不断变化的气候中固定和非固定沉积物负荷频率的分析框架

IF 3.9 3区 环境科学与生态学 Q1 ENGINEERING, CIVIL Stochastic Environmental Research and Risk Assessment Pub Date : 2024-07-02 DOI:10.1007/s00477-024-02763-7
Xi Yang, Min Qin, Zhihe Chen
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引用次数: 0

摘要

非静态泥沙负荷分析对于河流工程设计和水资源管理至关重要。传统的泥沙负荷频率分析方法通常假定是静态的,但由于无法考虑时间变化等因素,在不断变化的环境中可能导致结果不一致。在此,我们利用位置、尺度和形状的广义加法模型(GAMLSS)建立了以时间、降水量和河水流量为协变量的非稳态模型(分别命名为模型 1 和模型 2),并比较了它们与稳态模型(参数不变:模型 0)的拟合效果。本研究分析了中国西南金沙江流域的泥沙负荷。结果表明(1)研究区泥沙量明显减少,2002 年为显著变化点(p < 0.1);(2)基于模型 2 的拟合优度指数(全局拟合偏差:GD、AIC 准则和 SBC 准则)均小于其他两个模型的值。其他两个模型的泥沙负荷量位设计值均在模型 2 的范围内。(3) 与模型 1 相比,模型 2 中以降水和河水为协变量更能捕捉泥沙负荷频率的非稳态特征。此外,在考虑外部物理因素的情况下,模型 2 能更准确地预测未来泥沙量的变化。该研究成果可为决策者进行水利规划设计和河流治理开发提供科学依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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An analysis framework for stationary and nonstationary sediment load frequency in a changing climate

Non-stationary sediment load analysis is critical for river engineering design and water resource management. Traditional sediment load frequency analysis methods usually assume stationarity, which can lead to inconsistent results in a changing environment because they cannot account for factors such as time variations. Here, we use generalized additive models for location, scale and shape (GAMLSS) to establish non-stationary models with time, precipitation and streamflow as covariates (named Model 1 and Model 2, respectively), and compare their fitting effects with stationary models (parameters unchanged: Model 0). In this study, the sediment load of the Jinsha River Basin in southwest China was analyzed. Outcomes indicate that: (1) the research area's sediment load decreased significantly, with a significant change point in 2002 (p < 0.1); (2) the goodness of fit indices (global fitting deviation: GD, AIC criterion and SBC criterion) based on Model 2 are smaller than the values of the other two models. The other two models' sediment load quantile design values are within Model 2's range. (3) Compared with Model1, precipitation and streamflow as covariates in Model 2 are more able to capture the non-stationary features of sediment load frequency. Furthermore, Model 2 can more accurately forecast future changes in sediment load when external physical factors are considered. The findings of this research can serve as a scientific foundation for decision makers to carry out water conservancy planning and design and river management and development.

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来源期刊
CiteScore
7.10
自引率
9.50%
发文量
189
审稿时长
3.8 months
期刊介绍: Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.
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