Changes of crop climate risk degree:a case study on cotton in Henan Province

The agricultural climate risk in the geographical transitional zones is very sensitive to climate change. Based on previous researches, this article established the cotton climate suitability model and the risk index to analyze the cotton climate risk degree in Henan Province. The result shows that, in the last 40 years, the cotton climate risk degree has a gradual increasing trend in which the precipitation plays a very important role, and the climate suitability and its deviation have different performances in the influence on the risk change in different phases; the increase rate of risk degree has significant regional and transitional characteristics, for example, from the eastern plain to the western mountains, the risk change rate becomes slower and slower, so the contrast between the central-southern Huang-Huai plain and the Funiu mountains is the most remarkable; and in the north-south direction, the contrast between the fast increase belt from the central-southern Huang-Huai plain to the middle Yellow River and the slow increase belt near the Yellow River is particularly remarkable, which is possibly the results of topographic and climatic transitional belt; and the risk change process also has obvious regional difference. For instance, abrupt change is an important characteristic in eastern Henan where this sudden change type has notable latitudinal difference, while in the western mountainous area it is very rare. Although the temperature change over the 40 years is still increasing in this area, the trend has reduced gradually since the end of the 1980s.

Estimating the Degree of Earthquake Risk from Fault Characteristics

A theoretical model considering the relationship between fault characteristics and tectonic stress is proposed to quantify earthquake risk degree. The model is applied in practice to the fault along the southern margin of Weihe basin in Shaanxi Province, Northwestern China and fitted well with reality.

Changes of citrus climate risk in subtropics of China

Based on the citrus temperature, precipitation, sunlight and climate risk degree, the article divides subtropics of China into three types： the low risk region, the moderate risk region and the high risk region. The citrus temperature risk increases with increasing latitude （except for the western mountainous area of subtropics of China）. The citrus precipitation risk in the central part of subtropics of China is higher than that in the northern and western parts. The distributions of citrus sunlight risk are not consistent to those of the citrus precipitation risk. The citrus climate risk is mainly influenced by temperature. There is latitudinal zonal law for the distribution of the climate risk, that is, the climate risk increases with increasing latitude At the same time the climate risk in mountainous area is high and that in eastern plain area is low. There are differences in the temporal and spatial changes of the citrus climate. In recent 46 years, the citrus climate risk presents a gradual increasing trend in subtropics of China, especially it has been increasing fast since the 1980s. Because of the global warming, the low risk region in the eastern and southern parts has a gradual decreasing trend, however, the high risk region in the northern and western parts has an increasing trend and the high risk region has been extending eastward and southward. The article analyses the distribution of the citrus climate risk degree of reduction rates of 〉10%, 〉20% and 〉30% in subtropics of China, and studies their changes in different time periods. Results show that the risk is increasing from southeast to northwest.

Prediction-based dynamic routing intelligent algorithm in power optical communication network

New energy power generation equipment has the characteristics of diurnal, perturbative, seasonal, and periodic power generation, which makes new power optical communication network(POCN) more dynamic and changeable. This is directly reflected in the dynamics of the link risk and service importance of the POCN. In this paper, aiming at the problem of the dynamic importance of service in POCN, and the resulting power optical communication network reliability decline problem, a new energy POCN dynamic routing intelligence algorithm based on service importance prediction is proposed. Based on the short-term power generation of new energy power station, the importance of each service and the risk degree of each link are predicted. Link weights are dynamically adjusted, and k-shortest path(KSP) algorithm is used to calculate route results. When network resources are insufficient, low-importance services can give way to prevent a large number of high-importance services from being blocked. Simulation results show that compared with the traditional KSP algorithm, the prediction-based dynamic routing intelligent(P-DRI) algorithm can reduce the service blocking probability by 55.59%, and reduce the average importance of blocking services by 44.77% at the cost of about 6.17% of the calculation delay.