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Automated Approach for Rainfall-Runoff Model Generation
Abstract Manually constructing hydrological model descriptions for urban areas tends to be laborious due to the detailed mosaic land cover and the required high-resolution model setup. Here, the performance of a novel automated subcatchment generator with a detailed DEM-based surface flow routing is assessed against observations and manually constructed models. In general, the auto-generated models perform well against observations and comparably to manually constructed models regardless of the detail of land cover information input. The introduced inter-subcatchment connections may require previously acquired model parameters to be re-calibrated. This is due to the calibrated parameters in manually constructed models, even with high-resolution landuse, partly compensating for missing flow routes due to the larger scale used in subcatchment description.
Automated Approach for Rainfall-Runoff Model Generation
Abstract Manually constructing hydrological model descriptions for urban areas tends to be laborious due to the detailed mosaic land cover and the required high-resolution model setup. Here, the performance of a novel automated subcatchment generator with a detailed DEM-based surface flow routing is assessed against observations and manually constructed models. In general, the auto-generated models perform well against observations and comparably to manually constructed models regardless of the detail of land cover information input. The introduced inter-subcatchment connections may require previously acquired model parameters to be re-calibrated. This is due to the calibrated parameters in manually constructed models, even with high-resolution landuse, partly compensating for missing flow routes due to the larger scale used in subcatchment description.
Automated Approach for Rainfall-Runoff Model Generation
Niemi, Tero J. (author) / Krebs, Gerald (author) / Kokkonen, Teemu (author)
2018-09-01
6 pages
Article/Chapter (Book)
Electronic Resource
English
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