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Benefits of Combining Satellite-Derived Snow Cover Data and Discharge Data to Calibrate a Glaciated Catchment in Sub-Arctic Iceland
Stockholm University, Faculty of Science, Department of Physical Geography.
Stockholm University, Faculty of Science, Department of Physical Geography. Stockholm Univ, Dept Phys Geog, S-10691 Stockholm, Sweden.
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Number of Authors: 62020 (English)In: Water, E-ISSN 2073-4441, Vol. 12, no 4, article id 975Article in journal (Refereed) Published
Abstract [en]

The benefits of fractional snow cover area, as an additional dataset for calibration, were evaluated for an Icelandic catchment with a low degree of glaciation and limited data. For this purpose, a Hydrological Projections for the Environment (HYPE) model was calibrated for the Geithellnaa catchment in south-east Iceland using daily discharge (Q) data and satellite-retrieved MODIS snow cover (SC) images, in a multi-dataset calibration (MDC) approach. By comparing model results using only daily discharge data with results obtained using both datasets, the value of SC data for model calibration was identified. Including SC data improved the performance of daily discharge simulations by 7% and fractional snow cover area simulations by 11%, compared with using only the daily discharge dataset (SDC). These results indicate that MDC improves the overall performance of the HYPE model, confirming previous findings. Therefore, MDC could improve discharge simulations in areas with extra sources of uncertainty, such as glaciers and snow cover. Since the change in fractional snow cover area was more accurate when MDC was applied, it can be concluded that MDC would also provide more realistic projections when calibrated parameter sets are extrapolated to different situations.

Place, publisher, year, edition, pages
2020. Vol. 12, no 4, article id 975
Keywords [en]
glaciated-catchment modeling, conceptual hydrological model, multi-dataset calibration, Hydrological Predictions for the Environment, Geithellnaa, Iceland
National Category
Earth and Related Environmental Sciences
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URN: urn:nbn:se:su:diva-183591DOI: 10.3390/w12040975ISI: 000539527500052OAI: oai:DiVA.org:su-183591DiVA, id: diva2:1455231
Available from: 2020-07-22 Created: 2020-07-22 Last updated: 2025-02-07Bibliographically approved

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Bring, ArvidKalantari, Zahra

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