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Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation
Author(s) -
Vrugt Jasper A.,
Diks Cees G. H.,
Gupta Hoshin V.,
Bouten Willem,
Verstraten Jacobus M.
Publication year - 2005
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 - 10.1029/2004wr003059
Subject(s) - data assimilation , watershed , hydrological modelling , calibration , forcing (mathematics) , aggregate (composite) , computer science , process (computing) , environmental science , propagation of uncertainty , hydrology (agriculture) , meteorology , climatology , mathematics , statistics , algorithm , machine learning , geology , geotechnical engineering , physics , materials science , composite material , operating system
Hydrologic models use relatively simple mathematical equations to conceptualize and aggregate the complex, spatially distributed, and highly interrelated water, energy, and vegetation processes in a watershed. A consequence of process aggregation is that the model parameters often do not represent directly measurable entities and must therefore be estimated using measurements of the system inputs and outputs. During this process, known as model calibration, the parameters are adjusted so that the behavior of the model approximates, as closely and consistently as possible, the observed response of the hydrologic system over some historical period of time. In practice, however, because of errors in the model structure and the input (forcing) and output data, this has proven to be difficult, leading to considerable uncertainty in the model predictions. This paper surveys the limitations of current model calibration methodologies, which treat the uncertainty in the input‐output relationship as being primarily attributable to uncertainty in the parameters and presents a simultaneous optimization and data assimilation (SODA) method, which improves the treatment of uncertainty in hydrologic modeling. The usefulness and applicability of SODA is demonstrated by means of a pilot study using data from the Leaf River watershed in Mississippi and a simple hydrologic model with typical conceptual components.