A novel Bayesian Hierarchical Network Model (BHNM) for ensemble forecasts of daily streamflow is presented that uses the spatial dependence induced by the river network topology and hydrometeorological variables from the upstream contributing area between station gauges. Model parameters are allowed to vary with time as functions of selected covariates for each day. Using the network structure to incorporate flow information from upstream gauges and precipitation from the immediate contributing area as covariates allows one to model the spatial correlation of flows simultaneously and parsimoniously. An application to daily monsoon period (July–August) streamflow at three gauges in the Narmada basin in central India for the period 1978–2014 is presented. The best set of covariates include daily streamflow from upstream gauges or from the gauge above the upstream gauges depending on travel times and daily precipitation from the area between two stations. The model validation indicates that the model is highly skillful relative to a null-model of generalized linear regression, which represents the analogous non-Bayesian forecast. The ensemble spread of BHNM accounts for the forecast uncertainty leading to reliable and skillful streamflow predictions.

中文翻译：

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用于每日流量集合预测的贝叶斯分层网络模型

提出了一种新的贝叶斯分层网络模型 (BHNM)，用于每日流量的集合预测，该模型使用由河网拓扑和站台之间上游贡献区的水文气象变量引起的空间依赖性。允许模型参数随时间变化，作为每天所选协变量的函数。使用网络结构将来自上游仪表的流量信息和来自直接贡献区域的降水作为协变量合并在一起，可以同时且简洁地模拟流量的空间相关性。介绍了 1978 年至 2014 年期间印度中部纳尔马达盆地三个仪表的每日季风期（7 月至 8 月）水流的应用。最佳协变量集包括来自上游仪表或来自上游仪表上方的仪表的每日流量，具体取决于行驶时间和两个站点之间区域的每日降水量。模型验证表明该模型相对于广义线性回归的空模型非常熟练，它代表了类似的非贝叶斯预测。BHNM 的集合传播解释了导致可靠和熟练的流量预测的预测不确定性。