Abstract's details
Assimilating SWOT virtual discharge and water levels into a large scale hydrological model
CoAuthors
Event: 2019 Ocean Surface Topography Science Team Meeting
Session: Science IV: Altimetry for Cryosphere and Hydrology
Presentation type: Type Poster
Contribution: not provided
Abstract:
Modelling continental waters at global scale is a crucial issue for water resources management in a continuously changing climate. In this scope, satellite measurements provide relevant information, especially in regions where in situ measurements are not readily available. The future Surface Water and Ocean Topography (SWOT) satellite mission will deliver maps of water surface elevation (WSE), discharge and slope with an unprecedented resolution for rivers wider than 100 m and water surface areas above 250 x 250 m2 over continental surfaces between 78°S and 78°N.
In this study, we investigate the potential contribution of SWOT observations to improve discharge forecasts from a hydrological modeling platform. To this aim, we use an ensemble Kalman filter (EnKF), which is a stochastic or Monte Carlo approach of the classic Extended Kalman Filter (EKF) to correct several state variables in a hydrological model (discharge, water level, soil humidity, water storage etc ...). The hydrological platform presented here handles all the consecutive tasks necessary for operational hydrology from forcing and observations retrieval to the launch of the large scale hydrological model over a specified period and domain. The model used is the semi-distributed rainfall-runoff “Modelo de Grandes Bacias” or “Large Basins Model” developped by the brazillian Institute of Hydraulic Research (IPH). The study focuses on the Niger basin, a trans-boundary river, which is the main source of fresh water for all the riparian countries and where geopolitical issues restrict the exchange of hydrological data.
Since the SWOT observations are not available yet and also to assess the skills of the
assimilation method, the study is carried out in the framework of an Observing System
Simulation Experiment (OSSE). Here, we assume that modeling errors are only due to
uncertainties in precipitations. The control ensemble is built by integrating the model forced by an ensemble of perturbed fields of precipitations leading to an ensemble of simulated hydrological states. A member of this ensemble is then randomly chosen and used to create virtual SWOT observations of discharge and water levels over the period 2012-2017.
Several configurations are tested in which we vary the number of control variables, the characteristics of the modeling and bservation errors and the number of observations used for the assimilation.
The impact of the assimilation method on the Niger River modeling is estimated using various statistical scores and shows good potential for the future satellite mission SWOT to improve hydrological forecasts at global scale.
In this study, we investigate the potential contribution of SWOT observations to improve discharge forecasts from a hydrological modeling platform. To this aim, we use an ensemble Kalman filter (EnKF), which is a stochastic or Monte Carlo approach of the classic Extended Kalman Filter (EKF) to correct several state variables in a hydrological model (discharge, water level, soil humidity, water storage etc ...). The hydrological platform presented here handles all the consecutive tasks necessary for operational hydrology from forcing and observations retrieval to the launch of the large scale hydrological model over a specified period and domain. The model used is the semi-distributed rainfall-runoff “Modelo de Grandes Bacias” or “Large Basins Model” developped by the brazillian Institute of Hydraulic Research (IPH). The study focuses on the Niger basin, a trans-boundary river, which is the main source of fresh water for all the riparian countries and where geopolitical issues restrict the exchange of hydrological data.
Since the SWOT observations are not available yet and also to assess the skills of the
assimilation method, the study is carried out in the framework of an Observing System
Simulation Experiment (OSSE). Here, we assume that modeling errors are only due to
uncertainties in precipitations. The control ensemble is built by integrating the model forced by an ensemble of perturbed fields of precipitations leading to an ensemble of simulated hydrological states. A member of this ensemble is then randomly chosen and used to create virtual SWOT observations of discharge and water levels over the period 2012-2017.
Several configurations are tested in which we vary the number of control variables, the characteristics of the modeling and bservation errors and the number of observations used for the assimilation.
The impact of the assimilation method on the Niger River modeling is estimated using various statistical scores and shows good potential for the future satellite mission SWOT to improve hydrological forecasts at global scale.