• Journal of Korean Society of Rural Planning
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• v.24 no.4
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• pp.99-119
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• 2018

Unit load factor, which is used for the quantification of non-point pollution in watersheds, has the limitation that it does not reflect spatial characteristics of soil, topography and temporal change due to the interannual or seasonal variability of precipitation. Therefore, we developed the method to estimate a watershed-scale non-point pollutant load using seasonal forecast data that forecast changes of precipitation up to 6 months from present time for watershed-scale water quality management. To establish a preemptive countermeasure against non-point pollution sources, it is possible to consider the unstructured management plan which is possible over several months timescale. Notably, it is possible to apply various management methods such as control of sowing and irrigation timing, control of irrigation through water management, and control of fertilizer through fertilization management. In this study, APEX-Paddy model, which can consider the farming method in field scale, was applied to evaluate the applicability of seasonal forecast data. It was confirmed that the rainfall amount during the growing season is an essential factor in the non-point pollution pollutant load. The APEX-Paddy model for quantifying non-point pollution according to various farming methods in paddy fields simulated similarly the annual variation tendency of TN and TP pollutant loads in rice paddies but showed a tendency to underestimate load quantitatively.

• Journal of Korea Water Resources Association
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• v.52 no.5
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• pp.325-335
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• 2019

To characterize historical droughts in the conterminous United States (CONUS), we estimated the actual evapotranspiration ($ET_a$) in the CONUS using the generalized complementary relationship (GCR) for 1895-2016. The $ET_a$ estimates were compared against simulations from the Noah land surface model (LSM). In this study, the evapotranspiration (ET) deficit defined as the difference between the wet-environment ET ($ET_w$) and $ET_a$ was then normalized to calculate the Standardized Evapotranspiration Deficit Index (SEDI) across the CONUS for the years 1895-2016. The SEDI was compared to the Standard Precipitation Index (SPI) at various time scales. The results showed that the GCR $ET_a$ was slightly higher than the Noah LSM-simualted $ET_a$. As time scales increased, the correlation between the SEDI and the SPI was higher. This study suggests that the GCR has promise as a tool in the estimation of $ET_a$ and SEDI can be useful for the drought characterization.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1041-1053
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• 2018

In this study, a real-time storage level and capacity monitoring and forecasting system for Yongdam Dam watershed was developed using high resolution satellite image. The drought indices such as Standardized Precipitation Index (SPI) from satellite data were used for storage level monitoring in case of drought. Moreover, to predict storage volume we used a statistical method based on Principle Component Analysis (PCA) of Singular Spectrum Analysis (SSA). According to this study, correlation coefficient between storage level and SPI (3) was highly calculated with CC=0.78, and the monitoring and predictability of storage level was diagnosed using the drought index calculated from satellite data. As a result of analysis of principal component analysis by SSA, correlation between SPI (3) and each Reconstructed Components (RCs) data were highly correlated with CC=0.87 to 0.99. And also, the correlations of RC data with Normalized Water Surface Level (N-W.S.L.) were confirmed that has highly correlated with CC=0.83 to 0.97. In terms of high resolution satellite image we developed a water detection algorithm by applying an exponential method to monitor the change of storage level by using Multi-Spectral Instrument (MSI) sensor of Sentinel-2 satellite. The materials of satellite image for water surface area detection in Yongdam dam watershed was considered from 2016 to 2018, respectively. Based on this, we proposed the possibility of real-time drought monitoring system using high resolution water surface area detection by Sentinel-2 satellite image. The results of this study can be applied to estimate of the reservoir volume calculated from various satellite observations, which can be used for monitoring and estimating hydrological droughts in an unmeasured area.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.409-416
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• 2020

An extreme climate monitoring is essential to the reduction of socioeconomic damages from extreme events. The objective of this study was to produce the near-realtime weekly root-zone Soil Moisture Index (SMI) on the basis of soil moisture using the Noah 3.3 Land Surface Model (LSM) for potentially monitoring extreme drought events. The Yangtze basin was selected to evaluate the Noah LSM performance for the East Asia region (15-60°N, 70-150°E) and the evapotranspiration (ET) and sensible heat flux (SH) were compared with ET and SH from FluxNet and with ET from FluxCom, Global Land Evaporation Amsterdam Model (GLEAM), ERA-5, and Generalized Complementary Relationship (GCR). For the ET, the coefficients of determination (R²) were higher than 0.96, while the R² value for the SH was 0.71 with slightly lower than those. A time series of the weekly root-zone SMI revealed that the regions with Extreme drought had been expanded from the northern part of East China to the entire East China between July to October 2019. The trend analysis of the number of extreme drought events showed that extreme drought events in spring had reduced in South Korea over the past 20 years, while those in fall had a tendency to increase. It is concluded that this study can be useful to reduce the socioeconomic damages resulted from climate extremes by comprehensively characterizing extreme drought events.

• The Plant Pathology Journal
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• v.36 no.5
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• pp.406-417
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• 2020

Early warning services for crop diseases are valuable when they provide timely forecasts that farmers can utilize to inform their disease management decisions. In South Korea, collaborative disease controls that utilize unmanned aerial vehicles are commonly performed for most rice paddies. However, such controls could benefit from seasonal disease early warnings with a lead time of a few months. As a first step to establish a seasonal disease early warning service using seasonal climate forecasts, we developed the EPIRICE Daily Risk Model for rice blast by extracting and modifying the core infection algorithms of the EPIRICE model. The daily risk scores generated by the EPIRICE Daily Risk Model were successfully converted into a realistic and measurable disease value through statistical analyses with 13 rice blast incidence datasets, and subsequently validated using the data from another rice blast experiment conducted in Icheon, South Korea, from 1974 to 2000. The sensitivity of the model to air temperature, relative humidity, and precipitation input variables was examined, and the relative humidity resulted in the most sensitive response from the model. Overall, our results indicate that the EPIRICE Daily Risk Model can be used to produce potential disease risk predictions for the seasonal disease early warning service.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.5
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• pp.91-99
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• 2016

High resolution rainfall data at 1-hour or a finer scale are essential for reliable flood analysis and forecasting; nevertheless, many observations, forecasts, and climate projections are still given at coarse temporal resolutions. This study aims to evaluate a chaotic method for disaggregation of 6-hour rainfall data sets so as to apply operational 6-hour rainfall forecasts of the Korean Meteorological Association to flood models. We computed parameters of a state-of-the-art multiplicative random cascade model with two combinations of cascades, namely uniform splitting and diversion, using rainfall observations at Seoul station, and compared statistical performance. We additionally disaggregated 6-hour rainfall time series at 58 stations with the uniform splitting and evaluated temporal transferability of the parameters and changes in multifractal properties. Results showed that the uniform splitting outperformed the diversion in reproduction of observed statistics, and hence is better to be used for disaggregation of 6-hour rainfall forecasts. We also found that multifractal properties of rainfall observations has adequate temporal consistency with an indication of gradually increasing rainfall intensity across South Korea.

• Atmosphere
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• v.33 no.4
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• pp.355-365
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• 2023

To proactively manage climate risk, near-term climate predictions on annual to decadal time scales are of great interest to various communities. This study evaluates the near-term climate prediction skills in East Asia with DePreSys4 retrospective decadal predictions. The model is initialized every November from 1960 to 2020, consisting of 61 initializations with ten ensemble members. The prediction skill is quantitatively evaluated using the deterministic and probabilistic metrics, particularly for annual mean near-surface temperature, land precipitation, and sea level pressure. The near-term climate predictions for May~September and November~March averages over the five years are also assessed. DePreSys4 successfully predicts the annual mean and the five-year mean near-surface temperatures in East Asia, as the long-term trend sourced from external radiative forcing is well reproduced. However, land precipitation predictions are statistically significant only in very limited sporadic regions. The sea level pressure predictions also show statistically significant skills only over the ocean due to the failure of predicting a long-term trend over the land.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.71-82
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• 2019

The purpose of this study is to analyze the Mekong River streamflow alteration due to climate change. The future climate change scenarios were produced by bias corrections of the data from East Asia RCP 4.5 and 8.5 scenarios, given by HadGEM3-RA. Then, SWAT model was used for discharge simulation of the Kratie, the main point of the Mekong River (watershed area: $646,000km^2$, 88% of the annual average flow rate of the Mekong River). As a result of the climate change analysis, the annual precipitation of the Kratie upper-watershed increase in both scenarios compared to the baseline yearly average precipitation. The monthly precipitation increase is relatively large from June to November. In particular, precipitation fluctuated greatly in the RCP 8.5 rather than RCP 4.5. Monthly average maximum and minimum temperature are predicted to be increased in both scenarios. As well as precipitation, the temperature increase in RCP 8.5 scenarios was found to be more significant than RCP 4.5. In addition, as a result of the duration curve comparison, the streamflow variation will become larger in low and high flow rate and the drought will be further intensified in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.224-224
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• 2019

국제기상기구의 Global Framework for Climate Services (GFCS)의 관점에서 살펴보면 국내의 기상 기후 정보는 기상청을 중심으로 관측 자료와 중장기 예측 및 기후변화 시나리오 정보 등의 다양한 시간규모로 생산되고 있다. 하지만 사용자가 직접적으로 다양한 기후정보를 상세화하여 활용하기 위해서는 기후정보의 구축 및 전처리를 수행해야하는 어려움이 있다. 따라서 APEC Climate Center (APCC)에서 다학제 융합 기반 기후정보 서비스를 중심으로 사용자 인터페이스 플랫폼 (User Interface Platform: UIP)의 기술적 플랫폼으로 APCC Integrated Modeling Solution (AIMS)를 개발하였다. AIMS는 사용자의 관점으로 상세화를 수행할 수 있고, 다양한 응용 분야에 적용하기 쉽게 데이터를 생성하여 연구에 도움을 주고 있다. 본 연구는 AIMS에서 제공하고 있는 기존의 국가별로 제공하는 제 5차 결합 기후모델 비교사업 (The $5^{th}$ phase of the coupled model intercomparision project, CMIP5)에서 해석한 전구기후모델 (General Circulation Model, GCM)의 통계적 상세화 방법인 Simple Quantile Mapping (SQM)과 Spatial Disaggregation Quantile Delta Mapping (SDQDM)를 포함하여 AIMS에 새롭게 추가 된 통계적 상세화 방법인 Bias Correction and Stochastic Analog (BCSA) 방법을 소개하고자 한다. 또한 60개의 종관기상관측 (Automated Surface Observing System, ASOS)자료를 중심으로 생성한 세 가지 통계적 상세화방법의 과거재현성과 RCP4.5, RCP8.5 시나리오를 활용한 미래 불확실성 평가 결과를 이용하여 연구자들의 맞춤형 자료를 생산하고 평가하는데 도움을 줌으로써 다양한 기후자료의 효과적인 활용이 가능할 것으로 기대된다.

• 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.