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Computational Water Quality Modelling of Western Lake Erie.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational Water Quality Modelling of Western Lake Erie./
Author:	Wang, Qi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	150 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
Subject:	Water quality. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29101653
ISBN:	9798426888852

Computational Water Quality Modelling of Western Lake Erie.
Wang, Qi.

Computational Water Quality Modelling of Western Lake Erie. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 150 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--Queen's University (Canada), 2022.

This item must not be sold to any third party vendors.

During the 1970s, harmful algal blooms (HABs) were common occurrences in western Lake Erie. Remediation strategies reduced total P loads and bloom frequency; however, HABs have reoccurred since the mid-1990s under increased system stress from climate change. Given these concurrent changes in nutrient loading and climate forcing, there is a need to develop management tools to investigate historical changes in the lake and predict future water quality. Herein, we applied coupled one-dimensional (1D, AED-GLM) and three-dimensional (3D, AEM3D) hydrodynamic and biogeochemical models to reproduce water quality conditions of western Lake Erie from 1979-2015 and 2002-2014, respectively. For the 1D model, the root-mean-square errors (RMSE) between simulations and observations for water levels (0.36 m), surface water temperature (2.5 ℃), and concentrations of total phosphorus (0.01 mg L-1 ), phosphate (0.01 mg L-1 ), ammonium (0.03 mg L-1 ), nitrate (0.68 mg L-1 ), total chlorophyll-a (18.74 µg L-1 ), chlorophytes (3.94 µg L-1 ), cyanobacteria (12.44 µg L-1 ), diatoms (3.17 µg L-1 ), and cryptophytes (3.18 µg L-1 ) were minimized using model-independent parameter estimation. A sensitivity analysis shows that 40% reductions of total P and dissolved reactive P loads would have been necessary to bring blooms under the mild threshold (9600 MTA cyanobacteria biomass) during recent years (2005-2015), consistent with the Annex 4 recommendation. The 3D model was calibrated/validated in 2008/2009 using temperature, phosphate, total phosphorus, and chlorophyll-a data, with RMSE of 2.77/1.97 ℃, 1.78/5.65, 3.18/9.30, and 1.75/2.84 µg L-1 . In addition, the model was calibrated/validated against phytoplankton succession data over 2008-09/2002-14 with RMSE of 2.79-2.67/4.80-4.89 µg L-1 for early diatoms, 0.46-1.67/0.88-2.81 µg L-1 for late diatoms, 0.59-0.83/0.47-0.78 µg L-1 for cryptophytes, 0.59-0.73/0.64-0.84 µg L-1 for chlorophytes, and 4.15-10.90/2.62-12.89 µg L-1 for cyanobacteria; depending on the biomass to chlorophyll-a conversion method. The RMSE were comparable to those from seasonal simulations, indicating that this model can be calibrated using a single parameter set for decade long simulations and that model drift was minimal. Finally, because 3D and 1D models require different computational power and have different agreement with observations, we cross-compared simulations from these two models against observations of water temperature, total phosphorus, phosphate, nitrate, total chlorophyll-a and cyanobacteria at three stations along a transect from near the Maumee River mouth to mid-basin (average RMSE of 1.18/3.28 ℃, 0.04/0.05 mg L-1 , 0.01/0.05 mg L-1 , 0.71/0.93 mg L-1 , 21.99/19.50 µg L-1 , and 5.76/14.74 µg L-1 for AEM3D-iWQ/AED-GLM, respectively). The results show that 1D AED-GLM performed better in capturing the cyanobacteria bloom years, as this horizontally-averaged model was automatically calibrated to basin-average values, while 3D AEM3D performed better in reproducing seasonal and spatial variations of nutrients and phytoplankton at discrete stations, especially the algal plume near the Maumee River mouth.

ISBN: 9798426888852Subjects--Topical Terms:

556913
Water quality.

Computational Water Quality Modelling of Western Lake Erie.
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035 $a (MiAaPQ)AAI29101653
035 $a (MiAaPQ)QueensUCan_197429966
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100 1 $a Wang, Qi. $3 818625
245 1 0 $a Computational Water Quality Modelling of Western Lake Erie.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 150 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Boegman, Leon.
502 $a Thesis (Ph.D.)--Queen's University (Canada), 2022.
506 $a This item must not be sold to any third party vendors.
520 $a During the 1970s, harmful algal blooms (HABs) were common occurrences in western Lake Erie. Remediation strategies reduced total P loads and bloom frequency; however, HABs have reoccurred since the mid-1990s under increased system stress from climate change. Given these concurrent changes in nutrient loading and climate forcing, there is a need to develop management tools to investigate historical changes in the lake and predict future water quality. Herein, we applied coupled one-dimensional (1D, AED-GLM) and three-dimensional (3D, AEM3D) hydrodynamic and biogeochemical models to reproduce water quality conditions of western Lake Erie from 1979-2015 and 2002-2014, respectively. For the 1D model, the root-mean-square errors (RMSE) between simulations and observations for water levels (0.36 m), surface water temperature (2.5 ℃), and concentrations of total phosphorus (0.01 mg L-1 ), phosphate (0.01 mg L-1 ), ammonium (0.03 mg L-1 ), nitrate (0.68 mg L-1 ), total chlorophyll-a (18.74 µg L-1 ), chlorophytes (3.94 µg L-1 ), cyanobacteria (12.44 µg L-1 ), diatoms (3.17 µg L-1 ), and cryptophytes (3.18 µg L-1 ) were minimized using model-independent parameter estimation. A sensitivity analysis shows that 40% reductions of total P and dissolved reactive P loads would have been necessary to bring blooms under the mild threshold (9600 MTA cyanobacteria biomass) during recent years (2005-2015), consistent with the Annex 4 recommendation. The 3D model was calibrated/validated in 2008/2009 using temperature, phosphate, total phosphorus, and chlorophyll-a data, with RMSE of 2.77/1.97 ℃, 1.78/5.65, 3.18/9.30, and 1.75/2.84 µg L-1 . In addition, the model was calibrated/validated against phytoplankton succession data over 2008-09/2002-14 with RMSE of 2.79-2.67/4.80-4.89 µg L-1 for early diatoms, 0.46-1.67/0.88-2.81 µg L-1 for late diatoms, 0.59-0.83/0.47-0.78 µg L-1 for cryptophytes, 0.59-0.73/0.64-0.84 µg L-1 for chlorophytes, and 4.15-10.90/2.62-12.89 µg L-1 for cyanobacteria; depending on the biomass to chlorophyll-a conversion method. The RMSE were comparable to those from seasonal simulations, indicating that this model can be calibrated using a single parameter set for decade long simulations and that model drift was minimal. Finally, because 3D and 1D models require different computational power and have different agreement with observations, we cross-compared simulations from these two models against observations of water temperature, total phosphorus, phosphate, nitrate, total chlorophyll-a and cyanobacteria at three stations along a transect from near the Maumee River mouth to mid-basin (average RMSE of 1.18/3.28 ℃, 0.04/0.05 mg L-1 , 0.01/0.05 mg L-1 , 0.71/0.93 mg L-1 , 21.99/19.50 µg L-1 , and 5.76/14.74 µg L-1 for AEM3D-iWQ/AED-GLM, respectively). The results show that 1D AED-GLM performed better in capturing the cyanobacteria bloom years, as this horizontally-averaged model was automatically calibrated to basin-average values, while 3D AEM3D performed better in reproducing seasonal and spatial variations of nutrients and phytoplankton at discrete stations, especially the algal plume near the Maumee River mouth.
590 $a School code: 0283.
650 4 $a Water quality. $3 556913
650 4 $a Best management practices. $3 3685326
650 4 $a Aquatic ecosystems. $3 3560371
650 4 $a Algae. $3 546231
650 4 $a Climate change. $2 bicssc $3 2079509
650 4 $a Water resources management. $3 794747
690 $a 0404
690 $a 0454
690 $a 0595
710 2 $a Queen's University (Canada). $3 1017786
773 0 $t Dissertations Abstracts International $g 83-11B.
790 $a 0283
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29101653