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Hydrological Model Parameter Dimensionality is a Weak Measure of Prediction Uncertainty : Volume 12, Issue 4 (16/04/2015)

By Pande, S.

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Book Id: WPLBN0004012330
Format Type: PDF Article :
File Size: Pages 60
Reproduction Date: 2015

Title: Hydrological Model Parameter Dimensionality is a Weak Measure of Prediction Uncertainty : Volume 12, Issue 4 (16/04/2015)  
Author: Pande, S.
Volume: Vol. 12, Issue 4
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Savenije, H., Bastidas, L. A., Pande, S., & Arkesteijn, L. (2015). Hydrological Model Parameter Dimensionality is a Weak Measure of Prediction Uncertainty : Volume 12, Issue 4 (16/04/2015). Retrieved from

Description: Department of Water Management, Delft University of Technology, Delft, the Netherlands. 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.

Hydrological model parameter dimensionality is a weak measure of prediction uncertainty

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