Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes

Bulygina, Nataliya, McIntyre, Neil and Wheater, Howard (2011) Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes. Water Resources Research, 47 2: . doi:10.1029/2010WR009240


Author Bulygina, Nataliya
McIntyre, Neil
Wheater, Howard
Title Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes
Journal name Water Resources Research   Check publisher's open access policy
ISSN 0043-1397
1944-7973
Publication date 2011-02
Year available 2011
Sub-type Article (original research)
DOI 10.1029/2010WR009240
Open Access Status
Volume 47
Issue 2
Total pages 13
Place of publication Hoboken, NJ United States
Publisher Wiley-Blackwell Publishing, Inc.
Collection year 2011
Language eng
Formatted abstract
[1] A novel method is presented for conditioning rainfall-runoff models for ungauged catchment and land use impact applications. The method conditions the model on information from multiple regionalized response indices using a formal Bayesian approach. Two indices that hold information about soil type and land use effects are the base flow index from the Hydrology of Soil Type (HOST) classification and curve number from the U.S. Department of Agriculture's Soil Conservation Service soil and land use classification. These indices are used to constrain a five-parameter probability distributed moisture model for subcatchments of the Wye (grazed grassland) and Severn (mainly afforested) catchments in the United Kingdom. The base flow index and curve number constrain only two of the five model parameters, indicating that ideally, other sources of information would be sought. Nevertheless, the procedure significantly reduces the prior uncertainty in runoff prediction and gives predictions close to those of the calibrated models. For the case study, the introduction of the curve number in addition to the base flow index has only a small effect on model performance and uncertainty; however, it allows a distinction between the effects of soil type and land management for the purpose of scenario analysis. The principal assumptions used in the method are the applicability of the curve number classification system and its mapping to UK soil types and the likelihood function used for Bayesian conditioning. 
Keyword Management
Impact
Wales
Regionalization
Identification
Calibration
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Centre for Water in the Minerals Industry
 
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