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Between 2019 and 2022, there was a marked rise in adolescents/young adults seeking urgent help for functional tic-like behaviours (FTLBs). Given the global scale of this phenomenon, we aimed to pool cases from different institutions in an international registry to better characterize this spectrum and facilitate future longitudinal observation.An international collaborative group from 10 tertiary referral centres for tic disorders collected retrospective data on FTLB patients who sought specialists' attention between the last quarter of 2019 and June 2022. An audit procedure was used for collection of data, which comprised demographics, course of presentation and duration, precipitating and predisposing factors, phenomenology, comorbidities, and pharmacological treatment outcome.During the study period, we collected data on 294 patients with FTLBs, 97% of whom were adolescents and young adults and 87% of whom were female. FTLBs were found to have a peak of severity within 1 month in 70% of patients, with spontaneous remissions in 20%, and a very high frequency of complex movements (85%) and vocalizations (81%). Less than one-fifth of patients had pre-existing primary tic disorder, 66% had comorbid anxiety disorders, 28% comorbid depressive disorders, 24% autism spectrum disorder and 23% attention deficit/hyperactivity disorder. Almost 60% explicitly reported exposure to tic-related social media content. The vast majority of pharmacologically treated patients did not report benefit with tic-suppressing medications.Our data from the largest multicentre registry of FTLBs to date confirm substantial clinical differences from primary tic disorders. Social modelling was the most relevant contributing factor during the pandemic. Future longitudinal analyses from this database may help understand treatment approaches and responsiveness.

Estimating karst water resources at a global scale represents a key step towards enhancing groundwater management. Although recharge estimations at global scale exist, the special karst features leading to different recharge processes, in particular the preferential flow path, have not been adequately accounted for. Representing these special recharge processes is crucial for realistically quantifying recharge in karst regions. In this study, we present a new version of a global karst recharge model and the estimation of its model parameters using multiple observations. The updated model includes a new routine to consider eight land cover types for calculating evapotranspiration, interception and infiltration, as well as a new routine to account for snow and glacier melt. To prepare parameter estimation, six karst landscapes are defined based on the properties of karst grids (spatial resolution of 0.25°). For each of these landscapes, model parameters are found separately using a Monte Carlo uncertainty estimation framework and observations from the international soil moisture network (ISMN) and actual evapotranspiration observations from FLUXNET. With the new calibration, the updated model provides more precise estimates of groundwater recharge in athe karst regions of the world. It will therefore serve as a too to improve water management in karst regions and identify areas potentially suffering from water shortage.

The overall aim of the MEWAC-FEMAR project (Middle East Regional Water Research Cooperation Program – Feasibility of Managed Aquifer Recharge), funded by the German Federal Ministry of Education and Research (BMBF), is to support and enhance a safe and sustainable water supply in the Middle East by Managed Aquifer Recharge (MAR). One target region is Syria, which is facing water insecurity regarding water availability, water supply, and agricultural water needs. In addition, the region is severely affected by war which results in extreme destruction of infrastructure and other resources. The project's objective is to provide a model-based framework for integrated water resources management (IWRM) to identify suitable sites for MAR and to predict their impact on the regional water balance and the local situation. The proposed framework incorporates different methods and model techniques with applications ranging from global to local scales. The model workflow is completely based on open-source tools to ensure sustainable use of the project efforts. Modeling on the global scale is mainly done by data-driven methods like GIS Multi-Criteria Decision Analysis (GIS-MCDA) to answer questions like where are suitable and feasible areas for MAR. Regional scale models aim to describe the hydrological cycle by distributed numerical methods (e.g., MODFLOW-OWHM) to predict and manage MAR under consideration of the regional groundwater situation. Local-scale numerical models can provide detailed and site-specific insights into the flow and transport processes of the unsaturated zone and the underlying aquifer. Specific activities in Syria include e.g., evaluation of wastewater reuse for irrigation. Finally, the current process understanding with regard to MAR methods is critically evaluated and expanded.

Where does water go when it rains? Where are floods generated and how? What controls stream water quality during events? These questions are important to many fields from engineering and flood protection to water and ecosystem management and prediction of impacts of global change. The most elusive processes in the process-ensemble underlying these questions is subsurface stormflow (SSF), the fast event response triggered by lateral subsurface flow. SSF is prevalent and a more important process than generally accounted for because a basic understanding based on systematic studies across scales and sites is still lacking. However, only with systematic studies will it be possible to really advance our understanding by discovering general principles of SSF functioning and to provide protocols and best practices for its assessment, both experimentally and with respect to modelling.In many natural landscapes, SSF, i.e. any subsurface flow that occurs in response to a precipitation event, plays a major role in runoff generation: either by contributing directly to streamflow or by producing saturated areas or return flow, which then is the underlying cause of saturation excess overland flow. Therefore, much of what we see as event response in the hydrograph might be the direct or indirect result of SSF. It is likely that the discharge signal of SSF, including the indirectly triggered response in the stream, is larger than we generally assume. While its importance is probably largest in the headwaters, headwaters make up 70% of the stream network and greatly influence the supply and transport of water and solutes downstream. However, SSF is elusive and poorly accounted for as measurements are difficult for several reasons: the inaccessibility of the subsurface, the large spatial variability and heterogeneity, the variable sources and the fact that it is a threshold-driven process that only occurs during certain events. Thus, systematic studies of SSF are lacking, mainly due to difficulties of quantification.We suggest such a systematic study of SSF in different environments, across scales, and using a well-designed and replicated selection of approaches including novel approaches. This will be followed by a systematic evaluation of methods and possible proxies as well as model intercomparison, evaluation and improvement. Thereby, we will focus on 4 challenges: 1) Development of novel experimental methods,2) Spatial patterns of SSF, 3) Thresholds and cascading effects of SSF, 4) Impacts of SSF.Whereas standard single research projects investigate part of this puzzle at a specific location, this Research Unit provides the unique opportunity of fitting a large number of puzzle pieces together. This Research Unit will have a strong emphasis on experimental work in four contrasting catchments from the low to high mountain ranges (Sauerland, Ore Mountains, Black Forest, Alps) that then directly feeds into a collaborative modelling effort, which in turn influences experimental design in an iterative process.

In addition to overland flow, subsurface stormflow (SSF) can play a major role for runoff generation during certain events. Even though SSF is a well-recognised process, there is a lack of systematic studies on SSF, partly because it is very difficult to quantify. In hydrological modelling, this can lead to an unclear distinction of SSF from other processes. If parameters that describe SSF processes or thresholds are implemented in hydrological models, they are often used as fitting parameters and can contribute to overall model uncertainty. So far, there has been no systematic benchmarking of SSF routines in hydrological models.This presentation discusses how we plan to address this research gap. In order to address the inconsistency of SSF representation in hydrological models, different existing lumped hydrological models will be set up for four study sites located in the Alps, Ore Mountains, Black Forest and Sauerland. In a first step, different models will be calibrated using only basic data like discharge observations, climate data and readily available geodata. Differences in SSF simulations will be detected and quantified and the models will be benchmarked regarding the simulation of SSF dynamics and associated uncertainties. In a next step, we will include new experimental data on SSF derived at the four study sites in the calibration of the lumped hydrological models by a multi-objective calibration and evaluation framework. In order to consider SSF observations collected at scales different than the scale of model application, new SSF metrics will be developed. Testing different combinations of these metrics for model calibration it will be possible to state which SSF proxies can lead to the most productive improvement of SSF simulations.Identifying current weaknesses in SSF representation of current models, and providing directions for improving them by including the most beneficial SSF metrics, this project will show potential for the improvement of SSF simulations through SSF data collection. In a final application of the most reliable SSF simulations to all study sites, we will show the impact of extreme wet or extreme dry conditions on SSF occurrence and SSF volumes.

Modelling solute mixing and transport processes is one of the key steps to effectively managing karstic groundwater resources, particularly under the threat of climate change and risk of contamination. For that reason, a considerable body of literature has been devoted to understanding and describing solute mixing and transport processes in karst aquifers. However, due to the strong multiscale heterogeneity (from microscale to aquifer scale), modelling solute mixing and transport processes in karst aquifers remains a challenging task. This presentation critically reviews the current state of knowledge and fundamental challenges in the modelling of solute mixing and transport processes in karst aquifers, thereby collocating and synthesizing the existing body of knowledge in the literature. To provide a holistic and objective picture of the state-of-the-art of the solute mixing and transport modelling, we performed a bibliometric analysis on the relevant literature for karst groundwater studies (over 2800 scientific papers). Further, with a meta-analysis of scientific papers focusing on the quantitative tracer tests, we evaluated the field-based transport parameters that are typically served for the solute mixing and transport models.The review unveils the fundamental modelling hinges underlying a successful modelling practice for the solute mixing and transport processes in karst, thereby discussing to what extent and in what ways we are dealing with these challenges. The major modelling challenges are defined as follows: (i) Model conceptualization based on data collection and system understanding (e.g., How well is the problem of interest defined? To what extent is the domain of interest described?), ii) Model selection considering the choice of a dominant physicochemical process (e.g., How well is the process of interest represented by a set of governing equations over the problem domain?), iii) Time-variability of solute mixing and transport processes (e.g., To what extent do the parameters represent the process of interest under the different time-scales?), iv) Model parametrization considering the parameter non-uniqueness and transferability (e.g., How realistic are the model parameters? To which extent are they transferable over the same aquifer?), v) Uncertainty quantification in model results (e.g., How robust are the model results? How much are we (un)certain about our model?). Finally, we address potential research directions and knowledge gaps by encouraging the community for building a protocol for solute transport modelling in karst aquifers, as well as providing more transparent and reproducible results.

Managed aquifer recharge (MAR) is a powerful approach to counteracting the negative impacts of overexploited groundwater resources and enhancing groundwater availability in water-scarce regions. One common MAR strategy is aquifer storage and recovery (ASR). Theoretically, the ASR techniques can be adopted for karst systems to store and recover water within the system or to increase the subsurface inflow into the alluvial system from the adjacent upland karst system. However, karst systems present a particular challenge for the application and technical implementation of ASR, due to the associated strong anisotropy and heterogeneity, high runoff dynamics, as well as the underlying uncertainties regarding the available data.This research is oriented to provide a regional scale model of a karst-alluvial system in Lebanon, analyzing the impact of MAR scenarios as well as quantifying boundary inflow to adjacent alluvial systems. For this reason, several model conceptualizations (considering multi-model concepts) and ASR application scenarios are adopted and tested. For transferability, real-world case studies and idealized yet generalizable systems are considered and assessed. The model pre- and postprocessing is entirely script-based and uses open-source tools to ensure sustainable useAn anisotropic fast-marching algorithm is used to create spatially distributed karst channel networks through the Python package pyKasso. In addition, a discrete continuum model is developed (e.g., CfPy), where the stochastic conduit networks are implemented as a large ensemble of the plausible and representative karstic system. The regional karst alluvial model results are also used to make general recommendations for MAR site selection in the karstic study site.

Groundwater resources are generally evaluated by quantifying groundwater recharge and its storage into the aquifer using for example conceptual or numerical models. Recharge modelling provides a preliminary estimation of the renewable part of the groundwater resource. Moreover, results of regional groundwater recharge models may be used as input data for models on a smaller scale, i.e. at the catchment scale dedicated to water management for present and future conditions. It is thus necessary to constrain the recharge models for example by comparing their outputs to historical long-term observations of groundwater flows that can be derived from time series of groundwater levels or spring discharge.Karst systems with their high infiltration rate and preferential flow in enlarged conduit networks, react quickly to climatic events and changes. Thus, they can be used as proxies to evaluate the impact of global change on groundwater resources. Karst systems are present in different climatic regions of Europe, which allows comparing long-term trends of groundwater recharge with spring discharge (similar or opposing trends). In our study, two different regional recharge models covering entire Europe – one calculating potential groundwater recharge using simple soil water balance methods and one calculating groundwater recharge over karst areas only using 1-D physical equations for infiltration – are compared to the measured spring discharge from a large European database of more than 100 monitored karst systems. To identify and highlight changes in dominant recharge processes related to changing climatic or physiographic (land cover / land use) conditions, different variables used in the regional recharge models will be correlated to indices describing dynamics of karst spring discharge. The results of our study will help to understand the impact of climatic and groundwater recharge related influences on various geographic locations and give insights on uncertainties in the model structure of the applied regional groundwater recharge models.

High mountains, which exhibit alpine and subalpine characteristics, represent 15% of the earth’s land area and are estimated to contribute about 17% of global runoff. Depending on hydrogeological setting, a significant amount of catchment water can be stored in high mountainous underground as groundwater, which can contribute substantially to streamflow and represent an important water source. However, high-alpine catchments are often characterized by great geological complexity and highly heterogeneous hydraulic properties. For that reason, proper system characterization, monitoring and modeling remain challenging. In this study, we investigated a geologically complex alpine catchment in the Dolomites (Italian Alps) by combining hydrogeological investigation, hydrological monitoring and numerical modelling. A process based but spatially lumped hydrological model was applied to simulate the continuous measured catchment discharge in a period of three years, which covers a large variation of hydrodynamic conditions. The current model structure couples the sequential hydrogeological units within the studied catchment: (1) the fractured dolomitic rocks as bedrock aquifer and 2) the unconsolidated deposits accumulating on the slopes and at the valley floor as porous aquifer. In order to evaluate the model structure and parameterization in depth, we applied a multi-step evaluation approach considering both parameter sensitivity and uncertainty. The current modelling results demonstrate that the newly developed model can reproduce most discharge behavior of aquifers. The model indicates a dynamic linkage between surface and subsurface storage units during different flow conditions. Besides the matrix and conduit flow in fractured dolomitic aquifer, it highlights the important role of unconsolidated sediments (porous aquifer) to the storage and discharge behavior of the entire groundwater system. Furthermore, with the comprehensive model evaluation we learned the model structure deficit during extreme high flow condition and proposed a more detailed hydrogeological conceptual model to improve the model realism.

Groundwater level forecasting with machine learning has been widely studied due to its generally accurate results and little input data requirements. Furthermore, machine learning models for this purpose are set up and trained in a short time when compared to the effort required for process-based, numerical models. Despite the high performance of models obtained at specific locations, applying the same model architecture to multiple sites across a regional area might lead to contrasting accuracies. Likely causalities of this discrepancy in model performance have been barely examined in previous studies. Here, we investigate the link between model performance and the effects of geospatial site characteristics and time series features. Using precipitation and temperature as predictors, we model groundwater levels at approximately 500 observation wells in Lower Saxony, Germany, using a 1-D convolutional neural network with a fixed architecture and hyperparameters tuned for each time series individually. The performances are evaluated against geospatial and time series features using correlation coefficients. Model performance is negatively influenced at sites near waterworks and densely vegetated areas. Besides, the more complex the time series, the higher the metrics, but autocorrelation reduces the model performance. The new insights evidence that further information is required at certain locations to improve model accuracy due to external impacts.

Protected areas are a key tool for conserving biodiversity, sustaining ecosystem services and improving human well-being. Global initiatives that aim to expand and connect protected areas generally focus on controlling ‘above ground’ impacts such as land use, overlooking the potential for human actions in adjacent areas to affect protected areas through groundwater flow. Here, we assess the potential footprint of these impacts by mapping groundwatersheds. We find that over five in six protected areas globally (85%) have groundwatersheds that are underprotected. Half of all protected areas have a groundwatershed with a spatial footprint that lies predominantly (i.e., at least 50%) outside of the protected area’s boundary. These findings highlight a widespread potential risk to protected areas from activities affecting groundwater within their groundwatersheds, underscoring the need for groundwatershed-based protection measures. Delineating groundwatersheds can catalyze needed discussions about protected area connectivity and robustness, and investments in groundwatershed conservation and management can help protect groundwater-dependent ecosystems from external threats.

The socioeconomic, technological, and environmental needs and developments call for effective infrastructure investment planning. Because infrastructures are interdependent, this requires the entanglement of their demand and performance development. We propose a scenario-based system-of-systems model to support strategic decision-makers in evaluating the effects of planned investments on transport infrastructure demand and performance under different plausible futures and identifying required investments across transport infrastructure sectors considering interdependencies. The model estimates freight and passenger demand and their consequent emission and energy performance for rail, road, and waterway networks. Through a case study, we show how the model can assist decision-makers in tracing the effects of silo-based planned investments on interconnected infrastructures. Moreover, the case demonstrates how possible investments can be detected to ensure desirable infrastructure performance. Finally, it presents how a better understanding can be created about the impacts of transport demand and energy transition policies on the performance of infrastructure system-of-systems.