As I’ve mentioned before, one of the key ingredients for a dependable flood model is elevation data. But with a wide range of elevation data available, how do you select the dataset that will help you build the right flood model for the task at hand — and do so in an efficient, cost effective way? This blog post takes a look at that question for national flood model initiatives.
Elevation data varies in price, precision, accuracy, coverage, and other technical attributes, so this choice is not simple. It really depends on what you need it for. For example: For a national flood infrastructure program, the “best” elevation data would include a dependable digital terrain model (DTM), consistency over a large area, matching resolution for accurate flood zone determination with the modeling technique to be used, and efficiency (which affects schedule and cost).
For flood models that cover large areas, like an entire country, it is very important that these data inputs are consistent over the whole area to ensure the uncertainty of the model is also uniform. Models with varying levels of uncertainty create great confusion for users because they can never be sure how output from the model can be used. Uncertainty can be managed, but uncertain uncertainty — or not knowing what you don’t know — is very difficult to manage.
Plus, when elevation datasets from different sources are used for flood models, there are inevitable artifacts in the flood models where the different data joins together. Having a uniform foundation layer for the flood modeling eliminates this problem. And the best way to ensure consistency is to use a single methodology to create the entire elevation dataset.
Another result of the inherent uncertainty in flood modeling is that there is a limit to the required resolution of elevation data needed for a model to work adequately. For the same reason a tape measure should not be used to measure the distance between mountains, LiDAR data is frequently too precise and high resolution to be used for flood models beyond a very small area (i.e. < 10km2). Likewise, flood modeling demands elevation data of adequate precision to realize its full potential. Sophisticated 3D flood modeling techniques would be wasted on low-resolution (>20m) elevation data.
If a country wanted to build flood protections along its waterways, LiDAR elevation data on and around those limited sites would be well suited for the sophisticated flood modeling that could be done on those very small catchment areas and river stretches. For flood modeling of that precision, site surveys are usually required to augment elevation data.
For a national flood modeling initiative, where public funds and public welfare are at stake, it is important to utilize elevation data that is most readily available at a cost that is defendable by spending authorities. The most ideal elevation dataset would be one that is already available. If a new DTM dataset needs to be acquired for the country, IFSAR (airborne or spaceborne) is by far the most efficient and cost effective, delivering the most suitable DTM for 3D flood modeling at a small fraction of the cost of LiDAR, and in a fraction of the total time required to build the dataset.