Eight typical sampling sites were chosen to investigate the phytoplankton community structure and to assess the eutrophication degree of Baiyangdian Lake in 2009. Our results showed that among the total 133 species identified, Cyanophyta, Chlorophyta, and Bacillariophyta dominated the phytoplankton community. In spring, Chlorophyta and Bacillariophyta were the dominant phyla, and the dominant species included Chlorella sp., Chroomonas acuta Uterm., and Microcystis incerta Lemm.; the density of the phytoplankton ranged from 496 × 10⁴ to 6256 × 10⁴ cells/L with an average of 2384 × 10⁴ cells/L. However, Chlorophyta and Cyanophyta became the dominant phyla in summer, and the dominant species were Chlorella sp., Leptolyngbya valderiana Anagn., and Nephrocytium agardhianum Nageli.; the density of the phytoplankton varied from 318 × 10⁴ to 4630 × 10⁴ cells/L with an average of 1785 × 10⁴ cells/L. The density of the phytoplankton has increased significantly compared to the previous investigations in 2005. The index of Carlson nutritional status (TSIM) and the dominant genus assessment indicated that the majority of Baiyangdian Lake was in eutrophic state.

This study established a WRF/WRF-Hydro coupled forecasting system for precipitation–runoff forecasting in the Daqing River basin in northern China. To fully enhance the forecasting skill of the coupled system, real-time updating was performed for both the WRF precipitation forecast and WRF-Hydro forecasted runoff. Three-dimensional variational (3Dvar) multi-source data assimilation was implemented using the WRF model by incorporating hourly weather radar reflectivity and conventional meteorological observations to improve the accuracy of the forecasted precipitation. A deep learning approach, i.e., long short-term memory (LSTM) networks, was adopted to improve the accuracy of the WRF-Hydro forecasted flow. The results showed that hourly data assimilation had a positive impact on the range and trends of the WRF precipitation forecasts. The quality of the WRF precipitation outputs had a significant impact on the performance of WRF-Hydro in forecasting the flow at the catchment outlet. With the runoff driven by precipitation forecasts being updated by 3Dvar data assimilation, the error of flood peak flow was decreased by 3.02–57.42%, the error of flood volume was decreased by 6.34–39.30%, and the Nash efficiency coefficient was increased by 0.15–0.52. The implementation of LSTM can effectively reduce the forecasting errors of the coupled system, particularly those of the time-to-peak and peak flow volumes.