Assessment of Uncertainty in Flood Flows under Climate Change Impacts in the Upper Thames River Basin, Canada

Das, Samiran; Simonović́, Slobodan P.

doi:10.9734/bjecc/2012/2813

Cited by 12 publications

(12 citation statements)

References 33 publications

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“…This assessment concludes that GCMs are indeed a significant source of uncertainty when only a single DSM is used. Another study has been conducted by Das and Simonovic () to assess uncertainty due to climate change in extreme flood flows for the Upper Thames River Basin, Ontario, Canada. In this study, three carbon emission scenarios and six GCMs with a single weather generator based on the k‐nearest neighbour (K‐NN) used for downscaling the climate variables.…”

Section: Introductionmentioning

confidence: 99%

Quantification of uncertainty in the assessment of future streamflow under changing climate conditions

Mandal

Simonović́

2017

Hydrological Processes

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Climate change has a significant influence on streamflow variation. The aim of this study is to quantify different sources of uncertainties in future streamflow projections due to climate change. For this purpose, 4 global climate models, 3 greenhouse gas emission scenarios (representative concentration pathways), 6 downscaling models, and a hydrologic model (UBCWM) are used. The assessment work is conducted for 2 different future time periods (2036 to 2065 and 2066 to 2095). Generalized extreme value distribution is used for the analysis of the flow frequency. Strathcona dam in the Campbell River basin, British Columbia, Canada, is used as a case study. The results show that the downscaling models contribute the highest amount of uncertainty to future streamflow predictions when compared to the contributions by global climate models or representative concentration pathways. It is also observed that the summer flows into Strathcona dam will decrease, and winter flows will increase in both future time periods. In addition to these, the flow magnitude becomes more uncertain for higher return periods in the Campbell River system under climate change.

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Section: Introductionmentioning

confidence: 99%

Quantification of uncertainty in the assessment of future streamflow under changing climate conditions

Mandal

Simonović́

2017

Hydrological Processes

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“…By expanding the risk index over space, we can then produce local peril maps, visualizing geographical areas of the highest insurance risk. By incorporating the peaks over thresholds and other related extreme value methodology for climate projections (see the review of the methods by Das & Simonovic, ; Cooley et al, ; Westra et al, ), such peril maps can be potentially extended to include the impact of much more rare severe weather events that fall outside of the range of the projected climate data. Another promising approach in this direction is to use the new semiparametric method of Gong, Li, Peng, and Yao () to estimate joint extreme quantiles of multiple atmospheric variables using copulas and bootstrap.…”

Section: Discussionmentioning

confidence: 99%

Can we weather proof our insurance?

Lyubchich

Gel

2016

Environmetrics

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The last few years were particularly volatile for the insurance industry in North America and Europe, bringing a record number of claims due to severe weather. According to a 2013 World Bank study, annual average losses from natural disasters have increased from $50 billion in the 1980s to about $200 billion nowadays. Adaptation to such changes requires early recognition of vulnerable areas and the extent of the future risk due to weather factors. Despite the well‐documented impact of climate change on the insurance sector, there exists a relatively limited number of studies addressing the effect of the so‐called “normal” extreme weather (i.e., higher frequency and lower individual but high cumulative impact events) on the insurance dynamics. To reduce financial repercussions of such weather events, we develop a nonlinear attribution analysis of integer‐valued insurance claims and atmospheric variables. Using data‐driven nonparametric procedures, we identify triggering thresholds, or tipping points, leading to an increase in the number of claims. We develop a new data‐adaptive method to compare tails of observed and projected weather variables and employ its outcomes to assess future dynamics of insurance claims. We illustrate our approach by application to modeling and forecasting of flood‐related home insurance claims in Norway.

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“…The factors can be calculated for the entire baseline period, or subdivided into calendar months to take into account seasonal variability for projected climate changes. Although monthly change factors take into account seasonal variability, they do not allow for accurate estimation of the changes in daily extreme climate data [30].…”

Section: Statistical Downscaling Of Future Precipitation and Temperatmentioning

confidence: 99%

Global Climate Model Selection for Analysis of Uncertainty in Climate Change Impact Assessments of Hydro-Climatic Extremes

Breach¹,

Simonović́²,

Yang³

2016

AJCC

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Regional climate change impact assessments are becoming increasingly important for developing adaptation strategies in an uncertain future with respect to hydroclimatic extremes. There are a number of Global Climate Models (GCMs) and emission scenarios providing predictions of future changes in climate. As a result, there is a level of uncertainty associated with the decision of which climate models to use for the assessment of climate change impacts. The IPCC has recommended using as many global climate model scenarios as possible; however, this approach may be impractical for regional assessments that are computationally demanding. Methods have been developed to select climate model scenarios, generally consisting of selecting a model with the highest skill (validation), creating an ensemble, or selecting one or more extremes. Validation methods limit analyses to models with higher skill in simulating historical climate, ensemble methods typically take multi model means, median, or percentiles, and extremes methods tend to use scenarios which bound the projected changes in precipitation and temperature. In this paper a quantile regression based validation method is developed and applied to generate a reduced set of GCM-scenarios to analyze daily maximum streamflow uncertainty in the Upper Thames River Basin, Canada, while extremes and percentile ensemble approaches are also used for comparison. Results indicate that the validation method was able to effectively rank and reduce the set of scenarios, while the extremes and percentile ensemble methods were found not to necessarily correlate well with the range of extreme flows for all calendar months and return periods.

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Assessment of Uncertainty in Flood Flows under Climate Change Impacts in the Upper Thames River Basin, Canada

Cited by 12 publications

References 33 publications

Quantification of uncertainty in the assessment of future streamflow under changing climate conditions

Quantification of uncertainty in the assessment of future streamflow under changing climate conditions

Can we weather proof our insurance?

Global Climate Model Selection for Analysis of Uncertainty in Climate Change Impact Assessments of Hydro-Climatic Extremes

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