Combined sewer overflows represent significant risks to human health as untreated water is discharged to the environment. Municipalities recently began collecting large amounts of water-related data and considering the adoption of deep learning solutions like recurrent neural network (RNN) for overflow prediction. In this paper, we develop a novel metamorphic relation to characterize RNN robustness in the presence of missing data. We show how this relation drives automated testing of three implementation variants: LSTM, GRU, and IndRNN thereby uncovering deficiencies and suggesting more robust solutions for overflow prediction.