RESUMEN
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. The Three Gorges Project (TGP) on the Yangtze River is selected as a case study to comprehensively evaluate the HM's sustainability from 1993 to 2020. Subsequently, the emergy-based indicators of TGP are compared with several hydropower projects in China and worldwide to analyze the multi-impacts of hydropower development. The results showed that the river chemical potential (2.35 E+24sej) and the emergy losses (L) (1.39 E+24sej) are the primary emergy inflow sections (U) of the TGP system, accounting for 51.1% and 30.4% of the U, respectively. The flood control function of the TGP produced tremendous socio-economic benefits (1.24 E+24sej), accounting for 37.8% of the total emergy yield. The resettlement and compensation, water pollution during operation, fish biodiversity loss, and sediment deposition are the main L of the TGP, accounting for 77.8%, 8.4%, 5.6%, and 2.6%, respectively. Based on the enhanced emergy-based indicators, the assessment reveals that the sustainability level of the TGP falls in the middle range compared to other hydropower projects. Thus, along with maximizing the benefits of the HM system, it is necessary to minimize the SEEL of the HM system, which is a critical approach to promote the coordinated development of the hydropower and ecological environment in the Yangtze River basin. This study helps to understand the complex relationship between human and water systems and provides a novel framework that can be used as an evaluation index and insights for hydropower sustainability assessment.
Asunto(s)
Conservación de los Recursos Naturales , Ecosistema , Humanos , Conservación de los Recursos Naturales/métodos , Contaminación del Agua , ChinaRESUMEN
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. Four empirical models with different data requirements, Albrecht, Hargreaves-Samani, Priestley-Taylor, and Penman, were applied and optimized the parameters by the Shuffled Complex Evolution-University of Arizona algorithm with the ET0 calculated by the Penman-Monteith model as the reference value at 600 meteorological stations in China. Two machine learning models, Random Forest (RF) and Multiple Linear Regression (MLR) were used to establish the regionalization of the parameter of the empirical model. The result showed that parameter optimization could significantly improve ET0 estimation in different climate regions of China. The Penman model has the strongest physical foundation and the highest estimation accuracy, followed by the Hargeaves-Samani and Priestley-Taylor model. The mass-transfer-based model, Albrecht, could only estimate regional ET0 efficiently after parameter optimization. Based on the more advanced RF machine learning regionalization method that considers complex linear relationships of variables, ET0 estimation in regions lacking data could be improved efficiently. Machine learning could be used to describe the ET0 model parameters in different regions because of the similarity. The combination of machine learning and empirical model could provide a new method for ET0 estimation in data deficient regions.