@article {bnh-7198, title = {Guidelines on the optimal use of remote sensing data to improve the accuracy of hydrologic and hydraulic models}, number = {600}, year = {2020}, month = {08/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

Floods are among the most damaging natural disasters in Australia. In order to limit the personal and economic damage caused by floods, land and emergency managers need to rely on flood forecasting systems. These systems consist of a hydrologic model and a hydraulic model. The hydrologic model calculates the amount of water that enters the river network, while the hydraulic model computes how that water moves throughout the river and floodplain. The accuracy and reliability of flood forecasting systems has significantly improved in the last decades. However, errors and/or uncertainties in model structures and parameters, input data, and/or meteorological forcings often hamper the accuracy of predictions. This document confirms that remote sensing data can be used to improve the accuracy of hydrologic and hydraulic models and thus ultimately improve the flood forecast accuracy.

More specifically, remotely sensed soil moisture data are used to improve the hydrologic forecast skill of ungauged sub-catchment streamflow locations through multi-objective calibration. A pragmatic approach to select the optimal hydrologic model, optimized rainfall product, and remotely sensed soil product is outlined. Routines to assimilate and smooth streamflow and remotely sensed soil moisture observations over the length of a unit hydrograph are provided for improving forecast capability. Further, remotely sensed inundation extent and water level are used to improve the accuracy of the hydraulic model. This spatially distributed information is essential for understanding the floodplain inundation dynamics, adequately setting-up the hydraulic model and effectively constraining its parameters. The research underpinning these guidelines is consistent with the findings of ongoing research efforts worldwide and has contributed to the development of knowledge and a pragmatic framework for application in the Australian context.

The methodologies presented in these guidelines for optimal use of remotely sensed data to improve the predictive skill of flood forecasting models can be applied by operational agencies. Moreover, the techniques for the analysis of remotely sensed data support and complement the existing capabilities of Geoscience Australia, and the hydrologic model assimilation has been implemented by the Australian Bureau of Meteorology.\ 

}, keywords = {flood forecast, hydrologic, hydrological models, remote sensing data}, issn = {600}, author = {Valentijn Pauwels and Jeffrey Walker and Stefania Grimaldi and Ashley Wright and Yuan Li} } @article {bnh-4461, title = {Effective Representation of River Geometry in Hydraulic Flood Forecast Models}, journal = {Water Resources Research}, volume = {54}, year = {2018}, month = {01/2018}, abstract = {

Bathymetric data are a critical input to hydraulic models. However, river depth and shape cannot be systematically observed remotely, and field data are both scarce and expensive to collect. In flood modeling, river roughness and geometry compensate for each other, with different parameter sets often being able to map model predictions equally well to the observed data, commonly known as equifinality. This study presents a numerical experiment to investigate an effective yet parsimonious representation of channel geometry that can be used for operational flood forecasting. The LISFLOOD-FP hydraulic model was used to simulate a hypothetical flood event in the Clarence catchment (Australia). A high-resolution model simulation based on accurate bathymetric field data was used to benchmark coarser model simulations based on simplified river geometries. These simplified river geometries were derived from a combination of globally available empirical formulations, remote sensing data, and a limited number of measurements. Model predictive discrepancy between simulations with field data and simplified geometries allowed an assessment of the geometry impact on inundation dynamics. In this study site, the channel geometrical representation for a reliable inundation forecast could be achieved using remote sensing-derived river width values combined with a few measurements of river depth sampled at strategic locations. Furthermore, this study showed that spatially distributed remote sensing-derived inundation levels at the very early stages of a flood event have the potential to support the effective diagnosis of errors in model implementations.

}, keywords = {artificial neural networks (ANNs), calibration and evaluation, data allocation, data splitting, Emergency management, floods., hydrological models, model evaluation bias}, doi = {https://doi.org/10.1002/2017WR021765}, url = {http://onlinelibrary.wiley.com/doi/10.1002/2017WR021765/full}, author = {Stefania Grimaldi and Yuan Li and Jeffrey Walker and Valentijn Pauwels} } @article {bnh-4627, title = {A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction}, journal = {Hydrology and Earth System Sciences}, year = {2017}, month = {02/2017}, abstract = {

The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The discrete cosine transform (DCT) and discrete wavelet transform (DWT) are used to reduce the dimensionality of observed rainfall time series for the 438\ catchments in the Model Parameter Estimation Experiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. The results convincingly demonstrate that the DWT is superior to the DCT in preserving and characterizing the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.

}, keywords = {communities, emergency management., Flood, hydrological models, wavelet}, doi = {10.5194/hess-21-3827-2017}, url = {https://www.hydrol-earth-syst-sci.net/21/3827/2017/hess-21-3827-2017.html}, author = {Ashley Wright and Jeffrey Walker and Valentijn Pauwels} }