The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model

Sampson, Chris; Fewtrell, Timothy; O’Loughlin, Fiachra; Pappenberger, Florian; Bates, Paul; Freer, J. E.; Cloke, Hannah

doi:10.5194/hess-18-2305-2014

Cited by 59 publications

(42 citation statements)

References 71 publications

(78 reference statements)

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“…To our knowledge, so far only Sampson et al (2014) have analysed the effects of different precipitation scenarios on flood losses in depth. However, the Sampson et al study focused on an urban area and on a (relatively) small scale.…”

Section: Introductionmentioning

confidence: 99%

“…Herein, the input data, the choice of the impact indicators, the scale, and the vulnerability models are relevant sources of uncertainty (Ward et al, 2013;Apel et al, 2008;Merz and Thieken, 2009;de Moel and Aerts, 2011). In particular, vulnerability functions are considered as one of the most relevant sources of uncertainty in flood loss estimation (Ward et al, 2013;Sampson et al, 2014). Thus, uncertainty analysis is a key aspect in flood risk assessment.…”

Section: Introductionmentioning

confidence: 99%

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Effects of variability in probable maximum precipitation patterns on flood losses

Zischg

Felder

Weingartner

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The assessment of the impacts of extreme floods is important for dealing with residual risk, particularly for critical infrastructure management and for insurance purposes. Thus, modelling of the probable maximum flood (PMF) from probable maximum precipitation (PMP) by coupling hydrological and hydraulic models has gained interest in recent years. Herein, we examine whether variability in precipitation patterns exceeds or is below selected uncertainty factors in flood loss estimation and if the flood losses within a river basin are related to the probable maximum discharge at the basin outlet. We developed a model experiment with an ensemble of probable maximum precipitation scenarios created by Monte Carlo simulations. For each rainfall pattern, we computed the flood losses with a model chain and benchmarked the effects of variability in rainfall distribution with other model uncertainties. The results show that flood losses vary considerably within the river basin and depend on the timing and superimposition of the flood peaks from the basin's sub-catchments. In addition to the flood hazard component, the other components of flood risk, exposure, and vulnerability contribute remarkably to the overall variability. This leads to the conclusion that the estimation of the probable maximum expectable flood losses in a river basin should not be based exclusively on the PMF. Consequently, the basin-specific sensitivities to different precipitation patterns and the spatial organization of the settlements within the river basin need to be considered in the analyses of probable maximum flood losses.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of variability in probable maximum precipitation patterns on flood losses

Zischg

Felder

Weingartner

et al. 2018

Hydrol. Earth Syst. Sci.

Self Cite

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“…For example, recent advances in reduced-10 complexity flood models (Bates et al, 2010;Coulthard et al, 2013a) with simplified physics have shown promise in estimating flood depths, extent and velocity in urban areas (Fewtrell et al, 2011;Ramirez et al, 2016;Sampson et al, 2014). These models are particularly suited for data sparse locations, with large spatial extents (> 500 km 2 ), and urban topography represented by fine spatial resolution (≤ 30 m) digital elevation models (DEMs).…”

mentioning

confidence: 99%

Flood modeling can make a difference: Disaster risk-reduction and resilience-building in urban areas

Ramı́rez

Rajasekar²,

Patel

et al. 2016

Preprint

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Abstract. Surat, India is a coastal city with a population of approximately 4.5 million people that lies on the banks of the river 10Tapi and is located 100 km downstream from the Ukai dam. Given Surat's geographic location the city is repeatedly exposed to flooding caused by large emergency dam releases into the Tapi river combined with high tide water levels. Flood events of this type occur twice a decade, but their frequency and magnitude may increase due to the urbanization, encroachment in flood plain and climate change. A first step towards strengthening resilience in Surat requires a robust method for mapping flood exposure at fine spatial resolution. Here, in this study we have developed such a method for Surat using a reduced-complexity 15 hydrodynamic model to simulate flooding, but is easily transferable to other urban locations. Our method features three distinct phases that involve: 1) modelling dam release discharge from the Ukai dam arriving at Surat, 2) modelling flooding within Surat caused by the combination of dam release and tides, and, 3) identifying Surat critical infrastructure, population, and income groups exposed to flooding. Our flood model of Surat utilizes topography produced using elevation data collected from an extensive survey. Within the city we have modelled flood scenarios that represent the uncertainty in flood peak discharge 20 and duration resulting from possible climate change. These scenarios include catastrophic conditions that flood 50% of the city and expose > 60% of the population and critical infrastructure to deep flooding. Finally, we highlight how our modelling has contributed to changes in flood risk management within the city following a major flood and resulted in actions that have increased community resilience to flood hazard.

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“…Dankers et al (2007) used an RCM to evaluate the benefits of using high spatial resolution climate information for the Danube basin. Sampson et al (2014) have also demonstrated the importance of the resolution of precipitation data for a region of Ireland for hydrological impact modelling.…”

Section: Mathison Et Al: South Asia River-flow Projections and Thmentioning

confidence: 99%

“…These factors are all important given the limited computational resources available. The GCMs are the following: the third version of the Met Office Hadley Centre Climate Model (HadCM3; Pope et al, 2000;Gordon et al, 2000, a version of the Met Office Unified Model) and ECHAM5 (third realization; Roeckner et al, 2003) are downscaled using the HadRM3 RCM . These two GCMs capture the uncertainty in the sign of the projected change in precipitation with one showing an increase (HadCM3) and the other a decrease (ECHAM5).…”

Section: Gcm and Rcm Forcingmentioning

confidence: 99%

South Asia river-flow projections and their implications for water resources

Mathison

Wiltshire

Falloon

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. South Asia is a region with a large and rising population, a high dependence on water intense industries, such as agriculture and a highly variable climate. In recent years, fears over the changing Asian summer monsoon (ASM) and rapidly retreating glaciers together with increasing demands for water resources have caused concern over the reliability of water resources and the potential impact on intensely irrigated crops in this region. Despite these concerns, there is a lack of climate simulations with a high enough resolution to capture the complex orography, and water resource analysis is limited by a lack of observations of the water cycle for the region. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. Two global climate models (GCMs), which represent the ASM reasonably well are downscaled (1960-2100) using a regional climate model (RCM). In the absence of robust observations, ERA-Interim reanalysis is also downscaled providing a constrained estimate of the water balance for the region for comparison against the GCMs (1990GCMs ( -2006. The RCM river flow is routed using a river-routing model to allow analysis of present-day and future river flows through comparison with available river gauge observations. We examine how useful these simulations are for understanding potential changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows but overestimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century. The future maximum river-flow rates still occur during the ASM period, with a magnitude in some cases, greater than the present-day natural variability. Increases in river flow could mean additional water resources for irrigation, the largest usage of water in this region, but has implications in terms of inundation risk. These projected increases could be more than countered by changes in demand due to depleted groundwater, increases in domestic use or expansion of water intense industries. Including missing hydrological processes in the model would make these projections more robust but could also change the sign of the projections.

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The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model

Cited by 59 publications

References 71 publications

Effects of variability in probable maximum precipitation patterns on flood losses

Effects of variability in probable maximum precipitation patterns on flood losses

Flood modeling can make a difference: Disaster risk-reduction and resilience-building in urban areas

South Asia river-flow projections and their implications for water resources

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