Affiliation:
1. School of Civil Engineering Sun Yat‐sen University Guangzhou People's Republic of China
2. Center of Water Resources and Environment Sun Yat‐sen University Guangzhou People's Republic of China
Abstract
AbstractThe phenomenon of sudden drought after pluvial (or heavy pluvial after drought) is known as the Dry–Wet Abrupt Alternation (DWAA). This kind of disaster has been evaluated globally based on various DWAA indices. However, few validations have been performed to compare the various DWAA indices. Given that directly validating the identified DWAA events remains difficult because of the limited historical records of DWAA events, in this study, we developed a simple weighting analysis method to cross‐validate the detected DWAA events using three widely‐used indices. The weighting analysis method can be used to estimate the effectiveness of the DWAA indices by calculating the ratio of the synchronous DWAA events (the DWAA events could be synchronously detected by different indices) to the total number of the detected DWAA events. Results suggest that the standard weighted average of precipitation (SWAP) index has better performance for detecting DWAA events in the Pearl River Basin (PRB) compared to other indices. The soil moisture synchronization, meteorological concentration and real DWAA records further testify that the SWAP is more capable of detecting the DWAA events in the PRB. Based on the validation, the SWAP‐derived DWAA events show that both dry–wet (D–W) events and wet–dry events are severe in the upstream sub‐basins in the PRB, while D–W events are more frequent in the downstream PRB. Our study provides a simple but effective method to validate the detected DWAA. The finding underscores the need to validate DWAA detection generated from multiple indicators before further research, and it could help develop adaptation strategies to improve resilience to combined extremes under changing climate.
Funder
China Postdoctoral Science Foundation
Basic and Applied Basic Research Foundation of Guangdong Province
National Natural Science Foundation of China