Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset
| Autor: | Yunqing Xuan, Salam A. Abbas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Hydraulic engineering
010504 meteorology & atmospheric sciences Meteorology Soil and Water Assessment Tool Calibration (statistics) Hydrological modelling Geography Planning and Development 0207 environmental engineering 02 engineering and technology Aquatic Science 01 natural sciences Biochemistry cross-validation Cross-validation lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 Point estimation Precipitation precipitation pre-processing 020701 environmental engineering 0105 earth and related environmental sciences Water Science and Technology lcsh:TD201-500 Centroid swat Grid hydrological modelling calibration Environmental science gridded rainfall dataset |
| Zdroj: | Water, Vol 12, Iss 3, p 840 (2020) Water Volume 12 Issue 3 |
| ISSN: | 2073-4441 |
| Popis: | Effective representation of precipitation inputs is one of the essential components in hydrological model structures, especially when gauge measurements for the modelled catchment are sparse. Assessment of the impact of precipitation pre-processing is often nontrivial as precipitation data are very limited in the first place. In this paper, we demonstrate a study using a semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT) to examine the impact of different precipitation pre-processing methods on model calibration and the overall model performance with regards to the operational use. A river catchment in the UK is modelled to test against the three pre-processing methods: the Centroid Point Estimation Method (CPEM), the Grid Area Method (GAM) and the Grid Point Method (GPM). Cross-calibration and validation are then carried out by using the high-resolution Centre for Ecology & Hydrology&ndash Gridded Estimate Areal Rainfall (CEH-GEAR) dataset. The results show that the proposed methods GAM and GPM can improve the model calibration significantly against the one calibrated with the existing CPEM method used by the model the performance differences in the validation among the calibrated models, however, remain small and become irrelevant. The findings indicate that it is preferable to always make use of high-quality rainfall data, when available, with a better pre-processing method, even with models that are previously calibrated with low-quality rainfall inputs. It is also shown that such improvements are affected by the size of catchment and become less significant for smaller catchments. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|