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Abstract. Cyanobacterial Harmful Algal Blooms (CyanoHAB) are considered one of the threatening issues for fresh water ecology across the world. Periodic blooms in Kansas and around the United States lakes, small ponds and water bodies have potential to produce toxins and taste-and-odor compounds that cause substantial economic, public health, and environmental concerns. Predictive tools integrating cyanobacteria dynamics model and regular water monitoring datasets can be used for short term forecasting to facilitate early warning system for algal bloom management. Multiyear daily dataset from Cheney Reservoir, and Kansas River (Wamego) in Kansas were used to statistically correlate cyanobacteria concentration with various environmental parameters which indicates water temperature, irradiation, phosphorus and nitrogen as major stimulating factors for cyanobacteria abundance. Findings reveal inverse correlation between dissolved oxygen and cyanobacteria which indicates CyanoHAB as one of the major factors to deteriorate ecological health. In this study we attempted to implement process-based modeling of cyanobacteria growth using driving in-situ environmental data sets. We consider a physical process based non-linear dynamic model including growth factors (phosphorus, nitrogen, temperature, and irradiation) and biological interactions (growth and decay rate) to forecast bloom events. Temperature dependency on growth and decay rate was incorporated with commonly used theta model. Michaelis-Menten kinetics was used in multiplicative form along with temperature dependent theta model to have the effects of nitrogen, phosphorus and irradiation on the growth rate. A narrow range of parameter values from literature were used during model development and site specific parameter calibration. The temperature-only dependent model was also developed which could be applicable in data scarce and nutrition abundant context where growth rate may not be limited due to nutrition availability. We have used both modeling approach for Cheney reservoir, and Kansas River (Wamego) in Kansas using available historical data to backcast cyanobacteria abundance at 15 days interval. This tool linking with climate and reservoir watershed model would help to conceptualize future CyanoHAB prevention strategies, short-term forecasting and its relation with climatic change, watershed condition, and nutrient abundance in the lake.