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The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate models, hydrological models, and regional snow disaster monitoring. Benefitting from its unique advantages of cost-effective and high spatiotemporal resolution (∼ 1000 m 2 , hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are (1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and control its quality automatically; and (2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h or 24 h, 2013–2022) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS constellations (mean r=0.98, RMSD = 0.99 cm, and nRMSD (snow depth > 5 cm) = 0.11), different frequency bands (mean r = 0.97, RMSD = 1.46 cm, and nRMSD (snow depth > 5 cm) = 0.16), and different GNSS receivers (mean r = 0.62). The data set also has high external consistency with the in situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. Additionally, the result show the potential of GNSS to derive hourly snow depth observations for better monitoring of snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and data files will be maintained and updated as more data become available in the future. The GSnow-CHINA v1.0 data set is available at the National Tibetan Plateau/Third Pole Environment Data Center via 10.11888/Cryos.tpdc.271839 (Wan et al., 2021). [ABSTRACT FROM AUTHOR]

The black-land in Northeast China is one of three black-lands in the world and has become the largest grain producing area and commercial grain base of China. Therefore, black-land soil erosion caused by natural factors and human activities has attracted much attention. The Revised Universal Soil Loss Equation (RUSLE) developed by the Agriculture Research Service of the United States Department of Agriculture provides a comprehensive framework for assessing soil erosion and has been widely applied all over the world. Compared with factors such as rainfall erosivity, soil erodibility, topography, and conservation practice, the vegetation coverage and management factor (C -factor in RUSLE) of cropland encompasses the most easily optimized measures. However, in some studies, the quantification of C -factor only considers vegetation cover and ignores other farmland management measures due to the limitation of field management information at regional scale, which implies a huge room for improvement in quantification of C -factor. In this study, the quantification of C -factor considered not only vegetation cover but also crop residue cover, agricultural machinery total power, and fertilizer application rate. Among them, the former is related to weakening soil erosivity, and the latter two are related to enhancing soil erosivity. Monitored sediment transport modulus (STM) at the sub-watershed scale was used to compare changes in the estimation accuracy of the soil erosion modulus (SEM) before and after improving the C -factor. The results showed that the improved cropland C -factor in RUSLE produced a better linear fit accuracy between SEM and STM, with an average increase of 0.115 for R 2. Moreover, the order of SEM from high to low in different years was: 12.01 t·ha−1·y−1 (2019), 11.43 t·ha−1·yr−1 (2005), 11.17 t·ha−1·yr−1 (2010), 11.01 t·ha−1·yr−1 (2015), and 10.30 t·ha−1·yr−1 (2000), which was positively correlated with interannual variation of precipitation in Northeast China. Spatially, the order of multi-year average SEM in agricultural zones from high to low was as follows: 22.53 t·ha−1·yr−1 (Liaoning Plain and Hilly Zone; LPH), 20.44 t·ha−1·yr−1 (Baekdu Mountain Zone; BM), 15.11 t·ha−1·yr−1 (Western Liao River Zone; WLR), 11.36 t·ha−1·yr−1 (Lesser Khingan Mountain Zone; LKM), 7.56 t·ha−1·yr−1 (Greater Khingan Mountain Zone; GKM), 3.41 t·ha−1·yr−1 (Sanjiang Plain Zone; SJP), 3.14 t·ha−1·yr−1 (Songnen Plain Zone; SNP), and 2.06 t·ha−1·yr−1 (Hulunbuir Grassland Zone; HG), whereas the soil loss amount from high to low in various agricultural zones was in the order of BM > LPH > WLR > GKM > LKM > SNP > SJP > HG. Our study verified the feasibility of improving the C -factor of croplands in areas where cropland is the dominant land-use type. Moreover, our method will contribute to the use of RUSLE with higher precision in other regional-scale soil erosion assessments worldwide. • Soil erosion in Northeast China from 2000 to 2019 was evaluated. • CRC from remote sensing acted as a preventing soil erosion indicator to improve cropland C -factor. • AMTP and FAR acted as intensifying soil erosion indicators to improve cropland C -factor. • Estimated soil erosion modulus was validated via monitored sediment transport modulus. [ABSTRACT FROM AUTHOR]

The global navigation satellite system reflectometry (GNSS-R) technique has been proven to be a powerful tool for retrieving geophysical parameters of ocean and land/hydrology processes. The ultimate goal for such GNSS-R applications is to achieve large-scale, all-weather, and full-time mapping using spaceborne platforms. In order to ensure both GNSS-R receiver and algorithm meet the requirements of spaceborne observations, airborne experimental campaigns need to be first carried out for early testing and validation purposes. This paper presents a first comprehensive overview of China's airborne GNSS-R campaign conducted on May 30, 2014. There were two objectives for this campaign: (1) to examine the capability of the GNSS-R receiver developed by the National Space Science Center, Chinese Academy of Sciences, for airborne observations and (2) to study algorithms for soil moisture and altimetry retrievals. In this paper, initial results of soil moisture retrievals are presented. The left-hand circularly polarized-predominant satellite information was successfully used to retrieval soil moisture over the cropland. The right-hand circularly polarized components of the reflected signals were also received and examined. The GPS-derived soil moisture results, on the one hand, correctly represented the spatial variations of the soil moisture along the tracking of the flight; on the other hand, the results underestimated the ground-truth. Errors from the retrieval model and from the positioning and effects from the vegetation layer and from the atmospheric water vapor were the primary causes of the uncertainties in soil moisture retrievals using the airborne GNSS-R data. This airborne experimental campaign firstly investigate that China has the capability to perform airborne GNSS-R observation using the self-developed receiver, although the receiver developed by the NSSC needs to be further examined for its capability for spaceborne observation. The early findings of this study will provide illustrations for planned future airborne campaigns. [ABSTRACT FROM AUTHOR]

• Distribution of maize planting areas were retrieved in a large spatiotemporal scale. • Spatiotemporal patterns of drought stress in the major maize planting areas of China were evaluated. • NPP which characterize maize biomass can determine the thresholds of maize drought stress. • Conceptual model of maize drought stress based on the TVDI and NPP was established for the first time. Droughts are characterized by limited water resources and extreme arid climates and have a notable influence on crop yield and food security. Therefore, evaluating the crop drought stress at a large spatiotemporal scale is important. In this study, maize planting areas in the Northeast China Plain (NECP) and North China Plain (NCP) over the past 20 years (2000–2019) were identified from the moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index, comprising 8-day time-series moving windows. The temperature vegetation drought index (TVDI) was retrieved using the MODIS normalized difference vegetation index (NDVI) and land surface temperature datasets to evaluate maize drought. Furthermore, by combining MODIS NDVI with measured meteorological data, monthly net primary productivity (NPP) was retrieved based on the Carnegie–Ames–Stanford approach (CASA) to characterize the maize biomass. Thus, the weight of drought stress in different growth stages of maize was obtained by combining the maize phenological information with TVDI and NPP. Moreover, TVDI drought stress was obtained to comprehensively assess maize drought stress. Finally, we fitted TVDI drought stress with NPP to establish a conceptual model for the response of maize biomass to drought stress in the NCP and NECP at a large spatiotemporal scale for the first time. The conceptual model revealed that the phenomenon of excessive moisture during maize growth was not evident (TVDI drought stress < 0.375) and that most maize plants were still affected by drought stress (TVDI drought stress > 0.375) in the NECP. Correspondingly, in the NCP, there was hardly any excessive moisture, and maize exhibited a certain resistance to low-intensity drought stress (TVDI drought stress was approximately 0.330). However, after the drought stress exceeded the tolerance range (TVDI drought stress > 0.375), the maize biomass was significantly affected by drought stress. Our findings illustrate the two response models of drought stress in the major maize producing areas of China for the first time, thereby providing new insights for preventing drought stress risk and improving maize yield on a large scale. [ABSTRACT FROM AUTHOR]

• CO 2 and SRB were the main factors of shale gas pipeline corrosion and perforation. • Pre-cultured with culture medium increased the corrosion rate of the steel, and SRB catalytic corrosion process. • CO 2 promotes bacterial film formation and accelerates corrosion. • Bacterial enrichment is the main cause of pitting corrosion. In the shale gas field in southern Sichuan Basin, the corrosion-induced failure has been observed. In this paper, first, the corrosion phenomenon was analyzed. Second, the causes of corrosion were analyzed thoroughly from the aspects of corrosive environment, materials, and corrosion products. Finally, the field water environment was simulated, and the result showed the following: SRB is one of the main causes of corrosion and perforation of shale gas pipelines, bacterial enrichment is the primary reason for local corrosion, and CO 2 promotes the formation of bacterial film and accelerates corrosion. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we use canonical correlation analysis (CCA) method to investigate the semi-diurnal tidal winds in mesosphere and low thermosphere (MLT) region, observed by a newly installed meteor radar at Wuhan (30.6°N, 114.4°E), during the year 2002. In general, 4(3) effective semi-diurnal tidal pairs of patterns are obtained, which represent ∼2/3 total variances of the origin data set. These patterns are expected to be corresponding to the atmospheric oscillations within the semi-diurnal frequency band excited or modulated by different sources, i.e., the seasonal variations, the modulations by the planetary wave oscillations or the solar 27-day activity. Among all the patterns, the 1st pattern, which represents ∼1/3 of total variances, is the most notable. Its amplitudes show maximum values in spring and autumn, and the vertical wavelengths are longer in summer and shorter in winter, which is in line with the results obtained from traditional harmonic analysis. The vertical wavelengths of the higher order patterns (∼50km) suggest the classic semi-tidal mode S(2,4)/S(2,5) is dominant. [Copyright &y& Elsevier]

Agricultural non-point source pollution (ANPSP) is a critical cause of global environmental problems. However, the estimation of ANPSP in typical orchard-dominated areas is lacking. In this study, the site conditions and fertilizer application patterns of apple orchard-dominated landscapes in Qixia, north China, were investigated, along with the spatial distribution of land use types, which were obtained through field survey and remote sensing interpretation. Combined with rainfall runoff experiments and an analysis of soil samples and statistical yearbook data, the results revealed that the total N (P) export coefficients of apple orchards, cropland, and built-up land were 68.90 (2.79), 41.58 (1.87), and 4.19 (0.47) kg ha–1 a–1, respectively. Moreover, the areas of extremely low, low, medium, high, and extremely high risks for ANPSP in Qixia were 35,232, 56,514, 62,106, 29,331, and 18 450 ha, respectively, under the current fertilizer application rates of N 545 kg ha–1 a–1 and P 2 O 5 569 kg ha–1 a–1 in apple orchard areas. Two fertilization scenarios were simulated according to local standards; the reduction of the extremely high-risk ANPSP area reached 73.39% in scenario I and 100% in scenario II. In contrast, extremely low-risk areas increased by 14.88% (scenario I) and 218.15% (scenario II). Eventually, a correlation analysis between the source, sink, and current status of ANPSP risks and various environmental factors was conducted. The results showed that ANPSP was significantly positively correlated with the drainage density, water erosion intensity, clay content, and SOC, while it was significantly negatively correlated with the slope, altitude, and the sand content, which indicates the good performance of the source–sink landscape method. Therefore, this method at a source–sink landscape scale may facilitate the evaluation of ANPSP in areas both regionally and worldwide. • "Source–sink" landscape method was used to evaluate ANPSP. • Moderate rainfall runoff experiment and large amount of field sampling data were used to estimate ANPSP. • Spatial patterns of ANPSP in orchard-dominated area were first determined. • ANPSP spatial changes were simulated under various fertilizer application scenarios. • Environmental factors–ANPSP relationships were analyzed. [ABSTRACT FROM AUTHOR]

Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs. [ABSTRACT FROM AUTHOR]

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a new opportunity for land observation. This study is the first to compare and evaluate the performance of the only two spaceborne GNSS-R satellite missions whose data are publicly available, i.e., the UK's TechdemoSat-1 (TDS-1) and the US's Cyclone Global Navigation Satellite System (CYGNSS), for sensitivity analysis with SMAP SM on a daily basis and soil moisture (SM) estimates on a monthly basis over Mainland China. For daily sensitivity analysis, the two data were matched up and compared for the period (i.e., May 2017 through April 2018) when they coexisted (R = 0.561 vs. R = 0.613). For monthly SM estimates, a back-propagation artificial neural network (BP-ANN) was used to construct a model using data from more than two years. The model was subsequently used to derive long-term and continuous SM maps over Mainland China. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China (R = 0.676, MAE = 0.052 m3m−3, and ubRMSE = 0.060 m3m−3 for TDS-1; R = 0.798, MAE = 0.040 m3m−3, and ubRMSE = 0.062 m3m−3 for CYGNSS). The retrieved results were further validated using monthly in situ SM data from dense sites across Mainland China. It was found that the SM derived from the TDS-1/CYGNSS also correlated well with in situ SM (R = 0.687, MAE = 0.066 m3m−3, and ubRMSE = 0.056 m3m−3 for TDS-1; R = 0.724, MAE = 0.052 m3m−3, and ubRMSE = 0.053 m3m−3 for CYGNSS). The results in this study suggested that TDS-1/CYGNSS and the upcoming spaceborne GNSS-R mission could be new and powerful data sources to produce SM data set at a large scale and with relatively high precision. [ABSTRACT FROM AUTHOR]

Lakes can be effective indicators of climate change, and this is especially so for the lakes over the Qinghai–Tibet Plateau (QTP), the highest plateau in the world, which undergoes little direct human influence. The QTP has warmed at twice the mean global rate, and the lakes there respond rapidly to climate and cryosphere changes. The QTP has ∼ 1200 lakes larger than 1 km2 with a total area of ∼ 46 000 km2 , accounting for approximately half the number and area of lakes in China. The lakes over the QTP have been selected as an essential example for global lakes or water body studies. However, concerning lake data over the QTP are limited to the Landsat era and/or available at sparse intervals. Here, we extend the record to provide comprehensive lake evolution data sets covering the past 100 years (from 1920 to 2020). Lake mapping in 1920 was derived from an early map of the Republic of China and in 1960 from the topographic map of China. The densest lake inventories produced so far between 1970 and 2020 (covering all lakes larger than 1 km2 in 14 epochs) are mapped from Landsat MSS, TM, ETM + , and OLI images. The lake evolution shows remarkable transitions between four phases: significant shrinkage in 1920–1995, rapid linear increase in 1995–2010, relative stability in 2010–2015, and further increase in 2015–2020. The spatial pattern indicates that the majority of lakes shrank in 1920–1995 and expanded in 1995–2020, with a dominant enlargement for central-north lakes in contrast to contraction for southern lakes in 1976–2020. The time series of precipitation between 1920 and 2017 indirectly supports the evolution trends of lakes identified in this study. The lake data set is freely available at 10.5281/zenodo.4678104 (Zhang et al., 2021a). [ABSTRACT FROM AUTHOR]

Snow depth is an essential meteorological indicator for monitoring snow disasters. The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has been proven to be a practical approach to retrieving snow depth. This study presents a case study to explore utilizing the GNSS-IR-derived snow depth to monitor the 2022 early February snowstorm over southern China. A snow depth retrieval framework considering data quality control and specific ground surface substances was developed using 8-day data from 13 operational GNSS/Meteorology stations. The daily snow depths retrieved from different ground surfaces, i.e., dry grass, wet grass, and concrete, agreed well with the measured snow depth, with Mean Absolute Error (MAE) of 2.79 cm, 3.36 cm, and 2.53 cm, respectively. The percentage MAE when snow depths > 5 cm for the three ground surface substances was 26.8%, 53.7%, and 35.0%, respectively. The 6 h snow depth results also showed a swift and significant response to the snowfall event. This study proves the potential of GNSS-IR, used as a new operational tool in the automatic meteorological system, to monitor snow disasters over southern China, particularly as an efficient and cost-effective framework for real-time and accurate monitoring. [ABSTRACT FROM AUTHOR]

[Display omitted] • NPSP in typical, hilly mountainous areas was evaluated at annual and monthly scales. • Critical source areas were determined based on the multifactor method. • NPSP reduction and cost efficiency in different BMPs were simulated. • BMPs for the control of NPSP in typical, hilly mountainous areas were proposed. Non-point source pollution (NPSP) significantly affects regional water environments worldwide. Identifying critical source areas (CSAs) and implementing best management practices (BMPs) are essential to prevent and control NPSP. This study used the Soil and Water Assessment Tool model to simulate and evaluate the characteristics of NPSP in the Gongjiang Watershed, a typical, hilly mountainous area in Southern China. The study identified CSAs of NPSP using total nitrogen (TN), total phosphorus (TP), water quality level, and population density factors. The reduction effect of different BMPs on the NPSP load was evaluated, and the cost-effectiveness (CE) of various BMPs was evaluated for the control of NPSP in the watershed. The interannual variation in NPSP load was high from 2013 to 2018, with the largest pollution load in 2016 (wet year) and the smallest in 2018 (dry year). The pollution load fluctuated throughout the year and was related to fertilization and precipitation runoff. The spatial heterogeneity of NPSP was large, with TN and TP losses showing a spatial pattern of high in the south, low in the north, and highest in the center. The CSAs of the NPSP covered 1566.66 km2, representing 9.77 % of the total watershed area. However, TN and TP loads accounted for 19.39 % and 23.53 % of total watershed production. The order of effectiveness of individual management measures in reducing TN was stubble coverage (SC) > vegetative buffer strips (VBS) > terrace engineering (TE) > grassed waterways (GW) > 20 % fertilizer reduction (FR20) > 10 % fertilizer reduction (FR10). The order of effectiveness of TP reduction was SC > VBS > TE > FR20 > GW > FR10. Combined management measures were much more effective in reducing TN and TP than individual measures, with an average increase in TN and TP reduction of 20.59 % and 25.23 %, respectively. Based on the results of the CE evaluation of BMPs, a combination of FR20, GW, and VBS was more effective than individual measures. We suggest a combination of FR20, GW, and VBS measures for effective NPSP prevention and control in Southern China's typical, hilly mountainous areas. [ABSTRACT FROM AUTHOR]

• The Carbon sink capacity of a typical ecotypic county was precisely estimated. • STARFM was employed to estimate interannual NEP at county scale. • The NEP dataset at a 30 m grid scale from 2000 to 2020 was generated. • Spatio-temporal dynamics of NEP in ecotype county were grasped. Net ecosystem productivity (NEP) serves as a pivotal metric for quantitatively elucidating the carbon sink function of terrestrial ecosystems. As a prototype county for the development of an ecological civilization in China, the quantitative estimation of the ecotypic county's ecosystem carbon sink capacity holds immense significance in comprehending the carbon cycle and facilitating the sustainable advancement of regional ecosystems. This study undertook the estimation of NEP in Wuning County from 2000 to 2020, employing a fusion of multi-source remote sensing data, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the improved Carnegie-Ames-Stanford Approach model, and the soil respiration model. Furthermore, we delved into the differences in NEP across various types of land cover. In addition, we employed the Theil-Sen Median trend analysis and Mann-Kendall test to discern the spatio-temporal trends of NEP. The findings indicated the following: (1) The downscaled NDVI generated by STARFM exhibited a remarkable consistency with Landsat NDVI overall (R 2 > 0.95, P < 0.01, 0 < RMSE < 0.1). (2) The gross NEP from 2000 to 2020 in the study area ranged from 542.78 to 720 Gg C, with a multi-year average NEP of 183.84 g C m−2 yr−1. The simulated NEP demonstrated higher accuracy when compared to the measured data (R 2 = 0.79, P < 0.01). (3) The NEP exhibited a spatial pattern characterized by lower values in the central area and higher values in the north and south. Approximately 89.60 % of the total area demonstrated an increase in NEP, with woodland acting as the primary contributor, while 4.50 % of the total area displayed a decreasing trend, predominantly due to the expansion of built-up land. (4) Notable variations in NEP existed among different types of land cover. In terms of vegetation types, the annual average NEP ranked as follows: woodland > grassland > cropland. The application of STARFM has provided valuable insights into the methodology for precise delineation of spatio-temporal dynamics of NEP at the county scale. The outcomes of this study have furnished support for implementing climate change mitigation strategies in ecologically-oriented counties and the bottom-up promotion of China's carbon peaking and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Exploring the spatiotemporal characteristics of ecosystem services (ESs) and their drivers is crucial for managers to develop significant scientific policies that further sustainable development. We used the Yangtze River Economic Belt (YREB) to explore the trends, hotspots, and drivers of water yield (WY), soil conservation (SC), carbon sequestration (CS), and food supply (FS) between 2000 and 2020. Similarly, we analyzed relationships among ESs and drivers of the multiple ecosystem services landscape index (MESLI). We used the self-organizing map method to obtain the types and distribution of the ES bundles, revealing the bundles, trade-offs, and synergies among ESs. The four ESs had an increasing trend, with CS having the highest increase; ES hotspot analysis showed differences among upper, middle, and lower reaches. Constraint lines among ESs and drivers were diverse; the corresponding SC and WY reached thresholds when CS values were 1477.81 and 460.5 t km−2, respectively. When FS values were 67.34 and 86.17 × 104 Yuan·km−2, CS and WY reached their thresholds. All critical drivers of the four ESs were natural factors. The thresholds that the MESLI reached with driver status were 1000 mm (evapotranspiration), 2121 mm (precipitation), 2.42° (slope), 1.46% (soil organic matter), 36.08% (sand), 30.75% (proportion of non-agricultural population), 18.57% (cropland proportion), 1.05 × 104 persons·km−2 (population density), and 84.84% (proportion of non-agricultural industries in total gross domestic product), respectively. FS, water supply, and ecological conservation bundles changed over the 20 years, and trade-offs and synergies among ESs within bundles differed. We revealed the complexity of ESs from multiple perspectives, which will enable the development of ecosystem management and conservation recommendations for the YREB and large-scale economic zones worldwide. [Display omitted] • Four types of ESs and MESLI showed an overall increasing trend from 2000 to 2020. • Characteristics and relationships of multi-ES were analyzed from multi-perspectives. • Thresholds were obtained between ES-ES, and ES-drivers. • Precipitation was the most important driver of MESLI. [ABSTRACT FROM AUTHOR]

[Display omitted] • Exhibited spatial heterogeneity of ecosystem services (ESs). • Drivers of ESs had clear spatial distribution zoning rules. • Revealed threshold curves for the transform of ESs drivers. • Proposed an EEs-enhancing ecological conservation strategy. Investigating large-scale spatial patterns of ecosystem services (ESs) and their underlying drivers can greatly contribute to policies-making and regional sustainability development. With water yield (WY), soil conservation (SC), and carbon sequestration (CS) as representative ESs, we aim to quantify their spatial patterns in the Yangtze River Economic Belt, China, and to identify their driving factors, and to formulate sound environmental management strategies. Spatial geography and socioeconomic data from 2000 to 2020 were mined and a range of research methods, including multiscale geographic weighted regression, self-organizing maps, and linear discriminant analysis, were employed for such a purpose. Annual average WY, SC, and CS were 403 mm, 9897 t·km−1, and 1071 g·CO 2 ·m−2. The three ESs examined exhibit spatial heterogeneity. WY exhibited significant patterns of variation along the north–south gradient, while SC and CS exhibited significant variation along the topographic gradient. In the context of high correlation of driving factors among ESs, WY and SC exhibited a greater sensitivity to natural factors (such as precipitation), while CS demonstrated a height sensitivity to human activities in addition to vegetation cover. Spatial heterogeneity is pronounced among the main driving factors of ESs. Three threshold equations were established to describe the manner in which driving factors of different regional ecosystem services undergo transformations, equations possessed a high level of credibility in this study (coincidence > 80 %). This study reveals spatial variations in ecosystem services and their natural and socio-economic drivers. More specifically, we quantitatively validated the threshold in the expression of ecosystem service drivers, establishing a strong scientific foundation for regional ecosystem conservation and management. [ABSTRACT FROM AUTHOR]

Digital Earth is an important field of information technology and a research frontier of geosciences in the 21st century. So far, the Grid computing technique is one of the best solutions for Digital Earth infrastructure. Digital Earth can only be realised through the interaction of people, heterogeneous computing resources, information systems, and instruments, all of which are geographically and organisationally dispersed. Earth observation (EO) includes information acquisition, processing and applications. Information acquisition provides a vast amount of spatial data for building the fabric resource infrastructure. Information processing means that spatial information processing middleware is used with large amounts of secure Grid computing resources for real-time processing of all kinds of spatial data. We are currently working on the development of core-middleware for EO data processing and applications for the Digital Earth Prototype System, which is available in the Institute of Remote Sensing Applications (IRSA), Chinese Academy of Sciences (CAS) The further results will be available soon. [ABSTRACT FROM AUTHOR]

• New method is proposed to assess accuracy of satellite products in ungauged basins. • Mutual cancellation in product errors is avoided in water budget closure. • Results of proposed method are examined by the application of FORM. • The proposed method showed lower dependence on precipitation. Remote sensing products have been widely used in water resources assessment and management. However, the accuracy varies in different products. Water budget closure provides an important breakthrough for better assessing the uncertainties and quantifying error sources of satellite-based data products using water budget imbalance, especially in ungauged basins where conventional verification methods are not applicable. However, the commonly used assessment methods mainly refer to closure residual of water budget Δ Re s or its proportion to corresponding mean precipitation (P) Δ Re s / P , which have apparent limitations due to the dimensional effects in the former method and the high dependence on precipitation in the latter one. More critically, these two methods do not consider measured values (referring to in-situ values in gauged basins or average values of multiple satellite products in ungauged basins as in this study) as benchmarks of water budget components in the assessment of satellite-based hydrological data products, resulting in a wrong choice because the closure result is subject to the mutual cancellation in the errors of water budget components from different satellite-based products. This study proposes a novel error-based method to assess the accuracy of satellite-based hydrological data products for both gauged and ungauged basins based on water budget equation, measured values, and the omission error (OE) which is generally overlooked in existing studies. The method is applied to a typical closed basin, the Tarim River Basin (TRB) of China, to verify the applicability and reliability of the proposed method. Sixty combinations of water budget components from various satellite sources can be designed based on five precipitation (P) products, four evapotranspiration (ET) products and three terrestrial water storage change (TWSC) products. First order reliability method (FORM) is employed to assess the reliability of water budget closure results caused by the errors of satellite sources. The results indicated that different combinations produced different annual Δ Re s and Δ Re s / P , indicating great water budget closure errors for different combinations due to errors in different satellite sources. The OE error from satellite-based hydrological data products of water budget components clearly showed a greater impact on water budget imbalance than water budget components, indicating that OE error is important for understanding quality of satellite products. By comparing the results of Δ Re s and Δ Re s / P , it can be seen that our proposed method had great advantages in assessment of satellite products with a low dependence on P, and a better comparability between different combinations. The value of our assessment criterion ranges from 0 to 1 which is easier to understand in practice. The combinations of water budget components from TRMM_3B43 (P), GPM_3IMERGHH (P), GLDAS_NOAH025 (ET), and GRACE (TWSC) products show satisfactory water budget closure results in TRB using our proposed method. The proposed method provides a new aspect for assessing the accuracy of satellite-based hydrological data products especially in ungauged basins, which provides insights into water resources management in basins. [ABSTRACT FROM AUTHOR]

Straightforward solutions have long been expected for the analysis of multiwell aquifer tests. In this paper, we derive series analytical solutions of steady-state groundwater flow in a rectangular-shaped aquifer with pumping/injection wells for both confined and unconfined conditions. Double Fourier Transform (DFT) technique is applied to deal with different combinations of impermeable and specified head boundaries on sides. The obtained solutions are compact and concise in mathematics and flexible in terms of well number, well locations, and pumping/injection rates. Hatoucaidang, a groundwater resource field in the Ordos Plateau, Northwestern China, is introduced as a field case study, where a multiwell aquifer test was conducted. One of the analytical solutions derived herein is used to estimate hydraulic conductivities by applying a direct calculation method and a least square estimation method regarding observed versus calculated drawdowns. By comparing with nearby single-well pumping tests, the reliability of the derived analytical solutions is proven. This study facilitates utilizing the multiwell aquifer test to analyze the general behavior of groundwater movement in aquifer systems. [ABSTRACT FROM AUTHOR]

• Rice planting areas in China from 2000 to 2021 were extracted. • Rice growth period in China from 2000 to 2021 were identified. • Rice biomass in China was evaluated during the growth period. • Driving mechanisms affecting rice biomass were quantified. China is a major rice-producing nation and understanding changes in rice planting areas and growth periods and the mechanisms driving rice biomass is important to ensure effective agricultural strategies and food security. In this study, rice planting areas from 2000 to 2021 in China were extracted using Savitzky–Golay filtering and the absolute distance index. Using MODIS remote sensing image data, we determined the rice growth period based on the inflection points of the enhanced vegetation index. In addition, the cumulative biomass of rice during the growth period since 2000 was determined based on the Carnegie–Ames–Stanford approach model. Finally, the key driving mechanisms and coupling relationships among the driving factors of rice biomass during the growth period were determined using geographically weighted regression and structural equation models. The results showed that the accumulation of net primary productivity (NPP) during the rice growth period in Northeast China, the middle and lower reaches of the Yangtze River, and Northwest China increased significantly (P < 0.01). The accumulation of NPP during the rice growth period in Huang-Huai-Hai, South China, and Southwest China decreased significantly (P < 0.01). In addition, Central Inner Mongolia, Qinghai–Tibet Plateau, and Loess Plateau have less rice planting area and no significant variation pattern in the accumulation of NPP during the rice growth period. In the nine major agricultural regions of China, precipitation, temperature, and evapotranspiration were among the most influential meteorological factors affecting rice biomass. For the inland areas with less precipitation, drainage density was an increasingly important influencing factor. Areas with high topographic relief were more likely to be affected by the slope. In areas with relatively poor soil, the fertilizer application rate influenced rice biomass growth. Our results provide insights into the spatio-temporal patterns of rice biomass and its driving mechanisms, and demonstrate the efficacy of using remote sensing data at a macro scale. [ABSTRACT FROM AUTHOR]

Lakes and reservoirs are essential elements of the hydrological and biochemical cycles, considered sentinels of global climate change. However, comprehensive quantifications of their water storage changes (∆V) at a large spatiotemporal scale are still rare. Here, we integrated a global surface water dataset and SRTM digital elevation models, both available from Google Earth Engine platform at a spatial resolution of 30 m, to evaluate ∆V for a total of 760 lakes and reservoirs across China at an annual timescale since 1984. The results indicated that (1) the aggregated water storage went through a slight increase of 41.5 Gt (1.7 Gt/yr) during 1985–2005, a significant decrease of 100 Gt (−20.6 Gt/yr) during 2005–2009, and then increased by 136.3 Gt (21.3 Gt/yr) during 2009–2015. (2) The increasing trend was largely attributed to lakes and reservoirs in the Tibetan Plateau Lake Zone, and the decreasing trend was mainly due to the North and Northwest Lake Zone, with little variations observed for the Northeast and Southwest Lake Zones. (3) Qinghai lake was associated with the largest increase (18.3 Gt) and Poyang lake presented the largest decline (−9.2 Gt). The results can help advance our understanding of the impact of climate change and improve future projection. [ABSTRACT FROM AUTHOR]

Abstract: Neural networks (NNs) have been applied to ionospheric predictions recently. This paper uses radial basis function neural network (RBF-NN) to forecast hourly values of the ionospheric F 2 layer critical frequency(foF 2), over Wuhan (30.5N, 114.3E), China. The false nearest neighbor method is used to determine the embedding dimension, and the principal component analysis (PCA) is used to reduce noise and dimension. The whole study is based on a sample of about 26,000 observations of foF 2 with 1-h time resolution, derived during the period from January 1981 to December 1983. The performance of RBF-NN is estimated by calculating the normalized root-mean-squared (NRMSE) error, and its results show that short-term predictions of foF 2 are improved. [Copyright &y& Elsevier]

Water clarity is a well-established first-order indicator of water quality and has been used globally by water regulators in their monitoring and management programs. Assessments of water clarity in lakes over large temporal and spatial scales, however, are rare, limiting our understanding of its variability and the driven forces. In this study, we developed and validated a robust Secchi disk depth (Z SD) algorithm for lakes across China based on two water color parameters, namely Forel-Ule Index (FUI) and hue angle α, retrieved from MODIS data. The MODIS Z SD model shows good results when compared with in-situ measurements from 17 lakes, with a 27.4% mean relative difference (MRD) in the validation dataset. Compared with other empirical Z SD models, our FUI and α-based model demonstrates improved performance and adaptability over a wide range of water clarity and trophic states. This algorithm was subsequently applied to MODIS measurements to provide a comprehensive assessment of water clarity in large lakes (N = 153) across China for the first time. The mean summer Z SD of the studied lakes between 2000 and 2017 demonstrated marked spatial and temporal variations. Spatially, the Z SD of large lakes presented a distinct spatial pattern of "high west and low east" over China. This spatial pattern was found to be associated with the significant differences in lake depth and altitude between west and east China while China's population, GDP, temperature, and precipitation distribution have also contributed to a certain extent. Temporally, the Z SD of most lakes increased during this period, with an overall mean rate of 3.3 cm/yr for all lakes. Here, 38.6% (N = 59) of the lakes experienced a significant increase in their Z SD value during the past 18 years while only 8.5% (N = 13) showed a significant decreasing trend. Significant increases in lake Z SD were observed in west China, which were found to correlate with the increase of air temperature and lake surface area. This is possibly a response of the lakes in west China to climate change. In the lake systems of east China, which are predominately used as a drinking water source, the increase in lake Z SD was found to be strongly correlated with changes in local GDP (gross domestic production), NDVI (normalized difference vegetation index) and lake surface area, suggesting a combined effect of the implemented management practices and climatic variability. The results of this study provide important information for water quality conservation and management in China, and also highlight the value of satellite remote sensing in monitoring water quality over lakes at a large scale and long-term. • Developed a Z SD estimation algorithm for lakes in a wide range of water clarity based on FUI and α retrieved from MODIS data. • Revealed significant spatial and temporal patterns of water clarity in 153 large lakes across China between 2000 and 2017. • Water clarity in most of the lakes increased during the past 18-year period, especially in west China. • Significant increases of lake clarity in west China are attributed to the climate change. • Local GDP growth can contribute to the increase of lake clarity in east China. [ABSTRACT FROM AUTHOR]

Widespread Mesozoic granitoids in the Cathaysia Block of South China are associated with intensive reworking of the lower crust as a result of magma underplating. This inference is based mainly on studies of mafic igneous rocks, whereas there is little evidence from lower-crustal rocks. Lower-crustal xenoliths in Mesozoic basalts in the Daoxian region within the Cathaysia Block might record information on the relationship between magma underplating and remelting of the pre-existing crust beneath the block. The xenoliths are mainly mafic granulites, with minor felsic granulites. The mafic granulites have low SiO 2 contents (47.22–49.46 wt%) and high Mg# values (77.8–82.4), suggesting that their protoliths were derived from a mantle source. The felsic granulite xenoliths have high SiO 2 (69.56–70.27 wt%) and low MgO (1.63–2.50 wt%) contents, and zircons in these xenoliths yield negative ε Hf (t) values (−6.1 to −12.6) and high δ18O values (6.8–7.6‰), consistent with a crustal source. Both mafic and felsic granulite xenoliths record magmatic (226–218 Ma) and metamorphic (218–193 Ma) events, suggesting a genetic link between mafic and felsic rocks in the lower crust. We propose that the magma underplating was responsible for the origin of the mafic granulites and partial melting of pre-existing lower crust, as recorded by the felsic xenoliths. The granulite xenoliths also contain abundant inherited zircons that formed during the Archean–Neoproterozoic (2584–659 Ma), early Paleozoic (peaking at ca. 425 Ma), and late Paleozoic (peaking at ca. 261 Ma). Zircons from the Daoxian granulite xenoliths have distinct Hf O isotopic compositions that record the multistage evolution of the lower crust beneath the Cathaysia Block. This evolution involved the accretion of juvenile crust during the late Archean (ε Hf (t) = +4.2 to +4.6) and late Paleozoic (ε Hf (t) = +1.3 to +5.3; δ18O = 5.8–6.2‰), crustal reworking during the Neoproterozoic (ε Hf (t) = −7.5 to −11.8; δ18O = 5.1–9.5‰) and early Paleozoic (ε Hf (t) = −0.5 to −2.2; δ18O = 7.3–7.5‰), and crustal accretion with significant reworking during the early Mesozoic (ε Hf (t) = −19.2 to +5.9; δ18O = 6.8–7.6‰). The U Pb ages and Hf O isotopic data of zircons from the Daoxian granulite xenoliths are consistent with the distribution of Phanerozoic igneous rocks in the Cathaysia Block. These data support the inference that Mesozoic magma underplating triggered the remelting of pre-existing crustal materials and produced extensive granitoid magmatism. • Daoxian granulite xenoliths record multistage accretion/reworking of lower crust. • Mafic xenoliths are related to early Mesozoic magma underplating. • Felsic xenoliths originated mainly from remelting of pre-existing lower crust. • Underplating triggered Mesozoic remelting of lower crust in the Cathaysia Block. [ABSTRACT FROM AUTHOR]

Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of ~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields. [ABSTRACT FROM AUTHOR]

Background: The residents' knowledge, attitudes and practices related to ambient air pollution and health will help to improve the understanding of environmental protection and make environmental health policies more targeted and effective. This study aimed at knowing the attitudes and behaviors towards ambient air pollution and health.Methods: A cross-sectional survey was conducted in Ningbo, China in January 2015. Personal information and questions pertaining to the knowledge, attitudes and practices towards ambient air pollution and health were collected through questionnaire investigations. Descriptive statistics, chi-square tests and multiple unconditional logistic regression analysis were used.Results: The questionnaire was completed by 1604 respondents (59.41 % women). The awareness rate was 64.59 % and varied significantly with age, levels of education, and occupation (all p < 0.05). Only 5.80 % of the total participants were satisfied with the air quality in Ningbo in 2014. Most respondents (78.80 %) expressed concern about the possible aggravation of the haze. More than 80 % of participants believed that it will take at least 3-5 years or longer before the air quality is improved. Television and internet resources have replaced books and newspapers as the primary sources for obtaining knowledge about haze and related protective measures. 85.22 % of respondents were concerned about air quality index (AQI). Most of the residents have taken protective measures indoors during haze weather. 48.50 % have worn face masks when going outside, the most frequently type of face masks selected were cotton (39.85 %) or gauze face masks (36.24 %). Age and occupation were the main factors associated with the level of knowledge about air pollution (p < 0.05).Conclusions: There were a relatively high knowledge awareness rate, strong health protection consciousness and high enthusiasm for air pollution control among Ningbo residents. The elderly people and less-educated residents are the targetable population for improving environment. [ABSTRACT FROM AUTHOR]