Combining Optical Remote Sensing, McFLI Discharge Estimation, Global Hydrologic Modeling, and Data Assimilation to Improve Daily Discharge Estimates Across an Entire Large Watershed

Abstract

Remote sensing has gained attention as a novel source of primary information for estimating river discharge, and the Mass‐conserved Flow Law Inversion (McFLI) approach has successfully estimated river discharge in ungauged basins solely from optical satellite data. However, McFLI currently suffers from two major drawbacks: (1) existing optical satellites lead to temporally and spatially sparse discharge estimates and (2) because of the assumptions required, McFLI cannot guarantee downstream flow continuity. Hydrological modeling has neither drawback, yet model accuracy is frequently limited by a lack of discharge observations. We therefore combine McFLI and models in a data assimilation framework applicable globally. We establish a daily “ungauged” baseline model for 28,998 reaches of the Missouri river basin forced by recently published global runoff data, which we do not calibrate. We estimate discharge via McFLI using ∼1 million width measurements made from 12,000 Landsat scenes and assimilate McFLI into the model before validating at 403 USGS gauges. Results show that assimilated discharges did not impair already accurate baseline flows and achieved median improvements of 28% normalized root mean square error, 0.50 Nash–Sutcliffe efficiency (NSE), and 0.23 Kling–Gupta efficiency where baseline performance was poor (defined as baseline negative NSE, 225/403 reaches). We ultimately improved flows at 92% of these originally poorly modeled gauges, even though Landsat images only provide McFLI discharges at 1.5% of reaches and 26% of simulated days. Our results suggest that the combination of McFLI and state‐of‐the‐art hydrology models can improve flow estimations in ungauged basins globally.

Recent changes to Arctic river discharge

“…The RivWidthCloud 23 code for Landsat river width extraction is available at https:// github.com/seanyx/RivWidthCloudPaper; The geoBAM 19 algorithm used in this study is available at https://github.com/craigbrinkerhoff/geoBAMr; The data assimilation 27 package is available at https://github.com/Fluvial-UMass/SIRD_Missouri.…”

“…Remotely sensed data, however, are sparse in space and time by definition given orbit geometry, and thus the best way to better understand Arctic rivers without new gauges is to combine remote sensing and hydrological modeling. Although recently modeled global river discharge products show promising skill in the Arctic 25,26 , fusing models with remote sensing would allow the primary data of remote sensing to drive improvements in hydrologic models, which would then propagate the information gained from satellites in space and time to all rivers using classic hydraulic physics 27 . Further, improvements to hydrologic models gained from incorporating remote sensing can be used to reduce uncertainty in predictions of the Arctic hydrologic state 28 .…”

“…1) into an optimal blend of two recent global hydrologic model simulations 25,26 . See Methods on how we generate discharge from Landsat 19 , the global hydrologic models we used to drive the assimilation 25,26 , and our data assimilation scheme 27 . All of our methods are based on techniques and model results described in recent literature.…”

Assimilation of Transformed Water Surface Elevation to Improve River Discharge Estimation in a Continental-Scale River

“…The LETKF is a commonly used DA algorithm (e.g., Feng et al, 2021;Ishitsuka et al, 2020;Revel et al, 2019Revel et al, , 2021bWongchuig-Correa et al, 2020) for nonlinear models, which are needed for modeling hydrodynamic processes. The nonlinear hydrodynamic model can be shown in discrete form as follows:…”

“…Using operation system simulation experiments, researchers have thoroughly investigated the potential for improving river discharge through the assimilation of remote sensing data (Andreadis et al, 2007;Andreadis and Schumann, 2014;Biancamaria et al, 2011;Revel et al, 2019Revel et al, , 2021. In situ (Clark et al, 2008;Paiva et al, 2013a;Wongchuig et al, 2019) or remotely sensed (Emery et al, 2020b;Feng et al, 2021;Ishitsuka et al, 2020) discharge assimilation performs better, but the unavailability of ground observations and the limitations of remotely sensed river discharge values may hamper the performance of these DA schemes. Thus, DA approaches based on remotely sensed data can be used to improve the performance of global hydrodynamic models.…”