The Value of Large‐Scale Climatic Indices for Monthly Forecasting Severity of Widespread Flooding Using Dilated Convolutional Neural Networks

Spatially co‐occurring floods pose a threat to the resilience and recovery of the communities. Their timely forecasting plays a crucial role for increasing flood preparedness and limiting associated losses. In this study we investigated the potential of a dilated Convolutional Neural Network (dCNN) model conditioned on large‐scale climatic indices and antecedent precipitation to forecast monthly severity of widespread flooding (i.e., spatially co‐occurring floods) in Germany with 1 month lead time. The severity was estimated from 63 years of daily streamflow series as the sum of concurrent exceedances of at‐site 2‐year return periods within a given month across 172 mesoscale catchments (median area 516 km2). The model was trained individually for the whole country and three diverse hydroclimatic regions to provide insights on heterogeneity of model performance and flood drivers. Our results showed a considerable potential for forecasting widespread flood severity using dCNN especially as the length of training series increases. However, event‐based evaluation of model skill indicates large underestimation for rainfall‐generated floods during dry conditions despite overall lower severity of these events compared to the rain‐on‐snow floods. Feature attribution and wavelet coherence analyses both indicated considerable difference in the major flood drivers in three regions. While the flooding in North‐Eastern region is strongly affected by the Baltic Sea, the North‐Western region is affected more by global patterns associated with the El‐Niño activity. In the Southern region in addition to global patterns we detected the effect of the Mediterranean Sea, while antecedent precipitation plays a less important role in this region. Plain Language Summary Floods that occur simultaneously at different locations in space are associated with higher damages and are more difficult for communities to cope. Forecasting of such events still remains a challenge. Here we investigate the potential of recently developed deep learning networks for forecasting widespread flooding 1 month ahead. We test this model on a large data set of long streamflow time series in Germany and its three geographical regions using climatic indices and past precipitation as supporting variables. We show that the model has a considerable potential for flood forecasting, especially for rain‐on‐snow floods, but its performance is limited for rain‐on‐dry events. Finally, using explainable machine lea