High-resolution circa-2020 map of urban lakes in China

A national-scale assessment on the spatial and temporal variations in water color for urban lakes in China

Monitoring the variations of lake water quality is essential for urban water security and sustainable eco-environment health. However, it is challenging to investigate the water quality of urban lakes at large scales due to the need for large-amount in situ data with diverse optical properties for developing the remote sensing inversion algorithms. Forel-Ule Index (FUI), a proxy of quantifying water color, whose calculation does not require in situ data of specific properties, can comprehensively reflect water quality conditions. However, the spatial and temporal distribution of water color in Chinese urban lakes is still poorly understood. To fill this research gap, this study investigated the spatial distribution of water color in 523 urban lakes (area > 0.5 km2) in China using the FUI derived from the high-quality Multi-Spectral Instrument (MSI) data onboard Sentinel-2 during the ice-free period (April-October) from 2019 to 2022. The monthly and seasonal variation patterns of water color in urban lakes were also analyzed. Our results show that green domain is the most common color of urban lakes, with about 86 % of urban lakes in China being green, and non-green lakes accounting for only 14 % of the total number of lakes. The monthly variation of FUI in urban lakes across the country and multiple geographic regions is basically the same. The monthly average FUI first increases, then decreases, and then rebounds. We also found that the seasonal variation of water color in most urban lakes in southern and northern China is opposite. This study helps to comprehensively understand the spatial and temporal variation of water color and quality of urban lakes in China, providing key basic information for the protection and governance of urban lakes.

Annual Impervious Surface Data from 2001-2020 for West African Countries: Ghana, Togo, Benin and Nigeria

Impervious surface data are increasingly important for research and planning. Despite the availability of global and local urban land cover maps, regional data are lacking in Africa. We generated annual 30 m impervious cover data from 2001-2020 for Ghana, Togo, Benin, and Nigeria using the Landsat archive. We used random forest to predict impervious cover using 11 spectral indices and applied pixel-level temporal segmentation with the LandTrendr algorithm. Processing with LandTrendr improved the accuracy of the random forest predictions, with higher predicted-observed r2 (0.81), and lower mean error (-0.03), mean absolute error (5.73%), and root mean squared error (9.93%). We classified pixels >20% impervious as developed and < = 20% impervious as undeveloped. This classification had 93% overall accuracy and similar producer's (79%) and user's (80%) accuracies for developed area. Our maps had higher accuracy and captured more developed areas than comparable global datasets. This is the first regionally calibrated 30 m resolution impervious dataset in West Africa, which can support research on drivers and impacts of urban expansion and planning for future growth.

Diel variability of carbon dioxide concentrations and emissions in a largest urban lake, Central China: Insights from continuous measurements

Accurately quantifying the carbon dioxide (CO2) emissions from lakes, especially in urban areas, remains challenging due to constrained temporal resolution in field monitoring. Current lake CO2 flux estimates primarily rely on daylight measurements, yet nighttime emissions is normally overlooked. In this study, a non-dispersive infrared CO2 sensor was applied to measure dissolved CO2 concentrations over a 24-h period in a largest urban lake (Tangxun Lake) in Wuhan City, Central China, yielding extensive data on diel variability of CO2 concentrations and emissions. We showed the practicality and efficiency of the sensor for real-time continuous measurements in lakes. Our findings revealed distinct diurnal variations in CO2 concentrations (Day: 38.58 ± 23.8 μmol L-1; Night: 42.01 ± 20.2 μmol L-1) and fluxes (Day: 7.68 ± 10.34 mmol m-2 d-1; Night: 9.68 ± 9.19 mmol m-2 d-1) in the Tangxun Lake. The balance of photosynthesis and respiration is of utmost importance in modulating diurnal CO2 dynamics and can be influenced by nutrient loadings and temperature. A diel variability correction factor of 1.14 was proposed, suggesting that daytime-only measurements could underestimate CO2 emissions in urban lakes. Our data suggested that samplings between 11:00 and 12:00 could better represent the average diel CO2 fluxes. This study offered valuable insights on the diel variability of CO2 fluxes, emphasizing the importance of in situ continuous measurements to accurately quantify CO2 emissions, facilitating selections of sampling strategies and formulation of management strategies for urban lakes.

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.