts on water quality. Whether connecting point source or waste storage locations to the stream or by

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changing the biogeochemical conditions in soils, the extent and duration of flooding is important for accurately representing controls on the water quality of a given river.

4. There are biological impacts for organisms like fish and the aquatic insects on which they rely for food. Better mapping of when and where floods occur could benefit natural resource managers throughout North Carolina.

Collaborations and Engagements

Our team worked with Kurt Golembesky, P.E., CFM, NCFMP Engineer, North Carolina Department of Public Safety, Division of Emergency Management, Risk Management Section. Mr. Golembesky was very interested in the work and he also assisted with addressing questions about the LIDAR data and technical aspects of North Carolina’s current flood mapping capabilities.

The project team also engaged with leading experts who are directly involved with both the HAND approach, Dr. David Tarboton, and the National Water Model, Dr. David Maidment. Dr. Maidment has worked closely with first responders related to using NWM output.

Future Plans

The project team is very interested in extending the work to date. More specifically we would like to finish the processing the data for the remainder of the basins in North Carolina. We would welcome suggestions from the Collaboratory for potential funding sources to pursue this work.

We are also investigating seeking funding to test moving the processing into the Environmental Data Commons which is a project of the Open Commons Consortium. We have also been in discussions with the NSF­funded South Big Data Hub, co­hosted by RENCI for developing a south region effort in this area. Finally, we are also engaged with the DHS­funded Coastal Resilience Center at UNC­CH which is engaged in a number of post­Hurricane Matthew efforts.

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