What drives the changing characteristics of phytoplankton in urban lakes: Climate, hydrology, or human disturbance?

Phytoplankton in shallow urban lakes are influenced by various environmental factors. However, the long-term coupling effects and impact pathways of these environmental variables on phytoplankton remain unclear. This is an emerging issue due to high urbanization and the resultant complex climate, lake hydrology and morphology, human interference, and water quality parameter changes. This study used Tangxun Lake, the largest urban lake in the Yangtze River Economic Belt, as an example to assess for the first time the individual contributions and coupled effects of four environmental variables and fourteen indicators on chlorophyll-a (Chla) concentrations under two scenarios from 2000 to 2019. Additionally, the influence pathways between the environmental variables and Chla concentration were quantified. The results indicated that the Chla concentration was most affected by lake hydrology and morphology, as were the total nitrogen, total phosphorus, and transparency. Especially after urbanization (2015-2019), the coupling effect of human interference, lake hydrology and morphology, and water quality parameters was strongest (18%). This is mainly due to fluctuations in the lake water level and an increase in the shape index of lake morphology, large amounts of nutrients were input, which reduced lake transparency and indirectly changed the Chla content. In addition, due to the rapid development of Wuhan city, the expansion of construction land has led to an increase in impervious surface area and a decrease in lake area. During periods of intense summer rainfall, a substantial amount of pollutants entered the lakes through surface runoff, resulting in decreased lake transparency, and elevated concentrations of nitrogen and phosphorus, indirectly increasing the Chla content. This study provides a scientific basis for aquatic ecological assessment and pollution control in urban shallow lakes.

The divergence between potential and actual evapotranspiration: An insight from climate, water, and vegetation change.

Recently, unprecedented extreme drought has appeared around the world. As the most direct signal of drought, evapotranspiration deserves a more systematic and comprehensive study. Further depicting their divergence of potential (ETp) and actual evapotranspiration (ETa) will help to explore the limitation of evapotranspiration. In this paper, the multi-source remote sensing datasets from the Climate Research Unit (CRU), Gravity Recovery and Climate Experiment (GRACE) and its follow-on experiment (GRACE-FO), the Global Land Data Assimilation System (GLDAS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002 to 2020 were employed to explore the influence of meteorological, hydrological and botanical factors on ETp, ETa and their divergence - reduction of evapotranspiration (Er) which represents regional vegetation and water limitations. According to the Pearson correlation analysis and the Boruta Algorithm based on Random Forest, the temperature is the first decisive promoter of evapotranspiration in the most area while the sparse vegetation is the primary or second determinant limiting the evapotranspiration in 61.84% of the world. In addition, the Coupled Model Intercomparison Project (CMIP6) data from 2030 to 2090 and the support vector machine regression (SVMR) model were applied to predict the future global ETp, ETa and Er on the pixel scale. Predicted results of the model considering the water change not only can highly improve the model performance (with higher R2), but also can simulate the drought in Europe and the more intense ETa in Africa. Thus, Er proposed in this study provide a good reference for regional ETa except for ETp. The future evapotranspiration value derived by introducing the water storage changes into the machine learning model in this study is also valuable for climate change adaptation and drought warning.