Abstract
Nonstationary calculation of sediment load is an important basis for reservoir operation and management. The Jinsha River Basin (JRB) is the most important sediment-producing section of the Yangtze River, and the main source of sediment into the Three Gorges Reservoir (TGR). In this study, three sediment transport nonstationary analysis models were constructed through the Generalized Additive Models for Location, Scale and Shape (GAMLSS) model, namely Mode0, Mode1 and Mode2. The study found that the correlation coefficients between sediment load with runoff and rainfall are 0.78 and 0.73, respectively. Runoff and precipitation can be used as covariates for the nonstationary analysis of sediment transport. The optimal edge distribution of Mode0, Mode1, and Mode2 is Gumbel, Weibull, and Logistic, respectively. Mode2 can accurately describe the nonstationary change of sediment load, and Mode1 can basically reflect the nonstationary change of sediment load. While Mode0 is a consistent model, which cannot reflect the nonstationary change of sediment load over time. Mode2 has a good ability to simulate the quantile changes of sediment load during the training period and the test period, while Mode1 has a weak generalization ability. Mode0 cannot reflect inconsistent changes, and the simulation result is a fixed value. This study provides an important reference for the nonstationary analysis of sediment load under inconsistency conditions in the JRB.
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The data used to support the findings of this study are available from the corresponding author upon request.
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The work described here is supported by National Natural Science Foundation of China (Grant Nos. U1911204, 51861125203), National Key R&D Program of China (2021YFC3001000).
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HJ: methodology, writing-original draft. RZ and ML: reviewed the manuscript. CY and XC: software and collect data. All authors reviewed the manuscript.
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Jin, H., Zhong, R., Liu, M. et al. Nonstationary analysis of water and sediment in the Jinsha River Basin based on GAMLSS model. Stoch Environ Res Risk Assess 37, 4765–4781 (2023). https://doi.org/10.1007/s00477-023-02540-y
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DOI: https://doi.org/10.1007/s00477-023-02540-y