| 1. |
- Di Baldassarre, Giuliano, et al.
(författare)
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An integrative research framework to unravel the interplay of natural hazards and vulnerabilities
- 2018
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Ingår i: Earth's Future. - : John Wiley & Sons. - 2328-4277. ; 6:3, s. 305-310
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Tidskriftsartikel (refereegranskat)abstract
- Climate change, globalization, urbanization, social isolation, and increased interconnectednessbetween physical, human, and technological systems pose major challenges to disaster risk reduction(DRR). Subsequently, economic losses caused by natural hazards are increasing in many regions of theworld, despite scientific progress, persistent policy action, and international cooperation. We argue thatthese dramatic figures call for novel scientific approaches and new types of data collection to integratethe two main approaches that still dominate the science underpinning DRR: the hazard paradigm and thevulnerability paradigm. Building from these two approaches, here we propose a research framework thatspecifies the scope of enquiry, concepts, and general relations among phenomena. We then discuss theessential steps to advance systematic empirical research and evidence-based DRR policy action.
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| 2. |
- Di Baldassarre, Giuliano, et al.
(författare)
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Water shortages worsened by reservoir effects
- 2018
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Ingår i: Nature Sustainability. - London : Nature Publishing Group. - 2398-9629 .- 2398-9629. ; 1, s. 617-622
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Tidskriftsartikel (refereegranskat)abstract
- The expansion of reservoirs to cope with droughts and water shortages is hotly debated in many places around the world. We argue that there are two counterintuitive dynamics that should be considered in this debate: supply–demand cycles and reservoir effects. Supply–demand cycles describe instances where increasing water supply enables higher water demand, which can quickly offset the initial benefits of reservoirs. Reservoir effects refer to cases where over-reliance on reservoirs increases vulnerability, and therefore increases the potential damage caused by droughts. Here we illustrate these counterintuitive dynamics with global and local examples, and discuss policy and research implications.
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| 3. |
- Okoli, Kenechukwu, et al.
(författare)
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Model averaging versus model selection : estimating design floods with uncertain river flow data
- 2018
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Ingår i: Hydrological Sciences Journal. - : Taylor & Francis. - 0262-6667 .- 2150-3435. ; 63:13-14, s. 1913-1926
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Tidskriftsartikel (refereegranskat)abstract
- This study compares model averaging and model selection methods to estimate design floods, while accounting for the observation error that is typically associated with annual maximum flow data. Model selection refers to methods where a single distribution function is chosen based on prior knowledge or by means of selection criteria. Model averaging refers to methods where the results of multiple distribution functions are combined. Numerical experiments were carried out by generating synthetic data using the Wakeby distribution function as the parent distribution. For this study, comparisons were made in terms of relative error and root mean square error (RMSE) referring to the 1-in-100 year flood. The experiments show that model averaging and model selection methods lead to similar results, especially when short samples are drawn from a highly asymmetric parent. Also, taking an arithmetic average of all design flood estimates gives estimated variances similar to those obtained with more complex weighted model averaging.
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