• Journal of Korea Water Resources Association
• /
• v.55 no.2
• /
• pp.121-133
• /
• 2022

The drought resulting from insufficient rainfall compared to the amount in an ordinary year can significantly impact a broad area at the same time. Another feature of this disaster is hard to recognize its onset and disappearance. Therefore, a reliable and fast way of predicting both the suffering area and the amount of water shortage from the upcoming drought is a key issue to develop a countermeasure of the disaster. However, the available drought scenarios are about 50 events that have been observed in the past. Due to the limited number of events, it is difficult to predict the water shortage in a case where the pattern of a natural disaster is different from the one in the past. To overcome the limitation, in this study, we applied the four RCP climate change scenarios to the water balance model and the annual amount of water shortage from 360 drought events was estimated. In the following chapter, the deep neural network model was trained with the SPEI values from the RCP scenarios and the amount of water shortage as the input and output, respectively. The trained model in each sub-basin enables us to easily and reliably predict the water shortage with the SPEI values in the past and the predicted meteorological conditions in the upcoming season. It can be helpful for decision-makers to respond to future droughts before their onset.

• Atmosphere
• /
• v.30 no.2
• /
• pp.141-154
• /
• 2020

Seasonal forecast is growing in demand, as it provides valuable information for decision making and potential to reduce impact on weather events. This study examines how operational climate prediction systems can be reliable, producing the probability forecast in seasonal scale. A reliability diagram was used, which is a tool for the reliability by comparing probabilities with the corresponding observed frequency. It is proposed for a method grading scales of 1-5 based on the reliability diagram to quantify the reliability. Probabilities are derived from ensemble members using hindcast data. The analysis is focused on skill for 2 m temperature and precipitation from climate prediction systems in KMA, UKMO, and ECMWF, NCEP and JMA. Five categorizations are found depending on variables, seasons and regions. The probability forecast for 2 m temperature can be relied on while that for precipitation is reliable only in few regions. The probabilistic skill in KMA and UKMO is comparable with ECMWF, and the reliabilities tend to increase as the ensemble size and hindcast period increasing.

• Journal of Korean Society of Disaster and Security
• /
• v.15 no.2
• /
• pp.45-56
• /
• 2022

The countermeasure for the shortage of water during dry season and drought period has not been considered with return flowrate in detail. In this study, the outflow of STP was predicted through a data-based machine learning model, LSTM. As the first step, outflow, inflow, precipitation and water elevation were utilized as input data, and the distribution of variance was additionally considered to improve the accuracy of the prediction. When considering the variability of the outflow data, the residual between the observed value and the distribution was assumed to be in the form of a complex trigonometric function and presented in the form of the optimal distribution of the outflow along with the theoretical probability distribution. It was apparently found that the degree of error was reduced when compared to the case not considering where the variance distribution. Therefore, it is expected that the outflow prediction model constructed in this study can be used as basic data for establishing an efficient river management system as more accurate prediction is possible.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.224-224
• /
• 2015

The East Asia monsoon is one of the most complex atmospheric phenomena caused by Land-Sea thermal contrast. It plays essential role in fulfilling the water needs of the region but also poses stern consequences in terms of flooding and droughts. This study analyzed the influence of Global Sea Surface Temperature Anomalies (SSTA) on occurrence of droughts in the East Asia monsoon region ($20^{\circ}N-50^{\circ}N$, $103^{\circ}E-149^{\circ}E$). Standardized Precipitation Index (SPI) was employed to characterize the droughts over the region using 30-year (1978-2007) gridded rainfall dataset at $0.5^{\circ}$ grid resolution. Due to high variability in intensity and spatial extent of monsoon rainfall the East Asia monsoon region was divided into the homogeneous rainfall zones using cluster analysis method. Seven zones were delineated that showed unique rainfall regimes over the region. The influence of SSTA was assessed by using lagged-correlation between global gridded SSTA ($0.2^{\circ}$ grid resolution) and SPI of each zone. Sea regions with potential influence on droughts in different zones were identified based on significant positive and negative correlation between SSTA and SPI with a lag period of 3-month. The results showed that SSTA have the potential to be used as predictor variables for prediction of droughts with a reasonable lead time. The findings of this study will assist to improve the drought prediction over the region.

• Journal of Korea Water Resources Association
• /
• v.52 no.7
• /
• pp.451-461
• /
• 2019

The dam reservoir inflow prediction is utilized to ensure for water supply and prevent future droughts. In this study, we predicted the dam reservoir inflow and analyzed how seasonal weather forecasting affected the accuracy of the inflow for even multi-purpose dams. The hindcast and forecast of GloSea5 from KMA were used as input for rainfall-runoff models. TANK, ABCD, K-DRUM and PRMS models which have individual characteristics were applied to simulate inflow prediction. The dam reservoir inflow prediction was assessed for the periods of 1996~2009 and 2015~2016 for the hindcast and forecast respectively. The results of assessment showed that the inflow prediction was underestimated by comparing with the observed inflow. If rainfall-runoff models were calibrated appropriately, the characteristics of the models were not vital for accuracy of the inflow prediction. However the accuracy of seasonal weather forecasting, especially precipitation data is highly connected to the accuracy of the dam inflow prediction. It is recommended to consider underestimation of the inflow prediction when it is used for operations. Futhermore, for accuracy enhancement of the predicted dam inflow, it is more effective to focus on improving a seasonal weather forecasting rather than a rainfall-runoff model.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.1457-1461
• /
• 2009

기후변화로 인한 강수의 양과 패턴의 변화는 가뭄이나 홍수와 같은 극한사상의 발생가능성을 점차 증가시키고 있다. 이러한 극한사상의 발생에 대비하고자 기후변화가 가뭄이나 홍수에 미치는 영향 평가에 대한 연구가 전 세계적으로 활발히 진행 중이다. 이에 본 연구에서는 월 단위로 IPCC를 통해서 제공되는 Global Climate Model(GCM)중 하나인 BCM2 모형(A2 시나리오 선택)을 기반으로 기후변화가 한반도 가뭄에 미치는 영향평가 방안을 제시하고자 한다. 우선 전구단위 기후모형인 BCM2 모형을 격자단위 관측자료인 NCEP(National Centers for Environmental Prediction)자료를 이용하여 서울기상관측소 지점으로 축소하였다. 또한 축소된 강우자료의 편의를 보정하기 위하여 Quantile mapping 기법을 적용하였으며, 최종적으로 제시된 서울지점의 월 강우를 대상으로 표준강수지수(SPI)를 산정하여 기후변화가 서울지점의 가뭄에 미치는 영향을 평가하였다. 분석결과 기후변화를 고려할 경우, 전반적인 가뭄의 심도는 크게 깊어지지 않았으나 가뭄의 지속기간이 길어져 가뭄으로 인한 피해가 더욱 증가할 것으로 예상되었다.

• Journal of Soil and Groundwater Environment
• /
• v.20 no.2
• /
• pp.1-9
• /
• 2015

Groundwater has been a very precious resource for human life and economic development in the world. With increasing population and food demand, the groundwater use especially for agriculture is largely elevated worldwide. The very much large groundwater use results in depletion of major aquifers, land subsidences in many large cities, anthropogenic groundwater contamination, seawater intrusion in coastal areas and accompanying severe conflicts for water security. Furthermore, with the advent of changing climate, securing freshwater supply including groundwater becomes a pressing and critical issue for sustainable societal development in every country because prediction of precipitation is more difficult, its uneven distribution is aggravating, weather extremes are more frequent, and rising sea level is also threatening the freshwater resource. Under these difficulties, can groundwater be sustaining its role as essential element for human and society in the near future? We have to focus our efforts and wisdom on answering the question. Korean government should increase its investment in securing groundwater resources for changing climate.

• Journal of Korea Water Resources Association
• /
• v.50 no.7
• /
• pp.489-502
• /
• 2017

The objective of this study is to propose and evaluate the BAYES-ESP, which is a dam inflow prediction method based on Ensemble Streamflow Prediction method (ESP) and Bayesian theory. ABCD rainfall-runoff model was used to predict monthly dam inflow. Monthly meteorological data collected from KMA, MOLIT and K-water and dam inflow data collected from K-water were used for the model calibration and verification. To estimate the performance of ABCD model, ESP and BAYES-ESP method, time series analysis and skill score (SS) during 1986~2015 were used. In time series analysis monthly ESP dam inflow prediction values were nearly similar for every years, particularly less accurate in wet and dry years. The proposed BAYES-ESP improved the performance of ESP, especially in wet year. The SS was used for quantitative analysis of monthly mean of observed dam inflows, predicted values from ESP and BAYES-ESP. The results indicated that the SS values of ESP were relatively high in January, February and March but negative values in the other months. It also showed that the BAYES-ESP improved ESP when the values from ESP and observation have a relatively apparent linear relationship. We concluded that the existing ESP method has a limitation to predict dam inflow in Korea due to the seasonality of precipitation pattern and the proposed BAYES-ESP is meaningful for improving dam inflow prediction accuracy of ESP.

• Journal of Korea Water Resources Association
• /
• v.51 no.7
• /
• pp.607-616
• /
• 2018

Flow regime-based ANFIS Dam Inflow Prediction (FADIP) model is developed and compared with ANFIS Dam Inflow Prediction (ADIP) model in this study. The selected study area is the Chungju and Soyang multi-purpose dam watersheds in South Korea. The dam inflow, precipitation and monthly weather forecast information are used as input variables of the models. The training and validation periods of the models are 1987~2010 for Chungju and 1984~2010 for Soyang dam watershed. The testing periods for both watersheds are 2011~2016. The results of training and validation indicate that FADIP has better training ability than ADIP for predicting dam inflow in normal and low flow regimes. In the result of testing, ADIP shows low predictability of dam inflow in the low flow regime due to the model tuning on all flow regime together. However, FADIP demonstrates the improved accuracy over the entire period compared to ADIP, especially during the normal and low flow seasons. It is concluded that FADIP is valuable for the prediction of dam inflow in the case of drought years, and useful for water supply management of the multi-purpose dam.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.57 no.2
• /
• pp.1-13
• /
• 2015

The Coupled Model Intercomparison Project Phase 5 (CMIP5), coordinated by the World Climate Research Programme in support of the Intergovernmental Panel on Climate Change (IPCC) AR5, is the most recent, provides projections of future climate change using various global climate models under four major greenhouse gas emission scenarios. There is a wide selection of climate models available to provide projections of future climate change. These provide for a wide range of possible outcomes when trying to inform managers about possible climate changes. Hence, future agrometeorological indicators estimation will be much impacted by which global climate model and climate change scenarios are used. Decision makers are increasingly expected to use climate information, but the uncertainties associated with global climate models pose substantial hurdles for agricultural resources planning. Although it is the most reasonable that quantifying of the future uncertainty using climate change scenarios, preliminary analysis using reasonable factors for selecting a subset for decision making are needed. In order to narrow the projections to a handful of models that could be used in a climate change impact study, we could provide effective information for selecting climate model and scenarios for climate change impact assessment using maximum/minimum temperature, precipitation, reference evapotranspiration, and moisture index of nine Representative Concentration Pathways (RCP) scenarios.