Streamflow simulations of the terrestrial Arctic domain
Predicting riverine discharge to the Arctic Ocean has become increasingly important because of the dominant role that river runoff plays in the freshwater balance of the Arctic Ocean, and the predicted high sensitivity of the region to global warming. The ability of land surface models to represent...
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Veröffentlicht in: | Journal of Geophysical Research: Atmospheres 2005-04, Vol.110 (D8), p.D08112.1-n/a |
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Sprache: | eng |
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Zusammenfassung: | Predicting riverine discharge to the Arctic Ocean has become increasingly important because of the dominant role that river runoff plays in the freshwater balance of the Arctic Ocean, and the predicted high sensitivity of the region to global warming. The ability of land surface models to represent runoff and streamflow from northern river basins is critical to an understanding of the Arctic hydrologic cycle. A set of simulations with the land surface scheme VIC (Variable Infiltration Capacity) implemented at 100 km EASE‐Grid across the pan‐Arctic domain was conducted to evaluate the model's representation of various hydrologic processes in the Arctic land region, and to provide a consistent baseline hydroclimatology for the region. The pan‐Arctic drainage basin system was partitioned into 12 regions for purposes of model implementation and testing. Streamflow observations at various basin outlets, satellite‐based snow cover extent, observed dates of lake freeze‐up and break‐up, and sited monitored summer permafrost maximum active layer thickness were used to evaluate various simulated hydrologic variables. The results indicate that the VIC model was able to reproduce these hydrologic processes in the Arctic region. A 21‐year average river inflow (1979–1999) to the Arctic Ocean from the AORB (Arctic Ocean River Basin) illustrated in Prowse and Flegg (2000), was estimated with the simulated streamflow as 3354 km3/yr, and 3596 km3/yr with the inclusion of the Arctic Archepelago, which are comparable to the previous estimates derived from the observed data. |
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ISSN: | 0148-0227 2156-2202 |
DOI: | 10.1029/2004JD005518 |