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.

Landslide disasters are one of the main risks involved with the operation of long-distance oil and gas pipelines. Because previously established disaster risk models are too subjective, this paper presents a quantitative model for regional risk assessment through an analysis of the patterns of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou–Chengdu–Chongqing (LCC) long-distance multiproduct oil pipeline (82 km) in China as a case study, we successively carried out two independent assessments: a susceptibility assessment and a vulnerability assessment. We used an entropy weight method to establish a system for the vulnerability assessment, whereas a Levenberg–Marquardt back propagation (LM-BP) neural network model was used to conduct the susceptibility assessment. The risk assessment was carried out on the basis of two assessments. The first, the system of the vulnerability assessment, considered the pipeline position and the angle between the pipe and the landslide (pipeline laying environmental factors). We also used an interpolation theory to generate the standard sample matrix of the LM-BP neural network. Accordingly, a landslide susceptibility risk zoning map was obtained based on susceptibility and vulnerability assessment. The results show that about 70 % of the slopes were in high-susceptibility areas with a comparatively high landslide possibility and that the southern section of the oil pipeline in the study area was in danger. These results can be used as a guide for preventing and reducing regional hazards, establishing safe routes for both existing and new pipelines, and safely operating pipelines in the Guangyuan area and other segments of the LCC oil pipeline.

In situ measurements of bidirectional reflectance factors (REFFs) are presented for submerged carbonate sediments at six sites in the vicinity of Lee Stocking Island. Sediment grain sizes ranged from 400 µm to ≫1000 µm. Several features were common to all data sets. Although overall sediment reflectance varied spectrally, normalized REFF was independent of wavelength within the natural sample variability. This allowed us to derive a model REFF which, when multiplied by REFF(Θ_i = 0°, Θ_r = 45°, Ø) at a specific wavelength, represented the data well. In addition, normally illuminated samples were almost Lambertian, but samples with larger grain sizes had an REFF that decreased with increasing view angles. As the illumination angle increased, samples became increasingly non‐Lambertian. The dominant feature of the REFF in these non‐Lambertian surfaces is in the backscattering direction. In this direction the REFF was significantly larger than the nadir value. The largest backscattering REFFs correspond to large grain sizes and increases with increasing illumination angles. The empirical model, which represents the data within one standard deviation of sample variation, is presented for these sediments. This model is well behaved at angles out to 90° and thus can be used in radiative transfer models. This model provides a realistic bottom reflectance that can be used to improve light field predictions in shallow water.

Evapotranspiration (ET) is a key factor to further our understanding of climate change processes, especially on the Tibetan Plateau, which is sensitive to global change. Herein, the spatial patterns of ET are examined, and the effects of environmental factors on ET at different scales are explored from the years 2000 to 2012. The results indicated that a steady trend in ET was detected over the past decade. Meanwhile, the spatial distribution shows an increase of ET from the northwest to the southeast, and the rate of change in ET is lower in the middle part of the Tibetan Plateau. Besides, the positive effect of radiation on ET existed mainly in the southwest. Based on the environment gradient transects, the ET had positive correlations with temperature (R>0.85, p<0.0001), precipitation (R > 0.89, p < 0.0001), and NDVI (R > 0.75, p < 0.0001), but a negative correlation between ET and radiation (R = 0.76, p < 0.0001) was observed. We also found that the relationships between environmental factors and ET differed in the different grassland ecosystems, which indicated that vegetation type is one factor that can affect ET. Generally, the results indicate that ET can serve as a valuable ecological indicator.

Regional ecosystem health is the basis for regular regional exploration, ecological protection, and sustainable development. This study explored ecosystem health at the southern end of the Hu Line (Sichuan and Yunnan provinces) using the pressure–state–response model and examined the spatial evolution of ecosystem health. The proportion of unhealthy and morbid cities decreased from 45.9% in 2000 to 35.1% in 2016. The imbalance of ecosystem health among cities has gradually increased since 2006, but more high-quality cities have emerged (Z of Moran’s Index < 1.96, p > 0.05). Overall, the regional ecosystem on the southeast side of the Hu Line was healthier than that on the northwest side. Differences in ecosystem health on both sides of the Hu Line showed decreasing trends over time except for the pressure score. The spatial pattern of ecosystem health moved along the Hu Line because the pressure and state scores of ecosystems were mainly determined by the natural environmental conditions. Based on the county-level assessment, the grade of imbalance within cities was divided, and those that were lagging were identified. To correct regional imbalances, a comprehensive and proactive policy framework for a smart development model was put forward in Sichuan and Yunnan.

Population is a crucial basis for the study of sociology, geography, environmental studies, and other disciplines; accurate estimates of population are of great significance for many countries. Many studies have developed population spatialization methods. However, little attention has been paid to the differential treatment of the spatial stationarity and non-stationarity of variables. Based on a semi-parametric, geographically weighted regression model (s-GWR), this paper attempts to construct a novel, precise population spatialization method considering parametric stationarity to enhance spatialization accuracy; the southwestern area of China is used as the study area for comparison and validation. In this study, the night-time light and land use data were integrated as weighting factors to establish the population model; based on the analysis of variables characteristics, the method uses an s-GWR model to deal with the spatial stationarity of variables and reduce regional errors. Finally, the spatial distribution of the population (SSDP) of the study area in 2010 was obtained. When assessed against the traditional regression models, the model that considers parametric stationarity is more accurate than the models without it. Furthermore, the comparison with three commonly-used population grids reveals that the SSDP has a percentage error close to zero at the county level, while at the township level, the mean relative error of SSDP is 33.63%, and that is >15% better than other population grids. Thus, this study suggests that the proposed method can produce a more accurate population distribution.