Dr. Aaron Berg
Dr. Paulin Coulibaly
Dr. Thian Gan
Objective 1: Assess whether the monitoring network and available data can meet enhanced flood forecasting requirements and use other sources of information (e.g., remote sensing, radar, gridded/reanalysis datasets) along with regionalization techniques to address data limitations.
Objective 2: Use available satellite derived soil moisture products to derive and assess near real time, spatially distributed, estimates of the status of soil water and snow water equivalence. Investigate bias correction techniques for estimating rainfall from the next generation of radar (NEXRAD) data.
Significance: This subproject will address the critical issue of the paucity of monitoring data. The limitation of Canadian hydrometric networks is well established. A general framework is needed to consistently address data limitation. In recent years satellite derived hydrological products have emerged that are of interest for the improvement of initial state estimation for hydrologic models used in flood forecasting. Similarly, merging radar rainfall with rain gauge data allows the generation of distributed rainfall fields needed for improved flood forecasting.
Outcomes: A robust tool for data estimation will be developed to address the common problem of data limitation due to inadequate monitoring networks.This subproject will generate practical tools for deriving distributed products (rainfall, soil moisture, snow water equivalent) from radar and satellite data for inclusion in flood forecasting systems. In addition, these products can have various applications including climate model initialization, reservoir operation planning, and crop yield prediction.