Chunyan Tang

Spatiotemporal patterns in nutrient loads, nutrient concentrations, and algal biomass in Lake Taihu, China

Abstract Lake Taihu, Chinas third largest freshwater lake, exemplifies the severity of eutrophication problems in rapidly developing regions. We used long term land use, water quality, and hydrologic data from 26 in-lake and 32 tributary locations to describe the spatiotemporal patterns in nutrient loads, nutrient concentration, algal biomass, measured as chlorophyll a (Chl-a), in Lake Taihu. Point and nonpoint sources, as determined by chemical oxygen demand, contributed approximately 75 and 25% of the total nutrient loads to the lake, respectively. Spatial patterns in total phosphorus (TP) and total nitrogen (TN) concentrations in Lake Taihu strongly corresponded with observed loads from adjoining rivers with high concentrations proximate to densely populated areas. Chl-a concentrations exhibited spatial patterns similar to TP and TN concentrations. Generally, nutrient and Chl-a concentrations were highest in the northwestern region of the lake and lowest in the southeastern region of the lake. Seasonally, the largest nutrient loads occurred during summer. The annual net retention rate of TP and TN in Lake Taihu was approximately 30% of the total load. This study identifies regions of the lake and the watershed that are producing more nutrients to develop targeted management strategies. Reducing external P and N input from both point and nonpoint sources is obviously critical to address water quality issues in the lake. In addition, atmospheric deposition and resuspension of existing lake sediments also likely play a role in eutrophication processes and harmful algal blooms occurrence.

Parametric uncertainty and sensitivity analysis of hydrodynamic processes for a large shallow freshwater lake

Abstract A parametric uncertainty and sensitivity analysis of hydrodynamic processes was conducted for a large shallow freshwater lake, Lake Taihu, China. Ten commonly used parameters in five groups were considered including: air–water interface factor, water–sediment interface factor, surrounding terrain factor, turbulent diffusion parameters and turbulent intensity parameters. Latin hypercube sampling (LHS) was used for sampling the parametric combinations, which gave predictive uncertainty results directly without using surrogate models, and the impacts of different parametric distribution functions on the results were investigated. The results showed that the different parametric distribution functions (e.g. uniform, normal, lognormal and triangular) for sampling had very little impact on the uncertainty and sensitivity analysis of the lake hydrodynamic model. The air–water interface factor (wind drag coefficient) and surrounding terrain factor (wind shelter coefficient) had the greatest influence on the spatial distribution of lake hydrodynamic processes, especially in semi-closed bays and lake regions with complex topography, accounting for about 60–70% and 20%, respectively, of the uncertainty on the results. Vertically, velocity in the surface layer was also largely influenced by the two factors, followed by velocity in the bottom layer; the middle velocity had minimal impact. Likewise, the water–sediment interface factor (i.e. bottom roughness height) ranked third, contributing about 10% to the uncertainty of the hydrodynamic processes of the lake. In contrast, turbulent diffusion parameters and turbulent intensity parameters in the lake hydrodynamic model had little effect on the uncertainty of simulated results (less than 1% contribution). The findings were sufficiently significant to reduce the parameter uncertainties and calibration workload of the hydrodynamic model in large shallow lakes. Editor Z. W. Kundzewicz; Associate editor S. Grimaldi

A coupled modeling approach to predict water quality in Lake Taihu, China: linkage to climate change projections

Climate change is expected to impact water quality and ecosystem health in water bodies. In this study, a modeling framework was developed to assess the response of environmental variables to climate change scenarios in Lake Taihu, China. A coupled hydrodynamic-water quality-sediment flux model was employed to simulate water quality processes under future climate scenarios projected from a general circulation model (GCM) forced by the Representative Concentration Pathway 8.5 (RCP 8.5). The results showed that the climate change impacted physico-chemical parameters and biological interactions in Lake Taihu. The annual average water temperature increased by 0.96, 2.09, and 3.2 °C by the 2020s, 2050s, and 2080s, respectively. Daily temperature stratification tended to occur earlier and the period of stratification increased, especially in summer during calm wind conditions. The sediment flux increased and dissolved oxygen (DO) concentration decreased with climate change. Additionally, climate change increased the onset time, duration, and areas of algal blooms. The results showed that the blooms activation time advanced approximately six days per decade. The onset time of algal blooms of 2080s was in February and March and the elevated concentration of chlorophyll a lasted until late autumn in the west lake region. The chlorophyll a concentration in summer did not increase substantially (about 10–15 μg/L). Overall, water management strategies and ecological restoration plans in Lake Taihu, mainly based on nutrient load reduction and hydrodynamic modification, should incorporate anticipated effects of climate change.