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It has been hypothesized that climate change will induce the areal extension of cyanobacterial blooms. However, this hypothesis lacks field-based observation. In the present study both long-term historical data and short-term field measurement were used to identify the importance of changes in wind patterns on the cyanobacterial bloom in Lake Taihu (China), a large, shallow, eutrophic lake located in a subtropical zone. The cyanobacterial bloom mainly composed of Microcystis spp. recurred frequently throughout the year. The regression analysis of multi-year satellite image data extracted by the Floating Algae Index revealed that both the annual mean monthly maximum cyanobacterial bloom area (MMCBA) increased year by year from 2000 to 2011, while the contemporaneous cyanobacterial biomass showed no significant change. However, the correlation analysis shows that MMCBA was negatively correlated with wind speed. Our short-term field measurements indicated that the influence of wind on surface cyanobacterial blooms is that the Chlorophyll-a (Chla) concentration is fully mixing throughout the water column when the wind speed exceed 7 m s(-1). At lower wind speeds, there was vertical stratification of Chla with high surface concentrations and an increase in bloom area. The regression analysis of wind speed indicates that the climate has changed over the last decade. Lake Taihu has become increasingly calm, with the decrease of strong wind frequency between 2000 and 2011, corresponding to the increase in the MMCBA over time. Therefore, we conclude that changes in wind patterns related to climate change have favored the increase of cyanobacterial blooms in Lake Taihu.

AbstractGlobal environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global data set of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985–2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues.

AbstractIn this first worldwide synthesis of in situ and satellite‐derived lake data, we find that lake summer surface water temperatures rose rapidly (global mean = 0.34°C decade−1) between 1985 and 2009. Our analyses show that surface water warming rates are dependent on combinations of climate and local characteristics, rather than just lake location, leading to the counterintuitive result that regional consistency in lake warming is the exception, rather than the rule. The most rapidly warming lakes are widely geographically distributed, and their warming is associated with interactions among different climatic factors—from seasonally ice‐covered lakes in areas where temperature and solar radiation are increasing while cloud cover is diminishing (0.72°C decade−1) to ice‐free lakes experiencing increases in air temperature and solar radiation (0.53°C decade−1). The pervasive and rapid warming observed here signals the urgent need to incorporate climate impacts into vulnerability assessments and adaptation efforts for lakes.

A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the “high hazardous” category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures.