River Flow Forecasting Model For Sturgeon River

Abstract

A forecasting technique for predicting river flow resulting from combined snowmelt and rainfall is presented The technique incorporates Kalman filtering techniques to reflect Uncertainty in the measured data as well as errors in the system model. A primary emphasis is given to how the forecasting algorithm is applied to a case study area, thus demonstrating the utility of the technique when applied to real‐world data. Methodologies are presented which can be used to calculate the covariance matrices associated with the Kalman filter algorithm utilized by the forecasting procedure.