Date of this Version
Jaimes-Correa, J. C. (2013). Improving Time Structure Patterns of Orthogonal Markov Chains and its Consequences in Hydraulic Simulations. MS thesis, University of Nebraska-Lincoln.
Rainfall1 occurrences understood as rain events are relevant for agricultural practices because temporal distribution of rainfall highly affects yield production. A few stochastic models satisfactorily generate daily rainfall events while preserving temporal and spatial dependence among multiple sites. I evaluated an extension on the traditional Orthogonal Markov chain (TOMC) model in reproducing the temporal structure of rainfall events at multiple sites in Florida (FL), Nebraska (NE) and California (CA). In addition, a simulation of watershed runoff from rainfall events, reproduced by a single- and multi-site weather generator, was conducted. Results shows that (i) a temporal structure extended Orthogonal Markov chain (EOMC) maintained the spatial correlation between observed and generated rainfall events; (ii) EOMC used a smaller number of yearlong simulations for generating the observed frequencies of wet spells than TOMC requires for similar accuracy; (iii) using EOMC generated rainfall data in SWMM produced similar median runoff values to those generated using observed data; and (iv) EOMC reduces 50% of computing time for generating rainfall data. EOMC can benefit modeling of future climate scenarios by economical reduction of hardware need.
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