Abstract
Estimating air–water gas transfer velocities (k) is integral to understand biogeochemical and ecological processes in aquatic systems. In lakes, k is commonly predicted using wind-based empirical models, however, their predictive performance under conditions that differ from their original calibration remains largely unassessed. Here, we collected 2222 published k estimates derived from various methods in 46 globally distributed lakes to (1) evaluate the predictions of a selection of six available wind-speed based k models for lakes and (2) explore and develop new empirical models to predict k over global lakes. We found that selected k models generally performed poorly in predicting k in lakes. Model predictions were more accurate than simply assuming a mean k in only 2–39% of all lakes, however, we could not identify with confidence the specific conditions in which some models outperformed others. We developed new wind-based models in which additional variables describing the spatial coverage of k estimates and the lake size and shape had a significant effect on the wind speed-k relationship. Although these new models did not fit the global dataset significantly better than previous k models, they generate overall less biased predictions for global lakes. We further provide explicit estimates of prediction errors that integrate methodological and lake-specific uncertainties. Our results highlight the potential limits when using wind-based models to predict k across lakes and urge scientists to properly account for prediction errors, or measure k directly in the field whenever possible.
Highlights
Estimating gas fluxes across the air–water interface in lakes is fundamental for understanding their biogeochemical, environmental and ecological functioning
We evaluated the fits of all candidate models using the Akaike Information Criterion (AIC) and selected, for each of the three model types, the model with the lowest AIC and all parameters significant as the final model
The compiled data covered k600 values from 0.01 to 57.62 cm h−1, U10 from 0 to 13 m s−1, LA from 181 m2 to 1342 km2 and shoreline development index (SDI) from 1.0 to 22.5 (Fig. 2). k600 increased with U10 and was generally < 10 cm h−1 for U10 < 2 m s−1 and > 10 cm h−1 for U10 > 8 m s−1
Summary
Estimating gas fluxes across the air–water interface in lakes is fundamental for understanding their biogeochemical, environmental and ecological functioning. There are methods to estimate gas fluxes such as the use of floating chambers (Engle and Melack 2000; Matthews et al 2003) or by eddy covariance (Anderson et al 1999). These methods can be time and/or cost consuming and may be difficult to be applied to capture potential spatial variation in multiple systems at the same time (Cole et al 2010; Schilder et al 2013; Erkkilä et al 2018).
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