Near real time flood monitoring over the Mackenzie River Basin using passive microwave data

The potential of a rating curve model for flood monitoring is examined using SSM/I passive microwave images and flow discharge data. Flooded areas are estimated by a linear combination of brightness temperatures measured by the SSM/I sensor in the 19, 37 and 85 GHz channels on each pixel at the reception of each new image. NOAA-AVHRR images are used to validate the estimated flooded areas. The used rating curve model is based on an existing correlation between flooded areas and measured discharge. However, a time lag between these two variables can be observed. Thus, the rating curve model was modified by the introduction of a lag term that can vary depending on flooding intensity and basin features. Hence, the lag term is computed dynamically using a crosscorrelation function between the flooded area and the discharge vectors. The rating curve model is based on two empirical parameters that depend on the river morphology. To overcome this dependency, the model was linked to a Kalman filter to dynamically estimate the empirical parameters according to the forecast errors at each time step. With the Kalman filter, the dynamic rating curve model continuously readjusts its parameters to satisfy the non-stationary behavior of hydrological processes. The model is thus sufficiently flexible and adapted to various conditions. Simulations have been carried out over the Mackenzie River Basin (1.8 million km²), in North-West Canada, during the summer seasons of 1998 and 1999. The predicted flooded areas agree well with those derived from the NOAA-AVHRR images. This implies that a combination of SSM/I passive microwave data and discharge data shows an interesting potential in flood monitoring