Effect of Water Column Stability on Surface Chlorophyll and Time Lags under Different Nutrient Backgrounds in a Deep Reservoir

The Global Trend of Microplastic Research in Freshwater Ecosystems

The study of microplastics and their impact on aquatic ecosystems has received increasing attention in recent years. Drawing from an analysis of 814 papers related to microplastics published between 2013 and 2022 in the Web of Science Core Repository, this paper explores trends, focal points, and national collaborations in freshwater microplastics research, providing valuable insights for future studies. The findings reveal three distinct stages of microplastics: nascent development (2013-2015), slow rise (2016-2018), and rapid development (2019-2022). Over time, the focus of research has shifted from "surface", "effect", "microplastic pollution", and "tributary" to "toxicity", "species", "organism", "threat", "risk", and "ingestion". While international cooperation has become more prevalent, the extent of collaboration remains limited, mostly concentrated among English-speaking countries or English and Spanish/Portuguese-speaking countries. Future research directions should encompass the bi-directional relationship between microplastics and watershed ecosystems, incorporating chemical and toxicological approaches. Long-term monitoring efforts are crucial to assessing the sustained impacts of microplastics.

Varying water column stability controls the denitrification process in a subtropical reservoir, Southwest China

Reservoirs are regarded as hotspots of nitrogen transformation and potential sources of nitrous oxide (N₂O). However, it remains unclear how the hydrological conditions due to dam construction control the processes of nitrogen transformation in reservoir waters. To address this issue, we examined the spatial-temporal characteristics of nitrate concentrations, δ¹⁵N-NO₃^-, δ¹⁸O-NO₃^-, δ¹⁸O-H₂O, relative water column stability (RWCS), and related environmental factors in a subtropical eutrophic reservoir (Hongfeng Reservoir, HFR), Southwest China. We found that denitrification was the most important nitrogen transformation process in the HFR and that higher denitrification intensity was associated with increased RWCS in summer, which suggested hydrological control of the denitrification process. In contrast, low RWCS conditions favored the nitrification process in the HFR in winter. Additionally, dissolved oxygen (DO; p < 0.05) and nitrate concentrations (p < 0.01) had significant impacts on the denitrification rate. We also found that the spatiotemporal RWCS variations were a prerequisite for regulating DO/nitrate stratification and the coupling/decoupling of nitrification-denitrification at the local and global scales. This study would advances our knowledge of the impacts of RWCS and thermal stratification on nitrogen transformation processes in reservoirs.

A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom

In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena in Florida’s coastal areas. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, we developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models the K. brevis abundance is used as the target, and 10 level-02 ocean color products extracted from daily archival MODIS satellite data are used as controlling factors. The adopted approach addresses two main shortcomings of earlier models: (1) the paucity of satellite data due to cloudy scenes and (2) the lag time between the period at which a variable reaches its highest correlation with the target and the time the bloom occurs. Eleven spatio-temporal models were generated, each from 3 consecutive day satellite datasets, with a forecasting span from 1 to 11 days. The 3-day models addressed the potential variations in lag time for some of the temporal variables. One or more of the generated 11 models could be used to predict HAB occurrences depending on availability of the cloud-free consecutive days. Findings indicate that XGBoost outperformed the other methods, and the forecasting models of 5–9 days achieved the best results. The most reliable model can forecast eight days ahead of time with balanced overall accuracy, Kappa coefficient, F-Score, and AUC of 96%, 0.93, 0.97, and 0.98 respectively. The euphotic depth, sea surface temperature, and chlorophyll-a are always among the most significant controlling factors. The proposed models could potentially be used to develop an “early warning system” for HABs in southwest Florida.

Precipitation Mediates the Distribution but Not the Taxonomic Composition of Phytoplankton Communities in a Tributary of Three Gorges Reservoir

Precipitation is a driver of changes in the spatiotemporal distribution of phytoplankton communities. The ecological consequence of precipitation is important, but the underlying processes are not clear. Here we conducted an immediate prior- and after-event short-interval investigation in the Three Gorges Reservoir region, to test whether the short-term changes in the phytoplankton communities and functional groups could be predicted based on the precipitation level. We found that precipitation of moderate and high levels immediately changed the phytoplankton distribution and altered functional groups. According to structural equation model, the vertical velocity (λ = -0.81), light availability (Zeu/Zmix, λ = 0.47) and relative water column stability (RWCS, λ = 0.38) were important parameters for phytoplankton distribution during the precipitation event. Water quality did not directly affect phytoplankton distribution (λ = -0.11) and effects of precipitation on the water quality only lasted 1-2 days. The phytoplankton community was redistributed with some tolerance functional groups appearance, such as groups F, Lo, M and groups M, MP, TB, W1 appeared during- and after- precipitation event, respectively. We also found that mixing rather than flushing was the driving force for the decrease of phytoplankton biomass. Our study provided valuable data for reservoir regulation and evidence for predictions of phytoplankton during the precipitation events under different climate change scenarios.