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Climate change potentially increases the severity, frequency, and duration of cyanobacterial blooms in freshwater lakes, thereby threatening the ecosystem service functions of these lakes and negatively impacting human health. This paper provides a comprehensive review of the external environmental factors and internal eco-physiological characteristics that may trigger these blooms. In particular, detailed discussions are provided to explain the processes leading to cyanobacterial blooms caused due to rising temperature, CO2 concentration, and extreme climate change. A preliminary research framework to determine the impact of climate change on the blooms, namely, the “mechanism analysis — model construction — simulated prediction — risk assessment” research framework, is established. Strategies for the control and prevention of bacterial blooms are developed. Several key future research themes, including the construction of big data monitoring platforms, mechanism models for development and risk prediction, and formulations of nutrient control standards and prevention policies, are proposed to tackle the current problem. This review provides scientific evidence for the prevention and control of cyanobacterial blooms in lakes. [ABSTRACT FROM AUTHOR]

As a primary tool in climate change research at the watershed scale, the role of horizontal resolution in regional climate simulations needs to be evaluated. This paper investigates the role over the Huang-Huai-Hai (HHH) River basin using a regional climate model, RegCM4, coupled with the National Center for Atmospheric Research (NCAR) Community Land Model, version 4.5 (CLM4.5) including the modules of CN and DV (RCM_CLM4.5). The model is driven by ERA-Interim reanalysis data, and the time period is 1990—2010. Model validation shows that RCM_CLM4. 5 at 50 km and 25 km resolutions can reproduce the present climate well, including the spatial distribution of winter and summer mean temperature and precipitation, the annual cycles of temperature and precipitation, and the climate extremes. However, some biases can also be found. For example, compared with the observation, cold and warm biases are found in the winter and summer mean temperature, respectively; wet biases are found in the precipitation. Comparison between the two simulations, better performance of the magnitude and spatial distribution of mean temperature is found in the simulation at 25 km resolution; while for precipitation, better performance is observed in the one at 50 km resolution; little difference is found for the climate extremes. In general, results from this study indicate a very low sensitivity of present climate in this region to model resolution and can provide a reference for the application of this model in future climate change research over the HHH River basin. [ABSTRACT FROM AUTHOR]