Flood mapping for large areas (e.g. from catchments to a national level) is relatively new and challenging; associating mapped flood-prone areas with an annual chance of occurrence (in probabilities, say 1%) of a flood event involves many kinds of uncertainties and is even harder.
Flood maps that suggest various hazard levels over a large area are often in short supply for applications in insurance, emergency, planning, etc. This creates a perplexing situation where some users resort to open and accessible flood maps, such as FEMA’s National Flood Hazard Layer dataset, for different applications and interpretations, while ignoring the lineage and caveats attached to the underlying dataset. No single flood map is a panacea!
There is a real need to create new, insightful information products that can be used to proactively investigate flood-prone areas across a whole range of scales – from sites to river basins to a national level. Elevation is the single most important variable in determining flood hazard levels, and we focus on this by developing three flood analysis tools. Each can be implemented at scale and is a significant undertaking. We hope these tools can provide more geographic context and shed light on some key hydrodynamic processes about flooding.
1. Address-level Location Profile Report: The Importance of Elevation for Flood Mapping
The USGS has been developing and constantly updating the critical National Elevation Dataset (NED) over the past few decades, including the DEMs at ~60m, ~30m, ~10m, ~3m and ~1m resolutions (note that the resolution is approximate as the original NED is in geographic coordinates in units of decimal degrees). It appears that the two nationwide DEMs at ~30m and ~10m resolutions have been widely used for large-area flood mapping by various vendors. As elevation is the most critical input for flood mapping, it is useful to keep the following two major issues in mind:
The DEMs at a given resolution may be produced in earlier years with different production methods and lower quality levels. Figure 1 shows one aspect of these, the DEMs currency by year. Full information can be found at the USGS 3D Elevation Program (3DEP) websites about their current and historical release notes.
The vertical accuracy of NED. For example, the vertical accuracy of ~10m NED is 3.04m at 95% confidence level, based on a USGS assessment report published in 2014. It is reasonable to expect improved vertical accuracies for more recent versions of the NED.
The very recent, highly-commendable showcase (hosted by the Argo Group) on the comparison of U.S. flood models reported that for the same set of exposure locations, large dispersion of underlying elevation values was observed among four independent vendors. This suggests that the DEMs, albeit all sourced from the USGS, are somehow different. Indeed, underlying elevation values could be modified due to projections, spatial interpolations, data types, etc. It is common to observe an elevation difference up to a couple of meters even for the same site after some levels of post-processing. All these have serious implications on flood mapping and potential loss estimation. A re-think is warranted on how to reconcile the large elevation differences in the very first stage of flood modelling.
Figure 1: The currency of the USGS National Elevation Dataset (August 2017 release). Source: USGS.
In Australia, the national elevation data has been provided by Geoscience Australia. About 75% of populated areas have been covered by LiDAR-derived DTMs, typically at 5m resolution.
Using the above publicly-available national elevation datasets, along with other data sources, we have developed cloud-based analytics platforms (PropertyLocation360.com and PropertyLocation.com.au) to rapidly make address-level location profile reports. In each report, elevation is examined at a granular level and from multiple perspectives, including 3D views, slopes and water flow directions. All of these are closely related to flood modelling. In addition, various visualisation methods being explored are useful for assessing the quality of underling elevation data, e.g. the identification of detailed ground features (e.g. levees and roads) and any artifacts or abnormal terrain patterns.
Figure 2 shows an example of various elevation metrics, for a flood-prone address in Houston, TX. Many sample reports for the contiguous United States (CONUS) and Australia are available at the above websites.
Figure 2: Flood-related elevation metrics included in a typical exposure location profile report. (Full report was prepared before the 2017 Hurricane Harvey hit the region.)
2. Flood Simulation by Elevation
Flood simulation by elevation is commonly referred to as the bathtub or bucket-fill approach. It is often under criticism for its simplification and inability to incorporate complex hydrodynamic processes. But for many applications involving large areas or in aggregate nature (e.g. exposure assessment), this easy-to-understand approach is always handy and exploratory. Whether it is for coastal inundation or inland flooding, low-lying or flat floodplains can be delineated efficiently (e.g. Figure 3).
Our main contribution here is to create a cloud-based, automated approach for such simulations. Like the first tool, flood simulation by elevation can be generated and delivered within seconds for all locations in the contiguous U.S. and Australia, using the above two cloud platforms.
Figure 3: Flood simulation by elevation for an address in Houston, TX. Related elevation metrics are shown in Figure 2.
3. Flood Mapping by Inundation Depth
We have developed new geospatial processing routines for this, by capturing two key flood attributes – catchment sizes (two-dimensional) and inundation depths (vertical dimension). We develop large-area flood mapping with aggregate analysis in mind, therefore it is different from event-based classic flood models. Externals factors, such as mitigation measures (say levees, unless clearly reflected in underlying elevation data) and drainage capacities, are not considered.
Figure 4 illustrates this approach: flood extent expands as inundation depth increases. It is adaptive as the mapped flood extent is up to the minimum catchment size chosen and the maximum inundation depth that is derived from historical records or statistical extrapolations. These properties determine the flood mapping across scales. The use of a small catchment size and a major inundation depth can delineate flood-prone areas that may be conservative for risk aversion. Potential riverine and coastal flooding is covered while flash flooding excluded. We apply this new approach for large-area flood mapping in the contiguous U.S. and Australia.
Figure 4: An example of flood mapping by inundation depth.