Stochastic characterization of droughts in stationary and periodic series
| dc.contributor.author | Cancelliere, Antonino, author | |
| dc.contributor.author | Salas, Jose D., advisor | |
| dc.contributor.author | Boes, Duane C., advisor | |
| dc.date.accessioned | 2024-03-13T18:50:56Z | |
| dc.date.available | 2024-03-13T18:50:56Z | |
| dc.date.issued | 2008 | |
| dc.description.abstract | Stochastic modelling of droughts is a topic of great interest in water resources management. For instance, estimating drought probabilities and return periods helps in implementing risk based management decisions of water supply systems and provide useful information for drafting drought management plans. Due to the limited number of droughts that can be generally observed in historical series, the inferential approach, e.g. fitting a probability distribution to drought characteristics from an observed hydrological sample, leads to unreliable results. Furthermore, the multiyear spanning of droughts, as well as their multivariate framework requires the development of concepts and tools that differ significantly from those generally adopted to analyze other hydrological extremes, such as floods. | |
| dc.description.abstract | The research described in this dissertation includes the stochastic modelling of drought characteristics, in cases where the underlying series is either periodic or with non-negligible autocorrelation. In addition, extension of the derived models to the regional case has been explored. Moments of drought characteristics, probability distribution functions and return period have been developed in all cases, thus providing tools to characterize droughts in a broad set of situations that may occur in practice. | |
| dc.description.abstract | Drought length, accumulated deficit and intensity in stationary dependent processes are investigated jointly by assuming an autoregressive model for the underlying hydrological series. Analysis of accumulated deficit indicates that although the underlying process is stationary, the sequence of deficits is not and thus a truncated multivariate distribution is proposed to model the deficits. Approximate expressions of the joint distribution of the characteristics are derived and employed to estimate analytically the return period of droughts. | |
| dc.description.abstract | Drought length in periodic stochastic hydrological series is analyzed by modelling the dependence structure of the underlying hydrological series through a periodic lag 1 Markov chain. Moments and probability mass function of drought length are derived analytically as well as return period of droughts with length greater or equal to a fixed value. In addition, the analysis includes the joint characterization of drought length and accumulated deficit (or intensity) in periodic dependent series. Approximate expressions for the moments and pdf 's of droughts and a new formulation that enables one estimating return periods of droughts starting at a given season is presented. | |
| dc.description.abstract | Regional droughts have been analyzed in order to estimate the corresponding probabilities of occurrence and return periods. Making use of copulas concepts, approximate analytical expressions are derived for the moments of areal coverage of deficit, of areal deficit and of accumulated deficit that improves previous formulations. The corresponding approximate pdf's are also derived and employed to compute return periods of regional droughts. | |
| dc.description.abstract | The developed models have been validated by applying them to several hydrological series, ranging from precipitation to streamflows and drought monitoring indices such as Standardized Precipition Index and Palmer Drought Hydrological Index from regions characterized by different climatological regimes. | |
| dc.description.abstract | The overall conclusion of the research is that exact and/or approximate analytical expressions of probability distributions of drought characteristics derived from the statistics of the underlying hydrological series enables one a more reliable probabilistic characterization than employing the inferential approach. In addition, such analytical derivations may be useful for checking approximations or results obtained for more complex cases. The examples using a variety of water supply series and climatologic and hydrological drought indices illustrate and confirm the applicability of the analytical derivations obtained for drought characteristics and associated return periods. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | ETDF_Cancelliere_2008_3321264.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/237623 | |
| 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 | drought | |
| dc.subject | precipitation | |
| dc.subject | return period | |
| dc.subject | stochastic hydrology | |
| dc.subject | stream flows | |
| dc.subject | time series | |
| dc.subject | hydrologic sciences | |
| dc.subject | civil engineering | |
| dc.title | Stochastic characterization of droughts in stationary and periodic series | |
| 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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