Statistical Ensemble Seasonal Streamflow Forecasting in the South Saskatchewan River Basin by a Modified Nearest Neighbors Resampling
Statistical Ensemble Seasonal Streamflow Forecasting in the South Saskatchewan River Basin by a Modified Nearest Neighbors Resampling
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DOI：
10.1061/(asce)he.1943-5584.0000021
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发表时间：
2009-02
影响因子：
2.4
通讯作者：
A. Gobena;T. Gan
中科院分区：
文献类型：
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作者：
A. Gobena;T. Gan
针对加拿大艾伯塔省南部南萨斯喀彻温河流域的两个流域开发了集合季节性水流预测模型。这些集合是使用改进的 K 最近邻算法根据平均预测生成的。平均预测由稳健的 M 回归模型生成，该模型使用雪水当量和大规模气候信息作为预测变量，其中通过广义交叉验证标准自动选择预测变量的最佳组合。结果表明，早在径流年之前的 12 月初就可以对 4 月至 9 月的流量进行熟练的预测，从而将当前的预测提前期延长最多两个月。对预测潜在经济价值的评估表明，与条件中值预测相比，使用相同的一组预测变量，集合预测为广泛的最终用户提供了卓越的经济价值。
An ensemble seasonal streamflow forecasting model is developed for two watersheds in the South Saskatchewan River Basin of southern Alberta, Canada. The ensembles are generated from a mean forecast by using a modified K -nearest neighbor algorithm. The mean forecasts are produced by a robust M-regression model that uses snow water equivalent and large-scale climate information as predictors where the best combination of predictors is automatically selected by the generalized cross-validation criterion. It is shown that skillful forecasts of the April–September flow can be obtained as early as the beginning of December preceding the runoff year, thus extending the current forecast lead time by up to two months. An assessment of the potential economic value of the forecasts shows that with the same set of predictors, ensemble forecasts offer superior economic value for a wide range of end-users as compared to conditional median forecasts.