Suchergebnisse

International audience Lake Kivu, East Africa, is well known for its huge reservoir of dissolved methane (CH 4) and carbon dioxide (CO 2) in the stratified deep waters (below 250 m). The methane concentrations of up to~20 mmol/l are sufficiently high for commercial gas extraction and power production. In view of the projected extraction capacity of up to several hundred MW in the next decades, reliable and accurate gas measurement techniques are required to closely monitor the evolution of gas concentrations. For this purpose, an intercomparison campaign for dissolved gas measurements was planned and conducted in March 2018. The applied measurement techniques included on-site mass spectrometry of continuously pumped sample water, gas chromatography of in-situ filled gas bags, an in-situ membrane inlet laser spectrometer sensor and a prototype sensor for total dissolved gas pressure (TDGP). We present the results of three datasets for CH 4 , two for CO 2 and one for TDGP. The resulting methane profiles show a good agreement within a range of around 5-10% in the deep water. We also observe that TDGP measurements in the deep waters are systematically around 5 to 10% lower than TDGP computed from gas concentrations. Part of this difference may be attributed to the non-trivial conversion of concentration to partial pressure in gasrich Lake Kivu. When comparing our data to past measurements, we cannot verify the previously suggested increase in methane concentrations since 1974. We therefore conclude that the methane and carbon dioxide concentrations in Lake Kivu are currently close to a steady state.

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 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