Analysis of future nitrogen and phosphorus loading in watershed and the risk of lake blooms under the influence of complex factors: Implications for management.

For the management of eutrophic lakes, watershed nitrogen and phosphorus control is oriented to future water quality. Assessing future nutrient dynamics and the risk of lake eutrophication is necessary. However, current assessments often lack integrated consideration of socioeconomic and climatic factors, which reduces the reference value of the results. In this study, a typical large shallow lake Chaohu, which is highly influenced by human activities, was selected as the study area, and the current and future total nitrogen (TN) and total phosphorus (TP) loading in the basin were analysed using the improved MARINA model, and the risk of water bloom were assessed. The results showed that socioeconomic factors alone varied future TN and TP loading by -24% to 32% and -40% to 34%, respectively, under different development patterns. After considering the effect of increased precipitation, the changes of TN and TP loading became -10% to 163% and -29% to 108%, respectively. The effect on loading reduction under the sustainable development pattern was weakened (58% and 28% for TN and TP loading, respectively) and the increase in loading under the brutal development pattern was significantly amplified (409% and 215% for TN and TP loading, respectively). The adoption of active environmental policies remained an effective way of loading control. However, the risk of water bloom in local lake areas might persist due to factors such as urbanization. Timely and comprehensive assessments can provide managers with more information to identify key factors that contribute to the risk of water blooms and to develop diverse water quality improvement measures. The insights from our study are applicable to other watersheds around the world with similar socio-economic background and climatic conditions.

Disentangling the effects of phosphorus loading on food web stability in a large shallow lake.

Excessive nutrient loads reduce ecosystem resilience, resulting in fundamental changes in ecosystem structure and function when exceeding a certain threshold. However, quantitative analysis of the processes by which nutrient loading affects ecosystem resilience requires further exploration. Food web stability is at the heart of ecosystem resilience. In this study, we simulated the dynamics of the food web under different phosphorus loads for Lake Baiyangdian using the PCLake model and calculated the food web stability. Our results showed that there was a good correspondence between the food web stability and ecosystem state response to phosphorus loads. This relationship confirmed that food web stability could be regarded as a signal for the state transition in a real lake ecosystem. Moreover, our estimates suggested that food web stability was influenced only by several functional groups and their interaction strength. Diatoms and zooplankton were the key functional groups that affected food web stability. Phosphorus loads alter the distribution of functional group biomass, which in turn affects energy delivery and, ultimately, the stability of the food web. Corresponding to functional groups, the interactions among zooplankton, diatoms and detritus had the greatest impact, and the interaction strength of the three was positively correlated with food web stability. Overall, our study explained that food-web stability was critical to characterize ecosystem resilience response to external disturbances and can be turned into a scientific tool for lake ecosystem management.

Generalized additive models for biomass simulation of submerged macrophytes in a shallow lake.

Submerged macrophytes are widely distributed primary producer that play important roles in maintaining healthy aquatic ecosystems. Generally, the relationships between macrophytes and environmental factors are complicated, so nonlinear nonparametric models with relatively flexible structures are optimal for macrophyte habitat simulation. In this study, generalized additive model (GAM) was used to evaluate the response of the submerged macrophytes biomass to water environmental factors in the Baiyangdian Lake. Forward stepwise method was used to implement model optimization. Likelihood ratio test was used to determine whether adding a variable enhances the model performance. Four individual variables (water depth, transparency, total nitrogen, and total phosphorus) and two interaction terms (water depth × transparency and water depth × total phosphorus) were included in the optimal GAM. The optimal model explained 70.5% of the biomass variation with a relatively low residual deviance value (22.40). There was a significant correlation between the measured and predicted data (R2 = 0.716, p = 0.0004). The response lines generated by the model indicated that macrophyte biomass had a positive correlation with transparency but negative correlations with total nitrogen and nitrite nitrogen in water. The response patterns of macrophyte biomass to water depth and total phosphorus were unimodal. The biomass reached the maximum value when the water depth was about 2.1 m and the total phosphorus concentration was 0.07 mg/L. Water depth and transparency, which affect light availability, are critical physical variables affecting the conditions associated with the submerged macrophytes, and excess nitrite and phosphorus limiting macrophyte biomass.