_{1}

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Trends in rainy/non-rainy days are investigated using the Mann-Kendall non-parametric test at 249 weather station sites of North Carolina, United States. Sen-Slope method has been applied to predict the trend magnitude. Inverse distance weighing interpolation technique is adopted to represent the spatial distribution of trend magnitude across the North Carolina. Quality controlled daily precipitation data sets from 1950 to 2009 have been used to analyze. The double-mass curve and autocorrelation were adopted to analyze the precipitation time series of each station to check the consistency and homogeneity. Standard Precipitation Index (SPI) has also been discussed for the study area. It is found in North Carolina that a number of rainy day trends are increasing both spatially and temporally. Eastern part of North Carolina shows the significant increasing rainy day trends. Trend significance has been checked at 1% and 5% significance level. Recent decades show the high SPI in both the extent of wetness and dryness.

Trends in precipitation have been observed for last one century in many parts of the globe. Over this period, precipitation increased significantly in eastern parts of North and South America [

This study analyzes the spatial and temporal rainy/non-rainy day trend in annual scale for North Carolina in the period of 1950-2009. The non-parametric Mann-Kendall (MK) test and Sen-Slope (SS) were applied to detect the trend significance and slope, respectively.

This study is carried out for the state of North Carolina which is located in Eastern part of the United States (75˚30' - 84˚15'W, 34˚ - 36˚21'N) (see ^{2}. North Carolina exhibits one of the most complex climates in the United States for its variant topographic features that range from 46 m from the eastern coastal area to the western mountain area of 1829 m height [

Precipitation from the 249 stations across North Carolina was analyzed for the period of 1950-2009. The data sets were obtained from the United States Department of Agriculture-Agriculture Research Service (USDA- ARS) [

Surface interpolation technique was used to prepare a spatial precipitation data map over North Carolina from the point precipitation measuring stations within the Arc-GIS framework, which was adopted in Sayemuzzaman and Jha’s investigations [

The MK statistical test has been frequently used to quantify the significance of trends in hydro-meteorological time series [

In Equation (1), n is the number of data points,

The variance is computed as

In Equation (3), n is the number of data points, m is the number of tied groups and

Positive and negative values of

The MK test does not provide an estimate of the magnitude of the trend. For this purpose in this study, a nonparametric method referred to as the Sen-Slope (SS) is used [

i) The interval between time series data points should be equally spaced.

ii) Data should be sorted in ascending order according to time, then the following formula is applied to calculate Sen’s slope (Q_{k}):

In Equation (5), X_{j} and X_{i} are the data values at times j and i (j > i), respectively.

iii) In the Sen’s vector matrix members of size

total N values of Q_{k} are ranked from smallest to largest and the median of slope or Sen’s slope estimator is com- puted as:

Q_{med} sign reflects data trend direction, while its value indicates the steepness of the trend.

The steps adopted in the sample data

1) The lag-1 serial coefficient _{i}, originally derived in Ref [

where

2) According to Gocicand Trajkovic [

testing the time series data sets of serial correlation have been used.

If

3) If time series data sets are independent, then the MK test and the SS can be applied to original values of time series.

4) If time series data sets are serially correlated, then the “Pre-whitened” time series may be obtained as

represent the seasonal effect. It is seen from this daily time series data North Carolina does not represent high variability/abnormality in precipitations.

The Standardized Precipitation Index (SPI), developed by [

annual SPI for 249 stations was averaged and represented from 1950 to 2009 in

Trends in rainy/non-rainy days have been investigated using the Mann-Kendall and Sen-Slope non-parametric methods at 249 weather station sites of North Carolina, United States. Inverse distance weighing interpolation technique is adopted to represent the spatial distribution of trend magnitude. Quality controlled daily precipitation data sets from 1950 to 2009 have been used in this study. The double-mass curve and autocorrelation were adopted to analyze the precipitation time series of each station to check the consistency and homogeneity. Standard Precipitation Index (SPI) has also been discussed for the study area. It is found in North Carolina that a number of rainy day trends are increasing both spatially and temporally. Eastern part of North Carolina shows the significant increasing rainy day trends. Trend significance has been checked at 1% and 5% significance level. Recent decades show the high SPI in both the extent of wetness and dryness.

The author acknowledges Dr. Sayemuzzaman, Florida State University, for constructive comments, data collection and analyses.