Simulating basin-scale linkages of the food-energy-water nexus with reduced complexity modeling

Abstract

There is a rapidly growing need to communicate to the public and policymakers on the nature and impact of climate change and its associated extremes, which manifest themselves across essential Food-Energy-Water Systems (FEWS). The complexity of this nexus demands analytical tools that can capture the essence of FEWS with the climate system, which may be difficult to stage and implement from a computationally efficient point-of-view. Reduced Complexity Models (RCMs) can synthesize important facets of a system quickly and with less dependence on difficult-to-assign inputs. We report on the development of an RCM framework for the FEWS nexus, to serve as a basic research tool in facilitating parameter sensitivity experiments as well as a means to establish more insightful dialogue with stakeholders through joint scenario construction. Three stand-alone and coupled models at the basin scale have been configured using Stella Architect software to simulate: 1) major flows and storage of water, 2) power plant operations and subsequent impacts on river reaches; and 3) nitrogen (N) mobilization and transport from atmospheric and landmass sources to riverine receiving waters. The Delaware River Basin is chosen for a contemporary simulation test case. Modeled results are calibrated and validated using observed stream gauge data, indicating reliable model performance at the monthly and annual time steps (0.57 < NSE < 0.98). A set of single and multi-factor climate, technology, and policy experiments are then explored using the RCM framework. Basin-scale system sensitivities are investigated across a set of intensified climate extremes, revealing the crucial roles of sewage treatment and energy infrastructure for climate resilience, significant exacerbations as well as mitigations of thermal and N pollution under opposing climate extremes, and important tradeoffs between river temperature and electricity production that are explored with technology and policy scenarios.

Embedded Complexity of Evolutionary Sequences

“…With respect to digital elevation models, for example, the highest available resolutions are not always optimal for ecosystem modeling or terrain representation (e.g., [ 46 , 47 , 48 ]). Several studies have also found that, in addition to advantages in computational efficiencies and implementation costs, reduced-complexity models provide more accurate and useful results in many environmental modeling contexts (e.g., [ 9 , 10 , 11 , 12 , 49 , 50 ]).…”