Data assimilation combines observational data with a numerical model. It is commonly used in numerical weather prediction, but is also applied in oceanography, hydrology and other areas of Earth system science. By integrating observations with models in a quantitative way, data assimilation allows to estimate model states with reduced uncertainty, e.g. to initialize model forecasts. Also, data assimilation can estimate parameters that control processes in the model or fluxes, which can be difficult or impossible to measure. As such, data assimilation can use observations to provide information about non-observable quantities if the model represents those. The combination of modelled and observed data requires error estimates for both sources of information. In ensemble data assimilation the error in the model state is estimated by an ensemble of model state realizations. This ensemble not only provides estimates of uncertainties, but also of cross-correlations between different model variables or parameters. The uncertainty estimate from the ensemble is then used by the assimilation method, and the most widely known is the ensemble Kalman filter.

To simplify the implementation and use of ensemble data assimilation, the Parallel Data Assimilation Framework - PDAF - has been developed. PDAF is a freely available open-source software (http://pdaf.awi.de) that provides ensemble-based data assimilation methods like the ensemble Kalman filter, but also allows to perform pure ensemble simulations. PDAF is designed such that it can be used from small toy problems running on notebook computers up to high-dimensional Earth system models running on supercomputers.

This course is both for the novices as well as for data-assimilation experts. It will be useful for novices who have a modelling application and observations and are interested in applying data assimilation, but haven't found a starting point yet. Data-assimilation experts who want to enhance the performance of their applications, or are keen to accelerate development of new data-assimilation methods and new applications will also benefit from the course.

The course will first provide an introduction to the ensemble data assimilation methodology. Then, it will explain the implementation concept of PDAF and finally provide a hands-on example of building a data assimilation system based on a numerical model. This practical introduction will prepare the participants to build a data assimilation system for their numerical models with PDAF and hence provide a quick start for applying ensemble data assimilation to their individual problems.

Participants are invited to bring their own notebook computer to run the hands-on examples themselves. For this, a Fortran compiler and the BLAS and LAPACK libraries are required. Matlab or Python would also be handy for plotting. Given the overall limited capacity of the Wifi network during the conference, it is recommended that you download PDAF from http://pdaf.awi.de before the short course if you like to do the hands-on example on your own notebook computer.

Public information:
Apart from the description above, we will provide in the Short Course a version of PDAF which only includes the relevant features for the hand-on examples and that does not require to register on the PDAF web site. If you like to run the hands-on example it would also be useful if you have OpenMPI installed (or any other MPI library), but there will also be an example that does not require MPI.

State estimation theory in geosciences is commonly referred to as data assimilation. This term encompasses the entire sequence of operations that, starting from the observations of a system, and from additional statistical and/or dynamical information (such as an evolution model), provides the best possible estimate of its state. Data assimilation is common practice in numerical weather prediction but its application is becoming widespread in many other areas of climate, atmosphere, ocean and environment modelling; in all those circumstances where one intends to estimate the state of a large dynamical system based on limited information. While the complexity of data assimilation, and of the methods thereof, stands on its interdisciplinary nature across statistics, dynamical systems and numerical optimisation, when applied to geosciences an additional difficulty arises by the, constantly increasing, sophistication of the environmental models.

This overview course is aimed at geoscientists, who are confronted with the model-to-data fusion issue and would benefit from the application of data assimilation techniques, but so far have not delved into their conceptual and methodological complexities.

The course will provide first the formulation of the problem from a Bayesian perspective and will then present the two popular families of Gaussian based approaches, the Kalman-filter/-smoother and the variational methods. Ensemble based methods will then be considered, starting from the well known Ensemble Kalman filter, in its stochastic or deterministic formulation, and then the state-of-the-art ensemble-variational methods.
The course will focus on the specific challenges that data assimilation has encountered to deal with high-dimensional chaotic systems, such as the atmosphere and ocean, and the countermeasures that have been taken and which have driven the dramatic development of the field experienced in the last decades.
It will then conclude by presenting some of the nowadays active lines of development and current challenges, including coupled data assimilation and the particle filters.

Research, especially for early career scientists, starts with the spark of an idea and is then often challenged by empirical or methodological road bumps and seemingly dead ends. A diverse range of challenges face those in earth science research, particularly for early career scientists (ECS). Challenges include (1) access difficulties, whether for field sites, equipment or data, (2) problems of scaling and extrapolation and (3) a lack of methodological understanding or knowledge. In this short course, we will raise engaging discussions, which aim to solve challenges, suggest new research approaches and methods, and encourage networks and possibilities for in-depth discussions amongst early career scientists at international conferences.

This short course will start with 2 minute ‘pop-up’ presentations outlining the questions or challenges submitted by attendees. These pop-ups are followed by chaired group discussions in which short course participants engage to crowd solve the presented challenges. To wrap up the session, solutions and suggestions from each topical group are presented to the whole session in a final discussion. A summary on last years’ crowd solving efforts can be found in the EGU GM blog post https://blogs.egu.eu/divisions/gm/2018/04/25/diving-under-the-scientific-iceberg/.

This short course lives by your input: i) by stating a research idea or challenge you would like to share, and ii) by participating in the discussion during the short course. To organize and prepare the discussions, please send a short statement of your idea or challenge related to geomorphic research, and your motivation for solving it (3-4 sentences) to geomorph-problems@geographie.uni-bonn.de, by March 1, 2019. The contributions within the short course are free of charge. If you want to discuss a specific problem, but rather stay anonymous, please let us know. We are all early career scientists and expect a non-hierarchic, respectful and constructive environment for the discussions, which will hopefully go some way to identifying and engaging with problems which face ECS geomorphologists.

Session organizers: Anne Voigtländer, Johannes Buckel, Eleanore Heasley, Felix Nieberding, Liseth Perez, Anna Schoch, Harry Sanders, Richard Mason,...

Public information:
We encourage meeting up before the short course during the Networking Time ~18h - so grab another drink and join us near room -2.62!

Overview of the short course:

This short course discusses a new approach for deriving stochastic fluid equations which describe the slow large-scale characteristics of GFD without having to resolve the small fast scales accurately via very costly high-resolution direct numerical simulations. Instead, we discuss parametrising the small fast scales by using a new approach based on the concept of stochastic transport, rather than stochastic diffusion.

Stochastic advection by Lie transport (SALT) -- Darryl D Holm

In this course, we introduce a class of stochastic fluid equations, whose smooth solutions are characterized by natural extensions of the Kelvin theorems of their deterministic counterparts, which hold along certain noisy flows. These equations are called the \emph{stochastic Euler--Poincar\'{e}} and \emph{stochastic Navier-Stokes--Poincar\'{e}} equations respectively. The stochastic Euler--Poincar\'{e} equations were previously derived from a stochastic variational principle by Holm in
(Holm, D. D.: Variational principles for stochastic fluid dynamics. Proc. R. Soc. A 471.2176 (2015): 20140963)
which we will briefly review.
Solutions of these equations do not obey pathwise energy conservation/dissipation in general. In contrast, we also discuss a class of stochastic fluid models, solutions of which possess energy theorems but do not, in general, preserve circulation theorems.


Stochastic modeling under location uncertainty (LU)-- Etienne M\'emin

In this lecture, we will describe a formalism to systematically derive large-scale stochastic representations of fluid flows dynamics which take into account the inherent uncertainty attached to the flow evolution. The uncertainty introduced here is described through a random field, and aims at representing principally the small-scale effects that are neglected in the large-scale evolution model. The resulting large-scale dynamics is built from a stochastic representation of the Reynolds transport theorem. This formalism enables, in the very same way as in the deterministic case, a physically relevant derivation (i.e. from the usual conservation law) of the sought evolution laws. We will in particular show how to derive stochastic representations of geophysical flow dynamics and reduced order stochastic dynamical systems. We will give several examples of computational simulations obtained from such systems and how they can be used in different contexts.


Particle Filters for Data Assimilation -- Dan Crisan

Particle filters are a set of probabilistic algorithms used to solve filtering problems arising in signal processing and Bayesian statistical inference. Their area of applicability is currently being extended to solve high dimensional problems such as those encountered in data assimilation problems for numerical weather prediction. The lecture will contain an elementary introduction to particle filters with emphasis on their applicability to such problems. I will discuss the specific difficulties encountered when applying particle filters to high dimensional problems as well as procedures required for their successful implementation. I will cover model reduction (high to low resolution), tempering, jittering, uncertainly quantification and initialization.

We will explain how stochastic transport rather than diffusion provides the balance between spread and accuracy that is needed for data assimilation method using particle filtering to be successful.
The running example covered in the lectures will be an application to a partially observed solution of a damped and driven incompressible 2D Euler equation with stochastic advection by Lie transport (SALT).

Public information:
This short course discusses a new approach for deriving stochastic fluid equations which describe the slow large-scale characteristics of geophysical fluid dynamics without having to resolve the small fast scales accurately via very costly high-resolution direct numerical simulations. Instead, we discuss parametrising the small fast scales by using a new approach based on the concept of stochastic transport, rather than stochastic diffusion. The following broad topics will be covered:

Stochastic advection by Lie transport (SALT) -- Darryl D Holm

In this section, we introduce a class of stochastic fluid equations, whose smooth solutions are characterized by natural extensions of the Kelvin theorems of their deterministic counterparts, which hold along certain noisy flows. These equations are called the stochastic Euler--Poincare and stochastic Navier-Stokes--Poincare equations respectively. The stochastic Euler--Poincare equations were previously derived from a stochastic variational principle by Holm in (Holm, D. D.: Variational principles for stochastic fluid dynamics. Proc. R. Soc. A 471.2176 (2015): 20140963) which we will briefly review. Solutions of these equations do not obey pathwise energy conservation/dissipation in general. In contrast, we also discuss a class of stochastic fluid models, solutions of which possess energy theorems but do not, in general, preserve circulation theorems.


Stochastic modeling under location uncertainty -- Etienne Memin

In this section, we will describe a formalism to systematically derive large-scale stochastic representations of fluid flows dynamics which take into account the inherent uncertainty attached to the flow evolution. The uncertainty introduced here is described through a random field, and aims at representing principally the small-scale effects that are neglected in the large-scale evolution model. The resulting large-scale dynamics is built from a stochastic representation of the Reynolds transport theorem. This formalism enables, in the very same way as in the deterministic case, a physically relevant derivation (i.e. from the usual conservation law) of the sought evolution laws. We will in particular show how to derive stochastic representations of geophysical flow dynamics and reduced order stochastic dynamical systems. We will give several examples of computational simulations obtained from such systems and how they can be used in different contexts.


Particle Filters for Data Assimilation -- Dan Crisan

Particle filters are a set of probabilistic algorithms used to solve filtering problems arising in signal processing and Bayesian statistical inference. Their area of applicability is currently being extended to solve high dimensional problems such as those encountered in data assimilation problems for numerical weather prediction. The section will contain a brief introduction to particle filters with emphasis on their applicability to such problems. I will discuss the specific difficulties encountered when applying particle filters to high dimensional problems as well as procedures required for their successful implementation. I will cover model reduction (high to low resolution), tempering, jittering, uncertainly quantification and initialization.

I will also explain how stochastic transport rather than diffusion provides the balance between spread and accuracy that is needed for data assimilation method using particle filtering to be successful. Two examples will be covered in the lectures: one to a partially observed solution of a damped and driven incompressible 2D Euler equation and one to a partially observed solution of a two-layer quasi-geostrophic equation both with stochastic advection by Lie transport (SALT).

This short course aim is to present FREEWAT platform for water management. The FREEWAT plugin (QGIS-based platform) is conceived as a canvas, where several codes (mainly belonging to the USGS MODFLOW family) for the simulation of the hydrological cycle, hydrochemical or economic processes, are integrated in the QGIS desktop and where large spatial datasets can be stored, managed and visualized. This aims at evaluating hydrologic balance and assessing groundwater status and availability to support planning and management activities.
The objective of this short course is to present FREEWAT modules and to demonstrate some of its capabilities through performing an exercise.

The course will consist in a short theoretical introduction followed by tutorials and exercises. The plugin executable will be provided (and available also in the official QGIS repository of experimental plugins) along with manuals and data for exercises. Participants to this course will work on their own laptop and they will be provided with all the necessary material prior the beginning of the conference.

Participants are invited to bring their own laptop to follow the examples. To follow properly the course you can install QGIS and then, from its internal experimental repository, you can download FREEWAT. Training material can be downloaded from the FREEWAT website: http://www.freewat.eu/download-information.

See you at the EGU2019!!

Public information:
This short course aim is to present FREEWAT platform for water management. The FREEWAT plugin (QGIS-based platform) is conceived as a canvas, where several codes (mainly belonging to the USGS MODFLOW family) for the simulation of the hydrological cycle, hydrochemical or economic processes, are integrated in the QGIS desktop and where large spatial datasets can be stored, managed and visualized. This aims at evaluating hydrologic balance and assessing groundwater status and availability to support planning and management activities.
The objective of this short course is to present FREEWAT modules and to demonstrate some of its capabilities through performing an exercise.

The course will consist in a short theoretical introduction followed by tutorials and exercises. The plugin executable will be provided (and available also in the official QGIS repository of experimental plugins) along with manuals and data for exercises. Participants to this course will work on their own laptop and they will be provided with all the necessary material prior the beginning of the conference.

Participants are invited to bring their own laptop to follow the examples. To follow properly the course you can install QGIS and then, from its internal experimental repository, you can download FREEWAT. Training material can be downloaded from the FREEWAT website: http://www.freewat.eu/download-information.

See you at the EGU2019!!

Satellite data provides information on the marine environment that can be used for many applications – from water quality and early warning systems, to climate change studies and marine spatial planning. The most modern generation of satellites offer improvements in spatial and temporal resolution as well as a constantly evolving suite of products.

Data from the European Union Copernicus programme is open and free for everyone to use however they wish - whether from academic, governance, or commercial backgrounds. The programme has an operational focus, with satellite constellations offering continuity of service for the foreseeable future. There is also a growing availability of open source tools that can be used to work with this data.

This short course is an opportunity to learn about the data available from the Copernicus Sentinel 3 satellite, and then, with support from marine Earth Observation experts, to develop your own workflows for using data from the EUMETSAT Copernicus Marine Data Stream and Copernicus Marine Environment Monitoring Service. The sessions will be interactive, using the WeKEO DIAS hosted processing, Sentinel Applications Platform (SNAP) software, and Python programming. No experience is necessary as various exercises will be provided for a wide range of skill levels and applications, however participants should bring their own laptops and be prepared to install open source software in advance.

Nowadays, researchers have to tailor their models, data and results into systems which can be used by non-experts, such as policy makers, stakeholders, farmers and the many professionals in need of clear answers to land management questions.

One way ahead to bridge the gap between R&D and real-life applications is the development of decision support systems (DSS) on top of geospatial cyberinfrastructures (GCI) that can handle end-user requests in real time with all the complexity being transparent to the user.

The short course will cover some developments carried out within the EU H2020 LandSupport Project. The implementation of an indicator of land-take is showed, both presenting how to deal with the technical steps on a more general level and proposing hands-on sessions on the implementation of specific components of the whole land-take workflow.

First an introduction is presented, covering a general overview about the GCI and the requirements of pipelines.
A brief description of the main tasks follows:

• Big spatio-temporal raster data are managed by means of rasdaman. Here a workflow is presented showcasing how to import and query multi-band Sentinel-2 data based on the OGC Big Data Standards.
• Cloud masking and filtering. Copernicus Sentinel-2 data are processed to obtain bottom of the atmosphere, cloud free, reflectance data. A theoretical and a hands-on session in R will be presented.
• Classification. A spectral-temporal datacube of Sentinel-2 data are used to get a binary map of imperviousness (1: urban pixel, 0: non-urban pixel). At least one classification model will be presented with hands-on in R and/or MatLab.
• Land-take. An algorithm to calculate land-take using a low-level programming language is showed, with more advanced insights about the opportunity to face GPU calculations.

Altogether, we motivate how the LandSupport approach aims at providing decision support based on multi-source spatiotemporal data in a user-centric manner.
Ample time will be available for answering questions and discussion.

Most often observations and measurements of geophysical systems and dynamical phenomena are obtained as time series. Dynamics of the system are inferred from characteristics of these time series which usually manifest a chaotic or stochastic behavior.
In this short course different approaches, based on dynamical systems theory, will be explained, including phase-space portraits, bifurcation theory, correlation dimension and entropic approaches, Langevin and Fokker-Planck equations, fractal analysis, and other concepts of nonlinear time series analysis, like recurrence quantification analysis. Methods will be illustrated in terms of recent successful applications from various fields of geosciences, ranging from climate to solar-terrestrial relations.
The focus will be on a comparison between different methods to investigate different aspects of both known and unknown physical processes.

Public information:
Peter Ditlevsen: "Dynamical system approaches: bifurcations and conceptual models"
Tommaso Alberti: "Chaotic approaches: fractals and their dimensions, self-organization, and turbulence"
Reik Donner: "Time series analysis: quantification of recurrence properties in geoscientific time series"

With the start of the SENTINEL era, a major challenge for users is the efficient extraction of valuable information from an unprecedented amount of data. To provide data products that allow scientists, commercial users and decision makers to efficiently exploit these novel data, new methods are required to estimate land surface information from data retrieval, and to provide novel approaches and data dissemination. In this view, the MULTIPLY platform enables users to synergistically combine different satellite observations (including optical and SAR) together with additional a priori knowledge to provide inferences on land surface quantities (such as leaf area index, soil moisture, radiative fluxes, pigment concentrations, etc.) , as well as provide tools for information extraction and visualisation.
The platform uses state-of-the-art physical models of radiative transfer between the atmosphere and the land surface. The models allow for a coherent interpretion of different observation types. Additional information that constrains the inversion is also included as priors, which include not only expert or database-derived estimates of parameters but also dynamic models. This results in a continuous (in space and time) stream of parameters at high resolution (10s of m) that characterise the land surface, together with an estimate of their uncertainties.

This course is aimed at scientists, who require consistent and gap-free retrieval of land surface parameters, but are confronted with the limitations of current state-of-art approaches. Using a mix of hands-on demonstrators with the MULTIPLY platform, as well as theoretical background information, the course will deal with
• The basic concepts behind radiative transfer models
• The integration of a priori knowledge to land surface parameter retrieval.
• Combining observations and prior information in a Bayesian retrieval scheme
The course will focus on the specific challenges of current state-of-art approaches, and show the potential of MULTIPLY as a beyond-state-of-art framework, and highlight the platform as a useful tool for ecologists, agronomists and climate scientists who require timely information about the state of the land surface.

In this short course, we present a tutorial on the basics of the daytime atmospheric boundary layer and its interaction with the land surface. The participants will use the interactive CLASS software (https://classmodel.github.io). Participants have to bring a laptop with Linux, macOS or Windows installed. Please send an email to chiel.vanheerwaarden@wur.nl in case you are interested.

The climate is highly variable over wide ranges of scale in both space and time so that the amplitude of changes systematically depends on the scale of observations. As a consequence, climate variations recorded in time series or spatial distributions, which are produced through modelling or empirical analyses are inextricably linked to their space-time scales and is a significant part of the uncertainties in the proxy approaches. Rather than treating the variability as a limitation to our knowledge, as a distraction from mechanistic explanations and theories, in this course the variability is treated as an important, fundamental aspect of the climate dynamics that must be understood and modelled in its own right. Long considered as no more than an uninteresting spectral “background”, modern data shows that in fact it contains most of the variance.
We review techniques that make it possible to systematically analyse and model the variability of instrumental and proxy data, the inferred climate variables and the outputs of GCM’s. These analyses enable us to cover wide ranges of scale in both space and in time - and jointly in space-time - without trivializing the links between the measurements, proxies and the state variables (temperature, precipitation etc.). They promise to systematically allow us to compare model outputs with data, to understand the climate processes from small to large and from fast to slow. Specific tools that will be covered include spectral analysis, scaling fluctuation analysis, wavelets, fractals, multifractals, and stochastic modeling; we discuss corresponding software.

Public information:
For the detailed programme, see:
http://www.physics.mcgill.ca/~gang/ftp.transfer/CVAS.course.synopsis.18.3-19.final.pdf

Presenting at a scientific conference can be daunting for early career scientist and established. How can you optimally take advantage of those 12 minutes to communicate your research effectively? How do you cope with nervousness? What happens if someone asks a question that you don’t think you can answer? Is your talk tailored to the audience?
Giving a scientific talk is a really effective way to communicate your research to the wider community and it is something anyone can learn to do well! This short course provides the audience with hands-on tips and tricks in order to make your talk memorable and enjoyable for both speaker and audience.

This short course is an introduction to structural and petrological geological principles, used by geologist to understand system earth. The data available to geologists is often minimal, incomplete and representative for only part of the geological history. Besides learning field techniques to acquire and measure data, geologists need to develop a logical way of thinking to close gaps in the data to understand the system. There is a difference in the reality observed from field observation and the final geological model that tells the story.

In this course we briefly introduce the following subjects:
1) Acquisition of field-data
2) From structural field data to paleostresses
3) Using petrological field data to identify tectonic phases (e.g. burial and exhumation)
4) Rock deformation - What happens in the lab?
5) Data publications and EPOS - What to do with your research data?
6) Creating geological models: how to make the story complete


Our aim is not to make you the next specialist in geology, but we would rather try and make you aware of the challenges a geologist faces when he/she goes out into the field. Also the quality of data and the methods used nowadays are addressed to give seismologists and geodynamicists a feel for the capabilities and limits of geological research. This course is given by Early Career Scientist geologists and geoscientists and forms a trilogy with the short course on ‘Geodynamics 101’ and ‘Seismology 101’. For this reason, will also explain what kind of information we expect from the fields of seismology and geodynamics and we hope to receive some feedback in what kind of information you could use from our side.

How do seismologists detect earthquakes? How do we locate them? Is seismology only about earthquakes? Seismology has been integrated into a wide variety of geo-disciplines to be complementary to many fields such as tectonics, geology, geodynamics, volcanology, hydrology, glaciology and planetology. This 90-minute course is part of the Solid Earth 101 short course series together with ‘Geodynamics 101 (A & B)’ and ‘Geology 101’ to better illustrate the link between these fields.

In ‘Seismology 101’, we will present an introduction to the basic concepts and methods in seismology. In previous years, this course was given as "Seismology for non-seismologists" and it is still aimed at those not familiar with seismology -- in particular early career scientists. An overview will be given on various methods and processing techniques, which are applicable to investigate surface processes, near-surface geological structures and the Earth’s interior. The course will highlight the role that advanced seismological techniques can play in the co-interpretation of results from other fields. The topics will include:
- the basics of seismology, including the detection and location of earthquakes
- understanding and interpreting those enigmatic "beachballs"
- an introduction to free seismo-live.org tutorials and other useful tools
- how seismic methods are used to learn about the Earth, such as for imaging the Earth’s interior (on all scales), deciphering tectonics, monitoring volcanoes, landslides and glaciers, etc...

We likely won’t turn you in the next Charles Richter in 90 minutes but would rather like to make you aware how seismology can help you in geoscience. The intention is to discuss each topic in a non-technical manner, emphasizing their strengths and potential shortcomings. This course will help non-seismologists to better understand seismic results and can facilitate more enriched discussion between different scientific disciplines. The short course is organised by early career scientist seismologists and geoscientists who will present examples from their own research experience and from high-impact reference studies for illustration. Questions from the audience on the topics covered will be highly encouraged.

With the eddy covariance (EC) technique, net fluxes of carbon dioxide (CO2) and other trace gases as well as water and energy fluxes can be measured at the ecosystem level. These flux measurements are a main source for understanding biosphere-atmosphere interactions and feedbacks by cross-site analysis, model-data integration, and up-scaling.
However, analysis of the the half-hourly data requires intensive post-processing.
The attendees get teaching and hands-on training in standard post-processing routines of estimating the u*-threshold, gap-filling, flux-partitioning, aggregating results to days, seasons, and years, and error propagation using the open REddyProc R package with a focus on CO2 fluxes.

To help design a better lecture tell us your expectations at the following survey:
https://survey3.gwdg.de/index.php?r=survey/index&sid=19&lang=en

Participants should come with a laptop with installed recent versions of R, RStudio, and REddyProc.
https://www.biogeosciences-discuss.net/bg-2018-56/
https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb

Public information:
8:30 Basic Carbon fluxes: uStar threshold, gapFilling, flux-partitioning
9:30 Uncertainty estimation and common challenges

The tenth short-course in this highly successful sequence of Fourier-focused short-courses will consider two important basic techniques for analysis of geoscience (and other) time-series with regard to periodic features. First, the Fast Fourier Transform (FFT) for equal-interval time-series. Second, the related Lomb-Scargle periodogram for unequal-interval time-series.

The FFT is a key underpinning technique of time-series analysis for the identification of periodic features. The session will overview the key properties of the FFT and the inherent constraints of discrete time-series and sampled data to provide a framework for understanding other, more advanced data-analytical techniques. The Lomb-Scargle periodogram is a least-squares spectral analysis (LSSA) technique and can be considered as a replacement for the FFT for unequal-interval time-series. The session will make the links between the Lomb-Scargle periodogram and the FFT and their common roots in the covariance of a time-series and sinusoids of given frequencies. Both techniques yield estimates of the power spectrum of the data in question and the session will include a consideration of the relationship between the power spectrum and the frequency distribution of the variance as a basis for assessing the statistical effect-size of periodic features in time-series.

Public information:
This is the tenth in a sequence of short-courses that has resulted in the book "A Primer on Fourier Analysis for the Geosciences", by Robin Crockett, Cambridge University Press. Publication 14 February 2019. https://doi.org/10.1017/9781316543818

The main goal of this short course is to provide an introduction into the basic concepts of numerical modelling of solid Earth processes in the Earth’s crust and mantle in a non-technical manner. Emphasis will be put on what numerical models are and how they work while taking into account the advantages and limitations of the different methods. We will go through the steps of building a numerical code and setting up the corresponding models, using specific examples from key papers to showcase:
(1) The motivation behind using numerical methods,
(2) The basic equations used in geodynamic modelling studies, what they mean, and their assumptions,
(3) How to choose appropriate numerical methods,
(4) How to benchmark the resulting code,
(5) How to go from the geological problem to the model setup,
(6) How to set initial and boundary conditions,
(7) How to interpret the model results.
Armed with the knowledge of a typical numerical modelling workflow, participants will then be able to better assess the use of a specific numerical model to answer their own research question.

The 90-minute short course is run by early career geodynamicists and is part of the Solid Earth 101 short course series together with Geodynamics 101B, Seismology 101 and Geology 101. It is dedicated to everyone who is interested in, but not necessarily experienced with, understanding numerical models; in particular early career scientists (BSc, MSc, PhD students and postdocs) and people who are new to the field of geodynamic modelling. The course "Geodynamics 101B: Scientific applications" focusses on the application of the numerical methods discussed in this short course to large scale dynamic processes on Earth. Discussion and questions will be greatly encouraged.

Writing a scientific paper is an essential part of research, and is a skill that needs practice. In this short course several invited scientists will advice early-career scientists on how to write a scientific paper and how to increase the chance of publishing their research.
This session is organized in cooperation with the Young Hydrologic Society (http://younghs.com/).

This year's expert panel:
Prof. Dr. Thorsten Wagener (University of Bristol)
Prof. Dr. Christine Stumpp (BOKU, University of Natural Resources and Life Sciences, Vienna)
Prof. Dr. Jan Fleckenstein (UFZ Leipzig and University of Bayreuth)

his session will discuss the ins & outs of convening or co-convening a session from proposing to a session, the promotion and abstract handling to the actual General Assembly. We will discuss what makes a good session abstract and what are your options. What happens are you suggest a session and what you can do to promote your session.

The climate system as a whole can be viewed as a highly complex thermal/heat engine, in which numerous processes continuously interact to transform heat into work and vice-versa. As any physical system, the climate system obeys the basic laws of thermodynamics, and we may therefore expect the tools of non-equilibrium thermodynamics to be particularly useful in describing and synthesising its properties. The main aim of this short course will be twofold. Part 1 will provide an advanced introduction to the fundamentals of equilibrium and non-equilibrium thermodynamics, irreversible processes and energetics of multicomponent stratified fluids. Part 2 will illustrate the usefulness of this viewpoint to summarize the main features of the climate system in terms of thermodynamic cycles, as well as a diagnostic tool to constrain the behaviour of climate models. Although the aim is for this to be a self-contained module, some basic knowledge of the subject would be beneficial to the participants. Registration is not needed, but indication of interest would be helpful for planning purposes.

Part 1 (2 hours) will have the following learning objectives:
• Equilibrium thermodynamics, master thermodynamic potentials, partial thermodynamic properties
• Interdependence of energy conservation and irreversible entropy production
• Mutually consistent definitions of heat and work in the atmosphere and oceans
• Convexity of the internal energy and the concept of exergy and available potential energy (APE). Local versus global theories of APE. Problems related to the definition and construction of reference states and of the ‘environment’.
• Standard and non-standard theories of irreversible processes. Are all irreversible processes necessarily dissipative? Irreversibility parameter.
• Non-equilibrium theory of sensible and latent heat fluxes at the air-sea interface, reversible and irreversible phase changes.
• Theories for the thermodynamic efficiency of the atmospheric and oceanic heat engines: APE versus entropy-based Carnot approaches. Does humidity really make the atmospheric heat engine less efficient? Maximum work versus maximum power.
• Exact partitions of potential energy into sign-definite components. Applications to exact mean/eddy partitions. Concepts of local baroclinic life cycle.

Part 2 (1 hour) will illustrate practical applications rooted in recent research and will cover topics such as:
• Means of energy exchange throughout the atmosphere and in the oceans
• Representation of irreversible processes in climate models.
• Importance of extratropical eddies in shaping the meridional energy transport, and how this links to the general circulation of the atmosphere
• Link to observations, consistency of current climate models with theory. Using theory to improve climate models in the future.

The main goal of this short course is to provide an overview of the large scale dynamic processes on Earth, recent advances in the study of these processes and future directions. The course focusses on numerical methods to explain and advance our knowledge of geodynamic large scale processes, but additional constraints and insights obtained from the geological record and seismology (e.g., tomography) are also touched upon. The basic dynamics, state of the art understanding and outstanding questions of the following geodynamic processes are discussed through key papers in the field:
(1) Mantle convection
(2) The start of plate tectonics
(3) Break-up of supercontinents
(4) Subduction dynamics
(5) Crustal deformation & mountain building
Using their newfound knowledge of geodynamical processes, participants will be better able to understand and use geodynamical papers to answer their own research question.
The 90-minute short course is run by early career geodynamicists and is part of the Solid Earth 101 short course series together with Geodynamics 101A, Seismology 101, and Geology 101. It is dedicated to everyone who is interested in, but not necessarily experienced with, the large scale dynamics of the Earth; in particular early career scientists (BSc, MSc, PhD students and postdocs) and people who are new to the field of geodynamic modelling. The course "Geodynamics 101A: Numerical methods" discusses the numerical methods that are often used to solve for and study the processes outlined in this course. Discussion and questions will be greatly encouraged.

The ENES Climate Analytics Service (ECAS) is a new service from the EOSCHUB project. It enables scientific end-users to perform data analysis experiments on large volumes of climate data, by exploiting a PID-enabled, server-side, and parallel approach.
It aims at providing a paradigm shift for the ENES community with a strong focus on data intensive analysis, provenance management, and server-side approaches as opposed to the current ones mostly client-based, sequential and with limited/missing end-to-end analytics workflow/provenance capabilities.

This short course is divided into a teaching as well as a hands on training part and includes:
- presentation(s) on the theoretical and technical background of ECAS. This covers the data cube concept and its operations (eg.: subset extraction, reduction, aggregation). Furthermore, we provide an introduction to the Ophidia framework, which is the components of ECAS for processing multidimensional data.
- tutorials and training materials. Participants will have the opportunity to dive into the ECAS software stack and learn how to manipulate multidimensional data through real world use cases from the climate domain.

This short course is open to everyone interested in processing multidimensional data. ECAS is server-based, thus all required software and tools are already available on our sites. Participants do not need to install any software stack on their laptop. All they need is a browser to access the ECAS portal. Only a prior registration is required and it is straightforward by following these links: https://ecaslab.dkrz.de/registerproc.html or https://ophidialab.cmcc.it/web/registration.html

During this short course, the participants will learn:
- what the data cube concept is and how is manipulated with ECAS/Ophidia
- how to perform analysis on multidimensional data
- how to publish, access and share data and workflows with ECAS
- how to implement/deploy their own scientific workflows

Public information:
When: 10 April 2019
Where: Room -2.31

Analysis of uncertainty has been one of the overarching themes of hydrology research. With ever increasing need for quantification and communication of uncertainty, uncertainty analysis is a fundamental part of any modelling study in hydrology, e.g. flood forecasting. This short course aims to provide a state-of-the-science overview of different approaches to analysis and modelling of uncertainty. The primary focus will be given to methods in the hydro-meteorological domain.

We kindly invite early career hydrologic researchers (MSc students, PhD candidates, post-doctoral researchers) to attend this short course designed to address fundamentals of most widely adopted approaches for uncertainty analysis.

This will be the fifth year that the Hydroinformatics for Hydrology short course is run. The previous themes of the course were data-driven and hybrid techniques, data assimilation, geostatistical modelling and extreme value modelling.

Please note that a pre-registration is not necessary. The course will be open to a limited number of participants selected on a first come-first served basis.

We are delighted to announce Dr. Francesca Pianosi from University of Bristol as the lecturer of this short course.

For any additional information, please contact the conveners. In cooperation with the Young Hydrologic Society (http://younghs.com/).

You have heard of Jupyter Notebooks already? But you do not quite understand the hype about it? Then this short course is exactly for you. We will show you the beauty in working with Jupyter Notebooks and the entire Jupyter environment.

With Jupyter Notebooks you have your code, visualisation and documentation all in one place. Widgets allow the setup of interactive visualisations, where you can e.g. include leaflet maps into your notebooks.
JupyterLab and JupyterHub provide the right working environments to create and host your Jupyter Notebooks and collaborate with others.

This short course will introduce you to Jupyter Notebooks and give you practical examples how environmental data (meteorological data and satellite images) can be analysed. After a general introduction to Jupyter Notebooks, we will give you examples how you are able to access large volumes of meteorological and satellite data from data repositories, such as ECMWF, and cloud environments, such as the Copernicus Climate Data Store or Google Earth Engine. We will analyse and interactively visualise the data with Jupyter widgets. Towards the end, we will introduce you to JupyterLab and JupyterHub, to better understand the full Jupyter environment.

The course will be structured as follows:
- Jupyter Notebooks - Data analysis made simple
- Data access with Jupyter Notebooks from different data repositories
- Jupyter widgets - Make your data analysis interactive
- Jupyterlab, JupyterHub, … - Getting to know the Jupyter environment

This short course is hands-on and you can bring your laptop along. All exercises are designed to be easy to follow. The Jupyter Notebooks of this course will be made available after the course.

Public information:
This short course will introduce you to Jupyter Notebooks and give you practical examples how environmental data (meteorological data and satellite images) can be analysed. After a general introduction to Jupyter Notebooks, we will give you examples how you are able to access large volumes of meteorological and satellite data from data repositories, such as ECMWF, and cloud environments, such as the Copernicus Climate Data Store (CDS) or Google Earth Engine (GEE). We will analyse and interactively visualise the data with Jupyter widgets. Towards the end, we will introduce you to JupyterLab and JupyterHub, to better understand the full Jupyter environment.

Image analysis has become a standard tool for shape and fabric analysis of a wide range of rock types (sedimentary, magmatic and metamorphic) and for microstructure analysis of natural and experimental samples at all scales. From quantified shape fabrics, rock properties may be inferred and related to the processes that created them.

In the first half of the short course, some basic techniques are outlined, in the second half, there will be demonstrations of selected applications.

The following topics will be covered:
1) image acquisition and pre-processing
2) segmentation: from picture to bitmap
3) shape analysis of individual grains or particles
4) fabric and strain analysis: looking at volumes and surfaces
5) analysis of spatial distribution: from clustered to random to ordered

Demonstrations will be made using ImageJ and Image SXM. Note, however, that familiarity with either of these programs is not required. - This is a short course, not a workshop.

Please send email if you want to participate (renee.heilbronner@unibas.ch)

Satellite-based climate data records play an increasing role in climate monitoring and help to answer climate-related questions. Nowadays satellite-based climate data records cover a time period of several decades. EUMETSAT’s Satellite Application Facilities (SAF) provide a number of high quality climate data records for various geophysical variables, such as solar radiation, land surface temperature, cloud fractional cover, cloud microphysical variables, and many more, derived from both, geostationary and polar orbiting satellites.

These climate data records are free and open to everyone. They continue to be reprocessed to account for improvements of the algorithm and to include recent time periods. In addition to the data, free software tools are developed and provided by the SAF’s for users to work with the data.

This short course in an opportunity to get an overview about the climate data records available from the EUMETSAT Satellite Application Facilities, learn how to access them and gain some first experiences in how to work with the software tools provided. Participants will have the opportunity do some hands-on exercises using the data and tools provided. Participants are also welcome to bring their own scientific questions, for which the satellite based CDR’s may help to find the answer. Data and software developers will be around to hep and answer questions.

Participants should be prepared to bring a laptop to the course, we suggest that the participants order and download data from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF, www.cmsaf.eu); technical support in visualizing and analysing the data and will be provided by using the CM SAF R-Toolbox (https://www.cmsaf.eu/EN/Products/Tools/R/R_node.html); however, the participants are invited to use other software tools that enable the reading of netcdf-files as well.

A small breakfast will be served to all course participants.

Public information:
We would appreciate your registration via: https://bit.ly/2GOQ2yp
A small breakfast will be served to all course participants.

This course is aimed at anyone who wants to better understand the origin of physical anisotropy in rocks. The principles and methods learned in the course can be applied to any anisotropy that is described by tensors and depends on the bulk properties of a sample rather than being dominated by grain boundary properties. As such, this course is relevant for researchers working in a range of fields, including those investigating seismic anisotropy, magnetic fabrics, or anisotropy of thermal conductivity.
We will discuss the intrinsic anisotropy of single crystals, the interplay of crystallographic preferred orientation and single crystal anisotropy to control the anisotropy in rocks, and give an introduction to how anisotropic physical properties can be predicted in rocks, including an introduction to the freely available Matlab toolbox MTex.
Participants will leave the course with a thorough and detailed understanding of factors controlling anisotropy in rocks, and have the necessary background to quantitatively predict anisotropy based on their own texture datasets or demonstration data sets.

Today's demand for ad-hoc extraction and analytics goes well beyond the traditional approach of serving a bunch of files in some provider chosen format and granularity. Rather, flexibility in access, independent from low-level issues like formats, server-side data organisation (such as directories, tiling, etc.), and rich functionality for use without programming skills are among key requirements on future-oriented services that are both powerful and user-centric.

The OGC coverage concept, also adopted by ISO and INSPIRE, represents a unifying model for spatio-temporal regular and irregular grids, point clouds, and general meshes. Based on this data model, the OGC Web Coverage Service (WCS), suite representing OGC’s “Big Data” standard, provides a streamlined service model with rich, versatile functionality.

Large and growing tool support, both open-source and proprietary, as well as the massive data offerings existing - such as the Petabyte services of the EarthServer initiative - prove feasibility and relevance of these standards. This wave of uptake is currently leading to a next level of harmonization and user-oriented service quality. However, there is still a general lack of knowledge and skills in modelling and using coverage services which makes some users and providers hesitant about choosing coverage-based software.

In this session, given by the editor of the coverage standards suite, we present the coverage data and service definitions and their use for building portals, with special emphasis on spatio-temporal datacubes. Ample room will be available for Q&A. Real-life examples will illustrate all details; online services will be used for demonstration, and participants with an Internet-connected device can rerun and modify examples.

Participants will learn about the status and direction of the coverage and datacube standards in OGC, ISO, and INSPIRE. They will understand how to model common raster data set as standards-conformant coverages, and how to serve and use these over Web services. Based on this, the audience will understand the relevance of standards for harmonized, flexible Spatial Data Infrastructures.

Public information:
- Introduction: Raster Data and Services
- Coverage Data
- Representing Raster Data as Coverages
- format encoding: from JSON over GeoTIFF to NetCDF
- Web Coverage Services (WCS)
- Easy as can be: WCS Core
- Extensible for all needs: WCS Extensions and Application Profiles
- Spatio-Temporal coverage analytics: WCPS
- Coverage data import
- Conformance testing
- OGC, ISO, and INSPIRE: standardization status, issues, trends
- Wrap-up

Recommended for: geoscientists, climate scientists, geostatisticians, engineers.

This course is an introduction to stochastic simulation using Multiple Point Statistics (MPS), a modelling approach based on the use of training images with the aim of generating realistic heterogeneity characterizing natural processes. This family of techniques has been shown to be particularly suited for preserving complex features, for example the connectivity and geometry of geological units [1], the seasonality and complex time dependence of climate time-series [2], or the small-scale variability of missing data from remote sensing images [3].

In the routine practice, MPS can be used to fill the gaps in spatial or temporal datasets, interpolate sparse data, or simulate random fields to study the uncertainty of a process outcome. We will present the theory behind MPS, demonstrate an open-source code, and give practical tutorials on how to use it.

The course will be organized in two parts: the first one is a short introduction on the theory at the base of stochastic simulation and interpolation. The second and main part is dedicated to practical cases related to time series modeling and remote sensing data.

References:

[1] dell’Arciprete, D., Bersezio, R., Felletti, F. et al., Comparison of three geostatistical methods for hydrofacies simulation: a test on alluvial sediments, Hydrogeol Journal (2012) 20: 299. https://doi.org/10.1007/s10040-011-0808-0

[2] Oriani F, Mehrotra R, Mariethoz G, Straubhaar J, Sharma A, Renard P (2017). Simulating rainfall time-series: how to account for statistical variability at multiple scales, Stochastic Environmental Research and Risk Assessment, doi: 10.1007/s00477-017-1414-z.

[3] Gaohong Yin, Gregoire Mariethoz, Ying Sun & Matthew F. McCabe (2017) A comparison of gap-filling approaches for Landsat-7 satellite data, International Journal of Remote Sensing, 38:23, 6653-6679, DOI: 10.1080/01431161.2017.1363432

Rationale:
The proper and deep education on ethical issues in geosciences has been evolving in recent times, although not as quickly and deeply as necessary. Many of the professionals dedicated to Earth Sciences have been not in touch with such new concepts and tendencies as the concept of Geoethics. Geoethics is the research and reflection on the values which underpin appropriate behaviors and practices, wherever human activities interact with the Earth system. Geoethics provides a framework from which to define ethical professional behaviors in both geosciences and engineering and to determine how these should be put into practice for the benefit of society and the environment. This Short Course goes is directed towards introducing and training geoscientists in those new concepts and ideas.
Targeted audience:
Most, if not all, of the EGU GA attendants are potential participants, although we will target, mostly, early-career practitioners and scientists, with enough basic background not to be overly challenged in these theoretical and practical issues.
Learning objectives
After completing this course, participants
1. Will know the basic principles of ethics and how these lead to geoethics.
2. Will be aware of the dilemmas involved in making geoethical decisions.
3. Will have gained some experience in taking a geoethical approach to real-world cases.
Course Content:
1. From Ethics to Geoethics: definition, values, tools.
2. Responsible conduct of research and professionalism.
3. Tools for Confronting (geo)ethical dilemmas.
4. Geoethics for society: sustainable development and responsible mining.
5. Geoethics in natural hazards.
6. Geoethics in geoscience communication.
Time Schedule:
2-time blocks (1 hour 45 min x 2).
Preference for the afternoon a day after session Session EOS5.2 - Geoethics: ethical, social and cultural implications of geoscience knowledge, education, communication, research and practice. It could be one block overlapping afternoon not EOS5.2 poster sessions (from 15:45 up to 17:30) followed for the second slot after 17:30 the same day. Monday and Friday cannot be used.

Proposed Schedule and lecturers (backups considered but not listed):
First Block:
1. From Ethics to Geoethics: definition, values, tools. SILVIA PEPPOLONI
2. Responsible conduct of research and professionalism. VITOR CORREIA
3. Tools for Confronting (geo)ethical dilemmas. EDUARDO MARONE
Second Block:
4. Geoethics for society: sustainable development and responsible mining. JAN BOON
5. Geoethics in natural hazards. GIUSEPPE DI CAPUA
6. Geoethics in geoscience communication. NIC BILHAM

Public information:
Proposed Schedule and lecturers:
First Block:
1. From Ethics to Geoethics: definition, values, tools. SILVIA PEPPOLONI
2. Responsible conduct of research and professionalism. VITOR CORREIA
3. Tools for Confronting (geo)ethical dilemmas. EDUARDO MARONE
Second Block:
4. Geoethics for society: sustainable development and responsible mining. JAN BOON
5. Geoethics in natural hazards. GIUSEPPE DI CAPUA
6. Geoethics in geoscience communication. NIC BILHAM

How do you peer-review? Apparently you are just supposed to miraculously know. Many of us never receive formal training in peer review, yet our peer-reviews are the cornerstone of scientific legitimacy. Constructive, respectful, coherent reviews nurture dialogue and advance research. So, how can we review papers in an efficient way? In this course, we suggest a process to help ensure that we give the authors the most useful feedback? We will hear from peer-review experts about how they go about the process and have an open discussion with the audience.

The course aims at introducing attendees to models that are able to spatially predict "where" and "how many" landslide may trigger in the future. No complex equations will be shown. We will focus instead on practically generate such models in a step-by-step tutorial. A dataset as well as the required R-code will be shared during the course to allow everyone to test the method in his/her laptop. No prior knowledge of R is required, just install it before joining the class. The course will essentially cover most of the analyses shown in https://arxiv.org/pdf/1807.08513.pdf.

So, you've been given a time series, e.g, of hourly precipitation. That's great, but how can you generate as many as you like with exactly the same statistical properties? In this short course you'll find out.

You'll be introduced to a unified method of stochastic modelling and downscaling that makes feasible the generation of time series that preserve any desired marginal probability distribution and correlation structure including features like intermittency. The workshop includes a rapid introduction in the stochastic properties of hydroclimatic processes like precipitation, flooding, wind, temperature, etc., and highlights features like stationarity, cyclostationarity, marginal distributions, correlations structures and intermittency. We'll develop and apply on-the-spot and step-by-step: (a) the iconic AR(1) model, (b) higher order AR models as a method to approach arbitrary correlations structures; (c) the parent-Gaussian framework to simulate time series with any marginal distribution and correlation; and (d) intermittent time series modelling (like precipitation) at any time scale.

Early Career Scientists (ECS) are specifically welcome, and of course, this short course is organized in cooperation with the Young Hydrologic Society (YHS; younghs.com)!

Past climate and environmental data provide critical tests of global and regional climate models. While there are a small number of high-profile records, such as the Greenland ice cores, which are critical for informing on the dynamic nature of past climate change, determining the nature of regional to local scale climate impacts is key to understanding the complexities of climate change. Terrestrial records (lakes, speleothems, peat, etc.) provide valuable information on how local or regional climate conditions changed and – in some cases – how local ecosystems responded to the changes. However, integrating various types of terrestrial together and/or along with marine records in a regional paleoclimate study hampers a deeper understanding of the processes and feedbacks active in the climate system. For example, when records from neighbouring locations are precisely compared, it is possible to identify possible leads and lags between the records and to set up time lines of events for past periods of climate change. Time lines like these are of important to understand the dynamics of the climate system because they are the starting points for making hypotheses about not only the dynamics, but the mechanisms, of past climate change, adding to our understanding of the ice-sea-atmosphere interactions and feedbacks during periods of abrupt and extreme change. A invited speaker in the field of paleoclimatology and from the INTIMATE network, Prof. Achim Brauer, will provide :
i) a general overview on how various terrestrial records in a regional paleoclimate study are generally integrated,
ii) what are the common problems generated from an integrated paleoclimate study : interpretation of proxy data, disentangling different climate signals, temporal sensitivity of proxies to climatic change, the value of qualitative terms.
iii) solutions proposed such as the establishment of protocols for comparing records based upon precise chronologies, statistical tools for comparing records on related timescales and new methods for incorporating temporal uncertainties involved in inter-site correlations.
This introductory short course is addressed to all scientists involved in paleoclimate research and using various types of records. Registration is not needed, but indication of interest would be helpful for planning purposes

Machine learning (ML) is a well-established approach to complex data analysis and modelling in different scientific fields and in many practical applications. Nowadays, ML algorithms are widely used as efficient tools in GI Sciences, remote sensing, environmental monitoring and space-time forecasting. The short course gives an overview of ML algorithms widely applied in data exploration and modelling of high dimensional and multivariate geoscientific data. The main topics of the course, presented within the framework of a generic data-driven methodology of modelling, include detection of patterns and predictability, feature selection, unsupervised, supervised and active learning, visual analytics. Real case studies consider environmental pollution, natural hazards and renewable energy resources assessments.

LSDTopoTools (https://lsdtopotools.github.io) is an open source software package used to analyse landscapes, with applications in geomorphology, ecology, hydrology, soil science and planetary science. The primary aims of the software are to enable efficient, reproducible analysis of high resolution topographic data and to support the development and implementation of novel analysis techniques. During the course, attendees will gain hands on experience performing common analyses on provided topographic datasets, learn about more advanced techniques provided by the software and will have the opportunity to discuss their research with lead developers and users of LSDTopoTools.

This short course will cover:

- The principles of reproducible topographic analysis
- The calculation of simple topographic metrics
- The extraction and analysis of channel networks from high resolution topographic data
- Publication quality visualisation of analysis results

By the end of the course attendees will:

- Have a working version of LSDTopoTools on their personal laptop, ready to be used for their own research
- Understand the benefits of making topographic analysis more reproducible
- Be able to run topographic analyses on their own datasets
- Be able to visualise the results of these analyses without commercial software

Attendees must bring a laptop and are not required to have any programming experience, although familiarity with a command line shell would be beneficial.

Grain size or grain size distributions (GSDs) play a major role in many fields of geoscience research. Paleopiezometry is based on the relation between grains size and flow stress. Environments of depositions have typical GSDs. Time temperature and grain size have characteristic relations during static grain growth. Fracture processes are associated with the fractal dimension of the GSD they produce, etc.. In all these cases, meaningful interpretations rest on the correct acquisition and quantification of grain size data.

The aim of this short course is to discuss with participants the following questions

1) when do we need grain size analysis ? what is it good for ? what are the limitations ?
2) how do we identify grains? what are the criteria for segmentation?
3) how do we define reliable measures for grain size ?
4) what do we mean by 'mean grain size' ?
5) how much data do we need ?
6) and what about errors ?

Handouts will be available in electronic form.

Please send email if you want to participate (renee.heilbronner@unibas.ch)

Research projects can be very messy. They start from an idea which then becomes a proposal and (hopefully) turns into a funded project which needs to be implemented and reported to the funding agency. Somewhen along the project lifetime it’s easy to lose track of the tasks and then get buried in paperwork when reporting time comes, especially if you are an early-career scientist with little or no experience in project management.
In this short course, experienced research project managers will share tips and tricks on how to successfully manage your scientific project like a pro. The course will cover the phases of a project lifetime, from concept to closure. The course will also offer an overview of some popular tools to keep track of deadlines, budget, risks and communications. Finally, the short course will also provide templates and guidelines for plans, meetings and report.

Why attend?

When the course was offered the first time at EGU18, it was attended by 80+ participants, most of whom early career scientists. The feedback was very positive with participants stating it was “clear and very informative”, “interesting” and with “many useful tips”, and that they “would recommend the session further to colleagues”. We have collected the participants’ suggestions and now offer an improved version of the same course with a more diverse training team covering a wide spectrum of expertise in project management.

Who is this training course for?

If you’re a scientist with no background in management this course is for you, as you will learn how to apply project management principles to a wide variety of research projects from field-trips to large international collaborations.
If you’re an early-career scientist this course is great to get a good grasp of the effort necessary to run scientific projects and learn how you can make your academic life easier from the start with smart, easy-to-use tools and templates.
If you’re an experienced research project manager we’d love to hear about your work and for you to share your tips and lessons learnt with us.

Number of expected participants: 80-90

Tracer techniques and solute transport models are frequently used to quantify the temporary detainment of solutes in hyporheic and surface storage zones. The physical process of "transient storage" has implications for a wide variety of constituents as the storage process affects residence time and the extent of biogeochemical processing. This 2-hour workshop provides an overview of the hydrologic processes underlying the OTIS solute transport model (One-dimensional Transport with Inflow and Storage), and how these processes are represented in the stream transport equations. Emphasis will be placed on fundamental concepts such as experimental design, data evaluation, and parameter estimation using tracer techniques. Beginner to intermediate model users are encouraged to attend. Additional information on OTIS is available at http://water.usgs.gov/software/OTIS/. The workshop will be presented by Rob Runkel, a Research Hydrologist from the U.S. Geological Survey. Please contact Rob at runkel@usgs.gov if you plan to attend the workshop.

Important note: Due to time constraints, the step-by-step OTIS example that was previously described on the EGU website will not be presented.

Public information:
Quantifying Solute Transport in Streams: An Overview of the Hydrologic Processes Underlying the OTIS Solute Transport Model

Tracer techniques and solute transport models are frequently used to quantify the temporary detainment of solutes in hyporheic and surface storage zones. The physical process of "transient storage" has implications for a wide variety of constituents as the storage process affects residence time and the extent of biogeochemical processing. This 2-hour workshop provides an overview of the hydrologic processes underlying the OTIS solute transport model (One-dimensional Transport with Inflow and Storage), and how these processes are represented in the stream transport equations. Emphasis will be placed on fundamental concepts such as experimental design, data evaluation, and parameter estimation using tracer techniques. Beginner to intermediate model users are encouraged to attend. Additional information on OTIS is available at http://water.usgs.gov/software/OTIS/. The workshop will be presented by Rob Runkel, a Research Hydrologist from the U.S. Geological Survey. Please contact Rob at runkel@usgs.gov if you plan to attend the workshop.

Important note: Due to time constraints, the step-by-step OTIS example that was previously described on the EGU website will not be presented.

Clouds come in all sizes, from millimetric wisps up to planetary undulations: a casual glance discloses structures within structures within structures that are constantly changing, evolving from milliseconds to the age of the earth. The structures’ collective behaviour results in variability that is so large that standard methods are utterly inadequate: in 2015, it was found that they had underestimated the variability by the factor of a million billion.
Taming such extreme variability requires physical laws that operate over enormous ranges of scales from small to large, from fast to slow. These scaling laws answer the question: “how big is a cloud?”, and they explain the origin of events that are so extreme that they have been termed “black swans”. They define a new “macroweather” regime that sits in between the weather and climate, finally settling the question: “What is Climate”? while posing another: is agriculture and hence civilization itself, the result of freak macroweather?
Scaling laws are often “universal”, so it isn’t surprising that the red planet turns out to be the statistical twin of our blue one. This new understanding of the statistics - including the black swans – enables us to close the scientific part of climate debate by statistically testing and rejecting the skeptics’ Giant Natural Fluctuation hypothesis. The scaling laws can also be used to make accurate monthly to decadal (macroweather) forecasts by exploiting an unsuspected but huge memory in the atmosphere-ocean system itself. The same scaling approach significantly reduces the large uncertainties in our current climate projections to 2050 and 2100.
This short course reviews the nonlinear geoscience behind this new understanding. This includes multifractals, generalized scale invariance, fluctuation analysis, intermittency, spectra and stochastic macroweather predictions and climate projections [Lovejoy, 2018].

Reference:

Lovejoy, S. (2018), Weather, Macroweather and Climate: our random yet predictable atmosphere, Oxford U. Press, Oxford.

Public information:
This session will focus on several topics in scale and scaling
It will be given by S. Lovejoy and F. Schmitt
A detailed synopsis may be found here:
http://www.physics.mcgill.ca/~gang/ftp.transfer/Flyer.short.course.5.4.19.pdf

Ecosystem Services (ESs) assessment is increasingly used as a decision guiding tool, with a high potentiality for many environmental impact assessment through its threefold valuation: i.e. social, biophysical and economic. ESs assessment is a way to obtain a more holistic view on a framework to bring human life to a more enhanced level of sustainability. Soil is at the heart of the assessment of ESs.
By answering the question of how the study of physical, chemical and biological processes in soil can contribute to ESs assessment, the purpose of the short-course is to review recent surveys through the eyes of the ESs user, taking stock of what we know, what we do not know, and what we need to know as soil scientist and hydrologist.
Speakers: Prof. Nunzio Romano, University of Naples, Italy, and Prof. David Ellison, Swedish University of Agriculture SLU & Ellison Consulting, Sweden.

IMPORTANT NOTICE: Please, send registration info (your name and e-mail address to Marina Karsanina: marina.karsanina@gmail.com), this is necessary to estimate the number of participants and redistribute training materials and software prior to the course!
Also note that you will need a laptop (preferably fully charged) for practical work.

Motivation: In numerous scientific areas dealing with flow and transport in porous media such as hydrology, soil and rock physics, petroleum engineering, X-ray microtomography (XCT) is the key tool to obtain information on rock/soil structure under study. If structural information is obtained, one can utilize so-called pore-scale modelling to simulate fluid flow directly in the pore space of the 3D porous media images. Even the simplest workflow to simulate single phase flow and compute permeability requires a number of steps, image processing including segmentation and solution of the Stokes equation in 3D geometry being the most critical or time consuming. Recent developments in the field of pore-scale modelling allow to perform decent simulations using a modern personal computer, but such tools are still not widespread in routine research work.

Aim: To provide an introduction and basic tools to perform all necessary steps from X-ray microtomography images to single-phase flow simulations.

Plan: 1) Introduction to 3D imaging, image processing and pore-scale modelling (20 min.); 2) Overview of available software/solutions and typical problems (10 min.); 3) Description of solutions developed by our group and available to the public (10 min.); 4) Hands-on image processing and segmentation (30 min.); 5) Hands-on single phase flow modelling (20 min.); 6) Interpretation and visualization of results (20 min.); 7) Interactive session with questions (5 min.).
For all hands-on sessions you will use free software developed by our research group (FaT iMP) and some other freely available packages. All necessary materials, including sample XCT images, will be distributed by organizers prior to the course.

What will you learn: 1) The basics of porous media imaging, 2) how to prepare and crop XCT images for pore-scale modelling, 3) how to segment images using current state-of-the-art local thresholding techniques, 4) how to simulate single phase flow and compute permeability of porous media samples from 3D images.
At the end of the course you will be able to simulate single-phase flow based on grey-scale XCT images of porous media.

Public information:
1) Introduction to 3D imaging, image processing and pore-scale modelling (20 min.); 2) Overview of available software/solutions and typical problems (10 min.); 3) Description of solutions developed by our group and available to the public (10 min.); 4) Hands-on image processing and segmentation (30 min.); 5) Hands-on single phase flow modelling (20 min.); 6) Interpretation and visualization of results (20 min.); 7) Interactive session with questions (5 min.).
For all hands-on sessions you will use free software developed by our research group (FaT iMP) and some other freely available packages. All necessary materials, including sample XCT images, will be distributed by organizers prior to the course.

IMPORTANT NOTICE: Please, send registration info (your name and e-mail address to Marina Karsanina: marina.karsanina@gmail.com), this is necessary to estimate the number of participants and redistribute training materials and software prior to the course!
Also note that you will need a laptop (preferably fully charged) for practical work.

Mapping is a fundamental process to understand landscape diversity and how it changes across different times and scales. Despite the advances in mapping methods and the availability of co-variates, several challenges arise when mapping at different scales and data is very heterogeneous. Reducing mapping error and identifying the most accurate map is still a challenge, especially in areas with a high degree of human impact. The objective of this short course is to present the most advanced techniques to model environmental variables at different scales.

8:30-8:40: Course opening.

8:40-9:10: “Pedons to Pixels: Adapting to technological advances” David Lindbo, Director, Soil Science Division at USDA-NRCS, The USA

9:10-9:40: "Soil mapping and modelling in Europe" Panos Panagos, European Commission, Joint Research Centre, Ispra, Italy

9:40-10:10: “Methods for mapping ecosystem services at multiple scales“. Miguel Villoslada, Estonian University of Life Sciences, Tartu, Estonia.

10:10-10:15: Course clausure.

This short course is supported by the project A09.3.3-LMT-K-712-01-0104 Lithuanian National Ecosystem Services Assessment and Mapping (LINESAM) is funded by the European Social Fund according to the activity “Improvement of researchers” qualification by implementing world-class R&D projects.

R is open-source, versatile and scales for analyses from just a few observations to big data and high-performance computing. Its growing, enthusiastic user-base (including hydrologists) is responsible for a continuous stream of ever more efficient and useful packages and workflows.

In this short course we wish to introduce and showcase to our peers a selection of recent developments, approaches and best practices that can be applied to data analyses in hydrology. The majority of these are readily transferred to other disciplines, hence interested participants in all fields of geoscience are welcome to join!

The course is delivered by guest lecturers with experience in flood risk modelling, streamflow and drought analyses, as well as ecohydrology. It is tailored for absolute newcomers, as well as advanced useRs, and provides a platform for open discussion. In its third installment, the course also continues to build up R resources for hydrologists that remain accessible in the future: https://github.com/hydrosoc.

This session is organised in cooperation with the Young Hydrologic Society (YHS; https://younghs.com/)

Public information:
R is open-source, versatile and scales for analyses from just a few observations to big data and high-performance computing. Its growing, enthusiastic user-base (including hydrologists) is responsible for a continuous stream of ever more efficient and useful packages and workflows.

In this short course we wish to introduce and showcase to our peers a selection of recent developments, approaches and best practices that can be applied to data analyses in hydrology. The majority of these are readily transferred to other disciplines, hence interested participants in all fields of geoscience are welcome to join!

The course is delivered by guest lecturers with experience in flood risk modelling, streamflow and drought analyses, as well as ecohydrology. Topics include:

- getting, cleaning and visualizing hydrological data
- automating data downloading and reporting
- Parallel and HPC computing for hydrologists
- developing custom apps for data exploration, analyses and visualization
- modelling of the hydrological cycle in snow dominated catchments
- open discussion and QA time

In its third installment, the course also continues to build up R resources for hydrologists that remain accessible in the future: https://github.com/hydrosoc.

This session is organised in cooperation with the Young Hydrologic Society (YHS; https://younghs.com/)

The analysis of grain-size distributions has a long tradition in sedimentology and related disciplines studying Earth surface processes. The decomposition of multimodal grain-size distributions into inherent subpopulations by grain-size end-member modelling analysis (EMMA) allows inferring the underlying sediment sources, transport, depositional and post-depositional processes.

This course aims to introduce the concept of EMMA and it fields of application. It will show and practice the major steps needed to decompose large data sets into robust grain size end-members using the EMMAgeo package in R.

Public information:
The course will be a mix of hands-on time and partly interactive information transfer. We prepared this course for enthusiasts that already have some experience with R (Do you know the difference between a matrix and a data frame? Have you installed and worked with packages?Have you already written and shared your own R scripts?).

Please make sure you have installed the latest version of R (3.5.3, March 2019) and RStudio (1.1.463).

In addition, please have installed the following packages (or simply install EMMAgeo and devtools) using install.packages("PAKCKAGENAME"):
- devtools
- EMMAgeo
- GPArotation
- limSolve
- caTools
- shiny
- matrixStats

You can find the short course materials and short course slides on

http://www.micha-dietze.de/pages/r_courses.html


Hope are fresh and prepared for a rush of information right at the beginning of the EGU 2019!


Lisa and Micha

R is probably the most important statistical computing language in academia. With more than 10,000 packages it has been extended in many directions, including a huge support for geospatial data (see https://cran.r-project.org/web/views/Spatial.html and Bivand, Pebesma, and Gómez-Rubio 2013). R’s flexibility and statistical capabilities have made it attractive for people working in Earth, planetary and space sciences and a need for geographic data science.

This course will introduce the audience to R’s geographical capabilities, building on the book Geocomputation with R (https://geocompr.robinlovelace.net/) by the workshop authors (Lovelace, Nowosad, and Muenchow 2018). It will cover four topics and provide a solid foundation for attendees to apply R to a range of geographic data:

1. R’s implementation of the two most important spatial data models - vector (Pebesma 2018) and raster (Hijmans 2017).
2. Spatial data visualization with R.
3. Bridges to dedicated GIS software such as QGIS.
4. Statistical learning with geographic data.

Understanding data models is vital for working with geographic data in R. Maps, based on the data, can display complex information in a beautiful way while allowing for first inferences about spatial relationships and patterns. R has already become a Geographic Information System (GIS) (Bivand, Pebesma, and Gómez-Rubio 2013) - a system for the analysis, manipulation and visualization of geographic data (Longley et al. 2015). However, R was not designed as a GIS, and therefore computing large amounts of geographic data in R can be cumbersome. Even more important, R is missing hundreds of geoalgorithms which are readily available in common Desktop GIS. To deal with these shortcomings R packages have been developed allowing R to interface with GIS software. As an example, we will introduce the RQGIS package (Muenchow, Schratz, and Brenning 2017) for this purpose but also comment on other R-GIS bridges such as RSAGA (Brenning, Bangs, and Becker 2018) and rgrass7 (Bivand 2017). We will use RQGIS to compute terrain attributes (catchment area, catchment slope, SAGA wetness index, etc.) which we will subsequently use to model and predict spatially landslide susceptibility with the help of statistical learning techniques such as GLMs, GAMs and random forests (James et al. 2013). Hence, we show by example how to combine the best of two worlds: the geoprocessing power of a GIS and the (geo-)statistical data science power of R. The short course will consist of a mixture of presentations, live code demos and short interactive exercises if time allows.

Learning objectives
By the end of this workshop, the participants should:

- Know how to handle the two spatial data models (vector and raster) in R.
- Import/export different geographic data formats.
- Know the importance of coordinate reference systems.
- Be able to visualize geographic data in a compelling fashion.
- Know about geospatial software interfaces and how they are integrated with R (GEOS, GDAL, QGIS, GRASS, SAGA).
- Know about the specific challenges when modeling geographic data.

Software requirements
1. Latest version of R and RStudio
2. R packages: sf, raster, RQGIS, RSAGA, spData, tmap, tidyverse, mlr
3. QGIS (including SAGA and GRASS), please follow our installation guide (http://jannes-m.github.io/RQGIS/articles/install_guide.html) to make sure that RQGIS can work with QGIS

References
Bivand, Roger. 2017. Rgrass7: Interface Between GRASS 7 Geographical Information System and R. https://CRAN.R-project.org/package=rgrass7.

Bivand, Roger S., Edzer Pebesma, and Virgilio Gómez-Rubio. 2013. Applied Spatial Data Analysis with R. 2nd ed. New York: Springer.

Brenning, Alexander, Donovan Bangs, and Marc Becker. 2018. RSAGA: SAGA Geoprocessing and Terrain Analysis. https://CRAN.R-project.org/package=RSAGA.

Hijmans, Robert J. 2017. Raster: Geographic Data Analysis and Modeling. https://CRAN.R-project.org/package=raster.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani, eds. 2013. An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics 103. New York: Springer.

Longley, Paul, Michael Goodchild, David Maguire, and David Rhind. 2015. Geographic Information Science & Systems. Fourth edition. Hoboken, NJ: Wiley.

Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2018. Geocomputation with R. The R Series. CRC Press.

Muenchow, Jannes, Patrick Schratz, and Alexander Brenning. 2017. “RQGIS: Integrating R with QGIS for Statistical Geocomputing.” The R Journal 9 (2): 409–28.

Pebesma, Edzer. 2018. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal. https://journal.r-project.org/archive/2018/RJ-2018-009/index.html.

R is a free and open software that gained paramount relevance in data science, including fields of Earth sciences such as climatology, hydrology, geomorphology and remote sensing. R heavily relies on thousands of user-contributed collections of functions tailored to specific problems, called packages. Such packages are self-consistent, platform independent sets of documented functions, along with their documentations, examples and extensive tutorials/vignettes, which form the backbone of quantitative research across disciplines.

This short course focuses on consolidated R users that have already written their functions and wish to i) start appropriately organizing these in packages and ii) keep track of the evolution of the changes the package experiences. While there are already plenty of introductory courses to R we identified a considerable gap in the next evolutionary step: writing and maintaining packages.

The course covers:
- reasons for building packages,
- the general package structure and their essential elements,
- efficient ways to write and document functions,
- adding and documenting example data sets and examples,
- approaches to checking, building and sharing packages,
- versioning of packages using git and GitHub.

The course is open to everyone who is interested in R and whose experiences go beyond basic scripting. Participants should be able to answer the following questions right away: What is the difference between data type and data structure? How do matrices differ from lists? How are S4-objects indexed and how are lists indexed? What is the difference between lapply() and mapply()? What are the functions missing(), on.exit() and return() good for?

People. Stakeholders. Other humans. If any of these may be involved in your work, and insight into what they may think or do could be useful, you are entering the realm of Social Science. This session is on the basics of social science methods, presented by geo-scientists with some experience of implementing Social Science investigations.

The content will include a selection from; data collection techniques, expectations from analysis, risk & ethics, and data storage. At least, there will be enough to demystify Social Science, and to get you started on an investigation. The focus will be on practicalities and examples from the published literature.

Examples of areas in which Social Science methods may be needed include 'Knowledge Exchange' - the process of co-designing, co-working, collaborating, and generally engaging with non-academic partners. Anything where you may need to formally report views of colleagues (e.g., expert elicitation).

AFTER the session, course materials will be available on the following link for a few weeks.
https://drive.google.com/drive/folders/1qOWhKgGxnLR3D-tZAkg8BbN_p1sOkuuQ?usp=sharing

Public information:
Social Science methods for natural scientists will run from 14:00 to 15:30, and comprise a single coherent course that is best experienced as a whole. So, please turn up at the start. Large parts are participatory, but absolutely no prior knowledge or experience of doing social science research is needed (indeed this is our working assumption).

The sessions structure is as follows:-

• 0. Introductions
• 1. Demystifying the concept of social science
• 2. Outlets & modes of publication
• 3. The basics: Ethics and doing …… (interactive & participatory)
• 4. Simple but useful: Mind-maps and dots – (interactive & participatory)
• 5. Selection of vignettes ….. i.e. examples of social science done by natural scientists.
• 6. Summary list of top tips

Resilience has been an increasingly popular theme in disaster research due to its implications on both policy and practice in terms of reducing the negative consequences of disasters. From a social science perspective, research on the conceptualization and promotion of resilience of communities to disasters is considered highly valuable as individuals are an important actor in disaster risk management. Within this context, psychological insight into social aspects of resilience have a great potential to inform work in this field. Particularly, understanding the psychological processes involved in risk perception and preparedness of individuals and communities has helped to delineate how resilience can be promoted. This knowledge is especially important for disaster risk management as it also involves the pre-disaster phases mitigation and preparedness.

This short course aims to introduce early career scientists as well as various stakeholders (including civil society and policy makers) to the social aspects of disaster resilience with a focus on risk perception and preparedness. Particularly, psychosocial theories and/or models on risk perception and preparedness behaviors at the individual level will be explained to better understand how people perceive and respond to disasters. In order to facilitate the interest of the participants, a mini-exercise and discussion will be conducted at the beginning of the session. Upon introduction of the theories and/or models, the topic will be further elaborated by giving an overview of the research findings of various psychology studies. During the session, there will also be an opportunity to discuss how to incorporate the social aspects of disaster resilience and their implications for risk communication and disaster risk management activities.

Participation of early career scientists as well as those interested in the social aspects of disaster resilience is highly encouraged. The short course is open to everyone.

The short course is organized in cooperation with NhET (Natural hazard Early career scientists Team).

In times of climate change, current debates about carbon dynamics make waves in both the science and policy community. Several international policy frameworks* spearhead global efforts to streamline state governments, industry, and civil society into agreements for a sustainable development while mitigating climate change. The contribution of science to this process is critical to better prepare, implement, and measure the ambitious goals. Geoscientists from all fields are welcome to join this debate at the science-policy interface.

We will start with a scientific introduction on a topic of increasing focus in the policy-sphere; land and soil carbon dynamics, highlighting recent findings on carbon fluxes, whether it be source or sink. After discussing how these relate with policy guidelines, from our second speaker we will learn how scientific findings enter the policy arena, how policy organizations work, and why targeted-reports are crucially important for policy-makers. Our third speaker will present on how policies are turned into agreements at national or regional scales. To conclude, in an open discussion, the keynote speakers and audience will have the opportunity to discuss how the policy frameworks can boost science, which burning research needs are missing out, and how to explore career opportunities, especially for early career scientists. During the discussion, the expertise of the audience will be crowdsourced in an exercise on how to get involved and integrate your research ideas into policy-making decisions.

* like the Sustainable Development Goals, the Intergovernmental Panel on Climate Change, the 4 per 1000 Initiative and others

Public information:
Speakers:
- Scientific perspective by Prof. Dr. Claire Chenu (AgroParisTech)
- European science-policy interface by Panos Panagos, PhD MBA (European Commission)
- IPCC science-policy interface by Chris Lennard, PhD (University of Cape Town, lead author chapter 2 IPCC Special Report on Land and Climate)
- Policy end users by Rebecca Hood-Nowotny, PD MBA Ph.D. (BOKU)

Fires are a complex phenomenon that may generate a chain of responses and processes that affect each part of the ecosystem. Some ecosystems need fire to be sustainable. Soils are a crucial element of the environment and are the base for forest development. Thus, is important to understand the magnitude of the impacts of fire on soil properties and the response of plants to this disturbance. Soils are a poor conductor, thus the direct impacts of fire on soils are limited to the first centimetres. These impacts are especially important after high severity fires as a consequence of the high temperatures reached and the high consumption of organic matter. In this case, the direct impacts of fire can last more time comparing to low and moderate fire severities. Post-fire and indirect impacts on soil depend on fire history, ash properties, topography, post-fire weather, topography, vegetation recuperation and post-fire management. Vegetation recuperation depends also very much from the impacts of fire on soil. The aim of this course is to give an overview of fire impacts on soil properties, the latest methods that we used to assess it and contribute to building proper management guidelines for managers.

10:45-10:55: Presentation of the book "Fire impacts on soil properties"

10:55-11:25: "Fire in northern boreal forests - effects on biogeochemical cycles"
Kajar Koster, University of Helsinki, Finland

11:25-11:50: "Effects of prescribed fires on soil and plant ecosystems" Manuel Lucas Borja, University of Castilla y la Mancha, Spain

11:50-12:20: "Ash and soils. A close relationship in fire-affected areas" Paulo Pereira, Mykolas Romeris University, Lithuania

12:20-12:30: Course clausure

This course is supported by the POSTFIRE Project (CGL2013-47862-C2-1 and 2-R) and the POSTFIRE_CARE Project (CGL2016-75178-C2-2-R) sponsored by the Spanish Ministry of Economy and Competitiveness and AEI/FEDER, EU. This course is also supported by the Academy of Finland project BOREALFIRE (294600, 307222).

Publication in Open Access is gradually becoming the norm. EGU has been fostering Open Access journals since 2001. But our journals go beyond open access publication. We provide an Open Discussion Forum for open review, open discussion and transparent evaluation. We also foster the objective of open science, whereby all relevant data are shared openly
with the community. This Short Course is meant for potential authors in the EGU journals to discuss the procedures and advantages of our open access publishing and the general move to Open Science. We'll also provide general tips and ways for scientists to get involved.