Development and evaluation of a stochastic daily rainfall model with long-term variability
The primary objective of this study is to develop a stochastic rainfall generation model that can match not only the short resolution (daily) variability but also the longer resolution (monthly to multiyear) variability of observed rainfall. This study has developed a Markov chain (MC) model, wh...
Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2017-12-01
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Series: | Hydrology and Earth System Sciences |
Online Access: | https://www.hydrol-earth-syst-sci.net/21/6541/2017/hess-21-6541-2017.pdf |
Summary: | The primary objective of this study is to develop a stochastic
rainfall generation model that can match not only the short resolution
(daily) variability but also the longer resolution (monthly to multiyear)
variability of observed rainfall. This study has developed a Markov chain
(MC) model, which uses a two-state MC process with two parameters (wet-to-wet
and dry-to-dry transition probabilities) to simulate rainfall occurrence and
a gamma distribution with two parameters (mean and standard deviation of wet
day rainfall) to simulate wet day rainfall depths. Starting with the
traditional MC-gamma model with deterministic parameters, this study has
developed and assessed four other variants of the MC-gamma model with
different parameterisations. The key finding is that if the parameters of the
gamma distribution are randomly sampled each year from fitted distributions
rather than fixed parameters with time, the variability of rainfall depths at
both short and longer temporal resolutions can be preserved, while the variability of
wet periods (i.e. number of wet days and mean length of wet spell) can be
preserved by decadally varied MC parameters. This is a straightforward
enhancement to the traditional simplest MC model and is both objective and
parsimonious. |
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ISSN: | 1027-5606 1607-7938 |