Estimating Uncertainty in HSPF based Water Quality Model: Application of Monte-Carlo Based Techniques

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dc.contributor.authorMishra, Anuragen
dc.contributor.committeechairBenham, Brian L.en
dc.contributor.committeememberGallagher, Daniel L.en
dc.contributor.committeememberReckhow, Kenneth H.en
dc.contributor.committeememberSmith, Eric P.en
dc.contributor.committeememberWolfe, Mary Leighen
dc.contributor.departmentBiological Systems Engineeringen
dc.date.accessioned2014-03-14T20:15:37Zen
dc.date.adate2011-09-15en
dc.date.available2014-03-14T20:15:37Zen
dc.date.issued2011-07-28en
dc.date.rdate2011-09-15en
dc.date.sdate2011-08-23en
dc.description.abstractTo propose a methodology for the uncertainty estimation in water quality modeling as related to TMDL development, four Monte Carlo (MC) based techniques—single-phase MC, two-phase MC, Generalized Likelihood Uncertainty Estimation (GLUE), and Markov Chain Monte Carlo (MCMC) —were applied to a Hydrological Simulation Program–FORTRAN (HSPF) model developed for the Mossy Creek bacterial TMDL in Virginia. Predictive uncertainty in percent violations of instantaneous fecal coliform concentration criteria for the prediction period under two TMDL pollutant allocation scenarios was estimated. The average percent violations of the applicable water quality criteria were less than 2% for all the evaluated techniques. Single-phase MC reported greater uncertainty in percent violations than the two-phase MC for one of the allocation scenarios. With the two-phase MC, it is computationally expensive to sample the complete parameter space, and with increased simulations, the estimates of single and two-phase MC may be similar. Two-phase MC reported significantly greater effect of knowledge uncertainty than stochastic variability on uncertainty estimates. Single and two-phase MC require manual model calibration as opposed to GLUE and MCMC that provide a framework to obtain posterior or calibrated parameter distributions based on a comparison between observed and simulated data and prior parameter distributions. Uncertainty estimates using GLUE and MCMC were similar when GLUE was applied following the log-transformation of observed and simulated FC concentrations. GLUE provides flexibility in selecting any model goodness of fit criteria for calculating the likelihood function and does not make any assumption about the distribution of residuals, but this flexibility is also a controversial aspect of GLUE. MCMC has a robust formulation that utilizes a statistical likelihood function, and requires normal distribution of model errors. However, MCMC is computationally expensive to apply in a watershed modeling application compared to GLUE. Overall, GLUE is the preferred approach among all the evaluated uncertainty estimation techniques, for the application of watershed modeling as related to bacterial TMDL development. However, the application of GLUE in watershed-scale water quality modeling requires further research to evaluate the effect of different likelihood functions, and different parameter set acceptance/rejection criteria.en
dc.description.degreePh. D.en
dc.identifier.otheretd-08232011-172249en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08232011-172249/en
dc.identifier.urihttp://hdl.handle.net/10919/28777en
dc.publisherVirginia Techen
dc.relation.haspartMishra_A_D_2011.pdfen
dc.relation.haspartMishra_A_D_2011_Fairuse.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectwater quality modelingen
dc.subjectTMDLen
dc.subjectfecal coliformen
dc.subjectHSPFen
dc.subjectuncertainty analysisen
dc.subjectMonte-Carloen
dc.subjectBayesian techniquesen
dc.subjectGLUEen
dc.subjectMCMCen
dc.subjecttwo-phase Monte Carlo analysisen
dc.titleEstimating Uncertainty in HSPF based Water Quality Model: Application of Monte-Carlo Based Techniquesen
dc.typeDissertationen
thesis.degree.disciplineBiological Systems Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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