Combining Water Fraction and DEM-Based Methods to Create a Coastal Flood Map: A Case Study of Hurricane Harvey
2. Materials and Methods
2.1. A Case Study: Hurricane Harvey
2.2. Data Collection
- Watershed boundary dataset (WBD) developed by USGS (https://water.usgs.gov/GIS/huc.html);
- Flood-reported high-water-marks (HWMs) collected by USGS;
- Storm Surge Hindcast (SSH) product created by Coastal Emergency Risks Assessment (CERA);
- Coastal zone shapefiles 2009 from Data.gov (https://catalog.data.gov/dataset/shapefile-for-coastal-zone-management-program-counties-of-the-united-states-and-its-territories).
- 100 m National Land Cover Database 2006 from USGS. (https://viewer.nationalmap.gov/basic/?category=nlcd).
2.4. Water Fraction Model
2.5. DEM-Based Water Fraction (DWF) Map Derived from DEM-Based Downscaling Model
2.6. DWF Flood Map Validation
3.1. DEM-Based WF (DWF) Map
3.2. Accuracy Assessment
3.2.1. Comparison with CERA SSH Product
3.2.2. Quantitative Assessment Using HWMs
4.1. Main Findings
- The wide swath and daily temporal resolution of ATMS data in this research demonstrate an excellent capability of monitoring and mapping large-scale flood extent. Specifically, ATMS wide swath is big enough to cover the South Texas Coast using just one scene of ATMS image.
- ATMS sensors on the satellites are not usually affected by atmospheric constituents such as water vapor, dust, clouds, etc. . In this case, ATMS data is free of cloud cover and dense vegetation to some extent and is widely used to measure land surface features in different weather within 24 h of measurement. However, high-frequency ATMS channels like the 3–4 channels used in this study cannot penetrate thick rain bands of hurricanes, which is why our study periods were selected before and after Hurricane Harvey passed over the region. Goldberg  used SNPP/VIIRS resolution and GOES-16 geostationary satellite data with resolutions of 375 m and 1 km respectively to monitor floods induced by Hurricane Harvey. Although these satellites have better resolution than the one applied, they cannot penetrate through cloud cover. This could create uncertainty in the result even though the use of GOES-16 dataset serves to partially reduce the influence of cloud cover. Also, the authors did not downscale the coarse satellite data using a DEM which was done in this study. It is therefore essential to downscale the satellite-derived flood maps. Brakenridge et al. , after studying Hurricane Katrina using satellite images like MODIS and Sea, Lake, and Overland Surges from Hurricanes (SLOSH), concluded that combining MODIS maps and DEMs from LiDAR will be helpful to increase the accuracy of estimating storm surge.
- The outcome of this study made it evident that the use of high-resolution DEM provides feasibility to develop high-resolution flood maps from downscaled passive microwave measurements. The results from the DWF illustrate that this method can integrate water surface estimates, elevation information, basin boundaries, and hydrological mechanism and develop a flood map relatively easier and faster with good quality for disaster response and recovery. Li , equally used MODIS 500 m water fraction map and then downscaled to the resolution of DEM-30. He argued that incorporating a low-resolution DEM into a model may greatly change the flow direction and accumulation of the water, resulting in an error when integrating the DEM data and the TEERA/MODIS derived water fraction. This procedure produces more accurate results when a finer resolution DEM is used as in the case of the current study. The vertical accuracy of the 30 m SRTM DEM, which is 6–8 m as reported by , used by  in their elevation model did cause a substantial difference, over a plane surface, in modeled inundation areas. Because of the finer resolution −10 m DEM—used in this research with a vertical accuracy of 3 m , the differences over plain sites are essentially minimal . Furthermore, when  performed similar research for Hurricane Sandy, using 100 m resolution, they opined that the resolution of the DEM used cannot depict detailed information and errors may emerge within the created flood-map. Although the 10 m data used in this study does not imply that extremely detailed topographic information can be depicted with high accuracy, we assert that the errors have been greatly reduced as compared to what is obtainable in the research by Li et al.  and Zheng et al.  since the vertical accuracy for the DEM used here is better than that used in other improved basin-based methods.
- The study provided an improved basin-based method. During a hurricane event, there is usually a transfer of mass and energy in the water element, so much so, that the ocean body increases in water level resulting from the atmosphere and water body interaction . These phenomena coincide with the tides and affect watersheds that are lying landward. The individual determination of catchment DWF (Table 2) reveal the exact extent to which the catchments were inundated. The study area was divided into regional sub-watersheds (Table 2) that were used as a unit of validation, resulting in better accuracy for individual basins. The results show the percentage of flooded and non-flooded zones for both DWF and CERA SSH models. It was necessary to downscale the ATMS data to the level of sub-watersheds as each area, represented here by the sub-watersheds has unique characteristics such as hydro-geomorphic, water volumes and anthropogenic influences. It was therefore expected that the flood from a hurricane would impact each sub-watershed differently because of their unique characteristics. Based on this scale we can observe the spatial differences of the impact of flood within the entire area. Furthermore, the use of sub-watershed units makes it easier to fit in the ATMS derived WF values when downscaling for improved accuracy.
- The sub-watershed basins grouped into five different regions (Table 2) examined the spatial distribution of similarity and dissimilarity of both DWF and CERA SSH flood maps. Using sub-watershed regions provided better accuracy for individual sub-watershed basins as spatial differences in the flood impacts were apparent. Therefore, we can conclude from the result, of the validation, that this technique is feasible for developing large scale flood analysis, especially when dividing the study area into multiple sub-watershed basins.
- Zenith angle limitation and georeferencing problem: The pixel value in primary ATMS data appears to have a relatively low spatial resolution if the zenith angle is bigger than 20°. The cross-track and along-track FOV size of ATMS data range from 60 km to 136.7 km . Therefore, it is nearly impossible to cover a large-scale study site with the best ATMS data near nadir. Furthermore, the AMTS sensor cannot capture the data at the same angle in the same place. In this case, georeferencing is highly recommended before water fraction values are calculated. A slight displacement in the georeferencing process will result in a critical negative impact on the quality of the DWF flood map.
- It is difficult to identify the factors that cause the uncertainty of : On the one hand, the large amount of rainfall falling on the South Texas Coast may vary in high temporal and spatial variability during Hurricane Harvey, which results in an even higher by soil absorption and water accumulation. Vegetation may reduce the value of greatly because soil moisture cannot be easily detected if soil features are covered by vegetation .
- Limitation of real-time datasets: Some regions in the DWF map, especially region 5, have substantial differences compared with CERA SSH product. The reason is that DWF used post-hurricane ATMS data collected before and after Hurricane Harvey dissipated on 3 September. The ATMS data are not available from when the coastal flooding was at its peak. In contrast, CERA SSH product was derived from ADCIRC model to estimate coastal flood extent using real-time observations, which is more likely to be accurate.
- DEM limitations: Even though 10 m 3DEP DEM dataset is good enough to downscale large-scale ATMS data, further study is needed to improve the resolution of elevation dataset because the DWF method presented relies on high-resolution elevation datasets. LiDAR data are an excellent choice due to their very high spatial resolution of less than 1 m with a vertical accuracy of less than 15 cm . LiDAR data is also widely used to develop DSM and DTM elevation products for vegetation and building detection, which is even better to estimate flood extent in coastal urban areas . Recent studies (e.g., ) also pointed out that as the DEM resolution becomes coarser, the estimated flood damages will be reduced. It would be better to quantify DEM data using ground control points if further risk assessments are needed.
- Neglect of other environmental factors: In this study, only topography information and passive microwave remote sensing images were considered for mapping coastal floods. However, coastal floods may become much more devastating due to other environmental factors like sea level rise (SLR), land subsidence (LS) and bathymetric change (BC). For instance, Wang et al.  applied MIKE 21, a flood simulation model, to estimate the flood intensity based on these three environmental factors. They found that BC is the main factor responsible for severe coastal floods while SLR and LS may affect more on coastal floods in the future due to climate change and urban development.
4.3. Coastal Flood Map: Communicating Risk
Conflicts of Interest
|Water Fraction||Levene’s Test||t-Test||95% Confidence Interval|
|F||Sig.||t||df||Sig. (2-tailed)||Mean Difference||Std. Error Difference||Lower||Upper|
|Equal variances assumed||0.002||0.964||2.578||128||0.011||0.110712||0.042952||0.025725||0.1957|
|Equal variances not assumed||2.578||127.576||0.011||0.110712||0.042952||0.025722||0.195703|
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|Atmospheric Contribution (%)|
|6||53.596 ± 0.115||QH||0.89|
|11||57.29 ± 0.217||QH||0.99|
|12||57.29 ± 0.322 ± 0.048||QH||0.99|
|13||57.29 ± 0.322 ± 0.022||QH||1.00|
|14||57.29 ± 0.322 ± 0.010||QH||1.00|
|15||57.29 ± 0.322 ± 0.0045||QH||1.00|
|18||183.31 ± 7.0||QH||0.94|
|19||183.31 ± 4.5||QH||0.98|
|20||183.31 ± 3.0||QH||0.99|
|21||183.31 ± 1.8||QH||0.99|
|22||183.31 ± 1.0||QH||0.99|
|No||DWF||Non-DWF||CERA SSH||Non-CERA SSH|
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Li, X.; Cummings, A.R.; Alruzuq, A.R.; Matyas, C.J.; Amanambu, A.C. Combining Water Fraction and DEM-Based Methods to Create a Coastal Flood Map: A Case Study of Hurricane Harvey. ISPRS Int. J. Geo-Inf. 2019, 8, 231. https://doi.org/10.3390/ijgi8050231
Li X, Cummings AR, Alruzuq AR, Matyas CJ, Amanambu AC. Combining Water Fraction and DEM-Based Methods to Create a Coastal Flood Map: A Case Study of Hurricane Harvey. ISPRS International Journal of Geo-Information. 2019; 8(5):231. https://doi.org/10.3390/ijgi8050231Chicago/Turabian Style
Li, Xiaoxuan, Anthony R. Cummings, Ali Rashed Alruzuq, Corene J. Matyas, and Amobichukwu Chukwudi Amanambu. 2019. "Combining Water Fraction and DEM-Based Methods to Create a Coastal Flood Map: A Case Study of Hurricane Harvey" ISPRS International Journal of Geo-Information 8, no. 5: 231. https://doi.org/10.3390/ijgi8050231