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Tosiyuki Nakaegawa MRI, Japan

Potential predictability of seasonal mean river discharge in dynamical ensemble prediction using MRI/JMA GCM. Tosiyuki Nakaegawa MRI, Japan. Potential predictability of potentially available water resources ( P-E ) is low in most of land areas. Background.

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Tosiyuki Nakaegawa MRI, Japan

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  1. Potential predictability of seasonal mean river discharge in dynamical ensemble prediction using MRI/JMA GCM Tosiyuki Nakaegawa MRI, Japan

  2. Potential predictability of potentially available water resources (P-E) is low in most of land areas. Background • Dependable seasonal predictions would facilitate the water resources managements. Are there any factors in improving the predictability? （Nakaegawa et al.2003）

  3. River discharge: accumulation P-E: each grid Physical characteristics of river discharge • River discharge is a collection of total runoffs in an upper river basin, which is similar to the area average process. The collection is likely to reduce the unpredictable variability and, as a result, to enhance the predictability.

  4. Objectives • Estimation of the potential predictability of river discharge based on an ensemble experiment • Examination of the effects of land surface hydrological processes on the predictability, in comparison with that of P-E. The collection effect

  5. C20C Experiment setup • AGCM: MJ98，T42 with 30 vertical layers • River Routing Model: GRiveT, 0.5o river channel network of TRIP, velocity: 0.4m/s • Member: 6 • SST & Sea Ice : HadISST (Rayner et al. 2003) • CO2 : annualy varying • Integration period: 1872-2005 • Analysis period：1951-2000

  6. Potential Predictability • Definition: The maximum value that an ensemble approach can reach, assuming that perfectly predicted SSTs are available and that the model perfectly reproduces atmospheric and hydrological processes. • Variance ratio： measure of • PP based on the ANOVA • (Rowell 1998).

  7. High in Tropics and Low in Extratropics and inland areas • Seasonal cycles in both Tropics and Extratropics High for JJA; high for DJF Variance Ratio of Seasonal Mean River Discharge

  8. Variance Ratio of Seasonal Mean River Discharge • Resemblance of geographical distributions of the variance ratios of precipitation and P-E A major factor in the predictability of river discharge

  9. Variance Ratio in the Amazon River Basin higher variance ratios along major stream channels Runoff collection through a river channel network may enhance the variance ratio.

  10. Discharge>P-E P-E> Discharge Latitudinal distribution of variance ratios Weak Strong Weak P-E for DJF > P-E for JJA The magnitude relation varies with season. ○： Variance ratio at river mouths of basins larger than 105km2 Solid line: Zonal mean of the variance ratio of P-E over land areas

  11. Collection Effect • How much influence does the collection effect over a river basin have on the potential predictability of river discharge? Variance Ratio: (Discharge)-(P-E) Improvement Basin areas >106km2 Does not work effectively Cause deterioration

  12. Relationship between morphometric properties and discharges • Morphometric properties change the precipitation-discharge responses for basins with the same drainage area (Jones, 1997).

  13. Variance Ratio Difference and Morphometirc Properties Total Length Form Factor The size of a river basin influences the collection effects. L L2/A Drainage Density Mainstream Length L/A Absolute properties Relative properties

  14. A X M The Amazon River Semi-annual cycle Discharge Variance Ratio Improvement P-E Amazon River P-E Reduction Mean travel time Madeira: 86 days Xingu: 45 days Discharge Month

  15. The Mackenzie River The peak of the variance ratio River discharge: MAM; P-E: DJF Discharge Variance Ratio The mean travel time: 68 days Improvement P-E P-E: accumulated as snow in winter and melted in spring

  16. The Ob River The peak of the variance ratio River discharge: JJA; P-E: SON Discharge Variance Ratio The mean travel time: 68 days Improvement P-E River discharge in JJA mostly originates from snow melt water, not from P-E.

  17. v=0.14m/s smoothed 0.25 v=0.40m/s 0.15 Further Experiment Further experiment: slower velocity v=0.14m/s (Hagemann and Dumenil 1998) • The collection effects: • Improvement • Phase shift, and • Smoothing

  18. Concluding Summary(1) • Estimation of the potential predictability of river discharge based on an ensemble experiment with the C20C setup. • Similar geographical distribution to P-E • High in Tropics and low in extratropics and in inland areas

  19. Concluding Summary (2) • Examination of the effects of land surface hydrological processes on the predictability, in comparison with that of P-E. Distinctive collection effects are identified in large basins with greaterthan 106km2. Improvement in the variance ratio, phase shift, and smoothing Snow processes significantly influences on the predictability for the mid- and high latitude river basins. Snow accumulation and snow-melting

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