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首页> 外文期刊>Journal of Hydroinformatics >Probabilistic streamflow forecasts based on hydrologic persistence and large-scale climate signals in central Texas
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Probabilistic streamflow forecasts based on hydrologic persistence and large-scale climate signals in central Texas

机译：基于水文持久性和德克萨斯中部大规模气候信号的概率流预报

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Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking tonprovide reliable water supplies and maximize system benefits. In this study, streamflownautocorrelation and large-scale climate information are used to generate probabilistic streamflownforecasts for the Lower Colorado River system in central Texas. A number of potential predictorsnare evaluated for forecasting flows in various seasons, including large-scale climate indices relatednto the El Nin ˜ o/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North AtlanticnOscillation (NAO) and others. Results indicate that, of the predictors evaluated, only hydrologicnpersistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDOnprovide forecasts which are statistically better than climatology. An ordinal polytomous logisticnregression approach is proposed as a means of incorporating multiple predictor variables into anprobabilistic forecast model. Forecast performance is assessed through a cross-validationnprocedure, using distribution-oriented metrics, and implications for decision making are discussed.
机译：在季节性提前期进行熟练的流量预测可能对寻求连续提供可靠水源并最大化系统效益的水管理者很有用。在这项研究中，利用河流自身的自相关性和大规模的气候信息来为德克萨斯州中部的下科罗拉多河系统生成概率河流自身的预报。对许多潜在的预报器进行了评估，以预测各个季节的流量，包括与厄尔尼诺/南方涛动（ENSO），年代际涛动（PDO），北大西洋涛动（NAO）等相关的大规模气候指数。结果表明，在所评估的预测因子中，只有与ENSO和PDOn有关的水文持久性和太平洋海表温度模式提供了统计学上比气候学更好的预测。提出了一种序数多对数logistic回归方法，作为将多个预测变量整合到概率预测模型中的一种方法。使用面向分布的度量标准，通过交叉验证过程评估了预测绩效，并讨论了决策的含义。

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