Final Project: Flood Hazard Analysis

Introduction:

Flooding is an issue that affects many cities and towns located on rivers and other water bodies.  In order to prevent damaging effects from floods including property damage, economic loss on farm fields, and loss of human life, there needs to not only be an understanding of the mechanisms of floods, but also of flood history (Karatzas, 2017).  The Chippewa River Valley in particular has been subject to floods since settlers first came to the area (Wisconsin Historical Society, 2017).  As technology has advanced so have flood prevention measures and the ability to determine flood hazard zones.  However, these flood prevention measures are only as effective as the data they reference.  This data is hindered by an insufficient instrumental record of river levels (Therrel and Bialecki, 2015).  One way to recreate flood history before instrumental records is by using tree rings. When a tree experiences a flood under the right conditions, it will create a flood ring, which can then be observed in a tree core sample (Therrell and Bialecki, 2015).  This method is a new development in dendrochronology and flood rings have very specific conditions under which they form including the timing of the flood and growth in trees, the temperature, the total time the tree was inundated, and the species of tree. To support this relatively new and uncertain flood ring analysis technique, a flood hazard map of Wisconsin, including the area surrounding Brush Island in the lower Chippewa River valley, will be created showing which areas are most likely to flood and thus indicate which trees might have flood rings.

Methods:

In this analysis, five different data layers were used: rainfall layer, DEM of Wisconsin, soil characteristics layer, land cover layer, and a rivers and streams layer.  Each layer was projected in the NAD_1983_HARN_WI_TM projection which is specific to Wisconsin. The DEM of Wisconsin was used to create a slope layer, flow accumulation layer, and an elevation layer.   To obtain the rainfall layer, monthly averages of rainfall for cities in Wisconsin obtained from the NOAA National Climatic Data Center (2017) were transferred to Excel. Then using the Modified Fournier Index (MFI), an equation that sums the square of the monthly average rainfall divided by the yearly rainfall for all twelve months, an MFI indicator was created that indicates the sum of the average monthly rainfall intensity for an area (Figure 1). MFI is used because it has been determined to be acceptable for mapping rainfall intensity (Kourgialas and Karatzas, 2017). These were incorporated into a new field of a geocoded Wisconsin cities feature class and the spline method of interpolation created a raster of average monthly rainfall intensity because it is best for creating a smooth surface of varying data (Kourgialas and Karatzas, 2017). 

Figure 1. MFI equation.

Using spatial analyst, the slope raster function was performed on the DEM layer to determine slope in the study area.  Flow accumulation was obtained by first creating a flow direction raster (using the flow direction tool in spatial analyst) from the DEM and subsequently using the flow accumulation tool.  The soil characteristics layer had to be converted using the polygon to raster tool in order to create a raster usable in the analysis.  For the rivers and streams raster, a Euclidean distance tool created a gradient of distance from the river and stream network.  The rainfall, slope, flow accumulation, and rivers and streams layer all had to be converted from a floating point to an integer raster because weighted overlay analysis only works on integer rasters.  Then, the rainfall, slope, flow accumulation, and elevation rasters had to be projected into the correct coordinate system to perform the analysis. 

Finally, all the rasters had to be separated into four classes using the reclassify tool. The elevation, flow accumulation, slope, rivers and streams, and rainfall layer were all separated into four classes using the natural breaks method, which was used based in previous studies (Kazakis et al., 2015). Land cover and geology (soil types) are composed of descriptive classes, and so their classes were determined based on the research results of both Kazakis et al. (2015) and Kourgialas and Karatzas (2017).  Each of these layers was then inputted to an extension called analytic hierarchy process.  This extension creates a weighted value for each raster based on a table showing the importance of a variable relative to others.  The values for this table came from Kazakis et al. 2015 (Figure 2). 

Figure 2. AHP values for weighted overlay analysis.

 Then all seven rasters were imported to weighted overlay analysis, each raster was assigned its weight (as a percentage), and each class previously created with the reclassify tool was assigned a ranking of either 1, 3, 5, or 7.  The rankings were determined using both Kazakis et al. (2015) and Kourgialas and Karatzas (2017) as a guide.  Finally, the tool was run and a map was produced showing flood hazard areas for all of Wisconsin.  The work flow for this map is pictured below.

 

Figure 3. Workflow for creating the flood hazard map.

Results:

Figure 4. Flood hazard map of Wisconsin.

Figure 5. Flood hazard map of the area surrounding Brush Island.

 The final product shown in figure 4 was a flood hazard analysis map of the state of Wisconsin with four categories: very high, high, medium, and low.  The categories were determined using ranks and weights assigned to each variable via analytic hierarchy process and previous research.  Figure 5 shows that the area surrounding Brush Island, the area where tree cores were obtained for recreating a flood history of the Chippewa River, is completely encased in the very high flood zone.  This indicates the Brush Island has a high likelihood of flooding and thus the trees are likely to contain flood rings.  In general, areas surrounding rivers like the Chippewa River are more likely to flood than areas farther away.  The lower half of Wisconsin is also generally more susceptible to flooding based on figure 2. Important to note is the raster analysis was limited by the 30 meter DEM and thus the precision of the findings is limited. 

Conclusion:

Figure 3 shows that the Brush Island area is likely to flood based on the raster analysis performed.  This indicates that tree cores from the island are likely to have flood rings and validates previous predictions that obtaining tree cores from this area will yield flood rings. Tree ring data from Brush Island can then be used to recreate a flood history of the Chippewa River before instrumentation records were present.  This data combined with instrumentation records will give government officials and other groups a more complete and comprehensive flood history that can be used for planning, insurance policies, and future flood prevention measures.  While the raster data used for this analysis is recent (DEM, soil characteristics, flow accumulation) and the flood rings are dated farther back, the Chippewa River has not drastically changed in the last 200 years and thus general comparisons and analysis can be done using previous flood data (tree rings) and recent raster data.  The final raster was limited by its 30 meter DEM and limited rainfall data so fine scale analysis is not possible, but general trends can be determined.  This map is recommended for general use and coarser scale trends in flood hazard analysis of Wisconsin. 

Sources:

NOAA National Climatic Data Center. (2017). NOAA’s 1981-2010 Climate Normals [data file]. Retrieved from https://www.ncdc.noaa.gov/cdo-web/datasets/NORMAL_MLY/locations/FIPS:55/detail

Current Results Publishing Ltd. (2017). Average Precipitation for Wisconsin. Retrieved from https://www.currentresults.com/Weather/Wisconsin/precipitation-january.php

Wisconsin Department of Natural Resources. (2017). DNR Geodatabase. (Geodatabase). Wisconsin: Wisconsin Department of Natural Resources. Web. 23 Sep 2017. http://dnr.wi.gov/maps/GetGISData.html

Therrell, M. D., & Bialecki M. B. (2015). A multi-century tree-ring record of spring flooding on the Mississippi River, Journal of Hydrology, 529(2), 490–498. doi:10.1016/j.jhydrol.2014.11.005.

Xiao, Yangfan, Shanzhen, Yi, Zhongquian, Tang. (2017). Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference, Science of the Total Environment, 599-600, 1034-1046. http://dx.doi.org/10.1016/j.scitotenv.2017.04.218

Kourgialas, N.N., & Karatzas, G.P. (2017). A national scale flood hazard mapping methodology:The case of Greece- protection and adaptation policy approaches, Science of the Total Environment, 601-602, 441-452. http://dx.doi.org/10.1016/j.scitotenv.2017.05.197

Kazakis, N., Kougias, I., and Patsialis, T. (2015). Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: application in Rhodope-Evros region, Greece, Science of the Total Environment, 538, 555-563.http://dx.doi.org/10.1016/j.scitotenv.2015.08.055

Wisconsin Historical Society. (2017). Floods in Wisconsin. Retrieved from             https://www.wisconsinhistory.org/Records/Article/CS2507