Water-Energy-Food-Ecosystems pathways towards reducing water scarcity in Europe

The LISFLOOD-EPIC European setup has now successfully run for the reference 1990-2018 scenario, for single and combined water saving measure scenarios. In this study, five water (policy) saving measures are considered, i.e., efficiency scenarios in irrigation, leakage in the urban water supply, re-using treated wastewater, reducing energy water withdrawals and desalination. This creates a big challenge when it comes to comparisons between the what-if and the reference scenarios because of the complexity of the issue and the lack of a single performance index that can show how much better or worse a scenario is, compared to another. The Water Exploitation Index Plus (WEI+) indicator is used to estimate the impact of water saving measures on water scarcity. It is calculated for the consumption of water and is defined as the ratio of the total water net consumption divided by the available freshwater resources in a region including upstream inflowing water. The differences in WEI+ between a reference scenario and the proposed measure or measures, is a clear indication of the improvement this measure is capable of achieving as a percentage of the total water availability. Therefore, in this report, the results focus on the differences in WEI+ values, rather than on absolute values. The results showed that the scenarios with combined measures are the most effective, and future work will focus to estimate if this improvement will be sufficient to tackle water scarcity issues if climate change is also taken into account.JRC.D.2 - Ocean and Wate

Outline of the dynamic baseline for the MSFD Impact Assessment analysis in the context of the Blue2 Modelling Framework initiative

As part of the support JRC is providing DG ENV on the coming review (impact assessment and potential revision) of the Marine Strategy Framework Directive (MSFD), it is necessary to develop a future scenario in which the Directive is not changed but that consider all other elements (policy and socio-economic developments) that are expected to happen in the next decades. This scenario is named the ‘dynamic baseline’ and it should provide (once simulated with the appropriate modelling tools) the likely environmental conditions of EU marine regions in 2050 in the case of ‘no-revision’ of the MSFD. The environmental conditions under this scenario should be compared against those derived from the different ‘revision scenarios’ in which different elements of the MSFD are changed. The difference between both scenarios should help quantify the impacts of the revision options. As a first step towards the realization of the dynamic baseline, it is necessary to identify the main elements (e.g., drivers, trends and policies) that should be considered for the different descriptors of the Good Environmental Status included in the MSFD. This report contains, for each individual descriptor, the drivers (e.g., socio-economic developments, climate changes and international cooperation) and legislations (both at EU and international level) that could influence the condition of those descriptors in the next few decades.JRC.D.2 - Ocean and Wate

Space-time groundwater level distribution estimation in a complex system of aquifers 

&amp;lt;p&amp;gt;A geostatistical analysis based on a machine learning method was conducted to generate reliable spatial maps of groundwater level variability and to identify groundwater level patterns over the island of Crete, Greece. Geostatistics plays an important role in model-related data analysis and preparation, but has specific limitations when the aquifer system is inhomogeneous. Self-Organizing Maps can be applied to identify locally similar input data and then by means of Ordinary Kriging to estimate the spatial distribution of groundwater level. The proposed methodology was tested on a large dataset of groundwater level data in a complex hydrogeological district, and the results were significantly improved if compared to the use of classical geostatistical approaches.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This work was developed under the scope of the InTheMED project. InTheMED is part of the PRIMA programme supported by the European Union&amp;amp;#8217;s Horizon 2020 research and innovation programme under grant agreement No 1923.&amp;lt;/p&amp;gt;</jats:p

Artificial Neural Networks and Multiple Linear Regression for Filling in Missing Daily Rainfall Data

As demand for more hydrological data has been increasing, there is a need for the development of more accurate and descriptive models. A pending issue regarding the input data of said models is the missing data from observation stations in the field. In this paper, a methodology utilizing ensembles of artificial neural networks is developed with the goal of estimating missing precipitation data in the extended region of Chania, Greece on a daily timestep. In the investigated stations, there have been multiple missing data events, as well as missing data prior to their installation. The methodology presented aims to generate precipitation time series based on observed data from neighboring stations and its results have been compared with a Multiple Linear Regression model as the basis for improvements to standard practice. For each combination of stations missing daily data, an ensemble has been developed. According to the statistical indexes that were calculated, ANN ensembles resulted in increased accuracy compared to the Multiple Linear Regression model. Despite this, the training time of the ensembles was quite long compared to that of the Multiple Linear Regression model, which suggests that increased accuracy comes at the cost of calculation time and processing power. In conclusion, when dealing with missing data in precipitation time series, ANNs yield more accurate results compared to MLR methods but require more time for producing them. The urgency of the required data in essence dictates which method should be used

Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation

A relatively new method of addressing different hydrological problems is the use of artificial neural networks (ANN). In groundwater management ANNs are usually used to predict the hydraulic head at a well location. ANNs can prove to be very useful because, unlike numerical groundwater models, they are very easy to implement in karstic regions without the need of explicit knowledge of the exact flow conduit geometry and they avoid the creation of extremely complex models in the rare cases when all the necessary information is available. With hydrological parameters like rainfall and temperature, as well as with hydrogeological parameters like pumping rates from nearby wells as input, the ANN applies a black box approach and yields the simulated hydraulic head. During the calibration process the network is trained using a set of available field data and its performance is evaluated with a different set. Available measured data from Edward’s aquifer in Texas, USA are used in this work to train and evaluate the proposed ANN. The Edwards Aquifer is a unique groundwater system and one of the most prolific artesian aquifers in the world. The present work focuses on simulation of hydraulic head change at an observation well in the area. The adopted ANN is a classic fully connected multilayer perceptron, with two hidden layers. All input parameters are directly or indirectly connected to the aquatic equilibrium and the ANN is treated as a sophisticated analogue to empirical models of the past. A correlation analysis of the measured data is used to determine the time lag between the current day and the day used for input of the measured rainfall levels. After the calibration process the testing data were used in order to check the ability of the ANN to interpolate or extrapolate in other regions, not used in the training procedure. The results show that there is a need for exact knowledge of pumping from each well in karstic aquifers as it is difficult to simulate the sudden drops and rises, which in this case can be more than 6 ft (approx. 2 m). That aside, the ANN is still a useful way to simulate karstic aquifers that are difficult to be simulated by numerical groundwater models

A narrative-driven role playing game for raising flood awareness

In the framework of a water resources management class in the Technical University of Crete, a narrative-driven role-playing game (RPG) was planned and tested in the classroom, with the in-tent to raise awareness among the students, on how floods can have an impact on the everyday lives of different citizens. During this game, the students had the opportunity to act as different stakeholders, and in order to assess the impact this game had on their thoughts of who might be affected by a flood event, two questionnaires were used, one before and one after the game. The results show that there was very positive feedback from the participants on how this RPG helped them realize the different implications a flood event might have on citizens, and decision mak-ers. The community-based aspect that was chosen for this RPG implementation, helped show the difficulties the specific roles would face as single individuals and as a community in general. Using a similar approach can help any stakeholder understand the challenges in a more direct way than with traditional lecturing and presentations.JRC.D.2 - Water and Marine Resource

A Narrative-Driven Role-Playing Game for Raising Flood Awareness

In the framework of a water resources management class in the Technical University of Crete, a narrative-driven role-playing game (RPG) was planned and tested in the classroom, with the intent to raise awareness among the students on how floods can have an impact on the everyday lives of different citizens. During this game, the students had the opportunity to act as different stakeholders. In order to assess the impact of this game on participants’ thoughts of who might be affected by a flood event, two questionnaires were used, one before and one after the game. The results show that there was very positive feedback from the participants on how this RPG helped them realize the different implications a flood event might have on citizens and decision makers. The community-based aspect that was chosen for this RPG implementation showed the difficulties the specific roles would face as single individuals and as a community in general. Using a similar approach can help any stakeholder understand the challenges in a more direct way than with traditional lecturing and presentations.</jats:p