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GEOSTATISTICAL REGIONALIZATION OF LOW-FLOWS: TOP-KRIGING VS. PSBI

EGU Leonardo Topical Conferences Series on the hydrological cycle 2010 Luxembourg, 10-12 November 2010. GEOSTATISTICAL REGIONALIZATION OF LOW-FLOWS: TOP-KRIGING VS. PSBI. S. Castiglioni 1 , A. Castellarin 1 , A. Montanari 1 , J. O. Skøien 2 , G. Laaha 3 , G. Blöschl 4

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GEOSTATISTICAL REGIONALIZATION OF LOW-FLOWS: TOP-KRIGING VS. PSBI

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  1. EGU Leonardo Topical Conferences Series on the hydrological cycle 2010Luxembourg, 10-12 November 2010 GEOSTATISTICAL REGIONALIZATION OF LOW-FLOWS: TOP-KRIGING VS. PSBI • S. Castiglioni1, A. Castellarin1, A. Montanari1, J. O. Skøien2, G. Laaha3, G. Blöschl4 • School of Civil Engineering (Dept. DICAM) University of Bologna, Italy. • Department of Physical Geography, University of Utrecht, Utrecht, Netherlands. • Institute of Applied Statistics and Computing, Univ. of Natural Resources and Applied Life Sciences, BOKU Vienna, Austria • (4) Inst. for Hydraul. and Water Resour. Eng., Vienna Univ. of Technology, Vienna, Austria.

  2. Introduction The prediction of low-flows indices in ungauged basins is a critical task in a number of engineering applications related to surface water resources planning and management. Hydrological predictions generally have to deal with the inadequacy or deficiency of observations for the site of interest (see the decade on Prediction in Ungauged Basins promoted by the International Association of Hydrological Sciences – IAHS; Sivapalan et al., 2003). Recent studies highlight that geostatistical interpolation can be effectively applied to the problem of regionalization of hydrometric information. Ridracoli Dam 2007 historical minimum “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  3. Introduction: Top-kriging Top-kriging, or Topological kriging, predicts the variable of interest along river networks taking both the area and nested nature of catchments into account. Example of catchment size effect (a) and the effect of nesting (b and c) in the estimation of i. (a) (b) (c) Example of the estimate of the normalised specific 100-year flood from Top-kriging colour coded on the stream network of the Mur region (Skøien et al., 2006). “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  4. Introduction: PSBI PSBI (Physiographic-Space Based Interpolation) performs the spatial interpolation of the desired streamflow index (e.g., annual streamflow, low-flow index, flood quantile, etc.) in the space of catchment descriptors. GEOMORPHOCLIMATIC CATCHMENT DESCRIPTORS (xi ;yi) = physiographical space The literature reports successful applications of PSBI to the problem of regionalization of flood frequency regime (Chokmani and Ouarda, 2004) or parameters of rainfall-runoff models (Hundecha et al., 2008) and low-flows (Castiglioni et al., 2009). X Y “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  5. Top-kriging vs. PSBI Top-kriging: Physiographic Space-Based Interpolation (PSBI): X Y Although rather different in their approach to regionalization, these methodologies share a common background idea: both methodologies perform the regionalization of streamflow indices without defining or identifying homogeneous regions or pooling-groups of sites. “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  6. Objectives This study compares these two innovative geostatistical approaches for the prediction of low-flows in ungauged basins. The comparison is performed at two different spatial scales: (a) regional scale; (b) catchment scale. • We assess the ability of each technique to predict Q355 through a comprehensive leave-one out cross validation procedure for an entire study region of interest. • We apply both methodologies to a large catchment of the study area to better analyse and interpret their accuracy and reliability for prediction of Q355 along the stream network. “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  7. Study area • The region includes 51 unregulated basins for which daily streamflows are available for different observation periods with a minimum record length of 5 years; • Concerning the catchment scale application, our study focuses on the Metauro catchment that counts 7stream gauges for1043.6km2 Metauro basin “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  8. Physiographic and climatic descriptors “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  9. Physiographical space Physiographic and climatic descriptors: A, L, P, Hmax, Hmean, Hmin, ΔH, τc, MAP Principal Component Analysis (PCA, Basilevsky, 1994). Explain about 70% of the variability of the original set of physiographic and climatic descriptors Physiographical space: (xi ;yi) = f(A, L, P, Hmax, Hmean, Hmin, ΔH, τc, MAP) “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  10. Spatial interpolation PSBI: (PC1; PC2) = physiographical space Universal Kriging: sphericalvariogram(Castiglioni et al., 2009) Top-kriging: (x; y) = geographical space Top-kriging: modified exponential variogram(Skøien et al., 2006) “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  11. Cross-validation procedure We assessed the reliability of the techniques and the uncertainty of the associated predictions of Q355 in ungauged basins by applying a leave-one-out cross-validation procedure (see e.g., Zhang and Kroll, 2007; Brath et al., 2003): • assume one of the N basins, let us say site i, to be ungauged; 2.predict the Q355 value for this site on the basis of the remaining N-1 observations; 3.repeating this step N times, considering in turn each of the basins as ungauged, we obtain N cross-validation estimates of Q355 which can be compared with the corresponding observations. “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  12. Top-kriging vs. PSBI: regional scale PSBI Top-kriging Performance indices from the exclusion of these three basins “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  13. Top-kriging vs. PSBI: catchment scale The catchment-scale application of both methodologies required the preliminary identification of the stream network and the evaluation of the nine considered geomorphologic and climatic descriptors for all identified subcatchments. Subcatchment boundaries (Amin>10 Km2) DEM, SRTM 90m Digital Elevation Data (http://csi.cgiar.org/index.asp) “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  14. Top-kriging vs. PSBI: dataset 1° Application of Top-kriging GIS Processing 4° GISProcessing P Lithological map DEM (90 x 90 m) Sub-basin divides 2° 3° GIS Processing GIS Processing MAP Thiessen Polygon A, L, Hmax, Hmean Hmin, ΔH, tc Rain Gauges Application of PSBI “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  15. Top-kriging vs. PSBI: along river network PSBI: Top-kriging: Q355(m3/s) Q355(m3/s) 0.00 - 0.05 0.05 - 0.10 0.10 - 0.15 0.15 - 0.25 0.25 - 0.35 0.35 - 0.55 0.55 - 0.75 0.75 - 0.95 0.95 - 1.50 1.50 - 2.20 0.00 - 0.05 0.05 - 0.10 0.10 - 0.15 0.15 - 0.25 0.25 - 0.35 0.35 - 0.55 0.55 - 0.75 0.75 - 0.95 0.95 - 1.50 1.50 - 2.20 “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  16. Top-kriging vs. PSBI: along river network Q355(m3/s) PSBI Top-kriging: 0.00 - 0.05 0.05 - 0.10 0.10 - 0.15 0.15 - 0.25 0.25 - 0.35 0.35 - 0.55 0.55 - 0.75 0.75 - 0.95 0.95 - 1.50 1.50 - 2.20 MRE =0.10 MRE =0.54 MRE =0.12 MRE =0.52 “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  17. Top-kriging vs. PSBI: along river network MRE =0.40 MRE =3.02 Q355(m3/s) PSBI Top-kriging: 0.00 - 0.05 0.05 - 0.10 0.10 - 0.15 0.15 - 0.25 0.25 - 0.35 0.35 - 0.55 0.55 - 0.75 0.75 - 0.95 0.95 - 1.50 1.50 - 2.20 MRE =1.20 MRE =0.01 “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  18. Larger river branches Headwater catchments Top-kriging vs. PSBI: complementary methods? Q355(m3/s) PSBI 0.00 - 0.05 0.05 - 0.10 0.10 - 0.15 0.15 - 0.25 0.25 - 0.35 0.35 - 0.55 0.55 - 0.75 0.75 - 0.95 0.95 - 1.50 1.50 - 2.20 Top-kriging: “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  19. Conclusions • The results of our study point out that the performances of Top-kriging and PSBI are very similar: both methodologies represent an effective alternative to traditional regionalization approaches (i.e. multiregression models). • Concerning the catchment scale, the Metauro application of PSBI and Top-kriging demonstrated that the latter technique is easier to implement. The comparison between jack-knife predictions and empirical values of Q355point out that PSBI slightly outperforms Top-kriging. • Finally the analysis shows a complementariness of the two estimation methods (complementary in terms of the basic principle of spatial interpolation, complementary in terms of data requirements and complementary in terms of predictive performances). “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

  20. Thanks for your attention! “Geostatistical regionalization of low-flows: Top-kriging vs. PSBI” S. Castiglioni, A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, G. Blöschl

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