Projection of Changes in Stream Water Use Due to Climate Change

This study investigates the impact of rising temperatures on residential water use (RWU) in Seoul from 2015 to 2021, addressing the challenges of urban water sustainability under climate change. Using advanced models—convolutional neural networks (CNNs), long short-term memory (LSTM) Networks, eXtreme Gradient Boosting (XGBoost), and Bayesian Neural Networks (BNNs)—we examined RWU prediction accuracy and incorporated a method to quantify prediction uncertainties. As a result, the BNN model emerged as a robust alternative, demonstrating competitive predictive accuracy and the capability to account for uncertainties in predictions. Recent studies highlight a strong correlation between rising temperatures and increased RWU, especially during summer, with tropical nights (with temperatures above 25 °C) becoming more common; Seoul experienced a record 26 consecutive tropical nights in July 2024, underscoring a trend toward higher RWU. To capture these dynamics, we employed Shared Socioeconomic Pathway (SSP) scenarios and downscaled the KACE-1-0-G Global Climate Model (GCM) for Seoul, projecting a progressive increase in RWU: 0.49% in the F1 period (2011–2040), 1.53% in F2 (2041–2070), and 2.95% in F3 (2071–2100), with significant rises in maximum RWU across these intervals. Our findings highlight an urgent need for proactive measures to secure water resources in response to the anticipated increase in urban water demand driven by climate change.