黄河下游河相关系与平滩流量特征关系的随机演化模拟

Extreme weather is an important noise factor in affecting dynamic access to river morphology information.The response characteristics of river channel on climate disturbances draw us to develop a method to investigate the dynamic evolution of bankfull channel geometries(including the hydraulic geometry variables and bankfull discharges)with stochastic differential equations in this study.Three different forms of random inputs,including single Gaussian white noise and compound Gaussian/Fractional white noise plus Poisson noise,are explored respectively on the basis of the classical deterministic models.The model parameters are consistently estimated by applying a composite nonparametric maximum likelihood estimation(MLE)method.Results of the model application in the Lower Yellow River reveal the potential responses of bankfull channel geometries to climate disturbances in a probabilistic way,and,the calculated average trends mainly run to synchronize with the measured values.Comparisons among the three models confirm the advantage of Fractional jump-diffusion model,and through further discussion,stream power based on such a model is concluded as a better systematic measure of river dynamics.The proposed method helps to offer an effective tool for analyzing fluvial relationships and improves the ability of crisis management of river system under varying environment conditions.

Simulation on the stochastic evolution of hydraulic geometry relationships with the stochastic changing bankfull discharges in the Lower Yellow River

Extreme weather is an important noise factor in affecting dynamic access to river morphology information. The response characteristics of river channel on climate disturbances draw us to develop a method to investigate the dynamic evolution of bankfull channel geometries(including the hydraulic geometry variables and bankfull discharges) with stochastic differential equations in this study. Three different forms of random inputs, including single Gaussian white noise and compound Gaussian/Fractional white noise plus Poisson noise, are explored respectively on the basis of the classical deterministic models. The model parameters are consistently estimated by applying a composite nonparametric maximum likelihood estimation(MLE) method. Results of the model application in the Lower Yellow River reveal the potential responses of bankfull channel geometries to climate disturbances in a probabilistic way, and, the calculated average trends mainly run to synchronize with the measured values. Comparisons among the three models confirm the advantage of Fractional jump-diffusion model, and through further discussion, stream power based on such a model is concluded as a better systematic measure of river dynamics. The proposed method helps to offer an effective tool for analyzing fluvial relationships and improves the ability of crisis management of river system under varying environment conditions.