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Sensitivity analysis, calibration, and testing of a distributed hydrological model using error‐based weighting and one objective function
Author(s) -
Foglia L.,
Hill M. C.,
Mehl S. W.,
Burlando P.
Publication year - 2009
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2008wr007255
Subject(s) - weighting , sensitivity (control systems) , calibration , leverage (statistics) , errors in variables models , computer science , statistics , regression analysis , hydrological modelling , function (biology) , identification (biology) , regression , data mining , mathematics , engineering , geology , medicine , botany , electronic engineering , evolutionary biology , biology , radiology , climatology
We evaluate the utility of three interrelated means of using data to calibrate the fully distributed rainfall‐runoff model TOPKAPI as applied to the Maggia Valley drainage area in Switzerland. The use of error‐based weighting of observation and prior information data, local sensitivity analysis, and single‐objective function nonlinear regression provides quantitative evaluation of sensitivity of the 35 model parameters to the data, identification of data types most important to the calibration, and identification of correlations among parameters that contribute to nonuniqueness. Sensitivity analysis required only 71 model runs, and regression required about 50 model runs. The approach presented appears to be ideal for evaluation of models with long run times or as a preliminary step to more computationally demanding methods. The statistics used include composite scaled sensitivities, parameter correlation coefficients, leverage, Cook's D, and DFBETAS. Tests suggest predictive ability of the calibrated model typical of hydrologic models.