Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/923D8DFD-79E6-42D9-93B7-FC49596359B9 | ||
| Year | 2018 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
|
||
| Bibliographic Citation | M. Grillakis, A. Koutroulis and I. Tsanis "Improving seasonal forecasts for basin scale hydrological applications," Water, vol. 10, no. 11, Nov. 2018. doi: 10.3390/w10111593 https://doi.org/10.3390/w10111593 | ||
| Appears in Collections |
Seasonal forecasting is a fast-growing climate prediction application that puts into practice the latest improvements in the climate modeling research. Skillful seasonal forecasts can drastically aid practical applications and productive sectors by reducing weather-related risks such as water availability. In this study two operational seasonal forecasting systems are tested in a water resource important watershed on the island of Crete. Hindcast precipitation and temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 and Met Office GloSea5 systems are tested for their forecast skill up to seven months ahead. Data of both systems are downscaled and corrected for biases towards the observations. Different correction methods are applied and evaluated. Post-processed data from these methods are used as an input to the hydrological model HYPE, to provide streamflow forecasts. Results show that a prior adjustment of the two systems' precipitation and temperature may improve their forecast skill. Adjusted GloSea5 forecasts are slightly better estimates than the corresponding forecasts based on System 4. The results show that both systems provide a skillful ensemble streamflow prediction for one month ahead, with the skill decreasing rapidly beyond that. Update of the initial state of HYPE results in the reduction of the variability of the ensemble flow predictions and improves the skill but only as far as two months of forecast. Finally, the two systems were tested for their ability to capture a limited number of historical streamflow drought events, with indications that GloSea5 has a slightly better skill.
Copyright © DIAS 2013
