Literature DB >> 25602409

Three-dimensional lake water quality modeling: sensitivity and uncertainty analyses.

Shahram Missaghi, Miki Hondzo, Charles Melching.   

Abstract

Two sensitivity and uncertainty analysis methods are applied to a three-dimensional coupled hydrodynamic-ecological model (ELCOM-CAEDYM) of a morphologically complex lake. The primary goals of the analyses are to increase confidence in the model predictions, identify influential model parameters, quantify the uncertainty of model prediction, and explore the spatial and temporal variabilities of model predictions. The influence of model parameters on four model-predicted variables (model output) and the contributions of each of the model-predicted variables to the total variations in model output are presented. The contributions of predicted water temperature, dissolved oxygen, total phosphorus, and algal biomass contributed 3, 13, 26, and 58% of total model output variance, respectively. The fraction of variance resulting from model parameter uncertainty was calculated by two methods and used for evaluation and ranking of the most influential model parameters. Nine out of the top 10 parameters identified by each method agreed, but their ranks were different. Spatial and temporal changes of model uncertainty were investigated and visualized. Model uncertainty appeared to be concentrated around specific water depths and dates that corresponded to significant storm events. The results suggest that spatial and temporal variations in the predicted water quality variables are sensitive to the hydrodynamics of physical perturbations such as those caused by stream inflows generated by storm events. The sensitivity and uncertainty analyses identified the mineralization of dissolved organic carbon, sediment phosphorus release rate, algal metabolic loss rate, internal phosphorus concentration, and phosphorus uptake rate as the most influential model parameters.
Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Entities:  

Year:  2013        PMID: 25602409     DOI: 10.2134/jeq2013.04.0120

Source DB:  PubMed          Journal:  J Environ Qual        ISSN: 0047-2425            Impact factor:   2.751


  1 in total

1.  Linking multi-media modeling with machine learning to assess and predict lake Chlorophyll a concentrations.

Authors:  Christina Feng Chang; Valerie Garcia; Chunling Tang; Penny Vlahos; David Wanik; Jun Yan; Jesse O Bash; Marina Astitha
Journal:  J Great Lakes Res       Date:  2021-12-13       Impact factor: 3.032

  1 in total

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