In this study, the climatic trend of the total cloud amount (TCA) and the low cloud amount (LCA) was studied using climate diagnostic methods while the correlations and possible physical mechanism between TCA, LCA and El Niño and Southern Oscillation (ENSO) were analysed and discussed. The results indicated that the annual average TCA and LCA reduced with increasing latitude while a significant decreasing trend has been derived for TCA and LCA; the trend rate is 0.9% per decade and 0.2% per decade, respectively. Moreover, the most obvious reduction area of TCA was focused in Northeast China and for LCA, it was located in the surrounding area of the Shaanxi province. The cloud amounts of the four seasons also showed a reducing tendency except the LCA of summer. Meanwhile, the spectrum analysis showed that TCA has 10, 5.7, 3.6 and 2.8 year periods and LCA has 10, 4, 2.7 and 2.1 year-long time cycles. Further analysis also revealed that the annual average TCA has a significant negative correlation with ENSO, and the most remarkable negative correlation centre is focused on central China. However, the relationship of the annual average LCA with ENSO is relatively weak. Except in spring, the spatial correlation distribution of TCA and LCA with ENSO in the other seasons was consistent with that of the annual average TCA and LCA, and 4-year periods of TCA and LCA coincide with the cycle of ENSO. This reveals that ENSO has an impact on inter-annual variations of TCA and LCA over China. It is generally considered that ENSO, through the atmospheric circulation situation, causes variation of cloud amounts.
The water resource system is a non-linear system, featuring variability and randomness. Its risk assessment is very different from that of a linear system. Considering the effects of river flow on the pollutant diffusion, migration, and maximum tolerable concentration, a modified non-linear model (MNLM) was established, while the forcing terms were introduced to model functions for water pollution risk assessment. Taking the Weihe River Basin in China as an example, the risk assessment values were divided into five levels: negligible risk, acceptable risk, marginal risk, unacceptable risk, and catastrophic risk. As such, the risk variation of the river pollution interval was analyzed. The results showed that the BOD 5 , COD, and nitrite nitrogen are the main pollutants, leading to great risks of river water pollution. Moreover, it was found that the risk in the dry season is higher than that in the flood season, while the risk based on MNLM is 10.9% higher than that of linear methods. Verification indicates that MNLM is considered more suitable for risk assessment of complex river water pollution. However, the forcing term coefficient should be corrected for actual situations in different river water systems. The explored MNLM is expected to give insights into regional river water environment management.
Flood disasters have occurred frequently in recent years, but there is no consensus on the mechanism and influencing factors. Taking the upper reaches of Weihe River Basin as a case in Western China, a soil and water assessment tool (SWAT) model was established to quantitatively simulate the impact of land use and climate change on runoff changes, while 4 extreme land-use scenarios and 24 temperature and precipitation scenarios assumptions were proposed to simulate the response of runoff to land use and climate changes. The SWAT simulation results showed that the sensitivity parameters affecting the model simulation were the CANMX, CN2, SOL_K, CH_N2, and SOL_AWC. The correlation index R2 and the efficiency coefficient ENS of the upper Weihe River were both in the range of 0.75–0.78, the relative error PS between the simulated results and the measured runoff was below 10%, suggesting the good applicability of the SWAT model in this study area. Using the improved SWAT model to simulate the peak runoff (flood) simulation value is generally smaller than the measured value, and the absolute value of the error is less than 6%. The expansion of wasteland increased the runoff by over 90% on average, the expansion of cultivated land increased the runoff by 8% on average, and the expansion of woodland and grassland increased the surface runoff by 6% on average. When the precipitation decreased by 25% and the temperature increased by 22%, the smallest runoff was obtained in the simulation. Accordingly, when the precipitation increased by 25% and the temperature decreased by 22%, the maximum annual runoff was obtained. By decomposing the contribution rate of human activities and climate change to runoff, it showed that the contribution rate of human activities to the reduction of runoff was greater than that of climate change. This study can provide scientific reference for the simulation and prediction of future floods.
Mountain glaciers are highly sensitive to climate change. In this paper, we systematically analyzed and discussed the responses of glaciers to climate change during 1960-2017 in western China by the methods of least squares and correlation analysis. Results show that the maximum temperature, minimum temperature, average temperature, and precipitation significantly increased in western China at the rates of 0.32°C/10a, 0.48°C/10a, 0.39°C/10a, and 11.20 mm/10a, respectively. However, the wind speed, hours of sunshine, snowfall, and snowy days displayed decreasing trends at the rates of-0.53 m/(s•10a), 3.72 h/10a,-2.90 mm/10a, and-0.10 d/10a, respectively. The annual percentage of glacier area decreased by approximately 0.42%, and the average glacier area decreased by 2.76 km 2 /a. Meanwhile, glacial shrinkages were greater in the Altay Mountains, Tanggula Mountains, and Qilian Mountains than in the other mountainous regions. Glacier accumulation decreased while melt volume increased at a rate of 2.7×10 4 m 3 /a. The area of melt volume was 1.3 times that of the glacier accumulation area. The glacier mass balance (GMB) decreased substantially at a rate of-14.0 mm/a, whereas the equilibrium line altitude (ELA) showed an increasing trend at a rate of 0.5 mm/a. After 1997, the mass was smaller than-500.0 mm, indicating a huge loss in glaciers. Furthermore, relationships between ELA and GMB and various climatic factors were established. Temperature and precipitation demonstrated a significantly negative correlation, whereas wind speed and snowy days had significantly positive correlations with GMB. Snowy days also exhibited a remarkably negative correlation with ELA. The strong warming trend and less snowy days were thought to be the main factors leading to glacial melting, whereas the increase in precipitation, and reductions of sunshine hours and wind speed might slow glacial melting.
Regional environmental carrying capacity (ECC) is nonlinear and spatially specific. A hierarchy index system including resources, environmental and socioeconomic elements was established using an analytic hierarchy process. Principal component analysis (PCA) was used to estimate the regional size and differences of environmental carrying capacities. Main information of four principal components, i.e., carrying capacity of resources supply, carrying capacity of environmental quality, carrying capacity of social economy and carrying capacity of infrastructure construction, was extracted. The ECC evaluation value was divided into five levels of lowest carrying capacity, low carrying capacity, medium carrying capacity, high carrying capacity and highest carrying capacity, respectively. The results showed that on the whole ECC was at the medium carrying capacity level. ECC was generally highest in Guanzhong plain, followed by Loess Plateau, and was lowest in Qiba mountain. The carrying capacity of water resources and environmental quality was relatively low, and the infrastructure carrying capacity was highest among the four components. The temporal spatial variation of ECC was closely related to vulnerability of the natural resources and environment in the regions. Verification was proven that PCA was a useful tool when applied to evaluate ECC and reflect the spatial distribution of large-quantity ECC indices on a large regional scale. This study provides a basis for comprehensive understanding of resources, environment and management for regional balanced development.
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