A significant increase in surface water salinization in low-lying deltas is expected globally due to saline groundwater exfiltration driven by rising sea levels and decreasing freshwater availability. Sustaining fresh water-dependent agriculture in such areas will entail an increased demand for fresh water flushing. Unfortunately, the flushing of surface water is not operationally optimised and results in excessive use of scarce freshwater. To meet the increased demand for flushing, while minimizing the need for diverted freshwater, new operational designs are required. This paper presents a novel network model based approach that uses De Saint Venant (SV) and Advection Dispersion (AD) equations to optimize multiple objectives on water level and salinity control using a Nonlinear Model Predictive Control (NMPC). The resulting NMPC problem is solved with a receding horizon implementation, where the nonlinear program (NLP) at each iteration is solved using state-of-the-art large scale interior point solver (IPOPT). We evaluate the performance of the proposed approach and compare it to the traditional fixed flushing for a representative Dutch polder. Firstly, the approach is shown to be capable of controlling the water level and salinity level in the polder. Secondly, the results highlight that the network of canals, which were originally made for drainage, could not be made sufficiently fresh with current intake capacity. A simple design approach was used to identify appropriate new capacities for two of the gates that allow optimal flushing to guarantee the required water level and salinity constraints.

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POLDERS are low-lying and artificially drained areas surrounded bywater storage canals. In low-lying delta areas such as theMississippi delta in Louisiana (USA), the Ganges-Brahmaputra delta (Bangladesh), or the Rhine-Meuse delta (The Netherlands), polders experience surface water salinization problem due to saline groundwater exfiltration, which is the upward flow of saline groundwater from the subsurface. A significant increase in surface water salinization is expected globally driven by rising sea levels, leading to a decreasing freshwater availability. Land subsidence, climate change induced decrease in precipitation and sea level rise are expected to accelerate salinization of groundwater and surface water systems. To counteract surface water salinization, freshwater diverted from rivers is used for flushing the canals and ditches in coastal areas.
Sustaining freshwater-dependent agriculture in such areas will entail an increased demand for flushing, while the demand of a better water quality will tend to increase. On the other hand, freshwater usage is not explicitly considered for polder operation and results in excessive use. Decreasing the amount of freshwater usage for polder flushing can create additional supply opportunities for industrial users, drinking water companies or other irrigation systems. To meet the increasing demand for flushing due to expected increase of salinization while the freshwater availability is decreasing, new operational designs are required for polders that will use the available freshwater resources
efficiently.@en

Worldwide, delta areas are under stress due to climate change. With rising sea
levels and decreasing freshwater availability, surface water salinization due to
groundwater exfiltration is expected to increase in these low-lying areas. To
counteract surface water salinization, freshwater diverted from rivers is used to flush agricultural ditches. In this paper, we demonstrate a Model Predictive Control (MPC) scheme to control salinity and water levels in a water course while minimizing freshwater usage. A state space description of the discretized De Saint Venant and advection-dispersion equations for water and salt transport, respectively, is used as the internal model of the controller. The developed MPC scheme is tested using groundwater exfiltration data from two different representative Dutch polders. The tests demonstrate that water levels and salinity concentrations can successfully be controlled within set limits while minimizing the freshwater used.@en

We present a systematic approach for salinity sensor placement in a polder network, where the objective is to estimate the unmeasured salinity levels in the main polder channels. We formulate this problem as optimization of the estimated salinity levels using root mean square error (RMSE) as the "goodness of fit" measure. Starting from a hydrodynamic and salt transport model of the Lissertocht catchment (a low-lying polder in the Netherlands), we use principal component analysis (PCA) to produce a low-order PCA model of the salinity distribution in the catchment. This model captures most of the relevant salinity dynamics and is capable of reconstructing the spatial and temporal salinity variation of the catchment. Just using three principal components (explaining 93% of the variance of the dataset) for the low-order PCA model, three optimally placed sensors with a greedy algorithm make the placement robust for modeling and measurement errors. The performance of the sensor placement for salinity reconstruction is evaluated against the detailed hydrodynamic and salt transport model and is shown to be close to the global optimum found by an exhaustive search with a RMSE of 82.2 mg/L.@en

Surface water salinization in deltaic areas due to saline groundwater exfiltration is an important issue. Fresh water diverted from the rivers is used for flushing the canals and the ditches in coastal areas to remove the low quality saline surface water mixed with saline groundwater. Worldwide, deltaic areas are under stress due to climate change, sea level increase and decrease in fresh water availability. The current fresh water management strategies in polders to overcome the salinization problem solely depends on uncontrolled freshwater use. However, this operation will not be effective during a scarce freshwater availability scenario and has to be revised for efficient management possibilities. With the advances in real time measurement of salinity and water level measurements, using a Model Predictive Control (MPC) scheme for the operation of a polder system is gaining popularity. MPC is a powerful control tool that can handle multiple objectives, consider the constraints and the uncertainties of the system. However, a MPC scheme requires a simple and reliable internal model that will be used to calculate the optimum control actions. The internal model should be robust, should reflect the system behaviour with enough detail and should not be computationally costly. In this paper, a MPC scheme is proposed using the discretized linearized De Saint Venant (SV) and Advection-Diffusion (AD) equations as the internal model of the controller. The proposed scheme will be able to control salinity and water level at any discretization point by manipulating the flushing and outflow discharges. This is an ongoing research with tests continuing on a realistic test case.@en

Surface water salinization in deltaic areas due to saline groundwater exfiltration is an important issue. Saline surface water will not be appropriate for drinking water production, agricultural and industrial use, and therefore, freshwater diverted from rivers is used for flushing the canals and ditches in coastal areas. The effects of climate change, sea level increase and fresh water availability increases the stress on deltaic areas resulting in questioning current fresh water management strategies. In this paper, a Model Predictive Control (MPC) scheme is developed and tested for combined salinity and water level control of a polder ditch. The MPC scheme is coupled with Rapid Saline Groundwater Exfiltration Model (RSGEM) developed for fast calculation of exfiltration flux and concentration in a low-lying polder. For the test case presented in this paper, real data from Lissertocht catchment in Netherlands is used for RSGEM to see the performance of the MPC scheme for a real scenario. With open space for further research, results presented on this paper show that MPC of salinity in polders is capable of dealing with saline groundwater exfiltration modeled by RSGEM. @en

Saline groundwater exfiltration to surface water increases the surface water salinization and degrades the surface water quality in low-lying deltaic areas. The use of surface water will be less appropriate for agricultural, industrial and drinking water production due to salinization, and therefore, freshwater diverted from river is used for flushing canals and ditches in these areas. Current water management strategies for flushing control in low lying polders have to be revised due to expecting negative effects of climate change, sea level increase and decreasing fresh water availability. Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. In this study, a MPC scheme is developed and tested for combined salinity and water level control of a polder ditch. Saline groundwater exfiltration flux and concentration is modelled and used as known disturbances for the MPC scheme by using Rapid Saline Groundwater Exfiltration Model (RSGEM). The developed control scheme is tested on a test case using real data from a Dutch polder affected by high saline groundwater exfiltration to observe the performance of the controller for a real scenario. Simulation results show that MPC can increase the operational efficiency of flushing operations significantly.@en

Model predictive control (MPC) is one of the most popular control techniques that has been widely used in many fields of water resources management, such as canal control for drainage, irrigation, and navigation. MPC uses an internal mathematical model to describe system dynamics over a given prediction horizon and then minimizes a hard-constrained optimization problem based on actual objectives. Due to the use of hard constraints, the optimization problem may occasionally be infeasible. A compromise is sometimes made to look for a feasible solution by softening the hard constraints, which means that the limit on water levels or flows is allowed to be violated to a certain extent. For example, water in a canal may go above the top of a dike during a high-discharge event, resulting in a spill. This amount of spilling water leaves the water system and does not flow back, which therefore should be deducted in the mathematical model of the water system. To deal with this spill, past studies often utilized a hybrid model and an integer optimization. However, the system in the hybrid model is usually nonlinear and nonsmooth, especially when it transits from one discrete state to another. In this paper, an alternative way is proposed to link the spill with the softened constraint, still maintaining the linearity of the water system. Results show that the proposed way to tackle the spilling water is easy to implement and the water level is more accurately regulated around the setpoint in a canal control problem.@en

More than 35% of the world’s population live within 100 km of the coast. Groundwater resources in these areas are the main source for domestic, industrial and agricultural use. Worldwide, deltaic areas are under stress due to climate change, sea level increase and decrease in fresh water availability. In addition, in deltaic coastal areas around mean sea level, the saline groundwater will move toward the ground surface and exflitrate to surface water. This saline surface water will not be appropriate for drinking water production, agricultural and industrial use, and therefore, freshwater diverted from rivers is used for flushing the canals and ditches in coastal areas. Due to decreasing fresh water availability and increasing surface water salinization, current saline-fresh water management strategies have to be reviewed, and new sustainable strategies must be developed. Using real time measurements to see the effect of disturbances to the system and updating the control actions in real time will decrease the use of fresh water for flushing operations. Real time control of salinity in polders will result in more effective water management. Control of surface water salinization in a polder is a multi-objective problem such that water quality and quantity have to be considered. Moreover, the constraints of the system and uncertainties must be taken into account. Model Predictive Control (MPC) is a state-of-the-art control technique showing the best performance for these kind of problems. In this study, a MPC configuration for combined water quantity and quality control is discussed on a polder flushing case study for a hypothetical test water course/canal. The exfiltration of saline groundwater to the surface water is also considered and the predictions of saline groundwater exfiltration are used to achieve better control of the local water system. @en

Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. However, due to the water loss in open water systems by seepage, leakage and evaporation a mismatch between the model and the real system will be created. These mismatch affects the performance of MPC and creates an offset from the reference set point of the water level. We present model predictive control based on moving horizon estimation (MHE-MPC) to achieve offset free control of water level for open water canals. MHE-MPC uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and the offset in the controlled water level is systematically removed. We numerically tested MHE-MPC on an accurate hydro-dynamic model of the laboratory canal UPC-PAC located in Barcelona. In addition, we also used well known disturbance modeling offset free control scheme for the same test case. Simulation experiments on a single canal reach show that MHE-MPC outperforms disturbance modeling offset free control scheme. @en

Open water systems such as irrigation canals are used to transport and deliver water from the source to the user.Water loss in these systems by seepage, leakage, evaporation, or unknown water offtakes can be large. If this loss is unknown to the model used, it will not be considered by the controller and create a real system model mismatch. This mismatch will affect the water level directly and create an offset from the reference set point of the water level. A control configuration for open water canals, model predictive control (MPC) based on moving horizon estimation (MHE-MPC), to deal with offset problems resulting from real system-model mismatch is described in this paper. MHE uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and systematically removes the offset in the controlled water level. This control configuration is numerically tested on an accurate hydrodynamic model of the Control Algorithms Test Canal of the Technical University of Catalonia (UPC-PAC). The results presented in this paper show that MHE-MPC can realize offset-free control and the results are better than those of the well-known disturbance modelling offset-free control scheme. @en

Model predictive control (MPC) is a powerful tool which is used more and more to managing water systems such as reservoirs over a short-term prediction horizon. However, due to unknown disturbances present in the water system and other uncertainties, there is always a mismatch between the model and the actual system. To overcome this mismatch and achieve offset free control of the water system, the internal model of the MPC is updated by adding the disturbance dynamics of the actual system by means of a disturbance model. In this paper, the conditions to achieve offset free control for an open water reach are provided. A disturbance model is designed and used to achieve offset free control in a test canal assessed from simulation results.@en