Bayesian model averaging by combining deep learning models to improve lake water level prediction.

Water level (WL) is an essential indicator of lakes and sensitive to climate change. Fluctuations of lake WL may significantly affect water supply security and ecosystem stability. Accurate prediction of lake WL is, therefore, crucial for water resource management and eco-environmental protection.

Emergy-based sustainability evaluation model of hydropower megaproject incorporating the social-economic-ecological losses.

The sustainable development of the hydropower megaproject (HM) is one of the critical components of sustainable water resources management. Hence, an accurate assessment of the impacts of social-economic-ecological losses (SEEL) on the sustainability of the HM system is of utmost importance. This study proposes an emergy-based sustainability evaluation model incorporating the social-economic-ecological losses (ESM-SEEL), which integrated the inputs and outputs during HM's construction and operation into an emergy calculation account.

Parameter regionalization based on machine learning optimizes the estimation of reference evapotranspiration in data deficient area.

Reference evapotranspiration (ET0), as one important variable in climatology, hydrology, and agricultural science, plays an important role in the terrestrial hydrological cycle and agricultural irrigation. However, the ET0 estimation process is inaccurate due to the lack of weather stations and historical data. In this study, a new method of ET0 estimation was proposed to improve the ET0 estimation performance in regions with limited data.