The future water vulnerability assessment of the Seoul metropolitan area using a hybrid framework composed of physically-based and deep-learning-based hydrologic models

摘要

Physically-based hydrologic models can accurately simulate flow discharge in natural environment, but they cannot precisely consider the anthropogenic disturbance caused by the operation of large-scale dams in a watershed. This study tried to overcome this issue by developing a hybrid modeling framework, consisting of physically-based models for simulating upstream natural watersheds and deep-learning-based models for simulating dam operation. The model was developed for the Paldang Dam watershed, a major water source for Seoul metropolitan area, where the importance of stable water supply has increased due to the increase of population and water use per capita. The prediction performance of the hybrid model was compared with that of models built based only on the physically-based hydrologic model, namely the Variable Infiltration Capacity model (VIC) model, with single and cascaded structure. For the validation period, Nash-Sutcliffe Efficiency from the developed hybrid model, the single model, and the cascaded model were 0.6410, - 0.1054, and 0.2564, respectively, suggesting that the consideration of dam operation aided by the machine learning algorithm is essential for accurate assessment of river flow discharge and the subsequent water resources vulnerability. In order to evaluate the impact of climate change, future meteorological data under RCP4.5 scenario was used as an input for the hybrid model simulation, of which result revealed that the drought flow value (the 10th lowest daily flow over a year) with the return period of 10-year, 20-year, 50-year, 100-year, and 200-year in the far future (2071-2100) were projected to decrease by 22%, 28%, 37%, 43%, and 50%, respectively, compared to the near future (2021-2040), which calls for a proper drought mitigation measures.