RESUMEN
Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk. In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling.
RESUMEN
This study investigated the impact of COVID-19 lockdown on particulate matter concentrations, specifically PM2.5 and PM10, in Kuwait. We studied the variations in PM2.5 and PM10 between the lockdown in 2020 with the corresponding periods of the years 2017-2019, and also investigated the differences in PM variations between the 'curfew' and 'non curfew' hours. We applied mixed-effect regression to investigate the factors that dictate PM variability (i.e., dust and meteorological covariates), and also processed satellite-based aerosol optical depths (AOD) to determine the spatial variability in aerosol loads. The results showed low PM2.5 concentration during the lockdown (33 µg/m3) compared to the corresponding previous three years (2017-2019); however, the PM10 concentration (122.5 µg/m3) increased relative to 2017 (116.6 µg/m3), and 2019 (92.8 µg/m3). After removing the 'dust effects', both PM2.5 and PM10 levels dropped by 18% and 31%, respectively. The mixed-effect regression model showed that high temperature and high wind speed were the main contributors to high PM2.5 and PM10, respectively, in addition to the dust haze and blowing dust. This study highlights that the reductions of anthropogenic source emissions are overwhelmed by dust events and adverse meteorology in arid regions, and that the lockdown did not reduce the high concentrations of PM in Kuwait.
RESUMEN
Although Kuwait is greatly impacted by sand and dust storms (SDS) from Southern Iraq, to date little is known about the nature of these storms. Kuwait is vulnerable to SDS trajectories from the middle Euphrates region, specifically, from two "hot spot" areas (Al-Batha and Mamlahat Al-Samawah) of 4550 km2 located 250 km from its northern border. This study explores the transboundary SDS jets originating from Southern Iraq using Moderate Resolution Imaging Spectroradiometer (MODIS) images obtained from Aqua and Terra satellites over a twelve-year period (2007-2018). Furthermore, an analysis of a 5-day diurnal variation (two days prior, the day of the SDS occurrence, and two days after) explored the hourly patterns of visibility and wind speed, as well as grain size distribution of soil samples to better understand grain size compositions and sediment transport mechanisms. Satellite images confirmed that dust storm jets originated from the "hot spot" in southern Iraq and spread over Kuwait and extended to neighboring Arab Gulf countries as far as Bahrain (900 km) and Qatar (1200 km). In general, the highest wind speed and lowest visibility values were recorded in Northern of Kuwait, with suspended dust sustained for two days following the dust storm. The largest silt and clay fractions (grains ≤63 µm) were identified at the center and west Sabkha region of the "hot spot" area. Very fine sand particles (63-250 µm) were identified within the crescent sand dunes (Barchans) and artificial sand dunes (Al-Fajr). It is recommended that sustainable rehabilitation and land restoration of the "hot spot" area will result in the elimination of the long-range transport of SDS jet streams affecting the downwind Gulf countries.
RESUMEN
The exposure of the Earth's surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha-1 h-1 yr-1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.
