nasa-gesdisc/es-publications-researchareas huggingface dataset:
On the environmental determinants of COVID‐19 seasonality (2021)

Viral respiratory diseases (VRDs), such as influenza and COVID-19, are thought to spread faster during winter than during summer. It has been previously argued that cold and dry conditions are more conducive to the transmission of VRDs than warm and humid climates, although this relationship appears restricted to temperate regions and the causal relationship is not well understood. The severe acute respiratory syndrome coronavirus 2 causing COVID-19 has emerged as a serious global public health problem after the first COVID-19 reports in Wuhan, China, in late 2019. It is still unclear whether this novel respiratory disease will ultimately prove to be a seasonal endemic disease. Here, we suggest that air drying capacity (ADC; an atmospheric state variable that controls the fate/evolution of the virus-laden droplets) and ultraviolet radiation (UV) are probable environmental determinants in shaping the transmission of COVID-19 at the seasonal time scale. These variables, unlike temperature and humidity, provide a physically based framework consistent with the apparent seasonal variability in COVID-19 and prevalent across a broad range of climates (e.g., Germany and India). Since this disease is known to be influenced by the compounding effect of social, biological, and environmental determinants, this study does not claim that these environmental determinants exclusively shape the seasonality of COVID-19. However, we argue that ADC and UV play a significant role in COVID-19 dynamics at the seasonal scale. These findings could help guide the development of a sound adaptation strategy against the pandemic over the coming seasons.

Research areas: Public Health
Evaluating urban–rural access to pathology and laboratory medicine services in Tanzania (2021)

Placement of pathology and laboratory medicine (PALM) services requires balancing efficiency (maximizing test volume) with equitable urbanrural access. We compared the association between population density (proxy for efficiency) and travel time to the closest facility (proxy for equitable access) across levels of Tanzanias public sector health system. We linked geospatial data for Tanzania from multiple sources. Data on facility locations and other geographic measures were collected from government and non-governmental databases. We classified facilities assuming increasing PALM availability by tier: (1) dispensaries, (2) health centres, (3) district hospitals and (4) regional/referral hospitals. We used the AccessMod 5 algorithm to estimate travel time to the closest facility for each tier across Tanzania with 500-m resolution. District-level average population density and travel time to the closest facility were calculated and presented using medians and interquartile ranges. Spatial correlations between these variables were estimated using the global Morans I and bivariate Local Indicator of Spatial Autocorrelation, specifying a queens neighbourhood matrix. Spatial analysis was restricted to 171 contiguous districts. The study included 5406 dispensaries, 675 health centres, 186 district hospitals and 37 regional/referral hospitals. District-level travel times were shortest for Tier 1 (median: [IQR]: 45.4 min [30.074.7]) and longest for Tier 4 facilities (160.2 min [107.3260.0]). There was a weak spatial autocorrelation across tiers (Tier 1: 0.289, Tier 2: 0.292, Tier 3: 0.271 and Tier 4: 0.258) and few districts were classified as significant spatial outliers. Across tiers, geographic patterns of populated districts surrounded by neighbours with short travel time and sparsely populated districts surrounded by neighbours with long travel time were observed. Similar spatial correlation measures across health system levels suggest that Tanzanias health system reflects equitable urbanrural access to different PALM services. Longer travel times to hospital-based care could be ameliorated by shifting specialized diagnostics to more accessible lower tiers.

Research areas: Public Health
Global patterns of aegyptism without arbovirus (2021)

The worlds most important mosquito vector of viruses, Aedes aegypti, is found around the world in tropical, subtropical and even some temperate locations. While climate change may limit populations of Ae. aegypti in some regions, increasing temperatures will likely expand its territory thus increasing risk of human exposure to arboviruses in places like Europe, Northern Australia and North America, among many others. Most studies of Ae. aegypti biology and virus transmission focus on locations with high endemicity or severe outbreaks of human amplified urban arboviruses, such as dengue, Zika, and chikungunya viruses, but rarely on areas at the margins of endemicity. The objective in this study is to explore previously published global patterns in the environmental suitability for Ae. aegypti and dengue virus to reveal deviations in the probability of the vector and human disease occurring. We developed a map showing one end of the gradient being higher suitability of Ae. aegypti with low suitability of dengue and the other end of the spectrum being equal and higher environmental suitability for both Ae. aegypti and dengue. The regions of the world with Ae. aegypti environmental suitability and no endemic dengue transmission exhibits a phenomenon we term aegyptism without arbovirus. We then tested what environmental and socioeconomic variables influence this deviation map revealing a significant association with human population density, suggesting that locations with lower human population density were more likely to have a higher probability of aegyptism without arbovirus. Characterizing regions of the world with established populations of Ae. aegypti but little to no autochthonous transmission of human-amplified arboviruses is an important step in understanding and achieving aegyptism without arbovirus.

Research areas: Public Health
Modelling the influence of naturally acquired immunity from subclinical infection on outbreak dynamics and persistence of rabies in domestic dogs (2021)

A number of mathematical models have been developed for canine rabies to explore dynamics and inform control strategies. A common assumption of these models is that naturally acquired immunity plays no role in rabies dynamics. However, empirical studies have detected rabies-specific antibodies in healthy, unvaccinated domestic dogs, potentially due to immunizing, non-lethal exposure. We developed a stochastic model for canine rabies, parameterised for Laikipia County, Kenya, to explore the implications of different scenarios for naturally acquired immunity to rabies in domestic dogs. Simulating these scenarios using a non-spatial model indicated that low levels of immunity can act to limit rabies incidence and prevent depletion of the domestic dog population, increasing the probability of disease persistence. However, incorporating spatial structure and human response to high rabies incidence allowed the virus to persist in the absence of immunity. While low levels of immunity therefore had limited influence under a more realistic approximation of rabies dynamics, high rates of exposure leading to immunizing non-lethal exposure were required to produce population-level seroprevalences comparable with those reported in empirical studies. False positives and/or spatial variation may contribute to high empirical seroprevalences. However, if high seroprevalences are related to high exposure rates, these findings support the need for high vaccination coverage to effectively control this disease.

Research areas: Public Health
Solar Ultraviolet Radiation in Pretoria and Its Relations to Aerosols (2021)

Biomass burning has an impact on atmospheric composition as well as human health and wellbeing. In South Africa, the biomass burning season extends from July to October and affects the aerosol loading and tropospheric ozone concentrations which in turn impact solar ultraviolet radiation (UVR) levels at the surface. Using ground-based observations of aerosols, tropospheric ozone and solar UVR (as well as modelled solar UVR) we investigated the impact of aerosols and tropospheric ozone on solar UVR in August, September, and October over Pretoria. Aerosol optical depth (AOD) and tropospheric ozone reached a peak between September and October each year. On clear-sky days, the average relative difference between the modelled and observed solar Ultraviolet Index (UVI) levels (a standard indicator of surface UVR) at solar noon was 7%. Using modelled UVRwhich included and excluded the effects of aerosols and tropospheric ozone from biomass burningaerosols had a larger radiative effect compared to tropospheric ozone on UVI levels during the biomass burning season. Excluding only aerosols resulted in a 10% difference between the modelled and observed UVI, while excluding only tropospheric ozone resulted in a difference of 2%. Further understanding of the radiative effect of aerosols and trace gases, particularly in regions that are affected by emissions from biomass burning, is considered important for future research.

Research areas: Environmental Impacts
Framework for mapping the drivers of coastal vulnerability and spatial decision making for climate-change adaptation: A case study from Maharashtra, India (2018)

The impacts of climate change are of particular concern to the coastal region of tropical countries like India, which are exposed to cyclones, floods, tsunami, seawater intrusion, etc. Climate-change adaptation presupposes comprehensive assessment of vulnerability status. Studies so far relied either on remote sensing-based spatial mapping of physical vulnerability or on certain socio-economic aspects with limited scope for upscaling or replication. The current study is an attempt to develop a holistic and robust framework to assess the vulnerability of coastal India at different levels. We propose and estimate cumulative vulnerability index (CVI) as a function of exposure, sensitivity and adaptive capacity, at the village level, using nationally comparable and credible datasets. The exposure index (EI) was determined at the village level by decomposing the spatial multi-hazard maps, while sensitivity (SI) and adaptive capacity indices (ACI) were estimated using 23 indicators, covering social and economic aspects. The indicators were identified through the literature review, expert consultations, opinion survey, and were further validated through statistical tests. The socio-economic vulnerability index (SEVI) was constructed as a function of sensitivity and adaptive capacity for planning grassroot-level interventions and adaptation strategies. The framework was piloted in Sindhudurg, a coastal district in Maharashtra, India. It comprises 317 villages, spread across three taluks viz., Devgad, Malvan and Vengurla. The villages in Sindhudurg were ranked based on this multi-criteria approach. Based on CVI values, 92 villages (30%) in Sindhudurg were identified as highly vulnerable. We propose a decision tool for identifying villages vulnerable to changing climate, based on their level of sensitivity and adaptive capacity in a two-dimensional matrix, thus aiding in planning location-specific interventions. Here, vulnerability indicators are classified and designated as drivers (indicators with significantly high values and intervention priority) and buffers (indicators with low-to-moderate values) at the village level. The framework provides for aggregation or decomposition of CVI and other sub-indices, in order to plan spatial contingency plans and enable swift action for climate adaptation.

Research areas: Habitat Conversion/Fragmentation
Using evapotranspiration to assess drought sensitivity on a subfield scale with HRMET, a high resolution surface energy balance model (2014)

Evapotranspiration (ET) rates provide a valuable within-season indicator of plant productivity, as well as data on fluxes of water in a landscape. Applying remote sensing for ET estimation has potential to improve the sustainable management of water resources in agricultural settings. Most current ET models, however, rely on dry and wet pixels within a given scene to partition turbulent fluxes between latent and sensible heat, thus limiting their ability to map ET throughout the growing season at extremely high (meter scale) spatial resolutions. Here, we develop a field-validated surface energy balance model, High Resolution Mapping of EvapoTranspiration (HRMET), which requires only basic meteorological data, spatial surface temperature and canopy structure data. We use HRMET to estimate ET rates over two commercial cornfields in south-central Wisconsin during the 2012 growing season, which was characterized by severe drought. HRMET results indicate that the magnitude of within-field variability in ET rates was primarily driven by water availability. The application of remotely sensed data to precision agriculture has also been hampered by turnaround time between image acquisition and availability. We introduce relative ET (ETR), which enables comparison of ET rates between image dates by normalizing for variability caused by weather and crop stage. ETR also provides an intuitive, index-like metric for evaluating spatial variability in ET on a subfield scale. ETR maps illuminate persistent patterns in ET across measurement dates that may be driven by soil water availability and topography. ETR is used to develop a novel paired-image technique that can map subfield sensitivity classes to stressors such as drought. Sensitivity class mapping can be used to circumvent issues related to turnaround time to facilitate the incorporation of remotely sensed data into precision agriculture.

Research areas: Droughts
Uncovering Dryland Woody Dynamics Using Optical, Microwave, and Field (2020)

Dryland ecosystems are frequently struck by droughts. Yet, woody vegetation is often able to recover from mortality events once precipitation returns to pre-drought conditions. Climate change, however, may impact woody vegetation resilience due to more extreme and frequent droughts. Thus, better understanding how woody vegetation responds to drought events is essential. We used a phenology-based remote sensing approach coupled with field data to estimate the severity and recovery rates of a large scale die-off event that occurred in 20142015 in Senegal. Novel low (L-band) and high-frequency (Ku-band) passive microwave vegetation optical depth (VOD), and optical MODIS data, were used to estimate woody vegetation dynamics. The relative importance of soil, human-pressure, and before-drought vegetation dynamics influencing the woody vegetation response to the drought were assessed. The die-off in 20142015 represented the highest dry season VOD drop for the studied period (19892017), even though the 2014 drought was not as severe as the droughts in the 1980s and 1990s. The spatially explicit Die-off Severity Index derived in this study, at 500 m resolution, highlights woody plants mortality in the study area. Soil physical characteristics highly affected die-off severity and post-disturbance recovery, but pre-drought biomass accumulation (i.e., in areas that benefited from above-normal rainfall conditions before the 2014 drought) was the most important variable in explaining die-off severity. This study provides new evidence supporting a better understanding of the greening Sahel, suggesting that a sudden increase in woody vegetation biomass does not necessarily imply a stable ecosystem recovery from the droughts in the 1980s. Instead, prolonged above-normal rainfall conditions prior to a drought may result in the accumulation of woody biomass, creating the basis for potentially large-scale woody vegetation die-off events due to even moderate dry spells.

Research areas: Droughts
Weather underground: Subsurface hydrologic processes mediate tree (2020)

AbstractDrought extent and severity have increased and are predicted to continue to increase in many parts of the world. Understanding tree vulnerability to drought at both individual and species levels is key to ongoing forest management and preparation for future transitions in community composition. The influence of subsurface hydrologic processes is particularly important in waterlimited ecosystems, and is an understudied aspect of tree drought vulnerability. With California's 20132016 extraordinary drought as a natural experiment, we studied four cooccurring woodland tree species, blue oak (Quercus douglasii), valley oak (Quercus lobata), gray pine (Pinus sabiniana), and California juniper (Juniperus californica), examining drought vulnerability as a function of climate, lithology and hydrology using regional aerial dieback surveys and sitescale field surveys. We found that in addition to climatic drought severity (i.e., rainfall), subsurface processes explained variation in drought vulnerability within and across species at both scales. Regionally for blue oak, severity of dieback was related to the bedrock lithology, with higher mortality on igneous and metamorphic substrates, and to regional reductions in groundwater. At the site scale, access to deep subsurface water, evidenced by stem water stable isotope composition, was related to canopy condition across all species. Along hillslope gradients, channel locations supported similar environments in terms of water stress across a wide climatic gradient, indicating that subsurface hydrology mediates species experience of drought, and that areas associated with persistent access to subsurface hydrologic resources may provide important refugia at species xeric range edges. Despite this persistent overall influence of the subsurface environment, individual species showed markedly different response patterns. We argue that hydrologic niche segregation can be a useful lens through which to interpret these differences in vulnerability to climatic drought and climate change.

Research areas: Droughts
Analysis of the relationship between yield in cereals and remotely sensed fAPAR in the framework of monitoring drought impacts in Europe (2022)

Abstract. This study focuses on the relationship between satellite-measured fraction of absorbed photosynthetically active radiation (fAPAR) and crop yield cereals in Europe. Different features of the relationship between annual yield and multiple time series of fAPAR, collected during different periods of the year, were investigated. The two key outcomes of the analysis are the identification of the period: (i) from March to October as the one having the highest positive correlation between fAPAR and yield and (ii) from February to May as the period characterised by most of the estimated negative correlation. While both periods align well with the commonly assumed dynamic of the growing season, spatial differences are also observed across Europe. On the one hand, the Mediterranean regions report the highest correlation values (r>0.8) and the longest continuous periods with positive statistically significant results (up to 7 months), covering most of the growing season. On the other hand, the central European region is characterised by the most limited positive correlation values, with only 2 months or less showing statistically significant results. While marked differences in the overall capability to capture the full dynamic of yield are observed across Europe, fAPAR anomalies seem capable of discriminating low-yield years from the rest in most of the cases.

Research areas: Agriculture
Global exposure of population and land‐use to meteorological droughts under different warming levels and SSPs: A CORDEX‐based study (2021)

Global warming is likely to cause a progressive drought increase in some regions, but how population and natural resources will be affected is still underexplored. This study focuses on global population, forests, croplands and pastures exposure to meteorological drought hazard in the 21st century, expressed as frequency and severity of drought events. As input, we use a large ensemble of climate simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), population projections from the NASA-SEDAC dataset and land-use projections from the Land-Use Harmonization 2 project for 1981-2100. The exposure to drought hazard is presented for five Shared Socioeconomic Pathways (SSP1-SSP5) at four Global Warming Levels (GWLs: 1.5°C to 4°C). Results show that considering only Standardized Precipitation Index (SPI; based on precipitation), the SSP3 at GWL4 projects the largest fraction of the global population (14%) to experience an increase in drought frequency and severity (versus 1981-2010), with this value increasing to 60% if temperature is considered (indirectly included in the Standardized Precipitation-Evapotranspiration Index, SPEI). With SPEI, considering the highest GWL for each SSP, 8 (for SSP2, SSP4, SSP5) and 11 (SSP3) billion people, that is, more than 90%, will be affected by at least one unprecedented drought. For SSP5 at GWL4, approximately 2 × 106 km2 of forests and croplands (respectively, 6% and 11%) and 1.5 × 106 km2 of pastures (19%) will be exposed to increased drought frequency and severity according to SPI, but for SPEI this extent will rise to 17 × 106 km2 of forests (49%), 6 × 106 km2 of pastures (78%) and 12 × 106 km2 of croplands (67%), being mid-latitudes the most affected. The projected likely increase of drought frequency and severity significantly increases population and land-use exposure to drought, even at low GWLs, thus extensive mitigation and adaptation efforts are needed to avoid the most severe impacts of climate change.

Research areas: Droughts
Effects of Increased Drought in Amazon Forests Under Climate Change: (2022)

The Amazon forests are one of the largest ecosystem carbon pools on Earth. Although more frequent and prolonged future droughts have been predicted, the impacts have remained largely uncertain, as most land surface models (LSMs) fail to capture the vegetation drought responses. In this study, the ability of the LSM JSBACH to simulate the drought responses of leaf area index (LAI) and leaf litter production in the Amazon forests is evaluated against artificial drought experiments. Based on the evaluation, improvements are implemented, including a dependency of leaf growth on leaf carbon allocation and a better representation of drought-dependent leaf shedding. The modified JSBACH is shown to capture the drought responses at two sites and across different regions of the basin. It is then coupled with an atmospheric model to simulate the carbon and biogeophysical feedbacks of drought under future climate. We separate the drought impacts into (a) the direct effect, resulting from drier soil and stomatal closure, which does not involve a change in canopy structure, and (b) the LAI effect, resulting from leaf shedding and involving canopy response. We show that the latter accounts for 35% of reduced land carbon uptake (9 ± 10 vs. 26 ± 7 g/m<SUP>2</SUP>/yr; mean ± 1 sd) and 12% of surface warming (0.09 ± 0.03 vs. 0.7 ± 0.07 K) during the late 21st century. A north-south dipole of precipitation change is found, which is largely attributable to the direct effect. The results highlight the importance of incorporating drought deciduousness of tropical rainforests in LSMs to better simulate land-atmosphere interactions in the future.

Research areas: Droughts