Informations et ressources scientifiques
sur le développement des zones arides et semi-arides

Accueil du site → Doctorat → Australie → Development and evaluation of stochastic rainfall models for urban drought security assessment

University of Newcastle (2017)

Development and evaluation of stochastic rainfall models for urban drought security assessment

Chowdhury, A. F. M. Kamal

Titre : Development and evaluation of stochastic rainfall models for urban drought security assessment

Auteur : Chowdhury, A. F. M. Kamal

Université de soutenance : University of Newcastle. (Australia)

Grade : Doctor of Philosophy (PhD) 2017

Résumé partiel
The key objective of this study is to develop a stochastic daily rainfall model, which can be used in streamflow and reservoir water simulation for urban drought security assessment. After critically reviewing the existing rainfall simulation techniques, this study has developed a Markov Chain (MC) model for stochastic generation of daily rainfall. The MC model 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. One of the major focuses of the study is to evaluate the ability of the stochastic model to preserve the rainfall variability and autocorrelation at daily, monthly and multiyear resolutions. Preserving monthly to multiyear variabilities in a daily rainfall model is always challenging, while those longer-term variabilities are critically important for the drought security analysis of reservoirs as the reservoir water levels usually vary in monthly to multiyear resolutions. The traditional models usually underestimate the monthly to multiyear variability, which results in the overestimation of reservoir reliability. On the other hand, the daily variability is also important in many parts of the world to take the influence of short-term extreme rainfall events into account (e.g. East Coast Lows in eastern Australia, which may occur for a few days or weeks, but substantially contribute to the reservoir water level). Five variants of the MC model with different parameterisation techniques have been tested in this study. The first model, referred to as the Average Parameter Markov Chain (APMC) model, uses deterministic parameters of MC and Gamma distribution, that is, the same parameter set is used to simulate the rainfall in all years. The second model, referred to as the Decadal Parameter Markov Chain (DPMC) model, also uses deterministic parameters of MC and Gamma distribution, but the parameters vary for each decade. The third model, referred to as the Compound Distribution Markov Chain (CDMC) model, uses deterministic parameters of MC (same as APMC) and stochastic parameters of the Gamma distribution by sampling the mean and standard deviation of wet day rainfall depths from fitted distributions for each month. The fourth model, referred to as the Hierarchical Markov Chain (HMC) model, uses stochastic parameters of both MC, by sampling wet-to-wet and dry-to-dry transition probabilities from fitted distributions, and Gamma distribution (same as CDMC). The fifth and final model, referred to as the Decadal and Hierarchical Markov Chain (DHMC) model, uses decade-varied parameters of MC (same as DPMC) and stochastic parameters of Gamma distribution (same as CDMC). To calibrate the model parameters and compare their performance, this study has used dynamically downscaled rainfall data produced by the NSW/ACT Regional Climate Modelling (NARCliM) project (reanalysis data for three Regional Climate Models (RCMs)), gridded data by the Australian Water Availability Project (AWAP), and ground-based data of raingauge stations. The MC models have been assessed in five catchments of coastal NSW – (i) Goulburn River site (ii) Williams River site (iii) Sydney site (iv) Richmond River site and (v) Bega River site using the NARCliM and AWAP datasets. In addition, raingauge data for 12 raingauge stations around Australia and 30 stations around Sydney have been used to compare the MC models with an existing model. To compare the model performance for streamflow generation, this study has used area-averaged rainfall data of NARCliM and AWAP in a SimHyd hydrology model for three sub-catchments of the Williams River site (i.e. Hunter Water System). The APMC satisfactorily reproduces the variability of rainfall depths and wet periods at daily resolution only, and significantly underestimates the variability at monthly to multiyear resolutions. The DPMC also significantly underestimates the variability of rainfall depths at monthly to multiyear resolutions, but mostly preserves the variability of wet periods at monthly to multiyear resolutions. The CDMC satisfactorily reproduces the variability of rainfall depth at daily to multiyear resolutions, but significantly underestimates the variability of wet periods at multiyear resolution. The performance of CDMC for wet period variability is consistent with the respective performance of APMC, as both models use the same deterministic parameters of the MC process.

Mots clés : stochastic rainfall model ; calibration ; validation

Présentation

Version intégrale (27 Mb)

Page publiée le 3 février 2018