Spatial computing perspective on food energy and water nexus

Abstract

In the coming decades, the increasing world population is expected to grow the demand for food, energy, and water resources. In addition, these resources will be under stress due to the climate change and urbanization. Previously, more problems were caused by piecemeal approaches analyzing and planning those resources independent of each other. The goal of the food, energy, and water (FEW) nexus approach is to prevent such problems by understanding, appreciating, and visualizing the interconnections and interdependencies of FEW resources at local, regional, and global levels. The nexus approach seeks to use the FEW resources as an interrelated system of systems, but data and modeling constraints make it a challenging task. In addition, the lack of complete knowledge and observability of FEW interactions exacerbates the problem. Related work focused on physical science solutions (e.g., desalination, bio-pesticides). No doubt these are necessary and worthwhile for FEW resource security. Overlooked in these work is that spatial computing may help domain scientists achieve their goals for the FEW nexus. In this paper, we describe our vision of the spatial computing's role in understanding the FEW nexus using a Nexus Dashboard analogy. From a spatial data lifecycle perspective, we provide more details on the spatial computing components behind the Nexus Dashboard vision. In each component, we list new technical challenges that are likely to drive future spatial computing research.

Integrated Solutions for the Water-Energy-Land Nexus: Are Global Models Rising to the Challenge?

“…Developing and linking global high-resolution frameworks is a grand challenge because of the significant data requirements and computational complexity associated with spatially-explicit long-term planning problems [160]. Alternative (inexact) solution methods using heuristics and machine learning methods may provide an efficient way to optimize multi-sector development at continental-and global-scales.…”

“…The use of globally-comprehensive datasets will facilitate comparable assessments across not only scales, but also regions. Bridging multiple spatial scales will be challenging for decision-making models due to the computation complexity of distributed resource systems [160]. While nexus frameworks should be capable of independent analysis, enabling linkages with other established climate and I&V models, such as global hydrological models, general circulation models and global gridded crop models should be kept in mind during development.…”

Water quality monitoring for coupled food, energy, and water systems

“…Various authors have noted that the complexity of FEW system requires a nexus approach . Numerous authors in recent days (especially since the launch of a major USA National Science Foundation (NSF) funding program ) have proposed predictive models of the coupled FEW system . Many of these studies take an approach that emphasizes the complexity of the system.…”

Supporting better decisions across the nexus of water, energy and food through earth observation data: case of the Zambezi basin

“…Operationalisation of the WEF Nexus in decision making comes with a set of challenges, some of which were outlined by Eftelioglu et al (2016) as data and modelling constraints. To understand key role of land and ecosystems for understanding and supporting WEF decision making, we need new data such as EO data and more integrated modelling (including web based information/knowledge platforms).…”

“…To understand key role of land and ecosystems for understanding and supporting WEF decision making, we need new data such as EO data and more integrated modelling (including web based information/knowledge platforms). Eftelioglu et al (2016) argue that remote sensing in the context of WEF nexus can highlight issues within the nexus and be used to identify resource use hotspots for targeted solutions. "SubSaharan Africa has experienced long term LULC but only a few and patchy studies exist on long term characterization of LULC" (Wasige et al, 2013).…”