Satellite observed dryland greening in Asian endorheic basins: Drivers and implications to sustainable development.

A large portion of Central-Western Asia is made up of contiguous closed basins, collectively termed as the Asian Endorheic Basins (AEBs). As these retention basins are only being replenished by the intermittent and scarce rainfall, global warming coupled with ever-rising human demand for water is exerting unprecedented pressures on local water and ecological security. Recent studies revealed a persistent and widespread water storage decline across the AEBs, yet the response of dryland vegetation to this recent hydroclimatic trend and a spatially explicit partitioning of the impact into the hydroclimatic factors and human activities remain largely unknown. To fill in this knowledge gap, we conducted trend and partial correlation analysis of vegetation and hydroclimatic change from 2001 to 2021 using multi-satellite observations, including vegetation greenness, total water storage anomalies (TWSA) and meteorological data. Here we show that much of the AEB (65.53 %), encompassing Mongolia Plateau, Northwest China, Qinghai Tibet Plateau, and Western Asia (except the Arabian Peninsula), exhibited a significant greening trend over the past two decades. In arid AEB, precipitation dominated the vegetation productivity trend. Such a rainfall dominance gave way to TWSA dominance in the hyper-arid AEB. We further showed that the decoupling of rainfall and hyper-arid vegetation greening was largely due to a significant expansion (17.3 %) in irrigated cropland across the hyper-arid AEB. Given the extremely harsh environment in the AEB, our results therefore raised a significant concern on the ecological and societal sustainability in this region, where a mild increase in precipitation cannot catch up the rising evaporative demand and water consumption resulted from global warming and agriculture intensification.

Environmental DNA analysis of airborne poaceae (grass) pollen reveals taxonomic diversity across seasons and climate zones.

BACKGROUND: Grasses populate most biogeographical zones, and their diversity influences allergic sensitisation to pollen. Previously, the contribution of different Poaceae subfamilies to airborne pollen has mostly been inferred from historical herbarium records. We recently applied environmental (e)DNA metabarcoding at one subtropical site revealing that successive airborne grass pollen peaks were derived from repeated flowering of Chloridoid and Panicoid grasses over a season. This study aimed to compare spatiotemporal patterns in grass pollen exposure across seasons and climate zones. METHODS: Airborne pollen concentrations across two austral pollen seasons spanning 2017-2019 at subtropical (Mutdapilly and Rocklea, Queensland) and temperate (Macquarie Park and Richmond, New South Wales) sites, were determined with a routine volumetric impaction sampler and counting by light microscopy. Poaceae rbcL metabarcode sequences amplified from daily pollen samples collected once per week were assigned to subfamily and genus using a ribosomal classifier and compared with Atlas of Living Australia sighting records. RESULTS: eDNA analysis revealed distinct dominance patterns of grass pollen at various sites: Panicoid grasses prevailed in both subtropical Mutdapilly and temperate Macquarie Park, whilst Chloridoid grasses dominated the subtropical Rocklea site. Overall, subtropical sites showed significantly higher proportion of pollen from Chloridoid grasses than temperate sites, whereas the temperate sites showed a significantly higher proportion of pollen from Pooideae grasses than subtropical sites. Timing of airborne Pooid (spring), Panicoid and Chloridoid (late spring to autumn), and Arundinoid (autumn) pollen were significantly related to number of days from mid-winter. Proportions of eDNA for subfamilies correlated with distributions grass sighting records between climate zones. CONCLUSIONS: eDNA analysis enabled finer taxonomic discernment of Poaceae pollen records across seasons and climate zones with implications for understanding adaptation of grasslands to climate change, and the complexity of pollen exposure for patients with allergic respiratory diseases.

Structural complexity biases vegetation greenness measures.

Vegetation 'greenness' characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun-sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.

Large diurnal compensatory effects mitigate the response of Amazonian forests to atmospheric warming and drying.

Photosynthesis and evapotranspiration in Amazonian forests are major contributors to the global carbon and water cycles. However, their diurnal patterns and responses to atmospheric warming and drying at regional scale remain unclear, hindering the understanding of global carbon and water cycles. Here, we used proxies of photosynthesis and evapotranspiration from the International Space Station to reveal a strong depression of dry season afternoon photosynthesis (by 6.7 ± 2.4%) and evapotranspiration (by 6.1 ± 3.1%). Photosynthesis positively responds to vapor pressure deficit (VPD) in the morning, but negatively in the afternoon. Furthermore, we projected that the regionally depressed afternoon photosynthesis will be compensated by their increases in the morning in future dry seasons. These results shed new light on the complex interplay of climate with carbon and water fluxes in Amazonian forests and provide evidence on the emerging environmental constraints of primary productivity that may improve the robustness of future projections.

Prediction of airborne pollen and sub-pollen particles for thunderstorm asthma outbreaks assessment.

When exposed to convective thunderstorm conditions, pollen grains can rupture and release large numbers of allergenic sub-pollen particles (SPPs). These sub-pollen particles easily enter deep into human lungs, causing an asthmatic response named thunderstorm asthma (TA). Up to now, efforts to numerically predict the airborne SPP process and to forecast the occurrence of TAs are unsatisfactory. To overcome this problem, we have developed a physically-based pollen model (DREAM-POLL) with parameterized formation of airborne SPPs caused by convective atmospheric conditions. We ran the model over the Southern Australian grass fields for 2010 and 2016 pollen seasons when four largest decadal TA epidemics happened in Melbourne. One of these TA events (in November 2016) was the worldwide most extreme one which resulted to nine deaths and hundreds of hospital patient presentations. By executing the model on a day-by-day basis in a hindcast real-time mode we predicted SPP peaks exclusively only when the four major TA outbreaks happened, thus achieving a high forecasting success rate. The proposed modelling system can be easily implemented for other geographical domains and for different pollen types.

The AusPollen partnership project: Allergenic airborne grass pollen seasonality and magnitude across temperate and subtropical eastern Australia, 2016-2020.

BACKGROUND: Allergic rhinitis affects half a billion people globally, including a fifth of the Australian population. As the foremost outdoor allergen source, ambient grass pollen exposure is likely to be altered by climate change. The AusPollen Partnership aimed to standardize pollen monitoring and examine broad-scale biogeographical and meteorological factors influencing interannual variation in seasonality of grass pollen aerobiology in Australia. METHODS: Daily airborne grass and other pollen concentrations in four eastern Australian cities separated by over 1700 km, were simultaneously monitored using Hirst-style samplers following the Australian Interim Pollen and Spore Monitoring Standard and Protocols over four seasons from 2016 to 2020. The grass seasonal pollen integral was determined. Gridded rainfall, temperature, and satellite-derived grassland sources up to 100 km from the monitoring site were analysed. RESULTS: The complexity of grass pollen seasons was related to latitude with multiple major summer-autumn peaks in Brisbane, major spring and minor summer peaks in Sydney and Canberra, and single major spring peaks occurring in Melbourne. The subtropical site of Brisbane showed a higher proportion of grass out of total pollen than more temperate sites. The magnitude of the grass seasonal pollen integral was correlated with pasture greenness, rainfall and number of days over 30 °C, preceding and within the season, up to 100 km radii from monitoring sites. CONCLUSIONS: Interannual fluctuations in Australian grass pollen season magnitude are strongly influenced by regional biogeography and both pre- and in-season weather. This first continental scale, Southern Hemisphere standardized aerobiology dataset forms the basis to track shifts in pollen seasonality, biodiversity and impacts on allergic respiratory diseases.

Spatial based drought assessment: Where are we heading? A review on the current status and future.

Droughts are the most spatially complex natural hazards that exert global impacts and are further aggravated by climate change. The investigation of drought events is challenging as it involves numerous factors ranging from detection and assessment to modelling, management and mitigation. The analysis of these factors and their quantitative assessments have significantly evolved in recent times. In this paper, we review recent methods used to examine and model droughts from a spatial viewpoint. Our analysis was conducted at three spatial scales (point-wise, regional and global) and we evaluated how recent spatial methods have advanced our understanding of drought through case study examples. Further, we also examine and provide a broad overview of relevant case studies related to future drought occurrences under climate change. This study is a comprehensive synthesis of the various quantitative techniques used to assess the spatial characteristics of droughts at different spatial scales, and not an exhaustive review of all drought aspects. However, this serves as a basis for understanding the key milestones and advances accomplished through new spatial concepts relative to the traditional approaches to study drought. This work also aims to address the gaps in knowledge that are in need of further attention and provides recommendations to improve our understanding of droughts.

Response of dryland vegetation under extreme wet events with satellite measures of greenness and fluorescence.

Extreme wet events in central Australia triggered large vegetation responses that contributed greatly to large global land carbon sink anomalies. There remain significant uncertainties on the extent to which these events over dryland vegetation can be monitored and assessed with satellite data. In this study, we investigated the vegetation responses of the major Australian semiarid biomes to two extreme wet events utilizing multi-satellite observations of (1) solar-induced chlorophyll fluorescence (SIF), as a proxy for photosynthetic activity and (2) the enhanced vegetation index (EVI), as a measure of canopy chlorophyll or greenness. We related these satellite observations with gross primary productivity (GPP) estimated from eddy covariance tower sites, as a performance benchmark. The C3-dominated Mulga woodland was the most responsive biome to both wet pulses and exhibited the highest sensitivity to soil moisture. The C4-dominated Hummock grassland was more responsive to the 2011 "big wet" event, relative to the later 2016-2017 wet pulse. EVI swiftly responded to the extreme wet events and showed markedly amplified seasonal amplitude, however, there was a time lag as compared with SIF during the post-wet period, presumably due to the relatively slower chlorophyll degradation in contrast with declines in photosynthetic activity. Despite a robust linear SIF-GPP relationship (r2 ranging from 0.59 to 0.85), the spatially coarse SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) yielded high retrieval noise over the xeric biomes, hindering its capacity to capture thoroughly the dryland vegetation dynamics in central Australia. Our study highlights that synchronous satellite observations of greenness and fluorescence can potentially offer an improved understanding of dryland vegetation dynamics and can advance our ability to detect ecosystem alterations under future changing climates.

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network.

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.

Forest fragmentation impacts the seasonality of Amazonian evergreen canopies.

Predictions of the magnitude and timing of leaf phenology in Amazonian forests remain highly controversial. Here, we use terrestrial LiDAR surveys every two weeks spanning wet and dry seasons in Central Amazonia to show that plant phenology varies strongly across vertical strata in old-growth forests, but is sensitive to disturbances arising from forest fragmentation. In combination with continuous microclimate measurements, we find that when maximum daily temperatures reached 35 °C in the latter part of the dry season, the upper canopy of large trees in undisturbed forests lost plant material. In contrast, the understory greened up with increased light availability driven by the upper canopy loss, alongside increases in solar radiation, even during periods of drier soil and atmospheric conditions. However, persistently high temperatures in forest edges exacerbated the upper canopy losses of large trees throughout the dry season, whereas the understory in these light-rich environments was less dependent on the altered upper canopy structure. Our findings reveal a strong influence of edge effects on phenological controls in wet forests of Central Amazonia.

Spatial modeling of soil erosion hazards and crop diversity change with rainfall variation in the Central Highlands of Sri Lanka.

The spatial variation of soil erosion is essential for farming system management and resilience development, specifically in the high climate hazard vulnerable tropical countries like Sri Lanka. This study aimed to investigate climate and human-induced soil erosion through spatial modeling. Remote sensing was used for spatial modeling to detect soil erosion, crop diversity, and rainfall variation. The study employed a time-series analysis of several variables such as rainfall, land-use land-cover (LULC) and crop diversity to detect the spatial variability of soil erosion in farming systems. Rain-use efficiency (RUE) and residual trend analysis (RESTREND) combined with a regression approach were applied to partition the soil erosion due to human and climate-induced land degradation. Results showed that soil erosion has increased from 9.08 Mg/ha/yr to 11.08 Mg/ha/yr from 2000 to 2019 in the Central Highlands of Sri Lanka. The average annual rainfall has increased in the western part of the Central Highlands, and soil erosion hazards such as landslides incidence also increased during this period. However, crop diversity has been decreasing in farming systems, namely wet zone low country (WL1a) and wet zone mid-country (WM1a), in the western part of the Central Highlands. The RUE and RESTREND analyses reveal climate-induced soil erosion is responsible for land degradation in these farming systems and is a threat to sustainable food production in the farming systems of the Central Highlands.

Proposing an ecologically viable and economically sound farming system using a matrix-based geo-informatics approach.

Healthy farming systems play a vital role in improving agricultural productivity and sustainable food production. The present study aimed to propose an efficient framework to evaluate ecologically viable and economically sound farming systems using a matrix-based analytic hierarchy process (AHP) and weighted linear combination method with geo-informatics tools. The proposed framework has been developed and tested in the Central Highlands of Sri Lanka. Results reveal that more than 50% of farming systems demonstrated moderate status in terms of ecological and economic aspects. However, two vulnerable farming systems on the western slopes of the Central Highlands, named WL1a and WM1a, were identified as very poor status. These farming systems should be a top priority for restoration planning and soil conservation to prevent further deterioration. Findings indicate that a combination of ecologically viable (nine indicators) and economical sound (four indicators) criteria are a practical method to scrutinize farming systems and decision making on soil conservation and sustainable land management. In addition, this research introduces a novel approach to delineate the farming systems based on agro-ecological regions and cropping areas using geo-informatics technology. This framework and methodology can be employed to evaluate the farming systems of other parts of the country and elsewhere to identify ecologically viable and economically sound farming systems concerning soil erosion hazards. The proposed approach addresses a new dimension of the decision-making process by evaluating the farming systems relating to soil erosion hazards and suggests introducing policies on priority-based planning for conservation with low-cost strategies for sustainable land management.

An improved SPEI drought forecasting approach using the long short-term memory neural network.

Droughts are slow-moving natural hazards that gradually spread over large areas and capable of extending to continental scales, leading to severe socio-economic damage. A key challenge is developing accurate drought forecast model and understanding a models' capability to examine different drought characteristics. Traditionally, forecasting techniques have used various time-series approaches and machine learning models. However, the use of deep learning methods have not been tested extensively despite its potential to improve our understanding of drought characteristics. The present study uses a deep learning approach, specifically the Long Short-Term Memory (LSTM) to predict a commonly used drought measure, the Standard Precipitation Evaporation Index (SPEI) at two different time scales (SPEI 1, SPEI 3). The model was compared with other common machine learning method, Random Forests, Artificial Neural Networks and applied over the New South Wales (NSW) region of Australia, using hydro-meteorological variables as predictors. The drought index and predictor data were collected from the Climatic Research Unit (CRU) dataset spanning from 1901 to 2018. We analysed the LSTM forecasted results in terms of several drought characteristics (drought intensity, drought category, or spatial variation) to better understand how drought forecasting was improved. Evaluation of the drought intensity forecasting capabilities of the model were based on three different statistical metrics, Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The model achieved R2 value of more than 0.99 for both SPEI 1 and SPEI 3 cases. The variation in drought category forecasted results were studied using a multi-class Receiver Operating Characteristic based Area under Curves (ROC-AUC) approach. The analysis revealed an AUC value of 0.83 and 0.82 for SPEI 1 and SPEI 3 respectively. The spatial variation between observed and forecasted values were analysed for the summer months of 2016-2018. The findings from the study show an improvement relative to machine learning models for a lead time of 1 month in terms of different drought characteristics. The results from this work can be used for drought mitigation purposes and different models need to be tested to further enhance our capabilities.

Urban-rural gradients reveal joint control of elevated CO2 and temperature on extended photosynthetic seasons.

Photosynthetic phenology has large effects on the land-atmosphere carbon exchange. Due to limited experimental assessments, a comprehensive understanding of the variations of photosynthetic phenology under future climate and its associated controlling factors is still missing, despite its high sensitivities to climate. Here, we develop an approach that uses cities as natural laboratories, since plants in urban areas are often exposed to higher temperatures and carbon dioxide (CO2) concentrations, which reflect expected future environmental conditions. Using more than 880 urban-rural gradients across the Northern Hemisphere (≥30° N), combined with concurrent satellite retrievals of Sun-induced chlorophyll fluorescence (SIF) and atmospheric CO2, we investigated the combined impacts of elevated CO2 and temperature on photosynthetic phenology at the large scale. The results showed that, under urban conditions of elevated CO2 and temperature, vegetation photosynthetic activity began earlier (-5.6 ± 0.7 d), peaked earlier (-4.9 ± 0.9 d) and ended later (4.6 ± 0.8 d) than in neighbouring rural areas, with a striking two- to fourfold higher climate sensitivity than greenness phenology. The earlier start and peak of season were sensitive to both the enhancements of CO2 and temperature, whereas the delayed end of season was mainly attributed to CO2 enrichments. We used these sensitivities to project phenology shifts under four Representative Concentration Pathway climate scenarios, predicting that vegetation will have prolonged photosynthetic seasons in the coming two decades. This observation-driven study indicates that realistic urban environments, together with SIF observations, provide a promising method for studying vegetation physiology under future climate change.

The Melbourne epidemic thunderstorm asthma event 2016: an investigation of environmental triggers, effect on health services, and patient risk factors.

BACKGROUND: A multidisciplinary collaboration investigated the world's largest, most catastrophic epidemic thunderstorm asthma event that took place in Melbourne, Australia, on Nov 21, 2016, to inform mechanisms and preventive strategies. METHODS: Meteorological and airborne pollen data, satellite-derived vegetation index, ambulance callouts, emergency department presentations, and data on hospital admissions for Nov 21, 2016, as well as leading up to and following the event were collected between Nov 21, 2016, and March 31, 2017, and analysed. We contacted patients who presented during the epidemic thunderstorm asthma event at eight metropolitan health services (each including up to three hospitals) via telephone questionnaire to determine patient characteristics, and investigated outcomes of intensive care unit (ICU) admissions. FINDINGS: Grass pollen concentrations on Nov 21, 2016, were extremely high (>100 grains/m3). At 1800 AEDT, a gust front crossed Melbourne, plunging temperatures 10°C, raising humidity above 70%, and concentrating particulate matter. Within 30 h, there were 3365 (672%) excess respiratory-related presentations to emergency departments, and 476 (992%) excess asthma-related admissions to hospital, especially individuals of Indian or Sri Lankan birth (10% vs 1%, p<0·0001) and south-east Asian birth (8% vs 1%, p<0·0001) compared with previous 3 years. Questionnaire data from 1435 (64%) of 2248 emergency department presentations showed a mean age of 32·0 years (SD 18·6), 56% of whom were male. Only 28% had current doctor-diagnosed asthma. 39% of the presentations were of Asian or Indian ethnicity (25% of the Melbourne population were of this ethnicity according to the 2016 census, relative risk [RR] 1·93, 95% CI 1·74-2·15, p <0·0001). Of ten individuals who died, six were Asian or Indian (RR 4·54, 95% CI 1·28-16·09; p=0·01). 35 individuals were admitted to an intensive care unit, all had asthma, 12 took inhaled preventers, and five died. INTERPRETATION: Convergent environmental factors triggered a thunderstorm asthma epidemic of unprecedented magnitude, tempo, and geographical range and severity on Nov 21, 2016, creating a new benchmark for emergency and health service escalation. Asian or Indian ethnicity and current doctor-diagnosed asthma portended life-threatening exacerbations such as those requiring admission to an ICU. Overall, the findings provide important public health lessons applicable to future event forecasting, health care response coordination, protection of at-risk populations, and medical management of epidemic thunderstorm asthma. FUNDING: None.

Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains.

Extremely high temperatures represent one of the most severe abiotic stresses limiting crop productivity. However, understanding crop responses to heat stress is still limited considering the increases in both the frequency and severity of heat wave events under climate change. This limited understanding is partly due to the lack of studies or tools for the timely and accurate monitoring of crop responses to extreme heat over broad spatial scales. In this work, we use novel spaceborne data of sun-induced chlorophyll fluorescence (SIF), which is a new proxy for photosynthetic activity, along with traditional vegetation indices (Normalized Difference Vegetation Index NDVI and Enhanced Vegetation Index EVI) to investigate the impacts of heat stress on winter wheat in northwestern India, one of the world's major wheat production areas. In 2010, an abrupt rise in temperature that began in March adversely affected the productivity of wheat and caused yield losses of 6% compared to previous year. The yield predicted by satellite observations of SIF decreased by approximately 13.9%, compared to the 1.2% and 0.4% changes in NDVI and EVI, respectively. During early stage of this heat wave event in early March 2010, the SIF observations showed a significant reduction and earlier response, while NDVI and EVI showed no changes and could not capture the heat stress until late March. The spatial patterns of SIF anomalies closely tracked the temporal evolution of the heat stress over the study area. Furthermore, our results show that SIF can provide large-scale, physiology-related wheat stress response as indicated by the larger reduction in fluorescence yield (SIFyield ) than fraction of photosynthetically active radiation during the grain-filling phase, which may have eventually led to the reduction in wheat yield in 2010. This study implies that satellite observations of SIF have great potential to detect heat stress conditions in wheat in a timely manner and assess their impacts on wheat yields at large scales.

Dynamic ecological observations from satellites inform aerobiology of allergenic grass pollen.

Allergic diseases, including respiratory conditions of allergic rhinitis (hay fever) and asthma, affect up to 500 million people worldwide. Grass pollen are one major source of aeroallergens globally. Pollen forecast methods are generally site-based and rely on empirical meteorological relationships and/or the use of labour-intensive pollen collection traps that are restricted to sparse sampling locations. The spatial and temporal dynamics of the grass pollen sources themselves, however, have received less attention. Here we utilised a consistent set of MODIS satellite measures of grass cover and seasonal greenness (EVI) over five contrasting urban environments, located in Northern (France) and Southern Hemispheres (Australia), to evaluate their utility for predicting airborne grass pollen concentrations. Strongly seasonal and pronounced pollinating periods, synchronous with satellite measures of grass cover greenness, were found at the higher latitude temperate sites in France (46-50° N. Lat.), with peak pollen activity lagging peak greenness, on average by 2-3weeks. In contrast, the Australian sites (34-38° S. Lat.) displayed pollinating periods that were less synchronous with satellite greenness measures as peak pollen concentrations lagged peak greenness by as much as 4 to 7weeks. The Australian sites exhibited much higher spatial and inter-annual variations compared to the French sites and at the Sydney site, broader and multiple peaks in both pollen concentrations and greenness data coincided with flowering of more diverse grasses including subtropical species. Utilising generalised additive models (GAMs) we found the satellite greenness data of grass cover areas explained 80-90% of airborne grass pollen concentrations across the three French sites (p<0.001) and accounted for 34 to 76% of grass pollen variations over the two sites in Australia (p<0.05). Our results demonstrate the potential of satellite sensing to augment forecast models of grass pollen aerobiology as a tool to reduce the health and socioeconomic burden of pollen-sensitive allergic diseases.

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest.

Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun-sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate-phenology relationships in the tropics.

Responses of LAI to rainfall explain contrasting sensitivities to carbon uptake between forest and non-forest ecosystems in Australia.

Non-forest ecosystems (predominant in semi-arid and arid regions) contribute significantly to the increasing trend and interannual variation of land carbon uptake over the last three decades, yet the mechanisms are poorly understood. By analysing the flux measurements from 23 ecosystems in Australia, we found the the correlation between gross primary production (GPP) and ecosystem respiration (Re) was significant for non-forest ecosystems, but was not for forests. In non-forest ecosystems, both GPP and Re increased with rainfall, and, consequently net ecosystem production (NEP) increased with rainfall. In forest ecosystems, GPP and Re were insensitive to rainfall. Furthermore sensitivity of GPP to rainfall was dominated by the rainfall-driven variation of LAI rather GPP per unit LAI in non-forest ecosystems, which was not correctly reproduced by current land models, indicating that the mechanisms underlying the response of LAI to rainfall should be targeted for future model development.

CO variability and its association with household cooking fuels consumption over the Indo-Gangetic Plains.

This study examines the spatio-temporal trends obtained from decade long (Jan 2003-Dec 2014) satellite observational data of Atmospheric Infrared Sounder (AIRS) and Measurements of Pollution in the Troposphere (MOPITT) on carbon monoxide (CO) concentration over the Indo-Gangetic Plains (IGP) region. The time sequence plots of columnar CO levels over the western, central and eastern IGP regions reveal marked seasonal behaviour, with lowest CO levels occurring during the monsoon months and the highest CO levels occurring during the pre-monsoon period. A negative correlation between CO levels and rainfall is observed. CO vertical profiles show relatively high values in the upper troposphere at ∼200 hPa level during the monsoon months, thus suggesting the role of convective transport and advection in addition to washout behind the decreased CO levels during this period. MOPITT and AIRS observations show a decreasing trend of 9.6 × 1015 and 1.5 × 1016 molecules cm-2 yr-1, respectively, in columnar CO levels over the IGP region. The results show the existence of a spatial gradient in CO from the eastern (higher levels) to western IGP region (lower levels). Data from the Census of India on the number of households using various cooking fuels in the IGP region shows the prevalence of biomass-fuel (i.e. firewood, crop residue, cowdung etc.) use over the eastern and central IGP regions and that of liquefied petroleum gas over the western IGP region. CO emission estimates from cooking activity over the three IGP regions are found to be in the order east > central > west, which support the existence of the spatial gradient in CO from eastern to the western IGP region. Our results support the intervention of present Indian government on limiting the use of biomass-fuels in domestic cooking to achieve the benefits in terms of the better air quality, household health and regional/global climate change mitigation.