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Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from - 2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at 10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021). [ABSTRACT FROM AUTHOR]

Three-River-Source (TRS) National Park stands as one of China's earliest established national parks, dedicated to significant ecological responsibilities that include conserving soil and water resources in the Tibetan Plateau region. Research on climate change's influence on the TRS region's grasslands is of great significance in our efforts to comprehend and conserve the grassland ecosystem. The most effective random forest (RF) model was chosen to invert the aboveground biomass (AGB) of grassland in the previous 6 years (2015-2020) and predict the grassland AGB in the following 20 years (2021-2040) by comparing linear regression and multivariate nonlinear regression models such as RF, support vector machine, decision tree, and artificial neural network. A Theil-Sen median trend analysis and a Mann-Kendal test were then used to examine the trends of grassland AGB. The results showed that (1) RF outperformed other models in estimating grassland AGB, with a test set decision coefficient of multiple determination (R²) of 0.722, a root mean square error of 42.596 g/m², and a mean absolute error of 35.619 g/m²; (2) over 6 years, the grassland AGB in TRS National Park had a spatial trend of a steady rise from the northwest to the southeast. The average annual grassland AGB was 247.333 g/m², with averages of 44.836 g/m² , 92.601 g/m² , and 120.217 g/m² in the Yangtze River, Yellow River, and Lancang River source parks respectively. The trend of the grassland AGB was primarily stabilized and slightly recovered, with a small portion of the slightly deteriorated areas; (3) climate change significantly affected grassland AGB, and when temperature and precipitation conditions were adequate, grassland AGB values increased with temperature and precipitation. In the scenarios of ssp119, ssp245, and ssp585, grassland AGB is projected to exhibit a dynamic upward trend over the next 20 years. Global warming is expected to boost grassland AGB. Comprehensive measures are essential to maintain grassland health and ensure a positive impact on global carbon and ecological balance. The study's findings hold great importance for the ecological security of the TRS region and contribute to our global understanding of sustainable grassland development. [ABSTRACT FROM AUTHOR]

As an exquisite asset of Chinese traditional culture, traditional opera occupies a place of high esteem within the world's cultural and artistic treasury. The impact of emerging cultures has threatened the future of traditional opera culture, necessitating a thorough examination of the historical context of the Grand Canal and traditional opera. There is insufficient research on the spatial evolution of the traditional opera culture along the Grand Canal; thus, this study takes ancient opera stages, a representative cultural relic of traditional opera, as an entry point and employs methods such as kernel density analysis and standard deviation ellipse analysis to analyze the spatial and temporal distribution patterns of the traditional opera culture along the Grand Canal. The results showed that: (i) Nationwide, opera stages in the areas along the Grand Canal exhibit a significant clustering characteristic. (ii) The changes in the number and locations of opera stages in the areas along the Grand Canal are closely related to the rise and fall of the Canal. The opera stages emerged along the Canal, gradually prospered with the development of the Canal, and finally clustered in a band-like cluster along the Grand Canal. (iii) From the Ming Dynasty to the founding of the People's Republic of China, the opera stages in the areas along the Grand Canal spread in the "southeast–northwest" direction, which was consistent with the main direction of the Grand Canal, indicating its driving influence. (iv) On the centennial scale, from the 14th century to the 20th century, the evolution characteristics of the distribution centroid of opera stages in the areas along the Grand Canal were closely related to the key time nodes of Grand Canal construction and basin expansion. This study reveals the relationship between the Grand Canal and the spatial pattern evolution of traditional opera culture, aiming to promote the construction of the Grand Canal cultural belt. [ABSTRACT FROM AUTHOR]

This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province, China. The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing. At the same time, the wind speed predicted by the EC model is also included for comparative analysis. The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A. The CMA-GD model exhibits a monthly average correlation coefficient of 0.56, root mean square error of 2.72 m s–1, and average absolute error of 2.11 m s–1. In contrast, the EC model shows a monthly average correlation coefficient of 0.51, root mean square error of 2.83 m s–1, and average absolute error of 2.21 m s–1. Conversely, in Wind Farm B, the EC model outperforms the CMA-GD model. The CMA-GD model achieves a monthly average correlation coefficient of 0.55, root mean square error of 2.61 m s–1, and average absolute error of 2.13 m s–1. By contrast, the EC model displays a monthly average correlation coefficient of 0.63, root mean square error of 2.04 m s–1, and average absolute error of 1.67 m s–1. [ABSTRACT FROM AUTHOR]

Evapotranspiration is one of the most critical features in hydrology. In order to address the prediction of evapotranspiration accurately and scientifically, this study proposes a novel deep learning-based evapotranspiration prediction model, the CNN-BiLSTM-Attention model, using the climatically complex Mount Tai region in China as a case study. The model integrates the feature extraction capabilities of Convolutional Neural Networks (CNN), the temporal dependency capturing of Bidirectional Long Short-Term Memory Networks (BiLSTM), and the feature weighting abilities of the Attention mechanism. In order to enhance prediction accuracy with fewer climate parameters, various input parameter combinations are explored and compared with other classical models in this study. The model's performance is assessed across daily, weekly, and monthly time increments, using evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2). This study holds significant implications for mountain hydrological cycles, as accurate evapotranspiration prediction aids in more informed decision-making for agricultural production, water resource management, and climate change research. [ABSTRACT FROM AUTHOR]

Prior to assimilating hyperspectral infrared data on the FengYun (FY) satellite in the numerical weather prediction (NWP) system, it is necessary to identify the quality and bias characteristics of these data, especially as China's first early-morning-orbit satellite data. Using the numerical model CMA-GFS (China Meteorological Administration Global Forecast System) and the observation of FY-3E HIRAS-II (Hyperspectral Infrared Atmospheric Sounder-II), the differences between observed and simulated brightness temperatures (O-Bs) are comprehensively analyzed, with a focus on evaluating the long-wave (LW) temperature sounding channels of HIRAS-II observation in the clear sky. The results show that the O-Bs in the LW channels are between ±1.0 K, except for the C O 2 absorption line peak at 667 c m − 1 . Only a tiny variation in O-Bs, with relative consistency, could be observed during the day, the line of dawn and dusk, and night. The difference in the standard deviations of O-Bs in the three cases is less than 0.1 K. The O-Bs of two typical channels (channels 14 and 47) in the stratosphere have disturbances at individual times, whereas the O-Bs are much more stable in time series in the tropospheric channels. The O-Bs in different channels show the characteristics of changing with the latitude, but the bias and standard deviations of O-Bs during the ascending and descending stages are not much different, except for the bias of channel 47 in low latitude. The optimal ranking of Fields of View (FOVs) in assimilation is derived from a priori analysis of O-Bs. The results demonstrate that FOV4 and FOV5 are the best in a Field of Regard (FOR) compared to all LW channels of HIRAS-II in constructions of their O-Bs and magnitude of O-B standard deviations, and they can be used as the preferred FOVs for assimilation. While the O-Bs in FOV1 and FOV2 are slightly larger, the O-Bs' characteristics also meet the assimilation requirements and can be used as assimilation FOVs in HIRAS-II LW channels after FOV4 and FOV5 lose their efficacy. [ABSTRACT FROM AUTHOR]

Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface is crucial for monitoring tillage intensity and crop yield performance. Thus, a novel research study is conducted to develop an innovative approach for accurately estimating and mapping the Wheat Straw Cover (WSC) percentage through two different multispectral satellites (Sentinel-2B MSI and Landsat-8 OLI-TIRS), using remote sensing-based techniques in Changshu County, China. The field measurements were collected from 80 distinct sites and eight images were acquired through both satellites for the analysis process by applying Crop Residue Indices (CRIs). The results indicate that the coefficients of determination (R2) of the Normalized Difference Tillage Index (NDTI) computed by Sentinel-2 and Landsat-8 were 0.80 and 0.70, respectively, and the root-mean-square deviation (RMSD) values were in the range from 6.88 to 12.04% for CRIs for both satellite data. Additionally, the comparative analysis of the developed model revealed that NDTI was R2 = 0.85 and R2 = 0.77, followed by STI, R2 = 0.82 and R2 = 0.80 and NDRI, R2 = 0.69 and R2 = 0.56 for Sentinel-2B and Landsat-8 data, respectively. Hence, the correlation strength of NDTI, STI and NDRI with WSC percentages was markedly superior by using Sentinel-2B spectral data compared to Landsat-8 ones. Moreover, the NDTI of Sentinel-2B data was the most accurate in mapping the WSC percentage in four categories, with an overall accuracy of 86.53% (κ = 0.78), surpassing the other CRI indices. Therefore, these findings suggest that the multispectral imagery of Sentinel-2B bolstered with enhanced temporal and spatial data was superior for precisely estimating and mapping the WSC percentage compared to Landsat-8 data over a large-scale agricultural region. [ABSTRACT FROM AUTHOR]

In wind resource assessment research, mixture models are gaining importance due to the complex characteristics of wind data. The precision of parameter estimations for these models is paramount, as it directly affects the reliability of wind energy forecasts. Traditionally, the expectation–maximization (EM) algorithm has served as a primary tool for such estimations. However, challenges are often encountered with this method when handling complex probability distributions. Given these limitations, the objective of this study is to propose a new clustering algorithm, designed to transform mixture distribution models into simpler probability clusters. To validate its efficacy, a numerical experiment was conducted, and its outcomes were compared with those derived from the established EM algorithm. The results demonstrated a significant alignment between the new method and the traditional EM approach, indicating that comparable accuracy can be achieved without the need for solving complex nonlinear equations. Moreover, the new algorithm was utilized to examine the joint probabilistic structure of wind speed and air density in China's coastal regions. Notably, the clustering algorithm demonstrated its robustness, with the root mean square error value being notably minimal and the coefficient of determination exceeding 0.9. The proposed approach is suggested as a compelling alternative for parameter estimation in mixture models, particularly when dealing with complex probability models. [ABSTRACT FROM AUTHOR]

In recent times, airborne and terrestrial laser scanning have been utilized to collect point cloud data for forest resource surveys, aiding in the estimation of tree and stand attributes over hectare-scale plots. In this study, an automated approach was devised to estimate the diameter at breast height (DBH) and tree height across the entire sample area, utilizing information acquired from terrestrial laser scanning (TLS) and airborne laser scanning (ULS). Centered around a meticulously managed artificial forest in Northern China, where Mongolian oak and Chinese Scots pine are the predominant species, both TLS and ULS operations were conducted concurrently on each plot. Subsequent to data collection, a detailed processing of the point cloud data was carried out, introducing an innovative algorithm to facilitate the matching of individual tree point clouds from ULS and TLS sources. To enhance the accuracy of DBH estimation, a weighted regression correction equation based on TLS data was introduced. The estimations obtained for the Chinese Scots pine plots showed a correlation of R2 = 0.789 and a root mean square error (RMSE) of 3.2 cm, while for the Mongolian oak plots, an improved correlation of R2 = 0.761 and a RMSE of 3.1 cm was observed between predicted and measured values. This research significantly augments the potential for non-destructive estimations of tree structural parameters on a hectare scale by integrating TLS and ULS technologies. The advancements hold paramount importance in the domain of large-scale forest surveys, particularly in the calibration and validation of aboveground biomass (AGB) estimations. [ABSTRACT FROM AUTHOR]

Collapsibility is a unique engineering geological property of loess. Choosing appropriate parameters to build the prediction model of loess collapsibility is an essential step toward solving the loess collapsibility problem. A case study was performed for the loess in Xinyuan County of the Yili River Basin, China. A large amount of data was collected from preliminary geotechnical tests in this region. Mathematical statistics were applied to analyse the correlations between the loess collapsibility and soil parameters. Multiple linear regression and neural network theories were adopted to build this region's prediction model of loess collapsibility. The results showed that microscopically, the soils in this region were predominantly flocculated structures. The soil particles were flaky and in bracket contact, and the pores were round or irregularly shaped. Regarding the material composition, the soils were primarily composed of quartz and albite, with a low hematite content. In the study area, the correlation coefficients between the collapsibility coefficient of the loess vs. the density, dry density, saturation, porosity ratio, and porosity varied between 0.628 and 0.857, indicating a strong or very strong correlation. In terms of predicting loess collapsibility, the effectiveness of neural networks based on RBF (radial basis function) and multiple linear regression models was contrasted. The latter was discovered to be more appropriate, dependable, and accurate, with an accuracy percentage of 94.42%. Simultaneously, the model's assessment index is 0.014 for the root mean squared error (RMSE), 0.962 for the correlation coefficient (CC), 0.919 for the Nash–Sutcliffe efficiency coefficient (NSE), and −1.494 percent for the percent bias (PBIAS). It works well for estimating whether local loess may collapse. Therefore, the RBF neural network model built in the present study has adequate precision and meets the engineering requirements. Our research sheds new light on loess collapsibility assessment in this region. [ABSTRACT FROM AUTHOR]

Copyright of Vadose Zone Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The conventional multi-temporal InSAR (MT-InSAR) technology suffers from the problems of low spatial density of monitoring points (MPs) and poor interferogram quality in monitoring reservoir bank landslides in mountainous areas. To address it, this study builds upon the Eigendecomposition-based Maximum-likelihood estimator of Interferometric phase (EMI) method, introduces Fisher information to adjust the weight of each interferometric pair, and further develops a new distributed scatterer (DS) phase optimization method, which is referred to as FEMI-DSInSAR in the text. We applied the FEMI-DSInSAR to retrieve the deformation history of landslides along a ~100 km section of the Lancang River using 33 C-band Sentinel-1 images (January 2019–January 2022). The accuracy and reliability were validated by comparing the results with those obtained with EMI-DSInSAR and Stanford Method for Persistent Scatterers-Small Baseline Subset (StaMPS-SBAS) methods in terms of both interferogram quality and large-area deformation results. The differential interferograms obtained by the FEMI-DSInSAR method not only show better quality fringes, but also reduce the sum of phase difference (SPD) and the standard deviation of the phase (PSD) values by 8.9% and 25.2%, respectively. Moreover, the FEMI-DSInSAR method yields the most significant number of MPs under various geomorphic regions and achieves reliable surface displacements, compared to 10 in situ GPS measurements. Finally, we projected the FEMI-DSInSAR-derived line of sight (LOS) deformation results onto the slope direction and successfully identified 4 previously mapped and 46 newly detected unstable slopes. The characteristics of whole-area landslide deformation characteristics and the influencing factors were analyzed. [ABSTRACT FROM AUTHOR]

Accurate wind speed prediction is crucial for the safe and efficient utilization of wind resources. However, current single-value deterministic numerical weather prediction methods employed by wind farms do not adequately meet the actual needs of power grid dispatching. In this study, we propose a new hybrid forecasting method for correcting 10 m wind speed predictions made by the Weather Research and Forecasting (WRF) model. Our approach incorporates variational mode decomposition (VMD), principal component analysis (PCA), and five artificial intelligence algorithms: deep belief network (DBN), multilayer perceptron (MLP), random forest (RF), eXtreme gradient boosting (XGBoost), light gradient boosting machine (lightGBM), and the Bayesian optimization algorithm (BOA). We first predict wind speeds using the WRF model, with initial and lateral boundary conditions from the Global Forecast System (GFS). We then perform two sets of experiments with different input factors and apply BOA optimization to tune the four artificial intelligence models, ultimately building the final models. Furthermore, we compare the aforementioned five optimal artificial intelligence models suitable for five provinces in southern China in the wintertime: VMD-PCA-RF in December 2021 and VMD-PCA-lightGBM in January 2022. We find that the VMD-PCA-RF evaluation indices exhibit relative stability over nearly a year: the correlation coefficient (R) is above 0.6, forecasting accuracy (FA) is above 85 %, mean absolute error (MAE) is below 0.6 m s -1 , root mean square error (RMSE) is below 0.8 m s -1 , relative mean absolute error (rMAE) is below 60 %, and relative root mean square error (rRMSE) is below 75 %. Thus, for its promising performance and excellent year-round robustness, we recommend adopting the proposed VMD-PCA-RF method for improved wind speed prediction in models. [ABSTRACT FROM AUTHOR]

Assessing the capability of sub-seasonal rainfall forecast of dynamic model and proposing correction method is quite an important topic in current climate research field. From the perspective of rainfall amount, rainy days and rainfall-belt evolution, the sub-seasonal forecast ability of the European Centre for Medium-Range Weather Forecasts (ECMWF) model for the main rainy-season rainfall in eastern China is evaluated. Evaluation results show that the forecast biases increase gradually with the increase of forecast lead time, characterized by the predicted rainfall amount being obviously higher and the rainy days being much longer than observation. In order to reduce the forecast biases of sub-seasonal rainfall forecast of ECMWF model, the rainy day-based correction (RDC) method is proposed in this study. Cross validation results indicate that RDC method can modify the number of rainy days forecast of ECMWF model with the SCC of rainy days increasing by 12.96% ~ 18.62%, and the RMSE decreasing by 56.49% ~ 63.78%. The problem of maximum continuous rainy days being too long in the model forecast can be also improved. Meanwhile, the spatial correlation coefficient (SCC) of rainfall amount forecast of the ECMWF model with the observation weakly increases by 0.61% ~ 1.56% and the root mean square error (RMSE) decreases by 3.50% ~ 7.60% after the RDC treatment. Therefore, RDC method presents a good performance on improving the sub-seasonal forecast of rainy days, and maximum continuous rainy days, which can be further applied in other models' sub-seasonal forecast error correction. [ABSTRACT FROM AUTHOR]

Using Global Navigation Satellite Systems (GNSS) observation data for developing a high-precision ionospheric Total Electron Content (TEC) model is one of the essential subjects in ionospheric physics research and the application of satellite navigation correction. In this study, we integrate the Empirical Orthogonal Function (EOF) method with the TEC data provided by the Center for Orbit Determination in Europe (CODE), and observed by the dense GNSS receivers operated by the Crustal Movement Observation Network of China (CMONOC) to construct a regional ionospheric TEC model over China. The EOF analysis of CODE TEC in China from 1998 to 2010 shows that the first-order EOF component accounts for 90.3813% of the total variation of the ionospheric TEC in China. Meanwhile, the average value of CODE TEC is consistent with the spatial and temporal distribution characteristics of the first-order EOF base function, which mainly reflects the latitude and diurnal variations of TEC in China. The first-order coefficient after EOF decomposition shows an obvious 11-year period and semi-annual variations. The maximum amplitude of semi-annual variation mainly appears in March and October, which is closely associated with the variation in geographical longitude, the semi-annual change of the low-latitude electric field, and the ionospheric fountain effect. The second-order coefficient has an evident annual variation, the minimum amplitude mainly occurs in March, August, and September, and the amplitude values in the high solar activity years are more significant than those in the low solar activity years. The third-order coefficient mainly shows the characteristics of annual variation, and the fourth-order coefficient shows the noticeable semi-annual and annual variations. The third and fourth-order coefficients are both modulated by the solar activity index F10.7. The ionospheric TEC model in China, driven by CMONOC real-time GNSS observation data, can better reflect the latitude, local time and seasonal variation characteristics of ionospheric TEC over China. In particular, it can clearly show the spring and autumn asymmetry of ionospheric TEC in the low latitudes. The root mean square error of the absolute error between the model and the actual observation is mainly distributed around 2.45 TECU (1 TECU = 1016 electrons/m2). The values of the TEC model constructed in this study are closer to the actual observed values than those of the CODE TEC in China. [ABSTRACT FROM AUTHOR]

Satellite observations have provided global and regional soil-moisture estimates in the last four decades. However, the accuracy of these observations largely depends on reducing uncertainties in the retrieval algorithms. In this study, we address two challenges affecting the quality of soil-moisture estimates from a widely used soil-moisture-retrieval model, the land parameter retrieval model (LPRM). We studied two improvement schemes that were aimed at reducing uncertainties in open water signals (the LPRMv6_OWF) and vegetation signals (the LPRMv6_Veg), as well as a scheme to reduce their combined impacts (the LPRMv6_OWFVeg) on LPRM-retrieved soil moisture using the FengYun-3B (FY-3B) satellite observations. To assess the impacts of the improvement schemes, we utilized in situ soil moisture from the Jiangsu and Jiangxi provinces in China. We found that the retrievals (Rs) of the LPRMv6_Veg and the LPRMv6_OWFVeg were mainly in the range of 0.2 to 0.5 in Jiangsu and Jiangxi, with increases of 0.1 compared to those of the LPRMv6. The standard deviation (SD) of the LPRMv6_OWFVeg increased in Jiangsu, while the R of the LPRMv6_OWF increased in Jiangsu by 0.05–0.1 compared to that of the LPRMv6, but the SD tended to become worse. In Jiangxi, there was an increase of 0.1 in R. The results show that each of these algorithms improved the accuracy of soil-moisture inversion to some extent, compared to the original algorithm, with the LPRMv6_OWFVeg showing the greatest improvement, followed by the LPRMv6_Veg. The accuracy of both the LPRMv6_OWF and the LPRMv6_OWFVeg decreased to some extent when the open-water fraction (OWF) was greater than 0.2. Full areal extent analyses based on triple collocation showed significant improvements in correlations and minimized errors across different vegetation scenarios over the entire region of China in both the LPRMv6_OWF and the LPRMv6_Veg. However, reduced qualities were found in arid regions in northern China because of the nonlinear relationships between land-surface temperature, vegetation, and soil moisture in the LPRM. These results highlight important lessons for developing comprehensive improvement schemes for soil-moisture retrievals from passive microwave satellite observations. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the characteristics of physical and chemical composition of municipal solid waste (MSW) in China were reviewed and the statistical indexes, namely mean value, standard deviation, coefficient of variation, and t-test, were applied to analyze the physical composition, proximate, ultimate analysis, and heating value. Listed in decreasing sequence, the physical components of Chinese MSW are in food residue, non-combustibles, plastics, paper, textiles, wood waste, and rubber. In food residue, the average elementary hydrogen (H), oxygen (O) and nitrogen (N) content varied greatly with samples and the chlorine (Cl) and moisture contents were extraordinarily high. While conversely, the components of wood waste were simple and different components displayed little disparity in characteristics. The elemental compositions of paper and textiles were also simple. The properties of chlorine-free plastics (polyethylene, polypropylene and polystyrene) were consistent, with high volatile matter, carbon (C) and H content. The mean higher heating value (HHV) of polrvinyl chloride (PVC) was about a half of that of chlorine-free plastics, because the Cl content of PVC was approximate 50%. It suggested that plastics with or without chlorine should be separated as possible. The HHV of different rubbers varied sharply, from 21,812 to 38,868kJ/kg. A model was proposed to predict the proximate and ultimate analysis and heating value from physical composition, in which the PVC fraction in plastics and the supplementary moisture coefficient were introduced. The results showed that the predicted results fitted well with the measured ones. [Copyright &y& Elsevier]

Purpose – The purpose of this paper is to examine the determinants of average health expenditures for inpatients in China with national data for period 2002-2010 and regional data during 2005-2010. Design/methodology/approach – The semi-parametric framework is established to identify the determinants of health expenditures with local-constant least squares (LCLS) and local-linear least squares (LLLS) techniques. The LCLS technique aims to identify correlative determinants among all considered variables, and LLLS technique aims to further distinguish linear decisive and nonlinear control variables among all correlative determinants. Findings – First, root mean square error tends to decrease with irrelative variables smoothed out in regression model, validating the modelling reasonability of the semi-parametric approach. Second, the determinants of average health expenditures for inpatients exhibit considerable variation among regions despite the fact that governmental health expenditure, GDP per-capita, and urbanization do impact average health expenditures for inpatients to a certain extent. Third, both linear decisive and nonlinear control variables vary greatly with national, provincial, and regional data. Practical implications – First, the illiteracy rate should be further reduced nationally. Second, urbanization development and the average treatment number of inpatients for each physician per day should be strictly controlled in region A and C, respectively, in order to control average health expenditure for inpatients. Originality/value – First, the semi-parametric framework with LCLS and LLLS techniques allows for data structure-oriented model in regions rather than a uniform and definite model for underlying structure. Second, the research undertakes for the first time a comprehensive data analysis of the determinants of average health expenditures for inpatients with national and regional data in China. [ABSTRACT FROM AUTHOR]

In this paper, we reexamine the mutual fund flow-performance relationship and star effect using mutual funds in China that have unique features of high risk and low performance persistence. Confirming prior studies, we find that fund performance is positively related to flows in subsequent periods. However, our results show that funds that performed well in the past do not attract additional inflows after controlling for performance. In addition, a star fund, a fund with a five-star Morningstar rating, does not have any significant effect on the fund's flows. These results suggest that it is important to recognize the difference in investor groups and factors that affect performance persistence when analyzing the mutual fund flow-performance relationship. [ABSTRACT FROM AUTHOR]

Deformation prediction for a salt solution mining area is essential to mining environmental protection. The combination of Synthetic Aperture Radar Interferometry (InSAR) technique with Probability Integral Method (PIM) has proven to be powerful in predicting mining-induced subsidence. However, traditional mathematical empirical models (such as linear model or linear model combined with periodical function) are mostly used in InSAR approaches, ignoring the underground mining mechanisms, which may limit the accuracy of the retrieved deformations. Inaccurate InSAR deformations will transmit an unavoidable error to the estimated PIM parameters and the forward predicted subsidence, which may induce more significant errors. Besides, theoretical contradictory and non-consistency between InSAR deformation model and future prediction model is another limitation. This paper introduces the Coordinate-Time (CT) function into InSAR deformation modeling. A novel time-series InSAR model (namely, CT-PIM) is proposed as a substitute for traditional InSAR mathematical empirical models and directly applied for future dynamic prediction. The unknown CT-PIM parameters can be estimated directly via InSAR phase observations, which can avoid the error propagation from the InSAR-generated deformations. The new approach has been tested by both simulated and real data experiments over a salt mine in China. The root mean square error (RMSE) is determined as ±10.9 mm, with an improvement of 37.2% compared to traditional static PIM prediction method. The new approach provides a more robust tool for the forecasting of mining-induced hazards in salt solution mining areas, as well as a reference for ensuring the environment protection and safety management. [ABSTRACT FROM AUTHOR]

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This paper presents a study dealing with soil organic carbon (SOC) estimation of soil through the combination of soil spectroscopy and multivariate stepwise linear regression. Soil samples were collected in the three sub-regions, dominated by brown calcic soil, in the northern Tianshan Mountains, China. Spectral measurements for all soil samples were performed in a controlled laboratory environment by a portable ASD FieldSpec FR spectrometer (350-2,500 nm). Twelve types of transformations were applied to the soil reflectance to remove the noise and to linearize the correlation between reflectance and SOC content. Based on the spectral reflectance and its derivatives, hyperspectral models can be built using correlation analysis and multivariable statistical methods. The results show that the main response range of soil organic carbon is between 400 and 750 nm. Correlation analysis indicated that SOC has stronger correlation with the second derivative than with the original reflectance and other transformations data. The two models developed with laboratory spectra gave good predictions of SOC, with root mean square error (RMSE) <5.0. The use of the full visible near-infrared spectral range gave better SOC predictions than using visible separately. The multivariate stepwise linear regression of second derivate model (model A) is optimal for estimating SOC content, with a determination coefficient of 0.894 and RMSE of 0.322. The results of this research study indicated that, for the grassland regions, combining soil spectroscopy and mathematical statistical methods does favor accurate prediction of SOC. [ABSTRACT FROM AUTHOR]

The paper setted two scenarios of A and B based on a model of multivariate empirical probability distributions (MVE) to forecast China's cereals production in a short run. Four measures of the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and Theil's inequality coefficient (Theft IC) were used to test ex post simulation fit. Result indicates that the MVE model's short-term prediction accuracy is better simulated, and if the deadline of historical data is closer to the starting time, the statistic fit is better. The paper also finds that according to historical data from 2000 to 2008 (B scenario), the simulated mean values of China's cereals production in 2010 and 2011 are 532.88 million tons and 538.87 million tons, and the standard deviation values are 19.67 million tons and 20.01 million tons, respectively. [ABSTRACT FROM AUTHOR]

The terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) is now a significant issue for scientific research in high-resolution time-variable gravity fields. This paper proposes the use of singular spectrum analysis (SSA) to predict the TWSA derived from GRACE. We designed a case study in six regions in China (North China Plain (NCP), Southwest China (SWC), Three-River Headwaters Region (TRHR), Tianshan Mountains Region (TSMR), Heihe River Basin (HRB), and Lishui and Wenzhou area (LSWZ)) using GRACE RL06 data from January 2003 to August 2016 for inversion, which were compared with Center for Space Research (CSR), Helmholtz-Centre Potsdam-German Research Centre for Geosciences (GFZ), Jet Propulsion Laboratory (JPL)'s Mascon (Mass Concentration) RL05, and JPL's Mascon RL06. We evaluated the accuracy of SSA prediction on different temporal scales based on the correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE), which were compared with that of an auto-regressive and moving average (ARMA) model. The TWSA from September 2016 to May 2019 were predicted using SSA, which was verified using Mascon RL06, the Global Land Data Assimilation System model, and GRACE-FO results. The results show that: (1) TWSA derived from GRACE agreed well with Mascon in most regions, with the highest consistency with Mascon RL06 and (2) prediction accuracy of GRACE in TRHR and SWC was higher. SSA reconstruction improved R, NSE, and RMSE compared with those of ARMA. The R values for predicting TWS in the six regions using the SSA method were 0.34–0.98, which was better than those for ARMA (0.26–0.97), and the RMSE values were 0.03–5.55 cm, which were better than the 2.29–5.11 cm RMSE for ARMA as a whole. (3) The SSA method produced better predictions for obvious periodic and trending characteristics in the TWSA in most regions, whereas the detailed signal could not be effectively predicted. (4) The predicted TWSA from September 2016 to May 2019 were basically consistent with Global Land Data Assimilation System (GLDAS) results, and the predicted TWSA during June 2018 to May 2019 agreed well with GRACE-FO results. The research method in this paper provides a reference for bridging the gap in the TWSA between GRACE and GRACE-FO. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA) is a blind source signal separation method which can effectively estimate high-order information and thus can effectively extract the common mode errors (CMEs) of a regional global navigation satellite system (GNSS) observation network. In this paper, ICA is used for the weighted filtering (WICA) and the extraction of CMEs of a regional GNSS observation network with the root mean square error (RMSE) of daily solution taken as the weighting factor. Through an analysis of the observed data from 19 valid stations of the Crustal Movement Observation Network of China (CMONOC) in North China, it is shown that the coordinate series precision of 13, 16 and 12 stations in the N, E and U directions, respectively, after filtering by WICA is higher than that by the traditional ICA method. The average correlation coefficient of the coordinate time series for each station after filtering is obviously decreased. Two simulation experiments are designed to extract known CMEs. It is shown that CMEs can be recovered better by WICA and that the standard deviations of most stations after filtering are smaller than those by ICA. The results from the real data and simulation experiments suggest that the RMSE of coordinate series be considered in spatio-temporal filtering. [ABSTRACT FROM AUTHOR]

The Zhuhai Formation of the Huizhou Oilfield has been interpreted as being deposited in a tide-dominated estuarine setting. This paper uses stratigraphic principles, cores and logging data to analyse the facies changes of tidal sedimentation in response to the base level cycle. We conclude that the mud flat progradational allocycle is the result of a long-term base level fall, while the fining-upwards autocycle is in response to a short-term base level fall. Region-wide mud layers of supratidal zones and scour surfaces of subtidal zones are stratigraphic surfaces of tidal sedimentation. By integrating high-resolution stratigraphic correlations with seismic attributes extracted from the stratal slicing of 3-D data volumes, we use an attribute map of RMS (root mean square) amplitude to image the distribution and evolution of tidal deposits in the study area. Intertidal zones and the upper parts of the tidal cycle are favourable areas for prospecting and oil reserves and thus provide a basis for petroleum exploration and development. [ABSTRACT FROM AUTHOR]

Expo 2010 Shanghai China was a successful, splendid, and unforgettable event, leaving us with valuable experiences. The visitor flow pattern of the Expo is investigated in this paper. The Hurst exponent, the mean value, and the standard deviation of visitor volume indicate that the visitor flow is fractal with long-term stability and correlation as well as obvious fluctuation in a short period. Then the time series of visitor volume is converted into a complex network by using the visibility algorithm. It can be inferred from the topological properties of the visibility graph that the network is scale-free, small-world, and hierarchically constructed, confirming that the time series are fractal and a close relationship exists among the visitor volumes on different days. Furthermore, it is inevitable that will be some extreme visitor volumes in the original visitor flow, and these extreme points may appear in a group to a great extent. All these properties are closely related to the feature of the complex network. Finally, the revised linear regression is performed to forecast the next-day visitor volume based on the previous 10-day data. [ABSTRACT FROM AUTHOR]

In this paper, a number of ports in Hong Kong and mainland China are grouped and the concentration ratio and Herfindahl-Hirschman index of the container throughputs are calculated to analyze the changes in the port and shipping industry in Hong Kong. By tracing the trajectory of the barycenter of the standard deviation ellipse of the annual container throughputs, we can analyze whether port transportation is shifting away from Hong Kong towards mainland China. The impacts of underlying policies of mainland China and Hong Kong on the port and shipping industry in Hong Kong are also revealed. The analyses show that the port and shipping industry in Hong Kong has experienced three development stages: rapid growth from the 1970s-1990s, solid growth from 2000 to 2005, and sluggish growth from 2006 to 2015. • The concentration ratio and Herfindahl-Hirschman index of the container throughputs are analyzed for a port group consisting Hong Kong port and a number of ports in mainland China. • The growth and down of the port and shipping industry in Hong Kong are described. • The impacts of underlying policies of mainland China and Hong Kong on the port and shipping industry in Hong Kong are revealed. • It is found that port transportation is shifting away from Hong Kong towards mainland China. [ABSTRACT FROM AUTHOR]

GPT2w model is commonly used to calculate the meteorological parameters at certain location, such as temperature, weighted mean temperature, pressure and vapor pressure. It is also the public empiri- cal model for tropospheric delay with the best nominal accuracy. In this paper, meteorological sounding da- ta from 2013—2015 of 86 stations in China is used, which have participated the global meteorological ex- change. Precisions of meteorological parameters from GPT2w are examined and analyzed. It turns out that the average bias (Bias) and root mean square error (RMS) of temperature are 1.31 ℃ and 3.62 ℃ , respec- tively. For weighted mean temperature, the Bias is －1.58 K and the RMS is 4.07 K. For pressure and va- por pressure, the absolute values of Bias are smaller than 1 hPa, and the RMS are 6.98 hPa and 3.04 hPa, respectively. Using the data from 2006—2015, periodic characterization of the accuracy of different latitude models are analyzed. It turns out that the RMS of temperature, weighted mean temperature, pressure and vapor pressure shows certain periodic patterns, and differs with different latitude regions. In general, GPT2w model exhibits high precision and stability within the area of China. [ABSTRACT FROM AUTHOR]

As the ecological climate deteriorates, common disasters including typhoons, earthquakes and ice disasters occur frequently in southern China. In particular, ice disasters can make severe ice coating on power line, resulting in power outage accidents. Not only do power supply disruptions cause major economic losses, but also they threaten people's lives in extremely cold conditions. Therefore, studying on the security early warning of ice coating damage to power line is significant and urgent. This paper adopts grey correlation analysis (GRA) to obtain 16 influence factors, of which the correlation degrees are over 0.74. In order to reduce the internal relevance among the impact factors, 5 common factors are extracted as the predictive model input values through factor analysis. With the input weight and hidden layer threshold optimized, a new extreme learning machine based on the adaptive whale optimization algorithm improved by chaotic sine cosine operator (CSCWOA-ELM) is established to forecast the ice coating damage to power line in southern China. To verify the accuracy and advancement of the proposed model, real data from the power repair projects under ice disaster are selected for experiments. The simulation results prove that the extraction of common factors can highly improve the prediction accuracy by approximately 27.80%. Compared with the 3 benchmark models, the CSCWOA-ELM model, of which the root mean square error (RMSE) is 0.02341 and the mean absolute percentage error (MAPE) is 1.82175%, shows a better performance in predicting the ice coating damage to power line. [ABSTRACT FROM AUTHOR]

Alfalfa plays an important role in dairy farms for economic and ecological reasons in northwest China. In order to manage the complex relations between alfalfa growth and environmental issues accurately and timely, the alfalfa simulation model has been widely studied. An alfalfa growth simulation model based on water and nitrogen factors (ALFSIM-WN) was proposed. The model was divided into 3 sub-models, including crop dynamic sub-model, water balance sub-model and nitrogen balance sub-model using modular design method. It took alfalfa in Ningxia irrigation area of Yellow River as research object and could simulate the growth dynamics of alfalfa under different water supply and variable fertilization. It also could estimate the yield of alfalfa. In order to verify the accuracy and applicability of ALFSIM-WN, experiments were carried out at grassland experiment area of Maosheng Grass Limited Company in Yinchuan (northwest China, 3855N, 10606 E). The experimental alfalfa was Medicago sativa No.7. The irrigation methods included surface irrigation and subsurface drip irrigation. A split plot was designed as 2 experimental treatments. The 1st treatment was the irrigation amount which was divided into 5 irrigation levels including the surface irrigation (1 199 mm) and the subsurface drip irrigation (525, 600, 675, 750 mm). The 2nd treatment was nitrogen application rate which included 4 nitrogen application levels (0, 60, 120, 180 kg/hm2). A total of 17 treatments were designed in experiment. Different irrigation levels were carried out in batches according to different cuts and different growth period of alfalfa. Different nitrogen application levels were carried out in different cuts. Through 2 years experiments (2016-2017), model parameters had been determined using data from 2016, and growth parameters of alfalfa (such as growth period, leaf area index, soil moisture dynamics and yield) had been predicted based on gathering meteorological date, soil data and field management data of 2017. The comparison between simulated and observed was taken in this paper. The results showed that the simulated values by the model was in agreement with the trend of the observed values under different irrigation and nitrogen treatments, such as growth period, leaf area index, soil water content and yield of the alfalfa. Alfalfa in Ningxia irrigation area of Yellow River could harvest 3 to 4 times per year, the mean relative error of the growth period of alfalfa in 4 cuts simulated by the model was between 1.9% and 5.7%, which was consistent with the local growth period for forage. In addition, through analyzing the 4 cuts data in 2017, the mean relative error of leaf area index simulated by the model was between 2.3% and 17.6%, the mean relative error of soil water content simulated by the model was between 2.3% and 17.6%, and the mean relative error of yield simulated by the model was between 1.7% and 16.2%. The root mean square error of the leaf area index was between 0.09 and 0.44, the root mean square error of soil water content is between 0.009 and 0.039 cm3/cm3, and the root mean square error of yield was between 0.3 and 2.3 t/hm2. Therefore, the ALFSIM-WN had higher simulation accuracy in simulated growth period, leaf area index, soil water content and yield, indicating that the model has good application in Ningxia irrigation area of Yellow River and can be used as an effective simulation tool for alfalfa growth in forage planting. [ABSTRACT FROM AUTHOR]

Abstract Air temperature is an important parameter affecting biosphere processes, air quality, regional climate, and human health, but is difficult to estimate, especially on a nationwide scale. Remote sensing provides high spatial resolution data that influence air temperature and can help overcome this problem in spatially heterogeneous environments. Generally, the simple linear relationships between air temperature, land surface temperature (LST), and auxiliary data, seldom consider the complex spatial variations in air temperature. The random forest (RF) regression model can characterize the complex temperature distribution by establishing the non-linear relationships among them. The main purpose of this paper is to present an RF regression method to estimate monthly average temperatures in China, using MODIS LST, normalized differential vegetation index (NDVI), night-time light, and a digital elevation model (DEM). Results based on cross-validation show that mean absolute error (MAE) and root mean square error (RMSE) at national level in different months ranged from 1.15 to 1.44 °C (mean ± standard deviation: 1.30 ± 0.10 °C) and from 1.57 to 1.99 °C (1.79 ± 0.13 °C), respectively. Potential sources of error and uncertainty were analyzed and investigated. Results show that regression model performs better in eastern (MAE: 0.68 ± 0.79 °C; RMSE: 0.75 ± 0.81 °C) and central China (MAE: 0.68 ± 0.41 °C; RMSE: 0.74 ± 0.41 °C). The model performance was strongly dependent on elevation, slope, land cover, and satellite data quality. In addition, the percentage increase in mean squared error from RF regression model was used to quantify the importance of variables. Positive importance scores indicated an improvement in accuracy with the assistance of NDVI, night-time lights, and the DEM. LST, and more auxiliary data sources, when integrated into an RF regression model, may be ways in which to improve the modeling of air temperature variations in the future. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of four-factor asset pricing model using Hong Kong stock returns. Our four-factor model is constructed by adding a momentum factor into the Fama and French’s (J Finance Econ 33(1):3–56, ) three-factor model. We find that the four-factor model may explain return variation using Hong Kong data. Our results show evidence that all the four factors are significant in the model and intercepts are not significant. In addition, the reasonably high values of adjusted R2 and the insignificance of an additional explanatory variable of residual standard deviation provide supportive evidence to the model. The robustness of the model is also checked for two effects: up- and down-market conditions and seasonal behavior. [ABSTRACT FROM AUTHOR]

The total suspended solid (TSS) concentration (mg/L) is an important parameter of water quality in coastal waters. It is of great significance to monitor the spatiotemporal distribution and variation of TSS as well as its influencing factors. In this study, a quantitative retrieval model of TSS in Jiaozhou Bay (JZB) was established based on Landsat images from 1984 to 2020 (coefficient of determination (R2) = 0.77, root mean square error (RMSE) = 1.82 mg/L). In this paper, first, the long-term spatiotemporal variation of TSSs in JZB is revealed and, next, its influencing factors are further analyzed. The results show that the annual average TSSs in JZB reached their highest level in 1993 and their lowest level in 2016, showing a decreasing trend during the past decades. The TSSs were high in spring and winter and low in summer and autumn. The spatial distribution of the TSSs in JZB was similar at different timepoints, i.e., high in the northwest and gradually decreasing to the southeast. Tidal elevation exerted a significant influence on the daily variation of TSSs, and wind speed had a significant influence on the seasonal variation of TSSs. The Dagu River's discharge only affected the TSSs at the river mouth. Tidal elevation, river discharge, and wind speed were major influence factors for TSSs' variation in JZB. The results showed that the empirical model based on Landsat satellite data could be used to effectively monitor the long-term variation of TSSs in JZB. [ABSTRACT FROM AUTHOR]

Digital Surface Model (DSM) derived from high resolution satellite imagery is important for various applications. GFDM is China's first civil optical remote sensing satellite with multiple agile imaging modes and sub-meter resolution. Its panchromatic resolution is 0.5 m and 1.68 m for multi-spectral images. Compared with the onboard stereo viewing instruments (0.8 m for forward image, 0.65 m for back image, and 2.6 m for back multi-spectrum images) of GF-7, a mapping satellite of China in the same period, their accuracy is very similar. However, the accuracy of GFDM DSM has not yet been verified or fully characterized, and the detailed difference between the two has not yet been assessed either. This paper evaluates the DSM accuracy generated by GFDM and GF-7 satellite imagery using high-precision reference DSM and the observations of Ground Control Points (GCPs) as the reference data. A method to evaluate the DSM accuracy based on regional DSM errors and GCPs errors is proposed. Through the analysis of DSM subtraction, profile lines, strips detection and residuals coupling differences, the differences of DSM overall accuracy, vertical accuracy, horizontal accuracy and the strips errors between GFDM DSM and GF-7 DSM are evaluated. The results show that the overall accuracy of both is close while the vertical accuracy is slightly different. When regional DSM is used as the benchmark, the GFDM DSM has a slight advantage in elevation accuracy, but there are some regular fluctuation strips with small amplitude. When GCPs are used as the reference, the elevation Root Mean Square Error (RMSE) of GFDM DSM is about 0.94 m, and that of GF-7 is 0.67 m. GF-7 DSM is more accurate, but both of the errors are within 1 m. The DSM image residuals of the GF-7 are within 0.5 pixel, while the residuals of GFDM are relatively large, reaching 0.8 pixel. [ABSTRACT FROM AUTHOR]

• The cause of 1-2m shortwave irregularity in bi-block ballastless track construction has been discovered. • A vehicle-track coupled dynamic model considering 1–2 m shortwave irregularities was established and validated. • The dynamic effects of different amount of 1–2 m shortwave irregularities under different running speed were evaluated. • Control standards for 1–2 m short wavelength irregularities are proposed under different running speed conditions. Compared with slab ballastless track, bi-block ballastless track has the advantages of simpler constructed components and better economy, and has been used in high-speed railways for nearly 20,000 km in China. During the construction, the position deviation between sleepers and the disturbance of sleeper position caused by track bed concrete pouring can easily lead to shortwave irregularity with wavelength of 1 ∼ 2 m of bi-block ballastless track. Massive detection data on several high-speed railway lines showed that the standard deviation of vertical wheel-rail contact forces on bi-block ballastless track was about 58% higher than that on slab ballastless track. In this paper, a vehicle-track coupled dynamic analysis model was established, which can consider 1 ∼ 2 m shortwave irregularities of the track. Under the current construction and operation control standards, the dynamic responses of vehicles and tracks under different running speeds were studied. The results showed that vertical shortwave irregularity mainly affected the dynamic response of the wheel and rail, and rarely affected the dynamic response in other components. Compared with no shortwave irregularity, when the track had shortwave irregularity with σ v = 0.8 mm, the peak value of the vertical acceleration of wheelset increased by 4.1 times, the standard deviation of wheel-rail contact force increased by 3.9 times, the vertical acceleration of frame increased by 1.7 times, and the vertical acceleration of track bed increased by 1.8 times, while the vibration acceleration of car body had almost no effect. When the track had shortwave irregularity with σ h = 0.8 mm, the lateral acceleration of wheelset increased by 2.3 times. When the track had shortwave irregularity with σ v = 0.8 mm and the vehicle speed increased to 450 km·h−1, the wheel load reduction rate reached 0.85, which exceeded the safety limit. Consider the results of this study, following suggestions can be obtained:(1) the shortwave irregularity with wavelength of 1 ∼ 2 m can be measured indirectly by the standard deviation of wheel-rail vertical contact force or wheelset vibration acceleration. (2) To control the wheel load reduction rate within 0.6, the standard deviation of random vertical irregularity should be controlled within 0.6 mm when running speed is 350 km/h, and within 0.4 mm when running speed is 400 km/h or above. [ABSTRACT FROM AUTHOR]

• A PV/wind power prediction model based on Time2Vec, WDCNN and BiLSTM is proposed. • WDCNN model is adopted to expand receptive field and improve feature extraction. • Case study validates the effectiveness and superiority of the proposed method. • The proposed model shows significance in processing data with strong volatility. Accurate prediction of photovoltaic (PV)/wind power is an effective solution for the grid stability, reasonable dispatching and power supply reliability. Nowadays, various deep learning prediction methods are developed, most of which are recurrent neural network (RNN)-based and convolutional neural network (CNN)-based. To further improve the prediction performance, a novel hybrid method based on deep CNN with wide first-layer kernels (WDCNN) and bidirection long short-term (BiLSTM) is presented in this paper, in which WDCNN is introduced for large receptive field and useful information extraction, and stacked BiLSTM layers is incorporated to extract temporal correlations of past and future datasets. Besides, Time2Vec is adopted for better feature extraction through decomposing the time series data into non-periodic and periodic components. Several ablation and comparison experiments are carried out with a case study in Yongxing Island, China, and the performance metrics including normalized mean absolute error (NMAE), normalized mean square error (NMSE), normalized root mean square error (NRMSE), and mean absolute scaled error (MASE) confirm the effectiveness and superiority of the proposed model. Compared with individual WDCNN and BiLSTM, the performance of the combined WDCNN-BiLSTM are improved by 5.79%, 6.61%, 3.36%, 5.79% and 2.03%, 3.54%, 1.78%, 2.03% for PV prediction, and 20.38%, 29.37%, 15.96%, 20.38% and 39.12%, 59.68%, 36.50%, 39.12% for wind prediction, respectively. The adoption of Time2Vec further improves the prediction performance by 5.96%, 8.10%, 4.14%, 5.96% for PV prediction, and 4.86%, 9.19%, 4.70%, 4.86% for wind prediction, respectively. The proposed model yields most accurate prediction compared with other competing models. The replacement of CNN with WDCNN improves the prediction accuracy by 6.64%, 8.47%, 4.33%, 6.64% for PV prediction, and 30.59%, 42.78%, 24.35%, 30.59% for wind prediction, respectively. Moreover, the proposed model significantly outperforms compared models in prediction with more random fluctuations, which demonstrates the superiority of the proposed model in mining complex relationships. [ABSTRACT FROM AUTHOR]

Background: Online learning presents unique challenges for students, such as reduced social support and increased distractions. Understanding the psychological factors that contribute to educational attainment in online classes is therefore important. Purpose: This study aimed to investigate the structural relations among the psychological factors: academic resilience, personality, academic well-being, and educational attainment in online classes using the Tencent Meeting application in China. The study also explored the mediating role of student engagement in the relationship between the variables. Methodology: This study used structural equation modeling (SEM) to investigate the relationships among the variables of the study. The participants were 384 undergraduate, graduate, and postgraduate students from Henan Polytechnic University in China. The participants completed self-report surveys of academic resilience, academic well-being, educational attainment, student engagement and personality types. Data analysis: The data were analyzed using structural equation modeling (SEM) to examine the relationships among variables. The goodness of fit of the SEM was assessed using several fit indices, including the chi-square test, the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA). The study also conducted mediation analyses to explore the potential mediating roles of learner enjoyment in the relationships between psychological factors and educational attainment. Findings: The results of the study showed that all variables of the study were positively related to educational attainment. The findings suggest that promoting academic resilience, academic well-being, and student engagement may be effective strategies for enhancing educational attainment in online classes using the Tencent Meeting application in China. Conclusions: Using the Tencent Meeting application in China, this study provides insights into the complex interplay among several psychological factors and educational attainment in online classes. The findings highlight the importance of promoting academic resilience, personality, academic well-being, and student engagement to enhance educational attainment. [ABSTRACT FROM AUTHOR]

Potato is one of the key food crops and China is the largest potato producer in the world. However water scarcity is the major constraint to increase the productivity of potato in the arid regions such as Ningxia in northwest China where this crop is extensively cultivated. The overall objective of this study was to optimize the irrigation for potato cultivated under the drip irrigation. To do this, the AquaCrop model was calibrated and validated using the data obtained from two years of field experiment. Then, the calibrated crop model was used to simulate growth and tuber yield of potato in response to 30 different irrigation schemes under two different irrigation scenarios. The crop model evaluation parameters namely, the root mean square error (RMSE), the index of agreement (d), the normalized root mean square error (NRMSE) and the coefficient of determination (R²) showed that the AquaCrop model could simulate the growth and yield of potato under the drip irrigation with different irrigation treatments with reasonable accuracy. Furthermore, yield of potato has increased with increasing amount of total irrigation under drip irrigation; however, yield begins to decline when the amount of total irrigation exceeds 2500 m³ ha-1. The study also found that the optimum irrigation schedule for potato was 20 mm of irrigation quota at 7 days of irrigation cycle (i.e., 1800 m³ ha-1 or 180 mm of total irrigation). The above irrigation scheduling has achieved 46.77 t ha-1 of tuber yield with 15.74 kg m-3 of water use efficiency. These findings may be evaluated in potato cultivation across different climate and soil conditions for wide applicability at different arid regions of the world. [ABSTRACT FROM AUTHOR]

Riverine dissolved inorganic nitrogen (DIN), when elevated by human activities (e.g., land-use change), can accelerate the nitrogen cycle and downstream dispersal. However, estimating DIN export coefficients for individual land-use types can be complex due to mosaic land-use patterns and interactions between fertilizers and hydrological processes. We propose a framework that integrates an empirical model, a moving-window method, and an elasticity method to quantify seasonal DIN export coefficients for each land use in the Shixi Creek catchment, southeast China. Our model showed good agreement with field observations according to root mean square error and a normalized objective function. The export coefficients of farmland and forest were the highest (9.16 mg L−1) and lowest (2.91 mg L−1) ones, resulting in annual DIN exports, respectively, for farmland and forests of 1951 kg km−2 yr−1 and 619 kg km−2 yr−1, respectively. Urbanization was a dominant factor influencing DIN export; the export coefficient of built-up areas showed the highest elasticity and highest uncertainty, with abrupt fluctuations from dry to wet years. Our framework revealed the complex role of built-up areas in nitrogen export. Our results can shed light on how to improve riverine N management in a catchment by considering the interactive effects of climate and land use. [ABSTRACT FROM AUTHOR]

Wheat is an important food crop in China. The quality of wheat affects the development of the agricultural economy. However, the high-quality wheat produced in China cannot meet the demand, so it would be an important direction for research to develop high-quality wheat. Grain protein content (GPC) is an important criterion for the quality of winter wheat and its content directly affects the quality of wheat. Studying the spatial heterogeneity of wheat grain proteins is beneficial to the prediction of wheat quality, and it plays a guiding role in the identification, grading, and processing of wheat quality. Due to the complexity and variability of wheat quality, conventional evaluation methods have shortcomings such as low accuracy and poor applicability. To better predict the GPC, geographically weighted regression (GWR) models, multiple linear regression, random forest (RF), BP neural networks, support vector machine, and long-and-short-term memory algorithms were used to analyze the meteorological data and soil data of Jiangsu Province from March to May in 2019–2022. It was found that the winter wheat GPC rises by 0.17% with every 0.1° increase in north latitude at the county level in Jiangsu. Comparison of the prediction accuracy of the coefficient of determination, mean deviation error, root mean square error, and mean absolute error by analyzing multiple algorithms showed that the GWR model was the most accurate, followed by the RF model. The regression coefficient of precipitation in April showed the smallest range of variation among all factors, indicating that precipitation in April had a more stable effect on GPC in the study area than the other meteorological factors. Therefore, consideration of spatial information might be beneficial in predicting county-level winter wheat GPC. GWR models based on meteorological and soil factors enrich the studies regarding the prediction of wheat GPC based on environmental data. It might be applied to predict winter wheat GPC and improve wheat quality to better guide large-scale production and processing. [ABSTRACT FROM AUTHOR]

China's first dynamic environment satellite constellation includes the HY-2B, HY-2C, and HY-2D satellites. In this study, the along track SLA, SWH, and SSWS of this satellite constellation were evaluated. SLA parameters are evaluated using self-crossing and dual-crossing methods. The SSWS and SWH data were evaluated by comparing with NDBC buoy and other available satellites' data. The evaluation revealed that the standard deviation of the SLA from the HY-2B/C/D satellites' single mission crossovers was 3.29 cm, 3.51 cm, and 3.72 cm, respectively. In addition, at the dual-crossovers of the Jason-3 satellite and the HY-2B satellite, the HY-2B satellite, and the HY-2C/D satellites, the standard deviation was determined to be 3.40 cm, 3.48 cm, and 4.25 cm, respectively. The accuracy of the SWH products of the HY-2B/C/D satellite radar altimeters was observed to be 0.23 m, 0.25 m, and 0.26 m, respectively. The accuracy of the SSWS data of the HY-2B/C/D satellite radar altimeters was observed to be 1.48 m/s, 1.59 m/s, and 1.35 m/s, respectively. In addition, this study also analyzed and compared the observation efficiency of the dynamic environment satellite constellation with the following six satellites: Sentinel-3(A, B), Jason-3, Sentinel-6A, Saral, and Cryosat-2. Observation efficiency refers to selection of any point on the globe to find a minimum radius of at least one observation point within a circle in a 14-day period. The analysis results demonstrated that observation efficiency of China's first dynamic environment satellite constellation was comparable to that of the six satellites. [ABSTRACT FROM AUTHOR]

Coal-fired power plants, as major anthropogenic CO2 emission sources, constitute one of the largest contributors to global greenhouse gas emissions. Accurately calculating the dispersion process of CO2 emissions from these point sources is crucial, as it will aid in quantifying CO2 emissions using remote sensing measurements. Employing the Lagrangian Particle Dispersion Theory Model (LPDTM), our study involves modeling CO2 diffusion from point sources. Firstly, we incorporated high-resolution DEM (Digital Elevation Model) and artificial building elements obtained through the Adaptive Deep Learning Location Matching Method, which is involved in CO2 simulation. The accuracy of the results was verified using meteorological stations and aircraft measurements. Additionally, we quantitatively analyzed the influence of terrain and artificial building characteristics on high spatial resolution atmospheric CO2 diffusion simulations, revealing the significance of surface characteristics in dispersion modeling. To validate the accuracy of the LPDTM in high-resolution CO2 diffusion simulation, a comparative experiment was conducted at a power plant in Yangzhou, Jiangsu Province, China. The simulated result was compared with observation from aerial flights, yielding the R2 (Correlation Coefficient) of 0.76, the RMSE (Root Mean Square Error) of 0.267 ppm, and the MAE (Mean Absolute Error) of 0.2315 ppm for the comparison of 73 pixels where the plume intersected with flight trajectories. The findings demonstrate a high level of consistency between the modeled CO2 point source plume morphology and concentration quantification and the actual observed outcomes. This study carried out a quantitative assessment of the influence of surface features on high-resolution atmospheric CO2 point source diffusion simulations, resulting in an enhanced accuracy of the simulated CO2 concentration field. It offers essential technological and theoretical foundations for the accurate quantification of anthropogenic CO2 emissions using top-down approaches. [ABSTRACT FROM AUTHOR]

Current hydrological models still face difficulties in accurately simulating vadose zone (VZ) hydrological processes in arid watersheds with shallow groundwater environments. Therefore, this article introduces a new mechanistic VZ module coupled with an improved groundwater‐balance (GWB) module to enhance the capabilities of the well‐known Soil Water Assessment Tools (SWAT) model. The main improvements and features of the modified SWAT model (M‐SWAT) include: a numerical VZ module to accurately simulate soil water‐salt transport under variably saturated conditions by solving the head‐formed Richards' equation and advection‐dispersion equation; a GWB module (on a sub‐basin scale for shallow aquifer) coupled with VZ module by proposing an effective way of boundary information exchanges; and a more reasonable water‐salt stress functions (i.e., based on matric head and osmotic head) to calculate root water uptake and plant growth. The M‐SWAT was then tested and evaluated with two‐year observation data from the Yangchang canal command area (YCA, an experimental site) and the Jiyuan Irrigation System (Jiyuan, region‐scale), located in the arid upper Yellow River basin of northwest China. The model produced a good agreement between the simulated and observed data in both calibration and validation with sufficiently small root mean square error (RMSE). The determination coefficient (R2) was greater than 0.51, 0.36, and 0.87 in simulating the soil water‐salt dynamics, groundwater depth fluctuations, and leaf area index development, respectively. Case testing also proved that the M‐SWAT had sufficient computational efficiency and stability for regional application. Further comparisons with previous studies indicated that the M‐SWAT could more accurately and rationally simulate the soil water‐salt dynamics and their interactions in arid watersheds with shallow groundwater tables. [ABSTRACT FROM AUTHOR]

Currently, satellite-based soil moisture (SM) products and land surface model assimilation techniques are widely utilized. However, the presence of systematic errors in the observation process, algorithmic discrepancies between products, and variations in spatial and temporal scales result in diverse accuracy characteristics and applicability. This study evaluates three prominent SM products in China, namely, the Essential Climate Variable Soil Moisture (ECV), the European Centre for Medium-Range Weather Forecasts' Fifth-Generation Land Surface Reanalysis Data (ERA5-Land), and the Global Land Surface Data Assimilation System (GLDAS). The evaluation was conducted using extended triple collocation (ETC) analysis and in situ validation methods at a monthly scale from 2000 to 2020. The ETC analysis results show that among the three products, GLDAS exhibits the highest correlation coefficient (CC) and the lowest standard deviation of error (ESD), indicating its superior performance in China. ECV and ERA5-Land follow, with slightly lower performance. In the in situ validation results, ERA5-Land displays the highest correlation, capturing the temporal trend of the ground SM well. Comparatively, in terms of overall accuracy, ECV performs the best, with a slightly smaller mean error (ME) and root mean square error (RMSE) than GLDAS, and ERA5-Land has the lowest accuracy. The discrepancy between the in situ validation results and ETC analysis can be attributed to the limited number of sites and their representativeness errors. Notably, ERA5-Land exhibits a highly consistent trend of interannual fluctuations between ESD and precipitation. Furthermore, a strong association is observed between the ME and RMSE of ECV and GLDAS and precipitation. These findings serve as valuable references for future SM studies in China. [ABSTRACT FROM AUTHOR]

Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Existing public yield datasets have a coarse spatial resolution, spanning from 1 to 43 km. Although these datasets are useful for analyzing large-scale temporal and spatial change in yield, they cannot deal with small-scale spatial heterogeneity, which happens to be the most significant characteristic of the Chinese farmers' economy. Hence, we generated a 30 m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter-wheat-producing provinces in China for the period 2016–2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel 2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated using in situ measurements and census statistics and indicated a stable performance of the HLM based on calibration datasets across China, with a correlation coefficient (r) of 0.81 and a relative root mean square error (rRMSE) of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and statistical data (p<0.01), indicated by an r (rRMSE) of 0.72** (15.34 %) and 0.69** (19.16 %). The ChinaWheatYield30m is a sophisticated dataset with both high spatial resolution and excellent accuracy; such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling and regional climate evaluation. The ChinaWheatYield30m dataset generated from this study can be downloaded from 10.5281/zenodo.7360753 (Zhao et al., 2022b). [ABSTRACT FROM AUTHOR]

To achieve high-quality development, it is crucial to encourage the coordinated development of new infrastructure (NI) and regional sustainability (RS). Based on the coupling mechanism of NI and RS, this study develops a comprehensive evaluation index system, coupling coordination degree model (CCDM), spatial autocorrelation model, and standard deviation ellipse model used to estimate the coupling coordination degree (CCD) of NI-RS in China mainland between 2013 and 2020 and analyze the temporal and spatial evolution characteristic. The results showed the following: (1) The NI index fluctuated and increased, while the RS index increased year by year, showing a gradient-decreasing distribution pattern from east to west; (2) CCD increases year by year, but the spatial distribution is unbalanced, and CCD in eastern coastal areas is significantly higher than that in western areas. (3) The CCD of NI and RS showed a significant spatial positive correlation, showing that the high-value cluster in the east and the low-value cluster in the west. (4) The standard deviation ellipse of the national CCD showed a northeast-southwest distribution, the center of gravity migrated to the southwest, and the spatial clustering characteristics were significant. (5) Finally, the GM (1,1) model indicates a steady increase in CCD from 2021 to 2025. Based on the location distribution and evolution of the CCD between NI and RS in China, corresponding policy suggestions are put forward. [ABSTRACT FROM AUTHOR]

Surface fine particulate matter (PM) with a diameter of less than 2.5 microns (PM2.5) negatively impacts human health and the economy. However, due to data and model limitations, obtaining high-quality, high-spatial-resolution surface PM2.5 concentration data is a challenging task, and it is difficult to accurately assess the temporal and spatial changes in PM2.5 levels at a small regional scale. Here, we combined multi-angle implementation of atmospheric correction (MAIAC) aerosol products, ERA5 reanalysis data, etc., to construct an STW-LightGBM model that considers the spatiotemporal characteristics of air pollution and estimate the PM2.5 concentration of China's surface at 1 km resolution from 2015 to 2020. Our model performed well, and the fitting accuracy of the 10-fold cross-validation between years was 0.877–0.917. The fitting accuracy of the model was >0.85 at different time scales (month, season, and year). The average slope of the regression prediction was 0.9 annually. The results showed that PM2.5 pollution improved from 2015 to 2020. The average PM2.5 concentration decreased by 4.55 μg/m3, and the maximum decrease reached 90.51 μg/m3. The areas with high PM2.5 concentrations were predominantly in the North China Plain, Sichuan Basin, and Xinjiang in the west, and the levels in areas with elevated PM2.5 levels were consistent across most study years. The standard deviation ellipse for PM2.5 in China showed a 'northeast–southwest' spatial distribution. From an interannual perspective, the average values of the four seasonal stations in the country showed a downward trend from 2015 to 2020, with the most obvious decline in winter, from 70.67 μg/m3 in 2015 to 46.75 μg/m3 in 2020. Compared to earlier inversion studies, this work provides a more stable and accurate method for obtaining high-resolution PM2.5 data, which is necessary for local air governance and environmental ecological construction at a fine scale. [ABSTRACT FROM AUTHOR]

Gas–particle (G–P) partitioning is a crucial atmospheric process for semi-volatile organic compounds (SVOCs), particularly polycyclic aromatic hydrocarbons (PAHs). However, accurately predicting the G–P partitioning of PAHs has remained a challenge. In this study, we established a new steady-state G–P partitioning model based on the level-III multimedia fugacity model, with a particular focus on the particulate proportion (ϕ0) of PAHs in emissions. Similar to previous steady-state models, our new model divided the G–P partitioning behavior into three domains based on the threshold values of log⁡KOA (octanol–air partitioning coefficient), with slopes of 1, from 1 to 0, and 0 for the three domains. However, our model differed significantly from previous models in different domains. We found that deviations from the equilibrium-state G–P partitioning models were caused by both gaseous interference and particulate interference, with ϕ0 determining the influence of this interference. Different forms of the new steady-state model were observed under different values of ϕ0 , highlighting its significant impact on the G–P partitioning of PAHs. Comparison of the G–P partitioning of PAHs between the prediction results of our new steady-state model and monitored results from 11 cities in China suggested varying prediction performances under different values of ϕ0 , with the lowest root mean square error observed when ϕ0 was set to 0.9 or 0.99. The results indicated that the ϕ0 was a crucial factor for the G–P partitioning of PAHs. Furthermore, our new steady-state model also demonstrated excellent performance in predicting the G–P partitioning of PAHs with entirely gaseous emission and polybrominated diphenyl ethers with entirely particulate emission. Therefore, we concluded that the ϕ0 should be considered in the study of G–P partitioning of PAHs, which also provided a new insight into other SVOCs. [ABSTRACT FROM AUTHOR]