River Discharge Estimation by Using Altimetry Data and Simplified Flood Routing Modeling

Abstract

A methodology to estimate the discharge along rivers, even poorly gauged ones, taking advantage of water level measurements derived from satellite altimetry is proposed. The procedure is based on the application of the Rating Curve Model (RCM), a simple method allowing for the estimation of the flow conditions in a river section using only water levels recorded at that site and the discharges observed at another upstream section. The European Remote-Sensing Satellite 2, ERS-2, and the Environmental Satellite, ENVISAT, altimetry data are used to provide time series of water levels needed for the application of RCM. In order to evaluate the usefulness of the approach, the results are compared with the ones obtained by applying an empirical formula that allows discharge estimation from remotely sensed hydraulic information. To test the proposed procedure, the 236 km-reach of the Po River is investigated, for which five in situ stations and four satellite tracks are available. Results show that RCM is able to appropriately represent the discharge, and its performance is better than the empirical formula, although this latter does not require upstream hydrometric data. Given its simple formal structure, the proposed approach can be conveniently utilized in ungauged sites where only the survey of the cross-section is needed.

A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation

“…The difference between altimeter and gauge measurements also increases with distance between the points, topography and river width (León, et al, 2006). When compared with gauge data, RMSEs of altimetry data measured over the Amazon have ranges from 30 to 70 cm using data from EN-VISAT, ERS2, and GeoSaT (Tarpanelli et al, 2013); however, at cross track situations where altimetry measurements are taken at the same location with a gauging station, the difference can be < 20 cm (Seyler et al, 2009). The accuracy of altimeter measurements over rivers is also affected by the river width and the morphology of the river banks so that data on narrow rivers and vegetated banks have lower accuracy (Papa et al, 2012).…”

“…The accuracy of altimeter measurements over rivers is also affected by the river width and the morphology of the river banks so that data on narrow rivers and vegetated banks have lower accuracy (Papa et al, 2012). Furthermore, the specifications of the altimetry system itself can affect the quality of measurements; for example, ENVISAT data have been shown to have lower RMSE compared to ERS2 data due to ENVISAT's ability to switch frequency modes in response to changes in terrain and its smaller bin width (Tarpanelli et al, 2013). The limitation of a satellite DEM is in the data quality.…”

“…Although satellite altimetry was developed and optimized to measure ocean level changes (not rivers), it has been demonstrated as a source of data over large rivers and lakes (Tarpanelli et al, 2013;Jarihani et al, 2013). Typical altimeter footprints are in kilometres; e.g.…”

“…RMSE, mean error), checking for correlation with measured data or measuring the coefficient of determination (e.g. Tarpanelli et al, 2013). There are multiple sources of error that can affect the data; for example, the uncertainties in using satellite river width for calibration include the satellite estimation of the river width, the power relation between the discharge and river width (which is an approximation of the conditions at a river cross section when there is no backwater effect) and the assumption of a stable/static river cross section.…”

“…The calibration results were affected by rainfall data spatial distribution. Although use of satellite altimetry for river stage monitoring is usually applied to large rivers with a few kilometres' width (Papa et al, 2012), altimetry data were used to estimate discharge in an ungauged part of the Po River basin (width: 200-300 m) using cross-sectional data (Tarpanelli et al, 2013). They used a simplified routing model (RCM) based on upstream data, wave travel time and hydraulic conditions on two river sections to get the flow in the second river section.…”