• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.85-95
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• 2020

According to the standard guidelines of design flood (MLTM, 2012; MOE, 2019), the design flood is calculated based on past precipitation. However, due to climate change, the frequency of extreme rainfall events is increasing. Therefore, it is necessary to analyze future floods' volume by using climate change scenarios. Meanwhile, the standard guideline was revised by MOE (Ministry of Environment) recently. MOE proposed modified Huff distribution and new CN (Curve Number) value of forest and paddy. The objective of this study was to analyze the change of flood volume by applying the modified Huff and newly proposed CN to the probabilistic precipitation based on SSP and RCP scenarios. The probabilistic rainfall under climate change was calculated through RCP 4.5/8.5 scenarios and SSP 245/585 scenarios. HEC-HMS (Hydrologic Engineering Center - Hydrologic Modeling System) was simulated for evaluating the flood volume. When RCP 4.5/8.5 scenario was changed to SSP 245/585 scenario, the average flood volume increased by 627 ㎥/s (15%) and 523 ㎥/s (13%), respectively. By the modified Huff distribution, the flood volume increased by 139 ㎥/s (3.76%) on a 200-yr frequency and 171 ㎥/s (4.05%) on a 500-yr frequency. The newly proposed CN made the future flood value increase by 9.5 ㎥/s (0.30%) on a 200-yr frequency and 8.5 ㎥/s (0.25%) on a 500-yr frequency. The selection of climate change scenario was the biggest factor that made the flood volume to transform. Also, the impact of change in Huff was larger than that of CN about 13-16 times.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.275-282
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• 2016

The objective of this study is to propose a new method to determine the drought event and the design drought severity. In order to define a drought event from precipitation data, theory of run was applied with the cumulative rainfall deficit. When we have a large amount of rainfall over the threshold level, in this study, we compare with the previous cumulative rainfall deficit to determine whether the drought is relieved or not. The recurrence characteristics of the drought severity on the specific duration was analyzed by the conditional bivariate copula function and confidence intervals were estimated to quantify uncertainties. The methodology was applied to Seoul station with the historical dataset (1909~2015). It was observed that the past droughts considered as extreme hydrological events had from 10 to 50 years of return period. On the other hand, the current on-going drought event started from 2013 showed the significantly higher return period. It is expected that the result of this study may be utilized as the reliable criteria based on the concept of return period for the drought contingency plan.

• Journal of Korea Water Resources Association
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• v.50 no.7
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• pp.475-488
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• 2017

To simulate accurate drought, a drought index is needed to reflect the hydrometeorological phenomenon. Several studies have been conducted in Korea using the Modified Surface Water Supply Index (MSWSI) to simulate hydrological drought. This study analyzed the limitations of MSWSI and quantified the uncertainties of MSWSI. The influence of hydrometeorological components selected as the MSWSI components was analyzed. Although the previous MSWSI dealt with only one observation for each input component such as streamflow, ground water level, precipitation, and dam inflow, this study included dam storage level and dam release as suitable characteristics of the sub-basins, and used the areal-average precipitation obtained from several observations. From the MSWSI simulations of 2001 and 2006 drought events, MSWSI of this study successfully simulated drought because MSWSI of this study followed the trend of observing the hydrometeorological data and then the accuracy of the drought simulation results was affected by the selection of the input component on the MSWSI. The influence of the selection of the probability distributions to input components on the MSWSI was analyzed, including various criteria: the Gumbel and Generalized Extreme Value (GEV) distributions for precipitation data; normal and Gumbel distributions for streamflow data; 2-parameter log-normal and Gumbel distributions for dam inflow, storage level, and release discharge data; and 3-parameter log-normal distribution for groundwater. Then, the maximum 36 MSWSIs were calculated for each sub-basin, and the ranges of MSWSI differed significantly according to the selection of probability distributions. Therefore, it was confirmed that the MSWSI results may differ depending on the probability distribution. The uncertainty occurred due to the selection of MSWSI input components and the probability distributions were quantified using the maximum entropy. The uncertainty thus increased as the number of input components increased and the uncertainty of MSWSI also increased with the application of probability distributions of input components during the flood season.

• Journal of Korea Water Resources Association
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• v.49 no.12
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• pp.995-1006
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• 2016

According to 5th IPCC Climate Change Report, there is a very high likelihood that the frequency and intensity of extreme rainfall events will increase. In reality, flood damage has increased, and it is necessary to estimate the future probabilistic design rainfall amount that climate change is reflected. In this study, the future probabilistic design precipitation amount is estimated by analyzing trends of future annual maximum daily rainfall derived by RCP 8.5 scenarios and using the scale-invariance technique. In the first step, after reviewing the time-scale characteristics of annual maximum rainfall amounts for each duration observed from 60 sites operating in Korea Meterological Administration, the feasibility of the scale-invariance technique are examined using annual daily maximum rainfall time series simulated under the present climate condition. Then future probabilistic design rainfall amounts for several durations reflecting the effects of climate change are estimated by applying future annual maximum daily rainfall time series in the IDF curve equation derived by scale-invariance properties. It is shown that the increasing trend on the probabilistic design rainfall amount has resulted on most sites, but the decreasing trend in some regions has been projected.

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.755-765
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• 2013

This study developed a real-time dam's hydrologic variables prediction model (DHVPM) and evaluated its performance for simulating historical dam inflow and outflow in the Chungju dam basin. The DHVPM consists of the Sejong University River Forecast (SURF) model for hydrologic modeling and an autoreservoir operation method (Auto ROM) for dam operation. SURF model is continuous rainfall-runoff model with data assimilation using an ensemble Kalman filter technique. The four extreme events including the maximum inflow of each year for 2006~2009 were selected to examine the performance of DHVPM. The statistical criteria, the relative error in peak flow, root mean square error, and model efficiency, demonstrated that DHVPM with data assimilation can simulate more close to observed inflow than those with no data assimilation at both 1-hour lead time, except the relative error in peak flow in 2007. Especially, DHVPM with data assimilation until 10-hour lead time reduced the biases of inflow forecast attributed to observed precipitation error. In conclusion, DHVPM with data assimilation can be useful to improve the accuracy of inflow forecast in the basin where real-time observed inflow are available.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.529-541
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• 2023

Due to the growing concern over forecasting extreme weather events such as droughts caused by climate change, there has been a rising interest in seasonal meteorological forecasts that offer ensemble predictions for the upcoming seven months. Nonetheless, limited research has been conducted in South Korea, particularly in assessing their effectiveness at the catchment-scale. In this study, we assessed the accuracy of ECMWF's seasonal forecasts (including precipitation, temperature, and evapotranspiration) for the period of 2011 to 2020. We focused on 12 multi-purpose reservoir catchments and compared the forecasts to climatology data. Continuous Ranked Probability Skill Score method is adopted to assess the forecast skill, and the linear scaling method was applied to evaluate its impact. The results showed that while the seasonal meteorological forecasts have similar skill to climatology for one month ahead, the skill decreased significantly as the forecast lead time increased. Compared to the climatology, better results were obtained in the Wet season than the Dry season. In particular, during the Wet seasons of the dry years (2015, 2017), the seasonal meteorological forecasts showed the highest skill for all lead times.

• Journal of Astronomy and Space Sciences
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• v.24 no.2
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• pp.125-134
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• 2007

In this paper, we analyze the orbital variation of the Korea Multi-Purpose SATellite-1(KOMPSAT-1) in a strong space environment due to satellite drag by solar and geomagnetic activities. The satellite drag usually occurs slowly, but becomes serious satellite drag when the space environment suddenly changes via strong solar activity like a big flare eruption or coronal mass ejections(CMEs). Especially, KOMPSAT-1 as a low earth orbit satellite has a distinct increase of the drag acceleration by the variations of atmospheric friction. We consider factors of solar activity to have serious effects on the satellite drag from two points of view. One is an effect of high energy radiation when the flare occurs in the Sun. This radiation heats and expands the upper atmosphere of the Earth as the number of neutral particles is suddenly increased. The other is an effect of Joule and precipitating particle heating caused by current of plasma and precipitation of particles during geomagnetic storms by CMEs. It also affects the density of neutral particles by heating the upper atmo-sphere. We investigate the satellite drag acceleration associated with the two factors for five events selected based on solar and geomagnetic data from 2001 to 2002. The major results can be summarized as follows. First, the drag acceleration started to increase with solar EUV radiation with the best cross-correlation (r = 0.92) for 1 day delayed F10.7. Second, the drag acceleration and Dst index have similar patterns when the geomagnetic storm is dominant and the drag acceleration abruptly increases during the strong geomagnetic storm. Third, the background variation of the drag accelerations is governed by the solar radiation, while their short term (less than a day) variations is governed by geomagnetic storms.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.264-272
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• 2013

Climate changes have caused not only changes in the frequency and intensity of extreme climate events, but also temperature and precipitation. The damages on agricultural production system will be increased by heavy rainfall and snow. In this study we assessed vulnerability of crop cultivation facility and animal husbandry facility by heavy rain in 232 agricultural districts. The climate data of 2000 years were used for vulnerability analysis on present status and the data derived from A1B scenario were used for the assessment in the years of 2020, 2050 and 2100, respectively. Vulnerability of local districts was evaluated by three indices such as climate exposure, sensitivity and adaptive capacity, and each index was determined from selected alternative variables. Collected data were normalized and then multiplied by weight value that was elicited in delphi investigation. Jeonla-do and Gangwon-do showed higher climate exposures than the other provinces. The higher sensitivity to abnormal weather was observed from the regions that have large-scale cultivation facility complex compared to the other regions and vulnerability to abnormal weather also was higher at these provinces. In the projected estimation based on the SRES A1B, the vulnerability of controlled agricultural facility in Korea totally increased, especially was dramatic between 2000's and 2020 year.