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Integrating Lateral Inflows Into a SWOT Mission River Discharge Algorithm
Author(s) -
Nickles Cassandra,
Beighley Edward,
Durand Michael,
Prata de Moraes Frasson Renato
Publication year - 2020
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2019wr026589
Subject(s) - inflow , tributary , discharge , swot analysis , hydrology (agriculture) , environmental science , geology , geography , drainage basin , geotechnical engineering , oceanography , cartography , marketing , business
Estimating river discharge from observed surface water extents and elevations is central to the Surface Water and Ocean Topography (SWOT) mission. Although near global in coverage, SWOT will only observe rivers wider than 50 to 100 m, overlooking smaller tributaries draining into observable river reaches. This is problematic for the Metropolis‐Manning (MetroMan) discharge algorithm, which assumes changes in discharge per location must be balanced by a change in cross‐sectional area, not accounting for potential flow contributions SWOT will not observe within the inversion region analyzed. Here, we quantify the effect of these lateral inflows on the performance of estimated discharges along the Muskingum River using MetroMan. Three scenarios are considered: (1) disregarding lateral inflows, (2) providing MetroMan with observed lateral inflows, and (3) providing MetroMan with uncertain model‐derived lateral inflows to assess the discharge algorithm's effectiveness. Scenarios are expanded to consider multiple lateral inflow magnitudes and distributions. Results indicate discharge retrievals were degraded once unaccounted lateral inflows exceeded 5% of average river discharge. When MetroMan is informed by observed lateral inflows, the derived discharges have a relative root‐mean‐square error (rRMSE) of 23% as compared to 360% when lateral inflows are neglected. More importantly, when MetroMan uses simulated lateral inflows, with peak flow condition percent errors as high as 93%, discharge retrieval performance is similar (rRMSE = 17%). These findings highlight the importance of accounting for lateral flows, even in the absence of perfect measurements.