• Journal of Korea Water Resources Association
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• v.44 no.8
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• pp.619-628
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• 2011

The purpose of this study is to evaluate the existing method of calculating rainfall-runoff erosivity using monthly precipitation, such as Fournier's index, modified Fournier's index, IAS (Institute of Agricultural Sciences) index, etc., and to present more reasonable regression model based on monthly rainfall data in Korea. This study introduced a new simplified method of calculating rainfall-runoff erosivity based on monthly precipitation, called by modified IAS index. It was expanded form IAS index which is the simple calculation method by summing up the rainfall amount of two months with maximum amount. Monthly precipitation and annual rainfall-runoff erosivity at 21 weather stations for over 25 years were used to analyze correlation relationship and regression model. The result shows that modified IAS index is the more reasonable parameter for estimating rainfall-runoff erosivity of the middle-western and south-western regions in Korea.

• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.3
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• pp.35-45
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• 1984

Generally speaking, agriculture exist in a climatic environment of uncertainty. Namely, normal rainfall value, as given by the mean values, does not exist. Thought on exists, itl does not affect like extreme Precipitation value on the part of agriculture and of others. Therefore, it is important that we measure the duration and severity index of drought caused by extreme precipitation deficit. In this purpose, this study was dealt with the calculation of drought duration and severity indexs by the method of monthly weighting coefficient. There is no quantitive definition of drought that is universally acceptable. Most of the criteria was used to identify drought have been arbitrary because a drought is a 'non-event' as opposed to a distinct event such as a flood. Therefore, confusion arises when an attempt is made to define the drought phenomenon, the calculation of duration, drought index is based on the following four fundamental question, and this study was dealt with the answers of these four questions as they related to this analytical method, as follows. First, the primary interest in this study is to be the lack of precipitation as it relates to agricultural effective rainfall. Second, the time interval was used to be month in this analysis. Third, Drought event, distinguished analytically from other event, is noted by monthly weighting coefficient method based on monthly rainfall data. Fin-ally, the seven regions used in this study have continually affected by drought on account of their rainfall deficit. The result from this method was very similar to the previous papers studied by many workers. Therefore, I think that this method is very available in Korea to identify the duration of drought, the deficit of precipitation and severity index of drought, But according to the climate of Korea exist the Asia Monsoon zone. The monthly weighting coefficient is modify a little, Because get out of 0.1-0.4 occasionally.

• Water for future
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• v.28 no.1
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• pp.81-90
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• 1995

This study attempts to develop a stochastic system model for extension and prediction of monthly runoff series in river basins where the observed runoff data are insufficient although there are long-term hydrometeorological records. For this purpose, univariate models of a seasonal ARIMA type are derived from the time series analysis of monthly runoff, monthly precipitation and monthly evaporation data with trend and periodicity. Also, a causual model of multiple input-single output relationship that take monthly precipitation and monthly evaporation as input variables-monthly runoff as output variable is built by the cross-correlation analysis of each series. The performance of the univariate model and the multiple input-output model were examined through comparisons between the historical and the generated monthly runoff series. The results reveals that the multiple input-output model leads to the improved accuracy and wide range of applicability when extension and prediction of monthly runoff series is required.

• Atmosphere
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• v.18 no.1
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• pp.71-81
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• 2008

In this study, in order to estimate high resolution precipitation with monthly time scales, Parameter-elevation Regressions on Independent Slopes Model (PRISM) was modified and configured for Korean precipitation based on elevation, distance, topographic facet, and coastal proximity. Applying this statistical downscaling model to Korean precipitation for 5 years from 2001 to 2005, we have compiled monthly grid data with a horizontal resolution of 5-km and evaluated the model using bias, root mean square error (RMSE), and correlation coefficient between the observed and the estimated. Results show that bias, RMSE, and correlation coefficient of the estimated value have a range from 0.2% to 1.0%, 19.6% (June) to 43.9% (January), and 0.73 to 0.84, respectively, indicating that the modified Korean PRISM (K-PRISM) is reasonably worked by weighting factors, i.e., topographic effect and rain shadow effect.

• Journal of Ecology and Environment
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• v.42 no.2
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• pp.77-84
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• 2018

Background: For understanding and evaluating a more realistic and accurate assessment of ecosystem carbon balance related with environmental change or difference, it is necessary to analyze the various interrelationships between soil respiration and environmental factors. However, the soil temperature is mainly used for gap filling and estimation of soil respiration (Rs) under environmental change. Under the fact that changes in precipitation patterns due to climate change are expected, the effects of soil moisture content (SMC) on soil respiration have not been well studied relative to soil temperature. In this study, we attempt to analyze relationship between precipitation and soil respiration in temperate deciduous broad-leaved forest for 2 years in Gwangneung. Results: The average soil temperature (Ts) measured at a depth of 5 cm during the full study period was $12.0^{\circ}C$. The minimum value for monthly Ts was $-0.4^{\circ}C$ in February 2015 and $2.0^{\circ}C$ in January 2016. The maximum monthly Ts was $23.6^{\circ}C$ in August in both years. In 2015, annual precipitation was 823.4 mm and it was 1003.8 mm in 2016. The amount of precipitation increased by 21.9% in 2016 compared to 2015, but in 2015, it rained for 8 days more than in 2016. In 2015, the pattern of low precipitation was continuously shown, and there was a long dry period as well as a period of concentrated precipitation in 2016. 473.7 mm of precipitation, which accounted for about 51.8% of the precipitation during study period, was concentrated during summer (June to August) in 2016. The maximum values of daily Rs in both years were observed on the day when precipitation of 20 mm or more. From this, the maximum Rs value in 2015 was $784.3mg\;CO_2\;m^{-2}\;h^{-1}$ in July when 26.8 mm of daily precipitation was measured. The maximum was $913.6mg\;CO_2\;m^{-2}\;h^{-1}$ in August in 2016, when 23.8 mm of daily precipitation was measured. Rs on a rainy day was 1.5~1.6 times higher than it without precipitation. Consequently, the annual Rs in 2016 was about 12% higher than it was in 2015. It was shown a result of a 14% increase in summer precipitation from 2015. Conclusions: In this study, it was concluded that the precipitation pattern has a great effect on soil respiration. We confirmed that short-term but intense precipitation suppressed soil respiration due to a rapid increase in soil moisture, while sustained and adequate precipitation activated Rs. In especially, it is very important role on Rs in potential activating period such as summer high temperature season. Therefore, the accuracy of the calculated values by functional equation can be improved by considering the precipitation in addition to the soil temperature applied as the main factor for long-term prediction of soil respiration. In addition to this, we believe that the accuracy can be further improved by introducing an estimation equation based on seasonal temperature and soil moisture.

• Journal of Environmental Science International
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• v.7 no.1
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• pp.1-7
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• 1998

This paper alms to estimate the change point of the precipitation in Pusan area using the several statistical approaches. The data concerning rainfall are extracted from the annual climatological report and monthly weather report issued by the Korean Meteorological Administration. The average annual precipitation at Pusan is 1471.6 mm, with a standard deviation of 406.0 mm, less than the normal(1486.0 mm). The trend of the annual precipitation is continuously decreasing after 1991 as a change point. And the statistical tests such as t-test and Wilcoxon rank sum test reveals that the average annual precipitation of after 1991 is less than that of before 1991 at 10% significance level. And the mean gnu성 precipitation In Kyongnam districts is also continuously decreasing after 1991 same as Pusan.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1620-1624
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• 2010

The NLCCA has been applied to analyze the East Asia sea surface temperature (SST) and Korea monthly precipitation, where the eight leading PCs of the SST and the eight PCs of the precipitation during 1973-2007 were inputs to an NLCCA model. The first NLCCA mode is plotted in the PC spaces of the Korea precipitation and the world SST present a curve linking the nonlinear relationship between the first three leading PCs of Korea precipitation and world SST forthright. The correlation coefficient between canonical variate time series u and v is 0.8538 for the first NLCCA mode. And there are some areas' climate variability have higher relationship with Korea precipitation, especially focus on the north of East Sea' climate variability have represented the higher canonical correlation with Korea precipitation, with the correlation coefficient is 0.871 and 0.838. Likewise in Korea, most stations display similarly uniform distributing characteristic and less difference, in particular the inshore stations have display identical distributing characteristic. In correlation variables' scores, the fluctuation and variation trend are also seasonal oscillation with high frequency.

• Journal of Korea Water Resources Association
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• v.48 no.1
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• pp.69-78
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• 2015

In this study, meteorological drought indices were examined to simulate hydrological drought. SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) was applied to represent meteorological drought. Further, in order to evaluate the hydrological drought, monthly total inflow and SDI (Streamflow Drought Index) was computed. Finally, the correlation between meteorological and hydrological drought indices were analyzed. As a results, in monthly correlation comparison, the correlation between meteorological drought index and monthly total inflow was highest with 0.67 in duration of 270-day. In addition, a meteorological drought index were correlated 0.72 to 0.87 with SDI. In compared to the annual extremes, the relationship between meteorological drought index and minimum monthly inflow was hardly confirmed. But SDI and SPEI showed a slightly higher correlation. There are limitation that analyze extreme hydrological drought using meteorological drought index. For the evaluation of the hydrological drought, drought index which included inflow directly is required.

• Journal of the Korean earth science society
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• v.33 no.5
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• pp.422-433
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• 2012

The purpose of this study is to investigate the characteristic changes of the Changma season in the 2000s. To accomplish this goal, we have used daily rainfall data collected over nearly 40 years (1971 to 2010). The average summer precipitation data including the Changma season were collected from 16 weather stations that are placed across the three major regions (i.e. central region, southern region, and Jeju region) as Korea Meteorological Administration divided. These precipitation data were analyzed to find out characteristic changes of the Changma season. Results of the precipitation data comparison among the major regions that, monthly average precipitation in the central region was the highest in July; its precipitation tended to increase from May to September. In the southern region, the precipitation amount was lowest in June and tended to increase in May, September, and August. In the Jeju region, the precipitation has been the highest in June and July for the past 30 years, whereas September has been highest month in the last 10 years. The precipitation amount in the Jeju region decreased both in June and July, whereas it tended to grow in May, August and September. A correlation coefficient formula by Karl Pearson has been used to find out correlations between the Changma season and the precipitation of the major regions in 2000s and normal years. It was found that the correlation coefficient has decreased from 0.723 to 0.524 in the 2000s (2001 to 2010) compared to normal years (1971 to 2000).

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.