CSDMS: a community platform for numerical modeling of Earth surface processes

Tucker, G. E.; Hutton, Eric; Piper, Mark; Campforts, Benjamin; Gan, Tian; Barnhart, Katherine R.; Kettner, Albert J.; Overeem, I.; Peckham, S. D.; McCready, Lynn; Syvitski, Jaia

doi:10.5194/gmd-15-1413-2022

Cited by 19 publications

(17 citation statements)

References 121 publications

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“…As laid out in Sect. 1, currently an ecosystem of services is emerging that makes it easier for hydrologists to do computational research, with each service focusing on different parts of the hydrological research cycle (Tucker et al, 2022;Tarboton et al, 2014). In this ecosystem, eWaterCycle is developed as a platform on which hydrologists can execute their computational hydrological experiments.…”

Section: Discussionmentioning

confidence: 99%

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The eWaterCycle platform for open and FAIR hydrological collaboration

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

“…The Community Surface Dynamics Modeling System (CSDMS) (Tucker et al, 2022) community gathers a large number of hydrological models in a model repository. This repository contains metadata on models and the source code.…”

Section: Introductionmentioning

confidence: 99%

The eWaterCycle platform for open and FAIR hydrological collaboration

et al. 2022

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“…Second, our remote sensing potential model is extremely simple and steady state, and yet captures the underlying tradeoff between scene acquisition and ground-truthing. Within a watershed, multiple process-scales exist, ranging from storms and coastal upwelling events that happen on hourly to daily timescales, land use and land cover changes that happen on annual to decadal timescales, and landform changes that happen over many decades to centuries [40]. Our maps do not factor in these different timescales.…”

Section: Discussionmentioning

confidence: 99%

Can Remote Sensing Fill the United States’ Monitoring Gap for Watershed Management?

Sridharan

Kumar²,

Kumar³

2022

Water

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Remote sensing has been heralded as the silver bullet in water quality modeling and watershed management, and yet a quantitative mapping of where its applicability is likely and most useful has not been undertaken so far. Here, we combine geospatial models of cloud cover as a proxy for the likelihood of acquiring remote scenes and the shortest time of travel to population centers as a proxy for accessibility to ground-truth remote sensing data for water quality monitoring and produce maps of the potential of remote sensing in watershed management in the United States. We generate several maps with different cost-payoff relationships to help stakeholders plan and incentivize remote sensing-based monitoring campaigns. Additionally, we combine these remote sensing potential maps with spatial indices of population, water demand, ecosystem services, pollution risk, and monitoring coverage deficits to identify where remote sensing likely has the greatest role to play. We find that the Southwestern United States and the Central plains regions are generally suitable for remote sensing for watershed management even under the most stringent costing projections, but that the potential for using remote sensing can extend further North and East as constraints are relaxed. We also find large areas in the Southern United States and sporadic watersheds in the Northeast and Northwest seaboards and the Midwest would likely benefit most from using remote sensing for watershed monitoring. Although developed herein for watershed decision support in the United States, our approach is readily generalizable to other environmental domains and across the world.

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“…Though both models exhibit comparable current predictions by over-estimating the smaller velocities and under-estimating the higher ones (Fig. inter-operable and continually developing research software ecosystem (Tucker et al, 2021).…”

Section: On the Depth-induced Wave-breaking And Wave Setupmentioning

confidence: 93%

Benchmarking a two-way coupled coastal wave-current hydrodynamics model

Fragkou¹,

Old²,

Venugopal³

et al. 2022

Preprint

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Wave-current interaction phenomena are often represented through coupled model frameworks in ocean modelling. However, the benchmarking of these models is scarce, revealing a substantial research challenge. We seek to address this through a selection of benchmark cases for coupled wave-current interaction modelling frameworks. This comprises a series of analytical and experimental test cases spanning three diverse conditions of wave run-up, one scenario of waves opposing a current flow, and a 2-D arrangement of waves propagating over a submerged bar. We simulate these through coupling of the spectral wave model, Simulating WAves Nearshore (SWAN), with the coastal hydrodynamics shallow-water equation model, Thetis, through the Basic Model Interface (BMI) structure. In our analysis, by comparing calibrated versus default parameter settings we identify and highlight calibration uncertainties that emerge across a range of potential applications. Calibrated model results exhibit good correlation against experimental and analytical data, alongside benchmarked wave-current model predictions, where available. Specifically, inter-model comparisons showcase equivalent accuracy. Finally, the coupled model we developed as part of this work showcases its ability to account for wave-current effects, in a manner extensible to other coupled processes through BMI and applicable to more complex geometries.

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CSDMS: a community platform for numerical modeling of Earth surface processes

Cited by 19 publications

References 121 publications

The eWaterCycle platform for open and FAIR hydrological collaboration

The eWaterCycle platform for open and FAIR hydrological collaboration

Can Remote Sensing Fill the United States’ Monitoring Gap for Watershed Management?

Benchmarking a two-way coupled coastal wave-current hydrodynamics model

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