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Model-driven forecasting, used for flood risks or big hydropower systems management, can produce results of unsatisfying accuracy even with best-calibrated hydrodynamic models. One of the biggest uncertainty sources is the inflow data, either produced by different hydrological models or obtained using unreliable rating curves. To keep the model in the up-to-date state, data assimilation techniques are used. The aim of the assimilation is to reduce the difference between simulated and observed state of selected variables by updating hydrodynamic model state variables according to observed water levels. The widely used data assimilation method applicable for nonlinear hydrodynamic models is Ensemble Kalman Filter (EnKF). However, this method can often increase the computational time due to complexity of mathematical apparatus, making it less applicable in everyday operations. This paper presents the novel, fast, tailor-made data assimilation method, suitable for 1D open channel hydraulic models, based on control theory. Using Proportional-Integrative-Derivative (PID) controllers, the difference between measured levels and simulated levels obtained by hydrodynamic model is reduced by adding or subtracting the flows in the junctions/sections where water levels are measured. The novel PID control-based data assimilation (PID-DA) is compared to EnKF. Benchmarking shows that PID-DA can be used for data assimilation, even coupled with simplified 1D hydraulic model, without significant sacrifice of stability and accuracy, and with reduction of computational time up to 63 times. © 2020 Elsevier B.V.