Towards the construction of representative regional hydro(geo)logical numerical models: Modelling the upper Danube basin as a starting point

Introduction: Pressure on groundwater resources is increasing rapidly by population growth and climate change effects. Thus, it is urgent to quantify their availability and determine their dynamics at a global scale to assess the impacts of climate change or anthropogenically induced pressure, and to support water management strategies. In this context, regional hydrogeological numerical models become essential to simulate the behavior of groundwater resources. However, the construction of global hydrogeological models faces a lot of challenges that affect their accuracy.Methods: In this work, using the German portion of the Upper Danube Basin (∼43,000 km2) we outline common challenges encountered in parameterizing a regional-scale groundwater model, and provide an innovative approach to efficiently tackle such challenges. The hydrogeological model of the Danube consists of the groundwater finite element code OpenGeoSys forced by the groundwater recharge of the surface hydrological model mHM.Results: The main novelties of the suggested approach are 1) the use of spectral analyses of the river baseflow and a steady state calibration taking as reference the topography to constraint the hydraulic parameters and facilitate the calibration process, and 2) the calibration of the hydraulic parameters for a transient state model by considering parameters derived from the piezometric head evolution.Discussion/conclusion: The results show that the proposed methodology is useful to build a reliable large-scale groundwater model. Finally, the suggested approach is compared with the standard one used by other authors for the construction of global models. The comparison shows that the proposed approach allows for obtaining more reliable results, especially in mountainous areas

From Dynamic Groundwater Level Measurements to Regional Aquifer Parameters— Assessing the Power of Spectral Analysis

Large-scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale. However, parameterization of large-scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational data and the mismatch between the scales of modeling and measurements. In this work, we propose to bridge the scale gap and derive regional hydraulic parameters by spectral analysis of groundwater level fluctuations. We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system. We first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity. Applying Liang and Zhang’s (2013), https://doi.org/10.1016/j.jhydrol.2012.11.044, semi-analytical solution for the spectrum of hydraulic head time series, we identify for each ensemble member and at different locations representative aquifer parameters. Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings. Our analyses indicate that the variance of inferred effective transmissivity and storativity values for stochastic aquifer ensembles is small for observation points which are far away from the Dirichlet boundary. Moreover, the head time series has to cover a period which is roughly 10 times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer equivalent, regionally valid parameters. A sensitivity analysis further reveals that as long as the aquifer length and the position of the groundwater measurement location is roughly known, the parameters can be robustly estimated.This work was funded by the Helmholtz Centre for Environmental Research (UFZ) in Leipzig. The authors would like to thank three anonymous reviewers for constructive feedback which improved the quality of this paper. Furthermore, we thank Rohini Kumar for providing the mHM recharge time series, Falk Hesse and Sebastian Müller for support with the stochastic analysis as well as for help with the model setup. Thanks to Lennart Schüler for mathematical support concerning the analytical solutions and Fanny Sarrazin for providing concepts for the sensitivity analysis. We would like to thank the group of the groundwater initiative at UFZ, especially Christian Siebert and Tino Rödiger for constructive feedback about the results of the spectral analysis, the selection of the groundwater wells, and corresponding data sets. The scientific results have been computed at the High-Performance Computing (HPC) Cluster EVE, a joint effort of both the Helmholtz Centre for Environmental Research - UFZ (http://www.ufz.de/) and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (http://www.idiv-biodiversity.de/). E. Pujades and S. Attinger greatfully acknowledge the support from the research initiative Global Resource Water (GRoW; 02WGR1423A-F). GRoW is part of the Sustainable Water Management (NaWaM) funding priority within the Research for Sustainable Development (FONA) framework of the German Federal Ministry of Education and Research (BMBF). E. Pujades acknowledges the financial support from IDAEA-CSIC, which is a Centre of Excellence Severo Ochoa (Spanish Ministry of Science and Innovation, Project CEX2018-000794-S), and the Barcelona City Council through the Award for Scientific Research into Urban Challenges in the City of Barcelona 2020 (20S08708). Open access funding enabled and organized by Projekt DEAL.Peer reviewe

From Dynamic Groundwater Level Measurements to Regional Aquifer Parameters— Assessing the Power of Spectral Analysis

Large‐scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale. However, parameterization of large‐scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational data and the mismatch between the scales of modeling and measurements. In this work, we propose to bridge the scale gap and derive regional hydraulic parameters by spectral analysis of groundwater level fluctuations. We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system. We first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity. Applying Liang and Zhang’s (2013), https://doi.org/10.1016/j.jhydrol.2012.11.044, semi‐analytical solution for the spectrum of hydraulic head time series, we identify for each ensemble member and at different locations representative aquifer parameters. Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings. Our analyses indicate that the variance of inferred effective transmissivity and storativity values for stochastic aquifer ensembles is small for observation points which are far away from the Dirichlet boundary. Moreover, the head time series has to cover a period which is roughly 10 times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer equivalent, regionally valid parameters. A sensitivity analysis further reveals that as long as the aquifer length and the position of the groundwater measurement location is roughly known, the parameters can be robustly estimated.Plain Language Summary: We build large‐scale (regional) computer models of the subsurface flow conditions in order to quantify the long‐term shift in groundwater storage and response on the national level under changing climatic conditions and increasing human water demands. These models must be fed with hydrogeological parameters obtained from subsurface observation wells, drilling logs, and hydraulic tests in conjunction with (hydro)geological and geostatistical methods. In some regions these wells are sparsely distributed and derived parameters are representative only for small areas. We hypothesize that groundwater level records can reveal regional aquifer information when analyzed in the spectral domain. In order to bridge that scale gap and because groundwater level time series are generally available, we propose to infer regional parameters by analyzing the frequency content (spectrum) of long groundwater level time series. The required parameters were determined using mathematical formulations of the theoretical spectrum for simplified settings. We tested the methodology in computer models with limited complexity and found that the groundwater level time series indeed contain regional information if the time of observation is sufficiently long. Lastly, we apply the spectral analysis to real groundwater data to test the capability of the method to infer regional aquifer parameters in real aquifers.Key Points: We successfully tested the spectral analysis of groundwater level fluctuations in numerical models and obtained regional aquifer parameters. In a sensitivity analysis of the spectral analysis using field data, the storativity and the response times could be robustly estimated. The application of the suggested methodology to the field data from a catchment in central Germany produced plausible results.Helmholtz Centre for Environmental Research (UFZ)Global Resource WaterGerman Federal Ministry of Education and Research (BMBF)IDAEA‐CSICBarcelona City Councilhttps://github.com/ufz/ogs5https://geostat-framework.github.io

City2020+ : assessing climate change impacts for the city of Aachen related to demographic change and health ; a progress report

The research initiative CITY 2020+ assesses the risks and opportunities for residents in urban built environments under projected demographic and climate change for the year 2020 and beyond, using the city of Aachen as a case study. CITY 2020+ develops strategies, options and tools for planning and developing sustainable future city structures. The investigation focuses on how urban environment, political structure and residential behaviour can best be adapted, with attention to the interactions among structural, political, and sociological configurations and their impacts on human health. The interdisciplinary research is organized in three clusters. Within the first cluster, strategies of older people exposed to heat stress, and their networks as well as environmental health risks according to atmospheric conditions are examined. The second cluster addresses governance questions, urban planning and building technologies as well as spatial patterns of the urban heat island. The third cluster includes studies on air quality related to particulate matter and a historical perspective of city development concerning environmental issues and climate variability. However, it turns out that research topics that require an interdisciplinary approach are best addressed not by pre-structuring the work into related sub-projects but through combining them according to shared methodological approaches. Examples illustrating this rather practical approach within ongoing research are presented in this paper

Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine

Background. Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided.Aim. The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementary alternatives to traditional evidence-based medicine, specifically rapid learning health care and cohort multiple randomised controlled trial design. Rapid learning health care is an approach that proposes to extract and apply knowledge from routine clinical care data rather than exclusively depending on clinical trial evidence, (please watch the animation: http://youtu.be/ZDJFOxpwqEA). The cohort multiple randomised controlled trial design is a pragmatic method which has been proposed to help overcome the weaknesses of conventional randomised trials, taking advantage of the standardised follow-up approaches more and more used in routine patient care. This approach is particularly useful when the new intervention is a priori attractive for the patient (i.e. proton therapy, patient decision aids or expensive medications), when the outcomes are easily collected, and when there is no need of a placebo arm.Discussion. Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly

5th Data Science Symposium, GEOMAR

Modern digital scientific workflows - often implying Big Data challenges - require data infrastructures and innovative data science methods across disciplines and technologies. Diverse activities within and outside HGF deal with these challenges, on all levels. The series of Data Science Symposia fosters knowledge exchange and collaboration in the Earth and Environment research community. We invited contributions to the overarching topics of data management, data science and data infrastructures. The series of Data Science Symposia is a joint initiative by the three Helmholtz Centers HZG, AWI and GEOMAR Organization: Hela Mehrtens and Daniela Henkel (GEOMAR