Surviving Disasters: Learning from Experience

Grigg, Neil S.

doi:10.1002/j.1551-8833.2003.tb10453.x

Cited by 11 publications

(12 citation statements)

References 11 publications

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“…Usually, most regions of the United States (U.S.) receive rainfall through a series of small precipitation events (Votteler, 2002). When precipitation events are concentrated over short time periods or small geographic regions, then flooding, the most destructive and costly natural disaster in the U.S. (Grigg, 2003), may occur. In Texas, the influence of physiography, precipitation, and temperature results in unpredict-able climatic weather cycles that vary widely across the state (Carr, 1967).…”

Section: Introductionmentioning

confidence: 99%

Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas¹

Sharif

Hassan

Xie

et al. 2010

J American Water Resour Assoc

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Sharif, Hatim O., Almoutaz A. Hassan, Sazzad Bin‐Shafique, Hongjie Xie, and Jon Zeitler, 2010. Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas. Journal of the American Water Resources Association (JAWRA) 1‐11. DOI: 10.1111/j.1752‐1688.2010.00459.x Abstract: Many of the storms creating the greatest rainfall depths in Texas, measured over durations ranging from one minute to 48 hours, have occurred in the Texas Hill Country area. The upstream portion of the Guadalupe River Basin, located in the Texas Hill Country, is susceptible to flooding and rapid runoff due to thin soils, exposed bedrock, and sparse vegetation, in addition to the Balcones Escarpment uplift contributing to precipitation enhancement. In November 2004, a moist air mass from the Gulf of Mexico combined with moist air from the Pacific Ocean resulted in the wettest November in Texas since 1895. Although the peak discharges were not the highest on record, the U.S. Geological Survey (USGS) stream gauge on the Guadalupe River at Gonzales, Texas reported a daily mean discharge of 2,304 m3/s on November 23, 2004 (average discharge is 53 m3/s). In this paper, we examine the meteorological conditions that led to this event and apply a two‐dimensional, physically based, distributed‐parameter hydrologic model to simulate the response of a portion of the basin during this event. The study results clearly demonstrate the ability of physically based, distributed‐parameter simulations, driven by operational radar rainfall products, to adequately model the cumulative effect of two rainfall events and route inflows from three upstream watersheds without the need for significant calibration.

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Section: Introductionmentioning

confidence: 99%

Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas¹

Sharif

Hassan

Xie

et al. 2010

J American Water Resour Assoc

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“…This article looks at both the past and the future to provide a comprehensive perspective on disaster preparedness (how utilities prepare for risk) and emergency response (how utilities respond to an emergency incident after it occurs). Some of the lessons cited have been reported elsewhere (Grigg, 2003a; 2002). New information has been gleaned from security studies, recent findings about emergency management, and utility experiences during natural disasters of the past few years.…”

Section: Us Water Systems Have a History Of Increasing Reliabilitymentioning

confidence: 89%

“…In the 1990s and since the year 2000, water utilities have experienced numerous disasters and emergencies, which provided new knowledge, insight, and lessons. Much of this experience base (with the exception of the most recent events) is reviewed elsewhere (Grigg, 2002).…”

Section: Us Water Systems Have a History Of Increasing Reliabilitymentioning

confidence: 99%

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Ready or not? Disaster preparedness and emergency response in the Water industry

Grigg¹

2006

Journal AWWA

Self Cite

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This article summarizes the history of the reliability of U.S. water systems, how the water supply developed as the nation grew, emergency preparedness and management emerged gradually, effect of earthquakes on water supply, the threat of terrorism, disruption of water supplies from river and coastal flooding and hurricanes, aging infrastructure, the threat of waterborne contaminants to public health and confidence, comprehensive risk management, emergency planning by utilities, organizational commitment and leadership, best practices essential to emergency management, and vision and commitment are requirements for future planning.

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“…Adachi & Ellingwood, 2008; Franchin & Cavalieri, 2015; Grigg, 2002, 2003; Kim et al, 2007). As presented in Kim et al (2007), the probability of the loss of functionality of a node in the WN due to a power outage is conditional to the simultaneous failure of each one of the supporting nodes of the EPN.…”

Section: Damage Functionality and Dependency Models For Water Anmentioning

confidence: 99%

Modeling the resilience of critical infrastructure: the role of network dependencies

Guidotti

Chmielewski

Unnikrishnan

et al. 2016

Sustainable and Resilient Infrastructure

196

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Water and wastewater network, electric power network, transportation network, communication network, and information technology network are among the critical infrastructure in our communities; their disruption during and after hazard events greatly affects communities’ well-being, economic security, social welfare, and public health. In addition, a disruption in one network may cause disruption to other networks and lead to their reduced functionality. This paper presents a unified theoretical methodology for the modeling of dependent/interdependent infrastructure networks and incorporates it in a six-step probabilistic procedure to assess their resilience. Both the methodology and the procedure are general, can be applied to any infrastructure network and hazard, and can model different types of dependencies between networks. As an illustration, the paper models the direct effects of seismic events on the functionality of a potable water distribution network and the cascading effects of the damage of the electric power network (EPN) on the potable water distribution network (WN). The results quantify the loss of functionality and delay in the recovery process due to dependency of the WN on the EPN. The results show the importance of capturing the dependency between networks in modeling the resilience of critical infrastructure.

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Surviving Disasters: Learning from Experience

Cited by 11 publications

References 11 publications

Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas¹

Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas¹

Ready or not? Disaster preparedness and emergency response in the Water industry

Modeling the resilience of critical infrastructure: the role of network dependencies

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Surviving Disasters: Learning from Experience

Cited by 11 publications

References 11 publications

Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas1

Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas1

Ready or not? Disaster preparedness and emergency response in the Water industry

Modeling the resilience of critical infrastructure: the role of network dependencies

Contact Info

Product

Resources

About

Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas¹

Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas¹