Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors

Water Resources Research

Volumn 46, Issue 5, 2010, Pages

  Renard, Benjamin ^a   Kavetski, Dmitri ^b   Kuczera, George ^b   Thyer, Mark ^b   Franks, Stewart W ^b  

^a CENTRE DE LYON VILLEURBANNE   (France)

^b UNIVERSITY OF NEWCASTLE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ADDITIVE ERRORS; BAYESIAN INFERENCE; DATA QUALITY; DATA UNCERTAINTY; ENGINEERING CHALLENGES; HYDROLOGIC MODELING; HYDROLOGIC MODELS; IDENTIFIABILITY; ILL POSED; ILL-POSEDNESS; INHERENT LIMITATIONS; INPUT AND OUTPUTS; MONTE CARLO SAMPLING ALGORITHMS; PREDICTIVE UNCERTAINTY; PRIOR INFORMATION; PRIOR KNOWLEDGE; PROBABILISTIC MODELS; RAINFALL RUNOFF; RAINFALL-RUNOFF MODELING; STRUCTURAL COMPONENT; STRUCTURAL ERRORS; STRUCTURAL UNCERTAINTY;

BAYESIAN NETWORKS; ERROR ANALYSIS; INFERENCE ENGINES; MATHEMATICAL MODELS; RAIN; RUNOFF;

UNCERTAINTY ANALYSIS;

ALGORITHM; BAYESIAN ANALYSIS; DATA SET; ENGINEERING; ERROR ANALYSIS; HYDROLOGICAL MODELING; IDENTIFICATION METHOD; INPUT-OUTPUT ANALYSIS; MONTE CARLO ANALYSIS; RAINFALL-RUNOFF MODELING; UNCERTAINTY ANALYSIS;

EID: 77950177246     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2009WR008328     Document Type: Article

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