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Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early actionGroundwater 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 sq.km. An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June–September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.
Document ID
20220009542
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Katie Fankhauser
(University of Colorado Boulder Boulder, Colorado, United States)
Denis Macharia
(University of Colorado Boulder Boulder, Colorado, United States)
Jeremy Coyle
(University of Colorado Boulder Boulder, Colorado, United States)
Styvers Kathuni
(SweetSense Inc.)
Amy McNally
(Science Applications International Corporation (United States) McLean, Virginia, United States)
Kimberly Slinski
(University of Maryland, College Park College Park, Maryland, United States)
Evan Thomas
(University of Colorado Boulder Boulder, Colorado, United States)
Date Acquired
June 20, 2022
Publication Date
March 25, 2022
Publication Information
Publication: Science of The Total Environment
Publisher: Elsevier
Volume: 831
Issue Publication Date: July 20, 2022
ISSN: 0048-9697
e-ISSN: 1879-1026
Subject Category
Earth Resources And Remote Sensing
Funding Number(s)
CONTRACT_GRANT: 80NSSC20K0150
INTERAGENCY: AID-FFP-T-17-00001
CONTRACT_GRANT: 80GSFC20C0044
CONTRACT_GRANT: NNX17AE79A
CONTRACT_GRANT: NSF 1738321
CONTRACT_GRANT: AID-615-A-15-00008
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
External Peer Committee
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