WATER TEMPERATURE PREDICTIONS IN A DAM-RESERVOIR BASED ON MULTIPLE CLIMATE CHANGE DATASETS USING RECURRENT NEURAL NETWORK

Abstract

Our study presents surface water temperature predictions on a reservoir using multiple climate model datasets as the inputs to recurrent neural network (RNN). We focused the reservoir in Hokkaido that is located in the mid-latitude region and is supposed to be strongly affected by future climate change. With past and future dynamical-downscaling data of the climate change data (Scenario RCP8.5 for future) to the reservior-located area, the RNN that was trained by observed data (temperature and water temperature) was run to predict the water temperature on the reservoir. The difference between past and future climate change data (i.e., Future minus Past) in each GCM showed that the future temperature increased by 2 to 4 °C in a monthly average, which supports RCP8.5 projection. This trend of the air-temperature difference was sim-ilar to the predicted difference by RNN in water temperature (i.e., the water-temperature difference between monthly averages of past and future was 0 to 1 °C). However, during spring and summer periods, the water temperature predicted by the past temperature was marginally higher than that by the future temperature.