A Vine Copula‐Based Polynomial Chaos Framework for Improving Multi‐Model Hydroclimatic Projections at a Multi‐Decadal Convection‐Permitting Scale

Abstract

Understanding regional hydrologic responses to changing climate is vital for effective water resources planning and risk assessment of water-related hazards (Gudmundsson et al., 2021;Zscheischler et al., 2020). Thus, improving hydroclimatic projections plays a crucial role in supporting decision-making by policymakers and stakeholders, also fostering actions to reduce potential risks and impacts on water resources under current and future climate conditions. Hydrologic prediction systems driven by regional climate models have been routinely used to assess future changes in catchment-scale hydrologic regimes in a warming climate, which has attracted widespread attention from the hydroclimate community in recent years (

A Vine Copula‐Based Ensemble Projection of Precipitation Intensity–Duration–Frequency Curves at Sub‐Daily to Multi‐Day Time Scales

“…The intensity and frequency of heavy rainfall events are expected to increase under a warming climate, as highlighted in recent studies and the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Qing et al., 2022; Seneviratne et al., 2021; Zhu et al., 2018). This raises the question of whether the conventional design of urban infrastructure is still adequate in the context of climate change (Mondal & Daniel, 2019; Switzman et al., 2017; Wright et al., 2021; You & Wang, 2021; Zhang et al., 2022).…”

Improving Risk Projection and Mapping of Coastal Flood Hazards Caused by Typhoon-Induced Storm Surges and Extreme Sea Levels

“…The total inundation areas would increase to up to 10,200 square meters by 2100, which is comparable with those induced by Typhoon Mangkhut and Hato. It should be noted that such an inundation projection is solely based on the mean sea level and the risks would further increase if the projected extreme sea level was taken into account [41][42][43][44][45][46][47][48][49][50][51].…”

Predicting Hydrological Drought With Bayesian Model Averaging Ensemble Vine Copula (BMAViC) Model

“…Recently, vine copulas have been employed for streamflow simulation and prediction, flood risk assessment, and drought prediction (Ahn, 2021; Daneshkhah et al., 2016; Liu et al., 2018; Wu et al., 2022; Zhang et al., 2022). For example, Bevacqua et al.…”

“…Zhang et al. (2022) demonstrated that vine copula multi‐model ensemble approach with the BMA technique improved the accuracy of daily streamflow prediction. In particular, Moradkhani and Madadgar (2013) and Madadgar and Moradkhani (2014b) developed a probabilistic drought forecast model incorporating copula functions into the Bayesian networks.…”