Real-time management of an urban groundwater well field threatened by pollution.

We present an optimal real-time control approach for the management of drinking water well fields. The methodology is applied to the Hardhof field in the city of Zurich, Switzerland, which is threatened by diffuse pollution. The risk of attracting pollutants is higher if the pumping rate is increased and can be reduced by increasing artificial recharge (AR) or by adaptive allocation of the AR. The method was first tested in offline simulations with a three-dimensional finite element variably saturated subsurface flow model for the period January 2004-August 2005. The simulations revealed that (1) optimal control results were more effective than the historical control results and (2) the spatial distribution of AR should be different from the historical one. Next, the methodology was extended to a real-time control method based on the Ensemble Kalman Filter method, using 87 online groundwater head measurements, and tested at the site. The real-time control of the well field resulted in a decrease of the electrical conductivity of the water at critical measurement points which indicates a reduced inflow of water originating from contaminated sites. It can be concluded that the simulation and the application confirm the feasibility of the real-time control concept.

[1]  Michela Robba,et al.  Decision models for sustainable groundwater planning and control , 2007 .

[2]  P. Bayer,et al.  Optimized groundwater drawdown in a subsiding urban mining area , 2009 .

[3]  Roko Andričević,et al.  A Real‐Time Approach to Management and Monitoring of Groundwater Hydraulics , 1990 .

[4]  Henrik Madsen,et al.  Energy Optimization of Well Fields , 2009, Ground water.

[5]  Michael Nikolaou,et al.  Real-time reservoir management: A multiscale adaptive optimization and control approach , 2006 .

[6]  Alberto Guadagnini,et al.  Delineation of Source Protection Zones Using Statistical Methods , 2005 .

[7]  Harrie-Jan Hendricks Franssen,et al.  Ensemble Kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems , 2009 .

[8]  Aristotelis Mantoglou,et al.  Management of coastal aquifers based on nonlinear optimization and evolutionary algorithms , 2004 .

[9]  Kim-Fung Man,et al.  An optimal fuzzy PID controller , 2001, IEEE Trans. Ind. Electron..

[10]  W. Yeh,et al.  Optimization of real time operation of a multiple-reservoir system , 1974 .

[11]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[12]  K. Konarczak,et al.  Hierarchical predictive control of integrated wastewater treatment systems , 2008 .

[13]  W. Kinzelbach,et al.  Real‐time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem , 2008 .

[14]  H. Franssen,et al.  Field evidence of a dynamic leakage coefficient for modelling river–aquifer interactions , 2007 .

[15]  Tobias Siegfried,et al.  A multiobjective discrete stochastic optimization approach to shared aquifer management: Methodology and application , 2006 .

[16]  Mietek A. Brdys,et al.  Hierarchical model predictive control of integrated quality and quantity in drinking water distribution systems , 2005 .

[17]  Qing Yang,et al.  Nitrogen removal via nitrite from municipal wastewater at low temperatures using real-time control to optimize nitrifying communities. , 2007, Environmental science & technology.

[18]  Liang-Cheng Chang,et al.  Optimal control algorithm and neural network for dynamic groundwater management , 2009 .

[19]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[20]  Andres Alcolea,et al.  Regularized pilot points method for reproducing the effect of small scale variability : Application to simulations of contaminant transport , 2008 .

[21]  Christoph Schär,et al.  Probabilistic Flood Forecasting with a Limited-Area Ensemble Prediction System: Selected Case Studies , 2007 .

[22]  Joseph Park,et al.  Multilayer Control Hierarchy for Water Management Decisions in Integrated Hydrologic Simulation Model , 2007 .

[23]  H. Franssen,et al.  The importance of coupled modelling of variably saturated groundwater flow-heat transport for assessing river–aquifer interactions , 2011 .

[24]  P. Gill,et al.  Aquifer Reclamation Design: The Use of Contaminant Transport Simulation Combined With Nonlinear Programing , 1984 .