Date of Award:
12-2008
Document Type:
Dissertation
Degree Name:
Doctor of Philosophy (PhD)
Department:
Civil and Environmental Engineering
Committee Chair(s)
David K, Stevens
Committee
David K, Stevens
Committee
R. Ryan Dupont
Committee
Thomas B. Hardy
Committee
Darwin L. Sorensen
Committee
Wayne A. Wurtsbaugh
Abstract
Daily time series-based models are required to estimate the higher frequency fluctuations of nutrient loads and concentrations. Some mechanistic mathematical models can provide daily time series outputs of nutrient concentrations but it is difficult to incorporate non-numerical data, such as management scenarios, to mechanistic mathematical models. Bayesian networks (BNs) were designed to accept and process inputs of varied types of both numerical and non-numerical inputs.
A Rank-Data distribution method (R-D method) was developed to provide large time series of daily predicted flows and Total Phosphorus (TP) loads to BNs driving daily time series estimates of T-P concentrations into Hyrum and Cutler Reservoirs, Cache County, Utah. Time series of water resources data may consist of data distributions and time series of the ranks of the data at the measurement times. The R-D method estimates the data distribution by interpolating cumulative failure probability (CFPs) plots of observations. This method also estimates cumulative failure probability of predictions on dates with no data by interpolating CFP time series of observations. The R-D method estimates time series of mean daily flows with less residual between predicted flows and observed flows than interpolation of observed flows using data sets sampled randomly at varying frequencies.
Two Bayesian Networks, BN 1 (Bayesian Network above Hyrum Reservoir) and BN 2 (Bayesian Network below Hyrum Reservoir) were used to simulate the effect of the Little Bear River Conservation Project (LBRCP) and exogenous variables on water quality to explore the causes of an observed reduction in Total Phosphorus (TP) concentration since 1990 at the mouth of the Little Bear River. A BN provided the fine data distribution of flows and T-P loads under scenarios of conservation practices or exogenous variables using daily flows and TP loads estimated by R-D method. When these BN outputs were connected with the rank time series estimated by interpolation of the ranks of existing observations at measurement dates, time series estimation of TP concentrations into Cutler Reservoir under two different conservation practice options was obtained. This time series showed duration and starting time of water quality criterion violation. The TMDL processes were executed based on daily TP loads from R-D instead of mean or median values.
Checksum
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Recommended Citation
Lee, Joon-Hee, "Rank-Data Distribution Method (R-D Method) for Daily Time-Series Bayesian Networks and Total Maximum Daily Load Estimation" (2008). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 132.
https://digitalcommons.usu.edu/etd/132
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