nasa-gesdisc/es-publications-researchareas huggingface dataset:
Surface temperature response to the major volcanic eruptions in multiple reanalysis data sets (2020)

Abstract. The global response of air temperature at 2 m above the surface to the eruptions of Mount Agung in March 1963, El Chichon in April 1982, and Mount Pinatubo in June 1991 is investigated using 11 global atmospheric reanalysis data sets (JRA-55, JRA-25, MERRA-2, MERRA, ERA-Interim, ERA-40, CFSR, NCEP-NCAR R-1, 20CR version 2c, ERA-20C, and CERA-20C). Multiple linear regression (MLR) is applied to the monthly mean time series of temperature for two periods  19802010 (for 10 reanalyses) and 19582001 (for 6 reanalyses)  by considering explanatory factors of seasonal harmonics, linear trends, quasi-biennial oscillation (QBO), solar cycle, tropical sea surface temperature (SST) variations in the Pacific, Indian, and Atlantic Oceans, and Arctic SST variations. Empirical orthogonal function (EOF) analysis is applied to these climatic indices to obtain a set of orthogonal indices to be used for the MLR. The residuals of the MLR are used to define the volcanic signals for the three eruptions separately. First, area-averaged time series of the residuals are investigated and compared with the results from previous studies. Then, the geographical distribution of the response during the peak cooling period after each eruption is investigated. In general, different reanalyses show similar geographical patterns of the response, but with the largest differences in the polar regions. The Pinatubo response shows the largest average cooling in the 60 N60 S region among the three eruptions, with a peak cooling of 0.100.15 K. The El Chichon response shows slightly larger cooling in the NH than in the Southern Hemisphere (SH), while the Agung response shows larger cooling in the SH. These hemispheric differences are consistent with the distribution of stratospheric aerosol optical depth after these eruptions; however, the peak cooling after these two eruptions is comparable in magnitude to unexplained cooling events in other periods without volcanic influence. Other methods in which the MLR model is used with different sets of indices are also tested, and it is found that careful treatment of tropical SST variability is necessary to evaluate the surface response to volcanic eruptions in observations and reanalyses.

Research areas: Volcanic Eruptions
Parametric analysis of lava dome-collapse events and pyroclastic deposits at Shiveluch volcano, Kamchatka, using visible and infrared satellite data (2018)

For the years 2001 to 2013 of the ongoing eruption of Shiveluch volcano, a combination of different satellite remote sensing data are used to investigate the dome-collapse events and the resulting pyroclastic deposits. Shiveluch volcano in Kamchatka, Russia, is one of the world's most active dome-building volcanoes, which has produced some of the largest known historical block-and-ash flows (BAFs). Globally, quantitative data for deposits resulting from such large and long-lived dome-forming eruptions, especially like those at Shiveluch, are scarce. We use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR), shortwave infrared (SWIR), and visible-near infrared (VNIR) data to analyze the dome-collapse scars and BAF deposits that were formed during eruptions and collapse events in 2001, 2004, 2005, 2007, 2009, 2010, and two events in 2013. These events produced flows with runout distances of as far as 19km from the dome, and with aerial extents of as much as 22.3km2. Over the 12years of this period of investigation, there is no trend in deposit area or runout distances of the flows through time. However, two potentially predictive features are apparent in our data set: 1) the largest dome-collapse events occurred when the dome exceeded a relative height (from dome base to top) of 500m; 2) collapses were preceded by thermal anomalies in six of the cases in which ASTER data were available, although the areal extent of these precursory thermal areas did not generally match the size of the collapse events as indicated by scar area (volumes are available for three collapse events). Linking the deposit distribution to the area, location, and temperature profiles of the dome-collapse scars provides a basis for determining similar future hazards at Shiveluch and at other dome-forming volcanoes. Because of these factors, we suggest that volcanic hazard analysis and mitigation at volcanoes with similar BAF emplacement behavior may be improved with detailed, synoptic studies, especially when it is possible to access and interpret appropriate remote sensing data in near-real time.

Research areas: Volcanic Eruptions
Wildfire Impacts on Snowpack Phenology in a Changing Climate Within the Western U.S. (2022)

Snowpack in the western U.S. is critical for water supply and is threatened by wildfires, which are becoming larger and more common. Numerous studies have examined impacts of wildfire on snow water equivalent (SWE), but many of these studies are limited in the number of observation locations, and they have sometimes produced conflicting results. The objective of this study is to distinguish the net effects of wildfires on snowpack from those of climate. We consider 45 burned sites from the Snow Telemetry network. For each burned site, unburned control sites are identified from the same level III ecoregion. Impacts of climate changes on snowpack are analyzed by comparing pre-fire and post-fire snow water equivalent at the unburned sites. Combined climate and wildfire effects are considered by comparing pre-fire and post-fire SWE at the burned sites. Wildfire impacts are then isolated by taking the difference between the burned and unburned sites. The wildfire-induced changes in SWE are also compared to several possible controlling variables including burn severity, leaf-area index change, dominant pre-fire tree genus, years since the fire, and site elevation. On average, wildfires have advanced melt-out dates 9 days and maximum SWE dates 6 days and reduced annual maximum SWE by 10% across the sites considered. On average, the combined effects of climate and wildfire have advanced melt-out and maximum SWE dates approximately 14 and 10 days, respectively, while decreasing annual maximum SWE by approximately 10%.

Research areas: Wildfires, Cryospheric Indicators
Canadian forest fires, Icelandic volcanoes and increased local dust observed in six shallow Greenland firn cores (2022)

Abstract. Greenland ice cores provide information about past climate. Few impurity records covering the past 2 decades exist from Greenland. Here we present results from six firn cores obtained during a 426 km long northern Greenland traverse made in 2015 between the NEEM and the EGRIP deep-drilling stations situated on the western side and eastern side of the Greenland ice sheet, respectively. The cores (9 to 14 m long) are analyzed for chemical impurities and cover time spans of 18 to 53 years (3 years) depending on local snow accumulation that decreases from west to east. The high temporal resolution allows for annual layers and seasons to be resolved. Insoluble dust, ammonium, and calcium concentrations in the six firn cores overlap, and the seasonal cycles are also similar in timing and magnitude across sites, while peroxide (H2O2) and conductivity both have spatial variations, H2O2 driven by the accumulation pattern, and conductivity likely influenced by sea salt. Overall, we determine a rather constant dust flux over the period, but in the data from recent years (19982015) we identify an increase in large dust particles that we ascribe to an activation of local Greenland sources. We observe an expected increase in acidity and conductivity in the mid-1970s as a result of anthropogenic emissions, followed by a decrease due to mitigation. Several volcanic horizons identified in the conductivity and acidity records can be associated with eruptions in Iceland and in the Barents Sea region. From a composite ammonium record we obtain a robust forest fire proxy associated primarily with Canadian forest fires (R=0.49).

Research areas: Wildfires, Volcanic Eruptions
A compound event-oriented framework to tropical fire risk assessment in a changing climate (2022)

Abstract
            Tropical fire activity closely follows the co-occurrence of multiple climate stressors. Yet, it remains challenging to quantify how changes in climate alter the likelihood of fire risks associated with compound events. Recent abrupt changes in fire regimes in iconic landscapes in Brazil (namely the Pantanal and Xingu) provide a key opportunity to evaluate how extremely dry and hot conditions, both together and individually, have influenced the probability of large fires. Here we quantify the relationships between climate and fire across these regions and provide evidence on the extent to which fire risk and the associated impacts could be constrained if anthropogenic global warming is limited. We investigate the burned area, differentiating between fire types according to land use (forest fires, savanna fires, farming fires and grassland and wetland fires), and derive present and future fire risks linked to multiple climate variables. We show that concurrent air dryness (high vapour-pressure deficit (VPD)) and low precipitation have driven fire occurrence in both Xingu and the Pantanal, with VPD playing a dominant role. Historical climatic change has already increased compound event-related (CE-related) fire risks of all fire types (5%10%), and these risks are likely to increase in the future due to global warming. The likelihood of CE-related increase in fire risk may be reduced by up to 14.4% if global warming is constrained to +1.5 C instead of +3 C. Nevertheless, substantially increased CE-related fire risks are still expected even if restricting global mean warming to 1.5 C, particularly in the Pantanal. We thus conclude that climate action should be coordinated with environmental protection to reduce ignition sources and promote effective conservation measures to preserve these biomes.

Research areas: Wildfires
Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents (2020)

AimAn emerging framework for tropical ecosystems states that fire activity is either "fuel build‑up limited" or "fuel moisture limited", that is, as you move up along rainfall gradients, the major control on fire occurrence switches from being the amount of fuel, to the moisture content of the fuel. Here we used remotely sensed datasets to assess whether interannual variability of burned area is better explained by annual rainfall totals driving fuel build‑up, or by dry season rainfall driving fuel moisture.LocationPantropical savannas and grasslands.Time period2002–2016.MethodsWe explored the response of annual burned area to interannual variability in rainfall. We compared several linear models to understand how fuel moisture and fuel build‑up effect (accumulated rainfall during 6 and 24 months prior to the end of the burning season, respectively) determine the interannual variability of burned area and explore if tree cover, dry season duration and human activity modified these relationships.ResultsFuel and moisture controls on fire occurrence in tropical savannas varied across continents. Only 24% of South American savannas were fuel build‑up limited against 61% of Australian savannas and 47% of African savannas. On average, South America switched from fuel limited to moisture limited at 500 mm/year, Africa at 800 mm/year and Australia at 1,000 mm/year of mean annual rainfall.Main conclusionsIn 42% of tropical savannas (accounting for 41% of current area burned) increased drought and higher temperatures will not increase fire, but there are savannas, particularly in South America, that are likely to become more flammable with increasing temperatures. These findings highlight that we cannot transfer knowledge of fire responses to global change across ecosystems/regions—local solutions to local fire management issues are required, and different tropical savanna regions may show contrasting responses to the same drivers of global change.

Research areas: Wildfires, Ecosystems
Quantifying the effects of environmental factors on wildfire burned area (2020)

Abstract. Occurrences of devastating wildfires have been increasing
in the United States for the past decades. While some environmental
controls, including weather, climate, and fuels, are known to play important
roles in controlling wildfires, the interrelationships between these factors
and wildfires are highly complex and may not be well represented by
traditional parametric regressions. Here we develop a model consisting of
multiple machine learning algorithms to predict 0.50.5 gridded
monthly wildfire burned area over the south central United States during
20022015 and then use this model to identify the relative importance of the
environmental drivers on the burned area for both the winterspring and
summer fire seasons of that region. The developed model alleviates the issue
of unevenly distributed burned-area data, predicts burned grids with area
under the curve (AUC) of 0.82 and 0.83 for the two seasons, and achieves
temporal correlations larger than 0.5 for more than 70 % of the grids and
spatial correlations larger than 0.5 (p<0.01) for more than 60 %
of the months. For the total burned area over the study domain, the model
can explain 50 % and 79 % of the observed interannual variability for
the winterspring and summer fire season, respectively. Variable importance
measures indicate that relative humidity (RH) anomalies and preceding
months' drought severity are the two most important predictor variables
controlling the spatial and temporal variation in gridded burned area for
both fire seasons. The model represents the effect of climate variability by
climate-anomaly variables, and these variables are found to contribute the
most to the magnitude of the total burned area across the whole domain for
both fire seasons. In addition, antecedent fuel amounts and conditions are
found to outweigh the weather effects on the amount of total burned area in
the winterspring fire season, while fire weather is more important for the
summer fire season likely due to relatively sufficient vegetation in this
season.

Research areas: Wildfires
Fire Behavior and Heat Release as Source Conditions for Smoke Modeling (2022)

Abstract
            Modeling smoke dispersion from wildland fires is a complex problem. Heat and emissions are released from a fire front as well as from post-frontal combustion, and both are continuously evolving in space and time, providing an emission source that is unlike the industrial sources for which most dispersion models were originally designed. Convective motions driven by the fires heat release strongly couple the fire to the atmosphere, influencing the development and dynamics of the smoke plume. This chapter examines how fire events are described in the smoke modeling process and explores new research tools that may offer potential improvements to these descriptions and can reduce uncertainty in smoke model inputs. Remote sensing will help transition these research tools to operations by providing a safe and reliable means of measuring the fire environment at the space and time scales relevant to fire behavior.

Research areas: Wildfires
Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling (2020)

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.  

Research areas: Wildfires
An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time (2020)

In the case of ongoing wildfire events, timely information on current fire parameters is crucial for informed decision making. Satellite imagery can provide valuable information in this regard, since thermal sensors can detect the exact location and intensity of an active fire at the moment the satellite passes over. This information can be derived and distributed in near-real time, allowing for a picture of current fire activity. However, the derivation of the size and shape of an already affected area is more complex and therefore most often not available within a short time frame. For urgent decision making though, it would be desirable to have this information available in near-real time, and on a large scale. The approach presented here works fully automatic and provides perimeters of burnt areas within two hours after the satellite scene acquisition. It uses the red and near-infrared bands of mid-resolution imagery to facilitate continental-scale monitoring of recently occurred burnt areas. To allow for a high detection capacity independent of the affected vegetation type, segmentation thresholds are derived dynamically from contextual information. This is done by using a Morphological Active Contour approach for perimeter determination. The results are validated against semi-automatically derived burnt areas for five wildfire incidents in Europe. Furthermore, these results are compared with three widely used burnt area datasets on a country-wide scale. It is shown that a high detection quality can be reached in near real-time. The large-scale inter-comparison shows that the results coincide with 63% to 76% of the burnt area in the reference datasets. While these established datasets are only available with a time lag of several months or are created by using manual interaction, the presented approach produces results in near-real time fully automatically. This work is therefore supposed to represent a valuable improvement in wildfire related rapid damage assessment.

Research areas: Wildfires
Wild, Tamed, and Domesticated: Three fire macroregimes for global pyrogeography in the Anthropocene (2022)

Climate and natural vegetation dynamics are key drivers of global vegetation fire, but anthropogenic burning now prevails over vast areas of the planet. Fire regime classification and mapping may contribute towards improved understanding of relationships between those fire drivers. We used 15 years of daily active fire data from the MODIS fire product (MCD14ML, collection 6) to create global maps of six fire descriptors (incidence, size inequality, season length, interannual variability, intensity, and fire season modality). Using multiple correspondence analysis (MCA) and hierarchical agglomerative clustering, we identified three fire macroregimes: Wild, Tamed, and Domesticated, each of which splitting into prototypical and transitional regimes. Interpretation of the six fire regimes in terms of their main drivers relied on the global maps of anthromes and Köppen climate types. The analysis yielded a two‑dimensional space where the principal dimension of variability is primarily defined by interannual variability in fire activity and fire season length, and the secondary axis is based mainly on fire incidence. The Wild fire macroregime occurs mostly in cold wildlands, where burning is sporadic and fire seasons are short. Tamed fires predominate in seasonally dry tropical rangelands and croplands with high fire incidence. Domesticated fires are characteristic of humid, warm temperate and tropical croplands and villages with low fire incidence. The Tamed and Domesticated fire macroregimes, representing managed burning, account for 86% of all active fires in our dataset and for 70% of the global burnable area. Fourteen percent of active fires were found in the cold wildlands, and in the rangelands and forests of steppe and desert climates of the Wild macroregime. These results highlight the extent of human control over global pyrogeography in the Anthropocene.

Research areas: Wildfires, Environmental Impacts
Patterns of population displacement during mega-fires in California (2020)

Abstract
            The Facebook Disaster Maps (FBDM) work presented here is the first time this platform has been used to provide analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. Due to usage habits, the elder population is underrepresented in FBDM data. During the two mega-fires in California, FBDM data effectively captured the temporal change of population arising from the placing and lifting of evacuation orders. Coupled with monotonic trends, the fall and rise of cold and hot spots of population revealed the areas with the greatest population drop and potential places to house the displaced residents. A comparison between the Mendocino Complex and Woolsey fires indicates that a densely populated region can be evacuated faster than a scarcely populated one, possibly due to better access to transportation. In sparsely populated fire-prone areas, resources should be prioritized to move people to shelters as the displaced residents do not have many alternative options, while their counterparts in densely populated areas can utilize their social connections to seek temporary stay at nearby locations during an evacuation. Integrated with an assessment on underrepresented communities, FBDM data and the derivatives can provide much needed information of near real-time population displacement for crisis response and disaster relief. As applications and data generation mature, FBDM will harness crowdsourced data and aid first responder decision-making.

Research areas: Wildfires, Habitat Conversion/Fragmentation