nasa-gesdisc/es-publications-researchareas huggingface dataset:
Variabilities in ozone at a semi-urban site in the Indo-Gangetic Plain region: Association with the meteorology and regional processes (2012)

The Indo-Gangetic Plain (IGP) region is one of the most densely populated regions in the World, but ground-based observations of air pollutants are highly limited in this region. Here, surface ozone observations made during March 2009June 2011 at a semi-urban site (Pantnagar; 29.0N, 79.5E, 231 m amsl) in the IGP region are presented. Ozone mixing ratios show a daytime photochemical buildup with ozone levels sometimes as high as 100 ppbv. Seasonal variation in 24-h average ozone shows a distinct spring maximum (39.3  18.9 ppbv in May) while daytime (11301630 h) average ozone shows an additional peak during autumn (48.7  13.8 ppbv in November). The daytime, but not daily average, observed ozone seasonality is in agreement with the space-borne observations of OMI tropospheric column NO2, TES CO (681 hPa), surface ozone observations at a nearby high altitude site (Nainital) in the central Himalayas and to an extent with results from a global chemistry transport model (MATCH-MPIC). It is suggested that spring and autumn ozone maximum are mainly due to photochemistry, involving local pollutants and small-scale dynamical processes. Biomass burning activity over the northern Indian region could act as an additional source of ozone precursors during spring. The seasonal ozone photochemical buildup is estimated to be 3241 ppbv during spring and autumn and 914 ppbv during AugustSeptember. A correlation analysis between ozone levels at Pantnagar and Nainital along with the mixing depth data suggests that emissions and photochemical processes in the IGP region influence the air quality of pristine Himalayan region, particularly during midday hours of spring. The evening rate of change (8.5 ppbv hr1) is higher than the morning rate of change, which is dissimilar to those at other urban or rural sites. Ozone seasonality over the IGP region is different than that over southern India. Results from the MATCH-MPIC model capture observed ozone seasonality but overestimate ozone levels. Model simulated daytime ratios of H2O2/HNO3 are higher and suggesting that this region is in a NOx-limited regime. A chemical box model (NACR Master Mechanism) is used to further corroborate this using a set of sensitivity simulations, and to estimate the integrated net ozone production in a day (72.9 ppbv) at this site.

Research areas: Air Quality, Environmental Impacts
Rainfall-runoff simulation using satellite rainfall in a scarce data catchment (2021)

This study investigates the performance of TRMM and PERSIANN satellite rainfall data as input in a reliable rainfall-runoff model in order to provide information to the flood early warning for the Voshmgir Dam, Iran. Calibration of both continuous daily rainfall-runoff and an event-based flood was done using HEC-HMS Nelder-Mead (NM) method. Furthermore, simulations based on daily versus 3-hourly TRMM were compared to evaluate the effect of input time-step of rainfall-runoff model. Results show that the deficit and constant loss method is able to successfully predict the observed runoff. In addition, the Green-Ampt method using 3-hourly TRMM data showed a good capability to simulate daily peak discharges. The current study demonstrates the suitability of HEC-HMS for continuous and event-based runoff simulation in a complex watershed. Therefore, this research will have a significant contribution to the future development of water resources planning in this catchment in particular and in other data-scarce catchments.

Research areas: Validation
Precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of Argentina pampas (2021)

Global patterns of precipitation have changed due to the increase in temperature as a result of climate change. Measuring the amount of precipitation at a given location using surface instruments is relatively simple. However, the great spatial and temporal variability of the intensity, type and occurrence of this phenomenon, makes direct and uniformly calibrated measurements difficult in large regions. Satellite information is an important alternative to describe precipitation events; the Global Precipitation Measurement (GPM) mission estimates precipitation, considering different time periods, with three products Integrated Multi-Satellite Retrievals for GPM (IMERG), in near real time. This study evaluates and quantifies, temporal and spatially, the monthly precipitation estimated by Early (IMERG-E), Late (IMERG-L) and Final (IMERG-F) products compared with data from weather stations located in agricultural areas of the Pampas region in Argentina. Data of precipitation belonging to meteorological stations located at four provinces: Buenos Aires, Cordoba, La Pampa and Santa Fe, for 20142018 periods, were considered. The spatial performance of IMERG was evaluated using statistical coefficients and Taylor diagrams, considering at region, province and stations level. The adjustment of the products increased from IMERG-E to IMERGF, obtaining R2 values between 0.86 and 0.95 and RMSE from 14.2 to 29.3 mm, the best results corresponding to Cordoba and the worst to La Pampa. The performance of GPM products varies temporally; IMERG-F presented a higher correlation coefficient and a lower percent root mean square error in warm than in cold seasons. The results indicate that GPM can effectively capture the amount and patterns of monthly precipitation over the Pampas region of Argentina, which is important for its application to agricultural production and disaster prevention.

Research areas: Validation
A study on optical properties, classification, and transport of aerosols during the smog period over South Asia using remote sensing (2023)

Over the past few years, South Asian region has experienced frequent and thick smog events because of rapid population growth and enhanced anthropogenic activities, particularly in the Indo-Gangetic Plain (IGP). Therefore, the present study investigates aerosol properties such as aerosol optical depth (AOD) (500 nm), Angstrom exponent (AE) (440870 nm), single scattering albedo (SSA), fine-mode fraction (FMF), absorption aerosol optical depth (AAOD), and absorption aerosol exponent (AAE) over selected AERONET sites namely Bhola (20122021), Dhaka (20122021), Jaipur (20112021), Kanpur (20112021), Karachi (20112021), Lahore (20112021), and Pokhara (20112021) in the IGP during the smog period (October, November, and December). Additionally, different aerosol types were categorized using AERONET direct sun (AOD, AE) and inversion products (VSD, SSA, RI, FMF, and ASY). The monthly mean AOD, AE, and FMF varied from 0.33 to 1.07, 0.3 to 1.4, and 0.60.9 m over all selected AERONET sites during the smog period. Moreover, the outcomes revealed the dominance of biomass-burning and urban/ industrial aerosols over Lahore, Karachi, Dhaka, and Bhola during the smog period. Contrary to this, dust and mixed aerosols were abundant over Jaipur and Karachi, respectively. Furthermore, HYSPLIT cluster analysis is used to trace the transmission paths and potential sources of aerosols over selected sites.

Research areas: Air Quality, Environmental Impacts
Integrating GIS-based and geophysical techniques for groundwater potential assessment in Halabja Said Sadiq sub-basin, Kurdistan, NE Iraq (2019)

Groundwater is an important resource in Halabja Said Sadiq sab-basin, Sulaymaniyah district for agricultural and other uses. Continuous dramatic extraction of groundwater from legal and illegal wells led to a severe decline in the water table for the last thirty years. The objectives of this study are to delineate the groundwater productivity zones by combining the geographic information system and geoelectrical survey, which serves to recognize the locations of good groundwater storage and recharge zones. The Halabja Said Sadiq sub-basin has been selected as a case study to delineate the groundwater productivity zones. Four geoelectrical resistivity profiles conducted with electrode spacing 10 m and the length of the profiles is equal to 710 m.  Themes such as hydrogeology, land use/land cover, topography, drainage density, soil type, slope, lineaments and rainfall maps are created. The thematic maps made with GIS platform and appropriate weights put to the attributes taking into account the influence on the storage potential of groundwater. The results of geoelectrical profiles revealed that the aquifer thickness is 150 m. Three zones of groundwater potential delineated which are low, moderate and high and cover 33 %, 24 %, and 42 % of the total area respectively. Spatially, the highest zone is located along with the Quaternary deposits which characterized by high lineament density, low slop, and pediment deposition
The output of the groundwater potential model is verified by testing the discharge rate of the existing 580 wells. The results are revealed that most of the high yield wells are located within the high groundwater potential zone. Results of such verifications proved that the groundwater productivity areas recognized by GIS (AHP) and geoelectrical techniques are dependable and practical.

Research areas: Validation
Evaluation of the Compatibility of TRMM Satellite Data with Precipitation Observation Data (2023)

The availability of hydrological data is one of the challenges associated with developing water infrastructure in different areas. This led to the TRMM (Tropical Precipitation Measurement Mission) design by NASA, which involves using satellite weather monitoring technology to monitor and analyze tropical precipitation in different parts of the world. Therefore, this validation study was conducted to compare TRMM precipitation data with observed precipitation to determine its application as an alternate source of hydrological data. The Kuranji watershed was selected as the study site due to the availability of suitable data. Moreover, the validation analyses applied include the Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Coefficient Correlation (R), and Relative Error (RE). These used two calculation forms: one for the uncorrected data and another for the corrected data. The results showed that the best-adjusted data validation from the Gunung Nago station in 2016 was recorded to be RMSE = 62,298, NSE = 0.044, R = 0.902, and RE = 11,328. The closeness of the R-value to one implies that the corrected TRMM data outperforms the uncorrected ones. Therefore, it was generally concluded that the TRMM data matches the observed precipitation data and can be used for hydrological study in the Kuranji watershed

Research areas: Validation
Aerosol demasking enhances climate warming over South Asia (2023)

Abstract
            
              Anthropogenic aerosols mask the climate warming caused by greenhouse gases (GHGs). In the absence of observational constraints, large uncertainties plague the estimates of this masking effect. Here we used the abrupt reduction in anthropogenic emissions observed during the COVID-19 societal slow-down to characterize the aerosol masking effect over South Asia. During this period, the aerosol loading decreased substantially and our observations reveal that the magnitude of this aerosol demasking corresponds to nearly three-fourths of the CO
              2
              -induced radiative forcing over South Asia. Concurrent measurements over the northern Indian Ocean unveiled a ~7% increase in the earths surface-reaching solar radiation (surface brightening). Aerosol-induced atmospheric solar heating decreased by ~0.4 K d
              1
              . Our results reveal that under clear sky conditions, anthropogenic emissions over South Asia lead to nearly 1.4 W m
              2
              heating at the top of the atmosphere during the period MarchMay. A complete phase-out of todays fossil fuel combustion to zero-emission renewables would result in rapid aerosol demasking, while the GHGs linger on.

Research areas: Sun-Earth Interactions, Atmospheric/Ocean Indicators
Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco (2023)

Water availability is a key factor in territorial sustainable development. Moreover, groundwater constitutes the survival element of human life and ecosystems in arid oasis areas. Therefore, groundwater potential (GWP) identification represents a crucial step for its management and sustainable development. This study aimed to map the GWP using ten algorithms, i.e., shallow models comprising: multilayer perceptron, k-nearest neighbor, decision tree, and support vector machine algorithms; hybrid models comprising: voting, random forest, adaptive boosting, gradient boosting (GraB), and extreme gradient boosting; and the deep learning neural network. The GWP inventory map was prepared using 884 binary data, with 1 indicating a high GWP and 0 indicating an extremely low GWP. Twenty-three GWP-influencing factors have been classified into numerical data using the frequency ration method. Afterwards, they were selected based on their importance and multi-collinearity tests. The predicted GWP maps show that, on average, only 11% of the total area was predicted as a very high GWP zone and 17% and 51% were estimated as low and very low GWP zones, respectively. The performance analyses demonstrate that the applied algorithms have satisfied the validation standards for both training and validation tests with an average area under curve of 0.89 for the receiver operating characteristic. Furthermore, the models prioritization has selected the GraB model as the outperforming algorithm for GWP mapping. This study provides decision support tools for sustainable development in an oasis area.

Research areas: Land Surface/Agriculture Indicators
GIS-Based Soil Erosion Risk Assessment in the Watersheds of Bukidnon, Philippines Using the RUSLE Model (2023)

The sustainability of watersheds for supplying water and for carbon sequestration and other environmental services depends to a large extent on their susceptibility to soil erosion, particularly under changing climate. This study aimed to assess the risk of soil erosion in the watersheds in Bukidnon, Philippines, determine the spatial distribution of soil loss based on recent land cover maps, and predict soil loss under various rainfall scenarios based on recently reported climate change projections. The soil erosion risk assessment and soil loss prediction made use of GIS and the RUSLE model, while the rainfall scenarios were formulated based on PAGASAs prediction of drier years for Bukidnon in the early-future to late-future. Results showed that a general increase in soil loss was observed in 2015, over the period from 2010 to 2020, although some watershed clusters also showed a declining trend of soil erosion, particularly the Agusan-Cugman and Maridugao watershed clusters. Nearly 60% of Bukidnon has high to very severe soil loss rates. Under extreme rainfall change scenario with 12.61% less annual rainfall, the soil loss changes were only +1.37% and 2.87% in the category of none-to-slight and very severe, respectively. Results showed that a decrease in rainfall would have little effect on resolving the excessive soil erosion problem in Bukidnon. Results of this study suggest that having more vegetative land cover and employing soil conservation measures may prove to be effective in minimizing the risk of soil erosion in the watersheds. This study provides valuable information to enhance the sustainability of the watersheds. The erosion-prone areas identified will help decision-makers identify priority areas for soil conservation and environmental protection.

Research areas: Land Surface/Agriculture Indicators
Forecasting Air Quality in Tripoli: An Evaluation of Deep Learning Models for Hourly PM2.5 Surface Mass Concentrations (2023)

In this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli.

Research areas: Air Quality
Multi-Sensor Remote Sensing Data and GIS Modeling for Mapping Groundwater Possibilities: A Case Study at the Western Side of Assiut Governorate, Egypt (2023)

Groundwater is an essential natural resource and it has a significant role in the development of dry lands. It is the main source of fresh water in arid and semi-arid regions. The present study investigates groundwater potentiality in the western part of Assiut Governorate, Egypt using advanced remote sensing and geospatial techniques along with hydrological data and field validation. The adopted method provides a low-cost and highly effective tool that can be combined with the conventional land-based approach for mapping Groundwater Potentiality (GWP). The study aims to determine the groundwater probability and recharging zones based on the contribution of some physiographic variables that influence groundwater storage. Therefore, multi-sensors remote sensing data from ASTER, Landsat-8, MODIS, Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), and Radarsat-1 were accustomed to extract several geospatial thematic layers (variables). These layers include elevation, slope, curvature, drainage density, topographic wetness index, surface roughness, frequency of thermal anomaly, accumulated precipitation, Land Use/Land Cover (LULC), and lineament density. The produced layers are then scaled and weighted based on their contributions to the recharge of near-surface (unconfined) groundwater aquifers through infiltration and percolation processes. The Simple Additive Weight (SAW) method was utilized to aggregate all the weighted layers for creating the GWP map. This aggregated grouped map was then classified into 5 classes, from very high to very low groundwater potentiality zones. The results show that the high GWP was associated with low terrain, high surface ruggedness, high drainage and lineament densities, and relatively close to thermal anomalies in wadi deposits, and adjacent sandy areas. The remote sensing results were validated using comprehensive field observations including, pumping tests, water wells data, and vegetation patterns in the study area. The study concluded that a groundwater possibility map based on geospatial techniques and remote sensing data can provide a robust tool in groundwater exploration, and consequently, it can be adopted elsewhere in arid regions.

Research areas: Land Surface/Agriculture Indicators
Groundwater Dynamics in Transboundary Aquifers of Southern Africa (2023)

Groundwater resources are indispensable not only in water scarce or water stressed countries, but globally as a dependable reservoir and an alternative resource of freshwater. This study assessed the spatio-temporal variability of groundwater resources within two of the biggest transboundary aquifers that South Africa shares with its neighbouring countries. Groundwater dynamics in the Karoo-Sedimentary Transboundary Aquifer (KSTA) as well as the Stampriet Transboundary Aquifer System (STAS) were studied over a period of 72 years from 1948-2020. The study explored the use of historical groundwater storage data acquired through the use of Remote Sensing (RS) techniques, coupled with the use of Geographical Information Systems (GIS) to map spatio-temporal variability in groundwater storage. Groundwater resources of the Karoo-Sedimentary Transboundary Aquifer were found to be declining over time, with an overall decline of just over 5.4 km3, whereas groundwater resources in the Stampriet remained relatively constant, with an overall increase of 0.2 km3 over the past 72 years. The results show that RS techniques coupled with GIS applications are invaluable where there is a dearth of scientific data and information, furthermore, their use in the monitoring, management and protection of groundwater resources can be applicable on the local, regional and international scales.

Research areas: Land Surface/Agriculture Indicators
Investigating the long-term trends in aerosol optical depth and its association with meteorological parameters and enhanced vegetation index over Turkey (2022)

Aerosol optical depth (AOD) provides useful information on particulate matter pollution at both regional and global levels. In this study, the long-term datasets of aerosols, meteorological parameters, and enhanced vegetation index (EVI) were used from September 2002 to December 2021 over Turkey. This study examined the spatiotemporal distribution of aerosols and their association with meteorological parameters (temperature (Temp), relative humidity (RH), wind speed (WS)), and EVI over Turkey from 2002 to 2021. Moreover, this study also performed a comparison of AOD retrieved from Aqua with other satellites (Terra, SeaWiFS, and MISR) and ground-based (AERONET) products. The higher mean seasonal AOD (> 0.3) was observed over Southeastern Anatolia Region due to the dust transport from the Saharan Desert and Arabian Peninsula. Moreover, AOD was positively correlated with Temp and WS in the east of Turkey, while negative correlations were observed in the coastal regions. The correlation between AOD and RH was also observed negative in most parts of Turkey. Furthermore, in the coastal region, the correlation between AOD and EVI was found to be positive, whereas a negative correlation was seen over less vegetative areas. The multi-seasonal AOD averages were calculated as 0.187, 0.183, 0.138, and 0.104 for the spring, summer, autumn, and winter seasons, respectively. The most important result of this study is the regional differences in AOD over Turkey. For new studies, AOD should be observed separately for coastal areas and the eastern part of Turkey.

Research areas: Air Quality