R-functions for Canadian hydrologists: a Canada-wide collaboration

Anderson, Emily; Chlumsky, Robert; McCaffrey, David R.; Trubilowicz, J. W.; Shook, Kevin; Whitfield, Paul H.

doi:10.1080/07011784.2018.1492884

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…All analyses were performed with R (R Development Core Team, 2014) using packages kendall (McLeod, 2015), CSHShydRology (Anderson et al, 2018), and dtwclust (Sarda-Espinosa, 2017. A threshold of 0.05 was used in tests of significance, and accordingly, 5 % was also used as an indicator that the number of trends exceeds the number expected by chance alone.…”

Section: Discussionmentioning

confidence: 99%

The spatial extent of hydrological and landscape changes across the mountains and prairies of Canada in the Mackenzie and Nelson River basins based on data from a warm-season time window

Whitfield

Kraaijenbrink

Shook

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. East of the Continental Divide in the cold interior of Western Canada, the Mackenzie and Nelson River basins have some of the world's most extreme and variable climates, and the warming climate is changing the landscape, vegetation, cryosphere, and hydrology. Available data consist of streamflow records from a large number (395) of natural (unmanaged) gauged basins, where flow may be perennial or temporary, collected either year-round or during only the warm season, for a different series of years between 1910 and 2012. An annual warm-season time window where observations were available across all stations was used to classify (1) streamflow regime and (2) seasonal trend patterns. Streamflow trends were compared to changes in satellite Normalized Difference Indices. Clustering using dynamic time warping, which overcomes differences in streamflow timing due to latitude or elevation, identified 12 regime types. Streamflow regime types exhibit a strong connection to location; there is a strong distinction between mountains and plains and associated with ecozones. Clustering of seasonal trends resulted in six trend patterns that also follow a distinct spatial organization. The trend patterns include one with decreasing streamflow, four with different patterns of increasing streamflow, and one without structure. The spatial patterns of trends in mean, minimum, and maximum of Normalized Difference Indices of water and snow (NDWI and NDSI) were similar to each other but different from Normalized Difference Index of vegetation (NDVI) trends. Regime types, trend patterns, and satellite indices trends each showed spatially coherent patterns separating the Canadian Rockies and other mountain ranges in the west from the poorly defined drainage basins in the east and north. Three specific areas of change were identified: (i) in the mountains and cold taiga-covered subarctic, streamflow and greenness were increasing while wetness and snowcover were decreasing, (ii) in the forested Boreal Plains, particularly in the mountainous west, streamflows and greenness were decreasing but wetness and snowcover were not changing, and (iii) in the semi-arid to sub-humid agricultural Prairies, three patterns of increasing streamflow and an increase in the wetness index were observed. The largest changes in streamflow occurred in the eastern Canadian Prairies.

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Section: Discussionmentioning

confidence: 99%

The spatial extent of hydrological and landscape changes across the mountains and prairies of Canada in the Mackenzie and Nelson River basins based on data from a warm-season time window

Whitfield

Kraaijenbrink

Shook

et al. 2021

Hydrol. Earth Syst. Sci.

Self Cite

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“…ca/hydroglyph/) for visualizing Raven time series output data. Hydrologic model support is also provided by many modelindependent packages, such as the CSHS-hydRology package (Anderson et al, 2018). However, RavenR is the most comprehensive tool for preparing input files and performing a range of analyses with Raven output files.…”

Section: Raven Hydrologic Modelling Frameworkmentioning

confidence: 99%

“…While the supporting data analysis may not require expert knowledge of hydrology per se, the data preparation can require a substantial amount of time and effort in the modelling process. Further, the reproducibility and merit of research may rest on the ability to access and reproduce the original and processed intermediate data, which is vastly improved by the use of a scripting environment that in effect documents the steps taken to prepare the data files (Anderson et al, 2018). As such, the use of scripting tools, such as those that will be discussed in this section, may be used to both reduce the effort required to prepare input data files and improve the reproducibility of the research or applied project.…”

Section: Input File Processingmentioning

confidence: 99%

“…Scripts, as a perfect record of the data manipulation, model setup, post-processing, and even figure creation steps, are the ideal tools to accomplish this. R, in particular, has gained significant ground in hydrology, entering the toolbox of many in both consulting and academia (Anderson et al, 2018;Slater et al, 2019;Astagneau et al, 2021). This is due in part to the already robust package ecosystem awaiting hydrologists; the Comprehensive R Archive Network (CRAN), which hosts and tests R packages, allows for easy access to packages for a variety of data processing tasks such as downloading data (tidyhydat (Albers, 2017)), examining data (trend (Pohlert, 2020)), manipulating shapefiles and spatial data (sf, raster (Pebesma, 2018;Hijmans et al, 2021)), evaluating model outputs (hydroGOF (Mauricio Zambrano-Bigiarini, 2020)), and visualizing data (ggplot2 (Wickham, 2016)).…”

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confidence: 99%

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RavenR v2.1.4: an open source R package to support flexible hydrologic modelling

Chlumsky

Craig

Lin

et al. 2021

Preprint

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Abstract. In recent decades, advances in the flexibility and complexity of hydrologic models has enhanced their utility in scientific studies and practice alike. However, the increasing complexity of these tools leads to a number of challenges, including steep learning curves for new users and in the reproducibility of modelling studies. Here, we present the RavenR package, an R package that leverages the power of scripting to both enhance the usability of the Raven hydrologic modelling framework and provide complimentary analyses that are useful for modellers. The RavenR package contains functions that may be useful in each step of the model-building process, particularly for preparing input files and analyzing model outputs, and these tools may be useful even for non-Raven users. The utility of the RavenR package is demonstrated with the presentation of six use cases for a model of the Liard River basin in Canada. These use cases provide examples of visually reviewing the model configuration, preparing input files for observation and forcing data, simplifying the model discretization, performing reality checks on the model output, and evaluating the performance of the model. All of the use cases are fully reproducible, with additional reproducible examples of RavenR functions included with the package distribution itself. It is anticipated that the RavenR package will continue to evolve with the Raven project, and will provide a useful tool to new and experienced users of Raven alike.

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“…The use of these scripting languages also improves the reproducibility of scientific studies, which is a noted challenge in hydrology (Hutton et al, 2016). R, in particular, has gained significant ground in hydrology, entering the toolbox of many in both consulting and academia (Anderson et al, 2018;Slater et al, 2019;Astagneau et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling

et al. 2022

Self Cite

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Abstract. In recent decades, advances in the flexibility and complexity of hydrologic models have enhanced their utility in scientific studies and practice alike. However, the increasing complexity of these tools leads to a number of challenges, including steep learning curves for new users and issues regarding the reproducibility of modelling studies. Here, we present the RavenR package, an R package that leverages the power of scripting to both enhance the usability of the Raven hydrologic modelling framework and provide complementary analyses that are useful for modellers. The RavenR package contains functions that may be useful in each step of the model-building process, particularly for preparing input files and analyzing model outputs. The utility of the RavenR package is demonstrated with the presentation of six use cases for a model of the Liard River basin in Canada. These use cases provide examples of visually reviewing the model configuration, preparing input files for observation and forcing data, simplifying the model discretization, performing realism checks on the model output, and evaluating the performance of the model. All of the use cases are fully reproducible, with additional reproducible examples of RavenR functions included with the package distribution itself. It is anticipated that the RavenR package will continue to evolve with the Raven project and will provide a useful tool to new and experienced users of Raven alike.

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R-functions for Canadian hydrologists: a Canada-wide collaboration

Cited by 6 publications

References 16 publications

The spatial extent of hydrological and landscape changes across the mountains and prairies of Canada in the Mackenzie and Nelson River basins based on data from a warm-season time window

The spatial extent of hydrological and landscape changes across the mountains and prairies of Canada in the Mackenzie and Nelson River basins based on data from a warm-season time window

RavenR v2.1.4: an open source R package to support flexible hydrologic modelling

RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling

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