The spatial extent and coverage of tornado sirens in San Marcos, Texas

Zunkel, Paul

doi:10.1016/j.apgeog.2014.11.006

Cited by 9 publications

(8 citation statements)

References 8 publications

(9 reference statements)

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“…According to the manufacturers, sirens should be audible in all directions (360 • ) over a distance of 4.5 km with a power of 7 kW, but our field tests indicated that the siren sounds could not be heard beyond 1 km (Douvinet, 2018). The actual radius of audibility around sirens is a function of sound propagation, which besides strength of the emitted sound also depends on factors like the strength and direction of winds, temperature, air density, nature of materials used for construction, and the ambient sound (Zunkel, 2015;Mathews et al, 2017). While the audibility distance for a siren with a power of 7 kW is considered to be up to 3 km, sirens may also be inaudible beyond a distance of 800 m from the source point even under ideal conditions.…”

Section: Do Sirens Cover the Targeted Population?mentioning

confidence: 76%

“…They should allow authorities to quickly advise people and to implement countermeasures in a short response time (Douvinet, 2020). They are effective in alerting an entire population, day or night (Zunkel, 2015;Mathews et al, 2017;Landry et al, 2019), provided that the distribution of sirens is dense enough to be heard by all. Sirens leave no time for hesitation and require immediate reactions (Reed et al, 2010;Cain et al, 2021;Fekete et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Are sirens effective tools to alert the population in France?

Douvinet

Serra‐Llobet

Bopp

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. In France, sirens have been the principal tool designated to alert the population in the case of danger. However, their efficacity has not been objectively tested. Using a geographical information system, questionnaires, and surveys, we analyzed (1) the spatial distribution of the siren network in relation to the covered population, the hazards threatening different areas, and the actual number of disasters that have occurred in the past, (2) the political dilemma of activating sirens, and (3) the population's trust in sirens, as well as its understanding of expected behavior in the case of an emergency. Results show that, with a few exceptions, siren coverage in France is primarily determined by population density, not by the expected hazards or the cumulative number of past disasters. Sirens are also rarely used by the authorities. However, surveyed members of the population identify sirens as the most effective alert system over other alternatives such as cell-phone-based alerting tools. In a “mock” emergency most members of the public did not know how to respond in the case of an emergency, and even most of those who correctly identified the appropriate response prior to the exercise did not react upon later hearing the siren. To improve the effectiveness of the French siren network, we recommend (1) relocating sirens to optimize their efficiency, (2) complementing the sound of sirens with a clear and unified message, (3) reorganizing the competencies to activate siren alerts, and finally (4) improving public education on different alert tools and expected behavior during an emergency.

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Section: Do Sirens Cover the Targeted Population?mentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Are sirens effective tools to alert the population in France?

Douvinet

Serra‐Llobet

Bopp

et al. 2021

Nat. Hazards Earth Syst. Sci.

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“…Arétouyap et al (2014a, b, 2015) used geostatistics to analyze the spatial distribution of climate parameters in central Africa, the groundwater quality index in the Adamwa-Cameroon region and to characterize aquifers in the Pan-African context; Binita et al (2015) to investigate temporal and spatial assessment of climate change vulnerability; Chaney and Rojas-Guyler (2015) to establish the geographic variability in adolescent drug use and to correlate factors of use; Keumseok et al (2015) to build up spatial patterns of simulated obesity prevalence were compared with measures of low income and food accessibility; Mishra and Chaudhuri (2015) to characterize spatio-temporal trends in vegetation greenness in Uttarakhand Himalayas; Zunkel (2015) to establish a network of all 14 tornado sirens and examined the number of residents included and not included in that network, Teikeu Assatse et al (2016) to assess water quality.…”

Section: Introductionmentioning

confidence: 99%

Lessening the adverse effect of the semivariogram model selection on an interpolative survey using kriging technique

et al. 2016

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Objective Many parameters in environmental, scientific and human sciences investigations need to be interpolated. Geostatistics, with its structural analysis step, is widely used for this purpose. This precious step that evaluates data correlation and dependency is performed thanks to semivariogram. However, an incorrect choice of a semivariogram model can skew all the prediction results. The main objectives of this paper are (1) to simply illustrate the influence of the choice of an inappropriate semivariogram model and (2) to show how a best-fitted model can be selected. This may lessen the adverse effect of the semivariogram model selection on an interpolation survey using kriging technique.MethodsThe influence of the semivariogram model selection is highlighted and illustrated by thematic maps drawn using four different models (Gaussian, magnetic, spherical and exponential). Then, a guideline to select the most suitable model, using mean error (ME), mean square error (MSE), root mean square error (RMSE), average standard error (ASE), and root mean square standardized error (RMSSE), is proposed.ResultsThe choice of a semivariogram model seriously influences the results of a kriging survey at both endpoints and amplitude of the range of the estimated values. However, the direction of variation of the interpolated values is independent of the semivariogram model: different semivariogram models (with the same characteristics) produce different thematic maps but, the areas of minimum and maximum values remain unchanged. Yet, the suitable model can be selected by means of ME, MSE, RMSE, ASE and RMSSE.ConclusionThe present article illustrates how the use of an inappropriate semivariogram model can seriously distort the results of an evaluation, assessment or prediction survey. To avoid such an inconveniency, a methodical approach based on the computation and analysis of ME, RMSE, ASE, RMSSE and MSE is proposed.

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“…Geographic information system (GIS)-based approaches are often utilized to determine ideal siren locations (Current and O'Kelly, 1992;Stimers, 2006) and assess gaps in siren network coverage (Murray and O'Kelly, 2002;Webster, 2014;Stimers, 2015;Zunkel, 2015;Mathews and Ellis, 2016). These assessments are methodologically straightforward, utilizing basic overlay analysis of siren coverage area (see Murray, 2005Murray, , 2010 based on Euclidean distance buffers and maximum sound propagation from the sirens' locations (Stimers, 2015), and U.S. Census population data to identify areas and demographic groups outside of audible siren coverage (Stimers, 2006(Stimers, , 2015Zunkel, 2015;Mathews and Ellis, 2016). While this type of approach is efficient, it is not without its limits.…”

Section: Gis-based Extent and Coverage Modelingmentioning

confidence: 99%

“…Inability to receive siren warnings can be caused by poor placement of infrastructure and/or an insufficient number of sirens to provide ample sound coverage (Zunkel, 2015).…”

Section: Tornado Occurrence and Warning Disseminationmentioning

confidence: 99%