Predicting the Net Basin Supply to the Great Lakes with a Hydrometeorological Model

Abstract

The paper presents the incremental improvement of the prediction of the Great Lakes net basin supply (NBS) with the hydrometeorological model Modélisation Environmentale-Surface et Hydrologie (MESH) by increasing the accuracy of the simulated NBS components (overlake precipitation, lake evaporation, and runoff into the lake). This was achieved through a series of experiments with MESH and its parent numerical weather prediction model [the Canadian Global Environmental Multiscale model in its regional configuration (GEM Regional)]. With forcing extracted from operational GEM Regional forecasts, MESH underestimated the NBS in fall and winter. The underestimation increased when the GEM precipitation was replaced with its corrected version provided by the Canadian Precipitation Analysis. This pointed to overestimated lake evaporation and prompted the revision of the parameterization of the surface turbulent fluxes over water used both in MESH and GEM. The revised parameterization was validated against turbulent fluxes measured at a point on Lake Superior. Its use in MESH reduced the lake evaporation and largely corrected the NBS underestimation. However, the Lake Superior NBS became overestimated, signaling an inconsistency between the reduced lake evaporation and the prescribed precipitation. To remove the inconsistency, a new forcing dataset (including precipitation) was generated with the GEM model using the revised flux parameterization. A major NBS simulation improvement was obtained with the new atmospheric forcing reflecting the atmospheric response to the modified surface fluxes over the lakes. Additional improvements resulted by correcting the runoff with a modified snowmelt rate and by insertion of observed streamflows. The study shows that accurate lake evaporation simulation is crucial for accurate NBS prediction.

Present and future Laurentian Great Lakes hydroclimatic conditions as simulated by regional climate models with an emphasis on Lake Michigan-Huron

“…The effect of connecting-channel flows on residual NBS uncertainty is amplified moving downstream through the system. For instance, for Lake Erie, the uncertainty in NBS for some months was found to be greater than the actual NBS estimate itself (Deacu et al 2012). …”

“…Here, ΔS is the change in storage derived from the difference in observed lake levels at the beginning and the end of a month, O is the outflow, I is connecting channel inflow from the upstream lake, and D is the man-made total diversion. Provisional monthly values of the residual NBS were provided by both EC and the U.S. Army Corps of Engineers, which were subsequently agreed upon by Canada and U.S. and became values of the so-called coordinated residual NBS (Deacu et al 2012). As GLM-HMD does not include evapotranspiration over the land part of the Great Lakes, we estimated the annual mean from the observation-based terrestrial water budget components (see Equation 7 in Music and Caya 2007).…”

The Estimation of the North American Great Lakes Turbulent Fluxes Using Satellite Remote Sensing and MERRA Reanalysis Data

“…The cold, dry air flowing down from the Canadian continental climate meets the relatively warm lake and produces strong vapor pressure gradients in addition to high winds, causing the very high lake evaporation rates. The intense convection and the strong local atmospheric respond through lake-effect precipitation [25]. The climate teleconnection patterns, such as the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), and the El Niño-Southern Oscillation (ENSO) might also impact on the Great Lakes ice cover [26][27][28][29][30][31][32].…”

Parameter sensitivity analysis of a 1-D cold region lake model for land-surface schemes

“…The present study was based on a combination of three factors underlining its relevance: firstly, by building upon existing literature that stresses the importance of lake clarity in modeling heat transfers (Heiskanen et al, 2015;Rose et al, 2016;Woolway et al, 2016) and evaluating against measured fluxes, as done by Deacu et al (2012) for large lakes.…”