Are multiscale Water-Energy-Land-Food nexus studies effective in assessing agricultural sustainability?

2021 ◽  
Author(s):  
Sai Jagadeesh Gaddam ◽  
Prasanna Venkatesh Sampath
Keyword(s):  
Energy Consumption ◽  
Energy Security ◽  
Multiple Scales ◽  
Spatial Scales ◽  
Critical Role ◽  
Vulnerability Index ◽  
Sustainable Food ◽  
Data Intensive ◽  
Agricultural Efficiency ◽  
Requirement Model

Abstract Several studies have highlighted the need for multiscale Water-Energy-Land-Food (WELF) nexus studies to ensure sustainable food production without endangering water and energy security. However, a systematic attempt to evaluate the efficiency of such multiscale studies has not yet been made. In this study, we used a data-intensive crop water requirement model to study the multiscale WELF nexus in southern India. In particular, we estimated the groundwater and energy consumption for cultivating five major crops between 2017 and 2019 at three distinct spatial scales ranging from 160,000 km2 (state) to 11,000 km2 (district) to 87 km2 (block). A two-at-one-time approach was used to develop six WELF interactions for each crop, which was used to evaluate the performance of each region. A Gross Vulnerability Index (GVI) was developed at multiple scales that integrated the WELF interactions to identify vulnerable hotspots from a nexus perspective. Results from this nexus study identified the regions that accounted for the largest groundwater and energy consumption, which were also adjudged to be vulnerable hotspots. Our results indicate that while a finer analysis may be necessary for drought-resistant crops like groundnut, a coarser scale analysis may be sufficient to evaluate the agricultural efficiency of water-intensive crops like paddy and sugarcane. We identified that vulnerable hotspots at local scales were often dependent on the crop under consideration, i.e., a hotspot for one crop may not necessarily be a hotspot for another. Clearly, policymaking decisions for improving irrigation efficiency through interventions such as crop-shifting would benefit from such insights. It is evident that such approaches will play a critical role in ensuring food-water-energy security in the coming decades.

scholarly journals FRAMEWORK FOR MEASURING URBAN ENERGY SECURITY : CITIZEN PERSPECTIVE

2019 ◽  
Vol 7 (1) ◽  
pp. 34
Author(s):  
Yudha Prambudia ◽  
Arief Rahmana ◽  
Anita Juraida
Keyword(s):  
Energy Consumption ◽  
Final Stage ◽  
Energy Security ◽  
Critical Role ◽  
Security Evaluation ◽  
Urban Context ◽  
Urban Energy ◽  
Urban Energy Consumption ◽  
Systematic Framework

The ever increasing urban energy consumption has always been an important issue of urban energy security. Citizen plays critical role in shaping the energy consumption. Therefore, citizen perspective can give significant impact to urban energy security evaluation. This research aims to provide a systematic framework to measure urban energy security taking into account the perspective of citizen and showcase its implementation in a case of Bandung city. The proposed framework is a straight five stages process as follow; (1) establishing the urban context, (2) defining energy security relevant to the context, (3), determining dimensions of energy security , (4) determining indicators and and metrics, and (5) the final stage is calculating energy security. The implementation case shows Bandung’s energy security is at Middle Low status. It also verify that the framework is operationally viable and it can capture the significance of citizen perspective.

SMART NETWORKS FOR DIGITALIZATION OF URBAN ECONOMY WITHIN THE FRAMEWORK OF THE “SMART CITY”

2020 ◽  
Vol 3 (8) ◽  
pp. 21-27
Author(s):  
S. V. PROKOPCHINA ◽  
Keyword(s):  
Energy Consumption ◽  
Internal Energy ◽  
Energy Management ◽  
Energy Security ◽  
Smart City ◽  
Urban Economy ◽  
Training Center ◽  
Energy Audit ◽  
Intelligent Networks ◽  
Industrial Energy

The article deals with methodological and practical issues of building Bayesian intelligent networks (BIS) for digitalization of urban economy based on the principles of the “Smart city” concept. The BIS complex as a whole corresponds to the architecture of urban household management complexes for construction and industrial energy purposes for solving the problems of internal energy audit, accounting for energy consumption, ensuring energy security of enterprises and territories, in Addition, the system can become the basis for the implementation of a training center for energy management and housing.

scholarly journals A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2742
Author(s):  
Fatih Ünal ◽  
Abdulaziz Almalaq ◽  
Sami Ekici
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Unified Framework ◽  
Short Term ◽  
Planning And Scheduling ◽  
Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

scholarly journals Behavior Patterns, Energy Consumption and Comfort during COVID-19 Lockdown Related to Home Features, Socioeconomic Factors and Energy Poverty in Madrid

2021 ◽  
Vol 13 (11) ◽  
pp. 5949
Author(s):  
Teresa Cuerdo-Vilches ◽  
Miguel Ángel Navas-Martín ◽  
Ignacio Oteiza
Keyword(s):  
Energy Consumption ◽  
Thermal Adaptation ◽  
Vulnerability Index ◽  
General Increase ◽  
Energy Poverty ◽  
Poor Households ◽  
Global Spread ◽  
Containment Measures ◽  
The Face ◽  
Future Harm

During spring 2020, the world was shocked at the imminent global spread of SARS-CoV-2, resorting to measures such as domestic confinement. This meant the reconfiguration of life in an unusual space; the home. However, not all households experienced it in the same way; many of them were vulnerable. A general increase in energy consumption and discomfort in many cases, led these families to suffer the ravages of confinement. This study analyzes the energy and comfort situation for the Madrid (Spain) population, according to the configuration of the homes, the characteristics of the dwellings, the vulnerability index by district, and energy poverty (measured with the 10% threshold of energy expenditure of home incomes). The results show a greater exposure, in confinement, of vulnerable and energy-poor households to scenarios of discomfort in the home, to which they could not respond, while energy consumption inevitably increased. Driven by need, energy-poor homes applied certain saving strategies, mainly resorting to thermal adaptation with clothing. This study shows the risk these households experienced in the face of an extreme situation, and invites reflection on preventive and containment measures that aim to avoid harming the disadvantaged in the future; harm that would also entail serious consequences on the health of their cohabitants.

scholarly journals A Review on Energy and Renewable Energy Policies in Iran

2021 ◽  
Vol 13 (13) ◽  
pp. 7328
Author(s):  
Saeed Solaymani
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Causal Relationship ◽  
Energy Security ◽  
Fossil Fuels ◽  
Agricultural Products ◽  
Energy Resources ◽  
Causality Analysis ◽  
Renewable Energy Consumption

Iran, endowed with abundant renewable and non-renewable energy resources, particularly non-renewable resources, faces challenges such as air pollution, climate change and energy security. As a leading exporter and consumer of fossil fuels, it is also attempting to use renewable energy as part of its energy mix toward energy security and sustainability. Due to its favorable geographic characteristics, Iran has diverse and accessible renewable sources, which provide appropriate substitutes to reduce dependence on fossil fuels. Therefore, this study aims to examine trends in energy demand, policies and development of renewable energies and the causal relationship between renewable and non-renewable energies and economic growth using two methodologies. This study first reviews the current state of energy and energy policies and then employs Granger causality analysis to test the relationships between the variables considered. Results showed that renewable energy technologies currently do not have a significant and adequate role in the energy supply of Iran. To encourage the use of renewable energy, especially in electricity production, fuel diversification policies and development program goals were introduced in the late 2000s and early 2010s. Diversifying energy resources is a key pillar of Iran’s new plan. In addition to solar and hydropower, biomass from the municipal waste from large cities and other agricultural products, including fruits, can be used to generate energy and renewable sources. While present policies indicate the incorporation of sustainable energy sources, further efforts are needed to offset the use of fossil fuels. Moreover, the study predicts that with the production capacity of agricultural products in 2018, approximately 4.8 billion liters of bioethanol can be obtained from crop residues and about 526 thousand tons of biodiesel from oilseeds annually. Granger’s causality analysis also shows that there is a unidirectional causal relationship between economic growth to renewable and non-renewable energy use. Labor force and gross fixed capital formation cause renewable energy consumption, and nonrenewable energy consumption causes renewable energy consumption.

Implications of temporal and spatial scale for Atlantic salmon (Salmo salar) research

1998 ◽  
Vol 55 (S1) ◽  
pp. 9-21 ◽  
Author(s):  
Carol L Folt ◽  
Keith H Nislow ◽  
Mary E Power
Keyword(s):  
Atlantic Salmon ◽  
Salmo Salar ◽  
Multiple Scales ◽  
Spatial Scales ◽  
Effective Means ◽  
Research Strategies ◽  
Atlantic Salmon Salmo Salar ◽  
Temporal And Spatial ◽  
And Migration ◽  
Dependent Processes

The Atlantic salmon (Salmo salar) is a model species for studying scale issues (i.e., the extent, duration, and resolution of a study or natural process) in ecology. Major shifts in behavior and habitat use over ontogeny, along with a relatively long life span and large dispersal and migration distances, make scale issues critical for effective conservation, management, and restoration of this species. The scale over which a process occurs must be linked to the research design and we illustrate this with a discussion of resource tracking by Atlantic salmon. Identifying scale inconsistencies (e.g., when a process is evident at one scale but not another) is shown to be an effective means by which some scale-dependent processes are understood. We review the literature to assess the temporal and spatial scales used in Atlantic salmon research and find most current studies appear to sacrifice spatial and temporal extent for increased resolution. Finally, we discuss research strategies for expanding the temporal and spatial scales in salmon research, such as conducting multiple scales studies to elucidate scale inconsistencies, identifying mechanisms, and using techniques and approaches to generalize across studies and over time and space.

Characterizing spatial structures of larval fish assemblages at multiple scales in relation to environmental heterogeneity in the Strait of Georgia (British Columbia, Canada)

2018 ◽  
Vol 75 (11) ◽  
pp. 1902-1914 ◽  
Author(s):  
Lu Guan ◽  
John F. Dower ◽  
Pierre Pepin
Keyword(s):  
British Columbia ◽  
Environmental Heterogeneity ◽  
Multiple Scales ◽  
Larval Fish ◽  
Critical Role ◽  
Multiscale Approach ◽  
Spatial Structures ◽  
Strait Of Georgia ◽  
Medium Scale

Spatial structures of larval fish in the Strait of Georgia (British Columbia, Canada) were quantified in the springs of 2009 and 2010 to investigate linkages to environmental heterogeneity at multiple scales. By applying a multiscale approach, principal coordinate neighborhood matrices, spatial variability was decomposed into three predefined scale categories: broad scale (>40 km), medium scale (20∼40 km), and fine scale (<20 km). Spatial variations in larval density of the three dominant fish taxa with different early life histories (Pacific herring (Clupea pallasii), Pacific hake (Merluccius productus), and northern smoothtongue (Leuroglossus schmidti)) were mainly structured at broad and medium scales, with scale-dependent associations with environmental descriptors varying interannually and among species. Larval distributions in the central-southern Strait were mainly associated with salinity, temperature, and vertical stability of the top 50 m of the water column on the medium scale. Our results emphasize the critical role of local estuarine circulation, especially at medium spatial scale, in structuring hierarchical spatial distributions of fish larvae in the Strait of Georgia and suggest the role of fundamental differences in life-history traits in influencing the formation and maintenance of larval spatial structures.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

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