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Mapping and modelling riverine sand mining at the sub-continental scale: a case study for India.

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Dujardin, Elise ULiège
Promotor(s) : Vanmaercke, Matthias ULiège ; Vercruysse, Kim
Date of defense : 3-Sep-2021/7-Sep-2021 • Permalink : http://hdl.handle.net/2268.2/12578
Details
Title : Mapping and modelling riverine sand mining at the sub-continental scale: a case study for India.
Translated title : [fr] Cartographie et modélisation de l'extraction du sable des rivières à l'échelle continentale: cas d'étude de l'Inde
Author : Dujardin, Elise ULiège
Date of defense  : 3-Sep-2021/7-Sep-2021
Advisor(s) : Vanmaercke, Matthias ULiège
Vercruysse, Kim 
Committee's member(s) : Schmitz, Serge ULiège
Houbrechts, Geoffrey ULiège
Language : English
Number of pages : 99
Keywords : [en] Sand mining
[en] River
[en] India
Discipline(s) : Physical, chemical, mathematical & earth Sciences > Earth sciences & physical geography
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] Throughout the world, sand is often mined from rivers, especially in the Global South where the demand for sand increases due to urbanization. Although riverine sand mining (RSM) can have positive impacts (i.e. support livelihoods), it also can lead to many negative impacts particularly on the environment. The large volumes of sand extracted may far exceed the natural rate of replenishment of rivers and can result in local sand scarcity. To avoid this problem, efficient policies to sustainably manage RSM need to be in place. However, few data exist on the location of RSM on large (continental to global) scales. In an attempt to fill this data gap, this thesis aims to gain more insight into the occurrences of RSM at subcontinental scale. The specific objectives are to: (1) Develop a systematic mapping procedure of RSM to collect the first large-scale dataset of RSM occurrences, using India as a case-study. (2) Based on this dataset, identify potential controlling factors of RSM through statistical analysis. Factors were included that represent the demand and supply of sand. (3) Develop a statistical model to estimate the probability of RSM occurrences using logistic regression.


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Author

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

Promotor(s)

Committee's member(s)

  • Schmitz, Serge ULiège Université de Liège - ULiège > Département de géographie > Service de géographie rurale (LAPLEC)
    ORBi View his publications on ORBi
  • Houbrechts, Geoffrey ULiège Université de Liège - ULiège > Département de géographie > Département de géographie
    ORBi View his publications on ORBi
  • Total number of views 40
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