Water Supply Portfolio Research Articles

Climate Change Adaptations for California’s San Francisco Bay Area Water Supplies

The impact of climate changes on both sea level and the temporal and spatial distribution of runoff will affect water supply reliability and operations in California. To meet future urban water demands in the San Francisco Bay Area, local water managers can adapt by changing water supply portfolios and operations. An en gineering economic model, CALVIN, which optimizes water supply operations and allocations, was used to explore the effects on water supply of a severely warmer drier climate and substantial sea level rise, and to identify economically promising long -term adaptations for San Francisco Bay Area water systems. This modeling suggests that Bay Area urban water demands can be largely met, even under severe forms of climate change, but at a cost. Costs are from purchasing water from agricultural users (with agricu ltural opportunity costs), expensive water recycling and desalination alternatives, and some increases in water scarcity (costs of water conservation). The modeling also demonstrates the importance of water transfer and intertie infrastructure to facilitat e flexible water management among Bay Area water agencies. The intertie capacity developed by Bay Area agencies for emergencies, such as earthquakes, becomes even more valuable for responding to severe changes in climate.

Estimating Carbon Emissions in Water Supply Portfolio Evaluations Including Recycled Water and Greywater

Estimating Carbon Emissions in Water Supply Portfolio Evaluations Including Recycled Water and Greywater

Evolutionary multiobjective optimization in water resources: The past, present, and future

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall–runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with four or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are given for the new algorithms that should serve as the benchmarks for innovations in the water resources literature. The future of MOEAs in water resources needs to emphasize self-adaptive search, new technologies for visualizing tradeoffs, and the next generation of computing technologies.

Prioritizing localized stormwater capture for water supply and water quality benefits

Prioritizing localized stormwater capture for water supply and water quality benefitsThe Water Replenishment District of Southern California (WRD) manages the Central and West Coast Groundwater Basins (Basins) for nearly four million residents in 43 cities of southern Los Angeles County. In an effort to increase its sustainable water supply portfolio and to decrease its reliance on imported water, WRD is investigating alternatives to capture more stormwater for groundwater...Author(s)Cathy ChangRobert CarrTed JohnsonMark HannaMichael AntosShelley LuceWing TamSourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Jan, 2012ISSN1938-6478DOI10.2175/193864712811698970Volume / Issue2012 / 5Content sourceStormwater SymposiumCopyright2012Word count301

Many-objective de Novo water supply portfolio planning under deep uncertainty

This paper proposes and demonstrates a new interactive framework for sensitivity-informed de Novo planning to confront the deep uncertainty within water management problems. The framework couples global sensitivity analysis using Sobol’ variance decomposition with multiobjective evolutionary algorithms (MOEAs) to generate planning alternatives and test their robustness to new modeling assumptions and scenarios. We explore these issues within the context of a risk-based water supply management problem, where a city seeks the most efficient use of a water market. The case study examines a single city’s water supply in the Lower Rio Grande Valley (LRGV) in Texas, using a suite of 6-objective problem formulations that have increasing decision complexity for both a 10-year planning horizon and an extreme single-year drought scenario. The de Novo planning framework demonstrated illustrates how to adaptively improve the value and robustness of our problem formulations by evolving our definition of optimality while discovering key tradeoffs.

Water Reuse Options to Expand Water Supply Portfolios

Water Reuse Options to Expand Water Supply Portfolios

More efficient optimization of long‐term water supply portfolios

The use of temporary transfers, such as options and leases, has grown as utilities attempt to meet increases in demand while reducing dependence on the expansion of costly infrastructure capacity (e.g., reservoirs). Earlier work has been done to construct optimal portfolios comprising firm capacity and transfers, using decision rules that determine the timing and volume of transfers. However, such work has only focused on the short‐term (e.g., 1‐year scenarios), which limits the utility of these planning efforts. Developing multiyear portfolios can lead to the exploration of a wider range of alternatives but also increases the computational burden. This work utilizes a coupled hydrologic‐economic model to simulate the long‐term performance of a city's water supply portfolio. This stochastic model is linked with an optimization search algorithm that is designed to handle the high‐frequency, low‐amplitude noise inherent in many simulations, particularly those involving expected values. This noise is detrimental to the accuracy and precision of the optimized solution and has traditionally been controlled by investing greater computational effort in the simulation. However, the increased computational effort can be substantial. This work describes the integration of a variance reduction technique (control variate method) within the simulation/optimization as a means of more efficiently identifying minimum cost portfolios. Random variation in model output (i.e., noise) is moderated using knowledge of random variations in stochastic input variables (e.g., reservoir inflows, demand), thereby reducing the computing time by 50% or more. Using these efficiency gains, water supply portfolios are evaluated over a 10‐year period in order to assess their ability to reduce costs and adapt to demand growth, while still meeting reliability goals. As a part of the evaluation, several multiyear option contract structures are explored and compared.