Automated Water Level Monitoring at the Continental Scale from ICESat-2 Photons

Of the approximately 6700 lakes and reservoirs larger than 1 km(2) in the Contiguous United States (CONUS), only similar to 430 (similar to 6%) are actively gaged by the United States Geological Survey (USGS) or their partners and are available for download through the National Water Information System database. Remote sensing analysis provides a means to fill in these data gaps in order to glean a better understanding of the spatiotemporal water level changes across the CONUS. This study takes advantage of two-plus years of NASA's ICESat-2 (IS-2) ATLAS photon data (ATL03 products) in order to derive water level changes for similar to 6200 overlapping lakes and reservoirs (>1 km(2)) in the CONUS. Interactive visualizations of large spatial datasets are becoming more commonplace as data volumes for new Earth observing sensors have markedly increased in recent years. We present such a visualization created from an automated cluster computing workflow that utilizes tens of billions of ATLAS photons which derives water level changes for all of the overlapping lakes and reservoirs in the CONUS. Furthermore, users of this interactive website can download segmented and clustered IS-2 ATL03 photons for each individual waterbody so that they may run their own analysis. We examine similar to 19,000 IS-2 derived water level changes that are spatially and temporally coincident with water level changes from USGS gages and find high agreement with our results as compared to the in situ gage data. The mean squared error (MSE) and the mean absolute error (MAE) between these two products are 1 cm and 6 cm, respectively.

Automated summary

Of the approximately 6700 lakes and reservoirs larger than 1 km(2) in the Contiguous United States, only around 6% are actively monitored and have data available for download. Remote sensing analysis using NASA's ICESat-2 data was used to derive water level changes for about 6200 lakes and reservoirs in order to understand the hydrological situation across the CONUS. The results show high agreement with in situ gage data, with a mean squared error of 1 cm and a mean absolute error of 6 cm.