Zhi-Qiang Deng

Downstream hydraulic geometry relations: 1. Theoretical development

An edited version of this paper was published by AGU. Copyright 2003 American Geophysical Union.

Scaling dispersion model for pollutant transport in rivers

This communication presents a scale-dependent model, called Scaling Dispersion (SD) Model, for simulating solute dispersion and transport in rivers without using a user-specified residence time distribution function. The SD model consists of (1) the advection dispersion equation with a transient storage term which is characterized by a variable residence time, (2) a new method for estimation of the longitudinal Fickian dispersion coefficient involved in the model, and (3) a split-operator method for numerical solution of the equations involved in the model. Comparisons between the SD model and the most widely used transient storage model against tracer test data observed in three US rivers show that the SD model is capable of simulating different types of residence time distributions commonly observed in streams with an accuracy higher than or at least comparable with existing solute transport models, demonstrating the efficacy of the SD model. The SD model unifies the residence time distributions observed in natural rivers within a single modelling framework for the first time. The SD model provides an efficient and cost effective tool for predicting solute dispersion and transport in streams and rivers.

Downstream hydraulic geometry relations: 2. Calibration and testing

An edited version of this paper was published by AGU. Copyright 2003 American Geophysical Union.

Hydrograph-based approach to modeling bacterial fate and transport in rivers

A new approach, called hydrograph-based approach, is proposed for predicting bacterial concentrations in rivers. The new approach is relatively simple and efficient in terms of data requirements. It uses widely available hydrographs as the main input data for estimating flow and sediment transport parameters responsible for bacterial transport under varying flow conditions. The major component of the hydrograph-based approach is a new model, called VARTBacT model which is an extension of the Variable Residence Time (VART) model by including effects of unsteady flow, sediment transport, and bacterial decay/growth processes on bacterial transport and fate in rivers. The applicability of the new hydrograph-based approach is demonstrated through three case studies, each with distinct sediment and flow conditions: (1) steady low flow without sediment transport, (2) flood events with significant sediment transport due to watershed inputs, and (3) sediment resuspension from the streambed. While the sediment resuspension from streambed may be an important process for bacterial transport during high flows, results from this study indicate that the most important mechanism responsible for bacterial transport in streams is watershed loading during flood events and hyporheic exchange during low flow periods.

VART Model–Based Method for Estimation of Instream Dissolved Oxygen and Reaeration Coefficient

AbstractDissolved oxygen (DO) is essential to maintaining flora and fauna in aquatic ecosystems. The DO replenishment through the surface reaeration mechanism is commonly described using the reaeration coefficient (K2). This paper presents a new approach to modeling instream DO and estimating K2. The new approach includes an extension of the Variable Residence Time (VART) model and optimization algorithms for inverse modeling. The VART model is modified in this paper to incorporate the DO reaeration mechanism across the air-water interface, forming the VART-DO model. A major advantage of the VART-DO model is that it is capable of simulating DO exchange across the water-sediment interface through the hyporheic exchange mechanism in addition to the air-water exchange. A sensitivity analysis is conducted to investigate the relative importance of key model parameters to DO modeling. It is found that the K2 value increases markedly with the dispersion coefficient. The simplex-simulated annealing and the geneti...

Event flow hydrograph-based method for shear velocity estimation

Due to the lack of a simple method for finding the shear velocity during flood events, most sediment resuspension or transport models either resort to complex numerical models requiring detailed input data or rely on uniform steady flow formula. Here a practical method for estimating the shear velocity of unsteady flow is presented by using the flow hydrograph and channel geometry data. Using equations for open channel flow, formulas for shear velocity are derived in terms of discharge gradient, representing the friction slope of unsteady flow in prismatic channels. Applications of the new method using published experimental data and simulated results indicate that the method is comparable with existing methods in terms of accuracy but requires less input data. This method is particularly applicable to natural floods in relatively straight reaches of lowland rivers where the non-inertia wave approximation to the Saint-Venant equations is theoretically appropriate.

Modeling sediment resuspension-induced DO variation in fine-grained streams

Dissolved Oxygen (DO) levels in streams with nutrient enriched fine-grained sediment are highly affected by sediment resuspension. This paper presents a new model, called VART-DOS model, for simulation of instream DO transport, DO exchanges across water-sediment and water-air interfaces, and DO variation in response to sediment resuspension. The sediment resuspension effect is described by introducing a lumped term as a product of DO concentration and a rate of sediment resuspension-induced DO consumption (Λ). The rate parameter Λ is defined as a nonlinear function of average summer temperature of water and several sediment erosion-related parameters. This is a novel and unique feature of the VART-DOS model. Based on sensitivity analysis, effects of BOD and Sediment Oxygen Demand (SOD) on DO consumption are not so important as compared to sediment resuspension which can cause up to 83% reduction in DO level during high flow. The VART-DOS model was applied to the Lower Amite River in Louisiana, USA to perform continuous simulations of DO fluctuations in the winter month January and the summer month July involving several flood-induced sediment resuspension events. Simulation results indicate that the VART-DOS model is capable of capturing overall variation trends in DO concentration. The Normalized Root Mean Square Error (RMSE) between VART-DOS simulated and observed DO levels was 0. 42 for January and 0.23 for July, demonstrating the efficacy of the VART-DOS model.

Development of genetic programming-based model for predicting oyster norovirus outbreak risks

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide but exact causes of the outbreaks are rarely identified, making it highly unlikely to reduce the risks. This paper presents a genetic programming (GP) based approach to identifying the primary cause of oyster norovirus outbreaks and predicting oyster norovirus outbreaks in order to reduce the risks. In terms of the primary cause, it was found that oyster norovirus outbreaks were controlled by cumulative effects of antecedent environmental conditions characterized by low solar radiation, low water temperature, low gage height (the height of water above a gage datum), low salinity, heavy rainfall, and strong offshore wind. The six environmental variables were determined by using Random Forest (RF) and Binary Logistic Regression (BLR) methods within the framework of the GP approach. In terms of predicting norovirus outbreaks, a risk-based GP model was developed using the six environmental variables and various combinations of the variables with different time lags. The results of local and global sensitivity analyses showed that gage height, temperature, and solar radiation were by far the three most important environmental predictors for oyster norovirus outbreaks, though other variables were also important. Specifically, very low temperature and gage height significantly increased the risk of norovirus outbreaks while high solar radiation markedly reduced the risk, suggesting that low temperature and gage height were associated with the norovirus source while solar radiation was the primary sink of norovirus. The GP model was utilized to hindcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast. The daily hindcasting results indicated that the GP model was capable of hindcasting all historical oyster norovirus outbreaks from January 2002 to June 2014 in the Gulf of Mexico with only two false positive outbreaks for the 12.5-year period. The performance of the GP model was characterized with the area under the Receiver Operating Characteristic curve of 0.86, the true positive rate (sensitivity) of 78.53% and the true negative rate (specificity) of 88.82%, respectively, demonstrating the efficacy of the GP model. The findings and results offered new insights into the oyster norovirus outbreaks in terms of source, sink, cause, and predictors. The GP model provided an efficient and effective tool for predicting potential oyster norovirus outbreaks and implementing management interventions to prevent or at least reduce norovirus risks to both the human health and the seafood industry.

Optimum channel pattern for environmentally sound training and management of alluvial rivers

Abstract Employing the theory of minimum stream power and the principle of maximum entropy as well as field data, this paper develops relationships between the river channel pattern and river functions. It is deduced that the channel pattern with a sinuosity of S=1.4–1.6 and a meander wave length Lm≈12 times the channel width is the optimal channel pattern for environmentally sound training and management of alluvial rivers. Taking into account (i) the optimal channel pattern, (ii) the principle of sustainable development of river systems, and (iii) field investigation on river functions and environment, the paper proposes a river management model, which is characterized by (a) a low flow channel with optimal channel pattern, (b) ecotype river banks, (c) riparian forests, (d) embankment dams located at an optimum distance away from river banks, and (e) accessibility to watersides. A river system possessing these characteristics produces maximum societal, economic, and environmental benefits.

Development of artificial intelligence approach to forecasting oyster norovirus outbreaks along Gulf of Mexico coast

This paper presents an artificial intelligence-based model, called ANN-2Day model, for forecasting, managing and ultimately eliminating the growing risk of oyster norovirus outbreaks. The ANN-2Day model was developed using Artificial Neural Network (ANN) Toolbox in MATLAB Program and 15-years of epidemiological and environmental data for six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall. It was found that oyster norovirus outbreaks can be forecasted with two-day lead time using the ANN-2Day model and daily data of the six environmental predictors. Forecasting results of the ANN-2Day model indicated that the model was capable of reproducing 19years of historical oyster norovirus outbreaks along the Northern Gulf of Mexico coast with the positive predictive value of 76.82%, the negative predictive value of 100.00%, the sensitivity of 100.00%, the specificity of 99.84%, and the overall accuracy of 99.83%, respectively, demonstrating the efficacy of the ANN-2Day model in predicting the risk of norovirus outbreaks to human health. The 2-day lead time enables public health agencies and oyster harvesters to plan for management interventions and thus makes it possible to achieve a paradigm shift of their daily management and operation from primarily reacting to epidemic incidents of norovirus infection after they have occurred to eliminating (or at least reducing) the risk of costly incidents.