AIFAS: Probabilistic Global River Discharge Forecasting

Hydrological models are vital in river discharge to issue timely flood warnings and mitigate hydrological risks. Recently, advanced techniques in deep learning have significantly enhanced flood prediction by improving the accuracy and efficiency of forecasts, enabling more reliable early warnings and decision-making in flood risk management. Nevertheless, current applications of deep learning methods are still more restricted to local-scale models or in the best case on selected river points at a global scale. Many studies also lack spatial and topological information for training deep learning models, which can limit their generalization ability when applied to large regions with heterogeneous hydrological conditions. In addition, the lack of probabilistic forecasting impedes the quantification of uncertainty in flood predictions. Here we present the Artificial Intelligence Flood Awareness System (AIFAS) for probabilistic global river discharge forecasting. AIFAS is a generative AI model that is trained with long-term historical reanalysis data and can provide grid-based global river discharge forecasting at 0.05°. At the core of our model are the built-in vision module upon state space model (SSM) [1] and the diffusion-based loss function [2]. The vision SSM allows the model to connect the routing of the channel networks globally, while the diffusion loss generates ensembles of stochastic river discharge forecasts. We evaluate the AIFAS forecast skill against other state-of-the-art deep learning models, such as Google LSTM [3], climatology baseline, persistence baseline, and operational GloFAS forecasts [4]. The impact of different hydrometeorological products that drive AIFAS performance on different forecasting lead times will also be discussed. Our results show that the new forecasting system achieves reliable predictions of extreme flood events across different return periods and lead times.

References

[1] Gu, A., and Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023.

[2] Ho, J., Jain, A., and Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840-6851, 2020.

[3] Nearing, G., Cohen, D., Dube, V. et al. Global prediction of extreme floods in ungauged watersheds. Nature 627, 559–563 2024.

[4] Harrigan, S., Zsoter, E., Cloke, H., Salamon, P., and Prudhomme, C.: Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System, Hydrol. Earth Syst. Sci., 27, 1–19, 2023.