License License GPL 2 GPL 2

airGR references

Article

  • Coron, L., Thirel, G., Delaigue, O., Perrin, C. and Andréassian, V. (2017). The Suite of Lumped GR Hydrological Models in an R package. Environmental Modelling and Software, 94, 166-171, doi: 10.1016/j.envsoft.2017.05.002.
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2021

2020

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2016

Use and mention of airGR

Articles

Upcoming

  • Bathelemy R., Brigode P., Andréassian V., Perrin C., Moron V., Gaucherel C., et al. (2023). Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti. Earth System Science Data Discussions 2023, 1–34. doi: 10.5194/essd-2023-259
  • Colleoni F., Garambois P.-A., Javelle P., Jay-Allemand M. & Arnaud P. (2022). Adjoint-based spatially distributed calibration of a grid GR-based parsimonious hydrological model over 312 French catchments with SMASH platform. EGUsphere 2022, 1–37. doi: 10.5194/egusphere-2022-506
  • Devers A., Vidal J.-P., Lauvernet C., Vannier O. & Caillouet L. (2024). 140-year daily ensemble streamflow reconstructions over 661 catchments in France. Hydrology and Earth System Sciences Discussions 2024, 1–28. doi: 10.5194/hess-2024-42
  • Mologni C., Revel M., Chaumillon E., Malet E., Coulombier T., Sabatier P., et al. (2024). Fifty-year seasonal variability of East African droughts and floods recorded in Central Afar lake sediments (Ethiopia) and their connections with ENSO. EGUsphere 2024, 1–38. doi: 10.5194/egusphere-2024-310
  • Siddik Ahmed Barbhuiya1, Raghuvanshi A.S. & Tiwari H.L. (2022). Assessment of Streamflow in Ungauged Basin by Using Physical similarity approach. In Review. doi: 10.21203/rs.3.rs-1838734/v1
  • Thirel G., Santos L., Delaigue O. & Perrin C. (2023). On the use of streamflow transformations for hydrological model calibration. EGUsphere 2023, 1–26. doi: 10.5194/egusphere-2023-775

2024

  • Andrade J.M., Neto A.R., Nóbrega R.L.B., Rico-Ramirez M.A. & Montenegro S.M.G.L. (2024). Efficiency of global precipitation datasets in tropical and subtropical catchments revealed by large sampling hydrological modelling. Journal of Hydrology 633, 131016. doi: 10.1016/j.jhydrol.2024.131016
  • McInerney D., Thyer M., Kavetski D., Westra S., Maier H.R., Shanafield M., et al. (2024). Neglecting hydrological errors can severely impact predictions of water resource system performance. Journal of Hydrology 634, 130853. doi: 10.1016/j.jhydrol.2024.130853
  • Manikanta V. & Umamahesh N.V. (2024). Unravelling the impact of spatial discretization and calibration strategies on event-based flood models. Modeling Earth Systems and Environment. doi: 10.1007/s40808-023-01936-7
  • Marques A.C., Veras C.E., Kumpel E., Tobiason J.E. & Guzman C.D. (2024). Assessment of nutrients and conductivity in the Wachusett Reservoir watershed: An investigation of land use contributions and trends. International Soil and Water Conservation Research 12, 337–350. doi: 10.1016/j.iswcr.2023.07.004
  • Matt Gibbs M.A. & Vaze J. (2023). The SWTools R package for SILO data acquisition, homogeneity testing and correction. Australasian Journal of Water Resources, 1–13. doi: 10.1080/13241583.2023.2214989
  • Poncet N., Lucas-Picher P., Tramblay Y., Thirel G., Vergara H., Gourley J., et al. (2024). Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods? Natural Hazards and Earth System Sciences 24, 1163–1183. doi: 10.5194/nhess-24-1163-2024
  • Saadi M. & Furusho-Percot C. (2024). Which range of streamflow data is most informative in the calibration of an hourly hydrological model? Hydrological Sciences Journal 69, 1–20. doi: 10.1080/02626667.2023.2277835
  • Sezen C. & Šraj M. (2024). Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models. Science of The Total Environment 926, 171684. doi: 10.1016/j.scitotenv.2024.171684
  • Thébault C., Perrin C., Andréassian V., Thirel G., Legrand S. & Delaigue O. (2024). Multi-model approach in a variable spatial framework for streamflow simulation. Hydrology and Earth System Sciences 28, 1539–1566. doi: 10.5194/hess-28-1539-2024
  • Tursun A., Xie X., Wang Y., Liu Y., Peng D. & Zheng B. (2024). Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes. Journal of Hydrology 630, 130771. doi: 10.1016/j.jhydrol.2024.130771

2023

  • Acuña P. & Pizarro A. (2023). Can continuous simulation be used as an alternative for flood regionalisation? A large sample example from Chile. Journal of Hydrology 626, 130118. doi: j.jhydrol.2023.130118
  • Aitken G., Beevers L., Parry S. & Facer-Childs K. (2023). Partitioning model uncertainty in multi-model ensemble river flow projections. Climatic Change 176, 153. doi: 10.1007/s10584-023-03621-1
  • Araya D., Mendoza P.A., Muñoz-Castro E. & McPhee J. (2023). Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling. Hydrology and Earth System Sciences 27, 4385–4408. doi: 10.5194/hess-27-4385-2023
  • Ascott M.J., Christelis V., Lapworth D.J., Macdonald D.M.J., Tindimugaya C., Iragena A., et al. (2023). On the application of rainfall projections from a convection-permitting climate model to lumped catchment models. Journal of Hydrology 617, 129097. doi: 10.1016/j.jhydrol.2023.129097
  • Barbhuiya S., Raghuvanshi A.S. & Tiwari H.L. (2023). Assessment of streamflow in ungauged basin by using physical similarity approach. Arabian Journal of Geosciences 16, 672. doi: 10.1007/s12517-023-11786-3
  • Clayer F., Jackson-Blake L., Mercado-Bettín D., Shikhani M., French A., Moore T., et al. (2023). Sources of skill in lake temperature, discharge and ice-off seasonal forecasting tools. Hydrology and Earth System Sciences 27, 1361–1381. doi: 10.5194/hess-27-1361-2023
  • Delaigue O., Brigode P., Thirel G. & Coron L. (2023). airGRteaching: an open-source tool for teaching hydrological modeling with R. Hydrology and Earth System Sciences 27, 3293–3327. doi: 10.5194/hess-27-3293-2023
  • Faty B., Sterk G., Ali A., Sy S., Dacosta H., Diop S., et al. (2023). Satellite-based rainfall estimates to simulate daily streamflow using a hydrological model over Gambia watershed. Water Science 37, 1–18. doi: 10.1080/23570008.2023.2225898
  • Hrour Y., Fovet O., Lacombe G., Rousseau-Gueutin P., Sebari K., Pichelin P., et al. (2023). A framework to assess future water-resource under climate change in northern Morocco using hydro-climatic modelling and water-withdrawal scenarios. Journal of Hydrology: Regional Studies 48, 101465. doi10.1016/j.ejrh.2023.101465
  • Jeantet A., Thirel G., Lemaitre-Basset T. & Tournebize J. (2023). Uncertainty propagation in a modelling chain of climate change impact for a representative French drainage site. Hydrological Sciences Journal, 1–17. doi: 10.1080/02626667.2023.2203322
  • Karki N., Shakya N.M., Pandey V.P., Devkota L.P., Pradhan A.M.S. & Lamichhane S. (2023). Comparative performance of regionalization methods for model parameterization in ungauged Himalayan watersheds. Journal of Hydrology: Regional Studies 47, 101359. doi: 10.1016/j.ejrh.2023.101359
  • Koné S., Mahé G., Bamba F., Paturel J.-E., Dezetter A. & Servat E. (2023). Building Tools for further Investigating Acid Mining Production: Intercomparison of Four Hydrological Model Versions through a Scoring Technique on the Niger River Basin, in west Africa. Earth & Environmental Science Research & Reviews 6, 566–594. doi: 10.33140/EESRR
  • Kouakou C., Paturel J.-E., Satgé F., Tramblay Y., Defrance D. & Rouché N. (2023). Comparison of gridded precipitation estimates for regional hydrological modeling in West and Central Africa. Journal of Hydrology: Regional Studies 47, 101409. doi: 10.1016/j.ejrh.2023.101409
  • Lee S.C. & Kim D. (2023). A comparative study of conceptual model and machine learning model for rainfall-runoff simulation. Journal of Korea Water Resources Association 56, 563–574. doi: 10.3741/JKWRA.2023.56.9.563
  • McInerney D., Westra S., Leonard M., Bennett B., Thyer M. & Maier H.R. (2023). A climate stress testing method for changes in spatially variable rainfall. Journal of Hydrology 625, 129876. doi: 10.1016/j.jhydrol.2023.129876
  • Marti B.S., Zhumabaev A. & Siegfried T. (2023). A comprehensive open-source course for teaching applied hydrological modelling in Central Asia. Hydrology and Earth System Sciences 27, 319–330. doi: 10.5194/hess-27-319-2023
  • Mathevet T., Le Moine N., Andréassian V., Gupta H. & Oudin L. (2023). Multi-objective assessment of hydrological model performances using Nash–Sutcliffe and Kling–Gupta efficiencies on a worldwide large sample of watersheds. Comptes Rendus. Géoscience 355, 1–25. doi: 10.5802/crgeos.189
  • Newcomer M.E., Underwood J., Murphy S.F., Ulrich C., Schram T., Maples S.R., et al. (2023). Prolonged Drought in a Northern California Coastal Region Suppresses Wildfire Impacts on Hydrology. Water Resources Research 59, e2022WR034206. doi: 10.1029/2022WR034206
  • Rodrigues A.L., Rodrigues L.N., Marques G.F. & Villa P.M. (2023). Simulation Model to Assess the Water Dynamics in Small Reservoirs. Water Resources Management. doi: s11269-023-03468-2
  • Rozos E. (2023). Assessing Hydrological Simulations with Machine Learning and Statistical Models. Hydrology 10. doi: 10.3390/hydrology10020049
  • Ruelland D. (2023). Development of the snow- and ice-accounting routine (SIAR). Journal of Hydrology 624, 129867. doi: 10.1016/j.jhydrol.2023.129867
  • Saadi M., Furusho-Percot C., Belleflamme A., Chen J.-Y., Trömel S. & Kollet S. (2023). How uncertain are precipitation and peak flow estimates for the July 2021 flooding event? Natural Hazards and Earth System Sciences 23, 159–177. doi: 10.5194/nhess-23-159-2023
  • Saadi M., Furusho-Percot C., Belleflamme A., Trömel S., Kollet S. & Reinoso-Rondinel R. (2023). Comparison of three radar-based precipitation nowcasts for the extreme July 2021 flooding event in Germany. Journal of Hydrometeorology. doi: 10.1175/JHM-D-22-0121.1
  • Sezen C. & Šraj M. (2023). Hourly rainfall-runoff modelling by combining the conceptual model with machine learning models in mostly karst Ljubljanica River catchment in Slovenia. Stochastic Environmental Research and Risk Assessment. doi: 10.1007/s00477-023-02607-w
  • Strohmenger L., Sauquet E., Bernard C., Bonneau J., Branger F., Bresson A., et al. (2023). On the visual detection of non-natural records in streamflow time series: challenges and impacts. Hydrology and Earth System Sciences 27, 3375–3391. doi: 10.5194/hess-27-3375-2023
  • Thébault C., Perrin C., Andréassian V., Thirel G., Legrand S. & Delaigue O. (2023). Impact of suspicious streamflow data on the efficiency and parameter estimates of rainfall–runoff models. Hydrological Sciences Journal 68, 1627–1647. doi: 10.1080/02626667.2023.2234893
  • Tyralis H. & Papacharalampous G. (2023). Hydrological post-processing for predicting extreme quantiles. Journal of Hydrology 617, 129082. doi: 10.1016/j.jhydrol.2023.129082
  • Tyralis H., Papacharalampous G. & Khatami S. (2023). Expectile-based hydrological modelling for uncertainty estimation: Life after mean. Journal of Hydrology 617, 128986. doi: 10.1016/j.jhydrol.2022.128986
  • Woo D.K., Jo J., Kang S. Boosik, Lee, Lee G. & Noh S.J. (2023). Sensitivity evaluation of runoff analysis for precipitation and temperature variability using concentrated models IHACRES and GR4J. Journal of the Korean Society of Civil Engineers 43, 43–54.
  • Yang Y. & Chui T.F.M. (2023). Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model. Water Resources Research 59, e2022WR033684. doi: 10.1029/2022WR033684
  • Yang Y. & Chui T.F.M. (2023). Learning to Generate Lumped Hydrological Models. 1–24. doi: 10.48550/arXiv.2309.09904

2022

  • Acharya S.C., Nathan R., Wang Q.J. & Su C.-H. (2022). Temporal disaggregation of daily rainfall measurements using regional reanalysis for hydrological applications. Journal of Hydrology 610, 127867. doi: 10.1016/j.jhydrol.2022.127867
  • Alp H., Demirel M.C. & Aşıkoğlu Ö.L. (2022). Effect of Model Structure and Calibration Algorithm on Discharge Simulation in the Acısu Basin, Turkey. Climate 10. doi: 10.3390/cli10120196
  • Althoff D., Rodrigues L.N. & Silva D.D. da (2022). Predicting runoff series in ungauged basins of the Brazilian Cerrado biome. Environmental Modelling & Software 149, 105315. doi: 10.1016/j.envsoft.2022.105315
  • Astagneau P.C., Bourgin F., Andréassian V. & Perrin C. (2022). Catchment response to intense rainfall: Evaluating modelling hypotheses. Hydrological Processes 36, e14676. doi: 10.1002/hyp.14676
  • Bérubé S., Brissette F. & Arsenault R. (2022). Optimal Hydrological Model Calibration Strategy for Climate Change Impact Studies. Journal of Hydrologic Engineering 27, 04021053. doi: 10.1061/(ASCE)HE.1943-5584.0002148
  • Chelil S., Oubanas H., Henine H., Gejadze I., Malaterre P.O. & Tournebize J. (2022). Variational data assimilation to improve subsurface drainage model parameters. Journal of Hydrology 610, 128006. doi: 10.1016/j.jhydrol.2022.128006
  • Hah D., Quilty J.M. & Sikorska-Senoner A.E. (2022). Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool. Environmental Modelling & Software 157, 105474. doi: 10.1016/j.envsoft.2022.105474
  • Hao Y., Sun F., Wang H., Liu W., Shen Y.-J., Li Z., et al. (2022). Understanding climate-induced changes of snow hydrological processes in the Kaidu River Basin through the CemaNeige-GR6J model. CATENA 212, 106082. doi: 10.1016/j.catena.2022.106082
  • Hashemi R., Brigode P., Garambois P.-A. & Javelle P. (2022). How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? Hydrology and Earth System Sciences 26, 5793–5816. doi: 10.5194/hess-26-5793-2022
  • Henine H., Jeantet A., Chaumont C., Chelil S., Lauvernet C. & Tournebize J. (2022). Coupling of a subsurface drainage model with a soil reservoir model to simulate drainage discharge and drain flow start. Agricultural Water Management 262, 107318. doi: 10.1016/j.agwat.2021.107318
  • Horton P., Schaefli B. & Kauzlaric M. (2022). Why do we have so many different hydrological models? A review based on the case of Switzerland. WIREs Water 9, e1574. doi: 10.1002/wat2.1574
  • Jackson-Blake L.A., Clayer F., de Eyto E., French A.S., Frías M.D., Mercado-Bettín D., et al. (2022). Opportunities for seasonal forecasting to support water management outside the tropics. Hydrology and Earth System Sciences 26, 1389–1406. doi: 10.5194/hess-26-1389-2022
  • Jougla R. & Leconte R. (2022). Short-Term Hydrological Forecast Using Artificial Neural Network Models with Different Combinations and Spatial Representations of Hydrometeorological Inputs. Water 14, 1556. doi:
  • Lemoine A., Ramos M.-H. & Andréassian V. (2022). Climate change impacts on the management of hydropower reservoirs using guide curves. LHB, 2101393. doi: 10.1080/27678490.2022.210139310.3390/w14040552
  • Manikanta V. & Vema V.K. (2022). Formulation of Wavelet Based Multi-Scale Multi-Objective Performance Evaluation (WMMPE) Metric for Improved Calibration of Hydrological Models. Water Resources Research 58, e2020WR029355. doi: 10.1029/2020WR029355
  • Mendez M., Calvo-Valverde L.-A., Imbach P., Maathuis B., Hein-Grigg D., Hidalgo-Madriz J.-A., et al. (2022). Hydrological Response of Tropical Catchments to Climate Change as Modeled by the GR2M Model: A Case Study in Costa Rica. Sustainability 14. doi: 10.3390/su142416938
  • Meresa H., Donegan S., Golian S. & Murphy C. (2022). Simulated Changes in Seasonal and Low Flows with Climate Change for Irish Catchments. Water 14. doi: 10.3390/w14101556
  • Nasreen S., Součková M., Vargas Godoy M.R., Singh U., Markonis Y., Kumar R., et al. (2022). A 500-year annual runoff reconstruction for 14 selected European catchments. Earth System Science Data 14, 4035–4056. doi: 10.5194/essd-14-4035-2022
  • Pastén-Zapata E., Pimentel R., Royer-Gaspard P., Sonnenborg T.O., Aparicio-Ibañez J., Lemoine A., et al. (2022). The effect of weighting hydrological projections based on the robustness of hydrological models under a changing climate. Journal of Hydrology: Regional Studies 41, 101113. doi: 10.1016/j.ejrh.2022.101113
  • Pelletier A. & Andréassian V. (2022). On constraining a lumped hydrological model with both piezometry and streamflow: results of a large sample evaluation. Hydrology and Earth System Sciences 26, 2733–2758. doi: 10.5194/hess-26-2733-2022
  • Peredo D., Ramos M.-H., Andréassian V. & Oudin L. (2022). Investigating hydrological model versatility to simulate extreme flood events. Hydrological Sciences Journal 67, 628–645. doi: 10.1080/02626667.2022.2030864
  • Pérez-Sánchez J., Senent-Aparicio J. & Jimeno-Sáez P. (2022). The application of spreadsheets for teaching hydrological modeling and climate change impacts on streamflow. Computer Applications in Engineering Education 30, 1510–1525. doi: 10.1002/cae.22541
  • Pujol L., Garambois P.-A. & Monnier J. (2022). Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains. Geoscientific Model Development 15, 6085–6113. doi: 10.5194/gmd-15-6085-2022
  • Qi W., Chen J., Li L., Xu C.-Y., Li J., Xiang Y., et al. (2022). Regionalization of catchment hydrological model parameters for global water resources simulations. Hydrology Research 53, 441–466. doi: 10.2166/nh.2022.118
  • Schürz C. & Schulz K. (2022). Reply to STOTEN 802 (2022) 149713: The fallacy in the use of the “best-fit” solution in hydrologic modeling. Science of The Total Environment 821, 153402. doi: 10.1016/j.scitotenv.2022.153402
  • Sezen C. & Partal T. (2022). New hybrid GR6J-wavelet-based genetic algorithm-artificial neural network (GR6J-WGANN) conceptual-data-driven model approaches for daily rainfall–runoff modelling. Neural Computing and Applications 34, 17231–17255. doi: 10.1007/s00521-022-07372-5
  • Sezen C. & Partal T. (2022). The utilisation of conceptual and data-driven models for hydrological modelling in semi-arid and humid areas of the Antalya basin in Turkey. Acta Geophysica 70, 897–915. doi: 10.1007/s11600-022-00746-2
  • Sezen C. & Partal T. (2022). Two integrated conceptual–wavelet-based data-driven model approaches for daily rainfall–runoff modelling. Journal of Hydroinformatics 24, 949–975. doi: 10.2166/hydro.2022.171
  • Shannon J., Kolka R., Van Grinsven M. & Liu F. (2022). Joint impacts of future climate conditions and invasive species on black ash forested wetlands. Frontiers in Forests and Global Change 5, 957526. doi: 10.3389/ffgc.2022.957526
  • Trinugroho M.W. & Prima Nugroho A. (2022). Pemodelan limpasan air hujan menggunakan GR2M berbasis R di hilir daerah aliran sungai Cimanuk. Buletin Hasil Penelitian Agroklimat dan Hidrologi 18, 14–21. ISSN: 0216-3934
  • Wanzala M.A., Ficchi A., Cloke H.L., Stephens E.M., Badjana H.M. & Lavers D.A. (2022). Assessment of global reanalysis precipitation for hydrological modelling in data-scarce regions: A case study of Kenya. Journal of Hydrology: Regional Studies 41, 101105. doi: 10.1016/j.ejrh.2022.101105
  • Yahiaoui S., Chibane B., Pistre S., Bentchakal M. & Ali-Rahmani S.-E. (2022). Rainfall-runoff modeling using airGR and airGRteaching: application to a catchment in Northeast Algeria. Modeling Earth Systems and Environment 8, 4985–4996. doi: 10.1007/s40808-022-01444-0

2021

  • Adane G.B., Hirpa B.A., Gebru B.M., Song C. & Lee W.-K. (2021). Integrating Satellite Rainfall Estimates with Hydrological Water Balance Model: Rainfall-Runoff Modeling in Awash River Basin, Ethiopia. Water 13. doi: 10.3390/w13060800
  • Althoff D. & Rodrigues L.N. (2021). Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment. Journal of Hydrology 600, 126674. doi: 10.1016/j.jhydrol.2021.126674
  • Althoff D., Rodrigues L.N. & Bazame H.C. (2021). Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble. Stochastic Environmental Research and Risk Assessment 35, 1051–1067. doi: 10.1007/s00477-021-01980-8
  • Arabzadeh R., Aberi P., Hesarkazzazi S., Hajibabaei M., Rauch W., Nikmehr S., et al. (2021). WRSS: An Object-Oriented R Package for Large-Scale Water Resources Operation. Water 13. doi: 10.3390/w13213037
  • Astagneau P.C., Bourgin F., Andréassian V. & Perrin C. (2021). When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias. Hydrological Sciences Journal 66, 1288–1305. doi: 10.1080/02626667.2021.1923720
  • Astagneau P.C., Thirel G., Delaigue O., Guillaume J.H.A., Parajka J., Brauer C.C., et al. (2021). Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective. Hydrology and Earth System Sciences 25, 3937–3973. doi: 10.5194/hess-25-3937-2021
  • Ayzel G., Kurochkina L., Abramov D. & Zhuravlev S. (2021). Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks. Hydrology 8, 6. doi: 10.3390/hydrology8010006
  • Bouaziz L.J.E., Fenicia F., Thirel G., de Boer-Euser T., Buitink J., Brauer C.C., et al. (2021). Behind the scenes of streamflow model performance. Hydrology and Earth System Sciences 25, 1069–1095. doi: 10.5194/hess-25-1069-2021
  • Caillouet L., Vidal J.-P., Sauquet E., Devers A., Lauvernet C., Graff B., et al. (2021). Intercomparaison des évènements d’étiage extrême en France depuis 1871. LHB 107, 1–9. doi: 10.1080/00186368.2021.1914463
  • Donegan S., Murphy C., Harrigan S., Broderick C., Foran Quinn D., Golian S., et al. (2021). Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times. Hydrology and Earth System Sciences 25, 4159–4183. doi: 10.5194/hess-25-4159-2021
  • Flores N., Rodríguez R., Yépez S., Osores V., Rau P., Rivera D., et al. (2021). Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile. Water 13. doi: 10.3390/w13223191
  • Gagnon-Poiré A., Brigode P., Francus P., Fortin D., Lajeunesse P., Dorion H., et al. (2021). Reconstructing past hydrology of eastern Canadian boreal catchments using clastic varved sediments and hydro-climatic modelling: 160 years of fluvial inflows. Climate of the Past 17, 653–673. doi: 10.5194/cp-17-653-2021
  • Gnann S.J., Coxon G., Woods R.A., Howden N.J.K. & McMillan H.K. (2021). TOSSH: A Toolbox for Streamflow Signatures in Hydrology. Environmental Modelling & Software 138, 104983. doi: 10.1016/j.envsoft.2021.104983
  • Golian S. & Murphy C. (2021). Evaluation of Sub-Selection Methods for Assessing Climate Change Impacts on Low-Flow and Hydrological Drought Conditions. Water Resources Management 35, 113–133. doi: s11269-020-02714-1
  • Golian S., Murphy C. & Meresa H. (2021). Regionalization of hydrological models for flow estimation in ungauged catchments in Ireland. Journal of Hydrology: Regional Studies 36, 100859. doi: 10.1016/j.ejrh.2021.100859
  • Guilpart E., Espanmanesh V., Tilmant A. & Anctil F. (2021). Combining split-sample testing and hidden Markov modelling to assess the robustness of hydrological models. Hydrology and Earth System Sciences 25, 4611–4629. doi: 10.5194/hess-25-4611-2021
  • Hunter J., Thyer M., McInerney D. & Kavetski D. (2021). Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions. Journal of Hydrology 603, 126578. doi: 10.1016/j.jhydrol.2021.126578
  • Jeantet A., Henine H., Chaumont C., Collet L., Thirel G. & Tournebize J. (2021). Robustness of a parsimonious subsurface drainage model at the French national scale. Hydrology and Earth System Sciences 25, 5447–5471. doi: 10.5194/hess-25-5447-2021
  • Lemaitre-Basset T., Collet L., Thirel G., Parajka J., Evin G. & Hingray B. (2021). Climate change impact and uncertainty analysis on hydrological extremes in a French Mediterranean catchment. Hydrological Sciences Journal 66, 888–903. doi: 10.1080/02626667.2021.1895437
  • Llauca H., Lavado-Casimiro W., León K., Jimenez J., Traverso K. & Rau P. (2021). Assessing Near Real-Time Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Peruvian Andes. Remote Sensing 13, 826. doi: 10.3390/rs13040826
  • Llauca H., Lavado-Casimiro W., Montesinos C., Santini W. & Rau P. (2021). PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020). Water 13. doi: 10.3390/w13081048
  • McDowell R.W., Simpson Z.P., Ausseil A.G., Etheridge Z. & Law R. (2021). The implications of lag times between nitrate leaching losses and riverine loads for water quality policy. Scientific Reports 11, 16450. doi: 10.1038/s41598-021-95302-1
  • Mercado-Bettín D., Clayer F., Shikhani M., Moore T.N., Frías M.D., Jackson-Blake L., et al. (2021). Forecasting water temperature in lakes and reservoirs using seasonal climate prediction. Water Research 201, 117286. doi: 10.1016/j.watres.2021.117286
  • Nicolle P., Andréassian V., Royer-Gaspard P., Perrin C., Thirel G., Coron L., et al. (2021). Technical note: RAT – a robustness assessment test for calibrated and uncalibrated hydrological models. Hydrology and Earth System Sciences 25, 5013–5027. doi: 10.5194/hess-25-5013-2021
  • Ollivier C., Olioso A., Carrière S.D., Boulet G., Chalikakis K., Chanzy A., et al. (2021). An evapotranspiration model driven by remote sensing data for assessing groundwater resource in karst watershed. Science of The Total Environment 781, 146706. doi: 10.1016/j.scitotenv.2021.146706
  • Piazzi G., Thirel G., Perrin C. & Delaigue O. (2021). Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale. Water Resources Research 57. doi: 10.1029/2020WR028390
  • Royer-Gaspard P., Andréassian V. & Thirel G. (2021). Technical note: PMR – a proxy metric to assess hydrological model robustness in a changing climate. Hydrology and Earth System Sciences 25, 5703–5716. doi: hess-25-5703-2021
  • Saadi M., Oudin L. & Ribstein P. (2021). Physically consistent conceptual rainfall–runoff model for urbanized catchments. Journal of Hydrology 599, 126394. doi: 10.1016/j.jhydrol.2021.126394
  • Sauquet E., Beaufort A., Sarremejane R. & Thirel G. (2021). Predicting flow intermittence in France under climate change. Hydrological Sciences Journal 66, 2046–2059. doi: 10.1080/02626667.2021.1963444
  • Soper J.J., Guzman C.D., Kumpel E. & Tobiason J.E. (2021). Long-term analysis of road salt loading and transport in a rural drinking water reservoir watershed. Journal of Hydrology 603, 127005. doi: 10.1016/j.jhydrol.2021.127005
  • Toum E., Masiokas M.H., Villalba R., Pitte P. & Ruiz L. (2021). The HBV.IANIGLA Hydrological Model. The R Journal 13, 378–395. doi: 10.32614/RJ-2021-059
  • Tyralis H. & Papacharalampous G. (2021). Quantile-Based Hydrological Modelling. Water 13. doi: 10.3390/w13233420
  • Wang H., Cao L. & Feng R. (2021). Hydrological Similarity-Based Parameter Regionalization under Different Climate and Underlying Surfaces in Ungauged Basins. Water 13. doi: 10.3390/w13182508

2020

  • Adeyeri O.E., Laux P., Arnault J., Lawin A.E. & Kunstmann H. (2020). Conceptual hydrological model calibration using multi-objective optimization techniques over the transboundary Komadugu-Yobe basin, Lake Chad Area, West Africa. Journal of Hydrology: Regional Studies 27, 100655. doi: 10.1016/j.ejrh.2019.100655
  • Aufar Y., Sitanggang I.S. & Annisa (2020). Parameter Optimization of Rainfall-runoff Model GR4J using Particle Swarm Optimization on Planting Calendar. International Journal on Advanced Science, Engineering and Information Technology 10, 2575. doi: 10.18517/ijaseit.10.6.9110
  • Ayzel G., Kurochkina L. & Zhuravlev S. (2020). The influence of regional hydrometric data incorporation on the accuracy of gridded reconstruction of monthly runoff. Hydrological Sciences Journal, 1–12. doi: 10.1080/02626667.2020.1762886
  • Bezak N., Cerović L. & Šraj M. (2020). Impact of the Mean Daily Air Temperature Calculation on the Rainfall-Runoff Modelling. Water 12, 3175. doi: 10.3390/w12113175
  • Citrini A., Camera C. & Beretta G.P. (2020). Nossana Spring (Northern Italy) under Climate Change: Projections of Future Discharge Rates and Water Availability. Water 12, 387. doi: 10.3390/w12020387
  • Crochemore L., Ramos M.-H. & Pechlivanidis I.G. (2020). Can Continental Models Convey Useful Seasonal Hydrologic Information at the Catchment Scale? Water Resources Research 56. doi: 10.1029/2019WR025700
  • Flores A.P., Giordano L. & Ruggerio C.A. (2020). A basin-level analysis of flood risk in urban and periurban areas: A case study in the metropolitan region of Buenos Aires, Argentina. Heliyon 6, e04517. doi: 10.1016/j.heliyon.2020.e04517
  • Ghimire U., Agarwal A., Shrestha N.K., Daggupati P., Srinivasan G. & Than H.H. (2020). Applicability of Lumped Hydrological Models in a Data-Constrained River Basin of Asia. Journal of Hydrologic Engineering 25, 05020018. doi: 10.1061/(ASCE)HE.1943-5584.0001950
  • Monteil C., Zaoui F., Le Moine N. & Hendrickx F. (2020). Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm. Hydrology and Earth System Sciences 24, 3189–3209. doi: hess-24-3189-2020
  • Neri M., Parajka J. & Toth E. (2020). Importance of the informative content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density. Hydrology and Earth System Sciences 24, 5149–5171. doi: 10.5194/hess-24-5149-2020
  • Nguyen H., Mehrotra R. & Sharma A. (2020). Assessment of Climate Change Impacts on Reservoir Storage Reliability, Resilience, and Vulnerability Using a Multivariate Frequency Bias Correction Approach. Water Resources Research 56. doi: 10.1029/2019WR026022
  • O’Connor P., Murphy C., Matthews T. & Wilby R.L. (2020). Reconstructed monthly river flows for Irish catchments 1766–2016. Geoscience Data Journal, gdj3.107. doi: 10.1002/gdj3.107
  • Papacharalampous G., Tyralis H., Koutsoyiannis D. & Montanari A. (2020). Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale. Advances in Water Resources 136, 103470. doi: 10.1016/j.advwatres.2019.103470
  • Pelletier A. & Andréassian V. (2020). Characterising catchments’ memory through a crossover approach between piezometry and hydrograph separation. La Houille Blanche, 30–37. doi: 10.1051/lhb/2020032
  • Pelletier A. & Andréassian V. (2020). Hydrograph separation: an impartial parametrisation for an imperfect method. Hydrology and Earth System Sciences 24, 1171–1187. doi: 10.5194/hess-24-1171-2020
  • Schmidt-Walter P., Trotsiuk V., Meusburger K., Zacios M. & Meesenburg H. (2020). Advancing simulations of water fluxes, soil moisture and drought stress by using the LWF-Brook90 hydrological model in R. Agricultural and Forest Meteorology, 108023. doi: 10.1016/j.agrformet.2020.108023
  • Sezen C., Šraj M., Medved A. & Bezak N. (2020). Investigation of Rain-On-Snow Floods under Climate Change. Applied Sciences 10, 1242. doi: 10.3390/app10041242
  • Wijayarathne D.B. & Coulibaly P. (2020). Identification of hydrological models for operational flood forecasting in St. John’s, Newfoundland, Canada. Journal of Hydrology: Regional Studies 27, 100646. doi: j.ejrh.2019.100646
  • Yang W., Yang H. & Yang D. (2020). Classifying floods by quantifying driver contributions in the Eastern Monsoon Region of China. Journal of Hydrology 585, 124767. doi: 10.1016/j.jhydrol.2020.124767
  • Zhong R., Zhao T. & Chen X. (2020). Hydrological model calibration for dammed basins using satellite altimetry information. Water Resources Research. doi: 10.1029/2020WR027442

2019

  • Allani M., Mezzi R., Zouabi A., Béji R., Joumade-Mansouri F., Hamza M.E., et al. (2019). Impact of future climate change on water supply and irrigation demand in a small mediterranean catchment. Case study: Nebhana dam system, Tunisia. Journal of Water and Climate Change, jwc2019131. doi: 10.2166/wcc.2019.131
  • Aminyavari S. & Saghafian B. (2019). Probabilistic streamflow forecast based on spatial post-processing of TIGGE precipitation forecasts. Stochastic Environmental Research and Risk Assessment 33, 1939–1950. doi: 10.1007/s00477-019-01737-4
  • Ayzel G., Varentsova N., Erina O., Sokolov D., Kurochkina L. & Moreydo V. (2019). OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia. Water 11, 1546. doi: 10.3390/w11081546
  • Barker L.J., Hannaford J., Parry S., Smith K.A., Tanguy M. & Prudhomme C. (2019). Historic hydrological droughts 1891-2015: systematic characterisation for a diverse set of catchments across the UK. Hydrology and Earth System Sciences 23, 4583–4602. doi: 10.5194/hess-2019-202
  • Coxon G., Freer J., Lane R., Dunne T., Knoben W.J.M., Howden N.J.K., et al. (2019). DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology. Geoscientific Model Development 12, 2285–2306. doi: 10.5194/gmd-12-2285-201
  • Ficchì A., Perrin C. & Andréassian V. (2019). Hydrological modelling at multiple sub-daily time steps: model improvement via flux-matching. Journal of Hydrology. doi: 10.1016/j.jhydrol.2019.05.084
  • García-Romero, L., Paredes-Arquiola, J., Solera, A., Belda, E., Andreu, J. & Sánchez-Quispe, S.T. (2019). Optimization of the Multi-Start Strategy of a Direct-Search Algorithm for the Calibration of Rainfall–Runoff Models for Water-Resource Assessment. Water 11, 1876. doi: 10.3390/w11091876
  • Givati A., Thirel G., Rosenfeld D. & Paz D. (2019). Climate change impacts on streamflow at the upper Jordan River based on an ensemble of regional climate models. Journal of Hydrology: Regional Studies 21, 92–109. doi: 10.1016/j.ejrh.2018.12.004
  • Knoben W.J.M., Freer J.E., Fowler K.J.A., Peel M.C. & Woods R.A. (2019). Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations. Geoscientific Model Development 12, 2463–2480. doi: 10.5194/gmd-12-2463-2019
  • Lavenne A., Andréassian V., Thirel G., Ramos M.-H. & Perrin C. (2019). A Regularization Approach to Improve the Sequential Calibration of a Semidistributed Hydrological Model. Water Resources Research 55, 8821–8839. doi: 10.1029/2018WR024266
  • Lima F.N., Fernandes W. & Nascimento N. (2019). Joint calibration of a hydrological model and rating curve parameters for simulation of flash flood in urban areas. RBRH 24. 10.1590/2318-0331.241920180066
  • Lavtar K., Bezak N. & Šraj M. (2019). Rainfall-Runoff Modeling of the Nested Non-Homogeneous Sava River Sub-Catchments in Slovenia. Water 12, 128. doi: 10.3390/w12010128
  • Ma, Q., Xiong, L., Xia, J., Xiong, B., Yang, H. & Xu, C.Y (2019). A Censored Shifted Mixture Distribution Mapping Method to Correct the Bias of Daily IMERG Satellite Precipitation Estimates. Remote Sensing 11, 1345. doi: 10.3390/rs11111345
  • Navas R., Alonso J., Gorgoglione A. & Vervoort R.W. (2019). Identifying Climate and Human Impact Trends in Streamflow: A Case Study in Uruguay. Water 11, 1433. doi: 10.3390/w11071433
  • Papacharalampous G., Tyralis H., Langousis A., Jayawardena A.W., Sivakumar B., Mamassis N., et al. (2019). Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms. Water 11, 2126. doi: 10.3390/w11102126
  • Pérez-Sánchez J., Senent-Aparicio J., Segura-Méndez F., Pulido-Velazquez D. & Srinivasan R. (2019). Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain. Sustainability 11, 2872. doi: 10.3390/su11102872
  • Riboust P., Thirel G., Le Moine N. & Ribstein P. (2019). Revisiting a simple degree-day model for integrating satellite data: implementation of SWE-SCA hystereses. Journal of Hydrology and Hydrodynamics 67(1), 70–81, 2019. doi: 10.2478/johh-2018-0004
  • Saadi M., Oudin L. & Ribstein P. (2019). Étude de la sensibilité des paramètres d’un modèle «rural» sur des bassins versants urbanisés. La Houille Blanche, 35–43. doi: 10.1051/lhb/2019013
  • Saadi M., Oudin L. & Ribstein P. (2019). Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters. Water 11, 1540. doi: 10.3390/w11081540
  • Sapač, C., Medved, A., Rusjan, S. & Bezak, N. (2019). Investigation of Low- and High-Flow Characteristics of Karst Catchments under Climate Change. Water 11, 925. doi: 10.3390/w11050925
  • Sauquet E., Richard B., Devers A. & Prudhomme C. (2019). Water restrictions under climate change: a Rhône–Mediterranean perspective combining bottom-up and top-down approaches. Hydrology and Earth System Sciences 23, 3683–3710. doi: 10.5194/hess-23-3683-2019
  • Sezen C., Bezak N., Bai Y. & Šraj M. (2019). Hydrological modelling of karst catchment using lumped conceptual and data mining models. Journal of Hydrology 576, 98–110. doi: 10.1016/j.jhydrol.2019.06.036
  • Slater L.J., Thirel G., Harrigan S., Delaigue O., Hurley A., Khouakhi A., et al. (2019). Using R in hydrology: a review of recent developments and future directions. Hydrology and Earth System Sciences 23, 2939–2963. doi: 10.5194/hess-23-2939-2019
  • Smith K.A., Barker L.J., Tanguy M., Parry S., Harrigan S., Legg T.P., et al. (2019). A multi-objective ensemble approach to hydrological modelling in the UK: an application to historic drought reconstruction. Hydrology and Earth System Sciences 23, 3247–3268. doi: 10.5194/hess-23-3247-2019
  • Tilloy A., Malamud B.D., Winter H. & Joly-Laugel A. (2019). A review of quantification methodologies for multi-hazard interrelationships. Earth-Science Reviews 196, 102881. doi: 10.1016/j.earscirev.2019.102881
  • Tyralis H., Papacharalampous G., Burnetas A. & Langousis A. (2019). Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS. Journal of Hydrology 577, 123957. doi: 10.1016/j.jhydrol.2019.123957
  • Visser A.G., Beevers L. & Patidar S. (2019). A coupled modelling framework to assess the hydroecological impact of climate change. Environmental Modelling & Software 114, 12–28. doi: 10.1016/j.envsoft.2019.01.004
  • Visser A., Beevers L. & Patidar S. (2019). The Impact of Climate Change on Hydroecological Response in Chalk Streams. Water 11, 596. doi: 10.3390/w11030596
  • Visser-Quinn A., Beevers L. & Patidar S. (2019). Replication of ecologically relevant hydrological indicators following a modified covariance approach to hydrological model parameterization. Hydrology and Earth System Sciences 23, 3279–3303. doi: 10.5194/hess-23-3279-2019

2018

  • Desclaux T., Lemonnier H., Genthon P., Soulard B. & Le Gendre R. (2018). Suitability of a lumped rainfall–runoff model for flashy tropical watersheds in New Caledonia. Hydrological Sciences Journal. doi: 10.1080/02626667.2018.1523613
  • Faty B., Ali A., Dacosta H., Bodian A., Diop S. & Descroix L. (2018). Assessment of satellite rainfall products for stream flow simulation in Gambia watershed. African Journal of Environmental Science and Technology 12, 501–513. doi: 10.5897/AJEST2018.2551
  • Harrigan S., Prudhomme C., Parry S., Smith K. & Tanguy M. (2018). Benchmarking ensemble streamflow prediction skill in the UK. Hydrology and Earth System Sciences 22, 2023–2039. doi: 10.5194/hess-22-2023-2018
  • Ma Q., Xiong L., Liu D., Xu C.-Y. & Guo S. (2018). Evaluating the Temporal Dynamics of Uncertainty Contribution from Satellite Precipitation Input in Rainfall-Runoff Modeling Using the Variance Decomposition Method. Remote Sensing 10, 1876. doi: 10.3390/rs10121876
  • McInerney D., Thyer M., Kavetski D., Bennett B., Lerat J., Gibbs M., et al. (2018). A simplified approach to produce probabilistic hydrological model predictions. Environmental Modelling & Software 109, 306–314. doi: 10.1016/j.envsoft.2018.07.001
  • Ogilvie A., Belaud G., Massuel S., Mulligan M., Le Goulven P., Malaterre P.-O., et al. (2018). Combining Landsat observations with hydrological modelling for improved surface water monitoring of small lakes. Journal of Hydrology 566, 109–121. doi: 10.1016/j.jhydrol.2018.08.076
  • Pedruco P., Szemis J.M., Brown R., Lett R., Ladson A.R., Kiem A.S., et al. (2018). Assessing climate change impacts on rural flooding in Victoria. In: Water and Communities. pp. 645–648. Melbourne.
  • Santos L., Thirel G. & Perrin C. (2018). Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0). Geoscientific Model Development 11, 1591–1605. doi: 10.5194/gmd-11-1591-2018
  • Santos, L., Thirel, G. & Perrin, C. (2018). Technical note: Pitfalls in using log-transformed flows within the KGE criterion, Hydrology and Earth System Sciences Discussions 22, 4583-4591. doi: 10.5194/hess-2018-298
  • Sezen C., Bezak N. & Šraj M. (2018). Hydrological modelling of the karst Ljubljanica River catchment using lumped conceptual model. Acta hydrotechnica, 87–100. doi: 10.15292/acta.hydro.2018.06
  • Sezen C. & Partal T. (2018). The utilization of GR4J model and wavelet based artificial neural network for rainfall-runoff modelling. Water Science and Technology: Water Supply. doi: 10.2166/ws.2018.189
  • Soldanova V. & Cisty, M. (2018). Extrapolation of carpatclim data for engineering purposes. In: SGEM2018. 18th International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, 18(3.1), pp. 305-312. doi: 10.5593/sgem2018/3.1/S12.040
  • Zhang Y., Li Y., Ji X., Luo X. & Li X. (2018). Evaluation and Hydrologic Validation of Three Satellite-Based Precipitation Products in the Upper Catchment of the Red River Basin, China. Remote Sensing 10, 1881. doi: 10.3390/rs10121881

2017

  • de Boer-Euser T., Bouaziz L., De Niel J., Brauer C., Dewals B., Drogue G., et al. (2017). Looking beyond general metrics for model comparison; lessons from an international model intercomparison study. Hydrology and Earth System Sciences 21, 423–440. doi: 10.5194/hess-21-423-2017
  • Caillouet L., Vidal J.-P., Sauquet E., Devers A. & Graff B. (2017). Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871. Hydrology and Earth System Sciences 21, 2923–2951. doi: 10.5194/hess-21-2923-2017
  • Odry J. & Arnaud P. (2017). Comparison of Flood Frequency Analysis Methods for Ungauged Catchments in France. Geosciences 7, 88. doi: 10.3390/geosciences7030088
  • Poncelet C., Merz R., Merz B., Parajka J., Oudin L., Andréassian V., et al. (2017). Process-based interpretation of conceptual hydrological model performance using a multinational catchment set. Water Resources Research 53, 7247–7268. doi: 10.1002/2016WR019991
  • Riboust P., Le Moine N., Thirel G. & Ribstein P. (2017). How to simulate radiative inputs in complex topographic areas, an analysis on 115 Swiss Alps weather stations. Hydrol. Earth Syst. Sci. Discuss. doi: 10.5194/hess-2017-539

2016

  • Ficchì A., Perrin C. & Andréassian V. (2016). Impact of temporal resolution of inputs on hydrological model performance: An analysis based on 2400 flood events. Journal of Hydrology 538, 454–470. doi: 10.1016/j.jhydrol.2016.04.016

Inproceedings

2023

  • Raghuvanshi A.S., Barbhuiya S.A. & Tiwari H.L. (2023). Performance Evaluation of Lumped Conceptual Rainfall-Runoff Genie Rural (GR) Hydrological Models for Streamflow Simulation. In: Hydrology and Hydrologic Modelling. (Eds P.V. Timbadiya, P.L. Patel, V.P. Singh & P.J. Sharma), pp. 283–292. Springer Nature Singapore, Singapore. doi: 10.1007/978-981-19-9147-9_22

2022

  • Aubert Y., Legay T., Verdonck J., Brunel D. & Delichere S. (2022). Les données spatiales au service du suivi des ressources en eau. E3S Web of Conferences 346, 04008. doi: e3sconf/202234604008
  • Delichère S., Bortoli J., Benatier B., Philippe E., Germain M., Thomas X., et al. (2022). Pilotage Intégré des Crues et des Transferts d’Eau (PICTO) - Un outil de gestion dynamique des retenues destinées à l’alimentation en eau potable sur le territoire de la Vendée. E3S Web of Conferences 346, 03010. doi: 10.1051/e3sconf/202234603010

2021

  • Bezak N., Peternel T., Medved A. & Mikoš M. (2021). Climate Change Impact Evaluation on the Water Balance of the Koroška Bela Area, NW Slovenia. In: Understanding and Reducing Landslide Disaster Risk. (Eds V. Vilímek, F. Wang, A. Strom, K. Sassa, P.T. Bobrowsky & K. Takara), pp. 221–228. Springer International Publishing, Cham. doi: 10.1007/978-3-030-60319-9_25
  • Biao E.I., Obada E., Alamou E.A., Zandagba J.E., Chabi A., Amoussou E., et al. (2021). Hydrological Modelling of the Mono River Basin at Athiémé. Proceedings of the International Association of Hydrological Sciences 384, 57–62. doi: 10.5194/piahs-384-57-2021
  • Koubodana H.D., Atchonouglo K., Adounkpe J.G., Amoussou E., Kodja D.J., Koungbanane D., et al. (2021). Surface runoff prediction and comparison using IHACRES and GR4J lumped models in the Mono catchment, West Africa. Proceedings of the International Association of Hydrological Sciences 384, 63–68. doi: 10.5194/piahs-384-63-2021

2020

  • Arriagada A., Riquelme J. & Garcia-Perez T. (2020). Evaluation of the Effects of Climate Change on Water Infiltration on Thickened Tailings in the Atacama Region. doi: 10.36487/ACG_repo/2052_99
  • Nicolle P., Besson F., Delaigue O., Etchevers P., François D., Le Lay M., et al. (2020). PREMHYCE: An operational tool for low-flow forecasting. Proceedings of the International Association of Hydrological Sciences 383, 381–389. doi: piahs-383-381-2020
  • Muñoz Castro E., Mendoza P.A., Hernandez D. & Vargas X. (2020). Comparación de métodos de ensemble forecasting aplicados al pronóstico de volúmenes de deshielo en Chile central. In: 24 congreso chileno de ingeniería hidráulica. Sociedad chilena de ingeniería hidráulica. Santiago (Chile). PDF proceedings
  • Vyshnevskyi V., Shevchuk S. & Matiash T.V. (2020). Water resources of the lower Danube river and their use within the territory ok Ukraine. In: Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. (Eds L. Gorbachova & B. Khrystiuk), pp. 199–201. Ukrainian Hydrometeorological Institute, Department of Hydrological Research, Kyiv (Ukraine). doi: 10.15407/uhmi.conference.01.22

2019

  • Astorayme Valenzuela M. & Felipe O. (2019). Hydrological Simulation Using Two High-Resolution Satellite Precipitation Products to Generate Hourly Discharge Rates in the Rimac Basin, Peru. In: World Environmental and Water Resources Congress 2019. Pittsburgh (United States of America). doi: 10.1061/9780784482339.029

PhD theses

2023

  • Soutif-Bellenger M. (2023). Eau, agriculture, changement climatique : apports d’une modélisation intégrée agro-hydrologique pour l’adaptation. Sorbonne Université. HAL
  • Thébault C. (2023). Quels apports d’une approche multi-modèle semi-distribuée pour la prévision des débits ? Sorbonne Université. HAL

2022

  • Astagneau P.C. (2022). Pistes d’amélioration de la généralité et de l’efficacité d’un modèle opérationnel de prévision des crues. Sorbonne Université. HAL
  • Hora T. (2022). Addressing groundwater over-extraction in India: assessments, monitoring methods and interventions. University of Waterloo. UWSpace
  • Jeantet A. (2022). Durabilité du drainage agricole français sous contrainte de changement climatique. Sorbonne Université. HAL
  • Lemaitre-Basset T. (2023). Importance de la demande en eau atmosphérique et anthropique en contexte de changement climatique sur la durabilité de la gestion de la ressource : cas d’étude du bassin versant de la Moselle en France. Sorbonne Université. HAL
  • Pujol L. (2022). Optimal synergy of multi-source data and hydraulic-hydrological models for the cartographic modeling of complex hydrosystems. Université de Strasbourg. HAL

2021

  • Lemoine A. (2021). Indicateurs d’impacts des changements hydroclimatiques sur la gestion des réservoirs hydroélectriques. Sorbonne Université. HAL
  • Pelletier A. (2021). Nappes et rivières : la piézométrie peut-elle améliorer la modélisation des étiages des cours d’eau ? Sorbonne Université. HAL
  • Peredo D. (2021). Quels gains d’une modélisation hydrologique adaptée et d’une approche d’ensemble pour la prévision des crues rapides ? Sorbonne Université. HAL
  • Royer-Gaspard P. (2021). De la robustesse des modèles hydrologiques face à des conditions climatiques variables. Sorbonne Université. HAL

2020

  • Cassagnole M. (2020). Analyse du lien entre la qualité des prévisions hydrologiques et leur valeur économique pour le secteur hydroélectrique. AgroParisTech. HAL
  • Papacharalampous G.A. (2020). Stochastic process-based modelling for hydrological systems. National Technical University of Athens. ResearchGate
  • Saadi M. (2020). Représentation de l’urbanisation dans la modélisation hydrologique à l’échelle du bassin versant. Sorbonne Université. HAL

2019

  • Devers A. (2019). Towards a 150-year hydrometeorological reanalysis over France through data assimilation in ensemble reconstructions. Université Grenoble Alpes. HAL

2018

  • Bellier J. (2018). Prévisions hydrologiques probabilistes dans un cadre multivarié : quels outils pour assurer fiabilité et cohérence spatio-temporelle ? Université Grenoble Alpes. HAL
  • Rebolho C. (2018). Modélisation conceptuelle de l’aléa inondation à l’échelle du bassin versant. AgroParisTech. HAL
  • Riboust P. (2018). De la neige au débit : de l’intérêt d’une meilleure contrainte et représentation de la neige dans les modèles. Sorbonne Université. HAL
  • Santos L. (2018). Que peut-on attendre des Super Modèles en hydrologie ? Évaluation d’une approche de combinaison dynamique de modèles pluie-débit. AgroParisTech. HAL

2017

  • Ficchi A. (2017). An adaptive hydrological model for multiple time-steps: diagnostics and improvements based on fluxes consistency. Université Pierre et Marie Curie, Paris 6. HAL

2016

  • Caillouet L. (2016). Reconstruction hydrométéorologique des étiages historiques en France entre 1871 et 2012. Université Grenoble Alpes. HAL
  • Crochemore L. (2016). Seasonal streamflow forecasting for reservoir management. AgroParisTech. HAL
  • Poncelet C. (2016). Du bassin au paramètre : jusqu’où peut-on régionaliser un modèle hydrologique conceptuel ? Université Pierre et Marie Curie, Paris 6. HAL

MSc reports

2023

  • Garzón Rodríguez E.A. (2023). Construcción, aplicación y evaluación crítica de una caja de herramientas para la formulación del componente hidrológico de un POMCA. Universidad Nacional de Colombia. PDF

2022

  • Abrahão Campos Salles T. (2022). Modélisation hydrologique prenant en compte la gestion pour l’évaluation de la vulnérabilité et de l’adaptation au changement climatique sur le bassin versant de la Seille. Grenoble INP-Ense3. PDF
  • Araya Reydet D.A. (2022). Evaluación de la metodología ESP para la generación de pronósticos de caudales de deshielo en cuencas de Chile Central. Universidad de Chile. PDF
  • Hah K. W. (2022). Improved Streamflow Simulation through Ensemble and Stochastic Conceptual Data-driven Approaches. UWSpace
  • Henrotin E. (2023). Quelle fonction de propagation choisir pour relier les différentes mailles d’un modèle hydrologique semi-distribué ? Université de Tours. PDF
  • Lindao V.G.S. (2022). Evaluación de la oferta hídrica bajo escenarios de cambio climático en la microcuenca del río Colonso, Ecuador. Universidad Regional Amazónica Ikiam. Dspace

2021

  • Kourakos V. (2021). Refining the working hypotheses of parameter identification in hydrological modelling: the concept of stochastic calibration. National Technical University of Athens, Athens. PDF
  • Neri M. (2021). Innovative methodologies for enhancing the regionalisation of rainfall-runoff model parameters. Alma Mater Studiorum - Università di Bologna. PDF
  • Nunez Torres, L. (2021). Simulation d’un bassin versant anthropisé à l’aide d’un modèle hydrologique semi-distribué : Le bassin de la Seine et ses réservoirs. Polytech Sorbonne. PDF
  • Witt M. (2021). Investigating discharge dynamics at catchment level using remote sensing timeseries. University of Würzburg. PDF

2020

  • Fayet L. (2020). Impact du changement climatique sur la variabilité hydrologique des bassins versants en amont de l’estuaire de la Gironde. ENTPE. PDF
  • Soutif–Bellenger, M. (2020). Développement d’un modèle couplé agro-hydrologique. Application au bassin versant de l’Hérault. Sorbonne Université. PDF
  • Kouyaté, S. (2020). Modélisation des glaciers pour l’amélioration des débits simulés en haute montagne : diagnostic sur des bassins versants alpin. Université de Tours. PDF
  • Vlavonou Zannou S.L.M. (2020). Integrated Water Resources Management in Burkina-Faso through numerical modeling: Case study of the Mouhoun Basin. Pan African University. PDF

2019

  • Astagneau, P. (2019). Comparison of hydrological modelling R packages. Polytech Sorbonne. PDF
  • Belbal, H. (2019). Quelle efficacité peut-on attendre des modèles hydrologiques pour la prévision des crues en Nouvelle–Calédonie ? Diagnostic sur un ensemble de bassins versants néo-calédoniens. Polytech Nice-Sophia. PDF
  • Boutouba R., Fougere M., Lamouri A., Leguemani A.M. & Roux Q. (2019). Peut-on améliorer les performances de modèles pluie-débit en utilisant les données satellites MODIS ? Application sur le bassin versant de la Roya. Polytech Nice-Sophia.
  • Cesarini C. (2019). Analysis of the importance of the snow module and of the simulation of extreme streamflows in the presence of a dam using the “GR” hydrological models. Università di Bologna. PDF
  • Conte, B. (2019). Quelles perspectives de l’intégration de l’expertise dans le calage de modèle hydrologique ? Université Paris-Sud, Paris 11. PDF
  • Sleziak P. (2019). Vývoj webovej aplikácie pre potreby hydrologického modelovania. Technická univerzita Ostrava. PDF

2018

  • Garnier S. (2018). Évaluation de la qualité des prévisions saisonnières de pluies, de températures et débits en France. Université de Montpellier, Irstea, Antony, France. PDF
  • Huang P. (2018). Impact of coupling an actual evapotranspiration model with a lumped hydrological model to improve hydrological simulations. PolyTech’ Nice-Sophia. PDF

2017

  • Bildstein, A. (2017). Tests exploratoires pour la mise en place de prévisions opérationnelles des crues sur l’île de la Réunion. ENTPE Lyon, Irstea, Antony. PDF
  • Jeantet A. (2017). Validation de l’utilisation des pluies satellitaires pour la modélisation hydrologique en Guyane française. Université Pierre et Marie Curie, Paris 6. PDF
  • Koné M.L. (2017). Évaluation du bilan hydrologique à l’aide du modèle GR6J : cas d’un sous-bassin du Cavally en Côte d’Ivoire. Université Nangui-Abrogoua.
  • Mata Espinoza S.V. (2017). airGR un package de modélisation hydrologique à améliorer ? Évaluation sur un large échantillon de bassins versants. Université Pierre et Marie Curie, Paris 6. PDF
  • Peredo D. (2017). Impact d’une meilleure prise en compte de l’évapotranspiration dans la modélisation hydrologique. Université Pierre et Marie Curie, Paris 6. PDF

2016

  • Haddadi I. (2016). Les tests statistiques de significativité appliqués à l’hydrologie. Université Blaise Pascal, Clermont-Ferrand 2. PDF
  • Terrier M. (2016). Évaluation des procédures de naturalisation pour la reconstitution de débits sur le bassin versant de la Seine. Polytech Nice-Sophia. PDF
  • Gosset C. (2014). Quel apport des données satellites d’enneigement pour le calage d’un modèle hydrologique sur des bassins de montagnes. Université Paris-Sud, Paris 11. PDF

Conferences

2023

  • Neri M. & Toth E. (2023). On the accurate simulation of hydrological droughts in Alpine regions: investigating the multiple role of rainfall-runoff model dynamics and basin characteristics. 20th edition of the European Geoscience Union General Assembly. Vienna (Austria), 24–28 April 2023. doi: 10.5194/egusphere-egu23-8153.

2022

  • Nonki R.M., Amoussou E., Tshimanga R.M., Koubodana H.D., Kemgang Ghomsi F.E. & Lenouo A. (2022). Performance assessment of a daily time-step HYMOD conceptual rainfall-runoff model for the Upper Benue River, Cameroon. 11th edition of the IAHS Scientific Assembly. Montpellier, France. doi: 10.5194/iahs2022-547
  • Poncet N., Lucas-Picher P., Tramblay Y. & Thirel G. (2022). Does a convection-permitting climate model improve the simulation of flash floods? A case study over a Mediterranean watershed. 11th edition of the IAHS Scientific Assembly. Montpellier, France. doi: 10.5194/iahs2022-589
  • Royer-Gaspard P., Bourgin F., de Lavenne A., Perrin C. & Thirel G. (2022). Seeking best streamflow assimilation scheme in a semi-distributed hydrological model for flood forecasting. 11th edition of the IAHS Scientific Assembly. Montpellier, France. doi: 10.5194/iahs2022-339
  • Thébault C., Perrin C., Andréassian V., Thirel G. & Legrand S. (2022). Combining multiple hydrological model structures in a semi-distributed modelling environment. 11th edition of the IAHS Scientific Assembly. Montpellier, France. doi: 10.5194/iahs2022-490

2021

  • Dorchies D., Delaigue O. & Thirel G. (2021). Prise en compte des influences avec le package airGRiwrm. 4th edition of the HydroGR Days. Antony (France), 7-8 Dec. 2021. PDF slideshow
  • Dorchies D., Delaigue O. & Thirel G. (2021). airGRiwrm: an extension of the airGR R-package for handling Integrated Water Resources Management modeling. 18th edition of the European Geoscience Union General Assembly. Online, 19-30 April 2021. doi: 10.5194/egusphere-egu21-2190. PDF abstract
  • Kourakos V., Efstratiadis A. & Tsoukalas I. (2021). Can hydrological model identifiability be improved? Stress-testing the concept of stochastic calibration. 18th edition of the European Geoscience Union General Assembly. Online, 19-30 April 2021. doi: 10.5194/egusphere-egu21-11704. PDF slideshow
  • Papacharalampous G., Tyralis H., Koutsoyiannis D. & Montanari A. (2021). Large-scale calibration of conceptual rainfall-runoff models for two-stage probabilistic hydrological post-processing. 18th edition of the European Geoscience Union General Assembly. Online, 19-30 April 2021. doi: 10.5194/egusphere-egu21-18. PDF slideshow

2019

  • Barria Sandoval I., Barria P., Carrasco J. & Casassa G. (2019). Open source tools as an instrument for decision- making for adaptation to climate change: airGR GR2M streamflow projections. 1st edition of the Congreso Internacional de Gestión Integral del Agua. Cochabamba (Bolivia), 2-4 October 2019. PDF slideshow
  • Sapač K., Rusjan S., Bezak N. & Šraj M. (2019). Analysis of low-flow conditions in a heterogeneous karst catchment as a basis for future planning of water resource management. 28th nedition of the Conference of the Danubian countries on hydrological forecasting and hydrological bases of water management. Kiev (Ukraine), 6-8 November 2019. PDF proceedings
  • Tyralis H., Papacharalampous G., Burnetas A. & Langousis A. (2019). Stacking of probabilistic predictions for improving hydrological forecasts. 17th edition of the European Geoscience Union General Assembly. Vienna (Austria), 7-12 April 2019. PDF slideshow

2018

  • Kodja D.J., Akognongbé A.J.S., Amoussou E., Mahé G., Expédit V., Paturel J.E., et al. (2018). Calibration of the hydrological model GR4J based on potential evapotranspiration estimates by the Penman-Monteith and Oudin methods in the Ouémé watershed (West Africa). 8th edition of the Global Friend-Water Conference. United Nations Educational, Scientific and Cultural Organization, Beijing (China), 6-9 November 2018.
  • Newcomb A. & Smith S. (2018). Dams and Hydrologic Regime in the Penobscot River: A reappraisal based on historical records and hydrologic modeling. 4th edition of the Maine Sustainability & Water Conference. Augusta (United States of America), 29 March 2018. PDF poster
  • Harrigan S., Smith K., Parry S., Tanguy M. & Prudhomme C. (2017). Benchmarking Ensemble Streamflow Prediction Skill in the UK. 16th edition of the European Geoscience Union General Assembly. Vienna (Austria), 23-28 April 2017. PDF abstract
  • Roux Q. & Brigode P. (2018). How long would we have to wait before (re)filling the Malpasset dam reservoir? An example of a teaching project done using R and airGR modeling packages. 16th edition of the European Geoscience Union General Assembly. Vienna (Austria), 8-13 april 2018. PDF poster

Book chapters

2024

  • Singh V.P., Singh R., Paul P.K., Bisht D.S. & Gaur S. (2024). Uncertainty Analysis in Hydrologic Modelling. In: Hydrological Processes Modelling and Data Analysis: A Primer. pp. 203–227. Springer Nature Singapore, Singapore. doi: 10.1007/978-981-97-1316-5_10

2021

  • Muñoz Castro E. & Mendoza P. (2021). Identificabilidad de parámetros en modelos hidrológicos GR4J: ¿Somos consistentes? In: Rutas Hidrólogicas. pp. 33–45. Ingeniería Civil - Universidad de Chile. PDF

Manuals

2020

2019

Technical reports

2024

  • Murphy C. & Grainger S. (2024). CROSSDRO: Cross-sectoral Drought Impacts in Complex European Basins. Environmental Protection Agency, Wexford. PDF

2023

  • Jaafar H., Hazimeh R., Mourad R., Pérez-Blanco C.D., González-López H., Marta Debolini D.D. andNina G., et al. (2023). Talanoa Water. Deliverable 2.2: Water accounting database V2.0. American University of Beirut, Universidad de Salamanca, INRAE, GECOsistema, GPAI, INA. PDF

2020

  • Grandry M., Degré A. & Gailliez S. (2020). HydroTrend 2. Analyse de l’évolution de l’amplitude et de la fréquence des débits de crue en Région Wallonne. ULiège - Gembloux Agro-Bio Tech. ORBi