scholarly journals Benchmarking the efficiency of a metamodeling-enabled algorithm for the calibration of surface water quality models

2020 ◽  
Vol 22 (6) ◽  
pp. 1718-1726
Author(s):  
K. Kandris ◽  
E. Romas ◽  
A. Tzimas
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Water Quality Modeling ◽  
Automatic Calibration ◽  
High Fidelity ◽  
Quality Models ◽  
Derivative Free Optimization ◽  
Tangible Evidence ◽  
Water Quality Models

Abstract Computational efficiency is a major obstacle imposed in the automatic calibration of numerical, high-fidelity surface water quality models. To surpass this obstacle, the present work formulated a metamodeling-enabled algorithm for the calibration of surface water quality models and assessed the computational gains from this approach compared to a benchmark alternative (a derivative-free optimization algorithm). A radial basis function was trained over multiple snapshots of the original high-fidelity model to emulate the latter's behavior. This data-driven proxy of the original model was subsequently employed in the automatic calibration of the water quality models of two water reservoirs and, finally, the computational gains over the benchmark alternative were estimated. The benchmark analysis revealed that the metamodeling-enabled optimizer reached a solution with the same quality compared to its benchmark alternative in 20–38% lower process times. Thereby, this work manifests tangible evidence of the potential of metamodeling-enabled strategies and sets out a discussion on how to maximize computational gains deriving from such strategies in surface water quality modeling.

scholarly journals A Review of Surface Water Quality Models

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Qinggai Wang ◽  
Shibei Li ◽  
Peng Jia ◽  
Changjun Qi ◽  
Feng Ding
Keyword(s):  
Water Quality ◽  
Environmental Management ◽  
Surface Water ◽  
Surface Water Quality ◽  
Developed Countries ◽  
Control Measures ◽  
Quality Models ◽  
The Status ◽  
Three Stages ◽  
Water Quality Models

Surface water quality models can be useful tools to simulate and predict the levels, distributions, and risks of chemical pollutants in a given water body. The modeling results from these models under different pollution scenarios are very important components of environmental impact assessment and can provide a basis and technique support for environmental management agencies to make right decisions. Whether the model results are right or not can impact the reasonability and scientificity of the authorized construct projects and the availability of pollution control measures. We reviewed the development of surface water quality models at three stages and analyzed the suitability, precisions, and methods among different models. Standardization of water quality models can help environmental management agencies guarantee the consistency in application of water quality models for regulatory purposes. We concluded the status of standardization of these models in developed countries and put forward available measures for the standardization of these surface water quality models, especially in developing countries.

On the effect of scaling conceptual model complexity on stochastic response for water quality modeling

2011 ◽  
Vol 63 (2) ◽  
pp. 360-366
Author(s):  
G. T. Parker
Keyword(s):  
Water Quality ◽  
Conceptual Model ◽  
Water Quality Modeling ◽  
Response Surfaces ◽  
Model Complexity ◽  
Quality Model ◽  
Real World Data ◽  
Quality Models ◽  
Modelling Framework ◽  
Water Quality Models

This paper extends previous work comparing the response of water quality models under uncertainty. A new model, the River Water Quality Model no. 1 (RWQM1), is compared to the previous work of two commonly used water quality models. Additionally, the effect of conceptual model scaling within a single modelling framework, as allowed by RWQM1, is explored under uncertainty. Model predictions are examined using against real-world data for the Potomac River with a Generalized Likelihood Uncertainty Estimation used to assess model response surfaces to uncertainty. Generally, it was found that there are tangible model characteristics that are closely tied to model complexity and thresholds for these characteristics were discussed. The novel work has yielded an illustrative example but also a conceptually scaleable water quality modelling tool, alongside defined metrics to assess when scaling is required under uncertainty. The resulting framework holds substantial, unique, promise for a new generation of modelling tools that are capable of addressing classically intractable problems.

scholarly journals Improving In-Stream Nutrient Routines in Water Quality Models Using Stable Isotope Tracers: A Review and Synthesis

2018 ◽  
Vol 61 (1) ◽  
pp. 139-157 ◽  
Author(s):  
Alexandria Jensen ◽  
William Ford ◽  
James Fox ◽  
Admin Husic
Keyword(s):  
Water Quality ◽  
Stable Isotopes ◽  
Stable Isotope ◽  
Water Quality Modeling ◽  
Water Quality Model ◽  
Model Structure ◽  
Quality Model ◽  
Quality Models ◽  
Stable Isotope Signatures ◽  
Water Quality Models

Abstract. Water quality models serve as an economically feasible alternative to quantify fluxes of nutrient pollution and to simulate effective mitigation strategies; however, their applicability is often questioned due to broad uncertainties in model structure and parameterization, leading to uncertain outputs. We argue that reduction of uncertainty is partially achieved by integrating stable isotope data streams within the water quality model architecture. This article outlines the use of stable isotopes as a response variable within water quality models to improve the model boundary conditions associated with nutrient source provenance, constrain model parameterization, and elucidate shortcomings in the model structure. To assist researchers in future modeling efforts, we provide an overview of stable isotope theory; review isotopic signatures and applications for relevant carbon, nitrogen, and phosphorus pools; identify biotic and abiotic processes that impact isotope transfer between pools; review existing models that have incorporated stable isotope signatures; and highlight recommendations based on synthesis of existing knowledge. Broadly, we find existing applications that use isotopes have high efficacy for reducing water quality model uncertainty. We make recommendations toward the future use of sediment stable isotope signatures, given their integrative capacity and practical analytical process. We also detail a method to incorporate stable isotopes into multi-objective modeling frameworks. Finally, we encourage watershed modelers to work closely with isotope geochemists to ensure proper integration of stable isotopes into in-stream nutrient fate and transport routines in water quality models. Keywords: Isotopes, Nutrients, Uncertainty analysis, Water quality modeling, Watershed.

Data assimilation in surface water quality modeling: A review

2020 ◽  
Vol 186 ◽  
pp. 116307 ◽  
Author(s):  
Kyung Hwa Cho ◽  
Yakov Pachepsky ◽  
Mayzonee Ligaray ◽  
Yongsung Kwon ◽  
Kyung Hyun Kim
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Data Assimilation ◽  
Surface Water Quality ◽  
Water Quality Modeling ◽  
Quality Modeling

Water quality prediction and probability network models

1999 ◽  
Vol 56 (7) ◽  
pp. 1150-1158 ◽  
Author(s):  
Kenneth H Reckhow
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
River Estuary ◽  
Surface Water Quality ◽  
Network Models ◽  
Water Quality Modeling ◽  
Neuse River Estuary ◽  
Bayesian Probability ◽  
Bayes Nets ◽  
Quality Modeling

It is a common strategy in surface water quality modeling to attempt to remedy predictive inadequacies by incorporating additional mechanistic detail into the model. This approach reflects the reasonable belief that enhanced scientific understanding of basic processes can be used to improve predictive modeling. However, nature is complex, and even the most detailed simulation model is extremely simple in comparison. At some point, additional detail exceeds our ability to simulate and predict with reasonable error levels. In those situations, an attractive alternative may be to express the complex behavior probabilistically, as in statistical mechanics, for example. This viewpoint is the basis for consideration of Bayesian probability networks for surface water quality assessment and prediction. To begin this examination of Bayes nets, some simple water quality examples are used for the illustration of basic ideas. This is followed by discussion of a set of proposed probability network models for the eutrophication of the Neuse River estuary in North Carolina. The presentation concludes with consideration of applications and opportunities for Bayes nets in predictive water quality modeling.

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