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Illicit discharges in urban stormwater drains are a major environmental concern that deteriorate downstream waterway health. Conventional detection methods such as stormwater drain visual inspection and dye testing have fundamental drawbacks and limitations which can prevent easy location and elimination of illegal discharges in a catchment. We deployed 22 novel low-cost level, temperature and conductivity sensors across an urban catchment in Melbourne for a year to monitor the distributed drainage network, thereby detecting likely illicit discharges ranging from a transitory flow with less than 10 minutes to persistent flows lasting longer than 20 hours. We discuss rapid deployment methods, real-time data collection and online processing. The ensemble analysis of all dry weather flow data across all sites indicates that: (i) large uncertainties are associated with discharge frequency, duration, and variation in water quality within industrial and residential land uses; (ii) most dry weather discharges are intermittent and transient flows which are difficult to detect and not simply due to cross-connections with the sewerage network; (iii) detectable diurnal discharge patterns can support mitigation efforts, including policies and regulatory measures (e.g., enforcement or education) to protect receiving waterways; and, (iv) that it is possible to cost effectively isolate sources of dry weather pollution using a distributed sensor network.

In many parts of the world, small towns are experiencing high levels of population growth and development. However, there is little understanding of how urban growth in these regional towns will impact urban runoff. We used the case study of Wangaratta, located in South-East Australia, between 2006 and 2016, to investigate land cover changes and their impacts on urban runoff discharge. Detailed spatio-temporal analysis (including neighbourhood composition analysis and supervised classification of aerial imagery) identified that population, land use and land cover changes in Wangaratta, although subtle, were mostly driven by residential growth in the outskirts of the town, where there were large increases in impervious surface area. Overall, the urban growth was minimal. However, in spite of these small changes, a sub-catchment only SWMM model showed that the increase in impervious surface area nevertheless resulted in a statistically significant increase in total runoff across the town. Particularly, this increase was most pronounced for frequent and shorter storms. The analysis of urban development pattern changes coupled with urban hydrological modelling indicated that land cover changes in regional towns, especially when analysed in detail, may result in hydrological changes in the urban region (likely to be exacerbated in coming years by changing climate) and that adaptation efforts will need to adopt a variety of approaches in both existing and growth zones. Our findings highlight the necessity of detailed fine-scale analyses in small towns as even subtle changes will have substantial future implications and robust planning and adaptation decisions are even more important when compared to larger cities due to the greater economic constraints that small towns face and their important relationship with the surrounding hinterlands.

High-resolution data collection of the urban stormwater network is crucial for future asset management and illicit discharge detection, but often too expensive as sensors and ongoing frequent maintenance works are not affordable. We developed an integrated water depth, electrical conductivity (EC), and temperature sensor that is inexpensive (USD 25), low power, and easily implemented in urban drainage networks. Our low-cost sensor reliably measures the rate-of-change of water level without any re-calibration by comparing with industry-standard instruments such as HACH and HORIBA's probes. To overcome the observed drift of level sensors, we developed an automated re-calibration approach, which significantly improved its accuracy. For applications like monitoring stormwater drains, such an approach will make higher-resolution sensing feasible from the budget control considerations, since the regular sensor re-calibration will no longer be required. For other applications like monitoring wetlands or wastewater networks, a manual re-calibration every two weeks is required to limit the sensor's inaccuracies to ±10 mm. Apart from only being used as a calibrator for the level sensor, the conductivity sensor in this study adequately monitored EC between 0 and 10 mS/cm with a 17% relative uncertainty, which is sufficient for stormwater monitoring, especially for real-time detection of poor stormwater quality inputs. Overall, our proposed sensor can be rapidly and densely deployed in the urban drainage network for revolutionised high-density monitoring that cannot be achieved before with high-end loggers and sensors.

Stormwater harvesting (SWH) provides multiple benefits to urban water management. Other than providing water for human use, it also reduces the volume of polluted stormwater discharge to the environment. There are currently no methods available to quantify the additional environmental benefits, which could encourage greater uptake of the practice. This paper investigates a number of factors (climate and catchment characteristics, pollutant reduction targets, etc.) that could impact upon the benefits of SWH for pollution reduction through sensitivity analyses. A method was developed and tested for quantification of the pollution mitigation benefits by SWH under different scenarios. A novel indicator, Impervious Area Offset (IAO), was proposed to reflect the additional impervious area that can be left untreated to achieve the equivalent pollution load reduction targets due to the introduction of SWH. Results indicate significant correlations (p < 0.01) between IAO values and extraction rate (proportion of total annual runoff removed due to the harvesting system and water use substitution), system type, and pollutant reduction targets. The proposed linear empirical relationships between IAO values and extraction rate for different types of system configurations and pollution reduction targets were well represented by observed linear regression (average R2 = 0.98 for all tested scenarios). Empirical relationships were validated successfully against different scenarios, with differences between predicted IAO and baseline IAO values being only ±4.5% for the majority of the validation scenarios. Using this simple and reliable method to rapidly quantify SWH benefits can further add to the growing business case of adopting SWH practices.

The Australian landscape is affected by abandoned mines that pose environmental, public health and safety risks. To promote the beneficial reuse, rehabilitation and/or remediation of these sites and understand their spatial arrangement, we compiled, classified and analysed a country-wide geospatial database of all known inactive hard rock mine sites. Following extensive review and classification of disparate records of such sites that have been terminated, neglected or classified as heritage, plus those under care and maintenance in Australia, we assessed state-by-state reporting and cross-border rehabilitation requirements. This was enabled by the development of the Mining Incidence Documentation & Assessment Scheme (MIDAS) that can be used to catalogue and compare active or inactive mine data regardless of reporting conventions. At a national level, and with four case studies, we performed GIS-based spatial analyses and environmental risk assessments to demonstrate potential uses of our database. Analyses considered the proximity of sites to factors such as infrastructure and sensitive environmental receptors. As Australia struggles to manage the ongoing technical, socioeconomic and environmental challenges of effective mine rehabilitation, the insights enabled by this national-level spatial database may be key to developing coordinated responses that extend beyond state boundaries. Our classification and methodology are easily transferable, thereby encouraging more formalized, systematic and widespread documentation of abandoned mines worldwide.

Recent developments in high- and middle-income countries have exhibited a shift from conventional urban water systems to alternative solutions that are more diverse in source separation, decentralization, and modularization. These solutions include nongrid, small-grid, and hybrid systems to address such pressing global challenges as climate change, eutrophication, and rapid urbanization. They close loops, recover valuable resources, and adapt quickly to changing boundary conditions such as population size. Moving to such alternative solutions requires both technical and social innovations to coevolve over time into integrated socio-technical urban water systems. Current implementations of alternative systems in high- and middle-income countries are promising, but they also underline the need for research questions to be addressed from technical, social, and transformative perspectives. Future research should pursue a transdisciplinary research approach to generating evidence through socio-technical "lighthouse" projects that apply alternative urban water systems at scale. Such research should leverage experiences from these projects in diverse socio-economic contexts, identify their potentials and limitations from an integrated perspective, and share their successes and failures across the urban water sector.

Recent developments in high- and middle-income countries have exhibited a shift from conventional urban water systems to alternative solutions that are more diverse in source separation, decentralization, and modularization. These solutions include nongrid, small-grid, and hybrid systems to address such pressing global challenges as climate change, eutrophication, and rapid urbanization. They close loops, recover valuable resources, and adapt quickly to changing boundary conditions such as population size. Moving to such alternative solutions requires both technical and social innovations to coevolve over time into integrated socio-technical urban water systems. Current implementations of alternative systems in high- and middle-income countries are promising, but they also underline the need for research questions to be addressed from technical, social, and transformative perspectives. Future research should pursue a transdisciplinary research approach to generating evidence through socio-technical "lighthouse" projects that apply alternative urban water systems at scale. Such research should leverage experiences from these projects in diverse socio-economic contexts, identify their potentials and limitations from an integrated perspective, and share their successes and failures across the urban water sector.

Recent developments in high- and middle-income countries have exhibited a shift from conventional urban water systems to alternative solutions that are more diverse in source separation, decentralization, and modularization. These solutions include nongrid, small-grid, and hybrid systems to address such pressing global challenges as climate change, eutrophication, and rapid urbanization. They close loops, recover valuable resources, and adapt quickly to changing boundary conditions such as population size. Moving to such alternative solutions requires both technical and social innovations to coevolve over time into integrated socio-technical urban water systems. Current implementations of alternative systems in high- and middle-income countries are promising, but they also underline the need for research questions to be addressed from technical, social, and transformative perspectives. Future research should pursue a transdisciplinary research approach to generating evidence through socio-technical "lighthouse" projects that apply alternative urban water systems at scale. Such research should leverage experiences from these projects in diverse socio-economic contexts, identify their potentials and limitations from an integrated perspective, and share their successes and failures across the urban water sector.

To support sustainable urban planning and the design of water pollution mitigation strategies, the spatial and temporal trends of water quality in urban streams needs to be further understood. This study analyses over ten years of surface water quality data from 53 upstream catchments (20 of them predominated by a single type of land use) and two lowland sites across Greater Melbourne, Australia. We evaluated the impact of various catchment characteristics, especially urban land uses, on spatial and temporal urban water quality trends. Here, we focused on common urban pollutants: total suspended solids (TSS), total phosphorous (TP), total nitrogen (TN), zinc (Zn), copper (Cu) and nickel (Ni). Site median nutrient and heavy metal concentrations were negatively correlated with the catchment's elevation and its average annual rainfall. Further analysis shows that such trends were driven by the geographical pattern of Melbourne – i.e. low-laying sites tend to have less rainfall and be more urbanised. Only median concentrations of heavy metals (Zn and Cu) were correlated to catchment imperviousness. Further characterising of the urban environment was done into specific land uses (residential, industrial and commercial), yet median concentrations of all pollutants were not significantly correlated with land uses. This is because simple metrics, such as land use proportions, do not adequately reflect the significant variability in pollution sources that can exist even within the same land use type. Indeed, our temporal analysis found that the water quality difference between catchments with similar land uses is likely caused by their site-specific pollutant sources (construction and illegal discharge) and environmental management actions (wastewater management actions) regardless of similarities in land use. A 3-stage urbanisation cycle (development, operation and renewal) is suggested to further explain the urban water quality variance, but more data from small areas of a

Pollution build-up and wash-off processes are often included in urban stormwater quality models. However, these models are often unreliable and have poor performance at large scales and in complicated catchments. This study tried to improve stormwater quality models by adopting the genetic programming (GP) approach to generate new build-up algorithms for three different pollutants (total suspend solids – TSS, total phosphorus – TP and total nitrogen – TN). This was followed by testing of the new models (also traditional build-up and wash-off models as benchmark) using data collected from different catchments in Australia and the USA. The GP approach informed new sets of build-up algorithms with the inclusion of not just the typical antecedent dry weather period (ADWP), but also other less ‘traditional’ variables - previous rainfall depth for TSS and maximum air temperatures for TP and TN simulation. The traditional models had relatively poor performance (Nash-Sutcliffe coefficient, E < 0.0), except for TP at Gilby Road (GR) (E = 0.21 in calibration and 0.43 in validation). Improved performance was observed using the models with new build-up algorithms informed by GP. Taking TP at GR for example, the best performing model had E of 0.46 in calibration and 0.54 in validation. The best performing models for TSS, TP, and TN are often different, suggesting that specific models shall be used for different pollutants. Insights into further improvements possible for stormwater quality models were given. It is recommended that in addition to the typical build-up and wash-off process, new generations of stormwater quality models should be able to account for the non-conventional pollutant sources (e.g. cross-connections, septic tank leakage, illegal discharges) through stochastic approaches. Emission inventories with information like intensity-frequency-duration (IFD) of pollutant loads from each type of non-conventional source are suggested to be built for stochastic modellin

Globally, surface water quality deterioration is an important issue exacerbated by increasing urbanisation, intensified agricultural activities and climate change. To mitigate this increase in waterway pollution there is a strong need for effective catchment management strategies. However, this is currently limited by: (1) our lack of understanding of the key processes and mechanisms driving water quality change in waterways, and (2) our inability to predict future water quality change. To address these, we need to improve our ability to understand and model water quality. This improved capacity will enable us to better identify important water quality changes under various land use, land management and climate scenarios. These improvements could thus assist waterway managers to better prioritise regions which require special management attention, as well as better assess the benefits of different management interventions and preventative actions to reduce pollution levels.