Long-term spatiotemporal variability of whitings in Lake Geneva from
multispectral remote sensing and machine learning
Abstract
Whiting events are massive calcite precipitation events turning
hardwater lake waters to a milky turquoise color. The transitory nature
of whitings and their variable spatial extent make them poorly captured
by traditional monitoring. Herein, we use a multispectral remote sensing
approach to describe the spatial and temporal occurrences of whitings in
Lake Geneva from 2013 to 2021. Landsat-8, Sentinel-2, and Sentinel-3
sensors are combined and intercalibrated to derive the AreaBGR index and
identify whitings using appropriate filters. 95% of the detected
whitings are located in the northeastern part of the lake and occur in a
highly reproducible environmental setting: a high Rhone River discharge
(358.6 +/- 102.1 m3 s-1), air and
water temperatures of 21.3 +/- 3.0 °C and 18.0 +/- 1.9 °C respectively,
and during the stratified period (thermocline depth of 11.1 +/- 0.6 m).
An extended time series of whitings in the last 60 years is
reconstructed from a random forest algorithm and analyzed through a
Bayesian decomposition for annual and seasonal trends in the number of
whiting days. Results show that the annual number of whiting days
between 1958 and 2021 does not follow any particular monotonic trend.
The inter-annual changes of whiting occurrences significantly correlate
to the Western Mediterranean Oscillation Index (WeMOI). Besides, spring
whitings have increased since 2000 and significantly follow the Atlantic
Multidecadal Oscillation index (AMO). Future climate change in the
Mediterranean Sea and the Atlantic Ocean could induce more variable and
earlier whiting events in Lake Geneva.