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Lionel Benoit

Research scientist (chargé de recherche) at INRAE in Biostatistics and Spatial Processes (BioSP) team.



Contact information:

Domaine St. Paul, 228 route de l'Aérodrome, 84914 Avignon (France).

Email: lionel.benoit@inrae.fr

Researcher ID: https://orcid.org/0000-0002-8182-0152


Research interests:

I study the spatiotemporal statistics of weather at the local scale, their linkages with climate, and their impacts on eco-, hydro-, and agro-systems.


  • Space-time geostatistics.
  • Stochastic weather generators.
  • Statistical meteorology.
  • Environmental Sensor Networks (ESNs) for local scale and in-situ weather monitoring.


  • Statistical enhancement (quality-check, filtering, interpolation, gap-filling) of ESN data.
  • Characterization and simulation of rainfall space-time bevahior, with focus on orographic effects and intermittency.
  • Hydro-meteorology and soil water resources.


Current research:

Orographic rain enhancement in Pacific islands (collab. with University of Hawai‘i - WRRC and University of French Polynesia - GePaSud):

This line of research aims at better understanding how topography and atmospheric circulation interact to generate the steep gradients of precipitation observed in high tropical islands. It encompasses three aspects:

  1. Rainfall observation at high resolution (<1km, 10 min) to assess sub-daily rain fluctuations.
  2. Rainfall mapping at daily resolution to characterize spatial rainfall patterns.
  3. Stochastic rainfall generation to explore the space-time variability of rainfall over Pacific islands, and to investigate the links between the vertical structure of the atmosphere and orographic rain enhancement.


Spatio-temporal multivariate stochastic weather generation on large grids (collab. with BEYOND project and #Geolearning):

This line of research aims at leveraging recent developments in the field of multivariate geostatistics and SPDE-based geostatistical modeling to design new stochastic weather generators able to:

  1. Simulate the joint fluctuations of a large number of climate variables (e.g. temperature, precipitation, solar radiation, wind).
  2. Simulate synthetic weather at daily resolution and on large grids (e.g. across all France at 8 km x 8 km resolution).
  3. Simulate weather scenarios in a changing climate, with application to stochastic downscaling of climate projections.


Hydro-meteorology of fast responding catchments (collab. with INRAE-RiverLy and University of Lausanne - IDYST):

This line of research aims at combining field observations and (geo-)statistical data enhancement to improve the space-time description of the hydro-meteorological variables used to run hydrological models of fast respondig watersheds such as urban or mountain catchments. A special attention is paid to the intermittency of hydrometeorological variables, in particular precipitation and stream flow. Two aspects are investigated:

  1. Enhancing observations from local networks of weather stations (distance between stations 100 m - 10 km) using stochastic gap-filling and interpolation in order to produce high resolution 2D+time reconstructions of meteorological variables (e.g. precipitation, temperature, solar radiation, wind) used as input for hydrology modeling.
  2. Combining in-situ stream intermittence observations and regional scale hydro-climatic projections through ML regression in view of evaluating the evolution of stream intermittence in a changing climate.



  • Peleg N, Torelló-Sentelles H, Mariethoz G, Benoit L, Leitão J, Marra F (PrePrint), Brief communication: the potential use of low-cost acoustic sensors in short-term urban flood warnings, Natural Hazards and Earth System Sciences Discussions, https://doi.org/10.5194/nhess-2022-257.
  • Benoit L, Sichoix L, Nugent A, Lucas M, Giambelluca T (2022), Stochastic daily rainfall generation on tropical islands with complex topography, Hydrology and Earth System Sciences, 26, 2113–2129, https://doi.org/10.5194/hess-26-2113-2022.
  • Nussbaumer R, Bauer S, Benoit L, Mariethoz G, Liechti F, Schmid B (2021), Quantifying year-round nocturnal bird migration with a fluid dynamics model, Journal of the Royal Society Interface, 18, 20210194, https://doi.org/10.1098/rsif.2021.0194.
  • Michelon A, Benoit L, Beria H, Ceperley N, Schaefli B (2021), Benefits from high-density rain gauge observations for hydrological response analysis in a small alpine catchment, Hydrology and Earth System Sciences, 25, 2301-2325, https://doi.org/10.5194/hess-25-2301-2021.
  • Benoit L (2021), Radar and rain gauge data fusion based on disaggregation of radar imagery, Water Resources Research, 27, e2020WR027899, https://doi.org/10.1029/2020WR027899.
  • Benoit LLucas M, Tseng H, Huang Y-F, Tsang Y-P, Nugent A, Giambelluca T, Mariethoz G (2021), High space-time resolution observation of extreme orographic rain gradients in a Pacific Island catchment, Frontiers in Earth Science: Hydrosphere, 8, 546246, https://doi.org/10.3389/feart.2020.546246.
  • Benoit L, Vrac M, Mariethoz G (2020), Nonstationary stochastic rain type generation: accounting for climate drivers, Hydrology and Earth System Sciences, 24, 2841-2854, https://doi.org/10.5194/hess-24-2841-2020.
  • Nussbaumer R, Benoit L, Mariethoz G, Liechti F, Bauer S, Schmid B (2019), A geostatistical approach to estimate high resolution nocturnal bird migration densities from a weather radar network, Remote Sensing, 11, 2233, https://doi.org/10.3390/rs11192233.
  • Benoit L, Gourdon A, Vallat R, Irarrazaval I, Gravey M, Lehmann B, Prasicek G, Graff D, Herman F, Mariethoz G (2019), A high-resolution image time series of the Gorner Glacier – Swiss Alps – derived from repeated UAV surveys, Earth System Science data, 11, 579-588, https://doi.org/10.5194/essd-11-579-2019.
  • Benoit L, Vrac M, Mariethoz G (2018), Dealing with non-stationarity in sub-daily stochastic rainfall models, Hydrology and Earth System Sciences, 22, 5919–5933, https://doi.org/10.5194/hess-22-5919-2018.
  • Benoit L, Allard D, Mariethoz G (2018), Stochastic Rainfall Modeling at Sub-kilometer Scale, Water Resources Research, 54, 4108-4130, https://doi.org/10.1029/2018WR022817.
  • Benoit L, Mariethoz G (2017), Generating synthetic rainfall with geostatistical simulations, Wiley Interdisciplinary Reviews: Water, https://doi.org/10.1002/wat2.1199.
  • Lombardi D, Benoit L, Camelbeeck T, Martin O, Meynard C, Thom C (2016), Bimodal pattern of seismicity detected at the ocean margin of an Antarctic ice shelf, Geophysical Journal International, 206, 1375–1381, https://doi.org/10.1093/gji/ggw214.
  • Benoit L, Dehecq A, Pham H-T, Vernier F, Trouve E, Moreau L, Martin O, Thom C, Pierrot-Deseilligny M, Briole P (2015), Multi-method monitoring of Glacier d’Argentiere dynamics, Annals of Glaciology, 56, 118-128, https://doi.org/10.3189/2015AoG70A985.
  • Benoit L, Briole P, Martin O, Thom C, Malet JP, Ulrich P (2015), Monitoring landslide displacements with the Geocube wireless network of low-cost GPS, Engineering Geology, 195, 111-121, https://doi.org/10.1016/j.enggeo.2015.05.020.
  • Benoit L, Briole P, Martin 0, Thom C (2014), Real-time deformation monitoring by a wireless network of low-cost GPS, Journal of Applied Geodesy, 119–128, https://doi.org/10.1515/jag-2013-0023.


Most of the software related to the above papers is open-source and freely available in my GitHub repository: https://github.com/LionelBenoit



    Since 2021: Research scientist at INRAE-BioSP (Avignon, France).

    2020-2021: Post-doctoral research fellow at University of Hawai‘i - WRRC (Honolulu, USA) and University of French Polynesia - GePaSud (Puna'auia, French Polynesia).

    2015-2019: Research assistant at University of Lausanne - IDYST (Lausanne, Switzerland).

    2011-2014: PhD student at the French mapping agency (IGN) - LASTIG (Saint-Mandé, France) and Ecole Normale Supérieure de Paris - Laboratoire de Géologie (Paris, France).

    2008-2010: Engineering School student at Ecole Nationale des Sciences Géographiques (Champs-sur-Marne, France).