Bias correction of temperature and precipitation data for regional climate model application to the Rhine basin
Nowadays hydrological models have become an important tool in predicting streamflow generation. Most of these models need to be calibrated with the correct meteorological input before they can predict reliable streamflow generation. This report focuses on the hydrological analysis of three different meteorological forcing data sets for the Rhine basin. These are observed data, downscaled ERA15 data and a regional climate model run, known as the reference scenario. The goal of this report is to analyse the difference between the last two and the observed data such that a bias correction can be applied to minimize these differences. The bias correction used here corrects for the mean and the coefficient of variation of the precipitation data. The temperature data is corrected for the mean and the standard deviation
| Main Authors: | , , , , |
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| Format: | External research report biblioteca |
| Language: | English |
| Published: |
Wageningen Universiteit
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| Subjects: | catchment hydrology, discharge, models, precipitation, river rhine, rivers, watersheds, afvoer, hydrologie van stroomgebieden, modellen, neerslag, rijn, rivieren, stroomgebieden, |
| Online Access: | https://research.wur.nl/en/publications/bias-correction-of-temperature-and-precipitation-data-for-regiona-2 |
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| Summary: | Nowadays hydrological models have become an important tool in predicting streamflow generation. Most of these models need to be calibrated with the correct meteorological input before they can predict reliable streamflow generation. This report focuses on the hydrological analysis of three different meteorological forcing data sets for the Rhine basin. These are observed data, downscaled ERA15 data and a regional climate model run, known as the reference scenario. The goal of this report is to analyse the difference between the last two and the observed data such that a bias correction can be applied to minimize these differences. The bias correction used here corrects for the mean and the coefficient of variation of the precipitation data. The temperature data is corrected for the mean and the standard deviation |
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