Browsing by Author "Riggs, Ryan M."

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    Extending global river gauge records using satellite observations
    Riggs, Ryan M.; Allen, George H.; Wang, Jida; Pavelsky, Tamlin M.; Gleason, Colin J.; David, Cedric H.; Durand, Michael (IOP, 2023-05-26)
    Long-term, continuous, and real-time streamflow records are essential for understanding and managing freshwater resources. However, we find that 37% of publicly available global gauge records (N = 45 837) are discontinuous and 77% of gauge records do not contain real-time data. Historical periods of social upheaval are associated with declines in gauge data availability. Using river width observations from Landsat and Sentinel-2 satellites, we fill in missing records at 2168 gauge locations worldwide with more than 275 000 daily discharge estimates. This task is accomplished with a river width-based rating curve technique that optimizes measurement location and rating function (median relative bias = 1.4%, median Kling-Gupta efficiency = 0.46). The rating curves presented here can be used to generate near real-time discharge measurements as new satellite images are acquired, improving our capabilities for monitoring and managing river resources.
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    Turning Lakes Into River Gauges Using the LakeFlow Algorithm
    Riggs, Ryan M.; Allen, George H.; Brinkerhoff, Craig B.; Sikder, Md. Safat; Wang, Jida (American Geophysical Union, 2023-05)
    Rivers and lakes are intrinsically connected waterbodies yet they are rarely used to hydrologically constrain one another with remote sensing. Here we begin to bridge the gap between river and lake hydrology with the introduction of the LakeFlow algorithm. LakeFlow uses river-lake mass conservation and observations from the Surface Water and Ocean Topography (SWOT) satellite to provide river discharge estimates of lake and reservoir inflows and outflows. We test LakeFlow performance at three lakes using a synthetic SWOT data set assuming the maximum measurement errors defined by the mission science requirements, and we include modeled lateral inflow and lake evaporation data to further constrain the mass balance. We find that LakeFlow produces promising discharge estimates (median Nash-Sutcliffe efficiency = 0.88, relative bias = 14%). LakeFlow can inform water resources management by providing global lake inflow and outflow estimates, highlighting a path for recognizing rivers and lakes as an interconnected system.