Prediction of climate variables by comparing the k-nearest neighbor method and MIROC5 outputs in an arid environment

Abstract

The goal of this study was to compare the ability of the k-nearest neighbors (k-NN) approach and the downscaled output from the MIROC5 model for generating daily precipitation (mm) and daily maximum and minimum temperature (Tmax and Tmin; °C) for an arid environment. For this study, data from the easternmost province of Iran, South Khorasan, were used for the period 1986 to 2015. We also used an ensemble method to decrease the uncertainty of the k-NN approach. Although, based on an initial evaluation, MIROC5 had better results, we also used the output results of k-NN alongside the MIROC5 data to generate future weather data for the period 2018 to 2047. Nash-Sutcliffe efficiency (NSE) between MIROC5 estimates and observed monthly Tmax ranged from 0.86 to 0.92, and from 0.89 to 0.93 for Tmin over the evaluation period (2006− 2015). k-NN performed less well, with NSE between k-NN estimates and observed Tmax ranging from 0.54 to 0.64, and from 0.75 to 0.78 for Tmin. The MIROC5 simulated precipitation was close to observed historical values (−0.06 < NSE < 0.07), but the k-NN simulated precipitation was less accurate (−0.36 < NSE < −0.14). For the studied arid regions, the k-NN precipitation results compared poorly to the MIROC5 downscaling results. MIROC5 predicts increases in monthly Tmin and Tmax in summer and autumn and decreases in winter and spring, and decreases in winter monthly precipitation under RCP4.5 over the 2018−2047 period of this study. This study showed that the k-NN method should be expected to have inaccurate results for generating future data in comparison to the outputs of the MIROC5 model for arid environments.