Downscaling of daily extreme temperatures in the Yarlung Zangbo River Basin using machine learning techniques

Abstract

The Yarlung Zangbo River Basin (YZRB) is the longest plateau river in China and is one of the highest rivers in the world. In the context of climate change, the ecological environment of the YZRB has become increasingly fragile because of its unique location and environment. In this study, four machine learning techniques, multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM), and random forest (RF) model, were applied to downscale the daily extreme temperatures (maximum and minimum) at 20 meteorological stations located in and around the YZRB. The performance of these methods was evaluated using four comparison criteria. The best identified model was adopted to simulate future temperatures under two extreme scenarios (the lowest rate emission scenario (RCP2.6) and the highest rate emission scenario (RCP8.5)) from 2016 to 2050 using outputs from the MPI-ESM-LR climate model. The four comparison criteria showed that the RF model yielded the highest efficiency; therefore, this model was chosen to simulate the future temperatures. The results indicate that the extreme temperatures at the 20 stations increase continually under both extreme scenarios. The increases in the maximum temperature at the 20 stations under the two extreme emission scenarios are 0.46 and 0.83 °C, and the increases in the minimum temperature at the 20 stations are 0.30 and 0.68 °C for the period 2016–2050, respectively.