Publications des scientifiques de l'IRD

Dumont M., Plagnes V., Lachassagne Patrick, Guérin R., Nugraha B., Mohamad F., Oudin L., Fadillah A., Valdès D., Brocard G., Bonjour J. L., Saadi M., Esneu A. S., Muhammad A., Hendarmawan, Dörfliger N. (2023). Water cycle modelling strengthened by probabilistic integration of field data for groundwater management of a quite unknown tropical volcanic hydrosystem. Comptes Rendus Geoscience, 355 (Special Issue 1), p. 207-229. ISSN 1631-0713.

Titre du document
Water cycle modelling strengthened by probabilistic integration of field data for groundwater management of a quite unknown tropical volcanic hydrosystem
Année de publication
2023
Type de document
Article référencé dans le Web of Science WOS:001167673400012
Auteurs
Dumont M., Plagnes V., Lachassagne Patrick, Guérin R., Nugraha B., Mohamad F., Oudin L., Fadillah A., Valdès D., Brocard G., Bonjour J. L., Saadi M., Esneu A. S., Muhammad A., Hendarmawan, Dörfliger N.
Source
Comptes Rendus Geoscience, 2023, 355 (Special Issue 1), p. 207-229 ISSN 1631-0713
volcanic hydrosystems in Indonesia are mostly hydrogeologically unknown despite their socio-economic importance. The development of robust and easy-to-implement methodologies to conceptualize and quantify the water cycle components becomes a prerequisite for their sustainable management. We developed a lumped hydrologicalmodel to mimic the structure and functioning of a previously unknown hydrosystem located on the flanks of the Salak volcano (West Java). The structure of the aquifers was revealedwith electrical resistivity tomography. The distinction between springs fed by the extensive artesian aquifer and others fed by shallow perched aquifers was obtained mostly using hydrochemistry. The elevation of the recharge area was identified using isotopic analysis of spring water. After designing the hydrologicalmodel structure, we carried out a probabilistic parameters exploration using the multiple-try differential evolution adaptive Metropolis algorithm to calibrate aquifer discharge. Multiple Markov chains allow a better exploration of the parameter values. The Bayesian approach provides the best water cycle simulation with a parameter uncertainty analysis, improving the accuracy and representation of the water cycle appropriate for previously unknown hydrosystems.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020] ; Hydrologie [062] ; Géologie et formations superficielles [064]
Description Géographique
INDONESIE
Localisation
Fonds IRD [F B010089615]
Identifiant IRD
fdi:010089615
Contact