Devineni Naresh, Sankarasubramanian A, Ghosh Sujit
Department of Civil, Construction and Environmental Engineering, North Carolina State University, 2501 Stinson Drive, Box 7908, Raleigh, NC 27695-7908, USA.
Water Resour Res. 2008 Sep;44(9):W09404. doi: 10.1029/2006WR005855.
A new approach for developing multimodel streamflow forecasts is presented. The methodology combines streamflow forecasts from individual models by evaluating their skill, represented by rank probability score (RPS), contingent on the predictor state. Using average RPS estimated over the chosen neighbors in the predictor state space, the methodology assigns higher weights for a model that has better predictability under similar predictor conditions. We assess the performance of the proposed algorithm by developing multimodel streamflow forecasts for Falls Lake Reservoir in the Neuse River Basin, North Carolina (NC), by combining streamflow forecasts developed from two low-dimensional statistical models that use sea-surface temperature conditions as underlying predictors. To evaluate the proposed scheme thoroughly, we consider a total of seven multimodels that include existing multimodel combination techniques such as combining based on long-term predictability of individual models and by simple pooling of ensembles. Detailed nonparametric hypothesis tests comparing the performance of seven multimodels with two individual models show that the reduced RPS from multimodel forecasts developed using the proposed algorithm is statistically significant from the RPSs of individual models and from the RPSs of existing multimodel techniques. The study also shows that adding climatological ensembles improves the multimodel performance resulting in reduced average RPS. Contingency analyses on categorical (tercile) forecasts show that the proposed multimodel combination technique reduces average Brier score and total number of false alarms, resulting in improved reliability of forecasts. However, adding multiple models with climatology also increases the number of missed targets (in comparison to individual models' forecasts) which primarily results from the reduction of increased resolution that is exhibited in the individual models' forecasts under various forecast probabilities.
提出了一种开发多模型径流预测的新方法。该方法通过评估各个模型的技能（以秩概率得分（RPS）表示），根据预测变量状态对来自单个模型的径流预测进行组合。使用在预测变量状态空间中选定邻域上估计的平均RPS，该方法为在相似预测变量条件下具有更好可预测性的模型赋予更高的权重。我们通过组合从两个低维统计模型开发的径流预测来评估所提出算法的性能，这两个模型以海表面温度条件作为潜在预测变量，用于北卡罗来纳州（NC）纽斯河流域的福尔斯湖水库的多模型径流预测。为了全面评估所提出的方案，我们总共考虑了七个多模型，其中包括现有的多模型组合技术，如基于单个模型的长期可预测性进行组合以及通过简单合并集合进行组合。将七个多模型与两个单个模型的性能进行比较的详细非参数假设检验表明，使用所提出算法开发的多模型预测的降低后的RPS与单个模型的RPS以及现有多模型技术的RPS相比具有统计学意义。该研究还表明，添加气候集合可改善多模型性能，从而降低平均RPS。对分类（三分位数）预测的列联分析表明，所提出的多模型组合技术降低了平均布里尔得分和误报总数，从而提高了预测的可靠性。然而，添加多个带有气候学的模型也会增加漏报目标的数量（与单个模型的预测相比），这主要是由于在各种预测概率下单个模型预测中显示的分辨率提高的降低所致。
