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Technical Note: Initial assessment of a multi-method approach to spring-flood forecasting in Sweden

机译:技术说明:瑞典对春季洪水预报的多种方法的初步评估

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Hydropower is a major energy source in Sweden, and proper reservoir management prior to the spring-flood onset is crucial for optimal production. This requires accurate forecasts of the accumulated discharge in the spring-flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialized set-up of the HBV model. In this study, a number of new approaches to spring-flood forecasting that reflect the latest developments with respect to analysis and modelling on seasonal timescales are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for the Swedish river Vindelalven over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring-flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for early forecasts improvements of up to 25% are found. This potential is reasonably well realized in a multi-method system, which over all forecast dates reduced the error in SFV by similar to 4 %. This improvement is limited but potentially significant for e.g. energy trading.
机译:水力发电是瑞典的主要能源,在春季洪水发生之前进行适当的水库管理对于优化产量至关重要。这就需要准确预测春季洪水期间的累计排放量(即春季洪水量,SFV)。当今的SFV预报是使用基于模型的气候集合方法生成的,该方法使用历史年份的降水和温度的时间序列来强制进行HBV模型的校准和初始化设置。在这项研究中,提出并评估了许多新的春季洪水预报方法,这些方法反映了有关季节时标分析和建模方面的最新发展。在十年内,瑞典河Vindelalven的SFV后预报中评估了三种以特定方法代表的主要方法,前置时间为0至4个月。在第一种方法中,确定与春季洪水之前的时期有关的历史上类似的年份,并将其用于组成一个减少的集合。第二,使用季节性气象系综预报来驱动春季洪水期间的HBV模型。在第三种方法中,SFV和大量销售的大气环流之间的统计关系用于建立预测模型。没有任何一种新方法能够始终如一地胜过气候集成方法,但对于早期预测,发现最多可提高25%。在多方法系统中可以很好地实现此潜力,该系统在所有预测日期内均将SFV的误差降低了约4%。这种改进是有限的,但对于例如能源交易。

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