A combined weather generator and K-nearest-neighbour approach for assessing climate change impact on regional rainfall extremes

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.