• Energy Research
• 2016-2025close
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• 6. Clean water close

Abstract Levee systems are an important part of California’s water infrastructure, engineered to provide resilience against flooding and reduce flood losses. The growth in California is partly associated with costly infrastructure developments that led to population expansion in the levee protected areas. Therefore, potential changes in the flood hazard could have significant socioeconomic consequences over levee protected areas, especially in the face of a changing climate. In this study, we examine the possible impacts of a warming climate on flood hazard over levee protected land in California. We use gridded maximum daily runoff from global circulation models (GCMs) that represent a wide range of variability among the climate projections, and are recommended by the California’s Fourth Climate Change Assessment Report, to investigate possible climate-induced changes. We also quantify the exposure of several critical infrastructure protected by the levee systems (e.g. roads, electric power transmission lines, natural gas pipelines, petroleum pipelines, and railroads) to flooding. Our results provide a detailed picture of change in flood risk for different levees and the potential societal consequences (e.g. exposure of people and critical infrastructure). Levee systems in the northern part of the Central Valley and coastal counties of Southern California are likely to observe the highest increase in flood hazard relative to the past. The most evident change is projected for the northern region of the Central Valley, including Butte, Glenn, Yuba, Sutter, Sacramento, and San Joaquin counties. In the leveed regions of these counties, based on the model simulations of the future, the historical 100-year runoff can potentially increase up to threefold under RCP8.5. We argue that levee operation and maintenance along with emergency preparation plans should take into account the changes in frequencies and intensities of flood hazard in a changing climate to ensure safety of levee systems and their protected infrastructure.

In recent years, the rapid development of the rare earth industry has had a serious impact on the environment. Some enterprises have taken measures to improve the production process. In order to explore the sustainability of this industry and these improvements’ environmental benefits, this paper combines emergy analysis and lifecycle assessment to evaluate and compare the production process of rare-earth oxides considering the three aspects of emergy flow, pollutant emissions, and emergy-based indicators. Changes in the emergy of pollutant emissions before and after improvement of the production process are discussed. The results show that the greatest inputs in the mining and beneficiation stage and smelting separation stage are labor force and service and non-renewable resources, respectively. These two production stages are highly dependent on external input and have weak competitiveness. Both stages place great pressure on the environment, so the bastnasite production process would be unsustainable in the long term. After the improvement, the environmental impact of the production process for bastnaesite changed significantly, indicating that the improvement effect of the wastewater treatment facilities and the change of fuel from coal to natural gas is remarkable.

In this paper, we propose and investigate an original approach to energy conversion based on polyzwitterionic hydrogels, which exhibit an antipolyelectrolyte effect that enables them to swell in salt water and shrink in water of a different (i.e., desalinated water) salinity. The swelling and shrinking processes run cyclically and can move a piston up or down reversibly, thus transforming the antipolyelectrolyte effect into a mechanical force based on the salinity gradient. This phenomenon makes polyzwitterionic hydrogels suitable for use in a smart, polymeric engine. We apply this approach to investigate energy recovery from a polysulfobetaine-based hydrogel. The cross-linking density, external load, particle size, and repeatability of energy recoverability of hydrogels are examined. The maximum energy recovery from 0.4 g of hydrogel in feed (calculated based on dry form) of 102 mJ/kg was obtained by a hydrogel with a 3% cross-linking density, a 200-300 μm particle size, and 100 g external load. Excellent reproducibility of engine cycles was achieved over 10 cycles. This concept is complementary to the osmotic engine concept based on a polyelectrolyte hydrogel. In addition, polyzwitterionic materials have become a benchmark material for preventing biofouling, and the swelling properties of such materials can be further modulated and tuned.

Scarcity of water resources in the entire country is more serious in the northern provinces like Golestan. Therefore, farmers have to use saline water or waste waters. One way to adapt to this condition is to use unconventional water such as Caspian Sea water, with lower salinity as compared with water from free seas, for common crops like soybean. Thus feasibility of using Caspian Sea water that has integrated with narrow common water and the calibration of Aquacrop model under the effect of salinity and water stress for soybean crop in Golestan province are more important. The Aquacrop model assumes a linear relationship between the biomass yield (BY) and crop transpiration one one hand and water productivity (WP) value on the other (Steduto and Albrizio, 2005). This model, as compared to other crop models, requires minimal input data and its new version 4.0 (June, 2012) has a salinity module which was used in this research to simulate the grain yield (GY) and WP of soybean under deficit and saline water irrigation. Furthermore, the AquaCrop model has not often been tested to simulate the yield of soybean under saline conditions in the semi-arid climate of Golestan province. This study aims at evaluating the efficiency of AquaCrop model in soybean yield simulation. The results are to be used for optimizing water consumption under water and saline stress. To this end, an experiment was carried out at the research farm of Gorgan University of Agricultural Sciences and Natural Resources.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

Sustainability of agro-food supply chains has recently become the subject of greater interest from consumers, firms, governmental organizations and academia as the environment continues to deteriorate. One of the most critical factors influencing the sustainability of an agro-food supply chain is its network design. A particularly challenging aspect in this context is the broad range of influencing indicators associated with the Triple Bottom Line (TBL) of sustainability that need to be considered. However, many of these indicators could not be fully integrated or measured by single-step optimization problems. This paper presents a critical literature review of operational research methods for the design of sustainable supply chains. A novel two-stage hybrid solution methodology is proposed. In the first stage, a partner selection is performed using a hybrid multi criteria decision making based on Analytic Hierarchy Process (AHP) method and the Ordered Weighted Averaging (OWA) aggregation method. The result obtained in the first stage is used in the second stage to develop a multi-objective mathematical model to optimize the design of the supply chain network. This approach allows the simultaneous consideration of all three dimensions of sustainability including carbon footprint, water footprint, number of jobs created and the total cost of the supply chain design. The proposed approach generates a Pareto frontier to aid users in making decisions. Numerical experiments are completed utilizing data from an agro-food company to demonstrate the efficiency and effectiveness of the proposed solution methodology. The analyzes of the numerical results provide important organizational, practical and policy insights on (1) the impact of financial and environmental sustainability on supply chain network design (2) the tradeoff analysis between environmental emission, water footprint, societal implications and associated cost for making informed decision on supply chain investment.

AbstractA large number of small- to medium-sized community ponds exist in most parts of the Terai region in Nepal. Such ponds could be a viable alternative for other forms of surface irrigation. But, with the lack of efficient management, many of these ponds remain underutilized. An effort was made to facilitate the rehabilitation of such a pond in a selected village of Rupandehi District in Western Terai region of Nepal. This paper aims to evaluate the changed water availability situation in post-monsoon seasons after the pond rehabilitation. The paper also evaluates the feasibility of such interventions especially focusing on the potential to provide additional water and improve agricultural productivity. Results showed small increases in quantifiable indicators such as water availability, cropping intensity, productivity and income. The new institutional setup improved water allocation, improved operation and maintenance, and increased social awareness among the people about the importance of underutilized water resources. The intervention has the potential to be replicated in similar contexts.