DischargeFromSpace background
Technical Background
Overview
Passive microwave satellite sensors provide near-daily, global coverage of the Earth’s land surface. At certain microwave frequencies, these instruments can penetrate cloud cover, making them particularly well suited for river monitoring. Extending earlier efforts based on wide-area optical observations (e.g., Brakenridge et al., 2005; Van Dijk et al., 2016), data from passive microwave missions such as AMSR-E, AMSR-2, TRMM, and GPM can be used to detect and track variations in river discharge.
As river discharge increases, the water-covered surface area within a river reach expands. Since open water emits less microwave radiation than surrounding land, the brightness temperature of a pixel containing both land and water decreases (Brakenridge et al., 2007, 2012). The DFO - Flood Observatory exploits this relationship by examining individual pixels of approximately 10 km × 10 km and tracking their microwave emissions over time. An increasing fraction of water within a pixel leads to a reduction in observed microwave radiation, producing a signal that responds directly to changes in inundated area and indirectly to variations in discharge. These microwave-derived signals are converted to discharge values through calibration against long-term (~10-year) records from in situ gauging stations or outputs from the Water Balance Model (WBM).
Calibration Approach
Like stage-only measurements from ground stations, the surface-water-area-sensitive microwave signal is useful even without conversion to units of discharge. It allows detection of flood peaks, low-flow duration, and relative changes in flow state. However, transforming the signal into m³/s adds substantial value.
Most DFO discharge estimates use WBM output as an independent reference. For this work, the model provides daily discharge values from 1998–2011 at a 10 km global resolution, matching the microwave pixel scale. By comparing modelled discharge with the remote sensing signal, a statistical rating curve can be generated. This curve (linear or low-order polynomial) converts each day’s signal into a discharge estimate.
For calibration, we use monthly maximum, minimum, and mean daily values from both sources (n = 504). Prior work (Brakenridge et al., 2012) showed this method reliably captures low, medium, and high flows. Some scatter is expected, because errors in both the model and the remote sensing data contribute. In some locations, the model may mis-time peak flows, even though the satellite signal closely tracks real discharge changes. Nonetheless, for most pixels, the correlation between WBM discharge and the satellite signal is strong because both independently detect the underlying hydrologic dynamics.