Emulation of a process-based estuarine hydrodynamic model

Emulation modelling can be an effective alternative to traditional mechanistic approaches for complex environmental systems and, if carefully conceived, can offer significantly reduced run times and user expertise requirements. We present a case study of dynamic emulation for the domain of estuarine water quality modelling, by reporting the development and evaluation of a one-dimensional hydrodynamic model emulator. The proposed "neuroemulator" retains the dynamic nature of the process-based model utilizing a set of artificial neural networks. The underlying hydrodynamic model is routinely used for analysis and management of the northern reach of the San Francisco Bay-Delta estuary, a large complex region of strategic importance for water supply and ecosystem services on the Pacific coast of California, USA. The reduced computational expense of the emulator affords opportunities for direct use, as well as embedded use within other modelling frameworks such as those developed for reservoir operations and socio-hydrology.