Spatial Distribution of Precipitation in Huang-Huai-Hai River Basin between 1961 to 2016, China

Yuan, Yanbin; Yan, Denghua; Yuan, Zhe; Yin, Jun; Zhao, Zhongnan

doi:10.3390/ijerph16183404

Cited by 25 publications

(15 citation statements)

References 26 publications

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“…Unlike the general “dry-get-drier, wet-get-wetter” paradigm under a warming climate, precipitation was found to decline during past decades and increase in future decades throughout the DQRB (Figs. 5 and 6 ), similar to the results obtained in previous studies (Song et al 2015 ; Yuan et al 2019 ; Li et al 2016a ; Li et al 2018b ). This result is considered to be related to the Western Pacific subtropical high, which determines the intensity of the East Asian monsoon (Wang et al 2019 ); i.e., moisture transport and convergence with weak large-scale circulation are insufficient to deliver abundant moisture, while the strengthened and unstable East Asian summer monsoon north of 30° N enhances the availability of water vapor.…”

Section: Discussionsupporting

confidence: 91%

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Gao

Liu

et al. 2021

Environ Sci Pollut Res

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Understanding dynamic future changes in precipitation can provide prior information for nonpoint source pollution simulations under global warming. However, the evolution of the dependence structure and the unevenness characteristics of precipitation are rarely considered. This study applied a two-stage bias correction to daily precipitation and max/min temperature data in the Daqing River Basin (DQRB) with the HadGEM3-RA climate model. Validated from 1981 to 2015, future scenarios under two emission paths covering 2031–2065 and 2066–2100 were projected to assess variations in both the amount and unevenness of precipitation. The results suggested that, overall, the two-stage bias correction could reproduce the marginal distributions of variables and the evolution process of the dependence structure. In the future, the amount of precipitation in the plains is expected to increase more than that in the mountains, while precipitation unevenness, as measured by relative entropy, shows a slight increase in the mountains and a decrease in the plains, with enhanced seasonality. Conditioned on rising temperatures, high-/low-intensity precipitation tends to intensify/weaken precipitation unevenness. Additionally, the potential application of the bias correction method used herein and the possible impacts of uneven precipitation on nonpoint source pollution are given for further analyses. This study can provide useful information for future nonpoint source pollution simulations in the DQRB. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-16066-9.

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Section: Discussionsupporting

confidence: 91%

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Gao

Liu

et al. 2021

Environ Sci Pollut Res

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“…In previous studies, there were many methods that focused on hazard factors used to study flood and drought disasters, such as the drought index and extreme precipitation (Guo et al, 2018;Yang et al, 2018a;Yuan et al, 2019;Zuo et al, 2019). The hazard factors indicate the intensity of the water anomalies, which represent the direct cause of the disaster.…”

Section: Discussionmentioning

confidence: 99%

Risk analysis and influencing factors of drought and flood disasters in China

Pei

Tian

Ren

et al. 2021

Preprint

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The risk analysis of flood and drought disasters and the study of their influencing factors enhance our understanding of the temporal and spatial variation law of disasters and help identify the main factors affecting disasters. This paper uses the provincial administrative region of China as the research area. The statistical distribution of flood and drought disasters was optimized from 10 alternative distributions by the KS test, and the disaster risk was analyzed. Thirty-five indicators were selected from nature, agriculture and the social economy as alternative factors. The main factors affecting flood and drought disasters were selected by Pearson, Spearman and Kendall correlation coefficient test. The results demonstrated that the distribution of floods and drought is right skewed, and the beta distribution is the best statistical distribution for fitting disasters. In terms of time, the risk of flood and drought disasters in all regions showed a downward trend. Economic development and the enhancement of the ability to resist disasters were the main reasons for the change in disasters. Spatially, the areas with high drought risk were mainly distributed in Northeast and North China, and the areas with high flood risk were mainly distributed in the south, especially in Hubei, Hunan, Jiangxi and Anhui. The distribution of floods and drought disasters was consistent with the distribution characteristics of precipitation and water resources in China. Among the natural factors, precipitation was the main factor causing changes in floods and drought disasters. Among the agricultural and socioeconomic factors, the indicators reflecting the disaster resistance ability and regional economic development level were closely related to flood and drought disasters. The research results have reference significance for disaster classification, disaster formation mechanisms and flood and drought resistance.

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“…Some studies recommend the Weibull distribution as the optimal PDF of the observed precipitation in the DQRB in comparison to the common parametric distributions (Shi et al, 2014;Li and Shi, 2019). It should be that the precipitation distristructure of the DQRB has changed in the past few decades (Yuan et al, 2019). Thus, DKDE may be more preferred to represent the PDF of the observed precipitation.…”

Section: Discussionmentioning

confidence: 99%

The impact of using different probability representations in application of equidistant quantile matching for bias adjustment of daily precipitation over the Daqing River Basin, North China

Gao

Liu

et al. 2021

Intl Journal of Climatology

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This study focuses on how the representation of daily precipitation probability distributions may affect the application of equidistant quantile matching (EDCDFm). Five representations, that is, two parametric distributions and three nonparametric approaches, are selected. The parametric distributions include Gamma and Weibull while the nonparametric approaches include empirical cumulative distribution function (ECDF) and kernel density estimation method (KDE) as well as an improved KDE named diffusion-based kernel density estimation (DKDE). All five methods were applied to correct the daily precipitation of 11 stations over the Daqing River Basin, North China in 1981-2015. The results demonstrated that DKDE is closer to ECDF than the other three methods in the goodness-of-fit evaluation. Furthermore, nonparametric methods present advantages over parametric methods; especially, DKDE and ECDF are skilful equally and both of them display impressive comprehensive performance than other methods by multi-index. These findings suggest that representing the precipitation probability distributions accurately is beneficial for the bias correction and selecting one or more suitable indexes is of great significance to the verification of EDCDFm. This study can provide guidance for future water resources management of the Daqing River Basin.

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Spatial Distribution of Precipitation in Huang-Huai-Hai River Basin between 1961 to 2016, China

Cited by 25 publications

References 26 publications

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Risk analysis and influencing factors of drought and flood disasters in China

The impact of using different probability representations in application of equidistant quantile matching for bias adjustment of daily precipitation over the Daqing River Basin, North China

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