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Phosphenium ions [R

Information relating to errors in evapotranspiration (ET) products, including satellite-derived ET products, is critical to their application but often challenging to obtain, with a limited number of flux towers available for the sufficient validation of measurements. Triple collocation (TC) methods can assess the inherent uncertainties of the above ET products using just three independent variables as a triplet input. However, both the severity with which the variables in the triplet violate the assumptions of zero error correlations and the corresponding impact on the error estimation are unknown. This study proposed a cross-correlation analysis approach to discover the optimal triplet of satellite-derived ET products with regard to providing the most reliable error estimation. All possible triple collocation solutions for the same product were first evaluated by the extended triple collocation (ETC), among which the optimum was selected based on the correlation between ETC-based and in-situ-based error metrics, and correspondingly, a statistic experiment based on ranked triplets demonstrated how the optimal triplet was valid for all pixels of the product. Six popular products (MOD16, PML_V2, GLASS, SSEBop, ERA5, and GLEAM) that were produced between 2003 to 2018 and which cover China’s mainland were chosen for the experiment, in which the error estimates were compared with measurements from 23 in-situ flux towers. The findings suggest that (1) there exists an optimal triplet in which a product as an input of TC with other collocating inputs together violate TC assumptions the least; (2) the error characteristics of the six ET products varied significantly across China, with GLASS performing the best (median error: 0.1 mm/day), followed by GLEAM, ERA5, and MOD16 (median errors below 0.2 mm/day), while PML_V2 and SSEBop had slightly higher median errors (0.24 mm/day and 0.27 mm/day, respectively); and (3) removing seasonal variations in ET signals has a substantial impact on enhancing the accuracy of error estimations.

Terrestrial vegetation is largely mediated by vegetation-climate coupling. Growing conditions control vegetation growth, which in turn feeds back to climate through changes in biophysical and biogeochemical properties and processes, such as canopy structure and carbon and water exchanges. The vegetation-climate coupling is thus highly variable in space and time. However, little is known on how the large-scale vegetation-climate coupling varies within growing season, and how vegetation responds to climate extremes. In this contribution, we present some recent findings on seasonal and intra-seasonal vegetation-climate coupling and vegetation sensitivity to droughts using multiple remote sensing products including MODIS EVI, GIMMS3g NDVI and VIP EVI2. We account for the differences in phenological stages of growing seasons affected by both climate and landscape heterogeneity. Based on a novel analytical framework incorporating meteorological and vegetation conditions to locally defined vegetation growing seasons, we analyse vegetation-climate couplings using both local climate conditions and teleconnection indices (e.g., Jet Latitude Index). In addition, vegetation sensitivity to droughts and post-drought vegetation changes are assessed. Our results highlight the importance of considering vegetation phenology in understanding sub-seasonal land-atmosphere interaction and vegetation dynamics. The developed analytical framework is suggested to be an effective approach for evaluating vegetation and climate dynamics simulated by Earth System Models.

Lake surface water temperature (LSWT) is a physical property of lakes. LSWT is a critical parameter for evaluating lakes' water quality and biodiversity. The change in LSWT can also be an indicator of climate change. Therefore, it is crucial to monitor LSWT to improve our understanding of the spatiotemporal dynamics of LSWT for many applications. Conventionally and ideally, we can install in-situ gauge stations or monitoring sites to measure surface water temperature in lakes, and these in-situ measurements are generally the most accurate. However, in-situ measurements in lakes are often sparse and limited in terms of spatial coverage and temporal length, which leaves many lakes with no measurements or lacking long-term continuous measurements. For example, Lake Vänern (surface area of about 5,655 km2, the largest lake in the European Union) has only two operational stations for measuring LSWT. The existing in-situ measurements are at irregular intervals (approximately bi-weekly) and have many data gaps. Many lakes globally have the same data situation as Lake Vänern. As a result, in-situ measurements cannot sufficiently capture the spatiotemporal dynamics of LSWT in large lakes.Satellite remote sensing has emerged as an essential method to monitor LSWT. Thermal infrared satellite data have been widely used to estimate the surface temperature at relatively high spatial resolution (higher and up to 1 km resolution). One of the most used satellite products for surface temperature is the MODIS (Moderate Resolution Imaging Spectroradiometer) global land surface temperature product, which is available from 2000 at 1 km-daily spatial-temporal resolutions. However, many studies stressed that cloud influence could significantly degrade the quality and availability of satellite-derived surface temperature for certain lakes, calling for a dedicated investigation to address this issue. Besides MODIS data, there are many other satellite-derived LSWT products at different spatial-temporal resolutions and spatial coverage, e.g., the ones available at http://www.laketemp.net. In addition, the recent ERA5-Land, a state-of-the-art reanalysis dataset, can also provide spatially complete and temporally continuous land surface variables, including the lake temperature at 0.1 degree-hourly spatial-temporal resolutions from 1950 to present. Each of the aforementioned products has its own advantages and limitations.Our initial analysis showed a significant data gap in LSWT from MODIS product for Lake Vänern due to cloud influence, which motivates us to conduct this study. This study aims to evaluate multiple existing LSWT products and, more importantly, to combine them with the advanced data fusion and bias correction method to develop a new spatially complete and temporally continuous LSWT dataset for Lake Vänern, Sweden. New in-situ measurements of LSWT will be collected from boats and drones at many locations of Lake Vänern; such measurements, together with existing data from the two stations, will be used to evaluate multiple LSWT products, the developed method, and the merged dataset. The newly developed LSWT dataset for Lake Vänern will benefit many applications, such as lake evaporation estimation, water balance analysis, air-lake interactions, and local climate forecasting.

Hydrological models as common simulation tools for water resources management play a key role in improving our understanding of hydrological processes on the catchment and global scales. The reliability of hydrological simulations depends on the model structure, the quality of input data, and the calibration of model parameters. A large number of model parameters and interactions among each hydrological variable increase the complexity of the model calibration. Multi-objective model calibration is beneficial to reduce hydrological modeling uncertainty by different calibration criteria, which can lead to more realistic simulations. The FLEXG model is a conceptual glacio-hydrological model within the flexible modeling framework. The FLEXG model considers the effects of topography on the spatial distribution of temperature, precipitation, and runoff generation to have a better understanding of the impacts of landscape on hydrological processes. The FLEXG can simulate various glacio-hydrological variables such as runoff from different sources (e.g. snow and glacier melt), glacier mass balance, snow cover area, and snow water equivalent. This study aims to evaluate the influences of several calibration strategies on the FLEXG model's performance in simulating simulated runoff, snow cover area, glacier mass balance, and snow water equivalent in a glacierized catchment in Sweden. To this end, the FLEXG model was calibrated based on remotely sensed snow cover area data and compared to the traditional calibration strategy (calibrating merely against gauged streamflow data). The FLEXG model was also calibrated based on both gauged streamflow data and satellite snow cover area data as a multi-objective calibration. Glacio-hydrological simulations from the FLEXG model using different calibration strategies were evaluated with multiple metrics and at different temporal scales. Results showed that calibrating the FLEXG using only one variable (runoff or snow cover area) can provide acceptable results for only one variable, while the multi-objective calibration strategy can have acceptable simulation for both runoff and snow cover area. In addition, calibrating the FLEXG model using only snow cover area may underestimate runoff simulation.

Meeting the food demand for the rising global population with current agricultural resources is a great challenge for the 21st century. Accurate crop yield estimation is crucial for food security planning. Traditional ground field measurements can be time-consuming and costly, which has limitations for providing temporally consistent yield information in large areas. During the past decades, satellite-based observations have become an important input for crop yield estimation, since they can capture eco-physiological conditions on the ground consistently and frequently over extensive areas.Three main independent satellite-based variables for estimating agriculture production can be summarized as: (1) vegetation indices (VIs); (2) biophysical variables; and (3) abiotic environmental factors. NDVI has been widely used in estimating agricultural production. The recently developed Plant Phenology Index (PPI) is shown highly related to vegetation productivity. PPI does not suffer from saturation effects in dense vegetation, and hence, has the potential to address the underestimation problems of crop productivity using other indices. Solar-Induced Fluorescence (SIF) is a direct proxy of plant photosynthesis, which is closely linked to crop yield. Furthermore, satellite-derived evapotranspiration (ET) integrates the effects of multiple environmental factors (e.g., precipitation, temperature, and wind speed) with soil moisture conditions, which has been demonstrated as an essential variable in crop growth monitoring.However, the performance of these satellite-based datasets for crop yield estimation in Sweden remains insufficiently explored, which motivates us to conduct this study. We will investigate relationships between four satellite-derived variables (i.e., NDVI, PPI, SIF, and ET) and ground-based crop yield data (e.g., wheat, barley, sugar beet) in Skåne county during 2003–2022. Both NDVI and PPI are derived using satellite imagery data from Landsat (16-day, 30 m temporal/spatial resolutions) and Sentinel-2 (~5-day, 10 m) missions. Contiguous SIF (4-day, 0.05°) is selected for providing long-term high-frequency data. The gridded ET dataset (8-day, 0.25°) is merged by multiple ET datasets (i.e., FLUXCOM, GLASS, PML-V2) based on the triple collocation method. The ground measurements of crop yield in multiple agricultural fields in Skåne are obtained from Statistics Sweden.For methodology, all satellite-derived variables will be firstly harmonized with the crop type map. The ground-based crop yield data will be divided to the training and testing samples in terms of temporal periods and spatial distribution. The linear/nonlinear relationships between four satellite-based variables (both individually and in varying combinations) and crop yield data (training samples) will be explored with different models including machine learning methods. Then the testing samples will be used for independent validation to determine the best variables/variable combinations and models for crop yield estimation. Finally, we will estimate crop yield at the regional scale and analyze its temporal and spatial patterns.

Inorganic salts usually demonstrate simple phasal behaviors in dilute aqueous solution mainly involving soluble (homogeneous) and insoluble (macrophase separation) scenarios. Herein, we report the discovery of complex phase behavior involving multiple phase transitions of clear solution – macrophase separation – gelation – solution – macrophase separation in the dilute aqueous solutions of a structurally well-defined molecular cluster [Mo7O24]6− macroanions with the continuous addition of Fe3+. No chemical reaction was involved. The transitions are closely related to the strong electrostatic interaction between [Mo7O24]6− and their Fe3+ counterions, the counterion-mediated attraction and the consequent charge inversion, leading to the formation of linear/branched supramolecular structures, as confirmed by experimental results and molecular dynamics simulations. The rich phase behavior demonstrated by the inorganic cluster [Mo7O24]6− expands our understanding of nanoscale ions in solution.

A divergent intramolecular phosphination of alkynes is developed. Fine tuning of chemo- and regioselectivity has been realized by just adding LiBr or H

Herein, we report a facile and highly atom-economic approach to 2-phosphafurans by using simple 2-chloroethylphosphine and acetylenic ketones. The key step of this protocol utilizes the Lewis acidity of electrophilic phosphinidenes to induce an intramolecular cyclization with enones. Dearomative hetero-Diels-Alder reactions of 2-phosphafurans provide two series of bicyclic phosphacycles. This rare synthetic application of Lewis acidity of electrophilic phosphinidene complexes represents a new frontier of phosphinidene chemistry.

One way of reducing environmental pollution is to reduce our dependence on fossil fuels by replacing them with solar radiation (Rs), which is one of the main sources of clean and renewable energy. In this study, daily Rs values at seven meteorological stations in Iran (Ahvaz, Isfahan, Kermanshah, Mashhad, Bandar Abbas, Kerman and Tabriz) over 2010-2019 were estimated using empirical models, support vector machine (SVM), SVM coupled with cuckoo search algorithm (SVM-CSA) and multi-model approach in the form of two structures. In structure 1, data from each station were divided into training and testing sets. In structure 2, data from the former four stations were used for model training, and those from the latter three stations were used to test the models. The results showed that using meteorological parameters improved estimation accuracy compared with the use of geographical parameters for both SVM and SVM-CSA models. Coupling the CSA to SVM did improve the accuracy of radiation estimates, reducing RMSE by up to 38% (Kermanshah station) and 36% (Tabriz station) for the first structure and about 42.4% (Tabriz station) for the second. Performance analysis of the models over three intervals including, the first, middle and last third of measured radiation values at each station showed that for both structures (except at Tabriz station), the best model performance in under- and over-estimation sets of radiation values was obtained, respectively, in the first third interval (first structure, Mashhad station, RMSE = 28.39 J.cm

Cooperative palladium-catalyzed and P(NEt2)3-mediated (4 + 1) annulations are developed, leading to a big library of spirooxindole derivatives in high structural diversity.

Both DFT calculations and experimental results revealed that the lone pair of the adjacent atom X facilitates the phosphirane fragmentation to form electrophilic phosphinidene complexes.

A facile access to mono-C-alkynyl-o-carboranes from o-carboranes and arylsulfonylacetylenes was developed. This facile process tolerates a wide variety of functional groups, occurs at mild conditions in one-pot procedure with short reaction time. The obtained mono-C-alkynyl-o-carboranes can be easily derivatized to synthesize 1,2-difunctionalized o-carboranes. This work provides a useful tool for the functionalization of o-carboranes.

Tandem [5 + 1] carbonyl cyclization/[8 + 2] cycloaddition reactions of phosphiranes and tropones were developed as a straightforward method to access 6,5,7-fused tricyclic scaffolds.

Herein we report a diastereodivergent synthesis of fully disubstituted spiro[indoline-3,2′-pyrrolidin]-2-ones through base-promoted (3 + 2) cycloadditions. Importantly, the catalysts are found to have full control over the configuration of the stereocenters. When a Lewis base (PCy3) is used as a catalyst, good yields and excellent diastereoselectivities are obtained, regardless of the properties of the substituents, whereas spiro[indoline-3,2′-pyrrolidin]-2-ones of a different diastereoisomer are produced in good yields when a Bronsted base (K2CO3) is used. ESI-MS experiments proved the existence of key zwitterionic intermediates.

Oxytocin (Oxt) and vasopressin (Avp) are two neuropeptides with many central actions related to social cognition. The oxytocin (Oxtr) and vasopressin 1b (Avpr1b) receptors are co-expressed in the pyramidal neurons of the hippocampal subfield CA2 and are known to play a critical role in social memory formation. How the neuropeptides perform this function in this region is not fully understood. Here, we report the behavioral effects of a life-long conditional removal (knockout, KO) of either the Oxtr alone or both Avpr1b and Oxtr from the pyramidal neurons of CA2 as well as the resultant changes in synaptic transmission within the different fields of the hippocampus. Surprisingly, the removal of both receptors results in mice that are unable to habituate to a familiar female presented for short duration over short intervals but are able to recognize and discriminate females when presented for a longer duration over a longer interval. Importantly, these double KO mice were unable to discriminate between a male littermate and a novel male. Synaptic transmission between CA3 and CA2 is enhanced in these mice, suggesting a compensatory mechanism is activated to make up for the loss of the receptors. Overall, our results demonstrate that co-expression of the receptors in CA2 is necessary to allow intact social memory processing.

Introduction: The Public-Private Partnership (PPP) model has the advantages of enhancing the efficiency of social infrastructure construction and reducing the risk of government liabilities, and has been widely used and developed in China. In the current securitization of PPP project assets, the question of how to price them is key to whether the Asset-backed Securities (ABS) products can be issued successfully.Methods: This study analyzes the pricing process and influencing factors of PPP project ABS in the context of PPP projects in China by collating the relevant literature on PPP project ABS products and collating the advantages and disadvantages of the static discounted cash flow (SCF), static spread (SS), and option-adjusted spread (OAS) methods. The study finds that the SCF and SS methods, primarily used in pricing PPP project ABS products, lack consideration of interest rate changes, early repayment rates, and multiple interest rate path options, leading to biased results. This study introduces the Monte Carlo simulation pricing method (MCSM) and conducts an empirical analysis based on the case of the Heating income right ABS of Huaxia Happiness PPP Project ABS.Results: It is concluded that the Monte Carlo simulation method can more flexibly and accurately price PPP project ABS products with revenue rights as the underlying assets.Discussion: This study has reference significance for the pricing of ABS products in future PPP projects and the construction of PPP projects in integrated energy service industrial parks, and can promote the development of new integrated energy service industrial parks, thereby helping to improve the energy consumption structure of enterprises in industrial parks, improve energy use efficiency, ensure the safety of energy use by enterprises, and further enhance the application of new energy by enterprises, thereby reducing their Greenhouse gas emissions.

Recent rapid warming has caused uneven impacts on the composition, structure, and functioning of northern ecosystems. It remains unknown how climatic drivers control linear and non-linear trends in ecosystem productivity. Based on a plant phenology index (PPI) product at a spatial resolution of 0.05° over 2000–2018, we used an automated polynomial fitting scheme to detect and characterize trend types (i.e., polynomial trends and no-trends) in the yearly-integrated PPI (PPIINT) for northern (> 30°N) ecosystems and their dependence on climatic drivers and ecosystem types. The averaged slope for the linear trends (p < 0.05) of PPIINT was positive across all the ecosystems, among which deciduous broadleaved forests and evergreen needle-leaved forests (ENF) showed the highest and lowest mean slopes, respectively. More than 50% of the pixels in ENF, arctic and boreal shrublands, and permanent wetlands (PW) had linear trends. A large fraction of PW also showed quadratic and cubic trends. These trend patterns agreed well with estimates of global vegetation productivity based on solar-induced chlorophyll fluorescence. Across all the biomes, PPIINT in pixels with linear trends showed lower mean values and higher partial correlation coefficients with temperature or precipitation than in pixels without linear trends. Overall, our study revealed the emergence of latitudinal convergence and divergence in climatic controls on the linear and non-linear trends of PPIINT, implying that northern shifts of vegetation and climate change may potentially increase the non-linear nature of climatic controls on ecosystem productivity. These results can improve our understanding and prediction of climate-induced changes in plant phenology and productivity and facilitate sustainable management of ecosystems by accounting for their resilience and vulnerability to future climate change.

We developed novel shackled P-chiral ligands based on 1-phosphanorbornenes and oxazolines. They were subsequently evaluated in palladium-catalyzed (4+2) annulations, producing enantioenriched tetrahydropyran scaffolds in good yields with high site selectivity and enantioselectivity. Moreover, chemoselective (4+4) products were also achieved by using acyclic imines. In addition, density functional theory calculations were performed to afford the energy profile of the Michael addition step and ring formation step. This demonstrated that the enantioselective (4+2) annulations and the chemoselective reaction between (4+2) and (4+4) products were mostly under thermodynamic control.

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring.

Cough variant asthma (CVA), also called concealed asthma or allergic asthma, is the most common cause of chronic cough in children. The disorder is mainly characterized by a nonproductive dry cough associated with a high recurrence rate that is conventionally treated with antibiotics, anti-inflammatory medications, cough suppressants, or expectorants. For millennia, Chinese herbal medicine (CHM) has been used widely in China to treat pediatric CVA cases, although high-quality evidence of CHM efficacy is lacking. In this study, the effectiveness and safety of Xiehuangjiejing (XHJJ) granule will be evaluated when used alone to treat children with CVA.A randomized, double-blind, parallel, placebo-controlled multicenter trial will be conducted over the course of 2 weeks. A total of 180 CVA patients of ages between 4 and 7 years old will be randomly assigned to the experimental group (XHJJ granules, 4.5 g administered 3 times daily) or control group (matched placebo, 4.5 g administered 3 times daily) in a 2:1 ratio based on subject number per group, respectively. The trial will consist of a 7-day medical interventional stage and a 7-day follow-up stage. On day 7 of the follow-up stage, an evaluation of all subjects will be carried out to assess cough symptom score as the primary outcome and several secondary outcomes, including TCM (traditional Chinese medicine) syndrome score, lung function, and dosage of salbutamol aerosol inhaler therapy. Safety assessments will also be evaluated during the trial.The aim of this study was to examine the effectiveness and safety of Xiehuangjiejing (XHJJ) granule using a trial protocol designed to yield high-quality, statistically robust results for use in evaluating CHM as a treatment for CVA in children.

Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterparts. In contrast, deep learning (DL) approaches provide a great computational capability for streamflow simulation, but they rely on data characteristics and the physics of the issue cannot be fully understood. To overcome these limitations, the current study provided a novel framework based on a combination of conceptual and DL techniques for enhancing the accuracy of streamflow simulation in a snow-covered basin. In this regard, the current study simulated daily streamflow in the Kalixälven river basin in northern Sweden by integrating a snow-based conceptual hydrological model (MISD) with a DL model. Daily precipitation, air temperature (average, minimum, and maximum), dew point temperature, evapotranspiration, relative humidity, sunshine duration, global solar radiation, and atmospheric pressure data were used as inputs for the DL model to examine the effect of each meteorological variable on the streamflow simulation. Results proved that adding meteorological variables to the conceptual hydrological model underframe of parallel settings can improve the accuracy of streamflow simulating by the DL model. The MISD model simulated streamflow had an MAE = 8.33 (cms), r = 0.88, and NSE = 0.77 for the validation phase. The proposed deep-conceptual learning-based framework also performed better than the standalone MISD model; the DL method had an MAE = 7.89 (cms), r = 0.90, and NSE = 0.80 for the validation phase when meteorological variables and MISD results were combined as inputs for the DL model. The integrated rainfall-runoff model proposed in this research is a new concept in rainfall-runoff modeling which can be used for accurate streamflow simulations.

Atmospheric levels of atomic mercury pollution were measured using spectroscopic techniques in the city of Guangzhou, Guangdong Province, China. Assessments were mainly performed at ground level using a portable (vehicle mounted or hand carried) Zeeman modulation correlation spectrometer (Lumex RA-915M), and the results are given in easily comprehensible diagrams. Measurements were made with continuous recording in car traverses along major roads which cross the city, but also at selected spots, such as at a university campus with laboratory buildings. Further, pollution levels at different locations were recorded when walking through a major and a small hospital. While concentrations in the city in the range 3–10 ng/m3 were typical, and strongly dependent on the traffic situation, very high concentrations (up to 1300 ng/m3) were found at certain indoor hospital locations, again drawing attention to the fact that high mercury levels due to inadequate handling routines can remain undetected but could readily be eliminated by adequate measurements and subsequent sanitation.

We present a joint experimental and theoretical study of parametric resonance of spin wave eigenmodes in Ni$_{80}$Fe$_{20}$/Pt bilayer nanowires. Using electrically detected magnetic resonance, we measure the spectrum of spin wave eigenmodes in transversely magnetized nanowires and study parametric excitation of these eigenmodes by a microwave magnetic field. We also develop an analytical theory of spin wave eigenmodes and their parametric excitation in the nanowire geometry that takes into account magnetic dilution at the nanowire edges. We measure tuning of the parametric resonance threshold by antidamping spin Hall torque from a direct current for the edge and bulk eigenmodes, which allows us to independently evaluate frequency, damping and ellipticity of the modes. We find good agreement between theory and experiment for parametric resonance of the bulk eigenmodes but significant discrepancies arise for the edge modes. The data reveals that ellipticity of the edge modes is significantly lower than expected, which can be attributed to strong modification of magnetism at the nanowire edges. Our work demonstrates that parametric resonance of spin wave eigenmodes is a sensitive probe of magnetic properties at edges of thin-film nanomagnets., Comment: 24 pages, 9 figures

There are widespread Mesozoic-Cenozoic terrestrial volcanic activities in East China, and they produced favorable geologic factors for the volcanic reservoirs. To reveal the spatio-temporal evolution of regional volcanisms and their tectonic setting, we subdivide Mesozoic-Cenozoic volcanic activities into 6 volcanic cycles (I-VI), and summarize the temporal-spatial distribution, rock association and tectonic setting of each cycle. The Cycle I forms a post-orogenic intraplate bimodal volcanic association. The cycles II and III include arc volcanic associations formed in compressional and extensional subduction environments, respectively. The Cycle IV contains a post-orogenic arc bimodal association. The Cycle V is a basaltic association of tholeiite series under initial rift setting, and the Cycle VI is basaltic association of alkaline series under typical rift setting. The volcanic strata between each cycle are bounded by regional unconformity. The above 6 volcanic cycles correspond to 6 sequential stages of tectonic evolutions from the Early Jurassic post-orogeny, the Mid-Jurassic-Cretaceous subduction of the paleo-Pacific Plate to the Cenozoic marginal rifting. According to the geological characteristics of volcanic reservoirs in different volcanic cycles, it is put forward that the Cycle V is the major formation period of volcanic reservoirs in East China and should be the focus of exploration, and that the volcanic reservoirs of the Cycle IV are also worthy of attention.

For satellite interactions missions such as autonomous docking, the key to successful completion of these missions is to autonomously and accurately detect targets’ information. The missions with h...

Dissolved Organic Carbon (DOC) in inland waters plays an essential role in the global carbon cycle and has significant public health effects. Machine learning (ML) together with remote sensing has emerged as a powerful and promising combination to quantify water quality parameters from space. However, inland water sample data for DOC is limited. Hence, little is known about the potential to quantify DOC content in inland waters, especially over large-scale areas. This study presents the first attempt to estimate DOC in inland waters over a large-scale area using satellite data and ML methods with the newly published open-source dataset AquaSat. Four ML approaches, namely Random Forest Regression (RFR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and a Multilayer Backpropagation Neural Network (MBPNN) were trained using more than 16 thousand samples across the continental United States matched with satellite data from Landsat 5, 7 and 8 missions. Satellite data from the Landsat missions were further extended with environmental data from the ERA5-Land product and used as input to train the ML algorithms. Our results show that including environmental data as inputs considerably improved the prediction of DOC for all ML algorithms, with GPR showing the most promising performance results with moderate estimation errors (RMSE: 4.08 mg/L). Permutation feature importance analysis showed that the wavelength range in the visible Green band (from Landsat) and the monthly average air temperature (from ERA5-Land) were the most important variables for the ML approaches. The results demonstrate the predictive strength of GPR and its useful feature to derive per pixel standard deviations for detailed analysis. Our results further highlight the important role of considering environmental processes to explain DOC variations over large scales. The application and performance of the GPR in mapping spatiotemporal variations of DOC in an entire water body were discussed by taking Lake Okeechobee (the 8

The management and conservation of lakes should be conducted in the context of catchments because lakes collect water and materials from their upstream catchments. Thus, the datasets of catchment-level characteristics are essential for limnology studies. Lakes are widely spread on the Tibetan Plateau (TP), with a total lake area exceeding 50 000 km2, accounting for more than half of the total lake area in China. However, there has been no dataset of lake-catchment characteristics in this region to date. This study constructed the first dataset of lake-catchment characteristics for 1525 lakes with areas from 0.2 to 4503 km2 on the TP. Considering that large lakes block the transport of materials from upstream to downstream, lake catchments are delineated in two ways: the full catchment, which refers to the full upstream-contributing area of each lake, and the inter-lake catchments, which are obtained by excluding the contributing areas of upstream lakes larger than 0.2 km2 from the full catchment. There are six categories (i.e., lake body, topography, climate, land cover/use, soil and geology, and anthropogenic activity) and a total of 721 attributes in the dataset. Besides multi-year average attributes, the time series of 16 hydrological and meteorological variables are extracted, which can be used to drive or validate lumped hydrological models and machine learning models for hydrological simulation. The dataset contains fundamental information for analyzing the impact of catchment-level characteristics on lake properties, which on the one hand, can deepen our understanding of the drivers of lake environment change, and on the other hand can be used to predict the water and sediment properties in unsampled lakes based on limited samples. This provides exciting opportunities for lake studies in a spatially explicit context and promotes the development of landscape limnology on the TP. The dataset of lake-catchment characteristics for the Tibetan Plateau (LCC-TP v1.0) is accessible at the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.272026, Liu, 2022).

The activation of CS