Nonstationary analysis of hydrological drought index in a coupled human-water system: Application of the GAMLSS with meteorological and anthropogenic covariates in the Wuding River basin, China
Nonstationary analysis of hydrological drought index in a coupled human-water system: Application of the GAMLSS with meteorological and anthropogenic covariates in the Wuding River basin, China
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DOI：
10.1016/j.jhydrol.2022.127692
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发表时间：
2022-05
影响因子：
6.4
通讯作者：
Shuting Shao;Hongbo Zhang;V. Singh;Hao Ding;Jingru Zhang;Yanrui Wu
中科院分区：
文献类型：
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作者：
Shuting Shao;Hongbo Zhang;V. Singh;Hao Ding;Jingru Zhang;Yanrui Wu
在气候变化和人类活动的双重影响下，水文序列的显著非平稳性对部分地区基于平稳性假设的传统水文干旱分析提出了挑战，对环境变化下的区域水文干旱分析提出了质疑。为了评估水文干旱特征，本文提出了一种考虑气候变化和人为影响的基于GAMLSS （Generalized Additive Models for Location, Scale, and Shape）的非平稳标准化径流指数（NSRI），用于研究武定河流域的水文干旱状况。为此，对水文序列采用非平稳检验方法，并根据Pettitt检验结果将数据分割为三部分。对具有气象协变量（降水、温度）和人为协变量（社会发展用水量和拦坝蓄水引发的用水量）的径流数据拟合出非平稳概率分布。选择最优协变量组合后，利用GAMLSS模型计算NSRI。在传统SRI阈值的基础上，提出了一种新的评价阈值。在无定江流域丁家沟站，对SRI和NSRI的性能进行了比较。指数识别与实测干旱事件的比较结果表明，NSRI在干旱事件识别方面表现较好。此外，NSRI还发现更频繁的严重干旱和极端干旱。因此，拟议的NSRI为干旱事件的识别提供了更准确的基础，可以为干旱规划、备灾和缓解提供有价值的信息。
Significant nonstationarity of hydrological sequences, driven by the coupled influence of climate change and anthropogenic activities, has challenged traditional hydrological drought analysis under the stationarity assumption in some regions, and has questioned regional hydrological drought analysis under the changing environment. To evaluate hydrological drought characteristics, the novelty of this paper is the proposition of a GAMLSS (Generalized Additive Models for Location, Scale, and Shape)-based nonstationary standardized runoff index (NSRI), considering climate change and anthropogenic influence, for investigating the hydrological drought regime in the Wuding River basin. To that end, nonstationary test methods were performed on hydrological sequences, and data was segmented into three parts according to the results of the Pettitt test. A nonstationary probability distribution was fitted to runoff data with meteorological covariates (precipitation, temperature) and anthropogenic covariates (water consumption for social development demand, and water consumption triggered by water impounding by check dams). After selecting an optimal combination of covariates, the NSRI was calculated utilizing the GAMLSS model. A newly constructed evaluation threshold was proposed, based on the traditional SRI threshold. The performance of SRI and NSRI were compared at the Dingjiagou station in the Wuding River basin. Results of comparison between index identification and recorded drought events demonstrated that the NSRI better performed in drought event identification. Moreover, the NSRI identified more frequent severe droughts and extreme droughts. Therefore, the proposed NSRI provided a more accurate basis for the identification of drought events, which can offer valuable information for drought planning, preparedness, and mitigation.