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Wetlands in the coastal catchments adjacent to the Great Barrier Reef lagoon play an important role in local hydrological processes and provide important ecological habitats for terrestrial and aquatic species. Although many wetlands have been removed or degraded by agricultural expansion, there is now great interest in their protection and restoration as important aquatic ecosystems and potential filters of pollutant runoff. However, the filtering capacity of tropical wetlands is largely unknown, so the current study was established to quantify the water, sediment and nutrient balance of a natural riverine wetland in tropical north Queensland. Surface and groundwater fluxes of water, sediment and nutrients into and out of the wetland were monitored for a 3-year period. This paper focuses on the water balance of this natural wetland and a companion paper presents its sediment and nutrient balance and estimates of water quality filtering. Wetland inflows and outflows were dominated by surface flows which varied by 3-4 orders of magnitude through the course of the year, with 90% of the annual flow occurring during the period January to March. Although groundwater inputs to the wetland were only 5% of the annual water balance, they are very important to sustaining the wetland during the dry season, when they can be the largest input of water (up to 90%). Water retention times in this type of wetland are very short, particularly when most of the flow and any associated materials are passing through it (i.e. 1-2 h), so there is little time to filter most of the annual flux of water through this wetland. Longer retention times occur at the end of the dry season (up to 8·5 days); but this is when the lowest fluxes of water pass through the wetland. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to develop a three dimensional (3D) geological model, based on geographic information system (GIS), of the Barwon Downs Graben aquifer system in Victoria, Australia, and to visualize the complex geometry as a decision support tool for sustainable water management. Design/methodology/approach - A 3D visualization of the aquifer is completed, based on subsurface geological modelling. The existing borehole database, hydrogeological data, geological information and surface topography are used to model the subsurface aquifer. ArcGIS 9.2 is employed for two-dimensional (2D) GIS analysis and for 3D visualization and modelling geological objects computer aided design (GOCAD) 2.5.2 is used. The developed methodology of ArcGIS and GOCAD is implemented for creating the 3D geological model of the aquifer system. Findings - The 3D geomodel of the Barwon Downs Graben provides a new perspective of the complex subsurface aquifer geometry and its relation with surface hydrogeology in a more interactive manner. Considering the geometry, estimated volume of the unconfined Eastern View aquifer is as 0.83 × 1010 m3 and for the confined aquifer is about 1.02 × 1010 m3. The total volume of overlying strata of this aquifer is about 3.09 × 1010 m3. The water resources of the study area are affected by the pumping from this aquifer. This is also significantly influenced by the geometry of the Graben. Originality/value - The 3D model utilises comprehensive and generally available datasets in the public domain. Although the used 3D geomodelling tools are mainly developed for applications in the petroleum industry, the current paper shows its ability to be adapted to hydrogeological investigations. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for up-scaling field irrigation losses and quantifying relative losses at the irrigation area level for potential water savings. Two levels of analysis were considered: First, the field level where irrigation is applied. Second, the irrigation area level, where the field level losses are aggregated, or up-scaled, using average loss functions. In this up-scaling approach, detailed crop-soil-water modelling can capture the variability of physical parameters (such as soils, crops, water table depth, and management practices) at the field level which are then used to derive loss functions for aggregating losses at higher scales (irrigation area level). This allows potential field-level adaptations and water management changes made by individual farmers to be assessed for impact at the larger irrigation area level. The APSIM farming systems model was used for simulation of crops (wheat, rice, and soybean) and their interaction with the wider system processes at the field level. Given the climate, soil, and management information (sowing, fertilisation, irrigation, and residue management), the model simulates infiltration, the soil moisture profile, plant water uptake, soil evaporation, and deep drainage on a daily basis. Then, by placing the field level analysis in the context of the wider irrigation system or catchment, it is possible to correlate field level interventions (e.g. water savings measures) with water requirements at these higher levels. Application of this method in the Coleambally Irrigation Area in NSW, Australia, demonstrated that an exponential function can describe the relationship between deep drainage losses and the water table depth for different soil, crop, and water table depth combinations. The rate of loss increase (slope of the curve) with the water table depth is higher on lighter (higher intake rates) soils than on heavy soils and is more pronounced in areas under rice cultivation. We also demonstrate that this analysis technique can assist in identifying spatial distribution of losses in irrigation areas, considering water table depth as an additional factor, leading to targeted areas for water-saving measures. [ABSTRACT FROM AUTHOR]

Water, or lack of it, has become an issue of national and international significance. Who gets it and for what purpose are critical decisions being made right around the globe. In this paper we argue that the bodies through which these decisions are made are significantly gendered, with men dominating the ranks of water decision making bodies. Further we argue that the dominant factors that drive decisions are the economic value of water followed closely by the environmental issues associated with low water supply. Drawing on earlier work, we argue that the social factors associated with water, or what we refer to as social flow, are largely overlooked in decisions about water allocation. We assert that a significant reason for this is the gendered make up of water bodies. We therefore conclude that it is essential that women become more prominent on these bodies and that greater attention be given to the gendering of decision making bodies and the factors that drive these decisions. [ABSTRACT FROM AUTHOR]

Despite the lack of adequate economic evidence, water pollution from economic activities in Great Barrier Reef (GBR) catchments is often regarded excessive. In this paper marginal benefits from terrestrial (agricultural) water pollution and associated marginal marine costs from GBR degradation are estimated and used in an optimal control approach to determine optimal levels of water quality. Results, for a case study in the Wet Tropics in Australia, show that locally optimal levels of (fine sediment) water pollution are close to current levels, indicating that increased rates of (agricultural) water pollution lead to a decrease in local economic welfare. Globally optimal levels of water pollution are, however, below current levels, indicating that (inter-) national compensation for beneficial spillovers from reduced GBR degradation can increase global economic welfare. [ABSTRACT FROM AUTHOR]

Accurately predicting estuaries water quality is essential to support immediate intervention to water quality problem management. Deep learning is used to improve forecasting of water quality parameters in many aquatic systems. However, it is frequently constrained by low data frequency and quality. High-frequency, continuous monitoring using integrated in-situ water quality and environmental sensors can be an input to deep learning models resulting in highly accurate water quality and environmental predictions. This paper proposes a novel approach to improve forecasting of water quality and environmental variables. The results of the real-world data from the Swan Canning Estuary sites show how well the suggested model works. With different sizes of training and testing sets, the model can still predict the increased number of hours in high scores. Eliminating highly correlated variables impact the model's performance, emphasising the usefulness of strongly correlated variables in scarce data scenarios. • This article proposes a framework to enhance water quality variable forecasting. • The framework reframes the time series problem as a supervised learning problem. • The results on the set of data from the estuary show how well the framework works. • The approach is envisaged to support management in detecting water quality issues. [ABSTRACT FROM AUTHOR]