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.

中文翻译：

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结合卫星衍生的积雪数据和流量数据校准亚北极冰岛冰川集水区的好处

部分积雪面积作为校准的附加数据集的好处在冰川化程度低且数据有限的冰岛集水区进行了评估。为此，在多数据集校准中，使用每日排放 (Q) 数据和卫星检索的 MODIS 积雪 (SC) 图像为冰岛东南部的 Geithellnaa 流域校准了环境水文预测 (HYPE) 模型(MDC) 方法。通过将仅使用每日排放数据的模型结果与使用两个数据集获得的结果进行比较，确定了模型校准的 SC 数据值。与仅使用每日排放数据集 (SDC) 相比，包括 SC 数据将每日排放模拟的性能提高了 7%，将部分积雪面积模拟的性能提高了 11%。这些结果表明 MDC 提高了 HYPE 模型的整体性能，证实了之前的发现。因此，MDC 可以改进具有额外不确定性来源（例如冰川和积雪）的区域的排放模拟。由于应用 MDC 时部分积雪面积的变化更准确，因此可以得出结论，当校准参数集外推到不同情况时，MDC 也将提供更真实的预测。