The datasets were generated for the Upper Choptank River Watershed (UCRW) in Maryland and Delaware. Depressions and the total stream network were derived from a lidar DEM collected during April to June 2003 (vertical accuracy root mean square error (RMSE) = 14.3 cm) and March to April 2006 (vertical accuracy RMSE = 18.5 cm) for Maryland (1 m resolution) and April 2007 (vertical accuracy RMSE = 18.5 cm) for Delaware (3 m resolution)). Depressions were identified using the Stochastic Depression Analysis Tool in Whitebox GAT. A random sample from the potential error values was iteratively added to the original DEM prior to depressions being filled and identified. Cells were considered part of a depression if identified as such in 80% of the 20 iterations. An edge preserving smoothing filter with a 3-pixel by 3-pixel window was applied to reduce noise, or random error, within the final depression raster. Depressions smaller than 50 m2 were removed. To map the stream network, flow accumulation was calculated on the filled lidar DEM using the FD8 flow accumulation algorithm. Flow accumulation was thresholded at 50,000 m2, a decision point guided by field-based points. Three Landsat images were used to create a surface-water extent map from Landsat including Landsat-8 images collected on April 4, 2015 (p14r33) and April 11, 2015 (p15r33) and a Landsat-7 ETM+ image collected on April 12, 2015 (p14r33). Surface-water was identified using the Matched Filtering algorithm. The output values were linearly stretched and a Frost filter with a 3-pixel by 3-pixel window was applied. Landsat pixels with a per-pixel fraction of >0.4 were classified as inundated.