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Introduction: Recent work examining the impact of climate-change induced extremes on food-energy-water systems (FEWS) estimates the potential changes in physical flows of multiple elements of the systems. Climate adaptation decisions can involve tradeoffs between different system outcomes. Thus, it is important for decision makers to consider the potential changes in monetary value attributed to the observed changes in physical flows from these events, since the value to society of a unit change in an outcome varies widely between thing like food and energy production, water quality, and carbon sequestration. Methods: We develop a valuation tool (FEWSVT) that applies theoretically sound valuation techniques to estimates changes in value for four parameters within the food-energy-water nexus. We demonstrate the utility of the tool through the application of a case study that analyzes the monetary changes in value of a modelled heat wave scenario relative to historic (baseline) conditions in two study regions in the United States. Results: We find that food (corn and soybeans) comprises the majority (89%) of total changes in value, as heatwaves trigger physical changes in corn and soybeans yields. We also find that specifying overly simplified and incorrect valuation methods lead to monetary values that largely differ from FEWSVT results that use accepted valuation methods. Discussion: These results demonstrate the value in considering changes in monetary value instead of just physical flows when making decisions on how to distribute investments and address the many potential impacts of climate change-induced extremes. 

The rate and extent of anthropogenic alteration of the global nitrogen cycle over the past four decades has been extensive, resulting in cascading negative impacts on riverine and coastal water quality. In this paper, we investigate the individual effects of a set of management, technology, and policy mechanisms that alter total reactive nitrogen (TN) flux through rivers, using a modified, spatially detailed SPARROW TN model, between 1980 and 2019 in the Northeast (NE) and Midwest (MW) of the United States. Using the recalibrated model, we simulate and validate a historical baseline, to which we compare a set of climate and non-climate single factor experiments (SFEs) in which individual factors are held at 1980s levels while all other factors change dynamically. We evaluate SFE performance in terms of differences in TN flux and willingness to pay. The largest effect on TN flux are related to reduction in cropland area and atmospheric nitrogen deposition. Multi-factor experiments (MFEs) suggest that increasingly efficient corn cultivars had a larger influence than increasing fertilizer application rate, while population growth has a larger influence than wastewater treatment. Extreme climate SFEs suggest that persistent wet conditions increase TN flux throughout the study region. Meanwhile, persistent hot years result in reduced TN flux. The persistent dry climate SFE leads to increased TN flux in the NE and reduced TN flux in the MW. We find that the potential for TN removal through aquatic decay is greatest in MW, due to the role of long travel time of rivers draining into the Lower Mississippi River. This paper sheds light on how a geographically and climatologically diverse region would respond to a representative selection of management options. 

In this work, we consider the network slice composition problem for Service Function Chains (SFCs), which addresses the issue of allocating bandwidth and VNF resources in a way that guarantees the availability of the SFC while minimizing cost. For the purpose of satisfying the availability requirement of the SFC, we adapt a traffic-weighted availability model which ensures that the long-term fraction of traffic supported by the slice topology remains above a desired threshold. We propose a method for composing a single or multi-path slice topology and for properly dimensioning VNF replicas and bandwidth on the slice paths. Through simulations, we show that our proposed algorithm can reduce the total cost of establishment compared to a dedicated protection approach in 5G networks. 

Change to global climate, including both its progressive character and episodic extremes, constitutes a critical societal challenge. We apply here a framework to analyze Climate-induced Extremes on the Food, Energy, Water System Nexus (C-FEWS), with particular emphasis on the roles and sensitivities of traditionally-engineered (TEI) and nature-based (NBI) infrastructures. The rationale and technical specifications for the overall C-FEWS framework, its component models and supporting datasets are detailed in an accompanying paper (Vörösmarty et al., this issue). We report here on initial results produced by applying this framework in two important macro-regions of the United States (Northeast, NE; Midwest, MW), where major decisions affecting global food production, biofuels, energy security and pollution abatement require critical scientific support. We present the essential FEWS-related hypotheses that organize our work with an overview of the methodologies and experimental designs applied. We report on initial C-FEWS framework results using five emblematic studies that highlight how various combinations of climate sensitivities, TEI-NBI deployments, technology, and environmental management have determined regional FEWS performance over a historical time period (1980–2019). Despite their relative simplicity, these initial scenario experiments yielded important insights. We found that FEWS performance was impacted by climate stress, but the sensitivity was strongly modified by technology choices applied to both ecosystems (e.g., cropland production using new cultivars) and engineered systems (e.g., thermoelectricity from different fuels and cooling types). We tabulated strong legacy effects stemming from decisions on managing NBI (e.g., multi-decade land conversions that limit long-term carbon sequestration). The framework also enabled us to reveal how broad-scale policies aimed at a particular net benefit can result in unintended and potentially negative consequences. For example, tradeoff modeling experiments identified the regional importance of TEI in the form wastewater treatment and NBI via aquatic self-purification. This finding, in turn, could be used to guide potential investments in point and/or non-point source water pollution control. Another example used a reduced complexity model to demonstrate a FEWS tradeoff in the context of water supply, electricity production, and thermal pollution. Such results demonstrated the importance of TEI and NBI in jointly determining historical FEWS performance, their vulnerabilities, and their resilience to extreme climate events. These infrastructures, plus technology and environmental management, constitute the “policy levers” which can actively be engaged to mitigate the challenge of contemporary and future climate change. 

Climate change continues to challenge food, energy, and water systems (FEWS) across the globe and will figure prominently in shaping future decisions on how best to manage this nexus. In turn, traditionally engineered and natural infrastructures jointly support and hence determine FEWS performance, their vulnerabilities, and their resilience in light of extreme climate events. We present here a research framework to advance the modeling, data integration, and assessment capabilities that support hypothesis-driven research on FEWS dynamics cast at the macro-regional scale. The framework was developed to support studies on climate-induced extremes on food, energy, and water systems (C-FEWS) and designed to identify and evaluate response options to extreme climate events in the context of managing traditionally engineered (TEI) and nature-based infrastructures (NBI). This paper presents our strategy for a first stage of research using the framework to analyze contemporary FEWS and their sensitivity to climate drivers shaped by historical conditions (1980–2019). We offer a description of the computational framework, working definitions of the climate extremes analyzed, and example configurations of numerical experiments aimed at evaluating the importance of individual and combined driving variables. Single and multiple factor experiments involving the historical time series enable two categories of outputs to be analyzed: the first involving biogeophysical entities (e.g., crop production, carbon sequestered, nutrient and thermal pollution loads) and the second reflecting a portfolio of services provided by the region’s TEI and NBI, evaluated in economic terms. The framework is exercised in a series of companion papers in this special issue that focus on the Northeast and Midwest regions of the United States. Use of the C-FEWS framework to simulate historical conditions facilitates research to better identify existing FEWS linkages and how they function. The framework also enables a next stage of analysis to be pursued using future scenario pathways that will vary land use, technology deployments, regulatory objectives, and climate trends and extremes. It also supports a stakeholder engagement effort to co-design scenarios of interest beyond the research domain.