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At the general assembly of the European Geophysical Union in 2022, the “Groundwater Time Series Modeling Challenge” was launched (Haaf et al., 2022). We challenged our colleagues in the field to model five time series of hydraulic heads measured in groundwater observations wells around Europe and North America. Part of the head data was not made available to the participants and held back as independent evaluation data. In this presentation, we will share and summarize the results from the challenge. The challenge attracted submissions from 17 teams using a variety of modeling techniques (https://github.com/gwmodeling/challenge). The models used in the submissions ranged from machine and deep learning models to empirical and bucket-type models. Many of the participants devoted notable attention to the uncertainty quantification, providing not only the results of the best-fit model but also uncertainty intervals for their models. The time to set up each model ranged from a couple of minutes to a couple of hours, indicating that models are generally set up in a limited amount of time. For most models, the majority of the time was used for the training and/or uncertainty quantification. The time spent on training showed larger differences between the models, ranging from a few minutes to more than a day for a single model. The data used to model the time series varied per model, with empirical models using less information than the machine learning models. A preliminary analysis of the modeling results showed that most of the models performed well (as measured by goodness-of-fit metrics such as NSE, MAE, KGE) in both the training and evaluation period. For one of the time series, none of the models showed a good fit with the data in the evaluation period and we suspect that a systematic change in the groundwater system may have occurred. The best-performing model differed between observation wells; none of the models outperformed all other models for all time series. In the coming months up to the EGU 2023 General Assembly we will further analyze and synthesize the results.Haaf, E., Collenteur, R., Liesch, T., & Bakker, M. (2022). Presenting the Groundwater Time Series Modeling Challenge(No. EGU22-12580). Copernicus Meetings.

Slow-moving landslides move downslope at velocities that range from mm year-1 to m year-1. Such deformations can be measured using satellite-based synthetic aperture radar interferometry (InSAR). We developed a new method to systematically detect and quantify accelerations and decelerations of slowly deforming areas using InSAR displacement time series. The displacement time series are filtered using an outlier detector and subsequently, piecewise linear functions are fitted to identify changes in the displacement rate (i.e., accelerations or decelerations). Grouped accelerations and decelerations are inventoried as indicators of potentially unstable areas. We tested and refined our new method using a high-quality dataset from the Mud Creek landslide, California, USA. Our method detects accelerations and decelerations that coincide with those previously detected by manual examination. Second, we tested our method in the region around the Mazar dam and reservoir in Southeast Ecuador, where the time series data were of considerably lower quality. We detected accelerations and decelerations occurring during the entire study period near and upslope of the reservoir. The application of our method results in a wealth of information on the dynamics of the surface displacement of hillslopes and provides an objective way to identify changes in displacement rates. The displacement rates, their spatial variation, and the timing of accelerations and decelerations can be used to study the physical behavior of a slow-moving slope or for regional hazard assessment by linking the timing of changes in displacement rates to landslide causal and triggering factors

Aquifer thermal energy storage (ATES) is an energy efficient technique to provide heating and cooling to buildings by storage of warm and cold water in aquifers. In regions with large demand for ATES, ATES adoption has lead to congestion problems in aquifers. The recovery of thermal energy stored in aquifers can be increased by reducing the distance between wells of the same temperature while safeguarding individual system performance. Although this approach is implemented in practice, the understanding of how this affects both the recovery efficiency and the needed pumping energy is lacking. In this research, the effect of well placement on the performance of individual systems is quantified, and guidelines for planning and design are developed. Results show an increase in thermal recovery efficiency of individual systems when the thermal zones of wells of the same temperature are combined, which is explained by reduced surface area of the thermal zone over which losses occur. The highest increase of the thermal recovery efficiency is found for systems with a small storage volume and long well screens. The relative increase of the thermal recovery efficiency is 12% for average-sized systems with a storage volume of 250,000 m3/year, and 25% for small systems (50,000 m3/year). The optimal distance between wells of the same temperature is 0.5 times the thermal radius, following the trade-off between an increase of the thermal recovery efficiency and the increase in pumping energy. The distance between wells of opposite temperature must be larger than three times the thermal radius to avoid negative interaction.

Geogenic arsenic in drinking water is a worldwide problem. For private well owners, testing (e.g., private or government laboratory) is the main method to determine arsenic concentration. However, the temporal variability of arsenic concentrations is not well characterized and it is not clear how often private wells should be tested. To answer this question, three datasets, two new and one publicly available, with temporal arsenic data were utilized: 6370 private wells from New Jersey tested at least twice since 2002, 2174 wells from the USGS NAWQA database, and 391 private wells sampled 14 years apart from Bangladesh. Two arsenic drinking water standards are used for the analysis: 10 µg/L, the WHO guideline and EPA standard or maximum contaminant level (MCL) and 5 µg/L, the New Jersey MCL. A rate of change was determined for each well and these rates were used to predict the temporal change in arsenic for a range of initial arsenic concentrations below an MCL. For each MCL and initial concentration, the probability of exceeding an MCL over time was predicted. Results show that to limit a person to below a 5% chance of drinking water above an MCL, wells that are ½ an MCL and above should be tested every year and wells below ½ an MCL should be tested every 5 years. These results indicate that one test result below an MCL is inadequate to ensure long-term compliance. Future recommendations should account for temporal variability when creating drinking water standards and guidance for private well owners.

In recent years, meteorological droughts over Northwestern Europe caused severe declines in groundwater levels with significant damage to groundwater-dependent ecosystems and agriculture. One possible solution to reduce the declines in groundwater levels is to temporarily lower the extraction rates of nearby well fields used for drinking water production. The effectiveness of such measures depends on the magnitude and time of the response of the groundwater system to changes in groundwater extraction, which is salient information for decision makers. The response of the groundwater system is commonly quantified using numerical groundwater models that are time-consuming to develop and can be difficult to calibrate. In this research, a quick data-driven approach is proposed, based on time series analysis, that serves as a complement to more traditional groundwater modeling approaches. A scripted workflow was developed using Pastas, an open-source Python module for Transfer Function Noise modeling. The approach was applied to 243 monitoring wells in an area of the Netherlands, a country where summer droughts can cause serious problems, even though the country is better known for problems with too much water. For each monitoring well, the best model structure and relevant hydrological forcings (rainfall, evaporation, river stages, and extraction rates of well fields) were selected iteratively. Model selection was performed through split-sample testing and diagnostic checking. The accepted model for each monitoring well represents an independent estimate of the contribution of different hydrological forcings and processes to the groundwater response and is based exclusively on observed data. The modeled responses to the pumping rates of the well fields were used to determine the feasibility of reducing extraction rates to control heads during droughts.

Time series analysis with response functions is a versatile approach to analyze measured head series in observation wells. In such an analysis, the response function of the groundwater does not change with time. This approach works well when the groundwater recharge is a linear function of the measured rainfall and evaporation, as was shown through the analysis of synthetic time series generated with a saturated groundwater model where the groundwater recharge is applied directly to the saturated zone. In this research, the method was evaluated for situations where the groundwater recharge cannot be approximated well as a linear function of the measured rainfall and evaporation. Synthetic time series were generated with a two-dimensional saturated/unsaturated zone model (Hydrus2D) and analyzed with response functions. Performance of the time series model was improved through inclusion of a new root zone model consisting of a single reservoir. Reservoir inflow is measured rainfall. Reservoir outflow is evaporation and groundwater recharge, where the evaporation is a function of the amount of water stored in the root zone and the recharge is a function of both the amount of water stored in the root zone and the groundwater table. The new root zone model is a promising tool for the analysis of head series in areas with thick unsaturated zones and/or high potential evaporative fluxes.

Groundwater level time series are the most common source of information on the dynamics of subsurface water resources. The modeling of such time series is of crucial importance to increase our understanding of the system and make predictions on future changes. This becomes ever more important with global challenges such as climate change and human over-exploitation of groundwater resources. Different types of models can be applied to model groundwater level time series, ranging from purely statistical models (black-box), through lumped conceptual models (grey-box), to physically based models (white-box). Traditionally, physically based, distributed models are predominantly used to solve groundwater problems. In recent years, the use of grey- and black-box models has been receiving increased attention. In this poster we will present the “Groundwater Time Series Modeling Challenge”, which aims to systematically compare different methodologies to model groundwater level time series. We challenge researchers to model a predefined set of groundwater time series observed in unconsolidated aquifers worldwide, set in a variety of physiographic and climatic conditions. Participating groups are free to use the model of their choice, but are required to use the same forcing data and periods for calibration. Model performance will be centrally assessed by the organizers using non-public validation data set, which will be made public after the challenge. A more detailed description of the rules for this challenge and all groundwater and forcing data is available in a GitHub repository at https://github.com/gwmodeling/challenge. The results of the challenge will be presented at the General Assembly of the EGU in 2023 and reported in a peer-reviewed paper. With this challenge, we hope to increase the awareness in the groundwater community about all the different types of models that are available to model groundwater level time series.

Aquifer thermal energy storage (ATES) is a technology to provide energy-efficient heating and cooling to buildings by storage of warm and cold water in aquifers. In regions with large demand for ATES, ATES adoption has led to congestion problems in aquifers. The aquifer utilisation and the recovery of thermal energy stored in aquifers can be increased by reducing the distance between wells of the same temperature. Hence, this approach is implemented in practice, but the understanding of how this affects both the recovery efficiency and the needed pumping energy is missing.In this research, the effect of well placement on the performance of individual systems is quantified using numerical modelling. Results show an increase in performance of individual systems when the thermal zones of wells of the same temperature are combined. The relative increase of the thermal recovery efficiency is 12% for average-sized systems with a storage volume of 250,000 m3/year, and 25% for small systems (50,000 m3/year). Performance of the combined system improves because the surface area of the thermal zone of the combined system, over which thermal losses occur, is smaller than the sum of the surface areas of the individual systems. Performance improvement is larger for systems with small storage volumes and long well screens. The optimal distance between wells of the same temperature is 0.5 times the thermal radius, following the trade-off between an increase of the thermal recovery efficiency and an increase in pumping energy. The distance between wells of opposite temperature must be larger than 3 times the thermal radius to avoid negative interaction.

Global mean sea-level rise (SLR) has accelerated since 1900 from less than 2 mm/year during most of the century to more than 3 mm/year since 1993. At the regional scale, detection of an acceleration in SLR is difficult, because the long-term sea-level signal is obscured by large inter-annual variations with multi-year trends that are easily one order of magnitude larger than global mean values. Here, we developed a time series approach to determine whether regional SLR is accelerating based on tide gauge data. We applied the approach to eight 100-year records in the southern North Sea and detected, for the first time, a common breakpoint in the early 1990s. The mean SLR rate at the eight stations increases from 1.7±0.3 mm/year before the breakpoint to 2.7±0.4 mm/year after the breakpoint (95% confidence interval), which is unprecedented in the regional instrumental record. These findings are robust provided that the record starts before 1970 and ends after 2015.

Horizontal wells play an often overlooked role in hydrogeology and aquifer remediation but can be an interesting option for many applications. This study reviews the constructional and hydraulic aspects that distinguish them from vertical wells. Flow patterns towards them are much more complicated than those for vertical wells, which makes their mathematical treatment more demanding. However, at some distance, the drawdown fields of both well types become practically identical, allowing simplified models to be used. Due to lower drawdowns, the yield of a horizontal well is usually higher than that of a vertical well, especially in thin aquifers of lower permeability, where they can replace several of the latter. The lower drawdown, which results in lower energy demand and slower ageing, and the centralized construction of horizontal wells can lead to lower operational costs, which can make them an economically feasible option.

Time series analysis is applied to identify and analyze a transition in the groundwater regime in the aquifer below the sand ridge of Salland in the Netherlands, where groundwater regime refers to the range of head variations throughout the seasons. Standard time series analysis revealed a discrepancy between modeled and observed heads in several piezometers indicating a possible change in the groundwater regime. A new time series modeling approach is developed to simulate the transition from the initial regime to the altered regime. The transition is modeled as a weighted sum of two responses, one representing the initial state of the system, the other representing the altered state. The inferred timing and magnitude of the change provided strong evidence that the transition was the result of significant dredging works that increased the river bed conductance of the main river draining the aquifer. The plausibility of this explanation is corroborated by an analytical model. This case study and the developed approach to identify a change in the groundwater regime are meant to stimulate a more systematic application of time series analysis to detect and understand changes in groundwater systems which may easily go unnoticed in groundwater flow modeling.

An approach is presented to determine groundwater flow in unconsolidated aquifers with a heat pulse response test using a heating cable and a fiber-optic cable. The cables are installed together using direct push so that the cables are in direct contact with the aquifer. The temperature response is measured for multiple days along the fiber-optic cable with Distributed Temperature Sensing (DTS). The new approach fits a two-dimensional analytical solution to the temperature measurements, so that the specific discharge can be estimated without knowledge of the position of the fiber-optic cable relative to the heating cable. Two case studies are presented. The first case study is at a managed aquifer recharge system where fiber-optic cables are inserted 15 m deep at various locations to test the fitting procedure. Similar and relatively large specific discharges are found at the different locations with little vertical variation (0.4–0.6 m/day). The second case study is at a polder, where the water level is maintained 2 m below the surrounding lakes, resulting in significant groundwater flow. The heating and fiber-optic cables are inserted to a depth of 45 m. The specific discharge varies 0.07–0.1 m/day and is significantly larger in a thin layer at 30-m depth. It is shown with numerical experiments that the estimated specific discharge is smoother than in reality due to vertical conduction, but the peak specific discharge is estimated correctly for layers thicker than ∼1.5 m.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a life-time risk of 1 in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry GWAS in ALS including 29,612 ALS patients and 122,656 controls which identified 15 risk loci in ALS. When combined with 8,953 whole-genome sequenced individuals (6,538 ALS patients, 2,415 controls) and the largest cortex-derived eQTL dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, repeat expansions or regulatory effects. ALS associated risk loci were shared with multiple traits within the neurodegenerative spectrum, but with distinct enrichment patterns across brain regions and cell-types. Across environmental and life-style risk factors obtained from literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. All ALS associated signals combined reveal a role for perturbations in vesicle mediated transport and autophagy, and provide evidence for cell-autonomous disease initiation in glutamatergic neurons.

Regional-scale landslide deformation can be measured using satellite-based synthetic aperture radar interferometry (InSAR). Our study focuses on the quantification of displacements of slow-moving landslides that impact a hydropower dam and reservoir in the tropical Ecuadorian Andes. We constructed ground surface deformation time series using data from the Copernicus Sentinel-1 A/B satellites between 2016 and 2020. We developed a new approach to automatically detect the onset of accelerations and/or decelerations within each active landslide. Our approach approximates the movement of a pixel as a piecewise linear function. Multiple linear segments are fitted to the cumulative deformation time series of each pixel. Each linear segment represents a constant movement. The point where one linear segment is connected to another linear segment represents the time when the pixel’s rate of movement has changed from one value to another value and is referred to as a breakpoint. As such, the breakpoints represent moments of acceleration or deceleration. Three criteria are used to determine the number of breakpoints: the timing and uncertainty of the breakpoints, the confidence intervals of the fitted segments’ slopes, and the Akaike Information Criterion (AIC). The suitable number of breakpoints for each pixel (i.e., the number of accelerations or decelerations) is determined by finding the largest number of breakpoints that complies with the three listed criteria. The application of this approach to landslides results in a wealth of information on the surface displacement of a slope and an objective way to identify changes in displacement rates. The displacement rates, their spatial variation, and the timing of acceleration and deceleration can further be used to study the physical behavior of a slow-moving slope or for (regional) hazard assessment linking the onset of change in displacement rate to causal and triggering factors.

Objective To identify and describe health literacy profiles of patients with rheumatic diseases and explore whether the identified health literacy profiles can be generalized to a broader rheumatology context. Methods Patients with rheumatoid arthritis, spondyloarthritis, and gout from 3 hospitals in different regions in The Netherlands completed the Health Literacy Questionnaire (HLQ). Hierarchical cluster analysis was used to identify patients' health literacy profiles based on 9 HLQ domains. A multinomial regression model with the identified health literacy profiles as the dependent variable was fitted to assess whether patients with a given disease type or attending a given hospital were more likely to belong to a specific profile. Results Among 895 participating patients, the lowest mean HLQ domain scores (indicating most difficulty) were found for "critical appraisal," "navigating the health system," and "finding good health information." The 10 identified profiles revealed substantial diversity in combinations of strengths and weaknesses. While 42% of patients scored moderate to high on all 9 domains (profiles 1 and 3), another 42% of patients (profiles 2, 4, 5, and 6) clearly struggled with 1 or several aspects of health literacy. Notably, 16% (profiles 7-10) exhibited difficulty across a majority of health literacy domains. The probability of belonging to one of the profiles was independent of the hospital where the patient was treated or the type of rheumatic disease. Conclusion Ten distinct health literacy profiles were identified among patients with rheumatic diseases, independent of disease type and treating hospital. These profiles can be used to facilitate the development of health literacy interventions in rheumatology.

The application of non-linear transfer function noise (TFN) models using impulse response functions is explored to estimate groundwater recharge and simulate groundwater levels. A non-linear root zone model that simulates recharge is developed and implemented in a TFN model, and is compared to a more commonly used linear recharge model. An additional novel aspect of this study is the use of an autoregressive-moving average noise model so that the remaining noise fulfills the statistical conditions to reliably estimate parameter uncertainties and compute the confidence intervals of the recharge estimates. The models are calibrated on groundwater level data observed at the Wagna hydrological research station in the southeastern part of Austria. The non-linear model improves the simulation of groundwater levels compared to the linear model. The annual recharge rates estimated with the non-linear model are comparable to the average seepage rates observed with two lysimeters. The recharges estimates from the non-linear model are also in reasonably good agreement with the lysimeter data at the smaller time scale of recharge per 10 days. This is an improvement over the results from previous studies that used comparable methods, but only reported annual recharge rates. The presented framework requires limited input data (precipitation, potential evaporation, and groundwater levels) and can easily be extended to support applications in different hydrogeological settings than those presented here.

Distributed temperature sensing (DTS) systems can be used to estimate the temperature along optic fibers of several kilometers at a sub-meter interval. DTS systems function by shooting laser pulses through a fiber and measuring its backscatter intensity at two distinct wavelengths in the Raman spectrum. The scattering-loss coefficients for these wavelengths are temperature-dependent, so that the temperature along the fiber can be estimated using calibration to fiber sections with a known temperature. A new calibration approach is developed that allows for an estimate of the uncertainty of the estimated temperature, which varies along the fiber and with time. The uncertainty is a result of the noise from the detectors and the uncertainty in the calibrated parameters that relate the backscatter intensity to temperature. Estimation of the confidence interval of the temperature requires an estimate of the distribution of the noise from the detectors and an estimate of the multi-variate distribution of the parameters. Both distributions are propagated with Monte Carlo sampling to approximate the probability density function of the estimated temperature, which is different at each point along the fiber and varies over time. Various summarizing statistics are computed from the approximate probability density function, such as the confidence intervals and the standard uncertainty (the estimated standard deviation) of the estimated temperature. An example is presented to demonstrate the approach and to assess the reasonableness of the estimated confidence intervals. The approach is implemented in the open-source Python package &ldquo, dtscalibration&rdquo

Tide gauges are the main source of information about sea-level changes in the Industrial Age. When looking at global mean values, century-long reconstructions produce rates between 1-2 mm/yr, while estimates over the last three decades reveal a much faster rise of about 3 mm/yr, as also indicated by satellite altimetry observations. In spite of this evidence for a recent acceleration, its quantification remains a challenging and relevant task, because results are highly dependent on the length of the record and on the reconstruction technique, whereas decision makers require clear proof to legitimise action.While global mean results are very important to understand climate change, regional to local variations are more relevant for the purpose of planning mitigation and adaptation measures. However, mainly due to natural variability, looking at individual tide gauge stations hampers the accurate determination of linear and non-linear trends.We analyse tide gauge records along the Dutch coast by means of advanced statistical techniques, with the main objective of determining whether and under which conditions it is possible to detect departures from secular trends. We particularly focus on how to handle noise in the natural system, which for the Dutch coast is mainly represented by local atmospheric effects and by variability in ocean dynamics in the NE Atlantic.

Groundwater recharge remains a notoriously difficult flux to estimate, despite ongoing scientific efforts. In recent years, time series modeling using impulse response functions has gained popularity to simulate groundwater levels and is quickly becoming a common tool for hydrogeologists. Several approaches have been developed to estimate recharge from time series models for both linear and non-linear systems (e.g., [1], [2], and [3]). In this study, we introduce a novel approach to estimate groundwater recharge from observed groundwater levels in nonlinear systems (i.e., twice the precipitation does not necessarily lead to twice the recharge). We extend a time series model using impulse response functions with a non-linear unsaturated zone module that simulates recharge. The model parameters are estimated by fitting the simulated to the observed groundwater levels, with the groundwater recharge as an intermediate model result. The method is tested on a time series of groundwater levels observed in Southeastern Austria (Wagna), where lysimeter data of seepage to the groundwater is available for model validation. The simulated groundwater recharge suggests an event-based recharge behavior, with most recharge occurring shortly after larger precipitation events. This finding agrees with the behavior observed in the lysimeter data. The estimated recharge fluxes show a high correlation with the observed seepage on time scales from years to months or weeks, while daily recharge rates show larger errors. Advantages of the method include limited data requirements (only precipitation, potential evapotranspiration, and groundwater time series are required) and the possibility to correct for other factors causing groundwater level fluctuations (e.g., pumping, river levels). This makes it possible to apply the method in locations where little system knowledge (e.g., soil profiles) is available.References:[1] Besbes, M. and De Marsily, G. (1984) From infiltration to recharge: use of a parametric transfer function, Journal of Hydrology.[2] Peterson, T.J. and Fulton, S. (2019) Joint estimation of gross recharge, groundwater usage, and hydraulic properties within HydroSight, Groundwater.[3] Obergfell, C., Bakker, M. and Maas, K. (2019) Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads, Groundwater.

The Salt Water Intrusion Meetings, or SWIMs, are a series of meetings that focus on seawater intrusion in coastal aquifers and other salinisation processes. 2018 marks the 50th year of the SWIM and the 25th biennial meeting. The SWIM proceedings record half a century of research progress on site characterisation, geophysical and geochemical techniques, variable-density flow, modelling, and water management. The SWIM is positioning itself to remain a viable platform for discussing the coastal aquifer management challenges of the next 50 years.

The influence of the length of the calibration period and observation frequency on the predictive uncertainty in time series modeling of groundwater dynamics is investigated. Studied series are from deltaic regions with predominantly shallow groundwater tables in a temperate maritime climate where heads vary due to precipitation and evaporation. Response times vary over a wide range from ∼60 to ∼1200 days. A Transfer Function-Noise model is calibrated with the Markov Chain Monte Carlo method to both synthetic series and measured series of heads. The model fit and uncertainty are evaluated for various calibration periods and observation frequencies. It is often assumed that the required length of the calibration period is related to the response time of the system. In this study, no strong relationship was observed. Results indicate, however, that the required length of the calibration period is related to the decay time of the noise. Furthermore, the length of the calibration period was much more important than the total number of observations. For the measured series, the credible intervals could commonly be reduced to ∼10% of the measured head range and the prediction intervals to ∼50% of the measured head range with calibration periods of 20 years with approximately two observations per month.

Background Delivery of rheumatological care is suggested to be compromised by patient health literacy. Prevalence of problematic health literacy in the Netherlands is estimated to be as high as 36%. “Health literacy represents the cognitive and social skills which determine the motivation and ability of individuals to gain access to, understand and use information in ways which promote and maintain good health” (Nutbeam, 1998). A deeper understanding of diverse health literacy profiles of patients attending rheumatology clinics could facilitate development of interventions tailored to patients’ needs, in order to improve health outcomes and reduce inequities. Objectives To identify health literacy strengths and weaknesses among patients with Spondyloarthritis (SpA) and Rheumatoid Arthritis (RA) attending a rheumatology clinic, and identify typical ‘health literacy profiles’ based on these strengths and weaknesses. Methods Consecutive RA and SpA patients attending the outpatient clinic at Maastricht UMC+, the Netherlands, were invited to participate. Health literacy was assessed using the Health Literacy Questionnaire (HLQ), which includes nine dimensions (Figure 1). Socio-demographics and health-related characteristics were also collected. Hierarchical cluster analysis following Ward’s method identified clusters based on HLQ scale scores. This method groups subjects into a predefined number of clusters, based on smallest differences to the mean of all scales, creating minimal variance within each cluster, and maximum variance between clusters. Three researchers jointly examined twenty cluster solutions for meaningfulness by interpreting HLQ scales and patient characteristics. Meaningful clusters are translated into health literacy profiles using HLQ patterns and demographic data. A patient representative confirmed the identified profiles. Results In total, 133 patients with RA and 106 with SpA completed the questionnaire. Of these, 61% (n=146) were female, mean age was 61.6 (±13.3), 27% (n=65) lived alone, 11% (n=26) did not speak Dutch at home, 28% (n=68) were employed, and 22% and 50% had received low and medium education, respectively. Figure 1 displays identified health literacy profiles and scale scores, with greener values representing strengths and red scores representing weaknesses. Overall, the pattern indicates RA and SpA patients have similar profiles, but some represent a clear majority of SpA or RA patients and could be considered disease-specific. For example, cluster 10 includes patients who typically feel comfortable in their relationship with their doctor (scales 1 and 6), and are rather confident in managing their health and getting the information they need (scale 3 and 7). However, they struggle in understanding health information (scale 9), and lack the social support needed to make health decisions and maintain good health (scale 4). This profile appeared restricted to RA patients. Conclusion This study identified potential typical health literacy profiles in patients with inflammatory arthritis in our clinic. Profiles reflected distinctive strengths and weaknesses on the nine health literacy dimensions. Some profiles appeared to be condition-specific, which warrants further investigation. Identified profiles will feed into co-design workshops with patients, professionals and other stakeholders, in order to develop interventions to tailor the care to patients’ needs. References [1] Nutbeam D. (1998). Health promotion glossary. Health Promotion International, 13, 349–364. Disclosure of Interests Mark Bakker: None declared, Polina Putrik: None declared, Jany Rademakers: None declared, Mart van de Laar Grant/research support from: AbbVie, Eli Lilly, Janssen-Cilag, Merck Inc, Pfizer Inc, Sanofi Genzyme, Speakers bureau: Eli Lilly, Pfizer Inc, Harald Vonkeman: None declared, Marc Kok: None declared, Hanneke Voorneveld: None declared, Sofia Ramiro Grant/research support from: MSD, Consultant for: AbbVie, Lilly, MSD, Novartis, Pfizer, Sanofi, Speakers bureau: AbbVie, Lilly, MSD, Novartis, Pfizer, Sanofi, Maarten de Wit: None declared, Richard Osborne: None declared, Roy Batterham: None declared, Rachelle Buchbinder: None declared, Annelies Boonen: None declared

A new method is presented to estimate average diffuse aquifer recharge of water table aquifers in temperate climates using time series analysis of water table level fluctuations. An accurate estimate of the recharge caused by rainfall requires an accurate estimate of the influence of evaporation. In temperate climates, evaporation imprints a seasonal component in the water table fluctuations. As such, recharge is estimated from time series models fitted to observed heads under the additional constraint that the seasonal harmonic of the observed head is reproduced as the sum of the transformed seasonal harmonics present in precipitation, evaporation, and pumping. An explicit equation is presented, in terms of the model parameters, for the damping and phase shift of the response to the seasonal harmonic of the stresses. Taking into account the seasonal harmonic of the observed heads results in more reliable recharge estimates compared to standard time series analysis. The method is limited to systems that are sufficiently linear and that remain unaltered over the analysis period. Head fluctuations and stresses should contain a seasonal harmonic that can be estimated with accurately. Runoff must be negligible or quantifiable. The method is applied to measured heads obtained from piezometers situated on and around the ice-pushed sand ridge of Salland in the Netherlands and compares well with recharge estimates based on the saturated zone chloride mass balance.

Time series analysis is an increasingly popular method to analyze heads measured in an observation well. Common applications include the quantification of the effect of different stresses (rainfall, pumping, etc.), and the detection of trends and outliers. Pastas is a new and open source Python package for the analysis of hydrogeological time series. The objective of Pastas is twofold: to provide a scientific framework to develop and test new methods, and to provide a reliable ready‐to‐use software tool for groundwater practitioners. Transfer function noise modeling is applied using predefined response functions. For example, the head response to rainfall is simulated through the convolution of measured rainfall with a Gamma response function. Pastas models are created and analyzed through scripts, ensuring reproducibility and providing a transparent report of the entire modeling process. A Pastas model can be constructed in seven simple steps: import Pastas, read the time series, create a model, specify the stresses and the types of response functions, estimate the model parameters, visualize output, and analyze the results. These seven steps, including the corresponding Python code, are applied to investigate how rainfall and reference evaporation can explain measured heads in an observation well in Kingstown, Rhode Island, USA. The second example demonstrates the use of scripts to analyze a large number of observation wells in batch to estimate the extent of the drawdown caused by a well field in the Netherlands. Pastas is free and open source software available under the MIT‐license at http://github.com/pastas/pastas., Article impact statement: Perform time series analysis of observed heads in a few lines of Python code with the open source software Pastas.

A new analytic solution is presented for tidal propagation in multilayer coastal aquifers consisting of an arbitrary number of layers. The solution is derived using matrix calculus and may be applied to simulate tidal propagation in systems where aquifer layers are separated by leaky layers of low permeability or in stratified aquifers. The head, amplitude, and phase shift may be simulated for arbitrary tidal components. The transmissivity, storage coefficient, and loading efficiency may be different for every aquifer layer. Similarly, the resistance to vertical flow, storage coefficient, and loading efficiency may be different for every leaky layer. The aquifer system extends an infinite distance below the sea or may end abruptly at the shore line. In the former case, different aquifer and leaky layer properties may be specified below the sea and below the land. A homogeneous unconfined aquifer is simulated with 80 layers to demonstrate that the tidal signal propagates much farther inland in the deeper part than in the shallower part of an unconfined aquifer. This effect is enhanced significantly when layers of low permeability are present in the aquifer, even when these layers are fairly thin, such as clay lenses. The solution is implemented in Python and may be used, for example, to estimate aquifer parameters from head measurements in coastal aquifers.

Time series analysis is a data-driven approach to analyze time series of heads measured in an observation well. Time series models are commonly much simpler and give much better fits than regular groundwater models. Time series analysis with response functions gives insight into why heads vary, while such insight is difficult to gain with black box models out of the artificial intelligence world. An important application is to quantify the contributions to the head variation of different stresses on the aquifer, such as rainfall and evaporation, pumping, and surface water levels. Time series analysis may be applied to answer many groundwater questions without the need for a regular groundwater model, such as what is the drawdown caused by a pumping station? Or, how long will it take before groundwater levels recover after a period of drought? Even when a regular groundwater model is needed to solve a groundwater problem, time series analysis can be of great value. It can be used to clean up the data, identify the major stresses on the aquifer, determine the most important processes that affect flow in the aquifer, and give an indication of the fit that can be expected. In addition, it can be used to determine calibration targets for steady-state models, and it can provide several alternative calibration methods for transient models. In summary, the overarching message of this paper is that it would be wise to do time series analysis for any application that uses measured groundwater heads.

Graphical user interfaces (GUIs) are commonly used to construct and postprocess numerical groundwater flow and transport models. Scripting model development with the programming language Python is presented here as an alternative approach. One advantage of Python is that there are many packages available to facilitate the model development process, including packages for plotting, array manipulation, optimization, and data analysis. For MODFLOW-based models, the FloPy package was developed by the authors to construct model input files, run the model, and read and plot simulation results. Use of Python with the available scientific packages and FloPy facilitates data exploration, alternative model evaluations, and model analyses that can be difficult to perform with GUIs. Furthermore, Python scripts are a complete, transparent, and repeatable record of the modeling process. The approach is introduced with a simple FloPy example to create and postprocess a MODFLOW model. A more complicated capture-fraction analysis with a real-world model is presented to demonstrate the types of analyses that can be performed using Python and FloPy.