AI-generated ensemble river flow forecasting: Using rollout and an additional noise input to build ensemble forecasts

Machine learning models have been used with success to produce accurate river discharge forecasts at multiple lead times. However, almost no research has been done to show if they are physically consistent across lead times. In the deterministic problem setting, where models output a single forecast with multiple leadtimes, these models are known to be mean-seeking, predicting the most likely river flow for each day, regardless of how likely the resulting trajectory is to occur. This is important for forecasters who need to look at the multi-day properties of a forecast, such as the accumulated flow or number of days over threshold. When each leadtime is described as an independent distribution, the model provides no insight into how to connect the uncertainties at each lead time, as an ensemble forecast would. In this paper, we show that temporal consistency in machine learning forecasts cannot be assumed, and develop two methods for enforcing temporal consistency, the Conditional-LSTM and Seeded-LSTM. Through this, we create ensemble forecasts that successfully predict temporal properties of the 10-day hydrographs. We find that by explicitly training the model to treat the prediction of previous lead times as truth, our model better predicts temporal properties of 10-day hydrographs than other standard methods. Our approach allows users to efficiently generate as many ensemble members as desired, and we use our results to highlight the important of developing temporally consistent ensembles.