1. 58 seconds in Tucson, Arizona June 22, 1977 Photos copyright Jack Sheaffer Arizona Daily Star Summer WAS*IS Wednesday

2. False Alarms: Warning Project Research Findings & Warning Accuracy Conceptual Model Lindsey Barnes WAS*IS July 2006

3. False Alarm Conventional Wisdom Cry wolf effect- Aesop's fable of “The Boy who Cried Wolf” False alarm effect- “The credibility loss [of a warning system] due to a false alarm” (Breznitz 1984)

4. Other False Alarm Research Dow & Cutter (1998): Repeated Response to Hurricane Evacuation Orders Carsell (2001): Impacts of a False Alarm The January 29, 2000 Ventura, California Experience

5. False Alarm Research Questions What are public perceptions of false alarms? Is “cry wolf” concept accurate for flash flood warnings? How do demographic characteristics influence perceptions about false alarms?

6. Survey Questions 1.Realizing it’s difficult to predict flash floods, I would prefer more warnings even if it means there are more false alarms or close calls. 2.One or two false alarms or close calls would reduce my confidence in future warnings. 3.Officials are too sensitive to the possibility of flash flooding. Demographic characteristics

7. Flash Flood False Alarms

8. Denver vs. Austin “Realizing it’s difficult to predict flash floods, I prefer more warnings even if there are more false alarms or close calls” N= 922 Not significantly different

9. Denver by Gender: “Realizing it’s difficult to predict flash floods, I prefer more warnings even if there are more false alarms or close calls” N= 419 X² =.011, p <.05

10. Austin by Gender: “Realizing it’s difficult to predict flash floods, I prefer more warnings even if there are more false alarms or close calls” X² =4.150, p <.05

11. Denver by Age: “Realizing it’s difficult to predict flash floods, I prefer more warnings even if there are more false alarms or close calls ” N=400 X² =.005, p <.05

12. Denver vs. Austin “One or two false alarms would reduce my confidence in future warnings” N=917 Not significantly different

13. Denver by Gender: “One or two false alarms would reduce my confidence in future warnings” N=399 X² =.072, p <.1

14. Denver by Age: “One or two false alarms would reduce my confidence in future warnings” N=400 X2 =.025, p <.05

15. Denver vs. Austin: “Officials are too sensitive to the possibility of flash flooding” X² =4.533, p <.05 N= 906

16. Conclusions People would rather have more warnings with the possibility of a false alarm or close call False alarms may not reduce confidence in the warning process Officials are not viewed as too sensitive –Need to re-evaluate conventional wisdom

17. Conclusions (continued) Demographic characteristics do matter: Flash flooding –Gender: females may be more tolerant of false alarms (both cities) –Age: older people may be more tolerant of false alarms (Denver only) Tornadoes –Having a basement or interior room: People with a place to seek shelter are less likely to have confidence reduced by tornado false alarms

18. Limitations and Future Work Interpretation of questions Survey answers vs. actions How much can we generalize from these case studies?

19. Conceptual Model of Warning Accuracy Event followed warning as specified Event occurred but was less severe than warning Event occurred but was more severe than warning Warning was issued but event did not occur Warning was not issued but event occurred 1985 Hurricane Elena False Alarm Perfect Warning Unwarned Event Red River Flood 1997 1999 Oklahoma Tornados Model developed by Lindsey Barnes 2004 1990 Plainfield Tornado 1996 Hurricanes Fran and Bertha Near-miss Evacuations

20. Questions for WAS*ISers What are the most successful ways to implement this social science research? –Policy? –Introduction of new performance measures? –Public education? How can this information be useful to practitioners? Other examples for model?

21. Lindsey Barnes lbarnes2@uccs.edu Public Perceptions of Flash Flood False Alarms www.colorado.edu/hazards/SPC/

22. Austin Demographic Variable Chi-square value P valueDemographic Variable Chi-square value P value FLASH FLOODING Realizing it’s difficult to predict flash floods, I prefer more warnings even if there are more false alarms or close calls Gender4.150.042Gender6.467.011 Age6.546.088Age12.670.005 Education17.284.001Education8.229.042 Income10.101.018Income.245.970 Flood experience.090.764Flood experience.001.970 Have tornado plan.524.469 One or two false alarms would reduce my confidence in future warnings Gender.756.385Gender3.359.067 Age3.663.300Age9.349.025 Education5.486.139Education4.085.252 Income.316.957Income5.007.171 Flood experience.168.682Flood experience1.035.309 Have tornado plan4.814.028 Officials are too sensitive to the possibility of flash flooding Gender2.199.138Gender2.847.092 Age2.659.447Age6.607.086 Education37.620.000Education1.967.579 Income5.958.114Income6.928.074 Flood experience1.441.230Flood experience.139.709 Have tornado plan9.055.003 AustinDenver Red p<.05 Blue p<.1

23. TORNADOES Demographic VariableChi-square valueP value Realizing it’s difficult to predict the exact location of tornadoes, I prefer more warnings even if there are more false alarms or close calls Gender.693.405 Age5.116.164 Education2.194.533 Income6.153.104 Have basement.272.602 Have tornado plan3.000.083 One or two tornado false alarms would reduce my confidence in future warnings Gender.223.636 Age.483.923 Education10.123.018 Income.912.822 Have basement4.254.039 Have tornado plan3.141.076 Officials are too sensitive to the possibility of tornadoes Gender1.412.235 Age4.024.259 Education28.639.000 Income3.422.331 Have basement.945.331 Have tornado plan2.970.085 Red p<.05 Blue p<.1 Austin- only