Jobs:Job-01263
Start reviewing process: 21 July 2023
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Posting:
Position: 1 PhD Position
Start reviewing process: 21 July 2023
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Abstract
This thesis addresses the issue of the predictability of drinking water volumes that can be drawn from groundwater, both for the current climate (with time horizons ranging from one week to six months) and in the context of climate change. To achieve this objective, the aim is not only to understand how groundwater is recharged, but also to model how underground abstraction of drinking water affects river flow, and conversely, how floods and droughts impact exploitable water resources.
Advising team
The work is part of a partnership between Suez and INRAE:
INRAE's HYCAR research unit - https://webgr.inrae.fr/
Suez's LYRE research unit - https://www.toutsurmesservices.fr/Le-LyRE
The academic PhD advisor is Vazken Andréassian (https://sites.google.com/view/vazken-andreassian), and academic affiliation will be at Sorbonne Université in Paris (GRNE Doctoral School)
Objectives of the thesis
This thesis pursues both practical and theoretical objectives:
The operational objectives of this thesis are to propose forecasting models that can handle 3 timeframes useful to managers:
- the short term (a few days), which is useful for operators to optimize multi-resource operations, notably by reducing their energy consumption or their impact on more fragile resources;
- medium-term (one to six months), which provides a seasonal vision useful to managers and local authorities (to anticipate problems, forecast tanker requirements and usage restrictions, and to communicate with the prefecture);
- the very long term, which enables us to anticipate the need for new resources and plan infrastructure investments, is situated on the time scales of current climate change (2050 & 2100): it is useful for providing municipalities with a "strategic" vision enabling them to assess the sustainability of their water supply systems.
The scientific objectives of this thesis are in the field of forecasting and mathematics applied to hydrogeology:
- in terms of forecasting, the question to be resolved is that of the trade-off between the different types of modelling available, depending on the timescales involved and the aquifer systems. In this thesis, we'll be looking in particular at the possibilities of blending (as required) "classical" conceptual hydrological approaches with data-driven artificial intelligence/statistical learning approaches.
- in terms of hydrogeology, we will use automatic classification methods to obtain a functional typology of recharge systems, for a better understanding of how they function. The aim will be to understand which forecast horizons are "useful" for which types of aquifers, taking into account water table-river relationships.
Available data for the thesis
This thesis will benefit from two original databases:
- The BDD-HydroClim database, maintained at INRAE, which brings together daily data from all river flow measurement stations in France, together with a set of daily meteorological data for the catchment areas concerned;
- Suez's Aquadvanced® WellWatch database, which brings together data from over 400 boreholes operated by Suez in a variety of hydrogeological contexts (alluvial, chalk, sandstone, limestone, basement).
Please contact me if you are interested, by sending a CV and a letter explaining what you would expect from a PhD and why you think that you would be a suitable candidate for this PhD project.
Please help me sort by e-mails by starting the object of your email with “PhD in Hydrology”!
