Elevation data shows how the height of the ground varies over an area. To create a flood map, water is placed over the surface of the elevation data which is used to dictate where the water will flow and accumulate, allowing users to identify areas that are more or less prone to flood risk.
The type, accuracy and resolution of the elevation data are extremely important in developing flood maps, with these factors widely believed to have a direct impact on the resulting flood mapping extents and depths (Saksena & Merwade, 2015). Flooding may be greatly over- or underestimated if poor quality elevation data is used. Compared to other perils such as wind or earthquake, the quality of elevation data has a far greater impact on the quality of a flood map.
Elevation data can vary greatly in quality. It’s important to use recent and good quality elevation data (Sanders, 2007) to get the best results and there are different factors that are considered when choosing elevation data for national-scale flood mapping (see Figure 1 for some of these).
In this blog, we will be focusing on type, resolution and accuracy, providing an introduction to elevation data and the importance of these factors.
Figure 1: some factors affecting the choice of elevation data
The importance of type
For the purposes of this blog, we are using the terminology below:
- A digital surface model (DSM) captures the Earth’s surface including features such as trees and buildings. For data built from remote sensing, this is typically the surface “seen” by the sensor.
- A digital terrain model (DTM) is a bare-earth representation of the Earth's surface with trees, buildings and other features removed.
- Digital elevation model (DEM) is the general term used to describe either a DSM or DTM.
Figure 2: the difference between DTM and DSM.
Flood mapping is best done on DTMs so that artificial barriers to the flow of water are removed. However, in doing this, we may also lose the impact that a building may have on the flow of water, which can produce an unrealistic view of flood. To rectify this issue, detailed flood studies can use hybrid DTMs where buildings may be stamped back into the bare earth surface to get a better representation of the urban environment.
The importance of accuracy
Accuracy, or vertical accuracy, of elevation data is the possible height difference between the recorded height and the actual height of the land. For example, if a recorded height is 0.3m with an accuracy of +/- 0.1m, then the actual height of the land could be between 0.2m – 0.4m.
Different methods for creating elevation data provide different levels of accuracy. Lidar (light detecting and ranging), photogrammetry and radar (radio detection and ranging) are common ways of sourcing elevation data.
Lidar uses a laser to scan the Earth’s surface. The transmitted laser pulse is reflected off a surface back to a sensor. The time between the transmission and reflection is recorded with the X, Y co-ordinates of the location. Multiple reflections can be recorded which can be used to filter out features such as trees.
Photogrammetry is elevation data derived from photographic images of the earth using measuring and trigonometry techniques. A series of overlapping photographs are captured and the elevation is then calculated based on where the data appears on the photograph compared to where it would appear on a flat surface.
Radar is an object-detection system which uses radio waves to determine the range and altitude of fixed objects such as terrain. The radar transmits pulses of radio waves which bounce off any object in their path; a tiny part of the wave's energy is returned to the radar and recorded.
Lidar is generally the preferred source for flood mapping due to its excellent horizontal resolution (explained below) and vertical accuracy, as well as the ability to separate out the bare earth by removing vegetation and buildings (Sanders, 2007; Casas et al., 2006). However, lidar data collected using aerial cameras cannot detect floodwater passages such as under bridges and these features need to be detected and edited before mapping commences (Meesuk et al. 2014).
Lidar is often flown over smaller, high-value areas such as cities and not on a large scale, due to the costs involved in creating the data. Photogrammetry is a more cost-effective method for collecting data at a larger scale. Recent technologies have improved the horizontal resolution and vertical accuracy of photogrammetry data making it a useful source of elevation data in national-scale flood mapping. Combining lidar and photogrammetry data can be used to create continuous elevation data of a good quality.
The importance of resolution
The resolution, or horizontal resolution, of elevation data is the size of each cell which is given the same height value, generally given in metres. The smaller the cell size, the higher the resolution of the data.
Figure 3: the difference between resolution, 5m versus 30m.
Different resolutions are available in different areas and often a variety of resolutions are available at a national scale (Bühler et al, 2012).
Increasing the resolution can help define areas better, such as identifying small streams and roads, giving more detailed flow paths for water. However, increasing resolution has drawbacks such as increased computational requirements (Werner, 2001), which mean some resolutions can become increasing difficult to create at a national scale.
Similarly, increased resolution does not always relate to an improvement in flood mapping if the accuracy of the data is poor. Without a good accuracy of the data, an improvement in resolution is unlikely to improve flood mapping outputs.
What does JBA use?
When selecting elevation data, we consider these different factors to ensure we choose the best available data for mapping, often combining multiple sources of data. Elevation data available to JBA are reviewed regularly and used within our flood mapping so that clients benefit from any updates to the elevation data.
For a more in-depth exploration of the importance of DEM resolution for a flood map, check out the next blog in our series. For more information on any of our flood mapping, including our new 5m US flood maps and updated 2018 UK and ROI flood maps, please get in touch.
References:
Bühler, Y., Marty, M. & Ginzler, C. (2012) High resolution DEM Generation in High-Alpine Terrain Using Airbourne Remote Sensing Techniques. Transactions in GIS. 16(5), 635-647.
Casas, A., Benito, G., Thorndycraft, V.R. & Rico, M. (2006) The topographic data source of digital terrain models as a key element in the accuracy of hydraulic flood modelling. Earth Surface Processes and Landforms. 31(4), 444-456.
Meesuk, V., Vojinovic, Z., Mynett, A.E., Abdullah, A.F. (2014) Urban flood modelling combined top-view LiDAR data with ground-view SfM observations. Advances in Water Resources. 75, 105-117
Saksena, S. & Merwade, V. (2015) Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. Journal of Hydrology. 530, 180-194.
Sanders, B.F. (2007) Evaluation of on-line DEMs for flood inundation modelling. Advances in Water Resources. 30(8), 1831-1843.
Werner, M.G.F. (2001) Impact of grid size in GIS based flood extent mapping using a 1D flow model. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere. 26(7-8), 517-522.
