An Observation-Driven Approach to Rainfall and 

Flood Frequency Analysis Using High-Resolution 

Radar Rainfall Fields and Stochastic Storm 

Transposition

http://www.flickriver.com/photos/leegillen/3843787296/

http://www.wallpapersfolder.com/wallpaper/largethunderstorm-over-the-city_w73.html

Hydrometeorology Research Group

Civil and Environmental Engineering

Daniel B. Wright, James A. Smith, 

Mary Lynn Baeck
2/21

The Hydrometeorology Research Group

Focus: understanding the 

atmospheric and land 

surface processes tied to 

hydrologic extremes 

using observations and 

numerical models

• Atmospheric Dynamics

• Sensors and Remote 

Sensing

• Surface Hydrology and 

Hydrometeorology

• Urban Hydrology and 

Meteorology
3/21

Photo: http://www.ultimatemetal.com/forum

Outline of talk

1. Motivation and 

research questions

2. A critical look at 

conventional flood 

risk assessment

3. High-resolution radar 

rainfall and stochastic 

storm transposition 

(SST) for rainfall 

frequency analysis

4. SST for flood 

frequency analysis

5. Summary and future 

directions
4/21

Motivation and research questions

• What are the dominant physical processes that 

control urban floods?

• Specifically, how does the spatial and temporal 

structure and motion of extreme rainfall interact 

with surface, subsurface, and drainage network 

characteristics to produce floods?

• How can we use modern data and computational 

resources to examine these processes?

• How can we use this scientific understanding to 

improve engineering practice, risk management, 

and policy?
5/21

Conventional methods

1. Spatially uniform, temporally idealized extreme rainfall 

2. Assumed return-period dependency between rainfall and 

peak discharge at all points in a drainage network

3. Stationary flood risk

Atlanta

1974

Atlanta

2005

Wright et al., The Hydroclimatology of Flash Flooding in 

Atlanta,Water Res. Research, 48
6/21

Conventional methods

Design storms

• Assume spatial and 

often temporal 

uniformity

• Neglect important 

realities of extreme 

rainfall structure and 

motion

• Do not consider 

important differences 

in storm types

3 hr rainfall: TP-29 vs. stormcentered Area Reduction Factors

TP-29

Tropical 

Nontropical

Wright et al., A Critical Examination of 

Area Reduction factors, in preparation
7/21

Charlotte, NC study watersheds

Dense USGS rain gage network, NWS WSR-88D weather 

radar, USGS stream gages, FEMA flood studies, detailed 

drainage network and land surface information
8/21

High-resolution radar rainfall

Wright et al., High-Resolution Radar Estimation of Extreme Rainfall for Urban 

Hydrology, JAWRA, in review

10 year (2001-2010) bias-corrected 1 km2, 15-minute 

Hydro-NEXRAD radar rainfall dataset
9/21

60 km

60 km

Little Sugar Creek

At Archdale

Radar rainfall + storm catalogs

Creating a basin- specific storm catalog:

1. Define 

climatologically 

homogeneous “domain” containing the basin

2. Identify largest 50 rain events of t-hour 

duration of the correct 

size and shape from the 10-year radar rainfall archive

23 July 1997

2:00-14:00 UTC 
10/21

Radar rainfall + storm catalogs

Example: 

12-hour storm 

catalog for 

Little Sugar 

Creek at 

Archdale
11/21

SST Procedure

Stochastic 

Storm 

Transposition 

(SST)

1. Randomly select 

a storm from 

catalog and 

randomly 

transpose it 

within domain

2. Compute rainfall 

over watershed
12/21

SST Procedure

Stochastic Storm Transposition (SST)

3. Repeat this procedure k times, where 

k~Poisson(λ=5.0 storms/year) 

• 50 storms from a 10-year radar archive  λ=5.0 storms/year

4. Retain the highest of the k values: synthetic 

annual rainfall maximum

5. Repeat this many times to generate rainfall 

frequency estimates: “resampling”

Wright, D.B., J. A. Smith, G. Villarini, and M. L. Baeck, Estimating the 

Frequency of Extreme Rainfall Using Weather Radar and Stochastic Storm 

Transposition, Journal of Hydrology, in review.
13/21

SST—IDF estimates
14/21

SST and spatial rainfall structure

SST and rainfallweighted flow distance 

statistics let us examine 

the spatial distribution 

of extreme rainfall 

relative to the drainage 

network
15/21

SST and temporal rainfall structure

Temporal rainfall 

variability shows 

substantial 

departure from 

idealized design 

storm principles.
16/21

SST for flood frequency

Gridded 

Surface 

Subsurface 

Hydrologic 

Model 

(GGSHA)
17/21

Flood SST

High variability in flood response at Little Sugar Creek at 

Medical Center simulated from SST-based 6-hour, 100-year 

rainfall
18/21

Flood SST

Inter-basin heterogeneity in flood peaks simulated 

from SST-based 6-hour, 100-year rainfall
19/21

SST-based flood frequency analysis

• With SST, we can 

estimate flood return 

period at any point in 

the drainage network:

– Independent of rainfall 

return period

– Dependence/ 

independence of return 

period across drainage 

network

Wright et al., Estimating Flood Frequency Using Weather 

Radar and Stochastic Storm Transposition, in preparation
20/21

Future directions for SST...

• How can SST be simplified for engineering practice?

• Can we use SST for developing reservoir operating 

rules?

• How can we combine SST with PMP/PMF?

• What can we learn from SST to improve flood risk 

estimation in data-poor regions?

• How can we use SST in a changing regional climate?
21/21

Summary

1. Conventional rainfall and flood frequency 

analysis techniques ignore variability in spatial 

and temporal rainfall structure and motion and 

its interactions with the land surface, 

subsurface, and drainage network.

2. Extreme rainfall can vary substantially in space 

and time, with important implications for flood 

risk.

3. SST coupled with radar rainfall fields allows us 

to examine rainfall variability and incorporate 

it into flood risk estimates.
Thank you!

Acknowledgements

Hydrometeorology Research Group

Civil and Environmental Engineering

Daniel B. Wright, James A. Smith, 

Mary Lynn Baeck

This research is funded by the Willis Research Network, the 

National Science Foundation, and the NOAA Cooperative 

Institute for Climate Sciences.