Water, energy, land, and food are vital elements with multiple interactions. In this context, the concept of a water–energy–food (WEF) nexus was manifested as a natural resource management approach, aiming at promoting sustainable development at the international, national, or local level and eliminating the negative effects that result from the use of each of the four resources against the other three. At the same time, the transition to green energy through the application of renewable energy technologies is changing and perplexing the relationships between the constituent elements of the nexus, introducing new conflicts, particularly related to land use for energy production vs. food. Specifically, one of the most widespread “green” technologies is photovoltaic (PV) solar energy, now being the third foremost renewable energy source in terms of global installed capacity. However, the growing development of PV systems results in ever expanding occupation of agricultural lands, which are most advantageous for siting PV parks. Using as study area the Thessaly Plain, the largest agricultural area in Greece, we investigate the relationship between photovoltaic power plant development and food production in an attempt to reveal both their conflicts and their synergies.
We propose a novel probabilistic approach to flood hazard assessment, aiming to address the major shortcomings of everyday deterministic engineering practices in a computationally efficient manner. In this context, the principal sources of uncertainty are defined across the overall modeling procedure, namely, the statistical uncertainty of inferring annual rainfall maxima through distribution models that are fitted to empirical data, and the inherently stochastic nature of the underlying hydrometeorological and hydrodynamic processes. Our work focuses on three key facets, i.e., the temporal profile of storm events, the dependence of flood generation mechanisms on antecedent soil moisture conditions, and the dependence of runoff propagation over the terrain and the stream network on the intensity of the flood event. These are addressed through the implementation of a series of cascade modules, based on publicly available and open-source software. Moreover, the hydrodynamic processes are simulated by a hybrid 1D/2D modeling approach, which offers a good compromise between computational efficiency and accuracy. The proposed framework enables the estimation of the uncertainty of all flood-related quantities, by means of empirically derived quantiles for given return periods. Lastly, a set of easily applicable flood hazard metrics are introduced for the quantification of flood hazard.
Abstract. Motivated by the challenges induced by the so-called Target Model and the
associated changes to the current structure of the energy market, we revisit
the problem of day-ahead prediction of power production from Small
Hydropower Plants (SHPPs) without storage capacity. Using as an example a
typical run-of-river SHPP in Western Greece, we test alternative forecasting
schemes (from regression-based to machine learning) that take advantage of
different levels of information. In this respect, we investigate whether it
is preferable to use as predictor the known energy production of previous
days, or to predict the day-ahead inflows and next estimate the resulting
energy production via simulation. Our analyses indicate that the second
approach becomes clearly more advantageous when the expert's knowledge about
the hydrological regime and the technical characteristics of the SHPP is
incorporated within the model training procedure. Beyond these, we also
focus on the predictive uncertainty that characterize such forecasts, with
overarching objective to move beyond the standard, yet risky, point
forecasting methods, providing a single expected value of power production.
Finally, we discuss the use of the proposed forecasting procedure under
uncertainty in the real-world electricity market.
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