East Coast Lows (ECLs) are intense low-pressure systems which occur over the subtropical east coasts of southern and northern hemisphere continents. ECLs are typically associated with gale force winds, large seas, storm surges, heavy rainfall and flooding. While ECL impacts are typically seen as negative the rainfall associated with ECLs is also very important for urban water security within the heavily populated eastern seaboard of Australia (ESA). This study investigates historical ECLs to gain insights into the timing, frequency, intensity and location of ECL occurrence as well as the magnitude and spatial extent of ECL impacts on rainfall. The different characteristics and impacts associated with different ECL sub-types are highlighted and it is proposed that this spatial and temporal variability in ECL behaviour at least partially explains why the ESA is hydroclimatically different to the rest of Australia and why different locations within the ESA have such different rainfall patterns-and therefore different levels of flood and drought risk. These insights are critical to the objectives of the New South Wales government funded Eastern Seaboard Climate Change Initiative (ESCCI), in particular Project 5 which focuses on the water security impacts of ECLs. The results of this work will be used to produce climate-informed stochastic daily rainfall simulations that are more realistic than existing stochastic rainfall simulation methods at preserving the statistics important for catchment-scale hydrology (e.g. clustering of extreme events, long-term persistence, frequency/duration/magnitude of wet and dry spells). These simulated rainfall sequences, that incorporate the spatial and temporal hydroclimatic variability caused by ECLs and other climate phenomena, are important inputs into the hydrological models used to determine current and future urban water security within the ESA.

[1] Previous studies of the recent drought in the MurrayDarling Basin (MDB) have noted that low rainfall totals have been accompanied by anomalously high air temperatures. Subsequent studies have interpreted an identified trend in the residual timeseries of non-rainfall related temperature variability as a signal of anthropogenic change, further speculating that increased air temperature has exacerbated the drought through increasing evapotranspiration rates. In this study, we explore an alternative explanation of the recent increases in air temperature. This study demonstrates that significant misunderstanding of known processes of land surface -atmosphere interactions has led to the incorrect attribution of the causes of the anomalous temperatures, as well as significant misunderstanding of their impact on evaporation within the Murray-Darling Basin.

Many empirical models have been developed that use sunshine hours (SSH) data to estimate global solar radiation. Most of these models use the Angstrom-Prescott equation to produce deterministic estimates of monthly radiation and do not provide uncertainty estimates in their predictions. This study develops five stochastic models that use daily SSH data to produce probabilistic simulations of global radiation, including associated uncertainties. These models can be used to estimate historical daily radiation or to estimate radiation without the use of satellite data. Two sources of predictive uncertainty are considered: (1) the timing of the SSH during the day (estimated using Monte Carlo simulation) and (2) external errors such as variability in cloud type and amount (estimated using residual error modelling). The models differ in the parameterization of the diffuse and direct radiation, using either no scaling, linear or quadratic scaling of the radiation by the daily SSH fraction to account for cloud attenuation. The models are calibrated under several residual error assumptions, including constant, linear and quadratic variances dependent on the SSH fraction and simulated radiation. The five models perform equally well in simulating global radiation, with an average error of approximately 9% for all locations studied. The results suggest that SSH uncertainty does not dominate predictive errors in global radiation. The residual errors appear to be best described by a linear heteroscedastic structure with larger variance during cloudy days and smaller variance during sunny days. The developed methodology provides a novel approach for estimating the uncertainty in radiation estimates based on SSH data.

East Coast Lows (ECLs) are intense low-pressure systems which occur over the subtropical east coasts of southern and northern hemisphere continents. ECLs are typically associated with gale force winds, large seas, storm surges, heavy rainfall and flooding. While ECL impacts are typically seen as negative the rainfall associated with ECLs is also very important for urban water security within the heavily populated eastern seaboard of Australia (ESA). This study investigates historical ECLs to gain insights into the timing, frequency, intensity and location of ECL occurrence as well as the magnitude and spatial extent of ECL impacts on rainfall. The different characteristics and impacts associated with different ECL sub-types are highlighted and it is proposed that this spatial and temporal variability in ECL behaviour at least partially explains why the ESA is hydroclimatically different to the rest of Australia and why different locations within the ESA have such different rainfall patterns-and therefore different levels of flood and drought risk. These insights are critical to the objectives of the New South Wales government funded Eastern Seaboard Climate Change Initiative (ESCCI), in particular Project 5 which focuses on the water security impacts of ECLs. The results of this work will be used to produce climate-informed stochastic daily rainfall simulations that are more realistic than existing stochastic rainfall simulation methods at preserving the statistics important for catchment-scale hydrology (e.g. clustering of extreme events, long-term persistence, frequency/duration/magnitude of wet and dry spells). These simulated rainfall sequences, that incorporate the spatial and temporal hydroclimatic variability caused by ECLs and other climate phenomena, are important inputs into the hydrological models used to determine current and future urban water security within the ESA.

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.