An integrated approach to conceptualise hydrological and socio-economic interaction for supporting management decisions of coupled groundwater–agricultural systems

The management of complex interacting hydrosystems is challenging if in addition to the physical processes also socio-economic and environmental aspects have to be considered. This causes conflicts of interests among various water actors with mostly contradicting objectives and uncertainties about the consequences of potential management interventions. The objective of this paper is to present a methodological framework to support decision making under uncertainties for the management of complex hydrosystems. The proposed framework conceptualises hydrological and socio-economic interactions by constructing a Bayesian network (BN)-based decision support tool for a typical management problem of agricultural coastal regions. Thereby, the paper demonstrates the value of combining two different commonly used integrated modelling approaches. Coupled domain models are applied to simulate the nonlinearities and feedbacks of a strongly interacting groundwater–agriculture hydrosystem. Afterwards, a BN is used to integrate their results together with empirical knowledge and expert opinions regarding potential management interventions. A prototype application is performed for a coastal arid region, which is affected by saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture. It addresses the issues of contradicting management objectives such as sustainable aquifer management vs. profitable agricultural production and the problem of finding appropriate management interventions or policies. Several policy combinations have been analysed regarding their efficiency within different management scenarios in a probabilistic way, which enables decision makers to assess the risks associated with implementing alternative management strategies. In addition, efficient metrics for evaluating performance and uncertainty of the developed task-specific BN are used which underline the reliability of the results.