주요어 : 청원-충주지역, 수막재배, 지하수 이용량, 지하수 배출량, 수막재배 일수 Korean agricultural areas that employ water curtain cultivation (WCC) have recently suffered extensive groundwater shortages due to an increase in the number of facilities. The primary focus of this study is to measure the daily groundwater use and discharge rates in the Cheongweon and Chungju pilot areas, while the second focus is to estimate the total amount of groundwater used in WCC areas nationwide in Korea. Taking into consideration several factors, including motor type, outflow abilities of wells, records of daily minimum temperatures below 0 o C, and the number of running wells according to weather variations, we estimated that 53,138 m 3 /ha of groundwater had been used in the 4-hectare Cheongweon pilot area during the winter period of late 2011 through early 2012. On a prorated areal basis, we can calculate that the total groundwater used nationwide was 0.57 billion m , equivalent to 34.9% of the total agricultural groundwater use in Korea. In the Chungju area, the groundwater discharge rate was estimated to be less than 805 m 3 /ha. This value, combined with weather data for 108 days with a daily minimum temperature below 0 o C in this area, can be applied to infer that the total groundwater volume used in WCC areas nationwide is no more than 55% of the total agricultural groundwater use in Korea.
It is important to predict the groundwater level fluctuation for effective management of groundwater monitoring system and groundwater resources. In the present study, three different time series models for the prediction of groundwater level in response to rainfall were built, those are transfer function noise model (TFNM), artificial neural network (ANN), and adaptive neuro fuzzy interference system (ANFIS). The models were applied to time series data of Boen, Cheolsan, and Hongcheon stations in National Groundwater Monitoring Network. The result shows that the model performance of ANN and ANFIS was higher than that of TFNM for the present case study. As lead time increased, prediction accuracy decreased with underestimation of peak values. The performance of the three models at Boen station was worst especially for TFNM, where the correlation between rainfall and groundwater data was lowest and the groundwater extraction is expected on account of agricultural activities. The sensitivity analysis for the input structure showed that ANFIS was most sensitive to input data combinations. It is expected that the time series model approach and results of the present study are meaningful and useful for the effective management of monitoring stations and groundwater resources.
A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river. Direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Using the time series models the effect of river stage on groundwater level data was filtered out by setting a constant value for river stage inputs. The filtered data were applied to the hybrid water table fluctuation method in order to estimate the groundwater recharge. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.
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