Stochastic Modeling of Daily Precipitation Process in the Context of Climate Change
Stochastic Modeling of Daily Precipitation Process in the Context of Climate Change

Author: Sarah El Outayek

Publisher:

Published: 2021

Total Pages:

ISBN-13:

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"Information on the variations of rainfall in space and time is essential for the design and management of different water resources systems. This thesis proposed a new stochastic model (referred herein as the MCME model) that is able to capture accurately the statistical properties of the observed daily precipitation process for the current and future climates under different climate change scenarios. The MCME model consists of two components: (i) the first component representing the daily precipitation occurrence process based on the first-order two-state Markov Chain (MC); and (ii) the second component describing the distribution of daily precipitation amounts using the Mixed Exponential (ME) distribution. A comparative study was carried out to assess the performance of the proposed model as compared to the popular LARS-WG model using observed daily precipitation data from a network of nine raingauges representing different climatic conditions across Quebec. Results of this study have indicated the better performance of the MCME model in terms of its accuracy and robustness in the modeling of the daily precipitation process. In addition, an improved perturbation method was developed for establishing the linkages between the proposed MCME model with the coarse-scale climate model outputs. Results of a comparative study using both MCME and LARS-WG models have demonstrated the best performance of the proposed perturbation method as compared with other existing perturbation methods in terms of its accuracy in capturing different statistical properties of the projected daily precipitation process for future periods. Finally, an assessment of the performance of the MCME and LARS-WG models based on the proposed perturbation technique was performed in the context of climate change using daily precipitation data from a network of five stations located in Quebec and Ontario and the downscaled simulation data from 21 different global climate models. Results of this assessment have indicated the feasibility and accuracy of the proposed MCME model and the proposed perturbation technique for downscaling daily precipitation processes for impact and adaptation studies in practice"--

Improving Time Structure Patterns of Orthogonal Markov Chains and Its Consequences in Hydraulic Simulations
Improving Time Structure Patterns of Orthogonal Markov Chains and Its Consequences in Hydraulic Simulations

Author: Juan Carlos Jaimes Correa

Publisher:

Published: 2013

Total Pages: 97

ISBN-13:

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Rainfall (liquid precipitation) 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.

Stochastic Simulation Of Daily Rainfall Data Using Matched Block Bootstrap
Stochastic Simulation Of Daily Rainfall Data Using Matched Block Bootstrap

Author:

Publisher:

Published: 2004

Total Pages:

ISBN-13:

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Characterizing the uncertainty in rainfall using stochastic models has been a challenging area of research in the field of operational hydrology for about half a century. Simulated sequences drawn from such models find use in a variety of hydrological applications. Traditionally, parametric models are used for simulating rainfall. But the parametric models are not parsimonious and have uncertainties associated with identification of model form, normalizing transformation, and parameter estimation. None of the models in vogue have gained universal acceptability among practising engineers. This may either be due to lack of confidence in the existing models, or the inability to adopt models proposed in literature because of their complexity or both. In the present study, a new nonparametric Matched Block Bootstrap (MABB) model is proposed for stochastic simulation of rainfall at daily time scale. It is based on conditional matching of blocks formed from the historical rainfall data using a set of predictors (conditioning variables) proposed for matching the blocks. The efficiency of the developed model is demonstrated through application to rainfall data from India, Australia, and USA. The performance of MABB is compared with two non-parametric rainfall simulation models, k-NN and ROG-RAG, for a site in Melbourne, Australia. The results showed that MABB model is a feasible alternative to ROG-RAG and k-NN models for simulating daily rainfall sequences for hydrologic applications. Further it is found that MABB and ROG-RAG models outperform k-NN model. The proposed MABB model preserved the summary statistics of rainfall and fraction of wet days at daily, monthly, seasonal and annual scales. It could also provide reasonable performance in simulating spell statistics. The MABB is parsimonious and requires less computational effort than ROG-RAG model. It reproduces probability density function (marginal distribution) fairly well due to its data driven nature. Results obtaine.