An evaluation of model output statistics for subseasonal streamflow forecasting in European catchments
AbstractSubseasonal and seasonal forecasts of the atmosphere, oceans, sea ice, or land surfaces often rely on earth system model (ESM) simulations. While the most recent generation of ESMs simulates runoff per land surface grid cell operationally, it does not typically simulate river streamflow dire...
| Main Authors: | , , |
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| Format: | Text |
| Language: | English |
| Published: |
American Meteorological Society
2019
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.7892/boris.131902 https://boris.unibe.ch/131902/ |
| Summary: | AbstractSubseasonal and seasonal forecasts of the atmosphere, oceans, sea ice, or land surfaces often rely on earth system model (ESM) simulations. While the most recent generation of ESMs simulates runoff per land surface grid cell operationally, it does not typically simulate river streamflow directly. Here, we apply the model output statistics (MOS) method to the hindcast archive of the European Centre for Medium-Range Weather Forecasts (ECMWF). Linear models are tested that regress observed river streamflow on surface runoff, subsurface runoff, total runoff, precipitation, and surface air temperature simulated by ECMWF's forecast systems S4 and SEAS5. In addition, the pool of candidate predictors contains observed precipitation and surface air temperature preceding the date of prediction. The experiment is conducted for 16 European catchments in the period 1981-2006 and focuses on monthly average streamflow at lead times of 0 and 20 days. The results show that skill against the streamflow climatology is frequently absent and varies considerably between predictor combinations, catchments, and seasons. Using streamflow persistence as a benchmark model further deteriorates skill. This is most pronounced for a catchment that features lakes, which extend to about 14% of the catchment area. On average, however, the predictor combinations using the ESM runoff simulations tend to perform best. |
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