Show simple item record

dc.contributor.authorFentie, Bantien_US
dc.contributor.authorYu, Bofuen_US
dc.contributor.authorSilburn, M.en_US
dc.contributor.authorCiesiolka, C.en_US
dc.date.accessioned2017-04-24T09:09:19Z
dc.date.available2017-04-24T09:09:19Z
dc.date.issued2002en_US
dc.date.modified2010-07-06T06:59:36Z
dc.identifier.issn0022-1694en_US
dc.identifier.doi10.1016/S0022-1694(02)00017-3en_AU
dc.identifier.urihttp://hdl.handle.net/10072/6719
dc.description.abstractUnlike the USLE/RUSLE models, which require only rainfall intensity data to quantify climatic effects on soil erosion, physically based erosion models require data on runoff rates as their input. However, runoff rate data are rarely measured in the field. This study evaluates eight models in terms of their performance in predicting peak (Qp) and effective (Qe) runoff rates required by erosion models. The eight models are: (1) a multiple regression model (MR), (2) a power function model (PF), (3) a scaling technique (ST), (4) a constant infiltration model (CI), (5) a constant runoff coefficient model (RC), (6) a spatially variable infiltration model (VI), (7) the CREAMS peak runoff rate equation (Qp_CREAMS), and (8) an empirical peak runoff rate equation (QP_SAL). Rainfall and runoff data from experimental plots in a grazing catchment in central Queensland (Australia) were used. A commonly used model efficiency statistic (E) was used to compare the performance of these models. Models resulting in high E values are said to perform better than models resulting in low values of E. Hence, with E values of 0.85 and 0.81 in predicting Qp and Qe, respectively, the PF model ranked first. On the other hand, with an E value of -12.7, the Qp_CREAMS performed the worst in predicting peak runoff rates. On the basis of input data requirements and number of free parameters involved in each model, however, the VI model, with E values of 0.82 and 0.79 for Qp and Qe, respectively, is found to be the best choice when breakpoint rainfall is available for an event. If only peak rainfall intensity is available, the ST with E values of 0.80 and 0.63 for Qp and Qe, respectively, would be the best model to use to predict these two runoff rate characteristics for the site.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherElsevieren_US
dc.publisher.placeAmsterdamen_US
dc.relation.ispartofpagefrom102en_US
dc.relation.ispartofpageto114en_US
dc.relation.ispartofjournalJournal of Hydrologyen_US
dc.relation.ispartofvolume261en_US
dc.subject.fieldofresearchcode260599en_US
dc.titleEvaluation of eight different methods to predict hillslope runoff rates fro a grazing catchment in Australiaen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2002
gro.hasfulltextNo Full Text


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record