The environmental degradation of lakes in China has become increasingly serious over the last 30 years and eutrophication resulting from enhanced nutrient inputs is considered a top threat. In this study, a quasi-mass balance method, net anthropogenic N inputs (NANI), was introduced to assess the human influence on N input into three typical Chinese lake basins. The resultant NANI exceeded 10 000 kg N km(-2) year(-1) for all three basins, and mineral fertilizers were generally the largest sources. However, rapid urbanization and shrinking agricultural production capability may significantly increase N inputs from food and feed imports. Higher percentages of NANI were observed to be exported at urban river outlets, suggesting the acceleration of NANI transfer to rivers by urbanization. Over the last decade, the N inputs have declined in the basins dominated by the fertilizer use but have increased in the basins dominated by the food and feed import. In the foreseeable future, urban areas may arise as new hotspots for nitrogen in China while fertilizer use may decline in importance in areas of high population density.

Lake eutrophication is associated with excessive nutrient enrichment and unobserved internal nutrient cycling. In spite of advances in understanding the role of nitrogen (N) and phosphorus (P) cycling in eutrophication, the relative importance of N and P limitation and release from sediment is still an open question. The complicated interaction between N and P cycling and external driving factors leads to dynamics in N or P limitation patterns and internal release that change over time. We developed a nutrient-driven model of phytoplankton dynamics including the critical nutrient cycling processes. It was fitted using Bayesian inference to explore the roles of N and P inputs from external sources, net sediment release, and internal dynamics in Lake Yilong, a shallow eutrophic lake in China. The model provided a good fit to observations, with time-varying parameters required to fit time-dependent variations in the sediment release process. The results demonstrated that, in Lake Yilong, the pattern of nutrient limitation showed a transformation from P limitation to N and P co-limitation after an observed regime shift occurred in 2008. After the shift in 2008, sediment release had an increasing influence on N and P supply, which could make eutrophication remediation more difficult. For Lake Yilong, it would not be possible to reverse eutrophication solely with watershed nutrient load reduction so in-lake manipulation of physical chemical conditions to inhibit the sediments release should also be considered. (C) 2015 Elsevier B.V. All rights reserved.

Both ammonia-oxidizing archaea (AOA) and bacteria (AOB) can be involved in biotransformation of ammonia to nitrite in soil ecosystems. However, the distribution of AOA and AOB in plateau soils and influential factors remain largely unclear. In the present study, the activity, abundance and structure of ammonia oxidizers in different soils on the Yunnan Plateau were assessed using potential nitrification rates (PNRs), quantitative PCR assay and clone library analysis, respectively. Wide variation was found in both AOA and AOB communities in plateau soils. PNRs showed a significant positive correlation with AOB abundance. Both were determined by the ratio of organic carbon to nitrogen (C/N) and total phosphorous (TP). AOB could play a more important role in ammonia oxidation. AOB community diversity was likely affected by soil total nitrogen (TN) and total organic carbon (TOC) and was usually higher than AOA community diversity. Moreover, Nitrososphaera- and Nitrosospira-like organisms, respectively, were the dominant AOA and AOB in plateau soils. AOA community structure was likely shaped by TP and C/N, while AOB community structure was determined by pH. (c) 2015 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Ammonia oxidation is known to be performed by both ammonia-oxidizing archaea (AOA) and bacteria (AOB), although their relative significance to nitrification process in soil ecosystems remains controversial. The distribution of AOA and AOB in plateau soils with different land use types and the influential factors remains unclear. The present study investigated the abundance and structure of AOA and AOB communities in upland soils adjacent to Erhai Lake in the Yunnan Plateau (China). Quantitative PCR assays indicated a large variation in the community size of AOA and AOB communities, with the numerical dominance of AOA over AOB in most of soils. Clone library analysis illustrated a marked shift in the structure of soil AOA and AOB communities. A high abundance of Nitrososphaera- and Nitrosotalea-like AOA was observed, while Nitrosospira-like species predominated in AOB. AOA and AOB abundance was positively influenced by total nitrogen and moisture content, respectively. Moreover, moisture content might be a key determinant of AOA community composition, while C/N and nitrate nitrogen played an important role in shaping AOB community composition. However, further efforts will be necessary in order to elucidate the links between soil AOA and AOB and land use.

Predictions in ungauged basins (PUBs) are an increasingly important question in the water resource and quality management fields, especially for small watersheds where the phenomenon of data scarcity is prevalent. The transfer of hydrological parameters on the basis of regionalization is a common approach for solving PUB problems, and plenty of research has been undertaken on how to transfer calibrated parameters from gauged to ungauged watersheds. However, hydrological parameters estimation is substantially influenced by calibration objectives, which may affect model performance on the recipient watershed as well. In this paper the influence of calibration objectives on the transfer of parameter sets from one watershed to another is discussed. The HSPF hydrological model and the PEST automatic calibration model with four calibration objectives (squared error of daily flow, squared error of monthly flow, squared error of exceedance flow time, and sum of these squared errors) were applied. One gauged watershed of the Lake Dianchi Basin with daily flow data from 1999 to 2010 was calibrated by the combined HSPF-PEST model to obtain the transferrable parameters. Then the entire parameter sets were transferred to another neighborhood watershed, and model performance was tested by conventional goodness-of-fit statistics. Results show that (1) parameters transferred from all four calibration objectives perform well on the target watershed; (2) selection of calibration objective has a significant impact on model performance for both donor and recipient watersheds; and (3) the differences among objectives are similar in the two watersheds, suggesting that the objectives' features are transferrable. Therefore, the selection of calibration objective should be considered as a significant factor when transferring parameters to ungauged watersheds. (C) 2014 American Society of Civil Engineers.

Recently, environmental pressures along coasts have increased substantially. Classification of estuaries according to their susceptibility to eutrophication nutrient load is a useful method to determine priority management objects and to enforce control measures. Using historical monitoring data from 2007 to 2012, from 65 estuaries, including 101 estuarine monitoring sections and 260 coastal monitoring stations, a nutrient-driven phytoplankton dynamic model was developed based on the relationship among phytoplankton biomass, Total Nitrogen (TN) load and physical features of estuaries. The ecological filter effect of estuaries was quantified by introducing conversion efficiency parameter values into the model. Markov Chain Monte Carlo algorithm of Bayesian inference was then employed to estimate parameters in the model. The developed model fitted well to the observed chlorophyll, primary production, grazing, and sinking rates. The analysis suggests that an estuary with Q/V (the ratio of river flow to estuarine volume) greater than 2.0 per year and E > (conversion efficiency ratio) less than 1.0 g C/g N can be classified as less susceptible to TN load, Q/V between 0.7 to 2.0 per year and E > between 1.0 to 3.0 g C/g N as moderately susceptible, and E > greater than 3.0 g C/g N as very susceptible. The estuaries with Q/V less than 0.7 per year vary greatly in their susceptibility. The estuaries with high and moderate susceptibility accounted for 67% of all the analyzed estuaries. They have relatively high eutrophication risks and should be the focus of environmental supervision and pollution prevention.

A reliable system simulation to relate socioeconomic development with water environment and to comprehensively represent a watershed's dynamic features is important. In this study, after identifying lake watershed system processes, we developed a system dynamics modeling framework for managing lake water quality at the watershed scale. Two reinforcing loops (Development and Investment Promotion) and three balancing loops (Pollution, Resource Consumption, and Pollution Control) were constituted. Based on this work, we constructed Stock and Flow Diagrams that embedded a pollutant load model and a lake water quality model into a socioeconomic system dynamics model. The Dianchi Lake in Yunnan Province, China, which is the sixth largest and among the most severely polluted freshwater lakes in China, was employed as a case study to demonstrate the applicability of the model. Water quality parameters considered in the model included chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP). The business-as-usual (BAU) scenario and three alternative management scenarios on spatial adjustment of industries and population (Si), wastewater treatment capacity construction (S2), and structural adjustment of agriculture (S3), were simulated to assess the effectiveness of certain policies in improving water quality. Results showed that S2 is most effective scenario, and the COD, TN, and TP concentrations in Caohai in 2030 are 52.5, 10.9, and 0.8 mg/L, while those in Waihai are 9.6, 1.2, and 0.08 mg/L, with sustained development in the watershed. Thus, the model can help support the decision making required in development and environmental protection strategies. (C) 2015 Elsevier Ltd. All rights reserved.

Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely ``optimal'' solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multiobjective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. (C) 2015 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.

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.

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.

The building industry in China has huge potential capacity for energy/resources conservation and pollutants reduction to achieve sustainable development However, stakeholders are hardly able to reach a consensus on preferential needs and effective solutions, which was a difficulty faced by policy makers. To better identify the common interests on sustainable development in this field, the Sustainability Solutions Navigator (SSN) was adopted in China for the first time to assess the sustainability needs and practices. Based on the participation of stakeholders from the government, businesses, academia, and non-government organizations, prioritized needs and practices were identified using SSN, and gap analyses were conducted for comparison to global benchmarks. According to the results, the top needs were mainly focused on improving government efficiency and implementation, maintaining healthy indoor environments and obtaining adequate funds; priority practices were mainly focused on governmental action, renewable energy development and pollutant source reduction. The gap analysis indicated that the government efficiency and performance had the largest gap to the benchmark. By using a simple interactive tool to bring different stakeholders into policy making process, this study produces all-around information for decision makers. The results imply that the sustainability of the building industry in China has a much better expectation than governmental performance. (C) 2013 Elsevier Ltd. All rights reserved.

Lake Yilong in Southwestern China has been under serious eutrophication threat during the past decades; however, the lake water remained clear until sudden sharp increase in Chlorophyll a (Chl a) and turbidity in 2009 without apparent change in external loading levels. To investigate the causes as well as examining the underlying mechanism, a three-dimensional hydrodynamic and water quality model was developed, simulating the flow circulation, pollutant fate and transport, and the interactions between nutrients, phytoplankton and macrophytes. The calibrated and validated model was used to conduct three sets of scenarios for understanding the water quality responses to various load reduction intensities and ecological restoration measures. The results showed that (a) even if the nutrient loads is reduced by as much as 77%, the Chl a concentration decreased only by 50%; and (b) aquatic vegetation has strong interaction with phytoplankton, therefore requiring combined watershed and in-lake management for lake restoration. (c) 2013 Elsevier Ltd. All rights reserved.

Lake Dianchi, one of the main water sources for Kunming, China, experiences severe cyanobacterial blooms due to rapid urbanization and local industrial development. Scientific interest in the mechanisms that cause blooms has been increasing. An integrated model combining rough set theory with binary logistic regression was used to examine the correlation between weather conditions and cyanobacterial blooms in Lake Dianchi based on daily monitoring data. The binary logistic regression yielded quantitative correlations between cyanobacterial blooms and the assessed meteorological variables, including temperature, wind velocity, and wind direction. The rough decision process connected the weather conditions and cyanobacterial blooms, which were used to verify the binary regression model results. It was shown that by comparing the methods, the rough decision-adjusted binary logistic regression model significantly improved model accuracy. The integrated model of cyanobacterial blooms in Lake Dianchi may inform decision-makers at local water purification plants of the water quality in the lake and assist them in making more cost-effective decisions.