Achieving the UN Sustainable Development Goals depends on using resources efficiently, avoiding fragmentation in decision-making, recognising the trade-offs and synergies across sectors and adopting an integrated Nexus thinking among policymakers. Nexus Informatics develops the science of recognising and quantifying nexus interlinkages. Nexus-coherent solutions enhance the effect of policymaking in achieving adequate governance, leading to successful strategic vision and efficient resource management. In this article, we present the structure of a System Dynamics Model-the Nexus_SDM-that maps sector-specific data from major databases (e.g., EUROSTAT) and scenario models (e.g., E3ME-FTT OSeMOSYS and SWIM) for the national case study of Greece. Disaggregation algorithms are employed on annual national-scale data, turning them into detailed spatial and temporal datasets, by converting them to monthly values spread among all 14 River Basin Districts (RBDs). The Nexus_SDM calculates Nexus Interlinkage Factors and quantifies interlinkages among Water, Energy, Food, Built Environment, Natural Land and greenhouse gas (GHG) emissions. It simulates the nexus in the national case study of Greece as a holistic multi-sectoral system and provides insights into the vulnerability of resources to future socio-economic scenarios. It calculates the link between crop type/area, irrigation water and agricultural value, revealing which crops have the highest agricultural value with the least water and crop area. It demonstrates that fossil fuel power generation and use of oil for transportation are responsible for the most GHG emissions in most RBDs and presents projections for years 2030 and 2050. The analysis showcases that to move from a general nexus thinking to an operational nexus concept, it is important to focus on data availability and scale. Advanced Sankey and Chord diagrams are introduced to show distribution of resource use among RBDs and an innovative visualisation tool is developed, the Nexus Directional Chord plot, which reveals Nexus hotspots and strong interlinkages among sectors, facilitating stakeholder awareness.

We present an analysis of historical water demand data from the utility of Skiathos, Greece and demonstrate suitable demand forecasting methodologies. We apply linear and nonlinear forecasting methods to a three-year time series water demand. The best fit for quarterly averaged data was observed for the Winters’ additive method; for monthly-averaged data, ARIMA, Artificial Neural Network and a hybrid approach performed best. Given the intense seasonality of demand in Skiathos, monthly time series proved to be the best data set for forecasting, while the best forecasting method was the hybrid, which combines the advantages of ARIMA and Artificial Neural Networks

The concept of the Water–Energy–Food nexus (WEF), as documented by the United Nations Food and Agriculture Organization (FAO), suggests that the three resources are thoroughly interrelated, shaping a complicated web of interlinkages. Perceiving the three commodities as an interdependent variable system, rather than isolated subsystems is a step towards a more holistic approach, and thus a prerequisite to introducing a sustainable scheme for better managing resources. In this work, the well-documented WEF nexus is broadened to a five-dimensional nexus, also involving land use and climate. A methodology for drawing the interrelations among the five dimensions and unreeling the complicated system of direct and indirect interlinkages is given. The intensity of interlinkages among nexus components is initially assessed through a three-point typology with interlinkage scoring corresponding to resource use in Greece. The typology is used and is further expanded to quantify successfully all interlinkages among nexus components with a proposed heuristic algorithm. Results are used to create the cross-interlinkage matrix that identifies food as the most influencing resource and water as the resource mostly influenced by other nexus elements. Results show that indirect interlinkages of multiple resources can be very significant and should not be ignored when planning nexus-coherent policy initiatives and investments in different sectors, in order to promote resource efficiency.

Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail under different and often complicated stressor regimes, predicting their abundance is challenging. A dataset from 822 Northern European lakes is used to determine which variables better explain the variation of cyanobacteria biomass (CBB) by means of stepwise multiple linear regression. Chlorophyll-a (Chl-a) and total nitrogen (TN) provided the best modelling structure for the entire dataset, while for subsets of shallow and deep lakes, Chl-a, mean depth, TN and TN/TP explained part of the variance in CBB. Path analysis was performed and corroborated these findings. Finally, CBB was translated to a categorical variable according to risk levels for human health associated with the use of lakes for recreational activities. Several machine learning methods, namely Decision Tree, K-Nearest Neighbors, Support-vector Machine and Random Forest, were applied showing a remarkable ability to predict the risk, while Random Forest parameters were tuned and optimized, achieving a 95.81% accuracy, exceeding the performance of all other machine learning methods tested. A confusion matrix analysis is performed for all machine learning methods, identifying the potential of each method to correctly predict CBB risk levels and assessing the extent of false alarms; random forest clearly outperforms the other methods with very promising results.

The goal of the present work is to introduce a framework to assess climate change impacts on water resources in rural basins. The proposed framework was applied and tested in the Platanovrisi river basin, located in Northern Greece. A hydrological model for the basin was developed and implemented using the GR2M, and calibrated-validated using observed rainfall, temperature and streamflow data. Climate change projections from three climate models and two Shared Socioeconomic Pathways (SSP) scenarios were used as drivers to the calibrated-validated hydrological model to assess the impact of climate change on the hydrological regime of the basin. Climate change impacts were assessed in terms of precipitation, temperature, evapotranspiration, and meteorological drought. Results indicated that, for the period 2015–2050, the annual precipitation and discharge will decrease by 13–23% and 32–47%, respectively, while temperature will increase on an average of about 13% (about 1 °C) compared to the reference period (historical period spanning from 1974 to 2014). The results revealed significant changes in the annual and seasonal water flow, with a net reduction in the river flow during winter and spring and a slight increase during autumn and summer. Therefore, difficulties may arise for ensuring hydropower production and storage, agricultural needs and ecological flows. The results revealed significant sensitivity and variability of rainfall, evapotranspiration and river flows based on the climate model and the climate scenario examined. Overall, the proposed framework constitutes a robust approach for the assessment of climate change impacts on water resources in different hydrological regimes, and can be easily modified and applied to diverse watersheds worldwide. In addition, the proposed methodology can help and guide practitioners and decision makers towards adaptation and mitigation efforts for sustainable water management under climate change conditions.