Risk-based flood zoning employing expected annual damages: the Chenab River case study

Tariq, Muhammad Atiq Ur Rehman

doi:10.1007/s00477-013-0730-1

Cited by 31 publications

(17 citation statements)

References 18 publications

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“…The annualised risk is calculated as the product of damage and the corresponding flood probability (Kalyanapu, Judi, McPherson, & Burian, ):

R = D \times P,

where R is the annualised flood risk, and P is the exceedance probability. Expected annual damage (EAD) was used to summarise the overall flood risk (FEMA, ; Tariq, ):

italicEAD = \sum_{i = 1}^{m} ([], \frac{D_{P_{i}} + D_{P_{i - 1}}}{2} \times ((), P_{i} - P_{i - 1})),

where i is the rank of the exceedance probability, m is the number of probability increments,

D_{P_{i}}

is the damage corresponding to the i th exceedance probability,

D_{P_{i - 1}}

is the damage corresponding to the ( i− 1) th exceedance probability, P i is the i th exceedance probability, and P i−1 is the ( i− 1) th exceedance probability.…”

Section: Methodsmentioning

confidence: 99%

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A partition of the combined impacts of socioeconomic development and climate variation on economic risks of riverine floods

Feng

Rakowski

McPherson

et al. 2018

J Flood Risk Management

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Nonstationarities in both climate and socioeconomic systems play a role in flood risk. Based on a data‐driven case study in an urbanised watershed subjected to nonstationary factors in climate (rain, snow, and rain on snow) and socioeconomic conditions (e.g., built environment market changes), a multiscale, multimodel approach was adopted to develop local‐scale flood hazard predictions and an analytical framework was developed to quantify the associated flood risk. The case study shows that socioeconomic development can have a comparable contribution as climate variability when evaluating the expected annual damage. The relative contributions from socioeconomic development, in some cases, do not necessarily compound the risk, but can, in fact, act as a mitigating factor in annualised risk. Timelines of risk management planning are an important factor to consider. Socioeconomic factors such as market value can produce nonlinear and reversed trends in flood impact assessments within the 10‐year time frame. Furthermore, the nonstationarity of climate and development conditions together was shown to cause up to 43% of the variation in risk estimates and up to 70% of the variation in the benefit–cost effectiveness.

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“…The annualised risk is calculated as the product of damage and the corresponding flood probability (Kalyanapu, Judi, McPherson, & Burian, ):

R = D \times P,

where R is the annualised flood risk, and P is the exceedance probability. Expected annual damage (EAD) was used to summarise the overall flood risk (FEMA, ; Tariq, ):

italicEAD = \sum_{i = 1}^{m} ([], \frac{D_{P_{i}} + D_{P_{i - 1}}}{2} \times ((), P_{i} - P_{i - 1})),

where i is the rank of the exceedance probability, m is the number of probability increments,

D_{P_{i}}

is the damage corresponding to the i th exceedance probability,

D_{P_{i - 1}}

is the damage corresponding to the ( i− 1) th exceedance probability, P i is the i th exceedance probability, and P i−1 is the ( i− 1) th exceedance probability.…”

Section: Methodsmentioning

confidence: 99%

“…where R is the annualised flood risk, and P is the exceedance probability. Expected annual damage (EAD) was used to summarise the overall flood risk (FEMA, 2016;Tariq, 2013):…”

Section: Damage Modellingmentioning

confidence: 99%

A partition of the combined impacts of socioeconomic development and climate variation on economic risks of riverine floods

Feng

Rakowski

McPherson

et al. 2018

J Flood Risk Management

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“…TPM tries to eliminate the different losses that adversely impact the effective operation of the system (Pintelon and Gelders 1992). Tajiri and Gotoh (1992) and Shirose (1992) state that a definition of TPM contains the following five points:…”

Section: Total Productive Maintenance (Tpm)mentioning

confidence: 99%

Basic Concepts on Risk Analysis, Reliability and Maintenance

Almeida

Cavalcante

Alencar

et al. 2015

International Series in Operations Research &Amp; Management Science

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Abstract:Man's level of dependence on equipment is increasing. This degree of dependence requires high levels of availability, which has been changing the impact that disruption of these systems causes. For many systems, an interruption has consequences that go beyond the dimension of financial loss, thus justifying a multidimensional consequence approach by using multicriteria (MCDM/A) models. Thus, understanding the relationships between and among risk, reliability and maintenance (RRM) is essential in order to offer more comprehensive solutions to the various problems often treated in isolation from each other, and which are the most important problems of the competitive market. This chapter discusses fundamental topics about RRM, including tools for risk analysis and hazard identification, concepts of reliability, maintenance techniques such as RCM and TPM and eliciting expert's knowledge. These topics are presented in order to provide a basis for structuring different MCDM/A problems that are addressed in several chapters. Some fundamental aspects could be used as input to decision models in different forms such as attributes, objectives, criteria, and problem context. Basic Concepts on Risk AnalysisThere are many concepts on risk found in the literature and also different perceptions to it. However, if a decision is being made and risk is involved, then, the risk concept should combine consequences and probabilities, incorporating the DM's preferences over that, as seen in Chap. 2.Actually, a 'decision process' with no DM's preference has no decision being made, as discussed at the end of Chap. 1. Instead of that, that process either: a) has some preference structure incorporated within the model, at random; b) is just arbitrary following a previous decision of someone else.Even so, in most of real cases, the consequences are multidimensional, and therefore, require an MCDM/A approach for building a decision model. The following topics are mainly based on the basic RRM literature and do not incorporate the idea of decision support, as given in Chap. 2. That is, DM's preferences are not necessarily considered in the model.

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“… Note : Based on Aerts, Botzen, and de Moel (), Aerts et al (), Aubé et al (), Brouwer and van Ek (), de Bruin et al (), de Loe and Wojtanoswski (), Godschalk, Rose, Mittler, Porter, and West (), Jonkman, Brinkhuis, and Kok (), Leblanc and Ouellette (), Millerd et al (), Ouellette et al (), Rose et al (), SEPA (), and Tarig ().…”

Section: Literature Review Approachunclassified

Cost–benefit analysis of flood‐zoning policies: A review of current practice

Hudson

Botzen

2019

WIREs Water

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One commonly proposed method to limit flood risk is land‐use or zoning policies which regulates construction in high‐risk areas, in order to reduce economic exposure and its vulnerability to flood events. Although such zoning regulations can be effective in limiting trends in flood risk, they also have adverse impacts on society, for instance by limiting local development of areas near the water. In order to judge whether proposed land‐use or zoning policies are a net benefit to society, they should be accepted or rejected based on a societal cost–benefit analysis (CBA). However, conducting a CBA of zoning regulation is complex and comprehensive guidelines of how to do such an analysis are lacking. We offer guidelines for good practice. In order to assess the costs and benefits of zoning as a climate change adaption strategy, they should be assessed at a societal level in order to account for public good features of flood risk reduction strategies, and because costs in one area can be benefits in another region. We propose a multistep process: first, determine the spatial extent of the zoning policy and how interconnected the zoned area is to other locations; second, conduct a CBA using monetary costs and benefits estimated from an integrated hydro‐economic model to investigate if total benefits exceed total costs; third, conduct a sensitivity analysis regarding the main assumptions; fourth, conduct a multicriteria analysis (MCA) of the normative outcomes of a zoning policy. A desirable policy is preferred in both the CBA and MCA. This article is categorized under: Engineering Water > Planning Water Human Water > Value of Water Science of Water > Water Extremes Human Water > Methods

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Risk-based flood zoning employing expected annual damages: the Chenab River case study

Cited by 31 publications

References 18 publications

A partition of the combined impacts of socioeconomic development and climate variation on economic risks of riverine floods

A partition of the combined impacts of socioeconomic development and climate variation on economic risks of riverine floods

Basic Concepts on Risk Analysis, Reliability and Maintenance

Cost–benefit analysis of flood‐zoning policies: A review of current practice

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