Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters

Abstract

Abstract. Land surface models combined with river routing models are widely used to study the continental part of the water cycle. They give global estimates of water flows and storages, but they are not without non-negligible uncertainties, among which inexact input parameters play a significant part. The incoming Surface Water and Ocean Topography (SWOT) satellite mission, with a launch scheduled for 2021 and with a required lifetime of at least 3 years, will be dedicated to the measuring of water surface elevations, widths and surface slopes of rivers wider than 100 m, at a global scale. SWOT will provide a significant number of new observations for river hydrology and maybe combined, through data assimilation, with global-scale models in order to correct their input parameters and reduce their associated uncertainty. Comparing simulated water depths with measured water surface elevations remains however a challenge and can introduce within the system large bias. A promising alternative for assimilating water surface elevations consists of assimilating water surface elevation anomalies which do not depend on a reference surface. The objective of this study is to present a data assimilation platform based on the asynchronous ensemble Kalman filter (AEnKF) that can assimilate synthetic SWOT observations of water depths and water elevation anomalies to correct the input parameters of a large-scale hydrologic model over a 21 d time window. The study is applied to the ISBA-CTRIP model over the Amazon basin and focuses on correcting the spatial distribution of the river Manning coefficients. The data assimilation algorithm, tested through a set of observing system simulation experiments (OSSEs), is able to retrieve the true value of the Manning coefficients within one assimilation cycle much of the time (basin-averaged Manning coefficient root mean square error, RMSEn, is reduced from 33 % to [1 %–10 %] after one assimilation cycle) and shows promising perspectives with assimilating water anomalies (basin-averaged Manning coefficient RMSEn is reduced from 33 % to [1 %–2 %] when assimilating water surface elevation anomalies over 1 year), which allows us to overcome the issue of unknown bathymetry.

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

“…We tested the 4D‐LETKF (Hunt et al., 2004; Kalnay & Yang, 2010) to increase the number of observations used in an assimilation window, yet this was not substantially better than our presented results and further increased the computation burden. More sophisticated treatment of localization, spatiotemporal errors, or variance inflation (Emery et al., 2020) should also be tested. Recent studies (e.g., David et al., 2019; Y. Yang et al., 2019) have shown an effective way to propagate errors in discharge to runoff fields by inverting routing schemes.…”

A Framework for Estimating Global‐Scale River Discharge by Assimilating Satellite Altimetry

“…Further studies are needed to define the geographical parameters for hydrodynamic modeling using satellite altimetry (Breda et al., 2019; Durand et al., 2010a, 2010b; Pedonetti et al., 2014; Revel et al., 2018; Emery et al., 2020a; Yoon et al., 2012). Before implementing a global assimilation framework, topography parameters for river hydrodynamic models must be carefully defined.…”

“…Methods to estimate channel bathymetric depth using SWOT observations have also been proposed (Brêda et al., 2019; Durand et al., 2008; Revel et al., 2018; Yoon et al., 2012). Estimating other hydrodynamic parameters, such as Manning's coefficient, is also essential and may be achieved using SWOT observations (Emery et al., 2020a; Pedinotti et al., 2014). River hydrodynamic models will be improved by accurately defining topography parameters.…”

Ensemble Streamflow Data Assimilation using WRF-Hydro and DART: Hurricane Florence Flooding

“…In surface water hydrology, however, the impact of inflation on streamflow predictions is not fully understood. We note that the approach of Emery et al (2020a), a temporally-fixed inflation parameter (scalar) as a means of tuning static background error covariances, is significantly different from the temporally and spatially adaptive inflation applied to time evolving background error covariances in this paper.…”

“…The filter operates by minimizing a weighted sum of error variances and Type-II squared errors, different from the conventional Kalman filter which is based on least square minimization (Seo et al, 2018;Lee et al, 2019;Jozaghi et al, 2019). In a recent study, Emery et al (2020a) proposed updating the boundary fluxes based on the 2 https://doi.org/10.5194/hess-2020-642 Preprint. Discussion started: 25 March 2021 c Author(s) 2021.…”