Journal of Water Resources Planning and Management

Distribution network modeling is proposed to investigate and manage water quality variations in a distribution network. Common practice in the modeling relies on pipe network simplification through skeletonization and uses water demand patterns that are often generalized or derived from historical monthly water usage records. Because automatic water meter reading (AMR) and the supervisory control and data acquisition (SCADA) technologies are widely used, it is possible now to explore the hydraulic complexity in the network. Processes such as stochastic and pulse water demand on solute transport characteristics can be investigated. Fidelity and appropriateness of network modeling by network simplification can be quantified. In this paper, the modeling performance on network simplification are assessed using real-time water demand measurements and comparative network simulations for an independent segment of a large water utility in the United States. An all-pipe all-demand (APAD) model and an hourly demand variation curve (HDVC) demand model are simulated for the same network operations. Results show the prevalence of intermittent and pulse water demand particularly in network perimeters and dead-end branches. The results also highlight different node hydraulic properties such as R, water age, and flow oscillation when water demand in the APAD model is replaced by HDVC-based time-continuous generalized demand patterns. The degree of such difference varies specific to the distribution network configurations such as H-loop, branches, and dead-ends. These additional insights provide further understanding of the varying flow properties and their impacts on the movement of water parcels in pipe configurations. It is suggested that APAD network simulation be used for accuracy-demanding water quality simulation.

Urban water security is a major concern in the context of urbanization and climate change. Water security goes beyond having good infrastructure or good governance. Systems thinking can help in understanding the mechanisms that influence the long-term water security of a city. Therefore, we developed a dashboard of 56 indicators based on the pressure-state-impact-response (PSIR) framework. We applied the dashboard to ten cities to capture different characteristics of their water security and ranked the cities based on their overall water security index score. We found the highest levels of water security in wealthy cities in water-abundant environments (Amsterdam and Toronto), in which security is determined by the ability of the city to mitigate flood risks and the sustainability of hinterland dependencies for water supply. The lowest security was found in developing cities (Nairobi, Lima, and Jakarta). Here, the combination of large socioeconomic pressures (e.g., rapid population growth, slums, low GDP, polluting industries) and an inadequate response (weak institutions, and poor planning and operational management) leads to inappropriate fulfilment of all functions of the urban water system.

Short-time water demand forecasting is essential for optimal control in a water distribution system (WDS). Current methods (e.g., time-series models and conventional artificial neural networks) have limited power in practice due to the nonlinear nature of changes in water demand. In particular, 15-min time-step forecasting may not be accurate when using conventional models. To tackle this problem, this paper investigates the potential of deep learning in short-term water demand forecasting, developing a gated recurrent unit network (GRUN) model to forecast water demand 15 min and 24 h into the future with a 15-min time step. The performance of GRUN was compared with a conventional artificial neural network (ANN) model and seasonal autoregressive integrated moving average (SARIMA) model. A correction module was used to reduce the cumulative error. The results show that the deep learning method improves the performance of water demand prediction. The correction module enhances the performance of ANN and GRUN models. In general, deep neural network models like GRUN outperform the ANN and SARIMA models for both 15-min and 24-h forecasts. These findings can provide more flexible and effective solutions for water demand forecasting.

Affected by intensive human activities, basin hydrologic systems show characteristics of a dualistic structure. Thus, simulation of hydrological processes needs to consider the social water cycle system to obtain a more accurate result. This study uses a dualistic water cycle simulation system to simulate the hydrological processes affected by intensive human activities, which comprises the natural water cycle system and social water cycle system. The social water cycle system includes the agricultural water cycle system, industrial water cycle system, domestic water cycle system, and cross-regional allocation system. As part of the dualistic water cycle simulation system, an integrated dualistic hydrological model is developed which couples a distributed hydrological simulation module with a water resource allocation module. The integrated modeling approach is applied to the Haihe River basin. The results show that the model performance can be improved when considering coupled simulation of natural and social water cycle systems. This model can also be used to investigate the evolutionary relationships among the natural and social water cycle systems in the study region, and to assess the regional water resources in other basins affected by intensive human activities.

Dependence on groundwater throughout the Central Valley of California has resulted in severe and unsustainable groundwater depletion. In this research, we used the Central Valley hydrologic model (CVHM) to model how crop types and drought scenarios impact groundwater storage throughout the Central Valley. Simulations that decreased the abundance of water-intensive crops, such as fruits and nuts, observed the largest recovery in groundwater storage. Two drought scenarios, which respectively decreased surface water availability by 25% and 75%, were also tested. Combined drought and crop type scenarios were explored to understand the relative influence of either surface condition on groundwater storage. The results showed that water-intensive crops can decrease groundwater storage by over 20 m under water deficit conditions, whereas water-saving crops can recover groundwater storage and alleviate the impact of droughts. This research suggests that under drought conditions, groundwater storage will decline substantially to compensate for the need of water-intensive crops due to loss of surface water. Policy and management implications from this research suggest decreasing water demands, such as decreasing crop water intensity, can mitigate groundwater depletion.

Environmental water is being embraced by governments around the world as a means to partly restore rivers impacted by excessive river regulation and to protect those that are not yet overregulated. In cases where environmental water is provided as a water right, managers can make ongoing active management decisions regarding the timing and magnitude of environmental flows in response to changing conditions and understanding of environmental demands. Such active management should lead to improved environmental outcomes but also comes with a significant ongoing management cost when compared to rules-based environmental water releases. This study uses an optimization tool to examine the environmental gain achievable with the active management of environmental water compared to rules-based releases. A case study of the Yarra River, Victoria, Australia, demonstrates the potential for substantial environmental gains achieved through active management of environmental water.

Optimizing reservoir refill operations is important for improved water use. This study developed a real-time refill operation model using scenario-based forecasts. To bridge the gap between forecast horizon and operation horizon, the future operation horizon was divided into two stages based on the forecast horizon point. The first stage (the forecast horizon) was provided with scenario-based forecasts, while the second stage (the remaining horizon) was described using historical streamflow scenarios. Based on deterministic dynamic programming (DDP), explicit stochastic optimization (ESO), and implicit stochastic optimization (ISO), three hybrid two-stage stochastic methods (ESO-DDP, ISO-ESO, and ISO-DDP) were proposed. Using China’s Three Gorges Reservoir as a case study, the performances of six schemes (DDP, stochastic dynamic programming, sampling stochastic dynamic programming, ESO-DDP, ISO-ESO, and ISO-DDP) were compared. Results showed that ISO-DDP performed best in terms of refill rate and hydropower generation. Specifically, the ISO-DDP scheme decreased the refill rate by 2.33% and decreased the hydropower generation by 2.31% compared with the DDP scheme. Therefore, the proposed ISO-DDP method could be useful in reservoir refill operations.

Lack of available resources has been identified as a significant barrier for community implementation of climate adaptation actions. Although a broad array of federal resources exists, it is difficult for communities to match their specific needs with appropriate agency programs. We produced an automated, easy-to-use, decision-support tool to facilitate communities’ finding of appropriate federal partners. The Automated StrAtegic Prioritization (ASAP) tool combines engineering design methods with semantic vector space models (VSMs) to calculate a similarity score between community climate vulnerabilities and related US federal agency strategic plans (SPs) based on their latent semantic similarities. The VSMs were trained using federal SPs and verified using the corresponding federal agencies’ climate change adaptation plans (CCAPs). Two commonly used VSM weighting schemes were tested, and the superior scheme was used in the final ASAP model. The ASAP model was then tested using sector-specific vulnerability statements developed by the community of Keene, New Hampshire for their CCAP. ASAP’s results compared well to published records of federal funding data for Keene. ASAP should be a useful tool for communities involved in adaptation planning. The results of ASAP will also be helpful for federal agencies seeking to collaborate on disaster relief efforts. The results presented here demonstrate synergies in community–federal partnerships and federal–federal partnerships that will enable more effective implementation of climate adaptation plans, improve community climate preparedness and resilience, and can help reduce the nation’s overall vulnerability to climate change.

Climate change has emerged as a key threat to river ecosystems. This study presents a generic, conceptual framework for assessing the significance of climate change impact for ecological outcomes. Within the framework, multiple sequences of streamflows with a length that is “critical” to target species are derived under current and future climates; ecological responses are assessed using models that consider the influence of flow sequencing; the ranges of ecological responses under future and current climates are compared to indicate the significance of impact. This framework allows the degree of future climate risks to ecosystems to be assessed with reference to the range of behavior under current conditions. An example application of the approach to the Yarra River, Australia, show that careful use of environmental water rights could provide a similar range of outcomes under future and current climate, where the range of outcomes due to natural variability are appreciably greater than the shifts induced by climate change. This study demonstrates the importance of incorporating natural variability in impact assessments and shows the flexibility of active water management.