Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California

Abstract Accurate monitoring of surface waterlocation and extent is critical for the management of diverse water resource phenomena. The multi-decadal archive of Landsat satellite imagery is punctuated by missing data due to cloud coverduring acquisition times, hindering the assembly of a continuous time series of inundation dynamics. This study investigated whether streamflowvolume measurements could be integrated with satellite data to fill gaps in monthly surface waterchronologies for the Central Valley region of California, USA, from 1984 to 2015. We aggregated measurements of maximum monthly water extent within each of the study area’s 50 8-digit hydrologic unit code (HUC) watersheds from two Landsat-derived datasets: the European Commission’s Joint Research Centre (JRC) Monthly Water History and the U.S. Geological Survey Dynamic Surface WaterExtent (DSWE). We calculated Spearman rank correlation coefficients between water extent values in each HUC and streamflowdischarge data. Linear regression fits of the water extent/ streamflowdata pairs with the highest correlations served as the basis for interpolation of missing imagery surface watervalues on a HUC-wise basis. Results show strong (ρ > 0.7) maximum correlations in 11 (22.4%) and 25 (51.0%) HUCs for the DSWE and JRC time series, respectively, when comparisons were restricted to imagery and gages co-located in each HUC. Strong maximum correlations occurred in 39 (79.6%; DSWE) and 42 (85.7%; JRC) HUCs when imagery was paired with discharge data from any study area gage, providing a solid basis for reconstruction of water extent values. We generated continuous time series of 30+ years in 35 HUCs, demonstrating that this technique can provide quantitative estimates of historical surface waterextents and elucidate flooding or drought events over the period of data collection. Results of a non-parametric trend analysisof the long- term timeseries on an annual, seasonal, and monthly basis varied among HUCs, though most trends indicate an increase in surface waterover the past 30 years.