@inbook {bnh-8346, title = {Connecting Weather and Hazard: A partnership of physical scientists in connected disciplines}, booktitle = {Towards the {\textquotedblleft}Perfect{\textquotedblright} Weather Warning: Bridging Disciplinary Gaps through Partnership and Communication}, year = {2022}, pages = {149}, publisher = {Springer Nature}, organization = {Springer Nature}, chapter = {6}, abstract = {

Achieving consistency in the prediction of the atmosphere and related environmental hazards requires careful design of forecasting systems. In this chapter, we identify the
benefits of seamless approaches to hazard prediction and the challenges of achieving them in a multi-institution situation. We see that different modelling structures are adopted in different disciplines and that these often relate to the user requirements for those hazards. We then explore the abilities of weather prediction to meet the requirements of these different disciplines. We find that differences in requirement and language can be major challenges to seamless data processing and look at some ways in which these can be resolved. We conclude with examples of partnerships in flood forecasting in the UK and wildfire forecasting in Australia.

}, keywords = {Cold, Coupled model, Evaluation, Fire, Forecaster, Heat, hydrology, Ocean Flood, Seamless, Winter}, issn = {978-3-030-98989-7}, doi = {doi.org/10.1007/978-3-030-98989-7_6}, url = {https://link.springer.com/chapter/10.1007/978-3-030-98989-7_6$\#$Abs1}, author = {Brian Golding and Jenny Sun and MIchael Riemer and Nusrat Yussouf and Helen Titley and Joanne Robbins and Beth Ebert and Tom Pagano and Huw Lewis and Claire Dashwood and Mika Peace} } @article {bnh-7471, title = {Diminishing CO2-driven gains in water-use efficiency of global forests}, journal = {Nature Climate Change}, volume = {10}, year = {2020}, month = {04/2020}, pages = {466{\textendash}471}, abstract = {

There is broad consensus that, via changes in stomatal conductance, plants moderate the exchanges of water and carbon between the biosphere and atmosphere, playing a major role in global hydroclimate. Tree rings record atmospheric CO2\ concentration (ca) and its isotopic composition (13C/12C){\textemdash}mediated by stomatal and photosynthetic influences{\textemdash}that can be expressed in terms of intrinsic water-use efficiency (W). Here, we compile a global\ W\ dataset based on 422 tree-ring isotope series and report that\ W\ increased with\ ca\ over the twentieth century, but the rates of increase (dW/dca) declined by half. Angiosperms contributed more than gymnosperms to the slowdown, and in recent decades, dW/dca\ for angiosperms was close to zero. dW/dca\ varies widely across climatic regions and reflects pauses in emissions during the Great Depression and after World War II. There is strong spatial variability in climate forcing via an increasing\ W, which is weakening globally with time.

}, keywords = {Biogeochemistry, Climate and Earth system modelling, Ecophysiology, hydrology}, doi = {https://doi.org/10.1038/s41558-020-0747-7}, url = {https://www.nature.com/articles/s41558-020-0747-7}, author = {Adams, Mark A. and Buckley, TN and Turnbull, Tarryn L.} } @conference {bnh-5396, title = {Evaluation of TanDEM-X and DEM-H digital elevation models over the Condamine-Balonne catchment}, booktitle = {Hydrology and Water Resources Symposium (HWRS 2018): Water and Communities }, year = {2018}, month = {2018}, publisher = {Monash University }, organization = {Monash University }, abstract = {

A digital elevation model (DEM) is a three-dimensional representation of the Earth surface showing mountain peaks, valley floors, rivers and lakes. DEMs are an essential input for the application of environmental and flood models and, for large catchments,these datasets are produced using data collected by instruments on-board satellite missions. However, data collected from such techniques are inevitably affected by inaccuracies which have to be accurately understood and evaluated in order to allow the implementation of any numerical model. This study evaluated the accuracy of the recently released 12 m resolution TanDEM-X (German Aerospace Center) and the widely used 30 m resolution DEM-H (Geoscience Australia). One of the most important sources of inaccuracy in satellite-derived DEMs is a dense vegetation canopy. This problem is particularly important in dryland catchments, where vegetation is not uniformly distributed across the landscape but often concentrates along the river corridors. The Condamine-Balonne catchment, a low-gradient dryland area in Queensland, offers a representative case study. Satellite-derived DEMs were compared with more than 600 ground control points, three LiDAR datasets, and over 60 ground measured river cross sections. This analysis allowed the evaluation of the capability of the satellite-derived DEMs to reproduce (i) surface elevation at the point scale, (ii) river channels and floodplain morphology, and (iii) catchment slope along the river network. TanDEM-X performed better at the point scale and in transects with sparse vegetation resulting in Root Mean Square Deviation (RMSD) values respectively 0.30 m and 0.12 m lower than the DEM-H. However, TanDEM-X was affected by large errors in densely vegetated areas. Moreover, the enforcement of hydrological connectivity is required. This analysis offers useful insight for the application of TanDEM-X and DEM-H data for environmental and flood modelling in the Condamine-Balonne catchment and in similar dryland areas.

}, keywords = {condamine-balonne, hydrology, resource, water}, isbn = {9781925627183}, url = {https://search.informit.com.au/documentSummary;dn=134740719984631;res=IELENG}, author = {Ankun Wang and Stefania Grimaldi and Saif Shaadman and Yuan Li and Valentijn Pauwels and Jeffrey Walker} } @mastersthesis {bnh-4629, title = {Estimating areal rainfall time series using input data reduction, model inversion, and data assimilation}, year = {2017}, month = {11/2017}, pages = {146}, school = {Monash University}, address = {Melbourne}, abstract = {

Floods are devastating natural hazards that can have severe socio-economic impacts and lead to a loss of life. Consequently, the key driver for this research is to provide techniques that will lead to an increase in flood forecast skill. As fluvial floods are a direct result of rainfall, detailed knowledge of uncertainties in rainfall observations provides a fundamental foundation for improving both rainfall and flood forecast skill. As the understanding of uncertainties present in rainfall time series is developed, so too will the confidence in rainfall forecasts, and short- and long-term streamflow forecasts. For a description of rainfall uncertainty to be complete it must take into account uncertainty when rainfall was not observed, thus allowing, model structural errors to be correctly identified, analyzed, and treated. Therefore the focus of this thesis is to develop a robust methodology to estimate rainfall time series and its uncertainty such that it is consistent with both streamflow and soil moisture observations. To effectively estimate rainfall time series, a method to reduce hydrological input data dimensionality was identified. The effective reduction of hydrological input data dimensionality allows modern parameter estimation algorithms to simultaneously estimate rainfall time series and model parameters. Due to their wide-spread use as model input data reduction techniques in other fields, the discrete cosine transform (DCT) and discrete wavelet transform (DWT) were used, for comparative purposes, to reduce the dimensionality of observed rainfall time series for the 438 catchments in the Model Parameter Estimation Experiment (MOPEX) data set. Once the time series were reduced to a small number of parameters, the rainfall time series were reconstructed for comparison with the observed hyetographs. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. Analysis of the results demonstrate that, when compared to the DCT, the DWT is superior at preserving both short- and long-term rainfall patterns. Second, the DWT was used to reduce the dimensionality of the input rainfall time x series for the catchment of Warwick, Queensland, Australia. The DREAM(ZS) sampling algorithm, in conjunction with a likelihood function that considers both rainfall and streamflow, was then used to estimate the input rainfall time series. Model parameters and rainfall time series were simultaneously estimated. The inclusion of rainfall in the estimation process improved the root mean square error (RMSE) of streamflow simulations by a factor of up to 1.78. This was achieved while estimating an entire rainfall time series, inclusive of days when none was observed. Last, rainfall time series for the catchment of Warwick were estimated using three different rainfall-runoff models. Using the rainfall time series and model parameter estimates, remotely sensed soil moisture observations from the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer - Earth observing system (AMSR-E) satellites were assimilated into each of the models using an ensemble Kalman filter (EnKF). Through analysis of the innovations from the observed and simulated soil moisture it was found that the combination of model choice and remotely sensed soil moisture product had a significant impact on the quality of rainfall estimated. When compared to streamflow simulations obtained via the sole estimation of model parameters, all models that jointly estimated rainfall time series and model parameters produced superior streamflow estimates. Rainfall estimates obtained using the Sacramento Soil Moisture Accounting (SAC-SMA) model were the most realistic. When the SMOS remotely sensed soil moisture product was assimilated into the SAC-SMA, innovations that indicated errors are of a Gaussian nature were obtained. Further, streamflow simulations obtained from the SAC-SMA had the best RMSE. The research presented in this thesis developed a methodology that can be used to estimate and evaluate rainfall estimates obtained using model input data reduction, model inversion, and data assimilation techniques. These rainfall estimates can be used to condition rainfall forecasts and consequently improve flood forecast skill.

}, keywords = {Emergency management, Flood, hydrology, modelling., rainfall}, url = {http://users.monash.edu.au/~jpwalker/theses/AshleyWright.pdf}, author = {Ashley Wright} }