Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action.

Drought Early action Early warning Groundwater Kenya Machine learning Remote sensing

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
20 Jul 2022
Historique:
received: 29 12 2021
revised: 02 03 2022
accepted: 06 03 2022
pubmed: 30 3 2022
medline: 7 6 2022
entrez: 29 3 2022
Statut: ppublish

Résumé

Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 km

Identifiants

pubmed: 35346702
pii: S0048-9697(22)01546-7
doi: 10.1016/j.scitotenv.2022.154453
pii:
doi:

Substances chimiques

Water 059QF0KO0R

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

154453

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Evan Thomas reports financial support was provided by NASA. Evan Thomas reports financial support was provided by USAID. Evan Thomas reports a relationship with SweetSense Inc. that includes: employment and equity or stocks. Styvers Kathuni reports a relationship with SweetSense Inc. that includes: employment.

Auteurs

Katie Fankhauser (K)

Mortenson Center in Global Engineering, University of Colorado Boulder, Boulder, CO, USA.

Denis Macharia (D)

Mortenson Center in Global Engineering, University of Colorado Boulder, Boulder, CO, USA; Regional Centre for Mapping of Resources for Development, Nairobi, Kenya; Environmental Studies, University of Colorado Boulder, Boulder, CO, USA.

Jeremy Coyle (J)

Mortenson Center in Global Engineering, University of Colorado Boulder, Boulder, CO, USA; SweetSense Inc., USA.

Styvers Kathuni (S)

SweetSense Inc., USA.

Amy McNally (A)

NASA Goddard Space Flight Center, Greenbelt, MD, USA; Science Applications International Corporation, Reston, VA, USA; US Agency for International Development, Washington, DC, USA.

Kimberly Slinski (K)

NASA Goddard Space Flight Center, Greenbelt, MD, USA; University of Maryland Earth Systems Science Interdisciplinary Center, College Park, MD, USA.

Evan Thomas (E)

Mortenson Center in Global Engineering, University of Colorado Boulder, Boulder, CO, USA; SweetSense Inc., USA. Electronic address: evan.thomas@colorado.edu.

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Classifications MeSH