Prediction Of Algal Biomass In Water Sources Based On Growth Model

As one of the important sources of freshwater storage in China,the ecological health of reservoirs is related to the health and safety of water use by residents in the water supply area.As a primary producer in water,changes in algal biomass are a rapid response to changes in the water environment and are directly related to water ecological health.Therefore,it is crucial to make reasonable predictions for algal growth in reservoirs.Logistic models,as a well-known growth theory model,contain ecological information on algal growth and have wide applications in describing population growth process.In this study,based on the findings of typical drinking water sources,we analyzed the change characteristics of algal biomass and environmental factors in Nanwan Reservoir and Yudong Reservoir.We performed regression analysis of algal growth using classical methods such as correlation analysis and the least squares method and combined the regression analysis results with the Logistic model to construct a growth-based algal biomass prediction model.The model can simulate the algal growth in the next 30 days using temperature data from weather forecasts as an input variable.In this study,we also simulated predicted algal biomass in Nanwan Reservoir and Yudong Reservoir and investigated the applicability of the model in different reservoirs.The main results are as follows:(1)In order to investigate the spatiotemporal pattern of algae communities and the water quality status in Nanwan Reservoir,the algal community and its influencing factors were investigated in 2022.There were 72 species belonging to 7 Phyla observed,Cyanophyta,Chlorophyta and Bacillariophyta were dominant species.And the biomass of algae in Nanwan Reservoir varied from 0.95 mg/L to 5.62 mg/L.The growth period was mainly concentrated from February to July,and the growth in the inlet area of the reservoir river was earlier than that in the dam area.The water temperature,pH,dissolved oxygen,total phosphorus,total nitrogen and permanganate index of Nanwan Reservoir were investigated,using the water environment factor evaluation method and trophic level index(TLI)to assess the water environment.Based on the analysis,it was confirmed that the water quality,except for total phosphorus,reached the standard ofⅠ～Ⅲand the TLI affirmed that the nutrient type was poor-medium.The changes of light intensity,rainfall and water level in Nanwan Reservoir were investigated.(2)Based on the analysis results of the monitoring data of Nanwan Reservoir,the construction of the prediction model of algal biomass in water sources and its prediction accuracy judgment were carried out.Based on the results of redundancy analysis,water temperature and initial concentration were screened as the input variables of the model,and the growth rate coefficient equation about water temperature and initial concentration of algae was constructed by the least squares method,and then the equation was embedded in the Logistic model to predict the target parameters.During the model operation,an air to water temperature prediction model applicable to Nanwan Reservoir was fitted by historical data,and the maximum air temperature data in the weather forecast was used to obtain the surface water temperature of the reservoir for the next 30 days.And by adding correction coefficients,the effect of different water depths in the reservoir on algae was corrected.The model was finally able to predict the spatial and temporal trends of algae in Nanwan Reservoir for the next 30 days.(3)To investigate the applicability of the prediction model for different prediction indexes and different water sources.The algal biomass and physiochemical parameters of water in Yudong Reservoir,Zhaotong City,Yunnan Province were investigated in 2021,and we analyzed the spatial and temporal trends of algae.Based on the actual monitoring data of Yudong reservoir,the model was validated by modifying the key parameters of the prediction model.The results of the prediction model of Yudong reservoir show that in the application of reservoirs in different areas,it is necessary to consider the differences between reservoirs and make adjustments to the key parameters.The final prediction results of the model can reach high accuracy(R~2>0.8),which proves that the constructed model has good prediction ability for different prediction objects.In this study,an algal biomass prediction model based on the growth mechanism was constructed through the quantitative calculation of algal growth,and a set of algal growth prediction and early warning system platform was built based on the constructed model,which can be directly applied to the algal bloom prediction of natural water bodies in multiple scenarios.This study makes up for the limitations of the existing model such as limited data volume and short prediction time,improves the prediction time and prediction accuracy of the model,and provides a new method for algal prediction and early warning in drinking water sources.