Prediction of runoff seasonality in ungauged basins

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Presentation at the 2009 AGU Meeting. Session on Prediction in Ungauged Basins (PUB)

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  • Prediction of runoff seasonality in ungauged basins

    1. 1. Prediction of Runoff Seasonality in Ungauged Basins Pierluigi Claps [claps@polito.it] Francesco Laio Politecnico di Torino, Italy. AGU Fall Meeting 2009 Thans to: Gianluca Vezzù, Daniele Ganora, Elisa Bartolini
    2. 2. Runoff Seasonality • (1) A hydrological signature in Catchment Classification • (2) A starting point for streamflow Prediction in Ungauged Basins • (3) A key pattern for assessing effects of global warming in critical (mountain) areas AGU Fall Meeting 2009 P Claps - F Laio
    3. 3. Runoff seasonality Mean annual runoff [mm] North-Western Italy 41 basins AGU Fall Meeting 2009 P Claps - F Laio
    4. 4. AGU Fall Meeting 2009 P Claps - F Laio
    5. 5. (1) Classification (and prediction) based on Distances Regime curves (treated like patterns) are related to each other by means of their similarity - dissimilarity • Dissimilarity = distance, e.g.: |qi,s1 – qi,s2| • “complex” basin descriptors can be used, as, e.g., precipitation regimes •Regime distances are put in a distance matrix •Analogous distance matrices are created for each descriptor AGU Fall Meeting 2009 P Claps - F Laio
    6. 6. Distance-based approach e.g. Ganora et al. (WRR 2009) applied to FDC • A regression model identifies the most significant descriptors Regime distance Descriptors’ distance matrix matrices • Significance of regression coefficients: Mantel test [Mantel and Valand , 1970], Lichstein [2007] (distance matrices contain dependent values) • Cluster analysis or nearest neighbors with the selected descriptors will allow for curve estimation in Ungauged Basins AGU Fall Meeting 2009 P Claps - F Laio
    7. 7. Significant Variables Centroid latitude, Mean elevation, main orientation angle Distance between avg. elevation Distance between regimes Best regression model: highest R2 with all the covariates being significant AGU Fall Meeting 2009 P Claps - F Laio
    8. 8. Nearest Neighbour prediction 5 Mean RMSE Nr. of neighbours AGU Fall Meeting 2009 P Claps - F Laio
    9. 9. (2) Prediction by Parametric (Fourier) approach e.g. Claps et al. (JHE 2008) applied to Temperatures in Italy AGU Fall Meeting 2009 P Claps - F Laio
    10. 10. Regression Model Selection based on cross-validation RMSE and MAE Optimal regressions: common set of descriptors R2adj=0.68-0.89 AGU Fall Meeting 2009 P Claps - F Laio
    11. 11. Regression Model Selection based on cross-validation RMSE and MAE Optimal regressions: common set of descriptors R2adj=0.68-0.89 Best regressions: highest R2 with all the covariates being still significant R2adj=0.84-0.93 AGU Fall Meeting 2009 P Claps - F Laio
    12. 12. Results ___n.n. ___fou .+.+.obs AGU Fall Meeting 2009 P Claps - F Laio
    13. 13. (3) Role of rainfall regime and prediction (mountain areas) Quasi-deterministic model (Snow-affected runoff) Snow storage effects on the runoff regime; Detection of unreliable rainfall measurement; Assessment of precipitation underestimation due to undercatch regime Model input Precipitation and temperature regime Digital Elevation Model Model output Runoff regime Snow storage and melting AGU Fall Meeting 2009 P Claps - F Laio
    14. 14. Model Features 1. Sub-monthly temperature variability Logistic distribution with m=mean temp and variance to calibrate 1-t temp < 0 The cumulative probability snow allows to partition different temp > 0 physical processes within melt and t ET the same month 2. Snowmelt based on degree-day approach d: number of days tmpj : monthly positive tmp tmpb : threshold tmp k : melting rate AGU Fall Meeting 2009 P Claps - F Laio
    15. 15. Model Features 1. Sub-monthly temperature variability Logistic distribution with m=mean temp and variance to calibrate 1-t temp < 0 The cumulative probability snow allows to partition different temp > 0 physical processes within melt and t ET the same month 2. Snowmelt based on degree-day approach d: number of days tmpj : monthly positive tmp tmpb : threshold tmp PARAMETERS TO CALIBRATE: - within-month variance of T k : melting rate - Melt factor k AGU Fall Meeting 2009 P Claps - F Laio
    16. 16. Application to 41 basins in North-Western Italy Mountain areas AGU Fall Meeting 2009 P Claps - F Laio
    17. 17. Precipitation (undercatch) correction Procedure: 1.Reference run (parameters taken from literature) 2.Correction evaluation and application 3.Parameters calibration (Minimizing MAE) AGU Fall Meeting 2009 P Claps - F Laio
    18. 18. Precipitation (undercatch) correction Procedure: 1.Reference run (parameters taken from literature) 2.Correction evaluation and application 3.Parameters calibration (Minimizing MAE) AGU Fall Meeting 2009 P Claps - F Laio
    19. 19. reconstruction after correction Global regional parameters will allow prediction (ongoing) AGU Fall Meeting 2009 P Claps - F Laio

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