基于二维湖泊藻类模型的洪泽湖藻类空间动态模拟

将洪泽湖分为北、东、西3区,基于二维湖泊藻类模型构建洪泽湖藻类空间动态模型.利用水文、气象、水质数据初始化模型、设定边界条件,并用2012年逐月叶绿素a实测浓度校正模型参数,模拟出洪泽湖2012年叶绿素a浓度连续变化过程.从时间维度看,叶绿素a浓度变化多呈“双峰型”趋势,分别在4月和8月左右出现峰值;湖心和出湖口站点呈单峰型或无明显峰值;颜圩站点出现多个浓度峰值.从空间维度看,西区叶绿素a平均浓度最低且空间差异不明显;北区因湖水流动性差,浓度分布均匀,且靠近湖岸地区浓度较高;东区叶绿素a浓度受过水通道影响较大,空间差异显著.虽然受大型浅水湖泊模拟不确定性等因素影响,个别点位模拟误差偏高,但不会影响整体分析结果,模拟结果具有较高可信度.

Spatial-temporal modeling of phytoplankton in Lake Hongze using a 2-D hydrodynamic-phytoplankton model.

The dynamics of algae biomass distribution in Lake Hongze was simulated using a two-dimensional hydrodynamic-phytoplankton model. Lake Hongze was divided into three zones, i.e., northern zone, eastern zone and western zone. Chlorophyll a (Chl a) was used to represent algal biomass in Lake Hongze. The initial and boundary data for the hydrodynamic-phytoplankton model included hydrological, meteorological and water quality data. The spatial distribution of Chl a, nitrogen and phosphorus concentrations in January was used as the initial data. The boundary data included daily water flow, temperature and precipitation. Ten most sensitive parameters (e.g., maximum growth rate of phytoplankton) were identified based on sensitivity analysis, and was optimized based on the value ranges from previous study. The calibration results showed good agreement between measured and simulated Chl a (r2=0.281, RMSE = 2.52 μg/L). Based on the calibrated model, the dynamics of Chl a distribution in Lake Hongze during 2012 were simulated. The simulation results generally showed two Chl a peaks in April and August. However, simulation Chl a at central and outlet areas had one peak or no obvious peak. Simulation Chl a at Yanwei station showed many peaks in the year. Simulation Chl a in three spatial zones was significantly different. In western zone, Chl a was low and showed low spatial heterogeneity. Spatial heterogeneity of Chl a in northern zone was low due to the low water flow. Chl a in the nearshore area was relatively higher, and was significantly affected by wind conditions. In eastern zone, spatial heterogeneity of Chl a was high due to the inflow rivers with large discharge, such as River Huai. The model well predicted the overall trend of Chl a in Lake Hongze during 2012. However, the model fits at some stations were relatively low due to the existing model uncertain. The modeling practice in this study improved our understanding of algae dynamics in Lake Hongze, and thus benefited us in controlling algal blooms.