Abstract. Hutton (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. We replied that to achieve this, hydrologists shouldn't ‘re-invent the water wheel’ but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as BMI) to do their computational science (Hut, 2017). With this paper and the associated release of the eWaterCycle platform and software package1 we are putting our money where our mouth is and provide the hydrological community with a ‘FAIR by design’ platform to do our science. eWaterCycle is a platform that separates the experiment done on the model from the model code. In eWaterCycle hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, TopoFlex HBV, MARRMoT and WALRUS. While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates this manuscript includes five case studies: from a simple ‘Hello World’ where only a hydrograph is generated to a complex coupling of models in different languages. In this manuscript we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydologist who want to work with eWaterCycle we also provide a video explaining the platform from a users point of view. With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research fully compatible with the principles of Open Science as well as FAIR science.1available on Zenodo: doi.org/10.5281/zenodo.5119389
Abstract. Hutton et al. (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not “re-invent the water wheel” but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science (Hut et al., 2017). With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: https://doi.org/10.5281/zenodo.5119389, Verhoeven et al., 2022), we are putting our money where our mouth is and providing the hydrological community with a “FAIR by design” (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science. The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages. In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022). With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science.
<p>Too often model evaluation has no impact on how a multi-model ensemble is analysed. It has been argued that projection and prediction uncertainties can be decreased by giving more weight to those models in multi-model ensembles that are more skillful and realistic for a specific process or application. In addition, some models in multi-model ensembles are not independent and it is not always clear how to include available initial condition ensemble members which are becoming larger in number e.g. in CMIP6.</p><p>A weighting approach has been proposed which takes into account both of these aspects (Climate model Weighting by Independence and Performance- ClimWIP) and is able to deal with included initial condition ensemble members. This approach has been shown to decrease uncertainties in multiple use cases such as projections of Arctic September sea ice, North American summer maximum temperatures, European temperature and precipitation, as well as projected global mean temperatures. Even though the basic equation to calculate a model's weight is straight forward, the user needs to make several decisions, such as which metric to use to measure performance or independence, which variables to include etc. and potential pitfalls were identified. For the actual implementation a range of points need to be considered: (1) data from different modelling centers need to be processed and compared in a consistent way, (2) the strength of the performance and independence contributions is determined through two parameters that must also be calibrated, (3) results should be provided in a form that allows backtracing to the original data and code to allow reproducability. To facilitate re-use for new applications, the method was recently implemented into the ESMValTool. We will discuss advantages and disadvantages of the method, show results from some of the use cases, explain how the implementation into ESMValTool was done and how the method can now be more easily used.</p>
<p>With the release of the ERA5 dataset, worldwide high resolution reanalysis data became available with open access for public use. The Copernicus CDS (Climate Data Store) offers two options for accessing the data: a web interface and a Python API. Consequently, automated downloading of the data requires advanced knowledge of Python and a lot of work. To make this process easier, we developed era5cli.&#160;</p><p>The command line interface tool era5cli enables automated downloading of ERA5 using a single command. All variables and options available in the CDS web form are now available for download in an efficient way. Both the monthly and hourly dataset are supported. Besides automation, era5cli adds several useful functionalities to the download pipeline.</p><p>One of the key options in era5cli is to spread one download command over multiple CDS requests, resulting in higher download speeds. Files can be saved in both GRIB and NETCDF format with automatic, yet customizable file names. The `info` command lists correct names of the available variables and pressure levels for 3D variables. For debugging purposes and testing the `--dryrun` option can be selected to return only the CDS request. An overview of all available options, including instructions on how to configure your CDS account, is available in our documentation. Source code is available on https://github.com/eWaterCycle/era5cli.</p><p>In this PICO presentation we will provide an overview of era5cli, as well as a short introduction on how to use era5cli.</p>
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