Towards improving river discharge estimation in ungauged basins: calibration of rainfall-runoff models based on satellite observations of river flow width at basin outlet

Abstract

Abstract. Rainfall-runoff models are common tools for river discharge estimation in the field of hydrology. In ungauged basins, the dependence on observed river discharge data for calibration restricts applications of rainfall-runoff models. The strong correlation between quantities of river cross-sectional water surface width obtained from remote sensing and corresponding in situ gauged river discharge has been verified by many researchers. In this study, a calibration scheme of rainfall-runoff models based on satellite observations of river width at basin outlet is illustrated. One distinct advantage is that this calibration is independent of river discharge information. The at-a-station hydraulic geometry is implemented to facilitate shifting the calibration objective from river discharge to river width. The generalized likelihood uncertainty estimation (GLUE) is applied to model calibration and uncertainty analysis. The calibration scheme is demonstrated through a case study for simulating river discharge at Pakse in the Mekong Basin. The effectiveness of the calibration scheme and uncertainties associated with utilization of river width observations from space are examined from model input-state-output behaviour, capability of reproducing river discharge and posterior parameter distribution. The results indicate that the satellite observation of the river width is a competent surrogate of observed discharge for the calibration of rainfall-runoff model at Pakse and the proposed method has the potential for improving reliability of river discharge estimation in basins without any discharge gauging.

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

“…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. However, these sources of uncertainty are lowest for the period of satellite data acquisition, and increase with change in season and hydraulic conditions (Sun et al, 2010).…”

“…Satellite data are used to calibrate hydrologic models, especially in un-gauged catchments (Vermeulen et al, 2005;Sun et al, 2009). Calibration of flood inundation models can be done using several model parameters, but the most sensitive parameter that shows a direct relationship with water stage and therefore flooding extent and timing is the channel roughness (Schumann et al, 2007).…”

“…For un-gauged basins where hydrological data are inaccessible, satellite measurement of river width can be used for hydrological model calibration (Schumann et al, 2013;Sun et al, 2009). River width can be estimated from several sources of satellite data, making it more readily available than discharge or water level.…”

“…River width can be estimated from several sources of satellite data, making it more readily available than discharge or water level. Sun et al (2009) used measured river width from satellite SAR imagery to calibrate HYMOD hydrological model. The model calibration based on river width gave an 88.24 % Nash coefficient, with a larger error during low-flow than high-flow periods, implying its usefulness for flood discharge calculations.…”

“…However, a small error in width measurement can lead to a large error in discharge estimation as the discharge variability was much larger than the width variability. Sun et al (2010) used the GLUE methodology to reduce this uncertainty in calibration of river width-to-discharge estimation with the HYMOD hydrological model. From 50 000 samples of the parameter sets, 151 (likelihood equals RMSE values) succeeded as behavioural sets to be used in the model to simulate the measured satellite river widths.…”