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dc.contributor.authorCarlin, G.en_US
dc.contributor.authorCook, F.en_US
dc.contributor.authorCropp, Rogeren_US
dc.contributor.authorHarcher, M.en_US
dc.contributor.authorSmajgl, A.en_US
dc.contributor.authorHechbert, S.en_US
dc.contributor.authorHodgen, M.en_US
dc.contributor.editorLes Oxley and Don Kulasirien_US
dc.date.accessioned2017-05-03T11:37:37Z
dc.date.available2017-05-03T11:37:37Z
dc.date.issued2007en_US
dc.date.modified2008-04-28T01:06:23Z
dc.identifier.refurihttp://mssanz.org.au/MODSIM07/MODSIM07.htmen_AU
dc.identifier.urihttp://hdl.handle.net/10072/17970
dc.description.abstractAgent based modelling (ABM) environments are becoming increasingly popular for investigating the effects of land use change. The ABM environment enables models to be developed that simulate biophysical, economic and social processes at different spatial and temporal scales. The smallest spatial area modelled here is the paddock at a daily temporal resolution, therefore enabling daily interaction of the biophysical and social processes at the paddock scale. Shown here are the steps towards the development of the large scale catchment model that captures the finer spatial and temporal paddock scale processes. These steps involve deciding on the catchment area that captures the land use change to be investigated. Dividing the catchment into subcatchments and defining an appropriate number of contour segments depending on the area of the paddocks to be modelled. Paddock runoff and groundwater flow is modelled using a water balance model. Within each contour segment, runoff is summed and groundwater flow is calculated in a representative radial crosssection using a groundwater model. Contours provide an improvement to catchment modelling as the number of contours generated could also depend on the availability of model parameter data therefore simplifying the reuse of the model within other catchments. The Recursive Porous Agent Simulation Toolkit (Repast), which is an ABM environment, is used to build the Single Entity Policy Impact Assessment (SEPIA) model. SEPIA provides a modelling platform that combines the social agents (land managers) with the biophysical agents (surface and groundwater hydrology models) and spatial agents (subcatchments, contours, paddocks, etc). The results presented here use the SEPIA model version that was established for the Bowen Broken catchment, Queensland, Australia. This version of SEPIA includes land managers for beef cattle (grazing) production. SEPIA models the social world of the Bowen Broken catchment by creating land manager beef cattle production agents and simulates their behaviour resulting in the enactment of one of a number of possible land-use strategies. The land managers make land-use decisions which in turn have effects on biophysical conditions, the level of financial payoffs associated with agricultural production and a desire to maintain or improve the state of the biophysical environment. The land manager's decision to enact a land-use strategy is also influenced by exposure to changes in the biophysical world like sediment, cover, climate and yield variations at the finer daily temporal and paddock spatial scale. This then affects the manager's sense of environmental wellbeing for the property. For the purpose of this paper we use the biophysical world within SEPIA to estimate paddock scale sediment results to demonstrate the importance of finer scale modelling. If SEPIA used an annual sediment model then the outcomes may be quite different where the relationship between sediment and precipitation may be assumed more linear. Here we are able to model daily pasture growth and expose slower winter and faster summer growth patterns. The effect of slower winter growth reduces total paddock biomass and may effect cover factor depending on a number of other modelled variables (stocking rate, etc). Higher winter rainfall combined with lower cover factors or extended dry periods preceding a wet year typically drive the increase in sediment export during those years. Although SEPIA does produce daily and annual sediment figures it is important to consider the uncertainty related to the model inputs and consequent outputs. This uncertainty is primarily driven by the lack of understanding of the biophysical processes at play and the deficiency in measured field data at required scale and frequency.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent607243 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherModelling and Simulation Society of Australia and New Zealanden_US
dc.publisher.placeThe Australian National University, Canberra, Australiaen_US
dc.publisher.urihttp://www.mssanz.org.au/en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameMODSIM07 International Congress on Modelling and Simulationen_US
dc.relation.ispartofconferencetitleMODSIM 2007 International Congress on Modelling and Simulation.en_US
dc.relation.ispartofdatefrom2007-12-10en_US
dc.relation.ispartofdateto2007-12-13en_US
dc.relation.ispartoflocationChristchurch, New Zealanden_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode300803en_US
dc.titleUsing a catchment contour approach for simulation of ground and surface water behaviour within agent based modelling platforms.en_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.rights.copyrightCopyright 2007 Modellling & Simulation Society of Australia & New Zealand. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference link for access to the definitive, published version.en_AU
gro.date.issued2007
gro.hasfulltextFull Text


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