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Monitoring Reservoir Drought Dynamics with Landsat and Radar/Lidar Altimetry Time Series in Persistently Cloudy Eastern Brazil

1
Geography Program, College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
2
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
3
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
4
Science Systems and Applications, Inc., Lanham, MD 20706, USA
5
Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77005, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 827; https://doi.org/10.3390/rs11070827
Received: 5 March 2019 / Revised: 1 April 2019 / Accepted: 4 April 2019 / Published: 6 April 2019
Tropical reservoirs are critical infrastructure for managing drinking and irrigation water and generating hydroelectric power. However, long-term spaceborne monitoring of reservoir storage is challenged by data scarcity from near-persistent cloud cover and drought, which may reduce volumes below those in the observational record. In evaluating our ability to accurately monitor long-term reservoir volume dynamics using spaceborne data and overcome such observational challenges, we integrated optical, lidar, and radar time series to estimate reservoir volume dynamics across 13 reservoirs in eastern Brazil over a 12-year (2003–2014) period affected by historic drought. We (i) used 1560 Landsat images to measure reservoir surface area; (ii) built reservoir-specific regression models relating surface area and elevation from ICESat GLAS and Envisat RA-2 data; (iii) modeled volume changes for each reservoir; and (iv) compared modeled and in situ reservoir volume changes. Regression models had high goodness-of-fit (median RMSE = 0.89 m and r = 0.88) across reservoirs. Even though 88% of an average reservoir’s volume time series was based on modeled area–elevation relationships, we found exceptional agreement (RMSE = 0.31 km3 and r = 0.95) with in situ volume time series, and accurately captured seasonal recharge/depletion dynamics and the drought’s prolonged drawdown. Disagreements in volume dynamics were neither driven by wet/dry season conditions nor reservoir capacity, indicating analytical efficacy across a range of monitoring scenarios. View Full-Text
Keywords: reservoir volume; clouds; Envisat; ICESat; time series reservoir volume; clouds; Envisat; ICESat; time series
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Van Den Hoek, J.; Getirana, A.; Jung, H.C.; Okeowo, M.A.; Lee, H. Monitoring Reservoir Drought Dynamics with Landsat and Radar/Lidar Altimetry Time Series in Persistently Cloudy Eastern Brazil. Remote Sens. 2019, 11, 827.

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