2021
DOI: 10.1029/2020wr028582
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Probabilistic Projections of Multidimensional Flood Risks at a Convection‐Permitting Scale

Abstract: Floods are one of the major natural disasters inflicting catastrophic damages on society and ecosystems and even causing millions of deaths (

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Cited by 25 publications
(13 citation statements)
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“…The driving GCM does capture the spatial patterns but holistically overestimates air temperature and relative humidity. Large model biases in simulating the present-day climate would be systematically propagated into future climate projections at regional scales (Liang et al, 2008;Xue et al, 2014;Zhang et al, 2021). The RCM simulates the WBGT relatively low over the Tibetan Plateau which could result in potential underestimations in labor capacity reduction.…”
Section: Projection Of Heat Stress Changesmentioning
confidence: 99%
“…The driving GCM does capture the spatial patterns but holistically overestimates air temperature and relative humidity. Large model biases in simulating the present-day climate would be systematically propagated into future climate projections at regional scales (Liang et al, 2008;Xue et al, 2014;Zhang et al, 2021). The RCM simulates the WBGT relatively low over the Tibetan Plateau which could result in potential underestimations in labor capacity reduction.…”
Section: Projection Of Heat Stress Changesmentioning
confidence: 99%
“…Another limitation refers to the used copula types; in our study, the bivariate (2D) copula functions were used, but there is a potential to use, e.g., the three-dimensional (3D) copulas, successfully applied in hydrological analyses. Multi-dimensional vine copulas were used for example in adopting a pair-copula function to analyze the encounter frequency of high-low annual runoff-sediment yields between different stations [43], developing probabilistic projections of multidimensional river flood risks at a convection-permitting scale [46], prediction of streamflow and the refinement of spatial precipitation estimates [47], or analyzing extreme storm surges induced by tropical cyclones [48].…”
Section: Discussionmentioning
confidence: 99%
“…The next significant issue, raised in the copula-related research, is uncertainty and reliability of the constructed models. It has to be noted that there is a lack of sufficient efforts, which should be made to uncover the underlying uncertainty, e.g., in the vine copulabased flood risk assessments [46]. On the other hand, there are proofs that probabilistic forecasting can be more informative (e.g., in providing the prediction uncertainty) than other deterministic models [47].…”
Section: Discussionmentioning
confidence: 99%
“…In this context, CPRCM forcing provides a major improvement for hydrological applications by providing further dynamical downscaling of several variables that require a fine spatio‐temporal resolution and in which the physical consistency between variables is preserved. Several studies have already used CPRCMs in hydrological applications to assess future flood risks in the United States (Dougherty & Rasmussen, 2020, 2021; Lackmann, 2013), Eastern Alps (Reszler et al, 2018), Texas (Wang & Wang, 2019; Zhang, Wang, & Wang, 2020), Colorado River basin (Mendoza et al, 2016), Ouagadougou (Senior et al, 2021), and in the UK (Kay et al, 2015; Rudd et al, 2020). CPRCMs have also been applied to estimate the water budget of East Africa (Finney et al, 2019), Himalayan basins (Li, Gochis, et al, 2017), California (Dougherty et al, 2020), but also for droughts and low flows analysis (Lee, Bae, & Im, 2019; Qing et al, 2020).…”
Section: Cprcm Benefits For Impact Studiesmentioning
confidence: 99%