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Faculté des Sciences
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Développement d'un outil opérationnel de prévision d'apparition de gel printanier dans un vignoble wallon, à l'aide du modèle MAR

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Louis, Simon ULiège
Promotor(s) : Doutreloup, Sébastien ULiège
Date of defense : 29-Jun-2023/30-Jun-2023 • Permalink : http://hdl.handle.net/2268.2/17338
Details
Title : Développement d'un outil opérationnel de prévision d'apparition de gel printanier dans un vignoble wallon, à l'aide du modèle MAR
Translated title : [en] Development of an operational tool for predicting the occurrence of spring frost in a Walloon vineyard, using the MAR model.
Author : Louis, Simon ULiège
Date of defense  : 29-Jun-2023/30-Jun-2023
Advisor(s) : Doutreloup, Sébastien ULiège
Committee's member(s) : Ghilain, Nicolas ULiège
Devillet, Guénaël ULiège
Language : French
Number of pages : 91
Keywords : [fr] Vigne, gel printanier, prévision de gel, MAR
Discipline(s) : Physical, chemical, mathematical & earth Sciences > Earth sciences & physical geography
Research unit : Laboratoire de climatologie ULiège
Target public : Professionals of domain
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en sciences géographiques, orientation global change, à finalité approfondie
Faculty: Master thesis of the Faculté des Sciences

Abstract

[en] With climate change and the northward shift of isotherms, Belgium is becoming an
increasingly suitable territory for vineyards. Indeed, Belgium possesses the bioclimatic
indices necessary for vine growth. As a result, winegrowing activity is increasing in Belgium.
However, Belgium is not immune to spring frosts, which cause a lot of damage to vineyards
both in northern France and elsewhere in the world. To avoid losses, winemakers must deploy
defense tools such as heating candles, wind towers, helicopters, aspersion, and many other
tools. These tools cost time and money and require precise frost forecasts for their
implementation. The purpose of this thesis is to develop an operational tool for spring frost
prediction. This tool has been created from two databases, obtained from chips that recorded
the temperature at the vine fruit level during the 2021-2022 year, as well as from the MAR
(regional atmospheric model, forced by ERA5). Two methods were used to attempt frost
prediction. These two methods were based on the years 2021-2022. A predictive model was
first generated using an empirically modified formula: the MAR output temperature was
transformed to be as close as possible to that of the vine fruit, estimated using the chips. Then,
a second model was created through multivariate logistic regression, taking into account, in
addition to the minimum temperature of the MAR, several variables that could impact the
occurrence of frost. This resulted in an improvement in the prediction of frost nights
compared to the raw outputs of the MAR. In fact, in 2022, from March onwards, out of 46
nights, the raw MAR (without modification) correctly predicted 7 frost nights, missed 9 frost
nights, and correctly predicted 30 nights without frost, whereas the best model in this thesis
(derived from logistic regression) predicted on the same 46 nights, 13 correct frost nights, 3
missed frost nights, and correctly predicted 30 nights without frost. The model still needs to
be tested on future forecasts with the MAR forced by the global GFS model, for example.


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Author

  • Louis, Simon ULiège Université de Liège > Mast. scienc. géogr. or. glob. chang. à fin.

Promotor(s)

Committee's member(s)

  • Ghilain, Nicolas ULiège Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie
    ORBi View his publications on ORBi
  • Devillet, Guénaël ULiège Université de Liège - ULiège > Département de géographie > Serv. d'étude en géographie éco. fond. et appliquée (Segefa)
    ORBi View his publications on ORBi
  • Total number of views 39
  • Total number of downloads 116










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