The Unsustainable Trend of Natural Hazard Losses in the United States

Gall, Melanie; Borden, Kevin A.; Emrich, Christopher T.; Cutter, Susan L.

doi:10.3390/su3112157

Cited by 150 publications

(106 citation statements)

References 37 publications

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“…The dire consequences of climate-related extremes, even in developed economies (Gall et al, 2011), may call for a range of wellinformed adaptation strategies from low-regret (Wilby and Keenan, 2012) to transformative (Kates et al, 2012). Improving regional projections (e.g., through variable selection or statistical downscaling) and characterizing natural variability (e.g., irreducible uncertainty at decadal scales: Sutton, 2009, 2011;Branstator and Teng, 2012;Deser et al, 2012a, b;Fischer et al, 2013;Hu and Deser, 2013;Rosner et al, 2014) are necessary for informing adaptation at stakeholder-relevant scales and planning horizons.…”

Section: Discussionmentioning

confidence: 99%

Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

Ganguly

Kodra

Agrawal

et al. 2014

Nonlin. Processes Geophys.

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Abstract. Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems and human communities worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean-land-atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "big data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

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Section: Discussionmentioning

confidence: 99%

Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

Ganguly

Kodra

Agrawal

et al. 2014

Nonlin. Processes Geophys.

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“…For any sustainable transportation system, safety is an important consideration, and there is considerable interest in ensuring the safety of the bimodal tram system. Natural disasters have been increasing worldwide in recent years, including floods, snow, and typhoon disasters [8,9]. Flooding is the most frequent natural disaster in China; it seriously affects people's lives and productivity, causing considerable economic loss and serious damage to towns [10].…”

Section: Introductionmentioning

confidence: 99%

Development of a Prototype Web GIS-Based Disaster Management System for Safe Operation of the Next Generation Bimodal Tram, South Korea—Focused Flooding and Snowfall

et al. 2014

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Abstract:The Korea Railroad Research Institute (KRRI) has developed a bimodal tram and advanced bus rapid transit (BRT) system which is an optimized public transit system created by mixing the railway's punctual operation and the bus' easy and convenient access. The bimodal tram system provides mass-transportation service with an eco-friendly and human-centered approach. Natural disasters have been increasing worldwide in recent years, including floods, snow, and typhoons disasters. Flooding is the most frequent natural disaster in many countries and is increasingly a concern with climate change; it seriously affects people's lives and productivity, causing considerable economic loss and significant damage. Enhanced conventional disaster management systems are needed to support comprehensive actions to secure safety and convenience. The objective of this study is to develop a prototype version of a Web GIS-based bimodal tram disaster management OPEN ACCESS Sustainability 2014, 6 1777 system (BTDMS) using the Storm Water Management Model (SWMM) 5.0 to enhance on-time operation and safety of the bimodal tram system. The BTDMS was tested at the bimodal tram test railroad by simulating probable maximum flood (PMF) and snow melting for forecasting flooding and snow covered roads. This result could provide the basis for plans to protect against flooding disasters and snow covered roads in operating the bimodal tram system. The BTDMS will be used to assess and predict weather impacts on roadway conditions and operations and thus has the potential to influence economic growth. The methodology presented in this paper makes it possible to manage impacts of flooding and snowfall on urban transportation and enhance operation of the bimodal tram system. Such a methodology based on modeling could be created for most metropolitan areas in Korea and in many other countries.

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“…In defining the negative effects, frequent natural disasters are linked to low economic growth rates (Benson and Clay [1] and Yu Xiao [2]) and enduring consequences (Gall et al [3], Coffman and Noy [4] and Okuyama [5]). Natural disasters reduce household consumption over time without any sign of economic recovery (Dercon [6] and Mechler [7]).…”

Section: Introductionmentioning

confidence: 99%

Natural Disasters, Economic Growth and Sustainable Development in China―An Empirical Study Using Provincial Panel Data

Guo

Liu

et al. 2015

Sustainability

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Using a newly developed integrated indicator system with entropy weighting, we analyzed the panel data of 577 recorded disasters in 30 provinces of China from 1985-2011 to identify their links with the subsequent economic growth. Meteorological disasters promote economic growth through human capital instead of physical capital. Geological disasters did not trigger local economic growth from 1999-2011. Generally, natural disasters overall had no significant impact on economic growth from 1985-1998. Thus, human capital reinvestment should be the aim in managing recoveries, and it should be used to regenerate the local economy based on long-term sustainable development.

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The Unsustainable Trend of Natural Hazard Losses in the United States

Cited by 150 publications

References 37 publications

Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

Development of a Prototype Web GIS-Based Disaster Management System for Safe Operation of the Next Generation Bimodal Tram, South Korea—Focused Flooding and Snowfall

Natural Disasters, Economic Growth and Sustainable Development in China―An Empirical Study Using Provincial Panel Data

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