Probable maximum loss of a pipe network due to earthquakes: a case study in Iloilo city, Philippines

Purpose Buried structures like pipeline systems or water distribution networks (WDN) are vulnerable to seismic activities and the risk of damages increases when there is liquefaction. This paper aims to propose a methodology on how to determine the probable maximum loss (PML) on pipeline systems when earthquakes and liquefaction occur in future scenarios. Design/methodology/approach The paper used historical data and presents a case study on how the methodology to estimate the PML was used. The estimation is analytic and relied on simulations to determine the seismic and liquefaction hazard in the study area. Statistical and numerical analysis was used to estimate the damages and losses. Findings The output shows the PML of a WDN at different earthquake scenarios. It also shows a comparison between the damages and losses of diameter sizes of the pipes. Research limitations/implications In this paper, the damages behaved independently in one area, and correlation was not considered. Practical implications This PML methodology can aid in pre-disaster planning to prepare for seismic countermeasures risk transfer such as insurance to reduce the loss. Originality/value This paper shows a methodology and example on how to estimate the damages and PMLs of an existing WDN of a projected earthquake and liquefaction hazard based on historical data.

Climate services for forest fire risk management

<p>The weather and its climatic evolution play the main role in generating hazard profiles of forest fires. The increased in magnitude and damage of last forest fire seasons has caused a larger concern of the insurance sector for this peril. Due to the lack of knowledge of this risk, there is a widespread low level of insurance coverage of forest fire risk. A first step forward is clearly needed to (1) propose simplified approaches showing how the risk links with its main weather drivers, and (2) re-incentivize the use of insurance by forest managers.</p><p>To answer this objective, ARIA Technologies and its partners have developed a geospatial web-based decision tool to support both forest owners and forest insurance actors in managing the vulnerability of their asset/portfolios to fire risk. RiskFP includes:</p><ul><li>A “realistic disaster scenarios generator module” that allows the generation of hundreds of scenarios of extreme wildfires to complete information from historical fires databases. This information can be used in damage and loss modelling to improve the estimation of the probable maximum loss (PML). In addition, the risk FP “impact module” provides to the users information on the different potential impact like the amount of biomass burnt or the economic losses.</li> <li>A precise mapping of the local forest fire risk through the graphical representation of an index including five risk levels (from low to extreme) that provides an overview of the most critical locations regarding the potential behavior of the fire in case of an hypothetical ignition.</li> <li>A forecasting/projection module to inform the users on the frequency of the severe-extreme days in the mid- and long-term horizons. It can be used by the forestry sector to better anticipate and prepare the next fire season and as a planning tool for long-term operation/investment.</li> </ul><p>At the heart of the platform lies the concept of critical landscape weather patterns (CLP), an empirical fire weather index that identifies severe-extreme weather days derived from hourly records of a representative weather station (Gellie, 2019). It could be computed from past records, seasonal forecast or climate projection allowing to provide fire risk assessment for these different time scales. The CLP module is coupled with a propagation model, the Wildfire Analyst® forest fire simulator at the resolution of about 40m, that is used to estimate the progression and behavior of the fire in space and time. It is based on the standardized and validated semi-empirical Rothermel propagation model (1972).</p><p><strong>Acknowledgements:</strong></p><p>We acknowledge the European Commission for sponsoring this work in the framework of the H2020-insurance project (Grant Agreement number 730381).</p>

Property Risk Assessment for Liquefied Natural Gas Liquefaction Plants

Liquefied Natural Gas (LNG) liquefaction plants have become increasingly important as natural gas is exported from the United States of America to markets world-wide. Downtime of any part of the process train (gas turbine, compressors, controls, etc.) due to failure of one or more of its components can result in high costs. The total cost of loss is of great concern to the LNG industry as it moves towards increased LNG exports with required operational efficiency, and downtime reduced to a minimum. This paper reports the application of a methodology of property risk assessment, providing insight into the use of PML (Probable Maximum Loss) and MFL (Maximum Foreseeable Loss) risk measures. Major sources of risk are analyzed, drawing from both technical literature and operational information on typical large LNG liquefaction plants. The outcome of this paper is an estimation of the economic loss associated with property risk for two hypothetical LNG liquefaction plants, based upon sample plants located in North America and characterized by different capacity. These plants represent recently built and commissioned plants and are chosen to take advantage of current technology and plant capacities.

Value-at-Risk and Models of Dependence in the U.S. Federal Crop Insurance Program

The federal crop insurance program covered more than 110 billion dollars in total liability in 2018. The program consists of policies across a wide range of crops, plans, and locations. Weather and other latent variables induce dependence among components of the portfolio. Computing value-at-risk (VaR) is important because the Standard Reinsurance Agreement (SRA) allows for a portion of the risk to be transferred to the federal government. Further, the international reinsurance industry is extensively involved in risk sharing arrangements with U.S. crop insurers. VaR is an important measure of the risk of an insurance portfolio. In this context, VaR is typically expressed in terms of probable maximum loss (PML) or as a return period, whereby a loss of certain magnitude is expected to return within a given period of time. Determining bounds on VaR is complicated by the non-homogeneous nature of crop insurance portfolios. We consider several different scenarios for the marginal distributions of losses and provide sharp bounds on VaR using a rearrangement algorithm. Our results are related to alternative measures of portfolio risks based on multivariate distribution functions and alternative copula specifications.

Changes in Tsunami Risk to Residential Buildings at Omaha Beach, New Zealand

Coastal settlements worldwide have suffered significant damage and loss to tsunami hazards in the last few decades. This period coincides with socio-economic changes that have heightened spatio-temporal risk through increased coastal development and infrastructure. In this study, we apply a spatio-temporal loss model to quantify the changes in direct economic losses to residential buildings from tsunami hazards over a 20-year period in Omaha Beach, New Zealand. The approach reconstructed temporal urban settlement patterns (1992, 1996, 2006 and 2012) for an area potentially exposed to regional source tsunami inundation hazard. Synthetic depth–damage functions for specific building classes were applied to estimate temporal damage and loss from tsunami inundation exposure at each building location. Temporal loss estimates were reported for a range of risk metrics, including probable maximum loss, loss exceedance and average annual loss. The results showed that an increase in the number of buildings and changes to building design (i.e., storeys, floor area, foundations) influenced the increasing risk to direct economic loss over the study period. These increases were driven by conversion from rural to urban land use since 1996. The spatio-temporal method presented in this study can be adapted to analyse changing risk patterns and trends for coastal settlements to inform future tsunami mitigation measures and manage direct economic losses.