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In this paper, we propose a hybrid centralized training and decentralized execution neural network architecture with deep reinforcement learning (DRL) to complete the multi-agent path-finding simulation. In the training of physical robots, collisions and other unintended accidents are very likely to occur in multi-agent cases, so it is required to train the networks within a deep deterministic policy gradient for the virtual environment of the simulator. The simple particle multi-agent simulator designed by OpenAI (Sacramento, CA, USA) for training platforms can easily obtain the state information of the environment. The overall system of the training cycle is designed with a self-designed reward function and is completed through a progressive learning approach from a simple to a complex environment. Finally, we carried out and presented the experiments of multi-agent path-finding simulations. The proposed methodology is better than the multi-agent model-based policy optimization (MAMBPO) and model-free multi-agent soft actor–critic models. [ABSTRACT FROM AUTHOR]

This paper is based on the work presented at EVS36 in Sacramento. The core of the work deals with the cabin climate control of battery electric vehicles (BEV) using model predictive control (MPC) approaches. These aim to reduce the energy demand for cabin air conditioning while maintaining comfort and air quality. The first step briefly overviews model predictive control approaches and the respective fundamentals. Afterward, the modeling for the system dynamics is explained. The challenge for the system model considering humid air is discussed, and the first implementation method is presented. With the added equations for the air quality and humidity, a logic to prevent window fogging was developed to improve safety. Ultimately, model-in-the-loop (MiL) investigations identified an energy-saving potential of up to 15.4% for cold and 39.7% for hot conditions compared to a rule-based strategy. In addition, the investigations carried out showed that it was also possible to improve indoor comfort by specifically influencing the air quality and humidity. Together with the safety criteria introduced to prevent window fogging, it was possible to present a strategy that can significantly improve thermal management for the cabin in modern BEVs. [ABSTRACT FROM AUTHOR]

Substantial increases in non-native aquatic vegetation have occurred in the upper San Francisco Estuary over the last 2 decades, largely from the explosive growth of a few submerged and floating aquatic plant species. Some of these species act as ecosystem engineers by creating conditions that favor their further growth and expansion as well as by modifying habitat for other organisms. Over the last decade, numerous studies have investigated patterns of expansion and turn-over of aquatic vegetation species; effects of vegetation on ecosystem health, water quality, and habitat; and effects of particular species or communities on physical processes such as carbon and sediment dynamics. Taking a synthetic approach to evaluate what has been learned over the last few years has shed light on just how significant aquatic plant species and communities are to ecosystems in the Sacramento-San Joaquin Delta. Aquatic vegetation affects every aspect of the physical and biotic environment, acting as ecosystem engineers on the landscape. Furthermore, their effects are constantly changing across space and time, leaving many unanswered questions about the full effects of aquatic vegetation on Delta ecosystems and what future effects may result, as species shift in distribution and new species are introduced. Remaining knowledge gaps underlie our understanding of aquatic macrophyte effects on Delta ecosystems, including their roles and relationships with respect to nutrients and nutrient cycling, evapotranspiration and water budgets, carbon and sediment, and emerging effects on fish species and their habitats. This paper explores our current understanding of submerged and floating aquatic vegetation (SAV and FAV) ecology with respect to major aquatic plant communities, observed patterns of change, interactions between aquatic vegetation and the physical environment, and how these factors affect ecosystem services and disservices within the upper San Francisco Estuary. [ABSTRACT FROM AUTHOR]

This paper presents the history and evolution of the California Department of Water Resources' Municipal Water Quality Investigations (MWQI) program. This program tracks source water quality in the Sacramento–San Joaquin Delta (Delta) for drinking water supply for nearly two-thirds of California. The program provides early warning of changing conditions in source water quality, provides data and knowledge-based support for operational decision making, and provides scientific support to a variety of urban water users. This retrospective (i) documents program formation, (ii) describes its evolution in response to regulations and technological advances in water treatment and field monitoring, and (iii) notes how the development of federal drinking water quality regulations such as the Disinfection By-Products Rule impacted the program. The MWQI program is believed to be the first drinking water supply program in the United States to conduct continuous, real-time monitoring of organic carbon, bromide, and other anions and to report these data on the internet. In addition to its regular use for operational decision making, the data may be used for evaluating long-term trends and responses to specific changes in the Delta and its watershed. Future program directions will likely be guided by factors that may trigger changes in treatment plant processes and operations, such as emerging contaminants, changes in land and water management practices, permanent Delta island flooding, sea level rise, and climate change. While this retrospective focuses on one region, its multi-decade interplay of science, treatment and monitoring technology, and regulations (as well as practical aspects of managing such a large-scale program) are broadly relevant to professionals engaged in drinking water quality management in other urbanized and developed regions of the world. [ABSTRACT FROM AUTHOR]

Allocating reservoir flows to meet societal and ecosystem needs under increasing water demands and climatic variability presents challenges to resource managers. Often, rivers have been regulated to meet flow and temperature compliance points or mimic historical patterns. Because it is difficult to assess if this approach is efficient, process‐based models are being used to design river operations. This paper describes a model for fish incubation survival based on the premise that mortality from thermal stress occurs over a critical window (CW) of embryo development. A model for the embryo CW based on metabolic studies of development is combined with density‐dependent and background mortalities to describe salmonid survival from egg fertilization to fry detection downstream. The model is calibrated with a two‐decade dataset of Sacramento River winter‐run Chinook salmon egg‐to‐fry survival. The effects of temperature exposure over a range of CWs were explored. Based on statistical and biological support, two alternative CWs were identified for temperature control: the entire incubation period and a short duration window prior to hatching. Survival under different CW assumptions and temperature control operations were simulated with an internet‐accessible form of the model. The analysis indicated that under years of limited cold‐water resources, targeting water releases to the CW prior to hatching would yield the highest incubation survival. [ABSTRACT FROM AUTHOR]

Effective species management depends on accurate estimates of population size. There are, however, no estimates of annual juvenile production for Central Valley spring-run Chinook Salmon ("spring run"), a highly imperiled species in California, making it difficult to evaluate population status and effectively manage key issues such as entrainment of this species at water diversions. In recognition of this critical information gap, we initiated an effort to develop a juvenile production estimate (JPE) for spring run, defined here as an annual forecast of the number of juvenile Central Valley spring-run Chinook Salmon that enter the Sacramento-San Joaquin Delta ("Delta") from the Sacramento Valley. This metric would allow for a more robust scientific assessment of the population, which is needed to effectively manage water to reduce effects on spring run, a key condition of state permit requirements. To help guide this effort, we organized a workshop for stake-holders, managers, and scientists to review some of the key aspects of spring-run biology, examine the management and conservation importance of a JPE, identify knowledge gaps, introduce new tools, and discuss alternative approaches to forecasting the number of spring run emigrating from the Sacramento River drainage and into the Delta. This paper summarizes the spring-run biology, monitoring, and emergent methods for assessment considered at the workshop, as well as the guiding concepts identified by workshop participants necessary to develop a JPE for springrun Chinook Salmon. [ABSTRACT FROM AUTHOR]

This work surveys the performance of several empirical models, all recalibrated to a common data set, that were developed over the past 25 years to relate freshwater flow and salinity in the San Francisco Estuary (estuary). The estuary's salinity regime--broadly regulated to meet urban, agricultural, and ecosystem beneficial uses--is managed in spring and certain fall months to meet ecosystem objectives by controlling the 2 parts per thousand bottom salinity isohaline position (referred to as X2). We tested five empirical models for accuracy, mean, and transient behavior. We included a sixth model, employing a machine learning framework and variables other than outflow, in this survey to compare fitting skill, but did not subject it to the full suite of tests applied to the other five empirical models. Model performance was observed to vary with hydrology, year, and season, and in some cases exhibited unique limitations as a result of mathematical formulation. However, no single model formulation was found to be consistently superior across a wide range of tests and applications. One test revealed that the models performed equally well when recalibrated to a uniformly perturbed input time-series. Thus, while the models may be used to identify anomalies or seasonal biases (the latter being the subject of a companion paper), their use as inverse models to infer freshwater outflow to the estuary from salinity observations is not expected to improve upon the absolute accuracy of existing outflow estimates. This survey suggests that, for analyses that span a long hydrologic record, an ensemble approach--rather than the use of any individual model on its own--may be preferable to exploit the strengths of individual models. [ABSTRACT FROM AUTHOR]

[Display omitted] • SAMA, a new open-source microgrid optimization software, was introduced an validated. • Validation compared SAMA's results with industry-standard, Homer Pro proved accuracy. • Results for different locations and climates, confirming SAMA's reliability and accuracy. • SAMA introduces unique emission optimization and top-down pricing methodology for first time. • SAMA is open-source and free, which ensures accessibility and fosters innovation. • SAMA is broadly applicable for renewable, off-grid, grid-tied, and backup power systems. Although there are many software tools for simulations of hybrid energy systems, Homer Pro and Homer Grid are the only tools offering optimal sizing of hybrid energy systems. Originally developed as open-source software, they have been closed, which limits user-developers abilities to adapt and improve the software. In addition, all current proprietary offering suffer from the following limitations: i) high costs that limit accessibility to low-resource labs and individuals, ii) incapable of conducting multi-objective optimization, iii) constrained on any new innovations in hybrid energy system design and operation, iv) difficulties for users to specifically define prices and costs in inputs, v) inability to model different electric utility billing structures, vi) complex pricing methodology, and vii) the absence of machine learning-based predictive modeling. To overcome these limitations, this paper introduces and validates SAMA (Solar Alone Multi-objective Advisor), an open-source microgrid optimization software program designed to optimize hybrid energy system sizes economically using metaheuristic algorithms based on specific load profiles and meteorological data. Through rigorous validation exercises, SAMA's outcomes are compared against those generated by the Homer Pro software across distinct climatic conditions and geographical locations (i.e., Sacramento, California in the marine west coast and New Bern in humid continental climate zones of U.S., respectively). The results demonstrate congruence between SAMA and Homer Pro. SAMA can be used for a diverse array of applications from renewable energy systems to off-grid and grid-tied configurations, and from policy formulation to feasibility assessment. Unique features of SAMA include multi-objective optimizer, levelized emission optimization, different pre-defined utility billing structures, and a top-down pricing methodology. These features further enhance SAMA's adaptability, allowing users to tailor the software to their specific needs and system configurations. Finally, the paper anticipates future advancements that could refine component modeling, accelerate optimization processes, and incorporate advanced data analysis techniques such as machine learning to predict energy demand and production patterns. [ABSTRACT FROM AUTHOR]

• Incentivizing users to walk farther is a way of rebalancing a bike-share fleet. • The impact of incentives on willingness-to-walk (WTW) to get a bike was examined. • Half of bike-share users would use a bike located 8.9-minutes away without an incentive. • WTW for incentives at origins and destinations were 3.8 min/US$ and 4.2 min/US$. • Results suggest the potential effectiveness of incentives as a rebalancing policy. Bike-share services will produce more limited benefits if users cannot find bikes when and where they need them. Bike-share operators must thus have process for "rebalancing" the bikes within the system to ensure that they are available where demanded. A potentially cost-effective strategy for rebalancing bikes is to offer incentives of some sort to users to walk farther to get a bike (origin-based incentive) or bring a bike to the undersupplied area (destination-based incentive). This paper aims to examine bike-share users' willingness-to-walk to pick up a bike or drop off a bike at some distance from their origins or destinations if rewarded and to identify characteristics influencing willingness-to-walk. We use data from a survey of dock-less e-bike-share users conducted in the Sacramento region. The analysis shows that half of the respondents use bike-share if the available bike is located 8.9 min away. Our estimates of willingness-to-walk farther than the mean distance for incentives at origins and destinations were 3.8 min and 4.2 min per dollar, respectively. Our results give operators and policy makers insights into the potential effectiveness of incentives as a strategy for spatially rebalancing bike-share fleets. [ABSTRACT FROM AUTHOR]

An innovative payload containing a sensitive mid-wave infrared camera was flown on an uncrewed aircraft system (UAS) to acquire thermal imagery along a reach of the Sacramento River, California, USA. The imagery was used as input for an ensemble particle image velocimetry (PIV) algorithm to produce near-continuous maps of surface flow velocity along a reach approximately 1 km in length. To assess the accuracy of PIV velocity estimates, in situ measurements of flow velocity were obtained with an acoustic Doppler current profiler (ADCP). ADCP measurements were collected along pre-planned cross-section lines within the area covered by the imagery. The PIV velocities showed good agreement with the depth-averaged velocity measured by the ADCP, with R 2 values ranging from 0.59–0.97 across eight transects. Velocity maps derived from the thermal image sequences acquired on consecutive days during a period of steady flow were compared. These maps showed consistent spatial patterns of velocity vector magnitude and orientation, indicating that the technique is repeatable and robust. PIV of thermal imagery can yield velocity estimates in situations where natural water-surface textures or tracers are either insufficient or absent in visible imagery. Future work could be directed toward defining optimal environmental conditions, as well as limitations for mapping flow velocities based on thermal images acquired via UAS. [ABSTRACT FROM AUTHOR]

Current estimates of the threatened southern distinct population segment of the North American green sturgeon (Acipenser medirostris) combine a plot-sampling density estimator with Dual frequency IDentification SONar (DIDSON) and adaptive resolution imaging sonar (ARIS) sonar data. From 2020 to 2022, we annually collected images of all known green sturgeon aggregations and compared the established method to an N-mixture model using side-scan sonar images. We compared 18 different N-mixture model combinations and chose an overdispersed Poisson model that produced estimated abundances of 742, 1286, and 1208 for 2020–2022, respectively. These numbers are ∼2 times greater than the previous method and, if sustained, would fulfill a key criterion for green sturgeon recovery. N-mixture models are known to be sensitive to violations of assumptions, such as the highly dispersed data from our study that caused serious issues, and we recommend practitioners make judicious use of overdispersion and goodness-of-fit tests and be able to identify parameter confounding between detectability and abundance estimates. For our green sturgeon, we recommend simpler population estimates and to focus future energy on reducing variability in the data collection process. [ABSTRACT FROM AUTHOR]

Changes in demand, various hydrological inputs, and environmental stressors are among the issues that reservoir managers and policymakers face on a regular basis. These concerns have sparked interest in applying different techniques to determine reservoir operation policy decisions. As the resolution of the analysis increases, it becomes more difficult to effectively represent a real-world system using traditional methods such as dynamic programming and stochastic dynamic programming for determining the best reservoir operation policy. One of the challenges is the "curse of dimensionality," which means the number of samples needed to estimate an arbitrary function with a given level of accuracy grows exponentially with respect to the number of input variables (i.e., dimensionality) of the function. Deep reinforcement learning (DRL) is an intelligent approach to overcome the curses of stochastic optimization problems for reservoir operation policy decisions. To our knowledge, this study is the first attempt that examines various novel DRL continuous-action policy gradient methods, including deep deterministic policy gradients, twin delayed DDPG (TD3), and two different versions of Soft Actor-Critic (SAC18 and SAC19) for optimizing reservoir operation policy. In this study, multiple DRL techniques were implemented to find an optimal operation policy for Folsom Reservoir in California. The reservoir system supplies agricultural, municipal, hydropower, and environmental flow demands and flood control operations to the City of Sacramento. Analysis suggests that the TD3 and SAC are robust to meet the Folsom Reservoir's demands and optimize reservoir operation policies. Experiments on continuous-action spaces of reservoir policy decisions demonstrated that the DRL techniques can efficiently learn strategic policies in spaces and can overcome the curse of dimensionality and modeling. [ABSTRACT FROM AUTHOR]

The loss of biodiversity and biotic homogenization are on the rise in ecosystems around the world as a result of species invasions, habitat degradation, and the effects of climate change. In the Sacramento–San Joaquin Delta, nonnative species make up the majority of the fish community, and declines in native species have been well documented; however, little is known about whether these trends have resulted in biotic homogenization. In this study, we used data from a long-term beach seine survey to analyze regional beta diversity trends of nearshore fish assemblages in the Delta from 1995 to 2019. Overall, we found no evidence of regional biotic homogenization occurring over the study period. Regional beta diversity increased moderately over time and was significantly influenced by the high interannual variability of freshwater inflow. These beta diversity patterns were driven by the non-native Mississippi Silverside that has proliferated in the system in recent years, but also by a handful of native fish species such as the Sacramento Sucker, Tule Perch, and Splittail. Overall, our results offer a contrast to other highly invaded ecosystems around the world and suggest that despite the near extinction of some native fish species, there remain pockets of suitable habitat in the Delta that may play a key role in the conservation of remnant native fish diversity. [ABSTRACT FROM AUTHOR]

In the Sacramento–San Joaquin Delta (Delta), widespread drainage of historical wetlands has led to extensive subsidence and peat carbon losses, as well as high ongoing greenhouse gas (GHG) emissions. Large-scale wetland restoration and conversion to rice fields has the potential to mitigate these effects while conferring flood protection and creating habitat for wetland species. To explore the scale of these potential benefits, this study evaluated the effects of seven Delta-wide land-use scenarios on carbon stocks, land-surface elevation, GHG emissions, and habitat. Peat mapping and data from peat cores indicate that soil carbon stocks have decreased between the early 1800s and 2010s from 288 ± 15 to 145 ± 14 million metric tons (megatonnes; Mt) of carbon (C). If existing land uses continue, the Delta could lose an additional 8.3 Mt C during the coming 40 years, equal to average GHG emissions of 1.2 Mt CO2 equivalents (CO2e) yr-1. Future restoration and rice-farming scenarios indicate that wetland restoration could theoretically halt GHG emissions, converting the Delta from a large GHG source to a weak net source or sink. Across three future scenarios based on existing restoration targets, wetland creation and conversion to rice fields reduced GHG emissions by 0.39 to 0.67 Mt CO2e yr-1, with per-area benefits of 16 to 28 metric tons (tonnes; t) CO2e per hectare (ha) yr-1. Differences among scenarios in extents of wetland types influenced their relative benefits for different management goals. Tidal restoration and conversion to rice fields enhanced habitat benefits and offered a source of agricultural income, but with reduced GHG mitigation compared with conversion to peat-building wetlands. This highlights the importance of clear objectives when developing land-use plans. A strategic land-management portfolio that includes rice fields and both impounded and tidal wetlands could be designed to provide GHG and subsidence mitigation while offering a diverse suite of benefits for ecosystems and people. [ABSTRACT FROM AUTHOR]

Among four extant and declining runs of Chinook Salmon Oncorhynchus tshawytscha in California's Central Valley, none has declined as precipitously as the Sacramento River winter run. Migratory winter‐run Chinook Salmon employ a life history strategy to reside and feed in stopover habitats on their way from freshwaters to the ocean. Migratory winter run, on their way from freshwaters to the ocean, employ a life history strategy to reside and feed in stopover habitats that have been affected by anthropogenic disturbance. Using acoustic telemetry, we examined conditions that influenced reach‐specific movement and survival of out‐migrating juveniles during a prolonged, multi‐year drought from 2013 to 2016, followed by one of the wettest years on record (2017). We modeled how time‐varying individual riverine covariates and reach‐specific habitat features influenced smolt survival. Model selection favored a model with mean annual flow, intra‐annual deviations from the mean flow at the reach scale, reach‐specific channel characteristics, and travel time. Mean annual flow had the strongest positive effect on survival. A negative interaction between mean annual flow and intra‐annual reach flow indicated that within‐year deviations at the reach scale from annual mean flow had larger effects on survival in low‐flow years. These factors resulted in higher survival during years with pulse flows or high flows. Changes in movement behavior in response to small‐scale changes in velocity were negatively associated with survival. Covariates of revetment and wooded bank habitat were positively associated with survival, but the effect of these fixed habitat features changed depending on whether they were situated in the upper or lower part of the river. Fish exhibited density‐dependent stopover behavior, with slowed downstream migration in the upper river in the wet years and extending to the lower river in the most critically dry year. This paper contributes two key findings for natural resource managers interested in flow management and targeted habitat restoration. The first is new insight into how the magnitude of pulse flows in dry and wet years affects survival of winter‐run fish. The second is that density dependence influences where stopover habitat is used. Despite this, we identified an area of the river where fish consistently exhibited stopover behavior in all years. [ABSTRACT FROM AUTHOR]

A new daily water balance model is developed and tested in this paper. The new model has a similar model structure to the existing probability distributed rainfall runoff models (PDM), such as HyMOD. However, the model utilizes a new distribution function for soil water storage capacity, which leads to the SCS (Soil Conservation Service) curve number (CN) method when the initial soil water storage is set to zero. Therefore, the developed model is a unification of the PDM and CN methods and is called the PDM–CN model in this paper. Besides runoff modeling, the calculation of daily evaporation in the model is also dependent on the distribution function, since the spatial variability of soil water storage affects the catchment-scale evaporation. The generated runoff is partitioned into direct runoff and groundwater recharge, which are then routed through quick and slow storage tanks, respectively. Total discharge is the summation of quick flow from the quick storage tank and base flow from the slow storage tank. The new model with 5 parameters is applied to 92 catchments for simulating daily streamflow and evaporation and compared with AWMB, SACRAMENTO, and SIMHYD models. The performance of the model is slightly better than HyMOD but is not better compared with the 14-parameter model (SACRAMENTO) in the calibration, and does not perform as well in the validation period as the 7-parameter model (SIMHYD) in some areas, based on the NSE values. The linkage between the PDM–CN model and long-term water balance model is also presented, and a two-parameter mean annual water balance equation is derived from the proposed PDM–CN model. [ABSTRACT FROM AUTHOR]

The Xin'anjiang model and the Sacramento model are two widely used short-term watershed rainfall-runoff forecasting models, each with their own unique model structure, strengths, weaknesses and applicability. This paper introduces a weight factor to integrate the two models into a combined model, and uses the cyclic coordinate method to calibrate the weight factor and the parameters of the two models to explore the possibility of the complementarity between the two models. With application to the Yuxiakou watershed in Qingjiang River, it is verified that the cyclic coordinate method, although simple, can converge rapidly to a satisfactory calibration accuracy, mostly after two iterations. Also, the results in case studies show that the forecast accuracy of the new combined rainfall-runoff model can improve the forecast precision by 4.3% in a testing period, better in runoff process fitting than the Xin'anjiang model that performs better than the Sacramento model. [ABSTRACT FROM AUTHOR]

Biosolids can build soil organic matter, but their ability to increase carbon and nitrogen in persistent fractions in deep soil is not well understood. We aimed to assess the mechanisms that influence soil carbon and nitrogen dynamics at three sites—Sacramento (irrigated, grazed grassland), Solano (rainfed, grazed grassland), and Merced (feed cropping system with alfalfa–corn rotation)—where soils were amended with biosolids for 20 years using density fractionations, organomineral extractions, and correlations between calcium and soil organic carbon at three depths (0–10, 30–50, and 75–100 cm). We found that amended soils had higher carbon and nitrogen content in the free‐ and occluded‐light fractions at all depths relative to the control in the Sacramento and Solano sites; however, the Merced site had a greater relative increase of carbon and nitrogen associated with the heavy fraction. Effect sizes show that biosolids increase carbon and nitrogen content in free‐ and occluded‐light fractions in the surface soil (0–10 cm), and in both light and heavy fractions in the deep soil layer (75–100 cm). Ratios of carbon to iron and aluminum show that chelation is an important mechanism of carbon stabilization in Sacramento and Solano sites throughout the soil profile. No (0–10 cm) to negative (75–100 cm) correlations were observed between calcium and soil carbon in the amended soils in the Merced site. Our results indicate that, while biosolids are typically incorporated at shallow depths, long‐term application of biosolids can increase the amount of free‐ and occluded‐light carbon fractions in deep soil. Core Ideas: Long‐term biosolids increased deep soil C and N concentrations.Density fractionation indicates amended soils had more labile C and N in surface soils.Organometal complexes were a stabilization mechanism in two of the three sites.Negative correlations between C and Ca2+ in amended soils indicate biosolids interact with C.Accounting for deep soil C demonstrated the importance of assessing the climate change mitigation potential of managed soil. [ABSTRACT FROM AUTHOR]

The San Francisco Estuary (estuary) and the Sacramento-San Joaquin Delta (the Delta) in California face significant challenges in managing water resources during extended droughts. Zooplankton are a vital trophic link between phytoplankton producers and higherlevel consumers such as predatory zooplankton and fish. However, there is still much to be learned about what drives zooplankton abundance and how they respond to drastic changes in environmental conditions, such as droughts. We found that during drought years zooplankton abundance and distribution changes varied for examined taxa. Significant declines in the abundance of Daphnia spp. and the copepod Pseudodiaptomus forbesi occurred in the Suisun Marsh and Suisun Bay regions. In contrast, abundance of the non-native copepod Limnoithona tetraspina increased in Suisun Marsh and the South-Central Delta during those same drought conditions. Salinity is a strong determinant of the presence and abundance of the studied taxa, and we showed that changes in the distribution of salinity as a result of low outflow conditions were an important factor in the regional abundance of zooplankton. Because of the expected increase in the frequency and severity of regional droughts, understanding how these conditions affect zooplankton in the estuary will benefit scientists and resource managers who aim to improve conditions for native fishes. [ABSTRACT FROM AUTHOR]

Cyanobacterial phytoplankton blooms are more prevalent in the freshwater Sacramento-San Joaquin Delta (Delta) since the late 1990s, including blooms driven by overgrowths of potentially toxigenic organisms of the genus Microcystis. Data from 2014 to 2021 were used to show how flow dynamics, water temperature, and water clarity drive occurrence of Microcystis. We used a Microcystis bloom in the central Delta from 2021 as a case study for how novel monitoring tools can track blooms in real-time and be used post hoc to evaluate the effects of management actions. Microcystis was detected throughout the Delta in all but the highest-flow years, and bloom incidence and severity increased during drier years. In the South Delta, Franks Tract, lower San Joaquin River, and Old River regions, where blooms are most prevalent, higher water temperatures and clarities, combined with lower exports from state and federal water projects, were the best explanatory factors for the occurrence of Microcystis blooms. Nutrient concentrations were lower in summer than in winter, but only became potentially limiting at high phytoplankton concentrations. We used satellite data and in situ continuous monitoring of flow, phytoplankton communities, and water quality to track hydro-biogeochemical conditions during the 2021 case study Microcystis bloom in the Central Delta. We did not find evidence that changes to Delta outflow regulatory standards contributed to this bloom, but changes in flow caused by a salinity barrier placed in west False River may have exacerbated the bloom. The frequency and severity of droughts are expected to increase in the future as a result of climate change, and our study demonstrates how continued monitoring of cyanotoxins, water quality, and phytoplankton communities could help improve management of cyanobacterial blooms in the Delta and other estuaries. [ABSTRACT FROM AUTHOR]

Droughts have major effects on estuaries because freshwater entry is one of the defining features of an estuary, and freshwater flow is an important variable that determines the interannual change in the environment. In the upper San Francisco Estuary (the Sacramento-San Joaquin Delta, Suisun Bay, and Suisun Marsh), the Mediterranean climate includes frequent multi-year droughts. To assess ecosystem responses to droughts in the upper estuary, the Interagency Ecological Program Drought Synthesis Team assembled a set of flow, water quality, chlorophyll, zooplankton, and fish data from 1975 to 2021 to test for differences between multi-year droughts and multi-year wet periods and tested for linear relationships between each variable and the Sacramento Valley Hydrologic Index (see definitions and relationships as outlined in Appendix A). Our models showed droughts decreased Delta outflow, project exports, zooplankton in Suisun Bay, and some fish species. We also found that droughts increased water clarity, salinity, nutrients, chlorophyll in the South Delta, zooplankton in the South Delta, and water residence time. Although our analyses only tested correlations, we hypothesized that most of the food-web responses could be traced to increased residence time, decreased transport rates, or both. However, some responses may have been caused by secondary effects, including shifts in salinity gradients, regional changes in water quality, or differences in top-down effects of increased predation and grazing rates. With increased frequency of droughts in the future, this increasingly low-outflow, warming, clearing estuary--which is invaded by non-native species and has low pelagic fish production--is rapidly becoming the new "normal.". [ABSTRACT FROM AUTHOR]

Multi-year droughts are ever-present and transformational features of California's Mediterranean climate and can fundamentally affect the water quality and the ecosystem responses of the San Francisco Estuary (estuary) and the Sacramento-San Joaquin Delta (Delta). This study assessed data collected by long-term monitoring programs over the past 46 water years (1975-2021) to evaluate how water quality in the estuary changes during multi-year droughts. Data were aggregated by region (South-Central Delta, North Delta, confluence, Suisun Bay, and Suisun Marsh) and season, then differences between multi-year drought periods, multiyear wet periods, and neutral periods were compared using generalized linear models. We found that multi-year drought periods altered multiple physical and chemical parameters in the estuary, increasing water temperature, salinity, water clarity, and nutrient levels. This trend was consistent across regions and seasons, with few exceptions. Increases in these parameters during drought periods were likely caused by reduced Delta inflows that intensified in each successive dry year because of reduced precipitation and managed estuarine inflows and outflows. Drought periods did not substantially affect tidal velocities within the estuary, which remained mostly consistent across wet and drought periods. Trends in chlorophyll concentrations during drought periods were more nuanced with higher concentrations occurring in the South-Central Delta region and during the winter and spring. Together, these results characterized drought in the estuary as warm, clear, high in nutrients, with patchy phytoplankton blooms (as indexed by chlorophyll), all of which have implications for higher trophic levels. Considering that droughts are expected to increase in frequency and intensity in California with climate change, understanding the effects of multi-year droughts on the water quality conditions of the estuary can help inform water management decisions. [ABSTRACT FROM AUTHOR]

Information on habitat transitions is critical to understand whether efforts to manage the freshwater phase of juvenile green sturgeon (Acipenser medirostris) rearing habitats are feasible or beneficial. We implanted microacoustic transmitters in naturally produced age-0 juvenile green sturgeon in the Sacramento River to evaluate residency, general spatial distribution, movement rates, and downstream migration patterns over 300 river kilometers. Furthermore, we investigated whether changes in three environmental variables (discharge, turbidity, and water temperature) were associated with the beginning of movements resulting in the transition from riverine to brackish waters during their outmigration to the Sacramento-San Joaquin Delta Estuary. We captured 183 juvenile green sturgeon by trawl of which 88 were implanted with microacoustic tags. An additional 10 individuals were collected and tagged from the Red Bluff Diversion Dam rotary-screw traps. Linear mixed models showed that individuals caught by trawl had longer upper river residence times, post-tagging, than those caught by trap. Intergate velocity models showed that trap-caught individuals relocated at higher upper river reach velocities than trawl-caught fish, but there were no differences between trap- and trawl-caught fish within the middle or lower reach. Detections showed that juvenile green sturgeon began the downstream transition to brackish water habitat when discharge and turbidity increased. Temperature was not found to significantly influence the initiation of downstream migration. Both continuous and stepped downstream migration patterns were observed in each of the four cohorts, with smaller individuals being more likely to exhibit stepped migration. These data provide information that was previously unknown about the life history of the southern distinct population segment of the North American green sturgeon and can be utilized to assist with water resource management and recovery of this threatened fish species. [ABSTRACT FROM AUTHOR]

Dynamical (i.e., model-based) methods are widely used by forecasting centers to generate seasonal streamflow forecasts, building upon process-based hydrological models that require parameter specification (i.e., calibration). Here, we investigate the extent to which the choice of calibration objective function affects the quality of seasonal (spring–summer) streamflow hindcasts produced with the traditional ensemble streamflow prediction (ESP) method and explore connections between hindcast skill and hydrological consistency – measured in terms of biases in hydrological signatures – obtained from the model parameter sets. To this end, we calibrate three popular conceptual rainfall-runoff models (GR4J, TUW, and Sacramento) using 12 different objective functions, including seasonal metrics that emphasize errors during the snowmelt period, and produce hindcasts for five initialization times over a 33-year period (April 1987–March 2020) in 22 mountain catchments that span diverse hydroclimatic conditions along the semiarid Andes Cordillera (28–37 ∘ S). The results show that the choice of calibration metric becomes relevant as the winter (snow accumulation) season begins (i.e., 1 July), enhancing inter-basin differences in hindcast skill as initializations approach the beginning of the snowmelt season (i.e., 1 September). The comparison of seasonal hindcasts shows that the hydrological consistency – quantified here through biases in streamflow signatures – obtained with some calibration metrics (e.g., Split KGE (Kling–Gupta efficiency), which gives equal weight to each water year in the calibration time series) does not ensure satisfactory seasonal ESP forecasts and that the metrics that provide skillful ESP forecasts (e.g., VE-Sep, which quantifies seasonal volume errors) do not necessarily yield hydrologically consistent model simulations. Among the options explored here, an objective function that combines the Kling–Gupta efficiency (KGE) and the Nash–Sutcliffe efficiency (NSE) with flows in log space provides the best compromise between hydrologically consistent simulations and hindcast performance. Finally, the choice of calibration metric generally affects the magnitude, rather than the sign, of correlations between hindcast quality attributes and catchment descriptors, the baseflow index and interannual runoff variability being the best predictors of forecast skill. Overall, this study highlights the need for careful parameter estimation strategies in the forecasting production chain to generate skillful forecasts from hydrologically consistent simulations and draw robust conclusions on streamflow predictability. [ABSTRACT FROM AUTHOR]

Traditional methods for estimating abundance of fish populations are not feasible in some systems due to complex population structure and constraints on sampling effort. Lincoln's estimator provides a technique that uses harvest and harvest rate to estimate abundance. Using angler catch data allows assumptions of the estimator to be addressed without relying on methods that could be prohibitively field-intensive or costly. Historic estimates of White Sturgeon Acipenser transmontanus abundance in the Sacramento–San Joaquin River basin have been obtained using mark–recapture methods; however, White Sturgeon population characteristics often cause violations of model assumptions, such as population closure and independent capture probabilities. We developed a version of Lincoln's estimator using a joint likelihood, estimated abundance of White Sturgeon in the Sacramento–San Joaquin River basin in 2015 using this method and empirical data and assessed accuracy and precision of estimates in a simulation study. Estimating abundance using harvest and harvest rate, as represented by our model framework, has the potential to be precise and accurate. The joint likelihood–based approach fitted using Bayesian methods is advantageous because it includes all sources of variation in a single model. Precision of abundance estimates was low with application of the model to White Sturgeon in the Sacramento–San Joaquin River basin and to similar conditions in a simulated dataset. Using simulation, precision and accuracy increased with increases in the number of high-reward and standard tags released, tag reporting rate, tag retention rate, and harvest rate. Results demonstrate potential sources of error when using this approach and suggest that increasing the number of tagged fish and tag reporting rate are potential actions to improve precision and accuracy of abundance estimates of the model. [ABSTRACT FROM AUTHOR]

Accurate traffic density plays a pivotal role in the Intelligent Transportation Systems (ITS). The current practice to obtain the traffic density is through specialized sensors. However, those sensors are placed in limited locations due to the cost of installation and maintenance. In most metropolitan areas, traffic surveillance cameras are widespread in road networks, and they are the potential data sources for estimating traffic density in the whole city. Unfortunately, such an application is challenging since surveillance cameras are affected by the 4L characteristics: Low frame rate, Low resolution, Lack of annotated data, and Located in complex road environments. To the best of our knowledge, there is a lack of holistic frameworks for estimating traffic density from traffic surveillance camera data with 4 L characteristics. Therefore, we propose a framework for estimating traffic density using uncalibrated traffic surveillance cameras. The proposed framework consists of two major components: camera calibration and vehicle detection. The camera calibration method estimates the actual length between pixels in the images and videos, and the vehicle counts are extracted from the deep-learning-based vehicle detection method. Combining the two components, high-granular traffic density can be estimated. To validate the proposed framework, two case studies were conducted in Hong Kong and Sacramento. The results show that the Mean Absolute Error (MAE) for the estimated traffic density is 9.04 veh/km/lane in Hong Kong and 7.03 veh/km/lane in Sacramento. The research outcomes can provide accurate traffic density without installing additional sensors. [ABSTRACT FROM AUTHOR]

The diffuse attenuation coefficient of photosynthetically active radiation (KdPAR) is commonly used to predict light attenuation in aquatic productivity models, but obtaining measurements of PAR to compute KdPAR is difficult. In situ calculations of KdPAR require multiple measurements of PAR through the water column, and these measurements are infeasible for real-time recording. Instead, predictive models using surface-water measurements may be used. Traditional KdPAR models are based on open-ocean habitats and rely on chlorophyll-- as a proxy measurement for phytoplankton abundance--as the main predictive parameter. However, elevated suspended sediments and dissolved organic materials may also affect KdPAR values of inland water bodies and estuaries. In this study, we leverage KdPAR calculations derived from in situ light measurements collected along with surface-water-quality parameters across the Sacramento-San Joaquin River Delta in California, USA (the Delta). Sampling occurred between January of 2013 and May of 2014. We also explored regional and seasonal effects, but these did not clearly affect the model. Ultimately, the best-performing model included surface-level turbidity only (R² = 0.91). The simplicity of the model facilitates use of KdPAR estimates for a variety of purposes throughout the Delta, including euphotic depth calculations, and as inputs to primary-productivity and habitat-suitability models. We demonstrate the model's usability with two open-sources data sets (one spatially dense, and one temporally dense), and estimate KdPAR, euphotic depth, and primary productivity within the Delta. We provide calculations for each estimation, allowing users to easily adopt these models and apply them to their own data or with open-sourced data, which are abundant. [ABSTRACT FROM AUTHOR]

This work presents a simplified approach for estimating ionic concentrations from specific electrical conductance (EC) data in the San Francisco Estuary. Monitoring the EC of water through electrodes is simple and inexpensive. As a result, a wealth of high-resolution time-series data is available to indirectly estimate salinity concentrations and, by extension, seawater intrusion throughout the study domain. However, scientists and managers are also interested in quantifying ionic (e.g., bromide, chloride) and total dissolved solids (TDS) concentrations to meet water-quality regulations, protect beneficial uses, support environmental analyses, and track source-water dominance. These constituent concentrations, reported with lower spatial and temporal resolution than EC, are typically measured in the laboratory from discrete (grab) water samples. We divided the study domain into four unique regions to estimate concentrations of major ions and TDS as mathematical functions of measured or model-simulated EC. Salinity relationships in three of the four regions-- regions that represent Sacramento-San Joaquin Delta (Delta) inflow and seawater-dominated boundaries--reflect ionic make-ups that are either independent of or weakly dependent on season and hydrologic condition, and are highly correlated with EC. The fourth region--represented by the interior Delta--exhibits salinity characteristics associated with complex-boundary source-water mixing that varies by season and hydrologic condition. We introduce a novel method to estimate ionic and dissolved solids concentrations within this fourth region, given month, water year type, and (optionally) X2 isohaline position, which allows for more accurate EC-based estimates than previously available. The resulting approach, while not a substitute for hydrodynamic modeling, can provide useful information under constrained schedules and budgets. [ABSTRACT FROM AUTHOR]

Central Valley Spring-run Chinook (CVSC) are listed as threatened under the California and federal Endangered Species Acts, but how ocean fisheries affect CVSC is not routinely monitored or managed, largely because of data limitations. Most tag data for CVSC are from a hatchery program that may not sufficiently represent natural-origin fish in ocean and inland fishery recovery data. However, a discontinued tagging program for Butte Creek Wild Spring-run Chinook (BCWSC) provides for estimation of fishery impacts and maturation schedules for a limited set of years, which we compared with estimates for hatchery-origin fish for common years, while extending the hatchery-origin estimates over a wider time-frame. Additional scale-age data from BCWSC allow inferences about more recent maturation rates, conditional on harvest-rate estimates borrowed from other stocks. Overall, CVSC appear to experience low age-3 ocean fishery impact rates, but age-4 impact rates can be comparable to ocean harvest rates estimated for Sacramento River Fall Chinook. Tagging data from the years available indicate that ocean fisheries may reduce spawning run sizes (all ages combined) by 40% to 60% during periods of high fishing effort. Effects of ocean fishing on spawner abundance are weaker in years of reduced fishing or for cohorts displaying earlier maturation. It appears that maturation rates of hatchery-origin CVSC may have increased (i.e., earlier maturation) over the full time-period examined, and there may be indications of increasing maturation rates for BCWSC as well. [ABSTRACT FROM AUTHOR]

Estuarine tides, currents and shelf phenomena are often non-stationary, with a frequency composition that evolves with time and forcing such as river flow and density structure. Such tides are difficult to characterize quantitatively, particularly against a background of significant, multi-scale sub-tidal and cusp energy. One challenge identified in past work is the separation of diurnal and subtidal components of the signal. The difficulty is exacerbated in basins such as the San Francisco Bay-Delta, where the diurnal species is subject to fortnightly fluctuation due to O1-K1 beating (the tropical spring-neap cycle) and the subsequent interaction with the semidiurnal species leads to the production of modulated terdiurnal energy. This paper extends the method of higher order stochastic cycles, a construct from the structural time series literature, for use in analyzing these nonstationary tides. Interpretable as a multi-species generalization of Butterworth low pass and bandpass filters, Stochastic Cycle Harmonic Analysis (SCHA) possesses an interpretable stationary frequency response and can accurately extract terdiurnal frequencies. Represented by a state space formulation and fit using a Markov Chain Monte Carlo (MCMC) method, the method can produce Bayesian confidence intervals for derived quantities such as amplitudes, is robust to patches of missing data, and is energy conserving. We evaluate the method on benchmark problems that test its time and frequency resolution. In a field application on the Sacramento River in the San Francisco Bay-Delta SCHA confirms that the declinational beating of O1 and K1, heavily modulated by river flow, dominates fortnightly variation in the tide and its terdiurnal response. • Describes higher order stochastic cycles for harmonic analysis of tides (SCHA). • The SCHA method is accurate when the first four species are extracted. • SCHA model works for tides with nonstationary diurnal or terdiurnal content. • Implemented through a state space method with numerous practical extensions. [ABSTRACT FROM AUTHOR]

The Yellow-billed Magpie (Pica nuttalli) declined substantially throughout its range after the West Nile virus arrived in California in the 2000s, and neither development of disease resistance nor population recovery have been reported. After surveying 8 occupied sites in 2020 and 22 occupied sites in 2021 for a study of the magpie's habitat use in urban Sacramento, I continued surveying 15 urban parks in 2022 and 2023 to evaluate population trends over 4 years. The Sacramento magpie population increased consistently by 3-7% per year, suggesting a localized recovery. Since the extent of foraging habitat remained stable, other factors, possibly including development of resistance to the virus (as seen in other species of birds), may be promoting a population increase. These results, however, are local and relatively short term. More work is needed to determine if resistance is developing and to understand longer-term population and disease trends in other parts of the Yellow-billed Magpie's range. [ABSTRACT FROM AUTHOR]

Hydrodynamic models are widely used in simulating water dynamics in riverine and estuarine systems. A reasonably realistic representation of the geometry (e.g., channel length, junctions, cross-sections, etc.) of the study area is imperative for any successful hydrodynamic modeling application. Typically, hydrodynamic models do not digest these data directly but rely on pre-processing tools to convert the data to a readable format. This study presents a parsimonious open-source and user-friendly Java software tool, the Cross-Section Development Program (CSDP), that is developed by the authors to prepare geometric inputs for hydrodynamic models. The CSDP allows the user to select bathymetry data collected in different years by different agencies and create cross-sections and computational points in a channel automatically. This study further illustrates the application of this tool to the Delta Simulation Model II, which is the operational forecasting and planning hydrodynamic and water quality model developed for the Sacramento–San Joaquin Delta in California, United States. Model simulations on water levels and flow rates at key stations are evaluated against corresponding observations. The simulations mimic the patterns of the corresponding observations very well. The square of the correlation coefficient is generally over 0.95 during the calibration period and over 0.80 during the validation period. The absolute bias is generally less than 5% and 10% during the calibration and validation periods, respectively. The Kling–Gupta efficiency index is generally over 0.70 during both calibration and validation periods. The results illustrate that CSDP can be efficiently applied to generate geometric inputs for hydrodynamic models. [ABSTRACT FROM AUTHOR]

This study focused on the mixing of a solute, assumed to be conservative, introduced to one arm of an afterbay reservoir, between Keswick and Shasta Dams on the Sacramento River near Redding, California. Rhodamine water tracer (WT) dye served as the solute in a field experiment, and was introduced over 4.5 h and monitored for 4 days by sondes moored in the reservoir. The scenario was modeled numerically using the Delft3D flexible mesh (FM) hydrodynamic and mixing model, with measured inflows, outflows, water level, water temperatures, and bathymetry as input. Manning's n and horizontal eddy viscosity served as the (constant) model calibration parameters, and each was adjusted an order of magnitude below the default values to force observed and modeled dye hydrographs to match in arrival time and duration. The low friction factor was concluded to be due to a combination of low flow speeds coupled with energy dissipation inherent to the model. The model and surface drifters equipped with dual-frequency Global Navigation Satellite System equipment revealed velocities in the 1–5 cm/s range in much of the domain during the experiment. Simple analytical expressions were shown to be useful for estimating distance to full cross-sectional mixing, steady-state concentrations, and time to reach them, but the numerical model is required for investigation of the approach to steady state, and at locations where flows intersect. Time to steady-state concentrations was 1.5–13 days for 10 simulations that spanned a wide range of inflow conditions. Model sensitivity tests suggest that wind and heat fluxes were not important during the field study, but simulations of a summer scenario with small inflows of cold water upstream in warm weather should consider water temperature. Both field observations and numerical model results showed inflow to one arm of the reservoir reaching full cross-sectional mixing before plunging below the water surface near the intersection of this arm with the reservoir's main stem. Model results are being used to guide management decisions related to inflows to the reservoir from a relic mining site that is also a USEPA Superfund site. [ABSTRACT FROM AUTHOR]

Salinity management in estuarine systems is crucial for developing effective water-management strategies to maintain compliance and understand the impact of salt intrusion on water quality and availability. Understanding the temporal and spatial variations of salinity is a keystone of salinity-management practices. Process-based numerical models have been traditionally used to estimate the variations in salinity in estuarine environments. Advances in data-driven models (e.g., deep learning models) make them effective and efficient alternatives to process-based models. However, a discernible research gap exists in applying these advanced techniques to salinity modeling. The current study seeks to address this gap by exploring the innovative use of deep learning with data augmentation and transfer learning in salinity modeling, exemplified at 23 key salinity locations in the Sacramento–San Joaquin Delta which is the hub of the water-supply system of California. Historical, simulated (via a hydrodynamics and water quality model), and perturbed (to create a range of hydroclimatic and operational scenarios for data-augmentation purposes) flow, and salinity data are used to train a baseline multi-layer perceptron (MLP) and a deep learning Residual Long-Short-Term Memory (Res-LSTM) network. Four other deep learning models including LSTM, Residual Network (ResNet), Gated Recurrent Unit (GRU), and Residual GRU (Res-GRU) are also examined. Results indicate that models pre-trained using augmented data demonstrate improved performance over models trained from scratch using only historical data (e.g., median Nash–Sutcliffe efficiency increased from around 0.5 to above 0.9). Moreover, the five deep learning models further boost the salinity estimation performance in comparison with the baseline MLP model, though the performance of the latter is acceptable. The models trained using augmented data are then (a) used to develop a web-based Salinity Dashboard (Dashboard) tool that allows the users (including those with no machine learning background) to quickly screen multiple management scenarios by altering inputs and visualizing the resulting salinity simulations interactively, and (b) transferred and adapted to estimate observed salinity. The study shows that transfer learning results more accurately replicate the observations compared to their counterparts from models trained from scratch without knowledge learned and transferred from augmented data (e.g., median Nash–Sutcliffe efficiency increased from around 0.4 to above 0.9). Overall, the study illustrates that deep learning models, particularly when pre-trained using augmented data, are promising supplements to existing process-based models in estuarine salinity modeling, while the Dashboard enables user engagement with those pre-trained models to inform decision-making efficiently and effectively. [ABSTRACT FROM AUTHOR]

Salinity in estuarine environments has been traditionally simulated using process-based models. More recently, data-driven models including artificial neural networks (ANNs) have been developed for simulating salinity. Compared to process-based models, ANNs yield faster salinity simulations with comparable accuracy. However, ANNs are often purely data-driven and not constrained by physical laws, making it difficult to interpret the causality between input and output data. Physics-informed neural networks (PINNs) are emerging machine-learning models to integrate the benefits of both process-based models and data-driven ANNs. PINNs can embed the knowledge of physical laws in terms of the partial differential equations (PDE) that govern the dynamics of salinity transport into the training of the neural networks. This study explores the application of PINNs in salinity modeling by incorporating the one-dimensional advection–dispersion salinity transport equation into the neural networks. Two PINN models are explored in this study, namely PINNs and FoNets. PINNs are multilayer perceptrons (MLPs) that incorporate the advection–dispersion equation, while FoNets are an extension of PINNs with an additional encoding layer. The exploration is exemplified at four study locations in the Sacramento–San Joaquin Delta of California: Pittsburg, Chipps Island, Port Chicago, and Martinez. Both PINN models and benchmark ANNs are trained and tested using simulated daily salinity from 1991 to 2015 at study locations. Results indicate that PINNs and FoNets outperform the benchmark ANNs in simulating salinity at the study locations. Specifically, PINNs and FoNets have lower absolute biases and higher correlation coefficients and Nash–Sutcliffe efficiency values than ANNs. In addition, PINN models overcome some limitations of purely data-driven ANNs (e.g., neuron saturation) and generate more realistic salinity simulations. Overall, this study demonstrates the potential of PINNs to supplement existing process-based and ANN models in providing accurate and timely salinity estimation. [ABSTRACT FROM AUTHOR]

The goal of sugar-sweetened beverage (SSB) taxes is to raise the prices of SSBs to decrease consumption. Price promotions play an important role in the sales of SSBs and could potentially be used by manufacturers to weaken the impact of such taxes. The purpose of this study is to determine how price promotions changed after the introduction of the 2017 Oakland SSB tax. A difference-in-differences study design was used to compare changes in prices and the prevalence and amount of price promotions for beverages in Oakland, California, relative to Sacramento, California, using two different datasets. Nielsen Retail Scanner data included price promotions for beverages sold and store audit data included price promotions offered by retailers. Changes were analyzed for SSBs, noncalorically sweetened beverages, and unsweetened beverages. After the implementation of the tax, the prevalence of price promotions for SSBs did not change significantly in Oakland relative to the comparison site of Sacramento. However, the depth of price promotions increased by an estimated 0.35 cents per ounce (P<0.001) based on the Nielsen retail scanner data and by 0.39 cents per ounce (P<0.001) based on the store audit data. This increase in the amount by which SSBs were price promoted following the introduction of the Oakland SSB tax may reflect a strategy by manufacturers to weaken the tax and/or retailers to bolster demand. [ABSTRACT FROM AUTHOR]

• Provided a platform to test various residential building control systems. • Used probability of occupancy to determine setback temperature when unoccupied. • Integrated optimizer with adaptive and occupancy control. • Generated dynamic electricity tariff based on wholesale electricity pricing. • Evaluated the peak load shaving potential with dynamic pricing and optimization. Energy consumption in buildings continues to rise with increased deployment of energy-consuming equipment such as Heating, Ventilation, and Air Conditioning (HVAC) amid a growing world economy. Renewable energy is projected to comprise a majority of the future electricity supply, but the intermittent nature of renewables means that consumption must respond to dynamic supply for optimal utilization. This paper proposes a novel HVAC control strategy for residential buildings using the adaptive comfort model, considering occupancy through probability and real-time information, and optimizing the HVAC schedule to reduce cost, maintain thermal comfort, and respond to the dynamic availability of renewable energy while being generalizable to different situations. To validate this approach, the Universal CPS Environment for Federation (UCEF) co-simulation platform is used to connect advanced building controls with the building energy simulation software EnergyPlus. Simulations are performed for a residential building in Sacramento, CA during a typical summer week. Economic impacts, energy consumption, and thermal comfort are analyzed for traditional, adaptive, and occupancy-based control strategies under demand-based, tiered, and fixed electric tariff systems. Simulation results show that occupancy consideration, adaptive thermal comfort, and optimization can reduce cost by 50.1 %, electricity consumption by 52.9 %, and discomfort by 56.2 % compared to traditional fixed setpoints. The ability of the proposed HVAC control strategy to shift energy consumption away from peak times under a demand-based tariff system is qualitatively analyzed and findings suggest that maximum load-shifting on a grid-scale is attained using occupancy consideration with optimized control and demand-based pricing. For individual residential buildings, similar economic benefits can be gained using the less-complex adaptive HVAC control strategy with existing tiered or simple electric tariff systems. [ABSTRACT FROM AUTHOR]

Managed aquifer recharge (MAR) can provide long‐term storage of excess surface water for later use. While decades of research have focused on the physical processes of MAR and identifying suitable MAR locations, very little research has been done on how to consider competing factors and tradeoffs in siting MAR facilities. This study proposes the use of a simulation‐optimization (SO) framework to map out a cost‐effectiveness frontier for MAR by combining an evolutionary algorithm with two objective functions that seek to maximize groundwater storage gains while minimizing MAR cost. We present the theoretical framework along with a real‐world application to California's Central Valley. The result of the SO framework is a Pareto front that allows identifying suitable MAR locations for different levels of groundwater storage gain and associated MAR project costs, so stakeholders can evaluate different choices based on cost, benefits, and tradeoffs of MAR sites. Application of the SO framework to the Central Valley shows groundwater can be recharged from high‐magnitude (95th percentile) flows at a marginal cost of $57 to $110 million per km3. If the 10 percent largest flows are recharged the total groundwater storage gain would double and the marginal costs would drop to between $30 and $50 million per km3. If recharge water is sourced from outside local basins (e.g., the Sacramento‐San Joaquin Delta), groundwater storage gain is approximately 25%–80% greater than can be achieved by recharging local flows, but the total cost is about 10%–15% higher because of additional lift cost. Key Points: Combining a simulation‐optimization framework with a groundwater model can identify suitable managed aquifer recharge locationsRecharging water from local rivers within the same basin is the most cost‐efficient managed aquifer recharge approach per unit of groundwater storage gainDiverting more water may not yield the most efficient groundwater storage gain due to higher transportation cost [ABSTRACT FROM AUTHOR]

Little is known about how farms and markets are connected. Identifying critical gaps and central hubs in food systems is of importance in addressing a variety of concerns, such as navigating rapid shifts in marketing practices as seen during the COVID-19 pandemic and related food shortages. The constellation of growers and markets can also reinforce opportunities to shift growing and eating policies and practices with attention to addressing racial and income inequities in food system ownership and access. With this research, we compare network methods for measuring centrality and sociospatial orientations in food systems using two of America's most high-producing agricultural counties. Though the counties are adjacent, we demonstrate that their community food systems have little overlap in contributing farms and markets. Our findings show that the community food system for Yolo County is tightly interwoven with Bay Area restaurants and farmers' markets. The adjacent county, Sacramento, branded itself as America's Farm-to-Fork capital in 2012 and possesses network hubs focused more on grocery stores and restaurants. In both counties, the most central actors differ and have been involved with the community food system for decades. Such findings have implications beyond the case studies, and we conclude with considerations for how our methods could be standardized in the national agricultural census. [ABSTRACT FROM AUTHOR]

Phytoplankton subsidies from river inputs and wetland habitats can be important food sources for pelagic organisms in the Sacramento--San Joaquin Delta (Delta). However, while the Sacramento River is a key contributor of water to the Delta, providing 80% of the mean annual inflow, the river is only a minor source of phytoplankton to the system. The reason for low phytoplankton biomass in the Sacramento River is not well understood but appears to be associated with a 65-km stretch of the lower river where chlorophyll-a (Chl-a) concentrations can decline by as much as 90%. We conducted two surveys along the lower Sacramento River, in spring and fall of 2016, to investigate the relative contributions of different factors potentially driving this Chl-a decline. Our study evaluated the change in Chl-a concentrations as a result of dilution from tributaries, light availability, nutrient concentrations, nutrient uptake, phytoplankton productivity, zooplankton grazing, and clam grazing. Chl-a concentration decreased from 14 µg L-1 to 1.8 µg L-1 in the spring and from 4.0 µg L-1 to 1.2 µg L-1 in the fall. Dilutions from the Feather River and American River contributed 39% and 11% of Chl-a declines, respectively, during the spring. Average water depths roughly doubled downstream of the American River confluence, reducing water column light availability and lowering productivity. Zooplankton and clam grazing rates were generally low. Using a mass balance analysis, the measured variables explained 76% of the observed decline in Chl-a in the spring, suggesting additional losses from unidentified factors. We found that phytoplankton biomass is regulated by multiple potential factors in the lower Sacramento River, emphasizing the need for practitioners of restoration and management programs to evaluate multiple potential factors when attempting to enhance phytoplankton production in the Delta, or other large river systems. [ABSTRACT FROM AUTHOR]

Kimberly Wooten is an historical archaeologist with the California Department of Transportation's Cultural Studies (Caltrans) Office in Sacramento, California. Dr. Dr. Maria Zolezzi Garibaldi is a fictional archaeologist with the California Department of Transportation & Interstellar Travel, specializing in ritualistic behavior in both human and non-human societies during the twentieth and early twenty-first centuries. Journey with us to the year 2167, where our intrepid archaeologist has made a fascinating discovery... a FOOB! Carefully cradled in its pale pink packaging, this breast prosthesis is thought to have had ritual purposes, and while the prostheses do not deteriorate over time, intact packaging has never been found before! This article uses humor and archaeology to discuss the author's personal cancer experience and the association of the color pink with femininity and breast cancer. The author's intention is to bring awareness and understanding to archaeologists working with disabilities on a daily basis. [ABSTRACT FROM AUTHOR]

The Gravity Recovery And Climate Experiments (GRACE) satellite mission has been instrumental in estimating large-scale groundwater storage changes across the globe. GRACE observations include significant errors, so pre-processing is normally required before the data are used. In particular, the terrestrial water storage anomalies (TWSA) are usually filtered to reduce the effects of measurement errors and then rescaled to reduce the unintended impacts of the filtering. The scaling is typically selected to maximize the Nash-Sutcliffe Efficiency (NSE) between the rescaled filtered TWSA and the original TWSA from large-scale hydrologic models that represent an incomplete water budget. The objectives of this study are as follows (1) to evaluate the use of NSE in the current GRACE rescaling methodology, (2) develop an improved methodology that incorporates a complete regional water budget, and (3) examine the impacts of the rescaling methodology on regional assessments of groundwater depletion. To evaluate the use of NSE as a performance metric, we compare it to an analytical solution that restores the relative variability between the rescaled filtered and original GRACE TWSA series. The relative variability approach produces more reliable estimates when comparing to TWSA estimates from global positioning systems (GPS) for the Sacramento and San Joaquin River basins in California. Rescaling the complete regional water budget results in a larger scale factor than the scale factor from the large-scale hydrologic model outputs, and the new TWSA results are more consistent with those from GPS. The large scale factor also suggests that regional groundwater depletion is more severe than previously estimated. [ABSTRACT FROM AUTHOR]

Invasive aquatic vegetation (IAV) is a management challenge in the Sacramento-San Joaquin Delta and the Suisun Marsh that has commanded major resource investment for 4 decades. We review the history and supporting science of chemical, biological, and mechanical control of IAV in the Delta and Suisun March, and in flowing waters outside the region. Outside the Delta, there is a significant history of research on IAV control in lotic systems, but few studies come from tidal environments, and we found no investigations at a spatial scale like that of the Delta. The science of control efforts in the Delta is nascent but has seen marked growth over the recent decade. Since 1983, control of invasive submerged and floating species has been centralized within the California State Parks Division of Boating and Waterways (CDBW). The program relies on herbicides, with an annual budget that has exceeded $12.5 million since 2015. However, the results have been mixed because of the challenge of applying herbicides effectively in a tidal system. In parallel, biological control agents for water hyacinth (Eichhornia crassipes) and giant reed (Arundo donax) have been released but have not provided an appreciable control benefit, likely because they are not suited for the temperate Delta climate. Over recent decades, regulatory complexity has increased, hampering efforts to innovate alternative methods or respond quickly to new invaders. Control efforts for giant reed and common reed (Phragmites australis), the main invasive emergent plants, have not been coordinated under a central program, and studies to investigate control strategies have only recently been permitted. As a result, no local studies have been published on control outcomes for these species. Based on this history and our review of the science, we develop recommendations for leadership and science actions to proactively manage IAV. [ABSTRACT FROM AUTHOR]

The Sacramento-San Joaquin Delta (Delta) is a case-study of the Anthropocene "great accelerations," with exponentially increasing temperatures and sea level over time, leading to rapid change in other ecosystem components. In nearly all these interconnected changes and across scales, primary producers play a major role, with diverse effects that mitigate or exacerbate the rapid change induced by climate or other human-driven perturbations. Through this anthropocentric lens, primary producers can be viewed as performing numerous ecosystem services--which ultimately benefit humans--as well as ecosystem disservices, which negatively affect human communities. For example, through carbon sequestration, wetlands can perform ecosystem services of mitigating warming at a global scale and combating relative sea-level rise at a local scale, while generating food that supports regional food webs and fisheries. On the other hand, invasive aquatic vegetation (IAV) can trap sediment before it reaches wetlands, exacerbating local subsidence and relative sealevel rise while incurring great costs to recreation, fishing, and agencies tasked with its control. Effectively managing these ecosystem services and disservices requires understanding how they are connected. For example, wetland restoration often creates opportunities for IAV, which may inhibit sediment deposition on the wetland and out-compete native species. As the Delta science community works toward a more integrative understanding of how different components of the Delta interact as a whole and across scales, the pervasive effects of the ecosystem services and disservices of primary producers serve as foundational knowledge. In this topically themed edition of State of Bay-Delta Science, we review these effects. Individual contributions focus on the historical ecology of the primary productivity of aquatic vegetation, the ecology and control of invasive aquatic vegetation, harmful algal blooms, carbon sequestration and subsidence reversal by wetlands, and remote sensing methods for quantifying the ecosystem services and disservices of Delta primary producers. [ABSTRACT FROM AUTHOR]

The size of an organism is an important factor for a variety of physiological and ecological processes. For fishes, larger size can increase long‐term survival and provide a population level benefit. Therefore, threatened and endangered species management often focuses on supporting high quality habitat that provides growth opportunities. There are numerous habitat characteristics that can affect growth, including food availability, temperature, and habitat complexity. Understanding how growth responds to habitat types of different quality is the first step in determining what could lead to increased growth and potentially increased individual survival. We use Bayesian techniques to determine which of the differing methods is best for modeling the effects of habitat type and temperature on growth. To apply this method, we gather data from previous studies of the growth benefits of differing habitats and temperature regimes on Chinook salmon on the Sacramento River, CA, USA. We find a consistent growth benefit of floodplain rearing across multiple studies and show that a Ratkowsky model is the best for modeling this growth data. This information can specifically help managers model and protect the endangered and threatened Chinook populations in this system and more generally understand fish growth across differing habitat types. [ABSTRACT FROM AUTHOR]