Wind-induced flow velocity effects on nutrient concentrations at Eastern Bay of Lake Taihu, China.

Shallow lakes are highly sensitive to respond internal nutrient loading due to wind-induced flow velocity effects. Wind-induced flow velocity effects on nutrient suspension were investigated at a long narrow bay of large shallow Lake Taihu, the third largest freshwater lake in China. Wind-induced reverse/compensation flow and consistent flow field probabilities at vertical column of the water were measured. The probabilities between the wind field and the flow velocities provided a strong correlation at the surface (80.6%) and the bottom (65.1%) layers of water profile. Vertical flow velocity profile analysis provided the evidence of delay response time to wind field at the bottom layer of lake water. Strong wind field generated by the west (W) and west-north-west (WNW) winds produced displaced water movements in opposite directions to the prevailing flow field. An exponential correlation was observed between the current velocities of the surface and the bottom layers while considering wind speed as a control factor. A linear model was developed to correlate the wind field-induced flow velocity impacts on nutrient concentration at the surface and bottom layers. Results showed that dominant wind directions (ENE, E, and ESE) had a maximum nutrient resuspension contribution (nutrient resuspension potential) of 34.7 and 43.6% at the surface and the bottom profile layers, respectively. Total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) average concentrations were 6.38, 1.5, and 0.03 mg/L during our field experiment at Eastern Bay of Lake Taihu. Overall, wind-induced low-to-moderate hydrodynamic disturbances contributed more in nutrient resuspension at Eastern Bay of Lake Taihu. The present study can be used to understand the linkage between wind-induced flow velocities and nutrient concentrations for shallow lakes (with uniform morphology and deep margins) water quality management and to develop further models.