Quantifying Uncertainty of Probable Maximum Flood

In this paper, an improved probabilistic method for flood analysis using the probable maximum flood, the beta function, and orthogonal Jacobi’s polynomials is proposed. The shape of the beta function depends on the sample's characteristics and the bounds of the phenomenon. On the other hand, a serial of Jacobi’s polynomials has been used improving the beta function and increasing its convergence degree toward the real flood probability density function. This mathematical model has been tested using a sample of 1000 generated beta random data. Finally, some practical applications with real data series, from important Quebec's rivers, have been performed; the model solutions for these rivers showed the accuracy of this new method in flood frequency estimation. Key words: probable maximum flood, beta function, orthogonal polynomials, distribution function, flood frequency estimation, data generation, convergency.

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Quantifying Uncertainty in Exposure to Coastal Hazards Associated with Both Climate Change and Adaptation Strategies: A U.S. Pacific Northwest Alternative Coastal Futures Analysis

Water ◽

10.3390/w13040545 ◽

2021 ◽

Vol 13 (4) ◽

pp. 545

Author(s):

Alexis K. Mills ◽

Peter Ruggiero ◽

John P. Bolte ◽

Katherine A. Serafin ◽

Eva Lipiec

Keyword(s):

Climate Change ◽

Sea Level Rise ◽

Sea Level ◽

Performance Metrics ◽

Coastal Flooding ◽

Water Levels ◽

Coastal Hazards ◽

Policy Decisions ◽

Management Decisions ◽

Quantifying Uncertainty

Coastal communities face heightened risk to coastal flooding and erosion hazards due to sea-level rise, changing storminess patterns, and evolving human development pressures. Incorporating uncertainty associated with both climate change and the range of possible adaptation measures is essential for projecting the evolving exposure to coastal flooding and erosion, as well as associated community vulnerability through time. A spatially explicit agent-based modeling platform, that provides a scenario-based framework for examining interactions between human and natural systems across a landscape, was used in Tillamook County, OR (USA) to explore strategies that may reduce exposure to coastal hazards within the context of climate change. Probabilistic simulations of extreme water levels were used to assess the impacts of variable projections of sea-level rise and storminess both as individual climate drivers and under a range of integrated climate change scenarios through the end of the century. Additionally, policy drivers, modeled both as individual management decisions and as policies integrated within adaptation scenarios, captured variability in possible human response to increased hazards risk. The relative contribution of variability and uncertainty from both climate change and policy decisions was quantified using three stakeholder relevant landscape performance metrics related to flooding, erosion, and recreational beach accessibility. In general, policy decisions introduced greater variability and uncertainty to the impacts of coastal hazards than climate change uncertainty. Quantifying uncertainty across a suite of coproduced performance metrics can help determine the relative impact of management decisions on the adaptive capacity of communities under future climate scenarios.

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