Project A2-2

Project Team: Mattei Georgescu, PI – Arizona State University

Zhihua Wang – Arizona State University

Elie Bou-Zeid – Princeton University

Mohamed Moustaoui – Arizona State University

Alex Mahalov – Arizona State University

Claire Welty – University of Maryland, Baltimore County


Project Overview

This project will quantify hydroclimatic impacts due to the combined and interacting effects of urban expansion and greenhouse gas emissions for the end of century continental U.S. (CONUS). Scenario-based impervious surface expansion projections (as provided by the EPA Integrated Climate and Land-Use Scenarios [ICLUS]) will be used as surface boundary conditions within the Weather Research and Forecasting system (WRF) to approximate the uncertainty associated with future urban expansion. To include the effect of greenhouse gas emissions, we will make use of appropriately selected Global Climate Model (GCM) data, which will be used as initial and lateral boundary conditions for WRF. While initial hydroclimate assessment of decadal timescale simulations will be conducted at medium-range resolution (e.g., 20 km) for CONUS, in order to provide a broad representation of impacts at large-scales, dynamical downscaling at high-resolution (e.g., 1-2 km) will be conducted for each of the U-WIN regions of interest to examine and quantify robustness of simulated results across multiple scales. Examination of simulations will focus on local to regional scale hydroclimatic effects (e.g., urban heat island [UHI] effect, regional water cycle) and consequences for energy demand associated with scenario-based climate projections across all U-WIN regions. The implementation of designated urban adaptation and mitigation choices will be assessed, and cross-site dis-similarity, implications for tradeoffs, and potential co-benefits, will be quantified. Uncertainty quantification owing to projection differences in spatial distribution of emerging and expanding population centers (as provided by various ICLUS expansion projections) will be accounted for within the modeling framework. Finally, prioritization of geographically dependent urban adaptation strategies will be made.