Abstract
Abstract. In the threshold of the appearance of global warming from theory to reality, extensive research has focused on predicting the impact of potential climate change on water resources using results from Global Circulation Models (GCMs). This research carries this further by statistical analyses of long term meteorological and hydrological data. Seventy years of historical trends in precipitation, temperature, and streamflows in the Great Lakes of North America are developed using long term regression analyses and Mann-Kendall statistics. The results generated by the two statistical procedures are in agreement and demonstrate that many of these variables are experiencing statistically significant increases over a seven-decade period. The trend lines of streamflows in the three rivers of St. Clair, Niagara and St. Lawrence, and precipitation levels over four of the five Great Lakes, show statistically significant increases in flows and precipitation. Further, precipitation rates as predicted using fitted regression lines are compared with scenarios from GCMs and demonstrate similar forecast predictions for Lake Superior. Trend projections from historical data are higher than GCM predictions for Lakes Michigan/Huron. Significant variability in predictions, as developed from alternative GCMs, is noted. Given the general agreement as derived from very different procedures, predictions extrapolated from historical trends and from GCMs, there is evidence that hydrologic changes particularly for the precipitation in the Great Lakes Basin may be demonstrating influences arising from global warming and climate change.
Highlights
The Great Lakes of North America, namely Lake Superior, Huron, Michigan, Erie and Ontario, represent one of the most important water resources in the world, and provide water for multipurpose for more than fifty million people in eastern North America
Montanari et al (1996) analysed six temporal meteorological series data observations to detect the presence of long memory and linear trends to predict the effects of potential climate change in the cities of Rome and Parma in Italy
A slope that is statistically significant under the hypothesis of uncorrelated data may become not significantly different from zero if correlation is properly taken into account
Summary
The Great Lakes of North America, namely Lake Superior, Huron, Michigan, Erie and Ontario, represent one of the most important water resources in the world, and provide water for multipurpose for more than fifty million people in eastern North America. Analysing the long-term series data for predicting the influence of potential climate changes is an important application of statistics in recent researches. Montanari et al (1996) analysed six temporal meteorological series data observations to detect the presence of long memory and linear trends to predict the effects of potential climate change in the cities of Rome and Parma in Italy Their results indicated that a decreasing trend, not statistically significant, is present in all six records and that long-term memories are significant in only two series. Chao (1999) conducted an assessment of the Great Lakes water resources impacts under transient climate change scenarios by an integrated model linking empirical regional climate downscaling, hydrologic and hydraulic models, and GCMs. The transient scenarios show that in the near-term (approximately 20 years) significant changes could occur. The 2007 report will likely have a smaller range of numbers for both predictions (IPCC, 2007)
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