# Analysis on the Characteristics of Dry and Wet Periods in The Yangtze River Basin

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

^{−1}in the last 57 years [8]. The frequency of drought in countries in Oceania has reduced because of the influence of air circulation [9]. In China, the northwest region is relatively arid, and Lian et al. (2019) [10] found that precipitation amount and precipitation frequency in the western region of northwest China have both increased, while rainfall in the eastern region of northwest China has become more concentrated in the past 58 years, which confirmed the view that the northwest region is changing into a warmer and moister region [11]. Drought is aggravated due to the decrease in persistent precipitation, and the increase in days without precipitation in north China and southwest China [12]. In southern China, although persistent precipitation events and precipitation amount had an increasing trend, moderate and mild droughts often occurred [13], resulting in short-duration droughts, which also had an increasing tendency [14].

## 2. Study Area and Data

#### 2.1. Study Area

#### 2.2. Data Sources

## 3. Method

#### 3.1. Calculation of the Standardized Precipitation Evaporation Index

^{−1}); R

_{n}is the net radiation (MJ m

^{−2}day

^{−1}); G is the soil heat flux density (MJ m

^{−2}day

^{−1}); γ is the psychrometric constant (kPa °C

^{−1}); u

_{2}is the wind speed at 2 m height (m s

^{−1}); T is the mean monthly temperature (°C); Δ is slope vapor pressure curve (kPa °C

^{−1}); e

_{a}is the actual vapor pressure (kPa); and e

_{s}is the saturation vapor pressure (kPa).

_{i}is the monthly precipitation, and PET

_{i}is the monthly potential evapotranspiration.

_{i}series, according to the log-logistic distribution. α, β, and γ are scale, shape, and origin parameters, respectively, for D

_{i}values in the range (g < x < ∞). They can be determined by using the L-moment method [32]:

_{s}(s = 0, 1, 2…) can be calculated by the probability weighted moments, through the L-moment method [33]:

_{i}.

_{0}= 2.515517, c

_{1}= 0.802853, c

_{2}= 0.010328, d

_{1}= 1.432788, d

_{2}= 0.189269, d

_{3}= 0.001308. When P > 0.5, $\omega =\sqrt{-2\mathrm{ln}(1-P)}$ and the sign of the resultant SPEI is reversed [34,35,36]. Then, the magnitude of drought and wetness can be seen in Table 1:

#### 3.2. Empirical Orthogonal Function (EOF) and Rotational Empirical Orthogonal Function (REOF)

_{ij}is the jth observation value at the ith meteorological station or grid point. And X

_{m×n}can be seen as a linear combination of k spatial feature vectors and corresponding time weight series:

#### 3.3. “Take the Minimum” Category

_{ij}is the classification value of the j meteorological station of the i spatial vector. V

_{ij}is the spatial vector value of the j meteorological station of the i spatial vector, i = 1,2,3, …, 6. The minimum classification value of CV

_{ij}is the final classification value [43], the purpose of which is to extract the maximum absolute spatial load of all patterns of each meteorological station, and find out the number of the pattern where the maximum absolute spatial load is located. The aggregate regions of each pattern can be extracted into a composite region, and the month scale SPEI patterns can be partitioned:

#### 3.4. Linear Trend Rate and Significance Calculation of Drought and Wetness Index

#### 3.5. Definition of Drought and Wetness Events

#### 3.6. Other Methods

## 4. Results

#### 4.1. Spatial Distribution of the Number of Dry/Wet Months and Drought/Wetness Events in the Yangtze River Basin

#### 4.2. Partitioning Based on REOF

#### 4.3. Change Characteristics of Drought and Wetness

#### 4.3.1. Distribution of Conditions of Drought and Wetness in Each Year-Month

#### 4.3.2. Change Trend of Drought and Wetness Indicators

#### 4.3.3. Abrupt Changes and Variation Periods of Drought and Wetness

## 5. Discussion

#### 5.1. Effects of AO and ENSO on Drought and Wetness

#### 5.2. Continuity of Drought and Wetness Changes in the Yangtze River Basin

## 6. Conclusions

- (a)
- The Yangtze River basin is characterized by the coexistence of drought and flooding in the same areas. Areas where there were more dry/wet months at the same levels, are more likely to occur in the same region. There were more mildly and moderately dry months in the middle and lower reaches of the Yangtze River, but also mildly and moderately wet months. The upper reaches of the Yangtze River were prone to extremely dry months as well as extremely wet months.
- (b)
- Using REOF to analyze the drought and wetness conditions of the Yangtze Riverb asin in time and space, it was found that there are six significant patterns in the Yangtze River basin. Through the “Take the minimum“ method and the Tyson polygon, the Yangtze River basin can be divided into six characteristic subregions: east, southeast, south, north, southwest, and northwest.
- (c)
- The distribution of SPEI values for the central load of each pattern from 1960 to 2017 shows that drought and wetness of a higher grade generally occur from May to September. The eastern parrern frequently changed between dry and wet status; the southeastern pattern had more normal periods of dry and wet; the southern pattern had higher levels of wet months; the northwestern pattern was consistent and relatively dry; the northern pattern and the southwestern pattern had a longer period of extreme drought/wetness in the 1970s and 1980s.
- (d)
- From 1960 to 2017, the inter-annual change showed that the number of dry months, the OTD, and the DI and DD increased significantly in fewer subregions. However, spatially, the southern part of the Northwestern pattern and the western part of the southern pattern showed a significant decrease in the OTW, WI, and WD, and a significant increase in the OTD, DI, and DD, and this region changed from wetness to dryness in the past 29 years.
- (e)
- According to the 5-year moving average of the central load SPEI value, the subregions I and II had experienced many dry-wet transitions, the subregion III had a long-term normal dry–wet state before 1998, and the subregions IV and VI had a relatively long dry–wet transition. However, these dry and wet state transitions can better correspond to the abrupt change of RPCs.

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

## References

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**Figure 1.**Digital elevation model and meteorological station distribution in the Yangtze River basin.

**Figure 2.**The spatial distribution of (

**a1**) all dry months, (

**a2**) all wet months, and the months with the magnitude of (

**b1**) mild drought, (

**b2**) mild wetness, (

**c1**) moderate drought, (

**c2**) moderate wetness, (

**d1**) severe drought, (

**d2**) severe wetness, (

**e1**) extreme drought, and (

**e2**) extreme wetness.

**Figure 3.**Spatial distribution of the occurrence times of drought (OTD) and occurrence times of wetness (OTW) with different durations. 1 is the spatial distribution of OTD; 2 is the spatial distribution of OTW; (

**a**) is all drought–wetness events, and (

**b**–

**d**) are OTD/OTW with different durations.

**Figure 4.**Distribution of the space load value and central load of the significant pattern in (

**a**) REOF1, (

**b**) REOF2, (

**c**) REOF3; (

**d**) REOF4, (

**e**) REOF5, and (

**f**) REOF6.

**Figure 5.**The spatial distribution of (

**a**) the Tyson polygon classification of meteorological stations, and (

**b**) classified load value and feature area extraction.

**Figure 6.**Year–month distribution of SPEI values of the central load meteorological stations in (

**a**) subregion I, (

**b**) subregion II, (

**c**) subregion III, (

**d**) subregion IV, (

**e**) subregion V, and (

**f**) subregion VI.

**Figure 7.**The spatial distribution of linear trend of (

**a1**) OTD from 1960 to 2017, (

**a2**) DI from 1960 to 2017, (

**a3**) DD from 1960 to 2017, (

**b1**) OTW from 1960 to 2017, (

**b2**) WI from 1960 to 2017, and (

**b3**) WD from 1960 to 2017.

**Figure 8.**The spatial distribution of the linear trend of (

**a1**) OTD from 1960 to 1988, (

**a2**) DI from 1960 to 1988, (

**a3**) DD from 1960 to 1988, (

**b1**) OTD from 1989 to 2017, (

**b2**) DI from 1989 to 2017, and (

**b3**) DD from 1989 to 2017.

**Figure 9.**The spatial distribution of the linear trend of (

**a1**) OTW from 1960 to 1988, (

**a2**) WI from 1960 to 1988, (

**a3**) WD from 1960 to 1988, (

**b1**) OTW from 1989 to 2017, (

**b2**) WI from 1989 to 2017, and (

**b3**) WD from 1989 to 2017.

**Figure 11.**The 5-year moving average of central load SPEI of (

**a**) subregion I, (

**b**) subregion II, (

**c**) subregion III, (

**d**) subregion IV, (

**e**) subregion V, and (

**f**) subregion VI.

**Figure 12.**Morlet wavelet variance of DI in (

**a**) subregion I, (

**b**) subregion II, (

**c**) subregion III, (

**d**) subregion IV, (

**e**) subregion V, and (

**f**) subregion VI.

**Figure 13.**Morlet wavelet variance of WI in (

**a**) subregion I, (

**b**) subregion II, (

**c**) subregion III, (

**d**) subregion IV, (

**e**) subregion V, and (

**f**) subregion VI.

**Figure 14.**Generalized extreme value distribution (GEVD) of (

**a**) OTD, (

**b**) DI, (

**c**) DD, (

**d**) OTW, (

**e**) WI, and (

**f**) WD in different phase years of ENSO, and GEVD of (

**g**) OTD, (

**h**) DI, (

**i**) DD, (

**j**) OTW, (

**k**) WI, and (

**l**) WD in different phase years of AO.

**Table 1.**Classifications of drought and wetness magnitude based on standardized precipitation evapotranspiration index (SPEI) value.

Mild Drought | Moderate Drought | Severe Drought | Extreme Drought |
---|---|---|---|

−1 < SPEI ≤ −0.5 | −1.5 < SPEI ≤ −1 | −2 < SPEI ≤ −1.5 | SPEI ≤ −2 |

Mild Wetness | Moderate Wetness | Severe Wetness | Extreme Wetness |

1 > SPEI ≥ 0.5 | 1.5 > SPEI ≥ 1 | 2 > SPEI ≥ 1.5 | SPEI ≥ 2 |

**Table 2.**1960–2017 contribution rate and cumulative contribution rate of rotating empirical orthogonal function (REOF) patterns.

Pattern | REOF1 | REOF2 | REOF3 | REOF4 | REOF5 | REOF6 | REOF7 |
---|---|---|---|---|---|---|---|

Contribution rate | 21.99% | 11.59% | 8.23% | 5.08% | 4.50% | 2.89% | 2.54% |

Cumulative contribution rate | 21.99% | 33.58% | 41.81% | 46.89% | 51.39% | 54.27% | 56.81% |

Is it significant? | Yes | Yes | Yes | Yes | Yes | Yes | No |

Pattern | REOF1 | REOF2 | REOF3 |
---|---|---|---|

Name | The Eastern Pattern | The Southeastern Pattern | The Southern Pattern |

Representative region | Subregion I | Subregion II | Subregion III |

Representative station | 58345 | 57896 | 57731 |

Pattern | REOF4 | REOF5 | REOF6 |

Name | The Northern pattern | The Southeastern pattern | The Northwestern pattern |

Representative region | Subregion IV | Subregion V | Subregion VI |

Representative station | 57154 | 56537 | 56196 |

**Table 4.**Correlation between the rotated principal components (RPCs) and SPEI value of the central load of meteorological stations.

Pattern | REOF1 | REOF2 | REOF3 | REOF4 | REOF5 | REOF6 |
---|---|---|---|---|---|---|

Station | 58345 | 57896 | 57731 | 57154 | 56357 | 56196 |

correlation coefficient | 0.83 | 0.78 | 0.72 | −0.79 | 0.65 | 0.6 |

Significant level | *** | *** | *** | *** | *** | *** |

Station | Dry Months | OTD | DD | DI |
---|---|---|---|---|

58345 | −0.093 | −0.027 | 0.008 | −0.055 |

57896 | −0.162 | −0.090 | −0.098 | −0.104 |

57731 | 0.009 | 0.002 | 0.008 | 0.004 |

57154 | −0.178 | −0.153 | −0.016 | 0.041 |

56357 | −0.129 | −0.048 | −0.038 | 0.047 |

56196 | 0.287 | 0.078 | 0.152 | 0.242 |

Station | Moist Months | OTW | WD | WI |

58345 | −0.005 | −0.024 | 0.023 | 0.097 |

57896 | 0.122 | −0.017 | 0.044 | 0.048 |

57731 | −0.039 | −0.008 | 0.001 | 0.007 |

57154 | 0.105 | 0.009 | 0.115 | 0.198 |

56357 | 0.247 | 0.103 | 0.084 | 0.040 |

56196 | −0.280 | −0.138 | −0.048 | −0.050 |

Index | Phase | Criterion | Year |
---|---|---|---|

ENSO | Warm phase | ≥0.5 °C | 1964 1966 1969 1970 1973 1977 1978 1980 1983 1987 |

1988 1992 1995 1998 2003 2005 2007 2010 2015 | |||

Cold phase | ≤−0.5 °C | 1965 1968 1971 1972 1974 1975 1976 1984 1985 1989 | |

1996 1997 1999 2000 2001 2006 2008 2009 2011 | |||

AO | Positive phase | ≥0.2 °C | 1971 1972 1974 1975 1983 1988 1989 1990 1991 1992 |

1994 1998 1999 2001 2006 2007 2008 2011 2015 2017 | |||

Negative phase | ≤−0.2 °C | 1960 1962 1963 1964 1965 1966 1967 1968 1969 1970 | |

1976 1977 1978 1979 1981 1984 1985 1986 1987 1993 | |||

1995 1997 2000 2002 2003 2005 2009 2010 |

Type | Index | 58345 | 57896 | 57731 | 57154 | 56357 | 56196 |
---|---|---|---|---|---|---|---|

Drought | OTD | 0.51 | 0.57 | 0.57 | 0.70 | 0.87 | 0.67 |

DI | 0.66 | 0.61 | 0.55 | 0.53 | 0.67 | 0.63 | |

DD | 0.65 | 0.59 | 0.50 | 0.55 | 0.69 | 0.58 | |

Wetness | OTW | 0.54 | 0.53 | 0.50 | 0.39 | 0.80 | 0.61 |

WI | 0.62 | 0.44 | 0.58 | 0.63 | 0.52 | 0.38 | |

WD | 0.53 | 0.40 | 0.57 | 0.62 | 0.50 | 0.41 |

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## Share and Cite

**MDPI and ACS Style**

Huang, H.; Zhang, B.; Cui, Y.; Ma, S.
Analysis on the Characteristics of Dry and Wet Periods in The Yangtze River Basin. *Water* **2020**, *12*, 2960.
https://doi.org/10.3390/w12112960

**AMA Style**

Huang H, Zhang B, Cui Y, Ma S.
Analysis on the Characteristics of Dry and Wet Periods in The Yangtze River Basin. *Water*. 2020; 12(11):2960.
https://doi.org/10.3390/w12112960

**Chicago/Turabian Style**

Huang, Hao, Bo Zhang, Yanqiang Cui, and Shangqian Ma.
2020. "Analysis on the Characteristics of Dry and Wet Periods in The Yangtze River Basin" *Water* 12, no. 11: 2960.
https://doi.org/10.3390/w12112960