Software and codes

The latest version of BaRatinAGE is available here. BaRatinAGE is a simple and intuitive graphical user interface to the BaRatin method and constitutes the recommended way to get started with BaRatin. It is also feature-rich and stable, so that it can routinely be used in the everyday operational practice.

The R package RBaM allows using BaRatin directly from R, without going through the BaRatinAGE interface. This can be useful for users who are more comfortable with scripts than with clickable interfaces, or who need to integrate BaRatin into a more complex workflow.

Two codes have been developed for detecting rating shifts. BayDERS is a R script that implements a set of tools developed by Matteo Darienzo (2021) to detect rating shifts using various sources of information, including gaugings and the stage time series. The R package RatingShiftHappens is currently in development and implements only some of the tools available in BayDERS - it has hence less features than BayDERS but it should be simpler to apply.

All codes and pieces of software above share the same computing core: BaM! (Bayesian Modeling). BaM! is open-source and can be accessed here.

Documentation

BaRatinAGE online help is a good starting point to discover the BaRatin method. You can also watch this webinar for a general description of the method and some tutorials (also available in French).

Slides describing BaRatin’s principles are also available (also in French and in Spanish).

To take one further step into the mathematics and the hydraulics behind BaRatin, two options are available: reading this document in French (used for a course on Advanced Statistics for uncertainty quantification in Hydrology), or browsing through the topics sheets of this website.

Finally, the BaRatin poster below can be downloaded and printed to get an overview of the method at a glance, or just to put some color in your office.

References

References on the BaRatin method and the associated rating curve models

Darienzo, M., Detection and estimation of stage-discharge rating shifts for retrospective and real-time streamflow quantification, PhD thesis, 2021.

Darienzo, M., Le Coz, J., Renard, B., Lang, M., Detection of stage-discharge rating shifts using gaugings: a recursive segmentation procedure accounting for observational and model uncertainties, Water Resources Research, 57, e2020WR028607, 2021.

Horner I., Renard B., Le Coz J., Branger F., McMillan H.K., Pierrefeu G., Impact of stage measurement errors on streamflow uncertainty, Water Resources Research, 54, 1952-1976, 2018.

Le Coz, J., Renard, B., Bonnifait, L., Branger, F., Le Boursicaud, R. Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: a Bayesian approach, Journal of Hydrology, 509, 573-587, 2014.

Le Coz, J., Renard, B., Bonnifait, L., Branger, F., Le Boursicaud, R. Uncertainty Analysis of Stage-Discharge Relations using the BaRatin Bayesian Framework. 35th IAHR World Congress 08/09/2013-13/09/2013, Chengdu, China, 9 p, 2013.

Le Coz, J., Moukandi N’kaya, G. D., Bricquet, J.-P., Laraque, A., Renard, B., Estimation bayésienne des courbes de tarage et des incertitudes associées : application de la méthode BaRatin au Congo à Brazzaville, Proc. IAHS, 384, 25-29, 2021.

Mansanarez, V. Non unique stage-discharge relations: Bayesian analysis of complex rating curves and their uncertainties, PhD thesis, 2016.

Mansanarez, V., Le Coz, J., Renard, B., Vauchel, P., Pierrefeu, G., Lang, M. Bayesian analysis of stage-fall-discharge rating curves and their uncertainties, Water Resources Research, 52, 7424-7443, 2016.

Mansanarez, V., Renard, B., Le Coz, J. Lang, M., Darienzo, M., Shift happens! Adjusting stage-discharge rating curves to riverbed morphological changes at known times, Water Resources Research, 55, 2876-2899, 2019.

Perret, E., Le Coz, J., Renard, B., A rating curve model accounting for cyclic stage-discharge shifts due to seasonal aquatic vegetation, Water Resources Research, 57, e2020WR027745, 2021.

References using BaRatin and associated tools

Ahrendt, S., Horner-Devine, A. R., Collins, B. D., Morgan, J. A., Istanbulluoglu, E., Channel conveyance variability can influence flood risk as much as streamflow variability in western Washington State. Water Resources Research, 58, e2021WR031890, 2022.

Francke, T., Foerster, S., Brosinsky, A., Sommerer, E., Lopez-Tarazon, J.A., Güntner, A., Batalla, R.J., Bronstert, A., Water and sediment fluxes in Mediterranean mountainous regions: Comprehensive dataset for hydro-sedimentological analyses and modelling in a mesoscale catchment (River Isábena, NE Spain), Earth System Science Data, 10(2), 1063-1075, 2018.

Garcia, R., Costa, V., Silva, F., Bayesian rating curve modeling: alternative error model to improve low-flow uncertainty estimation, Journal of Hydrological Engineering, 25(5): 04020012, 2020.

Gouy, V., Liger, L., Ahrouch, S., Bonnineau, C., Carluer, N., Chaumot, A., Coquery, M., Dabrin, A., Margoum, C., Pesce, S., Ardières-Morcille in the Beaujolais, France: A research catchment dedicated to study of the transport and impacts of diffuse agricultural pollution in rivers, Hydrological Processes, 35(10), e14384, 2021.

Henn, B., Painter, T.H., Bormann, K.J., McGurk, B., Flint, A.L., Flint, L.E., White, V., Lundquist, J.D., High-Elevation Evapotranspiration Estimates During Drought: Using Streamflow and NASA Airborne Snow Observatory SWE Observations to Close the Upper Tuolumne River Basin Water Balance, Water Resources Research, 54(2), 746-766, 2018.

Horner, I., Le Coz, J., Renard, B., Branger, F., Lagouy, M., Streamflow uncertainty due to the limited sensitivity of controls at hydrometric stations, Hydrological Processes, 36(2), e14497, 2022.

Kastali, A., Zeroual, A., Remaoun, M., Serrano-Notivoli, R., Moramarco, T. Design Flood and Flood-Prone Areas under Rating Curve Uncertainty: Area of Vieux-Ténès, Algeria, Journal of Hydrologic Engineering, 26(3), 05020054, 2020.

Kastali, A., Zeroual, A., Zeroual, S., Hamitouche, Y., Auto-calibration of HEC-HMS Model for Historic Flood Event under Rating Curve Uncertainty. Case Study: Allala Watershed, Algeria, KSCE Journal of Civil Engineering, 26(1), 482-493, 2022.

Kazimierski, L.D., García, P.E., Ortiz, N., Morale, M., Re, M., Aforos de ríos y arroyos en la Cuenca Matanza-Riachuelo. Elaboración de relaciones altura - caudal (curvas HQ). Informe LHA 05-397-21, Instituto Nacional del Agua (INA) – ACUMAR, Ezeiza, Argentina, 2021.

Kiang, J.E., Gazoorian, C., McMillan, H., Coxon, G., Le Coz, J., Westerberg, I., Belleville, A., Sevrez, D., Sikorska, A.E., Petersen-Øverleir, A., Reitan, T., Freer, J., Renard, B., Mansanarez, V., Mason, R. A comparison of methods for streamflow uncertainty estimation, Water Resources Research, 54(10), 7149-7176, 2018.

Lang, M., Darienzo, M., Le Coz, J., Renard, B., Evaluation des incertitudes et de l’homogénéité de longues séries de débits de crue sur le Rhin à Bâle (1225-2017) et Maxau (1815-2018), LHB-Hydroscience, 108:1, 2053313, 2022.

Laraque, A., Le Coz, J., Moukandi N’kaya, G.D., Bissemo, G., Ayissou, L., Rouché, N., Bricquet, J.-P., Gulemvuga, G., Courbes de tarage du fleuve Congo à Brazzaville-Kinshasa, LHB-Hydroscience, 108:1, 2022.

Lucas, M., Renard, B., Le Coz, J., Lang, M., Bard, A., Pierrefeu, G., Are historical stage records useful to decrease the uncertainty of flood frequency analysis? A 200-year long case study, Journal of Hydrology, 624, 129840, 2023.

Lundquist, J.D., Roche, J.W., Forrester, H., Moore, C., Keenan, E., Perry, G., Cristea, N., Henn, B., Lapo, K., McGurk, B., Cayan, D.R., Dettinger, M.D. Yosemite hydroclimate network: distributed stream and atmospheric data for the Tuolumne River watershed and surroundings, Water Resources Research, 52, 7478-7489, 2016.

Maldonado, L.H., Firmo Kazay, D., Romero Lopez, E.E., The estimation of the uncertainty associated with rating curves of the river Ivinhema in the state of Paraná/Brazil, IAHR RiverFlow 2018 conference, E3S Web of Conferences, 40, 06029, 2018.

Masafu, C., Williams, R., Satellite video remote sensing for flood model validation. Water Resources Research, 60, e2023WR034545, 2024.

Mason, R.R. Jr., Kiang, J.E., Cohn, T.A. Rating curve uncertainty: An illustration of two estimation methods, IAHR River Flow conference, St. Louis, Missouri, USA, 12-15 July, 729-734, 2016.

Ocio, D., Le Vine, N., Westerberg, I., Pappenberger, F., Buytaert, W. The role of rating curve uncertainty in real-time flood forecasting, Water Resources Research, 53, 4197-4213, 2017.

Osorio, A.L.N.A., Reis, D.S. A Bayesian approach for the evaluation of rating curve uncertainties in flood frequency analyses, World Environmental and Water Resources Congress, West Palm Beach, Florida, USA, May 22-26, 482-491, 2016.

Osorio, A.L.N.A. Modelo bayesiano completo para análise de frequência de cheias com incorporação do conhecimento hidráulico na modelagem das incertezas na curva-chave [Full Bayesian model for flood frequency analysis with incorporation of hydraulic knowledge in the modeling of uncertainties in the rating curve], Master thesis, Universidade de Brasília, Brazil, 161 p, 2017.

Perret, E., Le Coz, J., Renard, B., Courbes de tarage dynamiques pour la végétation aquatique saisonnière, LHB-Hydroscience, 108:1, 2082339, 2022.

Qiu, J., Liu, B., Yang, Z., Peng, W., Uncertainty analysis of estimated discharge based on stage-discharge rating curves [in Chinese], Shuikexue Jinzhan/Advances in Water Science, 31(2), 214-223, 2020.

Qiu, J., Liu, B., Yu, X., Yang, Z., Combining a segmentation procedure and the BaRatin stationary model to estimate nonstationary rating curves and the associated uncertainties, Journal of Hydrology, 597, 126168, 2021.

Sikorska, A.E., Renard, R. Calibrating a hydrological model in stage space to account for rating curve uncertainties: general framework and key challenges, Advances in Water Resources, 105, 51-66, 2017.

Storz, S.M. Stage-discharge relationships for two nested research catchments of the high-mountain observatory in the Simen Mountains National Park in Ethiopia, Master thesis, Bern University, Switzerland, 87 p, 2016.

Vieira, L.M.D.S., Sampaio, J.C.L., Costa, V.A.F., Eleutério, J.C., Assessing the effects of rating curve uncertainty in flood frequency analysis, Revista Brasileira de Recursos Hidricos, 27, e11, 2022.

Zeroual, A., Meddi, M., Assani, A.A. Artificial neural network rainfall-discharge model assessment under rating curve uncertainty and monthly discharge volume predictions, Water Resources Management, 30, 3191-3205, 2016.