黄河流域降水的时空变化(考虑气候和仪器干扰

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2024-09-10 DOI:10.1016/j.envsoft.2024.106204
Wenzhuo Wang , Ningpeng Dong , Jinjun You , Zengchuan Dong , Li Ren , Lianqing Xue
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引用次数: 0

摘要

气候变化和仪器干扰使得准确识别水文气象周期变得十分困难。本研究提出了分层离散-连续小波分解(HDCWD)模型,在考虑气候和仪器干扰的情况下识别时段。该方法结合离散小波变换和连续小波变换,提供了去趋势、去噪和挖掘的三层识别框架。利用 HDCWD 识别了黄河流域降水的主导时段及其时空特征。结果表明(1) 黄河流域降水的主导时段为 2-4 年和 7-9 年(1956-1984 年),以及 2 年(1998-2002 年)。(2)纬度较高流域的降水周期较长,纬度较低流域的降水周期较短。结果表明,黄河流域降水量虽然在时空上存在差异,但有一定的演变规律。这些结果可为水资源管理提供信息。
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Spatiotemporal variations of the precipitation in the Yellow River Basin considering climate and instrumental disturbance
Climate change and instrumental disturbance make accurate identification of hydrometeorological period challenging. This study presents the hierarchical discrete-continuous wavelet decomposition (HDCWD) model to identify period with considering climate and instrumental disturbance. The method provides a three-layer identification framework of detrending, denoising and mining by combining discrete wavelet transform and continuous wavelet transform. The dominating periods and their spatiotemporal features of precipitation in the Yellow River Basin are identified by HDCWD. Results show the following: (1) Precipitation in the Yellow River Basin has the dominating periods of 2–4 years and 7–9 years (1956–1984), and period of 2 years from (1998–2002). (2) The periods of catchments in higher latitude exhibit longer and those in the lower east exhibit shorter. The results illustrate that although the precipitation in the Yellow River Basin differs in space and time, there is a certain evolution law. The results can provide information for water resources management.
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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