Assessment of seasonal warming trends at the Nile Delta: a paradigm for human-induced climate change.

Given its modern geographical and geomorphological characteristics, along with rapid socio-economic changes, the Nile Delta stands out as one of the world's most dynamic landscapes. The key drivers of the land use change in this region have been the reclamation of delta margins, changes in agricultural practices, and urban expansion. The present study aims to explore the variations in the seasonal daytime and nighttime trends of the land surface temperatures (LST) at this active agronomic system in response to the seasonal variations of vegetation cover as revealed by the normalized difference vegetation index (NDVI) during the past two decades. The data were exclusively acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument for the period from January 2001 to December 2021, where geospatial and statistical analyses were accomplished to construct a LST/NDVI spatio-temporal pattern throughout the Nile Delta. Results revealed a robust negative and a significant relationship between the NDVI and the diurnal LST with high regression coefficients (R2) ranging from 0.78 to 0.97 (p value < 0.05). Maximal seasonal warming trends occurred during harvesting seasons (springs and falls), while the least warming was recorded during winters (the growing seasons). It was also observed that the nocturnal warming (0.72°C/decade) was almost as double as the corresponding value of the daytime trend (0.33°C/decade). The study recognized a seasonal climatic warming throughout the Nile Delta influenced by the human-induced land use change and agricultural practices.

Impact of COVID-19 lockdown upon the air quality and surface urban heat island intensity over the United Arab Emirates.

The 2019 pandemic of Severe Acute Respiratory Syndrome-Corona Virus Diseases (COVID-19) has posed a substantial threat to public health and major global economic losses. The Northern Emirates of the United Arab Emirates (NEUAE) had imposed intense preventive lockdown measures. On the first of April 2020, a lockdown was implemented. It was assumed, due to lower emissions, that the air quality and Surface Urban Heat Island Intensity (SUHII) had been strengthened significantly. In this research, three parameters for Nitrogen Dioxide (NO2), Aerosol Optical Depth (AOD), and SUHII variables were examined through the NEUAE. we evaluated the percentage of the change in these parameters as revealed by satellite data for 2 cycles in 2019 (March 1st to June 30th) and 2020 (March 1st to June 30th). The core results showed that during lockdown periods, the average of NO2, AOD, and SUHII levels declined by 23.7%, 3.7%, and 19.2%, respectively, compared to the same period in 2019. Validation for results demonstrates a high agreement between the predicted and measured values. The agreement was as high as R2=0.7, R2=0.6, and R2=0.68 for NO2, AOD, and night LST, respectively, indicating significant positive linear correlations. The current study concludes that due to declining automobile and industrial emissions in the NEUAE, the lockdown initiatives substantially lowered NO2, AOD, and SUHII. In addition, the aerosols did not alter significantly since they are often linked to the natural occurrence of dust storms throughout this time of the year. The pandemic is likely to influence several policy decisions to introduce strategies to control air pollution and SUHII. Lockdown experiences may theoretically play a key role in the future as a possible solution for air pollution and SUHII abatement.

Estimation of monthly surface air temperatures from MODIS LST time series data: application to the deserts in the Sultanate of Oman.

Air temperature records in remote deserts and inaccessible mountainous regions rely upon data acquired from the nearest meteorological stations, which could be at tens of kilometers apart. The present study provides a reliable approach to extract air temperatures for any distant region using thermal data of satellite images. The study postulates that if there is a strong correlation between land surface temperatures (LST) from satellite images and air temperature records from ground meteorological stations, hence, air temperatures (day/night) could be directly extrapolated from regression equations with high confidence results. Data utilized in this study were obtained from 12 meteorological stations settled and distributed upon different physiographic units of Oman. Satellite images were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product. Regression analysis of max and min air temperatures from weather stations was conducted versus day and night LST from MODIS Aqua LST (MYD11A2) images. Results showed that the regression coefficients for the selected locations are strong for the night/min (R2 = 0.81-0.94) and day/max (R2 = 0.72-0.92) correlations of the 12 stations. The root mean square errors (RMSE) of the statistical models are 0.97 and 1.98 for the night and day temperatures, respectively. Moreover, the association between each pair of the data is significant at the 99% level (p < 0.01). As MODIS data cover large geographic extents, it was possible to produce national diurnal and annual air temperature maps of accurate records with considering the variation of the physiographic setting.