Mapping Flash Flood Potential in Indiana Evan Bentley 03/02/2011

Background Midnight shift - conversation about inadequate flash flood guidance. Indiana has variable geography which will affect flash flooding. Photo courtesy of Indiana Geological Survey

Background Hydrologists and experienced forecasters are aware of areas which are most prone to flash flooding, but where precisely are these areas and which areas are the most vulnerable. Creating a “Flash Flood Potential Index (FFPI)” will help to represent how “flashy” the land is in certain areas. An index such as this will greatly improve National Weather Service (NWS) flash flood warning accuracy.

What is a Flash Flood? flash flood—A flood that rises and falls quite rapidly with little or no advance warning, usually as the result of intense rainfall over a relatively small area.floodwarningrainfall Some possible causes are ice jams, dam failure, and topography.ice jamstopography

Introduction The FFPI was developed by hydrologist Greg Smith (CBRFC). He understood that flash flood guidance did not properly represent geographical features which play a key role in flash flooding. FFPI visualizes the role of land, urbanization, and vegetation. “Guesswork” to flash flood prediction is reduced.

Current Flash Flood Guidance Flash Flood Guidance for Indiana INCHES OF RAINFALL FOR SPECIFIED DURATIONS REQUIRED TO PRODUCE FLASH FLOODING IN FORECAST ZONES. LOWER AMOUNTS MAY CAUSE FLASH FLOODING IN URBAN OR MOUNTAINOUS AREAS. IDNAME 1-HR 3-HR 6-HR 12-HR 24-HR ADAMS CO ALLEN CO BARTHOLOMEW CO BENTON BLACKFORD CO BOONE CO BROWN CO CARROLL CO

Current Flash Flood Guidance

Introduction Flash Flood potential was mapped by resampling, reclassifying, and combining high resolution (30 m) GIS layers to create a final map output for the entire state of Indiana. Final output was clipped to size of the Indianapolis NWS’s County Warning Area (CWA), and then to an increased resolution using the NWS’s Flash Flood Monitoring and Prediction (FFMP) sub-basins file.

Data Rasters  United States Geological Survey (USGS) Seamless Data Warehouse ( Canopy Elevation Land Use  United States Department of Agriculture (USDA) Natural Resources Conservation Service (NCRS) Soil Data Mart ( Soil Type (STATSGO)

Methodology 1)Collect datasets (AVHRR Forest Canopy, MRLC Land Cover, USGS DEM, and STATSGO Soil Type). 2)Project all datasets to the same projection (NAD 1983 UTM 16N). 3)Clip all datasets to Indiana shapefile. 4)Resample all rasters to same spatial resolution (30 meters). 5)Reclassify all datasets to standard index 1 to 10 with 10 being most “flashy” and 1 being the least “flashy.” 6)Use raster calculator to combine the datasets into one final mean output. 7)Take mean of FFPI output per county to generate county grid. 8)Take mean of FFPI output per FFMP sub-basin to generate FFMP grid.

Classification and Indexing Slope  100% slope = 45 o angle  Percent slope greater than 30% given a 10 on the scale.  Equal division between 0% and 30% for scales  USGS and Engineering studies have shown 30% slope to be strong-very strong.

Slope Index

Slope is the most important geographic feature which effects flash flooding.

Classification and Indexing Land Use  Water/Wetlands given a 1 on FFPI.  Urban areas range from  Greatest area is grassland, cultivated land, and forests with values 4-6.  Urbanization responsible for the majority of significant variability in this layer.

Land Use Index

Classification and Indexing Forest Canopy  Less Forest Density leads to quicker runoff and higher values.  More forest density allows for slower runoff and less flash flood risk.  0-10% forest canopy has value of 10.  % forest canopy has value of 1.

Forest Canopy Index

Classification and Indexing Soil Type ClassFFPI Sand2 Loamy Sand4 Sandy Loam3 Silty Loam4 Silt5 Loam6 Sandy Clay Loam7 Silty Clay Loam7 Clay Loam8 Sandy Clay7 Silty Clay8 Clay9 Organic Matter5 Bedrock10

Soil Type Index

Calculation 1st Try…[(Slope)+(LC)+(Canopy)+(Soils)]/4  Not very useful  Did not show enough variation within geography 2 nd Try…[(2*Slope)+(LC)+(Canopy)+(Soils)]/5  Better, but could still be improved

Calculation Final Calculation [(2*Slope)+(LC)+(Soils)+(Canopy/2)]/4.5  More dependence on terrain, less dependence on canopy.  This fits our conceptual model.

FFPI

Urban Effects on FFPI Cities have many impervious surfaces  Parking Lots  Roads  Buildings Can anyone guess why there are lower values north of Indianapolis inside 465?

Zonal Averaging Shows counties with the least/greatest overall flood risk. Radar images such as reflectivity and storm total precipitation can be overlaid to aid in warning process. Matches our conceptual model.

Zonal Averaging FFPI per Flash Flood Monitoring and Prediction (FFMP) sub-basin. FFMP- existing guidance used by NWS forecasters. Shows areas within the same county with differing flash flood risk.

FFPI Versatility Maps could be printed to place on forecasting desks. FFPI can be output into many radar software packages such as:  GRLevelX  AWIPS (NWS forecasting software)

Conclusions Hope FFPI leads to lower false alarm rate and greater lead time for flash flood warnings. The FFPI should be used concurrently with radar overlays. The FFPI can be created by any NWS office and classification values can be adjusted accordingly. Does not necessarily represent flash flooding due to ice jams or dam failures well.

Recent Work In late December 2010, the OHRFC unveiled “new and improved” flash flood prediction guidance. The new guidance includes:  Slope  Land Use  Forest Canopy Dynamic Layers of Soil Moisture, and Recent Rainfall were also included.

Acknowledgements The FFPI concept was developed by Greg Smith (CBRFC) Used classification and indexing methodologies from Jim Brewster (WFO Binghamton, NY) Bharath Ganesh-Babu (Valparaiso University) as a GIS advisor over this project.

Questions?