Stochastic Modeling of Suspended Sediment Load in Alluvial Rivers

Sediment is a major source of non-point pollution. Suspended sediment can transport nutrients, toxicants and pesticides, and can contribute to eutrophication of rivers and lakes. Modeling suspended sediment in rivers is of particular importance in the field of environmental science and engineering. However, understanding and quantifying nonlinear interactions between river discharge and sediment dynamics has always been a challenge. In this paper, we introduce a parsimonious probabilistic model to describe the relationship between Suspended Sediment Load (SSL) and discharge volume. 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, climatic forcings, and the nature of rainfall-runoff transformation, by drawing samples from the probability distribution functions (PDFs) of the underlying process (here, discharge and SSL data). Discharge and SSL PDFs can be simplified into a joint distribution that describes the relationship between SSL and discharge, in which the latter acts as a proxy for different predictors of SSL. The joint distribution is created based on historical discharge and SSL data, and stores information about the discharge-SSL relationship and sediment transport process of the watershed of interest. 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

Probabilistic Hazard Assessment of Contaminated Sediment in Rivers

We propose a probabilistic framework rooted in multivariate and copula theory to assess heavy metal hazard associated with contaminated sediment in freshwater rivers that provide crucial ecosystem services such as municipal water source, eco-tourism, and agricultural irrigation. Exploiting the dependence structure between suspended sediment concentration (SSC) and different heavy metals, we estimate the hazard probability associated with each heavy metal at different SSC levels. We derive these relationships for warm (spring-summer) and cold (fall-winter) seasons, as well as stormflow condition, to unpack their nonlinear associations under different environmental conditions. To demonstrate its efficacy, we apply our proposed generic framework to Fountain Creek, CO, and show heavy metal concentration in warm season and under stormflow condition bears a higher hazard likelihood compared to the cold season. Under both warm season and stormflow conditions, probability of exceeding maximum allowable threshold for all studied heavy metals (Cu, Zn, and Pb, in recoverable form) at a standard hardness of 100 mg/l CaCo3 and at a high level of SSC (95th percentile) is consistently more than 80% in our study site. Moreover, a longitudinal study along the Fountain Creek demonstrates that urban and agricultural land use considerably increase likelihoods of violating water quality standards compared to natural land cover. The novelty of this study lies in introducing a probabilistic hazard assessment framework that enables robust risk assessment with important policy implications about the likelihood of different heavy metals violating water quality standards under various SSC levels

Process-Constrained Statistical Modeling of Sediment Yield

Sediment transport is a major contributor to a non-point source of pollution impacted by various factors that are modulated by climatic changes and anthropogenic influences. Quantifying and disentangling the contribution of these factors to sediment yield at large scales and across different flow regimes has not been fully explored. Here we present a framework to fine-tune a stochastic sediment yield model by classifying discharge and Suspended Sediment Load (SSL) observations based on the underlying governing processes in unregulated streams with various hydrological regimes. This stochastic model, rooted in copula theory, constructs a joint distribution between discharge and SSL storm events using historical time series of observations, classified based on seasonality, hysteresis patterns, and hydrograph components of the sediment transport processes. We include hydrological, land use, and geological properties of the watersheds to describe and discuss the effects of different factors on applying the underlying dynamics to enhance sediment yield estimation/prediction accuracy. We evaluated the proposed method on 67 streams across the United States. Our results show significant improvements in sediment yield modeling performance

A Novel Bayesian Maximum Entropy-Based Approach for Optimal Design of Water Quality Monitoring Networks in Rivers

Comprehensive river water quality monitoring and assessment helps to identify emerging water quality problems as well as developing sustainable water management strategies to maintain and protect healthy rivers and ecosystems. However, the cost of these efforts is a major concern due to large monitoring networks in rivers and watersheds. This paper presents a Bayesian Maximum Entropy (BME)-based framework to optimize the locations of Water Quality Monitoring Stations (WQMS) in rivers to obtain the highest value of information with the lowest number of monitoring stations. In this study, BME is employed as a flexible, accurate, and effective approach in geostatistics to optimize the spatiotemporal coverage of potential WQMS. In addition, an information-entropy model is proposed, using Value of Information (VOI) and Transinformation Entropy (TE), in a multi-objective optimization model to relax the computational burden and allow the entire decision space to be explored. The proposed model provides a set of Pareto-optimal solutions (WQMS locations) with tradeoffs between VOI (highest information) and TE (lowest overlap). This framework was applied to the Rappahannock River in eastern Virginia, United States. The results of this study revealed that only 5 monitoring stations, optimally placed along the river, could capture 76% of the information that 45 monitoring stations provided. This significantly reduces the costs of deploying and maintaining monitoring stations. Our approach will provide improved estimates of water quality in a cost-effective manner and can be transferrable to other regions to develop an accurate spatiotemporal estimation of potential WQMS