Abstract Duration and severity are the two most important parameters used for drought characterization. In this study, we used a bivariate copula‐based approach to understand the joint dependence of drought duration and severity of three different drought types. Three types of bivariate copulas (Gumbel, Frank and Plackett) are estimated for modelling and the best fit copula is selected over 1,162 grid points (at a resolution of 0.5° × 0.5°) of India. Further, the joint dependence of drought duration and severity are analysed to infer important properties in terms of exceedance probability and return periods. Finally, conditional probability and conditional return periods of drought characteristics are also derived, which could be useful for proper planning and management of the water resource system. From the investigation, it is observed that drought events in the Western and Central India are longer and more severe whereas the ones in the south Indian river basins are more frequent but less severe. Moreover, similar results were also obtained for the conditional probability and conditional return periods. This study provides information regarding the severe and longer drought event hotspots all over the study area and thus helpful for the policymakers in developing effective drought prevention and mitigation strategies comprehensively at a national scale.
Atmospheric rivers (ARs) are filamentary regions of high-water vapour flux in the lower troposphere that contribute significantly to poleward moisture movement in mid-latitude regions. Key characteristics (frequency, duration, and intensity) of ARs have been explored to recognize the regions vulnerable to AR-flood. To investigate the association of ARs with large-scale climate oscillations (LSCOs), precipitation extremes (PEs) maximum 1-day precipitation (Rx1day), maximum consecutive 5-day precipitation (Rx5day), precipitation amount from very wet days (R95pTOT) are explored in a non-stationary framework of generalized extreme value distribution, taking the Arctic Oscillation, North Atlantic Oscillation, El Niño Southern Oscillation, and Pacific Decadal Oscillation (PDO) as covariates. In almost 30% of regions around the globe, May-June-July-August-September (MJJAS) season PDO was found to be the relatively most influential covariate for capturing PEs. The west coast of North America and of Europe, southernmost South America, central East Asia, New Zealand, and Australia have been identified as the most critical regions associated with AR linked with PE-associated LSCOs.
Abstract A comprehensive assessment of compound hot and dry extremes based on different drought conditions (low precipitation, runoff, or soil moisture) and associated uncertainties is necessary to fully understand the possible risks. Here, we analyze changes in the likelihood of compound hot and dry conditions associated with low precipitation, runoff, and soil moisture using Coupled Model Intercomparison Project Phase6 (CMIP6) simulations for present‐day climate (+1°C) and additional global warming levels (+1.5°C, +2°C, +3°C). Further, we investigate the contributions of different components (e.g., global warming levels, climate models, copula types) to the total spread in their future projections. Results show the significance of global warming levels in governing risks of rising compound hot and dry extremes. The hotspot regions include the Mediterranean, South Central America, Amazonia, and Sahara. The rising risks are also accompanied by rising uncertainty as the spread in changing likelihood is significantly contributed by Earth System Models (ESMs), global warming levels, their interactions, and the statistical estimation error. The uncertainty due to ESMs spread was observed to be most significant in the case of compound hot and low soil moisture extremes, which also corresponds to some of the most impactful conditions. It was observed that the estimation error dominates the uncertainty in compound hot and low precipitation extremes as compared to the two other combinations. Our findings indicate that the regional likelihood and associated uncertainties of compound hot‐dry events in CMIP6 projections are functions of both the selection of drought types and the methodology of deriving the joint extremes.
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