The prediction of geothermal high-temperature anomalies along the plateau railway will be helpful in the construction of the project and its later management. Taking the Sichuan–Tibet railway as the study area and based on Landsat8 thermal infrared images, map data, and measured data regarding the cause and distribution of geothermal high-temperature anomalies, through correlation analysis, we selected six impact factors including the LST, combined entropy of geological formation, fault density, buffer distance to rivers, magnetic anomaly, and earthquake peak acceleration as the input maps of the model. The index-overlay information model, the weights of the entropy information model, and the weights of the evidence information model were established to quantitatively predict the geothermal anomaly in the study area, and the prediction maps were divided into four classes. The results show that the weights of the evidence information model achieved a high prediction accuracy; the success index and the ratio of the high anomaly area reached 0.0053% and 0.872, respectively, and the spatial distribution of the geothermal points is basically consistent with the prediction results. This research can act as a reference for the design and construction of the Sichuan–Tibet railway.

The greatest warm body in the world exists in the tropical oceans, which stimulates deep convection, resulting in abundant water vapor and precipitation in the tropical atmosphere. Using multiple SST datasets and related precipitation and atmospheric parameter data, this study examines multi-scale variabilities of the Indo-Pacific warm pool (IPWP) as well as the associated rain pool (IPRP). The results show that the IPWP and IPRP are spatially analogous and have significant increasing trends of intensity and coverage. Seasonal variations of the IPWP and IPRP are the strongest and almost coincident with each other. Our results also confirm previous findings that the most important interannual variations of the IPWP and IPRP are associated with various types of ENSO. The composite analysis reveals that the IPWP’s SST structure is linked to the ENSO-induced trade wind anomaly and that SST structural changes cause changes in the position and intensity of the ascending branch of the Walker circulation, which in turn drives changes in the position and intensity of the IPRP.

To investigate the main modes of interannual variation of chlorophyll-a (Chla) with seasonal evolution and its variation cycle in the North Indian Ocean based on satellite-derived products during 1998–2016, a season-reliant empirical orthogonal function (S-EOF) analysis and power spectrum analysis based on Fourier transform are applied in the study. The first three dominate modes reveal distinct Chla variability, as the S-EOF1 features by one dipole pattern have a negative anomaly in the central western Indian Ocean and a positive anomaly off the Java–Sumatra coasts, which is mainly synchronously associated with the climate indices of the positive Indian Ocean dipole (IOD) and eastern Pacific El Nino (EP-El Niño). The S-EOF2 indicates a tripolar structure with positive anomalies located in the central Indian Ocean surrounded by two negative anomalies, which is one year behind a positive IOD and EP-El Niño event. The S-EOF3 exhibits a different dipole distribution, with a positive anomaly in the central west and a negative anomaly in the southeast, synchronized or lagging behind the central Pacific El Nino (CP-El Niño). Moreover, regarding the correlation between the main modes of interannual variation and the IOD and El Nino events, the dynamic parameters (such as SST, SLA, rain, and wind) of the tropical Indo-Pacific Ocean are discussed using time-delay correlation and linear regression analysis to explain the key factors and possible influencing mechanism of the joint seasonal and interannual variations of Chla in the northern Indian Ocean.