Darrell G. Fontane's research while affiliated with Colorado State University and other places

While advection, dispersion, and their hydraulic background are broadly explored in water quality modelling, conversion processes (reaction and decay rates) usually lack of attention. They represent physical, chemical and biological processes that reactive substances go through in contact with water, typically assumed as calibration parameters. This study introduces a method for model calibration considering a temporal aspect of transformation coefficients: TRATS – Transformation Rates Time Series. Daily decay rates are generated as result of: (i) attributes of the river station, (ii) reference values from literature, and (iii) random variability. These transformation parameters are then applied in water quality simulations under unsteady state in a river, for biochemical oxygen demand (BOD), organic nitrogen (N-org) and dissolved oxygen (DO). The proposed method for integrated modelling suggests that temporal variation of kinetic processes may play an import role in transport and fate of pollutants in terms of overall variability and attenuation over time and space, and this aspect can be included in the calibration phase.

Recent water resources planning and management strategies state that the concepts of risk and variable inputs should be appraised in order to comply with multiple conditions. This becomes evident especially in environments with diverse uses of water, land use and climate change. In such a context, modelling of discharges and concentrations in rivers are valuable strategies to predict different scenarios. This research proposes an integrated analysis for modelling of flow and contaminant transport in rivers, based on hydrodynamics, time series, and water quality simulations. The first module estimates water volume and velocity, that have direct impact in pollutants transport; time series of concentrations are generated as synthetic pollutographs, using techniques based on flow conditions, time and statistical factors of a historical monitoring dataset – the objective is to match temporal scales of boundary conditions, since water quality data is usually available as irregular samples; the third module solves the advection-dispersion-reaction equation, exploring the different synthetic series as input. Results evidence that the input pollutograph, usually not explored in similar studies, may have a significant role in simulations for transport of substance in rivers under unsteady state; as consequence, corroborate with better estimates for planning strategies where temporal dynamic is relevant. The contributions lay the basis for further assessment of riverine systems linked to watershed dynamics, with multiple scenarios of data availability and input conditions.

This paper presents a new multiobjective approach to solve sedimentation problems behind weirs and low-head dams. The multicriteria decision analysis (MCDA) framework is used to improve reservoir operation rules for Sangju Weir in South Korea. A series of stage and discharge constraints can be developed to include consideration for reservoir sedimentation, hydropower generation, flood control, water supply, irrigation and drainage, and environment. Seasonally changing operation rules can help mitigate reservoir sedimentation while improving hydropower production, water supply, water quality, and environmental issues. Based on a 22-year daily reservoir operation simulation, improved operation rules to mitigate reservoir sedimentation include: (1) a nonflood season stage kept high (EL 47.0 m); (2) a flood season stage between EL 47.0 m and EL 44.5 m depending on the magnitude of the upstream flow discharge; and (3) gates should be opened during floods (Q > 600 m³/s).

The presence of highly urbanized and polluted areas affects both the quantity and the composition of organic matter in rivers through effluent loads and urban runoff discharges in watersheds. In such context, this paper aims to evaluate the biodegradability of anthropogenic organic matter in polluted rivers. Stream water samples were collected in three different sites considering a non-impacted area, a highly urbanized site located after a sewage treatment plant, and a site downstream of the watershed. For the biodegradation experiment, two adaptations of biodegradable dissolved organic carbon (BDOC) essay were evaluated to assess the decomposition rates between 10 days, with added nutrients, in the dark at 20 °C. The organic matter biodegradation was monitored by distinct parameters such as dissolved organic carbon (DOC), total organic carbon (TOC), particulate organic carbon (POC), fluorescence excitation-emission matrix (EEM), and UV absorbance measurements. The measured BDOC ranged from 0.8 mg/L at site IG01 (low anthropogenic occupation) to 4.2 mg/L at site IG02 (high impacted area), with averaged percentage of initial DOC ranging from 20 to 56 %, while an average of 28 % up to 95 % of POC can be considered as biodegradable. This pattern of biodegradation of fluorescent components was also observed through a decrease of tryptophan-like and tyrosine-like fluorescence peak intensity during the incubation time. The results also showed a higher decrease of humic-like fluorescence peak intensity at polluted sites (IG02 and IG05). Our experimental approach and monitoring strategy suggests that the evaluation of the organic matter biodegradability is essential to understand the fate and transformation mechanism of organic matter in urbanized and polluted rivers. And, considering a water quality planning and management perspective, this approach is important to identify the presence and location of organic compounds potentially important for dissolved oxygen depletion in stream waters.

Control of agricultural nonpoint sources of pollution is achievable through implementation of conservation practices at the farm or field level. There are several approaches to achieve a targeted implementation of conservation practices at the watershed scale. Recent studies have shown that optimization methods hold great promise for optimal allocation of nonpoint source pollution control measures. However, the use of optimization is a computationally intensive task and ultimately depends upon availability of automated optimization tools and expertise to analyze the results. In this study, a novel multicriteria decision analysis (MCDA) framework is proposed to identify a near-optimal suite of conservation practices at the watershed scale using a priori knowledge about the system. The proposed framework requires: (1) selecting a set of criteria, depending upon the objectives of the study, that should be considered in ranking the alternatives; (2) constructing an evaluation matrix; and (3) using a computational MCDA method to aggregate the scores based upon the various criteria and rank the alternatives. The framework was used to identify optimal placement of four types of conservation practices for nutrient and pesticide load control at minimum cost in the Eagle Creek Watershed, Indiana, United States. Results were compared with optimal solutions obtained from an optimization framework coupled with the soil and water assessment tool (SWAT). The results of this study showed that the proposed framework can be an effective and efficient approach in identifying near-optimal solutions for nonpoint source pollution control. The MCDA framework outperformed the optimization method by identifying similar solutions with more diversity without any need for iterative search algorithms. For highly complex problems or for a poorly established evaluation matrix, the MCDA framework may fail to identify near-optimal solutions; however, the results can effectively serve as a good initial population in a hybrid MCDA and optimization framework. A hybrid framework substantially improved efficiency of the search algorithm, optimality of the Pareto-front, and diversity of the solutions. This study also highlighted importance of the defining proper decision variables and accurate scoring of the conservation practices for successful implementation of conservation plans at the watershed scale. (C) 2014 American Society of Civil Engineers.

This paper presents a methodology for obtaining and adjusting of efficiency curves for hydroelectric generating units. It is based on measured data of power, gross head, and water discharge recorded by the company that manages the plant operation. The objective is to determine the actual performance characteristics of the set: turbine, generator, and penstock. In order to obtain the efficiency functions, an iterative calculation is used. Its input data are the functions currently in use of turbine efficiency, generator efficiency, and penstock head losses. For the adjustment of the efficiency functions, the Generalized Reduced Gradient optimization method is employed. A case study was applied to the data from a large Brazilian hydroelectric plant whose operation is under the coordination of the Electric System National Operator. The benefits of the proposed methodology are analyzed using a simulation tool for the hydroelectric operation. The simulator is used to reproduce the past operation of the plant, first with current data and second with adjusted data. The results show that the optimal unit efficiency functions significantly contribute to bring the real and simulated operation closer.

The power generated by a hydroelectric plant depends on the penstock head loss, the turbine efficiency, and the generator efficiency among other factors. Initially, the functions related to these variables are provided by the equipment manufacturer; however, over time they change as the plant ages. This paper presents a methodology to adjust an efficiency function for each generating unit based on measured data. It is applied using two optimization methods: the generalized reduced gradient and the evolutionary algorithm. A case study with data from a large Brazilian hydroelectric plant demonstrates how the methodology can be employed and compares the performance of the optimization tools. A comparison metric is used to show that the optimal unit efficiency function significantly improves the performance of simulation models to reproduce observed data and better describe the actual operation of the hydroelectric plant. DOI: 10.1061/(ASCE)EY.1943-7897.0000074. (C) 2012 American Society of Civil Engineers.