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HighlightsThe transient water table fluctuation method (TWTFM) is revisited.A novel application of linking SWAT model and TWTFM is suggested.A method is proposed to estimate daily groundwater recharge distribution.The method is demonstrated for the Jeju Island in Korea.Abstract. Estimating groundwater recharge remains a difficult but necessary task as part of managing available groundwater supplies. For example, the average groundwater recharge rate of Jeju Island is 54%, which is considerably higher than the inland recharge rate (~15%) in Korea. Although groundwater is the main water source of this and many other islands, quantifying temporal groundwater recharge for water resources planning remains difficult. To estimate groundwater recharge based on rainfall, a simple and straightforward method is proposed that uses an application of the Transient Water Table Fluctuation Method (TWTFM) linked with the Soil and Water Assessment Tool (SWAT). By using the computed annual percolation from the SWAT as input, two parameters (reaction factor and specific yield) could be estimated by assuming that the sum of daily recharge via the TWTFM was approximately equal to the annual percolation near the water table. This methodology was applied to the Hancheon watershed of Jeju Island, South Korea. Runoff time series data for two years (2009 and 2010) were used to calibrate SWAT and another two years of data were used to validate computed discharges from SWAT. For the calibration of the combined SWAT and TWTFM model, groundwater level data from 2009 and 2010 were used, and then data from 2011 and 2012 were used to predict groundwater recharge using the calibrated TWTFM parameters. The proposed methodology can be used as an efficient tool for estimating the temporal distribution of groundwater recharge using only groundwater data and the annual percolation rate. This methodology can be beneficial for regions where the vadose zone depth is deeply formed and temporal recharge predictions are essential for water management. Keywords: Reaction factor, Specific yield, SWAT, Transient water table fluctuation method (TWTFM).

Flood data on a high temporal scale are required for the design of hydraulic structures, flood risk assessment, flood protection, and reservoir operations. Such flood data are typically generated using rainfall-runoff models through an accurate calibration process. The data also can be estimated using a simple relationship between the daily and the sub-daily flow records as an alternative to rainfall&ndash, runoff modelling. In this study, we propose an approach combining an artificial neural network (ANN) model for peak flow estimation and the steepness index unit volume flood hydrograph (SIUVFH) method for sub-daily flow disaggregation to generate hydrographs on an hourly time scale. The SIUVFH method is based on the strong relationship between the flood peak and the steepness index, which is defined as the difference between the daily flood peak and daily flow several days before the peak, it is also used for selecting a reference unit volume flood hydrograph to be scaled to obtain the sub-daily flood hydrograph. In this study, to improve the applicability of the SIUVFH method for locations with a weak relationship between the flood peak and steepness index, the ANN-based flood peak estimation was used as an additional indicator to determine a reference unit volume flood hydrograph. To apply the proposed method, ANN models for estimating the peak flows from the mean daily flows during peak and adjacent days were constructed for the studied dam sites. The optimal ANN structures were determined through Monte Carlo cross-validation. The results showed a good performance with statistical measurements of relative root mean square errors of 0.155&ndash, 0.224, 0.208&ndash, 0.301, and 0.244&ndash, 0.382 for the training, validation, and testing datasets, respectively. An application of the combined use of the ANN-based peak estimation and the SIUVFH-based flow disaggregation revealed that the disaggregated hourly flows satisfactorily matched the observed flood hydrograph.

Rainfall runoff is a complex phenomenon in nature. It differs from place to place due to different topographical features and rainfall patterns. The Natural Resources Conservation Service - Curve Number (NRCS-CN) is a well-adopted model to account for direct runoff volume from storm events. There are several studies on determining the initial abstraction and the CN from observed rainfall-runoff data; however, few studies demonstrate their statistical characteristics. The major aim of this study is to determine the feasible range and the confidence intervals of the CN. We examined 660 rainfall-runoff events collected from six medium sized watersheds in South Korea. The interior point optimization algorithm was adopted to ascertain the optimum value of CN and the initial abstraction coefficient (λ). The obtained results show that the CN value ranged from 45 to 90 and the average λ = 0.12 was best suited for Korean watersheds. The estimated confidence intervals were highly significant and strongly recommended for Korean watersheds.

Background There is a controversy about late-onset congestive heart failure (CHF) among breast cancer survivors. This study investigated the incidence rate and risk factors of late-onset CHF more than 2 years after the breast cancer diagnosis. Methods A nationwide, retrospective study was conducted with the National Health Information Database. With 1:3 age- and sex-matched noncancer controls, Cox proportional hazard regression models were used to analyze the incidence and risk factors of late CHF. The cumulative incidence rate of late CHF was evaluated with a Kaplan-Meier analysis and a log-rank test. Results A total of 91,227 cases (286,480 person-years) and 273,681 controls (884,349 person-years) were evaluated between January 2007 and December 2013. The risks of late CHF were higher in cases than controls (hazard ratio [HR], 1.396; 95% confidence interval [CI], 1.268-1.538). Younger survivors (age ≤ 50 years) showed a higher risk of late CHF than their younger counterparts (HR, 2.903; 95% CI, 2.425-3.474). Although older age was a risk factor for late CHF, older survivors (age ≥ 66 years) showed no difference in the risk of late CHF in comparison with their counterparts (HR, 0.906; 95% CI, 0.757-1.084). Anthracyclines and taxanes were risk factors for late CHF, although trastuzumab, radiation, and endocrine therapy were not. Conclusions Young breast cancer survivors have a greater risk of late CHF than the young population without cancer. More attention should be paid to young breast cancer survivors who receive taxane- or anthracycline-based regimens over the long term.

The Soil Conservation Service Curve Number (CN) method is widely used to calculate the flood runoff in ungauged catchments. However, the existing CN calculation method has a disadvantage in that it cannot calculate the CNs considering the spatiotemporal variability of rainfall in the ungauged catchments. In this study, the authors used a distributed rainfall-runoff model and a simple runoff generation method to generate the hourly runoffs of the ungauged catchments considering the spatiotemporal variability of rainfall, and estimate the CNs of the ungauged catchments using the generated runoff and the CN back calculation method. As a result of calculating the CNs of the ungauged catchments for past 20 rainfall events, the CNs had a large variability even in the same catchment. In addition, the mean CNs for the 20 rainfall events of independent ungauged catchments differed even if the ungauged catchments were close to each other. The method of estimating the CNs of the ungauged catchments presented in this study properly reflects the characteristics of actual rainfall and catchment, and thus can be usefully used to estimate valid CN.

Total organic carbon (TOC) has vital significance for measuring water quality in river streamflow. The detection of TOC can be considered as an important evaluation because of issues on human health and environmental indicators. This research utilized the novel hybrid models to improve the predictive accuracy of TOC at Andong and Changnyeong stations in the Nakdong River, South Korea. A data pre-processing approach (i.e., complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)) and evolutionary optimization algorithm (i.e., crow search algorithm (CSA)) were implemented for enhancing the accuracy and robustness of standalone models (i.e., multivariate adaptive regression spline (MARS) and M5Tree). Various water quality indicators (i.e., TOC, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), and suspended solids (SS)) were utilized for developing the standalone and hybrid models based on three input combinations (i.e., categories 1~3). The developed models were evaluated utilizing the correlation coefficient (CC), root-mean-square error (RMSE), and Nash-Sutcliffe efficiency (NSE). The CEEMDAN-MARS-CSA based on category 2 (C-M-CSA2) model (CC = 0.762, RMSE = 0.570 mg/L, and NSE = 0.520) was the most accurate for predicting TOC at Andong station, whereas the CEEMDAN-MARS-CSA based on category 3 (C-M-CSA3) model (CC = 0.900, RMSE = 0.675 mg/L, and NSE = 0.680) was the best at Changnyeong station.

In this study, artificial neural network (ANN) models were constructed to predict the rainfall during May and June for the Han River basin, South Korea. This was achieved using the lagged global climate indices and historical rainfall data. Monte-Carlo cross-validation and aggregation (MCCVA) was applied to create an ensemble of forecasts. The input-output patterns were randomly divided into training, validation, and test datasets. This was done 100 times to achieve diverse data splitting. In each data splitting, ANN training was repeated 100 times using randomly assigned initial weight vectors of the network to construct 10,000 prediction ensembles and estimate their prediction uncertainty interval. The optimal ANN model that was used to forecast the monthly rainfall in May had 11 input variables of the lagged climate indices such as the Arctic Oscillation (AO), East Atlantic/Western Russia Pattern (EAWR), Polar/Eurasia Pattern (POL), Quasi-Biennial Oscillation (QBO), Sahel Precipitation Index (SPI), and Western Pacific Index (WP). The ensemble of the rainfall forecasts exhibited the values of the averaged root mean squared error (RMSE) of 27.4, 33.6, and 39.5 mm, and the averaged correlation coefficient (CC) of 0.809, 0.725, and 0.641 for the training, validation, and test sets, respectively. The estimated uncertainty band has covered 58.5% of observed rainfall data with an average band width of 50.0 mm, exhibiting acceptable results. The ANN forecasting model for June has 9 input variables, which differed from May, of the Atlantic Meridional Mode (AMM), East Pacific/North Pacific Oscillation (EPNP), North Atlantic Oscillation (NAO), Scandinavia Pattern (SCAND), Equatorial Eastern Pacific SLP (SLP_EEP), and POL. The averaged RMSE values are 39.5, 46.1, and 62.1 mm, and the averaged CC values are 0.853, 0.771, and 0.683 for the training, validation, and test sets, respectively. The estimated uncertainty band for June rainfall forecasts generally has a coverage of 67.9% with an average band width of 83.0 mm. It can be concluded that the neural network with MCCVA enables us to provide acceptable medium-term rainfall forecasts and define the prediction uncertainty interval.

Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models’ outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.

A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.

The design of hydraulic structures and the assessment of flood control measures require the estimation of flood quantiles. Since observed flood data are rarely available at the specific location, flood estimation in un-gauged or poorly gauged basins is a common problem in engineering hydrology. We investigated the flood estimation method in a poorly gauged basin. The flood estimation method applied the combination of rainfall-runoff model simulation and regional flood frequency analysis (RFFA). The L-moment based index flood method was performed using the annual maximum flood (AMF) data simulated by the rainfall-runoff model. The regional flood frequency distribution with 90% error bounds was derived in the Chungju dam basin of Korea, which has a drainage area of 6648 km2. The flood quantile estimates based on the simulated AMF data were consistent with the flood quantile estimates based on the observed AMF data. The widths of error bounds of regional flood frequency distribution increased sharply as the return period increased. The results suggest that the flood estimation approach applied in this study has the potential to estimate flood quantiles when the hourly rainfall measurements during major storms are widely available and the observed flood data are limited.

The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.

In this study, we use factor analysis and spatial analysis to study the spatio-temporal distribution of daily precipitation on Jeju Island, which includes marine and mountainous areas. The precipitation time series from 82 weather stations were used to fill in missing and ungauged data for some periods at 38 stations, and then the daily spatial distribution was analyzed from 1992 to 2013. Factor analysis and multiple regression showed that the statistical characteristics of the extended data fit well to those of the observed time series. The point precipitation characteristics, such as the mean and standard deviation, show small differences between observed and extended data, and the relationships to elevation also have similar behavior for seasonal and annual precipitation. However, the spatial precipitation characteristics for observed and extended data show significant differences with respect to elevation because the data for high-elevation areas are mainly interpolated for the period 1992 to 2013. For annual and five-year moving-average spatial precipitation, the amount of precipitation shows seasonal and spatial variability. Annual spatial precipitation in high-elevation areas usually shows high variation over time. These results suggest the need to consider completing missing and ungauged data series for assessment the spatio-temporal variation of precipitation.

Reliable estimation of groundwater recharge rate is crucial for the assessment of groundwater resource potential in Africa. In this study, we reviewed existing studies on groundwater recharge, especially in the semi-arid and humid regions of Africa. After the assessment of the main advantages and disadvantages of each method, we strongly agree that among the distinct existing methods, Water-Table Fluctuations (WTF), Recession-Curve Displacement, and Chloride Methods can be used with a better certainty of improved estimation of groundwater recharge in these regions. In addition, the features of existing studies on groundwater recharge are outlined. The major challenge of these regions on recharge study is the lack of basic data. Therefore, this paper suggests methods for dealing with this limitation and also the future outlook using recently developed technologies such as Remote Sensing (RS) and Geographical Information System (GIS). Watershed hydrologic modeling, which is a robust method for recharge estimation that is widely applied around the world, should also be applied for future perspective by solving the problems of its use and data requirements to find a better result. Strictly speaking, the key to the successful estimation of groundwater recharge lies in the utilization of a variety of independent methods. Therefore, by bringing together the advantages, limitations, and cost of each method, the study of the recharge estimation in different climatic environments of African regions can enter a new era.

The objective of this study is to suggest a methodology for estimating the time-variant probable maximum precipitation. First, the greatest amount of precipitation for a relatively long duration, e.g., a month or longer, defined as the Long-term Probable Maximum Precipitation (LPMP) in the present work was estimated. Then, the procedure for using the LPMP estimates for determining the temporally varied upper limit of precipitation during a certain period of the year, defined as the time-variant PMP (TPMP) in this study was implemented. Hershfield's statistical approach was used to estimate LPMP, i.e., monthly, calendar monthly, and cumulative monthly, for each of 61 weather stations located in South Korea, and nationwide distribution maps of three types of LPMP were made. In particular, using the cumulative monthly PMP estimates and total antecedent precipitation prior to a certain time, the TPMP for a certain duration was forecast for example problems. From the case study for precipitation data at Chungju weather station, TPMP forecasts at certain stormy periods were substantially smaller than the design PMP, which implies that reservoir operation for flood control based on the design PMP may be too restrictive for some period. It is expected that the TPMP can serve as a complementary index to increase the water supply of a reservoir.

Paper number: 16-028Received: 11 March 2016; Revised: 8 April 2016; Accepted: 8 April 2016AbstractIn order to infer regional flood frequency for ungauged watersheds, index flood method was applied for this study. To pursuit this given purpose, annual peak flood data for 22 watersheds located at the upstream of the Chungju Dam watershed were obtained from the spatial extension technique. The regionalization of mean annual flood was performed from extended flood data at 22 points. Based on the theory that flood discharge and watershed size follows the power law the regionalization generated the empirical relationship. These analyses were executed for the full size of the Chungju Dam watershed as one group and three different mid-size watersheds groups. From the results, the relationship between mean annual flood and watershed sizes follow the power law. We demonstrated that it is appropriate to use the relationship between specific flood discharges from the upper and lower watersheds in terms of estimating the floods for the ungaged watersheds. Therefore, not only the procedure of regional frequency analysis but also regionalizaion analaysis using finer discretization of the regions interest with respect to the regional frequency analyisis for the ungauged watersheds is important.Keywords: Regional flood frequency analysis, Index flood method, Power law, Regionalization, Ungaged watershed

Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1&ndash, M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.

Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water resource management. This study investigates kernel extreme learning machine (KELM), boosted regression tree (BRT), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and multivariate adaptive regression spline (MARS) models for daily dew point temperature estimation at Durham and UC Riverside stations in the United States. Daily time scale measured hydrometeorological data, including wind speed (WS), maximum air temperature (TMAX), minimum air temperature (TMIN), maximum relative humidity (RHMAX), minimum relative humidity (RHMIN), vapor pressure (VP), soil temperature (ST), solar radiation (SR), and dew point temperature (Tdew) were utilized to investigate the applied predictive models. Results of the KELM model were compared with other models using eight different input combinations with respect to root mean square error (RMSE), coefficient of determination (R2), and Nash&ndash, Sutcliffe efficiency (NSE) statistical indices. Results showed that the KELM models, using three input parameters, VP, TMAX, and RHMIN, with RMSE = 0.419 &deg, C, NSE = 0.995, and R2 = 0.995 at Durham station, and seven input parameters, VP, ST, RHMAX, TMIN, RHMIN, TMAX, and WS, with RMSE = 0.485 &deg, C, NSE = 0.994, and R2 = 0.994 at UC Riverside station, exhibited better performance in the modeling of daily Tdew. Finally, it was concluded from a comparison of the results that out of the five models applied, the KELM model was found to be the most robust by improving the performance of BRT, RBFNN, MLPNN, and MARS models in the testing phase at both stations.

In this study, long-term precipitation forecasting models capable of reflecting constantly changing climate characteristics and providing forecasts for up to 12 months in advance were developed using lagged correlations with global and local climate indices. These models were applied to predict monthly precipitation in the Han River basin, South Korea. Based on the lead month of forecast, 10 climate indices with high correlations were selected and combined to construct four-variable multiple regression models for monthly precipitation forecasting. The forecast results for the analytical period (2010&ndash, 2019) showed that predictability was low for some summer seasons but satisfactory for other seasons and long periods. In the goodness-of-fit test results, the Nash&ndash, Sutcliffe efficiency (0.48&ndash, 0.57) and the ratio of the root mean square error to the standard deviation of the observation (0.66&ndash, 0.72) were evaluated to be satisfactory while the percent bias (9.4%&ndash, 15.5%) was evaluated to be between very good and good. Due to the nature of the statistical models, however, the predictability is highly likely to be reduced if climate phenomena that are different from the statistical characteristics of the past appear in the forecast targets or predictors. The forecast results were also presented as tercile probability information (below normal, normal, above normal) through a comparison with the observation data of the past 30 years. The results are expected to be utilized as useful forecast information in practice if the predictability for some periods is improved.

This study develops a late spring-early summer rainfall forecasting model using an artificial neural network (ANN) for the Geum River Basin in South Korea. After identifying the lagged correlation between climate indices and the rainfall amount in May and June, 11 significant input variables were selected for the preliminary ANN structure. From quantification of the relative importance of the input variables, the lagged climate indices of East Atlantic Pattern (EA), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), East Pacific/North Pacific Oscillation (EP/NP), and Tropical Northern Atlantic Index (TNA) were identified as significant predictors and were used to construct a much simpler ANN model. The final best ANN model, with five input variables, showed acceptable performance with relative root mean square errors of 25.84%, 32.72%, and 34.75% for training, validation, and testing data sets, respectively. The hit score, which is the number of hit years divided by the total number of years, was more than 60%, which indicates that the ANN model successfully predicts rainfall in the study area. The developed ANN model, incorporated with lagged global climate indices, could allow for more timely and flexible management of water resources and better preparation against potential droughts in the study region.

The KSTAR PF cryo-circuit is a quasi-closed circulation system in which more than 370 g/s of supercritical helium (SHe) is circulated using a SHe circulator. The heated helium from superconducting magnet is cooled through sub cooler (4.3 K). The circulator is operated at 4.5 K and 6.5 bar, and the pressure drop of the circuit is kept at 2 bar in order to maintain the supercritical state and circulator stability. The circuit is connected with helium refrigerator system, distribution system, and supercritical magnet system. It has a hundred branches to supply supercritical helium to the poloidal field superconducting magnet. The branch was designed to optimize the operation conditions and they are grouped for one cryogenic valve has the same length within the cardinal principle of the optimization. Five cryogenic valves are installed to control the mass flow rate, and seven orifice mass flow meters, differential pressure gauges and temperature sensors were installed in front of the magnet in the distribution because upper magnet and lower magnet is symmetric theoretically. The cryogenic pipe line was manufactured with elevation about 10 m between upper magnet and lower magnet. The inlet and outlet helium feed-through were installed at the coil inside in case of KSTAR PF1–PF5 upper magnet and lower magnet. The flow imbalance is caused by void fraction and it could be changed due to manufacturing process even if it has the same length of cooling channel. This creates an imbalance among cooling channels and temperatures are slightly different. The flow was reduced and detoured due to the pressure rise rapidly at inlet and outlet during magnet operation. Therefore, the orifice mass flow meters are installed in front of the PF1–PF5 upper magnet and lower magnet in order to investigate flow trend and the thermos-hydraulic behavior is analyzed during PF magnet operation.

A method of estimating irrigation water need based on water balance and net water consumption concept is proposed, and applied to four watersheds in order to assess the regional and altitudinal characteristics of evapotranspiration and water need for upland crops in Jeju Island. Potential and actual evapotranspiration, and net water need were calculated during the period 1992 to 2013 using SWAT-K watershed model. The annual potential evapotranspiration decreased linearly with increasing elevation, while actual evapotranspiration showed increase with elevation to 400 m around and gradual decrease at higher elevation due to vegetation species, water availability, and cold limitation. Altitudinal pattern of net water need showed linear decrease with increasing elevation for three watersheds (Han-cheon, Cheonmi-cheon, and Oedo-cheon), and annual values of net water need for upland areas (below 200 m in elevation) were 559~680mm/yr. The comparison between actual pumping rate from wells and net water need for irrigation area showed that the amount of pumping water significantly increased during summer season (June to August), while net water need for crop cultivation relatively decreased during this period. To ensure these results, more water use data from pumping wells and additional watersheds should be investigated in the next study.

The streamflow depletion due to groundwater pumping from deep aquifer near the Juksan stream has been simulated, in this study, by using the surface water and groundwater integrated model, SWAT-ODFLOW in order to analyze the relationship between the stream depletion and hydraulic properties of aquifer and streambed, and to spatially assess the streamflow depletion. The simulated results showed that the streamflow depletion rate divided by the pumping rate for each well location ranges from 10% to 90% with reflecting the various well-stream distance, transmissivity, storativity, and streambed hydraulic conductance. In particular, the streamflow depletion exceeds about 50% of pumping rate for conditions with transmissivity higher than or storage coefficient lower than 0.1. The simulated results in the form of spatial maps indicated that the spatially averaged percent depletion of streamflow is about 53.6% for five years of pumping which is lower than that for shallow aquifer pumping by 12.9%. From the spatially distributed stream depletion, it was found that higher and more rapid stream depletion to pumping occurs near middle-downstream reach.

This study assessed the impact of uncertainties in flood data on the results of flood frequency analysis for Han river basin. To meet this aim, this study quantified assessment focused on the index flood and quantile by regional flood frequency analysis using the flood data from 17 water level gauges in Han river basin. We analysed the results categorized by three cases according to the characteristics of the measured data. Firstly, we analyzed the regional flood frequency for the water level gauge in the Pyungchang river basin to investigate the impact of water level data. The results has the error of 0.240 with respect to the mean flood. Secondly, we examined the impact of uncertainty in measurement data generated by the application of rating on the results of regional flood frequency analysis. We have compared the results by applying the rating estimated for each year to the one by the recently estimated rating. The results showed that the mean error has 0.246 in terms of the mean flood. Finally, we have inferred the regional flood frequency analysis results with the regulated flow in the downstream area of dams. The regulated specific discharge in the downstream area of dams controlled by dam operation showed a large difference to the estimated specific discharge in the downstream area of dams by extension of the natural specific discharge in the upstream area using the regionalization method.

Hydrologic component analysis was conducted to investigate water budget characteristics the Oedocheon watershed, Jeju Island. For this purpose, integrated SWAT-MODFLOW model was applied to this watershed for continuous surface water-groundwater modeling. Pasture and forest-deciduous are the major land use types and these affect general hydrologic component ratio. The spatio-temporal groundwater recharge can be obtained from SWAT and then distributed groundwater recharge can be reproduced by MODFLOW. The groundwater level variation was simulated with distributed groundwater pumping data. The water budget in this watershed was compared with the previous estimated result by Jeju-Do(2013). As this result considered discharge to the coastal side, the discrepancy was found. However, it was found that the overall tendency of both analyses were similar.

Purpose Breast cancer survivors have slightly increased the risk of second primary cancers. Breast, colon, uterine, and ovarian cancers are common secondary cancers in breast cancer survivors. In this study, we assessed the development of second primary cancers of breast cancer survivors in Korea. Methods Medical records of patients with breast cancer in 3 tertiary medical institutions were reviewed retrospectively. We evaluated secondary malignancy diagnosed at least 2 months after the breast cancer diagnosis. Based on the International Classification of Disease-9 codes of malignancies, secondary primary breast cancer records were evaluated with person-year adjustment. The standardized incidence ratio (SIR) was assessed using national cancer incidence. Results A total of 3,444 treatment records were included from 3 medical centers. The cumulative incidence of overall second primary cancers was 2.8% (n = 93). The SIR was significantly higher in all sites (1.56; 95% confidence interval [CI], 1.26-1.91), endometrial cancer (5.65; 95% CI, 2.06-12.31), biliary tract cancer (3.96; 95% CI, 1.19-8.60), and thyroid cancer (2.29; 95% CI, 1.67-3.08). Conclusion The incidence of cancer was higher in breast cancer survivors compared to general population. Surveillance of secondary cancer in this group should be recommended individually considering the benefit related to the prognosis of primary breast cancer.

The effects of initial conditions and input values of the rainfall-runoff model were studied in the applications of a lumped concept model for flood event data extension. For the initial conditions of the rainfall-runoff model, baseflow effects and spatial distributions of saturation points () for the storage function methods (SFM) were analyzed. In addition, researches on the effects of rainfall data conditions as input values for the rainfall-runoff model were performed. The Chungju Dam watershed was selected and divided into 3 catchments including smaller size of 22 sub-catchments. The observed discharge and inflow amounts at Yeongwol 1, Chungju Dam, and Yeongwol 2 water level stations were individually operated as criteria for flood data extension in 30 flood events from 1993 to 2009. Direct and base flow were distinguished from a stream flow. In order to test capability of flood data extension, obtained base flow was applied to the rainfall-runoff model for three water level stations. When base flow was adopted in the model, the Nash-Sutcliffe Efficiency(NSE) was increased. The numbers of over satisfaction for model performance (>0.5) were increased over 10%. Saturation points () which strongly influence the runoff amount when rainfall starts were optimized based on the runoff amount at three water level stations. The sizes of saturation points for three locations were similar which means saturation point size is not depending on the runoff amount. The effects of rainfall information for flood runoff were tested at 2002ev1 and 2008ev1. When increased the amount of rainfall information, the runoff simulations were closer to the simulations with full of rainfall information. However, the size of improvement was not substantial on rainfall-runoff simulations in terms of the size of total amount of rainfall.

Temporal variation of groundwater levels in Jeju Island reveals time-delaying and dispersive process of recharge, mainly caused by the hydrogeological feature that thickness of the unsaturated zone is highly variable. Most groundwater flow models have limitations on delineating temporal variation of recharge, although it is a major component of the groundwater flow system. A new mathematical model was developed to generate time series of recharge from precipitation data. The model uses a convolution technique to simulate the time-delaying and dispersive process of recharge. The vertical velocity and the dispersivity are two parameters determining the time series of recharge for a given thickness of the unsaturated zone. The model determines two parameters by correlating the generated recharge time series with measured groundwater levels. The model was applied to observation wells of Jeju Island, and revealed distinctive variations of recharge depending on location of wells. The suggested model demonstrated capability of the convolution method in dealing with recharge undergoing the time-delaying and dispersive process. Therefore, it can be used in many groundwater flow models for generating a time series of recharge.

The groundwater level varies according to the characteristics and composite materials of aquifer. In this study, specific yield and reaction factor which are the major two hydrogeological parameters in the WTF(Water Table Fluctuation) method were estimated and analyzed spatial characteristics. 8 groundwater level stations which have enough measuring period and high correlation with rainfall in the Hancheon watershed were used. The results showed that specific yield was randomly distributed and reaction factor showed inverse trend with altitude. If the enough data were collected, reaction factor according to altitude in ungauged points could be estimated by using these parameter characteristics. Key words : Groundwater level, Parameters, Reaction factor, Specific yield 1. 서 론 1) 제주지역은 화산활동으로 인한 현무암 지역으로 대수층의 투수성과 지하수 함양량이 매우 높다. 기상청자료(www.kma.go.kr)에 따르면 제주지역의 연평균 강우량이 약 1,900 mm 이상으로 다우 지역임에도 불구하고 급한 지형경사로 인해 유로연장이 짧으며, 고투성의 매질로 구성되어 다량의 강우가 집중될 경우에만 유출이 발생하므로, 이 지역의 유출률은 내륙에 비해 낮은 수준이다. 다시 말해 제주지역은 지표에 도달한 강우가 하천을 통해 바다로 직접 유출되기보다는 대수층에 유입되는 양이 많아 상시하천이나 강이 존재하기 어려워 건천이 형성되는 특징이 있다. 따라서 제주지역은 수자원 이용시 지하수에 대한 의존도가 매우 높다(Kim 등, 2013a; Sustainable Water Resources Research Center, 2010).지하수의 안전채수량(safe yield)은 수량 감소 또는 고갈, 수질악화 등의 지하수의 악영향을 유발하지 않는 범위 내에서 대수층으로부터 채취할 수 있는 한계채수량을 말한다. 안전채수량을 선정하기 위해서는 정확한 함양량 산정이 선행되어야 한다. 현재 함양량 산정에는 물수지법, 기저유출분리법, 지하수위 감수곡선법, 추적자 시험법 및 지하수위 변동법 등의 다양한 방법이 이용되고 있다(Healy, 2010). 이 중 지하수

In this work, the delay time for groundwater recharge was estimated by comparing simulated recharges by means of SWAT(Soil and Water Assessment Tool) model and WTF(Water Table Fluctuation) method. The delay time for groundwater recharge means that the time when the water from rainfall travelled through vadose zone just after getting out of soil zone bottom. As measuring this delay time is almost impossible, we used to compare the estimated values from modeling(SWAT) and analytic method(WTF). The test site is Hancheon watershed which has 8 groundwater measurement stations. The results show that the altitude has a linear relationship with the estimated delay time values. To validate these results, we conducted corelation analysis between transformed groundwater levels and observed ones. The results showed that computed groundwater levels have good corelation(R 2 =0.97, 0.87, respectively). The estimated delay time would be used for the groundwater behaviour characteristics in vadose zone. As recharge rates vary according to the height, the delay time is thought to be an import variable for the proper groundwater recharge estimation.

Minimally invasive cardiac surgery (MICS) requires lung isolation. Lung isolation is usually achieved with double-lumen endotracheal tube (DLT). Patients with idiopathic thrombocytopenic purpura (ITP) have an increased risk of bleeding events. We suspected endobronchial hemorrhage after exchange of DLT during induction of anesthesia for replacement of mitral valve in a 62-year-old man with a known ITP. The MICS was stopped and bronchial artery embolization was performed in the angiographic room. In the present case, in order to reduce the risk of bronchial arterial injury in ITP patient we intubated with single lumen endotracheal tube. Lung isolation led to achievement of intermittent total lung deflation. Based on the results, we recommend a high-dose intravenous immunoglobulin therapy and platelet transfusion prior to cardiac surgery in patients with ITP to increase platelet count. Moreover, it is proposed that in order to clear the vision during the operation, ventilation can be held or made intermittent both prior to cardiopulmonary bypass or at its conclusion to permit exposure.

Groundwater pumping from a well has different impacts on streamflow depletion because hydraulic properties of the aquifer and the stream bed differ depending on its location. Therefore, quantitative assessment of streamflow depletion due to each groundwater pumping with different well locations is needed for the effective groundwater development and streamflow management. In this study, a watershed-based surface water and groundwater integrated model, SWAT-MODFLOW was used to assess the streamflow depletion near stream reach due to groundwater pumping from a well located within the Sinduncheon watershed. The arbitrary 50 wells among the currently used groundwater pumping wells were selected within the study area and the streamflow responses to each groundwater pumping were simulated at nearby and downstream reaches. In particular, the applicability of the Stream Depletion Factor (SDF) and Stream Bed Factor (SBF), which are widely used for evaluating the degree of streamflow depletion due to groundwater pumping, was evaluated. The simulated results demonstrated that the streamflow depletion rate divided by the pumping rate significantly differ depending on well locations and distance between well and stream, showing a wide range of values from below 20% to above 90%. From the simulated results, it was found out that the SDF or the SBF can be a partial referred value but not an absolute criterion in determining whether a pumping well has a great impact on streamflow depletion or not.

The impact on hydrological components including evapotranspiration, soil moisture content, groundwater recharge, and dam inflow by the future potential climate and vegetation canopy changes was assessed for a dam watershed using Soil and Water Assessment Tool (SWAT) model. The SWAT model was calibrated and verified using 9 years (1997–2006) and another 7 years (1990–1996) daily dam inflow data, respectively for a 6,585.1 km2 dam watershed located in the mountainous northeastern part of South Korea. The second generation coupled global climate model (CGCM2) data of Canadian Centre for Climate modelling and analysis (CCCma) from two Special Reports on Emissions Scenarios (SRES) climate change scenarios (A2 and B2) of the Intergovernmental Panel on Climate Change (IPCC) were adopted. The future vegetation canopy was developed by the nonlinear regression between monthly Leaf Area Index (LAI) from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images and monthly mean temperature of 7 years (2000–2006) data. The future prediction results with A2 and B2 scenarios showed that the maximum changes in annual dam inflow were predicted to be −18.2% in 2090s A2 scenario compared to 2000 baseline data. The future seasonal maximum dam inflow changes appeared in fall period to be −31.0% for the A2 scenario. From the contribution analysis of climate change and vegetation canopy for the overall future predicted results, the climate change primarily led the future impact on the predicted results. For the future vegetation impact on hydrological components, soil moisture was more sensitive than dam inflow and evapotranspiration.

Runoff data availability is a substantial factor for precise flood control such as flood frequency or flood forecasting. However, runoff depths and/or peak discharges for small watersheds are rarely measured which are necessary components for hydrological analysis. To compensate for this discrepancy, a lumped concept such as a Storage Function Method (SFM) was applied for the partitioned Choongju Dam Watershed in Korea. This area was divided into 22 small watersheds for measuring the capability of spatial extension of runoff data. The chosen total number of flood events for searching parameters of SFM was 21 from 1991 to 2009. The parameters for 22 small watersheds consist of physical property based (storage coefficient: k, storage exponent: p, lag time: ) and flood event based parameters (primary runoff ratio: , saturated rainfall: ). Saturated rainfall and base flow from event based parameters were explored with respect to inflow at Choongju Dam while other parameters for each small watershed were fixed. When inflow of Choongju Dam was optimized, Youngchoon and Panwoon stations obtained average of Nash-Sutcliffe Efficiency (NSE) were 0.67 and 0.52, respectively, which are in the satisfaction condition (NSE > 0.5) for model evaluation. This result is showing the possibility of spatial data extension using a lumped concept model.

In this study, a watershed-based surface-water and groundwater integrated model, SWAT-MODFLOW was used to evaluate streamflow depletion induced by groundwater withdrawals and irrigation reservoirs for the Juksan-cheon watershed in South Korea. The streamflow responses to groundwater pumping and irrigation reservoirs were simulated under several different scenarios. The scenarios were (1) current pumping well withdrawals with reservoirs; (2) current pumping well withdrawals without reservoirs; (3) no pumping well withdrawals with reservoirs; (4) no pumping well withdrawals without reservoirs (natural condition). The simulated results indicated that the effects of groundwater pumping on streamflow depletion are a little more significant than those of irrigation reservoirs. Particularly, the groundwater withdrawals with irrigation reservoirs at current status (scenario 1) has induced the decrease of more than 20% in drought flow against the natural condition (scenario 4) at the outlet of the watershed. The specific drought flows through the main stream of Juksan-cheon watershed were simulated in order to assess the irrigation effects on downstream flows. It was found out that the specific drought flows are increasing as the distance from the reservoir increases due to the accumulation of the return flows to stream.