Jun Guo

Extreme weather events, such as extreme cold events (ECEs), are occurring more frequently due to climate change and variability. The ECEs have severe impacts on people’s lives and the economy. In this study, we used observational data for the period 1978-2020 from three national meteorological stations and forty-nine automatic weather stations located in the Tianjin Binhai New Area (TBNA), China, to analyze the spatio-temporal characteristics of ECEs and evaluate their impacts on population and economy. Our results indicate that the annual extreme minimum temperature showed a slight upward trend in the TBNA during the past 43 years. Extreme minimum temperatures were higher in the central part (–15 to –19ºC) and lower in the northern and southern parts of the TBNA (–19 to –23ºC). The cumulative decreases in temperature were higher in the west and lower in the east. ECEs were most frequent in autumn, followed by winter, and least frequent in summer. The months with the most ECEs were October and November. The ECEs lasted longer in the southern part of the TBNA and were shorter in the central and northern parts. The ECEs lasted, on average, approximately 15 days in the south and 7–10 days in most other areas. Places in the TBNA at a high risk of experiencing the ECEs include Gulin subdistrict and other areas in the central part of the TBNA, whereas Zhongtang town and other places in the southern TBNA are low-risk areas. In terms of the impact of ECEs on populations, the central parts of the TBNA such as Hangzhou subdistrict and the northern parts of the TBNA such as Hangu subdistrict are areas of moderate risk; most others are low-risk areas. In terms of the impact of ECEs on Gross Domestic Product (GDP), central parts of the TBNA such as Hujiayuan subdistrict are moderate-risk areas while most others are low-risk areas. Our findings serve as a basis for decision-making and provide a reference for local governments to mitigate the negative impacts of ECEs.

The variations in summer precipitation according to different grades and their effects on summer drought/flooding in the Haihe River basin were analyzed using the daily precipitation data from 161 meteorological stations from 1972 to 2021. The results showed that the number of rainy days (NRD) in summer in the Haihe River basin significantly declined in the past 50 years, mainly due to the reduction in the number of light-rain days. The precipitation amount (PA) exhibited prominent interdecadal characteristics, showing an upward tendency in the past 20 years accompanied by a remarkable increase in the proportion of torrential rain. The NRD in the northern part of the basin significantly decreased, while the PA in the southeast showed an increasing trend. Summer drought/flooding was strongly linked to the changes in the NRD and was predominantly affected by intense precipitation, with contribution rates of 5.5%, 16.8%, 31.2%, and 46.5% from light, moderate, heavy, and torrential rain, respectively. The effects of torrential rain increased in recent decades, particularly in the flooding scenarios. In addition, July was the critical period for summer drought/flooding, with the major influence of heavy and torrential rain. The most intense summer rainfall event in the Haihe River basin could contribute from 15% to 29% of total precipitation, resulting in changes in the severity and state of summer drought/flooding, which indicated that the precipitation process had a decisive impact on seasonal drought/flooding. Therefore, when predicting summer precipitation in the Haihe River basin, it is necessary to pay attention to the intense rainfall events during critical periods.

The Haihe River basin of North China is characterized by extremely low per capita water resources and a consistently long-term decreasing trend of precipitation and runoff over the last few decades. This study analyzes the climatological features of rainy season (May–September) precipitation in the Haihe River basin and its branch systems based on a high-density hourly observational dataset during 2007–2017. We show that there are two high-rainfall zones in the basin, with one along the south of the Yanshan Mountains to Taihang Mountains and another along the Tuma River in the south. Rainstorm centers exist amidst the two zones. July generally sees the highest precipitation, followed by August, and May has the lowest precipitation. The major flood season is reached between the third pentad of July and the fourth pentad of August. The precipitation is high at night but low in the daytime. In the pre-flood season before early July, rainfalls mostly arrive at 16:00–21:00 h. After entering the major flood season, the diurnal precipitation has two peaks, one at 17:00–22:00 h and the other at 0:00–7:00 h. In the post-flood season after mid-August, the most rain occurs at night, with the peak appearing at 0:00–8:00 h. The short-duration precipitation is mainly distributed in the mountainous areas, and the long-duration precipitation that contributes most to seasonal rainfalls appears in the plain areas, and the continuous precipitation mostly occurs in the windward slopes of the Taihang Mountains and the Yanshan Mountains. In addition, urbanization process around large city stations may have affected the rainy season precipitation to a certain extent in the Haihe River basin, with large and medium city stations experiencing around 10% higher precipitation than small city stations. However, this issue needs to be investigated exclusively.

An accurate gridded climatological temperature data-set can be a reliable basis for studying the issues that concern climate change, natural disasters, among others. In this study, the climate standard value data and annual observed data collected by 104 meteorological sites in Northeast China from 1971 to 2000 are used to interpolate the annual mean air temperature of the region with different spatial interpolation techniques. Efforts have also been made to verify the accuracy of the results generated by each interpolation method with error indicator, temperature characteristic value and temperature variation curves. The results show at the spatial scale, the partial thin-plate smoothing spline (PTPSS) scheme generates the most desirable temperature interpolations with a root-mean-square error at 0.34 °C and an average standard error at 0.52 °C. At the temporal scale, the PTPSS-based 1971–2000 temperature curves agree the best with the gridded temperature curves, with the correlation coefficients of means, minimums and maximums between the two being larger than 0.9. Meanwhile, the PTPSS produce the smallest error approaching zero among all the interpolation schemes tested in the study. In this context, the PTPSS scheme stands out as the most desirable interpolation method for the annual mean temperature in Northeast China.