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Modelling Monthly Water Balance: The...

LeGrand, Matthew.

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Modelling Monthly Water Balance: The Role of Lake Storage and Snow-related Processes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modelling Monthly Water Balance: The Role of Lake Storage and Snow-related Processes./
作者:	LeGrand, Matthew.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	141 p.
附註:	Source: Masters Abstracts International, Volume: 81-06.
Contained By:	Masters Abstracts International81-06.
標題:	Hydrologic sciences. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27541064
ISBN:	9781687993502

Modelling Monthly Water Balance: The Role of Lake Storage and Snow-related Processes.
LeGrand, Matthew.

Modelling Monthly Water Balance: The Role of Lake Storage and Snow-related Processes. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 141 p.

Source: Masters Abstracts International, Volume: 81-06.

Thesis (M.S.)--Trent University (Canada), 2019.

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

Water balance models calculate water storage and movement within drainage basins, a primary concern for many hydrologists. A Thornthwaite water balance model (H2OBAAS) has shown poor accuracy in predicting low flows in the Petawawa River basin in Ontario, so lake storage and winter snow processes were investigated to improve the accuracy of the model. Lake storage coefficients, represented by the slopes of lake stage vs. lake runoff relationships, were estimated for 19 lakes in the Petawawa River basin and compared on a seasonal and inter-lake basis to determine the factors controlling lake runoff behaviour. Storage coefficients varied between seasons, with spring having the highest coefficients, summer and fall having equal magnitude, and winter having the lowest coefficients. Storage coefficients showed positive correlation with lake watershed area, and negative correlation with lake surface area during summer, fall, and winter. Lake storage was integrated into the H2OBAAS and improved model accuracy, especially in late summer, with large increases in LogNSE, a statistical measure sensitive to low flows. However, varying storage coefficients with respect to seasonal lake storage, watershed area, and surface area did not improve runoff predictions in the model. Modified precipitation partitioning and snowmelt methods using monthly minimum and maximum temperatures were incorporated into the H2OBAAS and compared to the original methods, which used only average temperatures. Methods using temperature extremes greatly improved simulations of winter runoff and snow water equivalent, with the precipitation partitioning threshold being the most important model parameter. This study provides methods for improving low flow accuracy in a monthly water balance model through the incorporation of simple snow processes and lake storages.

ISBN: 9781687993502Subjects--Topical Terms:

3168407
Hydrologic sciences.
Subjects--Index Terms:

Lake Storage

Modelling Monthly Water Balance: The Role of Lake Storage and Snow-related Processes.
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500 $a Source: Masters Abstracts International, Volume: 81-06.
500 $a Advisor: Buttle, James M;Metcalfe, Robert A.
502 $a Thesis (M.S.)--Trent University (Canada), 2019.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Water balance models calculate water storage and movement within drainage basins, a primary concern for many hydrologists. A Thornthwaite water balance model (H2OBAAS) has shown poor accuracy in predicting low flows in the Petawawa River basin in Ontario, so lake storage and winter snow processes were investigated to improve the accuracy of the model. Lake storage coefficients, represented by the slopes of lake stage vs. lake runoff relationships, were estimated for 19 lakes in the Petawawa River basin and compared on a seasonal and inter-lake basis to determine the factors controlling lake runoff behaviour. Storage coefficients varied between seasons, with spring having the highest coefficients, summer and fall having equal magnitude, and winter having the lowest coefficients. Storage coefficients showed positive correlation with lake watershed area, and negative correlation with lake surface area during summer, fall, and winter. Lake storage was integrated into the H2OBAAS and improved model accuracy, especially in late summer, with large increases in LogNSE, a statistical measure sensitive to low flows. However, varying storage coefficients with respect to seasonal lake storage, watershed area, and surface area did not improve runoff predictions in the model. Modified precipitation partitioning and snowmelt methods using monthly minimum and maximum temperatures were incorporated into the H2OBAAS and compared to the original methods, which used only average temperatures. Methods using temperature extremes greatly improved simulations of winter runoff and snow water equivalent, with the precipitation partitioning threshold being the most important model parameter. This study provides methods for improving low flow accuracy in a monthly water balance model through the incorporation of simple snow processes and lake storages.
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650 4 $a Water resources management. $3 794747
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653 $a Monthly Water Balance
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653 $a Precipitation Partitioning
653 $a Snow Melt
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