Streamflow forecasts are essential for water resources management. Although there are many methods for forecasting streamflow, real-time forecasts remain challenging. This study evaluates streamflow forecasts using a process-based model (Soil and Water Assessment Tool-Variable Source Area model-SWAT-VSA), a stochastic model (Artificial Neural Network -ANN), an Auto-Regressive Moving-Average (ARMA) model, and a Bayesian ensemble model that utilizes the SWAT-VSA, ANN, and ARMA results. Streamflow is forecast from 1-8 d, forced with Quantitative Precipitation Forecasts from the US National Weather Service. Of the individual models, SWAT-VSA and the ANN provide better predictions of total streamflow (NSE 0.60-0.70) and peak flow, but underpredicted low flows. During the forecast period the ANN had the highest predictive power (NSE 0.44-0.64), however all three models underpredicted peak flow. The Bayesian ensemble forecast streamflow with the most skill for all forecast lead times (NSE 0.49-0.67) and provided a quantification of prediction uncertainty.

流量预报对于水资源管理至关重要。尽管有许多预测流量的方法，但实时预测仍然具有挑战性。本研究使用基于过程的模型（土壤和水评估工具-可变源面积模型-SWAT-VSA），随机模型（人工神经网络-ANN），自回归移动平均（ARMA）模型来评估流量预测，以及利用SWAT-VSA，ANN和ARMA结果的贝叶斯集成模型。在美国国家气象局的定量降水预报的推动下，预计1-8 d的水流量。在各个模型中，SWAT-VSA和ANN可以更好地预测总流量（NSE 0.60-0.70）和峰值流量，但低估了流量。在预测期内，人工神经网络的预测能力最高（NSE 0.44-0.64），但是，这三个模型均未预测峰流量。在所有预测提前期（NSE 0.49-0.67）中使用最熟练的贝叶斯合奏预测流，并提供了预测不确定性的量化。