For clean hydropower generation while sustaining ecosystems, minimizing harmful impacts and balancing multiple water needs is an integral component. One particularly harmful effect not managed explicitly by hydropower operations is thermal destabilization of downstream waters. To demonstrate that the thermal destabilization by hydropower dams can be managed while maximizing energy production, we modelled thermal change in downstream waters as a function of decision variables for hydropower operation (reservoir level, powered/spillway release, storage), forecast reservoir inflow and air temperature for a dam site with in situ thermal measurements. For data-limited regions, remote sensing-based temperature estimation algorithm was established using thermal infrared band of Landsat ETM+ over multiple dams. The model for water temperature change was used to impose additional constraints of tolerable downstream cooling or warming (1-6 °C of change) on multi-objective optimization to maximize hydropower. A reservoir release policy adaptive to thermally optimum levels for aquatic species was derived. The novel concept was implemented for Detroit dam in Oregon (USA). Resulting benefits to hydropower generation strongly correlated with allowable flexibility in temperature constraints. Wet years were able to satisfy stringent temperature constraints and produce substantial hydropower benefits, while dry years, in contrast, were challenging to adhere to the upstream thermal regime.
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http://dx.doi.org/10.1186/s40807-020-00060-9 | DOI Listing |
Heliyon
December 2024
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Basalt, which is a geological medium used for engineering construction in Southwest China, contains defect structures at various scales. In particular, the widespread presence of mesoscale hidden joints significantly affects the mechanical properties of basalt and the stability of engineering structures. However, research in this specific subject has been limited.
View Article and Find Full Text PDFOrg Lett
December 2024
Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, P. R. China.
Although methods for synthesizing chiral phosphorus scaffolds are available, the potential of this molecular chirality remains largely unexplored. Herein, we present a remote desymmetrization of prochiral biaryl phosphine oxides through an organocatalytic asymmetric arylation. This metal-free approach enables the efficient synthesis of a wide range of densely functionalized P(V)-stereogenic compounds with good to excellent yields and satisfactory enantioselectivities.
View Article and Find Full Text PDFEnviron Res
December 2024
School of Hydraulic Engineering, Dalian University of Technology, Dalian, 116024, China.
Groundwater pollution has become a global challenge, posing significant threats to human health and ecological environments. Machine learning, with its superior ability to capture non-linear relationships in data, has shown significant potential in addressing the groundwater pollution issues. This review presents a comprehensive bibliometric analysis of 1,462 articles published between 2000 and 2023, offering an overview of the current state of research, analyzing development trends, and suggesting future directions.
View Article and Find Full Text PDFAmbio
December 2024
Department of Social Sciences, Technology and Arts, Luleå University of Technology, 971 87, Luleå, Sweden.
Our study explores governing of European eel in Sweden. The paper aims to analyze and tentatively explain the degree of policy coherence between different political levels and discuss implications for management. The study focuses on the Advocacy Coalition Framework and a qualitative methodology.
View Article and Find Full Text PDFJ Environ Manage
December 2024
Department of Civil, Construction and Environmental Engineering, North Dakota State University, ND, United States.
The negative impacts of large hydroelectric reservoirs on downstream ecosystems have attracted worldwide attention. Few attempts have been made to dynamically predict ecological benefits and rationally negotiation in the reservoir-river-lake (RRL) system. This study addresses these gaps by developing an integrated framework with machine learning and game theory to balanced hydropower and ecological benefits.
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