Society For Risk Analysis Annual Meeting 2017

Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

M2-F
Water Water Everywhere

Room: Salon FG   10:30 am–12:00 pm

Chair(s): Hiba Baroud, Roshi Nateghi   hiba.baroud@vanderbilt.edu

Sponsored by Engineering and Infrastructure Specialty Group



M2-F.1  10:30 am  Why the well runs dry: Assessing global trends in groundwater stress. Bruss B.C, Nateghi R*, Zaitchik B; Purdue University   rnateghi@purdue.edu

Abstract: Groundwater is a key element of global access to water resources; and has important implications for political stability, economic growth and public health in every society. Rapid population growth, and accelerated rates of urbanization in recent decades have led to groundwater stress in many parts of the world. Identifying the national-scale drivers of groundwater storage trends is the key first step for understanding water availability and devising sustainable water management policy in a changing climate. In this research, we conducted extensive data mining and predictive analytics to identify the key predictors of the observed groundwater storage trends using agricultural, climate, demographic, land-use and economic variables in 81 countries across the globe.

M2-F.2  10:50 am  Water-Energy Nexus: Impact on Electrical Energy Conversion and Mitigation by Smart Water Resources Management. Gjorgiev B, Sansavini G*; ETH Zurich   sansavig@ethz.ch

Abstract: The water-energy nexus refers to the water used to generate electricity and to the electric energy used to collect, clean, move, store, and dispose of water. Water is used in all stages of electric energy conversion making power systems vulnerable to water scarcity and warming. In particular, a water flow decrease and temperature increase in rivers can significantly limit the generation of electricity. This paper investigates the issues to energy conversion stemming from the water-energy nexus and mitigates them by developing a model for the smart utilization of water resources. The objective is to minimize power curtailments caused by a river water flow decrease and a temperature increase. The developed water-energy nexus model integrates the operational characteristics of hydro power plants, the environmental conditions, the river water temperature prediction and thermal load release in river bodies. The application to a hydraulic cascade of hydro and a thermal power plants under drought conditions shows that smart water management entails a significant reduction of power curtailments. In general, the full coordination of the power outputs of the units affected by the hydrological link provides the most effective mitigations of the potential issues stemming from the water-energy nexus. Finally, critical temperature and flow regimes are identified which severely impact the energy conversion and may cause systemic risks in case the generators in one region must be simultaneously curtailed.

M2-F.3  11:10 am  Tsunamis, sea walls, and memory - vulnerability in coastal communities. Logan TM*, Bricker JD, Guikema SD; University of Michigan and TU Delft   tomlogan@umich.edu

Abstract: Our coastal communities are increasingly vulnerable due to global environmental change. Tsunamis are one example of a coastal hazard, and the north-east coast of Japan has been struck by four in the past 110 years. Seawalls are commonly used to defend against tsunamis, making it is essential we understand whether they truly mitigate damage. A model combining a cellular automaton and hydrodynamic models simulates possible land change development over time and under different scenarios. These scenarios include different seawall heights, aversion to developing in inundation prone areas, and coastal living preferences. The results include development patterns, frequency-number curves, and damage over time. We examine how the development changes the vulnerability of the community, and find evidence that high sea walls fail the inhabitants when black swan events arise. These insights further our understanding of suitable strategies to reduce risk of coastal communities.

M2-F.4  11:30 am  Extreme Precipitation Analysis and Prediction for a Changing Climate. Hu H*, Ayyub BM; University of Maryland, College Park   hhu3@umd.edu

Abstract: Extreme precipitation is one of the most important climate hazards that pose significant threat to various infrastructures. Understanding extreme precipitation events helps to manage its risk to society, and hence reduce potential losses. Many previous methods model daily precipitation as a series of independently and identically distributed random variables, in which case the day-to-day dependency is ignored. Although it does not pose significant impact on the analysis of average precipitation, the influence to extreme precipitation analysis is high. This work provides two new stochastic methods to analyze and predict various extreme precipitation events based on non-stationary models with or without the consideration of serial dependency associated with different days. The methods feature a novel way by incorporating Markov Chains with dynamic optimization. These methods bridge non-extreme precipitation and extreme precipitation so that abundant non-extreme precipitation data can be used for extreme precipitation analysis thus obtaining prediction with higher accuracy and reliability. On an annual basis, the analysis produces distributions for three important extreme precipitation indicators: maximum daily precipitation, number of days with heavy rainfall, and maximum number of consecutive days with heavy rainfall. The accuracy of the new methods is examined, using ten decades of empirical data in the Washington metropolitan area. The analysis shows that there is a significant dependency for precipitation between different days. Based on the new methods, predictions of various extreme events are also provided under different assumptions about serial dependency. Finally the impact of serial dependency on the analysis is also discussed. The results show that for the area studied, taking serial dependency into consideration improves the accuracy of the analysis by up to 16 percent.



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