EOS5.3

Science and engineering rest on the concept of reproducibility. Yet across numerous fields like psychology, computer systems, and water resources there are great problems to reproduce research results. In this presentation, I identify reasons for low reproducibility in science. I share tools to make results more reproducible. I introduce financial incentives and awards to encourage you and our colleagues to make our research more reproducible. Finally, I advance a vision for what our future reproducible science should look like and I ask each attendee to identify and commit to take at least one step to make their research results more reproducible.

How to cite: Rosenberg, D.: Can you make your science more reproducible?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16547, https://doi.org/10.5194/egusphere-egu21-16547, 2021.

To have lasting impact on the scientific community and broader society, hydrologic research must be open, accessible, reusable, and reproducible. With so many different perspectives on and constant evolution of open science approaches and technologies, it can be overwhelming for hydrologists to start down the path towards or grow one’s own push for open research. Open hydrology practices are becoming more widely embraced by members of the community and key organizations, yet, technical (e.g., limited coding experience), resource (e.g., open access fees), and social barriers (e.g., fear of being scooped) still exist. These barriers may seem insurmountable without practical suggestions on how to proceed. Here, we propose the Open Hydrology Principles to guide individual and community progress toward open science. To increase accessibility and make the Open Hydrology Principles more tangible and actionable, we also present the Open Hydrology Practical Guidelines. Our aim is to help hydrologists transition from closed, inaccessible, not reusable, and not reproducible ways of conducting scientific work to open hydrology and empower researchers by providing information and resources to equitably grow the openness of hydrological sciences. We provide the first version of a practical open hydrology resource that may evolve with open science infrastructures, workflows, and research experiences. We discuss some of the benefits of open science and common reservations to open science, and how hydrologists can pursue an appropriate level of openness in the presence of barriers. Further, we highlight how the practice of open hydrology can be expanded. The Open Hydrology Principles, Practical Guide, and additional resources reflect our knowledge of the current state of open hydrology and we recognize that recommendations and suggestions will evolve. Therefore, we encourage hydrologists all over the globe to join the open science conversation by contributing to the living version of this document and sharing open hydrology resources at the community-supported repository at open-hydrology.github.io.

How to cite: Hall, C., Saia, S., Popp, A., Schymanski, S., Drost, N., Dogulu, N., van Emmerik, T., Hut, R., and Melsen, L.: A Hydrologist’s Guide to Open Science, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-605, https://doi.org/10.5194/egusphere-egu21-605, 2021.

How to cite: Reinecke, R., Trautmann, T., Wagener, T., and Schüler, K.: A Community Perspective on Research Software in the Hydrological Sciences, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-801, https://doi.org/10.5194/egusphere-egu21-801, 2021.

The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte datasets. Datasets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence (AI/ML) are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to the data without specialized cloud computing knowledge. Practically, for scientists, the effect of these changes is to vastly shrink the amount of time spent acquiring and processing data, freeing up more time for science. This shift in paradigm is lowering the threshold for entry, expanding the science community, and increasing opportunities for collaboration, while promoting scientific innovation, transparency, and reproducibility. These changes are increasing the speed of science, broadening the possibilities of what questions science can answer, and expanding participation in science.

How to cite: Gentemann, C., Holdgraf, C., Abernathey, R., Crichton, D., Colliander, J., Kearns, E., Panda, Y., and Signell, R.: Science storms the cloud, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1005, https://doi.org/10.5194/egusphere-egu21-1005, 2021.

The virtual research environment V-FOR-WaTer aims at simplifying data access for environmental sciences, fostering data publications and facilitating data analyses. By giving scientists from universities, research facilities and state offices easy access to data, appropriate pre-processing and analysis tools and workflows, we want to accelerate scientific work and facilitate the reproducibility of analyses.

The prototype of the virtual research environment consists of a database with a detailed metadata scheme that is adapted to water and terrestrial environmental data. Present datasets in the web portal originate from university projects and state offices. We are also finalising the connection of V-FOR-WaTer to GFZ Data Services, an established repository for geoscientific data. This will ease publication of data from the portal and in turn give access to datasets stored in this repository. Key to being compatible with GFZ Data Services and other systems is the compliance of the metadata scheme with international standards (INSPIRE, ISO19115).

The web portal is designed to facilitate typical workflows in environmental sciences. Map operations and filter options ensure easy selection of the data, while the workspace area provides tools for data pre-processing, scaling, and common hydrological applications. The toolbox also contains more specific tools, e.g. for geostatistics and soon for evapotranspiration. It is easily extendable and will ultimately also include user-developed tools, reflecting the current research topics and methodologies in the hydrology community. Tools are accessed through Web Processing Services (WPS) and can be joined, saved and shared as workflows, enabling more complex analyses and ensuring reproducibility of the results.

How to cite: Strobl, M., Azmi, E., Hassler, S. K., Mälicke, M., Meyer, J., and Zehe, E.: V-FOR-WaTer: A Virtual Research Environment for Environmental Research, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3356, https://doi.org/10.5194/egusphere-egu21-3356, 2021.

The eWaterCycle platform (https://www.ewatercycle.org/) is a fully Open Source system designed explicitly to advance the state of Open and FAIR Hydrological modelling. While working with Hydrologists to create a fully Open and FAIR comparison study, we noticed that many ad-hoc tools and scripts are used to create input (forcing, parameters) for a hydrological model from the source datasets such as climate reanalysis and land-use data. To make this part of the modelling process better reproducible and more transparent we have created a common forcing input processing pipeline based on an existing climate model analysis tool: ESMValTool (https://www.esmvaltool.org/). 

Using ESMValTool, the eWaterCycle platform can perform commonly required preprocessing steps such as cropping, re-gridding, and variable derivation in a standardized manner. If needed, it also allows for custom steps for a hydrological model. Our pre-processing pipeline directly supports commonly used datasets such as ERA-5, ERA-Interim, and CMIP climate model data, and creates ready-to-run forcing data for a number of Hydrological models.

Besides creating forcing data, the eWaterCycle platform allows scientists to run Hydrological models in a standardized way using Jupyter notebooks, wrapping the models inside a container environment, and interfacing to these using BMI, the Basic Model Interface (https://bmi.readthedocs.io/). The container environment (based on Docker) stores the entire software stack, including the operating system and libraries, in such a way that a model run can be reproduced using an identical software environment on any other computer.

The reproducible processing of forcing and a reproducible software environment are important steps towards our goal of fully reproducible, Open, and FAIR Hydrological modelling. Ultimately, we hope to make it possible to fully reproduce a hydrological model experiment from data pre-processing to analysis, using only a few clicks.

How to cite: Drost, N., Aerts, J. P. M., Alidoost, F., Andela, B., Camphuijsen, J., van de Giesen, N., Hut, R., Hutton, E., Kalverla, P., van den Oord, G., Pelupessy, I., Smeets, S., Verhoeven, S., and van Werkhoven, B.: Towards Open and FAIR Hydrological Modelling with eWaterCycle, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7797, https://doi.org/10.5194/egusphere-egu21-7797, 2021.

Good scientific practice requires good documentation and traceability of every research step in order to ensure reproducibility and repeatability of our research. However, with increasing data availability and ability to record big data, experiments and data analysis become more complex. This complexity often requires many pre- and post-processing steps that all need to be documented for reproducibility of final results. This poses very different challenges for numerical experiments, laboratory work and field-data analysis. The platform Renku (https://renkulab.io/), developed by the Swiss Data Science Center, aims at facilitating reproducibility and repeatability of all these scientific workflows. Renku stores all data, code and scripts in an online repository, and records in their history how these files are generated, interlinked and modified. The linkages between files (inputs, code and outputs) lead to the so-called knowledge graph, used to record the provenance of results and connecting those with all other relevant entities in the project.

We will discuss here several use examples, including mathematical analysis, laboratory experiments, data analysis and numerical experiments, all related to scientific projects presented separately. Reproducibility of mathematical analysis is facilitated by clear variable definitions and a computer algebra package that enables reproducible symbolic derivations. We will present the use of the Python package ESSM (https://essm.readthedocs.io) for this purpose, and how it can be integrated into a Renku workflow. Reproducibility of laboratory results is facilitated by tracking of experimental conditions for each data record and instrument re-calibration activities, mainly through Jupyter notebooks. Data analysis based on different data sources requires the preservation of links to external datasets and snapshots of the dataset versions imported into the project, that is facilitated by Renku. Renku also takes care of clear links between input, code and output of large numerical experiments, our last use example, and enables systematic updating if any of the input or code files are changed.

These different examples demonstrate how Renku can assist in documenting the scientific process from input to output and the final paper. All code and data are directly available online, and the recording of the workflows ensures reproducibility and repeatability.

How to cite: Krieger, L., Nijzink, R., Thakur, G., Ramakrishnan, C., Roskar, R., and Schymanski, S.: Repeatable and reproducible workflows using the RENKU open science platform, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7655, https://doi.org/10.5194/egusphere-egu21-7655, 2021.

Reproducibility and re-usability of research requires giving access to data and numerical code but, equally importantly, helping others to understand how inputs, models and outputs are linked together. Jupyter Notebooks is a programming environment that dramatically facilitates this task, by enabling to create stronger and more transparent links between data, model and results. Within a single document where all data, code, comments and results are brought together, Jupyter Notebooks provide an interactive computing environment in which users can read, run or modify the code, and visualise the resulting outputs. In this presentation, we will explain the philosophy that we have applied for the development of interactive Jupyter Notebooks for two Python toolboxes, iRONS (a package of functions for reservoir modelling and optimisation) and SAFE (a package of functions for global sensitivity analysis). The purposes of the Jupyter Notebooks are two: some Notebooks target current users by demonstrating the key functionalities of the toolbox (‘how’ to use it), effectively replacing the technical documentation of the software; other Notebooks target potential users by demonstrating the general value of the methodologies implemented in the toolbox (‘why’ use it). In all cases, the Notebooks integrate the following features: 1) the code is written in a math-like style to make it readable to a wide variety of users, 2) they integrate interactive results visualization to facilitate the conversation between the data, the model and the user, even when the user does not have the time or expertise to read the code, 3) they can be run on the cloud by using online computational environments, such as Binder, so that they are accessible by a web browser without requiring the installation of Python. We will discuss the feedback received from users and our preliminary results of measuring the effectiveness of the Notebooks in transferring knowledge of the different modelling tasks.

How to cite: Peñuela, A. and Pianosi, F.: Creating a more fluent conversation between data, model and users through interactive Jupyter Notebooks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7755, https://doi.org/10.5194/egusphere-egu21-7755, 2021.

Open Science has established itself as a movement across all scientific disciplines in recent years. It supports good practices in science and research that lead to more robust, comprehensible, and reusable results. The aim is to improve the transparency and quality of scientific results so that more trust is achieved, both in the sciences themselves and in society. Transparency requires that uncertainties and assumptions are made explicit and disclosed openly. 
Currently, the Open Science movement is largely driven by grassroots initiatives and small scale projects. We discuss some examples that have taken on different facets of the topic:

• The software developed and used in the research process is playing an increasingly important role. The Research Software Engineers (RSE) communities have therefore organized themselves in national and international initiatives to increase the quality of research software.
• Evaluating reproducibility of scientific articles as part of peer review requires proper creditation and incentives for both authors and specialised reviewers to spend extra efforts to facilitate workflow execution. The Reproducible AGILE initiative has established a reproducibility review at a major community conference in GIScience.
• Technological advances for more reproducible scholarly communication beyond PDFs, such as containerisation, exist, but are often inaccessible to domain experts who are not programmers. Targeting geoscience and geography, the project Opening Reproducible Research (o2r) develops infrastructure to support publication of research compendia, which capture data, software (incl. execution environment), text, and interactive figures and maps.

At the core of scientific work lie replicability and reproducibility. Even if different scientific communities use these terms differently, the recognition that these aspects need more attention is commonly shared and individual communities can learn a lot from each other. Networking is therefore of great importance. The newly founded initiative German Reproducibility Network (GRN) wants to be a platform for such networking and targets all of the above initiatives. GRN is embedded in a growing network of similar initiatives, e.g. in the UK, Switzerland and Australia. Its goals include 

• Support of local open science groups
• Connecting local or topic-centered initiatives for the exchange of experiences
• Attracting facilities for the goals of Open Science 
• Cultivate contacts to funding organizations, publishers and other actors in the scientific landscape

In particular, the GRN aims to promote the dissemination of best practices through various formats of further education, in order to sensitize particularly early career researchers to the topic. By providing a platform for networking, local and domain-specific groups should be able to learn from one another, strengthen one another, and shape policies at a local level.

We present the GRN in order to address the existing local initiatives and to win them for membership in the GRN or sibling networks in other countries.

How to cite: Fritzsch, B. and Nüst, D.: German Reproducibility Network - a new platform for Open Science in Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14724, https://doi.org/10.5194/egusphere-egu21-14724, 2021.

Setting up earth system models can be cumbersome and time-consuming. Model-agnostic tasks are typically the same regardless of model used and include definition and delineation of the modeling domain and preprocessing of forcing data and parameter fields. Model-specific tasks include conversion of preprocessed data into model-specific formats and generation of model inputs and run scripts. We present a workflow that includes both model-agnostic and model-specific steps needed to set up the Structure for Unifying Multiple Modeling Alternatives (SUMMA) anywhere on the planet, with the goal of providing a baseline SUMMA set up that can easily be adapted for specific study purposes. The workflow therefore uses open source data with global coverage to derive basin delineations, climatic forcing, and geophysical inputs such as topography, soil and land use parameters. The use of open source data, an open source model and an open source workflow that relies on established software packages results in transparent and reproducible scientific outputs, open to verification and adaptation by the community. The workflow substantially reduces model configuration time for new studies and paves the way for more and stronger scientific contributions in the long term, as it lets the modeler focus on science instead of set up.

How to cite: Knoben, W., Gharari, S., and Clark, M.: Facilitating reproducible science: a workflow for setting up SUMMA simulations anywhere on the globe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3091, https://doi.org/10.5194/egusphere-egu21-3091, 2021.

The scope of this work is to present new insights of the GEOframe system. GEOframe is an open-source, semi-distributed, component-based hydrological modeling system. It is developed in Java and in Python and based on the environmental modeling framework Object Modeling System V3 (OMS3). Each part of the hydrological cycle is implemented in a self-contained building block, commonly called component. Components can be joined together to obtain multiple modeling solutions that can accomplish from simple to very complicated tasks. More than 50 components are available for the estimation of all the variables of the hydrological cycle. Starting from the geomorphic and DEM analyses, GEOframe allows the spatial interpolation of the meteorological forcing data, the simulation of the radiation budget, the estimation of the ET and of the snow processes. Runoff production is performed by using the Embedded Reservoir Model (ERM) or a combination of its reservoirs. Model parameters can be calibrated using two algorithms and several objective functions. The graph-based structure, called NET3, is employed for the management of process simulations. NET3 is designed using a river network/graph structure analogy, where each HRU is a node of the graph, and the channel links are the connections between the nodes. In any NET3 node, a different modeling solution can be implemented and nodes (HRUs or channels) can be connected or disconnected at run time through scripting.  Thanks to its solid informatics infrastructure and physical base, GEOframe proved a great flexibility and a great robustness in several applications, from small to big scale catchments. GEOframe is open source, is chain of development is based on open source products, and its codes are engineered to be inspectionable. This because it helps the reproducibility and replicability of research. Developers and users can easily collaborate, share documentation, and archive examples and data within the GEOframe community. We believe that these are a priori condition to verify the reliability and the robustness of models. GEOframe modular structure allows for the fair comparison of model structure units and algorithms implementations because just the component performing that specific task has to be changed. In this contribution we list the components available and discuss some applications at different scales whit different modeling tools which return what we think realistic results. We show that there exist no perfect model of a process but that the modelling art and science can  be made more evolutionary even when they are revolutionary. 

How to cite: Rigon, R., Bancheri, M., Formetta, G., Serafin, F., Bottazzi, M., Tubini, N., and D'Amato, C.: The GEOframe system: a modular, expandible, open-source system for doing hydrology by computer according to the open science paradigms., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7070, https://doi.org/10.5194/egusphere-egu21-7070, 2021.

Inverse problems lie at the core of many geophysical algorithms, from earthquake and exploration seismology, all the way to electromagnetics and gravity potential methods.

In 2018, we open-sourced PyLops, a Python-based framework for large-scale inverse problems. By leveraging the concept of matrix-free linear operators – together with the efficiency of numerical libraries such as NumPy, SciPy, and Numba – PyLops solves computationally intensive inverse problems with high-level code that is highly readable and resembles the underlying mathematical formulation. While initially aimed at researchers, its parsimonious software design choices, large test suite, and thorough documentation render PyLops a reliable and scalable software package easy to run both locally and in the cloud.

Since its initial release, PyLops has incorporated several advancements in scientific computing leading to the creation of an entire ecosystem of tools: operators can now run on GPUs via CuPy, scale to distributed computing through Dask, and be seamlessly integrated into PyTorch’s autograd to facilitate research in machine-learning-aided inverse problems. Moreover, PyLops contains a large variety of inverse solvers including least-squares, sparsity-promoting algorithms, and proximal solvers highly-suited to convex, possibly nonsmooth problems. PyLops also contains sparsifying transforms (e.g., wavelets, curvelets, seislets) which can be used in conjunction with the solvers. By offering a diverse set of tools for inverse problems under one unified framework, it expedites the use of state-of-the-art optimization methods and compressive sensing techniques in the geoscience domain.

Beyond our initial expectations, the framework is currently used to solve problems beyond geoscience, including astrophysics and medical imaging. Likewise, it has inspired the development of the occamypy framework for nonlinear inversion in geophysics. In this talk, we share our experience in building such an ecosystem and offer further insights into the needs and interests of the EGU community to help guide future development as well as achieve wider adoption.

How to cite: Ravasi, M., da Costa Filho, C. A., Vasconcelos, I., and Vargas, D.: Developing open-source tools for reproducible inverse problems: the PyLops journey, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5772, https://doi.org/10.5194/egusphere-egu21-5772, 2021.

Probabilistic predictions provide crucial information regarding the uncertainty of hydrological predictions, which are a key input for risk-based decision-making. However, they are often excluded from hydrological modelling applications because suitable probabilistic error models can be both challenging to construct and interpret, and the quality of results are often reliant on the objective function used to calibrate the hydrological model.

We present an open-source R-package and an online web application that achieves the following two aims. Firstly, these resources are easy-to-use and accessible, so that users need not have specialised knowledge in probabilistic modelling to apply them. Secondly, the probabilistic error model that we describe provides high-quality probabilistic predictions for a wide range of commonly-used hydrological objective functions, which it is only able to do by including a new innovation that resolves a long-standing issue relating to model assumptions that previously prevented this broad application.  

We demonstrate our methods by comparing our new probabilistic error model with an existing reference error model in an empirical case study that uses 54 perennial Australian catchments, the hydrological model GR4J, 8 common objective functions and 4 performance metrics (reliability, precision, volumetric bias and errors in the flow duration curve). The existing reference error model introduces additional flow dependencies into the residual error structure when it is used with most of the study objective functions, which in turn leads to poor-quality probabilistic predictions. In contrast, the new probabilistic error model achieves high-quality probabilistic predictions for all objective functions used in this case study.

The new probabilistic error model and the open-source software and web application aims to facilitate the adoption of probabilistic predictions in the hydrological modelling community, and to improve the quality of predictions and decisions that are made using those predictions. In particular, our methods can be used to achieve high-quality probabilistic predictions from hydrological models that are calibrated with a wide range of common objective functions.

How to cite: Hunter, J., Thyer, M., Kavetski, D., and McInerney, D.: An open-source R-package and web application for high-quality probabilistic predictions in hydrology, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8549, https://doi.org/10.5194/egusphere-egu21-8549, 2021.

An efficient method to solve a significant weakness in hydrological modelling to compute backwater effects in low lying catchments is presented. The re-usable and transferable method is implemented in the open source software KalypsoNA (KalypsoHydrology) and validated with results of a tidal influenced low lying catchment study. 
Especially in low lying (marshy) catchments, the pressure on current storm water drainage systems raises due to combined impacts of enlarged urbanisation on the one hand and mean sea level rise and heavy storm events on the other hand. Models are applied to analyse and assess the resulting consequences by these impacts on the flood routing along a stream using different hydrological approaches: (i) pure black box (namely empirical, lumped), (ii) hydrological conceptual or (iii) hydrodynamic-numerical approaches. The computation of flow depths, velocities and backwater effects in streams as well as on forelands are not yet modelled with hydrological approaches, but using simplified hydrodynamic-numerical approaches. A requirement for accurate hydrodynamic-numerical modelling is high resolution data of the topography of the main channel and the flood plain in case of bank overflow. Hence, the availability of suitable detailed profile data from measurements is crucial for hydrodynamic-numerical modelling. The comparatively long computing time for hydrodynamic-numerical model simulations is no limitation for answering special research questions, but it poses a limitation in real-time operational application and for meso to regional scale catchment modelling (>100 km²). 
To resolve the shortcomings in hydrological approaches to model water depths and backwater effects, new concepts are required which are applicable for catchments with scarce data availability, efficient for real-time operational model application, open for further model developments and re-useable for other hydrological model implementations.
This contribution presents the development, implementation and evaluation of a method for modelling backwater effects based on a hydrological flood routing approach and a backwater volume routing according to the water level slope. The developed method computes the backwater effects in two steps. First, the inflow from sub-catchments and the non-backwater affected flood routing processes are computed. Secondly, the afflux conditions are calculated which cause backwater effects in upstream direction. Afflux conditions occur mainly at tributary inlets or control structures (for example, tide gates, weirs, retention ponds or sluices). The input parameters comprise simplified or complex geometrical data per stream segment. Therefore, the model is applicable for catchments with a good or scarce availability of data. Computation time is in the range of max 3 minutes even for large catchments (> 150 km² with several sub- and sub-sub-catchments) using a time step size of 15 minutes for a 14 days simulation and is therefore applicable for real-time operational simulations in flood forecasting. 
The proposed method is re-useable and transferable to other hydrological numerical models which use conceptual hydrological flood routing approaches (e.g. Muskingum-Cunge or Kalinin-Miljukov). The open source software model KalypsoHydrology and the calculation core KalypsoNA are available at https://sourceforge.net/projects/kalypso/ and http://kalypso.wb.tu-harburg.de/downloads/. Open access for developments and user application is supported by an online accessible commitment management via SourceForge and a wiki as an online manual.

How to cite: Hellmers, S., Sauer, C., and Fröhle, P.: Computation of backwater effects in low lying (marshland) catchments – a re-usable and efficient method in an open source hydrological model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11714, https://doi.org/10.5194/egusphere-egu21-11714, 2021.

Evapotranspiration (ET) is a major component of the hydrological cycle and accurate estimates of the flux are important to the water and agricultural sector, among others. Due to difficulties in the direct observation of ET in the field, the flux is often estimated from other meteorological data using empirical formulas. There is a wide variety of such formulas, with different levels of input data and parameter requirements. While some Python packages are available in the Python ecosystem for these tasks, they typically focus on one specific formula or data type. The goal of PyEt is to provide a Python package for the estimation of ET that works with many different data types, is well documented and tested, and simple to use. The source code is hosted at GitHub (https://github.com/phydrus/PyEt) and Pypi can be used to install the package. PyEt currently contains nine different methods to estimate ET and various methods to estimate surface and aerodynamic resistance. The methods are tested against other open source data to ensure proper functioning of the methods. While the methods currently are only implemented for 1D data (e.g. time series data), future work will focus on enabling the methods on 2D and 3D data as well (such as Numpy Arrays, XArray, and NetCDF files). The package allows hydrologists to compute and compare evapotranspiration estimates using different approaches with minimum effort. The presentation will focus on the problems associated with reproducibility in ET estimation and linkage with existing Python libraries to perform complex sensitivity and uncertainty analyses.

How to cite: Vremec, M. and Collenteur, R.: PyEt - a Python package to estimate potential and reference evapotranspiration, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15008, https://doi.org/10.5194/egusphere-egu21-15008, 2021.

On 24 November 2020, the Springer Nature publishing group announced the introduction of Open Access (OA) journals in Nature and its sibling journals. The corresponding OA publication fee (charged directly to the authors) was set to €9,500/$11,390/£8,290, an amount that may be well out of reach for researchers with limited financial means. This is especially a problem for researchers in developing countries, and for early-career researchers on small, personal fellowships. Funding agencies often demand that research be published under an OA license, forcing authors to accept the high publication fees.

The high cost of these and similar OA fees for other Earth science journals prompted a discussion among the seismological community on Twitter, during which the idea was raised to start a free-to-publish, free-to-read journal for seismological research. The concept of Diamond Open Access was previously adopted by Volcanica (www.jvolcanica.org) for volcanological research, providing a precedent and directives for similar initiatives (like Seismica, but also Tektonika for the structural geology community). Following the community discussion on Slack with over 100 participants, a small "task force" was formed to investigate in detail the possibility of starting a Diamond OA seismology journal, taking Volcanica as a model. In this contribution, we report the progress that has been made by the task force and the seismological community in the conceptualisation of the journal, and the steps that remain to be taken. Once the initiation of the journal is completed, Seismica will offer a platform for researchers to publish and access peer-reviewed work with no financial barriers, promoting seismological research in an inclusive manner. We invite all interested members of the seismological and earthquake community to participate in the discussions and development of this OA journal, by contacting the authors listed on this abstract.

How to cite: van den Ende, M., Bruhat, L., Funning, G., Gabriel, A.-A., Hicks, S., Jolivet, R., Lecocq, T., Rowe, C., and Seismological Community, T.: The Seismica initiative: a community-led Diamond Open Access journal for seismological research, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2519, https://doi.org/10.5194/egusphere-egu21-2519, 2021.

How to cite: Jex, C., Colgan, W., Fyhn, M. B. W., Garde, A. A., Ineson, J. R., Hambly, A., Hyojin, K., Koch, J., Kokfelt, T. F., Larsen, S. H., Lindström, S., Lode, S., Madsen, R. B., Olivarius, M., Saalmann, K., Salehi, S., Seidenkrantz, M.-S., Stemmerik, L., and Svennevig, K.: A recipe for launching a diamond open-access journal with a century of geological knowledge in the pantry: Lessons learned from GEUS Bulletin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7168, https://doi.org/10.5194/egusphere-egu21-7168, 2021.

τeκτoniκa is an up-coming community-led diamond open access (DOA) journal, which aims to publish high-quality research in structural geology and tectonics. It is a grass-roots community-driven initiative that relies on the involvement of Earth Scientists from around the globe; that together represent the wide and diverse spectrum of the structural geology and tectonics community. 

Beyond the obvious objective of publishing novel research on structural geology and tectonics, it is intended to offer an alternative to traditional publishing models, which hide scholarly work behind exclusive and expensive paywalls. τeκτoniκa is a new addition to the growing set of DOA journals that have appeared in recent years. Along with preprint platforms, data and software repositories, it is part of an expanding movement within academia focused on breaking the barriers inherited from the pre-internet publishing era, to ensure free and open access to knowledge.

This contribution aims to showcase the value of this ambitious project as well as our vision for how DOA journals in general (and Tektonika in particular) might shape the future of geoscience publishing.

How to cite: Gouiza, M., Fernández Blanco, D., Bond, C., McCarthy, D., Lee, A., Pérez-Díaz, L., and community, Τ.: τeκτoniκa, a journal for an open future, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11146, https://doi.org/10.5194/egusphere-egu21-11146, 2021.