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Quantifying the effects of LUCCs on local temperatures, precipitation, and wind using the WRF model

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Land use/cover changes (LUCCs) are an important cause of regional climate changes, but the contribution of LUCCs to regional climate changes is not clear. In this study, the Weather Research… Click to show full abstract

Land use/cover changes (LUCCs) are an important cause of regional climate changes, but the contribution of LUCCs to regional climate changes is not clear. In this study, the Weather Research and Forecasting (WRF) model and statistical methods were used to investigate changes in meteorologic variables in January, April, July, and October 2013 due to local LUCCs from 1990 to 2010 in southern Shandong province, China. The results indicate that the WRF model simulates temperatures in the region well, with high correlation coefficients (0.86–0.97, p < 0.001) between the modeled and measured values. The model simulates precipitation less well, with correlation coefficients of 0.41–0.91, but they are all at statistically significant levels, with p < 0.05. During the 20-year period, the LUCCs in the study area consisted mainly of conversions from dry land to urbanized land (747.3 km2) and bare/sparse vegetation (132.4 km2). The LUCCs caused a 0.16 °C temperature increase in January and October and 0.01 and 0.18 °C temperature decreases in April and July, respectively. The range of temperature changes over mixed forest and water bodies due to the LUCCs was wide (0.39–1.31 °C) and was narrower over deciduous broadleaf forest and wetland (0.01 to 0.06 °C). The LUCCs did not change the precipitation greatly in January, April, and October but did affect the precipitation in July substantially, causing a decrease of 23.71 mm. The LUCCs did not affect wind speed and direction substantially during these four months: average wind speeds increased by 0.02 and 0.01 m/s in January and October, respectively, and decreased by 0.02 and 0.05 m/s in April and July, respectively. Overall, The LUCCs affected spring temperatures the least and summer precipitation the most.

Keywords: april july; precipitation; luccs; quantifying effects; wrf model

Journal Title: Environmental Monitoring and Assessment
Year Published: 2017

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