In this time of a changing climate, it is important to know whether lake levels will rise, potentially causing flooding, or river flows will dry up during abnormally dry weather. The Great Lakes region is the largest freshwater lake system in the world. Moreover, agriculture, industry, commerce, and shipping are active in this densely populated region. Environment and Climate Change Canada (ECCC) recently implemented the Water Cycle Prediction System (WCPS) over the Great Lakes and St. Lawrence River watershed (WCPS-GLS version 1.0) following a decade of research and development. WCPS, a network of linked models, simulates the complete water cycle, following water as it moves from the atmosphere to the surface, through the river network and into lakes, and back to the atmosphere. Information concerning the water cycle is passed between the models. WCPS is the first short-to-medium-range prediction system of the complete water cycle to be run on an operational basis anywhere. It currently produces two forecasts per day for the next three days. WCPS generally provides reliable results throughout the length of the forecast. The transmission of errors between the component models is reduced by data assimilation. Interactions between the environmental compartments are active. This ongoing intercommunication is valuable for extreme events such as rapid ice freeze-up and flooding or drought caused by abnormal amounts of precipitation. Products include precipitation; evaporation; lake water levels, temperatures, and currents; ice cover; and river flows. These products are of interest to a wide variety of governmental, commercial, and industrial groups, as well as the public.
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 recent 2-year surge represents one of the most rapid rates of water level change on the Great Lakes in recorded history and marks the end of an unprecedented period of low water levels.
<p><span class="TextRun SCXW169027254 BCX8" lang="EN-CA" xml:lang="EN-CA" data-contrast="auto"><span class="NormalTextRun SCXW169027254 BCX8">Environment and Climate Change Canada (ECCC) is in the process of deploying a continental-scale hydrological prediction system </span><span class="NormalTextRun SCXW169027254 BCX8">known as the</span><span class="NormalTextRun SCXW169027254 BCX8"> National Surface and River Prediction System (NSRPS). <span class="TextRun SCXW224052350 BCX8" lang="EN-CA" xml:lang="EN-CA" data-contrast="auto"><span class="NormalTextRun SCXW224052350 BCX8">Operating at a resolution of 30 arc seconds, </span></span></span><span class="NormalTextRun SCXW169027254 BCX8">NSRPS currently generates</span><span class="NormalTextRun SCXW169027254 BCX8"> Ensemble Streamflow Predictions</span> <span class="NormalTextRun SCXW169027254 BCX8">(</span><span class="NormalTextRun SCXW169027254 BCX8">ESPs</span><span class="NormalTextRun SCXW169027254 BCX8">)</span><span class="NormalTextRun SCXW169027254 BCX8"> for over </span><span class="NormalTextRun SCXW169027254 BCX8">four</span><span class="NormalTextRun SCXW169027254 BCX8"> million grid </span><span class="NormalTextRun SCXW169027254 BCX8">points</span><span class="NormalTextRun SCXW169027254 BCX8">. </span><span class="NormalTextRun SCXW169027254 BCX8">Issued once per day, the ensemble is composed of twenty members and provides 16-day forecasts.</span> <span class="NormalTextRun SCXW169027254 BCX8">NSRPS</span><span class="NormalTextRun SCXW169027254 BCX8"> differs from other hydrological forecasting </span><span class="NormalTextRun SCXW169027254 BCX8">systems </span><span class="NormalTextRun SCXW169027254 BCX8">in </span><span class="NormalTextRun SCXW169027254 BCX8">operational </span><span class="NormalTextRun SCXW169027254 BCX8">use in Canada by being continental in scope and by relying on an Earth System Modelling (ESM) approach </span><span class="NormalTextRun SCXW169027254 BCX8">for </span><span class="NormalTextRun SCXW169027254 BCX8">prediction</span><span class="NormalTextRun SCXW169027254 BCX8">. </span><span class="NormalTextRun SCXW169027254 BCX8">I</span><span class="NormalTextRun SCXW169027254 BCX8">n order to assess the value of </span><span class="NormalTextRun SCXW169027254 BCX8">forecasts </span><span class="NormalTextRun SCXW169027254 BCX8">issued</span><span class="NormalTextRun SCXW169027254 BCX8"> by NSRPS, </span><span class="NormalTextRun SCXW169027254 BCX8">a comparison is performed </span><span class="NormalTextRun SCXW169027254 BCX8">with </span><span class="NormalTextRun SCXW169027254 BCX8">a</span><span class="NormalTextRun SCXW169027254 BCX8"> simila</span><span class="NormalTextRun SCXW169027254 BCX8">r </span><span class="NormalTextRun SCXW169027254 BCX8">ESP</span><span class="NormalTextRun SCXW169027254 BCX8"> system</span><span class="NormalTextRun SCXW169027254 BCX8"> available over all of Canada: </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW169027254 BCX8">the</span><span class="NormalTextRun SCXW169027254 BCX8"> Global Flood Awareness System (</span><span class="NormalTextRun SpellingErrorV2Themed SCXW169027254 BCX8">GloFAS</span><span class="NormalTextRun SCXW169027254 BCX8">) from the European Centre for Medium-Range Weather Forecasts (ECMWF). The evaluation focusses on the Great Lakes and St. Lawrence watershed as well as the Nelson and Churchill watershed</span><span class="NormalTextRun SCXW169027254 BCX8">s</span><span class="NormalTextRun SCXW169027254 BCX8">, each over </span><span class="NormalTextRun SCXW169027254 BCX8">one</span><span class="NormalTextRun SCXW169027254 BCX8"> million km&#178; in size. 393 streamflow stations are identified where NSRPS and </span><span class="NormalTextRun SpellingErrorV2Themed SCXW169027254 BCX8">GloFAS</span><span class="NormalTextRun SCXW169027254 BCX8"> agree </span><span class="NormalTextRun SCXW169027254 BCX8">on</span><span class="NormalTextRun SCXW169027254 BCX8"> the watershed delineation</span><span class="NormalTextRun SCXW169027254 BCX8">. The comparison</span><span class="NormalTextRun SCXW169027254 BCX8">&#160;is limited to</span><span class="NormalTextRun SCXW169027254 BCX8"> the Spring, Summer and Fall of 2022</span><span class="NormalTextRun SCXW169027254 BCX8"> due to NSRPS forecast data availability</span><span class="NormalTextRun SCXW169027254 BCX8">. For most stations, NSRPS performs better than </span><span class="NormalTextRun SpellingErrorV2Themed SCXW169027254 BCX8">GloFAS</span><span class="NormalTextRun SCXW169027254 BCX8"> in terms of Continuous Ranked Probability Score (CRPS), but the median of the potential CRPS across the 393 stations is very similar for days 3-16.</span><span class="NormalTextRun SCXW169027254 BCX8"> Both systems suffer from a lack of </span><span class="NormalTextRun SCXW169027254 BCX8">spread</span><span class="NormalTextRun SCXW169027254 BCX8">,</span> <span class="NormalTextRun SCXW169027254 BCX8">particular</span><span class="NormalTextRun SCXW169027254 BCX8">ly</span><span class="NormalTextRun SCXW169027254 BCX8"> for</span><span class="NormalTextRun SCXW169027254 BCX8"> short lead times, but the problem is slightly more acute for </span><span class="NormalTextRun SpellingErrorV2Themed SCXW169027254 BCX8">GloFAS</span><span class="NormalTextRun SCXW169027254 BCX8">.<span class="NormalTextRun SCXW224052350 BCX8">&#160;</span></span><span class="NormalTextRun SCXW169027254 BCX8">Bayesian Model Averaging (BMA)</span><span class="NormalTextRun SCXW169027254 BCX8"> is explored in order to obtain calibrated probabilistic forecasts that perform better than both NSRPS and </span><span class="NormalTextRun SpellingErrorV2Themed SCXW169027254 BCX8">GloFAS</span><span class="NormalTextRun SCXW169027254 BCX8">.</span></span><span class="EOP SCXW169027254 BCX8" data-ccp-props="{">&#160;</span></p>
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