Abstract Seasonal streamflow forecasting, which is important for water resources management, is generally a challenging task in hydrology. This paper focuses on the consideration of base and fast flow for the forecasting of seasonal streamflow. Taking the three tributaries of the Pearl River in South China as case study, five forecasting experiments, which forecast the average daily flow in the three-month period by using the average daily flow in the antecedent month, are devised to elucidate the different roles of base, fast, and total flow. The recursive digital filter (RDF) model is employed for the separation of base flow from fast flow; the Bayesian joint probability (BJP) model is applied to generate ensemble forecasts under cross validation; and the Schaake shuffle is used to establish temporal structure to link ensemble members when adding up forecasts of base flow and fast flow. Forecasts in the different experiments are evaluated in terms of bias, reliability, and skill, and the relationship between correlation coefficient and forecast skill is illustrated. The results show that unbiased and reliable seasonal forecasts are generally obtained in the experiments. Correlation coefficients of base flow are overall higher than those of total flow while correlation coefficients of fast flow are lower. Forecast skill tends to increase with correlation coefficient. Meanwhile, fast flow can lead to extremely large total flow in the antecedent month, which can substantially enlarge the forecasts and diminish the forecast skill when the corresponding observation is not as large. By contrast, base flow tends to always contribute to forecast skill. The separation of base flow from fast flow, which accounts for the different dynamical processes of fast and base flow, can reduce the impact of fast flow and improve the forecast skill of total flow.

中文翻译：

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基流和快流在集合季节流预测中的不同作用：一项实验研究

摘要 季节性流量预测对于水资源管理很重要，通常是水文学中的一项具有挑战性的任务。本文着重考虑基流和快流在季节流量预测中的作用。以华南珠江三大支流为例，设计五个预测实验，利用前一个月的平均日流量预测三个月期间的平均日流量，以阐明其不同作用。基本流量、快速流量和总流量。采用递归数字滤波器（RDF）模型将基流与快流分离；应用贝叶斯联合概率（BJP）模型在交叉验证下生成集合预测；Schaake shuffle 用于建立时间结构，在将基流和快速流的预测相加时链接集合成员。从偏差、可靠性和技能方面评估不同实验中的预测，并说明相关系数与预测技能之间的关系。结果表明，在实验中普遍获得了无偏、可靠的季节预测。基流的相关系数总体上高于总流的相关系数，而快速流的相关系数较低。预测技巧往往随着相关系数的增加而增加。同时，快速的流量会导致前一个月的总流量非常大，在相应的观测值不是很大时，这会大大扩大预测并降低预测技巧。相比之下，基本流量往往有助于预测技能。基流与快速流的分离，解释了快速流和基流不同的动力过程，可以减少快速流的影响，提高总流量的预测能力。