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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.

Flooding is one of the world's most common and costly natural hazards, inflicting billions in damages each year. However, current hydraulic models to support flood mapping are not well suited for large-scale applications or frequent updates, either due to limited accuracy of simple methods or lack of scalability (i.e., large computational requirements) for more sophisticated hydrodynamic models. This results in flood maps being decades out of date or simply nonexistent in some areas. Recent advances in generating flood maps have been made seemingly in parallel between geospatial methods and hydrodynamic models, such as the Height Above Nearest Drainage (HAND) method, hybrid 1D-2D hydrodynamic models and more efficient computing of 2D models. This study presents the Geospatially Augmented Standard Step (GASS) method, which combines a novel improvement to the HAND method, Dynamic Height Above Nearest Drainage (DHAND), with a 1D hydraulic model for rapid flood inundation mapping at large scales while maintaining the accuracy of hydrodynamic models. This method is implemented into a new modelling software package called Blackbird, and is benchmarked in two case studies, including a verification of the code and a benchmark test comparing multiple approaches in the ability to approximate 2D model results. The Blackbird model vastly outperforms the simpler HAND-Manning method and also outperforms a traditional HEC-RAS 1D model when evaluated for accuracy of approximating a 2D model benchmark. This new method is shown to reduce the incidence of falsely predicting flooded areas through improved resolution of landscape connections over HAND-based or 1D hydraulic models. Blackbird also streamlines the model development effort relative to existing 1D or 2D models while maintaining a computational speed that was 10, 000 times less than a comparable 2D model in one case study. The method also allows for future integration of hydraulic structures, ice jams and other features that are unavailable in HAND-based models. Overall, the GASS method provides a viable option for large-scale and real-time fluvial flood mapping applications. [ABSTRACT FROM AUTHOR]

A blended model structure has emerged as an alternative to the traditional representation of model structure in a hydrologic model, in which multiple algorithmic choices are used to represent some hydrologic process within a model, and are combined within a single model run using a weighted average of process fluxes. This approach has been shown to improve overall model performance, as well as provide an efficient way to test multiple model structures. We propose that a blended model may also be at least a partial solution to the calls for a more robust Community Hydrologic Model, which can mitigate the need for developing new hydrologic models for each catchment and application. We develop an updated version of the blended model configuration that defines the suite of all possible hydrologic process options in the blended model. Configuration development was guided by model performance for more than 30 different discrete model configurations across 12 Model Parameter Estimation Experiment (MOPEX) catchments. Improvements to the blended model include the introduction of blended potential melt and potential evapotranspiration as new process groups, inclusion of nonblended structural changes, and a revision of the process options within each existing group. This leads to a very high-performing model with a mean calibration Kling–Gupta efficiency (KGE) score of 0.90 and mean validation KGE score of 0.80 across all 12 MOPEX catchments, a substantial improvement in model performance relative to the initial version. We tested for overfitting of models and found little statistical evidence that increasing the complexity of blended models reduces validation performance. We then selected the preferred model configuration as Version 2 of the blended model and tested it with 24 independent catchments against the original configuration. This test showed a statistically significant improvement or statistically similar performance in 22 of the 24 catchments in calibration and 21 of the 24 catchments in validation. The results also suggested a greater improvement in drier catchments. Version 2 of the blended model is robust across a range of catchments without the need for adjusting its flexible model structure and may be useful in future hydrology studies and applications alike. [ABSTRACT FROM AUTHOR]

A blended model structure has emerged as an alternative to the traditional representation of model structure in a hydrologic model, in which multiple algorithmic choices are used to represent some hydrologic process within a model, and are combined within a single model run using a weighted average of process fluxes. This approach has been shown to improve overall model performance, as well as provide an efficient way to test multiple model structures. We propose that a blended model may also be at least a partial solution to the calls for a more robust Community Hydrologic Model, which can mitigate the need for developing new hydrologic models for each catchment and application. We develop an updated version of the blended model configuration which defines the suite of all possible hydrologic process options in the blended model. Configuration development was guided by model performance for more than 30 different discrete model configurations across 12 MOPEX catchments. Improvements to the blended model include the introduction of blended potential melt and potential evapotranspiration as new process groups, inclusion of non-blended structural changes, and a revision of the process options within each existing group. This leads to a very high-performing model with a mean calibration Kling-Gupta Efficiency (KGE) score of 0.90 and mean validation KGE score of 0.80 across all 12 MOPEX catchments, a substantial improvement in model performance relative to the initial version of 0.06 and 0.07 in calibration and validation, respectively. We test for overfitting of models and find little statistical evidence that increasing the complexity of blended models reduces validation performance. We then select the preferred model configuration as version 2 of the blended model, and test it with 12 independent catchments, which shows a mean calibration and validation score of 0.89 and 0.76, respectively, and improvement over the original model (0.03 in mean calibration KGE score). Version 2 of the blended model is robust across a range of catchments without the need for adjusting its flexible model structure, and may be useful in future hydrology studies and applications alike. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

R is an open-source statistical language that is supported by a large user community with many benefits for use in watershed analysis. R has been used widely in the Canadian research community and Canadian researchers have contributed numerous R packages with specific applications (e.g. seas, GEVcdn, RHtests, tidyhydat, FlowScreen). Here a new R package, CSHShydRology, is introduced that combines functions developed by hydrologists across Canada. [ABSTRACT FROM AUTHOR]