Spatial and seasonal variations in evapotranspiration over Canada's landmass
A 30 yr (1979–2008) dataset of actual evapotranspiration (ET) at 1 km resolution was generated over Canada's landmass by integrating remote sensing land surface data and gridded climate data using the EALCO model run at a 30 min time step. This long-term high-resolution dataset was used to char...
| Published in: | Hydrology and Earth System Sciences |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2013
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/hess-17-3561-2013 http://www.hydrol-earth-syst-sci.net/17/3561/2013/hess-17-3561-2013.pdf https://doaj.org/article/acf30681c6df4d219169596ff1eb653b |
| Summary: | A 30 yr (1979–2008) dataset of actual evapotranspiration (ET) at 1 km resolution was generated over Canada's landmass by integrating remote sensing land surface data and gridded climate data using the EALCO model run at a 30 min time step. This long-term high-resolution dataset was used to characterize the spatiotemporal variations in ET across Canada. The results show that annual ET varied from 600 mm yr−1 over several regions in the south to less than 100 mm yr−1 in the northern Arctic. Nationally, ET in summer (i.e., June to August) comprised 65% of the annual total amount. ET in the cold season remained mostly below 10 mm month−1 over the country. Negative monthly ET was obtained over the Arctic region in winter, indicating EALCO simulated a larger amount of condensation than ET. Overall, the mean ET over the entire Canadian landmass for the 30 yr was 239 mm yr−1, or 44% of its corresponding precipitation. Comparisons of available ET studies in Canada revealed large uncertainties in ET estimates associated with using different approaches. The scarcity of ET measurements for the diverse ecosystems in Canada remains a significant challenge for reducing the uncertainties; this gap needs to be addressed in future studies to improve capabilities in climate/weather modeling and water resource management. |
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