STOCHASTIC OPTIMIZATION OF IRRIGATION SYSTEMS UNDER WATER RESOURCE CONSTRAINTS FROM PLOT TO REGIONAL SCALE USING DECOMPOSITION

Summary. Many regions in the world which are used for agriculture are highly water stressed and water resources often overexploited. In this contribution a new stochastic simulation optimization framework for decision support for optimal planning and opera-tion of water supply in irrigation is introduced. This consists of (i) a weather generator for simulating climate variability and regional impacts of climate change on the basis of IPCC scenarios; (ii) a tailor-made evolutionary optimization algorithm for optimal irri-gation management with limited water supply; (iii) mechanistic models for simulating water transport and crop growth in a sound manner; and (iv) a kernel density estimator for estimating stochastic productivity, profit and demand functions by a nonparametric method. A decomposition solution strategy is developed which allows the use of numer-ical process models together with Monte Carlo simulations for a reliable estimation of higher quantiles of the minimum agricultural water demand of deficit irrigation strategies at plot and regional scale. As a result of several simulation/optimization runs within the framework stochastic crop-water production functions (SCWPF) for different crops are presented which can be used as a basic tool for assessing the impact of climate variability on the risk for the potential yield. In addition, microeconomic impacts of climate change and the vulnerability of the agro-ecological systems are discussed. Finally, an application to a real case problem in south Al-Batinah region in the Sultanate of Oman is presented where a coastal aquifer is affected by saltwater intrusion due to excessive groundwater withdrawal for irrigated agriculture.