Resolving structural errors in a spatially distributed hydrologic model using ensemble Kalman filter state updates
| Autor: | Willem Bouten, Jurriaan H. Spaaks |
|---|---|
| Přispěvatelé: | Computational Geo-Ecology (IBED, FNWI) |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Technology
010504 meteorology & atmospheric sciences Computer science Interface (computing) Hydrological modelling 0207 environmental engineering 02 engineering and technology computer.software_genre Environmental technology. Sanitary engineering 01 natural sciences lcsh:Technology lcsh:TD1-1066 Data assimilation Geography. Anthropology. Recreation GE1-350 lcsh:Environmental technology. Sanitary engineering 020701 environmental engineering TD1-1066 lcsh:Environmental sciences 0105 earth and related environmental sciences lcsh:GE1-350 Estimation theory lcsh:T lcsh:Geography. Anthropology. Recreation Environmental sciences Metropolis–Hastings algorithm lcsh:G 13. Climate action Ensemble Kalman filter Data mining State (computer science) Noise (video) computer |
| Zdroj: | Hydrology and Earth System Sciences, 17(9), 3455-3472. European Geosciences Union Hydrology and Earth System Sciences, Vol 17, Iss 9, Pp 3455-3472 (2013) Hydrology and Earth System Sciences, 17(9), 3455-3472 NARCIS OpenAIRE DOAJ-Articles |
| ISSN: | 1027-5606 |
| Popis: | In hydrological modeling, model structures are developed in an iterative cycle as more and different types of measurements become available and our understanding of the hillslope or watershed improves. However, with increasing complexity of the model, it becomes more and more difficult to detect which parts of the model are deficient, or which processes should also be incorporated into the model during the next development step. In this study, we first compare two methods (the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) and the Simultaneous parameter Optimization and Data Assimilation algorithm (SODA)) to calibrate a purposely deficient 3-D hillslope-scale model to error-free, artificially generated measurements. We use a multi-objective approach based on distributed pressure head at the soil–bedrock interface and hillslope-scale discharge and water balance. For these idealized circumstances, SODA's usefulness as a diagnostic methodology is demonstrated by its ability to identify the timing and location of processes that are missing in the model. We show that SODA's state updates provide information that could readily be incorporated into an improved model structure, and that this type of information cannot be gained from parameter estimation methods such as SCEM-UA. We then expand on the SODA result by performing yet another calibration, in which we investigate whether SODA's state updating patterns are still capable of providing insight into model structure deficiencies when there are fewer measurements, which are moreover subject to measurement noise. We conclude that SODA can help guide the discussion between experimentalists and modelers by providing accurate and detailed information on how to improve spatially distributed hydrologic models. |
| Databáze: | OpenAIRE |
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