Like for many river basins around the world, climate projections foretell a hotter, drier future for the Murray-Darling Basin (MDB). If the most drastic scenarios play out, the current suite of economies, industries, and ecosystems is unlikely to remain viable in the coming decades. Such changes will stress already fractured decision-making processes, where institutions are working to regain trust in a highly contested policy sphere. The upcoming review and revision of the National Water Initiative (2004), the Water Act (2007), and the Murray-Darling Basin Plan (2012) provide an opportunity to address critiques that they currently do not adequately address climate change. In this paper, we analyse qualitative interviews with 42 experts from diverse sectors in the Basin - research, government, advocacy, and industry - to articulate the challenges associated with the governance of climate risks and adaptation in the MDB. We draw on the concept of anticipatory governance - distinguishing it from both adaptation governance and adaptive governance - to identify four governance transitions and three governance interventions that could be mobilised to address these challenges. We argue that governing the deep uncertainty of climate change requires moving beyond adaptive governance's reactive learning to anticipatory governance that institutionalises foresight, redistributes power, and acts on plausible futures before thresholds are crossed.
The Hattah Lakes floodplain system in north-west Victoria, Australia, is a highly modified, climate-constrained wetland and one of the Murray-Darling Basin's 'Living Murray' icon sites. Over the past century, river regulation and water over-allocation have reduced overbank flooding, contributing to ecological decline. This paper reviews 50 years of adaptive management, focusing on environmental water delivery as the key mechanism for sustaining ecological function. Three management phases are identified: a passive conservation phase (1975-2000) with limited intervention and declining condition; an active intervention phase (2000-2013), involving environmental water recovery and temporary pumping; and a contemporary infrastructure-enabled phase (2013-present), characterised by permanent pumping, flow-control infrastructure, and adaptive, hypothesis-driven watering supported by monitoring. This progression reflects broader institutional reforms under programs such as The Living Murray and the Murray-Darling Basin Plan. Results show that infrastructure combined with targeted monitoring enables a shift from opportunistic watering to deliberate, learning-based flow design. However, climate change introduces increasing hydrological variability, reduced water availability, and heightened ecological risk. Adaptive management must therefore move beyond replicating historical flooding to designing functional flow regimes that maintain ecosystem resilience under drier conditions. The Hattah Lakes case highlights how policy, infrastructure, and long-term monitoring can build adaptive capacity in regulated floodplains.
Smart water metres provide near real-time consumption data and enable early leak detection. Although they have been available for nearly three decades, limited research has examined their practical adoption, integration, and long-term impacts, particularly in the Australian context. This study offers new insights by analysing the adoption and implementation of smart water metres in Queensland, focusing on key drivers, barriers, and emerging opportunities. Findings show that adoption is steadily increasing; however, high implementation and maintenance costs remain the major barrier to adoption, especially when indirect or non-financial benefits are excluded. The study identifies significant potential to enhance the value of smart metres through integration with the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics (DA). Insights from experts highlight the importance of selecting appropriate technologies, ensuring compatibility with existing systems, and embedding smart metres within broader water demand management strategies.
Since the 1960s, irrigated cotton production has expanded considerably in the northern Murray-Darling Basin, with substantial losses to evaporation from large on-farm storages used for floodplain water harvesting. Diversion of this water negatively impacts downstream communities and ecosystems. We quantified area and capacity of 2,786 storages and 10,173 km of irrigation channels and estimated annual evaporation (1987-88 to 2023-24) using remote sensing time series and meteorological data Annual evaporation was 1,247 GL; 39% of surface water take from cotton-producing catchments. Storages in 2024 covered 94,758 ha (capacity 3,300 GL); a two-fold increase since 1995 when a cap on irrigation diversions was introduced. Storages contained water for 89% of the time and were 38-100% full for 34% of the time; longer than needed for irrigation requirements. Including evaporation, it takes 11.8 ML to grow a hectare of cotton; twice the 6-7 ML ha-1 the cotton industry estimates is applied to the crop. Evaporation is not accounted for in water policy reforms, yet we consider it is just another form of water take. Under increasing water scarcity due to climate change and irrigation diversions, there is a clear need to reduce losses. We discuss implications for water policy and options for management of evaporation on-farm.
This study critically explores spatial and temporal variations of water quality in Melaka River Basin from 2019 to 2023 with the application of advanced multivariate statistical techniques: HCA, DA and PCA. HCA has efficiently stratified the monitoring sites as high medium low pollution clusters that has revealed seasonally driven pollutant dispersion patterns particularly intensified during monsoon periods. DA validated these classes by having strong discrimination ability with R2 = 0.694 wherein as coliform, NH3N, COD and turbidity were found as critical indicators. PCA further identified the main pollution drivers as sediment load and microbial input under wet conditions, with organic enrichment and salinity stress during drier periods. More importantly, forward stepwise DA revealed that even fewer variables comprising only salinity, temperature, COD, and NH3N could still maintain high predictive power for practical use in simplified versions of cost-effective water monitoring strategies. Based on study findings, this multivariate demonstrates powerful tools towards breaking down complex environmental data sets to identify specific remediation activities to reveal seasonal and spatial pollution dynamics within a river system of such historical and ecological importance that advocate evidence-based water governance and targeted policy intervention.
With pressures on water supplies increasing globally, readily accessible data on water availability and consumption is of growing importance to policymakers and water managers. In urban areas, water metres, which can provide information about water use and infrastructure resilience, as well as enable volumetric charging for water use, have become core demand-side management tools for ensuring more efficient, cost-effective, and equitable decision-making. Yet, how water metres are used in Aotearoa New Zealand and their impact on pricing structures and water supply and use has not been widely studied. For decision-makers and researchers, the lack of readily accessible quantitative data on residential consumption could be constraining how urban drinking water policy is being developed to ensure it delivers targeted wellbeing improvements for communities. Here, we explore how metering is being used in Aotearoa New Zealand and estimate the possible impact of volumetric pricing on urban drinking water consumption using case studies from Tauranga and Wellington. Our methods reveal the challenges of accessing data on drinking water availability and consumption in Aotearoa New Zealand using Official Information Act channels. We conclude with recommendations for urban drinking water supply reform.
This Oration is presented from the perspective of a woman belonging to Martuwarra, the Fitzroy River in the remote Kimberley region of Western Australia. The narrative introduces both the Martuwarra Fitzroy River Council and the Martuwarra Fitzroy River within this region. As the storyteller, she reclaims water narrative to articulate her insider worldview, values, and ethics, emphasising a holistic understanding where land, water, and people are intrinsically interconnected, rather than managed as separate entities. She contextualise colonialism within an ongoing meta-crisis, offering pathways to shift colonial perspectives on water policy, law, and integrated and adaptive management. Climate change is introduced as a concern at national and global levels, underscoring the importance of embracing opportunities for climate action. In Australia, we are learning to decolonise our thinking and practices regarding climate issues, acknowledging their impacts on ourselves, our communities, and broader national and international contexts. The concept of law as a transtemporal obligation is referenced as a mechanism to promote justice, equity, and the greater good. The conclusion calls the audience and the nation to stand with the Martuwarra Council in advocating for our collective human right to live free from harm, as has been practiced for thousands of years.
This article investigates the application of factors that normalise laboratory data for developing predictive formulae for wave-overtopping discharges of rock-armoured coastal revetments and seawalls. The investigation has utilised data from a comprehensive scale model study of various rock-armoured coastal revetments and seawalls. Normalising the laboratory data using factors based on wave energy has resulted in predictions of average overtopping discharges that were more accurate than those in the current use, which are based on normalising with wave height alone, particularly for lower discharges, which are relevant for design. The coefficient of determination (R2) was 0.87 for the wave energy method and 0.76 for current practice. The influence of the wave period has been examined, and the wave period has been incorporated into a predictive equation. Some model and scale effects have been identified.
The essential role of water and the need to protect this valuable resource are well recognised worldwide. Leakage in water distribution networks (WDNs) has serious economic, social, and environmental consequences. Because conventional leak detection methods face several technical and practical limitations, recent studies have increasingly focused on field-simulation-based methods. Among these, hydraulic model calibration has proven to be one of the most effective approaches. However, many existing studies depend on idealised or fully defined hydraulic models, which restrict their applicability to actual WDNs. This study presents a calibration-based leak detection method that employs a modified simulated annealing (SA) algorithm to identify both the location and magnitude of leaks in a WDN. To represent model uncertainty more realistically, two approaches - Monte Carlo simulation (MCS) and simultaneous calibration (SC) - were incorporated into the proposed framework. The method was first tested on a benchmark WDN, where the results confirmed its ability to detect leaks effectively. The SC approach demonstrated higher accuracy and computational efficiency compared with MCS. The methodology was also applied to an actual WDN, where it successfully located a deliberately introduced leak. The findings indicate that the proposed method offers a practical and reliable tool for water utilities aiming to enhance leak detection and improve WDN management.
Timor-Leste experienced severe floods in 2020 and 2021. This study aims to build a general sustainable flood management framework in Dili, which serves as a blueprint for implementing the national strategy to manage flood risk. This research involved a literature review and stakeholder survey conducted in Dili to understand the perceptions of decision makers and experts regarding causes of floods, possible preventive measures, and the role of stakeholders in planning, implementing, and assessing flood management. Respondents attributed flooding to events caused by climate change and heavy rainfall, which were exacerbated by inadequate controls on flood structures and land use in river basins. The flood management framework includes governance, methodology, monitoring and evaluation, which addresses sustainability aspects such as socio-economic factors, environmental issues, natural resources and infrastructure.
The contribution of stakeholder engagement to improving the social, economic, and ecological outcomes of natural resource governance depends upon who are involved, what their interests and opinions are, and what institutions they refer to. These factors, however, have seldom been investigated in the real large-scale projects, compromising our capacity to meaningfully inform practice with scientific insights. This paper aims to develop an understanding of stakeholder engagement within water governance in the Murray-Darling Basin by drawing on public comments (submissions) on the policy initiatives in the Basin from 2007 to 2021. We used the sentiment of the submissions to represent the stakeholders' opinions, categorising them into dissatisfied, neutral, and satisfied. The sentiments were manually extracted and analysed in regard to the three themes: governance, socio-ecological benefits, and policy initiative implementation among the policy initiatives. Findings revealed 1) an unbalanced participation represented by the domination of agriculture/landownership and irrigation/water supply and a large difference among the policy initiatives; 2) an extremely negative opinion with a majority of themes exhibiting dissatisfaction; and 3) reference of previous water acts in support of their objections to the policy initiative mandated by the Water Act (2007). These findings indicate that stakeholder engagement in MDB was poorly implemented. Endeavours in a more collaborative approach are required.
"Beautiful to see the brolgas, they are here to welcome us because they know that we're here to help them, cause they're our people & mldr;" Uncle Badger Bates The imperative to incorporate Aboriginal knowledge into Australian freshwater management has never been clearer. The 2024 Menindee fish kill highlight failures in current management and monitoring. Federal and select State Governments have recognised Aboriginal cultural water practices as a component of regional water sharing plans and as an ingredient in sustainable freshwater catchment management. Drawing on stories, experiences and conversations gathered during an 'on Country' knowledge-sharing day at Toorale Station near Bourke, New South Wales, this paper explores the interplay between Aboriginal and non-Aboriginal scientist knowledge. Four major themes emerged from the day regarding the current state and future management of the Warriku (Warrego River) and Baaka (Darling River): 1) the experience of welcome and being on Country; 2) the holism associated with a Country perspective; 3) the threats posed to the Baaka and the Warriku and their peoples by settler-colonial land and water management policies and practices; and 4) future directions. This case study aims to help non-Aboriginal scientists bring Aboriginal peoples and their knowledge into environmental monitoring and management of Australian freshwater environments.
Australian consulting engineers widely use the Australian Water Balance Model (AWBM) to estimate runoff at a daily time-step. AWBM is a simple conceptual rainfall-runoff model that has similar complexity to many other models of this type. Here, we compare the performance of AWBM against three other conceptual rainfall-runoff models - SIMHYD, IHACRES and GR4J - that are used in Australia and around the world. The comparison is based on calibration and evaluation of model performance under current conditions as well as under changing conditions, indicative of likely future hydroclimate. Across all the comparisons, GR4J performed the best of the four models. SIMHYD and IHACRES had similar performance to each other, but were slightly inferior to GR4J, while AWBM was the worst performing model. GR4J was clearly the best model under current conditions, but its outperformance reduced under contrasting conditions. We recommend industry practitioners consider replacing AWBM with GR4J for their modelling requirements when modelling current conditions. For contrasting conditions GR4J is a very good selection, but care needs to be taken when catchment annual rainfall-runoff relationships shift under prolonged drying, when IHACRES may also prove to be a good selection.
Drought is a persistent issue in Australia that will continue in the future with the changing climate. National and regional drought resilience plans have proposed many strategies to reduce the effect of drought, however, they have not considered the potential of atmospheric water generation. Atmospheric water generation is the process of removing water vapour from the air. In this study, water production and energy consumption of different desiccant and refrigeration-based atmospheric water generation systems were estimated for a range of Australian climatic regions, using weather data from the Bureau of Meteorology for 2001-2021. The energy consumption was similar between the investigated desiccants and regions. In dry inland regions, desiccant-based systems consumed less energy than refrigeration-based systems. Water can be produced even in remote regions with low annual precipitation. Hence, desiccant-based atmospheric water generation should be considered as an alternative drinking water source during drought resilience planning for dry remote regions.
To reduce flood risk, it is critical to accurately estimate design floods, which are associated with specific annual exceedance probabilities. This study aims to develop a Regional Flood Frequency Analysis (RFFA) approach that clusters hydrologically diverse catchments into more homogeneous groups, thereby improving the reliability of design flood estimates. Traditional regionalisation often fails due to high inhomogeneity. The RFFA approach was applied and evaluated using 363 catchments in New Zealand. It was found that incorporating climate zones and catchment characteristics improved homogeneity. Cluster analysis based on catchment attributes was applied to delineate homogenous regions. The two-parameter Log-Normal and Pearson 3 distributions were identified as dominant regional probability distributions. The Generalised Additive Model and Index Flood L-moment approach were used to estimate regionalised design floods. Model performance, assessed with Jackknife Resampling, showed significantly smaller error estimates than previous RFFA studies in New Zealand. This approach provides region-specific design values for flood risk management in both gauged and ungauged catchments.
The PROQ transform converts rainfall P to runoff RO, which is then factored to peak discharge Q. This study evaluates the PROQ transform combined with SILO gridded daily rainfalls and the peaks over threshold series (POTS) at a stream gauge to estimate at-site design floods. A multi-day search window was developed to extract the POTS and PROQ model inputs from gauge data. Design flood analyses for the 1 EY to 1% AEP frequency range were performed at three gauges representing different Australian climes. Two approaches; Simple Design Event and Regression Fit, were tested against benchmark estimates based on Australian Rainfall and Runoff guidelines. The Simple Design Event method performed poorly as it was difficult to establish probability-neutral PROQ parameter inputs. As outputs were consistent with the benchmark, the PROQ Regression Fit approach could be a viable alternative for at-site flood frequency analysis. Due to its simplicity, the PROQ transform is easy to apply. Extra information such as a priori expectations of flood behaviour can be readily incorporated into the analysis.
Environmental pollution and waterborne diseases underscore the need for accurate water quality prediction to ensure public health. Therefore, Intelligent algorithms play a crucial role in estimating contaminant levels. However, the presence of outliers in the data can hurt the predictions. Therefore, dealing with these outliers is essential. In addition, regression algorithms often have problems with predicting data with overlapping features. This paper proposes an Adaptive Clustering Regression (ACR) model to enhance water quality index prediction. The proposed model has three stages: feature selection, data segmentation, and prediction. In the feature selection stage, the proposed model selects features based on their high correlation with the target variable (water quality index). The data is dynamically subdivided in the second phase based on density. The model trains a specific regression algorithm on each cluster. Evaluations of datasets from 37 monitoring stations demonstrate that ACR outperforms existing regression models, achieving over 5% improvement in predictive accuracy. The proposed model achieved good results and an improvement of over 5% compared to other regression algorithms.
