Bridging technology transfer boundaries: Integrated cloud services deliver results of nonlinear process models as surrogate model ensembles

Serafin, Francesco; David, Olaf; Carlson, Jonathan O.; Green, Timothy R.; Rigon, Riccardo

doi:10.1016/j.envsoft.2021.105231

Cited by 11 publications

(7 citation statements)

References 60 publications

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“…A similar search solely on “SNOTEL” leads to almost 8000 publications. These numbers are minimum estimates of the impact of SNOTEL data, as Google Scholar omits non‐documented data uses and does not consistently include many public and private documents generated by government and industry in association with day‐to‐day activities across the U.S. West, such as environmental and engineering consultant reports, WSF reports routinely issued by practical service‐delivery organizations (SDOs; Serafin et al, 2021) like the NRCS and National Weather Service River Forecast Centers (RFCs), and so forth.…”

Section: Snotel and Snolite Automated Stationsmentioning

confidence: 99%

SNOTEL, the Soil Climate Analysis Network, and water supply forecasting at the Natural Resources Conservation Service: Past, present, and future

Fleming

Zukiewicz

Ströbel

et al. 2023

J American Water Resour Assoc

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The Snow Survey and Water Supply Forecasting (SSWSF) Program and the Soil Climate Analysis Network (SCAN) of the United States Department of Agriculture's Natural Resources Conservation Service (NRCS) generate key observational and predictive information for water managers. Examples include mountain climate and snow monitoring through manual snow surveys and the SNOw TELemetry (SNOTEL) and SNOtel LITE networks, in situ soil moisture data acquisition through the SCAN and SNOTEL networks, and water supply forecasting using river runoff prediction models. The SSWSF Program has advanced continuously over the decades and is a major source of valuable water management information across the western United States, and the SCAN network supports agricultural and other water users nationwide. Product users and their management goals are diverse, and use‐cases range from guiding crop selection to seasonal flood risk assessment, drought monitoring and prediction, avalanche and fire prediction, hydropower optimization, tracking climate variability and change, environmental management, satisfying international treaty and domestic legal requirements, and more. Priorities going forward are to continue innovating to enhance the accuracy and completeness of the observational and model‐generated data products these programs deliver, including expanded synergies with the remote sensing community and uptake of artificial intelligence while maintaining long‐term operational reliability and consistency at scale.

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Section: Snotel and Snolite Automated Stationsmentioning

confidence: 99%

SNOTEL, the Soil Climate Analysis Network, and water supply forecasting at the Natural Resources Conservation Service: Past, present, and future

Fleming

Zukiewicz

Ströbel

et al. 2023

J American Water Resour Assoc

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“…The idea can no doubt be improved, and the organization provided is very simple, based on a file system organization; much more sophisticated architectures, using databases and web services, could be deployed to obtain the same functionalities with automatic procedures, similar to those used in the version control system for codes. If we then assigned a unique identifier to any catchment worldwide, the procedure could even be decentralized and certified by using, for instance, blockchain-like technologies (Serafin et al, 2021). However, as simple as it is, it is a proof of concept that such a strategy of participatory modeling is possible.…”

Section: Participatory Needsmentioning

confidence: 99%

“…Testing new features, which is an intrinsically problematic issue (Kelly and Sanders, 2008), becomes almost unviable in MaaA. In fact, these models have been implemented as monolithic codes, and the absence of separation of concerns makes it difficult to read/debug them (Serafin, 2019). If a modeler is interested in using a particular MaaA function, this is actually not possible, and coders are re-implementing the same things over and over.…”

Section: Four Plus One Steps Towards Darthsmentioning

confidence: 99%

“…7, 32 hydrologic response units (HRUs) and two lakes are distinguished, and each of them can be partially processed in parallel. For instance, the Net3 infrastructure (Serafin, 2019) has been deployed in the OMS core and serves this scope. It treats the external HRUs (shown in orange at the bottom of Fig.…”

Section: Dealing With the Computational Burdenmentioning

confidence: 99%

“…In principle, when using MBC, it would be conceptually easy to say that, in a given modeling solution, some of the components can be PB and some others can be ML based and that the workflow envisioned in the cited papers can, thus, be implemented. For instance, Serafin et al (2021) have shown that it can be done by adopting an artificial neural network (ANN) model for runoff purposes, in the OMS/CSIP system. In this case, the ANN libraries were integrated into the framework, and this made the integration easier, although not trivial; however, in general, a more loose coupling through "bridges" should be envisioned.…”

Section: The Grand Challenge Of Hybrid Models In Darthsmentioning

confidence: 99%

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HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists

Rigon¹,

Formetta²,

Bancheri³

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. The “Digital Earth” (DE) metaphor is very useful for both end users and hydrological modelers (i.e., the coders). In this opinion paper, we analyze different categories of models with the view of making them part of Digital eARth Twin Hydrology systems (DARTHs). We stress the idea that DARTHs are not models, rather they are an appropriate infrastructure that hosts (certain types of) models and provides some basic services for connecting to input data. We also argue that a modeling-by-component strategy is the right one for accomplishing the requirements of the DE. Five technological steps are envisioned to move from the current state of the art of modeling. In step 1, models are decomposed into interacting modules with, for instance, the agnostic parts dealing with inputs and outputs separated from the model-specific parts that contain the algorithms. In steps 2 to 4, the appropriate software layers are added to gain transparent model execution in the cloud, independently of the hardware and the operating system of computer, without human intervention. Finally, step 5 allows models to be selected as if they were interchangeable with others without giving deceptive answers. This step includes the use of hypothesis testing, the inclusion of error of estimates, the adoption of literate programming and guidelines to obtain informative clean code. The urgency for DARTHs to be open source is supported here in light of the open-science movement and its ideas. Therefore, it is argued that DARTHs must promote a new participatory way of performing hydrological science, in which researchers can contribute cooperatively to characterize and control model outcomes in various territories. Finally, three enabling technologies are also discussed in the context of DARTHs – Earth observations (EOs), high-performance computing (HPC) and machine learning (ML) – as well as how these technologies can be integrated in the overall system to both boost the research activity of scientists and generate knowledge.

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Generalized and Simulated Architecture for Seamless Experiment Conditions in Cloud Computing

Kumar¹,

Raja²,

Ramu³

2022

Advanced Technologies and Societal Change

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Bridging technology transfer boundaries: Integrated cloud services deliver results of nonlinear process models as surrogate model ensembles

Cited by 11 publications

References 60 publications

SNOTEL, the Soil Climate Analysis Network, and water supply forecasting at the Natural Resources Conservation Service: Past, present, and future

SNOTEL, the Soil Climate Analysis Network, and water supply forecasting at the Natural Resources Conservation Service: Past, present, and future

HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists

Generalized and Simulated Architecture for Seamless Experiment Conditions in Cloud Computing

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