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Parametric emulation and inference in computationally expensive integrated urban water quality simulators
Antonio M Moreno-Rodenas1,2, Jeroen G Langeveld3,4, Francois H L R Clemens3,5
1Section Sanitary Engineering, Water Management Department, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN, Delft, The Netherlands. a.m.morenorodenas@tudelft.nl.
Summary
Data-driven emulators accelerate complex water quality modeling by approximating pollutant dynamics. This approach enables efficient uncertainty analysis for dissolved oxygen in rivers, improving environmental assessments.
Area of Science:
- Environmental Science and Engineering
- Water Resource Management
- Computational Hydrology
Background:
- Integrated catchment models for water quality assessment are complex and computationally expensive.
- Uncertainty in physical and biochemical parameters of receiving water bodies significantly impacts dissolved oxygen depletion simulations.
- Computational burden hinders intensive applications like parametric inference in real-world water quality modeling.
Purpose of the Study:
- To develop and apply a data-driven emulator for approximating dissolved oxygen depletion processes in a large-scale urban water quality model.
- To investigate the use of emulators for sensitivity analysis and parametric inference using system observations.
- To assess the impact of different likelihood assumptions on dissolved oxygen process inference.
Main Methods:
- Utilized a large-scale integrated urban water quality model to simulate dissolved oxygen dynamics.
- Developed a polynomial expansion emulator to approximate the relationship between river parameters and water quality outputs (flow, dissolved oxygen).
- Employed the emulator for sensitivity analysis and formal parametric inference with local system observations, considering various likelihood functions.
Main Results:
- Demonstrated that a polynomial expansion emulator can effectively approximate the link between river physical/biochemical parameters and dissolved oxygen concentration dynamics.
- Successfully used the emulator to perform sensitivity analysis and parametric inference, reducing computational demands.
- Highlighted the influence of likelihood assumptions (heteroscedasticity, normality, autocorrelation) on the inference of dissolved oxygen processes.
Conclusions:
- Data-driven emulators offer a viable solution to overcome the computational challenges in complex water quality modeling.
- Emulators facilitate the integration of formal uncertainty analysis, including parametric inference, into hydrological and water quality studies.
- This approach enhances the efficiency and applicability of water quality models for environmental assessment and decision-making.
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