Abstract
Abstract. River basin management can greatly benefit from short-term river discharge predictions. In order to improve model produced discharge forecasts, data assimilation allows for the integration of current observations of the hydrological system to produce improved forecasts and reduce prediction uncertainty. Data assimilation is widely used in operational applications to update hydrological models with in situ discharge or level measurements. In areas where timely access to in situ data is not possible, remote sensing data products can be used in assimilation schemes. While river discharge itself cannot be measured from space, radar altimetry can track surface water level variations at crossing locations between the satellite ground track and the river system called virtual stations (VS). Use of radar altimetry versus traditional monitoring in operational settings is complicated by the low temporal resolution of the data (between 10 and 35 days revisit time at a VS depending on the satellite) as well as the fact that the location of the measurements is not necessarily at the point of interest. However, combining radar altimetry from multiple VS with hydrological models can help overcome these limitations. In this study, a rainfall runoff model of the Zambezi River basin is built using remote sensing data sets and used to drive a routing scheme coupled to a simple floodplain model. The extended Kalman filter is used to update the states in the routing model with data from 9 Envisat VS. Model fit was improved through assimilation with the Nash–Sutcliffe model efficiencies increasing from 0.19 to 0.62 and from 0.82 to 0.88 at the outlets of two distinct watersheds, the initial NSE (Nash–Sutcliffe efficiency) being low at one outlet due to large errors in the precipitation data set. However, model reliability was poor in one watershed with only 58 and 44% of observations falling in the 90% confidence bounds, for the open loop and assimilation runs respectively, pointing to problems with the simple approach used to represent model error.
Highlights
Accurate short-term predictions of river flows are necessary for optimal river basin management, in particular for river systems with large reservoirs or in areas subject to flooding
The improvements obtained from assimilation of in situ data to hydrological models, in particular water levels and discharge, have been successfully proven since the 1980s and data assimilation is commonly used in operational flood forecasting models (e.g., Kitanidis and Bras, 1980; Refsgaard, 1997; Madsen and Skotner, 2005)
Model calibration was carried out over the years 2001–2004
Summary
Accurate short-term predictions of river flows are necessary for optimal river basin management, in particular for river systems with large reservoirs or in areas subject to flooding. The improvements obtained from assimilation of in situ data to hydrological models, in particular water levels and discharge, have been successfully proven since the 1980s and data assimilation is commonly used in operational flood forecasting models (e.g., Kitanidis and Bras, 1980; Refsgaard, 1997; Madsen and Skotner, 2005). Such applications require the availability of timely in situ data, which can be challenging in large remote river basins or in situations where riparian countries are unwilling to share their data. A solution to bypass such challenges is the use of remote sensing data
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