@article {bnh-5008, title = {Evaluation and calibration of a high-resolution soil moisture product for wildfire prediction and management}, journal = { Agricultural and Forest Meteorology}, volume = {264}, year = {2019}, month = {01/2019}, pages = {12}, chapter = {27}, abstract = {

Soil moisture deficit is a key variable used in operational fire prediction and management applications. In Australia, operational fire management practices use simple, empirical water balances models to estimate soil moisture deficit. The Bureau of Meteorology has recently developed a prototype, high-resolution, land surface modelling based, state-of-the-art soil moisture analyses for Australia. The present study examines this new product for use in operational fire prediction and management practices in Australia. The approach used is twofold. First, the new soil moisture product is evaluated against observations from ground based networks. Among the results, the mean Pearson{\textquoteright}s correlation for surface soil moisture across the three in-situ networks is found to be between 0.78 and 0.85. Secondly, the study evaluate a few different calibration methods to facilitate the ready utilization of the new soil moisture product in the current operational fire prediction framework. The calibration approaches investigated here are: minimum-maximum matching, mean-variance matching and, cumulative distribution function matching. Validation of the calibrated products using extended triple collocation technique shows that the minimum-maximum method has the highest skill. Evaluation of the calibrated products against MODIS fire radiative power data highlights that large fires correspond to a drier soil in minimum-maximum outputs compared to other calibration results and the current operational method.

}, keywords = {Calibration, Fire danger rating, Land surface model, Soil moisture deficit, Triple collocation, Verification}, doi = {10.1016/j.agrformet.2018.09.012}, url = {https://www.sciencedirect.com/science/article/pii/S0168192318303071}, author = {Vinod Kumar and Imtiaz Dharssi} } @article {bnh-4462, title = {Hydrologic model calibration using remotely sensed soil moisture and discharge measurements: The impact on predictions at gauged and ungauged locations}, journal = {Journal of Hydrology}, volume = {557}, year = {2018}, month = {01/2018}, chapter = {897}, abstract = {

The skill of hydrologic models, such as those used in operational flood prediction, is currently restricted by the availability of flow gauges and by the quality of the streamflow data used for calibration. The increased availability of remote sensing products provides the opportunity to further improve the model forecasting skill. A joint calibration scheme using streamflow measurements and remote sensing derived soil moisture values was examined and compared with a streamflow only calibration scheme. The efficacy of the two calibration schemes was tested in three modelling setups: 1) a lumped model; 2) a semi-distributed model with only the outlet gauge available for calibration; and 3) a semi-distributed model with multiple gauges available for calibration. The joint calibration scheme was found to slightly degrade the streamflow prediction at gauged sites during the calibration period compared with streamflow only calibration, but improvement was found at the same gauged sites during the independent validation period. A more consistent and statistically significant improvement was achieved at gauged sites not used in the calibration, due to the spatial information introduced by the remotely sensed soil moisture data. It was also found that the impact of using soil moisture for calibration tended to be stronger at the upstream and tributary sub-catchments than at the downstream sub-catchments.

}, keywords = {Calibration, Remote sensing., Soil moisture, Streamflow forecasting}, url = {https://www.sciencedirect.com/science/article/pii/S0022169418300131}, author = {Yuan Li and Stefania Grimaldi and R N Pauwels and Jeffrey Walker} }