Research leader

A/Prof Valentijn Pauwels Research Leader

Research team

Jeffrey Walker
Prof Jeffrey Walker Research Team
Dr Stefania Grimaldi
Dr Stefania Grimaldi Research Team
Ashley Wright
Dr Ashley Wright Research Team

End User representatives

Norman Mueller End-User
Fang Yuan End-User

Accurate flood predictions are critically important for limiting the damage caused by floods. Flood forecasting systems are based on models that require large volumes of data, such as rainfall forecasts, detailed measurements and high-resolution topography. However, flood forecasts are prone to uncertainty due to a lack of detailed measurements, and possible errors or oversimplifications in the models and/or data sets. Remote sensing is the science of obtaining information about objects or areas from a distance, typically from aircraft or satellites. This research is integrating this type of data on soil moisture and flood extent with rainfall and runoff models, which will lead to more accurate flood predictions. It will develop a remote sensing-aided methodology that can eventually enable forecasting models that predict the volume of water entering the river network to be applied anywhere in Australia.

The team set up a forecasting system for two test basins: the Clarence in northern New South Wales and Condamine-Balonne-Maranoa in southern Queensland. Both areas were chosen because they are prone to frequent flooding. The team has determined the parameters of the hydrologic model using discharge data and remotely sensed soil moisture data and are developing strategies to correct model outputs automatically. The hydraulic model calibration and incorporation of remotely sensed data is ongoing. Specifically, the project is developing a method to determine effective river cross-sections because it is difficult to measure the river bathymetry (riverbed topography) in a detailed way for large basins. The team has acquired river cross-section data in strategic locations on two field visits.

For the hydrologic model, it was found that joint calibration using discharge and soil moisture leads to more robust results than traditional calibration using only discharge data. In other words, the model degraded slightly during the calibration period but improved during the validation period. Including soil moisture in the calibration improved the simulations for the ungauged sub-basins.

Because rainfall is highly uncertain, streamflow data was used to estimate the rainfall volumes for the duration of the flood.

The team have also completed a preliminary analysis of a proposed new method for improving the detection of flooded areas in densely vegetated catchments. It involves using simplified river geometries that are based on a combination of limited field data sampled at strategic locations, global databases and remote sensing data.

A workshop at Geoscience Australia was held in October 2016, streamlining the use of the remote sensing techniques developed in this project for the Geoscience Australia Water Observations from Space product. Geoscience Australia will use the method developed in this project to classify the areas monitored as being flooded or not flooded. This will start in the second phase of the project.

By improving real-time flood prediction, this research is expected to improve the accuracy of flood warnings, resulting in a decrease in flood damage and potentially loss of life.

The researchers are completing phase one of the study and have a broad program planned for phase two. It includes a comparison of different remote sensing-based, soil-moisture products, such as surface soil-moisture retrievals and root-zone, and soil-moisture analysis, for hydrologic model updating. The team will also develop a model-data fusion algorithm for a hydrologic forecasting system to optimally use both remotely sensed soil moisture and stream-flow measurements.

The project will validate rainfall estimations using remotely sensed soil moisture observations. It will also develop a remote sensing-aided methodology to derive effective river-transect data for large catchments, and to improve the accuracy of digital elevation models for large catchments. This methodology will eventually enable hydraulic models to be applied anywhere in Australia.

Year Type Citation
2018 Journal Article Liu, S., Li, Y., Pauwels, V. & Walker, J. Impact of rain gauge quality control and interpolation on streamflow simulation: an application to the Warwick Catchment, Australia. Frontiers in Earth Science (2018). doi:https://doi.org/10.3389/feart.2017.00114
2018 Journal Article Wright, A., Walker, J. & Pauwels, V. Identification of hydrologic models, optimized parameteres, and rainfall inputs consistent with in situ streamflow and rainfall and remotely sensed soil moisture. Journal of Hydrometeorology 19, (2018).
2017 Conference Paper Li, Y., Grimaldi, S., Wright, A., Walker, J. & Pauwels, V. Improving flood forecast skill using remote sensing data. AFAC17 (Bushfire and Natural Hazards CRC, 2017).
2017 Conference Paper Rumsewicz, M. Research proceedings from the 2017 Bushfire and Natural Hazards CRC and AFAC Conference. Bushfire and Natural Hazards CRC & AFAC annual conference 2017 (Bushfire and Natural Hazards CRC, 2017).
2017 Journal Article Wright, A., Walker, J. & Pauwels, V. A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction. Hydrology and Earth System Sciences (2017). doi:10.5194/hess-21-3827-2017
2016 Conference Paper Li, Y., Grimaldi, S., Wright, A., Walker, J. & Pauwels, V. Improving flood forecast skill using remote sensing data. AFAC16 (Bushfire and Natural Hazards CRC, 2016).
2016 Journal Article Grimaldi, S., Li, Y., Pauwels, V. & Walker, J. Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges. Surveys in Geophysics 37, (2016).
2016 Journal Article Li, Y., Grimaldi, S., Walker, J. & Pauwels, V. Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review. Remote Sensing 8, (2016).
2016 Magazine Article Jones, F. Remote-sensing flood data is filling the gaps. The Australian Journal of Emergency Management 31, (2016).
2015 Conference Paper Li, Y., Grimaldi, S., Pauwels, V., Walker, J. & Wright, A. Combining hydrologic and hydraulic models for real time flood forecasting - non peer reviewed extended abstract. Adelaide Conference 2015 (2015).
2015 Report Pauwels, V., Walker, J., Li, Y., Grimaldi, S. & Wright, A. Improving flood forecast skill using remote sensing data: Annual project report 2014-2015. (Bushfire and Natural Hazards CRC, 2015).
2015 Report Pauwels, V. Improving Flood Forecast Skill Using Remote Sensing Data Annual Report 2014. (2015).
Improving flood forecasting skill using remote sensing data
25 Aug 2014
Accurate, timely and precise Forecast precipitation is the “holy grail” of flood forecasting; this project...
Improving flood forecasting skills using remote sensing data: precipitation retrieval
18 Aug 2015
Accurate, timely and precise forecast precipitation is the "Holy Grail" of flood forecasting; this project...
Improving Flood Forecast Skill Using Remote Sensing Data - Hydraulic Component
18 Aug 2015
Accurate flood forecast is essential to save lives and reduce damages.How far can we get using remote sensing...
Improving Flood Forecasting Skill Using Remote Sensing Data - Hydrological Component
18 Aug 2015
Flood forecasts suffer from various SOURCES of uncertainties. This project investigates the benefit of using...
Stefania Grimaldi Conference Poster 2016
14 Aug 2016
The use of remote sensing data in operational flood forecasting is currently receiving increasing attention.
Improving flood forecasting skill using remote sensing data: rainfall estimation
30 Jun 2017
This project aims to use hydrologic models and data assimilation theory to estimate catchment wide rainfall.
Improving flood forecast skill using remote sensing data: model/remote sensing data fusion
30 Jun 2017
This project investigates the use of remotely sensed soil moisture data and flood extent/level to improve...
Improving flood forecast skill using Remote Sensing data
19 Sep 2018
“The outcomes from this research will provide information for us to use remotely sensed data to improve our...