Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hessd-12-3945-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
16 Apr 2015
Review status
This discussion paper has been under review for the journal Hydrology and Earth System Sciences (HESS). The revised manuscript was not accepted.
Hydrological model parameter dimensionality is a weak measure of prediction uncertainty
S. Pande1, L. Arkesteijn1, H. Savenije1, and L. A. Bastidas2 1Department of Water Management, Delft University of Technology, Delft, the Netherlands
2ENERCON Services Inc., Pittsburgh Office, Murrysville, PA, USA
Abstract. This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting) and its simplified version SIXPAR (Six Parameter Model), are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters) does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.

Citation: Pande, S., Arkesteijn, L., Savenije, H., and Bastidas, L. A.: Hydrological model parameter dimensionality is a weak measure of prediction uncertainty, Hydrol. Earth Syst. Sci. Discuss., 12, 3945-4004, doi:10.5194/hessd-12-3945-2015, 2015.
S. Pande et al.
S. Pande et al.

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