A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes

Abhishek Kumar; Allison H. Roy; Konstantinos M. Andreadis; Xinchen He; Caitlyn Butler

doi:10.3390/rs16060947

Remote Sensing (Mar 2024)

A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes

Abhishek Kumar,
Allison H. Roy,
Konstantinos M. Andreadis,
Xinchen He,
Caitlyn Butler

Affiliations

Abhishek Kumar: Massachusetts Cooperative Fish and Wildlife Research Unit, Department of Environmental Conservation, University of Massachusetts, Amherst, MA 01003, USA
Allison H. Roy: U.S. Geological Survey, Massachusetts Cooperative Fish and Wildlife Research Unit, Department of Environmental Conservation, University of Massachusetts, Amherst, MA 01003, USA
Konstantinos M. Andreadis: Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA 01003, USA
Xinchen He: Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA 01003, USA
Caitlyn Butler: Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA 01003, USA

DOI: https://doi.org/10.3390/rs16060947
Journal volume & issue: Vol. 16, no. 6
p. 947

Abstract

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Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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