Constructed flood mitigation and drainage systems are integral to the development of many estuarine floodplains. These systems function throughout the tidal range, protecting from high water levels while draining excess catchment flows to the low water level. However, drainage can only be achieved under gravity when suitable water levels are available for discharge. Changes to the tidal range and symmetry that occur throughout estuarine waters mean that the window of opportunity for gravity discharge will vary dynamically within and between different catchments. It will also be affected by sea level rise (SLR). Concerns regarding the impacts of SLR have focussed on the acute effects of higher water levels, but SLR will affect the full tidal range and drainage systems will be particularly vulnerable to changes in the low tide. This study introduces the concept of the “drainage window”; to assess how the tidal regime may influence the drainage of estuarine floodplains, and particularly the potential impact of changing tidal regimes under SLR. The results of applying the drainage window to two different estuaries indicate that SLR may substantially reduce the opportunity for discharging many estuarine floodplain drainage systems. Additionally, measures proposed to mitigate flood risks may exacerbate drainage risks. Reduced drainage creates a host of chronic problems that may necessitate changes to existing land uses. A holistic assessment of future changes to all water levels (including low tide water levels) is required to inform strategic land use planning and management.

A method is presented to address model state uncertainty in hydrologic model simulation. This is achieved by introducing tuneable parameters that allow adjustments to the model states. Excessive dimensionality is avoided by introducing only a limited number of parameters that control the index (timing) and size of the state adjustments. The method is designed to compensate for issues with hydrologic model structures, particularly those relevant to the soil moisture state in a rainfall-runoff model. In the context of water resource planning and management, errors in the model states have often been overlooked as an important source of uncertainty and have the potential to significantly degrade model simulations. A synthetic study shows that a classical parameter estimation approach will produce biased distributions when state errors exist, and that the proposed state and parameter uncertainty estimation (SPUE) can remove the bias in parameter estimates for improved model simulations. In a real case study, SPUE and the classical approach are implemented in 46 sites around Australia. The results show that hydrologic parameter distributions for a selected conceptual model can be significantly different when accounting for state uncertainty. This has large implications for scenario modelling since it puts into dispute how to determine appropriate parameters for such studies. SPUE parameters outperform the classical approach in a range of metrics including the Akaike Information Criterion. Additionally, SPUE parameters performed better during validation periods. Future work involves testing SPUE with different hydrologic model and likelihood formulations, and enhancing rigor by explicitly accounting for observational data uncertainty.

We present herein a new basis for measuring river discharge in ungauged catchments. Surrogate runoff (SR) is created using remotely sensed data to compensate for the absence of ground streamflow measurements. Because of their widespread availability, remotely sensed SR products are attractive, with approaches such as satellite-derived measurement-calibration ratio (C/M ratio). However, the use of the C/M ratio suffers from its limited penetration through ground vegetation canopies. While a microwave signal with a longer wavelength has been used to enhance the penetration capability, the coarseness of the spatial resolution of the microwave signal offsets its improvement due to the inherent assumptions in the C/M ratio, i.e., selecting two contrasting pixels (i.e., measurement and calibration) at the same time. To address both issues, this study proposes a new SR formulation using a longer wavelength (L-band microwave) with a better assumption for handling coarse grids, whereby the temporal variability of dryness against the driest state in each grid is used. The performance of the new SR is assessed for 467 Australian Hydrologic Reference Station catchments. Results show considerable improvements in the Pearson linear correlation (R) between the proposed SR and streamflow: 44% of the study areas show R higher than 0.4 with the new approach, whereas only 13% of the study areas show R higher than 0.4 with the currently used alternative (C/M ratio derived from Ka-band microwave). Overall, the resulting SR is dramatically improved by using the newly designed SR approach with the L-band microwave signal.