Development and evaluation of a stochastic daily rainfall model with long-term variability

A F M Kamal Chowdhury,Nadeeka Parana Manage,Anthony S Kiem,Natalie Lockart,Garry Willgoose,George Kuczera

doi:10.5194/hess-21-6541-2017

A F M Kamal Chowdhury, Nadeeka Parana Manage + Show 4 more

Open Access

https://doi.org/10.5194/hess-21-6541-2017

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Abstract

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 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.

Highlights

Observed rainfall data generally provide a single realisation of a short record, often not more than a few decades
The WGEN simulates temperature and solar radiation. While other models such as point process models (Cowpertwait et al, 1996) are used for stochastic rainfall generation, this study has focused on Markov chain (MC)-type models
This study examined the autocorrelation of wet day rainfall depths, and only found very weak lag–1 autocorrelations (r2 < 0.1) for both Sydney and Adelaide

Summary

Introduction

Observed rainfall data generally provide a single realisation of a short record, often not more than a few decades. For urban water security analysis of reservoirs, long-term hydrologic records are required to sample extreme droughts that drive the security of the urban system (Mortazavi et al, 2013). The daily models generally preserve the shortterm daily rainfall variability (since they are calibrated to the daily resolution data) but tend to underestimate the longerterm rainfall variability of monthly and multiyear resolutions (Wang and Nathan, 2007). Such underestimation is critically important for the application of these models in hydrological planning and design.

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