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Groundwater dependent ecosystems (GDEs) include valuable ecosystems such as springs, wetlands, rivers, lakes and lagoons. The protection of these systems and services they provide is highlighted by international agreements, i.e. Ramsar convention on wetlands, and regional legislation, i.e. the European Water Framework Directive. Groundwater provides water, nutrients and a relatively stable temperature. However, the role of groundwater in surface ecosystems is not fully understood. The ecosystem can depend on groundwater directly or indirectly, and the reliance can be continuous, seasonal or occasional. This has implications for the vulnerability of ecosystems, as some may be easily affected by external pressure. Conceptual models and quantitative assessments of how groundwater interacts with the environment are needed. GDEs are also threatened by different land use activities and climate change. Hence, we need to understand how GDEs are affected by changes in

Groundwater in sufficient amounts and of suitable quality is essential for potable water supplies, crop irrigation and healthy habitats for plant and animal biocenoses. The groundwater resource is currently under severe pressure from land use and pollution and there is evidence of dramatic changes in aquifer resources in Europe and elsewhere, despite numerous policy measures on sustainable use and protection of groundwater. Little is known about how such changes affect groundwater dependent ecosystems (GDEs), which include various aquatic and terrestrial ecosystems above ground and inside the aquifer. Future management must take this uncertainty into account. This paper focuses on multiple aspects of groundwater science, policy and sustainable management. Examples of current management methods and practices are presented for selected aquifers in Europe and an assessment is made of the effectiveness of existing policies in practice and of how groundwaters and GDEs are managed in various conditions. The paper highlights a number of issues that should be considered in an integrated and holistic approach to future management of groundwater and its dependent ecosystems.

Elsevier Groenendijk, P.; Heinen, M.; Klammler, G.;Kupfersberger, H.; Pisinaras, V.; Gemitzi, A.... (2014). Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data. Science of the Total Environment. 499: 463-480. doi:10.1016/j.scitotenv.2014.07.002 . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 2 The agricultural sector faces the challenge of ensuring food security without an excessive burden on the 24 environment. Simulation models provide excellent instruments for researchers to gain more insight into relevant 25 processes and best agricultural practices and provide tools for planners for decision making support. The extent 26 to which models are capable of reliable extrapolation and prediction is important for exploring new farming 27 systems or assessing the impacts of future land and climate changes. 28A performance assessment was conducted by testing six detailed state-of-the-art models for simulation of nitrate 29 leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for 30 lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to 31 nitrate leaching. 32Three consecutive phases were distinguished to gain insight in the predictive power of the models: 1) a blind test 33 for 2005 -2008 in which only soil hydraulic characteristics, meteorological data and information about the 34 agricultural management were accessible; 2) a calibration for the same period in which essential information on 35 field observations was additionally available to the modellers; and 3) a validation for 2009 -2011 with the 36 corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root 37 mean squared error, index of agreement, model efficiency, root relative squared error, Pearson's linear 38 correlation coefficient) was applied for testing the results and comparing the models. 39None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high 40 input farming systems had difficulties in accurate predicting leaching in low input farming systems that are 41 strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate 42 calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to 43 identify years and crops with high and low leaching rates. 44 Keywords 45Lysimeter, model comparison, nitrate leaching, performance assessment, predictive power, simula...

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