Application of Recurrent Neural Networks for Drought Projections in California

We use recurrent neural networks (RNNs) to investigate the complex interactions between the long-term trend in dryness and a projected, short but intense, period of wetness due to the 2015-2016 El Niño. Although it was forecasted that this El Niño season would bring significant rainfall to the region, our long-term projections of the Palmer Z Index (PZI) showed a continuing drought trend, contrasting with the 1998-1999 El Niño event. RNN training considered PZI data during 1896-2006 that was validated against the 2006-2015 period to evaluate the potential of extreme precipitation forecast. We achieved a statistically significant correlation of 0.610 between forecasted and observed PZI on the validation set for a lead time of 1 month. This gives strong confidence to the forecasted precipitation indicator. The 2015-2016 El Niño season proved to be relatively weak as compared with the 1997-1998, with a peak PZI anomaly of 0.242 standard deviations below historical averages, continuing drought conditions

Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data

Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries are utilized here to study the aerosol optical depth (AOD) and Ångström exponent (AE) with other available observations. Additionally, a total of 65 different machine learning models of four categories, namely: linear regression, ensemble, decision tree, and deep neural network (DNN), were built from multiple data sources (MODIS, MISR, OMI, and MERRA-2) to predict the best usable AOD product as compared to AERONET data. DNN validates well against AERONET data and proves to be the best model to generate optimized aerosol products when the ground observations are insufficient. This approach can improve the knowledge of air pollutant variability and intensity in the MENA region for decision makers to operate proper mitigation strategies

Aerosol Climatology Over Nile Delta Based on MODIS, MISR and OMI Satellite Data

Since 1999 Cairo and the Nile delta region have suffered from air pollution episodes called the “black cloud” during the fall season. These have been attributed to either burning of agriculture waste or long-range transport of desert dust. Here we present a detailed analysis of the optical and microphysical aerosol properties, based on satellite data. Monthly mean values of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) at 550 nm were examined for the 10 yr period from 2000–2009. Significant monthly variability is observed in the AOD with maxima in April or May (_0.5) and October (_0.45), and a minimum in December and January (_0.2). Monthly mean values of UV Aerosol Index (UVAI) retrieved by the Ozone Monitoring Instrument (OMI) for 4 yr (2005–2008) exhibit the same AOD pattern. The carbonaceous aerosols during the black cloud periods are confined to the planetary boundary layer (PBL), while dust aerosols exist over a wider range of altitudes, as shown by Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) aerosol profiles. The monthly climatology of Multi-angle Imaging Spectro-Radiometer (MISR) data show that the aerosols during the black cloud periods are spherical with a higher percentage of small and medium size particles, whereas the spring aerosols are mostly large non-spherical particles. All of the results show that the air quality in Cairo and the Nile delta region is subject to a complex mixture of air pollution types, especially in the fall season, when biomass burning contributes to a background of urban pollution and desert dust

Using Multi-indices Approach to Quantify Mangrove Changes Over the Western Arabian Gulf along Saudi Arabia Coast

Mangroves habitat present an important resource for large coastal communities benefiting from activities such as fisheries, forest products and clean water as well as protection against coastal erosion and climate related extreme events. Yet they are increasingly threatened by natural pressure and anthropogenic activities. We observed an inaccurate distribution of mangroves over the Western Arabian Gulf (WAG) which is a vital habitat and resource for the local ecosystem, according to the United Stated Geological Survey (USGS) mangrove database through spectral analysis. Change detection analysis is conducted on mangrove forests along the Saudi Arabian coast of the WAG for the years 2000, 2010 and 2018 using Landsat 7 & 8 data. Three supervised classification methodologies are employed for mangrove mapping, including Supported Vector Machine (SVM), Decision Tree (DT), referred to as Classification and Regression Trees (CART) and Random Forest (RF). CART’s accuracy was recorded to be \u3e95% while other classifiers were \u3e90%. The CART supervised learning classifier, mapping mangroves’ distribution and biomass using Google Earth Engine (GEE) online platform, indicates an overall increase in the northern Tarut Bay and Tarut Island, by 0.21 km2 from 2000 to 2010 and by 1.4 km2 from 2010 to 2018. The increase might be due to mitigation strategies such as mangrove breeding and plantation. It can be challenging to detect changes in certain regions due to the inadequate resolution of Landsat where submerged mangroves can be confused with salt marshes and macro algae. We employed a new method to identify and analyze submerged mangrove forests distribution via a submerged mangrove recognition index (SMRI) and Normalized Difference Vegetation Index (NDVI) in Abu Ali Island. Our results show the robustness of SMRI as an effective indicator to detect submerged mangroves in both high and medium spatial resolution satellite images. NDVI values differentiated submerged mangroves from tidal flats between Landsat 7 & 8 as well as during conditions of low and high tides. High resolution WorldView-2 image showed agreement of mangroves distribution with the SMRI and NDVI results

Abl Kinase Inhibitors from Egyptian Spinach Leaves in the Treatment of Chronic Myeloid Leukemia

Presenter: Ospanov Meirambekhttps://egrove.olemiss.edu/pharm_annual_posters_2021/1013/thumbnail.jp

An Assessment of Atmospheric and Meteorological Factors Regulating Red Sea Phytoplankton Growth

This study considers the various factors that regulate nutrients supply in the Red Sea. Multi-sensor observation and reanalysis datasets are used to examine the relationships among dust deposition, sea surface temperature (SST), and wind speed, as they may contribute to anomalous phytoplankton blooms, through time-series and correlation analyses. A positive correlation was found at 0–3 months lag between chlorophyll-a (Chl-a) anomalies and dust anomalies over the Red Sea regions. Dust deposition process was further examined with dust aerosols’ vertical distribution using satellite lidar data. Conversely, a negative correlation was found at 0–3 months lag between SST anomalies and Chl-a that was particularly strong in the southern Red Sea during summertime. The negative relationship between SST and phytoplankton is also evident in the continuously low levels of Chl-a during 2015 to 2016, which were the warmest years in the region on record. The overall positive correlation between wind speed and Chl-a relate to the nutritious water supply from the Gulf of Aden to the southern Red Sea and the vertical mixing encountered in the northern part. Ocean Color Climate Change Initiative (OC-CCI) dataset experience some temporal inconsistencies due to the inclusion of different datasets. We addressed those issues in our analysis with a valid interpretation of these complex relationships

Long-Term NDVI and Recent Vegetation Cover Profiles of Major Offshore Island Nesting Sites of Sea Turtles in Saudi Waters of the Northern Arabian Gulf

Vegetation is an important ecological component of offshore islands in the Arabian Gulf (AG), which maintains long-term resilience of these islands. This is achieved by influencing sediment retention and moisture acquisition via condensation during periods of high humidity and by providing a variety of microhabitats for island fauna. The resilience of offshore islands’ ecosystems in the Saudi waters is important because they host the largest number of nesting hawksbill and green turtles in the AG. This study defines the characteristics and the long-term trends in vegetation cover of the offshore islands used by sea turtles as nesting grounds in the northern AG. To establish a ground-validated baseline for vegetation profiles, a 50 m × 50 m grid system is developed on Karan and Jana islands (Is.) with photo-quadrats taken at each grid intersection. The 1,317 and 444 photo-quadrats, for Karan and Jana Is., respectively, were analyzed for maximum plant height and percent cover of living (green) plants, dead plants, and bare sand. Landsat 7 and 8 satellite top-of-atmosphere reflectance images were used to calculate the Normalized Difference Vegetation Index (NDVI) from 1999 through 2018 to analyze the long-term vegetation profiles of the islands. Monthly rainfall data from five meteorological stations along the Eastern Province of Saudi Arabia and Oceanic Niño Index (ONI) are presented to provide a context of the long-term NDVI time series variability. The ground-validated vegetation profiles provided baseline data during the onset of summer in 2017 and revealed differences in maximum plant height and the extent of living, dead vegetation and sand cover on Jana Is. (28.3 cm, 19.9%, 63.3%, and 16.8%) and Karan Is. (21.7 cm, 20.6%, 48.7%, and 30.7%), respectively. The NDVI data for both islands are grouped into three periods, namely: 2001–2007 - high winter, low summer; 2008–2013 – low winter, low summer; 2014–2018 – irregular high/low winter, low summer. The long-term trend showed a slightly decreasing NDVI when compared in the context of the high NDVI measured for the two islands during the early 2000 s, particularly during the winter time. An extended reduction in winter NDVI was recorded for six years from 2008 to 2013, which coincided with reduced rainfall in the region and prolonged La Niña. Five extreme dips in winter NDVI values coincided with strong (2000, 2008, and 2011) and moderate (2012 and 2018) La Niña events. Long-term vegetation profiles of the offshore islands seemed to be tightly coupled with long-term rainfall patterns

Multidecadal Analysis of Beach Loss at the Major Offshore Sea Turtle Nesting Islands in the Northern Arabian Gulf

Undocumented historical losses of sea turtle nesting beaches worldwide could overestimate the successes of conservation measures and misrepresent the actual status of the sea turtle population. In addition, the suitability of many sea turtle nesting sites continues to decline even without in-depth scientific studies of the extent of losses and impacts to the population. In this study, multidecadal changes in the outlines and area of Jana and Karan islands, major sea turtle nesting sites in the Arabian Gulf, were compared using available Kodak aerographic images, USGS EROS Declassified satellite imagery, and ESRI satellite images. A decrease of 5.1% and 1.7% of the area of Jana and Karan islands, respectively, were observed between 1965 and 2017. This translated to 14,146 m2 of beach loss at Jana Is. and 16,376 m2 of beach loss at Karan Is. There was an increase of island extent for Karan Is. from 1965 to 1968 by 9098 m2 but comparing 2017 with 1968, Karan Is. lost as much as 25,474 m2 or 2.6% of the island extent in 1968. The decrease in island aerial extent was attributed to loss of beach sand. The southern tips of the island lost the most significant amount of sand. There was also thinning of beach sand along the middle and northern sections that exposed the rock outcrops underneath the beach. The process of beach changes of both islands was tracked by the satellite imagery from Landsat 1,3,5,7 and Sentinel-2 during 1972 to 2020. Other factors including the distribution of beach slope, sea level changes, as well as wind & current from both northward and eastward components were analyzed to show its impact on the beach changes. The loss of beach sand could potentially impact the quality and availability of nesting beach for sea turtles utilizing the islands as main nesting grounds. Drivers of beach loss at the offshore islands are discussed in the context of sea level rise, dust storms, extreme wave heights and island desertification

Annual Patterns of Atmospheric Pollutions and Episodes over Cairo Egypt

The Nile Delta major cities, particularly Cairo, experienced stagnant air pollution episodes, known as Black Cloud, every year over the past decade during autumn. Low-elevated thermal inversion layers play a crucial role in intensifying pollution impacts. Carbon monoxide, ozone, atmospheric temperature, water vapor, and methane measurements from the tropospheric emission spectrometer (TES) on board the Aura have been used to assess the dominant component below the inversion layer. In this study, time series analysis, autocorrelations, and cross correlations are performed to gain a better understanding of the connections between those parameters and their local effect. Satellite-based data were obtained for the years 2005–2010. The parameters mentioned were investigated throughout the whole year in order to study the possible episodes that take place in addition to their change from year to year. Ozone and carbon monoxide were the two major indicators to the most basic episodes that occur over Cairo and the Delta region

Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data

Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries are utilized here to study the aerosol optical depth (AOD) and Ångström exponent (AE) with other available observations. Additionally, a total of 65 different machine learning models of four categories, namely: linear regression, ensemble, decision tree, and deep neural network (DNN), were built from multiple data sources (MODIS, MISR, OMI, and MERRA-2) to predict the best usable AOD product as compared to AERONET data. DNN validates well against AERONET data and proves to be the best model to generate optimized aerosol products when the ground observations are insufficient. This approach can improve the knowledge of air pollutant variability and intensity in the MENA region for decision makers to operate proper mitigation strategies