The Tibetan Plateau is one of the most vulnerable areas to extreme precipitation. In recent decades, water cycles have accelerated, and the temporal and spatial characteristics of extreme precipitation have undergone dramatic changes across the Tibetan Plateau, especially in its various ecosystems. However, there are few studies that considered the variation of extreme precipitation in various ecosystems, and the impact of El Niño-Southern Oscillation (ENSO), and few researchers have made a quantitative analysis between them. In this study, we analyzed the spatial and temporal pattern of 10 extreme precipitation indices across the Tibetan Plateau (including its four main ecosystems: Forest, alpine meadow, alpine steppe, and desert steppe) based on daily precipitation from 76 meteorological stations over the past 30 years. We used the linear least squares method and Pearson correlation coefficient to examine variation magnitudes of 10 extreme precipitation indices and correlation. Temporal pattern indicated that consecutive wet days (CWD) had a slightly decreasing trend (slope = −0.006), consecutive dry days (CDD), simple daily intensity (SDII), and extreme wet day precipitation (R99) displayed significant increasing trends, while the trends of other indices were not significant. For spatial patterns, the increasing trends of nine extreme precipitation indices (excluding CDD) occurred in the southwestern, middle and northern regions of the Tibetan Plateau; decreasing trends were distributed in the southeastern region, while the spatial pattern of CDD showed the opposite distribution. As to the four different ecosystems, the number of moderate precipitation days (R10mm), number of heavy precipitation days (R20mm), wet day precipitation (PRCPTOT), and very wet day precipitation (R95) in forest ecosystems showed decreasing trends, but CDD exhibited a significant increasing trend (slope = 0.625, P < 0.05). In the other three ecosystems, all extreme precipitation indices generally exhibited increasing trends, except for CWD in alpine meadow (slope = −0.001) and desert steppe (slope = −0.005). Furthermore, the crossover wavelet transform indicated that the ENSO had a 4-year resonance cycle with R95, SDII, R20mm, and CWD. These results provided additional evidence that ENSO play an important remote driver for extreme precipitation variation in the Tibetan Plateau.

Poverty alleviation is one of the most important tasks facing human social development. It is necessary to make accurate monitoring and evaluations for areas with poverty to improve capability of implementing poverty alleviation policies. Here, this study introduced nighttime light (NTL) data to estimate county-level poverty in southwest China. First, this study used particle swarm optimization-back propagation hybrid algorithm to explore the potential relationship between two NTL data (the Defense Meteorological Satellite Program’s Operational Line Scan System data and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite data). Then, we integrated two NTL data at the pixel level to establish a consistent time-series of NTL dataset from 2000 to 2019. Next, an actual comprehensive poverty index (ACPI) was employed as an indicator of multidimensional poverty at county level based on 11 socioeconomic and natural variables, and which could be the reference to explore the poverty evaluation using NTL data. Based on the correlation between the ACPI and NTL characteristic variables, a poverty evaluation model was developed to evaluate the poverty situation. The result showed the great matching relationship between DMSP-OLS and NPP-VIIRS data (R² = 0.84). After calibration, the continuity and comparability of DMSP-OLS data were significantly improved. The integrated NTL data also reflected great consistency with socioeconomic development (r = 0.99). The RMSE between ACPI and the estimated comprehensive poverty index (ECPI) based on the integrated NTL data is approximately 0.19 (R² = 0.96), which revealed the poverty evaluation model was feasible and reliable. According to the ECPI, we found that the magnitude of poverty eradication increased in southwest China until 2011, but slowed down from 2011 to 2019. Regarding the spatial scale, geographic barriers are a key factor for poverty, with high altitude and mountainous areas typically having a high incidence of poverty. Our approach offers an effective model for evaluation poverty based on the NTL data, which can contribute a more reliable and efficient monitoring of poverty dynamic and a better understanding of socioeconomic development.

We investigate the geodetic data set of Interferometric Synthetic Aperture Radar (InSAR) including Advanced Land Observation Satellite (ALOS)‐2 and Sentinel‐1A/1B satellites for inferring the fault model of the 2017 $Mw$ 7.3 Sarpol Zahab, Iran, earthquake. The InSAR deformation fields show that the seismogenic fault does not reach the ground surface, but some shallow folds have been triggered by the mainshock. Our preferred faulting model suggests that the coseismic rupture occurs on a single planar fault surface with a strike angle of 337.5°. Two significant slip sources are determined by the geodetic data: one is located within the 11.8‐ to 13.5‐km depth range with a peak slip of 4.9 m, and the other occurs at the shallower depth (10.5–12.5 km) with a peak slip of 4.5 m. Both of them are responsible for the primary deformation signals in the geodetic imagery. The significant fault slip concentrates at the 10‐ to 14‐km depth within the Pan‐African basement. However, most of the aftershocks have depths between 3 and 12 km in the shallow sedimentary section. We hypothesize that the Hormuz Salt section with a depth of 12–13 km detaches the high‐slip zones from the aftershock cluster, by which the fault slip is not transferred through the intervening salt section to the surface. The predicted static Coulomb stress change by our preferred faulting model at a depth of 10 km could encourage the occurrence of aftershocks. Moreover, the triggered fault‐related folding in the southwest of the seismic zone has a positive Coulomb stress change and aseismic slip caused by the mainshock.

High-quality and equitable primary education services promote the building of a harmonious socialist society and are an important basis for improving people’s quality of life and promoting high-quality and sustainable regional development. Here, we take Chengdu City as a test area, integrate data from multiple sources, use the random forest model to simulate the distribution data of primary school-age children in Chengdu City in 2020, and use the kernel density estimation method and the multi-traffic mode two-stage floating catchment area method to measure the spatial distribution characteristics and accessibility of primary school educational facilities in Chengdu City and combine the imbalance index and spatial autocorrelation analysis, examination of the equalization of the distribution of primary school educational facilities, and the correlation between school-age population and accessibility. The results show that in the past decade, the population of Chengdu has grown rapidly, and the number of primary school-age children has also been increasing. The overall distribution of primary school-age children in Chengdu presents a decentralized pattern of “one point with multiple cores”, with the population decreasing from the center to the periphery, and the population distribution dominates the spatial distribution of primary school facilities, which also highlights the imbalance in the construction of primary school facilities to some extent (S = 0.257), which was mainly manifested by the fact that the central-eastern part of the city has more primary school facilities, while the western part has fewer. In addition, the results of both accessibility and autocorrelation analyses show that the overall accessibility of the central circle of Chengdu was high, while the accessibility of the second and third circles was at a lower level and below, with very obvious cross-regional and cross-circle differences. This study can not only provide more accurate recommendations for the allocation of educational facilities but also serve as a reference for evaluating the spatial equity of other public services in the city.

In recent years, the tourism industry has developed rapidly. However, traditional tourism information has the disadvantages of slow response speed and limited information content, which cannot reflect the evolution trend of spatial and temporal patterns of tourism information in time. Here, based on the Baidu Index, we construct an evaluation framework to analyse the spatial and temporal flow of tourism information in the Chengdu–Chongqing urban cluster from 2011 to 2021. Then, we analyse the urban links between different network levels from the evolution pattern. Finally, we use the geodetector model to analyse its driving mechanism. The results show that Chengdu and Chongqing are the most active cities in the study area in terms of tourism information. The unbalanced development of tourism information between Chengdu and Chongqing and other cities in the region gradually deepens during the period 2011–2019 (polarization effect), but the unbalanced development moderates after 2019. On the other hand, cities in the middle of the Chengdu–Chongqing cluster always have weak agglomeration effects of tourism information. Cities with high tourism information outflow rates in the Chengdu–Chongqing city cluster are mainly concentrated around Chengdu. The average outflow rate of Deyang is the highest, at 27.8%. Cities with low tourist information outflow rates are primarily located in the west, central and south. Ya’an is the city with the lowest outflow rate, with an average of −62.2%. Specifically, Chengdu is the dominant and most radiantly influential city. The tourism information of the Chengdu–Chongqing urban cluster shows a radial network with Chengdu and Chongqing as the core. The driving force analysis shows that the push factor of tourist source, such as the number of people buying pension insurance, is the core driving mechanism, while the pull factor of destination, such as the park green area, and resistance factors such as psychological distance, are in the secondary position. In general, this paper uses Internet tourism data to expand the traditional tourism information research of the Chengdu–Chongqing urban cluster, which can better respond to the changes and needs of the tourism market and provide reference for the spatial optimization of tourism destinations.