Stochastic Models for the Disaggregation of Precipitation Time Series on Sub-Daily Scale
Stochastic Models for the Disaggregation of Precipitation Time Series on Sub-Daily Scale

Author: Veronica Villani

Publisher:

Published: 2015

Total Pages: 0

ISBN-13:

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Stochastic disaggregation model, based on coupling of the modified version of the Bartlett-Lewis Rectangular Pulse stochastic rainfall model and proportional adjusting procedure, is shown to disaggregate daily observed precipitation to hourly scale. Furthermore synthetic hourly time series are generated.This model requires the identification of a set of parameters that allow to reproduce, as well as possible, the statistical properties of the observed precipitation. The identification is formulated as a global optimization problem. A comparison between observed and modeled statistics of the precipitation time series is presented for the weather station of San Martino Valle Caudina (Southern Italy).

Stochastic Disaggregation Modelling of Rainfall Series
Stochastic Disaggregation Modelling of Rainfall Series

Author: Shashank Singh

Publisher: LAP Lambert Academic Publishing

Published: 2013

Total Pages: 140

ISBN-13: 9783659435782

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Meteorological models generate fields of precipitation and other climatological variables as spatial averages at the scale of the grid used for numerical solution. The grid-scale can be large, particularly for general circulation models and disaggregation is required. Disaggregation models were introduced in hydrology by the pioneering work of Valencia and Schaake (1972, 1973). Disaggregation models are widely used tools for the stochastic simulation of hydrologic series. They divide known higher-level values (e.g. annual) into lower level ones (e.g. seasonal), which add up to the given higher level. Thus ability to transform a series from a higher time scales to a lower one. Artificial Neural Network that mimics working of human neurons has proved to be a better performing model compared to stochastic and mathematical modeling of hydrological series. The result identified for Valencia-Schaake Model, Lane's Model and using ANN technique have been thoroughly discussed for their application and better understanding of Disaggregation modeling.

Chaos in Hydrology
Chaos in Hydrology

Author: Bellie Sivakumar

Publisher: Springer

Published: 2016-11-16

Total Pages: 408

ISBN-13: 9048125529

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This authoritative book presents a comprehensive account of the essential roles of nonlinear dynamic and chaos theories in understanding, modeling, and forecasting hydrologic systems. This is done through a systematic presentation of: (1) information on the salient characteristics of hydrologic systems and on the existing theories for their modeling; (2) the fundamentals of nonlinear dynamic and chaos theories, methods for chaos identification and prediction, and associated issues; (3) a review of the applications of chaos theory in hydrology; and (4) the scope and potential directions for the future. This book bridges the divide between the deterministic and the stochastic schools in hydrology, and is well suited as a textbook for hydrology courses.

Artificial Intelligence
Artificial Intelligence

Author: David L. Poole

Publisher: Cambridge University Press

Published: 2017-09-25

Total Pages: 821

ISBN-13: 110719539X

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Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Empirical-statistical Downscaling
Empirical-statistical Downscaling

Author: Rasmus E. Benestad

Publisher: World Scientific

Published: 2008

Total Pages: 228

ISBN-13: 9812819126

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Empirical-statistical downscaling (ESD) is a method for estimating how local climatic variables are affected by large-scale climatic conditions. ESD has been applied to local climate/weather studies for years, but there are few ? if any ? textbooks on the subject. It is also anticipated that ESD will become more important and commonplace in the future, as anthropogenic global warming proceeds. Thus, a textbook on ESD will be important for next-generation climate scientists.

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.

Rainfall
Rainfall

Author: Firat Y. Testik

Publisher: John Wiley & Sons

Published: 2013-05-02

Total Pages: 500

ISBN-13: 1118671546

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Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 191. Rainfall: State of the Science offers the most up-to-date knowledge on the fundamental and practical aspects of rainfall. Each chapter, self-contained and written by prominent scientists in their respective fields, provides three forms of information: fundamental principles, detailed overview of current knowledge and description of existing methods, and emerging techniques and future research directions. The book discusses Rainfall microphysics: raindrop morphodynamics, interactions, size distribution, and evolution Rainfall measurement and estimation: ground-based direct measurement (disdrometer and rain gauge), weather radar rainfall estimation, polarimetric radar rainfall estimation, and satellite rainfall estimation Statistical analyses: intensity-duration-frequency curves, frequency analysis of extreme events, spatial analyses, simulation and disaggregation, ensemble approach for radar rainfall uncertainty, and uncertainty analysis of satellite rainfall products The book is tailored to be an indispensable reference for researchers, practitioners, and graduate students who study any aspect of rainfall or utilize rainfall information in various science and engineering disciplines.

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"--