Flood forecasting and adaptive sampling with spatially distributed dynamic depth sensors

Abstract

Measurements of water level taken by a network of wireless sensors called ‘FloodNet’ were assimilated into a one-dimensional hydrodynamic model using an ensemble Kalman filter, to create a forecasting model.  The ensemble Kalman filter led to an increase in forecast accuracy of between 50% and 70% depending on location for forecast lead times of less than 4 hours.

This research then focused on methods for targeting measurements in real-time, such that the power limited but flexible resources deployed by the FloodNet project could be used optimally.  Two targeting methods were developed.  The first targeted measurements systematically over space and time until the forecasting model predicted that the probability of the water level exceeding a pre-defined threshold was less than 5%.  The second method targeted measurements based on the expected decrease in forecasted water level error variance at a validation time and location, quickly calculated for various sets of measurements by an ensemble transform Kalman filter.  Estimates of forecast error covariance from the ensemble Kalman filter and ensemble transform Kalman filter were significantly correlated, with correlations ranging between 0.979 and 0.292.  Targeting measurements based on the decrease in forecast error variance was found to be more efficient than the systematic sampling method.  The ensemble transform Kalman filter based targeting method was also used to estimate the ‘signal variance’ of theoretical measurements at any computational node in the hydrodynamic model.  Furthermore, time series data, different sensors types and measurements of floodplain stage could all be taken into account either as part of the targeting process or prior to measurement targeting.

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