Spatial and Temporal Trend Analysis of Precipitation and Drought in South Korea

Abstract

High spatial and temporal variation in precipitation in South Korea leads to an increase in the frequency and duration of drought. In this study, the spatial characteristics of temporal trends for precipitation and drought severity time series were analyzed at 55 stations across South Korea for the period 1980-2015. This study also reviewed the usefulness of different trend tests while addressing the issue of serial correlation, which has often received less attention in previous studies. Results showed that most significant trends in precipitation were detected along the south coast of South Korea, especially during winter, late spring and summer, whereas no significant trend was detected in annual precipitation. The Sen's slope of the trends increased from January to August and decreased from August onward. Principal component analysis applied on Standardized Precipitation Index (SPI) at a 12-month time scale divides the whole of South Korea into four subregions with different temporal behaviors of drought severity. Moreover, drought severity showed a significant increasing trend, mainly on the northeast coast. Drought frequency analysis showed more frequent droughts in late winter, early spring and early autumn, with less frequent droughts in summer.

Aridity Trends in Central America: A Spatial Correlation Analysis

“…There are important differences between the trend significance when assuming independence, compared to those obtained using a spatial dependence correction in all variables, as pointed out by [8] and [31]. Differences are due to the clear spatial dependence that can be noted in Figures 3 and 5.…”

“…is an indicator function meaning that the nugget parameter is only added to the diagonal cells. Then, a confidence interval can be calculated using parametric bootstrap for all trends, as recommended in [31]. Equivalently, what we propose is to estimate Σ(s i , s j ) for all i and j locations, to then use it to generate samples of a multivariate normal centered in zero, as the distribution of Z(s) when its mean is zero.…”

A Proposal to Evaluate Drought Characteristics Using Multiple Climate Models for Multiple Timescales

“…A probability transformation is then applied to transform monthly precipitation to a standard normal distribution with a zero mean and standard deviation of one to yield SPI values by preserving probabilities [45]. Figure 2 shows the fitness of the SPI data [46]. corresponding to the best climate simulation which has the largest rank sum to N and aij = 1 as i = j. aij is determined by the difference of rank sum between sets of models plus 1.…”

“…A probability transformation is then applied to transform monthly precipitation to a standard normal distribution with a zero mean and standard deviation of one to yield SPI values by preserving probabilities [45]. Figure 2 shows the fitness of the SPI data [46]. The non-parametric Mann-Kendall (MK) test [25,26] is widely applied to detect the possible trends in many countries such as in China [47], in Serbia [48] in Brazil [49], in Canada [50].…”

Insights on Climate-Driven Fluctuations of Cave 222Rn and CO2 Concentrations Using Statistical and Wavelet Analyses

“…In addition, multivariate statistical analysis provides a useful tool to empirically establish easy and understandable correlations between different parameters, highlighting the influence between one principal parameter and those that are related. Although these techniques have been applied to different subjects such as evaluating groundwater pollution [45], assessing the spatial and temporal trend of precipitation in a local area [46], or predicting water permeability in rocks [47], they have been discreetly applied to cave data analysis. However, recently, this type of analysis showed satisfactory results in the study of the spatial variability of cave-air carbon dioxide and methane concentrations in Gaden and Cathedral caves [35].…”