Mark Bakker
Please Note
14 records found
1
The ASR system in Hoorn faces strict requirements, which address the challenge of maintaining water quality standards and optimising recovery efficiency. These requirements must ensure that the extracted water remains suitable for consumption, with no more than 1% dilution with ambient groundwater. The objective of this study is to identify a method to improve the recovery efficiency of the ASR system in Hoorn. The ASR system operates by injecting drinking water into an aquifer during periods of water availability and recovering it when needed. Compared to installing a new pipeline, the ASR system offers a more cost-effective solution with additional benefits such as space efficiency and temperature stability. However, the ASR system in Hoorn faces challenges related to maintaining water quality standards and optimising recovery efficiency. Processes such as lateral flow, dispersion, and buoyancy affect the system’s performance, and a thorough understanding of these processes is crucial for accurately predicting recovery efficiency. A comprehensive analysis of a pilot ASR system in Hoorn was conducted by PWN to address these challenges. The pilot system consists of a single well where two pumps operate at two different filter depths in an aquifer. In the final layout of the ASR, additional wells are necessary to achieve the desired capacity.
A radially symmetric model was used to simulate groundwater flow, conservative solute transport, and heat transport. Due to the stringent water quality requirements, the radially symmetric model must accurately capture essential processes in an Aquifer Storage and Recovery (ASR) system, such as flow, dispersion, retardation, and buoyancy. The performance of a radial symmetric model in SEAWAT and MODFLOW 6 was assessed based on analytical methods and 3D models. Through this analysis, it was decided to utilise a radial symmetric model in SEAWAT due to the presence of numerical dispersion in a model using MODFLOW 6.
After this analysis, the model’s performance was tested against various measurements, including hydraulic head, temperature, and electrical conductivity. It is evident that the model effectively captures both solute transport and heat transport. Discrepancies between measurements and the model can be attributed to assumptions made during the study and uncertainties in the measured values. However, the presence of clay layers between the deep and shallow filters in the pumping well significantly contributes to local differences between the model and the measurements. The main reason for this difference is that these layers are not homogeneous throughout the depths, allowing water to flow between them. This heterogeneity cannot be simulated with a radially symmetric model. However, despite this heterogeneity, these clay layers consistently result in low recovery efficiency in the current system.
The objective of this study was to identify a method to improve the recovery efficiency of the ASR system in Hoorn. The current system has a recovery efficiency of about 30%. This can improved by implementing a check valve in the shallow filter of the pump well, with 60% to 65% of the filter dedicated to recovery to achieve a recovery efficiency of 80%. During the testing of this system, an injection period was followed by a recovery period with specific pumping rates. It took three cycles to achieve the desired recovery efficiency. It is important to note that these cycles did not include storage and rest phases. The system’s recovery efficiency may change when these phases are incorporated. However, an important assumption is that homogeneous layers are present. Heterogeneity of layers can lead to deviations from the modelled recovery efficiency. This research contributes to a better understanding of the pilot ASR system in Hoorn and provides insights into improving its recovery efficiency. With the lessons learned from this study,
PWN can assist in developing the final design for the ASR system. This design will involve multiple wells to meet the required capacity. ...
The ASR system in Hoorn faces strict requirements, which address the challenge of maintaining water quality standards and optimising recovery efficiency. These requirements must ensure that the extracted water remains suitable for consumption, with no more than 1% dilution with ambient groundwater. The objective of this study is to identify a method to improve the recovery efficiency of the ASR system in Hoorn. The ASR system operates by injecting drinking water into an aquifer during periods of water availability and recovering it when needed. Compared to installing a new pipeline, the ASR system offers a more cost-effective solution with additional benefits such as space efficiency and temperature stability. However, the ASR system in Hoorn faces challenges related to maintaining water quality standards and optimising recovery efficiency. Processes such as lateral flow, dispersion, and buoyancy affect the system’s performance, and a thorough understanding of these processes is crucial for accurately predicting recovery efficiency. A comprehensive analysis of a pilot ASR system in Hoorn was conducted by PWN to address these challenges. The pilot system consists of a single well where two pumps operate at two different filter depths in an aquifer. In the final layout of the ASR, additional wells are necessary to achieve the desired capacity.
A radially symmetric model was used to simulate groundwater flow, conservative solute transport, and heat transport. Due to the stringent water quality requirements, the radially symmetric model must accurately capture essential processes in an Aquifer Storage and Recovery (ASR) system, such as flow, dispersion, retardation, and buoyancy. The performance of a radial symmetric model in SEAWAT and MODFLOW 6 was assessed based on analytical methods and 3D models. Through this analysis, it was decided to utilise a radial symmetric model in SEAWAT due to the presence of numerical dispersion in a model using MODFLOW 6.
After this analysis, the model’s performance was tested against various measurements, including hydraulic head, temperature, and electrical conductivity. It is evident that the model effectively captures both solute transport and heat transport. Discrepancies between measurements and the model can be attributed to assumptions made during the study and uncertainties in the measured values. However, the presence of clay layers between the deep and shallow filters in the pumping well significantly contributes to local differences between the model and the measurements. The main reason for this difference is that these layers are not homogeneous throughout the depths, allowing water to flow between them. This heterogeneity cannot be simulated with a radially symmetric model. However, despite this heterogeneity, these clay layers consistently result in low recovery efficiency in the current system.
The objective of this study was to identify a method to improve the recovery efficiency of the ASR system in Hoorn. The current system has a recovery efficiency of about 30%. This can improved by implementing a check valve in the shallow filter of the pump well, with 60% to 65% of the filter dedicated to recovery to achieve a recovery efficiency of 80%. During the testing of this system, an injection period was followed by a recovery period with specific pumping rates. It took three cycles to achieve the desired recovery efficiency. It is important to note that these cycles did not include storage and rest phases. The system’s recovery efficiency may change when these phases are incorporated. However, an important assumption is that homogeneous layers are present. Heterogeneity of layers can lead to deviations from the modelled recovery efficiency. This research contributes to a better understanding of the pilot ASR system in Hoorn and provides insights into improving its recovery efficiency. With the lessons learned from this study,
PWN can assist in developing the final design for the ASR system. This design will involve multiple wells to meet the required capacity.
The Effect of Brackish Water Extraction on the Brackish Upconing Below the Horstermeer Polder
Creating a 3D Regional Variable-Density Groundwater Model using MODFLOW 6 and FloPy
Van Hondsbossche dijk naar Hondsbossche duinen
Een modelstudie naar de effecten van een zandsuppletie op het grondwater regime
Estimating hydraulic aquifer parameters from tide-induced groundwater fluctuations
A case study in Schouwen-Duiveland
In this report the performance of a linear and nonlinear recharge model, inside the TFN model, are tested against synthetic time series of the groundwater table. These time series for the groundwater table are created with the unsaturated/saturated zone model HYDRUS-1D. With HYDRUS-1D, thirty-five synthetic time series are created for five different soil types and seven different unsaturated zone thicknesses (up to 5 m). The three most commonly used response functions, exponential, gamma and four-parameters are also tested for these thirty-five time series.
The results show that TFN models using the nonlinear recharge model are almost always better in estimating the groundwater table time series than the linear recharge model. This is confirmed in both the calibration and validation period. The common disadvantage of the linear recharge model, undershooting the groundwater table in (dry) summers, is not observed for the nonlinear recharge model. This can improve the forecasting abilities of TFN models during droughts.
Additionally, the nonlinear recharge model gives a more realistic representation of the fluxes in the root zone. This is confirmed goodness-of-fit parameters when comparing of the recharge flux and evaporation reduction calculated by HYDRUS-1D and the nonlinear recharge model. Especially when using the exponential response function, the recharge flux can be estimated quite well by the nonlinear recharge model. However, the nonlinear recharge model is currently not able to estimate groundwater uptake (upwards recharge) while it is observed in the HYDRUS-1D simulations.
The linear model does perform decently for shallow groundwater tables down to a depth of 150 cm since that is where large groundwater fluctuations and more days with groundwater uptake (upward recharge) are observed. The use of the gamma and four-parameter response functions significantly improves the performance of the linear recharge model. This can be explained by the compensation of these response functions for dispersion and retardation in the root zone. Nevertheless, when performing groundwater table time series analysis on synthetic time series created with HYDRUS-1D, the nonlinear recharge model is preferred to simulate the groundwater table. ...
In this report the performance of a linear and nonlinear recharge model, inside the TFN model, are tested against synthetic time series of the groundwater table. These time series for the groundwater table are created with the unsaturated/saturated zone model HYDRUS-1D. With HYDRUS-1D, thirty-five synthetic time series are created for five different soil types and seven different unsaturated zone thicknesses (up to 5 m). The three most commonly used response functions, exponential, gamma and four-parameters are also tested for these thirty-five time series.
The results show that TFN models using the nonlinear recharge model are almost always better in estimating the groundwater table time series than the linear recharge model. This is confirmed in both the calibration and validation period. The common disadvantage of the linear recharge model, undershooting the groundwater table in (dry) summers, is not observed for the nonlinear recharge model. This can improve the forecasting abilities of TFN models during droughts.
Additionally, the nonlinear recharge model gives a more realistic representation of the fluxes in the root zone. This is confirmed goodness-of-fit parameters when comparing of the recharge flux and evaporation reduction calculated by HYDRUS-1D and the nonlinear recharge model. Especially when using the exponential response function, the recharge flux can be estimated quite well by the nonlinear recharge model. However, the nonlinear recharge model is currently not able to estimate groundwater uptake (upwards recharge) while it is observed in the HYDRUS-1D simulations.
The linear model does perform decently for shallow groundwater tables down to a depth of 150 cm since that is where large groundwater fluctuations and more days with groundwater uptake (upward recharge) are observed. The use of the gamma and four-parameter response functions significantly improves the performance of the linear recharge model. This can be explained by the compensation of these response functions for dispersion and retardation in the root zone. Nevertheless, when performing groundwater table time series analysis on synthetic time series created with HYDRUS-1D, the nonlinear recharge model is preferred to simulate the groundwater table.
Relating groundwater heads to stream discharge by using machine learning techniques
A case study in subcatchment Chaamse Beken
Four different machine learning algorithms are used: decision tree regression (DTR), random forest regression (RFR), gradient boosting regression (GBR) and support vector regression (SVR). The training set of these models is set from 1985-1999, whereas the test set is from 1999-2003. Moreover, different input variables and combinations of these variables are chosen for the models: shallow wells (screen-1 wells), deeper wells (screen-2 wells), precipitation and potential evaporation. The model performance is evaluated with the metrics Nash-Sutcliffe Efficiency (NSE), mean absolute error (MAE), fourth root mean quadrupled error (R4MS4E) and mean squared logarithmic error (MSLE). The first two are considered for overall model performance, whereas the latter two are for high flow and low flow model performance.
The best overall and low model performance is obtained by using the algorithm SVR and using inputs groundwater heads of shallow wells, precipitation and potential evaporation (a NSE of 0.75). In order to examine if this machine learning model can be used in the future for stream discharge simulation, the SVR model is compared with an existing conceptual hydrological model GR4J. The GR4J model has a NSE value of 0.80 and can be rated as good. It has a larger NSE value than the SVR model and performs better than the SVR machine learning model.
It is important to stress that for the GR4J model the memory (or state) of the system is included. This inclusion of the memory of the system is not the case for the machine learning algorithms. Furthermore, an important difference is the fact that groundwater heads play a significant role in the simulation of the stream discharge by using machine learning algorithms. These groundwater heads are not directly used in the GR4J model. Lastly, it is stressed that for building the GR4J model physical understanding of the hydrological system is needed, whereas for machine learning this is not the case.
Overall, it can be concluded that GR4J is still favoured above SVR, but SVR shows promising results for further research in simulating stream discharge.
...
Four different machine learning algorithms are used: decision tree regression (DTR), random forest regression (RFR), gradient boosting regression (GBR) and support vector regression (SVR). The training set of these models is set from 1985-1999, whereas the test set is from 1999-2003. Moreover, different input variables and combinations of these variables are chosen for the models: shallow wells (screen-1 wells), deeper wells (screen-2 wells), precipitation and potential evaporation. The model performance is evaluated with the metrics Nash-Sutcliffe Efficiency (NSE), mean absolute error (MAE), fourth root mean quadrupled error (R4MS4E) and mean squared logarithmic error (MSLE). The first two are considered for overall model performance, whereas the latter two are for high flow and low flow model performance.
The best overall and low model performance is obtained by using the algorithm SVR and using inputs groundwater heads of shallow wells, precipitation and potential evaporation (a NSE of 0.75). In order to examine if this machine learning model can be used in the future for stream discharge simulation, the SVR model is compared with an existing conceptual hydrological model GR4J. The GR4J model has a NSE value of 0.80 and can be rated as good. It has a larger NSE value than the SVR model and performs better than the SVR machine learning model.
It is important to stress that for the GR4J model the memory (or state) of the system is included. This inclusion of the memory of the system is not the case for the machine learning algorithms. Furthermore, an important difference is the fact that groundwater heads play a significant role in the simulation of the stream discharge by using machine learning algorithms. These groundwater heads are not directly used in the GR4J model. Lastly, it is stressed that for building the GR4J model physical understanding of the hydrological system is needed, whereas for machine learning this is not the case.
Overall, it can be concluded that GR4J is still favoured above SVR, but SVR shows promising results for further research in simulating stream discharge.
From observation well to model area
Estimating groundwater levels spatially using time series analysis
Aquifer tests are carried out at five study sites in northern Ghana to determine local geohydrological conditions. The TTim analytic element modelling environment is used to analyze the obtained groundwater drawdown data and derive parameters for subsurface characteristics. TTim allows for the inclusion of additional model parameters (e.g. borehole storage, well skin resistance and multiple model layers) and outperforms the analytic Theis method in this research. Although some uncertainties are present in the derived subsurface parameters, plausible values for transmissivity (T) and storativity (S) are suggested to be present in the ranges of respectively 1 to 100 (m2/d) and 1e-3 to 1e-2 (-).
The year-round performance of a northern Ghana single ASR system is studied with a MODFLOW model. The potential types of ASR system improvements that are examined are (a) the extension of daily pumping time, (b) the enlargement of the borehole diameter, and (c) the reduction of the well skin resistance. The ASR systems sensitivities to changing environmental conditions are explored by (a) the degradation of well depth by clogging, (b) the shortening of the wet season inundation time, and (c) the reduction of the wet season inundation levels. Research results show that well maintenance is key for the performance of existing (and new) ASR systems. The recharge and discharge volumes can be improved by cleaning of the borehole depth and well screen. In the case of a new ASR system, the performance can positively be influenced by an enlargement of the borehole diameter. Furthermore, the construction of a proper permeable well skin (screen and gravel-pack around the well) can also result in increased system capacities. Despite the imposed options of system modifications, the geographic position of an ASR system remains of utmost importance for system performance. The construction of an ASR system at a location sensitive to flooding (riverbank overtopping or rainfall based) can be beneficial from a sustainable perspective. Recharge volumes are normative for the sustainable use of an ASR system. The recharges are (approximately linear) dependent on the time-span and levels of inundation. Moreover, the research contains soil scenarios, and demonstrates that the ASR system performs significantly better in regions with higher transmissivity (T) values.
To give insight on some financial aspects of an operational ASR system, the obtained (improved) ASR system discharge capacities are transformed to agricultural and financial yields. A subdivision of the dry season into a tomato and a groundnut cropping season demonstrates that financial yields are crop type dependent. The ASR system revenues are dominantly affected by the choice in crop type(s) and crop-specific market prices. The yields are compared to the ASR system pumping costs. The importance of pump selection is demonstrated by the implementation of the Pedrollo 4" submersible pump efficiencies. The use of a pump that is tuned to local conditions can be beneficial for the operational costs of an ASR system. Although no distinctive conclusion on the financial feasibility can be drawn, the examined system improvements are substantially beneficial for the revenues of a northern Ghana ASR system.
...
Aquifer tests are carried out at five study sites in northern Ghana to determine local geohydrological conditions. The TTim analytic element modelling environment is used to analyze the obtained groundwater drawdown data and derive parameters for subsurface characteristics. TTim allows for the inclusion of additional model parameters (e.g. borehole storage, well skin resistance and multiple model layers) and outperforms the analytic Theis method in this research. Although some uncertainties are present in the derived subsurface parameters, plausible values for transmissivity (T) and storativity (S) are suggested to be present in the ranges of respectively 1 to 100 (m2/d) and 1e-3 to 1e-2 (-).
The year-round performance of a northern Ghana single ASR system is studied with a MODFLOW model. The potential types of ASR system improvements that are examined are (a) the extension of daily pumping time, (b) the enlargement of the borehole diameter, and (c) the reduction of the well skin resistance. The ASR systems sensitivities to changing environmental conditions are explored by (a) the degradation of well depth by clogging, (b) the shortening of the wet season inundation time, and (c) the reduction of the wet season inundation levels. Research results show that well maintenance is key for the performance of existing (and new) ASR systems. The recharge and discharge volumes can be improved by cleaning of the borehole depth and well screen. In the case of a new ASR system, the performance can positively be influenced by an enlargement of the borehole diameter. Furthermore, the construction of a proper permeable well skin (screen and gravel-pack around the well) can also result in increased system capacities. Despite the imposed options of system modifications, the geographic position of an ASR system remains of utmost importance for system performance. The construction of an ASR system at a location sensitive to flooding (riverbank overtopping or rainfall based) can be beneficial from a sustainable perspective. Recharge volumes are normative for the sustainable use of an ASR system. The recharges are (approximately linear) dependent on the time-span and levels of inundation. Moreover, the research contains soil scenarios, and demonstrates that the ASR system performs significantly better in regions with higher transmissivity (T) values.
To give insight on some financial aspects of an operational ASR system, the obtained (improved) ASR system discharge capacities are transformed to agricultural and financial yields. A subdivision of the dry season into a tomato and a groundnut cropping season demonstrates that financial yields are crop type dependent. The ASR system revenues are dominantly affected by the choice in crop type(s) and crop-specific market prices. The yields are compared to the ASR system pumping costs. The importance of pump selection is demonstrated by the implementation of the Pedrollo 4" submersible pump efficiencies. The use of a pump that is tuned to local conditions can be beneficial for the operational costs of an ASR system. Although no distinctive conclusion on the financial feasibility can be drawn, the examined system improvements are substantially beneficial for the revenues of a northern Ghana ASR system.