ABSTRACT
A review is made of current methods for assessing future changes in urban rainfall extremes and their effects on urban drainage systems, due to anthropogenic-induced climate change. The review concludes that in spite of significant advances there are still many limitations in our understanding of how to describe precipitation patterns in a changing climate in order to design and operate urban drainage infrastructure. Climate change may well be the driver that ensures that changes in urban drainage paradigms are identified and suitable solutions implemented. Design and optimization of urban drainage infrastructure considering climate change impacts and co-optimizing these with other objectives will become ever more important to keep our cities habitable into the future.
Subject(s)
Climate Change , Drainage, Sanitary , Rain , CitiesABSTRACT
The main objective of the present study is to propose a method for estimating an optimal temporal storm pattern for urban drainage design in southern Quebec (Canada) in the context of climate change. Following a systematic evaluation of the performance of eight popular design storm models for different typical urban basins, it was found that the Canadian Atmospheric Environment Service (AES) storm pattern and the Desbordes model (with a peak intensity duration of 30 min) were the most accurate for estimating runoff peak flows while the Watt model gave the best estimation of runoff volumes. Based on these analyses, an optimal storm pattern was derived for southern Quebec region. The proposed storm pattern was found to be the most suitable for urban drainage design in southern Quebec since it could provide accurate estimation of both runoff peak flow and volume. Finally, a spatial-temporal downscaling method, based on a combination of the spatial statistical downscaling SDSM technique and the temporal scaling General Extreme Value distribution, was used to assess the climate change impacts on the proposed optimal design storm pattern and the resulting runoff properties.
Subject(s)
Cities , Climate Change , Drainage, Sanitary , Rain , Time FactorsABSTRACT
This paper discusses the comparative assessment of eight candidate distributions in providing accurate and reliable maximum rainfall estimates for Malaysia. The models considered were the Gamma, Generalised Normal, Generalised Pareto, Generalised Extreme Value, Gumbel, Log Pearson Type III, Pearson Type III and Wakeby. Annual maximum rainfall series for one-hour resolution from a network of seventeen automatic gauging stations located throughout Peninsular Malaysia were selected for this study. The length of rainfall records varies from twenty-three to twenty-eight years. Model parameters were estimated using the L-moment method. The quantitative assessment of the descriptive ability of each model was based on the Probability Plot Correlation Coefficient test combined with root mean squared error, relative root mean squared error and maximum absolute deviation. Bootstrap resampling was employed to investigate the extrapolative ability of each distribution. On the basis of these comparisons, it can be concluded that the GEV distribution is the most appropriate distribution for describing the annual maximum rainfall series in Malaysia.
Subject(s)
Models, Statistical , Rain , Environmental Monitoring , MalaysiaABSTRACT
This study proposes two alternative methods for estimating the distribution of extreme rainfalls for sites where rainfall data are available (gaged sites) and for locations without data (ungaged sites). The first method deals with the estimation of short-duration rainfall extremes from available rainfall data for longer durations using the "scale-invariance" concept to account for the relationship between statistical properties of extreme rainfall processes for different time scales. The second method is concerned with the estimation of extreme rainfalls for ungaged sites. This method relies on a new definition of homogeneous sites. Results of the numerical application using data from a network of 10 recording rain gauges in Quebec (Canada) indicate that the proposed methods are able to provide extreme rainfall estimates that are comparable with those based on observed at-site rainfall data.
