A Method to Filter Out the Effect of River Stage Fluctuations using Time Series Model for Forecasting Groundwater Level and its Application to Groundwater Recharge Estimation

doi:10.7857/JSGE.2015.20.3.074

All Issue

2015 Vol.20, Issue 3 Preview Page Next Page

A Method to Filter Out the Effect of River Stage Fluctuations using Time Series Model for Forecasting Groundwater Level and its Application to Groundwater Recharge Estimation 지하수위 시계열 예측 모델 기반 하천수위 영향 필터링 기법 개발 및 지하수 함양률 산정 연구: 윤희성;박은규;김규범;하규철;윤필선;이승현;
Yoon, Heesung;Park, Eungyu;Kim, Gyoo-Bum;Ha, Kyoochul;Yoon, Pilsun;Lee, Seung-Hyun;

30 June 2015. pp. 74 ~ 82

PDF

Abstract

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.

Keywords

Groundwater level;River stage;Time series model;Recharge;

References

An, J.H., Hamm, S.Y., Lee, J.H., Kim, N.H., Yang, D.B., and Hwang, J.G., 2008, Estimation of groundwater recharge in Junggwae-Boeun area in Ulsan City using the water balance and hydrogeological analyses, Econ. Environ. Geol., 41(4), 427-442.
Andreo, B., Vias, J., Duran, J.J., Jimenez, P., Lopez-Geta, J.A., and Carrasco, F., 2008, Methodology for groundwater recharge assessment in carbonate aquifers: application to pilot sites in southern Spain, Hydrogeol. J., 16, 911-925.10.1007/s10040-008-0274-5
Arnold, J.G. and Allen, P.M., 1999, Automated methods for estimating baseflow and groundwater recharge from streamflow records, J. Am. Water Resour. Assoc., 35, 411-424.10.1111/j.1752-1688.1999.tb03599.x
Bae, S.K. and Kim, Y.H., 2006, Estimation of groundwater recharge rate using the NRCS-CN and the baseflow separation methods, J. Environ. Sci., 15(3), 253-260.
Box, G.E.P. and Jenkins, G.M., 1976, Time Series Analysis-Forecasting and Control, Holden-Day, San Francisco, California, USA, 575 p.
Charbeneau, R.J., 2000, Ground Water Hydraulics and Pollutant Transport, Prentice-Hall, Old Tappan, N.J., 593 p.
Chio, I.H., Woo, N.C., Kim, S.J., Moon, S.K., and Kim, J., 2007, Estimation of the groundwater recharge rate during a rainy season at a headwater catchment in Gwangneung, Korea, Kor. J. Agr. For. Mateorol., 9, 75-87.
Constantz, J., Tyler, S.W., and Kwicklis, E.M., 2003, Temperature-profile methods for estimating percolation rates in arid environments, Vadose Zone J., 2, 12-24.10.2136/vzj2003.0012
Coppola, E., Rana, A.J., Poulton, M.M., Szidarovszky, F., and Uhl, V.V., 2005, A neural network model for predicting aquifer water level elevations, Ground Water, 43(2), 231-241.10.1111/j.1745-6584.2005.0003.x
Crosbie, R.S., Binning, P., and Kalma, J.D., 2005, A time series approach to inferring groundwater recharge using the water table fluctuation, Water Resour. Res., 41, 1-9.
Dages, C., Voltz, M., and Bsaibes, A., 2009, Estimating the role of a ditch network in groundwater recharge in a Mediterranean catchment using a water balance approach, J. Hydrol., 375, 495-512.
Healy, R.W. and Cook, P.G., 2002, Using groundwater levels to estimate recharge, Hydrogeol. J., 10, 91-109.10.1007/s10040-001-0178-0
Herrera, L.J., Pomares, H., Rojas, I., Guillen, A., Prieto, A., and Valenzuela, O., 2007, Recursive prediction for long term time series forecasting using advanced models, Neurocomputing, 70(16-18), 2870-2880.10.1016/j.neucom.2006.04.015
Huang, T. and Pang, Z., 2011, Estimating groundwater recharge following land-use change using chloride mass balance of soil profiles: a case study at Guyuan and Xifeng in the Loess Plateau of China, Hydrogeol. J., 19, 177-186.10.1007/s10040-010-0643-8
Ji, Y., Hao, J., Reyhani, N., and Lendasse, A., 2005, Direct and recursive prediction of time series using mutual information selection, Lect. Notes Comput. Sc., 3512, 1010-1017.
Jung, Y., Lim, K.J., and Kim, H., 2014, Estimation of baseflow considering recession characteristic of hydrograph, J. Wetl., Res., 16(2), 161-171.10.17663/JWR.2014.16.2.161
Kim, G.B., Yi, M.J., Kim, J.W., Lee, J.Y., and Lee, K.K., 2004, Evaluation of estimating groundwater recharge using water level data obtained from the national groundwater monitoring stations, J. Eng. Geol., 14(3), 313-323.
Koo, M.H. and Lee, D.H., 2002, A numerical analysis of the water level fluctuation method for quantifying groundwater recharge, J. Geol. Soc. Korea, 38(3), 407-420.
Koo, M.H., Kim, T.K., Kim, S.S., Chung, S.R., Kang, I.O., Lee, C.J., and Kim, Y., 2013, Estimating groundwater recharge using the water-table fluctuation method: Effect of stream-aquifer interactions, J. Soil Groundw. Environ., 18(5), 65-76.
K-water, 2013, Research on flooding vulnerability assessment using groundwater level, MOLIT, Water Resources Management Project Report, Korea, 334 p.
Lee, M.J. and Lee, J.H., 2011, Coupled model development between groundwater recharge quantity and climate change using GIS, J. Korean Assoc. Geogr. Info. Stud., 14(3), 36-51.10.11108/kagis.2011.14.3.036
Lim, K.J., Engel, B.A., Tang, Z., Choi, J., Kim, K.S., Muthukrishnan, S. and Tripathy, D., 2005, Automated WEB GIS based hydrograph analysis tool, WHAT, J. Amer. Water Resour. Assoc., 41, 1407-1416.10.1111/j.1752-1688.2005.tb03808.x
Maier, H.R., Jain A., Dandy, G.C., and Sudheer, K.P., 2010, Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions, Environ. Modell. Softw., 25, 891-909.10.1016/j.envsoft.2010.02.003
Nayak, P.C., Satyaji Rao, Y.R., and Sudheer, K.P., 2006, Groundwater level forecasting in a shallow aquifer using artificial neural network approach, Water Resour. Manag., 20, 77-90.10.1007/s11269-006-4007-z
Park, E., 2012, Delineation of recharge rate from a hybrid water table fluctuation method, Water Reour. Res., 48, W07503, doi:10.1029/2011WR011696.
Park, J.S., Kim, K.H., Jeon, M.W., and Kim, J.S., 1999, Estimation methods of groundwater recharge rate in small basin, J. Korean Soc. Groundw. Environ., 6(2), 76-86.
Rawat, K.S., Mishra, A.K., Paul, G., and Kumar, R., 2012, Estimation of ground water recharge in Shankergarh block of Allahabad(India) using remote sensing and statistical approach, Global J. Sci. Eng. Techn., 1, 34-48.
Rumelhart, D.E., McClelland, J.L., and The PDP Research Group, 1986, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge, Massachusetts, USA, 516 p.
Sanford, W.E., 2002, Recharge and groundwater models: an overview, Hydrogeol. J., 10, 110-120.10.1007/s10040-001-0173-5
Scanlon, B.R., Reedy, R.C., and Tachovsky, J.A., 2007, Semiarid unsaturated zone chloride profiles: archives of past land use changes impacts on water reources in the southern High Plains, United States, Water Resour. Res., 43, W06423, doi:10.1029/2006WR005769.
Stoertz, M.W. and Bradbury, K.R., 1989, Mapping recharge areas using a groundwater-flow model: a case study, Ground Water, 27, 220-228.10.1111/j.1745-6584.1989.tb00443.x
Tankersley, C.D., Graham, W.D., and Hatfield, K., 1993, Comparison of univariate and transfer function models of groundwater fluctuations, Water Resour. Res., 29, 3517-3533.10.1029/93WR01527
van Genuchten, M.Th., 1980, A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892-898.10.2136/sssaj1980.03615995004400050002x
Yi, M.J. and Lee, K.K., 2004, Transfer function-noise modeling of irregularly observed groundwater heads using precipitation data, J. Hydrol., 288, 272-287.10.1016/j.jhydrol.2003.10.020
Yoon, H., Jun, S.C., Hyun, Y., Bae, G.O., and Lee, K.K., 2011, A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, J. Hydrol., 396, 128-138.10.1016/j.jhydrol.2010.11.002
Yoon, H., Kim, Y., Ha, K., and Kim, G.B., 2013, Application of groundwater-level prediction model using data-based learning algorithms to National Groundwater Monitoring Network data, J. Eng. Geol., 23(2), 137-147.10.9720/kseg.2013.2.137
Yun, S.W., Jo, Y.J., and Lee, J.Y., 2009, Comparison of groundwater recharges estimated by waterlevel fluctuation and hydrograph separation in Haean basin of Yanggu, J. Geol. Soc. Korea, 45, 391-404.

Information

Publisher :The Korean Society of Soil and Groundwater Environment
Publisher(Ko) :한국지하수토양환경학회
Journal Title :Journal of Soil and Groundwater Environment
Journal Title(Ko) :지하수토양환경
Volume : 20
No :3
Pages :74 ~ 82
DOI :https://doi.org/10.7857/JSGE.2015.20.3.074

[1] An, J.H., Hamm, S.Y., Lee, J.H., Kim, N.H., Yang, D.B., and Hwang, J.G., 2008, Estimation of groundwater recharge in Junggwae-Boeun area in Ulsan City using the water balance and hydrogeological analyses, Econ. Environ. Geol., 41(4), 427-442.

[2] Andreo, B., Vias, J., Duran, J.J., Jimenez, P., Lopez-Geta, J.A., and Carrasco, F., 2008, Methodology for groundwater recharge assessment in carbonate aquifers: application to pilot sites in southern Spain, Hydrogeol. J., 16, 911-925.10.1007/s10040-008-0274-5

[3] Arnold, J.G. and Allen, P.M., 1999, Automated methods for estimating baseflow and groundwater recharge from streamflow records, J. Am. Water Resour. Assoc., 35, 411-424.10.1111/j.1752-1688.1999.tb03599.x

[4] Bae, S.K. and Kim, Y.H., 2006, Estimation of groundwater recharge rate using the NRCS-CN and the baseflow separation methods, J. Environ. Sci., 15(3), 253-260.

[5] Box, G.E.P. and Jenkins, G.M., 1976, Time Series Analysis-Forecasting and Control, Holden-Day, San Francisco, California, USA, 575 p.

[6] Charbeneau, R.J., 2000, Ground Water Hydraulics and Pollutant Transport, Prentice-Hall, Old Tappan, N.J., 593 p.

[7] Chio, I.H., Woo, N.C., Kim, S.J., Moon, S.K., and Kim, J., 2007, Estimation of the groundwater recharge rate during a rainy season at a headwater catchment in Gwangneung, Korea, Kor. J. Agr. For. Mateorol., 9, 75-87.

[8] Constantz, J., Tyler, S.W., and Kwicklis, E.M., 2003, Temperature-profile methods for estimating percolation rates in arid environments, Vadose Zone J., 2, 12-24.10.2136/vzj2003.0012

[9] Coppola, E., Rana, A.J., Poulton, M.M., Szidarovszky, F., and Uhl, V.V., 2005, A neural network model for predicting aquifer water level elevations, Ground Water, 43(2), 231-241.10.1111/j.1745-6584.2005.0003.x

[10] Crosbie, R.S., Binning, P., and Kalma, J.D., 2005, A time series approach to inferring groundwater recharge using the water table fluctuation, Water Resour. Res., 41, 1-9.

[11] Dages, C., Voltz, M., and Bsaibes, A., 2009, Estimating the role of a ditch network in groundwater recharge in a Mediterranean catchment using a water balance approach, J. Hydrol., 375, 495-512.

[12] Healy, R.W. and Cook, P.G., 2002, Using groundwater levels to estimate recharge, Hydrogeol. J., 10, 91-109.10.1007/s10040-001-0178-0

[13] Herrera, L.J., Pomares, H., Rojas, I., Guillen, A., Prieto, A., and Valenzuela, O., 2007, Recursive prediction for long term time series forecasting using advanced models, Neurocomputing, 70(16-18), 2870-2880.10.1016/j.neucom.2006.04.015

[14] Huang, T. and Pang, Z., 2011, Estimating groundwater recharge following land-use change using chloride mass balance of soil profiles: a case study at Guyuan and Xifeng in the Loess Plateau of China, Hydrogeol. J., 19, 177-186.10.1007/s10040-010-0643-8

[15] Ji, Y., Hao, J., Reyhani, N., and Lendasse, A., 2005, Direct and recursive prediction of time series using mutual information selection, Lect. Notes Comput. Sc., 3512, 1010-1017.

[16] Jung, Y., Lim, K.J., and Kim, H., 2014, Estimation of baseflow considering recession characteristic of hydrograph, J. Wetl., Res., 16(2), 161-171.10.17663/JWR.2014.16.2.161

[17] Kim, G.B., Yi, M.J., Kim, J.W., Lee, J.Y., and Lee, K.K., 2004, Evaluation of estimating groundwater recharge using water level data obtained from the national groundwater monitoring stations, J. Eng. Geol., 14(3), 313-323.

[18] Koo, M.H. and Lee, D.H., 2002, A numerical analysis of the water level fluctuation method for quantifying groundwater recharge, J. Geol. Soc. Korea, 38(3), 407-420.

[19] Koo, M.H., Kim, T.K., Kim, S.S., Chung, S.R., Kang, I.O., Lee, C.J., and Kim, Y., 2013, Estimating groundwater recharge using the water-table fluctuation method: Effect of stream-aquifer interactions, J. Soil Groundw. Environ., 18(5), 65-76.

[20] K-water, 2013, Research on flooding vulnerability assessment using groundwater level, MOLIT, Water Resources Management Project Report, Korea, 334 p.

[21] Lee, M.J. and Lee, J.H., 2011, Coupled model development between groundwater recharge quantity and climate change using GIS, J. Korean Assoc. Geogr. Info. Stud., 14(3), 36-51.10.11108/kagis.2011.14.3.036

[22] Lim, K.J., Engel, B.A., Tang, Z., Choi, J., Kim, K.S., Muthukrishnan, S. and Tripathy, D., 2005, Automated WEB GIS based hydrograph analysis tool, WHAT, J. Amer. Water Resour. Assoc., 41, 1407-1416.10.1111/j.1752-1688.2005.tb03808.x

[23] Maier, H.R., Jain A., Dandy, G.C., and Sudheer, K.P., 2010, Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions, Environ. Modell. Softw., 25, 891-909.10.1016/j.envsoft.2010.02.003

[24] Nayak, P.C., Satyaji Rao, Y.R., and Sudheer, K.P., 2006, Groundwater level forecasting in a shallow aquifer using artificial neural network approach, Water Resour. Manag., 20, 77-90.10.1007/s11269-006-4007-z

[25] Park, E., 2012, Delineation of recharge rate from a hybrid water table fluctuation method, Water Reour. Res., 48, W07503, doi:10.1029/2011WR011696.

[26] Park, J.S., Kim, K.H., Jeon, M.W., and Kim, J.S., 1999, Estimation methods of groundwater recharge rate in small basin, J. Korean Soc. Groundw. Environ., 6(2), 76-86.

[27] Rawat, K.S., Mishra, A.K., Paul, G., and Kumar, R., 2012, Estimation of ground water recharge in Shankergarh block of Allahabad(India) using remote sensing and statistical approach, Global J. Sci. Eng. Techn., 1, 34-48.

[28] Rumelhart, D.E., McClelland, J.L., and The PDP Research Group, 1986, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge, Massachusetts, USA, 516 p.

[29] Sanford, W.E., 2002, Recharge and groundwater models: an overview, Hydrogeol. J., 10, 110-120.10.1007/s10040-001-0173-5

[30] Scanlon, B.R., Reedy, R.C., and Tachovsky, J.A., 2007, Semiarid unsaturated zone chloride profiles: archives of past land use changes impacts on water reources in the southern High Plains, United States, Water Resour. Res., 43, W06423, doi:10.1029/2006WR005769.

[31] Stoertz, M.W. and Bradbury, K.R., 1989, Mapping recharge areas using a groundwater-flow model: a case study, Ground Water, 27, 220-228.10.1111/j.1745-6584.1989.tb00443.x

[32] Tankersley, C.D., Graham, W.D., and Hatfield, K., 1993, Comparison of univariate and transfer function models of groundwater fluctuations, Water Resour. Res., 29, 3517-3533.10.1029/93WR01527

[33] van Genuchten, M.Th., 1980, A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892-898.10.2136/sssaj1980.03615995004400050002x

[34] Yi, M.J. and Lee, K.K., 2004, Transfer function-noise modeling of irregularly observed groundwater heads using precipitation data, J. Hydrol., 288, 272-287.10.1016/j.jhydrol.2003.10.020

[35] Yoon, H., Jun, S.C., Hyun, Y., Bae, G.O., and Lee, K.K., 2011, A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, J. Hydrol., 396, 128-138.10.1016/j.jhydrol.2010.11.002

[36] Yoon, H., Kim, Y., Ha, K., and Kim, G.B., 2013, Application of groundwater-level prediction model using data-based learning algorithms to National Groundwater Monitoring Network data, J. Eng. Geol., 23(2), 137-147.10.9720/kseg.2013.2.137

[37] Yun, S.W., Jo, Y.J., and Lee, J.Y., 2009, Comparison of groundwater recharges estimated by waterlevel fluctuation and hydrograph separation in Haean basin of Yanggu, J. Geol. Soc. Korea, 45, 391-404.

Journal of Soil and Groundwater Environment ISSN:1598-6438(Print) 2287-8831(Online) 지하수토양환경

All Issue