An electricity market-based approach to finance environmental flow restoration.

As environmental flow demands become better characterized, improved water allocation and reservoir operating solutions can be devised to meet them. However, significant economic trade-offs are still expected, especially in hydropower-dominated basins. This study explores the use of the electricity market as both an institutional arrangement and an alternative financing source to handle the costs of implementing environmental flows in river systems managed for hydropower benefits. A framework is proposed to identify hydropower plants with sustainable operation within the portfolio of power sources, including a cost-sharing mechanism based on the electricity market trading to manage a time-step compensation fund. The objective is to address a common limitation in the implementation of environmental flows by reducing the dependence on government funding and the necessity for new arrangements. Compensation amounts can vary depending on ecosystem restoration goals (level of flow regime restoration), hydrological conditions, and hydropower sites characteristics. The application in the Paraná River Basin, Brazil, shows basin-wide compensation requirements ranging from zero in favorable hydrological years to thousands of dollars per gigawatt-hour generated in others. Each electricity consumer's contribution to the compensation fund is determined by their share of energy consumption, resulting in values ranging from cents for residential users to thousands of dollars for industrial facilities. Finally, the compensation fund signals the economic value of externalities in energy production. For residential users, achieving varying levels of ecosystem restoration led to an electricity bill increase of less than 1 %. For larger companies, the increase ranged from less than 1 %-12 %.

Identifying robust adaptive irrigation operating policies to balance deeply uncertain economic food production and groundwater sustainability trade-offs.

Increasing irrigation demand has heavily relied on groundwater use, especially in places with highly variable water supplies that are vulnerable to drought. Groundwater management in agriculture is becoming increasingly challenging given the growing effects from overdraft and groundwater depletion worldwide. However, multiple challenges emerge when seeking to develop sustainable groundwater management in irrigated systems, such as trade-offs between the economic revenues from food production and groundwater resources, as well as the broad array of uncertainties in food-water systems. In this study we explore the applicability of Evolutionary Multi-Objective Direct Policy Search (EMODPS) to identify adaptive irrigation policies that water agencies and farmers can implement including operational decisions related to land use and groundwater use controls as well as groundwater pumping fees. The EMODPS framework yields state-aware, adaptive policies that respond dynamically as system state conditions change, for example with variable surface water (e.g., shifting management strategies across wet versus dry years). For this study, we focus on the Semitropic Water Storage district located in the San Joaquin Valley, California to provide broader insights relevant to ongoing efforts to improve groundwater sustainability in the state. Our findings demonstrate that adaptive irrigation policies can achieve sufficiently flexible groundwater management to acceptably balance revenue and sustainability goals across a wide range of uncertain future scenarios. Among the evaluated policy decisions, pumping restrictions and reductions in inflexible irrigation demands from tree crops are actions that can support dry-year pumping while maximizing groundwater storage recovery during wet years. Policies suggest that an adaptive pumping fee is the most flexible decision to control groundwater pumping and land use.

Water, environment, and socioeconomic justice in California: A multi-benefit cropland repurposing framework.

Low-income, rural frontline communities of California's Central Valley experience environmental and socioeconomic injustice, water insecurity, extremely poor air quality, and lack of fundamental infrastructure (sewage, green areas, health services), which makes them less resilient. Many communities depend financially on agriculture, while water scarcity and associated policy may trigger farmland retirement further hindering socioeconomic opportunities. Here we propose a multi-benefit framework to repurpose cropland in buffers inside and around (400-m and 1600-m buffers) 154 rural disadvantaged communities of the Central Valley to promote socioeconomic opportunities, environmental benefits, and business diversification. We estimate the potential for (1) reductions in water and pesticide use, nitrogen leaching, and nitrogen gas emissions, (2) managed aquifer recharge, and (3) economic and employment impacts associated with clean industries and solar energy. Retiring cropland within 1600-m buffers can result in reductions in water use of 2.18 km3/year, nitrate leaching into local aquifers of 105,500 t/year, greenhouse gas emissions of 2,232,000 t CO2-equivalent/year, and 5388 t pesticides/year, with accompanying losses in agricultural revenue of US$4213 million/year and employment of 25,682 positions. Buffer repurposing investments of US$27 million/year per community for ten years show potential to generate US$101 million/year per community (total US$15,578 million/year) for 30 years and 407 new jobs/year (total 62,697 jobs/year) paying 67 % more than prior farmworker jobs. In the San Joaquin Valley (southern Central Valley), where groundwater overdraft averages 2.3 km3/year, potential water use reduction is 1.8 km3/year. We have identified 99 communities with surficial soils adequate for aquifer recharge and canals/rivers within 1600 m. This demonstrates the potential of managed aquifer recharge in buffered zones to substantially reduce overdraft. The buffers framework shows that well-planned land repurposing near disadvantaged communities can create multiple benefits for farmers and industry stakeholders, while improving quality of life in disadvantaged communities and producing positive externalities for society.

A coupled human-natural system analysis of freshwater security under climate and population change.

Limited water availability, population growth, and climate change have resulted in freshwater crises in many countries. Jordan's situation is emblematic, compounded by conflict-induced population shocks. Integrating knowledge across hydrology, climatology, agriculture, political science, geography, and economics, we present the Jordan Water Model, a nationwide coupled human-natural-engineered systems model that is used to evaluate Jordan's freshwater security under climate and socioeconomic changes. The complex systems model simulates the trajectory of Jordan's water system, representing dynamic interactions between a hierarchy of actors and the natural and engineered water environment. A multiagent modeling approach enables the quantification of impacts at the level of thousands of representative agents across sectors, allowing for the evaluation of both systemwide and distributional outcomes translated into a suite of water-security metrics (vulnerability, equity, shortage duration, and economic well-being). Model results indicate severe, potentially destabilizing, declines in freshwater security. Per capita water availability decreases by approximately 50% by the end of the century. Without intervening measures, >90% of the low-income household population experiences critical insecurity by the end of the century, receiving <40 L per capita per day. Widening disparity in freshwater use, lengthening shortage durations, and declining economic welfare are prevalent across narratives. To gain a foothold on its freshwater future, Jordan must enact a sweeping portfolio of ambitious interventions that include large-scale desalinization and comprehensive water sector reform, with model results revealing exponential improvements in water security through the coordination of supply- and demand-side measures.

Bayesian machine learning ensemble approach to quantify model uncertainty in predicting groundwater storage change.

Agricultural water demand, groundwater extraction, surface water delivery and climate have complex nonlinear relationships with groundwater storage in agricultural regions. As an alternative to elaborate computationally intensive physical models, machine learning methods are often adopted as surrogate to capture such complex relationships due to their high computational efficiency. Inevitably, using only one machine learning model is prone to underestimate prediction uncertainty and subjected to poor accuracy. This study presents a novel machine learning-based groundwater ensemble modeling framework in conjunction with a Bayesian model averaging approach to predict groundwater storage change and improve overall model predicting reliability. Three different machine learning models have been developed namely artificial neural network, support vector machine and response surface regression. To explicitly quantify uncertainty from machine learning model parameter and structure, Bayesian model averaging is employed to produce a forecast distribution associated with each machine learning prediction. Model weights and variances are obtained based on model performance to construct ensemble models. Then, the developed individual and Bayesian model averaging machine learning ensemble models are applied, evaluated and validated at different spatial scales including subregional and regional scales in an overdrafted agricultural region-the San Joaquin River Basin, through independent training and testing dataset. Results shows the machine learning models have remarkable predicting capability without sacrificing accuracy but with higher computational efficiency. Compared to a single-model approach, the ensemble model is able to produce consistently reliable predictions across the basin, yet it does not always outperform the best model in the ensemble. Additionally, model results suggest that groundwater pumping for agricultural irrigation is the primary driving force of groundwater storage change across the region. The modeling framework can serve as an alternative approach to simulating groundwater response, especially in those agricultural regions where lack of subsurface data hinders physically-based modeling.

A glass half empty: Limited voices, limited groundwater security for California.

Groundwater is a common pool resource that supports agriculture, human communities, and the environment. Public participation in common pool natural resources management can be affected by media representation of stakeholders and perceptions of identity as a stakeholder. Newspaper media has an outsized influence on framing subject matter, expertise, organizations, and who should participate. Media shapes individual, local, and regional perspectives around resource management and defines potential solutions to natural resources management. This study analyzes media coverage about California's new Sustainable Groundwater Management Act (SGMA) to understand impacts on public participation in common pool natural resources management and to identify represented stakeholders and solutions involved in groundwater sustainability. A total of 365 newspaper articles were collected from California newspapers in three readership locations. We also searched for representation of SGMA in Spanish-language publications. Article characteristics were analyzed through qualitative content analysis and quantitative nonparametric analysis. Results indicate bias for featuring agricultural industry, politician, and water managers' voices. Solutions for managing water resources were focused on new supply, demand reduction and infrastructure investment, though novel solutions were also presented. Most newspaper articles included few stakeholders and solutions, illustrating isolated, short narratives about a common pool resource. The trends and gaps in representation in California media coverage may contribute to the public's low levels of engagement in groundwater planning.

Optimizing the dammed: water supply losses and fish habitat gains from dam removal in California.

Dams provide water supply, flood protection, and hydropower generation benefits, but also harm native species by altering the natural flow regime and degrading aquatic and riparian habitat. Restoring some rivers reaches to free-flowing conditions may restore substantial environmental benefits, but at some economic cost. This study uses a systems analysis approach to preliminarily evaluate removing rim dams in California's Central Valley to highlight promising habitat and unpromising economic use tradeoffs for water supply and hydropower. CALVIN, an economic-engineering optimization model, is used to evaluate water storage and scarcity from removing dams. A warm and dry climate model for a 30-year period centered at 2085, and a population growth scenario for year 2050 water demands represent future conditions. Tradeoffs between hydropower generation and water scarcity to urban, agricultural, and instream flow requirements were compared with additional river kilometers of habitat accessible to anadromous fish species following dam removal. Results show that existing infrastructure is most beneficial if operated as a system (ignoring many current institutional constraints). Removing all rim dams is not beneficial for California, but a subset of existing dams are potentially promising candidates for removal from an optimized water supply and free-flowing river perspective. Removing individual dams decreases statewide delivered water by 0-2282 million cubic meters and provides access to 0 to 3200 km of salmonid habitat upstream of dams. The method described here can help prioritize dam removal, although more detailed, project-specific studies also are needed. Similarly, improving environmental protection can come at substantially lower economic cost, when evaluated and operated as a system.

Estimating economic value of agricultural water under changing conditions and the effects of spatial aggregation.

Given the high proportion of water used for agriculture in certain regions, the economic value of agricultural water can be an important tool for water management and policy development. This value is quantified using economic demand curves for irrigation water. Such demand functions show the incremental contribution of water to agricultural production. Water demand curves are estimated using econometric or optimisation techniques. Calibrated agricultural optimisation models allow the derivation of demand curves using smaller datasets than econometric models. This paper introduces these subject areas then explores the effect of spatial aggregation (upscaling) on the valuation of water for irrigated agriculture. A case study from the Rio Grande-Rio Bravo Basin in North Mexico investigates differences in valuation at farm and regional aggregated levels under four scenarios: technological change, warm-dry climate change, changes in agricultural commodity prices, and water costs for agriculture. The scenarios consider changes due to external shocks or new policies. Positive mathematical programming (PMP), a calibrated optimisation method, is the deductive valuation method used. An exponential cost function is compared to the quadratic cost functions typically used in PMP. Results indicate that the economic value of water at the farm level and the regionally aggregated level are similar, but that the variability and distributional effects of each scenario are affected by aggregation. Moderately aggregated agricultural production models are effective at capturing average-farm adaptation to policy changes and external shocks. Farm-level models best reveal the distribution of scenario impacts.