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Integrating social equity considerations into analyses of the food-energy-water systems nexus (FEWS) could improve understanding of how to meet increasing resource demands without impacting social vulnerabilities. Effective integration requires a robust definition of equity and an enhanced understanding of reliable FEWS analysis methods. By exploring how equity has been incorporated into FEWS research in the United States and countries with similar national development, this systematic literature review builds a knowledge base to address a critical research need. Our objectives were to 1) catalog analysis methods and metrics relevant to assessing FEWS equity at varying scales; 2) characterize current studies and interpret shared themes; and 3) identify opportunities for future research and the advancement of equitable FEWS governance. FEWS equity definitions and metrics were categorized by respective system (food, energy, water, overall nexus) and common governance scales (local, regional, national, global). Two central issues were climate change, which increases FEWS risks for vulnerable populations, and sustainable development, which offers a promising framework for integrating equity and FEWS in policy-making contexts. Social equity in FEWS was integrated into studies through affordability, access, and sociocultural elements. This framework could support researchers and practitioners to include equity in FEWS analysis tools based on study scale, purpose, and resource availability. Research gaps identified during the review included a lack of studies effectively integrating all three systems, a need for publicly available datasets, omission of issues related to energy conversion facilities, and opportunities for integration of environmental justice modalities into FEWS research. This paper synthesized how social equity has previously been incorporated into FEWS and outlines pathways for further consideration of equity within nexus studies. Our findings suggested that continued exploration of connections between FEWS, equity, and policy development across scales could reduce social risks and vulnerabilities associated with these systems. 

In this study, we evaluate the implications of a bias correction method on a combination of Global/Regional Climate Models (GCM and RCM) for simulating precipitation and, subsequently, streamflow, surface runoff, and water yield in the Soil and Water Assessment Tool (SWAT). The study area is the Des Moines River Basin, U.S.A. The climate projections are two RCMs driven by two GCMs for historical simulations (1981–2005) and future projections (2030–2050). Bias correction improves historical precipitation for annual volumes, seasonality, spatial distribution, and mean error. Simulated monthly historical streamflow was compared across 26 monitoring stations with mostly satisfactory results for percent bias (Pbias). There were no changes in annual trends for future scenarios except for raw WRF models. Seasonal variability remained the same; however, most models predicted an increase in monthly precipitation from January to March and a reduction for June and July. Meanwhile, the bias-corrected models showed changes in prediction signals. In some cases, raw models projected an increase in surface runoff and water yield, but the bias-corrected models projected a reduction in these variables. This suggests the bias correction may be larger than the climate-change signal and indicates the procedure is not a small correction but a major factor. 

RCMs produced at ~0.5° (available in the NA-CORDEX database esgf-node.ipsl.upmc.fr/search/cordex-ipsl/) address issues related to coarse resolution of GCMs (produced at 2° to 4°). Nevertheless, due to systematic and random model errors, bias correction is needed for regional study applications. However, an acceptable threshold for magnitude of bias correction that will not affect future RCM projection behavior is unknown. The goal of this study is to evaluate the implications of a bias correction technique (distribution mapping) for four GCM-RCM combinations for simulating regional precipitation and, subsequently, streamflow, surface runoff, and water yield when integrated into Soil and Water Assessment Tool (SWAT) applications for the Des Moines River basin (31,893 km²) in Iowa-Minnesota, U.S. The climate projections tested in this study are an ensemble of 2 GCMs (MPI-ESM-MR and GFDL-ESM2M) and 2 RCMs (WRF and RegCM4) for historical (1981-2005) and future (2030-2050) projections in the NA-CORDEX CMIP5 archive. The PRISM dataset was used for bias correction of GCM-RCM historical precipitation and for SWAT baseline simulations. We found bias correction improves historical total annual volumes for precipitation, seasonality, spatial distribution and mean error for all GCM-RCM combinations. However, improvement of correlation coefficient occurred only for the RegCM4 simulations. Monthly precipitation was overestimated for all raw models from January to April, and WRF overestimated monthly precipitation from January to August. The bias correction method improved monthly average precipitation for all four GCM-RCM combinations. The ability to detect occurrence of precipitation events was slightly better for the raw models, especially for the GCM-WRF combinations. Simulated historical streamflow was compared across 26 monitoring stations: Historical GCM-RCM outputs were unable to replicate PRISM KGE statistical results (KGE>0.5). However, the Pbias streamflow results matched the PRISM simulation for all bias-corrected models and for the raw GFDL-RegCM4 combination. For future scenarios there was no change in the annual trend, except for raw WRF models that estimated an increase of about 35% in annual precipitation. Seasonal variability remained the same, indicating wetter summers and drier winters. However, most models predicted an increase in monthly precipitation from January to March, and a reduction in June and July (except for raw WRF models). The impact on hydrological simulations based on future projected conditions was observed for surface runoff and water yield. Both variables were characterized by monthly volume overestimation; the raw WRF models predicted up to three times greater volume compared to the historical run. RegCM4 projected increased surface runoff and water yield for winter and spring by two times, and a slight volume reduction in summer and autumn. Meanwhile, the bias-corrected models showed changes in prediction signals: In some cases, raw models projected an increase in surface runoff and water yield but the bias-corrected models projected a reduction of these variables. These findings underscore the need for more extended research on bias correction and transposition between historical and future data. 

Abstract Building cooling loads are driven by heat gains through enclosures. This research identifies possible ways of reducing the building cooling loads through vegetative shading. Vegetative shading reduces heat gains by blocking radiation and by evaporative air cooling. Few measured data exist, so we gathered thermal data from a vegetative wall grown in front of a Mobile Diagnostics Lab (MDL), a trailer with one conditioned room with instrumentation that collects thermal data from heat flux sensors and thermistors within its walls. In spring 2020 a variety of plants were cultivated in a greenhouse and planted in front of the south façade of the MDL, which was placed in direct sunlight to collect heat flux data. The plants acted as a barrier for solar radiation and reduced the amount of thermal energy affecting the trailer surface. Data were collected through the use of 16 heat flux sensors and development of continuous infrared (IR) images indicating surface temperature with and without plant cover. The façade surface beneath the plants was 10-30 °C cooler than exposed façade areas. In further analyses, the heat-flux data were compared to IR temperature data. 

Although vegetables are important for healthy diets, there are concerns about the sustainability of food systems that provide them. For example, half of fresh-market vegetables sold in the United States (US) are produced in California, leading to negative impacts associated with transportation. In Iowa, the focus of this study, 90% of food is imported from outside the state. Previous life cycle assessment (LCA) studies indicate that food consumption patterns affect global warming potential (GWP), with animal products having more negative impacts than vegetables. However, studies focused on how GWP, energy, and water use vary between food systems and vegetable types are less common. The purpose of this study was to examine these environmental impacts to inform decisions to buy locally or grow vegetables in the Midwest. We used a life cycle approach to examine three food systems (large-, mid-, and small-scale) and 18 vegetables commonly grown in/near Des Moines, Iowa. We found differences in GWP, energy, and water use (p ≤ 0.001 for each) for the three food systems with the large-scale scenario producing more emissions. There were also differences among vegetables, with the highest GWP for romaine lettuce (1.92 CO2eq/kg vegetable) approximately three times that of leaf lettuce (0.65 CO2eq/kg vegetable) at the large scale. Hotspots and tradeoffs between GWP, energy, and water use were also identified and could inform vegetable production/consumption based on carbon and water use footprints for the US Midwest. 

Most people in the world live in urban areas, and their high population densities, heavy reliance on external sources of food, energy, and water, and disproportionately large waste production result in severe and cumulative negative environmental effects. Integrated study of urban areas requires a system-of-systems analytical framework that includes modeling with social and biophysical data. We describe preliminary work toward an integrated urban food-energy-water systems (FEWS) analysis using co-simulation for assessment of current and future conditions, with an emphasis on local (urban and urban-adjacent) food production. We create a framework to enable simultaneous analyses of climate dynamics, changes in land cover, built forms, energy use, and environmental outcomes associated with a set of drivers of system change related to policy, crop management, technology, social interaction, and market forces affecting food production. The ultimate goal of our research program is to enhance understanding of the urban FEWS nexus so as to improve system function and management, increase resilience, and enhance sustainability. Our approach involves data-driven co-simulation to enable coupling of disparate food, energy and water simulation models across a range of spatial and temporal scales. When complete, these models will quantify energy use and water quality outcomes for current systems, and determine if undesirable environmental effects are decreased and local food supply is increased with different configurations of socioeconomic and biophysical factors in urban and urban-adjacent areas. The effort emphasizes use of open-source simulation models and expert knowledge to guide modeling for individual and combined systems in the urban FEWS nexus.