• Journal of the Korean Society of Hazard Mitigation
• /
• v.9 no.6
• /
• pp.99-109
• /
• 2009

In this study, often the spatial distribution of precipitation was analyzed using the quantile with regional frequency analysis and spatial analysis to find out the detail distribution of extreme precipitation for preventing the disaster in the region of Gangwon. The hourly precipitation data of 66 stations in Gangwon were used. As the results of regional frequency analysis, it shows that the generalized logistic (GLO) distribution is the best for the region of Gangwon. As the results of spatial analysis, the quaniles have high vaules nearby Seolakdong, Daegwallyeong and Cheongil as the duration of precipitation increase, and the change of spatial distribution occurs severely according to the duration of precipitation. The spatial characteristics of precipitation appears clearly as the return period of quantile increases. As the results of the spatial distribution of precipitation in Gangwon heavy quantiles usually are appeared in Yongdong, and the spatial distributions of quantile in Yongseo are various according to the duration and the return period of quantile. Therefore, to estimate more accurate quantiles in Gangwon, various geographical and weather conditions are considered additionally for the regional precipitation frequency analysis.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.132-132
• /
• 2021

Access to accurate spatial precipitation in many hydrological studies is necessary. Existence of many mountains with diverse topography in South Korea causes different spatial distribution of precipitation. Rain gauge stations show accurate precipitation information in points, but due to the limited use of rain gauge stations and the difficulty of accessing them, there is not enough accurate information in the whole area. Weather radars can provide an integrated precipitation information spatially. Despite this, weather radar data have some errors that can not provide accurate data, especially in heavy rainfall. In this study, some location-based variable like aspect, elevation, plan curvature, profile curvature, slope and distance from the sea which has most effect on rainfall was considered. Then Automatic Weather Station data was used for spatial training of variables in each event. According to this, K-fold cross-validation method was combined with Adaptive Neuro-Fuzzy Inference System. Based on this, 80% of Automatic Weather Station data was used for training and validation of model and 20% was used for testing and evaluation of model. Finally, spatial distribution of precipitation for 1×1 km resolution in Gwangdeoksan radar station was estimates. The results showed a significant decrease in RMSE and an increase in correlation with the observed amount of precipitation.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.329-329
• /
• 2022

Accurate characterization of terrestrial precipitation variation from high spatial resolution satellite sensors is beneficial for urban hydrology and microscale agriculture modeling, as well as natural disasters (e.g., urban flooding) early warning. However, the widely-used top-down approach for precipitation retrieval from microwave satellites is limited in several hydrological and agricultural applications due to their coarse spatial resolution. In this research, we aim to apply a novel bottom-up method, the parameterized SM2RAIN, where precipitation can be estimated from soil moisture signals based on an inversion of water balance model, to generate high spatial resolution terrestrial precipitation estimates at 0.01º grid (roughly 1-km) from the C-band SAR Sentinel-1. This product was then tested against a common reanalysis-based precipitation data and a domestic rain gauge network from the Korean Meteorological Administration (KMA) over central South Korea, since a clear difference between climatic types (coasts and mainlands) and land covers (croplands and mixed forests) was reported in this area. The results showed that seasonal precipitation variability strongly affected the SM2RAIN performances, and the product derived from separated parameters (rainy and non-rainy seasons) outperformed that estimated considering the entire year. In addition, the product retrieved over the mainland mixed forest region showed slightly superior performance compared to that over the coastal cropland region, suggesting that the 6-day time resolution of S1 data is suitable for capturing the stable precipitation pattern in mainland mixed forests rather than the highly variable precipitation pattern in coastal croplands. Future studies suggest comparing this product to the traditional top-down products, as well as evaluating their integration for enhancing high spatial resolution precipitation over entire South Korea.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.120-120
• /
• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.1260-1264
• /
• 2009

Precipitation time series is a mixture of complicate fluctuation and changes. The monthly precipitation data of 61 stations during 36 years (1973-2008) in Korea are comprehensively analyzed using the EOFs technique and CSEOFs technique respectively. The main motivation for employing this technique in the present study is to investigate the physical processes associated with the evolution of the precipitation from observation data. The twenty-five leading EOF modes account for 98.05% of the total monthly variance, and the first two modes account for 83.68% of total variation. The first mode exhibits traditional spatial pattern with annual cycle of corresponding PC time series and second mode shows strong North South gradient. In CSEOF analysis, the twenty-five leading CSEOF modes account for 98.58% of the total monthly variance, and the first two modes account for 78.69% of total variation, these first two patterns' spatial distribution show monthly spatial variation. The corresponding mode's PC time series reveals the annual cycle on a monthly time scale and long-term fluctuation and first mode's PC time series shows increasing linear trend which represents that spatial and temporal variability of first mode pattern has strengthened. Compared with the EOFs analysis, the CSEOFs analysis preferably exhibits the spatial distribution and temporal evolution characteristics and variability of Korean historical precipitation.

• Journal of Korea Water Resources Association
• /
• v.45 no.9
• /
• pp.875-885
• /
• 2012

In this study, the total precipitation amount in Jeju Island was estimated with the simulated precipitation for ungauged stations missing precipitation data using the spatial precipitation analysis. The missing data were generated through the modified multiple linear regression in this study, and the analysis of spatial precipitation was conducted with the PRISM(Parameter-elevation Regression on Independent Slope Model). The generated data with modified multiple linear regression model have similar pattern with original data. Thus, the model in this study shows good applicability to estimate the missing data. The difference of annual average precipitation between Case 1 (original data) and Case 2 (modified data) appears very small ratio which is about 1.5%. However, the difference of annual average precipitation according to elevation shows the large ratio up to 37.4%. As the results, the method of estimating missing data in this study would be useful to calculate the total precipitation amount at the low station density area and the places with the high spatial variation of precipitation.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.6
• /
• pp.101-112
• /
• 2022

Extreme precipitation events have recently become a leading cause of disasters. Thus, investigating the variability and trends of extreme precipitation is crucial to mitigate the increasing impact of such events. Spatial distribution and temporal trends in annual precipitation and four extreme precipitation indices of duration (CWD), frequency (R10 mm), intensity (Rx1day), and percentile-based threshold (R95pTOT) were analyzed using the daily precipitation data of 10 observation stations in Chungcheong province during 1974-2020. The precipitation at all observation stations, except the Boryeong station, showed nonsignificant increasing trends at 95% confidence level (CL) and increasing magnitudes from the west to east regions. The high variability in mean annual precipitation was more pronounced around the northeast and northwest regions. Similarly, there were moderate to high patterns in extreme precipitation indices around the northeast region. However, the precipitation indices of duration and frequency consistently increased from the west to east regions, while those of intensity and percentile-based threshold increased from the south to east regions. Nonsignificant increasing trends dominated in CWD, R10 mm, and Rx1day at all stations, except for R10 mm at Boeun station and Rx1day at Cheongju and Jecheon stations, which showed a significantly increasing trend. The spatial distribution of trend magnitude shows that R10 mm increased from the west to east regions. Furthermore, variations in precipitation were very strongly correlated (99% CL) with R10 mm, Rx1day, and R95pTOT at all stations, except with wR10 mm at Cheongju station, which was strongly correlated with a 95% CL.

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.738-739
• /
• 2015

The main objective of this study, "Spatial Downscaling of Precipitation from GCMs for Assessing Climate Change over Han River and Imjin River Watersheds", is to carry out over Han River and Imjin River watersheds. To this end, a statistical regression method with MOS (Model Output Statistics) corrections at every downscaling step was developed and applied for downscaling the spatially-coarse Global Climate Model Projections (GCMPs) from CCSM3 and CSIRO with respect to precipitation into 0.1 degree (about 11 km) spatial grid over study regions. The spatially archived hydro-climate data sets such as Willmott, GsMap and APHRODITE datasets were used for MOS corrections by means of monthly climatology between observations and downscaled values. Precipitation values downscaled in this study were validated against ground observations and then future climate simulation results on precipitation were evaluated for the projections.

• Korean Journal of Remote Sensing
• /
• v.32 no.3
• /
• pp.331-337
• /
• 2016

Water vapor is main absorption factor of outgoing longwave radiation. So, it is essential to monitoring the changes in the amount of water vapor and to understanding the causes of such changes. In this study, we monitor temporal variability of Total Precipitable Water (TPW) which observed by satellite. Among climate variables, precipitation play an important part to analyze temporal variability of water vapor because it is produced by water vapor. And El $Ni{\tilde{n}}o$ is one of climate variables which appear regularly in comparison with the others. Through them, we analyze relationship between temporal variability of TPW and climate variable. In this study, we analyzed long-term change of TPW from Moderate-Resolution Imaging Spectroadiometer (MODIS) data and change of precipitation in middle area of Korea peninsula quantitatively. After these analysis, we compared relation of TPW and precipitation with El $Ni{\tilde{n}}o$. The aim of study is to research El $Ni{\tilde{n}}o$ has an impact on TPW and precipitation change in middle area of Korea peninsula. First of all, we calculated TPW and precipitation from time series analysis quantitatively, and anomaly analysis is performed to analyze their correlation. As a result, TPW and precipitation has correlation mostly but the part had inverse correlation was found. This was compared with El $Ni{\tilde{n}}o$ of anomaly results. As a result, TPW and precipitation had inverse correlation after El $Ni{\tilde{n}}o$ occurred. It was found that El $Ni{\tilde{n}}o$ have a decisive effect on change of TPW and precipitation.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.9 no.5
• /
• pp.103-113
• /
• 2009

In this study, we separated the precipitation zones using the geographic location of stations and precipitation characteristics (monthly, seasonal, annual) in Gangwon province. Precipitation data of 66 weather stations (meterological office: 11 locations, auto weather system (AWS): 55 places) were used, and statistical method, K-means cluster method, was conducted for division of the precipitation regions. As the results of regional classification, the five zones of precipitation (Yongdong: 1 region, Youngseo: 4 regions) were separated. Seasonal average precipitation in spring is similar throughout Gangwon Province, seasonal average precipitation in summer has high values at Youngseo, and seasonal average precipitation in autumn and winter have high values at Youngdong. The some areas, the vicinity of Misiryeong and Daegwallyeong, happens the orographic precipitation in spatial analysis, but the orographic effects didn't occur for the whole Gangwon areas. However, to achieve more accurate results, the expansion of observatories per elevation and AWS data are demanded.