Improving runoff simulations using satellite-observed time-series of snow covered area
Improving runoff simulations using satellite-observed time-series of snow covered area
复制标题
利用卫星观测的积雪区域时间序列改进径流模拟
DOI：
10.2166/nh.2003.0008
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
E. Alfnes
中科院分区：
文献类型：
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作者：
R. Engeset;H. Udnæs;T. Guneriussen;H. Koren;E. Malnes;R. Solberg;E. Alfnes
融雪可能是重大洪水的重要因素，因此最新的积雪信息对洪水预报服务非常重要。本研究评估业务径流模拟是否可以通过应用卫星推导的积雪覆盖面积（SCA）从光学和雷达传感器得到改善。目前，HBV模型用于径流预测在挪威，卫星观测的SCA是定性的，但不直接在模型中使用。在挪威南部的三个集水区进行了研究，使用1995年至2002年的数据。结果表明，卫星观测的SCA可以用来检测模型没有正确模拟雪水库。在融雪季节早期发现错误将有助于预报服务在可能发生的破坏性洪水之前更新和纠正模型。该方法需要对SCA以及径流模型校准。使用了卫星传感器NOAA AVHRR和ERS SAR的时间序列。其中，AVHRR与模拟的SCA显示出良好的相关性，而SAR则较差。2000年5月AVHRR、SAR和Landsat ETM+同步数据的比较显示出良好的相互相关性。在卫星观测的1，088平方公里的总面积中，高级甚高分辨率辐射计观测到823平方公里的特殊大气层覆盖面积和720平方公里的合成孔径雷达，而使用ETM+观测到的面积为889平方公里。
Snowmelt can be a significant contributor to major floods, and hence updated snow information is very important to flood forecasting services. This study assesses whether operational runoff simulations could be improved by applying satellite-derived snow covered area (SCA) from both optical and radar sensors. Currently the HBV model is used for runoff forecasting in Norway, and satellite-observed SCA is used qualitatively but not directly in the model. Three catchments in southern Norway are studied using data from 1995 to 2002. The results show that satellite-observed SCA can be used to detect when the models do not simulate the snow reservoir correctly. Detecting errors early in the snowmelt season will help the forecasting services to update and correct the models before possible damaging floods. The method requires model calibration against SCA as well as runoff. Time-series from the satellite sensors NOAA AVHRR and ERS SAR are used. Of these, AVHRR shows good correlation with the simulated SCA, and SAR less so. Comparison of simultaneous data from AVHRR, SAR and Landsat ETM+ for May 2000 shows good inter-correlation. Of a total satellite-observed area of 1,088 km 2 , AVHRR observed a SCA of 823 km 2 and SAR 720 km 2 , as compared to 889 km 2 using ETM+.