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Hydraulic lift (HL) is the passive movement of water through the roots from deep wet to dry shallow soil layers when stomata are closed. HL has been shown in different ecosystems and species, and it depends on plant physiology and soil properties. In this study we explored HL patterns in several arid land shrubs, and developed a simple model to simulate the temporal evolution and magnitude of HL during a soil drying cycle under relatively stable climatic conditions. This model was then used to evaluate the influence of soil texture on the quantity of water lifted by shrubs in different soil types. We conducted transpiration suppression experiments during spring 2005 in Chile and spring 2008 in Spain on five shrub species that performed HL, Flourensia thurifera, Senna cumingii and Pleocarphus revolutus (Chile), Retama sphaerocarpa and Artemisia barrelieri (Spain). Shrubs were covered with a black, opaque plastic fabric for a period of 48-72 h, and soil water potential was recorded at different depths under the shrubs. While the shrubs remained covered, water potential continuously increased in shallow soil layers until the cover was removed. The model output indicated that the amount of water lifted by shrubs is heavily dependent on soil texture, as shrubs growing in loamy soils redistributed up to 3.6 times more water than shrubs growing on sandy soils. This could be an important consideration for species growing in soils with different textures, as their ability to perform HL would be context dependent.

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This tutorial is based on modification of the professor nomination lecture presented two years ago in front of the Scientific Council of the Czech Technical University in Prague [16].It is devoted to the techniques for the models developing suitable for processes forecasting in complex systems. Because of the high sensitivity of the processes to the initial conditions and, consequently, due to our limited possibilities to forecast the processes for the long-term horizon, the attention is focused on the techniques leading to practical applications of the short term prediction models. The aim of this tutorial paper is to bring attention to possible difficulties which designers of the predicting models and their users meet and which have to be solved during the prediction model developing, validation, testing, and applications. The presented overview is not complete, it only reflects the author's experience with developing of the prediction models for practical tasks solving in banking, meteorology, air pollution and energy sector. The paper is completed by an example of the global solar radiation prediction which forms an important input for the electrical energy production forecast from renewable sources. The global solar radiation forecasting is based on numerical weather prediction models. The time-lagged ensemble technique for uncertainty quantification is demonstrated on a simple example.