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- Title
Development and Evaluation of a Stochastic Daily Rainfall Model with Long Term Variability.
- Authors
Kamal Chowdhury, A. F. M.; Lockart, Natalie; Willgoose, Garry; Kuczera, George; Kiem, Anthony S.; Manage, Nadeeka Parana
- Abstract
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 from fitted distributions prior to simulating the rainfall for each year, the variability of rainfall depths at longer 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 decade-varied MC parameters. This is a straightforward enhancement to the traditional simplest MC model and is both objective and parsimonious.
- Subjects
RAINFALL; STOCHASTIC analysis; MARKOV processes; GAMMA distributions; COMPUTER simulation; MATHEMATICAL models
- Publication
Hydrology & Earth System Sciences Discussions, 2017, p1
- ISSN
1812-2108
- Publication type
Article
- DOI
10.5194/hess-2017-84