Reservoir regulation-induced variations in water level impacts cyanobacterial bloom by the changing physiochemical conditions.

Gaining insight into the impact of reservoir regulation on algal blooms is essential for comprehending the dynamic changes and response mechanisms in the reservoir ecosystem. In this study, we conducted a comprehensive field investigation linking physiochemical parameters, and phytoplankton community to different water regimes in the Three Gorges Reservoir. Our aim was to explore the effects of reservoir regulation on the extinction of cyanobacterial blooms. The results showed that during the four regulatory events, the water levels decreased by 2.02-4.33 m, and the average water velocity increased 68 % compared to before. The average total phosphorus and total nitrogen concentrations reduced by up to 20 %, and the cyanobacterial biomass correspondingly declined dramatically, between 66.94 % and 75.17 %. As the change of water level decline increasing, there was a significant increase of algal diversity and a notable decrease of algal cell density. Additionally, a shift in the dominant phytoplankton community from Cyanobacteria to Chlorophyceae was observed. Our analysis indicated that water level fluctuations had a pronounced effect on cyanobacterial extinction, with hydrodynamic changes resulting in a reduction of cyanobacterial biomass. This research underlined the potential for employing hydrodynamic management as a viable strategy to mitigate the adverse ecological impacts of cyanobacterial blooms, providing a solution for reservoir's eco-environmental management.

Albedo changes caused by future urbanization contribute to global warming.

The replacement of natural lands with urban structures has multiple environmental consequences, yet little is known about the magnitude and extent of albedo-induced warming contributions from urbanization at the global scale in the past and future. Here, we apply an empirical approach to quantify the climate effects of past urbanization and future urbanization projected under different shared socioeconomic pathways (SSPs). We find an albedo-induced warming effect of urbanization for both the past and the projected futures under three illustrative scenarios. The albedo decease from urbanization in 2018 relative to 2001 has yielded a 100-year average annual global warming of 0.00014 [0.00008, 0.00021] °C. Without proper mitigation, future urbanization in 2050 relative to 2018 and that in 2100 relative to 2018 under the intermediate emission scenario (SSP2-4.5) would yield a 100-year average warming effect of 0.00107 [0.00057,0.00179] °C and 0.00152 [0.00078,0.00259] °C, respectively, through altering the Earth's albedo.

Challenging a Global Land Surface Model in a Local Socio-Environmental System.

Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson's R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI; phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses.