Simulating climate change scenarios using an improved K-nearest neighbor model

An improved weather-generating model that allows nearest neighbour resampling with perturbation of the historic data is applied to generate weather data based upon plausible climate scenarios. The intent is to create an ensemble of climate scenarios that can be used for the assessment of the vulnerability of a watershed to extreme events, including both floods and droughts. Analysis of the results clearly indicates that the model adequately simulated extreme unprecedented events for five different climate change scenarios. Based on the simulation results, the increasing precipitation scenario is identified as the critical scenario for the assessment of risks associated with the occurrence of floods in the basin. The increasing temperature scenario appears to be the critical scenario for the analysis of droughts in the basin. Frequency analysis was carried out to determine the impact of potential climatic change on the occurrence of storm depths of any given magnitude. A promising potential application of the model is in rainfall-runoff modelling where the storms depths could be related to the occurrence of extreme events in the basin. The proposed model, in conjunction with a rainfall-runoff model, has the potential of providing valuable aid in developing efficient management strategies for a watershed. The model produces spatially correlated data, which is crucial for accurate runoff estimation. Although the model is applied to the Upper Thames River Basin in the Canadian province of Ontario, it is generic and transportable to any other watershed with minimal changes.