Abstract:Water demand forecasts are needed for the design, operation and management of urban water supply systems. In this study, the relative performance of regression, time series analysis and artificial neural network (ANN) models are investigated for short-term peak water demand forecasting. The significance of climatic variables (rainfall and maximum air temperature, in addition to past water demand) on water demand management is also investigated.Numerical analysis was performed on data from the city of Ottawa, Ontario, Canada. The existing water supply infrastructure will not be able to meet the demand for projected population growth; thus, a study is needed to determine the effect of peak water demand management on the sizing and staging of facilities for developing an expansion strategy. Three different ANNs and regression models and seven time-series models have been developed and compared. The ANN models consistently outperformed the regression and time-series models developed in this study. It has been found that water demand on a weekly basis is more significantly correlated with the rainfall amount than the occurrence of rainfall.

Abstract:Information on intensity-duration-frequency of rainfall is commonly required for a variety of hydrologic applications. In this study, trends are estimated for different durations of annual extreme rainfall using the regional average Mann-Kendall S trend test. The method of L-moments was employed to delineate homogeneous regions. The trend test was modified to account for observed autocorrelation, and a bootstrap methodology was used to account for the observed spatial correlation.Numerical analysis was performed on 44 rainfall stations from the province of Ontario, Canada, for a 20 year time frame. This was done using data from homogeneous regions established using the L-moments procedure for the annual maximum observations for the following durations: 5, 10, 15 and 30 min, and 1, 2, 6 and 12 h. Depending on different rainfall durations, four or five homogeneous regions were delineated. Based on a 5% significance level, approximately 23% of the regions tested had a significant trend, predominantly for short-duration storms. Serial dependency was observed in 2Ð3% of data sets and spatial correlation was found in 18% of the regions. The presence of serial and spatial correlation had a significant impact on trend determination.

Abstract:Intensity-duration-frequency (IDF) relationships are currently constructed based on an at-site frequency analysis of rainfall data separately for different durations. These relationships are not accurate and reliable since they depend on many assumptions such as distribution selection for each duration; they require a large number of parameters, and are not time-independent.In this study, scaling properties of extreme rainfall are examined to establish scaling behaviour of statistical noncentral moments over different durations. A scale invariance concept is explored for disaggregation (or downscaling) of rainfall intensity from low to high resolution and is applied to the derivation of scaling IDF curves. These curves are developed for gauged sites based on scaling of the generalized extreme value (GEV) and Gumbel probability distributions.Numerical analysis was performed on annual maximum rainfall series for the province of Ontario, for storm durations of 5, 10, 15, and 30 min (the typical time of concentration for small urban catchments) and 1, 2, 6, 12, and 24 h (the typical time of concentration for larger rural watersheds).Results show that rainfall does follow a simple scaling process. Estimates found from the scaling procedure are comparable to estimates obtained from traditional techniques; however, the scaled approach was more efficient and gives more accurate estimates compared with the observed rainfall total at all stations.

Design storms (DS) that are determined from intensity-durationfrequency (IDF) relationships are required in many water resources engineering applications. Short duration DS are of particular importance in municipal applications. In this paper, linear trends were estimated for different combinations of durations and frequencies (return periods) of annual short-duration extreme rainfall. Numerical analysis was performed for 15 meteorological stations from the province of Ontario, Canada. The estimated magnitude (rate mm/h) and direction of trend (increasing, decreasing, or no trend) were estimated and then used to quantify the effect of trend on the frequency of design storms. Significant trends were detected for all durations. It was determined that due to the existence of trends (which might be attributed to climate change), the design storms of a given duration might occur more frequently in the future by approximately as much as 36 years depending on the duration and return period.

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