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A Remote Sensing Approach to Characterize Winter Water Level Drawdown Patterns in Lakes

Dates

Publication Date
Start Date
2016-01-01
End Date
2021-06-30

Citation

Kumar, A., Roy, A., Andreadis, K., He, X., and Butler, C., 2024, A remote sensing approach to characterize winter water level drawdown patterns in lakes: U.S. Geological Survey data release, https://doi.org/10.5066/P965R4AM.

Summary

This data release consists of four datasets that were used for evaluating winter drawdown patterns in 166 Massachusetts lakes greater than 0.3 km2 surface area. The first dataset (“Water area and level.csv”) provides water area and water level time series data of 166 lakes from 2016 to 2021. Water area and water level time-series data were derived from European Space Agency’s Sentinel 1 synthetic aperture radar satellite sensor using the JavaScript code in Google Earth Engine platform. Details of this code were described in the software release (https://doi.org/10.5066/P9ZA5I1U). The second dataset (“Water area interpolated.csv”) is the linearly-interpolated daily water area time series data of the 166 lakes from the first dataset [...]

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Attached Files

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Water area and level.csv 5.42 MB text/csv
Water area interpolated.csv 3.8 MB text/csv
Winter drawdown classification.csv 7.28 KB text/csv
Winter drawdown metrics_2016.csv 5.98 KB text/csv
Winter drawdown metrics_2017.csv 7.44 KB text/csv
Winter drawdown metrics_2018.csv 5.19 KB text/csv
Winter drawdown metrics_2019.csv 6.86 KB text/csv
Winter drawdown metrics_2020.csv 4.98 KB text/csv

Purpose

The data was used to characterize winter lake drawdown pattern and compute drawdown metrics including timing, duration, magnitude, and percentage lake area exposure during drawdown for 166 Massachusetts lakes.

Map

Communities

  • Cooperative Fish and Wildlife Research Units
  • National and Regional Climate Adaptation Science Centers
  • Northeast CASC
  • USGS Data Release Products

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Additional Information

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Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P965R4AM

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