Previous studies have shown that natural disasters, and hurricanes in particular, have led to more deaths than those usually documented in short post-storm surveys. Such indirect deaths, thought to be related to dietary, stress or pre-existing medical conditions, can exceed the number of direct deaths and may persist for weeks or even months beyond the event itself. In the present study, cumulative sum of deviations plots are used to quantify the number of direct and indirect deaths resulting from Hurricanes Charley, Frances, Ivan and Jeanne that made landfall in Florida in 2004. Results suggest that there was an elevated mortality for up to 2 months following each storm, resulting in a total of 624 direct and indirect deaths attributable to the storm. Trauma-related deaths that can be associated directly with the storm account for only ∼4% of the total storm-related mortality, while indirect mortality accounts for most storm-related deaths. Specifically, a large percentage of the elevated mortality was associated with heart (34%) and cancer-related deaths (19%), while diabetes (5%) and accident-related deaths (9%) account for a smaller but still significant percentage of the elevated mortality. The results further suggest that the elevated mortality was the result of additional deaths that would not have otherwise occurred within that 5 month period, and not simply a clustering of deaths that were inevitable between 1 August and 31 December 2004. The elevated mortality identified in this study is significantly greater than the official count of 31 direct and 113 indirect deaths resulting from the four hurricanes combined. This suggests a need for improved mortality counts and surveillance in order to better evaluate and identify effective prevention policies, and to identify preventable deaths.

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Geographic Information Systems (GIS) software is used to analyze rainwater harvesting potential in Escambia County, Florida, USA. The approach presented can be replicated using LiDAR data, and the infrared spectrum of National Agriculture Imagery Program (NAIP) imagery. GIS surface maps are analyzed in combination with local utility consumption data to determine potential reductions in potable water consumption for households. The results indicate an extensive urban catchment of rooftop surfaces, and commensurate potential for rainwater harvesting and stormwater attenuation. Sixty two percent of the households analyzed consumed less water than could be potentially harvested. The remaining 38% consumed more water than could be potentially harvested. There are noted and significant differences between the two sample populations, including differences in water consumed and roof size. A comparison of lot size between the two sample populations did not yield any significant difference. The conclusions indicate that the widespread implementation of rainwater harvesting could substantially reduce potable water use in urban areas, and are of use to policy makers, planners, engineers and property owners everywhere.

The advances of remote sensing techniques allow for the generation of dense point clouds to detect detailed surface changes up to centimeter/millimeter levels. However, there is still a need for an easy method to derive such surface changes based on digital elevation models generated from dense point clouds while taking into consideration spatial varied uncertainty. We present a straightforward method, Las2DoD, to quantify surface change directly from point clouds with spatially varied uncertainty. This method uses a cell-based Welch’s t-test to determine whether each cell of a surface experienced a significant elevation change based on the points measured within the cell. Las2DoD is coded in Python with a simple graphic user interface. It was applied in a case study to quantify hillslope erosion on two plots: one dominated by rill erosion, and the other by sheet erosion, in southeastern United States. The results from the rilled plot indicate that Las2DoD can estimate 90% of the total measured sediment, in comparison to 58% and 70% from two other commonly used methods. The Las2DOD-derived result is less accurate (65%) but still outperforms the other two methods (30% and 48%) for the plot dominated by sheet erosion. Las2DoD captures more low-magnitude changes and is particularly useful where surface changes are small but contribute significantly to the total surface change when summed.