On the visual detection of non-natural records in streamflow time series: challenges and impacts

Large datasets of long-term streamflow measurements are widely used to infer and model hydrological processes. However, streamflow measurements may suffer from what users can consider anomalies, i.e. non-natural records that may be erroneous streamflow values or anthropogenic influences that can lead to misinterpretation of actual hydrological processes. Since identifying anomalies is time consuming for humans, no study has investigated their proportion, temporal distribution, and influence on hydrological indicators over large datasets. This study summarizes the results of a large visual inspection campaign of 674 streamflow time series in France made by 43 evaluators, who were asked to identify anomalies falling under five categories, namely, linear interpolation, drops, noise, point anomalies, and other. We examined the evaluators' individual behaviour in terms of severity and agreement with other evaluators, as well as the temporal distributions of the anomalies and their influence on commonly used hydrological indicators. We found that inter-evaluator agreement was surprisingly low, with an average of 12 % of overlapping periods reported as anomalies. These anomalies were mostly identified as linear interpolation and noise, and they were more frequently reported during the low-flow periods in summer. The impact of cleaning data from the identified anomaly values was higher on low-flow indicators than on high-flow indicators, with change rates lower than 5 % most of the time. We conclude that the identification of anomalies in streamflow time series is highly dependent on the aims and skills of each evaluator, which raises questions about the best practices to adopt for data cleaning.</p

<span style="" class="text typewriter">airGRteaching</span>: an open-source tool for teaching hydrological modeling with R

Hydrological modeling is at the core of most studies related to water, especially for anticipating disasters, managing water resources, and planning adaptation strategies. Consequently, teaching hydrological modeling is an important, but difficult, matter. Teaching hydrological modeling requires appropriate software and teaching material (exercises, projects); however, although many hydrological modeling tools exist today, only a few are adapted to teaching purposes. In this article, we present the airGRteaching package, which is an open-source R package. The hydrological models that can be used in airGRteaching are the GR rainfall-runoff models, i.e., lumped processed-based models, allowing streamflows to be simulated, including the GR4J model. In this package, thanks to a graphical user interface and a limited number of functions, numerous hydrological modeling exercises representing a wide range of hydrological applications are proposed. To ease its use by students and teachers, the package contains several vignettes describing complete projects that can be proposed to investigate various topics such as streamflow reconstruction, hydrological forecasting, and assessment of climate change impact.</p

airGRteaching: enseigner la modélisation hydrologique à l'aide des modèles pluie-débit GR (interface 'Shiny' incluse) (v. 0.2.2.2)

ManuelAdd-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables

airGRteaching: enseigner la modélisation hydrologique à l'aide des modèles pluie-débit GR (interface 'Shiny' incluse) (v. 0.2.3.2)

ManuelAdd-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables

airGRteaching: enseigner la modélisation hydrologique à l'aide des modèles pluie-débit GR (interface 'Shiny' incluse) (v. 0.2.3.2)

ManuelAdd-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables

airGRteaching: enseigner la modélisation hydrologique à l'aide des modèles pluie-débit GR (interface 'Shiny' incluse) (v. 0.2.6.29)

ManuelAdd-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables

airGRdatasets: Hydro-Meteorological Catchments Datasets for the 'airGR' Packages (v. 0.1.4)

ManuelSample of hydro-meteorological datasets extracted from the 'CAMELS-FR' French database . It provides metadata and catchment-scale aggregated hydro-meteorological time series on a pool of French catchments for use by the 'airGR' packages

Enseigner l'hydrologie à l'aide des modèles hydrologiques globaux en utilisant le package R airGRteaching

International audienceApplying hydrological models during tutorial classes is a way of making hydrology students use on concrete examples the knowledge they acquired during lectures. Most of the time, this is done through the application of either single components of a hydrological model (e.g. a unit hydrograph) or of more complex models, which suffer from difficult understanding and limited application due to the need of large datasets or low computational time. To overcome this issue, Irstea has also developed an R-package, airGRteaching (Delaigue et al., 2017), based on a suite of daily rainfall-runoff models (i.e. catchment scale representations of the precipitation-discharge relationship) already included in the airGR R-package (Coron et al., 2017a,b) (see abstract EGU2018-13049). This package includes the following features: - three very simple functions to prepare data, calibrate a model and run a simulation, - additional static and dynamic graphical functions, - a Shiny (Chang et al., 2016) interface that connects this R package to a browser-based visualization tool This presentation will focus on how the interface can be used by students to apply what they learnt. The airGRteaching graphical user interface presents a sidebar panel, which allows to select the basin and model and also to modify the model parameters with a slider widget. It is also possible to automatically calibrate the model (choosing objective function and flows transformation). The students can see the results on different time series reactive graphics or model scheme, and as a table presenting efficiency criteria values. The previous simulations can be viewed in order to better understand the role of the changed parameter(s). Static charts and model outputs can be exported respectively in the PNG and the CSV formats, and can be used by students for reporting. One exercise that can be given is to ask students to calibrate manually the model on a snowy basin, without telling them it is a snowy basin and without activating the snow model. By analyzing the fact that it is not possible to manually calibrate the model in such conditions and by analyzing the hydrological regime of the river, the students should be able to identify that the snow model should be activated. Other exercises include the calibration of a hydrological model on subperiods and wet or dry year and application on a validation period, which highlights the importance of the calibration conditions

airGR et airGRteaching : deux packages pour la modélisation pluie-débit et l'enseignement de l'hydrologie

International audienceThe use of R is growing fast on every step of hydrological studies, from the retrieval of hydro-meteorological data, to spatial analysis and cartography, hydrological modeling, statistics, and the design of static and dynamic visualizations (Slater et al., 2019, HESSD). Recently, IRSTEA developed an R package called airGR (Coron et al, 2017, EM&S, and 2018), to make the GR rainfall-runoff models widely available and facilitate reproducible science. It is available on the CRAN and includes efficient and fast-running hydrological models. The airGR package was designed to facilitate the use by non-expert users and allows the user to customize evaluation criteria, models or calibration algorithm. To help students learning and because the GR models are widely used in hydrology courses in French universities or engineering schools, we developed a package called airGRteaching (Delaigue et al., 2018, HIC, and 2018) based on airGR. This package reduces modeling to only three functions. In addition, the package offers various graphical outputs, static and dynamic (using the dygraphs package) to easily explore the model input data, as well as the results obtained during the model calibration or simulation phase. Finally, airGRteaching offers a Shiny interface allowing students to fully understand the role of each parameter or internal state of the models