Quantifying model output uncertainty from sparse input data: a case study in the Mississippi sound and mobile bay
Abstract A major limitation in hydrodynamic-water quality modeling is the sparse availability of observed data for model inputs. In this study, a hydrodynamic-water quality model, EFDC+, was used to develop a water quality model for the shallow estuaries, Mississippi Sound and Mobile Bay. This study...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
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| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-025-07538-5 |
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| Summary: | Abstract A major limitation in hydrodynamic-water quality modeling is the sparse availability of observed data for model inputs. In this study, a hydrodynamic-water quality model, EFDC+, was used to develop a water quality model for the shallow estuaries, Mississippi Sound and Mobile Bay. This study investigated four interpolation methods to augment sparse input data: (1) last observation carried forward (LOCF), (2) linear interpolation (LI), (3) natural cubic spline interpolation (Spline), and (4) linear weighted moving average (WMA). These methods were used to construct daily water quality time series from sparse monthly data at five different boundary conditions. Statistical measures of performance were used to: (1) compare interpolated inputs across methods, (2) assess model outputs based on each interpolation method, and (3) compare modeled outputs with observed data. The study results indicate that the (1) LOCF and Spline interpolated inputs did not perform well with increased data gaps and outliers, and (2) the LI and WMA methods produced the most similar interpolated inputs and model outputs. Among the methods, LI was the most preferred due to its low RMSE and better agreement with observed data. Spline showed the least agreement, with the highest RMSE. Future work will use the study results for calibrating a hydrodynamic-water quality model for simulating water quality scenarios in the study area. |
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| ISSN: | 3004-9261 |