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Through the method of Linear trend, Cumulative anomaly and Morlet wavelet, the interdecadal variation, annual, seasonal, monthly variation, abrupt change of climate and cycle of rainfall in Dali Prefecture from 1962 to 2015 were analyzed. The start date of rainy season occurred on May 8th, 2001 is the earliest; The start date of rainy season occurred on July 1st, 2015 is the latest; The average value of the start date of rainy season from 1962 to 2015 is May 31st. During the past 54 years, the annual, seasonal and monthly rainfall showed the different degrees of reduction. Linear trend analysis showed that the trend of annual rainfall was decreasing with linear trend rate of -14.02 mm/10a, the annual rainfall decreases with the number of years is not significant. Seasonal rainfall decrease with the year number is not significant. The rainfall in summer and autumn decreased with the year number. The decreasing trend of rainfall in summer is greater than that in autumn. The rainfall in Winter and Spring increased with the year number. The increasing trend of rainfall in summer is greater than that in autumn. By using the method of cumulative anomaly and Morlet wavelet, the abrupt change of annual rainfall was analyzed, and the change of annual rainfall were found in 1974, 2002 and 2008. The annual rainfall was mainly in the period of 6 and 30. On the 6a and 10a period scales, the annual rainfall from 2015 to 2019 is increasing, which predicts that Dali Prefecture is in the wet period from 2015 to 2019.

In recent years, in the context of global warming, various types of extreme weather and climate events, meteorological disasters caused by the loss and the impact of increasing, climate change caused by the community and all levels of government’s general concern and attention [

The annual rainfall data of Dali Prefecture from 1962 to 2015 were analyzed by using the meteorological station of Dali Prefecture. The seasonal and monthly rainfall of meteorological station in Weishan County was used to analyze the correlation coefficient of seasonal and monthly rainfall.

Spring is from March to May, spring rainfall accounts for 5% - 17% of annual rainfall; Summer is from June to August, summer rainfall accounts for 50% - 66% of annual rainfall; Autumn is from September to November, autumn rainfall accounts for 25% - 30% of annual rainfall; Winter is from December to February, winter rainfall accounts for 1% - 6% of annual rainfall [

As can be seen from

The study of the annual variation of rainfall in Dali Prefecture is helpful to the drought resistance in the dry weather. Based on the linear regression analysis of annual rainfall from 1962 to 2015, the curve of annual rainfall change with time was obtained (

It is concluded that the trend of annual rainfall tends to decrease, and the decreasing trend is very obvious. The trend of annual rainfall was decreasing with linear trend rate of −14.02 mm/10a. The average rainfall of 1962~2015 is 809.9

Decade | 1960s | 1970s | 1980s | 1990s | 2000s | 2010s |
---|---|---|---|---|---|---|

Precipitation | 882 | 817 | 780 | 882 | 831 | 708 |

Difference | 56 | −9 | −46 | 56 | 5 | −118 |

mm. The annual rainfall was the largest in 1966 and the smallest in 2009. The correlation coefficient between annual rainfall and year number from 1962 to 2015 was 0.18, that has passed the significance test of the confidence level of 0.05. The annual rainfall trend decreases with the year number is significant.

The study of the seasonal variation of rainfall in Dali Prefecture is helpful to guide agricultural production, especially for the local agricultural production. The linear regression and correlation of the monthly rainfall from 1962 to 2015 show the average seasonal rainfall values, linear trend and correlation coefficient. F-test analysis of double sample variance was performed to confirm whe- ther the significance P of the rainfall group and the year number group reached the significance level α, α = 0.05. α is used to test the reliability of their correlation coefficients. The unit of rainfall is mm. The unit of linear propensity is mm/a.r is the correlation coefficient.

The linear tendency of rainfall in winter and spring is positive, that indicates that the rainfall in winter and spring increases with the year number. The increasing trend of rainfall is spring > winter. The correlation coefficient of winter and spring between rainfall and the year number did not pass the F-test with significance level of 0.05, indicates that the increase of rainfall in winter and spring was not significant.

The study of monthly precipitation variation in Dali Prefecture is helpful for the relevant departments to provide meteorological decision service. As can be seen from

July, August, October, November and December, indicating that their monthly rainfall decreased with the year number. The correlation coefficients between rainfall and year number in February, June, August, October, November and December were tested by the significance level with confidence level of 0.05, indicating that their monthly rainfall was significantly reduced with the year number. The correlation coefficient of rainfall in July did not pass the significance test of 0.05 confidence level, indicating that its monthly rainfall is not significant decrease with the year number. The Linear tendency of the monthly rainfall in January, March, April, May and September were positive, indicating that their monthly rainfall increased with the year number. The correlation coefficient between rainfall and year number in January, April and May has been tested by the significance level of 0.05 confidence level, indicating that their monthly rainfall increase with the year number is significant. The correlation coefficient between the rainfall and the year number in March and September did not pass the test with the confidence level of 0.05, that indicates that their monthly rainfall is not significant with the year number.

The analysis method for the year periodic variation of rainfall from 1962 to 2015 by using Morlet wavelet analysis. It is found that the annual rainfall change exists long period that contains short cycle. The annual rainfall is formed by different periodic shocks superimposed. The climate change is a reflection of the nonlinear climate system. In recent years, it has attracted more and more attention, which has become an important topic. Abrupt climate change exists in the climate change process, which is not a continuous phenomenon. The cumulative curve of climatic factors was used to determine it. Using the following Equation (1):

Equation (1) is

If the absolute value of the Equation (1) is the largest, the corresponding t is the mutation year. In order to test whether the climate transition year is up to the standard of abrupt climate change, the corresponding signal to noise ratio is calculated in the turning year. Using the following Equation (2):

Equation (2) is

In Equation (2),

cumulative peak of annual rainfall anomalies occurred in 1974, 2002 and 2008, and the annual rainfall changed from the multiple stage to the minor phase. In order to test whether the above transition years have reached the standard of abrupt climate change, according to Equation (2), the signal-to-noise ratio corresponding to the transition years were calculated. By studying the annual rainfall of 10 years before and after the climate turning year, we found that the corresponding SNRS of rainfall in 1974, 2002 and 2008 were 0.16, 0.83 and 0.78. The abrupt climate change was not significant. The turning years could only be used as the climatic transition years.

The analysis method for the year periodic variation of rainfall from 1962 to 2015 by using Morlet wavelet analysis. It found that the annual rainfall change exists long period that contains short cycle.

periods with an average change period of 16a. On the 6a and 10a period scales, the annual rainfall from 2015 to 2019 is increasing, which predicts that Dali Prefecture is in the wet period from 2015 to 2019.

Wavelet variance gets changed with the period scale.

that is the negative peak. The real part of the wavelet transform of the end of year number is 9 and 5 with the following three years, that is a decreasing function. The real part of the wavelet transform of the end of year number is 2 with the following three years, that is an increasing function.

Based on the above analysis, the following conclusions can be drawn:

(1) The annual rainfall decreases with the linear tendency of −14.02 mm/10a, the annual rainfall decreases with the year number is not significant. The rainfall

of the seasons decrease with the year number is not significant. The rainfall in summer and autumn decreased with the year number, and the decreasing trend between them was summer > autumn. The rainfall in winter and spring increased with the year number, and the increasing trend between them was spring > winter.

(2) The monthly rainfall in February, June, July, August, October, November, and December decreases with the year number, that is significant. The monthly rainfall in July decrease with the year number is not significant. The monthly rainfall in January, March, May and September increases with the year number is significant.

(3) The summer rainfall decrease with the year number, that is significant. The rainfall in January, March, May and September increase with the year number, that is significant. The rainfall in January increase significantly, that could lead to the chilling injury of crops. The increase of rainfall in March is favorable for the growth of spring crops. The increase of rainfall in May will lead to rainy season ahead of schedule. The increase of rainfall in September will lead to that autumn rainy weather lasted longer. The rainfall in February, October, November and December decrease with the year number, but the decreasing trend is not significant. It indicates that their monthly rainfall is almost the same as the average monthly rainfall.

(4) It is found that the corresponding SNRS of rainfall in 1974, 2002 and 2008 were 0.16, 0.83 and 0.78. The abrupt climate change was not significant.

(5) On the 6a and 10a period scales, the annual rainfall from 2015 to 2019 is increasing, which predicts that Dali Prefecture is in the wet period from 2015 to 2019.

(6) The real part of the 6a and 30a period scales wavelet transform changes periodically with the year number. The amplitude from 1979 to 1999 is larger than that from 1962 to 1978 and from 2000 to 2015. 1962~1990 is the first cycle, 1990~2015 is the second cycle.

Zhang, R.W. and Zhang, L.F. (2017) Analysis on Characteristics of Rainfall Change in Dali Prefecture in Recent 54 Years. Journal of Geoscience and Environment Protection, 5, 125-136. https://doi.org/10.4236/gep.2017.55010