Monitoring the health status of water mains using a scorecard modelling approach

There has been considerable research into prediction of water mains failure, however, those models are very complex and fail to convey the message of the health status of an asset to the relevant stakeholders. The study developed a ‘pipe health scorecard’ based on historical failure data which could be used for operation, maintenance, refurbishment, or replacement decisions by a water utility. This scorecard model was developed by using 160,413 pipe-condition datasets from the South Australian Water Corporation over 10 years. Measures such as the Kolmogorov–Smirnov (KS) statistic, Area Under the ROC Curve (AUC), and Population Stability Index (PSI) showed the model is strong enough to predict the health status of water mains. The study found that the factors influencing water mains failure to be in the order of importance: length, material, age, location (road vs verge), diameter, and operating parameters. The development of a simple but reliable model for the assessment of the health status of water mains will have major benefits to the water utility with the ability to identify and potentially replace water pipes prior to failure. Additional benefits of flexible scheduling of the maintenance and replacement programs would contribute to cost savings.

Advancing Urban Water Security in the Indus Basin, Pakistan—Priority Actions for Karachi and Lahore

Despite growing recognition of solutions to water scarcity challenges, decision-makers across the world continue to face barriers to effective implementation of water planning, governance and management. This is evident in the cases of Lahore and Karachi in the Indus Basin in Pakistan and illustrated through the experiences of the provincial government departments and utilities. Water scarcity and associated challenges are continuing to impose significant costs on these cities, which continue to grow as water availability further declines, demand increases, water quality deteriorates, and infrastructure degrades. A team of Australian water experts was commissioned by the Australian Water Partnership to diagnose urban water challenges and identify priority actions for improved water security, in collaboration with Pakistani partners. This paper presents the outcomes of that work. This includes a synthesis of the published literature and data on the geographical, climatic, and water scarcity contexts of both Karachi and Lahore. It then identifies responses to water insecurity that have been considered or implemented in the past and the barriers that have inhibited the effectiveness of these efforts. Finally, it presents actions within five priority action areas that Pakistani stakeholders have identified as being most practical and impactful for improving water security outcomes.

Modeling Vegetation Water Stress over the Forest from Space: Temperature Vegetation Water Stress Index (TVWSI)

The conventional Land Surface Temperature (LST)–Normalized Difference Vegetation Index (NDVI) trapezoid model has been widely used to retrieve vegetation water stress. However, it has two inherent limitations: (1) its complex and computationally intensive parameterization for multi-temporal observations and (2) deficiency in canopy water content information. We tested the hypothesis that an improved water stress index could be constructed by the representation of canopy water content information to the LST–NDVI trapezoid model. Therefore, this study proposes a new index that combines three indicators associated with vegetation water stress: canopy temperature through LST, canopy water content through Surface Water Content Index (SWCI), and canopy fractional cover through NDVI in one temporally transferrable index. Firstly, a new optical space of SWCI–NDVI was conceptualized based on the linear physical relationship between shortwave infrared (SWIR) and soil moisture. Secondly, the SWCI–NDVI feature space was parameterized, and an index d(SWCI, NDVI) was computed based on the distribution of the observations in the SWCI–NDVI spectral space. Finally, standardized LST (LST/long term mean of LST) was combined to d(SWCI, NDVI) to give a new water stress index, Temperature Vegetation Water Stress Index (TVWSI). The modeled soil moisture from the Australian Water Resource Assessment—Landscape (AWRA-L) and Soil Water Fraction (SWF) from four FLUXNET sites across Victoria and New South Wales were used to evaluate TVWSI. The index TVWSI exhibited a high correlation with AWRA-L soil moisture (R2 of 0.71 with p < 0.001) and the ground-based SWF (R2 of 0.25–0.51 with p < 0.001). TVWSI predicted soil moisture more accurately with RMSE of 21.82 mm (AWRA-L) and 0.02–0.04 (SWF) compared to the RMSE ranging 28.98–36.68 mm (AWRA-L) and 0.03–0.05 (SWF) were obtained for some widely used water stress indices. The TVWSI could also be a useful input parameter for other environmental models.

We Have Plenty of Water, Don’t We? Social Norms, Practices, and Contentions in a Drought-Ridden Country

Climate and land use change pose global challenges to water policy and management. This article furthers calls for integrated research conceptualizing water management as a holistic, interdependent system that may benefit from sociological research. To better understand how socioenvironmental change affects lifestyle expectations and experiences, interviews with in-migrants (relocated to inland Australia from metropolitan cities), industry and government informants are thematically analyzed. Results show in-migrants engage in adaptive water management and conservation strategies to enhance water security, yet call for council provision of water management education to minimize vulnerability. Industry informants perceive few water supply or pollution issues, favoring technological solutions to support unfettered growth and water amenities, while de-prioritizing environmental sustainability goals. Government priorities reflect drought narratives in Australian water policy reform and show concern about meeting consumer water supply and preserving water quality. With predictions of greater weather severity, including flooding, and in-migrants’ difficulty managing heavy rainfall, national legislation and policy modifications are necessary. Specifically, normalizing climate variability in policy and social identities is desirable. Finally, practices prioritizing water scarcity and trading management over environmental protection indicate a need to surpass environmental commodification by depoliticizing water management.

Satellite soil moisture data assimilation for improved operational continental water balance prediction

A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is a Kalman-filter-type sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model using tangent linear modelling theory in a post-analysis adjustment after the state updating at each time step. In this study, we assimilate satellite soil moisture retrievals from both Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions simultaneously into the Australian Water Resources Assessment Landscape model (AWRA-L) using the proposed framework and evaluate its impact on the model's accuracy against in situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in situ observation increases from 0.54 (open loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed Enhanced Vegetation Index (EVI) time series across cropland areas results in significant improvements from 0.52 to 0.64 in correlation. The improvements gained from data assimilation can persist for more than 1 week in surface soil moisture estimates and 1 month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

Improving the performance of rainfall-runoff models using the gene expression programming approach

In this study, five hydrological models, including the soil and water assessment tool (SWAT), identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow (IHACRES), Hydrologiska Byråns Vattenbalansavdelning (HBV), Australian water balance model (AWBM), and Soil Moisture Accounting (SMA), were used to simulate the flow of the Hablehroud River, north-central Iran. All the models were validated based on the root mean square error (RMSE), coefficient of determination (R2), Nash-Sutcliffe model efficiency coefficient (NS), and Kling-Gupta efficiency (KGE). It was found that SWAT, IHACRES, and HBV had satisfactory results in the calibration phase. However, only the SWAT model had good performance in the validation phase and outperformed the other models. It was also observed that peak flows were generally underestimated by the models. The sensitivity analysis results of the model parameters were also evaluated. A hybrid model was developed using gene expression programming (GEP). According to the error measures, the ensemble model had the best performance in both calibration (NS = 0.79) and validation (NS = 0.56). The GEP combination method can combine model outputs from less accurate individual models and produce a superior river flow estimate.