Laboratory model studies of flushing of trapped salt water from a blocked tidal estuary

Results are presented from a series of laboratory model studies of the flushing of saline water from a partially- or fully-closed estuary. Experiments have been carried out to determine quantitatively the response of the trapped saline volume to fresh water flushing discharges Q for different values of the estuary bed slope α and the density difference (∆ρ)_o between the saline and fresh water. The trapped saline water forms a wedge within the estuary and for maintained steady discharges, flow visualisation and density profile data confirm that its response to the imposition of the freshwater purging flow occurs in two stages, namely (i) an initial phase characterised by intense shear-induced mixing at the nose of the wedge and (ii) a relatively quiescent second phase where the mixing is significantly reduced and the wedge is forced relatively slowly down and along the bed slope. Scalings based upon simple energy balance considerations are shown to be successful in (i) describing the time-dependent wedge behaviour and (ii) quantifying the proportion of input kinetic energy converted into increasing the potential energy of the wedge/river system. Measurements show that the asymptotic value of the energy conversion factor increases with increasing value of the river Froude number Fr_o at small values of Fr_o, thereafter reaching a maximum value and a gradual decrease at the highest values of Fr_o. Dimensional analysis considerations indicate that the normalised, time-dependent wedge position (x_w)³(g')o/q² can be represented empirically by a power-law relationship of the form (x_w)[(g')_o/q²]^1/3 =C [(t)[(g')_o²/q]^1/3]"where the proportionality coefficient C is a function of both Fr_o and the slope angle α and the exponent n has a value of 0.24. Successful attempts are made to relate the model data to existing field observations from a microtidal estuary.

Experiments with multiple, intermittent periodic flushing flows confirm the importance of the starting phase of each flushing event for the time dependent behaviour of the saline wedge after reaching equilibrium in the intervals between such events. For the parameter ranges investigated and for otherwise-identical external conditions, no significant differences are found in the position of the wedge between cases of sequential multiple flushing flows and steady single discharges of the same total duration.

The water harvesting landscape of Budj Bim and Lake Condah : whither world heritage recognition

In July 2004 the Budj Bim National Heritage Landscape was inscribed onto the National Heritage List. The place accorded with the criterion of A. Events, Processes (in demonstrating a place of Indigenous-European colonization conflict), B. Rarity (in demonstrating the context, historical and philosophy of benevolence of Governments to Indigenous people), F. Creative or technical achievement (in demonstrating technical accomplishment in construction the system), and, I. Indigenous tradition (in demonstrating longevity and continuity of cultural practices). Such affords Budj Bim, that hosts a unique Indigenous water harvesting and aquaculture infrastructure system dating some 7,000-10,000 years within a country that the Gunditjmara have managed for some 20,000-50,000 years, national standing. Within the lands gazetted is a complex and multi-faceted system that would today be categorised as a major integrated landscape planning and catchment management scheme that includes demonstrable major site engineering, hydraulic engineering, and aquaculture and water management scientific evidence and process knowledge and application.

Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km2) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling.