It is imperative for cities to develop sustainable water management and planning strategies in order to best serve urban communities that are currently facing increasing population and water demand. Water resources managers are often chastened by experiencing failures attributed to natural extreme droughts and floods. However, recent changes in water management systems have been responding to these uncertain conditions.Water managers have become thoughtful about the adverse effects of uncertain extreme events on the performance of water supply systems. Natural hydrologic variability and inherent uncertainties associated with the future climate variations make the simulation and management of water supplies a greater challenge. The hydrologic simulation process is one of the main components in integrated water resources management. Hydrologic simulations incorporate uncertain input values, model parameters, and a model structure. Therefore, stochastic streamflow simulation and prediction, and consideration of uncertainty propagation on performance of water supply systems (WSSs) are essential phases for efficient management of these systems. The proposed integrated framework in this study models a WSS by taking into account the dynamic nature of the system and utilizing a Markov chain Monte Carlo (MCMC) algorithm to capture the uncertainties associated with hydrologic simulation. Hydrologic responses from the results of a rainfall-runoff model for three watersheds of Karaj, Latyan, and Lar in Tehran, Iran, as the case study are used as inputs to the reservoirs. Results confirm that uncertainties associated with the hydrologic model's parameters propagate through the simulation and lead to a wide variation in reservoir storage and WSS performance metrics such as vulnerability and reliability. For example, water storage simulation in the Karaj Reservoir can vary up to 70% compared with the observed values. This causes contradiction and conflict in the management of reservoirs and water systems and decision making. The results emphasize the importance of analyzing WSS performance under uncertain conditions to improve the simulation of natural processes and support water managers for a more efficient decision-making process.

As is much of the world, California increasingly is challenged by water scarcity. A recent multiyear drought depleted surface reservoir and groundwater storage in many places of the state. The Sustainable Groundwater Management Act (SGMA), passed in 2014, promises sustainable groundwater management in California and suggests managed aquifer recharge (MAR) as one of the key practices to eliminate groundwater overdraft by groundwater sustainability agencies. Questions remain, however, about the amount of water available for MAR. Conjunctive management provides the opportunity to modify reservoir operations and enhance recharge long before any drought occurs. However, the amount by which reoperation of surface reservoirs can increase the available water for MAR has not been thoroughly investigated. Folsom reservoir is operated to meet a variety of objectives, including flood control, water supply, hydropower, and environmental flow. The inclusion of water discharge for groundwater recharge adds another objective for the operation of the reservoir and complicates the decision-making. Various management strategies were developed and applied to evaluate performance of the system during a historical period, and a new objective was added to maximize the available water for recharge from Folsom reservoir. Although the reoperation strategy offers additional storage in the system and increases the expected value of recharge from 280 to 430 million cubic meter (mcm) per year, trade-offs between different objectives showed that new operating rules perform quite satisfactorily, with nonsignificant deficits and violations of old objectives.

While hydropower in California is one the main sources of renewable energy, population growth has continuously increased demand for energy. In addition, recent droughts reduced the amount of available water behind the hydropower dams to provide the water head needed to run the turbines in hydropower plants. A more sustainable alternative, instead of developing new infrastructure, is to enhance the daily operation of reservoirs to support hydropower generation. This study suggests a new optimal operation policy for Folsom Reservoir in California and hydropower plants, which maximizes hydropower generation and reduces flood risk. This study demonstrates the application of the cellular automata (CeA) approach to optimize the daily hydropower operation of Folsom Reservoir. The reservoir operation is a nonlinear problem, where the hydropower generation and elevation-area-storage functions are the main nonlinearity to accurately represent the daily operation of the system. Moreover, the performance of the CeA approach under two extreme climate conditions, wet and dry, was evaluated and compared to the operation during normal conditions. Results showed that the CeA approach provides more efficient solutions in comparison to the commonly used evolutionary optimization algorithms. For the size of the non-linear optimization problem designed in this study, CeA outperformed genetic algorithm for finding optimal solutions for different climate conditions. Results of CeA showed that although the annual average in-flow to the reservoir during the dry period was about 30% less than the normal condition, CeA offered about a 20% reduction in average hydropower generation. The new operation policy offered by CeA can partly compensate for the loss of the snowpack in California’s Sierra Nevada under a warming climate. The approach and its outcomes support an informed decision-making process and provide practical reservoir operational guideline to remediate the adverse effects of hydroclimatic changes in the future.

Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.

The San Francisco Estuary (California, USA) had abundant pelagic fish in the late 1960s, but has few pelagic fish today. A primary cause for this decline in fish is thought to be a trophic cascade, triggered by declining phytoplankton. Here, we describe the changes in pelagic community structure of the San Francisco Estuary. Then, we examine whether changes in hydrodynamics due to freshwater exports, which increased exponentially beginning in 1967, in addition to the 1986 invasion by the clam Potamocorbula amurensis, explain the phytoplankton loss. Hydrodynamic variables were reconstructed back to 1956 using statistical models fit to, and cross-validated against, output from a hydrodynamic model. Then, we regressed mean summer/fall chlorophyll a-the season with the largest phytoplankton decline-against the reconstructed hydrodynamic variables and the presence/absence of P. amurensis for 1969-2014. The regression model, which explained 78% of the interannual variation in chlorophyll a, was then used to quantify the influence of P. amurensis and exports on chlorophyll a. Based on monitoring data, chlorophyll a declined 22-fold from 1969-2014, zooplankton declined 32-fold from 1972-2014, and pelagic fish declined 92-fold from 1968-2014. Averaged over 1990-2014, the chlorophyll a model ascribed an 88% decline in chlorophyll a to P. amurensis, a 74% decline to exports (at minimum), and a 97% decline to the combined influence of P. amurensis and exports (at minimum). Thus, the decline in pelagic productivity in the San Francisco Estuary has occurred largely due to the combined impacts of the P. amurensis invasion and increased freshwater exports.

Frequent and prolonged droughts challenge groundwater sustainability in California but managing aquifer recharge can help to partially offset groundwater overdraft. Here, we use managed aquifer recharge (MAR) to examine potential benefits of adding an artificial recharge facility downstream from California’s Lower American River Basin, in part to prepare for drought. We use a statewide hydroeconomic model, CALVIN, which integrates hydrology, the economics of water scarcity cost and operations, environmental flow requirements, and other operational constraints, and allocates water monthly to minimize total scarcity and operating costs. This study considers a recharge facility with unconstrained and constrained flows. The results show that adding a recharge facility increases groundwater storage, reduces groundwater overdraft, and increases hydropower without substantially impacting environmental flows. Further, artificial recharge adds economic benefits by (1) reducing the combined costs of water shortage and surface water storage and (2) by increasing hydropower revenue. This study provides a benchmark tool to evaluate the economic feasibility and water supply reliability impacts of artificial recharge in California.