• 한국농공학회논문집
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• 제62권2호
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• pp.39-52
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• 2020

A high quality, long-term, high-resolution precipitation dataset is an essential in climate analyses and global water cycles. Rainfall data from station observations are inadequate over many parts of the world, especially North Korea, due to non-existent observation networks, or limited reporting of gauge observations. As a result, satellite-based rainfall estimates have been used as an alternative as a supplement to station observations. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and global coverage. CHIRPS is a global precipitation product and is made available at daily to seasonal time scales with a spatial resolution of 0.05° and a 1981 to near real-time period of record. In this study, we analyze the applicability of CHIRPS data on the Korean Peninsula by supplementing the lack of precipitation data of North Korea. We compared the daily precipitation estimates from CHIRPS with 81 rain gauges across Korea using several statistical metrics in the long-term period of 1981-2017. To summarize the results, the CHIRPS product for the Korean Peninsula was shown an acceptable performance when it is used for hydrological applications based on monthly rainfall amounts. Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and climate, hydrological application, for example, drought monitoring in this region.

• 한국농공학회지
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• 제12권4호
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• pp.2063-2077
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• 1970

The Purpose of this study is to give the hydrologically basic data for the development of water resources in Korea and a quantity of daily average precipitation and its frequency in a year are investigated to study the presumption which is affected to river flow. Characteristics of precipitation is poor as source of water resources compared with its efficiency. So, because of such characteristics of precipitation, river flow also is in harmony and distribution of river flow comes to the result of irregularity, that is, range of river coefficiet between the quantity of maximum river flow and others river flow is big, and it is insufficient as source of water resources. Yearly river flow being expressed by daily unit indicates the ratio(%) of distribution to total yearly river flow, and the model of hydrograph is drawn up. The gives the basis to make yearly water balance sheet. This study is not completed, yet but in forth-coming days, the water will try continuously to give more correct basis for the development of water resources according to a great deal of data.

• 한국자원식물학회:학술대회논문집
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• 한국자원식물학회 2000년도 춘계임시총회 및 학술발표대회
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• pp.26-33
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• 2000

This study was conducted to investigate the relationships between yearly variations of elimatic elements and yearly variations of productivity in potato. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were investigated for 9 years from 1987 to 1995. The meteorological data what gathered at the Goheung Weather Station for the same period of crop growing season were used to find out the relationships between climatic elements and crop productivity. Yearly variation of the daily minimum temperature in March and April were large with coefficients of variation (C.V.) of 126.0%, 368%, respectively, but the variation of the daily mean and maximum temperature in May and June were relative small. Stem length and number of stem show more C.V. of 9.3%, 14.3%, respectively, but the variation of the yield was relative small with 3.7%. Correlation coefficients between the amount of precipitation in April and yield, yield and daily mean temperature in June were negatively significant at the level of 5, 1 %, respectively. Correlation coefficients between the growth habits and yield are positively significant at the level of 5, 1 %, respectively. Simple linear regression equations by the least square method are estimated for stem length (Yl) and the precipitation in April(X) as Y,=82.47-0.11x (R2=0.3959), and for yield(Y2) and the precipitation in April(X) as Y,=2003.61-0.94X (R2=0.5418).

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.151-151
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• 2018

The present study is aimed to quantifying the uncertainty in the general circulation model (GCM) selection and its impacts on hydrology studies in the basins. For this reason, 13 GCMs was selected among the 26 GCM models of the Fifth Assessment Report (AR5) scenarios. Then, the climate data and hydrologic data with two Representative Concentration Pathways (RCPs) of the best model (INMCM4) and worst model (HadGEM2-AO) were compared to understand the uncertainty associated with GCM models. In order to project the runoff, the Precipitation-Runoff Modelling System (PRMS) was driven to simulate daily river discharge by using daily precipitation, maximum and minimum temperature as inputs of this model. For simulating the discharge, the model has been calibrated and validated for daily data. Root mean square error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were applied as evaluation criteria. Then parameters of the model were applied for the periods 2011-2040, and 2070-2099 to project the future discharge the five large basins of South Korea. Then, uncertainty caused by projected temperature, precipitation and runoff changes were compared in seasonal and annual time scale for two future periods and RCPs compared to the reference period (1976-2005). The findings of this study indicated that more caution will be needed for selecting the GCMs and using the results of the climate change analysis.

• 한국수자원학회논문집
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• 제49권10호
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• pp.863-876
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• 2016

현재 국내외에서 제공되고 있는 기후변화 시나리오 자료의 경우 일단위로 제공되고 있다. 그러나 수문 설계 및 계획 시 중요한 입력자료 중 하나는 시간단위 강우 자료로서 기후변화 시나리오에 따른 수자원 변동성을 평가하기 위해선 신뢰성 있는 상세화 기법이 필요하다. 국내외에서는 일단 위에서 일단위로 상세화 하는 기법, 또는 공간상세화 기법 연구는 다수 진행된바 있는 반면, 시간단위 상세화 기법 연구는 일단위 연구에 비해 상대적으로 미진한 실정이다. 이러한 점에서 본 연구에서는 기후변화 시나리오에 따른 영향 평가가 가능한 자료생성을 위해 Conditional Copula 모형을 활용하여 극치시간단위 강우량 상세화 기법을 개발하였으며, 미래 RCP 8.5 시나리오를 활용하여 연대별 극치시간강우량을 생성하였다. 생성된 결과는 우리나라 기상청 지점별로 빈도해석을 통해 결과를 제시하였으며, 본 연구결과는 수자원 분야에서 미래 기후변화 영향을 평가하기 위한 기초자료로 활용 될 수 있을 것으로 기대된다.

• 물과 미래
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• 제24권3호
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• pp.95-104
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• 1991

본 연구에서는 장기 수자원계획시 유역내 가용 수자원을 파악하는데 이용할 수 있는 새로운 연유출량 추정모형의 개발을 시도하였다. 연구범위는 우리나라 전역으로 1945년부터 1988년 까지의 육수량, 유출량 자료를 이용하였다. 모형개발을 위한 표준유역은 유출의 인공조작이 없고 수위자료가 양호하며, 수위-유량 관계곡선이 작성되어 있는 46개 지점을 택하였으며, 표본 유역별로 일수위 자료를 수집, 정리하여 일유출량을 산정하고 합산하여 연유출량을 산정하였다. 또한, 연평균강수량을 산정하여 지점별로 연유출율을 계산하고 이것을 기초로 우리나라 연평균 유출율을 추정하였다. 그리고, 연유출량과 역특성인자들을 이용하여 연유출량 추정모형을 개발하였으며, 실제유역에 적용하여 모형의 합리성을 검토하였다.

• 농촌계획
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• 제21권2호
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• pp.127-135
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• 2015

In recent years, meteorological disasters have frequently occurred in rural areas. As a result, there have been growing concerns over the protective measures needed. In order to avoid natural risks and damage, and to strengthen countermeasure to meteorological disasters, local governments needs to be prepared. Therefore, this paper seeks to prevent meteorological disasters through mapping of inundation vulnerability in agricultural land, Chungcheongnam-do. In doing so, this study were considered 5 variables (i.e. precipitation, region of altitude below 50m, region of slope gradient is below 10 degree, distance from river within less 50m) for creating vulnerability map. The precipitation was excluded in five variables. Since, the precipitation which include Daily maximum precipitation, 2-Daily maximum precipitation, summer precipitation was not any correlation among them. The results of analysing four variables, exclusive of precipitation, were showed that the agricultural lands where located in Dangjin, Buyeo, Hongseong and Asan were low correlation of inundation vulnerability by overlapping analysis. Moreover, The correlation analysis was showed low correlation between each factors and the annual average area of agricultural lands' inundation, whereas, the correlation analysis which was overlapping each factor showed high correlation. In conclusion, in order to create reliable vulnerability map in agricultural lands, Chungcheongnam-do, it must be considered to overlap analysis of the four main factors such region of altitude below 50m, region of slope gradient is below 10 degree, distance from river within less 50m. We suppose that this study's analysis can help to set the preparedness site of agricultural lands inundation.

• 대기
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• 제31권5호
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.180-180
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• 2023

To improve global risk management, understanding the characteristics and distribution of precipitation is crucial. However, obtaining spatially and temporally resolved climatic data remains challenging due to sparse gauge observations and limited data availability, despite the use of satellite and reanalysis products. To address this challenge, merging available precipitation products has been introduced to generate spatially and temporally reliable data by taking advantage of the strength of the individual products. However, most of the existing studies utilize all the available products without considering the varying performances of each dataset in different regions. Comprehensively considering the relative contributions of each parent dataset is necessary since their contributions may vary significantly and utilizing all the available datasets for data merging may lead to significant data redundancy issues. Hence, for this study, we introduce a site-specific precipitation merging method that utilizes the Quadruple Collocation (QC) approach, which acknowledges the existence of error-cross correlation between the parent datasets, to create a high-resolution global daily precipitation data from 2001-2020. The performance of multiple gridded precipitation products are first evaluated per region to determine the best combination of quadruplets to be utilized in estimating the error variances through the QC approach and computation of merging weights. The merged precipitation is then computed by adding the precipitation from each dataset in the quadruplet multiplied by each respective merging weight. Our results show that our approach holds promise for generating reliable global precipitation data for data-scarce regions lacking spatially and temporally resolved precipitation data.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.