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A large portion of Central-Western Asia is made up of contiguous closed basins, collectively termed as the Asian Endorheic Basins (AEB). As these retention basins are only being replenished by the intermittent precipitation, increasing droughts in the region and a growing demand for water have been presumed to jointly contributed to the land degradation. To understand the impact of climate change and human activities on dryland vegetation over the AEB, 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%) exhibited a greening trend over the past two decades. Partial correlation analyses indicated that climatic factors had varying effects on vegetation productivity as a function of vegetation types and aridity. 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 hyper-arid AEB, our results therefore raised the concerns on the ecological and societal sustainability in this region, where a mild increase in precipitation might not be able to catch up the rising evaporative demand and water consumption resulted from global warming and irrigation intensification.

China has implemented some of the world's most ambitious ecological restoration (ER) programs over the past two decades. These large-scale multi-billion-dollar projects have achieved widespread greening across the country. However, the impacts of different ER-driven land cover modifications on total water resources remain largely unknown, particularly over areas characterized by complex environments of coupled human and natural systems. Here we quantified ecohydrological impacts of multiple ER programs applied over various parts of China's Yellow River Basin (YRB), with their lumped effects being partitioned into individual ones. ER-related drivers were disentangled, leading in individual attribution of inter-annual climatic variability and agricultural activity with cross-sensor satellite observations and statistical modelling. Our results showed an ecohydrological decoupling of water storage and vegetation greenness attributed to ER programs. Increases in natural land cover types (e.g. forest and grassland) were found at the expense of human intensive land use, such as farmland, with the combined changes resulting in a concurrent dramatic water storage loss of 3.70 billion tons/year from 2002 to 2021. ER was the dominant driver of water loss (>66 %) among all factors considered, commensurate with areal land-cover change and ER program cost. The entire basin was divided into two regions with opposite water trends by the ER activities with different restoration strategies. This study's framework is applicable to substantial part of the globe like YRB and is encouraged to be applied for more wholistic ER impact evaluations.

Most of the worlds’ population relies on the processes and ecosystems in the coastal zone. Understanding the long-term change of coastlines is critical for the effective management of these complex, and heavily utilised regions. There has been a recent increase of studies focused on large-scale shoreline change mapping. However, most current methods are optimized for extracting shorelines of wave-dominated sandy beaches, which are only 30% of the global coasts, resulting in uncertainty for other environments such as tidal flats and bedrock. Here, we propose a new shoreline change mapping workflow, using the Landsat archive and Google Earth Engine, which increases compute efficiency and is suitable for retrieving shoreline changes for various coastal landforms at high tide instead of mean sea level. By validating against regional and continental datasets in Australia, we found the approach here produced high mapping accuracy and showed particularly better performance at tide-dominated coasts, where tidal flats and intertidal bars and ridges are present, when compared to past approaches. This is an important step forward since tide-dominated and tide-modified coasts are widely distributed at tropical low latitudes. We also explored the global application of the proposed method and derived hotspots of shoreline erosion and accretion that agreed with multiple regional studies across the world. Most of these hotspots were related to river sediment discharge and human intervention on the coast, as expected. Although it requires further validation, the global application of our method demonstrates the significance of this approach in identifying potential threats to coastal zones, especially in complex tide-dominated environments, which can facilitate effective coastal management.

Big data reveals new, stark pictures of the state of our environments. It also reveals ‘bright spots’ amongst the broad pattern of decline and—crucially—the key conditions for these cases. Big data analyses could benefit the planet if tightly coupled with ongoing sustainability efforts.

Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established PV power plants. However, a comprehensive map regarding the PV power plants' locations and extent remains scarce on the country scale. This study developed a workflow, combining machine learning and visual interpretation methods with big satellite data, to map PV power plants across China. We applied a pixel-based random forest (RF) model to classify the PV power plants from composite images in 2020 with a 30 m spatial resolution on the Google Earth Engine (GEE). The resulting classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km2. We found that most PV power plants were situated on cropland, followed by barren land and grassland, based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants. The dataset of photovoltaic power plant distribution in China by 2020 is available to the public at https://doi.org/10.5281/zenodo.6849477 (Zhang et al., 2022).

Competition for land resources is intense, with growing demand for food and resources to support humanity. Despite this, global agricultural area has significantly declined over the past two decades due to socio–political trends, market changes and environmental degradation. Although expensive, restoring degraded lands that are no longer contested for agricultural use due to low productivity may present a major conservation opportunity with minimal social or political opposition. Here we present a new perspective for conservation that highlights the potential conservation value of uncontested lands. We highlight key factors that make lands uncontested, define an approach for evaluating costs and benefits on uncontested land parcels and propose methods for identifying these lands. Land use is one of the most contested issues facing global conservation, but degraded lands should be the focus of governments and trusts to take and conserve uncontested areas for nature.

Heavy metal(loid)s (HMs) have been consistently entering the food chain, imposing great harm on environment and public health. However, previous studies on the spatial dynamics and transport mechanism of HMs have been profoundly limited by the field sampling issues, such as the uneven observations of individual carriers and their spatial mismatch, especially over large-scale catchments with complex environment. In this study, a novel methodological framework for mapping HMs at catchment scale was proposed and applied, combining a species distribution model (SDM) with physical environment and human variables. Based on the field observations, we ecologicalized HMs in different carriers as different species. This enabled the proposed framework to model the 'enrichment area' of individual HMs in the geographic space (termed as the HM 'habitat') and identify their 'hotspots' (peak value points) within the catchment. Results showed the output maps of HM habitats from secondary carriers (soil, sediment, and wet deposition) well agreed with the influence of industry contaminants, hydraulic sorting, and precipitation washout process respectively, indicating the potential of SDM in modeling the spatial distributions of the HM. The derived maps of HMs from secondary carriers, along with the human and environmental variables were then input as explanatory variables in SDM to predict the spatial patterns of the final HM accumulation in river water, which was observed to have largely improved the prediction quality. These results confirmed the value of our framework to leverage SDMs from ecology perspective to study HM contamination transport at catchment scale, offering new insights not only to map the spatial HM habitats but also help locate the HM transport chains among different carriers.

Glacial meltwater and ice calving contribute to the flood volume of glacial lakes such as Lake Merzbacher in the Tian Shan Mountains of central Asia. In this study, we simulated the lake's volume by constructing an empirical relationship between the area of Lake Merzbacher, determined from satellite images, and the lake's water storage, derived from digital elevation models. Results showed that the lake water supply rate before Glacial Lake Outburst Floods (GLOFs) generally agreed well with those during the GLOFs from 2009 to 2012 but not in 2008 and 2015. Furthermore, we found that the combination of glacial meltwater and ice calving is not enough to fully explain the supply rate during GLOFs in 1996 and 1999, suggesting other factors affect the supply rate during GLOFs as well. To examine this further, we compared the water supply rate before and during GLOF events in 1999 and 2008. We inferred that quickly released short-term and intermediate-term water storage by glaciers have likely contributed to both flood events in those years. This study highlights the need to improve our understanding of the supply component of outburst floods, such as irregularly released stored water may lead to GLOF events with generally three different types: case I (singular event-triggered englacial water release), case II (glacier melt due to temperature changes), and case III (englacial water release mixed with glacier melt).

© 2016 Elsevier B.V. Arid wetlands are important for biodiversity conservation, but sensitive and vulnerable to climate variability and hydroclimatic events. Amplification of the water cycle, including the increasing frequency and severity of droughts and wet extremes, is expected to alter spatial and temporal hydrological patterns in arid wetlands globally, with potential threats to ecosystem services and their functioning. Despite these pressing challenges, the ecohydrological interactions and resilience of arid wetlands to highly variable water regimes over long time periods remain largely unknown. Recent broad-scale drought and floods over Australia provide unique opportunities to improve our understanding of arid wetland ecosystem responses to hydroclimatic extremes. Here we analysed the ecohydrological dynamics of the Coongie Lakes arid wetland in central Australia, one of the world's largest Ramsar-designated wetlands, using more than two decades (1988–2011) of vegetation and floodwater extent retrievals derived from Landsat satellite observations. To explore the impacts of large-scale hydrological fluctuations on the arid wetland, we further coupled Landsat measurements with Total Water Storage Anomaly (TWSA) data obtained from the Gravity Recovery and Climate Experiment (GRACE) satellites. Pronounced seasonal and inter-annual variabilities of flood and vegetation activities were observed over the wetland, with variations in vegetation growth extent highly correlated with flood extent (r = 0.64, p < 0.05) that ranged from nearly zero to 3456 km2. We reported the hydrological dynamics and associated ecosystem responses to be largely driven by the two phases (El Niño and La Niña) of the El Nino-Southern Oscillation (ENSO) ocean-atmosphere system. Changes in flood and vegetation extent were better explained by GRACE–TWSA (r = 0.8, lag = 0 month) than rainfall (r = 0.34, lag = 3 months) over the water source area, demonstrating that TWS is a valuable hydrological indicator for complex dryland river systems. The protracted Millennium Drought from 2001 to 2009 resulted in long-term absence of major flood events, which substantially suppressed wetland vegetation growth. However, the 2010–11 La Niña induced flooding events led to an exceptionally large resurgence of vegetation, with a mean vegetation growth extent anomaly exceeding the historical average (1988–2011) by more than 1.5 standard deviations, suggesting a significant resilience of arid wetland ecosystems to climate variability. This study showed the ecological functioning of arid wetlands is particularly sensitive to large-scale hydrological fluctuations and extreme drought conditions, and vulnerable to future altered water regimes due to climate change. The methods developed herein can be applied to arid wetlands located in other dryland river systems across the globe.

Realistic representations and simulation of mass and energy exchanges across heterogeneous landscapes can be a challenge in land surface and dynamic vegetation models. For mixed life-form biomes such as savannas, plant function is very difficult to parameterise due to the distinct physiological characteristics of tree and grass plant functional types (PFTs) that vary dramatically across space and time. The partitioning of their fractional contributions to ecosystem gross primary production (GPP) remains to be achieved at regional scale using remote sensing. The objective of this study was to partition savanna gross primary production (GPP) into tree and grass functional components based on their distinctive phenological characteristics. Comparison of the remote sensing partitioned GPPtree and GPPgrass against field measurements from eddy covariance (EC) towers showed an overall good agreement in terms of both GPP seasonality and magnitude. We found total GPP, as well as its tree and grass components, decreased dramatically with rainfall over the North Australian Tropical Transect (NATT), from the Eucalyptus forest and woodland in the northern humid coast to the grasslands, Acacia woodlands and shrublands in the southern xeric interior. Spatially, GPPtree showed a steeper decrease with precipitation along the NATT compared to GPPgrass, thus tree/grass GPP ratios also decreased from the northern mesic region to the arid south region of the NATT. However, results also showed a second trend at the southern part of the transect, where tree-grass ratios and total GPP increased with decreasing mean annual precipitation, and this occurred in the physiognomic transition from hummock grasslands to Acacia woodland savannas. Total GPP and tree-grass GPP ratios across climate extremes were found to be primarily driven by grass layer response to rainfall dynamics. The grass-containing xeric savannas exhibited a higher hydroclimatic sensitivity, whereas GPP in the northern mesic savannas was fairly stable across years despite large variations in rainfall amount. The pronounced spatiotemporal variations in savanna vegetation productivity encountered along the NATT study area suggests that the savanna biome is particularly sensitive and vulnerable to predicted future climate change and hydroclimatic variability.

The authors regret that two co-authors were unintentionally missed in the previous paper. We have added the two co-authors to recognize their contribution to the validation data - the biophysical field observations of land conditions. The error does not change the science of this study. Zunyi Xie, Stuart R. Phinn, Edward T. Game, David J. Pannell, Richard J. Hobbs, Peter R. Briggs, Terrence S. Beutel, Christopher Holloway, Eve McDonald-MaddenKey Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China Centre for Biodiversity and Conservation Science, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD 4072, Australia Remote Sensing Research Centre, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD 4072, Australia The Nature Conservancy, Brisbane, QLD 4101, Australia School of Agriculture and Environment, The University of Western Australia, Perth, WA 6009, Australia School of Biological Science, The University of Western Australia, Perth, WA 6009, Australia CSIRO Oceans and Atmosphere, Canberra, ACT 2601, Australia Department of Agriculture and Fisheries, Parkhurst, Brisbane, QLD 4701, Australia Department of Agriculture and Fisheries, Dutton Park, Brisbane, QLD 4102, Australia The authors would like to apologise for any inconvenience caused.

© 2018 Elsevier B.V. Ecosystems in arid and semi-arid regions are vulnerable to climatic and anthropogenic disturbances. However, our understanding of vegetation stability (including resistance and resilience, which are the abilities of ecosystems to resist perturbations and return to pre-disturbance structure or function, respectively) in response to environmental changes in dryland ecosystems remains insufficient, particularly in the absence of large-scale observations of water availability. Here we introduced GRACE monthly total water storage anomaly (TWSA) data into an autoregressive model with remote sensed EVI, air temperature and precipitation to investigate the short-term vegetation stability and its influencing factors in Central Asia (CA) during 2003–2015. The results showed that the grid-level vegetation resilience in CA increased logarithmically as mean annual precipitation (R 2 = 0.33, P < 0.05) but decreased linearly with increasing mean annual temperature (R 2 = 0.41, P < 0.05). Vegetation resilience was not correlated with TWSA, due to the decoupling of TWSA with precipitation both spatially and temporally in the majority of CA. At the biome level, vegetation resilience also increased as a logarithmical function of aridity index (R 2 = 0.80, P < 0.05). Vegetation resistance to TWSA showed minor difference across biomes, while vegetation resistance to precipitation functioned as a parabolic curve along the aridity gradient (R 2 = 0.59, P < 0.05). Our results suggest that accounting for the effects of total water column instead of precipitation only is critical in understanding vegetation-water relationships in drylands. The steep decrease in vegetation resilience in areas with high temperature and low water availability implies a high risk of collapse for these water-limited ecosystems if there are severe droughts. Furthermore, reduction in total water storage, induced by, e.g., large-scale extraction of surface runoff or shallow-layer groundwater for irrigation, can result in negative influences to natural biomes in dryland regions.

Globally, the area of agricultural land is shrinking in part due to environmental degradation. Acquisition and restoration of degraded lands no longer used for agriculture may present a major conservation opportunity with minimal social and political opposition. The ability to efficiently and accurately identify these lands from regional to global scales will aid conservation management, ultimately enhancing the global prospects of achieving the Sustainable Development Goals (SDGs). Remote Sensing provides a potential tool to identify areas where surface property changes can be mapped and linked with land degradation. In this study, we begin to tackle a small section of this challenge by presenting novel approach to mapping changes in vegetation cover amounts at the pixel level (30 m), using Google Earth Engine (GEE). We illustrate our approach across large-scale rangelands in Queensland Australia, using three decades of Landsat satellite imagery (1988–2017) along with field observations of land condition scores for validation. The approach used an existing method for dynamic reference cover to remove the rainfall variability and focused on the human management effects on the vegetation cover changes. Results showed the identified vegetation cover changes could be categorized into five classes of decrease, increase or stable cover compared with a set reference level, which was obtained from locations of the most persistent ground cover across all dry years. In total, vegetation cover decrease was observed in 20% of our study area, with similar portion of lands recovering and the rest (~60%) staying stable. The lands with decrease in vegetation cover, covering a considerable area of ~2 × 105 km2, exhibited a markedly reduced resilience to droughts. The accuracy assessment yielded an overall classification accuracy of 82.6% (±3.32 standard error) with 75.0% (±5.16%) and 70.0% (±4.13%) producer's and user's accuracy for areas experiencing a significant decrease in vegetation cover, respectively. Identifying areas of degraded land will require multiple stages of spatial data analysis and this work provided the first stage for identifying vegetation cover changes in large-scale rangeland environment, and provides a platform for future research and development to identify degraded lands and their utility for achieving conservation endeavours.

Australia has experienced a large frequency of hydroclimatic events since the early 21st century, with multiple large-scale droughts and flooding rains exerting dramatic impacts on water resources and ecosystems. Despite these pronounced consequences, the coupling of ecosystem functioning to extreme climate variability remains elusive due to the lack of complete understanding of hydrological connections. In this study, we investigated the spatiotemporal trends of Australia's hydrological and vegetation responses to three climate modes: El Nino-Southern Oscillation, the Indian Ocean dipole and the Southern Annular Mode, utilizing climate indices, satellite-derived total water storage anomaly (TWSA) from GRACE, precipitation from TRMM and vegetation greenness from MODIS. Using partial cross-correlation and vegetation sensitivity analyses to interpret the interactions among climate modes, water resources and vegetation across Australia, three hydroclimatic extreme events from 2002 to 2017 were analyzed: (i) a prolonged drought (2002–09, colloquially known as the ‘big dry’); (ii) a dramatic wet pulse (2010–11, the ‘big wet’); and (iii) another anomalous El Nino event (2015). Our results showed the entire continent partitioned into three geographic zones with diverse drying and wetting trends in total water storage, precipitation and vegetation greenness, reflecting varying and fundamental influences from the individual climate modes. Ecosystem productivity was found to be better related and more sensitive to TWSA than precipitation across different hydroclimate zones and during both extreme dry and wet conditions. We also observed TWSA increased rapidly during wet extremes, and these gains in water resources persisted for an additional four years (i.e., TWSA remained positive until 2015 following the 2011 ‘big wet’). Lastly, findings from another hydroclimatic event (the 2015 El Nino drought) further confirmed the relationships among climate, water and ecosystems, demonstrating the feasibility of predicting ecohydrological implications of future hydroclimatic extremes in Australia.

Previous studies show that the area of a moraine-dammed lake can provide a good indicator of the significance of its outburst. For a glacier-dammed lake however, because its area and depth fluctuates with the melting of its ice dam, it is difficult to predict the outburst of the glacier-dammed lake by using its area alone. A characteristic of the surface of Lake Merzbacher is a large amount of floating ice therefore, a method is proposed in this article to extract the area of floating ice on the lake and the area of ice free water in the lake by using Environment and Disaster Monitoring Small Satellite images respectively. Furthermore, based on the area of floating ice extracted through the image information of Lake Merzbacher in 2009 and 2010, we determined the relationship between the ice area and the outburst of the lake, then formulated the Index for hazard of Glacier Lake Outburst Flood (IGLOF) of Lake Merzbacher, which cannot only predict the flood outburst, but also determine the specific outburst period after the lake drainage had occurred. This can be shown in a recalculation of the lake drainages in the years 2009 and 2010. Research results indicate that when IGLOF is less than 0.5 and the lake area is larger than 3 km(2), the outburst process is in early-warning period and GLOF will occur in the next 5-8 days. Also, the successful outburst prediction of Lake Merzbacher in 2011 showed that the index described in this paper provides a quick methodology for forecasting and warning against Lake Merzbacher outburst floods. However as our research was based on a short observation period (2009-2011) and also cannot be supplemented by other images, it will still be needed to be checked and validated by continuous observation and improvement in future. (C) 2012 Elsevier B.V. All rights reserved.

By inhibiting target gene expression, microRNAs (miRNAs) play major roles in various physiological and pathological processes. miR-146a, a miRNA induced upon lipopolysaccharide (LPS) stimulation and virus infection, is also highly expressed in patients with immune disorders such as rheumatoid arthritis, Sjögren's syndrome, and psoriasis. Whether the high level of miR-146a contributes to any of these pathogenesis-related processes remains unknown. To elucidate the function of miR-146a in vivo, we generated a transgenic (TG) mouse line overexpressing miR-146a. Starting at an early age, these TG mice developed spontaneous immune disorders that mimicked human autoimmune lymphoproliferative syndrome (ALPS) with distinct manifestations, including enlarged spleens and lymph nodes, inflammatory infiltration in the livers and lungs, increased levels of double-negative T cells in peripheral blood, and increased serum immunoglobulin G levels. Moreover, with the adoptive transfer approach, we found that the B-cell population was the major etiological factor and that the expression of Fas, a direct target of miR-146a, was significantly dampened in TG germinal center B cells. These results indicate that miR-146a may be involved in the pathogenesis of ALPS by targeting Fas and may therefore serve as a novel therapeutic target.

Australia experienced one of the worst droughts in history during the early 21st-century (termed the ‘big dry’), exerting negative impacts on food production and water supply, with increased forest die-back and bushfires across large areas. Following the ‘big dry’, one of the largest La Niña events in the past century, in conjunction with an extreme positive excursion of the Southern Annular Mode (SAM), resulted in dramatic increased precipitation from 2010 to 2011 (termed the ‘big wet’), causing widespread flooding and a recorded sea level drop. Despite these extreme hydroclimatic impacts, the spatial partitioning and temporal evolution of total water storage across Australia remains unknown. In this study we investigated the spatial-temporal impacts of the recent ‘big dry’ and ‘big wet’ events on Australia's water storage dynamics using the total water storage anomaly (TWSA) data derived from the Gravity Recovery and Climate Experiment (GRACE) satellites. Results showed widespread, continental-scale decreases in TWS during the ‘big dry’, resulting in a net loss of 3.89 ± 0.47 cm (299 km3) total water, while the ‘big wet’ induced a sharp increase in TWS, equivalent to 11.68 ± 0.52 cm (898 km3) of water, or three times the total water loss during the ‘big dry’. We found highly variable continental patterns in water resources, involving differences in the direction, magnitude, and duration of TWS responses to drought and wet periods. These responses clustered into three distinct geographic zones that correlated well with the influences from multiple large-scale climate modes. Specifically, a persistent increasing trend in TWS was recorded over northern and northeastern Australia, where the climate is strongly influenced by El Niño-Southern Oscillation (ENSO). By contrast, western Australia, a region predominantly controlled by the Indian Ocean Dipole (IOD), exhibited a continuous decline in TWS during the ‘big dry’ and only a subtle increase during the ‘big wet’, indicating a weak recovery of water storage. Southeastern Australia, influenced by combined ENSO, IOD and SAM interactions, exhibited a pronounced TWS drying trend during the ‘big dry’ followed by rapid TWS increases during the ‘big wet’, with complete water storage recoveries. A spatial intensification of the water cycle was further identified, with a wetting trend over wetter regions (northern and northeastern Australia) and a drying trend over drier regions (western Australia). Our results highlight the value of GRACE derived TWSA as an important indicator of hydrological system performance for improved water impact assessments and management of water resources across space and time. Refereed/Peer-reviewed

International audience; Each year, terrestrial ecosystems absorb more than a quarter of the anthropogenic carbon emissions, termed as land carbon sink. An exceptionally large land carbon sink anomaly was recorded in 2011, of which more than half was attributed to Australia. However, the persistence and spatially attribution of this carbon sink remain largely unknown. Here we conducted an observation-based study to characterize the Australian land carbon sink through the novel coupling of satellite retrievals of atmospheric CO 2 and photosynthesis and in-situ flux tower measures. We show the 2010-11 carbon sink was primarily ascribed to savannas and grasslands. When all biomes were normalized by rainfall, shrublands however, were most efficient in absorbing carbon. We found the 2010-11 net CO 2 uptake was highly transient with rapid dissipation through drought. The size of the 2010-11 carbon sink over Australia (0.97 Pg) was reduced to 0.48 Pg in 2011-12, and was nearly eliminated in 2012-13 (0.08 Pg). We further report evidence of an earlier 2000-01 large net CO 2 uptake, demonstrating a repetitive nature of this land carbon sink. Given a significant increasing trend in extreme wet year precipitation over Australia, we suggest that carbon sink episodes will exert greater future impacts on global carbon cycle. Since the beginning of the industrial age, human activities (fossil fuel combustion, land use change, etc.) have driven the atmospheric CO 2 concentration from about 280 parts per million (ppm) in around 1780 to over 400 ppm in 2015 1,2. The burning of fossil fuels and other human activities are currently adding more than 36 billion metric tons of CO 2 to the atmosphere each year 3 , producing an unprecedented build-up of this important greenhouse-forcing agent. Each year, terrestrial ecosystems sequester on average about a quarter of fossil fuel emissions and help mitigate global warming 1-5. However, the nature, geographic distribution of land carbon sinks, and how their efficiencies change from year to year are not adequately understood, precluding an accurate prediction of their responses to future climate change and subsequent influences on climate through carbon cycle-climate feedbacks 6,7. Recent evidence suggests that global semi-arid ecosystems provide an important contribution to the global land carbon sink and can dominate inter-annual variability and the trend of global terrestrial carbon cycle 8,9. Previous studies of the semi-arid carbon sink primarily relied on model outputs 8,9. The results can be subject to uncertainties in input variables such as the assimilation of datasets that are sparse in many regions of the world, and may be further confounded by model assumptions, as suggested by a previous study finding that models can

Australia recently experienced a long-term continental drought (“big dry”, 2001–2009) followed by an anomalous wet two-year period (“big wet”, 2010–2011). Despite the significance of the two extreme events, continental-wide information regarding the effects of the high and low precipitation conditions on the hydrological components, stress and recovery is not available. In this paper, we use terrestrial total water storage changes (ATWS) from the Gravity Recovery and Climate Experiment (GRACE) and precipitation data from the Tropical Rainfall Measuring Mission (TRMM) spanning from 2002 to 2013, where ATWS represents the main source of water available for human consumption, agriculture and natural ecosystems. We rely on a combination of temporal trend analysis and spatial statistics methods in order to evaluate the terrestrial total water storage (TWS) dynamics and the relationship between TWS and rainfall during the “big dry” and “big wet” events. Here we report the occurrence of hydrological cycle intensification during the study period in Australia which exhibited strong spatial variations: the wet areas (the northern and northeast regions) got wetter while the dry areas (the west and interior of the continent) became drier. By contrast, in southeastern Australia TWS changes over time showed sudden extreme responses to both events. Our results constitute a step beyond quantifying droughts/anomalous wet years that rely solely on precipitation data. This work demonstrates the ability of TWS observations as a significant indicator of hydrological system performance during hydroclimatic events and also an important tool for understanding continental-wide and regional spatial and temporal patterns of water availability.

The Merzbacher Glacial Lake located in the Sary Jaz-Kumarik River Basin has severe impacts on the environment and human well-being in downstream of the AKSU River in China. The Merzbacker Lake has flooded with increased frequency in recent years with the climate warming. It is vital to strengthen observation and monitoring of the Merzbacker Lake, especially by remote sensing data. Large portion by floating ice is the key issues of monitoring the lake. Based on the high temporal and spatial resolution Environment and Disaster Monitoring Satellite images, an improved method focused on threshold and mask technique were presented. Parts of the lake which is clear of ice was extracted mainly by Normalized Difference Water Index method. Parts of the lake covered by floating ice were relarively accurately extracted by using the Band B image of the HSV images. The area change information of Merzbacher Lake then were extracted by the post-processing of the areas in different periods. As an example, the changing areas series of Merbacher Lake in 2009 were analyzed. The results suggests that the date of the Merbacher Lake flood was around 30th July, and the flood lasted for no more than a week. The application indicates that the method can provid quick and relatively accurate monitoring of the lake flood, which is very import to construct forecasting and warning systems of Merzbacher Lake.