Effects of climate indices on extreme rainfall in Queensland, Australia

Abstract

Over the last 100 years, global surface temperature has increased approximately 0.75 0C. One of the main concerns regarding global warming is the increased frequency and magnitude of extreme weather events, e.g. extreme rainfall which has already been recorded in many regions of the world. During the last decades, losses in economy and society have increased due to extreme climatic events. In certain parts of the world, society and its natural resources are damaged by extreme rainfall causing loss of lives and destruction of infrastructures. Increased frequency and magnitude of extreme rainfall led to the failure of existing drainage system and risk of increased flooding. Therefore, it is necessary to obtain information on extreme rainfall events for designing hydrologic and hydraulic infrastructures, e.g. stormwater management, urban drainage systems, water sensitive urban design (WSUD). The increased frequency and magnitude of extreme rainfall events throughout the world led to increase concerns amongst hydrologists and water resources engineers in estimating the design rainfall derived from extreme rainfall frequency analysis. Since design rainfalls are important input in water resources engineering, errors in the selection of design rainfall event will cause devastating consequences for water infrastructure projects and flood mitigations works. Although several studies on extreme rainfall events have been conducted and ongoing, regional dependency of the phenomena hinder in drawing generic conclusion. This paper investigates the effects of climate indices on extreme rainfall and its prediction. The extreme rainfall analysis will be performed using data from a rainfall station in Queensland. The analysis focusing on extreme events is easily understandable and manageable on impact studies and design rainfall estimation for water infrastructures. Comparing the modelled extreme rainfall with the observed outputs, it was found good predictive capability of the developed MLR models. The findings of the study also have significant importance for other regions of the world where there is considerable hydroclimatic variability.