Simultaneous estimation of model parameters and diffuse pollution sources for river water quality modeling

2007 ◽  
Vol 56 (1) ◽  
pp. 155-162 ◽  
Author(s):  
K.S. Jun ◽  
J.W. Kang ◽  
K.S. Lee
Keyword(s):  
Water Quality ◽  
River Water ◽  
Optimal Estimation ◽  
Water Quality Modeling ◽  
Diffuse Pollution ◽  
Kinetic Constants ◽  
Simultaneous Estimation ◽  
Model Parameters ◽  
Diffuse Sources ◽  
Quality Modeling

Diffuse pollution sources along a stream reach are very difficult to both monitor and estimate. In this paper, a systematic method using an optimal estimation algorithm is presented for simultaneous estimation of diffuse pollution and model parameters in a stream water quality model. It was applied with the QUAL2E model to the South Han River in South Korea for optimal estimation of kinetic constants and diffuse loads along the river. Initial calibration results for kinetic constants selected from a sensitivity analysis reveal that diffuse source inputs for nitrogen and phosphorus are essential to satisfy the system mass balance. Diffuse loads for total nitrogen and total phosphorus were estimated by solving the expanded inverse problem. Comparison of kinetic constants estimated simultaneously with diffuse sources to those estimated without diffuse loads, suggests that diffuse sources must be included in the optimization not only for its own estimation but also for adequate estimation of the model parameters. Application of the optimization method to river water quality modeling is discussed in terms of the sensitivity coefficient matrix structure.

Process-based or Probabilistic Models?

2020 ◽  
Author(s):  
Craig Stow
Keyword(s):  
Water Quality ◽  
Analytical Solutions ◽  
Probabilistic Models ◽  
Likelihood Function ◽  
Simulation Models ◽  
Water Quality Modeling ◽  
Process Models ◽  
Model Parameters ◽  
Bayesian Approaches ◽  
Quality Modeling

<p>The historical adoption of Bayesian approaches was limited by two main impediments: 1) the requirement for subjective prior information, and 2) the unavailability of analytical solutions for all but a few simple model forms. However, water quality modeling has always been subjective; selecting point values for model parameters, undertaking some “judicious diddling” to adjust them so that model output more closely matches observed data, and declaring the model to be “reasonable” is a long-standing practice. Water quality modeling in a Bayesian framework can actually reduce this subjectivity as it provides a rigorous and transparent approach for model parameter estimation. The second impediment, lack of analytical solutions, has for many applications, been largely reduced by the increasing availability of fast, cheap computing and concurrent evolution of efficient algorithms to sample the posterior distribution. In water quality modeling, however, the increasing computational availability may be reinforcing the dichotomy between probabilistic and “process-based” models. When I was a graduate student we couldn’t do both process and probability because computers weren’t fast enough. However, current computers unimaginably faster and we still rarely do both. It seems that our increasing computational capacity has been absorbed either in more complex and highly resolved, but still deterministic, process models, or more structurally complex probabilistic models (like hierarchical models) that are still light process. In principal, Bayes Theorem is quite general; any model could constitute the likelihood function, but practically, running Monte Carlo-based methods on simulation models that require hours, days, or even longer to run is not feasible. Developing models that capture the essential (and best understood processes) and that still allow a meaningful uncertainty analysis is an area that invites renewed attention.</p>

Water Resources Planning – A Research Program

1987 ◽  
Vol 19 (9) ◽  
pp. 119-124
Author(s):  
M. A. Santos ◽  
J. R. Costa
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
River Water ◽  
Drainage Basin ◽  
National Laboratory ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Quality Modeling ◽  
Resources Planning

A research project on “Methodologies for Water Resources Policy Analysis” is under current development at the National Laboratory for Civil Engineering, Portugal. Its main objectives are to develop and test techniques, computational tools and procedures which may help in the design of water resources plans, in the comparison and evaluation of alternative strategies and in real time drainage basin management and operation. In order to achieve these objectives the technical activity of the project has tackled such water resources problems as the assessment of water availability and demands, the characterization of river water quality and wastewater, water pollution control and river water quality modeling. Also, effective technology transfer from technicians to local, regional and national managers and decision-makers has been tried. In this paper, the main project activities are summarized, some of the achievements are pointed out and its most significant results are presented.

scholarly journals Evaluation of Uncertainty Propagation Predictions in River Water Quality Modeling

2021 ◽  
Author(s):  
André Fonseca ◽  
Cidália Botelho ◽  
Rui Boaventura ◽  
Vitor Vilar
Keyword(s):  
Water Quality ◽  
Monte Carlo ◽  
River Water ◽  
Fecal Coliform ◽  
Statistical Evaluation ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Evaluation Framework ◽  
Sensitivity Analyses ◽  
Quality Modeling

Abstract The uncertainty on model predictions to evaluate river water quality is often high to delineate appropriate conclusions. This study presents the statistical evaluation of the water quality modeling system Hydrologic Simulation Program FORTRAN as a tool to improve monitoring planning and mitigate uncertainty in water quality predictions. It also presents findings in determining HSPF model’s sensitivity analysis concerning water quality predictions. The computer model was applied to Ave River watershed, Portugal. The hydrology was calibrated at two stations from January 1990 to December 1994 and validated from January 1995 to December 1999. A two-step statistical evaluation framework is presented based on the most common hydrology criteria for model calibration and validation and, a Monte Carlo methodology uncertainty evaluation approach coupled with multi parametric sensitivity analyses to assess model uncertainty and parameter sensitivity. Fourteen HSPF water quality parameters probability distributions are used as input factors for the Monte Carlo simulation. The simulation results for in stream fecal coliform concentrations was found to be most sensitive to parameters that represent first order decay rate and surface runoff that removes 90 percent of fecal coliform from pervious land surface rather than accumulation and maximum storage rates. Regarding oxygen governing process (DO, BOD, NO3, PO4), benthal oxygen demand and nitrification/denitrification rates were the most sensitive parameters.

River water quality modeling

2021 ◽  
pp. 157-204
Author(s):  
Clark C.K. Liu ◽  
Pengzhi Lin ◽  
Hong Xiao
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Quality Modeling
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