China is confronting the challenge of accelerated lake eutrophication, where Lake Dianchi is considered as the most serious one. Eutrophication control for Lake Dianchi began in the mid-1980s. However, decision makers have been puzzled by the lack of visible water quality response to past efforts given the tremendous investment. Therefore, decision makers desperately need a scientifically sound way to quantitatively evaluate the response of lake water quality to proposed management measures and engineering works. We used a water quality modeling based scenario analysis approach to quantitatively evaluate the eutrophication responses of Lake Dianchi to an under-construction water diversion project. The primary analytic framework was built on a three-dimensional hydrodynamic, nutrient fate and transport, as well as algae dynamics model, which has previously been calibrated and validated using historical data. We designed 16 scenarios to analyze the water quality effects of three driving forces, including watershed nutrient loading, variations in diverted inflow water, and lake water level. A two-step statistical analysis consisting of an orthogonal test analysis and linear regression was then conducted to distinguish the contributions of various driving forces to lake water quality. The analysis results show that (a) the different ways of managing the diversion projects would result in different water quality response in Lake Dianchi, though the differences do not appear to be significant; (b) the maximum reduction in annual average and peak Chl-a concentration from the various ways of diversion project operation are respectively 11% and 5%; (c) a combined 66% watershed load reduction and water diversion can eliminate the lake hypoxia volume percentage from the existing 6.82% to 3.00%; and (d) the water diversion will decrease the occurrence of algal blooms, and the effect of algae reduction can be enhanced if diverted water are seasonally allocated such that wet season has more flows. (C) 2013 Elsevier B.V. All rights reserved.

Sediment microorganisms play a crucial role in a variety of biogeochemical processes in freshwater ecosystems. The objective of the current study was to investigate the spatial distribution of sediment bacterial community structure in Luoshijiang Wetland, located in Yunnan-Kweichow Plateau (China). Wetland sediments at different sites and depths were collected. Clone library analysis indicates bacterial communities varied with both sampling site and sediment depth. A total of fourteen bacterial phyla were identified in sediment samples, including Proteobacteria, Acidobacteria, Actinobacteria, Armatimonadetes, Bacteroidetes, Chlorobi, Chloroflexi, Cyanobacteria, Firmicutes, Gemmatimonadetes, Nitrospirae, Planctomycetes, Spirochaetes, and Verrucomicrobia. Proteobacteria (mainly Betaproteobacteria and Deltaproteobacteria) predominated in wetland sediments. Moreover, the proportions of Alphaproteobacteria, Acidobacteria, Bacteroidetes, Gemmatimonadete, and Planctomycetes were significantly correlated with chemical properties.

Nonylphenol (NP) is one of commonly detected contaminants in the environment. Biological degradation is mainly responsible for remediation of NP-contaminated site. Knowledge about the structure of NP-degrading microbial community is still very limited. Microcosms were constructed to investigate the structure of microbial community in NP-contaminated river sediment and its change with NP biodegradation. A high level of NP was significantly dissipated in 6-9 days. Bacteria and ammonia-oxidizing archaea (AOA) were more responsive to NP amendment compared to ammonia-oxidizing bacteria (AOB). Gammaproteobacteria, Alphaproteobacteria and Bacteroidetes were the largest bacterial groups in NP-degrading sediment. Microorganisms from bacterial genera Brevundimonas, Flavobacterium, Lysobacter and Rhodobacter might be involved in NP degradation in river sediment. This study provides some new insights towards NP biodegradation and microbial ecology in NP-contaminated environment. (C) 2014 Elsevier Inc. All rights reserved.

While water diversion and dilution are often proposed and implemented for lake eutrophication management, their effectiveness and efficiency in achieving water quality goals is often questionable. Although water quality modeling (WQM) has been applied to quantify lake responses to water diversion and dilution in practice, it is necessary to improve the existing analysis approaches with an uncertainty-based decision-support framework to address the situation of severe data limitation that exists in many realworld cases. This study implemented an enhanced multiple-pattern inverse water quality modeling (MPIWQM) approach in a water diversion study for a terminal plateau lake in southwestern China to address the difficulty of developing robust water-diversion decision support under data limitation and model uncertainty. A two-dimensional, longitudinal and vertical hydrodynamic and water quality model was developed to simulate water circulation and nutrient fate and transport in the lake. To overcome a severe limitation of data, this study employed a multiple-pattern load-parameter estimation (MPLE) method that couples the numerical model with a Genetic Algorithm (GA) and a cluster algorithm to construct an uncertainty-based decision support system. Execution of the MPLE approach resulted in 27 load-parameter patterns for the case study to represent all possible combinations of loading-parameter patterns conditioned on the available water quality data in the lake. The uncertainty-based decision-support framework was then applied to evaluate three realistic water diversion scenarios proposed by local management authorities, and the system was able to predict a range of possibilities given a specific water diversion condition. The scenario analysis results showed that (a) within the range of uncertainties represented by the 27 load-parameter patterns, the model consistently predict that the water diversions would unlikely cause significant water quality improvement in the lake; (b) the water quality response to water diversion demonstrates clear spatial variability, temporal variability, and the effect is in general cumulative over time; (c) different water quality constituents respond to the diversions differently, where the chemical oxygen demand (COD) demonstrates the strongest response, while the total phosphorus (TP) the weakest; and (d) none of the proposed water diversion scenarios is able to reverse, or significantly mitigate the water quality deterioration trend in the lake. (C) 2014 Elsevier B.V. All rights reserved.

Both ammonia-oxidizing bacteria (AOB) and archaea (AOA) might be the key microorganisms in ammonia conversion in ecosystems. However, the depth-related change of AOA and AOB in sediment ecosystem is still not clear. The relative contribution of AOA and AOB to nitrification in wetland sediment remains also unclear. Moreover, information about ammonia-oxidizing microorganisms in high-altitude freshwater wetland is still lacking. The present study investigated the relative abundances and community structures of AOA and AOB in sediments of a high-altitude freshwater wetland in Yunnan Province (China). Variations of the relative abundances and community structures of AOA and AOB were found in the wetland sediments, dependent on both sampling site and sediment depth. The relative abundances of AOA and AOB (0.04-3.84 and 0.01-0.52 %) and the AOA/AOB ratio (0.12-4.65) showed different depth-related change patterns. AOB community size was usually larger than AOA community size. AOB diversity was usually higher than AOA diversity. AOA diversity decreased with the increase of sediment depth, while AOB diversity showed no obvious link with the sediment depth. Pearson's correlation analysis showed that AOA diversity had a positive significant correlation with available phosphorus. Nitrosospira-like sequences, with different compositions, predominated in the wetland sediment AOB communities. This work could provide some new insights toward nitrification in freshwater sediment ecosystems.

Streams play an important role in linking the land with lakes. Nutrients released from agricultural or urban sources flow via streams to lakes, causing water quality deterioration and eutrophication. Therefore, accurate simulation of streamflow is helpful for water quality improvement in lake basins. Lake Dianchi has been listed in the Three Important Lakes Restoration Act' in China, and the degradation of its water quality has been of great concern since the 1980s. To assist environmental decision making, it is important to assess and predict hydrological processes at the basin scale. This study evaluated the performance of the soil and water assessment tool (SWAT) and the feasibility of using this model as a decision support tool for predicting streamflow in the Lake Dianchi Basin. The model was calibrated and validated using monthly observed streamflow values at three flow stations within the Lake Dianchi Basin through application of the sequential uncertainty fitting algorithm (SUFI-2). The results of the autocalibration method for calibrating and the prediction uncertainty from different sources were also examined. Together, the p-factor (the percentage of measured data bracketed by 95% prediction of uncertainty, or 95PPU) and the r-factor (the average thickness of the 95PPU band divided by the standard deviation of the measured data) indicated the strength of the calibration and uncertainty analysis. The results showed that the SUFI-2 algorithm performed better than the autocalibration method. Comparison of the SUFI-2 algorithm and autocalibration results showed that some snowmelt factors were sensitive to model output upstream at the Panlongjiang flow station. The 95PPU captured more than 70% of the observed streamflow at the three flow stations. The corresponding p-factors and r-factors suggested that some flow stations had relatively large uncertainty, especially in the prediction of some peak flows. Although uncertainty existed, statistical criteria including R-2 and Nash-Sutcliffe efficiency were reasonably determined. The model produced a useful result and can be used for further applications. Copyright (c) 2012 John Wiley & Sons, Ltd.

Ammonia oxidation is known to be carried out by ammonia-oxidizing bacteria (AOB) and archaea (AOA), while methanotrophs (methane-oxidizing bacteria (MOB)) play an important role in mitigating methane emissions from the environment. However, the difference of AOA, AOB, and MOB distribution in wetland sediment and adjacent upland soil remains unclear. The present study investigated the abundances and community structures of AOA, AOB, and MOB in sediments of a high-altitude freshwater wetland in Yunnan Province (China) and adjacent agricultural soils. Variations of AOA, AOB, and MOB community sizes and structures were found in water lily-vegetated and Acorus calamus-vegetated sediments and agricultural soils (unflooded rice soil, cabbage soil, and garlic soil and flooded rice soil). AOB community size was higher than AOA in agricultural soils and lily-vegetated sediment, but lower in A. calamus-vegetated sediment. MOB showed a much higher abundance than AOA and AOB. Flooded rice soil had the largest AOA, AOB, and MOB community sizes. Principal coordinate analyses and Jackknife Environment Clusters analyses suggested that unflooded and flooded rice soils had relatively similar AOA, AOB, and MOB structures. Cabbage soil and A. calamus-vegetated sediment had relatively similar AOA and AOB structures, but their MOB structures showed a large difference. Nitrososphaera-like microorganisms were the predominant AOA species in garlic soil but were present with a low abundance in unflooded rice soil and cabbage soil. Nitrosospira-like AOB were dominant in wetland sediments and agricultural soils. Type I MOB Methylocaldum and type II MOB Methylocystis were dominant in wetland sediments and agricultural soils. Moreover, Pearson's correlation analysis indicated that AOA Shannon diversity was positively correlated with the ratio of organic carbon to nitrogen (p < 0.05). This work could provide some new insights toward ammonia and methane oxidation in soil and wetland sediment ecosystems.

Nutrient load reduction is a well-recognized requirement for aquatic ecosystem restoration. However, decision making is difficult due to challenges related to uncertainty and the interaction between decision makers and modelers, including (a) the quantitative relationship between risks arising from different aspects and the fact that cost is not usually revealed and (b) the fact that decision makers are not significantly involved in the modeling process. In this study, an interactive optimal-decision procedure with risk-cost tradeoff is proposed to overcome these limitations. It consists of chance-constrained programming (CCP) models, risk scenario analysis using the Taguchi method, risk-cost tradeoff and feedback for model adaption. A hybrid intelligent algorithm (HIA) integrating Monte Carlo simulation, artificial neural networks, and an augmented Lagrangian genetic algorithm was developed and applied to solve the CCP model. The proposed decision procedure and HIA are illustrated through a case study of uncertainty-based optimal nutrient load reduction in the Lake Qionghai Watershed, China. The CCP model has four constraints associated with risk levels indicating the possibility of constraint violation. Sixteen risk scenarios were designed with the Taguchi method to recognize the interactions between multiple constraint risks and total cost. The results were analyzed using the signal-to-noise ratio, analysis of variance, and multivariate regression. The model results demonstrate how cost is affected by risk for the four constraints and show that the proposed approach can provide effective support for decision making on risk-cost tradeoffs. (C) 2014 Elsevier Ltd. All rights reserved.

Microbial communities play a crucial role in various biogeochemical processes in aquatic ecosystems. However, existing knowledge on microbial communities in the waters of wetlands is still very scant. The objective of the present study was to investigate the bacterioplankton community in the Luoshijiang Wetland, a high-altitude freshwater wetland in the Yunnan-Kweichow Plateau. Water samples were collected from different sites. The bacterioplankton community was characterized using 16S rRNA gene clone library analysis. A spatial variation of the diversity and composition of the bacterioplankton community was observed. Verrucomicrobia and Proteobacteria were the most abundant components. Proteobacteria might play an important role in water self-purification, but the significance of Verrucomicrobia remained unclear. Moreover, Pearson's correlation analysis showed that Actinobacteria and Gemmatimonadetes were positively correlated with nitrite nitrogen in waters, while Alphaproteobacteria with dissolved phosphorous.