Skip to main content

Description

The Global Lake/Reservoir Storage Time Series is derived from the Surface Water Height Time Series and Surface Water Extent Mask Time Series products. The purpose of this dataset is to provide surface water storage estimates for several hundred lakes and reservoirs across the globe. These time series potentially span a 25 year time period, from late 1992 to 2017, satisfying the project goal of ESDR creation with a suitable level of quality that supports long-term trend analysis and global water dynamics models. This product is readily accessible and is of direct use to both water managers and the scientific community worldwide, and allows for improved assessment and modeling of the human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.

Product Summary

Platforms
OSTM/JASON-2
Instruments
POSEIDON-3
Spatial Extent

N: 66 S: -66 E: 180 W: -180

Spatial Resolution
0.001 Decimal Degrees x 0.001 Decimal Degrees
Location
GLOBAL
Coordinate System
CARTESIAN
Granule Spatial Representation
CARTESIAN
Temporal Extent
1992-09-25 to 2019-12-23
Data Partner
Physical Oceanography Distributed Active Archive Center, Jet Propulsion Laboratory, NASA (NASA/JPL/PODAAC)
Concept ID
C2036882391-POCLOUD
Data State
COMPLETE
Number of Files/Granules
372
Processing Level
4
Published
Updated
Science Keywords
LAKES

Citation

Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.

Copy Citation

Tortini, R., Noujdina, N., Yeo, S., Khandelwal, A., Kumar, V., Birkett, C., Ricko, M., Yang, X., & Lettenmaier, D. (2019). Pre SWOT Hydrology Global Lake/Reservoir Storage Time Series V2 [Data set]. NASA Physical Oceanography Distributed Active Archive Center. https://doi.org/10.5067/UCLRS-STOV2 Date Accessed: 2026-03-18

Documents

Variables

The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.

For questions on a specific variable, please use the Earthdata Forum.

Name Description Units Data Type Fill Value Valid Range Scale Factor Offset
altimeter_source Flag to identify the data source from G-REALM10, G-REALM35, DAHITI, or Hydroweb N/A float -40000000000 N/A 1 N/A
ice_flag Flag to indicate ice presence on a monthly basis. This flag was determined using a combination of bibliographical resources and MODIS data (MOD10A1.005) in Google Earth Engine. For the MODIS derived ice dates the Bitmask for Snow_Cover_Daily_Tile (100: Snow Covered Lake Ice) on the Terra Snow Cover Daily Global 500m product was used. A ratio was made using cumulative monthly ice coverage per lake and lake surface area. If the monthly ice cover for each lake exceeded half of the maximum ice coverage for the range of the product the lake the data was flagged. Bibliographical data was selected preferentially over MODIS derived data. Due to the spectral confusion between ice with high turbidity and algal blooms, lakes that were highly turbid or had high chlorophyllic activity in areas that do not freeze were hand edited. N/A float -40000000000 N/A 1 N/A
model_flag Flag to identify whether or not the data was modeled using extrapolation methods based on the correlation of altimetry and surface area data. N/A float -40000000000 N/A 1 N/A
outlier_flag_hypsometry Flag to indicate statistical outliers in hypsometry N/A float -40000000000 N/A 1 N/A
outlier_flag_surface_area Flag to indicate statistical outliers in surface area N/A float -40000000000 N/A 1 N/A
surface_water_extent Water extent is calculated by classifying pixels as either water or land using machine learning algorithms and post-classification refinement. This is applied on each lake for each MODIS timestep (Khandelwal et al., 2017). Data Processing Version ID: TPJOJ.2.3 m2 float -40000000000 32022688000 to 34914492000 1 N/A
surface_water_height The Global Lake/Reservoir Surface Water Height Time Series is derived from the NASA/USDA G-REALM 10-day, G-REALM 35-day, as well as LEGOS and DAHITI lake level products. Data Processing Version ID for G-REALM 10-day product: TPJOJ.2.3 m float -40000000000 -1.13 to 2.863313 1 N/A
time The provided date indicates the beginning of the time stamp. Altimeter data is acquird from different agencies and merged together. Therefore, the altimeter data has variable temporal resolution and is dependent upon which data source was used (see variable altimeter_source). days since 1900-01-01T00:00:00 int N/A N/A 1 N/A
water_storage Storage variation is estimated using derived elevation-area relationships for each reservoir by combining the MODIS-based surface area estimates with the radar altimetry elevations as outlined in Gao, et al. 2012. Data Processing Version ID: TPJOJ.2.3 m3 float -40000000000 -128745390000 0 1 N/A
You're viewing 9 of 9