Characteristics of the precipitation concentration and their relationship with the precipitation structure: A case study in the Huai River basin, China

Yin, Yixing; Chen, Haishan; Wang, Guojie; Xu, Weixin; Wang, Shenmin; Yu, Wenjun

doi:10.1016/j.atmosres.2021.105484

Cited by 10 publications

(6 citation statements)

References 39 publications

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“…Te greater GC means the greater national income gap. In this paper, the GC was used to measure daily precipitation concentration, and the DPCI can be calculated as follows [16,32,33]:…”

Section: Dpci Calculationmentioning

confidence: 99%

Changing Properties of Daily Precipitation Concentration in the Hai River Basin, China

Ren

Zhao

Ling

et al. 2023

Scientific Programming

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Understanding the spatiotemporal pattern of precipitation concentration is important for the assessment of flood and drought risk and utilization of water resources. In this study, the daily precipitation concentration index in the Hai River basin in China was calculated based on the Gini coefficient obtained from the observed data of 51 meteorological stations from 1951 to 2018 and spatiotemporal pattern variations were investigated. The trends and abrupt changes of DPCI were tested by the Mann–Kendall and the Pettitt methods, respectively. The relationships among DPCI, percentage of precipitation contributed by the rainiest days, and disaster losses were discussed by the linear correlation analysis. The results showed that the DPCI value ranges between 0.6471 and 0.7938, decreases westward and northward, which is negatively related to latitude and elevation, and is positively related to longitude. Negative trends of the DPCI were found at most stations, and the PCI trends of more than 80% stations were statistically significant. Abrupt changes of the DPCI have a postponement trend from west to east with time. Daily heterogeneity of the rainfall in a year is highly correlated with the heavy rainfall amount of the 25% rainiest days. For a year, higher DPCI coupled with more precipitation is easy to cause a flood disaster; conversely, higher DPCI along with less precipitation is easy to cause a drought disaster. In the future, the risk of flood disasters would be reduced, but the drought disasters would be increased in the Hai River basin.

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“…Te greater GC means the greater national income gap. In this paper, the GC was used to measure daily precipitation concentration, and the DPCI can be calculated as follows [16,32,33]:…”

Section: Dpci Calculationmentioning

confidence: 99%

Changing Properties of Daily Precipitation Concentration in the Hai River Basin, China

Ren

Zhao

Ling

et al. 2023

Scientific Programming

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“…In addition, the annual precipitation in this area is uneven and is mainly concentrated in summer. The low precipitation levels in winter accentuate the air pollution problems and contribute to smog formation (Yin et al, 2021;Ji and Li, 2016). Furthermore, in recent years, climate change has led to periods of extremely high temperatures and cold snaps in Linyi (Wu et al, 2022).…”

Section: Study Areamentioning

confidence: 99%

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

Yang

et al. 2023

Preprint

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Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluated the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprised meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China from January 1, 2016 to August 20, 2022. We used support vector regression (SVR) to build regression models to analyze the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. To evaluate the model performance, 70% of the dataset was used for training and 30% was used for testing. The Spearman correlation and SVR model results indicated that NO2, PM2.5, and PM10 were correlated with the occurrence of respiratory diseases, and the strongest correlation was for pneumonia. An increase in the daily average temperature and daily relative humidity decreased the number of patients with pneumonia and chronic lower respiratory diseases but increased the number of patients with acute upper respiratory infections. The SVR modeling showed potential for predicting the number of respiratory-related hospital visits. This work demonstrated that combining machine learning with meteorological and air pollution data can be used for disease prediction and can serve as a useful tool for policymakers to take preventive measures.

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“…However, according to Cortesi et al (2012), daily rainfall distribution has not substantially changed over Europe from 1971 to 2010. In addition, these methods help identify the effect of teleconnections indices on droughts and ood events (Xie et al, 2022;Yang et al, 2020a;Yin et al, 2021). However, in most of the studies, researchers have focused on the changes in the amount of precipitation and negative trends in precipitation over the regions, but spatiotemporal precipitation changes in many regions are still unknown (Yang et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the Gini coe cient can be employed to determine the precipitation concentration (Eq. 4) (Jolliffe and Hope, 1996;Rajah et al, 2014;Yin et al, 2021).…”

mentioning

confidence: 99%

Identification of features and variations of precipitation concentration indicators over Poland and two large-scale transboundary river basins in the Baltic Sea region

Eini

Darand

Malekzadeh

et al. 2023

Preprint

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In recent years, climate change affected several regions over the globe by changing the temporal and spatial pattern of precipitation, increasing the temperature, and causing severe droughts and floods. In this regard, understanding the changes and characteristics of precipitation in different aspects is an urgent practice. In this study, by employing four precipitation concentration indicators, including CI (concentration index), PCI (precipitation concentration index), PCP (precipitation concentration period), and PCD (precipitation concentration degree), various features of precipitation concentration over two large-scale transboundary river basins (Vistula and Odra river basins) and union of the territory of Poland in Central Europe are investigated. For this aim, a 2 km × 2 km regional gridded dataset was employed from 1990–2019 at daily time steps. Results show that the number of rainy days varies between 160 and 250 days. In general, the effects of sea and high elevation on precipitation concentration are vivid in the south (mountainous area) and north (Baltic Sea). Over the foothills of the mountainous area, CI has a more extensive range than other areas; this indicates that precipitation over these regions happened over fewer days. The variation of PCI is tiny and shows that almost all months contribute to the annual precipitation. PCP, by varying between 40˚ to 130˚, shows that the most considerable amount of precipitation in the short periods occurs between February and May. PCD denotes that precipitation concentration is highly distributed on rainy days. These indicators have shown that almost precipitation is consistently distributed over the year, and there are no significant changes in these indicators. However, these indicators have different patterns relative to the central parts of the region over the northern and southern parts.

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Characteristics of the precipitation concentration and their relationship with the precipitation structure: A case study in the Huai River basin, China

Cited by 10 publications

References 39 publications

Changing Properties of Daily Precipitation Concentration in the Hai River Basin, China

Changing Properties of Daily Precipitation Concentration in the Hai River Basin, China

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

Identification of features and variations of precipitation concentration indicators over Poland and two large-scale transboundary river basins in the Baltic Sea region

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