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- Author or Editor: Yaling Liu x
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Abstract
Precipitation is one of the most important meteorological factors affecting the water cycle and ecological system over the Source Region of the Three Rivers (SRTR), where the Yangtze River, Yellow River, and Lantsang River originate. The characteristics of annual and summer water vapor transport and budget over the SRTR are analyzed using monthly observational and reanalysis datasets during 1980–2019. The linkage between water vapor transport and summer precipitation is also explored in this study. The results show that the Global Precipitation Climatology Project (GPCP) data are in agreement with the measured precipitation. The SRTR is a sink region for water vapor, where the water vapor content shows an increasing trend with a rate of 0.2 mm (10 yr)−1 annually and 0.3 mm (10 yr)−1 in the summer. The water vapor mainly flows into the SRTR from the lower (521.2 × 106 kg s−1) and the middle (195.7 × 106 kg s−1) layers of the southern boundary in summer, while it exports from the middle (208.1 × 106 kg s−1) layer of the eastern boundary. The abnormal wind convergence and the low pressure system, combined with the effects of the western Pacific subtropical high and the Mongolian high, provide conditions for the transport of water vapor and precipitation over the SRTR. A close relationship is found between water vapor flux and precipitation from the singular value decomposition (SVD) analysis. The Brahmaputra River basin is the key region of water vapor transport over the SRTR, which contributes to further understanding the mechanisms of water vapor transport and the regional water cycle.
Significance Statement
Under the background of global warming, the Tibetan Plateau has an obvious trend of warming and humidification. The purpose of this study was to investigate the characteristics of water vapor transport and its linkage with summer precipitation over Source Region of the Three Rivers, which is located in the hinterland of the Tibetan Plateau. We found that the Brahmaputra River basin is the key region affecting the precipitation. These findings contribute to the understanding of the regional water cycle characteristics and the mechanism of the synergistic effect of westerly wind and monsoon on the change of “Water Tower of Asia.”
Abstract
Precipitation is one of the most important meteorological factors affecting the water cycle and ecological system over the Source Region of the Three Rivers (SRTR), where the Yangtze River, Yellow River, and Lantsang River originate. The characteristics of annual and summer water vapor transport and budget over the SRTR are analyzed using monthly observational and reanalysis datasets during 1980–2019. The linkage between water vapor transport and summer precipitation is also explored in this study. The results show that the Global Precipitation Climatology Project (GPCP) data are in agreement with the measured precipitation. The SRTR is a sink region for water vapor, where the water vapor content shows an increasing trend with a rate of 0.2 mm (10 yr)−1 annually and 0.3 mm (10 yr)−1 in the summer. The water vapor mainly flows into the SRTR from the lower (521.2 × 106 kg s−1) and the middle (195.7 × 106 kg s−1) layers of the southern boundary in summer, while it exports from the middle (208.1 × 106 kg s−1) layer of the eastern boundary. The abnormal wind convergence and the low pressure system, combined with the effects of the western Pacific subtropical high and the Mongolian high, provide conditions for the transport of water vapor and precipitation over the SRTR. A close relationship is found between water vapor flux and precipitation from the singular value decomposition (SVD) analysis. The Brahmaputra River basin is the key region of water vapor transport over the SRTR, which contributes to further understanding the mechanisms of water vapor transport and the regional water cycle.
Significance Statement
Under the background of global warming, the Tibetan Plateau has an obvious trend of warming and humidification. The purpose of this study was to investigate the characteristics of water vapor transport and its linkage with summer precipitation over Source Region of the Three Rivers, which is located in the hinterland of the Tibetan Plateau. We found that the Brahmaputra River basin is the key region affecting the precipitation. These findings contribute to the understanding of the regional water cycle characteristics and the mechanism of the synergistic effect of westerly wind and monsoon on the change of “Water Tower of Asia.”
Abstract
Soil moisture (SM) links the water and energy cycles over the land–atmosphere interface and largely determines ecosystem functionality, positioning it as an essential player in the Earth system. Despite its importance, accurate estimation of large-scale SM remains a challenge. Here we leverage the strength of neural network (NN) and fidelity of long-term measurements to develop a daily multilayer cropland SM dataset for China from 1981 to 2013, implemented for a range of different cropping patterns. The training and testing of the NN for the five soil layers (0–50 cm, 10-cm depth each) yield R 2 values of 0.65–0.70 and 0.64–0.69, respectively. Our analysis reveals that precipitation and soil properties are the two dominant factors determining SM, but cropping pattern is also crucial. In addition, our simulations of alternative cropping patterns indicate that winter wheat followed by fallow will largely alleviate the SM depletion in most parts of China. On the other hand, cropping patterns of fallow in the winter followed by maize/soybean seem to further aggravate SM decline in the Huang-Huai-Hai region and southwestern China, relative to prevalent practices of double cropping. This may be due to their low soil porosity, which results in more soil water drainage, as opposed to the case that winter crop roots help maintain SM. This multilayer cropland SM dataset with granularity of cropping patterns provides an important alternative and is complementary to modeled and satellite-retrieved products.
Abstract
Soil moisture (SM) links the water and energy cycles over the land–atmosphere interface and largely determines ecosystem functionality, positioning it as an essential player in the Earth system. Despite its importance, accurate estimation of large-scale SM remains a challenge. Here we leverage the strength of neural network (NN) and fidelity of long-term measurements to develop a daily multilayer cropland SM dataset for China from 1981 to 2013, implemented for a range of different cropping patterns. The training and testing of the NN for the five soil layers (0–50 cm, 10-cm depth each) yield R 2 values of 0.65–0.70 and 0.64–0.69, respectively. Our analysis reveals that precipitation and soil properties are the two dominant factors determining SM, but cropping pattern is also crucial. In addition, our simulations of alternative cropping patterns indicate that winter wheat followed by fallow will largely alleviate the SM depletion in most parts of China. On the other hand, cropping patterns of fallow in the winter followed by maize/soybean seem to further aggravate SM decline in the Huang-Huai-Hai region and southwestern China, relative to prevalent practices of double cropping. This may be due to their low soil porosity, which results in more soil water drainage, as opposed to the case that winter crop roots help maintain SM. This multilayer cropland SM dataset with granularity of cropping patterns provides an important alternative and is complementary to modeled and satellite-retrieved products.
