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Mapping Model Behaviour Using Self-organizing Maps : Volume 13, Issue 3 (18/03/2009)

By Herbst, M.

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Book Id: WPLBN0003978372
Format Type: PDF Article :
File Size: Pages 15
Reproduction Date: 2015

Title: Mapping Model Behaviour Using Self-organizing Maps : Volume 13, Issue 3 (18/03/2009)  
Author: Herbst, M.
Volume: Vol. 13, Issue 3
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2009
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Gupta, H. V., Casper, M. C., & Herbst, M. (2009). Mapping Model Behaviour Using Self-organizing Maps : Volume 13, Issue 3 (18/03/2009). Retrieved from http://hawaiilibrary.net/


Description
Description: Department of Physical Geography, University of Trier, Germany. Hydrological model evaluation and identification essentially involves extracting and processing information from model time series. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by the distributed conceptual watershed model NASIM. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.

Summary
Mapping model behaviour using Self-Organizing Maps

Excerpt
Abramowitz, G., Gupta, H. V., Pitman, A., Wang, Y., Leuning, R., Cleugh, H., and Hsu, K.-l.: Neural Error Regression Diagnosis (NERD): A Tool for Model Bias Identification and Prognostic Data Assimilation, J. Hydrometeorol., 7, 160–177, doi:10.1175/JHM479.1, 2006.; Abramowitz, G. and Gupta, H. V.: Toward a model space and model independence metric, Geophys. Res. Lett., 35, L05705, doi:10.1029/2007GL032834, 2008.; Abramowitz, G., Leuning, R., Clark, M., and Pitman, A.: Evaluating the Performance of Land Surface Models, J. Climate, 21, 5468–5481, doi:10.1175/2008JCLI2378.1, 2008.; Abramowitz, G., Pitman, A., Gupta, H. V., Kowalczyk, E., and Wang, Y.: Systematic Bias in Land Surface Models, J. Hydrometeorol., 8, 989–1001, doi:10.1175/JHM628.1, 2007.; Alhoniemi, E., Hollmén, J., Simula, O., and Vesanto, J.: Process Monitoring and Modeling using the Self-Organizing Map, Integr. Comput. Aid. E., 6, 3–14, 1999.; Ambroise, B., Perrin, J. L., and Reutenauer, D.: Multicriterion validation of a semidistributed conceptual model of the water cycle in the Fecht Catchment (Vosges Massif, France), Water Resour. Res., 31, 1467–1482, 1995.; Boogaard, H. F. P. v. d., Mynett, A. E., and Ali, M. S.: Self organizing feature maps for the analysis of hydrological and ecological data sets, in: Hydroinformatics '98, edited by: Babovic, V. M. and Larsen, L. C., Balkema, Rotterdam, The Netherlands, 733–740, 1998.; Herbst, M. and Casper, M. C.: Towards model evaluation and identification using Self-Organizing Maps, Hydrol. Earth Syst. Sci., 12, 657–667, 2008.; Boyle, D. P., Gupta, H. V., and Sorooshian, S.: Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods, Water Resour. Res., 36, 3663–3674, 2000.; Chang, D.-H.: Analysis and modeling of space-time organization of remotely sensed soil moisture, Department of Civil and Environmental Engineering, University of Cincinnati, Cincinnati, Ohio, USA, 169 pp., Ph. D. thesis, 2001.; Duan, Q., Sorooshian, S., and Gupta, V. K.: Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015–1031, doi:10.1029/91WR02985, 1992.; Franks, S. W., Gineste, P., Beven, K. J., and Merot, P.: On constraining the predictions of a distributed model: the incorporation of fuzzy estimates of saturated areas into the calibration process, Water Resour. Res., 34, 787–797, doi:10.1029/97WR03041, 1998.; Gallart, F., Latron, J., Llorens, P., and Beven, K.: Using internal catchment information to reduce the uncertainty of discharge and baseflow predictions, Adv. Water Resour., 30, 808–823, doi:10.1016/j.advwatres.2006.06.005, 2007.; Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34, 751–764, 1998.; Gupta, H. V., Sorooshian, S., Hogue, T. S., and Boyle, D. P.: Advances in Automatic Calibration of Watershed Models, in Calibration of Watershed Models, edited by Duan, Q., Gupta, H. V., Sorooshian, S., Rousseau, A. N., and Turcotte, R., Water Science and Application Series Vol. 6, AGU, Washington DC, USA, 9–28, 2003.; Gupta, H. V., Wagener, T., and Liu, Y.: Reconciling theory with observations: elements of a diagnostic approach to model evaluation, Hydrol. Process., 22, 3802–3813, doi:10.1002/hyp.6989, 2008.; Hall, M. J.: How well does your model fit the data? J. Hydroinform., 3, 49–55, 2001.; Haykin, S.: Neural networks - a comprehensive foundation, 2nd ed., New Jersey, USA, 842 pp., 1999.; Hsu, K.-L., Gupta, H. V., Gao, X., Sorooshian, S., and Imam, B.: Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis, Water Resour. Res., 38, 1302, doi:10.1029/2001WR000795, 2002.; Huang, M., Liang, X., and Liang, Y.: A transferability study of model parameters for the variable infiltration capacity land surface scheme, J. Geophys. Res., 108(D22), 8864, doi:10.1029/200

 

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