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Modeling the Effects of Past and Future Climate Change on GLKN Lakes

Sector: Geothermal • Location: United States of America

Source: Grants.gov

Project
Archived

Remote interior lakes in national park units of the Great Lakes Network (GLKN) are experiencing unexpected ecological change, including blooms of noxious blue-green algae. Sediment-core data indicate that these changes are unique in the recent history of the lakes. However, land-use change and related increases in phosphorus input are not the likely cause. Rather, the lakes may be responding to a

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The project “Modeling the Effects of Past and Future Climate Change on GLKN Lakes” is an infrastructure initiative in the Geothermal sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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Description

Description

Remote interior lakes in national park units of the Great Lakes Network (GLKN) are experiencing unexpected ecological change, including blooms of noxious blue-green algae. Sediment-core data indicate that these changes are unique in the recent history of the lakes. However, land-use change and related increases in phosphorus input are not the likely cause. Rather, the lakes may be responding to a warming climate, as indicated by a lengthening of the ice-free season and stronger thermal stratification during summer. This project will explore whether there is a causal link between observed temperature increases and ecological conditions in GLKN lakes, the likely physical and biological controls, and how these effects vary among different types of lakes. This study will: (1) Compare diatom-based reconstructions of ecological change among four general lake types likely to represent a range of sensitivity to climate warming; specifically shallow and deep lakes and lakes with small and large surface areas; and (2) Reconstruct the thermal conditions (stratification, ice-free season, temperatures) of the study lakes based on local climate records and hydrodynamic lake models. These lake-thermal records will then be compared with ecological reconstructions from the sediment cores to develop predictive relationships between lake type and climate-induced ecological risk.

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High

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100%

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