Detecting human interventions by spatial dependence of extreme water levels using a high dimensional conditional probability approach over the Pearl River Delta

Abstract

The Pearl River Delta (PRD) is one of the largest urbanized areas in both area and population and also suffers increasing flooding hazards in the context of climate change and anthropogenic interventions. The co-occurrence of multiple upstream flooding drivers (compound flooding) over different upstream channels often exacerbates stronger impacts on downstream water levels compared with their isolated occurrence. It is particularly important to derive more reliable risk estimations of extreme water levels in downstream channels of a nested river system by considering the spatial dependence between multiple upstream floods and downstream water levels. In this study, we present an approach, termed the high-dimensional conditional probability (HDCP) approach, to capture the probabilistic dependence behavior of upstream floods and downstream water level events at the PRD, and estimate the extreme water levels' likelihood and event-impacted areas under the multiple upstream flooding. The PRD, as one of the most vulnerable regions to extreme floods in China, was used as a case study to test the applicability of the HDCP approach. First, the HDCP is used to model the conditional dependence of upstream floods and downstream water levels based on the canonical vine copula. Then, different flood scenarios are investigated by estimating the occurrence likelihood of downstream water level events and their spatial patterns. The final part identifies the changes in the occurrence likelihood variability of extreme water level events after strong human interventions. Results indicate that the upstream flooding over the Xijiang River dominates the occurrence likelihood of extreme water level events over the PRD, and the Beijiang River plays a secondary role. After strong human interventions, the effect of the Beijiang River has been strengthened, while the effect of the Xijaing River has been weakened. The proposed HDCP approach is not only applicable to the PRD area dominated by multiple upstream rivers, as mentioned in this study, but it is also promising in detecting the spatial occurrence likelihood of other hydrometeorological variables in other areas with different climates and topographical conditions, and formulating regional development strategies for policymakers in water resources and disaster drought risk managements against single/compound extreme events.