Understanding Water Level Changes in the Great Lakes by an ICA-Based Merging of Multi-Mission Altimetry Measurements

Accurately monitoring spatio-temporal changes in lake water levels is important for studying the impacts of climate change on freshwater resources, and predicting natural hazards. In this study, we applied multi-mission radar satellite altimetry data from Laurentian Great Lakes, North America to optimally reconstruct multi-decadal lake-wide level. We used results study physical processes such as teleconnections El Niño southern oscillation (ENSO) episodes over approximately past three-and-a-half decades (1985–2018). First, assessed three reconstruction methods, namely standard empirical orthogonal function (EOF), complex EOF (CEOF), independent component analysis (CICA), model The performance these techniques was evaluated using in-situ gauge data, after correcting Glacial Isostatic Adjustment (GIA) process a contemporary GIA forward model. While altimeter-measured level much less affected by GIA, averaged gauge-measured found have increased up 14 cm decades. Our indicate that CICA-reconstructed 35-year more accurate than other two techniques. correlation coefficients between CICA situ water-level were 0.96, 0.99, 0.97, 0.95, Lake Superior, Michigan, Huron, Erie, Ontario, respectively; ~7% higher original data. root mean squares errors (RMSE) 6.07 cm, 4.89 9.27 7.71 9.88 respectively, each lakes, ~44% differencing with Furthermore, indicated Lakes significantly correlated ENSO, 0.5–0.8. ~25 (~30 lower) normal during EI (La Niña) events.