**4. Predicting droughts and floods**

In addition to general circulation models (GCMs) [38–40] that attempt to implement physical models of climatic systems, considerable research has been reported over the past decade using machine learning, particularly neural networks, to forecast rainfall [41–48]. The results suggest that the skill of the forecasts using the machine learning approach for medium-term forecast is superior to GCMs [48]. With GCMs, forecasts are usually initially generated for extended grid areas for defined geographical areas. Forecasts for more specific locations can then be generated through a process of downscaling. With the machine learning approach, forecasts are generated for specific locations for which historical data are available. If there are sufficient locations over a geographical region, contour maps representing observed and forecast rainfall can then be generated. This is illustrated for Tasmania in **Figures 2** and **3** for observed and forecast monthly rainfall, 12 months, in advance.

**7**

**Figure 2.**

**Figure 3.**

**5. Rainfall reconstructions**

*Forecast monthly rainfall for Tasmania August 2008.*

degree of certainty, to anthropogenic climate change.

Instrumental records of rainfall and temperature generally extend back only about 100 years in Australia. Reconstructions of past temperatures, extending back hundreds or thousands of years, are available for many parts of the world [49]. These are derived from palaeo data, including tree rings, corals, ice cores and stalagmites. Such reconstructions are much rarer for rainfall and, comparatively, few exist currently for Australia. However, it is important to consider the examples that exist as they enable episodes of droughts and flooding rains to be put into a wider context before asserting particular events are unprecedented, or ascribing them, with a high

Climate proxy data are ideally derived from sources that are located within, or in close proximity to, the region of interest. However, in cases where such proxy

*Introductory Chapter: Australia—A Land of Drought and Flooding Rain*

*DOI: http://dx.doi.org/10.5772/intechopen.89549*

*Observed monthly rainfall for Tasmania August 2008.*

*Introductory Chapter: Australia—A Land of Drought and Flooding Rain DOI: http://dx.doi.org/10.5772/intechopen.89549*

*Rainfall - Extremes, Distribution and Properties*

excessive rainfall.

**3. Flooding rains**

parts of the urban area [3, 33].

repeating 40 year cycle [37].

of herbicides as suggested previously [36].

**4. Predicting droughts and floods**

Australia.

of rainfall in eastern Australia created a greater threat to crop growth than

There are many impacts of droughts other than agriculture [20]. For example, droughts in Australia have had effects on wildlife populations including waterbirds [27, 28]. Studies have shown that droughts have an effect on mental health of the population, particularly in rural areas of Australia [29]. Li et al. investigated the ecological effects of extreme drought [30], including water acidification and eutrophication in the Lower Lakes (Lakes Alexandrina and Albert) in South

Flooding rains are also recurring feature of the Australian climate. For example, prolonged rainfall over large areas of Queensland led to flooding of historic proportions in December 2010, extending into January 2011 [31, 32]. About 33 people died as a result of those floods, with more than 78% of the state (an area larger than France and Germany combined) declared a disaster zone. More than 2.5 million people were affected [31] with approximately 29,000 homes and businesses experiencing some form of inundation, with the cost of flooding estimated to be over A\$5 billion [31]. In January 2011, Brisbane, the state capital of Queensland, experienced its second highest flood in over a century. Major flooding occurred throughout most of the Brisbane River catchment, with an estimated 18,000 properties inundated [32]. More recently in 2018, extreme rainfall conditions inundated the city of Townsville, located in coastal north Queensland, experiencing flooding of large

In addition to the impacts on urban infrastructure, floods may have a substantial impact on the ecosystem. For example, runoff following extreme rainfall has been associated with detrimental impacts on coral of the Great Barrier Reef [34]. Flooding can also impact on the establishment of tree seedlings [35] and has been implicated in the dieback of mangroves in Queensland rivers, rather than the effect

With the devastating impacts of floods, there is interest in understanding and potentially improving predictive capabilities. Studies by McMahon suggest that floods in south-eastern Queensland do not occur randomly but are associated with a

In addition to general circulation models (GCMs) [38–40] that attempt to implement physical models of climatic systems, considerable research has been reported over the past decade using machine learning, particularly neural networks, to forecast rainfall [41–48]. The results suggest that the skill of the forecasts using the machine learning approach for medium-term forecast is superior to GCMs [48]. With GCMs, forecasts are usually initially generated for extended grid areas for defined geographical areas. Forecasts for more specific locations can then be generated through a process of downscaling. With the machine learning approach, forecasts are generated for specific locations for which historical data are available. If there are sufficient locations over a geographical region, contour maps representing observed and forecast rainfall can then be generated. This is illustrated for Tasmania in **Figures 2** and **3** for observed and forecast monthly rainfall, 12 months,

**6**

in advance.

#### **Figure 2.** *Observed monthly rainfall for Tasmania August 2008.*

**Figure 3.** *Forecast monthly rainfall for Tasmania August 2008.*
