Earth's nitrogen (N) cycle is imbalanced because of excessive anthropogenic inputs. Freshwater lakes efficiently remove N from surface waters by transformation of NO to atmospheric N and/or NO (denitrification; DN) and by burial of organic N in sediments (net sedimentation; NS). However, relatively little is known about the controlling environmental conditions, and few long-term measurements on individual lakes are available to quantify conversion rates. We report N-elimination rates in 21 Swiss lakes estimated from whole-lake N budgets covering up to ∼20 years of monitoring. The NO concentration in the bottom water was the main predictor of DN. Additionally, DN rates were positively correlated with external N load and the area-specific hydraulic loading rate (mean depth/water residence time; ). NS of N was strongly related to total phosphorus (P) concentration. Nitrogen removal efficiency (NRE), the fraction of the load of dissolved N to a lake removed by DN and NS, was strongly negatively related to . This previously unconsidered variable improves the predictability of NRE and does not require knowledge of N and P loading rates or concentrations. We conclude that P management alone intended to oligotrophy lakes only slightly increases N export unless it is accompanied by N management.
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Life Sciences, Hamburg University of Applied Science, Ulmenliet 20, Hamburg D-21033, Germany.
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Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, Stirling, United Kingdom.
Bloom-forming algae present a unique challenge to water managers as they can significantly impair provision of important ecosystem services and cause health risks to humans and animals. Consequently, effective short-term algae forecasts are important as they provide early warnings and enable implementation of mitigation strategies. In this context, machine learning (ML) emerges as a promising forecasting tool.
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Eawag - Swiss Federal Institute for Aquatic Science and Technology, Dübendorf, Switzerland; Department of Civil and Environmental Engineering and Earth Science, University of Notre Dame, IN, USA. Electronic address:
This study investigates the drought of three major terminal lakes: Great Salt Lake, Salton Sea, and Lake Urmia, driven by socio-hydrological lock-in-a phenomenon characterized by feedback loops between human activities and environmental processes. Previous research has linked this drying to socio-hydrological lock-in, where rational actions by individuals collectively lead to suboptimal outcomes, exacerbating water scarcity and ecological degradation. Despite existing studies, a critical knowledge gap remains in understanding how these feedback mechanisms operate across different socio-economic and ecological contexts.
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Utrecht University, Princetonlaan 8A, 3584CB Utrecht, The Netherlands.
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