Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Session Overview
FEW-1: Dynamic Systems Modeling in the Food-Energy-Water Nexus
Tuesday, 25/Jun/2019:
2:00pm - 3:30pm

Session Chair: Weiwei Mo
Location: Weidler/Halsey

2:00pm - 2:30pm

Balancing hydropower, fish population, and fish biodiversity through temporal and spatial coordination

Cuihong Song, Weiwei Mo

University of New Hampshire, United States of America

A diverse set of dam management projects, such as dam removals, fishway installations, and periodic turbine shutdowns, have been implemented to restore the endangered or threatened diadromous fish species and preserve a healthy ecosystem. However, conducting these activities could inevitably lead to the loss of hydropower generation capacities. Our understandings of the quantitative tradeoffs and synergies among energy, fish abundance, and fish biodiversity (hereafter referred to as energy-fish tradeoffs) under different dam decisions remain limited. In this study, we explored the dynamic energy-fish tradeoffs associated with five run-of-river hydroelectric dams located in the Penobscot River basin of the United States. Specifically, we investigated the influence of removing dams, installing fishways, and shutting down turbines in different temporal and spatial scales on the energy-fish tradeoffs using an integrative system dynamics model. This model ran across 150 years on a daily time step. Four types of diadromous fish species that are biological, commercial, and recreational important were chosen to represent the local fish diversity, including alewife (Alosa pseudoharengus), American shad (Alosa sapidissima), Atlantic salmon (Salmo salar), and sea lamprey (Petromyzon marinus). We find that diadromous fish populations are very sensitive to even a short period of river fragmentation. For example, alewives need 18 years to recover if they only experience a one-year blockage of the upstream critical habitats. While constructing the five hydroelectric dams (without fishway installations) can produce around 427 GWh/year of energy, they could potentially cause up to a 90% reduction in total fish populations and have a catastrophic impact on fish biodiversity. Around 90% of these fishery losses were happening in five years of dam construction. Thereafter, fish decline went slow until it reached the lowest value at around 20th year of dam construction. Corresponding to this dynamic fish population, conducting fish restoration efforts (e.g., fishway installation, turbine shutdown) in this short time period is an effective way to eliminate dams’ negative impacts on riverine fisheries. It should be noted that the effectiveness of fishway installations is largely influenced by the size of reopened habitat areas and the actual upstream passage rate of the fishways. Operating turbine shutdowns during the peak downstream migration periods of different fish species in addition to other dam management strategies can effectively increase spawner population by 480-550% while preserving 65% of the hydropower generation capacity. Our results highlight that in a river system where active hydropower dams operate, a coordination of multiple dam management strategies at both space and time scales can best balance the tradeoffs between energy production, fish population, and biodiversity.

2:30pm - 3:00pm

Potential Emissions Changes from Refrigerated Supply Chain Introduction and Mitigation Opportunities

Brent R. Heard, Shelie A. Miller

University of Michigan, United States of America

Refrigeration is a transformative technology in the food-energy-water nexus, changing the underlying logistics of food storage and transportation. This study assesses changes in the pre-retail food supply chain following the introduction of an integrated refrigerated supply chain, or “cold chain.” Drivers of emissions changes are identified, and the relative effectiveness of interventions for mitigating emissions increases is assessed.

This study models the introduction of an integrated cold chain into sub-Saharan Africa and estimates changes in pre-retail greenhouse gas (GHG) emissions if the cold chain develops similarly to North America or Europe. GHG emissions (in CO2e) required to supply food to retail are estimated for seven categories: cereals, roots and tubers, fruit, vegetables, meat, fish and seafood, and milk. The food supply chain (FSC) is modeled with four key parameters: loss rates (% of food loss at FSC stages), demand (kg type consumed per capita), agricultural emissions factors (kg CO2e/kg food), and cold chain emissions factor (kg CO2e/kg food).

Refrigeration presents an important and understudied trade-off: the ability to reduce food losses and their associated environmental impacts, but increasing energy use and creating GHG emissions. It is estimated that postharvest emissions added from cold chain operation are larger than food loss emissions avoided, by 10% in the North American scenario and 2% in the European scenario. However, the cold chain also enables changes in agricultural production and diets. Connected agricultural production changes decrease emissions, while dietary shifts facilitated by refrigeration may increase emissions. These system-wide changes brought about by the cold chain may increase the embodied emissions of food supplied to retail by 10% or decrease them by 15%, depending whether shifts towards a North American or European diet is modeled.

Motivated by these findings, we then examine potential interventions to mitigate postharvest emissions increases from cold chain introduction and operation. Preliminary results for fresh and frozen broccoli are reported here, with results for additional foods including chicken, apples, fish, and milk to be presented. Emissions discussed are for kg CO2e added after agricultural production per 1 kg food reaching retail in an integrated cold chain. For fresh broccoli, the most-effective mitigations modeled include decreasing truck transportation distance by 25% and replacing its standard R404a refrigeration unit with a more-efficient unit, both decreasing pre-retail emissions by 24%. Frozen broccoli has higher pre-retail emissions than fresh, due to more-intensive processing. A 25% decrease in electricity grid emissions-intensity for food processing provides a 5% emissions decrease for this product. For frozen broccoli, a 25% decrease in trucking distance yields a 19% decrease in pre-retail emissions. Using an R452a refrigeration unit provides a notable emissions decrease for frozen broccoli (12%) through providing increased energy efficiency and using a refrigerant with lower global warming potential, as does using the more-efficient R404a refrigeration unit (-10%). The efficiency improvement from the R452a unit only occurs at lower temperatures, with this unit only providing a 2% decrease for fresh broccoli. Additional technical interventions, changes in supply chain logistics, and the effects of potential diet shifts will be assessed.

3:00pm - 3:30pm

Assessing impacts of climate change on maize and soybean yields in the southeastern United States using historical linear regression model and an ensemble of emulators

Hai Nguyen1, Christine Costello1, Kangwon Seo1, Paulina Jaramillo2

1University of Missouri, United States of America; 2Carnegie Mellon University, United States of America

As part of an NSF-funded project that examines climate change implications of interconnected infrastructure systems of the food-water-energy nexus in the southeastern US, this paper focuses on analyzing how production of two major food crops in the US – maize and soybeans – respond to climate change scenarios. Despite technological advances, crop production is still vulnerable to climate, therefore understanding climate change impacts on crop production is crucial to anticipating future production and designing appropriate adaptation plans. One of the common approaches for analyzing yield response to climate is to develop functional relationship between yield and climate by linear regression using historical data observations. However, since future conditions are often not observed in the past, this presents a limitation when using historical datasets introducing uncertainty about the extent of yield change in response to climate. To address this uncertainty, simulations from crop models are used to generate ‘emulators’, which use simulated climate and yield data as ‘true’ observations to develop statistical models. This approach takes advantage of the dynamics of yield and future climate and, therefore provides insight about possible yield responses to climate change. In this paper, two different datasets were used to develop an ensemble of statistical models. The first set of data is historical crop yields and climate data, taken from the USDA NASS database and University of Idaho Gridded Surface Meteorological Dataset, to construct a set of statistical models based on actual observations (called observation model). The second set of data are simulations taken from the Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP) to develop emulators. This set of data is comprised of results from the three different global gridded crop models (GGCMs) with climate input from three bias-corrected CMIP5 global circulation models (GCMs).

It is found that yield change based on observation models tend to be more pessimistic than predictions of emulators as the limitations in historical data tend to overestimate yield response to higher temperature. In general, yield change ranges from 40-70% decline in yield due to climate for the mid-century, and 60-91% for the end of the century in response to climate changes according to the three GCMs with the representative concentration pathway (RCP 8.5. The disparity in yield response to climate as a whole and to each climate component, i.e., precipitation and temperature, between the observation models and emulators are due to differences in the structure of the GGCMs as well as in the climate data.