Keywords
Suspended Sediment Load, Stochastic Modeling
Start Date
25-6-2018 9:00 AM
End Date
25-6-2018 10:20 AM
Abstract
Sediment is a major source of non-point pollution. Suspended sediment can transport nutrients, toxicants and pesticides, and can contribute to the eutrophication of rivers and lakes. Modeling the suspended sediment in rivers is, hence, of particular importance in the field of environmental science and engineering. However, understanding and quantifying the nonlinear interactions between river discharge and sediment dynamics is historically a challenge. In this paper, we introduce a parsimonious probabilistic model to describe the relationship between Suspended Sediment Load (SSL) and river discharge. This model, rooted in multivariate probability theory and Bayesian Network, infers conditional marginal distribution of SSL for a given discharge level. The proposed framework relaxes the need for detailed information about the physical characteristics of the watershed, the climatic forcing, and the nature of rainfall-runoff transformation, by drawing samples from the Joint Probability Distribution Function (JPDF) that describes the relationship between SSL and discharge. Such JPDF is created from the historical data for a river section, and is dependent on, and stores critical information about the governing sedimentation processes of a watershed. We test this framework for seven major rivers in the U.S., results of which show promising performance to predict SSL and its likelihood given different discharge levels.
Invited Presentation: A new approach to model suspended sediment load: Stochastic prediction and uncertainty estimation
Sediment is a major source of non-point pollution. Suspended sediment can transport nutrients, toxicants and pesticides, and can contribute to the eutrophication of rivers and lakes. Modeling the suspended sediment in rivers is, hence, of particular importance in the field of environmental science and engineering. However, understanding and quantifying the nonlinear interactions between river discharge and sediment dynamics is historically a challenge. In this paper, we introduce a parsimonious probabilistic model to describe the relationship between Suspended Sediment Load (SSL) and river discharge. This model, rooted in multivariate probability theory and Bayesian Network, infers conditional marginal distribution of SSL for a given discharge level. The proposed framework relaxes the need for detailed information about the physical characteristics of the watershed, the climatic forcing, and the nature of rainfall-runoff transformation, by drawing samples from the Joint Probability Distribution Function (JPDF) that describes the relationship between SSL and discharge. Such JPDF is created from the historical data for a river section, and is dependent on, and stores critical information about the governing sedimentation processes of a watershed. We test this framework for seven major rivers in the U.S., results of which show promising performance to predict SSL and its likelihood given different discharge levels.
Stream and Session
Stream C: Integrated Social, Economic, Ecological, and Infrastructural Modeling
Session C12: Connecting Environment, Technology, and Society: Integrated Decision Support Tools for System-Level Analysis