Quantifying the Impact of Dust Sources on Urban Physical Growth and Vegetation Status: A Case Study of Saudi Arabia

Recently, dust has created many problems, including negative effects on health, and environmental and economic costs, for people who live both near to and far from sources of dust. The aim of this study is to evaluate and quantify the impact of dust sources located inside Saudi Arabia on the physical growth and vegetation status of cities. In order to do so, satellite data sets, simulated surface data, and soil data for Saudi Arabia from 2000 to 2021 were used. In the first step, a dust sources map of the study area was generated using multi-criteria decision analysis. Land surface temperature (LST), vegetation cover, soil moisture, precipitation, air humidity, wind speed, and soil erodibility factors were considered as effective criteria in identifying dust sources. In the second step, built-up land and vegetation status maps of major cities located at different distances from dust sources were generated for different years based on spectral indicators. Then, the spatiaotemporal change of built-up land and vegetation status of the study area and major cities were extracted. Finally, impacts of major dust sources on urban physical growth and vegetation were quantified. The importance degrees of soil erodibility, wind speed, soil moisture, vegetation cover, LST, air humidity, and precipitation to identify dust sources were 0.22, 0.20, 0.16, 0.15, 0.14, 0.07, and 0.05, respectively. Thirteen major dust sources (with at least 8 years of repetition) were identified in the study area based on the overlap of the effective criteria. The identified major dust sources had about 300 days with Aerosol Optical Depth (AOD) values greater than 0.85, which indicates that these dust sources are active. The location of the nine major dust sources identified in this study corresponds to the location of the dust sources identified in previous studies. The physical growth rates of cities located 400 km from a major dust source (DMDS) are 46.2% and 95.4%, respectively. The reduction rates of average annual normalized difference vegetation index (NDVI) in these sub-regions are 0.006 and 0.002, respectively. The reduction rate of the intensity of vegetation cover in the sub-region close to dust sources is three times higher than that of the sub-region farther from dust sources. The coefficients of determination (R2) between the DMDS and urban growth rate and the NDVI change rate are 0.52 and 0.73, respectively, which indicates that dust sources have a significant impact on the physical growth of cities and their vegetation status.Institutional Fund ProjectsPeer Reviewe

A Multi-Source Data Fusion Method to Improve the Accuracy of Precipitation Products: A Machine Learning Algorithm

In recent decades, several products have been proposed for estimating precipitation amounts. However, due to the complexity of climatic conditions, topography, etc., providing more accurate and stable precipitation products is of great importance. Therefore, the purpose of this study was to develop a multi-source data fusion method to improve the accuracy of precipitation products. In this study, data from 14 existing precipitation products, a digital elevation model (DEM), land surface temperature (LST) and soil water index (SWI) and precipitation data recorded at 256 gauge stations in Saudi Arabia were used. In the first step, the accuracy of existing precipitation products was assessed. In the second step, the importance degree of various independent variables, such as precipitation interpolation maps obtained from gauge stations, elevation, LST and SWI in improving the accuracy of precipitation modelling, was evaluated. Finally, to produce a precipitation product with higher accuracy, information obtained from independent variables were combined using a machine learning algorithm. Random forest regression with 150 trees was used as a machine learning algorithm. The highest and lowest degree of importance in the production of precipitation maps based on the proposed method was for existing precipitation products and surface characteristics, respectively. The importance degree of surface properties including SWI, DEM and LST were 65%, 22% and 13%, respectively. The products of IMERGFinal (9.7), TRMM3B43 (10.6), PRECL (11.5), GSMaP-Gauge (12.5), and CHIRPS (13.0 mm/mo) had the lowest RMSE values. The KGE values of these products in precipitation estimation were 0.56, 0.48, 0.52, 0.44 and 0.37, respectively. The RMSE and KGE values of the proposed precipitation product were 6.6 mm/mo and 0.75, respectively, which indicated the higher accuracy of this product compared to existing precipitation products. The results of this study showed that the fusion of information obtained from different existing precipitation products improved the accuracy of precipitation estimation