• Energy Research
• 2021-2025close
• 6. Clean water close
• Polytechnique Montréal close

Assessing the impact of climate change on water systems often requires employing a hydrological model to estimate streamflow. However, the choice of hydrological model, process representation, input data resolution, and catchment discretization can potentially influence such analyses. This study aims to evaluate the sensitivity of climate change impact assessments to various hydrological modeling configurations in a snow-dominated headwater system in Alberta, Canada. The HBV-MTL and GR4J models, coupled with the Degree-Day and CemaNeige snowmelt modules, were utilized and calibrated using point- and grid-based climate data on lumped and semi-distributed catchment discretization. The hydrological models, in conjunction with a water allocation model, were supplied with climate model outputs to project changes in the basin. While all models revealed a unanimous increase in peak flow, the difference between their estimations could be as substantial as 42%. In contrast, their divergence was minimal in projecting median flow. Furthermore, most models projected an aggravated water supply deficit between 16% and 40%. Overall, the quantified climate change impacts were the most sensitive to the choice of snow routine module, followed by the model type, catchment discretization, and data resolution in this snow-dominant basin. Therefore, particular attention should be given to the proper representation of snowmelt processes.

Changes in climate are altering the historical characteristics of the streamflow regime and affecting the performance of water systems. Here, the role of representing natural streamflow conditions on quantification of water system vulnerability under changing climate is evaluated in the Oldman River Basin, Canada. Four hydrological models are developed using point- and grid-based climate data and considering lumped and semi-distributed representations of the watershed. These hydrological models are then coupled with a reservoir water allocation model. Using an ensemble of climate model projections fed into these integrated models, changes in the water system���s behavior are evaluated. Although intensified and earlier peak flows and more critical water deficits are projected, the estimated risks of failure strongly depend on the considered hydrological model configuration. The divergence among models��� projections for water deficit can be as high as 300%. Therefore, usage of all configurations is recommended to revise the reservoir operational policies.

Life Cycle Impact Assessment (LCIA) links the emissions and resource abstractions of a product system or process to potential impacts on the environment through characterization factors (CF). For regionalized impact categories like water-use, the regional CFs can vary over several orders of magnitude within the same country. The aggregated country-level CF, often used in LCIA, represents an average of local CF weighted by the local water consumption of all (or most) human water use including water use by all (or most) economic sectors. There is, however, great variability in spatio-temporal distribution of human water consumption across different industries. This study provides industry-specific water-use CFs for the electricity sector across the US. Our analysis shows that for electricity generation, the use of all-sector aggregated water-use CF would lead to an underestimation of impact scores compared to industry-specific CFs, by two folds. Even within the electricity sector, for two of the major subsectors, electricity based on natural gas and hydroelectricity, the country-level CFs can be significantly different due to the geographic distribution of powerplants. Our findings signify that the use of industry-specific CF can have a high influence in LCIA, especially for impact categories, such as water-use, with great spatio-temporal heterogeneity.

This paper represents a streamflow prediction model with the approach of ensemble multi-GCM downscaling and system dynamics (SD) for the Aji-Chay watershed located in northwest Iran. In this study, firstly, the precipitation and temperature projection for the future was assessed according to the climate change impact using a statistical downscaling technique, i.e., Long Ashton Research Station Weather Generator (LARS-WG); secondly, a rainfall-runoff model for future horizons was developed according to artificial neural networks (ANN); finally, an SD model was developed according to plausible reclamation scenarios, i.e., cloud seeding, increasing the irrigation efficiency and reducing agricultural production, controlling policies on groundwater withdrawal as well as environmental awareness, and cultivation to reduce domestic consumption to achieve sustainable development. For downscaling purposes, the outputs of four general circulation models (GCMs) including EC-EARTH, HadGEM2, MIROC5, MPI-ESM from Coupled Model Intercomparison Project 5 (CMIP5) were applied. The results of multi-GCM downscaling indicated an ascending trend of 0.1 °C to +1.3 °C for temperature and a descending trend of 17 to 23% for precipitation by 2040 under representative concentration pathways (RCPs) of 4.5 and 8.5, respectively. Moreover, the results of the SD model revealed that none of the individual reclamation scenarios were impressive on water balance sustainable conditions; instead, the simultaneous implementation of all plausible scenarios managed to meet the requirements of socio-environment aspects as well as sustainability approaches.

The versatility of microalgae biomass as candidates for various products and bioremediation needs motivates interests towards design and implementation of novel microalgae bioreactors. Conventional open-reactors are reliant on large quantities of sunlight and space while yields are constrained by outdoor environment conditions. Conversely, closed-reactor systems like bubble columns reduces these constrains on microalgae growth while occupying far less space at the expense of high energy demands, notably from lighting systems. A novel patented closed reactor design has recently been proposed that improves the bubble column concept with an efficient and effective lighting system. The present study uses Life Cycle Assessment approach to compare the environmental performance of conventional reactors and the proposed internally luminated novel closed reactor design, expressing impacts per kg biostimulant for the Scenedesmus almeriensis harvest from such units. All performance data was collected from a pilot facility in Almeria, Spain. Urban-industrial symbiosis scenarios are also portrayed in the study using wastewater and incinerator flue gas. Results show that under synthetic nutrient and carbon inputs in Spanish pilot operations, the cumulative energy demand for the novel photobioreactors is similar to conventional vertically-stacked horizon bioreactors but are substantially more demanding than conventional open reactors. However, when leveraging renewable energy sources and the photosynthesis process to consume wastestreams in urban-industrial symbiosis scenarios, the novel photobioreactor was able to achieve up to 80 % improvements in several impact categories e.g. eutrophication and climate change. Impact mitigation credits per kg dwt biomass across all energy scenarios in symbiosis amount to ≈1.8 kg CO2eq and ≈0.09 kg PO4 eq. This highlights that such closed and internally illuminated photobioreactors can be competitive with conventional reactors, and have potential to harness photosynthesis to reduce environmental burdens in an urban-industrial symbiosis setting. Possible economies of scale and the associated potential gains in efficiencies are further discussed.

Purpose: Understanding the main factors affecting the environmental impacts of milk production and consumption along the value chain is key towards reducing these impacts. This paper aims to present detailed spatialized distributions of impacts associated with milk production and consumption across the United States (U.S.), accounting for locations of both feed and on-farm activities, as well as variations in impact intensity. Using a Life Cycle Analysis (LCA) approach, focus is given to impacts related to (a) water consumption, (b) eutrophication of marine and freshwater, (c) land use, (d) human toxicity and ecotoxicity, and (e) greenhouse gases. Methods: Drawing on data representing regional agricultural practices, feed production is modelled for 50 states and 18 main watersheds and linked to regions of milk production in a spatialized matrix-based approach to yield milk produced at farm gate. Milk processing, distribution, retail, and consumption are then modelled at a national level, accounting for retail and consumer losses. Custom characterization factors are developed for freshwater and marine eutrophication in the U.S. context. Results and discussion: In the overall life cycle, up to 30% of the impact per kg milk consumed is due to milk losses that occur during the retail and consumption phases (i.e., after production), emphasizing the importance of differentiating between farm gate and consumer estimates. Water scarcity is the impact category with the highest spatial variability. Watersheds in the western part of the U.S. are the dominant contributors to the total water consumed, with 80% of water scarcity impacts driven by only 40% of the total milk production. Freshwater eutrophication also has strong spatial variation, with high persistence of emitted phosphorus in Midwest and Great Lakes area, but high freshwater eutrophication impacts associated with extant phosphorus concentration above 100 µg/L in the California, Missouri, and Upper Mississippi water basins. Overall, normalized impacts of fluid milk consumption represent 0.25% to 0.8% of the annual average impact of a person living in the U.S. As milk at farm gate is used for fluid milk and other dairy products, the production of milk at farm gate represents 0.5% to 3% of this annual impact. Dominant contributions to human health impacts are from fine particulate matter and from climate change, whereas ecosystem impacts of milk are mostly due to land use and water consumption. Conclusion: This study provides a systematic, national perspective on the environmental impacts of milk production and consumption in the United States, showing high spatial variation in inputs, farm practices, and impacts.