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.
Some possible causes are ice jams, dam failure,
and topography.
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
http://www.srh.noaa.gov/rfcshare/ffg.php?duration=3&location=OHRFC
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
1.3 1.3
1.4
1.4
1.5
ALLEN CO
1.4
1.4
1.4 1.4
1.6
BARTHOLOMEW CO 1.9
2.0
2.1
2.2 2.2
BENTON
3.4
3.9
4.4
BLACKFORD CO
1.8 1.9
2.0
1.9 1.9
BOONE CO
1.8 1.9
2.0
2.0
2.1
BROWN CO
1.9
2.0
2.1
2.1
2.2
CARROLL CO
2.2 2.5
2.6
2.8 3.1
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
(http://seamless.usgs.gov)




Canopy
Elevation
Land Use
United States Department of Agriculture (USDA)
Natural Resources Conservation Service (NCRS)
Soil Data Mart (http://soildatamart.ncrs.usda.gov)

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 = 45o angle

Percent slope greater than 30% given a 10 on the
scale.

Equal division between 0% and 30% for scales 19.

USGS and Engineering studies have shown 30%
slope to be strong-very strong.
Slope Index
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 8-10.
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.
90-100% forest canopy has value of 1.
Forest Canopy Index
Classification and Indexing

Soil Type
Class
FFPI
Sand
2
Loamy Sand
4
Sandy Loam
3
Silty Loam
4
Silt
5
Loam
6
Sandy Clay Loam
7
Silty Clay Loam
7
Clay Loam
8
Sandy Clay
7
Silty Clay
8
Clay
9
Organic Matter
5
Bedrock
10
Soil Type Index
Calculation

1st Try…[(Slope)+(LC)+(Canopy)+(Soils)]/4



Not very useful
Did not show enough variation within geography
2nd 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?