Description

Feedback loops and nonlinear interactions interconnect physical and human processes, and understanding of emergent regional climate modifiers (e.g., agriculture, urbanization, etc.) on decadal scales cannot be realized simply by studying these components in isolation. Thus integrative climate modeling of agricultural and urban processes is imperative. This project focuses on three inter-connected research thrusts: mathematical/physics-based predictive modeling and data development; characterization of decadal and regional impacts associated with agriculture/urban expansion; and examination of resulting socio-economic impacts. We will: (1) create fast and accurate numerical algorithms and solvers for nested simulations grounded in mathematical advances; (2) develop a robust physics-based modeling system for integrated agricultural and urban applications; (3) improve input data accuracy by aggregating high-quality land use/cover (LULC) information into computational scales of the physical model; (4) predictively model decadal timescale regional climate/environmental trends, and characterize low frequency variability of regional climate driven by changes in agricultural and urban landscapes, with particular focus on modulation of diurnal cycle and its seasonal variability, impact on the length of growing seasons and yield implications for a variety of crops; (5) characterize potential of urban farms and community gardens as an urban heat island and air quality mitigation strategy; (6) examine socioeconomic benefits/tradeoffs and perform social network analysis to assess potential of urban gardens as a viable future agri-urban development pathway. Validation and verification efforts will focus on a limited collection of focused scenarios within the continental United States, nonetheless the techniques we develop will be crosscutting. Decadal timescale regional climate trends through 2050 will be examined. Variability associated with agricultural and urban LULC change will focus on selected regions whose built environment is rapidly expanding at the expense of agriculture, where native landscapes such as forest and marshlands are being replaced with urban land use, and areas whose decaying urban condition has encouraged replacement of dilapidated infrastructure with farmland. We will estimate economic and social benefits/tradeoffs for these regions using socio-economic analysis and survey tools developed with USDA data and propose strategies for sustainable integrated agri-urban development. An interdisciplinary team consisting of computational and climate scientists, mathematicians, statisticians and geoscientists from ASU and NCAR will carry out the proposed research. The two groups will collaborate in two key areas: development of a fast and accurate high-resolution model (coupled WRF-Crop and WRF-Urban) for agricultural and urban applications and providing multi-disciplinary training in computational geosciences and climate sciences for graduate students and a post-doctoral researcher.

This study will advance our understanding and modeling capabilities of linked LULC agricultural and urban processes on decadal and regional scales. It undertakes cutting-edge research of physical processes and high performance computing in multi-scale nested simulations, with one cross-fertilizing the other, thus enabling studies of feedback loops and emergent properties using physics-based predictive climate modeling for integrated agricultural and urban applications. It will also produce fundamental knowledge regarding techniques for generating economic and social impact projections of urban agriculture/community gardens, which are increasingly critical to the social fabric, food security and wellness of local communities.

This study will advance our understanding and modeling capabilities of linked LULC agricultural and urban processes on decadal and regional scales. It undertakes cutting-edge research of physical processes and high performance computing in multi-scale nested simulations, with one cross-fertilizing the other, thus enabling studies of feedback loops and emergent properties using physics-based predictive climate modeling for integrated agricultural and urban applications. It will also produce fundamental knowledge regarding techniques for generating economic and social impact projections of urban agriculture/community gardens, which are increasingly critical to the social fabric, food security and wellness of local communities.
StatusActive
Effective start/end date5/1/154/30/20

Funding

  • USDA: National Institute of Food and Agriculture (NIFA): $751,860.00

Fingerprint

regional climate
garden
physics
economic impact
urban agriculture
climate modeling
social impact
food security
agriculture
urban development
modeling
agricultural land
timescale
simulation
land use
crop
heat island
network analysis
social network
climate