Long-Term Modelling of Stratification in Large Lakes: Application to Lake Constance

Author(s):  
Eckard Hollan ◽  
Paul F. Hamblin ◽  
Hubert Lehn
Keyword(s):  
2018 ◽  
Vol 10 (8) ◽  
pp. 1210
Author(s):  
Charles White ◽  
Andrew Heidinger ◽  
Steven Ackerman ◽  
Peter McIntyre

Inland waters are warming at highly variable rates that often differ from regional air temperature trends. This variable warming is partially attributable to an individual lake’s geographical and morphological characteristics. In very large lakes, significant intralake variability in long-term warming trends has also been observed. In light of this intralake and interlake heterogeneity of lake surface water temperature (LSWT) and LSWT trends, we revisit the 1.1 km Advanced Very High Resolution Radiometer (AVHRR) record for the Laurentian Great Lakes. In this work, we have assembled a long-term (1986–2016) and high-spatial-resolution (0.018°) daily LSWT dataset using AVHRR record. Subtracting an empirically-determined mean diurnal cycle mitigates the effects of varying observation times. Adjustments in the georegistration of the images are made to reduce the impact of AVHRR navigational errors on the earlier platforms. Both the original daily composites, and a gap-filled product using locally weighted interpolation methods will be made available to support fine-scale physical and environmental research in the region.


2010 ◽  
Vol 17 (5) ◽  
pp. 386-393 ◽  
Author(s):  
G. THOMAS ◽  
R. RÖSCH ◽  
R. ECKMANN

1993 ◽  
Vol 24 (2-3) ◽  
pp. 135-150 ◽  
Author(s):  
Geoff Kite

Considerable scientific attention has been focused on a measured increase in atmospheric CO2 and a suspected corresponding change in climate. Such a change in climate, if it occurred, might be expected to have a magnified effect on hydrologic time series and, indeed, projections have been made of major changes in water resources. If the climatic changes are indeed magnified in hydrologic time series then, by detecting trends in such series, it should be possible to work backwards and identify the causative climatic change. This paper looks at two data sets: 1) long-term temperature, precipitation and streamflow data from sites across Canada and 2) long-term levels of large lakes in Africa and North America. The study assumes that time series may be modelled by trend, periodic, autoregressive and random residual components. The trend component of a time series is generally associated with changes in the structure of the time series caused by cumulative natural or manmade phenomena. Periodicities in natural time series are usually due to astronomical cycles such as the earth's rotation around the sun. Autoregressive components reflect the tendency for an event to be dependent on the magnitude of the previous event(s), a memory effect. The analyses of temperature, precipitation and streamflow data show some significant linear trends but no pattern is apparent. The analyses of longterm lake levels also identify linear trends but these are all explainable without invoking climate change due to greenhouse gases.


AMBIO ◽  
2017 ◽  
Vol 46 (5) ◽  
pp. 554-565 ◽  
Author(s):  
Justin Rhodes ◽  
Harald Hetzenauer ◽  
Marieke A. Frassl ◽  
Karl-Otto Rothhaupt ◽  
Karsten Rinke

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