Automatic parameterization of a flow routing scheme driven by radar altimetry data: Evaluation in the Amazon basin

ISI Document Delivery No.: 129GR Times Cited: 2 Cited Reference Count: 36 Cited References: BAMBER JL, 1994, INT J REMOTE SENS, V15, P925 Birkett CM, 2000, REMOTE SENS ENVIRON, V72, P218, DOI 10.1016/S0034-4257(99)00105-4 Boone A, 1999, J APPL METEOROL, V38, P1611, DOI 10.1175/1520-0450(1999)0382.0.CO;2 Boyle DP, 2000, WATER RESOUR RES, V36, P3663, DOI 10.1029/2000WR900207 Calmant S, 2008, SURV GEOPHYS, V29, P247, DOI 10.1007/s10712-008-9051-1 Chow V. T., 1988, APPL HYDROLOGY Coe MT, 2008, HYDROL PROCESS, V22, P2542, DOI 10.1002/hyp.6850 Cogley J. G., 2003, 20031 TRENT U DEP GE Cretaux JF, 2011, ADV SPACE RES, V47, P1497, DOI 10.1016/j.asr.2011.01.004 Dadson SJ, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014474 Decharme B, 2012, CLIM DYNAM, V38, P1389, DOI 10.1007/s00382-011-1054-9 Durand M, 2010, IEEE J-STARS, V3, P20, DOI 10.1109/JSTARS.2009.2033453 Getirana A. C. V., 2012, HYDROL EARTH SYST SC, V9, P7591, DOI [10.5194/hessd-9-7591-2012, DOI 10.5194/HESSD-9-7591-2012] Getirana ACV, 2010, J HYDROL, V387, P244, DOI 10.1016/j.jhydrol.2010.04.013 Getirana ACV, 2009, J HYDROL, V379, P205, DOI 10.1016/j.jhydrol.2009.09.049 Getirana ACV, 2012, J HYDROMETEOROL, V13, P1641, DOI 10.1175/JHM-D-12-021.1 Getirana ACV, 2010, HYDROL PROCESS, V24, P3219, DOI 10.1002/hyp.7747 Goldberg D.E., 1989, GENETIC ALGORITHMS S Gupta VK, 1998, WATER RESOUR RES, V34, P751, DOI DOI 10.1029/97WR03495 Habets F, 1999, J HYDROL, V217, P75, DOI 10.1016/S0022-1694(99)00019-0 Kirpich Z. P., 1940, CIVIL ENG, V10, P362 KOBLINSKY CJ, 1993, WATER RESOUR RES, V29, P1839, DOI 10.1029/93WR00542 Leon JG, 2006, J HYDROL, V328, P481, DOI 10.1016/j.hydrol/2005.12.006 Masson V, 2003, J CLIMATE, V16, P1261, DOI 10.1175/1520-0442-16.9.1261 Masutomi Y, 2009, HYDROL PROCESS, V23, P572, DOI 10.1002/hyp.7186 Michailovsky C. I., 2012, HYDROL EARTH SYST SC, V9, P3203, DOI [10.5194/hessd-9-3203-2012, DOI 10.5194/HESSD-9-3203-2012] Noilhan J, 1996, GLOBAL PLANET CHANGE, V13, P145, DOI 10.1016/0921-8181(95)00043-7 Pereira-Cardenal SJ, 2011, HYDROL EARTH SYST SC, V15, P241, DOI 10.5194/hess-15-241-2011 Roux E, 2010, HYDROLOG SCI J, V55, P104, DOI 10.1080/02626660903529023 Sheffield J, 2006, J CLIMATE, V19, P3088, DOI 10.1175/JCLI3790.1 Shuttleworth W. J., 1993, HDB HYDROLOGY, P1 SOROOSHIAN S, 1983, WATER RESOUR RES, V19, P251, DOI 10.1029/WR019i001p00251 Wilson M, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL030156 Yamazaki D, 2011, WATER RESOUR RES, V47, DOI 10.1029/2010WR009726 Yamazaki D, 2009, HYDROL EARTH SYST SC, V13, P2241 Yapo PO, 1998, J HYDROL, V204, P83, DOI 10.1016/S0022-1694(97)00107-8 Getirana, Augusto C. V. Boone, Aaron Yamazaki, Dai Mognard, Nelly Yamazaki, Dai/J-3029-2012 Centre National d'Etudes Spatiales (CNES) The first author thanks the Centre National d'Etudes Spatiales (CNES) for the financial support. The study benefited from data made available by Agencia Nacional de Aguas (ANA) and by the European Space Agency (ESA) under the form of Geophysical Data Records (GDRs). The multimission database of GDRs is maintained by the Centre de Topographie des Oceans et de l'Hydrosphere (CTOH) at LEGOS. The authors also thank G. Cochonneau (IRD) and M. C. Gennero (IRD) for their help in data acquisition and processing and three anonymous reviewers for their valuable comments. 2 AMER GEOPHYSICAL UNION WASHINGTON WATER RESOUR RESThis paper describes and evaluates a procedure that integrates radar altimetry data into the automatic calibration of large-scale flow routing schemes (LFRS). The Hydrological Modeling and Analysis Platform, coupled in off-line mode with the Interactions between Soil, Biosphere, and Atmosphere land surface model, is used to simulate daily surface water dynamics of the Amazon basin at a 0.25 degrees spatial resolution. The Multiobjective Complex Evolution optimization algorithm is used to optimize one parameter (subsurface runoff time delay) and other three parameter multiplier factors (Manning roughness coefficient for rivers, river width, and bankfull height) by minimizing two objective functions for the 2002 to 2006 period. Four calibration experiments are performed by combining water discharge observations and Envisat data to evaluate the potential of using radar altimetry in the automatic calibration of LFRS. One experiment is based on daily discharge observations, other combines discharge with altimetric data, and the other two ones are driven exclusively by radar altimetry data, at 16 or four virtual stations, depending on the experiment. The calibration process is validated against discharge observations at five gauging stations located on the main tributaries. This study shows the feasibility of calibrating LFRS using radar altimetry data. Results demonstrate that reasonable parameters can be obtained by using radar altimetry in an optimization procedure with competitive computational costs. However, there is evidence of equifinality among model parameters. Furthermore, the automatic calibration driven by altimetric data can reliably reproduce discharges time series, and significant improvements are noticed in simulated water level variations. Citation: Getirana, A. C. V., A. Boone, D. Yamazaki, and N. Mognard (2013), Automatic parameterization of a flow routing scheme driven by radar altimetry data: Evaluation in the Amazon basin, Water Resour. Res., 49, doi: 10.1002/wrcr.20077