涪江流域日输沙率的时间尺度变化效应

Temporal Scale Effects on Daily Sediment Yield in the Fu River Basin

[目的]解析日输沙率与关键环境因子之间的尺度依赖关系,探索有效的预测方法,以应对由于泥沙输运机制的复杂性和水文过程的非线性所带来的预测挑战。[方法]采用集合经验模态分解与自适应噪声(CEEMDAN)方法,分析了西南地区涪江流域4个水文站点控制区域2013—2018年的日输沙率与5个潜在影响变量(流量、降水量、平均气温、潜在蒸散发量和NDVI)间多时间尺度变异特征,识别不同时间尺度下日输沙率与相关变量相互作用关系,在此基础上,对日输沙率作出预测。[结果]CEEMDAN方法成功将日输沙率及其潜在影响变量分解为10-11本征模态函数(IMF)和残差项,揭示出日输沙率在3~730天不同时间尺度上的显著变化。分解后的日输沙率与流量、降水量在各个时间尺度上均显示出显著的相关性增强,而气温、潜在蒸散发量和NDVI主要与长时间尺度的日输沙率显著相关。通过逐步多元线性回归预测日输沙率,模型的预测性能显著,R2值在所有站点均超过0.55。[结论]CEEMDAN方法能够有效揭示输沙率及其影响变量之间的尺度依赖性,为理解河流输沙动态提供了新的视角。

[Objective]To analyze the scale-dependent relationships between daily sediment yield and key environmental factors and to explore effective prediction method to cope with the prediction challenges caused by the complexity of sediment transport mechanisms and the nonlinearity of hydrological processes.[Methods]Employing the Composite Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)approach,we analyzed the multi-temporal scale variability characteristics between daily sediment yield and five potential influencing variables(flow rate,precipitation,average temperature,potential evapotranspiration,and NDVI)in the controled regions of four hydrological stations in the Fu River basin of Southwest China from2013 to 2018.Interactions between daily sediment yield and related variables at different temporal scales were identified,upon which predictions of daily sediment yield were predicted.[Results]The CEEMDAN method successfully decomposed the daily sediment yield and its potential influencing variables into 10-11 intrinsic mode functions(IMFs)and residual item,revealing the significant changes of the daily sediment yield at different temporal scales ranging from 3 to 730 days.After decomposition,daily sediment yield showed significantly enhanced correlation with howrate and precipitation across all temporal scales,whereas temperature,potential evapotranspiration,and NDVI were primarily significantly correlated with long-term scale sediment yields.By using stepwise multiple linear regression to predict the daily sediment yield,the prediction performance of the model is significant,and the R 2 values exceeds 0.55 at all stations.[Conclusion]The CEEMDAN method effectively revealed the scale dependency between sediment yield and its influencing variables,offering a new perspective for understanding the dynamics of river sediment transport.