Application of a modified conceptual rainfall-runoff model to simulation of groundwater level in an undefined watershed.

Groundwater level simulation models can help ensure the proper management and use of urban and rural water supply. In this paper, we propose a groundwater level tank model (GLTM) based on a conceptual rainfall-runoff model (tank model) to simulate fluctuations in groundwater level. The variables used in the simulations consist of daily rainfall and daily groundwater level, which were recorded between April 2011 and March 2015 at two representative observation wells in Kumamoto City, Japan. We determined the best-fit model parameters by root-mean-square error through use of the Shuffled Complex Evolution-University of Arizona algorithm on a simulated data set. Calibration and validation results were evaluated by their coefficients of determination, Nash-Sutcliffe efficiency coefficients, and root-mean-square error values. The GLTM provided accurate results in both the calibration and validation of fluctuations in groundwater level. The split sample test results indicate a good reliability. These results indicate that this model can provide a simple approach to the accurate simulation of groundwater levels.

[1]  Z. Hou,et al.  A comparative study of shallow groundwater level simulation with WA–ANN and ITS model in a coastal island of south China , 2015, Arabian Journal of Geosciences.

[2]  Keith Beven,et al.  The Institute of Hydrology distributed model , 1987 .

[3]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[4]  Sobri Harun,et al.  Global optimization methods for calibration and optimization of the hydrologic tank model's parameters , 2010 .

[5]  P. C. Nayak,et al.  Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach , 2006 .

[6]  Vijay P. Singh,et al.  MIKE 11 - a generalized river modelling package. , 1995 .

[7]  Shakeel Ahmed,et al.  Forecasting groundwater level using artificial neural networks. , 2009 .

[8]  Vijay P. Singh,et al.  Tank Model Using Kalman Filter , 1999 .

[9]  Wolfgang Kinzelbach,et al.  Groundwater Modelling: An Introduction With Sample Programs in Basic , 1986 .

[10]  M. Sugawara,et al.  Automatic calibration of the tank model / L'étalonnage automatique d'un modèle à cisterne , 1979 .

[11]  E. Reichard.  Groundwater–Surface Water Management With Stochastic Surface Water Supplies: A Simulation Optimization Approach , 1995 .

[12]  Leticia B. Rodríguez,et al.  Modeling stream-aquifer interactions in a shallow aquifer, Choele Choel Island, Patagonia, Argentina , 2006 .

[13]  V. Klemeš,et al.  Operational Testing of Hydrological Simulation Models , 2022 .

[14]  Samad Emamgholizadeh,et al.  Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) , 2014, Water Resources Management.

[15]  Soroosh Sorooshian,et al.  Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model , 1993 .

[16]  Y. Kawagoshi,et al.  Present Status and Feature of Groundwater Contamination by Nitrate-nitrogen in Kumamoto City , 2009 .

[17]  J. Refsgaard,et al.  Large scale modelling of groundwater contamination from nitrate leaching , 1999 .

[18]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[19]  Kyungrock Paik,et al.  A conceptual rainfall‐runoff model considering seasonal variation , 2005 .

[20]  K. Lee,et al.  A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer , 2011 .

[21]  Ashwani Kumar,et al.  Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi–Surua Inter-basin of Odisha, India , 2013 .

[22]  E. Hansen,et al.  NUMERICAL SIMULATION OF THE RAINFALL-RUNOFF PROCESS ON A DAILY BASIS , 1973 .

[23]  M. Franchini,et al.  Comparative analysis of several conceptual rainfall-runoff models , 1991 .

[24]  Tawatchai Tingsanchali,et al.  Application of tank, NAM, ARMA and neural network models to flood forecasting , 2000 .

[25]  Vijay P. Singh,et al.  Tank Model for Sediment Yield , 2005 .

[26]  George Kuczera,et al.  Assessment of hydrologic parameter uncertainty and the worth of multiresponse data , 1998 .

[27]  Xu Xu,et al.  Integration of SWAP and MODFLOW-2000 for modeling groundwater dynamics in shallow water table areas , 2012 .

[28]  A. Gupta,et al.  Regional management modeling of a complex groundwater system for land subsidence control , 1995 .

[30]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[31]  K. P. Sudheer,et al.  Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India , 2010 .

[32]  X. R. Liu,et al.  The Xinanjiang model. , 1995 .

[33]  Takahashi Kenji,et al.  TANK MODEL AND ITS APPLICATION TO PREDICTING GROUNDWATER TABLE IN SLOPE , 2008 .

[34]  Issei Oshima,et al.  Administration for groundwater management in the Kumamoto area , 2010 .

[35]  Barry J. Adams,et al.  Semidistributed Form of the Tank Model Coupled with Artificial Neural Networks , 2006 .

[36]  R. Moore,et al.  Hierarchical testing of three rainfall-runoff models in small forested catchments , 1999 .

[37]  Alon Rimmer,et al.  Simplified Conceptual Structures and Analytical Solutions for Groundwater Discharge Using Reservoir Equations , 2012 .

[38]  Zhao Ren-jun,et al.  The Xinanjiang model applied in China , 1992 .

[39]  A. Ghumman,et al.  Groundwater modeling of Saq Aquifer Buraydah Al Qassim for better water management strategies , 2011, Environmental monitoring and assessment.

[40]  Paulin Coulibaly,et al.  Groundwater level forecasting using artificial neural networks , 2005 .