Stochastic simulation of hydrologic data based on nonparametric approaches
| dc.contributor.author | Lee, Taesam, author | |
| dc.contributor.author | Salas, Jose D., advisor | |
| dc.date.accessioned | 2024-03-13T19:53:57Z | |
| dc.date.available | 2024-03-13T19:53:57Z | |
| dc.date.issued | 2008 | |
| dc.description.abstract | Stochastic simulation of hydrologic data has been widely developed for several decades. However, despite the several advances made in literature still a number of limitations and problems remain. The major research topic in this dissertation is to develop stochastic simulation approaches to tackle some of the existing problems such as the preservation of the long-term variability and the joint modeling of intermittent and non-intermittent stations. For this purpose, nonparametric techniques have been applied. For simulating univariate seasonal streamflows, a model is suggested based on k-nearest neighbors resampling (KNNR). Gamma kernel density estimate (KDE) perturbation is employed to generate realistic values of streamflow that are not part of the historical data. Further, aggregate and pilot variables are included in KNNR so as to reproduce the long-term variability. For multivariate streamflows, the moving block bootstrapping procedure is employed considering a random block length, KNNR block selection to avoid the discontinuity between blocks, a Genetic Algorithm mixture, and Gamma KDE perturbation. In addition, the drawbacks of an existing nonparametric disaggregation scheme have been examined and appropriate modifications developed that include accurate adjusting for the disaggregate variable, KNNR, and Genetic Algorithm mixture. The suggested univariate, multivariate, and disaggregation models have been compared with existing nonparametric models using several cases of streamflow data of the Colorado River System. In all cases, the results showed major improvements. Furthermore, disaggregation from daily to hourly rainfall for a single site has been studied based on three disaggregation models so as to account for the diurnal cycle in hourly data. Those models are (1) Conditional Markov Chain and Simulated Annealing (CMSA), (2) Product Model (GAR(1)-PDAR(1)) with Accurate Adjusting (PGAA), and (3) Stochastic Selection Method with Weighted Storm Distribution (SSMW). Various tests and comparisons have been performed to validate the models and it revealed that PGAA is superior to the others for preserving the diurnal cycle and the key statistics of hourly rainfall. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | ETDF_Lee_2008_3346465.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/237836 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.rights.license | Per the terms of a contractual agreement, all use of this item is limited to the non-commercial use of Colorado State University and its authorized users. | |
| dc.subject | disaggregation | |
| dc.subject | nonparametric | |
| dc.subject | simulation | |
| dc.subject | stochastic hydrology | |
| dc.subject | streamflow | |
| dc.subject | time series | |
| dc.subject | hydrologic sciences | |
| dc.title | Stochastic simulation of hydrologic data based on nonparametric approaches | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Civil and Environmental Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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