PhD Dissertation Defense: Mahdad Talebpour

This work built and evaluated a fully-coupled urban atmosphere-land surface-subsurface model, WRF-PUCM-PF, by coupling Weather Research and Forecasting (WRF), Princeton Urban Canopy Model (PUCM), and ParFlow. The WRF-PUCM-PF model was evaluated against WRF-PUCM, in application to a small watershed (Dead Run, 10.8 km by 10.8 km) in Baltimore, Maryland, (90-m grid resolution). WRF-PUCM-PF realistically turned most of the generated rain over impervious surfaces into runoff and had a 2% increase in area-averaged soil moisture content. WRF-PUCM gained 10 times higher soil moisture content through infiltration over impervious surfaces. WRF-PUCM-PF’s soil moisture distribution was influenced realistically by topography and land cover. However, WRF-PUCM’s spatial soil moisture distribution was similar to accumulated rain spatial distribution, neglecting any lateral flow. The sensitivity of WRF’s fine-scale (150 m) urban simulation to (1) leading time in soil moisture spinup in WRF (14, 7, and 4 days before the analysis period), and (2) the conversion algorithm between the National Land Cover Dataset (NLCD) Developed categories to WRF’s urban categories, was evaluated by running 12 scenarios. Overall, starting 7 days earlier resulted in better performance in LST prediction by perturbing soil moisture distribution and not diverging from atmospheric observations. WRF’s conversion algorithm of NLCD had the strongest impact on WRF’s LST performance. The role of incorporating a realistically simulated soil moisture distribution (50-m grid resolution) by ParFlow into WRF was evaluated and validated for the Baltimore city (36 km by 36 km). ParFlow-simulated soil moisture was injected into WRF and compared to a simulation using interpolated soil moisture from the parent (outer) domain output. Validated against Landsat 8 land surface temperature (LST) on August 22, 2017, 12:00 EDT, the model with ParFlow soil moisture input performed significantly better by achieving smaller biases in area-averaged LST (0.41 ˚C and 1.06 ˚C) over non-urban areas compared to the model with the interpolated soil moisture distribution (1.2 ˚C and 3.2 ˚C). Overall, this work underlines the importance of incorporating realistic terrestrial hydrology dynamics in the simulation of urban soil moisture distribution and microclimatic variation.