An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models
This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, t...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-06-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/20/2453/2016/hess-20-2453-2016.pdf |
| _version_ | 1818319014291570688 |
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| author | X. Yuan |
| author_facet | X. Yuan |
| author_sort | X. Yuan |
| collection | DOAJ |
| description | This is the second paper of a two-part series on
introducing an experimental seasonal hydrological forecasting system over
the Yellow River basin in northern China. While the natural hydrological
predictability in terms of initial hydrological conditions (ICs) is
investigated in a companion paper, the added value from eight North American
Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of
99 members is assessed in this paper, with an implicit consideration of
human-induced uncertainty in the hydrological models through a
post-processing procedure. The forecast skill in terms of anomaly
correlation (AC) for 2 m air temperature and precipitation does not
necessarily decrease over leads but is dependent on the target month due to
a strong seasonality for the climate over the Yellow River basin. As there
is more diversity in the model performance for the temperature forecasts
than the precipitation forecasts, the grand NMME ensemble mean forecast has
consistently higher skill than the best single model up to 6 months for
the temperature but up to 2 months for the precipitation. The NMME
climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model
regionalized over the Yellow River basin to produce forecasts of soil
moisture, runoff and streamflow. And the NMME/VIC forecasts are compared
with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month
hindcast experiments for each calendar month during 1982–2010. As verified
by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC
for the soil moisture forecasts, and the former has higher skill than the
latter only for the forecasts at long leads and for those initialized in the
rainy season. The forecast skill for runoff is lower for both forecast
approaches, but the added value from NMME/VIC is more obvious, with an
increase of the average AC by 0.08–0.2. To compare with the observed
streamflow, both the hindcasts from NMME/VIC and ESP/VIC are post-processed
through a linear regression model fitted by using VIC offline-simulated
streamflow. The post-processed NMME/VIC reduces the root mean squared
error (RMSE) from the post-processed ESP/VIC by 5–15 %. And the reduction occurs mostly during the transition from wet to dry seasons. With the consideration of the uncertainty in the hydrological models, the added value from climate forecast models is decreased especially at short leads, suggesting the necessity of improving the large-scale hydrological models in human-intervened river basins. |
| first_indexed | 2024-12-13T10:02:22Z |
| format | Article |
| id | doaj.art-7b49209d19bf4937805b975ab3c5c7e4 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-13T10:02:22Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-7b49209d19bf4937805b975ab3c5c7e42022-12-21T23:51:37ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382016-06-012062453246610.5194/hess-20-2453-2016An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast modelsX. Yuan0RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaThis is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982–2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08–0.2. To compare with the observed streamflow, both the hindcasts from NMME/VIC and ESP/VIC are post-processed through a linear regression model fitted by using VIC offline-simulated streamflow. The post-processed NMME/VIC reduces the root mean squared error (RMSE) from the post-processed ESP/VIC by 5–15 %. And the reduction occurs mostly during the transition from wet to dry seasons. With the consideration of the uncertainty in the hydrological models, the added value from climate forecast models is decreased especially at short leads, suggesting the necessity of improving the large-scale hydrological models in human-intervened river basins.http://www.hydrol-earth-syst-sci.net/20/2453/2016/hess-20-2453-2016.pdf |
| spellingShingle | X. Yuan An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models Hydrology and Earth System Sciences |
| title | An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models |
| title_full | An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models |
| title_fullStr | An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models |
| title_full_unstemmed | An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models |
| title_short | An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models |
| title_sort | experimental seasonal hydrological forecasting system over the yellow river basin ndash part 2 the added value from climate forecast models |
| url | http://www.hydrol-earth-syst-sci.net/20/2453/2016/hess-20-2453-2016.pdf |
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