# Mutation Characteristics of Precipitation Concentration Spatiotemporal Variation and Its Potential Correlation with Low-Frequency Climate Factors in the LRB Area from 1960 to 2020

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Research Area, Data and Methods

#### 2.1. Research Area

^{2}. It is one of the seven major rivers in China. The LRB has a semi-humid monsoon climate in most parts. Floods in the basin are frequent, and a large flood occurs every 7–8 years on average. The LRB is becoming a key research objective in the field of climate and hydrology. The range of 116° E–129° E, 38° N–46° N was selected as the research area.

#### 2.2. Data

#### 2.3. Methodology

#### 2.3.1. The PCD and PCP

- where, n is the total number of days in year i;
- j is the daily ordinal number in year i;
- r
_{ij}is the precipitation of a station in year i on day j; - R
_{i}is the total precipitation of the station in year i, Divide $\left[-\mathsf{\pi},\mathsf{\pi}\right]$ equally according to the number of days in year i, and θ_{j}is the azimuth of the jth day.

#### 2.3.2. Sliding t-Test

_{i}is the i

_{th}element in the time series, and the sample length n before and after the mutation point can be set artificially to make the test more reliable.

_{i}with n

_{i}sample sizes, a certain time was artificially set as the reference point, and two sub-sequences x

_{i}

_{1}and x

_{i}

_{2}before and after the reference point, with ${\overline{{x}_{i}}}_{1}$ and ${\overline{{x}_{i}}}_{2}$ are the mean values, variances of s

_{i}

_{1}

^{2}and s

_{i}

_{2}

^{2}and H: ${\overline{{x}_{i}}}_{2}-{\overline{{x}_{i}}}_{1}=0$, t meets the distribution of (n

_{1}+ n

_{2}− 2) t; and $\alpha $ is the given significance level (this study takes n

_{1}= n

_{2}= 10, α = 0.05). If $\left|t\right|>{t}_{\alpha}$, then H is false, that means if the mean difference between two sequences exceeds a certain level of significance ${t}_{\alpha}$, a mutation can be considered to have occurred.

#### 2.3.3. Student t-Test

_{1}and x

_{2}have different sample numbers n

_{1}and n

_{2}and their respective variances s

_{1}

^{2}and s

_{2}

^{2}are not equal, then the following statistic t-test can be used:

_{0}: μ

_{1}− μ

_{2}= μ

_{0}is true, where μ

_{0}is some specified difference that you wish to test. The critical values of t, t

_{α/}

_{2}(α/2 = 0.05) are based on (n

_{1}+ n

_{2}− 2) df [34].

#### 2.3.4. Cross Wavelet Transform (CWT) Analysis

_{X}and σ

_{Y}are the standard deviations of time series X and Y, respectively. The freedom degree (v) of the Morlet wavelet transform is set as 2. When the left term exceeds the upper bound of 95% confidence limit of the power spectrum of red noise, it is considered to pass the test of the standard spectrum of red noise with a significance level of 0.05.

## 3. Results and Discussions

#### 3.1. Mutation Points Identification of the PCD and PCP

#### 3.2. Spatial Pattern of PCD and PCP

#### 3.3. Relationship between Precipitation Indexes and Low-Frequency Climate Factors

#### 3.3.1. PCD

#### 3.3.2. PCP

## 4. Conclusions

- (1)
- Mutations occurred in the PCD sequence in 1980 and the PCP sequence in 2005 in the LRB area from 1960 to 2020.
- (2)
- Over the past 60 years, the annual PCD variation range was between 0.53 and 0.80 and it tended to decrease. The decrease in PCD was −0.03/10 a before the mutation (1960–1979), and −0.01/10 a after the mutation (1980–2020). The PCP decreased by −0.09/a before the mutation (1960–2004) and increased by 1.01/a after the mutation (2005–2020). The daily sequence of PCP in this basin was quite concentrated and ranged from 184th to 218th d, that is, from early July to early August.
- (3)
- In the LRB, PCD increased from southeast to northwest. Two high PCD (>0.72) areas were concentrated separately in the northwest of the upstream and downstream in Changchun. The spatial distribution of the PCD generally tended to flatten over the entire study period.
- (4)
- PDO, SS, and AO were the important climate factors driving the abrupt change of PCD, and the resonance between climate factors and the PCD was characterized by complexity and diversity. Before the mutation year 2005, the PCP was mainly affected by AO and SS, both of them showed anti-phase resonance with the PCP, and evolution lagged. ENSO had an important effect on both PCD and PCP but had no significant correlation with the occurrence of the mutations.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Xie, Z.; Du, Y.; Zeng, Y.; Miao, Q. Classification of yearly extreme precipitation events and associated flood risk in the Yangtze-Huaihe River Valley. Sci. China Earth Sci.
**2018**, 61, 1341–1356. [Google Scholar] [CrossRef] - Cui, H.; Jiang, S.; Ren, L.; Xiao, W.; Yuan, F.; Wang, M.; Wei, L. Dynamics and potential synchronization of regional precipitation concentration and drought-flood abrupt alternation under the influence of reservoir climate. J. Hydrol. Reg. Stud.
**2022**, 42, 101–147. [Google Scholar] [CrossRef] - Zhang, L.; Li, Y.; Zhang, F.; Chen, L.; Pan, T.; Wang, B.; Ren, C. Changes of winter extreme precipitation in Heilongjiang province and the diagnostic analysis of its circulation features. Atmos. Res.
**2020**, 245, 105094. [Google Scholar] [CrossRef] - He, W.; Bu, R.; Xiong, Z.; Hu, Y. Characteristics of temperature and precipitation in Northeastern China from 1961 to 2005. Acta Ecol. Sin.
**2013**, 33, 519–531. [Google Scholar] [CrossRef] - Xie, Y.; Liu, S.; Fang, H.; Ding, M.; Liu, D. A study on the precipitation concentration in a Chinese region and its relationship with teleconnections indices. J. Hydrol.
**2022**, 612, 128203. [Google Scholar] [CrossRef] - Darand, M.; Pazhoh, F. Spatiotemporal changes in precipitation concentration over Iran during 1962–2019. Clim. Change
**2022**, 173, 25. [Google Scholar] [CrossRef] - Liu, Y.; Yan, J.; Cen, M.; Fang, Q.; Liu, Z.; Li, Y. A graded index for evaluating precipitation heterogeneity in China. J. Geogr. Sci.
**2016**, 26, 673–693. [Google Scholar] [CrossRef] [Green Version] - Yin, Y.; Xu, C.-Y.; Chen, H.; Li, L.; Xu, H.; Li, H.; Jain, S.K. Trend and concentration characteristics of precipitation and related climatic teleconnections from 1982 to 2010 in the Beas River basin, India. Glob. Planet. Change
**2016**, 145, 116–129. [Google Scholar] [CrossRef] - Huang, Y.; Wang, H.; Xiao, W.-H.; Chen, L.-H.; Yang, H. Spatiotemporal characteristics of precipitation concentration and the possible links of precipitation to monsoons in China from 1960 to 2015. Theor. Appl. Clim.
**2019**, 138, 135–152. [Google Scholar] [CrossRef] - Sarricolea, P.; Meseguer-Ruiz, Ó.; Serrano-Notivoli, R.; Soto, M.V.; Martin-Vide, J. Trends of daily precipitation concentration in Central-Southern Chile. Atmos. Res.
**2019**, 215, 85–98. [Google Scholar] [CrossRef] - Wang, R.; Zhang, J.; Guo, E.; Zhao, C.; Cao, T. Spatial and temporal variations of precipitation concentration and their relationships with large-scale atmospheric circulations across Northeast China. Atmos. Res.
**2019**, 222, 62–73. [Google Scholar] [CrossRef] - Yang, P.; Zhang, Y.; Xia, J.; Sun, S. Investigation of precipitation concentration and trends and their potential drivers in the major river basins of Central Asia. Atmos. Res.
**2020**, 245, 105128. [Google Scholar] [CrossRef] - Mei, C.; Liu, J.; Huang, Z.; Wang, H.; Wang, K.; Shao, W.; Li, M. Spatiotemporal pattern variations of daily precipitation concentration and their relationship with possible causes in the Yangtze River Delta, China. J. Water Clim. Change
**2022**, 13, 1583–1598. [Google Scholar] [CrossRef] - Gao, L.; Huang, J.; Chen, X.; Chen, Y.; Liu, M. Contributions of natural climate changes and human activities to the trend of extreme precipitation. Atmos. Res.
**2018**, 205, 60–69. [Google Scholar] [CrossRef] - Hao, W.; Shao, Q.; Hao, Z.; Ju, Q.; Baima, W.; Zhang, D. Non-stationary modelling of extreme precipitation by climate indices during rainy season in Hanjiang River Basin, China. Int. J. Clim.
**2019**, 39, 4154–4169. [Google Scholar] [CrossRef] - Zhang, X.; Duan, K.; Dong, Q. Comparison of nonstationary models in analyzing bivariate flood frequency at the Three Gorges Dam. J. Hydrol.
**2019**, 579, 124208. [Google Scholar] [CrossRef] - Yadav, R.K.; Kumar, K.R.; Rajeevan, M. Increasing influence of ENSO and decreasing influence of AO/NAO in the recent decades over northwest India winter precipitation. J. Geophys. Res. Atmos.
**2009**, 114. [Google Scholar] [CrossRef] [Green Version] - Fuentes-Franco, R.; Giorgi, F.; Coppola, E.; Kucharski, F. The role of ENSO and PDO in variability of winter precipitation over North America from twenty first century CMIP5 projections. Clim. Dyn.
**2016**, 46, 3259–3277. [Google Scholar] [CrossRef] - Zhang, K.; Yao, Y.; Qian, X.; Wang, J. Various characteristics of precipitation concentration index and its cause analysis in China between 1960 and 2016. Int. J. Climatol.
**2019**, 39, 4648–4658. [Google Scholar] [CrossRef] - Sun, Q.; Miao, C.; Qiao, Y.; Duan, Q. The nonstationary impact of local temperature changes and ENSO on extreme precipitation at the global scale. Clim. Dyn.
**2017**, 49, 4281–4292. [Google Scholar] [CrossRef] - Zhang, L.; Liu, Y.; Zhan, H.; Jin, M.; Liang, X. Influence of solar activity and EI Niño-Southern Oscillation on precipitation extremes, streamflow variability and flooding events in an arid-semiarid region of China. J. Hydrol.
**2021**, 601, 126630. [Google Scholar] [CrossRef] - Nazari-Sharabian, M.; Karakouzian, M. Relationship between Sunspot Numbers and Mean Annual Precipitation: Application of Cross-Wavelet Transform—A Case Study. J
**2020**, 3, 67–78. [Google Scholar] [CrossRef] [Green Version] - Dong, Q.; Wang, W.; Kunkel, K.E.; Shao, Q.; Xing, W.; Wei, J. Heterogeneous response of global precipitation concentration to global warming. Int. J. Clim.
**2021**, 41, E2347–E2359. [Google Scholar] [CrossRef] - Huan, W.; Er, L.; Wei, Z. A new method to reflect the intra-seasonal heterogeneity of the precipitation in China. J. Trop. Meteorol.
**2015**, 31, 655–663. [Google Scholar] [CrossRef] - Cui, L.; Wang, L.; Lai, Z.; Tian, Q.; Liu, W.; Li, J. Innovative trend analysis of annual and seasonal air temperature and rainfall in the Yangtze River Basin, China during 1960–2015. J. Atmosph. Sol. Terr. Phys.
**2017**, 164, 48–59. [Google Scholar] [CrossRef] - Sangüesa, C.; Pizarro, R.; Ibañez, A.; Pino, J.; Rivera, D.; García-Chevesich, P.; Ingram, B. Spatial and Temporal Analysis of Rainfall Concentration Using the Gini Index and PCI. Water
**2018**, 10, 112. [Google Scholar] [CrossRef] [Green Version] - Cheng, Z.; Chen, X.; Zhang, Y.; Jin, L. Spatio-temporal evolution characteristics of precipitation in the north and south of Qin-ba Mountain area in recent 43 years. Arab. J. Geosci.
**2020**, 13, 848. [Google Scholar] [CrossRef] - Liu, X.; Tong, X.; Jia, Q.; Liu, X.; Xiang, J.; Xue, Z. Characteristics of daily precipitation concentration in Liaohe River Basin from 1960 to 2018. J. Meteorol. Environ.
**2020**, 36, 18–24. [Google Scholar] [CrossRef] - Zhang, L.J.; Qian, Y.P. Annual distribution features of precipitation in China and their interannual variations. Acta Meteorol. Sin.
**2003**, 146–163. [Google Scholar] - Dourado, C.S.; Oliveira, S.R.M.; Avila, A.M.H. Análise de zonas homogêneas em séries temporais de precipitação no Estado da Bahia. Bragantia
**2013**, 72, 192–198. [Google Scholar] [CrossRef] [Green Version] - Silva, B.K.N.; Lucio, P.S. Characterization of risk/exposure to climate extremes for the Brazilian Northeast—Case study: Rio Grande do Norte. Theor. Appl. Clim.
**2015**, 122, 59–67. [Google Scholar] [CrossRef] - Chatterjee, S.; Khan, A.; Akbari, H.; Wang, Y. Monotonic trends in spatio-temporal distribution and concentration of monsoon precipitation (1901–2002), West Bengal, India. Atmos. Res.
**2016**, 182, 54–75. [Google Scholar] [CrossRef] - Du, R.; Shang, F.; Ma, N. Automatic mutation feature identification from well logging curves based on sliding t test algorithm. Clust. Comput.
**2019**, 22, 14193–14200. [Google Scholar] [CrossRef] - Kim, T.K. T test as a parametric statistic. Korean J. Anesthesiol.
**2015**, 68, 540–546. [Google Scholar] [CrossRef] [Green Version] - Grinsted, A.; Moore, J.C.; Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys.
**2004**, 11, 561–566. [Google Scholar] [CrossRef] - Jevrejeva, S.; Moore, J.C.; Grinsted, A. Influence of the Arctic Oscillation and El Niño-Southern Oscillation (ENSO) on ice conditions in the Baltic Sea: The wavelet approach. J. Geophys. Res. Atmos.
**2003**, 108, 4617. [Google Scholar] [CrossRef] [Green Version] - Deng, S.; Chen, T.; Yang, N.; Qu, L.; Li, M.; Chen, D. Spatial and temporal distribution of rainfall and drought characteristics across the Pearl River basin. Sci. Total. Environ.
**2018**, 619, 28–41. [Google Scholar] [CrossRef] - Sun, L.; Cai, Y.; Yang, W.; Yi, Y.; Yang, Z. Climatic variations within the dry valleys in southwestern China and the influences of artificial reservoirs. Clim. Change
**2019**, 155, 111–125. [Google Scholar] [CrossRef] - Li, Q.; Liu, X.; Zhong, Y.; Wang, M.; Shi, M. Precipitation Changes in the Three Gorges Reservoir Area and the Relationship with Water Level Change. Sensors
**2021**, 21, 6110. [Google Scholar] [CrossRef] - Li, C.; Zhang, H.; Singh, V.P.; Fan, J.; Wei, X.; Yang, J.; Wei, X. Investigating variations of precipitation concentration in the transitional zone between Qinling Mountains and Loess Plateau in China: Implications for regional impacts of AO and WPSH. PLoS ONE
**2020**, 15, e0238709. [Google Scholar] [CrossRef] - Trenberth, K.E.; Caron, J.M.; Stepaniak, D.P.; Worley, S. Evolution of El Niño–Southern Oscillation and global atmospheric surface temperatures. J. Geophys. Res. Atmos.
**2002**, 107, AAC 5-1–AAC 5-17. [Google Scholar] [CrossRef] - Dai, A.; Fyfe, J.C.; Xie, S.-P.; Dai, X. Decadal modulation of global surface temperature by internal climate variability. Nat. Clim. Change
**2015**, 5, 555–559. [Google Scholar] [CrossRef] - Dong, B.; Dai, A. The influence of the Interdecadal Pacific Oscillation on Temperature and Precipitation over the Globe. Clim. Dyn.
**2015**, 45, 2667–2681. [Google Scholar] [CrossRef] - Zhang, X.N.; Shi, X.M.; Yang, S.Y. Relationship between number of sunspots and rainfall in Xi’an in summer and autumn. Arid Zone Res.
**2013**, 30, 485–490. [Google Scholar] - Li, H.J.; Gao, J.E.; Zhang, H.C.; Zhang, Y.X. Response of Extreme Precipitation to Solar Activity and El Nino Events in Typical Regions of the Loess Plateau. Adv. Meteorol.
**2017**, 2017, 9823865. [Google Scholar] [CrossRef] [Green Version] - Rahman, M.S.; Islam, A.R.M.T. Are precipitation concentration and intensity changing in Bangladesh overtimes? Analysis of the possible causes of changes in precipitation systems. Sci. Total Environ.
**2019**, 690, 370–387. [Google Scholar] [CrossRef] - Daoyi, G.; Shaowu, W. Influence of Arctic Oscillation on winter climate over China. J. Geogr. Sci.
**2003**, 13, 208–216. [Google Scholar] [CrossRef]

**Figure 2.**Moving t-test schematics of (

**a**) PCD and (

**b**) PCP; (The dashed line is the annual PCD/PCP series, the black solid line is the 10-year moving average, and the blue (red) solid line shows the linear regression before (after) the mutation point) and the t values of (

**c**) PCD and (

**d**) PCP (The black solid lines represent the t-values, and the red (blue) dashed lines represent the 0.05 significance upper (lower) bound).

**Figure 3.**Spatial distribution maps of PCD&PCP: Spatial distribution of (

**a**) PCD and (

**b**) PCP before mutation year; spatial distribution of (

**c**) PCD and (

**d**) PCP after mutation year; differences before and after the mutation of (

**e**) PCD and (

**f**) PCP.

**Figure 4.**CWT spectrum between PCD and PDO/AO/ENSO/SS. (

**a**,

**c**,

**e**,

**g**) show the results of XWT; (

**b**,

**d**,

**f**,

**h**) show the results of WTC.

**Figure 5.**CWT spectrum between PCP and PDO/AO/ENSO/SS. (

**a**,

**c**,

**e**,

**g**) show the results of XWT; (

**b**,

**d**,

**f**,

**h**) show the results of WTC.

PCD | XWT | WTC | |||

Period | Years | Period | Years | ||

PDO | 1–4 a 1–4 a 8–11 a | 1981–2001 2003–2007 1988–2004 | 3.5–5 a 1–3 a 8–10 a | 1968–1974 1988–2001 1980–2019 | |

AO | / | / | 3.5–5.5 a 8–10 a | 1968–1971 1980–1994 | |

ENSO | 0–5 a | 1964–2013 | 1–6 a | 2006–2014 | |

SS | 8–12 a | 1973–2003 | 2–3.5 a 0–3.5 a 1–3 a 8–15 a | 1974–1981 1988–1992 2010–2012 1975–2005 | |

PCP | XWT | WTC | |||

Period | Years | Period | Years | ||

PDO | 2–6 a 5–7 a 8–9 a | 1986–2009 2008–2011 1986–2008 | 0–1.5 a 7 a 2 a 5.5 a | 1964–1968 1981–1988 2008–2009 2009–2011 | |

AO | / | / | 3.5–5.5 a 3–10 a | 1968–1974 1971–1999 | |

ENSO | 0–4.5 a 1–6 a | 1964–1972 1980–2013 | 0.5–4 a 11–14 a 2–6 a | 1964–1973 1978–1984 2009–2013 | |

SS | 7.5–14 a | 1974–2001 | 1.5 a | 2009 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhang, L.; Cao, Q.; Liu, K.
Mutation Characteristics of Precipitation Concentration Spatiotemporal Variation and Its Potential Correlation with Low-Frequency Climate Factors in the LRB Area from 1960 to 2020. *Water* **2023**, *15*, 955.
https://doi.org/10.3390/w15050955

**AMA Style**

Zhang L, Cao Q, Liu K.
Mutation Characteristics of Precipitation Concentration Spatiotemporal Variation and Its Potential Correlation with Low-Frequency Climate Factors in the LRB Area from 1960 to 2020. *Water*. 2023; 15(5):955.
https://doi.org/10.3390/w15050955

**Chicago/Turabian Style**

Zhang, Lu, Qing Cao, and Kanglong Liu.
2023. "Mutation Characteristics of Precipitation Concentration Spatiotemporal Variation and Its Potential Correlation with Low-Frequency Climate Factors in the LRB Area from 1960 to 2020" *Water* 15, no. 5: 955.
https://doi.org/10.3390/w15050955