The Impact of Surface Data Accuracy on Floodplain Mapping

University of Texas

CE 394K.3 GIS in Water Resources

Final Report Fall 2007

Friday, December 7, 2007

Prepared By Chelsea Cohen

Abstract

LIDAR remote sensing technology is a valuable resource that can increase the accuracy of floodplain mapping.  The mapping accuracy on a LIDAR generated TIN surface is far more accurate than alternative mapping accuracies on interpolated 2ft and 10ft contour surfaces or NED 1 arc second DEM surfaces.   While there are some computational limits to HEC-GeoRAS processing floodplains at low raster cell sizes, the current capacity of HEC-GeoRAS far outweighs the alternative of manually digitizing floodplains.  This project will show that coupling LIDAR TIN data and HEC-GeoRAS to map floodplains can significantly reduce mapping effort and increase efficiency and accuracy.

Introduction

With the advance of new LIDAR remote sensing technology, digital terrain models can be produced with a much higher degree of accuracy.  Coupling LIDAR terrain data and HEC-GeoRAS could potentially improve the accuracy of floodplain mapping.  The objective of this project is to compare the plotting accuracy of HEC-RAS water surface elevations (WSE) when plotted on these different terrain models:

1)     1 arc second DEMs

2)     10ft contours

3)     2ft contours

4)     LIDAR TIN

In addition, the floodplain plotting accuracy will be analyzed in:

1)     Rural area

2)     Urban area

Study Area and Data

Data for this project was collected from the author’s internship at Freese and Nichols, Inc. in Dallas, TX in the spring of 2007.  The Study area is in Cleburne, TX, which is approximately 50 miles southwest of Dallas.  Approximately 2 miles along Stone Lake Stream was analyzed.  Stone Lake is a tributary that connects to East Buffalo Creek.

The LIDAR TIN data for use in this project was collected by the City of Cleburne in 2005.  The file contained mass point and hard breaklines files.  The LIDAR data was clipped to reduce processing time of the TIN, shown in Figure 1.

Figure 1: Stream Centerline and Surface LIDAR TIN clipped to Study Area

Next, 2ft and 10ft contours from the TIN surface using 3D Analyst.  The Topo to Raster feature in Spatial Analysis was used to create raster surface from contour lines.  The reason for this was to create an interpolated tin based just on the contour data.  Therefore, this surface would not have the breakline data that the LIDAR TIN contained.  Lastly, the Rasters were converted to TINs using 3D Analyst.  Figure 2 compares the LIDAR TIN to the 2ft TIN.  In the LIDAR TIN, there is a noticeable neighborhood where the streets can easily be identified as well as a stream centerline.  However, the 2ft surface does not preserve the streets and the neighborhood is smoothed out.

Figure 2: LIDAR TIN surface (left) compared to 2ft surface (right)

In Figure 3, the LIDAR TIN and 10ft TIN are compared.  The 10ft surface interpolation gets rid of any trace of a neighborhood.  In addition, there stream centerline is not well defined.

Figure 3: LIDAR TIN surface (left) compared to 10ft surface (right)

The USGS NED 1arc second DEM for the area was downloaded from The National Map Seamless Server (http://seamless.usgs.gov/).  The resolution of NED DEM is approximately 30m or 100ft.  The NED DEM was interpolated to 2ft and 10ft accuracy tins using raster calculator and 3D Analyst Convert Raster to Tin tool.  Figure 4 and 5 compare LIDAR TIN surface and the NED surfaces.  The NED surfaces again show no signs of a neighborhood.  More importantly, they also do not accurately represent the stream centerline.

Figure 4: LIDAR TIN surface (left) compared to 2ft interpolated NED surface (right)

Figure 5: LIDAR TIN surface (left) compared to 10ft interpolated NED surface (right)

Methodology

Considering that there are several factors that affect WSE, it was assumed that the hydrologic and hydraulic data is accurate.  If the hydrology and hydraulics do not produce accurate water surface elevations, even with detailed terrain, the result will be an inaccurate floodplain.  This assumption was made to prove that if accurate water surface elevations are coupled with good terrain, the most accurate floodplain can be produced.

A hydraulics model using was created using HEC-GeoRAS Geometry tools and HEC-RAS.  The hydraulic model did not incorporate structures, ineffective flow or levees.  The 100YR peak flow values were obtained from the hydrologic data developed at Freese and Nichols, Inc.  Lastly, the floodplains were plotted using HEC-GeoRAS Mapping.

While conducting these calculations, several benefits of HEC-GeoRAS mapping became clear.  It greatly reduced time and effort of manually digitizing floodplain boundaries.  The process of manually digitizing floodplains can be tedious because of the need to interpolate WSE between cross-sections using 2ft contours.  GeoRAS automates plotting floodplains on TIN or raster surfaces, which are more accurate than contours.  In addition, this reduces the human error involved with digitizing.  GeoRAS can digitized floodplains in minutes, which used to take hours and days.

There was one drawback of HEC-GeoRAS mapping that came to light with the project.  The higher the raster cell size specified for the floodplain, the quicker that GeoRAS can process the floodplain delineation.  A higher raster cell size will result in and less accurate floodplain.  The original intent of the project was to plot the LIDAR TIN using at least 0.5ft to 1ft raster cell size.  The result would be a smooth floodplain boundary that mapped well between the 2ft contours.  However, the smallest raster cell size that HEC-GeoRAS would process was 2ft.  Figure 6 shows where the raster cell size is specified.  A 1 ft raster cell size generated an error shown in Figure 7.  The LIDAR TIN used was only 3.5 sq. miles.  For larger study areas, this cell size could be restricted even more.  In order to reduce the need to process different sections of the model at a time, the capabilities of HEC-GeoRAS need to be increased.

Figure 6: HEC-GeoRAS Post-processing Layer Setup specifying LIDAR TIN surface and 1ft raster cell size

Figure 7: HEC-GeoRAS mapping error when specify LIDAR TIN surface and 1ft raster cell size

A representation of what the raster cell size represents in shown in Figure 8.  The boundary is digitized in lengths specified by the raster cell size.

Figure 8: Comparison of 2ft and 10ft Raster Cell Size for Floodplain Generation

Lastly, there is still a need to go through and double-check the floodplain boundaries that are generated.  Common editing that usually needs to be done is to edit out little low areas outside the floodplain HEC-GeoRAS maps, an example is shown in Figure 9.  It is important to note that the time it takes to double-check the floodplain is significantly less the time it would take to manually digitize the floodplain.

Figure 9: Low area outside the floodplain that needs to be manually deleted

Results

FEMA checks floodplain delineation to make sure that water surface elevation at cross-sections follows the 2ft contours.  Therefore, it is important to make sure that floodplains do not improperly cross 2ft contours between cross-sections.

Mapping Accuracy of 10ft Surface

As can be seen in Figure 10 and Figure 11, this is an extremely inaccurate surface to use.  The floodplain created is smoothed out covers much more area than is represented with the LIDAR TIN surface.  Figure 12 was created by with the union tool in Analysis tools.  This highlights all the areas where the floodplain boundary is at a higher elevation than the LIDAR TIN and where it is lower.

Figure 10: LIDAR TIN vs 10ft Contour Surface Floodplain Delineation

Figure 11: LIDAR TIN vs 10ft Contour Surface

Floodplain Delineation

Figure 12: Difference in Floodplain Boundaries for the LIDAR TIN and 10ft Contour Surface

Mapping Accuracy of 2ft Surface

The 2ft contour surface was the most accurate out of the contour and NED surfaces.  However, it still frequently crossed contours between cross-sections, as seen in Figure 13.  This would take up time to manually go back in and correct the floodplain boundary to follow the contours.  Figure 14 shows how the 2ft surface varied from the LIDAR TIN mainly in the urban area.

Figure 13: LIDAR TIN vs 2ft Contour Surface

Figure 14: Difference in Floodplain Boundaries for the LIDAR TIN and 2ft Contour Surface

Mapping Accuracy of NED 10ft Surface

The NED 10ft surface overestimates the floodplain boundary in the urban area and underestimates it in the rural area.  Figure 17 shows the difference between the NED 10ft surface and LIDAR TIN is too significant and shows how interpolating a surfaces low accuracy surfaces is not a preferred method for floodplain mapping.

Figure 15: LIDAR TIN vs NED 10ft Surface

Figure 16: LIDAR TIN vs NED 10ft Surface

Figure 17: Difference in Floodplain Boundaries for the LIDAR TIN and NED 10ft Surface

Mapping Accuracy of NED 2ft Surface

NED data is readily available for download on several websites.  However, this project shows that NED data is not accurate enough to be used on floodplain mapping.  Figure 18 and Figure 19 show how smoothed out the NED 2ft floodplain boundary is.  This data was interpolated from 100ft to 2ft accuracy.  However, this shows how important it is to have several data points, like LIDAR collects, rather than to interpolate from a few data points.  Figure 20 shows how most of the variation was in the urban area.

Figure 18: LIDAR TIN vs NED 2ft Surface

Figure 19: LIDAR TIN vs NED 2ft Surface

Figure 20: Difference in Floodplain Boundaries for the LIDAR TIN and NED 2ft Surface

Mapping Accuracy of LIDAR TIN Surface

The floodplain boundary created from the LIDAR Tin more consistently followed 2ft contour lines than any other surface.  Even though the raster cell size could not be set lower than 2ft, the floodplain boundary was still very accurate.   The LIDAR TIN surface would require little editing to make it FEMA turn-in ready.  Figure 21 shows how this floodplain also seems the most reasonable because the shape of the flow follows along streets, ditches and building lots similar to how it flows in reality.  The LIDAR TIN surface is definitely the most accurate terrain model to be used for floodplain mapping.

Figure 21: LIDAR TIN Surface Floodplain Boundary

Mapping Accuracy in Rural and Urban Areas

In rural areas, crops can be ruined where flooding occurs.  Therefore, it is important to be able to show how much of the overbank will be flooded.  Figure 22 shows how the 2ft Contour surface and the LIDAR TIN surface vary in a rural area.  There was not as much discrepancy between the 2ft Contour surface and the LIDAR TIN surface in the rural areas, as compared to the urban areas.

Figure 22: LIDAR TIN vs 2ft Contour Surface in Rural Area

Impact of detailed topography felt greater in urban areas that have structures and streets close to the floodplain.  The LIDAR TIN is detailed enough to be able to show housing lots that are in and out of the floodplain, as seen in Figure 23.  Also, Figure 24 shows how water floods up through streets and ditches.  This is very important because it could be used in the future to determine road closures and evacuation needs.

Figure 23: LIDAR TIN vs 2ft Contour Surface in Urban Area

Figure 24: LIDAR TIN vs 2ft Contour Surface in Urban Area

Conclusions

Considering the higher-rate for flood insurance if your home is located in a floodplain, it is important to make the best assessment of where water is going to go during a flood.  There is not enough terrain detail by preparing floodplain maps with contours or NED 1arc second DEMs.  As was shown in the results, there was significant area that is mapped improperly using these types of surfaces.  By having inaccurate maps, people are paying high rates for flood insurance who might not need it and vice versa.

In Texas, as a result of the significant flooding in 2007, there is a greater demand for better knowledge and prediction of flood areas.  In the future, a flood prediction model could be developed linking detailed floodplain mapping and weather data to warning systems for evacuation during storms.  This would be most helpful if there was surface LIDAR TIN data available so that the model could represent the most detailed terrain.

No matter how good the hydrologic and hydraulic design is, if a floodplain is mapped on an inadequate terrain surface, the result is going to be an inaccurate floodplain.  Industry needs to embrace new technology available for floodplain modeling and mapping.  Coupling LIDAR TIN data and HEC-GeoRAS to map floodplains can significantly reduce mapping effort and increase efficiency and accuracy.

References

US Army Corps of Engineers, HEC-GeoRAS: GIS Tools for support of HEC-RAS using ArcGIS User’s Manual, Version 4, September 2005