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首页> 外文期刊>Water resources research >Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model
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Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model

机译：可靠的长期总体流量预报：将校准的气候预报与概念性径流模型和分段误差模型相结合

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We present a new streamflow forecasting system called forecast guided stochastic scenarios (FoGSS). FoGSS makes use of ensemble seasonal precipitation forecasts from a coupled ocean-atmosphere general circulation model (CGCM). The CGCM forecasts are post-processed with the method of calibration, bridging and merging (CBaM) to produce ensemble precipitation forecasts over river catchments. CBaM corrects biases and removes noise from the CGCM forecasts, and produces highly reliable ensemble precipitation forecasts. The post-processed CGCM forecasts are used to force the Wapaba monthly rainfall-runoff model. Uncertainty in the hydrological modeling is accounted for with a three-stage error model. Stage 1 applies the log-sinh transformation to normalize residuals and homogenize their variance; Stage 2 applies a conditional bias-correction to correct biases and help remove negative forecast skill; Stage 3 applies an autoregressive model to improve forecast accuracy at short lead-times and propagate uncertainty through the forecast. FoGSS generates ensemble forecasts in the form of time series for the coming 12 months. In a case study of two catchments, FoGSS produces reliable forecasts at all lead-times. Forecast skill with respect to climatology is evident to lead-times of about 3 months. At longer lead-times, forecast skill approximates that of climatology forecasts; that is, forecasts become like stochastic scenarios. Because forecast skill is virtually never negative at long lead-times, forecasts of accumulated volumes can be skillful. Forecasts of accumulated 12 month streamflow volumes are significantly skillful in several instances, and ensembles of accumulated volumes are reliable. We conclude that FoGSS forecasts could be highly useful to water managers.
机译：我们提出了一种新的流量预测系统，称为预测引导随机情景（FoGSS）。 FoGSS利用来自耦合的海洋-大气总环流模型（CGCM）的整体季节降水预报。 CGCM预报通过标定，桥接和合并（CBaM）方法进行后处理，以生成河流集水区的整体降水预报。 CBaM纠正了偏差并消除了CGCM预报中的噪声，并生成了高度可靠的整体降水预报。后处理的CGCM预测用于强制Wapaba月降雨径流模型。水文模型的不确定性由三阶段误差模型解决。第1阶段应用log-sinh变换对残差进行归一化并均化其方差；第2阶段应用条件偏差校正来校正偏差并帮助消除负面的预测技巧；第3阶段应用自回归模型来提高短交货期的预测准确性，并通过预测传播不确定性。 FoGSS以时间序列的形式生成未来12个月的整体预测。在两个流域的案例研究中，FoGSS在所有提前期都可以提供可靠的预测。大约3个月的交货时间证明了对气候学的预测能力。在较长的交货时间中，预报技能近似于气候预报的技能。也就是说，预测变得像随机情景。由于预测技能实际上在长交货时间上永远不会为负，因此对累积量的预测可能是熟练的。在某些情况下，对12个月累积流量的预测非常熟练，并且累积流量的集合是可靠的。我们得出结论，FoGSS预测对水管理人员可能非常有用。

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