Title of article :
A simulation-based interval two-stage stochastic model
for agricultural nonpoint source pollution control
through land retirement
Author/Authors :
Y. B. Luo ، نويسنده , , *، نويسنده , , J.B. Li، نويسنده , , G.H. Huang، نويسنده , , H.L. Li، نويسنده ,
Issue Information :
هفته نامه با شماره پیاپی سال 2006
Abstract :
This study presents a simulation-based interval two-stage stochastic programming (SITSP) model for agricultural nonpoint
source (NPS) pollution control through land retirement under uncertain conditions. The modeling framework was established by
the development of an interval two-stage stochastic program, with its random parameters being provided by the statistical analysis
of the simulation outcomes of a distributed water quality approach. The developed model can deal with the tradeoff between
agricultural revenue and boff-siteQ water quality concern under random effluent discharge for a land retirement scheme through
minimizing the expected value of long-term total economic and environmental cost. In addition, the uncertainties presented as
interval numbers in the agriculture-water system can be effectively quantified with the interval programming. By subdividing the
whole agricultural watershed into different zones, the most pollution-related sensitive cropland can be identified and an optimal
land retirement scheme can be obtained through the modeling approach. The developed method was applied to the Swift Current
Creek watershed in Canada for soil erosion control through land retirement. The Hydrological Simulation Program-FORTRAN
(HSPF) was used to simulate the sediment information for this case study. Obtained results indicate that the total economic and
environmental cost of the entire agriculture-water system can be limited within an interval value for the optimal land retirement
schemes. Meanwhile, a best and worst land retirement scheme was obtained for the study watershed under various uncertainties.
Keywords :
Land retirement , Nonpoint source pollution , Two-stage stochastic , Uncertainty , simulation , optimization
Journal title :
Science of the Total Environment
Journal title :
Science of the Total Environment