Abstract.Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures -for example, by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow for a more detailed analysis of the dynamic behaviour of the soil-plant interface.We coupled two of such high-process-oriented independent models and calibrated both models simultaneously. The catchment modelling framework (CMF) simulated soil hydrology based on the Richards equation and the van Genuchten-Mualem model of the soil hydraulic properties. CMF was coupled with the plant growth modelling framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation.The Monte Carlo-based generalized likelihood uncertainty estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 10 6 model runs randomly drawn from a uniform distribution.The model was applied to three sites with different management in Müncheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matter of roots, storages, stems and leaves. The shape parameter of the retention curve n was highly constrained, whereas other parameters of the retention curve showed a large equifinality. We attribute this slightly poorer model performance to missing leaf senescence, which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need for including agricultural management options in the coupled model.

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Abstract. The agricultural water footprint addresses the quantification of water consumption in agriculture, whereby three types of water to grow crops are considered, namely green water (consumed rainfall), blue water (irrigation from surface or groundwater) and grey water (water needed to dilute pollutants). By considering site-specific properties when calculating the crop water footprint, this methodology can be used to support decision making in the agricultural sector on local to regional scale. We therefore developed the spatial decision support system SPARE:WATER that allows us to quantify green, blue and grey water footprints on regional scale. SPARE:WATER is programmed in VB.NET, with geographic information system functionality implemented by the MapWinGIS library. Water requirements and water footprints are assessed on a grid basis and can then be aggregated for spatial entities such as political boundaries, catchments or irrigation districts. We assume inefficient irrigation methods rather than optimal conditions to account for irrigation methods with efficiencies other than 100 %. Furthermore, grey water is defined as the water needed to leach out salt from the rooting zone in order to maintain soil quality, an important management task in irrigation agriculture. Apart from a thorough representation of the modelling concept, we provide a proof of concept where we assess the agricultural water footprint of Saudi Arabia. The entire water footprint is 17.0 km 3 yr −1 for 2008, with a blue water dominance of 86 %. Using SPARE:WATER we are able to delineate regional hot spots as well as crop types with large water footprints, e.g. sesame or dates. Results differ from previous studies of national-scale resolution, underlining the need for regional estimation of crop water footprints.

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