Control theory-based update of water levels in 1D hydrodynamic models

Abstract

Model-driven forecasting used for flood risk assessment or river hydropower systems management, can produce bad results due to many model uncertainties. False inflow and lateral inflow data and/or poor estimation of initial conditions are some of the uncertainty sources. To improve model-driven forecasting, data assimilation methods are used for updating model (e.g., water levels) according to measurements. Widespread data assimilation methods (EnKF, Particle Filter) often increase computational time, which creates difficulties in everyday application of these methods in hydraulic modelling. This paper presents novel approach based on indirect model update adding correction flows at observation locations. This novel, tailor-made, assimilation approach uses proportional-integrative-derivative controller’s theory as algorithm for correction flow calculation. Using indirect approach for model update has justification in models where multiple inflows, including lateral inflows, are bad estimated or even neglected. This novel approach is tested on 170km long section of Danube model in Serbia, showing good performance.