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【目的】在气候变化与人类活动加剧的背景下，传统基于平稳性假设的水文干旱指数难以准确表征实际干旱状况。为科学评估非平稳条件下的水文干旱，亟需构建一种能够融合自然与人为影响因子的新型干旱评估方法。【方法】基于多源数据，在对比LSTM与RF机器学习模型性能的基础上，重构了扰动期的天然径流序列并量化了人类活动指数(HI);借助GAMLSS模型，以降水量、气温和HI为协变量，分别构建了平稳与非平稳模型，计算平稳水文干旱指数(SRI)和非平稳水文干旱指数(NSRI);在对比二者性能的基础上，结合Copula函数揭示了非平稳条件下的水文干旱特征。【结果】结果显示：(1)研究区径流序列在1988年发生突变，呈现显著非平稳特征，突变点后径流量增幅达35.5%。(2)非平稳模型的拟合性能在所有月份均优于平稳模型，其中包含HI的模型组合表现最佳；相较于SRI,NSRI在识别典型干旱事件的过程与强度方面更为准确，而SRI存在明显低估。(3)1989—2020年期间，流域共发生37次水文干旱事件，平均历时和烈度分别为2.97个月和3.27;干旱整体呈加剧趋势，且春季干旱具有长历时、高烈度特征，夏季则表现为短历时、高频次。(4)基于Copula的联合概率分析表明，干旱历时与烈度呈负相关；水文干旱的联合重现期(12.9a)显著短于同现重现期(113.4a)。【结论】构建的NSRI指数通过耦合气候与人类活动因子，较传统方法更准确表征非平稳干旱过程，且人类活动是干旱演变的关键驱动因素；在人为干扰期流域水文干旱加剧，研究成果可为干旱区水资源管理与风险防控提供科学依据。
Abstract：[Objective]In the context of climate change and intensified human activities, traditional hydrological drought indices based on the stationarity assumption struggle to accurately characterize the actual drought conditions. To scientifically evaluate hydrological drought under non-stationary conditions, it is necessary to develop a novel drought assessment method that can integrate both natural and human impact factors.[Methods]Based on multi-source data, the natural runoff series during the disturbance period was reconstructed, and the human activity index(HI) was quantified after comparing the performance of long short-term memory(LSTM) and random forest(RF) machine learning models. Using the generalized additive models for location, scale and shape(GAMLSS) model, stationary and non-stationary models were constructed with precipitation, temperature, and HI as covariates, and the stationary hydrological drought index(SRI) and non-stationary hydrological drought index(NSRI) were calculated. Based on the performance comparison of the two indices, the characteristics of hydrological drought under non-stationary conditions were revealed using Copula functions.[Results]The result showed that:(1) the runoff series in the study area underwent an abrupt change in 1988, exhibiting significant non-stationary characteristics, with runoff increasing by 35.5% after the change point.(2) The fitting performance of the non-stationary model was superior to that of the stationary model in all months, with the model combination including HI showing the best performance. Compared with SRI, NSRI was more accurate in identifying the process and severity of typical drought events, while the SRI showed significant underestimation.(3) From 1989 to 2020, a total of 37 hydrological drought events occurred in the river basin, with an average duration of 2.97 months and an average severity of 3.27. The overall drought trend was intensifying, with spring droughts characterized by long duration and high severity, while summer droughts exhibited short duration and high frequency.(4) Joint probability analysis based on Copula functions indicated a negative correlation between drought duration and severity. The joint return period(12.9 years) of hydrological drought was significantly shorter than the co-occurrence return period(113.4 years).[Conclusion]The constructed NSRI characterizes non-stationary drought processes more accurately than traditional method by coupling climate and human activity factors, and human activity is identified as a key driver of drought evolution. During the disturbance period, hydrological drought in the river basin has intensified. The findings can provide a scientific basis for water resource management and risk prevention in arid areas.
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基本信息:
DOI：10.13928/j.cnki.wrahe.2026.07.002
中图分类号:P333
引用信息:
[1]朱晓钰,穆振侠,宋志林,等.基于GAMLSS模型的吐鲁番阿拉沟河流域非平稳水文干旱分析[J].水利水电技术(中英文),2026,57(07):17-32.DOI:10.13928/j.cnki.wrahe.2026.07.002.
基金信息:
国家自然科学基金项目(52269007,52569005); 新疆水利工程安全与水灾害防治重点实验室实践创新项目(ZDSYS-YJS-2023-26)
2026-02-02
2026-02-02
2026-02-02