Search

A novel combined statistical downscaling and disaggregation framework (CSDD) based on Long Ashton Research Station-Weather Generator (LARS-WG) and K-nearest neighbour (KNN) was proposed to examine the climate change impact on regional extreme rainfall in Singapore. The approach could generate high-resolution rainfall sequences both spatially (e.g. station level) and temporally (e.g. 5-min) based on 12 emission scenarios under 4 general circulation models. Three different routes were proposed to generate high-resolution synthetic rainfall time series: (1) the rainfall records at all stations were combined and treated as one station (Route 1); (2) the rainfall record at each station was treated individually (Route 2); and (3) the total areal rainfall at daily and hourly timescales was used to keep inter-site correlations (Route 3). The results indicated that all routes under CSDD framework could effectively reproduce the regional rainfall at various timescales. This study further examined the impacts of climate change on regional extremes. The results show that the maximum increase rates of 100-year extremes at the end of this century at durations of 24-h, 1-h and 5-min would be 14.8, 16.3 and 16.8%, respectively. The proposed framework takes the full advantage of both LARS-WG and KNN and could effectively help provide continuous high-resolution synthetic rainfall data (with consideration of spatial dependence and uncertainty) under climate-change conditions for hydrological impact study.

A coupled K-nearest neighbour (KNN) and Bayesian neural network (BNN) model was developed for downscaling daily rainfall at a single site. The KNN was used for classification of dry/wet day and rainfall typing based on rainfall magnitude. The BNN was applied for prediction of rainfall amount. The proposed method was applied to rainfall downscaling at Singapore Island. The Climate Forecast System Reanalysis (CFSR) data were used for providing large-scale predictors at a high spatial resolution; 31-years daily rainfall record at two typical weather stations on the island was used as predictand. The performance of KNN–BNN was compared with two classical downscaling tools including automated statistical downscaling tool (ASD) and generalized linear model (GLM). The study results indicated that, the proposed model performed equally good or better than both ASD and GLM, in terms of prediction of basic statistical indicators (i.e. mean, SD, probability of wet days, 90th percentile rainfall amount, and maximum rainfall); it notably outperformed others in generating narrower uncertainty intervals for all indicators, especially for monthly mean and maximum rainfall. It was also demonstrated that separation of yearly data into monthly or seasonal could considerably enhance the performance of KNN–BNN.