Hydrological drivers of record‐setting water level rise on Earth's largest lake system

Abstract

Between January 2013 and December 2014, water levels on Lake Superior and Lake MichiganHuron, the two largest lakes on Earth by surface area, rose at the highest rate ever recorded for a 2 year period beginning in January and ending in December of the following year. This historic event coincided with below-average air temperatures and extensive winter ice cover across the Great Lakes. It also brought an end to a 15 year period of persistently below-average water levels on Lakes Superior and MichiganHuron that included several months of record-low water levels. To differentiate hydrological drivers behind the recent water level rise, we developed a Bayesian Markov chain Monte Carlo (MCMC) routine for inferring historical estimates of the major components of each lake's water budget. Our results indicate that, in 2013, the water level rise on Lake Superior was driven by increased spring runoff and over-lake precipitation. In 2014, reduced over-lake evaporation played a more significant role in Lake Superior's water level rise. The water level rise on Lake Michigan-Huron in 2013 was also due to above-average spring runoff and persistent over-lake precipitation, while in 2014, it was due to a rare combination of below-average evaporation, above-average runoff and precipitation, and very high inflow rates from Lake Superior through the St. Marys River. We expect, in future research, to apply our new framework across the other Laurentian Great Lakes, and to Earth's other large freshwater basins as well.

The changing water cycle: Burabay National Nature Park, Northern Kazakhstan

“…51 Since 2013, the water levels of Burabay and Shortandy Lakes have been steadily increasing while Ulken Shabakty water levels remained approximately stable ( Figure 5). There are similar trends between long-term water level changes in endorheic lakes of Canadian praries 34 and BNNP lakes.…”

Hydroclimatological Drivers of Extreme Floods on Lake Ontario

“…A substantial body of work has sought to identify anomalies in the water balance components-overlake precipitation, overlake evaporation, and runoff-that contribute to interannual fluctuation in lake levels, as well as the circulation anomalies driving these fluctuations (Assel, 1998;Assel et al, 2004;Biron et al, 2014;Ghanbari & Bravo, 2008;Gronewold et al, 2016;Hanrahan et al, 2010Hanrahan et al, , 2014Polderman & Pryor, 2004;Van Cleave et al, 2014). The polar jet stream, which is influenced by a variety of larger-scale teleconnections in the Pacific and Atlantic basins, has been identified as a dominant forcing of wintertime climatic variability in the Great Lakes region (Assel, 1998;Assel & Rodionov, 1998;Bai et al, 2012Bai et al, , 2015Gronewold et al, 2015;Rodionov, 1997;Rodionov & Assel, 2000Rodionov et al, 2001).…”

Evaluating Operational Hydrodynamic Models for Real‐time Simulation of Evaporation From Large Lakes

“…This surge has been attributed to a combination of persistent above-average precipitation across the Great Lakes region, and below-average evaporation (Gronewold et al, 2016). During this latter period, hydrologic and hydrodynamic conditions across the Great Lakes were characterized by an abrupt record-setting rise in Lake Superior and Lake Michigan-Huron water levels.…”

“…This model, commonly referred to as the large lake statistical water balance model, or L2SWBM (Gronewold et al, 2016), employs a Bayesian modeling framework (Press, 2003;Van Dongen, 2006) to infer posterior probability distributions for monthly total values of major water balance components of the Great Lakes. This model, commonly referred to as the large lake statistical water balance model, or L2SWBM (Gronewold et al, 2016), employs a Bayesian modeling framework (Press, 2003;Van Dongen, 2006) to infer posterior probability distributions for monthly total values of major water balance components of the Great Lakes.…”

“…We then compare simulated lakewide evaporation from each model to monthly total lakewide evaporation estimates from a recently developed statistical water balance model. This model, commonly referred to as the large lake statistical water balance model, or L2SWBM (Gronewold et al, 2016), employs a Bayesian modeling framework (Press, 2003;Van Dongen, 2006) to infer posterior probability distributions for monthly total values of major water balance components of the Great Lakes. The L2SWBM reconciles each lake's long-term water balance using historical water levels and readily available historical data sets of the water balance.…”