• Proceedings of the Korea Water Resources Association Conference
• /
• 2004.05b
• /
• pp.256-261
• /
• 2004

Due to the problems of global warming, the frequency of meteorological extremes such as droughts, floods and the annual rainfall amount are suddenly increasing. Even though the increase of greenhouse gases, for example, is thought to be the main factor for global warming, its impact on global climate has not yet been revealed clearly in rather quantitative manners. Therefore, tile objective of this study is to inquire the change of precipitation condition due to climate change by global warming. In brief, this study want to see its assumption if rainfall quantile estimates are really changing. In order to analyze the temporal change, the rainfall quantile estimates at the Seoul rain gauge stations are estimated for the 21-year data period being moved from 1908 to 2002 with 1-year lag. The main objective of this study is to analyze the variability of rainfall quantile estimates using four methods. Next, The changes in confidence interval of rainfall quantile are evaluated by increasing the data period. It has been found that confidence interval of rainfall quantile estimates is reduced as the data period increases. When the hydraulic structures are to be designed, it is important to select the data size and to re-estimate the flood prevention capacity in existing river systems.

• Journal of Wetlands Research
• /
• v.21 no.spc
• /
• pp.51-60
• /
• 2019

In this study, we performed At-site Frequency Analysis(AFA) and Regional Frequency Analysis(RFA) using the observed and climate change scenario data, and the relative root mean squared error(RMMSE) was compared and analyzed for both approaches through Monte Carlo simulation. To evaluate the rainfall quantile, the daily rainfall data were extracted for 615 points in Korea from HadGEM3-RA(12.5km) climate model data, one of the RCM(Regional Climate Model) data provided by the Korea Meteorological Administration(KMA). Quantile mapping(QM) and inverse distance squared methods(IDSM) were applied for bias correction and spatial disaggregation. As a result, it is shown that the RFA estimates more accurate rainfall quantile than AFA, and it is expected that the RFA could be reasonable when estimating the rainfall quantile based on climate change scenarios.

• Journal of Korea Water Resources Association
• /
• v.45 no.8
• /
• pp.827-837
• /
• 2012

The estimation of the rainfall quantile is of great importance in designing hydrologic structures. Conventionally, the rainfall quantile is estimated by univariate frequency analysis with an appropriate probability distribution. There is a limitation in which duration of rainfall is restrictive. To overcome this limitation, bivariate frequency analysis by using 3 copula models is performed in this study. Annual maximum rainfall events in 5 stations are used for frequency analysis and rainfall depth and duration are used as random variables. Gumbel (GUM), generalized logistic (GLO) distributions are applied for rainfall depth and generalized extreme value (GEV), GUM, GLO distributions are applied for rainfall duration. Copula models used in this study are Frank, Joe, and Gumbel-Hougaard models. Maximum pseudo-likelihood estimation method is used to estimate the parameter of copula, and the method of probability weighted moments is used to estimate the parameters of marginal distributions. Rainfall quantile from this procedure is compared with various marginal distributions and copula models. As a result, in change of marginal distribution, distribution of duration does not significantly affect on rainfall quantile. There are slight differences depending on the distribution of rainfall depth. In the case which the marginal distribution of rainfall depth is GUM, there is more significantly increasing along the return period than GLO. Comparing with rainfall quantiles from each copula model, Joe and Gumbel-Hougaard models show similar trend while Frank model shows rapidly increasing trend with increment of return period.

• Journal of Korea Water Resources Association
• /
• v.33 no.5
• /
• pp.569-580
• /
• 2000

To account for the influence of heavy storm in Korea by climate change like global warming, the frequency analyses for annual maximum rainfall sequence in 12 rainfall gauge stations are carried out. In order to analyze the temporal change, the rainfall quantile of each station is estimated by the 30-yr data period being moved from 1954 to 1998 with 1-yr lag. Through the analysis for l00-yr rainfall quantile it has been shown that the recent heavy storms increase comparing with storms in the past. From the additional estimating of the rainfall quantile of each station by the 30-yr data period being cumulated from 1954 to 1998 with 1-yr, the change of the probable rainfall by including the heavy storm duration is realized. When the hydraulic structures are determined, it is important to select the data size and necessary to reestimate the flood prevention capacity in existing river systems.ystems.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.228-232
• /
• 2017

Extreme hydrological events can cause serious threats to the society. Hence, the selection of probability distributions for extreme rainfall is a fundamental issue. For this reason, this study was focused on understanding possible distributional changes in annual daily maximum rainfalls (AMRs) over time in Sri Lanka using quantile regression. A simplified nine-category distributional-change scheme based on comparing empirical probability density function of two years (i.e. the first year and the last year), was used to determine the distributional changes in AMRs. Daily rainfall series of 13 station over Sri Lanka were analyzed for the period of 1960-2015. 4 distributional change categories were identified for the AMRs. 5 stations showed an upward trend in all the quantiles (i.e. 9 quantiles: from 0.05 to 0.95 with an increment of 0.01 for the AMR) which could give high probability of extreme rainfall. On the other hand, 8 stations showed a downward trend in all the quantiles which could lead to high probability of the low rainfall. Further, we identified a considerable spatial diversity in distributional changes of AMRs over Sri Lanka.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.1
• /
• pp.117-133
• /
• 2014

In this study, we analysed impacts of the recent increasing trend of exceedance rainfall thresholds for separation of data set and different research periods using Quantile Regression (QR) approach. And also we performed significant test for time series data using linear regression, Mann-Kendall test and Sen test over the Korean major 8-city. Spring and summer precipitation was tend to significant increase, fall and winter precipitation was tend to decrease, and heavy rainy days in last 30 years have increased from 3.1 to 15 percent average. In addition, according to the annual ranking of rainfall occurs Top $10^{th}$ percentile of precipitation for 3IQR (inter quartile range) of the increasing trend, most of the precipitation at the point of increasing trend was confirmed. Quantile 90% percentile of the average rainfall 43.5mm, the increasing trend 0.1412mm/yr, Quantile 99% percentile of the average rainfall 68.0mm, the increasing trend in the 0.1314mm/yr were analyzed. The results can be used to analyze the recent increasing trend for the annual maximum value series information and the threshold extreme hydrologic information. And also can be used as a basis data for hydraulic structures design on reflect recent changes in climate characteristics.

• Journal of Korea Water Resources Association
• /
• v.41 no.9
• /
• pp.873-884
• /
• 2008

In this study, rainfall quantile was estimated using scale invariance property of rainfall data with different durations and the applicability of such property was evaluated for the rainfall data of South Korea. For this purpose, maximum annual rainfall at 22 recording sites of Korea Meteorological Administration (KMA) having relatively long records were used to compare rainfall quantiles between at-site frequency analysis and scale invariance property. As the results, the absolute relative errors of rainfall quantiles between two methods show at most 10 % for hourly rainfall data. The estimated quantiles by scale invariance property can be generally applied in the 8 of 14 return periods used in this study. As an example of down-scaling method, rainfall quantiles of $10{\sim}50$ minutes duration were estimated by scale invariance property based on index duration of 1 hour. These results show less than 10 % of absolute relative errors except 10 minutes duration. It is found that scale invariance property can be applied to estimate rainfall quantile for unmeasured rainfall durations.

• Journal of Korea Water Resources Association
• /
• v.50 no.1
• /
• pp.1-15
• /
• 2017

Information on radar rainfall with high spatio-temporal resolution over large areas has been used to mitigate climate-related disasters such as flash floods. On the other hand, a well-known problem associated with the radar rainfall using the Marshall-Palmer relationship is the underestimation. In this study, we develop a new bias correction scheme based on the quantile regression method. This study employed a bivariate copula function method for the joint simulation between radar and ground gauge rainfall data to better characterize the error distribution. The proposed quantile regression based bias corrected rainfall showed a good agreement with that of observed. Moreover, the results of our case studies suggest that the copula function approach was useful to functionalize the error distribution of radar rainfall in an effective way.

• Journal of Korean Society of Forest Science
• /
• v.102 no.3
• /
• pp.463-466
• /
• 2013

We examined relationship between rainfall and triggering of shallow landslides in South Korea, based on hourly rainfall data for 478 shallow landslides during 1963-2012. Rainfall intensity(I) and duration(D) relationship was analyzed to obtain the I-D threshold for the initiation of a shallow landslide using the quantile regression analysis. The I-D threshold equation from in this study is: $I=9.64D^{-0.27}$($4{\leq}D{\leq}76$), where I and D are expressed in millimeters per hour and hours, respectively. In addition, rainfall criteria were proposed to predict the potential to cause landslides, based on values of I-D and cumulative rainfall derived from quantile regression analysis. Our findings may provide essential data and important evidences for the improvement of landslide warning and evacuation system.

• Journal of the Korean Geotechnical Society
• /
• v.30 no.12
• /
• pp.5-14
• /
• 2014

Most of slope failures have taken place between June and September in Korea, which cause a considerable damage to society. Rainfall intensity and duration are very significant triggering factors for landslide. In this paper, landslide-triggering rainfall threshold consisting of rainfall intensity-duration (I-D) was proposed. For this study, total 255 landslides were collected in landslide inventory during 1999 to 2012 from NDMI (National Disaster Management Institute), various reports, newspapers and field survey. And most of the required rainfall data were collected from KMA (Korea Meteorological Administration). The collected landslides were classified into three categories: debris flow, shallow landslide and unconfirmed. A rainfall threshold was proposed based on landslide type using statistical method such as quantile-regression method. Its validation was carried out based on 2013 landslide database. The proposed rainfall threshold was also compared with previous rainfall thresholds. The proposed landslide-triggering rainfall thresholds could be used in landslide early warning system in Korea.