Quantifying the Influence of Updated Land Use/Land Cover in Simulating Urban Climate: A Case Study of Metro Manila, Philippines

Document Type

Article

Publication Date

11-2024

Abstract

This study quantifies the impact of using an updated Land Use/Land Cover (LULC) dataset in the Weather Research and Forecasting (WRF) model on simulating temperature, relative humidity (RH), heat index (HI), wind speed (WS) and wind direction (WD) over Metro Manila (MM), Philippines. The 2015 LULC dataset from the National Mapping and Resource Information Authority (NAMRIA) of the Philippines was used to update the default LULC datasets in the WRF model. Model outputs from four simulations for April 2015 using the default USGS and MODIS LULC, and their updated counterparts (USGS_NAMRIA and MODIS_NAMRIA) were compared with data from 28 automated weather stations distributed across MM. The results show that the MODIS LULC experiment performed better in simulating the overall temperature, HI, and WS, but performed worse in simulating RH and WD, compared to the USGS LULC experiment. On the other hand, the two experiments with updated LULC have nearly similar results for all simulated variables. Both experiments show lower mean percentage biases (MPB), lower Root Mean Square Errors (RMSE), and higher Index of Agreement (IOA) with respect to the observational data in temperatures and nighttime HI, compared to their default counterparts. However, they worsen the overall RH. All simulations tended to overestimate WS, though the overall WS in the USGS_NAMRIA and daytime WS in MODIS_NAMRIA simulations improved, compared to their default counterparts. Lower MB and RMSE values are also evident in the simulated daytime WD in the updated experiments. The results of this study demonstrate potential improvements in reducing biases in temperature (0.6 to 2.4%), nighttime HI (5 to 7%), wind speed (25 to 38%) and wind direction (up to 4°) when using an updated LULC in the WRF model over MM during April 2015. This information will be useful in climate research and weather forecasting.

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