Spatiotemporal Rainfall Variability and Drought Assessment during Past Five Decades in South Korea Using SPI and SPEI

Abstract

About 41% of the earth is drought-affected, which has impacted nearly 2 billion people, and it is expected that more than 90% of terrestrial areas will be degraded by 2050. To evade and mitigate the harmful impacts of drought, it is necessary to study the rainfall variability and assess the drought trend at a global and regional level. This study utilized 70 meteorological stations in South Korea to evaluate the rainfall variability, drought, and its trend during the past five decades using the standardized precipitation evapotranspiration index (SPEI) and the standardized precipitation index (SPI). Rainfall data normality was assessed with mean, standard deviation, skewness, and kurtosis. The highest amount of rainfall was observed in the months of June, July, and August. The SPI and SPEI 12-month results revealed that 1982, 1988, 2008, 2015, and 2017 were dry years throughout the country, while from 2013 to 2017 mixed drought events were observed for the 6-month time series. The Mann-Kendall trend test was applied to the 1- and 12-month time series, and the results revealed that the months of January, March, April, May, June, and August had a significant negative trend, which means drought is increasing in these months, while the months of September, October, and December had a significant positive trend, which means wetter conditions prevailed in these months during the study period. It was observed in the 12-month time series that only two met stations had a significant negative trend, while only one had a significant positive trend. It was found that January and March were the driest months, and October was the wettest month. The detected drought events in this research are consistent with ENSO events. We have observed differences in drought characteristics (duration and frequency) for both indices. Climatic data revealed that South Korea has faced drought conditions (rainfall deficit) due to a shortened monsoon season. This study can provide guidance on water management strategies under the changing pattern of drought in South Korea.

Trends of Rainfall Variability and Drought Monitoring Using Standardized Precipitation Index in a Scarcely Gauged Basin of Northern Pakistan

“…The data were saved in a tiff format to obtain the monthly precipitation dat tually, the raster data were converted from raster to point data and further used ysis. We calculated the descriptive statistics (standard deviation (SD), skewness, a tosis) in XLSTAT (XLSTAT is an add-on for Excel use for data analysis in Excel) A in Excel [51]. In the next step, we evaluated the drought on a 1-and 12-month ti using the SPI method.…”

Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe

“…However, changes in erosivity class from low to moderate were reported in the same study. These changes in rainfall erosivity in Europe can be mainly explained by climate change (i.e., extreme events: flood and drought), which largely affects the precipitation patterns, not only in Europe but all over the world [ 58 , 59 , 60 , 61 ].…”

“…There are various kinds of neural network (NN) models, but usually, two models are used in prediction applications, i.e., recurrent network and feedforward network. The backpropagation algorithm is used to train both models [ 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ]. When the backpropagation algorithm is used to change the weight of neurons, it works on the gradient descent method (weights change in downward direction).…”

Impacts of Spatial Interpolation Methods on Daily Streamflow Predictions with SWAT

“…The results of the cross-validation were utilized to determine the optimal interpolation method according to the meteorological conditions of the basin. In this study, the standardized precipitation index recommended by WMO for drought assessments [33], was utilized to identify the long-term meteorological conditions at the Yongdam watershed; the SPI-12 at the study area that has been previously presented by Moazzam et al [34] was applied in this study. The SPI-12 index [34] at the Yongdam watershed and its respective classification (henceforth, called meteorological conditions) according to WMO [33] are summarized in Table 2.…”

“…The overall accuracy of SWAT in simulating daily streamflow greatly increased through the utilization of spatial interpolation methods on precipitation inputs, wherein the NSE values increased and the RMSE values decreased. The default daily simulations (i.e., NN) performed worst in majority of the cases and only outperformed all the other methods in three instances, two when the SPI-12 classification falls under moderately wet conditions (i.e., 2011 and 2020), and one under wet conditions (i.e., 2004; the preceding year 2003 is classified under extremely wet conditions [34]). In addition, during 2004, 2011, and 2020, the NN-based areal precipitation was observed with the highest occurrence probability of extreme precipitation, which is contrary to the findings presented by Cheng et al [7], who presented that NN-based precipitation performed best for light precipitation patterns.…”