ABSTRACT
Water evaporates from reservoirs of hydropower plants (HPPs), often in significant volumes. Reservoir evaporation is a dynamic phenomenon depending on climate, varying size of open water surfaces (OWS), and electricity production. Due to a lack of data and methods to estimate the OWS's size variation, previous studies assessed HPPs water footprints (WFs) considering static OWSs acknowledging the uncertainty of this omission. This study estimates WFs of HPPs, considering dynamic OWSs for four plant types in Ecuador, Flooded lakes, and Flooded rivers, with dam heights lower or higher than their Gross Static Head (GSH). It quantifies OWSs size variation using a Digital Elevation Model and GSH data, assessing OWS evaporation, effects on electricity production and WFs. There are large differences among the evaporation of HPPs when OWS size variations are considered. HPP operation, geographical features, and climate determine temporal differences. Flooded lake HPPs have relatively large WFs. Flooded River HPPs, with dam heights below their GSH, have the smallest WFs, but water storage capacity is limited. Static area approaches underestimated annual WFs by 10% (Flooded Lake HPPs) to 80% (Flooded River HPPs). Earlier studies showed effects of HPPs on water from a water management perspective, suggesting that less water-intensive HPP technologies are favorable, or that other water-efficient electricity-generating technologies, like solar or wind, should replace HPPs. This study also included the electricity perspective, indicating that energy management and water storage are important factors for WFs. The most water-effective technology cannot fulfill current electricity production due to a lack of storage options. The system dynamics analysis indicates that aiming for small WFs is not always the best option from an energy and water perspective.
ABSTRACT
Freshwater has spatial and temporal constraints, affecting possibilities to generate electricity. Previous studies approached this from a water perspective quantifying water consumption of electricity to optimize water use, or from an electricity perspective using modeling methods to optimize electricity output. However, power plants consume different water volumes per unit of electricity, depending on the applied technology, and supply systems often include a mix of different technologies with a different water footprint (WF), an indicator of water consumption, per unit of electricity. When water availability varies in time, probably the contribution of different electricity generating technologies also varies in time, resulting in WF fluctuations. Focusing on electricity generation from the water perspective, we assessed how water availability affects an electricity mix's dynamics and its blue WF using Ecuador as a case study. We studied the Amazon and Pacific basins, which have different temporal and spatial water availability fluctuations, assessing monthly water availability, electricity production, and blue WFs per plant. The Amazon basin has smaller temporal and spatial availability fluctuations than the Pacific. The difference between the largest and smallest water availability in the Amazon basin is two-fold, in the Pacific four-fold. Hydropower generation in the Amazon basin contributes more than 60% to the electricity mix. However, hydropower is directly affected by water availability, and its production decreases in water-limited periods. For biomass plants, limited water availability affects the fuel source, sugarcane bagasse. As water availability decreases, other technologies in the mix take over, causing WF variation (from 4.8 to 8.6 103 m3 per month). Usually, less water-availability means more water-efficiency, implying fossil-fueled plants in the Pacific take over from hydropower in the Amazon. It is relevant to assess the water-electricity nexus in countries with electricity mixes dominated by hydropower because energy planning needs to consider water availability and electricity mix dynamics.
ABSTRACT
This study analyzes relationships between food supply, consumption and income, taking supply, meat and dairy, and consumption composition (in macronutrients) as indicators, with annual per capita GDP as indicator for income. It compares food consumption patterns for 57 countries (2001) and gives time trends for western and southern Europe. Cross-sectional and time series relationships show similar patterns of change. For low income countries, GDP increase is accompanied by changes towards food consumption patterns with large gaps between supply and actual consumption. Total supply differs by a factor of two between low and high income countries. People in low income countries derive nutritional energy mainly from carbohydrates; the contribution of fats is small, that of protein the same as for high income countries and that of meat and dairy negligible. People in high income countries derive nutritional energy mainly from carbohydrates and fat, with substantial contribution of meat and dairy. Whenever and wherever economic growth occurs, food consumption shows similar change in direction. The European nutrition transition happened gradually, enabling agriculture and trade to keep pace with demand growth. Continuation of present economic trends might cause significant pressure on natural resources, because changes in food demand occur much faster than in the past, especially in Asia.
