Franz Lennartz

Optimal planning and operation of irrigation systems under water resource constraints in Oman considering climatic uncertainty

In this contribution, we introduce a stochastic framework for decision support for optimal planning and operation of water supply in irrigation. This consists of (1) a weather generator for simulating regional impacts of climate change on the basis of IPCC scenarios, (2) a tailor-made evolutionary optimization algorithm for optimal irrigation scheduling with limited water supply, (3) a mechanistic model for simulating water transport and crop growth in a sound manner, and (4) a kernel density estimator for estimating stochastic productivity, profit, and demand functions by a nonparametric method. As a result of several simulation/optimization runs within the framework, we present stochastic crop-water production functions (SCWPF) for different crops which can be used as a basic tool for assessing the impact of climate variability on the risk for the potential yield for specific crops and specific agricultural areas. A case study for an agricultural area in the Al Batinah region of the Sultanate of Oman is used to illustrate these methodologies. In addition, microeconomic impacts of climate change and the vulnerability of the agro-ecological system are discussed.

Sustainable management of a coupled groundwater-agriculture hydrosystem using multi-criteria simulation based optimisation.

In this paper we present a new simulation-based integrated water management tool for sustainable water resources management in arid coastal environments. This tool delivers optimised groundwater withdrawal scenarios considering saltwater intrusion as a result of agricultural and municipal water abstraction. It also yields a substantially improved water use efficiency of irrigated agriculture. To allow for a robust and fast operation we unified process modelling with artificial intelligence tools and evolutionary optimisation techniques. The aquifer behaviour is represented using an artificial neural network (ANN) which emulates a numerical density-dependent groundwater flow model. The impact of agriculture is represented by stochastic crop water production functions (SCWPF). Simulation-based optimisation techniques together with the SCWPF and ANN deliver optimal groundwater abstraction and cropping patterns. To address contradicting objectives, e.g. profit-oriented agriculture vs. sustainable abstraction scenarios, we performed multi-objective optimisations using a multi-criteria optimisation algorithm.