Both aerobic methane-oxidizing bacteria (MOB) and nitrite-dependent anaerobic methane oxidation (n-damo) bacteria can play an important role in mitigating the methane emission produced in anoxic sediment layers to the atmosphere. However, the environmental factors regulating the distribution of these methane-oxidizing microorganisms in lacustrine ecosystems remain essentially unclear. The present study investigated the distribution of aerobic MOB and n-damo bacteria in sediments of various freshwater lakes on the Yunnan Plateau (China). Quantitative PCR assay and clone library analysis illustrated the spatial variations in the abundances and structures of aerobic MOB and n-damo bacterial communities. Type I MOB (Methylosoma and Methylobacter) and type II MOB (Methylocystis) were detected, while type I MOB was more abundant than type II MOB. Lake sediments n-damo bacterial communities were composed of novel Methylomirabilis oxyfera-like pmoA genes. Lake sediments in the same geographic region could share a relatively similar aerobic MOB community structure. Moreover, Pearson's correlation analysis indicated that n-damo pmoA gene diversity showed a positive correlation with the ratio of organic matter to total nitrogen in lake sediment.

Integrated continuous simulation-optimization models can be effective predictors of a process-based responses for cost-benefit optimization of best management practices (BMPs) selection and placement. However, practical application of simulation-optimization model is computationally prohibitive for large-scale systems. This study proposes an enhanced Nonlinearity Interval Mapping Scheme (NIMS) to solve large-scale watershed simulation-optimization problems several orders of magnitude faster than other commonly used algorithms. An efficient interval response coefficient (IRC) derivation method was incorporated into the NIMS framework to overcome a computational bottleneck. The proposed algorithm was evaluated using a case study watershed in the Los Angeles County Flood Control District. Using a continuous simulation watershed/stream-transport model, Loading Simulation Program in C++ (LSPC), three nested in-stream compliance points (CP)each with multiple Total Maximum Daily Loads (TMDL) targetswere selected to derive optimal treatment levels for each of the 28 subwatersheds, so that the TMDL targets at all the CP were met with the lowest possible BMP implementation cost. Genetic Algorithm (GA) and NIMS were both applied and compared. The results showed that the NIMS took 11 iterations (about 11 min) to complete with the resulting optimal solution having a total cost of \$67.2 million, while each of the multiple GA executions took 21-38 days to reach near optimal solutions. The best solution obtained among all the GA executions compared had a minimized cost of \$67.7 millionmarginally higher, but approximately equal to that of the NIMS solution. The results highlight the utility for decision making in large-scale watershed simulation-optimization formulations.

Eutrophication has become a top environmental issue for most lake ecosystems in the world and enhanced phosphorus (P) input is usually considered the primary stressor. Focused on the role of phosphorus in eutrophic lakes, a bibliometric approach was applied to quantitatively evaluate the main interests of research and trends in this area. Using data from the Science Citation Index Expanded database between 1900 and 2013, a total of 3,875 publications was returned by searching topic keywords. Spatial, temporal, and interactive characteristics of the articles, countries, and keywords are presented using time series, frequency, and co-occurrence analysis. Result shows that the annual publications on P in eutrophic lakes keep an exponential growth (R (2) = 0.93; p < 0.0001) over the last two decades, reflecting an increasing attraction in this area. However, publications of phosphorus research make up only 40 % of total records in eutrophic lakes, indicating that there are other significant topics in eutrophication problems of lakes. The USA is the largest output country in this area, contributing 23 % of the total articles, followed by China with a proportion of 15 %. However. China has replaced the USA as the largest output country in the world since 2011, but its citation per paper is significantly lower than the USA, indicating its' favor on quantity over quality. Based on international cooperation analysis, five regional groups were found, and the USA, the UK, P.R. China, Sweden, and German are the centers of their groups. The top 20 title keywords, author keywords and keywords plus were identified according to their frequency to assist our understanding of interests of research and modes. Surprisingly, nitrogen is a high co-occurrence keyword in this study, and its share of publications with P research in eutrophic lakes is increasing rapidly. Furthermore, the high correlation between P and N research in spatial distribution also indicates the increasing significance of N research in eutrophic lakes.

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.

Meteorological trend analysis is a useful tool for understanding climate change and can provide useful information on the possibility of future change. Lake Dianchi is the sixth largest freshwater body in China with serious eutrophication. Algal blooms outbreak was proven to be closely associated with some climatic factors in Lake Dianchi. It is therefore essential to explore the trends of climatic time series to understand the mechanism of climate change on lake eutrophication. We proposed an integrated method of Mann-Kendall (MK) test, seasonal-trend decomposition using locally weighted regression (LOESS) (STL), and regime shift index (RSI) to decompose the trend analysis and identify the stable and abrupt changes of some climate variables from 1951 to 2009. The variables include mean air temperature (Tm), maximum air temperatures (Tmax), minimum air temperatures (Tmin), precipitation (Prec), average relative humidity (Hum), and average wind speed (Wind). The results showed that (a) annual Tm, Tmax, and Tmin have a significant increasing trend with the increasing rates of 0.26, 0.15and 0.43 A degrees C per decade, respectively; (b) annual precipitation has an insignificant decreasing trend with the decreasing rate of 3.17 mm per decade; (c) annual Hum has a significant decreasing trend in all seasons; and (d) there are two turning points for temperature rise around 1980 and 1995 and two abrupt change periods for precipitation with the extreme points appearing in 1963 and 1976. Temperature rise and precipitation decline in summer and autumn as well as wind speed decrease after the 1990s may be an important reason for algal blooms outbreak in Lake Dianchi. This study was expected to provide foundation and reference for regional water resource management.

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.

Both Bacteria and Archaea might be involved in various biogeochemical processes in lacustrine sediment ecosystems. However, the factors governing the intra-lake distribution of sediment bacterial and archaeal communities in various freshwater lakes remain unclear. The present study investigated the sediment bacterial and archaeal communities in 13 freshwater lakes on the Yunnan Plateau. Quantitative PCR assay showed a large variation in bacterial and archaeal abundances. Illumina MiSeq sequencing illustrated high bacterial and archaeal diversities. Bacterial abundance was regulated by sediment total organic carbon and total nitrogen, and water depth, while nitrate nitrogen was an important determinant of bacterial diversity. Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes, Chlorobi, Chloroflexi, Cyanobacteria, Firmicutes, Gemmatimonadetes, Nitrospirae, Planctomycetes, and Verrucomicrobia were the major components of sediment bacterial communities. Proteobacteria was the largest phylum, but its major classes and their proportions varied greatly among different lakes, affected by sediment nitrate nitrogen. In addition, both Euryarchaeota and Crenarchaeota were important members in sediment archaeal communities, while unclassified Archaea usually showed the dominance.

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.

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.

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.

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.

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.

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.