Research

1. Integrated Food-Energy-Water Systems Modeling

There is an increasing recognition that integrated models representing many constituent systems and their interactions are needed to address 21st-century challenges (e.g., the various “nexus” initiatives promoted by numerous funding agencies). An example of these models is the Global Change Analysis Model (GCAM), developed by the Pacific Northwest National Laboratory (Figure 1(a)). GCAM represents a world-class integrated assessment model, used to develop global climate change scenarios widely used by the Intergovernmental Panel on Climate Change (IPCC) and the federal government in climate studies. My research efforts to date using GCAM include scenario development and uncertainty analysis centered on future energy systems transitioning to a low-carbon future (Kim et al. 2025), global hydropower expansion (Kim et al. in review), and resource security outcomes (e.g., food-energy-water security) (Kim et al. in revision). 

   The current community climate scenarios adopted in nearly every climate-oriented assessment (e.g., the Shared Socioeconomic Pathways, the SSPs) risk overlooking important regional and sectoral challenges and may not be suitable under specific circumstances. For example, societal challenges represented in the scenarios for human health in the rural Midwest may differ from those arising from future hydropower generation scenarios in New England. For instance, Figure 1(b) shows the regional food burden (i.e., the share of income spent on food) for the lowest income decile in each country in 2050 when a global low-carbon transition is achieved. The results highlight that the positive climate outcomes on a global scale can take place at the expense of food security in the regions with higher economic poverty and population (e.g., Eastern & Western Africa and India), demonstrating the danger of focusing too narrowly on the climate system. I then pair the outcomes from the GCAM simulations with machine learning-based exploratory modeling capabilities to tailor scenarios to specific sectors and regions. By providing scientists and policymakers with the outcomes and tools to identify critical challenges for their complex systems, I believe that these works can help better characterize multi-sector dynamics under future uncertainties. 

Water resources are among the most critical civil infrastructure systems, playing an integral role in nearly every sector of our society. Nevertheless, adverse effects arising from climate change have caused traditional planning and management frameworks for water resources infrastructure to evolve. To this end, my past works address two challenges from climate change (i.e., hydrologic non-stationarity and deep uncertainty) with robust and adaptive reservoir operation strategies (Kim et al. 2021; Kim et al. 2022) (Figure 2(a), (b)). Through my work, I proved that including the concept of adaptive and robust strategies yielded more resilient performance over a wider range of uncertainties, especially under extreme climatic conditions. Since the challenges from unprecedented climate-driven extremes on water resources are expected to happen at a global scale, these novel frameworks have the potential to be expanded to other water infrastructure systems. 

   Another direction the water resources community is pursuing under climate change is to include interactive participation from the local stakeholders during modeling through collaborative modeling. During my doctoral research, I developed a simulation-visualization decision support tool for the Boryeong Multipurpose Reservoir using STELLA Architect as a lead technical analyst (Figure 2(c)). This study was the first of its kind in which a collaborative modeling process was implemented in the context of climate change adaptation in South Korea (Kim et al. 2019). I believe that these tools have the potential to better include perspectives from diverse and underrepresented communities during the scientific processes.