Assessing the impacts of climate change on rainwater harvesting : a case study for eight Australian capital cities

Due to climate change, freshwater supply will be limited at many locations around the globe. Rainwater harvesting (RWH) has emerged as an alternative and sustainable freshwater source. In this study, the impacts of climate change on water saving as well as the reliability of a RWH system are investigated using data from eight Australian capital cities. Both historical and projected rainfall data were incorporated into a daily water balance model to evaluate the performance of a RWH system in relation to its reliability, water savings and scarcity. Indoor (toilet and laundry), outdoor (irrigation) and combined (indoor plus outdoor) water demands were considered for a 5 m(3) tank size. It has been found that in the future period, the water savings and reliability of a RWH system will reduce slightly across the selected cities. Different capital cities of Australia will experience different level of performance for a RWH system depending on their locations, water uses and seasons. The findings of this study will be useful to water authorities and policy makers to plan for a sustainable RWH system under changing climate conditions

Regional flood frequency analysis based on peaks-over-threshold approach : a case study for South-Eastern Australia

Study region: Southeast Australia Study focus: Regional flood frequency analysis (RFFA) is a widely adopted statistical method to estimate design floods in ungauged catchments. Annual maximum flood (AMF) model is the most popular method in developing RFFA techniques. However, the AMF-based approaches are criticised for its limitations in the range of very frequent to frequent flood estimation. As an alternative, the peaks-over-threshold (POT) based approach has shown theoretical advantages in this flood range. POT based RFFA is currently underemployed internationally due to its complexity in modelling. This study develops POT-based RFFA techniques for south-eastern Australia using data from 151 catchments. A comparison is made between ordinary least squares (OLS) and weighted least squares (WLS) methods in developing POT-based RFFA techniques. New hydrological insights for the region: The OLS based method is found to perform better than the WLS. The median relative error values of the developed prediction equations range 31–38%. The new POT-based RFFA technique overcomes the limitations of the current Australian Rainfall and Runoff, which does not have any RFFA technique for very frequent floods. It is expected that these new POT-based RFFA technique will be used in practice in south-east Australia

Peaks-over-threshold based regional flood frequency analysis using regularised linear models

Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Most of the RFFA techniques are based on the annual maximum (AM) flood model; however, research has shown that the peaks-over-threshold (POT) model has greater flexibility than the AM model. There is a lack of studies on POT-based RFFA techniques. This paper presents the development of POT-based RFFA techniques, using regularised linear models (least absolute shrinkage and selection operator, ridge regression and elastic net regression). The results of these regularised linear models are compared with multiple linear regression. Data from 145 stream gauging stations of south-east Australia are used in this study. A leave-one-out cross-validation is adopted to compare these regression models. It has been found that the regularised linear models provide quite accurate flood quantile estimates, with a median relative error in the range of 37 to 47%, which outperform the AM-based RFFA techniques currently recommended in the Australian Rainfall and Runoff guideline. The developed RFFA technique can be used to estimate flood quantiles in ungauged catchments in the study region

Comparison between quantile regression technique and generalised additive model for regional flood frequency analysis : a case study for Victoria, Australia

For design flood estimation in ungauged catchments, Regional Flood Frequency Analysis (RFFA) is commonly used. Most of the RFFA methods are primarily based on linear modelling approaches, which do not account for the inherent nonlinearity of rainfall-runoff processes. Using data from 114 catchments in Victoria, Australia, this study employs the Generalised Additive Model (GAM) in RFFA and compares the results with linear method known as Quantile Regression Technique (QRT). The GAM model performance is found to be better for smaller return periods (i.e., 2, 5 and 10 years) with a median relative error ranging 16–41%. For higher return periods (i.e., 20, 50 and 100 years), log-log linear regression model (QRT) outperforms the GAM model with a median relative error ranging 31–59%

Artificial intelligence-based regional flood frequency analysis methods : a scoping review

Flood is one of the most destructive natural disasters, causing significant economic damage and loss of lives. Numerous methods have been introduced to estimate design floods, which include linear and non-linear techniques. Since flood generation is a non-linear process, the use of linear techniques has inherent weaknesses. To overcome these, artificial intelligence (AI)-based non-linear regional flood frequency analysis (RFFA) techniques have been introduced over the last two decades. There are limited articles available in the literature discussing the relative merits/demerits of these AI-based RFFA techniques. To fill this knowledge gap, a scoping review on the AI-based RFFA techniques is presented. Based on the Scopus database, more than 1000 articles were initially selected, which were then screened manually to select the most relevant articles. The accuracy and efficiency of the selected RFFA techniques based on a set of evaluation statistics were compared. Furthermore, the relationships among countries and researchers focusing on AI-based RFFA techniques are illustrated. In terms of performance, artificial neural networks (ANN) are found to be the best performing techniques among all the selected AI-based RFFA techniques. It is also found that Australia, Canada, and Iran have published the highest number of articles in this research field, followed by Turkey, the United Arab Emirates (UAE), India, and China. Future research should be directed towards identification of the impacts of data quantity and quality, model uncertainty and climate change on the AI-based RFFA techniques

Selection of the best fit probability distributions for temperature data and the use of L-moment ratio diagram method : a case study for NSW in Australia

This paper explores different goodness-of-fit (GOF) criteria’s used in the various fields of science to compare candidate probability density functions (pdfs) to annual maximum temperature records and discusses their usefulness and drawbacks. The L-moment ratio diagram method is also proposed as alternative approach for the GOF of the pdfs. The advantage this method allows for an easy comparison of the fit of many pdfs for several stations on a single diagram. To gain knowledge about higher order moments (i.e. shape, skewness and kurtosis) of the station data set, plotting the position of a given temperature data set in L-moment ratio diagram space is prompt and effective and can provide a useful addition to the GOF criterion. Both the L-moment ratio diagrams and many GOF criteria are used on real data to assess the fit of the pdfs for temperature data in the state of New South Wales, Australia. The analysis of the L-moment ratio diagrams reveals that the generalized extreme value and normal distributions generally fit best the annual maximum temperature series. The other two- and three-parameter distributions also showed viable fits in some instances. Results obtained from L-moment diagrams, temperature frequency histograms, cumulative density plots and the simulation study are compared with those obtained from GOF statistics, and a good agreement is generally observed between all these approaches. In conclusion the L-moment ratio diagram can represent a simple, effective and efficient approach to be used as a complementary method along with the traditional GOF criteria

Air quality pollutants and their relationship with meteorological variables in four suburbs of Greater Sydney, Australia

Meteorological variability plays a pivotal role in ambient air pollution. An in-depth analysis of air pollutants including meteorological variables in four suburbs of greater Sydney, Australia, was carried out for a continuous period of 24 months from January 2016 to 2018. Results revealed significant air quality problems with seasonal trends, for all six pollutants, in all suburbs. Maximum 24-h average PM10 concentrations for the four suburbs were 49.4, 55.3, 74.0 and 102.8 μg/m3 demonstrating severe PM10 air pollution events. NO2 concentrations exceeded national guideline limits and all four suburbs showed higher than recommended concentrations of O3. Generalised additive model analysis displayed varying dependencies between air pollutants and meteorological variables influenced by season and location. Different plots were used to interpret data in terms of meteorological variables. Generally, easterly and southerly winds led to the highest average concentrations of air pollutants for all suburbs. Extremes in air quality pollution concentrations were related to east and west winds and higher wind speeds (4–8 m/s). Wide variations existed in air pollutants between the 10th and 95th percentile values, especially PM10. Minimum and maximum concentration of all analysed pollutants occurred during low temperatures (11.7–18.4 °C) with the exception of ozone favouring higher temperatures (24–38 °C) during hotter months. Results show pollution formation varies in different seasons and suburbs, in relation to meteorological variables. This study can be used to mitigate, improve prediction and forecast accuracy of air pollution. Such studies open the possibilities to explore the effects of air quality and its impact on public health

Design rainfall estimation and changes

Design rainfall estimation is frequently needed in practice. Most nations develop their own design rainfall atlas for nationwide application. This chapter intends to provide insights of design rainfall estimation issues to researchers and practitioner to enable them to understand some of the fundamental statistical concepts behind the development of design rainfalls in the form of intensity–duration–frequency (IDF) data. We have attempted to cover essential aspects of design rainfall estimation, which include (1) at-site and regional perspectives, (2) regional homogeneity and spatial dependence, (3) parametric and nonparametric approaches to fit probability distributions and model selection using different goodness-of-fit tests, (4) data collation, (5) gauged and ungauged site estimation, (6) uncertainty analysis, (7) IDF smoothing, (8) presentation of IDF data for practical application, and (9) impact of climate change on design rainfall estimation. We would like to acknowledge the supports of the editor-in-chief associate professor Saeid Eslamian and the anonymous reviewers for making constructive comments and suggestions, which have improved the materials presented in this chapter. We would also like to acknowledge the members of our family for supporting us in writing this chapter

Regional flood estimation in New South Wales Australia using generalized least squares quantile regression

This paper investigates the applicability of quantile regression technique (QRT) as a viable regional flood frequency analysis (RFFA) method for the state of New South Wales in Australia. The study uses data from 96 small to medium-sized unregulated basins across New South Wales to develop a generalized least squares (GLS)-based QRT. An independent test employing a wide range of statistical diagnostics indicates that the developed regression equations based on the GLS regression can provide quite accurate flood quantile estimates with median relative error values in the range of 13-42%. The developed regression equations are relatively easy to apply and require data for only three predictors-basin area, design rainfall intensity, and stream density

Dimensionality reduction for regional flood frequency analysis : linear versus nonlinear methods

Regional flood frequency analysis is still an important area of hydrology research as there are many ungauged catchments. The majority of hydrological methods in regional flood frequency analysis involve complex non-linear relationships between predictor variables and flood characteristics. In the past, dimensionality reduction techniques based on linear methods such as canonical correlation analysis (CCA) were used in regional flood frequency analysis to delineate hydrological clusters. Non-linear dimensionality reduction techniques, such as KCCA and multidimensional scaling (MDS), have been used in several fields of science, but not explicitly in regional flood frequency analysis. To determine hydrologically similar clusters, the approaches considered in this article use CCA, KCCA, and MDS as dimensionality reduction techniques in conjunction with Gaussian mixture models (GMM). Log-linear regression and generalized additive models are then applied to the hydrological clusters to evaluate regional flood frequency analysis. A comparison of linear and non-linear (NL) methods is performed using data from Victoria, Australia, to demonstrate the benefit of these methods. It has been found that the non-linear frameworks of multi-dimensional scaling with Gaussian mixture model-non-linear (MDSGMM-NL) and KCCA with Gaussian mixture model-non-linear (KCCAGMM-NL), as well as the mixed frameworks (i.e. KCCA and Gaussian mixture model-non-linear [CCAGMM-NL]), can be used to represent the non-linear complexities of hydrological processes in regional flood frequency analysis