Over the last decade our answer to the question: “but what is eWaterCycle?” has changed considerably. In 2014 we presented the first iteration of eWaterCycle: we showed that it was feasible to build a real time hydrological forecasting system that ran an ensemble of global models, forced with weather forecasts, assimilating satellite observations at every timestep, all from pre-existing openly available components.In the light of the discussion in the hydrological community on reproducible science [Hutton, 2016] we build the next iteration of eWaterCycle: a platform that allows everyone to use commonly available hydrological models. Years (and a pandemic) later this platform is now openly available [Hut, 2022]. The vision behind the platform is to take as much as possible the computer-related headaches of running other people’s models away to let hydrologists focus on the hydrology. Furthermore, eWaterCycle is ‘FAIR by Design’: it should be easy to make any analysis done by eWaterCycle adhere to the FAIR principals. Using eWaterCycle MSc students have been able to do the type of research that previously was done by a PhD and PhDs have done the type of research that previously would require a whole team of people. Large Sample hydrology studies, Model coupling and climate change impact studies have all been done using eWaterCycle.Adding one’s own model to the platform, however, still required considerable effort which limited the uptake by the broader hydrological community. That’s why recently we released v2.0 of eWaterCycle which fixes this: it is now significantly easier to add models to eWaterCycle!Looking forward, among other things we will be:Making teaching material on hydrological modelling available as Open Educational Resources through eWaterCycle [funded project] Adding data assimilation as a module to eWaterCycle [funded project] Add easy access to Large Sample Hydrology datasets (camels / caravan) [looking for students] Study the impact of climate change on all catchments of the world, using many different hydrological models [looking for students] Connect or host eWaterCycle on the infrastructure currently being developed for Destination Earth (DestinE) [looking for funds and collaborations] In this presentation I will reflect on the achievements of the last decade, highlight the scientific results generated with eWaterCycle and look forward to the next decade. Hutton, C., T. Wagener, J. Freer, D. Han, C. Duffy, and B. Arheimer (2016), Most computational hydrology is not reproducible, so is it really science?, Water Resour. Res., 52, 7548–7555, doi:10.1002/2016WR019285.Hut, R., Drost, N., van de Giesen, N., van Werkhoven, B., Abdollahi, B., Aerts, J., Albers, T., Alidoost, F., Andela, B., Camphuijsen, J., Dzigan, Y., van Haren, R., Hutton, E., Kalverla, P., van Meersbergen, M., van den Oord, G., Pelupessy, I., Smeets, S., Verhoeven, S., de Vos, M., and Weel, B.: The eWaterCycle platform for open and FAIR hydrological collaboration, Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, 2022.

For users of hydrological models, the suitability of models can depend on how well their simulated outputs align with observed discharge. This study emphasizes the crucial role of factoring in discharge observation uncertainty when assessing the performance of hydrological models. We introduce an ad hoc approach, implemented through the eWaterCycle platform, to evaluate the significance of differences in model performance while considering the uncertainty associated with discharge observations. The analysis of the results encompasses 299 catchments from the Catchment Attributes and MEteorology for Large-sample Studies Great Britain (CAMELS-GB) large-sample catchment dataset, addressing three practical use cases for model users. These use cases involve assessing the impact of additional calibration on model performance using discharge observations, conducting conventional model comparisons, and examining how the variations in discharge simulations resulting from model structural differences compare with the uncertainties inherent in discharge observations. Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations. Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users.

Large-sample hydrology datasets provide an excellent test-bed for evaluating and comparing hydrological models. The validity of the results from studies that use large-sample hydrology datasets, however, can be undermined when observation uncertainty is not taken into account in the analyses. The differences between model simulations might well be within the observation uncertainty bounds and are, therefore, inconclusive on model performance.To this end, we highlight the importance of including streamflow observation uncertainty when conducting hydrological evaluation and model comparison experiments based on the CAMELS-GB dataset (Coxon et al., 2015) . We introduce a generic flexible workflow that accounts for streamflow observation uncertainty, but is also applicable for other sources of observation uncertainty. This workflow is implemented in the ‘FAIR by design’ eWaterCycle platform (Hut et al., 2022). Two experiments are conducted to demonstrate the effect that streamflow observation uncertainty has on large-sample dataset based conclusions. The first experiment is an inter-model comparison experiment of the distributed PCR-GLOBWB and wflow_sbm hydrological models (Hoch et al. (2022) & van Verseveld et al. (2022)). The second experiment is an inner-model evaluation of the impact of additional streamflow based calibration on the results of the distributed wflow_sbm hydrological model. For the latter we found that approximately one third of the catchment simulations resulted in model differences that fell within the bounds of streamflow observation uncertainty.

Climate change and increases in extremes, such as heatwaves and droughts, threaten crop production and food security in various regions worldwide. Irrigation is increasingly used to secure stable yields, increasing the competition for available water resources with other sectors. To assess the vulnerability of crop production under present and future drought and heatwave events, the two-sided interactions between crop growth and hydrology should be represented by a coupled model system, combining the strength of both a crop model and a global water resource model.Our main objective, therefore, is to quantify the mutual feedback between crop production and hydrology under climate extremes (i.e., droughts and heatwaves) in various regions globally over the historical period 1990-2019. To this end, we have developed a coupled hydrological-crop model framework, coupling the PCR-GLOBWB2 water resources model to the WOFOST crop model. The coupled model framework operates on high spatiotemporal resolution (daily time step up to 5 arc minutes) to assess the two-way interaction between hydrology and crop production (maize, wheat, rice, and soybean) for irrigated and rainfed agriculture. We first established a one-way coupling to evaluate the effect of the simulated water availability in terms of soil moisture of PCR-GLOBWB2 on crop production in WOFOST. Next, we established a two-way coupling in which the vegetation dynamics of WOFOST determine the evapotranspiration, which is fed back into PCR-GLOBWB2 and affects the soil moisture status. The individual WOFOST and PCR-GLOBWB2 runs and the coupled one-way and two-way model runs were compared in terms of crop production, dynamic vegetation growth, and hydrological response. The results of our simulations will be corroborated with reported yield statistics, observed discharge data, soil moisture, evaporation data obtained from satellite remote sensing, and reported annual irrigation withdrawals to assess their validity. In addition, we will evaluate the additional variance that can be explained by the more complete process description in the coupled hydrological – crop production model framework. For example, we hypothesize that the one-way coupling overestimates the crop yields under drought-heatwave events.

In our digital age, connecting models and data from various components of the earth system becomes more and more important. To this end, it is important to define standards and interfaces facilitate such integration. Here, we present the approach we took for eWaterCycle [1]. eWaterCycle is a fully Open Source system designed explicitly to advance the state of Open and FAIR hydrological modelling. It gives users access to a centralized platform where they can perform hydrological experiments. Complete with data, a suite of models, an interactive scripting environment, and a graphical explorer to quickly setup an experiment. Hydrological models vary in programming language, their setup and configuration. They require differently defined inputs, and do not have a standard form of output. This makes it challenging to compare and exchange models and their inputs and output. eWaterCycle aims to solve this, so scientists can build upon each other’s work and focus on scientific questions instead of technical details. Inside eWaterCycle, models are wrapped in a Basic Model Interface and live in their own isolated containers. We developed grpc4bmi to enable communication with models inside their containers. This means that you can talk to different models, written in a range of programming languages, in a standardized way. ESMValTool is used to generate the meteorological forcing data with reproducible recipes. Discharge observations from the USGS and GRDC are available to validate the models. The platform comes with comprehensive documentation, including a suite of example notebooks. It also includes setup instructions for system administrators and guidance for incorporating new models. The interface between meteorology and hydrology is most prominent in the forcing generation module. Beyond that, the standards and technology used for the hydrological models in eWaterCycle can be extended to other components of the land-atmosphere continuum. [1] https://doi.org/10.5194/gmd-15-5371-2022

The eWaterCycle platform introduced in 2022 (https://doi.org/10.5194/gmd-15-5371-2022) provides hydrologists with an online platform to conduct numerical studies involving hydrological models. It allows hydrologists to work with each other's data and datasets directly from a webbrowser. The workflow of the experiment done is clearly visible, reproducible and easily adaptable because of how eWaterCycle separates the model (the algorithm) used from the experiment done with the model. eWaterCycle is designed such that research conducted on the platform is ‘FAIR by design’. Using eWaterCycle, studies can be done in less time, more transparently and by more junior members of the hydrological community than was possible a few years ago. In this presentation, we will explain the capabilities of the eWaterCycle platform and show them by describing recently (published) works of MSc and PhD members of our team, including a model coupling study, a large sample hydrology study and a climate impact assessment study.

Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, rainfall nowcasting can provide an alternative. This observation-based method, however, quickly loses skill after the first few hours of the forecast due to growth and dissipation processes that are not accounted for. In addition, providing an additional forecasting method can let users drown in the amount of available information. A promising way forward is a seamless forecasting system, which combines the aforementioned forecasting methods. By optimally combining (blending) rainfall nowcasts with NWP forecasts, we can extend the skillful lead time of short-term rainfall forecasts and provide users with more consistent, seamless forecasts.We implemented an adaptive scale-dependent ensemble blending method in the open-source pysteps library. In this implementation, the blending of the extrapolation (ensemble) nowcast, (ensemble) NWP and noise components is performed level-by-level, which means that the blending weights vary per spatial cascade level. These scale-dependent blending weights are computed from the recent skill of the forecast components, and converge to a climatological value, which is computed from a multi-day rolling window and can be adjusted to the (operational) needs of the user. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy.We evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments, focusing on both the rainfall forecasts and the resulting discharge forecasts using the fully distributed wflow_sbm hydrological model. We benchmark the results of the 48-member blended forecasts against the deterministic Belgian NWP forecast, a 48-member nowcast and a simple 48-member linear blending approach. When focusing on the resulting rainfall forecasts, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event-averaged CRPS and CSI values) and adds value compared to NWP for the first hours of the forecast. This holds for both the radar domain and catchment scale, although the difference, particularly with the linear blending method, reduces when we focus on catchment-average cumulative rainfall sums instead of instantaneous rainfall rates. We find similar results for the resulting discharge forecasts, although the effect of the catchment size and corresponding lag times becomes influential and determines the added value of nowcasting over NWP. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless hydrometeorological forecasting systems.

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: "Tipping points and thresholds in hydrology," "Intensification of the water cycle," and "Water services under pressure." Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other early-career scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement.

<p>The ERA5 meteorological reanalysis dataset, from the European Centre for Medium-Range Weather Forecasts (ECMWF), is widely used in areas such as meteorology, hydrology and land-surface modelling. The Copernicus Climate Data Store (CDS) offers two options for accessing the data: a web interface and a Python API. However, automated downloading of the data requires advanced knowledge of Python, and can prove challenging to people less familiar with programming.</p> <p>Many climate scientists have their own Python scripts to download data from the CDS, all responsible for their own creation and maintenance. A quick search for Python scripts that call the CDS API on GitHub yields 1802 results, and this is not even counting scripts stored privately. However, these are by and large not reusable. A few years ago we created <em>era5cli</em>, as a byproduct of a project we were working on, to try to break this pattern of single-use scripts. <em>era5cli </em>enables automated downloading of ERA5 data using a single command.</p> <p>It is inefficient that everyone writes their own copy of the same, or at least similar code. That why we asked ourselves whether era5cli is still filling a niche and if so, what we could do to make it easier to re-use for others. In this presentation we give an overview of our recent efforts into turning era5cli from a utility script into a reusable software package.</p> <p>Despite the relatively small size of <em>era5cli</em>, around 1000 lines of Python code and comments, maintenance is not trivial. Changes are occasionally made to ERA5 and the CDS, and new Python versions are released while old ones are deprecated. Users of era5cli have helped here by submitting fixes to issues they have found in a Github pull request, but still require guidance and/or approval of administrators. By reducing the maintenance load of <em>era5cli</em>, through targeted streamlining of the code and a clean-up of the repository, as well as adding to the developer instructions in the documentation, we lower the threshold for community contributions and successful future maintenance. With this, we aim to make <em>era5cli</em> future-proof.</p> <p><em>era5cli </em>can be installed using Python’s <em>pip</em>, as well as using conda/mamba (<em>conda install</em> <em>era5cli</em> -c <em>conda</em>-<em>forge</em><em>). The source code for era5cli is available on </em>https://github.com/eWaterCycle/era5cli, and the documentation can be found on https://era5cli.readthedocs.io/.</p>

We conduct an inner-basin evaluation of the (lateral) fluxes of the wflow_sbm model at 3km, 1km and 200m spatial resolutions the CAMELS dataset. Previous work (Aerts 2021) has shown that while the quality of streamflow predictions at basin outlets might show small differences between basins for the different model resolutions, inner basin lateral flows can differ greatly over different resolutions.In this work we study the impact of model resolution on rainfall partitioning and subsequent impact on lateral flows. To quantify terrain characteristics, we apply the method of Gharari et al. (2011) to classify parts of each basin as wetland, hillslope, or plateau. For the different model resolutions, we calculate how much rain falls on the different classifications and study lateral flow within the basin per terrain classification type.The results of this work will shed light on how models run at different resolutions have different internal lateral flows while still generating similar and adequate streamflow predictions. This insight will help in making informed decisions on what resolution to run a model at for a given problem to optimize both output and internal realism of the model estimations.This study is carried out within the eWaterCycle framework; allowing for a FAIR by design research setup that is scalable in terms of case study areas and hydrological models.

Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora of scaling-related challenges that remain unsolved. To the user, in light of model result interpretation, finer-resolution output might imply an increase in understanding of the complex interplay of heterogeneity within the hydrological system. Here we investigate spatial scaling in the form of varying spatial resolution by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model based on 454 basins from the large-sample CAMELS data set. Model instances are derived at three spatial resolutions, namely 3 km, 1 km, and 200 m. The results show that a finer spatial resolution does not necessarily lead to better streamflow estimates at the basin outlet. Statistical testing of the objective function distributions (Kling–Gupta efficiency (KGE) score) of the three model instances resulted in only a statistical difference between the 3 km and 200 m streamflow estimates. However, an assessment of sampling uncertainty shows high uncertainties surrounding the KGE score throughout the domain. This makes the conclusion based on the statistical testing inconclusive. The results do indicate strong locality in the differences between model instances expressed by differences in KGE scores of on average 0.22 with values larger than 0.5. The results of this study open up research paths that can investigate the changes in flux and state partitioning due to spatial scaling. This will help to further understand the challenges that need to be resolved for hyper-resolution hydrological modelling.

Global hydrological models have become a valuable tool for a range of global impact studies related to water resources. However, glacier parameterization is often simplistic or non-existent in global hydrological models. By contrast, global glacier models do represent complex glacier dynamics and glacier evolution, and as such, they hold the promise of better resolving glacier runoff estimates. In this study, we test the hypothesis that coupling a global glacier model with a global hydrological model leads to a more realistic glacier representation and, consequently, to improved runoff predictions in the global hydrological model. To this end, the Global Glacier Evolution Model (GloGEM) is coupled with the PCRaster GLOBal Water Balance model, version 2.0 (PCR-GLOBWB 2), using the eWaterCycle platform. For the period 2001–2012, the coupled model is evaluated against the uncoupled PCR-GLOBWB 2 in 25 large-scale (>50 000 km2), glacierized basins. The coupled model produces higher runoff estimates across all basins and throughout the melt season. In summer, the runoff differences range from 0.07 % for weakly glacier-influenced basins to 252 % for strongly glacier-influenced basins. The difference can primarily be explained by PCR-GLOBWB 2 not accounting for glacier flow and glacier mass loss, thereby causing an underestimation of glacier runoff. The coupled model performs better in reproducing basin runoff observations mostly in strongly glacier-influenced basins, which is where the coupling has the most impact. This study underlines the importance of glacier representation in global hydrological models and demonstrates the potential of coupling a global hydrological model with a global glacier model for better glacier representation and runoff predictions in glacierized basins.

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

<div>The eWaterCycle platform (https://www.ewatercycle.org/) is a fully Open Source system designed explicitly to advance the state of Open and FAIR Hydrological modelling. It allows scientists to set up experiments in a standardized way and run them interactively in a Jupyter environment.</div><div> </div><div>Previously we have presented various components that constitute the system: a preprocessing pipeline using ESMValTool (https://www.esmvaltool.org/) to generate meteorological forcing data, containerized models implementing the Basic Model Interface (https://bmi.readthedocs.io/), gRPC4BMI (https://github.com/eWaterCycle/grpc4bmi) to communicate with these containers from a Python environment, a visual explorer that lets the user set up an experiment with a few clicks and automatically generates a notebook based on the selected settings, and utilities to work with observations and analyse results.</div><div> </div><div>Recently we have officially released the eWaterCycle Python package (https://ewatercycle.readthedocs.io/en/latest/) that connects these components to provide a simple and clean user interface. The core of the package is modelled after PyMT (https://pymt.readthedocs.io/en/latest/index.html), extended with convenience functions to make the interface more user friendly, e.g. using xarray (http://xarray.pydata.org/en/stable/index.html) for spatial data and providing more user-friendly time accessors. Separate modules are available to load forcing data and parameter sets from the system and configure them correctly for the target model.</div><div> </div><div>The package comes with comprehensive documentation, including a suite of example notebooks. It also includes setup instructions for system administrators and guidance for incorporating new models. Currently, the following models are available through the eWaterCycle system: wflow, lisflood, marrmot m01, marrmot m14 and pcrglobwb.</div><div> </div><div>The release of the package marks a milestone in the development towards our goal of fully reproducible, open, and FAIR Hydrological modelling.</div>

The eWaterCycle platform (https://www.ewatercycle.org/) is a fully Open Source and ‘FAIR by Design’ Platform where hydrologists can do computational hydrological research using their own, or other’s, models and data. Using eWaterCycle, computational hydrologist can focus on the hydrological part of their work, without the headache that often comes with the computational part. In eWaterCycle experiments are separated from models: experiments are build and run in Jupyter notebooks and models can be accessed as objects in these notebooks. The models themselves are ‘hidden’ in (Docker) containers and accessed through an easy interface. This interface and the technology behind it that we’ve build allows computational hydrologists to work with (each others) models written in different programming languages without having to access that code. Currently PCRGlobWB 2.0, Hype, LISFlood, WFLOW and MARMoT are among the models supported by eWaterCycle. Furthermore, pre-processing of atmospheric forcing data is handled transparently by ESMValTool, which separates the process of selecting and standardising variables from the steps needed to make forcing compatible with a specific model. If a source of forcing data has been made ready for one model in eWaterCycle it is easy to use it for any other. If a model has been used with one forcing data source, it is easy to swap it with another. Currently ERA5 and ERA-Interim are supported in eWaterCycle. With eWaterCycle use cases such as (but not limited to!) these are now easier to implement: * Comparing two models for the same region against observation data (GRDC discharge observations are standard supported) to determine which model performs best for a given research question * Coupling two models (in different programming languages) to exchange information at every timestep, for example making a * Running a multi-model ensemble (including adding data assimilation of observations) Previously we have announced eWaterCycle as work in progress. At the 2022 General Assembly we will demonstrate the release of v1.0 of the eWaterCycle platform, giving the computational hydrological community access to a platform that supports fully reproducible, open, and FAIR Hydrological modelling. This work is currently under open review for publication in GMD: https://gmd.copernicus.org/preprints/gmd-2021-344/ and parts of this work have been presented at the AGU 2021 Fall Meeting.

In this study, we investigate the effect of spatial resolution discretization at 3km, 1km, and 200m by evaluating the streamflow estimation of the model. A hypothesis driven approach is used to investigate why changes in states and fluxes are taking place at different spatial resolutions and how they relate to model performance. These changes are evaluated in the context of landscape and climate characteristics as well as hydrological signatures. Answering the research question: can landscape, climate and hydrological characteristics dictate appropriate spatial modelling resolution a priori? We use a spatially distributed wflow_sbm model (Imhoff et al., 2020, code: https://zenodo.org/record/4291730) together with the CAMELS dataset (Addor et al., 2017), covering the Continental United States. The wflow_sbm model is chosen due to flexibility in the spatial resolution of the watershed discretization while maintaining run time performance suitable for large-sample studies. The flexibility in spatial resolution is achieved by the use of point-scale (pedo)transfer functions (PTFs) with upscaling rules to global datasets to ensure flux matching across scales (Imhoff et al., 2020; Samaniego et al., 2010, 2017). The model relies on open datasets for parameter estimation and requires minimal calibration efforts as it is most sensitive to two model parameters, rooting depth and horizontal conductivity . This study is carried out within the eWaterCycle framework; allowing for a FAIR by design research setup that is scalable in terms of case study areas and hydrological models.

The eWaterCycle platform(https://www.ewatercycle.org/) is a fully Open Source system designed explicitly to advance the state of Open and FAIR Hydrological modelling.Reproducibility is a key ingredient of FAIR, and one of the driving principles of eWaterCycle.While working with Hydrologists to create a fully Open and FAIR comparison study, we noticed that many ad-hoc tools and scripts are used to create input (forcing, parameters) for a hydrological model from the source datasets such as climate reanalysis and land-use data.To make this part of the modelling process better reproducible and more transparent we have created a common forcing input processing pipeline based on an existing climate model analysis tool: ESMValTool (https://www.esmvaltool.org/).Using ESMValTool the eWaterCycle platform can perform commonly required pre-processing steps such as cropping, re-gridding, and variable derivation in a standardized manner.If needed, it also allows for custom steps for a Hydrological model.Our pre-processing pipeline directly supports commonly used datasets such as ERA-5, ERA-Interim, and CMIP climate model data, and creates ready-to-run forcing data for a number of Hydrological models.Besides creating forcing data, the eWaterCycle platform allows scientists to run Hydrological models in a standardized way using Jupyter notebooks, wrapping the models inside a container environment, and interfacing to these using BMI, the Basic Model Interface (https://bmi.readthedocs.io/).The container environment (based on Docker) stores the entire software stack, including the operating system and libraries, in such a way that a model run can be reproduced using an identical software environment on any other computer.The reproducible processing of forcing and a reproducible software environment are important steps towards our goal of fully reproducible, Open, and FAIR Hydrological modelling.Ultimately, we hope to make it possible to fully reproduce a Hydrological model experiment from data pre-processing to analysis, using only a few clicks.

The dataset contains 3 simulated timeseries at the basin outlet for the CAMELS dataset created with the wflow_sbm model at various spatial resolutions (3km, 1km, 200m). The data set includes the calculated objective functions NSE, KGE 2009, KGE 2012, and KGE NP.