Research on total electron content (TEC) empirical models is one of the important topics in the field of space weather services. Global TEC empirical models based on Global Ionospheric Maps (GIMs) TEC data released by the International GNSS Service (IGS) have developed rapidly in recent years. However, the accuracy of such global empirical models has a crucial restriction arising from the non-uniform accuracy of IGS TEC data in the global scope. Specifically, IGS TEC data accuracy is higher on land and lower over the ocean due to the lack of stations in the latter. Using uneven precision GIMs TEC data as a whole for model fitting is unreasonable. Aiming at the limitation of global ionospheric TEC modelling, this paper proposes a new global ionospheric TEC empirical model named the TECM-GRID model. The model consists of 5183 sections, corresponding to 5183 grid points (longitude 5°, latitude 2.5°) of GIM. Two kinds of single point empirical TEC models, SSM-T1 and SSM-T2, are used for TECM-GRID. According to the locations of grid points, the SSM-T2 model is selected as the sub-model in the Mid-Latitude Summer Night Anomaly (MSNA) region, and SSM-T1 is selected as the sub-model in other regions. The fitting ability of the TECM-GRID model for modelling data was tested in accordance with root mean square (RMS) and relative RMS values. Then, the TECM-GRID model was validated and compared with the NTCM-GL model and Center for Orbit Determination in Europe (CODE) GIMs at time points other than modelling time. Results show that TECM-GRID can effectively describe the Equatorial Ionization Anomaly (EIA) and the MSNA phenomena of the ionosphere, which puts it in good agreement with CODE GIMs and means that it has better prediction ability than the NTCM-GL model.

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Urban ecosystem health evaluation can assist in sustainable ecological management at a regional level. This study examined urban agglomeration ecosystem health in the middle reaches of the Yangtze River with entropy weight and extension theories. The model overcomes information omissions and subjectivity problems in the evaluation process of urban ecosystem health. Results showed that human capital and education, economic development level as well as urban infrastructure have a significant effect on the health states of urban agglomerations. The health status of the urban agglomeration's ecosystem was not optimistic in 2013. The majority of the cities were unhealthy or verging on unhealthy, accounting for 64.52% of the total number of cities in the urban agglomeration. The regional differences of the 31 cities' ecosystem health are significant. The cause originated from an imbalance in economic development and the policy guidance of city development. It is necessary to speed up the integration process to promote coordinated regional development. The present study will aid us in understanding and advancing the health situation of the urban ecosystem in the middle reaches of the Yangtze River and will provide an efficient urban ecosystem health evaluation method that can be used in other areas.

Long-term variation of estimated global solar radiation (E g; ) and its relationship with total cloud cover (TCC), low cloud cover (LCC), water vapor content (WVC) and aerosol optical depth (AOD) were investigated based on the observations at 21 meteorological stations in Hunan province, China. Long-term variations of all variables were calculated for each station; the Mann-Kendall trend test was used to detect the significant level of temporal development trend for each variable; the Pearson correlation analysis was used to measure their linear relationships. Annual E g; generally decreased at the rate of -2.11 9 10 -3 MJ m -2 decade -1 in Hunan province during 1980-2013. Seasonal mean E g; decreased at the rate of -11.99 9 10 -3 , -4.71 9 10 -3 and -4.51 9 10 -3 MJ m -2 decade -1 in summer, autumn and winter, respectively, while the increasing trend was observed in spring (15.74 9 10 -3 MJ m -2 decade -1 ). The annual variation of E g; in Hunan province was dominantly determined by the variations of AOD (0.33 9 10 -3 decade -1 ) and LCC (0.24 % p decade -1 ). But the spatial variation of E g; in Hunan province was complex. All 21 stations were divided into four groups according to the long-term trends of E g; , TCC, LCC, AOD and WVC. An increasing E g; was observed at stations in group 1, which was determined by the variability of TCC. The variability of AOD and TCC might contribute to the increasing E g; in group 2. There were decreasing trends of E g; for the stations in group 3 and group 4, which were largely determined by the increases of AOD and LCC.

Purpose The purpose of this paper is to analyze the characteristics of spatio-temporal dynamics and the evolution of land use change is essential for understanding and assessing the status and transition of ecosystems. Such analysis, when applied to Horqin sandy land, can also provide basic information for appropriate decision-making. Design/methodology/approach By integrating long time series Landsat imageries and geographic information system (GIS) technology, this paper explored the spatio-temporal dynamics and evolution-induced land use change of the largest sandy land in China from 1983 to 2016. Accurate and consistent land use information and land use change information was first extracted by using the maximum likelihood classifier and the post-classification change detection method, respectively. The spatio-temporal dynamics and evolution were then analyzed using three kinds of index models: the dynamic degree model to analyze the change of regional land resources, the dynamic change transfer matrix and flow direction rate to analyze the change direction, and the barycenter transfer model to analyze the spatial pattern of land use change. Findings The results indicated that land use in Horqin sandy land during the study period changed dramatically. Vegetation and sandy land showed fluctuating changes, cropland and construction land steadily increased, water body decreased continuously, and the spatial distribution patterns of land use were generally unbalanced. Vegetation, sandy land and cropland were transferred frequently. The amount of vegetation loss was the largest. Water body loss was 473.6 km2, which accounted for 41.7 per cent of the total water body. The loss amount of construction land was only 1.0 km2. Considerable differences were noted in the rate of gravity center migration among the land use types in different periods, and the overall rate of construction land migration was the smallest. Moreover, the gravity center migration rates of the water body and sandy land were relatively high and were related to the fragile ecological environment of Horqin sandy land. Originality/value The results not only confirmed the applicability and effectiveness of the combined method of remote sensing and GIS technology but also revealed notable spatio-temporal dynamics and evolution-induced land use change throughout the different time periods (1983-1990, 1990-2000, 2000-2010, 2010-2014, 2014-2016 and 1983-2016).

Globally distributed GPS (global positioning system) stations have been continuously running for nearly 20 years, thereby accumulating numerous observations. These long-time recorded GPS data can be used to calculate continuous total electron content (TEC) values at single stations and provide an effective modeling dataset to establish single-station empirical TEC models. In this paper, a new empirical TEC model called SSM-T1 for single stations is proposed on the basis of GPS data calculated by IONOLAB-TEC application from 2004 to 2015. The SSM-T1 model consists of three parts: diurnal, seasonal, and solar dependency variations, with 18 coefficients fitted by the nonlinear least-squares method. The SSM-T1 model is tested at four stations: Paris (opmt), France; Bangalore (iisc), India; Ceduna (cedu), Australia; and O’Higgins (ohi3) over the Antarctic Peninsula. The RMS values of the model residuals at these four stations are 3.22, 4.46, 3.28, and 3.83 TECU. Assessment results show that the SSM-T1 model is in good agreement with the observed GPS-TEC data and exhibits good prediction ability at the Paris, Bangalore, and Ceduna stations. However, at the O’Higgins station, the SSM-T1 model seriously deviates from the observed GPS-TEC data and cannot effectively describe the variation of mid-latitude summer night anomaly.

Compared with regional or global total electron content (TEC) empirical models, single‐station TEC empirical models may exhibit higher accuracy in describing TEC spatial and temporal variations for a single station. In this paper, a new single‐station empirical total electron content (TEC) model, called SSM‐month, for the O'Higgins Station in the Antarctic Peninsula is proposed by using Global Positioning System (GPS)‐TEC data from 01 January 2004 to 30 June 2015. The diurnal variation of TEC in the O'Higgins Station may have changing features in different months, sometimes even in opposite forms, because of ionospheric phenomena, such as the Mid‐latitude Summer Nighttime Anomaly (MSNA). To avoid the influence of different diurnal variations, the concept of monthly modeling is proposed in this study. The SSM‐month model, which is established by month (including 12 submodels that correspond to the 12 months), can effectively describe the diurnal variation of TEC in different months. Each submodel of the SSM‐month model exhibits good agreement with GPS‐TEC input data. Overall, the SSM‐month model fits the input data with a bias of 0.03 TECU (total electron content unit, 1 TECU = 1016 el m−2) and a standard deviation of 2.78 TECU. This model, which benefits from the modeling method, can effectively describe the MSNA phenomenon without implementing any modeling correction. TEC data derived from Center for Orbit Determination in Europe global ionosphere maps (CODE GIMs), International Reference Ionosphere 2012 (IRI2012), and NeQuick are compared with the SSM‐month model in the years of 2001 and 2015–2016. Result shows that the SSM‐month model exhibits good consistency with CODE GIMs, which is better than that of IRI2012 and NeQuick, in the O'Higgins Station on the test days.