Kumar Nikhil, Patel Piyush, Singh Shivam, Goyal Manish Kumar
Department of Civil Engineering, Indian Institute of Technology, Indore, 453552, India.
Sci Rep. 2023 Aug 2;13(1):12524. doi: 10.1038/s41598-023-38771-w.
Climate change significantly impacts the global hydrological cycle, leading to pronounced shifts in hydroclimatic extremes such as increased duration, occurrence, and intensity. Despite these significant changes, our understanding of hydroclimatic risks and hydrological resilience remains limited, particularly at the catchment scale in peninsular India. This study aims to address this gap by examining hydroclimatic extremes and resilience in 54 peninsular catchments from 1988 to 2011. We initially assess extreme precipitation and discharge indices and estimate design return levels using non-stationary Generalized Extreme Value (GEV) models that use global climate modes (ENSO, IOD, and AMO) as covariates. Further, hydrological resilience is evaluated using a convex model that inputs simulated discharge from the best hydrological model among SVM, RVM, random forest, and a conceptual model (abcd). Our analysis shows that the spatial patterns of mean extreme precipitation indices (R1 and R5) mostly resemble with extreme discharge indices (Q1 and Q5). Additionally, all extreme indices, including R1, Q1, R5, and Q5, demonstrate non-stationary behavior, indicating the substantial influence of global climate modes on extreme precipitation and flooding across the catchments. Our results indicate that the random forest model outperforms the others. Furthermore, we find that 68.52% of the catchments exhibit low to moderate hydrological resilience. Our findings emphasize the importance of understanding hydroclimatic risks and catchment resilience for accurate climate change impact predictions and effective adaptation strategies.
气候变化对全球水文循环产生重大影响，导致水文气候极端事件发生明显变化，如持续时间、发生频率和强度增加。尽管发生了这些重大变化，但我们对水文气候风险和水文恢复力的理解仍然有限，特别是在印度半岛的流域尺度上。本研究旨在通过考察1988年至2011年期间印度半岛54个流域的水文气候极端事件和恢复力来填补这一空白。我们首先评估极端降水和流量指数，并使用非平稳广义极值（GEV）模型估计设计重现期水平，该模型将全球气候模式（厄尔尼诺-南方涛动、印度洋偶极子和大西洋多年代际振荡）作为协变量。此外，使用一个凸模型评估水文恢复力，该模型输入支持向量机、相关向量机、随机森林和概念模型（abcd）中最佳水文模型模拟的流量。我们的分析表明，平均极端降水指数（R1和R5）的空间格局大多与极端流量指数（Q1和Q5）相似。此外，所有极端指数，包括R1、Q1、R5和Q5，都表现出非平稳行为，表明全球气候模式对各流域极端降水和洪水有重大影响。我们的结果表明，随机森林模型优于其他模型。此外，我们发现68.52%的流域表现出低到中等的水文恢复力。我们的研究结果强调了了解水文气候风险和流域恢复力对于准确预测气候变化影响和制定有效适应策略的重要性。
