Timo Jaakko Heimovaara

In the Netherlands a long-term experiment is being carried to actively reduce the emission potential from landfill waste bodies. At the Kragge landfill, a pilot is performed to test enhanced leaching by recirculating treated leachate on one of the cells. We present an analysis of the water and chloride mass balance based on long-term time series and a recently developed modelling approach. Using this approach we have an accurate mass balance, showing that the landfill Cell 3 is hydrologically connected to its neighbouring cell: about 22 % of the infiltrated leachate discharged through the drainage system of Cell 4. While the water storage in Cell 4 remained close to the initial storage, the water storage in Cell 3 consistently shows an increase, which is still ongoing after 6.65 years, about 44 % of the infiltrated water remains in the waste body of cell 3. The mass of chloride in Cell 3 remains constant because of the recirculation of leachate. On Cell 4, the chloride mass decreases because of the flushing with fresh rainwater. The approach gives accurate results for the change in mass and water. However, the absolute magnitude of remaining chloride mass and water storage in the waste body remains uncertain.

Accurate estimation of water storage in municipal solid waste landfills is critical for assessing leachate‐generation risk yet remains challenging due to pronounced heterogeneity. Here we apply Bayesian Evidential Learning (BEL) to directly relate Electrical Resistivity Tomography (ERT) data to total water storage (TWS), bypassing explicit inversion. A semi‐parametric forward model generates 100,000 synthetic TWS–ERT pairs spanning stochastic saturation fields and petrophysical uncertainty. A Bayesian neural network captures data‐dependent predictive uncertainty, while stratified resampling and adaptive weighting mitigate class imbalance across the TWS range. The framework yields well‐calibrated posterior estimates and consistent agreement with independent water‐balance benchmarks from four field transects. The BEL–ERT workflow provides a rapid, open‐source alternative for landfill monitoring and highlights the potential of uncertainty‐aware learning from synthetic ensembles to quantify water storage in heterogeneous near‐surface systems.

Understanding and accurately predicting landfill emissions are critical for sustainable landfill management and environmental protection. This study introduces a novel approach using Particle Filter coupled with Markov Chain Monte Carlo (PF-MCMC) in a weakly coupled data assimilation framework, applied within a travel time distribution based landfill leachate emission model. The proposed method integrates sequential observations of leachate production rate (LPR) and chloride concentrations to estimate hidden model states, parameters, and underlying processes. Synthetic experiments demonstrated the effectiveness of the PF-MCMC method in significantly reducing uncertainty and improving predictions of chloride concentration, releasable leachate storage in the waste body, and total releasable solute mass compared to open-loop simulations. Real-world application to data from Braambergen landfill in the Netherlands further confirmed these advantages, notably improving concentration predictions while offering insights into hidden landfill processes like baseflow dynamics and preferential flow distributions. The study highlights the potential of PF-MCMC not only as a predictive tool but also as an analytical instrument to diagnose model limitations and dynamically track changes in landfill behavior, thereby enhancing long-term landfill aftercare strategies.

Accurate estimation of water stored in municipal solid waste landfills is essential for leachate-risk management but remains difficult to achieve due to high heterogeneity. We apply Bayesian Evidential Learning (BEL) to relate Electrical Resistivity Tomography (ERT) data directly to total water storage (TWS), bypassing explicit inversion. A semi-parametric forward model generates 5,000 synthetic TWS—ERT pairs spanning stochastic saturation fields and petrophysical uncertainty. Dimensionality reduction, prior falsification via one-class SVM, and a last-layer Bayesian neural network yield posterior TWS distributions with uncertainty bounds. Synthetic validation shows accurate mid-range predictions, with 95\% intervals covering most true values; underestimation occurs at high TWS due to parameter uncertainty. Application to four field transects shows reasonable agreement with independent water-balance estimates, despite the absence of direct ground truth. BEL—ERT offers a rapid, open-source alternative for landfill monitoring and demonstrates how uncertainty-aware learning from synthetic ensembles can support water-content estimation in heterogeneous near-surface systems.

Sanitary engineered landfills require extensive aftercare to safeguard human health and the environment. This involves monitoring emissions like leachate and gas, maintaining cover layers, and managing leachate and gas collection systems. Researchers have explored methods to conclude or extend aftercare. Quantifying emission potential, a key concept integrating various processes influencing emissions, is essential for managing and predicting landfill impacts. In this study we developed a stochastic travel time model based on water life expectancies. The model is used to predict leachate production rates and leachate chloride concentrations from landfill waste bodies. Unknown parameters are quantified by matching model output to measured time series using Bayesian inference. Once parameter distributions have been obtained, we are able to describe the measured long‐term leachate dynamics. By analyzing the parameters and evolution of model states, we obtain a deeper understanding of the water and mass balance of the waste bodies. We demonstrate that the model can be used to quantify the chloride emission potential and the estimated values of total chloride mass match data quantified by sampling from the waste body. The results confirm that emissions with leachate are dominated by preferential flow infiltrating from the cover layer. Similar results have been obtained by applying the model to datasets from four different waste bodies, demonstrating that the approach is generally applicable for conservative solutes. Understanding of the water balance of the landfill together with conservative solute leaching is a necessary first step for further evaluating emission of reactive species.

The emission potential, which represents the total leachable mass in landfill waste body, is hard to measure directly. Therefore we propose to quantify it by assimilating available measurements. The leachate production rate is influenced by the total water storage in the waste body, while both total chloride mass and total water storage in the waste body influence the chloride concentration in the leachate. Thus assimilating leachate volume and chloride concentration simultaneously will help quantify the uncertainties in emission potential. This study investigated the feasibility of using a particle filter in a concentration‐volume coupled travel time distribution model to estimate the emission potential. Leachate production rates and chloride concentrations were assimilated simultaneously by a weakly coupled data assimilation method. The time lag issue in the travel time distribution model was solved by adding a daily model error to cover layer states. The proposed method was tested in synthetic experiments first to investigate the performance. The results show that the uncertainties in chloride mass and waste body total water storage were quantified and reduced. The predictions of chloride concentrations were also improved.

Canal dikes in low‐lying polders, as well as in other regions worldwide, are critical infrastructure for flood protection and water management. The subsurface water conditions can cause dike failures during excessive rainfall and prolonged periods of drought. There is a lack of multi‐year monitoring of subsurface water conditions in canal dikes and an insufficient understanding of their geohydrological behavior. This study provides and analyses a novel multiyear data set of soil moisture and hydraulic heads (from February 2020 until March 2023) from a monitoring network covering various canal dikes with different characteristics in the western Netherlands. The data, including two extremely dry summers, highlight the impact of meteorological variations on the subsurface water conditions. Non‐hydrostatic hydraulic head levels were observed during droughts that can be detrimental to dike stability and that are often not accounted for in safety assessments for drought situations. The effectiveness of various meteorological drought indicators applied to subsurface water conditions was evaluated: the precipitation deficit is the most reliable measure and outperforms the standardized drought indicators (SPEI and SPI). The drought recovery of dikes was analyzed to understand seasonal transitions and the sequence of different failure mechanisms, during dry and wet situations. This analysis also reveals differences between meteorological, soil moisture, and groundwater droughts, highlighting soil's storage capacity after drought and the limitations of meteorological drought indicators as proxies for soil moisture and groundwater. The insights from this study enhance assessments, inspection procedures and the identification of weak spots of dikes and other earthworks of infrastructure.

Landfills require extensive aftercare to safeguard human health and the environment. This involves monitoring emissions like leachate and gas, maintaining cover layers, and managing leachate and gas collection systems. Researchers have explored methods to conclude or extend aftercare. Experimental programs in the Netherlands are testing stabilization techniques for landfill waste bodies. Quantifying emission potential, a key concept integrating various processes influencing emissions, is essential for managing and predicting landfill impacts. In this study we developed a stochastic travel time model based on water life expectancies. The model is used to predict leachate production rates and leachate chloride concentrations from landfill waste bodies. We present new data for long-term time series of leachate production and leachate quality for four different waste bodies in the Netherlands. Unknown parameters are quantified by matching model output to measured time series using Bayesian inference. Once parameter distributions have been obtained, we are able to describe the measured long-term leachate dynamics. By analyzing the parameters and evolution of model states, we obtain a deeper understanding of the water and mass balance of the waste bodies. We demonstrate that the model can be used to quantify the emission potential and the estimated values of total mass match data quantified by sampling from the waste body. The results confirm that emissions with leachate are dominated by preferential flow infiltrating from the cover layer. Similar results have been obtained by applying the model to datasets from four different waste bodies, demonstrating that the approach is generally applicable.

The emission potential, which represents the total leachable mass in landfill waste body, is hard to measure directly. Therefore we propose to quantify it by assimilating available measurements. The leachate production rate is influenced by the total water storage in the waste body, while both total chloride mass and total water storage in the waste body influence the chloride concentration in the leachate. Thus assimilating leachate volume and chloride concentration simultaneously will help quantify the uncertainties in emission potential. This study investigated the feasibility of using particle filter in a concentration-volume coupled travel time distribution model to estimate the emission potential. Leachate production rates and chloride concentrations were assimilated simultaneously by a weakly coupled data assimilation(WCDA) method. The time lag issue in the travel time distribution model was solved by adding a daily model error to cover layer states. The proposed method was tested in synthetic experiments firstly to investigate the performance. The results show that the uncertainties in chloride mass and waste body total water storage were quantified and reduced. The predictions of chloride concentrations were also improved.