Groundwater vulnerability maps were created for the Corridor wellfield (~300 km<sup><span>2</span></sup><span>) in the eastern Jordan using the DRASTIC and modified DRASTIC groundwater vu...Groundwater vulnerability maps were created for the Corridor wellfield (~300 km<sup><span>2</span></sup><span>) in the eastern Jordan using the DRASTIC and modified DRASTIC groundwater vulnerability assessment models. Th</span><span>e</span><span> study area is considered as one of the most important well fields therein providing partially three governorates with the needed drinking water. Detailed geological and hydrogeological parameters as well as the land-use map of the area were obtained from various sources to utilize both models. ArcGIS software was used for calculations and maps preparation. As a result, the generic DRASTIC vulnerability index ranges between 109 and 168. Thus, two vulnerability classes were observed, moderate (9.9%) and high (90.1%) vulnerability classes. On the other hand, the modified DRASTIC model (risk map) is taking into account the land-use map classes in the study area. The output risk map reveals two main classes, the moderate and high-risk areas. The moderate-risk areas occupy 9.3% of the total volume of the study area while the high-risk areas are 90.7%. Due to the high depth to groundwater within the area (between 90 m and 390 m), the depth to groundwater intervals was modified in the model to become more comfortable with the situation in Jordan. The high percentage of the high vulnerable areas against pollutants reflect</span><span>s</span><span> the need to do more investigation for the studied area.</span>展开更多
High-resolution(HR)climate data are indispensable for studying regional climate trends,disaster prediction,and urban development planning in the face of climate change.However,state-of-the-art long-term global climate...High-resolution(HR)climate data are indispensable for studying regional climate trends,disaster prediction,and urban development planning in the face of climate change.However,state-of-the-art long-term global climate simulations do not provide appropriate HR climate data.Deep learning models are often used to obtain high-resolution climate data.However,due to the fact that these models require sufficient low-resolution(LR)and HR data pairs for the training process,they cannot be applied to scenario with inadequate training data.In this paper,we explore the applicability of a single image generative adversarial network(SinGAN)in generating HR climate data.SinGAN relies on single LR input data to obtain the corresponding HR data.To improve the performance for extreme-value regions,we propose a SinGAN combined with the weighted patchGAN discriminator(WSinGAN).The proposed WSinGAN outperforms comparable models in generating HR precipitation data,and its results are close to real HR data with sharp gradients and more refined small-scale features.We also test the scalability of the pre-trained WSinGAN for unseen samples and show that although only a single LR sample is used to train WSinGAN,it can still produce reliable HR data for unseen data.展开更多
The massive lockdown of human socioeconomic activities and vehicle movements due to the COVID-19 pandemic in 2020 has resulted in an unprecedented reduction in pollutant gases such as Nitrogen Dioxide(NO_(2))and Carbo...The massive lockdown of human socioeconomic activities and vehicle movements due to the COVID-19 pandemic in 2020 has resulted in an unprecedented reduction in pollutant gases such as Nitrogen Dioxide(NO_(2))and Carbon Monoxide(CO)as well as Land Surface Temperature(LST)in Amman as well as all countries around the globe.In this study,the spatial and temporal variability/stability of NO_(2),CO,and LST throughout the lockdown period over Amman city have been analyzed.The NO_(2) and CO column density values were acquired from Sentinel-5p while the LST data were obtained from MODIS satellite during the lockdown period from 20 March to 24 April in 2019,2020,and 2021.The statistical analysis showed an overall reduction in NO_(2) in 2020 by around 27% and 48% compared to 2019 and 2021,respectively.However,an increase of 7% in 2021 compared to 2019 was observed because almost all anthropogenic activities were allowed during the daytime.The temporal persistence showed almost constant NO2 values in 2020 over the study area throughout the lockdown period.In addition,a slight decrease in CO(around 1%)was recorded in 2020 and 2021 compared to the same period in 2019.Restrictions on human activities resulted in an evident drop in LST in 2020 by around 13%and 18% less than the 5-year average and 2021 respectively.The study concludes that due to the restrictions imposed on industrial activities and automobile movements in Amman city,an unprecedented reduction in NO_(2),CO,and LST was recorded.展开更多
文摘Groundwater vulnerability maps were created for the Corridor wellfield (~300 km<sup><span>2</span></sup><span>) in the eastern Jordan using the DRASTIC and modified DRASTIC groundwater vulnerability assessment models. Th</span><span>e</span><span> study area is considered as one of the most important well fields therein providing partially three governorates with the needed drinking water. Detailed geological and hydrogeological parameters as well as the land-use map of the area were obtained from various sources to utilize both models. ArcGIS software was used for calculations and maps preparation. As a result, the generic DRASTIC vulnerability index ranges between 109 and 168. Thus, two vulnerability classes were observed, moderate (9.9%) and high (90.1%) vulnerability classes. On the other hand, the modified DRASTIC model (risk map) is taking into account the land-use map classes in the study area. The output risk map reveals two main classes, the moderate and high-risk areas. The moderate-risk areas occupy 9.3% of the total volume of the study area while the high-risk areas are 90.7%. Due to the high depth to groundwater within the area (between 90 m and 390 m), the depth to groundwater intervals was modified in the model to become more comfortable with the situation in Jordan. The high percentage of the high vulnerable areas against pollutants reflect</span><span>s</span><span> the need to do more investigation for the studied area.</span>
文摘High-resolution(HR)climate data are indispensable for studying regional climate trends,disaster prediction,and urban development planning in the face of climate change.However,state-of-the-art long-term global climate simulations do not provide appropriate HR climate data.Deep learning models are often used to obtain high-resolution climate data.However,due to the fact that these models require sufficient low-resolution(LR)and HR data pairs for the training process,they cannot be applied to scenario with inadequate training data.In this paper,we explore the applicability of a single image generative adversarial network(SinGAN)in generating HR climate data.SinGAN relies on single LR input data to obtain the corresponding HR data.To improve the performance for extreme-value regions,we propose a SinGAN combined with the weighted patchGAN discriminator(WSinGAN).The proposed WSinGAN outperforms comparable models in generating HR precipitation data,and its results are close to real HR data with sharp gradients and more refined small-scale features.We also test the scalability of the pre-trained WSinGAN for unseen samples and show that although only a single LR sample is used to train WSinGAN,it can still produce reliable HR data for unseen data.
文摘The massive lockdown of human socioeconomic activities and vehicle movements due to the COVID-19 pandemic in 2020 has resulted in an unprecedented reduction in pollutant gases such as Nitrogen Dioxide(NO_(2))and Carbon Monoxide(CO)as well as Land Surface Temperature(LST)in Amman as well as all countries around the globe.In this study,the spatial and temporal variability/stability of NO_(2),CO,and LST throughout the lockdown period over Amman city have been analyzed.The NO_(2) and CO column density values were acquired from Sentinel-5p while the LST data were obtained from MODIS satellite during the lockdown period from 20 March to 24 April in 2019,2020,and 2021.The statistical analysis showed an overall reduction in NO_(2) in 2020 by around 27% and 48% compared to 2019 and 2021,respectively.However,an increase of 7% in 2021 compared to 2019 was observed because almost all anthropogenic activities were allowed during the daytime.The temporal persistence showed almost constant NO2 values in 2020 over the study area throughout the lockdown period.In addition,a slight decrease in CO(around 1%)was recorded in 2020 and 2021 compared to the same period in 2019.Restrictions on human activities resulted in an evident drop in LST in 2020 by around 13%and 18% less than the 5-year average and 2021 respectively.The study concludes that due to the restrictions imposed on industrial activities and automobile movements in Amman city,an unprecedented reduction in NO_(2),CO,and LST was recorded.
