Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018

Abstract

Abstract. The recent availability of freely and openly available satellite remote sensing products has enabled the implementation of global surface water monitoring to a level not previously possible. Here we present a global set of satellite-derived time series of surface water storage variations for lakes and reservoirs for a period that covers the satellite altimetry era. Our goal is to promote the use of satellite-derived products for the study of large inland water bodies, and to set the stage for the expected availability of products from the Surface Water and Ocean Topography (SWOT) mission, which will vastly expand the spatial coverage of such products, expected from 2021 on. Our general strategy is to estimate global surface water storage changes (ΔV) in large lakes and reservoirs using a combination of paired water surface elevation (WSE) and water surface area (WSA) extent products. Specifically, we use data produced by multiple satellite altimetry missions (TOPEX-Poseidon, Jason-1, Jason-2, Jason-3, and ENVISAT) from 1992 on, with surface extent estimated from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 on. We leverage from relationships between elevation and surface area (i.e., hypsometry) to produce estimates of ΔV even during periods when either of the variables was not available. This approach is successful provided that there are strong relationships between the two variables during an overlapping period. Our target is to produce time series of ΔV as well as WSE and WSA for a set of 347 lakes and reservoirs globally for the 1992–2018 period. The data sets presented are publicly available and distributed via NASA’s Jet Propulsion Laboratory’s Physical Oceanography Distributed Active Archive Center (PO DAAC; https://podaac.jpl.nasa.gov/). Specifically, the WSE data set is available at https://doi.org/10.5067/UCLRS-GREV2 (Birkett et al., 2019), the WSA data set is available at https://doi.org/10.5067/UCLRS-AREV2 (Khandelwal and Kumar, 2019), and the ΔV data set is available at https://doi.org/10.5067/UCLRS-STOV2 (Tortini et al., 2019). The records we describe represent the most complete global surface water time series available from the launch of TOPEX-Poseidon in 1992 (beginning of the satellite altimetry era) to near-present. The production of long-term, consistent, and calibrated records of surface water cycle variables such as the data set presented here is of fundamental importance to baseline future SWOT products.

Remotely sensed reservoir water storage dynamics (1984–2015) and the influence of climate variability and management at global scale

“…3. The average Pearson correlation between our Landsat-derived water volumes and published MODIS-derived estimates (Tortini et al 2020) from 1992 to 2015 for 100 reservoirs achieved 0.87, and the R values does not differ remarkably from different sizes of reservoirs.…”

Gainers and losers of surface and terrestrial water resources in China during 1989–2016

“…However, some of these studies used images for specific dates in different years for comparison [18][19][20] , which might lead to very different results and temporal uncertainties in SWA due to the strong seasonal dynamics and interannual variation of surface water bodies 23 , and some of these studies only focused on certain hotspots in China 21,22 . Furthermore, several studies have reported the area changes of only lakes, ponds, or reservoirs 13,[24][25][26] , but did not include other surface water bodies such as rivers and streams.…”

Remotely sensed reservoir water storage dynamics (1984–2015) and the influence of climate variability and management at a global scale

“…The higher hypsometric correlation we used, the less uncertainties volume estimations would have (Crétaux et al, 2016). We selected a correlation threshold of 0.7 in this study, which is lower than Tortini et al (2020) (R ≥ 0.85) and Busker et al (2019) (R ≥ 0.9), but higher than Gao et al (2012) (R ≥0.5). The selection of an appropriate correlation threshold can also depend on the purpose of the study.…”

“…3. We investigated the influence of Landsat image quality on the volume time series estimation by comparing time series derived from images with different contamination ratios (0 %-95 %) against the MODIS-derived lake product (Tortini et al, 2020). The temporal accuracy slightly decreases as the contamination ratio increases (Table S3).…”