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Titolo:
Bayesian recursive parameter estimation for hydrologic models
Autore:
Thiemann, M; Trosset, M; Gupta, H; Sorooshian, S;
Indirizzi:
Univ Arizona, Dept Hydrol & Water Resources, Tucson, AZ 85721 USA Univ Arizona Tucson AZ USA 85721 & Water Resources, Tucson, AZ 85721 USA
Titolo Testata:
WATER RESOURCES RESEARCH
fascicolo: 10, volume: 37, anno: 2001,
pagine: 2521 - 2535
SICI:
0043-1397(200110)37:10<2521:BRPEFH>2.0.ZU;2-3
Fonte:
ISI
Lingua:
ENG
Soggetto:
RAINFALL-RUNOFF MODELS; GLOBAL OPTIMIZATION; AUTOMATIC CALIBRATION; MULTICRITERIA METHODS; SENSITIVITY ANALYSIS; UNCERTAINTY; PREDICTION; VALIDATION; CATCHMENT;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Engineering, Computing & Technology
Citazioni:
33
Recensione:
Indirizzi per estratti:
Indirizzo: Thiemann, M Riverside Technol Inc, 2290 E Prospect Rd,Suite 1, Ft Collins,CO 80525 USA Riverside Technol Inc 2290 E Prospect Rd,Suite 1 Ft Collins CO USA 80525
Citazione:
M. Thiemann et al., "Bayesian recursive parameter estimation for hydrologic models", WATER RES R, 37(10), 2001, pp. 2521-2535

Abstract

The uncertainty in a given hydrologic prediction is the compound effect ofthe parameter, data, and structural uncertainties associated with the underlying model. In general, therefore, the confidence in a hydrologic prediction can be improved by reducing the uncertainty associated with the parameter estimates. However, the classical approach to doing this via model calibration typically requires that, considerable amounts of data be collected and assimilated before the model can be used. This limitation becomes immediately apparent when hydrologic predictions must be generated for a previously ungauged watershed that has only recently been instrumented. This paper presents the framework for a Bayesian recursive estimation approach to hydrologic prediction that can be used for simultaneous parameter estimation and prediction in an operational setting. The prediction is described in terms of the probabilities associated with different output values. The uncertainty associated with the parameter estimates is updated (reduced) recursively, resulting in smaller prediction uncertainties as measurement data are successively assimilated. The effectiveness and efficiency of the method areillustrated in the context of two models: a simple unit hydrograph model and the more complex Sacramento soil moisture accounting model, using data from the Leaf River basin in Mississippi.

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Documento generato il 29/11/20 alle ore 23:33:18