Modelling the Vegetation Response to Climate Changes in the Yarlung Zangbo River Basin Using Random Forest

Vegetation coverage variation may influence watershed water balance and water resource availability. Yarlung Zangbo River, the longest river on the Tibetan Plateau, has high spatial heterogeneity in vegetation coverage and is the main freshwater resource of local residents and downstream countries. In this study, we proposed a model based on random forest (RF) to predict the Normalized Difference Vegetation Index (NDVI) of the Yarlung Zangbo River Basin and explore its relationship with climatic factors. High-resolution datasets of NDVI and monthly meteorological observation data from 2000 to 2015 were used to calibrate and validate the proposed model. The proposed model was then compared with artificial neural network and support vector machine models, and principal component analysis and partial correlation analysis were also used for predictor selection of artificial neural network and support vector machine models for comparative study. The results show that RF had the highest model efficiency among the compared models. The Nash–Sutcliffe coefficients of the proposed model in the calibration period and verification period were all higher than 0.8 for the five subzones; this indicated that the proposed model can successfully simulate the relationship between the NDVI and climatic factors. By using built-in variable importance evaluation, RF chose appropriate predictor combinations without principle component analysis or partial correlation analysis. Our research is valuable because it can be integrated into water resource management and elucidates ecological processes in Yarlung Zangbo River Basin

Experimental Study on the Mechanical Properties of Sandstone under the Action of Chemical Erosion and Freeze-Thaw Cycles

In order to study the mechanical properties of sandstone under the coupling action of chemical erosion and freeze-thaw cycles, the fine-grained yellow sandstone in a mining area in Zigong, China, is collected as the research object. The changes in mechanical properties of yellow sandstone under the coupling action of chemical solution erosion and freeze-thaw cycles are analyzed based on uniaxial compression tests (UCTs) and triaxial compression tests (TCTs). The results show that, with the increase in freeze-thaw cycles, the compressive strength, elastic modulus, and cohesion of the sandstone samples decrease with varying degrees. Under constant freeze-thaw cycles, the most serious mechanical properties of degradation are observed in acidic solution, followed by alkaline solution and neutral solution. Under different confining pressures, the compressive strength and elastic modulus of the sandstone samples decrease exponentially with the increase in freeze-thaw cycles. Under the action of the chemical solution erosion and freeze-thaw cycles, the internal friction angle fluctuates around 30°. For the cohesion degradation, 35.4%, 29.3%, and 27.2% degradation are observed under acidic, alkaline, and neutral solutions. Nuclear magnetic resonance imaging shows that the chemical erosion and freeze-thaw cycles both promote the degradation of rock properties from surface to interior; after 45 freeze-thaw cycles, the mechanical properties drop sharply. To properly design rock tunneling support and long-term protection in the cold region, the impact of both freeze-thaw cycles and chemical erosion should be considered

Study on Deformation Characteristics of Low-Highway Subgrade under Traffic Load

Highway subgrade bears millions of traffic loads over the years, and its strength, stiffness, and long-term stability gradually decline. In this paper, dynamic triaxial tests were carried out to study the time evolution and spatial distribution of strain and pore pressure of highway-subgrade soil under the action of traffic load. The influence of traffic load on subgrade deformation was analyzed. Furthermore, a numerical-calculation model of the subgrade was established. The deformation characteristics of subgrade under driving load were analyzed. The main conclusions can be drawn as follows: (1) With the increase in loading times, the cumulative strain and pore pressure can be roughly divided into three stages: rapid-growth stage, slow-growth stage, and equilibrium stage. (2) The influence of traffic load on the cumulative strain and pore-water pressure of subgrade soil decreases rapidly with the increase in depth. (3) The amplitude of traffic load has a tremendous influence on the strain and pore pressure of subgrade soil, especially for shallow subgrade. (4) As the distance from the subgrade surface increases, the maximum deformation appears at the edge of the subgrade

Study on Deformation Characteristics of Low-Highway Subgrade under Traffic Load

Highway subgrade bears millions of traffic loads over the years, and its strength, stiffness, and long-term stability gradually decline. In this paper, dynamic triaxial tests were carried out to study the time evolution and spatial distribution of strain and pore pressure of highway-subgrade soil under the action of traffic load. The influence of traffic load on subgrade deformation was analyzed. Furthermore, a numerical-calculation model of the subgrade was established. The deformation characteristics of subgrade under driving load were analyzed. The main conclusions can be drawn as follows: (1) With the increase in loading times, the cumulative strain and pore pressure can be roughly divided into three stages: rapid-growth stage, slow-growth stage, and equilibrium stage. (2) The influence of traffic load on the cumulative strain and pore-water pressure of subgrade soil decreases rapidly with the increase in depth. (3) The amplitude of traffic load has a tremendous influence on the strain and pore pressure of subgrade soil, especially for shallow subgrade. (4) As the distance from the subgrade surface increases, the maximum deformation appears at the edge of the subgrade

Assessing the Sensitivity of Vegetation Cover to Climate Change in the Yarlung Zangbo River Basin Using Machine Learning Algorithms

Vegetation is a key indicator of the health of most terrestrial ecosystems and different types of vegetation exhibit different sensitivity to climate change. The Yarlung Zangbo River Basin (YZRB) is one of the highest basins in the world and has a wide variety of vegetation types because of its complex topographic and climatic conditions. In this paper, the sensitivity to climate change for different vegetation types, as reflected by the Normalized Difference Vegetation Index (NDVI), was assessed in the YZRB. Three machine learning models, including multiple linear regression, support vector machine, and random forest, were adopted to simulate the response of each vegetation type to climatic variables. We selected random forest, which showed the highest performance in both the calibration and validation periods, to assess the sensitivity of the NDVI to temperature and precipitation changes on an annual and monthly scale using hypothetical climatic scenarios. The results indicated there were positive responses of the NDVI to temperature and precipitation changes, and the NDVI was more sensitive to temperature than to precipitation on an annual scale. The NDVI was predicted to increase by 1.60%–4.68% when the temperature increased by 1.5 °C, while it only changed by 0.06%–0.24% when the precipitation increased by 10% in the YZRB. Monthly, the vegetation was more sensitive to temperature changes in spring and summer. Spatially, the vegetation was more sensitive to temperature increases in the upper and middle reaches, where the existing temperatures were cooler. The time-lag effects of climate were also analyzed in detail. For both temperature and precipitation, Needleleaf Forest and Broadleaf Forest had longer time lags than those of other vegetation types. These findings are useful for understanding the eco-hydrological processes of the Tibetan Plateau

Assessing the Sensitivity of Vegetation Cover to Climate Change in the Yarlung Zangbo River Basin Using Machine Learning Algorithms

Vegetation is a key indicator of the health of most terrestrial ecosystems and different types of vegetation exhibit different sensitivity to climate change. The Yarlung Zangbo River Basin (YZRB) is one of the highest basins in the world and has a wide variety of vegetation types because of its complex topographic and climatic conditions. In this paper, the sensitivity to climate change for different vegetation types, as reflected by the Normalized Difference Vegetation Index (NDVI), was assessed in the YZRB. Three machine learning models, including multiple linear regression, support vector machine, and random forest, were adopted to simulate the response of each vegetation type to climatic variables. We selected random forest, which showed the highest performance in both the calibration and validation periods, to assess the sensitivity of the NDVI to temperature and precipitation changes on an annual and monthly scale using hypothetical climatic scenarios. The results indicated there were positive responses of the NDVI to temperature and precipitation changes, and the NDVI was more sensitive to temperature than to precipitation on an annual scale. The NDVI was predicted to increase by 1.60%–4.68% when the temperature increased by 1.5 °C, while it only changed by 0.06%–0.24% when the precipitation increased by 10% in the YZRB. Monthly, the vegetation was more sensitive to temperature changes in spring and summer. Spatially, the vegetation was more sensitive to temperature increases in the upper and middle reaches, where the existing temperatures were cooler. The time-lag effects of climate were also analyzed in detail. For both temperature and precipitation, Needleleaf Forest and Broadleaf Forest had longer time lags than those of other vegetation types. These findings are useful for understanding the eco-hydrological processes of the Tibetan Plateau