Ana Gonzalez-Nicolas

The Terrestrial Systems Modeling Platform (TSMP, github.com/HPSCTerrSys/TSMP2) is a scale-consistent, highly modular, physics-based, massively parallel, and fully integrated coupled groundwater-vegetation-atmosphere modeling system for regional Earth system modeling. TSMP is composed of the atmospheric models ICON or COSMO, the land surface model Community Land Model (CLM), and the ParFlow subsurface-surface hydrological model, linked to each other through the OASIS3-MCT coupler. TSMP is used across a wide range of spatio-temporal scales, ranging from individual field plots, large eddy simulations to continental scale climate mode runs in a variety of applied research topics, such as water resources, land-atmosphere coupling, or climate change projections. In version 2, or short TSMP2, we have comprehensively modernized the coupled model system. One of the main changes from version 1 is the replacement of component models with their current state-of-the-art successors for the atmosphere and the land surface, and a modified coupling design to improve the mass and energy balances between the compartments. TSMP2 now relies entirely on a CMake build-system, which enhances the system’s portability across different computer infrastructures. Through a Git-based framework TSMP2 includes auxiliary tools for model setup, a workflow engine and case study simulation experiments to meet reproducibility, research software engineering, and FAIR data best practices. The TSMP2 coupled model system and all its components are free open-source software. Aside from a description of the TSMP2 infrastructure, we present the impact of coupling approaches between the compartments on model states in TSMP2, and outline our development strategy along with technical and performance aspects arising from the coupling.

The Terrestrial Systems Modeling Platform (TSMP, github.com/HPSCTerrSys/TSMP2) is a scale-consistent, highly modular, physics-based, massively parallel, and fully integrated coupled groundwater-vegetation-atmosphere modeling system for regional Earth system modeling. TSMP is composed of the atmospheric models ICON or COSMO, the land surface model Community Land Model (CLM), and the ParFlow subsurface-surface hydrological model, linked to each other through the OASIS3-MCT coupler. TSMP is used across a wide range of spatio-temporal scales, ranging from individual field plots, large eddy simulations to continental scale climate mode runs in a variety of applied research topics, such as water resources, land-atmosphere coupling, or climate change projections. In version 2, or short TSMP2, we have comprehensively modernized the coupled model system. One of the main changes from version 1 is the replacement of component models with their current state-of-the-art successors for the atmosphere and the land surface, and a modified coupling design to improve the mass and energy balances between the compartments. TSMP2 now relies entirely on a CMake build-system, which enhances the system’s portability across different computer infrastructures. Through a Git-based framework TSMP2 includes auxiliary tools for model setup, a workflow engine and case study simulation experiments to meet reproducibility, research software engineering, and FAIR data best practices. The TSMP2 coupled model system and all its components are free open-source software. Aside from a description of the TSMP2 infrastructure, we present the impact of coupling approaches between the compartments on model states in TSMP2, and outline our development strategy along with technical and performance aspects arising from the coupling.

Benchmarks are essential in the design of modern HPC installations, as they define key aspects of system components. Beyond synthetic workloads, it is crucial to include real applications that represent user requirements into benchmark suites, to guarantee high usability and widespread adoption of a new system. Given the significant investments in leadership-class supercomputers of the exascale era, this is even more important and necessitates alignment with a vision of Open Science and reproducibility. In this work, we present the JUPITER Benchmark Suite, which incorporates 16 applications from various domains. It was designed for and used in the procurement of JUPITER, the first European exascale supercomputer. We identify requirements and challenges and outline the project and software infrastructure setup. We provide descriptions and scalability studies of selected applications and a set of key takeaways. The JUPITER Benchmark Suite is released as open source software with this work at github.com/FZJ-JSC/jubench

Benchmarks are essential in the design of modern HPC installations, as they define key aspects of system components. Beyond synthetic workloads, it is crucial to include real applications that represent user requirements into benchmark suites, to guarantee high usability and widespread adoption of a new system. Given the significant investments in leadership-class supercomputers of the exascale era, this is even more important and necessitates alignment with a vision of Open Science and reproducibility. In this work, we present the JUPITER Benchmark Suite, which incorporates 16 applications from various domains. It was designed for and used in the procurement of JUPITER, the first European exascale supercomputer. We identify requirements and challenges and outline the project and software infrastructure setup. We provide descriptions and scalability studies of selected applications and a set of key takeaways. The JUPITER Benchmark Suite is released as open source software with this work at https://github.com/FZJ-JSC/jubench.

An accurate assessment of the physical states of the Earth system is an essential component of many scientific, societal, and economical considerations. These assessments are becoming an increasingly challenging computational task since we aim to resolve models with high resolutions in space and time, to consider complex coupled partial differential equations, and to estimate uncertainties, which often requires many realizations. Machine learning methods are becoming a very popular method for the construction of surrogate models to address these computational issues. However, they also face major challenges in producing explainable, scalable, interpretable, and robust models. In this paper, we evaluate the perspectives of geoscience applications of physics-based machine learning, which combines physics-based and data-driven methods to overcome the limitations of each approach taken alone. Through three designated examples (from the fields of geothermal energy, geodynamics, and hydrology), we show that the non-intrusive reduced-basis method as a physics-based machine learning approach is able to produce highly precise surrogate models that are explainable, scalable, interpretable, and robust.

Several environmental phenomena require monitoring time-dependent densities in porous media, e.g., clogging of river sediments, mineral dissolution/precipitation, or variably-saturated multiphase flow. Gamma-ray attenuation (GRA) can monitor time-dependent densities without being destructive or invasive under laboratory conditions. GRA sends gamma rays through a material, where they are attenuated by photoelectric absorption and then recorded by a photon detector. The attenuated intensity of the emerging beam relates to the density of the traversed material via Beer–Lambert’s law. An important parameter for designing time-variable GRA is the exposure time, the time the detector takes to gather and count photons before converting the recorded intensity to a density. Large exposure times capture the time evolution poorly (temporal raster error, inaccurate temporal discretization), while small exposure times yield imprecise intensity values (noise-related error, i.e. small signal-to-noise ratio). Together, these two make up the total error of observing time-dependent densities by GRA. Our goal is to provide an optimization framework for time-dependent GRA experiments with respect to exposure time and other key parameters, thus facilitating neater experimental data for improved process understanding. Experimentalists set, or iterate over, several experimental input parameters (e.g., Beer–Lambert parameters) and expectations on the yet unknown dynamics (e.g., mean and amplitude of density and characteristic time of density changes). We model the yet unknown dynamics as a random Gaussian Process to derive expressions for expected errors prior to the experiment as a function of key experimental parameters. Based on this, we provide an optimization framework that allows finding the optimal (minimal-total-error) setup and demonstrate its application on synthetic experiments.

Currently, the export regime of a catchment is often characterized by the relationship between compound concentration and discharge in the catchment outlet or, more specifically, by the regression slope in log-concentrations versus log-discharge plots. However, the scattered points in these plots usually do not follow a plain linear regression representation because of different processes (e.g., hysteresis effects). This work proposes a simple stochastic time-series model for simulating compound concentrations in a river based on river discharge. Our model has an explicit transition parameter that can morph the model between chemostatic behavior and chemodynamic behavior. As opposed to the typically used linear regression approach, our model has an additional parameter to account for hysteresis by including correlation over time. We demonstrate the advantages of our model using a high-frequency data series of nitrate concentrations collected with in situ analyzers in a catchment in Germany. Furthermore, we identify event-based optimal scheduling rules for sampling strategies. Overall, our results show that (i) our model is much more robust for estimating the export regime than the usually used regression approach, and (ii) sampling strategies based on extreme events (including both high and low discharge rates) are key to reducing the prediction uncertainty of the catchment behavior. Thus, the results of this study can help characterize the export regime of a catchment and manage water pollution in rivers at lower monitoring costs. We propose a simple stochastic time-series model to represent the export regime of a catchment beyond simple regression. We propose how to get the required data with the least effort when the use of high-frequency in situ analyzers is not feasible or restricted. Sampling strategies based on extreme events are essential for reducing the prediction uncertainty of the catchment behavior.

When applying environmental models, the choice of model complexity and the design of field campaigns depend on each other and on the modeling/prediction goal. We propose jointly optimizing model complexity and data collection (design) by maximizing the expected performance for the modeling goal. We use ensembles of highly resolved virtual realities and of less complex modeling variants that differ in their degrees of upscaling and simplified parameterization. For each design under consideration, we simulate hypothetical measurement data (subject to noise) with all realizations of all models. To mimic model calibration with hypothetical data, we identify pairs of best fitting realizations between virtual reality and each model variant for each design. Then, we emulate model choice by selecting (across the model variants, for each design and for each virtual reality) the pair that shows the best predictive match in the modeling goal. Finally, we identify the model/design combination that offers, on average over all virtual realities, the best predictive match. As a test application, we consider a heterogeneous, stratified aquifer, in which the stratification enhances hydraulic anisotropy on the macroscale. We define two different modeling goals: (a) estimating the hydraulic conductivity tensor upscaled to the full aquifer thickness and (b) predicting the pumping rate needed to dewater a construction pit. Our results indicate that jointly optimizing observation networks and model selection can reduce the prediction uncertainty of parameters at lower experimental costs. We also show that the involved trade‐offs between model complexity and required design depend on the target quantity.

The boundaries of surface-water catchments can be delineated by analyzing digital elevation models using geographic information systems. Surface-water divides and groundwater divides, however, might significantly differ from each other because the groundwater surface does not necessarily follow the surface topography. Hydraulic-head measurements are needed to properly delineate a groundwater divide and thereby the subsurface boundary of a catchment, but piezometers are expensive. It is therefore vital to optimize the placement of the necessary piezometers. In this work, we introduce an optimal design analysis, which can identify the best configuration of potential piezometer placements within a given set. The method is based on the formal minimization of the expected posterior uncertainty within a sampling-based Bayesian framework. It makes use of a random ensemble of behavioral steady-state groundwater flow models. For each behavioral realization we compute virtual hydraulic-head measurements at all potential well points and delineate the groundwater divide by particle tracking. We minimize the uncertainty of the groundwater-divide location by marginalizing over the virtual measurements. We test the method mimicking a real aquifer in South-West Germany. Previous works in this aquifer indicated a groundwater divide that is shifted compared to the surface-water divide. The analysis shows that the uncertainty in the localization of the groundwater divide can be reduced with each new well. A comparison of the maximum uncertainty reduction at different numbers of wells quantifies the added value of information for each new well. In our case study, the uncertainty reduction obtained by three monitoring points is maximized when the first well is close to the topographic surface water divide, the second one in the valley, and the third one in between. 

Commonly, chemical catchment regimes are described by a simple regression slope of log-concentrations versus log-discharges measured in the catchment outlet river. The slope value of these plots defines the chemical regime of a catchment. A slope=-1 corresponds to a constant contaminant release subject to dilution by rainfall (an unrealistic extreme but needed as a base flow); whereas a slope=0 means that there are chemostatic effects in the catchment or a washout of contaminants at a constant concentration. However, reality shows that actual time-series measurements of discharge and concentrations conflict with this naive representation since the measurements show temporal hysteresis that defies regression assumptions (i.e. that regression residuals must be uncorrelated). To represent this time interaction beyond regression, we design a simple stochastic time-series model that accounts for fluctuating concentration release and transport with memory. In this work, we also establish how to get the observation data required for a robust estimation of the slope with the least effort. To show the capability of our proposed model and method, we apply a retrospective optimal design of experiments to a high-frequency data series of nitrate concentration (collected by online probes) and discharge of a real catchment in Germany. We thin out the data by applying frequency and event-based monitoring strategies to find out the key components of the strategies that best predict the catchment behavior. Results indicate that our catchment under study (the Ammer catchment in southwestern Germany) is relatively close to a chemostatic type catchment and that our stochastic model, in fact, provides more accurate results for small data sets. Also, optimal data collection schemes for this purpose should be event-based, considering both high and low extremes of discharge that are spread out over time.