Keywords
Hydrologic complexity; robust model selection; complexity regularization
Location
Session C1: Complexity, Sensitivity, and Uncertainty Issues in Integrated Environmental Models
Start Date
16-6-2014 3:40 PM
End Date
16-6-2014 5:20 PM
Abstract
This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available. here by a robust model, we mean a model that predicts a variable of interest conditioned on future input forcing better than a model that is fitted on limited data. We demonstrate this on a rainfall-runoff model structure, SAC-SMA, using MOPEX data set of Guadalupe river basin.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Complexity regularized hydrological model selection
Session C1: Complexity, Sensitivity, and Uncertainty Issues in Integrated Environmental Models
This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available. here by a robust model, we mean a model that predicts a variable of interest conditioned on future input forcing better than a model that is fitted on limited data. We demonstrate this on a rainfall-runoff model structure, SAC-SMA, using MOPEX data set of Guadalupe river basin.
