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dc.contributor.supervisorDow, Karen (Civil Engineering)en_US
dc.contributor.authorAbbasnezhadi, Kian
dc.date.accessioned2017-03-31T13:27:11Z
dc.date.available2017-03-31T13:27:11Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/1993/32177
dc.description.abstractThe Canadian Precipitation Analysis (CaPA) system has been developed by Environment and Climate Change Canada (ECCC) to optimally combine different sources of information to estimate precipitation accumulation across Canada. The system combines observations from different networks of weather stations and radar measurements with the background information generated by ECCC's Regional Deterministic Prediction System (RDPS), derived from the Global Environmental Multiscale (GEM) model. The main scope of this study is to assess the importance of weather stations when combined with the background information for hydrological modeling. A new approach to meteorological network design, considered to be a stochastic hydro-geostatistical scheme, is proposed and investigated which is particularly useful for augmenting data-sparse networks. The approach stands out from similar approaches of its kind in that it is comprised of a data assimilation component included based on the paradigm of an Observing System Simulation Experiment (OSSE), a technique used to simulate data assimilation systems in order to evaluate the sensitivity of the analysis to new observation network. The proposed OSSE-based algorithm develops gridded stochastic precipitation and temperature models to generate synthetic time-series assumed to represent the 'reference' atmosphere over the basin. The precipitation realizations are used to simulate synthetic observations, associated with hypothetical station networks of various densities, and synthetic background data, which in turn are assimilated in CaPA to realize various pseudo-analyses. The reference atmosphere and the pseudo-analyses are then compared through hydrological modeling in WATFLOOD. By comparing the flow rates, the relative performance of each pseudo-analysis associated with a specific network density is assessed. The simulations show that as the network density increases, the accuracy of the hydrological signature of the CaPA precipitation products improves hyperbolically to a certain limit beyond which adding more stations to the network does not result in further accuracy. This study identifies an observation network density that can satisfy the hydrological criteria as well as the threshold at which assimilated products outperforms numerical weather prediction outputs. It also underlines the importance of augmenting observation networks in small river basins to better resolve mesoscale weather patterns and thus improve the predictive accuracy of streamflow simulation.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCaPA, GEM, geostatistics, precipitation, stochastic model, meteorological network, assimilation system, flow simulation, hydrologic modeling, WATFLOOD, precipitation gauge, precipitation analysisen_US
dc.titleInfluence of meteorological network density on hydrological modeling using input from the Canadian Precipitation Analysis (CaPA)en_US
dc.typeinfo:eu-repo/semantics/doctoralThesis
dc.typedoctoral thesisen_US
dc.degree.disciplineCivil Engineeringen_US
dc.contributor.examiningcommitteeStadnyk, Tricia (Civil Engineering) Hanesiak, John (Environment and Geography) Rousseau, Alain (Modelisation hydrologique, Institut National de la Recherche Scientifique - Centre Eau Terre Environnement)en_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.noteMay 2017en_US
local.subject.manitobayesen_US


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