Investigation of the Effects of Different Land Use and Land Cover Patterns on Mesoscale Meteorological Simulations in the Taiwan Area

Abstract

The U.S. Geological Survey (USGS) land use (LU) data employed in the Weather Research and Forecasting (WRF) model classify most LU types in Taiwan as mixtures of irrigated cropland and forest, which is not an accurate representation of current conditions. The WRF model released after version 3.1 provides an alternative LU dataset retrieved from 2001 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products. The MODIS data correctly identify most LU-type distributions, except that they represent western Taiwan as being extremely urbanized. A new LU dataset, obtained using 2007 Système Probatoire d’Observation de la Terre (SPOT) satellite imagery [from the National Central University of Taiwan (NCU)], accurately shows the major metropolitan cities as well as other land types. Three WRF simulations were performed, each with a different LU dataset. Owing to the overestimation of urban area in the MODIS data, WRF-MODIS overpredicts daytime temperatures in western Taiwan. Conversely, WRF-USGS underpredicts daytime temperatures. The temperature variation estimated by WRF-NCU falls between those estimated by the other two simulations. Over the ocean, WRF-MODIS predicts the strongest onshore sea breezes, owing to the enhanced temperature gradient between land and sea, while WRF-USGS predicts the weakest onshore flow. The intensity of the onshore breeze predicted by WRF-NCU is between those predicted by WRF-MODIS and WRF-USGS. Over Taiwan, roughness length is the key parameter influencing wind speed. WRF-USGS significantly overpredicts the surface wind speed owing to the shorter roughness length of its elements, while the surface wind speeds estimated by WRF-NCU and WRF-MODIS are in better agreement with the observed data.

Effects of a Detailed Vegetation Database on Simulated Meteorological Fields, Biogenic VOC Emissions, and Ambient Pollutant Concentrations over Japan

“…In the case of Austria, Schicker et al [12] found many more urban grid cells in their updated land-use database compared with the default USGS data, resulting in higher and better-simulated temperature when using their updated input. Cheng et al [13] also indicated that most regions of Taiwan were not classified as urban areas in the USGS data, contrary to the actual situation, thereby under-predicting temperature in urban areas. The present findings are consistent with these examples.…”

WRF/UCM Simulations of Urban Heat Island in Guangzhou with an Extracted Land-use Map from the Remote Sensing Data

“…Land use and land cover change associated with urbanization considerable impact on climate [1]. One of the results is a consistent rise in temperature in the urban atmosphere because of the land-use changes and accompanying effects on physical processes governing energy, momentum and matter exchange between land surface and the atmosphere [2,3]. Guangzhou, the capital and largest city of Guangdong province and the 3rd largest Chinese city, is undergoing a rapid and widespread urbanization and a deterioration of urban environments caused by urban heat islands.…”

Urban Heat Island Simulations in Guangzhou, China, Using the Coupled WRF/UCM Model with a Land Use Map Extracted from Remote Sensing Data

“…Such tremendous urban expansion accompanied with increasing built-up areas and intensifying human activities causes remarkable modifications of the underlying surface properties, thereby significantly enhancing the UHI effect [17]. Previous studies show that the UHI effect noticeably contributes to regional warming in Guangzhou area [21].…”

“…The coupled WRF/UCM modeling system has been applied to many regions, such as Houston, USA [6], Nanjing, China [7], and New York, USA [8], and its performance has been evaluated against observations. expansion accompanied with increasing built-up areas and intensifying human activities causes remarkable modifications of the underlying surface properties, thereby significantly enhancing the UHI effect [17]. Previous studies show that the UHI effect noticeably contributes to regional warming in Guangzhou area [21].…”