Suchergebnisse

Abstract
Background
Aggregations of cyanobacteria in lakes and reservoirs are commonly associated with surface blooms, but may also occur in the metalimnion as subsurface or deep chlorophyll maxima. Metalimnetic cyanobacteria blooms are of great concern when potentially toxic species, such as Planktothrix rubescens, are involved. Metalimnetic blooms of P. rubescens have apparently increased in frequency and severity in recent years, so there is a strong need to identify reservoir management options to control it. We hypothesized that P. rubescens blooms in reservoirs can be suppressed using selective withdrawal to maximize its export from the reservoir. We also expect that altering the light climate can affect the dynamics of this species. We tested our hypothesis in Rappbode Reservoir (the largest drinking water reservoir in Germany) by establishing a series of withdrawal and light scenarios based on a calibrated water quality model (CE-QUAL-W2).

Results
The novel withdrawal strategy, in which water is withdrawn from a certain depth below the surface within the metalimnion instead of at a fixed elevation relative to the dam wall, significantly reduced P. rubescens biomass in the reservoir. According to the simulation results, we defined an optimal withdrawal volume to control P. rubescens blooms in the reservoir as approximately 10 million m3 (10% of the reservoir volume) during its bloom phase. The results also illustrated that P. rubescens growth can be most effectively suppressed if the metalimnetic withdrawal is applied in the early stage of its rapid growth, i.e., before the bloom occurs. In addition, our study showed that P. rubescens biomass gradually decreased with increasing light extinction and nearly disappeared when the extinction coefficient exceeded 0.55 m−1.

Conclusions
Our study indicates the rise in P. rubescens biomass can be effectively offset by selective withdrawal as well as by reducing light intensity beneath the water surface. Considering the widespread occurrence of P. rubescens in stratified lakes and reservoirs worldwide, we believe the results will be helpful for scientists and managers working on other water bodies to minimize the negative impacts of this harmful cyanobacteria. Our model may serve as a transferable tool to explore local dynamics in other standing waters.

The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow-outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants. © 2019 Author(s). ; Acknowledgements. The primary code for GLM has been developed by Matthew R. Hipsey, Louise C. Bruce, Casper Boon, Brendan Busch, and David P. Hamilton at the University of Western Australia in collaboration with researchers participating in GLEON, with support provided by a National Science Foundation (NSF) (USA) Research Coordination Network Award. Whilst GLM is a new code, it is based on the large body of historical research and publications produced by the Centre for Water Research at the University of Western Australia, which we acknowledge for the inspiration, development, and testing of several of the model approaches that have been adopted. Funding for the initial development of the GLM code was from the U.S. NSF Cyber-enabled Discovery and Innovation grant awarded to Paul C. Hanson (lead investigator) and colleagues from 20092014 (NSF CDI-0941510); subsequent development was supported by the Australian Research Council projects awarded to Matthew R. Hipsey and colleagues (ARC projects LP0990428, LP130100756, and DP130104078). Funding for the optimization and improvement of the snow and ice model was provided by NSF MSB-1638704. Funding for the development of the GLM teaching module and GRAPLEr was provided by NSF ACI-1234983 and NSF EF-1702506 awarded to Cayelan C. Carey. Funding for glmtools was provided by the Department of the Interior Northeast Climate Science Center. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Provision of the environmental symbols used for the GLM scientific diagrams are courtesy of the Integration and Application Network, University of Maryland Center for Environmental Science. Joanne Moo and Aditya Singh also provided support in model set-up and testing. We gratefully acknowledge the anonymous reviewers whose contribution and editing have significantly improved the paper and model.

The modelling community has identified challenges for the integration and assessment of lake models due to the diversity of modelling approaches and lakes. In this study, we develop and assess a one-dimensional lake model and apply it to 32 lakes from a global observatory network. The data set included lakes over broad ranges in latitude, climatic zones, size, residence time, mixing regime and trophic level. Model performance was evaluated using several error assessment metrics, and a sensitivity analysis was conducted for nine parameters that governed the surface heat exchange and mixing efficiency. There was low correlation between input data uncertainty and model performance and predictions of temperature were less sensitive to model parameters than prediction of thermocline depth and Schmidt stability. The study provides guidance to where the general model approach and associated assumptions work, and cases where adjustments to model parameterisations and/or structure are required. (c) 2017 Published by Elsevier Ltd. ; Australian Research Council (ARC)Australian Research Council [DP130104078, LP130100756] ; GLM development and funding support for LCB, BDB, CB and MRH was provided by the Australian Research Council (ARC) (grants DP130104078 & LP130100756). Additional contributions from individuals and organisations as well as sources of data, provided from a variety of organisations are summarised in Appendix D. This study was made possible through the sharing of ideas, data and models across the AEMON and GLEON networks as well as discussions and working groups held during AEMON workshops and GLEON meetings. ; Public domain authored by a U.S. government employee