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Titolo:
TOWARD IMPROVED CALIBRATION OF HYDROLOGIC-MODELS - MULTIPLE AND NONCOMMENSURABLE MEASURES OF INFORMATION
Autore:
GUPTA HV; SOROOSHIAN S; YAPO PO;
Indirizzi:
UNIV ARIZONA,DEPT HYDROL & WATER RESOURCES,BOX 210011 TUCSON AZ 85721 UNIV ARIZONA,DEPT SYST & IND ENGN TUCSON AZ 85721
Titolo Testata:
Water resources research
fascicolo: 4, volume: 34, anno: 1998,
pagine: 751 - 763
SICI:
0043-1397(1998)34:4<751:TICOH->2.0.ZU;2-Q
Fonte:
ISI
Lingua:
ENG
Soggetto:
RAINFALL-RUNOFF MODELS; MULTIOBJECTIVE PARAMETER-ESTIMATION; OBJECTIVE DECISION-MAKING; AUTOMATIC CALIBRATION; GLOBAL OPTIMIZATION; CATCHMENT MODELS; UNCERTAINTY; PREDICTION; IDENTIFICATION; EUTROPHICATION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
70
Recensione:
Indirizzi per estratti:
Citazione:
H.V. Gupta et al., "TOWARD IMPROVED CALIBRATION OF HYDROLOGIC-MODELS - MULTIPLE AND NONCOMMENSURABLE MEASURES OF INFORMATION", Water resources research, 34(4), 1998, pp. 751-763

Abstract

Several contributions to the hydrological literature have brought into question the continued usefulness of the classical paradigm for hydrologic model calibration. With the growing popularity of sophisticated''physically based'' watershed models (e.g., land-surface hydrology and hydrochemical models) the complexity of the calibration problem hasbeen multiplied many fold. We disagree with the seemingly widespread conviction that the model calibration problem will simply disappear with the availability of more and better field measurements. This paper suggests that the emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multiobjective nature of the problem and must explicitly recognize the role of model error. The results of our preliminary studies are presented. Through an illustrative case study we show that the multiobjective approach is not only practical and relatively simple to implement but can also provide useful information about the limitations of a model.

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Documento generato il 03/12/20 alle ore 21:02:39