Coordinated dynamics of aquaculture ponds and water eutrophication owing to policy: A case of Jiangsu province, China.

Aquaculture ponds (APs) are rapidly expanding globally and are considered crucial for guaranteeing the supply of food, population growth, and economic development. However, the rapid expansion of aquaculture not only brought benefits but also a series of eco-environmental issues, such as water eutrophication. To achieve sustainable development, it is essential to gain a profound understanding of the spatiotemporal evolution of APs, the drivers behind their dynamics, and their relationship with the aquatic environment. Jiangsu Province (JS) in China, a historically significant aquaculture region, encompasses two prominent river basins: the Huai River Basin (HRB) and the Yangtze River Basin (YRB). In light of the construction of an ecological civilization, JS serves as a demonstration and pioneering area for basin protection and development. Therefore, this study focuses on JS, aiming to elucidate the spatiotemporal dynamics of APs, the corresponding relationship with basin management policies, and the impact on water eutrophication. The results revealed that: (1) in 2022, APs in JS were unevenly distributed, with a total area of 3278.78 km2, of which 79 % was located in the HRB. (2) During 2016-2022, APs exhibited an initial growth trend before 2019, followed by a decrease. (3) Due to policy interventions, AP changes within different basins showed opposite trends, and the corresponding water eutrophic state aligned with AP dynamics. The findings of this study can serve as a typical case to provide scientific evidence for the formulation and implementation of policies to improve the water environment in eutrophic basins.

Mapping particulate organic carbon in lakes across China using OLCI/Sentinel-3 imagery.

Remote sensing monitoring of particulate organic carbon (POC) concentration is essential for understanding phytoplankton productivity, carbon storage, and water quality in global lakes. Some algorithms have been proposed, but only for regional eutrophic lakes. Based on in-situ data (N = 1269) in 49 lakes across China, we developed a blended POC algorithm by distinguishing Type-I and Type-II waters. Compared to Type-I, Type-II waters had higher reflectance peak around 560 nm (>0.0125 sr-1) and mean POC (4.65 ± 4.11 vs. 2.66 ± 3.37 mg/L). Furthermore, because POC was highly related to algal production (r = 0.85), a three-band index (R2 = 0.65) and the phytoplankton fluorescence peak height (R2 = 0.63) were adopted to estimate POC in Type-I and Type-II waters, respectively. The novel algorithm got a mean absolute percent difference (MAPD) of 35.93 % and outperformed three state-of-the-art formulas with MAPD values of 40.56-76.42 %. Then, the novel algorithm was applied to OLCI/Sentinel-3 imagery, and we first obtained a national map of POC in 450 Chinese lakes (> 20 km2), which presented an apparent spatial pattern of "low in the west and high in the east". In brief, water classification should be considered when remotely monitoring lake POC concentration over a large area. Moreover, a process-oriented method is required when calculating water column POC storage from satellite-derived POC concentrations in type-II waters. Our results contribute substantially to advancing the dynamic observation of the lake carbon cycle using satellite data.

Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation.

Cyanobacterial blooms release a large number of algal toxins (e.g., Microcystins, MCs) and seriously threaten the safety of drinking water sources what the SDG 6.1 pursues (to provide universal access to safe drinking water by 2030, United Nations Sustainable Development Goal). Nevertheless, algal toxins in lake water have not been routinely monitored and evaluated well and frequently so far. In this study, a total of 100 large lakes (>25 km2) in densely populated eastern China were studied, and a remote sensing scheme of human health risks from MCs based on Sentinel-3 OLCI data was developed. The spatial and temporal dynamics of MCs risk in eastern China lakes since OLCI satellite observation data (2016-2021) were first mapped. The results showed that most of the large lakes in eastern China (80 out of 100) were detected with the occurrence of a high risk of more than 1 pixel (300×300 m) at least once. Fortunately, in terms of lake areas, the frequency of high human health risks in most waters (70.93% of total lake areas) was as less as 1%. This indicates that drinking water intakes can be set in most waters from the perspective of MCs, yet the management departments are required to reduce cyanobacterial blooms. This study highlights the potential of satellite in monitoring and assessing the risk of algal toxins and ensuring drinking water safety. It is also an important reference for SDG 6.1 reporting for lakes that lack routine monitoring.

Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery.

Lakes are major emitters of methane (CH4); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003-2020) and in situ measured data (2011-2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH4 emissions and found that the random forest (RF) model achieved the best fitting accuracy (R2 = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH4 emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH4 emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003-2020) daily time series of CH4 emissions. This study demonstrates the great potential of satellites to map lake CH4 emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.

Management actions mitigate the risk of carbon dioxide emissions from urban lakes.

Lakes are recognized as important sources of carbon dioxide (CO2) emissions, which vary greatly across land use type. However, CO2 emissions from lakes in urban landscapes are generally overlooked despite their daily connections to human activity. Furthermore, the role of management actions in CO2 emissions remained unclear mostly because of the lack of long-term observations. Here, the CO2 partial pressure (pCO2) from two urban lakes (Lake Wuli and Lake Donghu) in eastern China were investigated based on 16-year (2002-2017) field measurements. This long-term measurements showed the annual mean pCO2 were 1150 ± 612 µatm for Lake Wuli and 1143 ± 887 µatm for Lake Donghu, with corresponding estimated flux of 21.12 ± 19.60 mmol m-2 d-1 and 16.42 ± 20.39 mmol m-2 d-1, respectively. This indicates significant CO2 evasion into the atmosphere. Strong links between CO2 and human-derived nutrients (e.g., ammonium) and dissolved organic carbon, dissolved oxygen, and trophic state index were found. Although pCO2 was relatively uniform across sites and seasons in each lake, substantial inter-annual variability with significant decreasing trends were found. The decrease in annual CO2 can be partly explained by the reduction of pollutant loadings with management actions, which held the hypotheses that management actions mitigated the CO2 emission risks. Overall, management actions (e.g., ecological restoration and municipal engineering) should be considered for better understanding the roles of anthropogenic aquatic ecosystems in carbon cycle.

Unbalanced impacts of nature and nurture factors on the phenology, area and intensity of algal blooms in global large lakes: MODIS observations.

Under the influence of climate warming and human activities, many large lakes have experienced an increase in eutrophication and algal blooms. Although these trends have been identified using low temporal resolution (~16 days) satellites such as those of the Landsat missions, the opportunity to compare high-frequency spatiotemporal variations of algal bloom characteristics between lakes has not been explored. In the present study, we explore daily satellite observations by developing a universal, practical, and robust algorithm to identify the spatiotemporal distribution of algal bloom dynamics in large lakes (>500 km2) across the globe. Data from 161 lakes, taken from 2000 to 2020 showed an average accuracy of 79.9 %. Algal blooms were detected in 44 % of all lakes, with a higher incidence in temperate lakes (67 % of all temperate lakes), followed by tropical lakes (59 %) compared to lakes in arid climates (23 %). We found positive trends in bloom area and frequency (p < 0.05), as well as an earlier bloom time (p < 0.05). Climate factors were found to be linked to changes in annual initial bloom time (44 %); while an increase in human activities was associated to bloom duration (49 %), area (max percent: 53 %, mean percent: 45 %), and frequency (46 %). The study shows the evolution of daily algal blooms and their phenology in global large lakes for the first time. Such information enhances our understanding of algal bloom dynamics and their drivers, with important considerations to improve the management of large lake ecosystems.

Satellite Estimation of Dissolved Carbon Dioxide Concentrations in China's Lake Taihu.

Lakes play an important role in the global carbon cycle; however, there are still large uncertainties in the estimation of global lake carbon emission due to the limitations in conducting field surveys at large geographic scales. Using long-term Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery and field observation data in eutrophic Lake Taihu, we developed a novel approach to estimate the concentration of dissolved carbon dioxide (cCO2) in lakes. Based on the MODIS-derived chlorophyll-a concentration, lake surface temperature, diffuse attenuation coefficient of photosynthetically active radiation, and photosynthetically active radiation, a spatially explicit cCO2 model was developed using multivariate quadratic polynomial regression (coefficient of determination (R2) = 0.84, root-mean-square error (RMSE) = 11.81 µmol L-1, unbiased percent difference (UPD) = 22.46%). Monte Carlo simulations indicated that the model is stable with relatively small deviations in cCO2 estimates caused by input variables (UPD = 26.14%). MODIS data from 2003 to 2018 showed a significant declining trend (0.42 µmol L-1 yr-1, p < 0.05) in the annual mean cCO2. This was associated with a complex balance between the increasing algae biomass and decreasing external inputs of inorganic carbon, nutrients, and organic matter. The high spatiotemporal variabilities in cCO2 were attributed to river inputs and seasonal changes in temperature and algae biomass. The study shows that satellite remote sensing can play an important role in the field of inland water carbon cycling, providing timely much-needed insights into the drivers of the spatial and temporal changes in dissolved CO2 concentrations in inland waters.

Spatial and seasonal variability of nitrous oxide in a large freshwater lake in the lower reaches of the Yangtze River, China.

Aquatic ecosystems are recognized as a source of N2O in accordance with the flux estimations of rivers and estuaries; however, limited research has been conducted on large lakes. In this study, we report the annual N2O dynamics of a large eutrophic freshwater lake located in the subtropical zone of East China. The dissolved N2O concentrations in Lake Chaohu were observed to be between 8.5 and 92.3 nmol L-1 with emission rates between 0.3 and 53.6 µmol m-2 d-1, exhibiting considerable spatiotemporal variability. The average seasonal N2O concentrations were obtained, with the highest value of 23.4 nmol L-1 found in winter and the lowest value of 12.7 nmol L-1 found in summer. In contrast to the N2O concentrations observed, the highest N2O emission rates occurred during summer, while the lowest emission rates occurred in autumn. The emissions of N2O were substantially high in the western part of the lake, which suffers from serious eutrophication. In addition, the hotspots of N2O emissions have been found around the inflowing mouth of the Nanfei River, which transports large amounts of nutrients into the lake. The results suggest that anthropogenically enhanced nutrient inputs may have a significant role in the production and emission of N2O. However, the negative relationship between the surface water temperature and the N2O concentration suggests that, N2O fluxes might be influenced by other inconspicuous mechanisms. In the future the nitrogen dynamics of water and sediment in the lake should be collated to reveal mechanisms controlling N2O emissions. In summary, Lake Chaohu acts as a source of N2O with its most eutrophic part contributing 54.9% of the total N2O emissions of the whole lake.

Environmental investments decreased partial pressure of CO2 in a small eutrophic urban lake: Evidence from long-term measurements.

Inland waters emit large amounts of carbon dioxide (CO2) to the atmosphere, but emissions from urban lakes are poorly understood. This study investigated seasonal and interannual variations in the partial pressure of CO2 (pCO2) and CO2 flux from Lake Wuli, a small eutrophic urban lake in the heart of the Yangtze River Delta, China, based on a long-term (2000-2015) dataset. The results showed that the annual mean pCO2 was 1030 ± 281 µatm (mean ± standard deviation) with a mean CO2 flux of 1.1 ± 0.6 g m-2 d-1 during 2000-2015, suggesting that compared with other lakes globally, Lake Wuli was a significant source of atmospheric CO2. Substantial interannual variability was observed, and the annual pCO2 exhibited a decreasing trend due to improvements in water quality driven by environmental investment. Changes in ammonia nitrogen and total phosphorus concentrations together explained 90% of the observed interannual variability in pCO2 (R2 = 0.90, p < 0.01). The lake was dominated by cyanobacterial blooms and showed nonseasonal variation in pCO2. This finding was different from those of other eutrophic lakes with seasonal variation in pCO2, mostly because the uptake of CO2 by algal-derived primary production was counterbalanced by the production of CO2 by algal-derived organic carbon decomposition. Our results suggested that anthropogenic activities strongly affect lake CO2 dynamics and that environmental investments, such as ecological restoration and reducing nutrient discharge, can significantly reduce CO2 emissions from inland lakes. This study provides valuable information on the reduction in carbon emissions from artificially controlled eutrophic lakes and an assessment of the impact of inland water on the global carbon cycle.

Eutrophic Lake Taihu as a significant CO2 source during 2000-2015.

Inland lakes receive growing attentions on eutrophication and their roles in global carbon cycle. However, understanding how inland lakes contribute to global carbon cycle is seriously hampered due to a shortage of long-term records. This study investigated the carbon dioxide (CO2) flux from the Lake Taihu, a large (2400 km2) and shallow (mean depth 1.9 m) eutrophic lake in subtropical region, based on a long-term (2000-2015) measurement of the partial pressure of carbon dioxide (pCO2) at high spatiotemporal resolution. We found that the Lake Taihu was a significant source of atmospheric CO2 with an average CO2 emission flux at 18.2 ± 8.4 mmol m-2 d-1 (mean±1standard deviation) and a mean annual pCO2 value of 778 ± 169 µatm. The highest pCO2 and CO2 flux were observed in eutrophic zone with a high external input of carbon and nutrient, and the lowest in non-eutrophic zones with no direct external input of nutrient and carbon. A substantial seasonal pattern in pCO2 was observed, particularly in eutrophic pelagic area, and was significantly negatively correlated with chlorophyll a. Long-term measurement showed the interannual variation in annual lake CO2 dynamics, which was highly sensitive to human-induced nutrient input. Watershed input of carbon and nutrient leads to the high CO2 level, counterbalancing the in-lake primary production. All lines of evidence suggest that human activities may have predominate contribution to CO2 source in the Lake Taihu, and this mechanism might be widespread in global freshwater lakes.