Lab 7 - Coastal Flooding

Coastal flooding and sea level rise is a hot topic these days, and for good reason. It's real, it's happening, and spatial analysis can help us gauge to what extent our communities and landscapes might be impacted. After reading literature on sea level rise and storm surge analysis, followed by a few videos by the NHA and NOAA, we explored NOAA's digital sea level rise viewer before starting the lab.

For the first part of the lab I determined the extent of area impacted by a 3ft and 6ft sea level rise and related flood depth along the southern coast of the District of Honolulu. I overlaid the floodzones with US Census tract data to get an idea of population density in the affected areas (see map). Switching to Census blocks, I further analyzed the data to look at specific population types. Out of a total population of 390,738 people in the entire district, 19% are white, 48% of homes are owner occupied, and 18% are 65 and older. Of the total population 2% would be affected by a 3 ft sea level rise, with 15% affected by a 6 ft rise. For whites, 37% are affected by a 3 ft rise and 30% affected by a 6 ft rise, compared to only 19% and 18% respectively that are unaffected. The results lead me to believe more whites live along the low-lying coastal areas than further inland. 49% and 50% of owner occupied homes were not impacted by a 3ft rise and a 6 ft rise, with a less than 20% difference in those that were affected-32% and 37% respectively, showing that there are slightly more owned homes further inland than along the immediate coast. For populations of 65 or older, there results were within 1% of those affected or unaffected for both scenarios, with 17% affected and 18% unaffected. So this small age group is evenly dispersed across the district. 

For the second part of the lab I compared storm surge accuracy between Lidar and USGS DEMs for Collier County, FL. I created floodzones for each DEM, used the Region Group tool to exclude disconnected areas, selected the main cell value area with the Equal To tool, overlaid a buildings dataset, and then used spatial overlays and queries to examine agreement of the impacted building results between the two elevation models. Using the Lidar DEM as the "true" count, I also calculated the errors of omission and commission for the two models.

Considering the topic was interesting and easy to understand, I had a lot of reworking in this lab. Likely because there was a lot of table joining and field calculations, where one little mistype or incorrect option could lead to incorrect results in the end.

Flood area and depth expected with 6 ft sea level rise and affected population based on census tracts for District of Honolulu.