nasa-gesdisc/es-publications-researchareas huggingface dataset:
How Accurately Can Satellite Products (TMPA and IMERG) Detect Precipitation Patterns, Extremities, and Drought Across the Nepalese Himalaya? (2020)

This study aims to assess the accuracy of two satellite-based precipitation products (SBPPs), that is, Tropical Rainfall Measurement Mission (TRMM)-based Multi-satellite Precipitation Analysis (TMPA) and its upgraded version Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), in capturing spatial and temporal variation of precipitation and their application for extreme events (high-intensity precipitation and drought). They were evaluated against 142-gauge stations from Nepal during 20012018. The results show that, in general, both SBPPs show the overall characteristics of precipitation patterns, although underestimated the mean annual precipitation during the study period. It was also noted that IMERG product yields better performance to detect precipitation events (probability of detection) and no-precipitation events (false alarm ratio) than TMPA. Based on four different extreme precipitation indices: heavy precipitation events (R10mm), extreme precipitation events (R25mm), five consecutive dry days (CDD), and five consecutive wet days (CWD), it was observed that the SBPPs underestimated the frequency of R25mm and CDD spells while overestimated R10mm and CWD spells. Additionally, both SBPPs exhibited considerable capabilities in capturing the drought events during the study period. Overall, the drought event, bias, and frequency show that the IMERG product has slightly better capabilities to capture drought than the TMPA product. In general, IMERG was found to be superior at a daily timescale, while TMPA shows consistent performance on a monthly scale during the study period. Furthermore, there is still space for further improvement of IMERG rainfall retrieval algorithms.

Research areas: Atmospheric/Ocean Indicators, Droughts
Carbonaceous aerosol emission reduction over Shandong province and the impact of air pollution control as observed from synthetic satellite data (2020)

Aerosol loading and trace gas emissions from agriculture crop residue burning has been one of the main factors leading to the deterioration of air quality especially during the harvest time in China. Here, we evaluate the contribution of atmospheric aerosol loading from biomass burning and its relationship to the air pollution control policy. We focused on Shandong province, China because it has the highest level of agriculture burning pollution in October. Satellite-based data related to biomass burning including aerosol optical depth (AOD) and fire pixel counts from Moderate Resolution Imaging Spectroradiometer (MODIS), carbon monoxide (CO) from Measurements Of Pollution In The Troposphere (MOPITT), aerosol index (AI) and tropospheric NO2 from Ozone Monitoring Instrument (OMI), and aerosol layer depths from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). These metrics were synthetically utilized to evaluate spatial and temporal variation of atmospheric aerosols during the agriculture crop residue burning month from 2006 to 2017. Carbonaceous aerosol emission reduction and the impact of the air pollution control policy can be found in the CALIPSO aerosol types and MODIS fire pixel counts data. The AOD, NO2, CO, and AI over three different land covers (cropland, forest and impervious surfaces) were further obtained according to the land cover data set. The results indicated that there was an obvious decrease in all carbonaceous aerosol-related quantities after year 2012 when the local government began to enact and implement strict air pollution control policies, although a period (23 years) of transition after the policy implementation time was identified. We also found that CO is stable and low over the cropland versus forest and impervious surfaces; NO2 exhibits a continuously high value in the northern areas of the oil field. The impact of industry activity rather than straw burning should be also considered for NO2 and CO in certain areas.

Research areas: Environmental Impacts, Air Quality
Interannual lake fluctuations in the Argentine Puna: relationships with its associated peatlands and climate change (2019)

High elevation ecosystems are likely more sensitive to climate change. But, due to paucity of instrumental records, such effects are poorly studied, particularly in mountains outside Europe and North America. Here, we quantified water body area fluctuations for the last 32 years in 15 lakes spread over an area of 14.3 million ha in the Argentine Puna, through the classification of Landsat images; and we quantified peatlands NDVI (a proxy of vegetation productivity) from MODIS images. We evaluated the pairwise similarity between lakes interannual fluctuations and their relationship with climate models (TRMM 3B43 v7; CRU TS 4.10) and potential controls (ENSO index); and the correlations between water body area and the NDVI variation of its associated peatlands. Lakes were grouped in two clusters defined by their synchronic water body area variability and these clusters define two main geographic zones: NE and SW. Consistent with previous observations of an overall aridization trend, water body area generally decreased but showed large variability among lakes. Peatlands productivity was more correlated with lake variability than with modeled precipitation, and lake water body area was weakly related to indices of ENSO, providing an additional tool to relate local climate with continental and global climate models. The analysis shows that lake behavior is highly variable spatially and temporally, and that satellite-based monitoring is a valuable tool for assessing ecological conditions of wetlands in the region, characterized by the lack of climatic instrumental records; and to explore the vulnerability of wetlands to climate change.

Research areas: Land Surface/Agriculture Indicators, Atmospheric/Ocean Indicators
High fire-derived nitrogen deposition on central African forests (2018)

Atmospheric nitrogen (N) deposition is an important determinant of N availability for natural ecosystems worldwide. Increased anthropogenic N deposition shifts the stoichiometric equilibrium of ecosystems, with direct and indirect impacts on ecosystem functioning and biogeochemical cycles. Current simulation data suggest that remote tropical forests still receive low atmospheric N deposition due to a lack of proximate industry, low rates of fossil fuel combustion, and absence of intensive agriculture. We present field-based N deposition data for forests of the central Congo Basin, and use ultrahigh-resolution mass spectrometry to characterize the organic N fraction. Additionally, we use satellite data and modeling for atmospheric N source apportionment. Our results indicate that these forests receive 18.2 kg N hectare1 years1 as wet deposition, with dry deposition via canopy interception adding considerably to this flux. We also show that roughly half of the N deposition is organic, which is often ignored in N deposition measurements and simulations. The source of atmospheric N is predominantly derived from intensive seasonal burning of biomass on the continent. This high N deposition has important implications for the ecology of the Congo Basin and for global biogeochemical cycles more broadly.

Research areas: Energy Production/Use
Landslide inventory for hazard assessment in a data-poor context: a regional-scale approach in a tropical African environment (2018)

Landslide hazard remains poorly characterized on regional and global scales. In the tropics in particular, the lack of knowledge on landslide hazard is in sharp contrast with the high landslide susceptibility of the region. Moreover, landslide hazard in the tropics is expected to increase in the future in response to growing demographic pressure and climate and land use changes. With precipitation as the primary trigger for landslides in the tropics, there is a need for an accurate determination of rainfall thresholds for landslide triggering based on regional rainfall information as well as reliable data on landslide occurrences. Here, we present the landslide inventory for the central section of the western branch of the East African Rift (LIWEAR). Specific attention is given to the spatial and temporal accuracy, reliability, and geomorphological meaning of the data. The LIWEAR comprises 143 landslide events with known location and date over a span of 48 years from 1968 to 2016. Reported landslides are found to be dominantly related to the annual precipitation patterns and increasing demographic pressure. Field observations in combination with local collaborations revealed substantial biases in the LIWEAR related to landslide processes, landslide impact, and the remote context of the study area. In order to optimize data collection and minimize biases and uncertainties, we propose a three-phase, Search-Store-Validate, workflow as a framework for data collection in a data-poor context. The validated results indicate that the proposed methodology can lead to a reliable landslide inventory in a data-poor context, valuable for regional landslide hazard assessment at the considered temporal and spatial resolutions.

Research areas: Land Surface/Agriculture Indicators, Habitat Conversion/Fragmentation
Hydrological appraisal of rainfall estimates from radar, satellite, raingauge and satellitegauge combination on the Qinhuai River Basin, China (2019)

Multisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellitegauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km2. All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.

Research areas: Validation
Observations of Precipitable Water Vapor along the Maritime Continent Associated with El Nino-Southern Oscillation Activity (2018)

The effect of El Nino and La Nina events is a major issue in the regional climate of Southeast Asia. This study uses the Precipitable Wa- ter Vapor (PWV) values estimated from Global Navigation Satellite System (GNSS; in particular Global Positioning System, GPS) to in- vestigate the El Nino-Southern Oscillation (ENSO) activity in the Southern coast of the South China Sea (SCS). For this study, GPS and meteorological systems are installed at the Universiti Malaysia Sabah, Kota Kinabalu (UMSK), which is located on the coast of the mar- itime continent. Subsequently, PWV from GPS and PWV obtained from Radiosonde, prior to the investigation of ENSO activity, are com- pared. To further investigate the relationship between PWV and ENSO activities, data from three additional GPS stations located along the coast of SCS during the 3-year period 2009-2011 were analyzed. The selection period considers the availability of GPS data. The obtained results show that during the increased intensity of El Nino in 2009/2010, PWV decreases and drops about 10-week later, i.e., after the sea surface temperature anomaly (SSTa) has peaked. PWV has a negative correlation with the El Nino index, which is comparable with pre- cipitation data obtained from NASAs Tropical Rainfall Measuring Mission (TRMM). In contrast, the amount of PWV was increased dur- ing the La Nina event of 2010/2011. This observation suggests the GPS is a suitable technique for studying the ENSO activity.

Research areas: Atmospheric/Ocean Indicators
A novel cross-satellite based assessment of the spatio-temporal development of a cyanobacterial harmful algal bloom (2018)

As the frequency of cyanobacterial harmful algal blooms (CyanoHABs) become more common in recreational
lakes and water supply reservoirs, demand for rapid detection and temporal monitoring will be imminent for
effective management. The goal of this study was to demonstrate a novel and potentially operational cross-
satellite based protocol for synoptic monitoring of rapidly evolving and increasingly common CyanoHABs in
inland waters. The analysis involved a novel way to cross-calibrate a chlorophyll-a (Chl-a) detection model for
the Landsat-8 OLI sensor from the relationship between the normalized difference chlorophyll index and the
floating algal index derived from Sentinel-2A on a coinciding overpass date during the summer CyanoHAB
bloom in Utah Lake. This aided in the construction of a time-series phenology of the Utah Lake CyanoHAB event.
Spatio-temporal cyanobacterial density maps from both Sentinel-2A and Landsat-8 sensors revealed that the
bloom started in the first week of July 2016 (July 3rd, mean cell count: 9163 cells/mL), reached peak in mid-
July (July 15th, mean cell count: 108176 cells/mL), and reduced in August (August 24th, mean cell count: 9145
cells/mL). Analysis of physical and meteorological factors suggested a complex interaction between landscape
processes (high surface runoff), climatic conditions (high temperature, high rainfall followed by negligible
rainfall, stable wind), and water quality (low water level, high Chl-a) which created a supportive environment
for triggering these blooms in Utah Lake. This cross satellitebased monitoring methods can be a great tool for
regular monitoring and will reduce the budget cost for monitoring and predicting CyanoHABs in large lakes.

Research areas: Water Quality
Spatio-temporal dynamics of phytoplankton functional groups in the South China Sea and their relative contributions to marine primary production (2019)

The sea-surface distribution of four phytoplankton functional groups in the South China Sea (SCS) namely Coccolithophores (Coc), Diatoms (Dia), Cyanobacteria (Cya) and Chlorophytes (Chlo) was studied across spatial and temporal scales. The time-series datasets derived from the National Aeronautic and Space Administration (NASA) Ocean Biogeochemical Model and retrieved via the Giovanni Portal were analysed statistically with well-known methods including Pearsons correlation coefficient, ordinary least square regression (OLR), Maximum Entropy Spectral Analysis (MESA) and Principal Components Analysis (PCA). The data were analysed for relative abundance, seasonality, cyclicity, long-term trends, spatial variability, inter-relationships, and relative contributions of individual phytoplankton groups to primary production (PP, using Chl-a as a proxy). The results reveal that the numerically dominant phytoplankton group in terms of relative abundance is Cya, comprising about 57% of the planktonic biomass, followed closely by Coc (about 40%), while Dia constitutes almost 3% of the total abundance. Dia was found to have the strongest annual cycle, and hence displayed the highest rate of seasonal variability. Clear spatial segregation patterns have also been uncovered; Coc and Dia are strongly correlated in the Northern SCS (NSCS) while Cya dominate in Southern SCS (SSCS). Coc and Dia display a strong positive correlation with PP, whereas the seasonal relationships between Cya and Chlo with PP are non-linear, and hence appear statistically non-significant. The rise in the trend of Cya and decrease in Coc, since 2004, may be indicative of changes in successional patterns of phytoplankton functional groups in the SCS. This study, therefore, sets the precedence for more robust research to uncover the immediate and remote causes of the observed spatiotemporal changes in marine phytoplankton dynamics and to develop predictive models of marine primary productivity (MPP) and ecological health.

Research areas: Ecosystems
Spatio-Temporal Rainfall Variability and Flood Prognosis Analysis Using Satellite Data over North Bihar during the August 2017 Flood Event (2019)

Flooding is one of the most common natural disasters in India. Typically, the Kosi and Gandak river basins are well-known for lingering flood affected basins in North Bihar every year, which lies in the eastern part of India. There were no such comprehensive studies available in North Bihar that discussed flood progression and regression at shorter time-scales like two day intervals. So in this study, we employed high temporal resolution data to capture inundation extent and further, the flood extent has been validated with high spatial resolution data. The specific objective of this study was to analyze the satellite-derived Near Real Time (NRT) MODIS flood product for spatiotemporal mapping of flood progression and regression over the North Bihar. The synthetic aperture RADAR (SAR) data were also used to validate the MODIS NRT Flood data. As a case study, we selected a recent flood event of AugustSeptember 2017 and captured the flood inundation spatial extent at two day intervals using the 2 day composite NRT flood data. The flood prognosis analysis has revealed that during the peak flooding period, 12% to 17% of the area was inundated and the most adversely affected districts were Darbhanga and Katihar in North Bihar. We estimated that in total nearly 6.5% area of the North Bihar was submerged. The method applied was simple, but it can still be suitable to be applied by the community involved in flood hazard management, not necessarily experts in hydrological modeling. It can be concluded that the NRT MODIS flood product was beneficial to monitor flood prognosis over a larger geographical area where observational data are limited. Nevertheless, it was noticed that the flood extent area derived from MODIS NRT data has overestimated areal extent, but preserved the spatial pattern of flood. Apparently, the present flood prognosis analysis can be improved by integrating microwave remote sensing data (SAR) and hydrological models.

Research areas: Floods
Role of interactions between cloud microphysics, dynamics and aerosol in the heavy rainfall event of June 2013 over Uttarakhand, India (2017)

In this study, we propose a hypothesis, supported by numerical model simulations, concerning the role of cloud microphysical processes and aerosols in the invigoration of an extreme rainfall event over Uttarakhand in June 2013. The interactions among dynamics, thermodynamics and microphysical processes and their feedbacks play a vital role in the occurrence of extreme events. To test the proposed hypothesis, Weather Research and Forecasting (WRF) simulations are carried out with three different microphysical schemes (i.e. WDM6, Morrison, and CLR). The role of aerosol indirect effects in the process of invigoration of precipitation is demonstrated with a high-resolution regional model for the extreme event over the foothills of the Himalayas. The extreme event is characterized by the strong northsouth tropospheric temperature gradient and strong moisture convergence. Forced uplift beyond the freezing level initiates a precipitation process which involves cloud ice and mixed-phase cloud microphysics and latent heat release; further, it invigorates convection and enhances precipitation. Results pinpoint that the role of microphysical processes are very crucial during such a type of extreme event. Additionally, the result accentuates the importance of aerosols on the deep convective cloud systems which have influence through invigoration and involvement of complex interactions between aerosol, large-scale dynamics and cloud microphysics.

Research areas: Atmospheric/Ocean Indicators
Dust emission and environmental changes in the dried bottom of the Aral Sea (2015)

In the 1990s, the western world became aware of the ecological disaster of what was once the fourth largest lake in the world  the Aral Sea. The drastic desiccation of the Aral Sea led to the intensive development of desertification processes in the region and the formation of a new desert, the Aralkum. In the last few decades, the Aralkum has become the new hot spot of dust and salt storms in the region. Dust storms and their source areas have been determined and analyzed by the NOAA AVHRR, TOMS and OMI data. An analysis of the land-cover changes in the dried bottom of the Aral Sea revealed that the north-eastern part of the Aralkum Desert is one of the most active dust sources in the region, responsible for high aerosol concentrations in the atmosphere. Dust plumes that sweep up from the dried bottom of the Aral Sea have become larger, and dust storms have become more powerful, since the bottom exposure. The main change that occurred in the land cover was the considerable reduction of vegetation and small water bodies, while the areas of solonchaks (salty pans) and sandy massifs increased significantly.

Research areas: Habitat Conversion/Fragmentation, Air Quality, Land Surface/Agriculture Indicators
Volcanic ash fall events identified using principal component analysis of a high-resolution speleothem trace element dataset (2015)

Large multivariate trace element datasets produced by LA-ICP-MS speleothem analysis can pose difficulties for analysis and interpretation. Processes acting on various timescales and magnitudes affect trace element concentrations, and deconvolving the most important controls is often complex. Here Principal Component Analysis (PCA) is applied to identify the modes and timings of variation which best explain the overall variability in an exceptionally high-resolution (10 m vertical resolution) multivariate trace element record produced by LA-ICP-MS from a modern (19792001) Belizean stalagmite with excellent age control. Principal Component 1 (PC1) in this dataset is defined by a weak correlation between multiple elements, and may reflect non-carbonate material incorporated within the speleothem. Elevated PC1 scores in ATM-7 occur following regional volcanic eruptions with ash clouds extending over the cave site, as demonstrated using NASA remote sensing data from the Total Ozone Mapping Spectrometer and HYSPLIT trajectory modelling. Spikes in PC1 occur at the beginning of the wet season, and this may reflect a seasonal flushing event that transports volcanogenic material through the karst and incorporates it within the speleothem. Our results suggest that PCA can simplify exploration of large laser ablation datasets, and that PCA is a valuable tool for identifying the dominant controls on stalagmite trace element chemistry. Future studies should evaluate how transferable this technique is to other sites with different environmental conditions where volcanic ashfall has occurred. This research potentially adds tephrochronology to the stalagmite dating toolkit or, conversely, opens the door to using stalagmites to identify previously unknown or uncertainly dated eruptions.

Research areas: Volcanic Eruptions