nasa-gesdisc/es-publications-researchareas huggingface dataset:
Stable isotopic variations (D and 18O) in a mountainous river with rapidly changing altitude: Insight into the hydrological processes and rainout in the basin (2022)

Hydrological processes play an important role in stable isotopic compositional variations of the river water. The present study includes spatial distributions of stable hydrogen and oxygen isotopes (D and 18O) of a Himalayan River (the Teesta) to trace various hydrological processes and their associated roles on isotopic fractionation of river water. The 18O compositions of the water samples varies between 12.2 and 5.4 (mean: 8.01  1.81) and that of D between 95.1 and 38.5 (mean: 57.6  15.6). The observed compositional variation in 18O, D and d values of river water is mainly governed through snowmelt input, evaporation, and recycled moisture contribution. The variation of 18O and D values point towards an early melting of snow in the basin and a significant contribution of snowmelt to the river water. The orographic induced precipitation front appears to control the hydrological regimes in a distinct manner as reflected in the trend between d values and elevation. The rain shadow affected northern reaches of the basin are characterized with dry & arid climate triggering evaporation whereas, the windward southern side witness temperature influenced evaporation. The study suggests that the differential degree of recycled moisture contribution to the local precipitation is also a key component in bringing the stable isotopic variation in the river water. This varied magnitude of moisture recycling could result from uneven distribution of evapotranspiration and surface water evaporation. Results found that a rapid change in the altitude of a river basin can give rise to different hydrometeorological conditions, thereby resulting in simultaneous operation of almost all the major hydrological processes and a complex isotopic compositional variation in the river water. Also, the rainout percentage in the Teesta basin is found to be higher owing to this rapid rate of elevation increase.

Research areas: Land Surface/Agriculture Indicators
Aedes aegypti in Southern Brazil: Spatiotemporal Distribution Dynamics and Association with Climate and Environmental Factors (2023)

In Brazil, the mosquito Aedes (Stegomyia) aegypti is considered the main vector of the dengue, chikungunya, and Zika arbovirus transmission. Recent epidemiological studies in southern Brazil have shown an increase in the incidence of dengue, raising concerns over epidemiological control, monitoring, and surveys. Therefore, this study aimed at performing a historical spatiotemporal analysis of the Ae. aegypti house indices (HI) in southern Brazil over the last 19 years. As vector infestation was associated with climatic and environmental variables, HI data from the Brazilian Ministry of Health, climate data from the Giovanni web-based application, and environmental data from the Mapbiomas project were used in this study. Our results showed an expressive increase in the number of HI surveys in the municipalities confirming the vector presence, as compared to those in 2017. Environmental variables, such as urban infrastructure, precipitation, temperature, and humidity, were positively correlated with the Ae. aegypti HI. This was the first study to analyze Ae. aegypti HI surveys in municipalities of southern Brazil, and our findings could help in developing and planning disease control strategies to improve public health.

Research areas: Public Health
Fitness effects of seasonal birth timing in a long-lived social primate living in the equatorial forest (2022)

Reproductive seasonality is the norm in mammals from temperate regions but less common at lower latitudes, where a broad diversity of reproductive phenology strategies is observed. Our knowledge of the evolutionary determinants shaping this diversity remains fragmentary and may reflect high phenotypic plasticity in individual strategies. Here we investigated the ecological determinants and fitness consequences of variation in birth timing across the annual cycle in a social primate endemic to the Congo basin, the mandrill, Mandrillus sphinx, which breeds seasonally. We further examined traits that modulate this variation within and across individuals. We used 9 years of ecological, life history and behavioural data from a natural population to characterize patterns of environmental and reproductive seasonality. We then investigated the consequences of variation in birth timing for pre- and postnatal offspring survival and maternal interbirth intervals. Finally, we studied the influence of within- (reproductive history and age) and between-individual (social rank) traits on variation in birth timing. We found that mandrills daily foraging time varied seasonally, with greater fluctuations for subordinate than dominant females. Birth timing was plastic, as females gave birth year round without detectable consequences for postnatal offspring survival. Giving birth within the birth peak, however, decreased interbirth intervals and probability of miscarriage. Finally, reproductive history and social rank mediated within- and between-individual variation in birth timing, respectively. Specifically, females that experienced a previous reproductive failure gave birth early in the next birthing season and dominant females bred less seasonally than subordinates, which may reflect their more even access to resources across the year. Overall, the selective pressures shaping mandrill reproductive seasonality differed from a classical scenario of seasonal fluctuations in resources limiting offspring survival. A complex interplay between social and ecological factors may thus determine within- and between-individual variation in phenology strategies of tropical and gregarious mammals.

Research areas: Ecosystems
Assessment of Drought Severity and Their Spatio-Temporal Variations in the Hyper Arid Regions of Kingdom of Saudi Arabia: A Case Study from Al-Lith and Khafji Watersheds (2022)

The goal of this study is to calculate meteorological drought using the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) for the Al-Lith and Khafji basins of the Kingdom of Saudi Arabia (KSA) from 2001 to 2020. The in situ (rain gauges, RGs) and Integrated Multi-satellite Retrievals for GPM (IMERG) data are used in the current study. The meteorological drought is monitored across the AL-Lith and Khafji watersheds. The climate of the Khafji watershed is like the climate of Al-Lith to some extent. Still, due to complex terrain, Al-Lith receives relatively high precipitation and has a higher average temperature than the Khafji watershed. Results show that the total drought periods observed are 166 and 139 months based on SPEI and SPI on a multiple time scale (1, 3, 6, and 12 months) in the Al-Lith watershed, respectively. While, based on SPEI and SPI, the Khafji watershed experienced a drought of 129 and 72 months, respectively. This finding indicates that the SPEI-calculated drought is more severe and persistent in both watersheds than the SPI-calculated drought. Additionally, the correlation coefficient (CC) between SPI and SPEI is investigated; a very low correlation is observed at a smaller scale. CC values of 0.86 and 0.93 for Al-Lith and 0.61 and 0.79 for the Khafji watershed are observed between SPEI-1/SPI-1 and SPEI-3/SPI-3. However, the correlation is significant at high temporal scales, i.e., 6 and 12 months, with CC values of 0.95 and 0.98 for Al-Lith and 0.86 to 0.94 for the Khafji watershed. Overall, the study compared the performance of IMERG with RGs to monitor meteorological drought, and IMERG performed well across both watersheds during the study period. Therefore, the current study recommends the application of IMERG for drought monitoring across data-scarce regions of KSA. Furthermore, SPEI estimates a more severe and long-lasting drought than SPI because of the temperature factor it considers.

Research areas: Droughts
Quantifying vegetation response to environmental changes on the Galapagos Islands, Ecuador using the Normalized Difference Vegetation Index (NDVI) (2021)

The vegetation of the Galapagos Islands (Ecuador) is strongly influenced by climate. El Nino events, seasonality, isolation, volcanism, and increasing human activity define the ecosystems of the archipelago. Given their socio-cultural and economic importance, it is critical to monitor the response of Galapagos vegetation to changes in climate and assess its vulnerability. This study explores the potential to use Normalized Difference Vegetation Index (NDVI) as a proxy to describe trends in primary productivity in the Galapagos (20002019) and models the relationship between NDVI and climate variables including evaporation and atmospheric carbon dioxide concentration. From numerous possible co-variates compiled from reanalysis and satellites, we identify the independent variables that most strongly influence NDVI using the least absolute shrinkage and selection operator (LASSO) method. Significant variables, including carbon dioxide concentration, evaporation, and autocorrelation (1-month and 12-months lagged NDVI) are then used to model NDVI in a generalized linear model (GLM) framework. The model predicts NDVI more effectively where values for NDVI are high (high elevation, lush vegetation), and clearly reflects seasonality. Validation of the model across pixels produces R 2 values ranging from 0.05 to 0.94, and the mean R 2 is 0.57 (0.65 for elevation >20 m). This methodology has the potential to continuously and non-intrusively monitor vegetation changes in sensitive ecological regions, such as the Galapagos.

Research areas: Land Surface/Agriculture Indicators
ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas (2022)

The Jammu-Srinagar National Highway is the critical road connection between Kashmir valley and the rest of India. It passes through extremely steep slopes and high mountains prone to mass movements, particularly landslides and rockslides. Most mountainous roads are constructed on fragile and rocky slopes, and any natural (e.g., precipitation) or human-triggered disturbance (e.g., heavy traffic) can cause a fatal and devastating landslide under the influence of gravity. Many landslide-prone sites along the Highway are responsible for the continuous blockade almost throughout the year but peaking particularly during winters. As a result, it has a high toll on the state's economy and is responsible for many human casualties yearly. The present study aims to characterize various factors and their threshold values responsible for triggering a landslide. Through extensive field surveys, we evaluated different geotechnical parameters of soils at the most landslide-prone site along the Highway and augmented it with the satellite remote sensing datasets to determine the threshold values that trigger a landslide and assess the probability of occurrence of landslide events in the future using Autoregressive Moving Average (ARIMA) model and IBM SPSS Forecasting Model. This work shall help devise countermeasures for managing the landslides in the study area locally and shall serve as the guiding framework for using artificial intelligence and machine learning techniques for hazard management in the Himalayas.

Research areas: Land Surface/Agriculture Indicators
Classification of Different Sky Conditions Based on Solar Radiation Extinction and the Variability of Aerosol Optical Depth, Angstrom Exponent, Fine Particles Over Tehri Garhwal, Uttarakhand, India (2022)

On entering the Earths atmosphere, a large amount of solar radiation gets absorbed and scattered by aerosols, clouds, atmospheric gases, and Earths surface. In this context, we discuss an approach to characterize the sky conditions using ground-based observations of solar radiation. The proposed approach is applied to the measured solar radiation for the duration 16 October 2018 to 31 December 2019, at Himalayan Clouds Observatory (HCO), which is situated at Swami Ram Tirtha (SRT) Campus (3034 N, 7841 E) Badshahithaul, Tehri Garhwal, Uttarakhand, India. The present work demonstrates the variation in Clearness Index (KT) associated with Global Horizontal Radiation (Ha), Extraterrestrial Horizontal Radiation (Ho) and associated variability of Aerosol Optical Depth (AOD), Angstrom Exponent (AE) and fine particles (PM2.5 (g m3)) in different sky conditions. Most of the period of 2018 shows clear sky (81.82%) as compared to partially cloudy (4.29%) and cloudy (3.9%) days. Whereas only 20.89% days having clear sky, 38.38% as partially cloudy sky and 34.73% as cloudy sky conditions were observed during 2019. From the variance of Ha and KT, we obtained minimum values in the monsoon and winter from which we get cloudier condition in these seasons. AOD and PM2.5 remains lower in clear sky condition and highest in cloudy condition due to several atmospheric factors. Also, the AE of cloudy cases is marginally lower, implying larger particles, most likely due to swelling in humid conditions. Finally, we observed that the negative correlation between AOD and AE which indicates the swelling particles nearby cloudy region. Similarly, a positive correlation between AOD and PM2.5 indicates the sources of air pollution can influence aerosol loading for all sky conditions.

Research areas: Sun-Earth Interactions
Remote sensing of vegetation prolonged drought at the salt playas of Hail  Saudi Arabia (2022)

Drought is a complex natural phenomenon with a wide variety of natural and socioeconomic impacts, including water scarcity, health, agriculture, etc. This study provides a short-term (20002019) trend assessment of drought in Hail region, Saudi Arabia, using the Normalized Difference Vegetation Index (NDVI) and rainfall dataset synchronized by the assessment of the decadal trends of the local land surface temperatures (LST). The NDVI and LST data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, while rainfall data were acquired from the Tropical Rainfal Measuring Mission (TRMM) precipitation database. The impacts of drought on soils and natural vegetation of the region were also considered. Results evidenced that natural vegetation witnessed a prolonged decline between 2000 and 2019, accompanied by a regional increase of the decadal LST trends and decline of rainfall. However, it seems that vegetation communities in this hyper-arid region are of high tolerance to soil salinity and drought. There is a general consensus that drought conditions prevailing in this region reflect the context of climate change prevailing in the region and worldwide.

Research areas: Droughts, Land Surface/Agriculture Indicators
Variability of air quality and aerosol over Indian region during 20032012 (2022)

The Indo-Gangetic basin is one of the biggest, most populated and polluted regions in the world. Satellite- and ground-based data show strong seasonal variability of aerosol loading with a maximum during pre-monsoon (summer) season. In this study, decadal (20032012) variability of aerosol optical depth (AOD) derived from AERONET measurements over Kanpur is presented which indicates maximum AOD during 2003 and minimum during 2012. The aerosol size distribution (ASD) exhibits an increase in the radius and a decrease in the width of distribution. It shows an enhancement during 2003 and 2010 and diminished values during 2004, 2007, 2009 and 2011. The ASD is found to show a peak during summer season (pre-monsoon) throughout 20032012 only except the years 2008 and 2011. Moreover, for years 2008 and 2011 ASD showed a peak value during a monsoon month (July). Changes in the spectrum of ASD are explained in terms of surface temperature and precipitation. Seasonal variation of aerosol radiative forcing and its climatic impacts have also been discussed.

Research areas: Air Quality, Atmospheric/Ocean Indicators
A scoping review of flash floods in Malaysia: current status and the way forward (2022)

Flash floods are not only the deadliest weather-related hazard but also one of the leading challenges with which governments and societies need to cope. Flash floods occur within a very limited time, which is insufficient to enable effective warnings and preparedness. Flash floods have become, for many reasons, the most frequent form of natural disaster in Malaysia, considerably affecting humans, property, and the economy. Modeling flash flood phenomena in the tropics is challenging due to the high topographic and meteorological complexity of these regions. The uncertain definition boundary of the monsoon flood and the multidisciplinary nature of flash flood studies also increase the challenge of the reviewing process. In this study, a systematic methodology was developed to review flash floods in Malaysia by considering all the possible related issues. This study revealed a gap in the data analysis of flash floods and that related studies in Malaysia are still not highly developed. Accordingly, the creation of a comprehensive Malaysian flash flood dataset is recommended to advance flash flood studies, modeling, and forecasting. Rainfall analysis based on Global Precipitation Measurement and Tropical Rainfall Measuring Mission data of different intensities also confirmed the high variability of rainfall in Malaysia. The highest variability in the hourly-based rainfall dataset was observed in the central region. The information and findings presented here will be useful for interested hydrologists and decision-makers by enabling better water management. Additionally, the proposed recommendations for future research could pave the way for a better understanding of flash floods in Malaysia, and the method could be applied in different river basins worldwide.

Research areas: Floods
Unprecedented heavy rainfall event over Yamunanagar, India during 14 July 2016: An observational and modelling study (2021)

Extreme rainfall events have posed several serious threats to many populated and urbanized areas in the world including the Indian subcontinent. Therefore, accurate predictions of their intensity and areas of influence are important for flood-prone risk assessments. On 14 July 2016, heavy to exceptionally heavy rainfall occurred in Yamunanagar (30.16 N, 77.29 E), located in the state of Haryana in North India, which led to widespread disruption of communication, electricity, inundation of houses, and so forth. The present paper aims at examining observational, synoptic, thermo-dynamical, and numerical features associated with this devastating rainfall episode. The analysis found that during extreme rainfall episodes, a trough in mid-tropospheric westerlies and a strong low-level atmospheric monsoonal flow seem to have strongly interacted with each other, creating a strong convergence zone near study areas that led to a severe rainstorm. The quasi-stationary supercells were also noticed due to continuous moisture incursions from the Bay of Bengal and orographic uplift over the Himalayas near Yamunanagar. A deep layer of wind shear interacts dynamically with the convergence zone and leads to a potential rainstorm. Thermodynamic indices indicate high instability over the heavy rainfall area. The dynamics of this event were studied in detail by using three-dimensional variational data assimilation within the weather research and forecasting model, configured with triple two-way nesting domains (27, 9, and 3 km). The model results show that the weather research and forecasting model satisfactorily captures the quantitative precipitation (300 mm) in 24 h over the Yamunanagar region as compared with observation (365 mm).

Research areas: Atmospheric/Ocean Indicators
Evaluation of various meteorological datasets in estimation yield and actual evapotranspiration of wheat and maize (case study: Qazvin plain) (2021)

Precipitation (P) and temperature (T) are important factors in agricultural studies, water resources, and ecosystems. Therefore, their accurate measurement and estimation are very important. In this study, GLDAS-AgMERRA, GLDAS-CRU, GLDAS-AgCFSR networked meteorological datasets were evaluated in estimating the yield and actual evapotranspiration (ETa) of wheat and maize plants and comparing them with Qazvin Synoptic Station information. For this purpose, the Qazvin Synoptic Station information from 1980 to 2010 and the climatic information of the mentioned sets was extracted for six scenarios (S1: P and potential evapotranspiration (ETp) of GLDAS-T of CRU,S2: P and ETP of GLDAS-T of AgMERRA, S3: P of GLDAS- T and ETP of CRU, S4: P of GLDAS- T and ETP of AgMERRA, S5: P and ETP of GLDAS- T of AgCFSR, S6: P of GLDAS- T and ETp of AgCFSR). These datasets data quality was evaluated using R2, NRMSE, and ME statistical parameters with Aqua Crop model outputs. The calculated statistical parameters showed that in estimating the yield of the simulated wheat plant using Scenario 2 and 4, for maize, scenarios 1 and 3 were better correlated with other scenarios. In terms of ETa, Scenario 3 for maize and Scenario 6 for wheat were better correlated. The results show that accurately networked meteorological datasets can be used to estimate crop yields.

Research areas: Agriculture
Satellite Assessment of Some Environmental Parameters in the Region of the Iskitim Coal Mines in 20132020 (2022)

This article discusses the use of multispectral data from Landsat 8, Sentinel 2, Aqua, and Terra satellites for monitoring the environment in areas of open-pit coal mines in the Iskitimsky district of Novosibirsk oblast for 20132020. The dynamics of changes in the values of the reflection coefficient (CR) from the surface and water bodies, the snow index NDSI during the snowmelt period, and the change in NDVI in summer are shown. The dynamics of changes in the aerosol optical depth (AOD) (aerosol optical thickness (AOT)) of CO and CH4 values in the atmosphere of the Iskitimsky district is shown using the Giovanni data analysis and visualization system. A satellite assessment of the state of the atmosphere revealed seasonal changes in the AOD with maximum values in April and July. It is shown that the highest concentrations of carbon monoxide in the atmosphere are observed in the winter months, as well as in the spring of March and April. A stable decrease in the concentration of carbon monoxide in the atmosphere over 7 years from 2014 to 2020 with a determination coefficient of R2 = 0.95 and an increase in the concentration of methane with a determination coefficient of R2 = 0.89 are shown.

Research areas: Energy Production/Use, Air Quality