Comprehensive evaluation of seasonal forecasts from NMME and statistical models over the Blue Nile Basin and the Grand Ethiopian Renaissance Dam (GERD) watershed
Study region: The Blue Nile basin, including the Grand Ethiopian Renaissance Dam (GERD) Study focus: This study evaluates the performance of seasonal precipitation forecasts from the North American Multi-Model Ensemble (NMME) over the Blue Nile Basin and compares them to newly developed statistical...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824005111 |
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| Summary: | Study region: The Blue Nile basin, including the Grand Ethiopian Renaissance Dam (GERD) Study focus: This study evaluates the performance of seasonal precipitation forecasts from the North American Multi-Model Ensemble (NMME) over the Blue Nile Basin and compares them to newly developed statistical forecasting models, using the satellite-based Integrated Multi-satellitE Retrievals for GPM (IMERG) as the reference product. New hydrological insights for the region: The NMME models exhibit considerable variability in bias. Some models significantly overestimate precipitation across the years, others show substantial underestimation, some indicate minimal bias across most years, and a few demonstrate generally low bias with large outlier biases in specific years. Depending on the model, NMME can explain only from 2 % to 24 % of the summer precipitation variability. A simple statistical model, which uses June-July-August forecasts and Indian Dipole Mode Index (DMI) observations from the prior September-October-November as predictors, outperforms the dynamic NMME models in many performance metrics. Both the NMME models and the statistical model possess some skill in forecasting dry or wet summers, explaining up to 40 % of the temporal precipitation variability. This study underscores the need for further enhancing the accuracy of seasonal precipitation forecasting models to make the forecasts useful for applications |
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| ISSN: | 2214-5818 |