Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Nobuaki Kimura,Kei Ishida,Daichi Baba

doi:10.3390/w13081109

Abstract

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

Highlights

Global warming may strongly affect mid-latitude regions by the end of the twentyfirst century [1]
The validation of the long short-term memory (LSTM) model with the observed data was conducted in certain cases, including a linear fitting function as a reference
To understand the impacts of long-term climate change on mid-latitude reservoir environments, we conducted past and future predictions of surface water temperature using locally downscaled climate data from general circulation model (GCM) projections (RCP8.5), along with the LSTM model coupled with the transfer learning (TL) approach

Summary

Introduction

Global warming may strongly affect mid-latitude regions by the end of the twentyfirst century [1]. The increase in air temperature can potentially cause significant changes in aquatic environments, as well as result in frequent occurrences of water-related disasters in hydrological systems in the mid-latitude regions. Past studies have reported the impacts of climatic change on the water temperature of water bodies, such as reservoirs and lakes, using a simple method with observed data. The linear regression shows a trend of the dependent variable that responds to the independent variable, it inappropriately predicts the dependent variable when the independent variable has strong nonlinearity, such as meteorological and limnological data, including uncertainties from complex climatic factors and aquatic systems (e.g., Mudelsee [7]). Certain studies applied the DNN to lakes in a continental subarctic climate in mid-latitude regions to predict water temperature profiles [9] and water quality [10]

Methods

Results

Discussion

Conclusion