The vast majority of stars with mass similar to that of the Sun are expected to destroy lithium (Li) gradually over the course of their lives, via low-temperature nuclear burning. This has now been supported by observations of hundreds of thousands of red giant stars(1-5). Here we perform a large-scale systematic investigation into the Li content of stars in the red clump phase of evolution, which directly follows the red giant branch phase. Surprisingly, we find that all red clump stars have high levels of Li for their evolutionary stage, with an increase of a factor of 40 over the end of the red giant branch stage, on average. This suggests that all low-mass stars undergo an Li production phase between the tip of the red giant branch and the red clump. We demonstrate that our finding is not predicted by stellar theory, revealing a stark tension between observations and models. We also show that the well-studied(1,2,4-6)very Li-rich giants, withA(Li) > +1.5 dex, represent only the extreme tail of the Li enhancement distribution, comprising 3% of red clump stars. Our findings suggest a new definition limit for Li-richness in red clump stars,A(Li) > -0.9 dex, which is much lower than the limit ofA(Li) > +1.5 dex used over many decades(1,5-9).

Process-based grass models (PBGMs) are widely used for predicting grass growth under potential climate change and different management practices. However, accurate predictions using PBGMs heavily rely on field observations for data assimilation. In data-limited areas, performing robust and reliable estimates of grass growth remains a challenge. In this paper, we incorporated satellite-based MODIS data products, including leaf area index, gross primary production and evapotranspiration, as an additional supplement to field observations. Popular data assimilation methods, including Bayesian calibration and the updating method ensemble Kalman filter, were applied to assimilate satellite derived information into the BASic GRAssland model (BASGRA). A range of different combinations of data assimilating methods and data availability were tested across four grassland sites in Norway, Finland and Canada to assess the corresponding accuracy and make recommendations regarding suitable approaches to incorporate MODIS data. The results demonstrated that optimizing the model parameters that are specific for grass species and cultivar should be targeted prior to updating model state variables. The MODIS derived data products were capable of constraining model's simulations on phenological development and biomass accumulation by parameter optimization with its performance exceeding model outputs driven by default parameters. By integrating even a small number of field measurements into the parameter calibration, the model's predictive accuracy was further improved - especially at sites with obvious biases in the input MODIS data. Overall, this comparative study has provided flexible solutions with the potential to strengthen the capacity of PBGMs for grass growth estimation in practical applications.

Global reservoir information can not only benefit local water management but can also improve our understanding of the hydrological cycle. This information includes water area, elevation, and storage; evaporation rate and volume values; and other characteristics. However, operational wall-to-wall reservoir storage and evaporation monitoring information is lacking on a global scale. Here we introduce NASA's new MODIS/VIIRS Global Water Reservoir product suite based on moderate resolution remote sensing data-the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). This product consists of 8-day (MxD28C2 and VNP28C2) and monthly (MxD28C3 and VNP28C3) measurements for 164 large reservoirs (MxD stands for the product from both Terra (MOD) or Aqua (MYD) satellites). The 8-day product provides area, elevation, and storage values, which were generated by first extracting water areas from surface reflectance data and then applying the area estimations to the pre-established Area-Elevation (A-E) relationships. These values were then further aggregated to monthly, with the evaporation rate and volume information added. The evaporation rate and volume values were calculated after the Lake Temperature and Evaporation Model (LTEM) using MODIS/VIIRS land surface temperature product and meteorological data from the Global Land Data Assimilation System (GLDAS). Validation results show that the 250 m area classifications from MODIS agree well with the high-resolution classifications from Landsat (R-2 = 0.99). Validation of elevation and storage products for twelve Indian reservoirs show good agreement in terms of R-2 values (0.71-0.96 for elevation, and 0.79-0.96 for storage) and normalized root-mean-square error (NRMSE) values (5.08-19.34% for elevation, and 6.39-18.77% for storage). The evaporation rate results for two reservoirs (Lake Nasser and Lake Mead) agree well with in situ measurements (R-2 values of 0.61 and 0.66, and NRMSE values of 16.25% and 21.76%). Furthermore, preliminary results from the VIIRS reservoir product have shown good consistency with the MODIS based product, confirming the continuity of this 20-year product suite. This new global water reservoir product suite can provide valuable information with regard to water-sources-related studies, applications, management, and hydrological modeling and change analysis such as drought monitoring.

Coastal aquaculture is an important supply of animal proteins for human consumption, which is expanding globally. Meanwhile, extensive aquaculture may increase nutrient loadings and environmental concerns along the coast. Accurate information on aquaculture pond location is essential for coastal management. Traditional methods use morphological parameters to characterize the geometry of surface waters to differentiate artificially constructed conventional aquaculture ponds from other water bodies. However, there are other water bodies with similar morphology (e.g., saltworks, rice fields, and small reservoirs) that are difficult to distinguish from aquaculture ponds, causing a lot of omission/commissioning errors in areas with complex land-use types. Here, we develop an extraction method with shape and water quality parameters to map aquaculture ponds, including three steps: (1) Sharpen normalized difference water index to detect and binarize water pixels by the Otsu method; (2) Connect independent water pixels into water objects through the four-neighbor connectivity algorithm; and (3) Calculate the shape features and water quality features of water objects and input them into the classifier for supervised classification. We selected eight sites along the coast of China to evaluate the accuracy and generalization of our method in an environment with heterogeneous pond morphology and landscape. The results showed that six transfer characteristics including water quality characteristics improved the accuracy of distinguishing aquaculture ponds from salt pans, rice fields, and wetland parks, which typically had F1 scores > 85%. Our method significantly improved extraction efficiency on average, especially when aquaculture ponds are mixed with other morphological similar water bodies. Our identified area was in agreement with statistics data of 12 coastal provinces in China. In addition, our approach can effectively improve water objects when high-resolution remote sensing images are unavailable. This work was applied to open-source remote sensing imagery and has the potential to extract long-term series and large-scale aquaculture ponds globally.

Greening of the Earth is observed during the past several decades and both climatic and non-climatic factors drive this process. However, the greening spatio-temporal patterns and the role of human activities such as agricultural intensification in hyper-arid regions remain unclear. This study aimed to (a) reveal the greening pattern in China's southern Xinjiang using satellite estimations of normalized difference vegetation index and leaf area index data during 1982-2019, and (b) examine the impacts of human activities in terms of land use land cover (LULC) data. Our multi-decadal analysis is ideal to reveal long-term trends and support a better understanding of the anthropogenic effects in this hyper-arid and endorheic region. The results showed that vegetation as a whole increased significantly in southern Xinjiang and the greening rate of cropland was much higher than the other LULC types. Significant greening was found over >90% of cropland, while insignificant changes and browning trends were found over nearly half the area of the other LULCs. The proportion of greening areas was more than 80% within 1 km from human-dominated areas while the proportion decreased to 40% with distances >15 km. The spatial heterogeneity of the greening indicated that, despite widely reported beneficial effects of warmer and wetter climate for a general greening trend, human activities could be the dominant factor modulating the greening rates disproportionately over different LULCs in arid and hyper-arid areas.

A warming climate will intensify the water cycle, resulting in an exacerbation of water resources crises and flooding risks in the Lancang-Mekong River Basin (LMRB). The mitigation of these risks requires accurate streamflow and flood simulations. Process-based and data-driven hydrological models are the two major approaches for streamflow simulations, while a hybrid of these two methods promises advantageous prediction accuracy. In this study, we developed a hybrid physics-data (HPD) methodology for streamflow and flood prediction under the physics-guided neural network modeling framework. The HPD methodology leveraged simulation information from a process-based model (i.e., VIC-CaMa-Flood) along with the meteorological forcing information (precipitation, maximum temperature, minimum temperature, and wind speed) to simulate the daily streamflow series and flood events, using a long short-term memory (LSTM) neural network. This HPD methodology outperformed the pure process-based VIC-CaMa-Flood model or the pure observational data driven LSTM model by a large margin, suggesting the usefulness of introducing physical regularization in data-driven modeling, and the necessity of observation-informed bias correction for process-based models. We further developed a gradient boosting tree method to measure the information contribution from the process-based model simulation and the meteorological forcing data in our HPD methodology. The results show that the process-based model simulation contributes about 30% to the HPD outcome, outweighing the information contribution from each of the meteorological forcing variables (<20%). Our HPD methodology inherited the physical mechanisms of the process-based model, and the high predictability capability of the LSTM model, offering a novel way for making use of incomplete physical understanding, and insufficient data, to enhance streamflow and flood predictions.

Climate change alters weather patterns and hydrological cycle, thus potentially aggravating water quality impairment. However, the direct relationships between climate variability and water quality are complicated by a multitude of hydrological and biochemical mechanisms dominate the process. Thus, little is known regarding how water quality responds to climate variability in the context of changing meteorological conditions and human activities. Here, a longitudinal study was conducted using trend, correlation, and redundancy analyses to explore stream water quality sensitivity to temperature, precipitation, streamflow, and how the sensitivity was affected by watershed climate, land cover percentage, landscape configuration, fertilizer application, and tillage types. Specifically, daily pollutant concentration data of suspended solid (SS), total phosphorus (TP), soluble reactive phosphorus (SRP), total Kjeldahl nitrogen (TKN), nitrate and nitrite (NOx), and chloride (Cl) were used as water quality indicators in four Lake Erie watersheds from 1985 to 2017, during which the average temperature has increased 0.5°C and the total precipitation has increased 9%. Results show that precipitation and flow were positively associated with SRP, NOx, TKN, TP, and SS, except for SRP and NOx in the urban basin. The rising temperatures led to increasing concentrations of SS, TKN, and TP in the urban basin. SRP and NOx sensitivity to precipitation was higher in the years with more precipitation and higher precipitation seasonality, and the basins with more spatially aggregated cropland. No-tillage and reduced tillage management could decrease both precipitation and temperature sensitivity for most pollutants. As one of the first studies leveraging multiple watershed environmental variables with long-term historical climate and water quality data, this study can assist target land use planning and management policy to mitigate future climate change effects on surface water quality.

While the evaporative water loss from global lakes is invisible, the volume is substantial. In recent decades, lake evaporation volume has been significantly increasing due to enhanced evaporation rate, melting lake ice, and expansion of water extent.

We present greater than or similar to 15,000 metal-rich ([Fe/H] > -0.2 dex) A and F stars whose surface abundances deviate strongly from solar abundance ratios and cannot plausibly reflect their birth material composition. These stars are identified by their high [Ba/Fe] abundance ratios ([Ba/Fe] > 1.0 dex) in the LAMOST DR5 spectra analyzed by Xiang et al. They are almost exclusively main-sequence and subgiant stars with T-eff greater than or similar to 6300 K. Their distribution in the Kiel diagram (T-eff-log g) traces a sharp border at low temperatures along a roughly fixed-mass trajectory (around 1.4M(circle dot)) that corresponds to an upper limit in convective envelope mass fraction of around 10(-4). Most of these stars exhibit distinctly enhanced abundances of iron-peak elements (Cr, Mn, Fe, Ni) but depleted abundances of Mg and Ca. Rotational velocity measurements from GALAH DR2 show that the majority of these stars rotate slower than typical stars in an equivalent temperature range. These characteristics suggest that they are related to the so-called Am/Fm stars. Their abundance patterns are qualitatively consistent with the predictions of stellar evolution models that incorporate radiative acceleration, suggesting they are a consequence of stellar internal evolution, particularly involving the competition between gravitational settling and radiative acceleration. These peculiar stars constitute 40% of the whole population of stars with mass above 1.5M(circle dot), affirming that "peculiar" photospheric abundances due to stellar evolution effects are a ubiquitous phenomenon for these intermediate-mass stars. This large sample of Ba-enhanced, chemically peculiar A/F stars with individual element abundances provides the statistics to test more stringently the mechanisms that alter the surface abundances in stars with radiative envelopes.

We report the discovery of a highly eccentric long-period Jovian planet orbiting the hot-Jupiter host HD 83443. By combining radial velocity data from four instruments (AAT/UCLES, Keck/HIRES, HARPS, Minerva-Australis) spanning more than two decades, we find evidence for a planet with m sin i = 1.35(-0.06)(+0.07) M-J, moving on an orbit with a = 8.0 +/- 0.8 au and eccentricity e = 0.76 +/- 0.05. We combine our radial velocity analysis with Gaia eDR3/Hipparcos proper motion anomalies and derive a dynamical mass of 1.5(-0.2)(+0.5)M(Jup). We perform a detailed dynamical simulation that reveals locations of stability within the system that may harbor additional planets, including stable regions within the habitable zone of the host star. HD 83443 is a rare example of a system hosting a hot Jupiter and an exterior planetary companion. The high eccentricity of HD 83443c suggests that a scattering event may have sent the hot Jupiter to its close orbit while leaving the outer planet on a wide and eccentric path.