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 (

Adapting to Changing Labor Productivity as a Result of Intensified Heat Stress in a Changing Climate

“…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.…”

Probabilistic Approach to Precipitation-Runoff Relation in a Mountain Catchment: A Case Study of the Kłodzka Valley in Poland

“…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].…”

“…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].…”

Convection‐permitting modeling with regional climate models: Latest developments and next steps

“…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).…”