Automated Water Supply Model (AWSM): Streamlining and standardizing application of a physically based snow model for water resources and reproducible science

Havens, S.; Marks, Danny; Sandusky, Micah; Hedrick, A. R.; Johnson, Micah; Robertson, Mark; Trujillo, Ernesto

doi:10.1016/j.cageo.2020.104571

Cited by 13 publications

(15 citation statements)

References 35 publications

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“…This is not satisfactory, as it leads to underperforming simulations and large numbers of questions directed to the model developers. The intrinsic complexity of configuring a numerical model may also make its end users highly dependent on direct support from the models' developers (Havens et al, 2020).…”

Section: Configuring Numerical Modelsmentioning

confidence: 99%

“…In order to increase the possibility of rerunning the exact same numerical model in the future, there are several approaches: for example, as a Docker image that contains every element necessary to the reproducibility of the numerical work as laid out by Havens et al (2020) or as another approach by working on the numerical model and its dependencies following the Reproducible Builds (https: //reproducible-builds.org/, last access: 14 January 2022) approach (Lamb and Zacchiroli, 2021) or ReScience efforts (Rougier et al, 2017). The former assumes that the users know how to use Docker, while the long-term stability of the container file format remains to be evaluated (Rougier et al, 2017;Navarro Leija et al, 2020), and the latter requires systematic tracking of all components of the toolchain.…”

Section: Reproducible Science Considerationsmentioning

confidence: 99%

See 1 more Smart Citation

Inishell 2.0: semantically driven automatic GUI generation for scientific models

Bavay¹,

Reisecker

Egger

et al. 2022

Geosci. Model Dev.

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Abstract. As numerical model developers, we have experienced first hand how most users struggle with the configuration of the models, leading to numerous support requests. Such issues are usually mitigated by offering a graphical user interface (GUI) that flattens the learning curve. Developing a GUI, however, requires a significant investment for the model developers, as well as a specific skill set. Moreover, this does not fit with the daily duties of model developers. As a consequence, when a GUI has been created – usually within a specific project and often relying on an intern – the maintenance either constitutes a major burden or is not performed. This also tends to limit the evolution of the numerical models themselves, since the model developers try to avoid having to change the GUI. In this paper we describe an approach based on an XML description of the required numerical model configuration elements (i.e., the data model of the configuration data) and a C++/Qt tool (Inishell) that populates a GUI based on this description on the fly. This makes the maintenance of the GUI very simple and enables users to easily get an up-to-date GUI for configuring the numerical model. The first version of this tool was written almost 10 years ago and showed that the concept works very well for our own surface process models. A full rewrite offering a more modern interface and extended capabilities is presented in this paper.

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Section: Configuring Numerical Modelsmentioning

confidence: 99%

Section: Reproducible Science Considerationsmentioning

confidence: 99%

Inishell 2.0: semantically driven automatic GUI generation for scientific models

Bavay¹,

Reisecker

Egger

et al. 2022

Geosci. Model Dev.

View full text Add to dashboard Cite

show abstract

“…We used the Automated Water Supply Model (AWSM; Havens et al, 2020) to obtain a spatially continuous estimation of the distribution and phase of precipitation, snowpack characteristics and surface water inputs (SWI). The two major components of AWSM are the Spatial Modeling for Resources Framework (SMRF; Havens et al, 2017) and iSnobal (Marks et al, 1999).…”

Section: Spatially Distributed Snowpack Modellingmentioning

confidence: 99%

“…This migration of the transition zone can affect precipitation patterns as well as discharge generation and timing across mountain ranges, with notable effects at the elevations surrounding the transition zone. Climate change also has the potential to increase annual climate variations (Seager et al, 2012), affecting annual runoff efficiency (Hedrick et al, 2020) and likely also influencing stream discharge timing and magnitude. In mid-elevation rainsnow transition zones the annual snowpack variability is already relatively large.…”

Section: Introductionmentioning

confidence: 99%

Stream discharge depends more on the temporal distribution of water inputs than on yearly snowfall fractions for a headwater catchment at the rain-snow transition zone

Kiewiet

Trujillo

Hedrick

et al. 2021

Preprint

Self Cite

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Abstract. Climate warming affects snowfall fractions and snowpack storage, displaces the rain-snow transition zone towards higher elevations, and impacts discharge timing and magnitude as well as low-flow patterns. However, it remains unknown how variations in the spatial and temporal distribution of precipitation at the rain-snow transition zone affect discharge. To investigate this, we used observations from eleven weather stations and snow depths measured in one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semi-arid, 1.8 km2 headwater catchment at the rain-snow transition zone. We focused on surface water inputs (SWI; the summation of rainfall and snowmelt) for four years with contrasting climatological conditions (wet, dry, rainy and snowy) and compared simulated SWI to measured discharge. We obtained a strong spatial agreement between snow depth from the lidar survey and model (r2: 0.88), and a median Nash-Sutcliffe Efficiency (NSE) of 0.65 for simulated and measured snow depths for all modelled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the four years, with north-facing slopes producing 1.09 to 1.25 times more SWI than south-facing slopes, and snow drifts producing up to six times more SWI than the catchment average. We found that discharge in a snowy year was almost twice as high as in a rainy year, despite similar SWI. However, years with a lower snowfall fraction did not always have lower annual discharge nor earlier stream drying. Instead, we found that the dry-out date at the catchment outlet was positively correlated to the snowpack melt-out date. These results highlight the heterogeneity of SWI at the rain-snow transition zone and emphasize the need for spatially distributed modelling or monitoring of both the snowpack and rainfall.

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“…Climate change also has the potential to increase annual climate variations (Seager et al, 2012), affecting annual runoff efficiency (Hedrick et al, 2020) and likely also influencing stream discharge timing and magnitude. In mid-elevation rainsnow transition zones the annual snowpack variability is already relatively large.…”

Automated Water Supply Model (AWSM): Streamlining and standardizing application of a physically based snow model for water resources and reproducible science

Cited by 13 publications

References 35 publications

Inishell 2.0: semantically driven automatic GUI generation for scientific models

Inishell 2.0: semantically driven automatic GUI generation for scientific models

Stream discharge depends more on the temporal distribution of water inputs than on yearly snowfall fractions for a headwater catchment at the rain-snow transition zone

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