Estimation of Discharge From Three Braided Rivers Using Synthetic Aperture Radar Satellite Imagery: Potential Application to Ungaged Basins

Abstract

Analysis of 41 ERS 1 synthetic aperture radar images and simultaneous ground measurements of discharge for three large braided rivers indicates that the area of active flow on braided river floodplains is primarily a function of discharge. A power law correlation is found between satellite‐derived effective width We and discharge Q, where We is the water surface area within a braided reach divided by the reach length. Synthetic values of We and Q generated from a cellular automata model of stream braiding display a similar power law correlation. Power functions that are fit through plots of We and Q represent satellite‐derived rating curves that can subsequently be used to estimate instantaneous river discharge from space, with errors ranging from tens to hundreds of cubic meters per second. For ungaged rivers, changes in relative discharge can be determined from satellite data alone to determine the shape and timing of annual flows in glacierized basins. Absolute discharge can probably be estimated within a factor of 2. More accurate estimates will require either (1) one or more ground measurements of discharge acquired simultaneously with a satellite image acquisition, or (2) successful parameterization of known morphologic controls such as total sinuosity ∑P, valley slope, bank material and stability, and braid channel hydraulic geometry. Values of total sinuosity ∑P derived from satellite imagery and field measurements from two rivers of braid channel width, depth, velocity, water surface slope, and bed material grain size indicate that while the shape of satellite‐derived We‐Q rating curves may be influenced by all of these variables, the sensitivity of flow area to changing discharge is most dependent upon the degree of braiding. Efforts to monitor river discharge from space will be most successful for intensely braided rivers with high values of total sinuosity. Subsampling of existing daily discharge records from the Iskut River suggests that satellite return times of about 1 week are sufficient for approximating the shape and timing of the seasonal hydrograph in large, glacierized basins. Although errors are large, the presented technique represents the only currently available way to estimate discharge in ungauged braided rivers.

Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels

“…Early results from Hess et al (1995) deal with the mapping of inundated areas of the central Amazon floodplain with the SIR-C SAR, and Wang et al (1995) compared C-and L-bands with respect to the detection of flooding in Amazonian forests. Smith et al (1995Smith et al ( , 1996 estimated discharge from braided glacial rivers with ERS-1 SAR images. Almost all studies dealing with the extent of inundation in the Amazon floodplain been with the central main stem of the Amazon except for Richey et al (2002), who used the Amazon basin wide inundation map based on the dual-season JERS-1 mosaic from (Hess et al, 2003) to estimate outgassing from Amazonian rivers and wetlands.…”

Experimental evidence of dynamic scaling and indications of self‐organized criticality in braided rivers

“…Even then, however, care must be taken to insure that data are taken from river sections in which downstream changes in discharge, grain size, vegetation, etc., are minimal so that each reach may be taken as statistically stationary. In the near future it is hoped that improved satellite technology will offer the means of obtaining accurate and frequent monitoring of the spatially and temporally variable flow distribution of natural braided river systems [e.g., see Smith et al, 1995Smith et al, , 1996. To allow study of the detailed structure of braided rivers, higher resolution satellite images than those available today and wider coverage of areas of interest are needed.…”

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

“…(v) discharge estimation (Callow and Boggs, 2013;Frappart et al, 2005;Kouraev et al, 2004;Smith et al, 1996;Zhang et al, 2004); (vi) evapotranspiration estimation (Donohue et al, 2010;Glenn et al, 2011;Mohamed et al, 2004;Smettem et al, 2013;Zhang et al, 2009);…”