nasa-gesdisc/es-publications-researchareas huggingface dataset:
Analyzing Precipitation Changes in the Northern Tip of the Antarctic Peninsula during the 19702019 Period (2020)

Five decades of precipitation data are available from the Chilean Antarctic weather stations located in the northern tip of the Antarctic Peninsula. Data include daily accumulation and type of precipitation registered at the time of the observation at the Meteorological Antarctic Center located at Base Eduardo Frei Montalva, King George Island. This information allowed us to analyze not only the precipitation accumulation changes (always questionable in cold and windy regions) but also changes in precipitation days and precipitation phases (rain versus snow). The expo nential filter was applied to the monthly data to obtain decadal-like changes. The analysis revealed an overall increase in precipitation from 1970 to the early-1990s 60  7 mm (10 year)1 (p < 0.05) and 31  4 mm (10 year)1 (p < 0.05) and a negative trend between 1991 and 1999 with decreasing precipitation of 95  9 mm (10 year)1 (p < 0.05). On the other hand, while an increase in precipitation events also took place from 1970 to the early-1990s, there was a decreasing trend in precipitation events during the 2010s. This implies that the positive trend in precipitation accumulation registered during this period is due to the increasing extreme precipitation events. The precipitation type analysis shows an increase (decrease) in snow (rain) events from around the mid-1990s to mid-2010s during the summer season. These opposite trends were related to the summer cooling affecting the AP region.

Research areas: Atmospheric/Ocean Indicators
Investigating and modelling ground settlement response to groundwater dynamic variation in parts of Lagos using space-based retrievals (2021)

Ground settlement has impacted many infrastructures in Lagos. Rapid urbanisation has further compounded this problem. Monitoring and modelling of ground settlement will provide actionable information that can help to mitigate this problem. In this study, space-based retrievals were used to investigate the magnitude and spatial distribution of ground settlement and the role of groundwater depletion in Lagos. Using the SBAS technique, the ground settlement rates were computed based on the Sentinel-1 data acquired between 20152019. The GRACE-derived groundwater head changes between 20022017 was quantified, and the ground settlement response to groundwater depletion was modelled. The highest recorded groundwater depletion rate was 11 mm/year, while the highest observed ground settlement rate was 94 mm/year, indicating an intense rate of ground settlement up to the end of 2019. The statistical correlation between ground settlement and groundwater head changes is in the order of 0.4, which is a moderate positive correlation indicating a partial correlation between ground settlement and groundwater depletion. To model settlement, a one-dimensional approach was used to simulate the response of ground settlement to changes in groundwater level. The simulated results fairly correspond to the magnitude of observed settlement, confirming that factors other than groundwater depletion partly control ground settlement in Lagos.

Research areas: Land Surface/Agriculture Indicators
Nontarget mass spectrometry and in silico molecular characterization of air pollution from the Indian subcontinent (2022)

Fine particulate-matter is an important component of air pollution that impacts health and climate, and which delivers anthropogenic contaminants to remote global regions. The complex composition of organic molecules in atmospheric particulates is poorly constrained, but has important implications for understanding pollutant sources, climate-aerosol interactions, and health risks of air pollution exposure. Here, comprehensive nontarget high-resolution mass spectrometry was combined with in silico structural prediction to achieve greater molecular-level insight for fine particulate samples (n = 40) collected at a remote receptor site in the Maldives during January to April 2018. Spectral database matching identified 0.5% of 60,030 molecular features observed, while a conservative computational workflow enabled structural annotation of 17% of organic structures among the remaining molecular dark matter. Compared to clean air from the southern Indian Ocean, molecular structures from highly-polluted regions were dominated by organic nitrogen compounds, many with computed physicochemical properties of high toxicological and climate relevance. We conclude that combining nontarget analysis with computational mass spectrometry can advance molecular-level understanding of the sources and impacts of polluted air.

Research areas: Air Quality
Satellite observations for monitoring atmospheric NO2 in correlation with the existing pollution sources under arid environment (2022)

Monitoring of air pollutants using satellite data has been largely improved over the past few decades, which can provide deeper insights into the effects of anthropogenic activities on the air quality. The observations and measurements of atmospheric NO2 are poorly investigated in North Africa, therefore, the current study applied a multi-proxy approach to better understand of the ambient environment. This approach is based on satellite observations, chemical and biological analyses, and investigative information during fieldworks. The Aura satellite provides the basic data for the current study with fine resolution of atmospheric NO2 and O3 concentrations. The obtained results reveal noticeable increases of atmospheric NO2 values since the 2011, where its emission reaches the peak during summer season that is characterized by high anthropogenic activities. The study area has many sources for NO2 emissions, such as the urban region, traffic, as well as the NH3 emission that is in turn converted to NO2. Although the discharged and spreading wastewater (80,000 m3/day in summer) has a limited role in NO2 emissions, it represents an indicator of the anthropogenic activities. The wastewater analyses confirm the occurrence of nitrate (NO3), nitrite (NO2), and ammonia (NH4+), which provide an appropriate condition for NO2 release. The analyses of multi-climate datasets (previous records and the expected scenarios) reveal an increase of temperature accompanied by decrease of precipitation which confirmed the existence of climate change. Therefore, the study presents a set of suggestions to mitigate the release of NOx gases and achieve Net-Zero emissions.

Research areas: Air Quality
Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction (2022)

A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the floods intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASAs Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.

Research areas: Floods
Spaceborne NO2 observations are sensitive to coal mining and processing in the largest coal basin of Russia (2022)

Coal use exacerbates several major environmental problems including build-up of greenhouse gases and air quality deterioration. Although Kuzbass (Siberia) is one of the largest exploited coal basins worldwide, the role of regional coal mining and processing in atmospheric pollution is unknown. We outlined the Kuzbass coal basin by spaceborne night-lights and revealed a regional, long-term tropospheric NO2 anomaly (20052018) by spaceborne NO2 column observations (hereafter  NO2). The spatial agreement between NO2 and night-lights indicates that the anomaly is attributable to an agglomeration of coal quarries and the cities in Kuzbass, that are heavily reliant on coal. A positive relationship between NO2 and interannual coal production suggested that the anomaly was related to coal in Kuzbass; ~ 1.0% of annual coal production increase induced ~ 0.50.6% of NO2 enhancement. As coal production accelerated since 2010, NO2 exhibited strikingly similar annual increases over Kuzbass in 20102014 (7%) and 20152019 (15%), compared to 20052009. Conversely, Siberian cities lacking a coal industry followed the global trend of reducing NO2 for the same periods (5% and 14%, respectively), driven by fuel combustion improvements. Overall, we demonstrated that coal mining, processing and utilization can induce distinct tropospheric NO2 anomalies, detectable from space.

Research areas: Energy Production/Use, Air Quality
Satellite remote sensing of environmental variables can predict acoustic activity of an orthopteran assemblage (2022)

Passive acoustic monitoring (PAM) is a promising method for biodiversity assessment, which allows for longer and less intrusive sampling when compared to traditional methods (e.g., collecting specimens), by using sound recordings as the primary data source. Insects have great potential as models for the study and monitoring of acoustic assemblages due to their sensitivity to environmental changes. Nevertheless, ecoacoustic studies focused on insects are still scarce when compared to more charismatic groups. Insects acoustic activity patterns respond to environmental factors, like temperature, moonlight, and precipitation, but community acoustic perspectives have been barely explored. Here, we provide an example of the usefulness of PAM to track temporal patterns of acoustic activity for a nocturnal assemblage of insects (Orthoptera). We integrate satellite remote sensing and astronomically measured environmental factors at a local scale in an Andean Forest of Colombia and evaluate the acoustic response of orthopterans through automated model detections of their songs for nine weeks (March and April of 2020). We describe the acoustic frequency range and diel period for the calling song of each representative species. Three species overlapped in frequency and diel acoustics but inhabit different strata: canopy, understory, and ground surface level. Based on the acoustic frequency and activity, we identified three trends: (i) both sampled cricket species call at lower frequency for shorter periods of time (dusk); (ii) all sampled katydid species call at higher frequency for longer time periods, including later hours at night; and (iii) the diel acoustic activity span window seems to increase proportionally with dominant acoustic frequency, but further research is required. We also identified a dusk chorus in which all the species sing at the same time. To quantify the acoustic response to environmental factors, we calculated a beta regression with the singing activity as a response variable and moon phase, surface temperature and daily precipitation as explanatory variables. The response to the moon phase was significant for the katydids but not for the crickets, possibly due to differences in diel activity periods. Crickets are active during dusk, thus the effects of moonlight on acoustic activity are negligible. The response to precipitation was significant for the two crickets and not for the katydids, possibly because of higher likelihood of rain interrupting crickets shorter diel activity period. Our study shows how the local survey of orthopteran acoustic assemblages, with a species taxonomic resolution coupled with remote-sensing environmental measurements can reveal responses to environmental factors. In addition, we demonstrate how satellite data might prove to be a useful alternative source of environmental data for community studies with geographical, financial, or other constraints.

Research areas: Ecosystems
Predicting Short-Term Deformation in the Central Valley Using Machine Learning (2023)

Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers integrity.

Research areas: Land Surface/Agriculture Indicators
Lithogenic particle flux to the subantarctic Southern Ocean: A multitracer estimate using sediment trap samples (2022)

Mineral dust is a key source of essential micronutrients, particularly iron (Fe), for phytoplankton in the Southern Ocean. However, observations of dust deposition over the Southern Ocean are sparse, hindering assessments of its influence on marine biogeochemistry. We present a time series (20102019) of lithogenic particle flux estimates using sediment trap samples collected at 1,000 m depth at the subantarctic Southern Ocean Time Series (SOTS) site. Lithogenic flux was estimated using individual Fe, aluminium (Al), titanium, and thorium concentrations in sediment trap particles less than 1 mm in size. These tracers showed good agreement with one another, and their average was investigated as a proxy for mineral dust deposition. This multi-tracer average lithogenic flux exhibited strong seasonality, peaking in late spring and summer. No significant Fe enrichment was observed compared to the average upper continental crust, indicating that lithogenic material dominates particulate Fe flux at SOTS. Similar Fe:Al ratios in our samples compared to those reported in marine aerosols off southern Australia, coupled with particle trajectory analysis, suggested Australian dust constitutes the primary lithogenic source to SOTS sinking particles. Lead enrichment in our samples also highlighted an anthropogenic contribution to sinking particles, which might represent an additional aeolian source of more bio-available Fe to subantarctic waters. This study contributes a new long-term estimate of lithogenic particle fluxes and aeolian deposition over the subantarctic Southern Ocean. These estimates may enhance model representation of trace metal contribution to biogeochemical processes in the Southern Ocean.

Research areas: Ecosystems
Studying the Dynamics of Wildfires in Russia in 20012020 Taking into Account Climatic Factors (2021)

The features of large wildfires that occurred in Russia in 20012020 were studied on the base of satellite monitoring data. The contribution of the regions of the European part of Russia, as well as the Ural, Siberian, and Far East federal districts, to , 2, and PM2.5 emissions due to wildfires is estimated. It is established that the contribution of the Siberian Federal District into total emission volumes due to wildfires was prevailing from 2011 to 2017, and in some years it reached 60%. It is revealed that in 2020 wildfire emission volumes in the territory of the Far East Federal District exceeded 55% of the all-Russian emissions. An analysis of the features of anomalous wildfires is performed taking into account weather and climate factors. The relationship between large wildfires and weather and climatic factors, including blocking anticyclones, has been revealed. Using OMI (Aura satellite) data, it has been found that an aerosol plume due to large wildfires in Russia can spread over long distances and reach other continents.

Research areas: Wildfires, Air Quality
Assessment of Runoff Pattern and Relationship to Sediment Yield of BhagirathiAlaknanda River Basin Using Geospatial Techniques (2018)

Erosion has been as a major issue in Bhagirathi and Alaknanda river basins due to high surface runoff. The river basins are located in the Uttarakhand state of India and have a total basin area of 19,066.5 km2. Sediment yield has been estimated and then correlated with the surface runoff using geospatial techniques during the period 20002015. Analytic hierarchy process (AHP) has been used to estimate the sediment yield. All required layers for assessment of runoff and sediment yield has been prepared in a geographical information system environment. Tropical Rain Measuring Mission (TRMM) rainfall data (0.25  0.25), Global Land Data Assimilation System (GLDAS) runoff data (0.25  0.25), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) elevation data (30 m), and Landsat 5 and Landsat 8 satellite images (30 m) have been used to prepare rainfall maps, runoff maps, relief maps, and land use/land cover maps respectively. The study has shown that the sediment yield, as well as runoff, has a non-monotonic trend during 2000 to 2015. The highest runoff has been observed in the year 2013 whereas minimum runoff was in the year 2009. Correspondingly, the highest and lowest sediment yields were observed in the years 2013 and 2009 respectively. The northern part of the catchment was observed to be more vulnerable with respect to high sediment yield as well as runoff. As a whole, both river basins have a strong positive correlation between sediment yield and runoff with R2 values of 0.8. With respect to the findings, it can be concluded that some of the reservoirs for hydropower projects within these river basins may be in danger in the near future due to high sediment yields.

Research areas: Land Surface/Agriculture Indicators
Tropospheric NO2 Trends over South Asia during the Last Decade (20042014) Using OMI Data (2015)

The focus of this study is to assess spatiotemporal variability of tropospheric NO2 over South Asia using data from spaceborne OMI during the past decade (20042015). We find an average value of NO2 1.0  0.05  1015 molec/cm2 and a significant decadal increase of 14%. The elevating NO2 pollution over the region is linked to rise in motor vehicles and industrial and agricultural activities and increase in biomass fuel usage. The observed seasonality of NO2 is associated with change in meteorological conditions and seasonal cycles of anthropogenic emissions. OMI data reveal a seasonal peak in spring followed by winter largely linked to metrological conditions and anthropogenic emissions from crop residue and biomass burning for heating purpose, and low concentration in summer is mostly attributed to meteorological conditions. Significant increase, up to 42%, in NO2 concentrations over northwestern IGB, is observed connected to large scale postmonsoon crop residue events of 2010 and 2012. It is seen that NO2 is mounting over all the hotspot locations and most of the cities. Dhaka shows the highest increase of 77% followed by Islamabad (69%), Kabul (68%), Korba (64%), Bardhaman (47%), and Lahore (40%). On the contrary, DG Khan has shown negative trend of 11%.

Research areas: Air Quality, Environmental Impacts
The relationship between African easterly waves and daily rainfall over West Africa: observations and regional climate simulations (2015)

The relationship between summer African easterly waves (AEWs) and daily rainfall is assessed in West Africa for 19982008 using various reanalyses, satellite-derived rainfall products, and a regional climate model (RCM) run at 90- and 30-km resolutions. 35 and 69 day AEWs are extracted by filtering daily 700 hPa meridional wind time series at 1W and 11.5N, and 1W and 17.5N, respectively. Both observed and simulated rainfall anomalies are of larger magnitude over West Africa during 35-d than 69-d AEWs. The RCM simulates larger rainfall rates in phase with the 35-d wave trough instead of ahead, unlike the observations, and overestimates the intensity and spatial coverage of rainfall associated with 69-d AEWs. The observed and simulated co-variability between 35-d (69-d) AEW activity and daily rainfall is strong (weak) and mostly located south (north) of 15N. However, the RCM overestimates the spatial coverage of the AEWrainfall relationship in the longitudinal (latitudinal) direction in the case of 35-d (69-d) AEWs. Observed and simulated daily intense rainfall events, extracted using a percentile threshold approach, are mostly located south of 15N during summer. The observed relationship between their frequency of occurrence and active 35-d AEWs is maximal west of 8E, while extends up to southern Chad in both RCM simulations. Their magnitude is also largely overestimated by the RCM, indicating an exaggerated coupling between the wave activity and the convection. Finally, observed and simulated 35-d AEWs establish the most favorable synoptic conditions for the development of intense rainfall events over West Africa.

Research areas: Atmospheric/Ocean Indicators
Relationship between rainfall and lightning over central Indian region in monsoon and premonsoon seasons (2009)

Lightning activity and rainfall over the central Indian region (lat, 15.5 N to 25.5 N and lon, 75 E to 85 E) from the TRMM satellite have been analyzed. Ten years' data of monthly lightning and hourly averaged monthly rainfall from 1998 to 2007 have been used for analysis, which shows quite different relationships between lightning and rainfall in monsoon and premonsoon seasons in this region. Very good positive correlation is observed between rainfall and lightning during the premonsoon period, however, in the monsoon period a correlation between them is not so good. The different relationship between lightning and rainfall in the monsoon and premonsoon has been attributed to the low updraft during the monsoon period due to low cloud base height and low aerosol concentration during this period. This analysis shows that deep electrified convective systems do form over the central Indian region during active monsoon periods; however the relationship between convective rainfall and lightning frequency during this period is not as consistent as during the premonsoon period.

Research areas: Atmospheric/Ocean Indicators