Uncertainty Quantification for Flood Forecasting in Small Catchments

The quantification of uncertainty in flood forecasting, especially in small catchments, presents significant challenges due to the inherent uncertainty in precipitation now- and forecasts. Several authors have investigated uncertainty quantification for time series forecasting in general and for flood forecasting specifically. To our knowledge, none have focused on small catchments, tidal influences, large hourly forecast horizons, or the incorporation of classic forecasting tools such as differencing. We implement and evaluate approaches using Conformal Prediction, Monte Carlo Dropout, Ensembles and direct distribution forecasting to quantify the uncertainty of LSTM networks trained to predict the change in water level for the next 48 h in three catchments in Northern Germany. We analyze the performance of the different approaches regarding the width and accuracy of the prediction intervals. Our study shows that the incorporation of differencing strongly influences which uncertainty quantification methods are suitable, with direct distribution forecasting ignoring correlations between the forecasting steps and Conformal Prediction being the most suitable for our specific datasets.