• Water Engineering Research
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• v.4 no.1
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• pp.31-43
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• 2003

The impacts of climate change on soil moisture in sub - Arctic watershed simulated by using the hydrologic model. A range of arbitrary changes in temperature and precipitation are applied to the runoff model to study the sensitivity of soil moisture due to potential changes in precipitation and temperature. The sensitivity analysis indicates that changes in precipitation are always amplified in soil moisture with the amplification factor for flow. The change in precipitation has effect on the soil moisture in the catchment. The percentage change in soil moisture levels can be greater than the percentage change in precipitation. Compared to precipitation, temperature increases or decreases alone have impacts on the soil moisture. These results show the potential for climate change to bring about soil moisture that may require a significant planning response. They are also indicative of the fact that hydrological impacts affecting water supply may be important in consider-ing the cost and benefits of potential climate change.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.25-28
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• 2016

Recently, because of the weather forecasts through the low-resolution data has been limited, the demand of the high-resolution data is sharply increasing. Therefore, in this study, we restore the ultra-high resolution synthetic precipitation and temperature data for 2000-2014 due to small-scale topographic effect using the QPM (Quantitative Precipitation Model)/QTM (Quantitative Temperature Model). First, we reproduce the detailed precipitation and temperature data with 1km resolution using the distribution of Automatic Weather System (AWS) data and Automatic Synoptic Observation System (ASOS) data, which is about 10km resolution with irregular grid over South Korea. Also, we recover the precipitation and temperature data with 1km resolution using the MERRA reanalysis data over North Korea, because there are insufficient observation data. The precipitation and temperature from restored current climate reflect more detailed topographic effect than irregular AWS/ASOS data and MERRA reanalysis data over the Korean peninsula. Based on this analysis, more detailed prospect of regional climate is investigated.

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

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.6
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• pp.44-53
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• 1999

The statistical characteristics of the factors related to the daily rainfall prediction model are analyzed . Records of daily precipitation, mean air temperature, relative humidity , dew-point temperature and air pressure from 1973∼1998 at 8 meteorological sttions in south-western part of Korea were used. 1. Serial correlatino of daily precipitaiton was significant with the lag less than 1 day. But , that of other variables were large enough until 10 day lag. 2. Crosscorrelation of air temperature, relative humidity , dew-point temperature showed similar distribution wiht the basin contrours and the others were different. 3. There were significant correlation between the meteorological variables and precipitation preceded more than 2 days. 4. Daily preciption of each station were treated as a truncated continuous random variable and the annual periodic components, mean and standard deviation were estimated for each day. 5. All of the results could be considered to select the input variables of regression model or neural network model for the prediction of daily precipitation and to construct the stochastic model of daily precipitation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.397-408
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• 2009

In this study, a stochastic precipitation generation framework for simultaneous simulation of daily precipitation at multiple sites is presented. The precipitation occurrence at individual sites is generated using hybrid-order Markov chain model which allows higher-order dependence for dry sequences. The precipitation amounts are reproduced using Anscombe residuals and gamma distributions. Multisite spatial correlations in the precipitation occurrence and amount series are represented with spatially correlated random numbers. The proposed model is applied for a network of 17 locations in the middle of Korean peninsular. Evaluation statistics are reported by generating 50 realizations of the precipitation of length equal to the observed record. The analysis of results show that the model reproduces wet day number, wet and dry day spell, and mean and standard deviation of wet day amount fairly well. However, mean values of 50 realizations of generated precipitation series yield around 23% Root Mean Square Errors (RMSE) of the average value of observed maximum numbers of consecutive wet and dry days and 17% RMSE of the average value of observed annual maximum precipitations for return periods of 100 and 200 years. The provided model also reproduces spatial correlations in observed precipitation occurrence and amount series accurately.

• Atmosphere
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• v.19 no.2
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• pp.199-211
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• 2009

In this paper, we examine the multiple change-point and change pattern in the 54 years (1954-2007) time series of the annual and the heavy precipitation characteristics (amount, days and intensity) averaged over South Korea. A Bayesian approach is used for detecting of mean and/or variance changes in a sequence of independent univariate normal observations. Using non-informative priors for the parameters, the Bayesian model selection is performed by the posterior probability through the intrinsic Bayes factor of Berger and Pericchi (1996). To investigate the significance of the changes in the precipitation characteristics between before and after the change-point, the posterior probability and 90% highest posterior density credible intervals are examined. The results showed that no significant changes have occurred in the annual precipitation characteristics (amount, days and intensity) and the heavy precipitation intensity. On the other hand, a statistically significant single change has occurred around 1996 or 1997 in the heavy precipitation days and amount. The heavy precipitation amount and days have increased after the change-point but no changes in the variances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

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

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.39-47
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• 2017

Precipitation is an important component for hydrological and water control study. In general, AWS data provides more accurate but low dense information for precipitation while radar data gives less accurate but high dense information. The objective of this study is to construct adjusted precipitation field based on hierarchical spatial model combining radar data and AWS data. Here, we consider a Bayesian hierarchical model with spatial structure for hourly accumulated precipitation. In addition, we also consider a redistribution of hourly precipitation to 2.5 minute precipitation. Through real data analysis, it has been shown that the proposed approach provides more reasonable precipitation field.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.263-274
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• 2013

In this study we evaluated the hydrological applicability of multi-satellite precipitation estimates. Three high-resolution global multi-satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), the Global Satellite Mapping of Precipitation (GSMaP), and the Climate Precipitation Center (CPC) Morphing technique (CMORPH), were applied to the Coupled Routing and Excess Storage (CREST) model for the evaluation of their hydrological utility. The CREST model was calibrated from 2002 to 2005 and validated from 2006 to 2009 in the Chungju Dam watershed, including two years of warm-up periods (2002-2003 and 2006-2007). Areal-averaged precipitation time series of the multi-satellite data were compared with those of the ground records. The results indicate that the multi-satellite precipitation can reflect the seasonal variation of precipitation in the Chungju Dam watershed. However, TMPA overestimates the amount of annual and monthly precipitation while GSMaP and CMORPH underestimate the precipitation during the period from 2002 to 2009. These biases of multi-satellite precipitation products induce poor performances in hydrological simulation, although TMPA is better than both of GSMaP and CMORPH. Our results indicate that advanced rainfall algorithms may be required to improve its hydrological applicability in South Korea.