Wei Xingchen, Wu Xinyu, Zhang Hongbo, Lan Tian, Cheng Chuntian, Wu Yanrui, Aggidis George
Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China; Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China.
Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China; Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China.
Sci Total Environ. 2024 Nov 25;953:175981. doi: 10.1016/j.scitotenv.2024.175981. Epub 2024 Sep 7.
According to the coupled influence of climate variation and anthropogenic activities, hydro-meteorological variables are hard to keep stationary in a changing environment. Consequently, the efficacy of traditional standardized drought indices, predicated upon the assumption of stationarity, has been called into question. In China, the challenge of drought monitoring and declaration is exacerbated by the need for multiple drought indices covering meteorological, agricultural, hydrological, and groundwater aspects, often lacking real-time availability. To address these challenges, we developed a framework for drought monitoring and assessment from a drought propagation perspective. Central to this is the Nonstationary Integrated Drought Index (NIDI), which integrates responses from meteorological, agricultural, hydrological, and groundwater droughts, accounting for climate change and anthropogenic influences. First, we analyse the process of drought propagation to select the suitable time scale standardized drought index. Subsequently, significant large-scale climatic indices are selected through linear and nonlinear correlation analyses to identify climate anomalies. Anthropogenic influences are assessed using indicators such as the Normalized Difference Vegetation Index (NDVI), Impervious Surface Ratio (ISR), and population density (POP). Nonstationary probability models are then developed for precipitation, soil moisture, runoff, and groundwater series, incorporating climatic and human-induced factors. Finally, the NIDI is calculated using a D-vine copula model, with parameter estimation and updating facilitated by a genetic algorithm, representing the temporal dependence structure among the variables. A case study in the Hulu River Basin of western China validated the NIDI. Results showed that the NIDI effectively accounts for nonstationary hydro-meteorological variables due to climate change and human activities, accurately reproducing their time-dependent structure. Compared to conventional indices like SPI, SSI, SRI, and SGI, the NIDI identifies more extreme drought events. In conclusion, the presented NIDI offers a more comprehensive approach to drought identification, providing valuable insights for accurate drought detection and effective drought-related policy-making.
受气候变化和人类活动的耦合影响，水文气象变量在不断变化的环境中难以保持稳定。因此，基于平稳性假设的传统标准化干旱指数的有效性受到了质疑。在中国，干旱监测和预警面临挑战，因为需要多种涵盖气象、农业、水文和地下水方面的干旱指数，而这些指数往往缺乏实时可用性。为应对这些挑战，我们从干旱传播的角度开发了一个干旱监测和评估框架。其核心是非平稳综合干旱指数（NIDI），该指数整合了气象干旱、农业干旱、水文干旱和地下水干旱的响应，同时考虑了气候变化和人为影响。首先，我们分析干旱传播过程，以选择合适时间尺度的标准化干旱指数。随后，通过线性和非线性相关分析选择显著的大尺度气候指数，以识别气候异常。利用归一化植被指数（NDVI）、不透水表面比率（ISR）和人口密度（POP）等指标评估人为影响。然后，结合气候和人为因素，为降水、土壤湿度、径流和地下水序列建立非平稳概率模型。最后，使用D-vine copula模型计算NIDI，通过遗传算法进行参数估计和更新，以体现变量之间的时间依赖结构。中国西部葫芦河流域的案例研究验证了NIDI。结果表明，NIDI有效地考虑了气候变化和人类活动导致的非平稳水文气象变量，准确地再现了它们的时间依赖结构。与SPI、SSI、SRI和SGI等传统指数相比，NIDI识别出更多极端干旱事件。总之，所提出的NIDI为干旱识别提供了一种更全面的方法，为准确的干旱检测和有效的干旱相关政策制定提供了有价值的见解。
