The University of Tokyo · Environmental Science
Professor Menaka Revel's research lab specializes in advancing global water cycle understanding through innovative data assimilation techniques that integrate satellite observations—particularly from the upcoming SWOT mission—with large-scale hydrodynamic models. The lab focuses on improving the accuracy of continental-scale river discharge and water surface elevation estimates by developing computationally efficient filtering methods, such as the Local Ensemble Transform Kalman Filter (LETKF), tailored for complex river systems. A key research direction involves optimizing local analysis domains and handling uncertainties in remote sensing data to enhance model performance in ungauged and data-scarce basins.
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Water resource management has faced challenges in recent decades due to limited in situ observations and the limitations of hydrodynamic modeling. Data assimilation techniques have been proposed to improve hydrodynamic model outputs of local rivers (river length ≤ 1500 km) using synthetic observations of the future Surface Water and Ocean Topography (SWOT) satellite mission to overcome limited in situ observations and the limitations of hydrodynamic modeling. However, large-scale data assimilati
Abstract Understanding spatial and temporal variations in terrestrial waters is key to assessing the global hydrological cycle. The future Surface Water and Ocean Topography (SWOT) satellite mission will observe the elevation and slope of surface waters at <100 m resolution. Methods for incorporating SWOT measurements into river hydrodynamic models have been developed to generate spatially and temporally continuous discharge estimates. However, most SWOT data assimilation studies have been co
Abstract. Quantifying continental-scale river discharge is essential for understanding the terrestrial water cycle, but it is susceptible to errors caused by a lack of observations and the limitations of hydrodynamic modeling. Data assimilation (DA) methods are increasingly used to estimate river discharge in combination with emerging river-related remote sensing products (e.g., water surface elevation (WSE), water surface slope, river width, and flood extent). However, directly comparing simula
Estimating spatial and temporal variations of surface waters is important for water resources management. The upcoming Surface Water and Ocean Topography (SWOT) mission will enhance our understanding on global water cycle by measuring water surface elevations at a high resolution. It will be benificial to combine SWOT observations to hydrodynamic modelling to overcome its limited observation frequency. We performed an observing system simulation experiment (OSSE) for estimating river channel bat
Data assimilation techniques are becoming popular in estimating hydraulic variables in ungauged basins with the recent advancements in the satellite technology. The Local Ensemble Transformation Kalman Filter (LETKF), which limits the assimilation domain by a “local patch”, is an efficient method for a global-scale data assimilation, but the optimization of the size and weighting function of the local patch is still challenging especially for river hydrodynamic models. Here we propose a method t
Earth and Space Science Open Archive This work was has been accepted for publication in Water Resources Research. Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary. Learn more about preprints. preprintOpen AccessYou are viewing the latest version by default [v1]A framework for estimating global-scale river discharge by assimilating satellite altimetryAuthors Menaka Revel iD Daiki Ikeshima Dai Yamazaki Shinjiro Kanae See all authors
Abstract. Quantifying continental-scale river discharge is essential to understanding the terrestrial water cycle but is susceptible to errors caused by a lack of observations and the limitations of hydrodynamic modeling. Data assimilation (DA) methods are increasingly used to estimate river discharge in combination with emerging river-related remote sensing products (e.g., water surface elevation [WSE], water surface slope, river width, and flood extent). However, directly comparing simulated W
<strong class="journal-contentHeaderColor">Abstract.</strong> Satellite altimetry data are useful for monitoring water surface dynamics, evaluating and calibrating hydrodynamic models, and enhancing river-related variables through optimization or assimilation approaches. However, comparing simulated water surface elevations (WSEs) using satellite altimetry data is challenging due to the difficulty of correctly matching the representative locations of satellite altimetry virtual stations (VSs) to
<strong class="journal-contentHeaderColor">Abstract.</strong> Satellite altimetry data are useful for monitoring water surface dynamics, evaluating and calibrating hydrodynamic models, and enhancing river-related variables through optimization or assimilation approaches. However, comparing simulated water surface elevations (WSEs) using satellite altimetry data is challenging due to the difficulty of correctly matching the representative locations of satellite altimetry virtual stations (VSs) to
Quantifying continental-scale river discharge is essential to understanding the terrestrial water cycle but is susceptible to errors caused by a lack of observations and the limitations of hydrodynamic modeling. Data assimilation (DA) methods are increasingly used to estimate river discharge in combination with emerging river-related remote sensing products (e.g., water surface elevation [WSE], water surface slope, river width, and flood extent). However, directly comparing simulated WSE to sate
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