Comparison of Unified Land Use and Land Cover Datasets for Urban Scale Modeling of Meteorology, Emissions, and Air Quality in the Houston-Galveston Area

In addition to the advanced modeling techniques and new formulations, urban modeling of air quality and environmental applications require appropriate input data, such as land use (LU) and land cover (LC) data. The LULC data can not only affect physical characteristics involved in surface flux and radiative energy exchanges, but also influence the amount of biogenic emissions produced by the vegetation. Houston-Galveston area (HGA) is one of the most severe ozone non-attainment regions in the US. Simulations of a mesoscale model with inadequate LULC data, such as the default LULC available with MM5, can lead to mischaracterization of local flow conditions and evolution pattern of the planetary boundary layer. Compared to other parts of Texas and the US, the LULC database available for Eastern Texas has been updated relatively recently. To estimate biogenic emissions for the HGA, Texas Commission on Environmental Quality utilized a composite land use database that includes a mapping of ground cover, vegetation species, and leaf mass densities for the state of Texas (TCEQ LULC). To overcome certain shortcomings of the LULC data, such that they were compiled with various data sources over many years of time span and had many components that depended on the county-based surrogate information, Texas Forest Service (TFS) prepared a detailed land cover and land use data set from LANDSAT-derived data that is based on multi-spectral land-surface characteristics basis for the HGA. In addition, recognizing that the meteorological modeling requires adequate model inputs far beyond the immediate areas of interest, Center for Space Research (CSR), University of Texas, has initiated a project to generate a moderate-resolution LULC data set for use as an input to the air quality modeling. In this paper we perform meteorological and emissions modeling with these different LULC datasets to quantify uncertainties in the model inputs. Finally, we simulated air quality model with the different meteorological and emissions inputs to study the overall impacts of using different LU/LC dataset in the photochemical modeling of the Houston-Galveston ozone nonattainment area.