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Abstract Accounting for temporal changes in carbon dioxide (CO ₂ ) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO ₂ based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO ₂ flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO ₂ . Annual CO ₂ emissions had an average of 25% (range 3%–58%) interannual variation. Similar tomore » studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO ₂ flux variability were delineated through mutual information analysis. Sample analysis of CO ₂ fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO ₂ fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.« less

While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (> 70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites inmore » the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (> 90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.« less

Evaporation (E) is a critical component of the water and energy budget in lake systems yet is challenging to quantify directly and continuously. We examined the magnitude and changes of E and its drivers over Lake Erie - the shallowest and most southern lake of the Laurentian Great Lakes. We deployed two eddy-covariance tower sites in the western Lake Erie Basin - one located nearshore (CB) and one offshore (LI) - from September 2011 through May 2016. Monthly E varied from 5 to 120 mm, with maximum E occurring in August–October. The annual E was 635 ± 42 (±SD) mmmore » at CB and 604 ± 32 mm at LI. Mean winter (October–March) E was 189 ± 61 mm at CB and 178 ± 25 mm at LI, accounting for 29.8% and 29.4% of annual E. Mean daily E was 1.8 mm during the coldest month (January) and 7.4 mm in the warmest month (July). Monthly E exhibited a strong positive linear relationship to the product of wind speed and vapor pressure deficit. Pronounced seasonal patterns in surface energy fluxes were observed with a 2-month lag in E from R_n, due to the lake's heat storage. This lag was shorter than reports regarding other Great Lakes. Difference in E between the offshore and nearshore sites reflected within-lake spatial heterogeneity, likely attributable to climatic and bathymetric differences between them. These findings suggest that predictive models need to consider lake-specific heat storage and spatial heterogeneity in order to accurately simulate lake E and its seasonal dynamics.« less

Correction to: Scientific Data https://doi.org/10.1038/s41597-020-0534-3, published online 09 July 2020

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such asmore » gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.« less

The Mongolian Plateau (MP) and Tibetan Plateau (TP) have experienced higher-than-global average warming in recent decades, resulting in many significant changes in ecosystem structure and function. Among them are the leaf area index (LAI) and albedo, which play a fundamental role in understanding many causes and consequences of land surface processes and climate. Here, we focused on the spatiotemporal changes of LAI, albedo, and their spatiotemporal relationships on the two roofing landscapes in Eurasia. Based on the MODIS products, we investigated the spatiotemporal changes of albedo(_{VIS, NIR and SHO}) and LAI from 2000 through 2016. We found that there existedmore » a general negative logarithmic relationship between LAI and three measures of albedo on both plateaus. No significant relationship was found for LAI-albedo_NIR on the TP, due to more complex land surface canopy characteristics affected by the NIR reflection there. During 2000–2016, overall, annual mean LAI increased significantly by 119.40 × 10³ km² on the MP and by 28.35 × 10³ km² on the TP, while the decreased areas for annual mean albedoVIS were 585.59 × 10³ km² and 235.73 × 10³ km² on the MP and TP, respectively. More importantly, the LAI-albedo relationships varied substantially across the space and over time, with mismatches found in some parts of the landscapes. Substantial additional efforts with observational and/or experimental investigations are needed to explore the underlying mechanisms responsible for these relationships, including the influences of vegetation characteristics and disturbances.« less

The impacts of extreme weather events on water-carbon (C) coupling and ecosystem-scale water use efficiency (WUE) over a long term are poorly understood. We analyzed the changes in ecosystem water use efficiency (WUE) from 10 years of eddy-covariance measurements (2004-2013) over an oak-dominated temperate forest in Ohio, USA. The aim was to investigate the long-term response of ecosystem WUE to measured changes in site-biophysical conditions and ecosystem attributes. The oak forest produced new plant biomass of 2.5 +/- 0.2 gC kg(-1) of water loss annually. Monthly evapotranspiration (ET) and gross ecosystem production (GEP) were tightly coupled over the 10-year studymore » period (R-2=0.94). Daily WUE had a linear relationship with air temperature (T-a) in low-temperature months and a unimodal relationship with T-a in high-temperature months during the growing season. On average, daily WUE ceased to increase when T-a exceeded 22 degrees C in warm months for both wet and dry years. Monthly WUE had a strong positive linear relationship with leaf area index (LAI), net radiation (R-n), and T-a and weak logarithmic relationship with water vapor pressure deficit (VPD) and precipitation (P) on a growing-season basis. When exploring the regulatory mechanisms on WUE within each season, spring LAI and P, summer R-n and T-a, and autumnal VPD and R-n were found to be the main explanatory variables for seasonal variation in WUE. The model developed in this study was able to capture 78% of growing-season variation in WUE on a monthly basis. The negative correlation between WUE and A in spring was mainly due to the high precipitation amounts in spring, decreasing GEP and WUE when LAI was still small, adding ET being observed to increase with high levels of evaporation as a result of high SWC in spring. Summer WUE had a significant decreasing trend across the 10 years mainly due to the combined effect of seasonal drought and increasing potential and available energy increasing ET, but decreasing GEP in summer. We concluded that seasonal dynamics of the interchange between precipitation and drought status of the system was an important variable in controlling seasonal WUE in wet years. In contrast, despite the negative impacts of unfavorable warming, available groundwater and an early start of the growing season were important contributing variables in high seasonal GEP, and thus, high seasonal WUE in dry years. (C) 2015 Elsevier B.V. All rights reserved.« less