nasa-gesdisc/es-publications-researchareas huggingface dataset:
A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models (2022)

This paper uses a Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) re-analysis to identify long-term Mediterranean Sea Offshore Wind (OW) classification possible locations. In particular, an OW classification based on the last 40-years period OW speeds highlighted the best areas for potential Offshore Wind Turbine Generators (OWTG) installations in the Mediterranean basin. Preliminary, long-term OW classification results show that several Mediterranean basin zones in the Aegean Sea, Gulf of Lyon, the Northern Morocco and Tunisia regions have attractive OW potential. Secondly, a combined forecasting model based on the wavelet decomposition method and long-term memory neural network has been developed to predict the short-term wind speed considering the last ten years of hourly data for Mediterranean areas. The results of the proposed model for wind speed prediction have been compared with other single models, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), highlighting a higher level of accuracy. Finally, three Weibull fitting algorithms have been provided to analyze the wind energy potential in the Mediterranean basin.

Research areas: Energy Production/Use
Photosynthetic patterns during autumn in three different Salix cultivars grown on a brownfield site (2022)

Leaf senescence at the end of the growing season is a complex process stimulated by changes in daylength and temperature that prepares deciduous trees for winter by reducing photosynthetic rates and remobilization of nutrients. Extending the duration of photosynthetic activity could have important consequences for the translocation of heavy metals in the phytoremediation of contaminated sites using deciduous trees like willow. In the present study, three Salix cultivars (India, SX67, and Fish Creek) that were observed to maintain green leaves late into autumn were evaluated over an 11-week period extending from mid-September to mid-November on a brownfield site in Montreal, Canada. Gas exchange rates, chlorophyll fluorescence, and leaf pigments were measured weekly. A general trend of declining stomatal conductance and transpiration were observed early in the trial, followed by reductions in photosynthetic efficiency and concentrations of chl a, chl b, and carotenoids, in agreement with other studies. In particular, the cultivar Fish Creek had higher rates of gas exchange and pigment concentrations than either SX67 or India, but values for these parameters also declined more rapidly over the course of the trial. Both photoperiod and soil and air temperatures were strong drivers of changes in photosynthetic activity in all three of these cultivars according to correlation analyses. Further studies should focus on their biomass production and heavy metal accumulation capacity in light of the observed variation in photosynthetic activity stimulated by seasonal changes in light and temperature.

Research areas: Environmental Impacts
Observed and Simulated Characteristics of 2015 Chennai Heavy Rain Event: Impact of Land-Use Change, SST, and High-Resolution Global Analyses (2022)

The southern Indian city of Chennai received more than 1200 mm of rainfall between 5 November and 10 December 2015. The record-breaking rain exceeded the previous 24-h precipitation record (290 mm) on 1 and 2 December and resulted in severe urban flooding. Meteorological analysis from observational and reanalysis data sets shows that this rainfall event was the culmination of three synoptic-scale systems with oceanic moisture trajectories. The city has also witnessed dramatic urban sprawl in recent decades, and the feedback of urban heating on regional convection is postulated to be another critical factor in modulating the mesoscale environment and heavy rains. Accordingly, the study seeks to assess whether the Weather Research Forecasting (WRF) modeling system typically used for heavy rain prediction can better simulate this event by considering enhanced meteorological setup and urban feedback. Simulations were conducted using the WRF modeling system with different model configurations for 40 days (00 UTC 1 November until 00 UTC 11 December 2015), with a triple-nested domain and the finest grid spacing of 1.2 km centered over Chennai. The results indicate that the WRF modeling system broadly agrees with the observations, such as from the GPM-IMERG rainfall products. Model experiments also reveal the impact of increased urban built-up area on the rainfall distribution and the improvement in the model results using high-resolution atmospheric and SST initialization. The findings indicate that even for this synoptic-driven event, representing realistic urban land cover can aid the simulation of the heavy urban rainfall.

Research areas: Atmospheric/Ocean Indicators, Habitat Conversion/Fragmentation
Comparison of development trends of Tehran and Isfahan cities and its effects on vegetation, climate and air pollution using GEE, Giovanni (2022)

Background and objective: Population growth and industrialization of cities have led to the rapid growth of urbanization and development of cities. Today, the evaluation of urban physical development using modern remote sensing techniques can provide basic information to planners. The purpose of this study is to investigate the trend of urban development and its effects on changes in vegetation, temperature, rainfall, and air pollution in the two metropolises of Tehran and Isfahan.Materials and methods: In this study, two systems Google Earth Engine (GEE) and Giovanni have been used to analyze indicators such as temperature, vegetation, humidity, and precipitation. NDVI maps and diagrams of two cities in 5- and 10-year periods were produced from MODIS satellite (2000 to 2021) and Landsat 8 (2013 to 2021).Results and conclusion: Urban development and population growth in Tehran have led to an increase in heat islands (LST) and temperature in the region, which has a direct effect on the reduction of snow and rain and, as a result, the reduction of runoff, vegetation, and soil moisture. Due to the development of industrial towns and residential constructions around Isfahan, destructive environmental effects related to vegetation, weather, and pollution in Mashhad, and the increase in urbanization and industry have led to a decrease in the level of vegetation and NDVI in this area. This conclusion was obtained that the obtained results confirm that the development of the cities of Tehran and Isfahan has destructive environmental effects on the weather and vegetation in all directions.

Research areas: Habitat Conversion/Fragmentation, Land Surface/Agriculture Indicators
Analysis of wildfires and associated emissions during the recent strong ENSO phases in Southern Africa using multi-source remotely-derived products (2022)

In southern Africa, drier conditions are more pronounced during the El Nino Southern Oscillation (ENSO) years, triggering wildfire activity and extreme drought conditions which, individually or together, lead to loss of crop productivity, deaths of livestock and wildlife, famine, degraded ecosystems, water quality and quantity. However, the fire characteristics in relation to the emissions from biomass burning and surface properties are only examined to a limited extent in the literature, especially in Africa, where anthropogenic activities largely determine the fire activity. This study uses the available data from multi-source remote sensing platforms to (1) analyse the spatial distribution of wildfires and associated emissions during strong El Nino (2015/2016) and La Nina (2010/2011) phases in southern Africa, and (2) examine the effects of the severe El Nino and La Nina years on the relationship between the emission parameters, vegetation parameters and climatic parameters. Generally, the results suggest more emissions from the wildfire in the El Nino phase than that of the La Nina. Overall, the Pearsons correlation clearly shows the influence and the relationship between the climate parameters themselves and also with emission parameters.

Research areas: Wildfires, Atmospheric/Ocean Indicators, Environmental Impacts
A deep insight into state-level aerosol pollution in India: Long-term (20052019) characteristics, source apportionment, and future projection (2023) (2022)

The present study provides a national scenario of aerosol pollution with the long-term (20052019) trend, source apportionment, and future scenario (2023) for each of the Indian states. We used MODIS AOD and FRP, differential AODs from MERRA-2, and trace gases (NO2, SO2) data from OMI. Almost all the states of IGP fall under the red zone (highly vulnerable; AOD >0.5) whereas central, western, and a few south-Indian states fall under the orange zone (vulnerable; 0.4 < AOD >0.5). The most alarming feature is that most of the southern Indian states exhibit a shift from blue/green (less vulnerable/safe; AOD <0.4) to vulnerable zones in 2023 as observed using the auto-regressive integrated moving average (ARIMA) model. Principal component analysis (PCA) revealed that the coal-fired thermal power plant (TPP), vehicular, solid fuel/waste, and biomass burning are the major sources of aerosols for the vulnerable states at present and in the future. We estimated and proposed the TPP capacity (GW) that needs to be reduced to bring down the AOD to move the vulnerable zones to less vulnerable and safe zones. The present study would complement and strengthen the ongoing national missions to combat air pollution in India.

Research areas: Environmental Impacts
Analysis of Wildfires in the Mid and High Latitudes Using a Multi-Dataset Approach: A Case Study in California and Krasnoyarsk Krai (2022)

In this study, we investigate the emissions from wildfires in the mid latitude (California) and high latitude (Krasnoyarsk Krai) during the periods of 1617 August 2020 and 28 July 2019, respectively. Wildfires are unique in themselves as they are driven by various factors such as fuel type, topology, and meteorology. In this study, we analyze whether there are any major variations in the emissions and transport of pollutants between two large wildfire cases in the mid latitude of California and high latitude of Krasnoyarsk Krai. The study is important to understand and characterize the emission regime from biomass burning of different land covers using a mutli-dataset approach. We analyze whether there are any major variations in the emissions and transport of pollutants from these wildfires. For example, the aerosol extinction coefficient profile showed smoke detected at the highest altitude of 9 km in Krasnoyarsk Krai, whereas in California the highest altitude was observed at approximately 6 km. Moreover, large values of black carbon (BC) concentration were observed in Krasnoyarsk Krai approximately 7 g/m3 compared to the 0.44 g/m3 observed in California. Areas with an immense dense vegetation are prone to large emissions. The results from this case study suggest that high latitude wildfires emit more pollutants than mid latitude wildfires. However, more studies in the future will be conducted to conclude this observation and finding with certainty.

Research areas: Wildfires, Environmental Impacts, Air Quality
Rapid Extreme Tropical Precipitation and Flood Inundation Mapping Framework (RETRACE): Initial Testing for the 20212022 Malaysia Flood (2022)

The 20212022 flood is one of the most serious flood events in Malaysian history, with approximately 70,000 victims evacuated daily, 54 killed and total losses up to MYR 6.1 billion. From this devastating event, we realized the lack of extreme precipitation and flood inundation information, which is a common problem in tropical regions. Therefore, we developed a Rapid Extreme TRopicAl preCipitation and flood inundation mapping framEwork (RETRACE) by utilizing: (1) a cloud computing platform, the Google Earth Engine (GEE); (2) open-source satellite images from missions such as Global Precipitation Measurement (GPM), Sentinel-1 SAR and Sentinel-2 optical satellites; and (3) flood victim information. The framework was demonstrated with the 20212022 Malaysia flood. The preliminary results were satisfactory with an optimal threshold of five for flood inundation mapping using the Sentinel-1 SAR data, as the accuracy of inundated floods was up to 70%. Extreme daily precipitation of up to 230 mm/day was observed and resulted in an inundated area of 77.43 km2 in Peninsular Malaysia. This framework can act as a useful tool for local authorities and scientists to retrace the extreme precipitation and flood information in a relatively short period for flood management and mitigation strategy development.

Research areas: Floods, Atmospheric/Ocean Indicators
Potential utilization of air temperature, total electron content, and air relative humidity as possible earthquake precursors: A case study of Mexico M7.4 earthquake (2022)

This study examines ionospheric total electron content (TEC) perturbations from six International Global Navigation Satellite System stations (GNSS) including GUAT-Guatemala, SSIA-El Salvador, INEG-Mexico, MANA-Nicaragua, MDO1-United States of America, and BOGT- Colombia for several days around the occurrence of a major earthquake (M 7.4 and depth 20.0 km) in Mexico, June 23, 2020 at 10:29 Local Time (LT). The INEG station in the North-Northwest of the epicenter at a distance of about 936 km indicated a positive TEC anomaly on June 18, 2020, which can be possibly viewed as an earthquake precursor due to its occurrence during a quiet geomagnetic storm and inactive solar activity. Study findings reveal that other TEC perturbations may not be related to the earthquake, because they appeared during geomagnetic activities. Moreover, the atmospheric parameters have significant and synchronous deviations from the earthquake epicenter on June 13, 2020. The highest atmospheric chemical potential (ACP) is about 0.010 eV, atmospheric air temperature has positive deviation of 3.937 C at 15:00 LT, the lowest atmospheric relative humidity has negative deviation of 25.387% at 13:00 LT and outgoing longwave radiation (OLR) 27.58 W m2. Observations validate that in the earthquake preparation zone, variations in atmospheric air temperature and relative humidity reach at peak value during 10 days prior to the impending earthquake event with the later perturbations in the ionosphere.

Research areas: Earthquakes
Physical control on the inter-annual variability of summer dissolved nutrient concentration and phytoplankton biomass in the Indian sector of the Southern Ocean (2022)

To understand the role of physical processes and their interannual variability on the dissolved nutrient concentration and phytoplankton biomass distribution, field data collected in the Indian sector of the Southern Ocean (ISSO) during the austral summer of 20092011 are used. In the subtropical zone, macronutrients were limited (N:P < 1, N:Si < 1, Si:P  1) and the phytoplankton biomass variability was mainly governed by the mesoscale eddy activity associated with the Agulhas Return Current. High nutrient low chlorophyll condition prevailed in the sub-Antarctic zone and further south. A South-North gradient of the upper layer dissolved SiO2 was higher than that of NO3. The sub-Antarctic zone was characterized by the highest N:Si ratio (>4) and it was associated with the enhanced draw down of silicate due to the winter/spring diatom blooms in the region. The chlorophyll-a (CHL) concentration in the Polar Frontal Zone was low (0.2 mg m3) in 2009 and 2010 but it was high (0.5 mg m3) in 2011. This increase in CHL in 2011 was due to the supply of dissolved iron from the strong winds and subsequent mixing during the winter of 2010. Further, the increased CHL values in the Antarctic zone (0.5 mg m3) in 2011 compared to 2009 and 2010 could be due to the increased sea ice melting associated with positive Southern Annular Mode. The increased phytoplankton biomass in the summer of 2011 coincides with an increase in nitrate utilization (N:P13) compared to 2009 and 2010. Observations showed that ISSO frontal zones are characterized by inter-annual variability in terms of nutrient utilization and phytoplankton biomass production.

Research areas: Ecosystems
Long-term trends in aerosol optical properties and their relationship with cloud properties over southern India and Sri Lanka (2022)

Asia is one of the continents where aerosol levels are comparatively higher across the world. India and Sri Lanka are some of the regions in the South Asian continent where pollution is increasing rapidly due to the rise in industrialization. The present study investigated the interactions between atmospheric aerosol and cloud microphysical properties and their spatial, temporal, and seasonal variation at local and regional scales during 20002020 using remotely sensed data sets in south India and Sri Lanka. High values (>0.5) of annual mean aerosol optical depth (AOD) were detected over southern India. On the other hand, lower values (0.2) were detected over Sri Lanka. In terms of seasonality, a high level of AOD was registered in both southern India and Sri Lanka during the premonsoon and monsoon periods. The angstrom exponent (AE412-470) revealed the dominance of fine-mode particles during winter and the postmonsoon, generally from biomass burning and industrial activities. The long-term analysis exhibited an increasing trend of atmospheric aerosol concentration over southern India and Sri Lanka. Interestingly, there was a decrease in AOD during the year 2020; the reduction in anthropogenic activities in the region was attributed to the COVID-19 lockdown, hence less accumulation of pollutants in the atmosphere. AOD showed a positive correlation with cloud effective radius over the western areas along with the Indian Ocean, north, southeast, and southern end of India, while negatively correlated with high AOD areas such as northeast of the study domain. The AOD and cloud optical depth were positively correlated over continental areas, while negative correlations were notable over the Indian Ocean around Sri Lanka, implying heterogeneities of aerosol's effect on cloud microphysical properties over the study area. Finally, the results from wind circulation and backward air mass trajectories reveal higher concentrations of fine-mode particles associated with the continent, whereas coarse-mode particles originate from the oceans.

Research areas: Atmospheric/Ocean Indicators, Environmental Impacts
Spatial and temporal variability of the physical, carbonate and CO2 properties in the Southern Ocean surface waters during austral summer (2005-2019) (2022)

In situ measurements of sea surface temperature (SST), salinity (SSS), Total Alkalinity (AT) and Total Carbon (CT) were obtained during austral summer (mid-February to mid-March) from 2005 to 2019 in the Southern Ocean (SO), along a transect between Hobart, Tasmania and Dumont dUrville French Antarctic Station. The studied transect is divided in four regions from North to South: the Subtropical Zone (STZ), the Subantarctic Region (SAR), the Antarctic Region (AAR) and the Coastal Antarctic Zone (CAZ). Latitudinal distribution of measured SST, SSS, AT, CT as well as calculated pH, CO2 parameters (seawater fugacity of CO2 (fCO2sw), difference between seawater and atmospheric fCO2 (fCO2), CO2 flux (FCO2)) and satellite-derived Chlorophyll a (Chl-a) are discussed. We show that the variability of physical and carbonate parameters in the STZ and north of the SAR are related to the mesoscale activity. In the CAZ, the freshwater inputs from sea-ice melting strongly impact the variability of all parameters. The comparison between physical and carbonate parameters highlights that AT and CT are directly related to the latitudinal variability of SST and SSS. Study of the CO2 parameters shows that the transect is a sink of CO2 during February and March, with a mean FCO2 of 4.0  2.8 mmol m2 d1. The most negative values of FCO2 are found in the STZ and SAR north of 50S and in the AAR south of 62S, where biological activity is high. New simple empirical relationships are developed for AT from SST and SSS and for CT using SST, SSS and atmospheric fCO2 (fCO2atm) for the austral summer in the studied area. Using high resolution SSS and SST from the SURVOSTRAL program, trends of AT and CT are determined in the SAR and the AAR from 2005 to 2019. SST, SSS and AT increase over this period in the SAR, which might be explained by the southward migration of the Subtropical Front. In the AAR, no clear trend is detected. CT increases by 1.0  0.2 and 0.8  0.3 mol kg1 yr1 in the SAR and AAR respectively. The trend in the AAR is attributed to the increase in anthropogenic CO2 emissions in the atmosphere while, in the SAR, hydrographic changes also contribute to the increase. Using the coefficient associated with fCO2atm in the equation of CT, we estimate the impact of atmospheric CO2 increase on CT at 1.18  0.14 mol kg1 yr1 and 1.07  0.13 mol kg1 yr1 in the SAR and AAR respectively. Decreases in pH are observed in both regions (0.0018  0.0001 and 0.0026  0.0003 yr1 in the SAR and AAR respectively), indicating the sensitivity of surface waters in the area towards the development of ocean acidification processes under rising anthropogenic emissions.

Research areas: Atmospheric/Ocean Indicators
AI-based ensemble modeling of landfill leakage employing a lysimeter, climatic data and transfer learning (2022)

Predicting leachate pollutants is of prime importance in detecting the amount of pollution in water resources adjacent to sources of leakage. In this study, Electrical Conductivity (EC) as a physicochemical water pollution parameter with the possibility of portable measurement was used as an indicator of leachate quality for the Tychy-Urbanowice operating and closed landfill complex. In order to simulate landfill conditions, two lysimeter experiments were conducted simultaneously. Using sensors mounted in the lysimeters, from the end of November 2018 to the end of December 2019, EC, waste temperature and waste moisture were measured for the open lysimeter and only waste moisture for the closed lysimeter. Additionally, meteorological data obtained from the nearest synoptic station and soil moisture and temperature acquired from the GLDAS satellite were employed as external data to analyze various conditions. Thereafter, Artificial Neural Network (ANN), Neuro-Fuzzy Inference System (ANFIS), and Emotional ANN (EANN) models were developed to determine the parameters affecting the EC value recorded for the open lysimeter and subsequently, predict the missing EC parameter of the closed lysimeter by employing the transfer learning method. Following that, in order to improve the precision of EC predictions, ensemble techniques were applied to the outputs of the models that were developed. The results showed that the moisture of the lysimeters made a significant contribution to the EC value prediction. It is worth mentioning that among ANN, ANFIS, and EANN, the EANN model yielded more precise results in EC estimation, with the average DC above 0.80 and 0.90 for individual and ensembled modeling in both the training and verification phases, respectively.

Research areas: Water Quality