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Abstract. The offline Eulerian AURAMS (A Unified Regional Air quality Modelling System) chemical transport model was adapted to simulate airborne concentrations of seven PAHs (polycyclic aromatic hydrocarbons): phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, chrysene + triphenylene, and benzo[a]pyrene. The model was then run for the year 2002 with hourly output on a grid covering southern Canada and the continental USA with 42 km horizontal grid spacing. Model predictions were compared to ~5000 24 h-average PAH measurements from 45 sites, most of which were located in urban or industrial areas. Eight of the measurement sites also provided data on particle/gas partitioning which had been modelled using two alternative schemes. This is the first known regional modelling study for PAHs over a North American domain and the first modelling study at any scale to compare alternative particle/gas partitioning schemes against paired field measurements. The goal of the study was to provide output concentration maps of use to assessing human inhalation exposure to PAHs in ambient air. Annual average modelled total (gas + particle) concentrations were statistically indistinguishable from measured values for fluoranthene, pyrene and benz[a]anthracene whereas the model underestimated concentrations of phenanthrene, anthracene and chrysene + triphenylene. Significance for benzo[a]pyrene performance was close to the statistical threshold and depended on the particle/gas partitioning scheme employed. On a day-to-day basis, the model simulated total PAH concentrations to the correct order of magnitude the majority of the time. The model showed seasonal differences in prediction quality for volatile species which suggests that a missing emission source such as air–surface exchange should be included in future versions. Model performance differed substantially between measurement locations and the limited available evidence suggests that the model's spatial resolution was too coarse to capture the distribution of concentrations in densely populated areas. A more detailed analysis of the factors influencing modelled particle/gas partitioning is warranted based on the findings in this study.

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Hydrologic models have been used for numerous applications in the last number of decades. These applications include streamflow prediction, flood forecasting, or reservoir level forecasting, or in a scientific capacity to advance our understanding of hydrologic systems. Whether used in a predictive or scientific capacity, models are an abstraction of the complex natural system being simulated, and necessarily simplify the treatment of hydrological processes occurring in a watershed, either to facilitate computational expediency or in recognition of the large degree of uncertainty regarding how the watershed functions. In most practical cases, model calibration is required in order to reconcile model output with historical observation; conventionally, this calibration process focuses on tuning only model parameters. However, hydrologic models are typically considered to have four main components that contribute to model uncertainty (e.g., Beven, 2005;Butts et al., 2004;Gupta, Clark, et al., 2012): (1) input forcing data, such as precipitation and temperature, (2) model structure, which includes the algorithmic functions used to simulate the hydrologic cycle, (3) model parameters related to the selected hydrologic process algorithms (often the only degree of freedom used to tune model performance), and (4) an error term, which represents any uncertainty or deviation from reality not captured in the other three components of model uncertainty.This study focuses upon the simultaneous calibration of model structure and model parameters. We refer to model structure here as a combination of (a) the number and type of stores represented in the model and (b) the collection of algorithms used to describe the relationships between fluxes and storage in each hydrological response unit (HRU). Model structure can be further extended to other modeling decisions such as spatial discretization, but this extended definition is not evaluated here. Historically, most hydrologic models have been designed with a fixed model structure while the input data and model parameters may vary from watershed to watershed. These fixed model structures were typically chosen because they (1) adequately represented the hydrologic response of one or more watersheds, (2) generally respected the physics of water flow and storage, (3) were consistent with conceptual models of the water cycle garnered from field investigation, and/or (4) convention (Addor & Melsen, 2019). In many cases, trial and error was used to determine final model structure (e.g., Perrin et al., 2003). However, there are many such model structures that can generally meet these criteria, and as such, there exist dozens of fixed structure models which used different algorithms to represent various components of the water cycle. The fixed model structure concept

The off-line Eulerian AURAMS chemical transport model was adapted to simulate the atmospheric fate of seven PAHs: phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, chrysene + triphenylene, and benzo[a]pyrene. The model was then run for the year 2002 with hourly output on a~grid covering southern Canada and the continental USA with 42 km horizontal grid spacing. Model predictions were compared to ~ 5000 24 h average PAH measurements from 45 sites, eight of which also provided data on particle/gas partitioning which had been modelled using two alternative schemes. This is the first known regional modelling study for PAHs over a North American domain and the first modelling study at any scale to compare alternative particle/gas partitioning schemes against paired field measurements.

Annual average modelled total (gas + particle) concentrations were statistically indistinguishable from measured values for fluoranthene, pyrene and benz[a]anthracene whereas the model underestimated concentrations of phenanthrene, anthracene and chrysene + triphenylene. Significance for benzo[a]pyrene performance was close to the statistical threshold and depended on the particle/gas partitioning scheme employed.

On a day-to-day basis, the model simulated total PAH concentrations to the correct order of magnitude the majority of the time. Model performance differed substantially between measurement locations and the limited available evidence suggests that the model spatial resolution was too coarse to capture the distribution of concentrations in densely populated areas. A more detailed analysis of the factors influencing modelled particle/gas partitioning is warranted based on the findings in this study

Abstract. 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.

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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.