What Did Really Improve Our Mesoscale Hydrological Model? A Multidimensional Analysis Based on Real Observations
| Autor: | Till Francke, Erik Sommerer, Axel Bronstert, Saskia Foerster, Gabriele Baroni, José Andrés López-Tarazón, Arlena Brosinsky |
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| Přispěvatelé: | Francke T., Baroni G., Brosinsky A., Foerster S., Lopez-Tarazon J.A., Sommerer E., Bronstert A. |
| Rok vydání: | 2018 |
| Předmět: |
Multidimensional analysis
Calibration (statistics) Computer science 0208 environmental biotechnology Mesoscale meteorology modeling 02 engineering and technology Work in process computer.software_genre 020801 environmental engineering Identification (information) sediment Temporal resolution model enhancement Data mining Sensitivity analyses computer sensitivity analyses Water Science and Technology |
| Zdroj: | Water Resources Research |
| ISSN: | 1944-7973 0043-1397 |
| DOI: | 10.1029/2018wr022813 |
| Popis: | Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro-sedimentological model WASA-SED for the Mediterranean-mountainous Isábena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases. |
| Databáze: | OpenAIRE |
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