Based on meteorological and hydrological observation and NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research) reanalysis data, the variation characteristics of compound dry and hot events, and their causes and impact on runoff in the main confluence area of the upper Yellow River in summer were analyzed. The results show that, from the average spatial distribution, the number of high temperature days in summer increases gradually from southwest to northeast in the main confluence area of the upper Yellow River, and the opposite is true for summer precipitation. From temporal and spatial distribution, the number of summer high temperature days in the main confluence area of the upper Yellow River has been increasing consistently since 1961. The precipitation shows fluctuating consistently changes from a long-term trend perspective, but increased consistently after 2000. The compound dry-hot events have increased significantly since this century. From the perspective of multi-time scale changes, the summer compound dry-hot events in the main confluence area of the upper Yellow River exist mainly inter-annual changes and trend changes, and the significant increase of summer compound dry-hot events since 2000 is mainly caused by trend changes. From the perspective of influencing factors, the changes of summer compound dry-hot events in the main confluence areas of the upper Yellow River are mainly influenced by multiple circulation factors, but the influencing factors differ greatly on different time scales. On the inter-annual scale, the influence of westerly circulation, East Asian summer monsoon, South Asian summer monsoon, plateau summer monsoon, and north wind circulation is relatively weak, on the inter-decadal scale, compound dry-hot evencs are mainly influenced by the Tibetan Plateau summer monsoon and they are also influenced by the westerly circulation, the East Asian summer monsoon, the South Asian summer monsoon, the Tibetan Plateau summer monsoon and the north wind circulation on the multi-decadal scale. From the background field of large-scale circulation, the West Pacific subtropical high is stronger and westward, the lack of abnormal water vapor transport in the southwest and the abnormal downward motion in the vertical field are the main reasons for the increase of summer compound dry-hot events in the main confluence area of the upper Yellow River since this century. The increase of compound dry-hot events in the main confluence area of the upper Yellow River will reduce the runoff of Lanzhou station in the basin, the main reason for the increase in runoff of the Yellow River Lanzhou section since 1998 is the increase in precipitation.
In order to construct an accurate drought prediction model, it is very important to select predictors with physical significance and adopt efficient prediction methods. Compared to the traditional prediction methods, more efficient and reliable machine learning algorithms have been more widely used in climate prediction. This study is based on the monthly meteorological element data of 70 national meteorological stations in Hubei Province from 1960 to 2022, as well as the atmospheric circulation and sea temperature indices provided by the National Climate Center and the National Oceanic and Atmospheric Administration (NOAA). The standardized precipitation evapotranspiration index was used to determine drought occurrence as the target variable, and 11 indices were selected as input variables using feature selection methods. On this basis, two machine learning algorithms, classification and regression tree (CART) and random forest (RF), were used to construct summer drought prediction models of Hubei Province. The 47 years data were randomly selected as the training set, while the remaining 16 years data were used as the test set to evaluate the prediction performance. The results show that the prediction accuracy of the CART and RF models for drought was 88% and 81%, respectively. Additionally, both algorithms identified the Asian zonal circulation index as the most important variable in their models, indicating that this index is crucial for predicting summer droughts in Hubei Province. By constructing these two machine learning algorithm prediction models, this study provides an objective and effective new approaches for summer drought prediction in Hubei Province, which will provide scientific information for drought prevention and mitigation in the region.
Based on the daily precipitation data at 18 meteorological stations of Hainan Island from 1969 to 2018, the spatio-temporal change characteristics of precipitation randomness were analyzed by using information entropy method, Mann-Kendall trend test and spatial interpolation technique of inverse distance weight. The results show that the uneveness of monthly apportionment of annual precipitation and precipitation days increased from east to west of Hainan Island. In recent 50 years, the monthly apportionment unevenness of annual precipitation and precipitation days showed an increasing trend in northern and western areas and part areas of southern Hainan Island, while it showed a decreasing trend in the rest areas on the whole. The spatial distribution of randomness of daily precipitation was significantly different in the whole year and four seasons in Hainan Island, and they were significantly and positively correlated with the proportion of rainstorm and above rainfall days. In terms of time, the randomness of daily precipitation in the whole year and four seasons increased in most cities (counties) of Hainan Island from 1969 to 2018, especially the probability of strong precipitation increased in four seasons. The rainstorms to torrential rains in central, northwestern, eastern and eastern Hainan Island should be paid enough attention in spring, summer, autumn and winter, respectively.
Based on aerological observations at the hail supp ression experimental stations of Yongdeng andMinxian counties of Gansu Province during 1979 - 1987, as well as surface meteorological observations at the same time, the upperwinds under the six different weather conditions of strong hailstorm, weak hailstorm, thunderstorm, shower, overcast and sunshine were analyzed. Results show that significant defference p rezented in low airwind direction shear, middle airwind velocity shear and jet in upper air before haill falling under the strong hailstorm weather condition compared with those of otherweather conditions, which is useful to forecast hailstorm and study its formation mechanism.
In this paper,.the spatial and temporal characteristics of drought in China are the analyzed. On the basis of the main factors of the influence of China climate anomaly .the causes of drought are also studied. It is shown that the causes of formation are mostly the global and regional climate warming .the inter一annual and decadal oscillation of the main factors.
