End User representatives
Flood forecasting systems aim at predicting the arrival time, water depth and velocity of the flood wave in each point downstream. They are an essential tool in emergency management, supporting local response actions and communication of warnings to the public.
Significant progress has been made internationally in the development of flood models, but they are still prone to a significant error, due to errors and uncertainties in the rainfall data and the model structure and parameters.
Remote sensing can be a helpful tool for operational water management, and particularly for flood forecasting. In this project remote sensing data is being used in two ways:
- Estimated soil moisture profiles from hydrologic models will be improved through the merging of these model predictions with remotely sensed surface soil moisture values. This is expected to have a beneficial impact on modelled hydrographs.
- Estimated flood inundations and water levels from hydraulic models will be improved through merging these model results with remotely sensed observations of flood inundations or water levels. This is expected to improve the predictive capability of the hydraulic model.
Overall, using remote sensing data in flood forecasting is expected to lead to better early warning systems, management of floods, and post-processing of flood damages.
The project has two test sites – on the Clarence River in New South Wales, and the Condamine River in Queensland – and is acquiring the required data to meet objectives. It has also selected the hydrologic and hydraulic models to be used in the study, and focused on research utilisation of the project by looking at the optimal application of the coupled models in a data assimilation framework. Publications are being written on this work.
The research is expected to lead to improved flood peak estimates, better mapping of flood extents, and improved flood warnings.
|2016||Conference Paper||Improving flood forecast skill using remote sensing data. AFAC16 (Bushfire and Natural Hazards CRC, 2016).|
|2016||Journal Article||Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review. Remote Sensing 8, (2016).|
|2016||Magazine Article||Remote-sensing flood data is filling the gaps. The Australian Journal of Emergency Management 31, (2016).|
|2015||Conference Paper||Combining hydrologic and hydraulic models for real time flood forecasting - non peer reviewed extended abstract. Adelaide Conference 2015 (2015).|
|2015||Report||Improving flood forecast skill using remote sensing data: Annual project report 2014-2015. (Bushfire and Natural Hazards CRC, 2015).|
|2015||Report||Improving Flood Forecast Skill Using Remote Sensing Data Annual Report 2014. (2015).|
|21 Mar 2014||Monitoring and prediction||7.35 MB (7.35 MB)||flood, modelling, multi-hazard|
|04 Dec 2014||Improving flood forecasting using remote sensing data - user perspective||1.06 MB (1.06 MB)||flood, forecasting|
|09 Dec 2014||Improving flood forecasting using remote sensing data||1.5 MB (1.5 MB)||flood, forecasting|
|03 Apr 2016||Monitoring and prediction - cluster overview||0 bytes (0 bytes)||forecasting, multi-hazard, scenario analysis|
|30 Aug 2016||Improving flood forecast skill using remote sensing data - Stefania Grimaldi||5.05 MB (5.05 MB)||engineering, flood, remote sensing|
Accurate, timely and precise Forecast precipitation is the “holy grail” of flood forecasting; this project aims to use observation constrained hydrologic models to estimate precipitation.
Accurate, timely and precise forecast precipitation is the "Holy Grail" of flood forecasting; this project aims to use observation constrained hydrologic models to estimate precipitation.
Accurate flood forecast is essential to save lives and reduce damages.How far can we get using remote sensing data to calibrate and constrain in real time a Coupled Hydrologic - hydraulic model?
Flood forecasts suffer from various SOURCES of uncertainties. This project investigates the benefit of using remotely sensed soil moisture data for hydrological model calibration and updating. A real-time forecasting system constrained by soil moisture and flow data is being developed.
The use of remote sensing data in operational flood forecasting is currently receiving increasing attention.
|Resilience to clustered disaster events on the coast - storm surge||Dr Scott Nichol||Geoscience Australia|
|Improving flood forecast skill using remote sensing data||Assoc Prof Valentijn Pauwels||Monash University|
|Mapping bushfire hazard and impacts||Prof Albert van Dijk||Australian National University|
|Disaster landscape attribution: thermal anomaly surveillance and hazard mapping, data scaling and validation||Prof Simon Jones||RMIT University|