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Owing to the significant societal value of inland water resources, there is a need for cost-effective monitoring of water quality on large scales. We tested the suitability of the recently launched Sentinel-2A to monitor a key water quality parameter, coloured dissolved organic matter (CDOM), in various types of lakes in northern Sweden. Values of a(420)CDOM (CDOM absorption at 420 nm wavelength) were obtained by analyzing water samples from 46 lakes in five districts across Sweden within an area of approximately 800 km2. We evaluated the relationships between a(420)CDOM and band ratios derived from Sentinel-2A Level-1C and Level-2A products. The band ratios B2/B3 (460 nm/560 nm) and B3/B5 (560 nm/705 nm) showed poor relationships with a(420)CDOM in Level-1C and 2A data both before and after the removal of outliers. However, there was a slightly stronger power relationship between the atmospherically-corrected B3/B4 ratio and a(420)CDOM (R2 = 0.28, n = 46), and this relationship was further improved (R2 = 0.65, n = 41) by removing observations affected by light haze and cirrus clouds. This study covered a wide range of lakes in different landscape settings and demonstrates the broad applicability of a(420)CDOM retrieval algorithms based on the B3/B4 ratio derived from Sentinel-2A.

The concentration of colored dissolved organic matter (CDOM) in lakes is strongly influenced by climate, land cover, and topographic settings, but it is not known how drought may affect the relative importance of these controls. Here, we evaluate the controls of CDOM during two summers with strongly contrasting values of the Palmer drought index (PDI), indicating wet vs. dry conditions. We hypothesized that lake CDOM during a wet summer season is regulated mainly by the surrounding land cover to which the lakes are hydrologically connected, while, during drought, the lakes are disconnected from the catchment and CDOM is regulated by climatic and morphometric factors that govern the internal turnover of CDOM in the lakes. A suite of climate, land cover, and morphometric variables was assembled and used to explain remotely sensed CDOM values for 255 boreal lakes distributed across broad environmental and geographic gradients in Sweden and Norway. We found that PDI explained the variability in CDOM among lakes in a dry year, but not in a wet year, and that severe drought strongly decreased CDOM during the dry year. Large lakes, especially, with a presumed high degree of catchment uncoupling, showed low CDOM during the dry year. However, in disagreement with our hypothesis, climate, land cover, and morphometry all showed a stronger impact on lake CDOM in wet vs. dry years. Thus, drought systematically weakened the predictability of CDOM variations at the same time as CDOM was offset toward lower values. Our results show that drought not only has a direct effect on CDOM, but also acts indirectly by changing the spatial regulation of CDOM in boreal lakes.

Water loss is a crucial factor for vegetation in the semi-arid Sahel region of Africa. Global satellite-driven estimates of plant CO2 uptake (gross primary productivity, GPP) have been found to not accurately account for Sahelian conditions, particularly the impact of canopy water stress. Here, we identify the main biophysical limitations that induce canopy water stress in Sahelian vegetation and evaluate the relationships between field data and Earth observation-derived spectral products for up-scaling GPP. We find that plant-available water and vapor pressure deficit together control the GPP of Sahelian vegetation through their impact on the greening and browning phases. Our results show that a multiple linear regression (MLR) GPP model that combines the enhanced vegetation index, land surface temperature, and the short-wave infrared reflectance (Band 7, 2105–2155 nm) of the moderate-resolution imaging spectroradiometer satellite sensor was able to explain between 88% and 96% of the variability of eddy covariance flux tower GPP at three Sahelian sites (overall = 89%). The MLR GPP model presented here is potentially scalable at a relatively high spatial and temporal resolution. Given the scarcity of field data on CO2 fluxes in the Sahel, this scalability is important due to the low number of flux towers in the region.

The authors wish to make the following correction to Table 7 in this paper [...]

It has been well established that numerous coastal areas are threatened by sea-level rise and coastal flooding. Some of these vulnerable lands contain significant archeological sites and cultural resources. The accurate calculation of shoreline rates of change and identification of coastal hazard zones for areas containing cultural resources is crucial for the development of effective coastal zone management strategies that address resource conservation and preservation. This investigation employed geospatial and analytical statistical techniques to conduct a shoreline change study on Rainsford Island occurring from 1944 to 2008. The 4.45-ha island, located in Boston Harbor, Massachusetts, consists of two heavily eroded drumlins connected by a low-lying bar. Past archeological surveys have concluded that Rainsford Island has numerous historical sites and is an area of high prehistoric sensitivity. A recent geophysical survey mapped a Revolutionary War era cemetery on the island. Multiple data sources were integrated, including historical maps, aerial photographs, and airborne laser topographic data for shoreline delineation over various temporal and spatial scales. The Digital Shoreline Analysis System was used to determine rate-of-change statistics and distances, and to identify hotspot areas of erosion and accretion. The results show that the island eroded during the study period at a rate of 0.05 m/y on average, with erosion rates as high as −0.59 m/y. The bar has migrated SE resulting in erosion along the island's northern shoreline. Predictive modeling indicates that 26% of the island would become inundated with 1 m of sea-level rise including the area containing the cemetery.

Effective societal responses to rapid climate change in the Arctic rely on an accurate representation of region-specific ecosystem properties and processes. However, this is limited by the scarcity and patchy distribution of field measurements. Here, we use a comprehensive, geo-referenced database of primary field measurements in 1,840 published studies across the Arctic to identify statistically significant spatial biases in field sampling and study citation across this globally important region. We find that 31% of all study citations are derived from sites located within 50 km of just two research sites: Toolik Lake in the USA and Abisko in Sweden. Furthermore, relatively colder, more rapidly warming and sparsely vegetated sites are under-sampled and under-recognized in terms of citations, particularly among microbiology-related studies. The poorly sampled and cited areas, mainly in the Canadian high-Arctic archipelago and the Arctic coastline of Russia, constitute a large fraction of the Arctic ice-free land area. Our results suggest that the current pattern of sampling and citation may bias the scientific consensuses that underpin attempts to accurately predict and effectively mitigate climate change in the region. Further work is required to increase both the quality and quantity of sampling, and incorporate existing literature from poorly cited areas to generate a more representative picture of Arctic climate change and its environmental impacts. Analysing a database of >1,800 field studies in the terrestrial Arctic, the authors identify large spatial biases in sampling, with nearly one-third of all citations derived from sites located within 50 km of two research stations.

Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour. Understanding the behaviour of the machines powered by artificial intelligence that increasingly mediate our social, cultural, economic and political interactions is essential to our ability to control the actions of these intelligent machines, reap their benefits and minimize their harms.

While advanced melanoma remains one of the most challenging cancers, recent developments in our understanding of the molecular drivers of this disease have uncovered exciting opportunities to guide personalized therapeutic decisions. Genetic analyses of melanoma have uncovered several key molecular pathways that are involved in disease onset and progression, as well as prognosis. These advances now make it possible to create a \"Molecular Disease Model\" (MDM) for melanoma that classifies individual tumors into molecular subtypes (in contrast to traditional histological subtypes), with proposed treatment guidelines for each subtype including specific assays, drugs, and clinical trials. This paper describes such a Melanoma Molecular Disease Model reflecting the latest scientific, clinical, and technological advances.

Net primary production (NPP) is the principal source of energy for ecosystems and, by extension, human populations that depend on them. The relationship between the supply and demand of NPP is important for the assessment of socio-ecological vulnerability. We present an analysis of the supply and demand of NPP in the Sahel using NPP estimates from the MODIS sensor and agri-environmental data from FAOSTAT. This synergistic approach allows for a spatially explicit estimation of human impact on ecosystems. We estimated the annual amount of NPP required to derive food, fuel and feed between 2000 and 2010 for 22 countries in sub-Saharan Africa. When comparing annual estimates of supply and demand of NPP, we found that demand increased from 0.44 PgC to 1.13 PgC, representing 19% and 41%, respectively, of available supply due to a 31% increase in the human population between 2000 and 2010. The demand for NPP has been increasing at an annual rate of 2.2% but NPP supply was near-constant with an inter-annual variability of approximately 1.7%. Overall, there were statistically significant (p < 0.05) increases in the NPP of cropland (+6.0%), woodland (+6.1%) and grassland savanna (+9.4%), and a decrease in the NPP of forests (−0.7%). On the demand side, the largest increase was for food (20.4%) followed by feed (16.7%) and fuel (5.5%). The supply-demand balance of NPP is a potentially important tool from the standpoint of sustainable development, and as an indicator of stresses on the environment stemming from increased consumption of biomass.

Populating the exoplanet mass–radius diagram in order to identify the underlying relationship that governs planet composition is driving an interdisciplinary effort within the exoplanet community. The discovery of hot super-Earths—a high-temperature, short-period subset of the super-Earth planet population—has presented many unresolved questions concerning the formation, evolution, and composition of rocky planets. We report the discovery of a transiting, ultra-short-period hot super-Earth orbiting TOI-1075 (TIC351601843), a nearby (d = 61.4 pc) late-K/early-M-dwarf star, using data from the Transiting Exoplanet Survey Satellite. The newly discovered planet has a radius of 1.791 −0.081+0.116 R ⊕ and an orbital period of 0.605 day (14.5 hr). We precisely measure the planet mass to be 9.95 −1.30+1.36 M ⊕ using radial velocity measurements obtained with the Planet Finder Spectrograph mounted on the Magellan II telescope. Our radial velocity data also show a long-term trend, suggesting an additional planet in the system. While TOI-1075 b is expected to have a substantial H/He atmosphere given its size relative to the radius gap, its high density ( 9.32−1.85+2.05 g cm−3) is likely inconsistent with this possibility. We explore TOI-1075 b’s location relative to the M-dwarf radius valley, evaluate the planet’s prospects for atmospheric characterization, and discuss potential planet formation mechanisms. Studying the TOI-1075 system in the broader context of ultra-short-period planetary systems is necessary for testing planet formation and evolution theories and density-enhancing mechanisms and for future atmospheric and surface characterization studies via emission spectroscopy with the JWST.