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Objectives. To quantify the impact of droughts on drinking water arsenic and nitrate levels provided by community water systems (CWSs) in California and to assess whether this effect varies across sociodemographic subgroups. Methods. I integrated CWS characteristics, drought records, sociodemographic data, and regulatory drinking water samples (n = 83 317) from 2378 water systems serving 34.8 million residents from 2007 to 2020. I analyzed differential drought effects using fixed-effect regression analyses that cumulatively accounted for CWS-level trends, income, and agricultural measures. Results. CWSs serving majority Latino/a communities show persistently higher and more variable drinking water nitrate levels. Drought increased nitrate concentrations in majority Latino/a communities, with the effect doubling for CWSs with more than 75% Latino/a populations served. Arsenic concentrations in surface sources also increased during drought for all groups. Differential effects are driven by very small (< 500) and privately owned systems. Conclusions. Impending droughts driven by climate change may further increase drinking water disparities and arsenic threats. This underscores the critical need to address existing inequities in climate resilience planning and grant making. (Am J Public Health. 2024;114(9):935–945. https://doi.org/10.2105/AJPH.2024.307758) [ABSTRACT FROM AUTHOR]

As water scarcity becomes the new norm in the Western United States, states such as California have increased their efforts to improve water resilience. Achieving water security under climate change, population growth, and urbanization requires an integrated multi-sectoral approach, where adaptation strategies combine supply and demand management interventions. Yet, most studies consider supply-side and demand-side management strategies separately. Water conservation efforts are mainly driven by policy requirements and publicly available data to assess the effectiveness of demand- and supply-side management policies is often hard to find and unstructured. Here we present CaRDS - the statewide California Residential water Demand and Supply open dataset. CaRDS encompasses nine years (2013-2021) of monthly water supply and demand time series for 404 water suppliers in California, USA, compiled from different open-access data sources. Access to detailed temporal and spatial water supply operations and demands at the state-level can be useful to researchers and practitioners to realize applications such as evaluating the effectiveness of water conservation policies and discovering regional differences in water resilience measures. [ABSTRACT FROM AUTHOR]

Objective: Low survival of emigrating salmonid smolts through the interior regions of California's Sacramento–San Joaquin River Delta (hereafter, "Delta") and large‐scale water extraction from that region have prompted managers to seek to predict and manipulate smolt route use through the tidal Delta. The local flow variables previously used in modeling are not the metrics used in management. Here, I investigate the predictive utility of variables representing both localized flow conditions and remote management metrics to predict routing of juvenile steelhead Oncorhynchus mykiss at two river junctions on the San Joaquin River in the south Delta. Methods: Individual‐based generalized linear models were used with detections of over 4000 acoustic‐tagged juvenile hatchery‐reared steelhead to relate routing to the presence of a rock barrier, simulated localized flow conditions from a hydrodynamic model, and daily management metrics, including upstream river discharge and water pumping rates elsewhere in the Delta. Models were developed for the first two diffluences encountered by smolts entering the Delta (head of Old River and Turner Cut). Result: Exclusive use of the management metrics in routing models underestimated the subdaily, tidally dominated fluctuations in fish routing compared to localized flow covariates. The daily rate of water extraction 20–30 km away contributed to use of non‐main‐stem routes, but the effect was small compared to subhourly flow conditions at the river junctions themselves. Conclusion: Water resource and fish managers are advised to monitor conditions at the locations of interest rather than depending solely on remote metrics. In the Delta, use of a flow barrier and reduction of water pumping operations when smolts are migrating should be combined with habitat improvement in interior Delta regions to optimize migratory survival through this complex and heavily modified system. Impact statementThis research developed a juvenile steelhead routing model that can be used in adaptive management. It highlighted the limitations of common management measures for predicting route use and the importance of monitoring local flow conditions for predictive purposes. [ABSTRACT FROM AUTHOR]

Spatial and temporal patterns of water supply and consumptive water use were analyzed from 475 Detailed Analysis Units by County (DAUCOs) spatial units across California during 2002 through 2016 to evaluate spatial and temporal variability and how it might associate with precipitation variability and other factors. Many, but not all, DAUCOs have relatively low total water supply variability compared to that of state-wide precipitation. Such low variability, in DAUCOs having sufficient diversity of water supply sources, is the result of switching between sources as needed to maintain a reliable total water supply. We used multiple approaches to explore these variations which involved four categories of water supply (local, groundwater, imported, and other) and two categories of water use (agricultural and urban). First, a cluster analysis of the volumetric water balance data identified a small set of clusters having similar magnitudes and proportions of water supply sources and water use—some of them composed of only a few DAUCOs but accounting for a disproportionate amount of the state’s water use. Second, a principal components analysis identified leading modes of anomalous water supply and water use among the 475 DAUCOs, capturing most of the time variation during 2002 to 2016. The most prominent mode exhibits a multi-year trend, most strongly involving increasing groundwater supply and agricultural water use, and decreasing urban water use and imported water supply. Over the study period, trends in both supply and use were pronounced, but differed considerably across California DAUCOs. One predominant subset of DAUCOs grew their agricultural water use with increased groundwater supply; in contrast to a widespread group of DAUCOs which reduced their urban water use. An important result for planners is our finding that variation in precipitation—itself important—is amplified by the human response to water supply availability and regulatory policy. [ABSTRACT FROM AUTHOR]

The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term "snow deluge" and use anomalously high snowpack in California's Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California's 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges--which we define as the 20 y April 1 SWE event--are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation. [ABSTRACT FROM AUTHOR]

Objectives: Reports of unsafe school drinking water in the United States highlight the importance of ensuring school water is safe for consumption. Our objectives were to describe (1) results from our recent school drinking water sampling of 5 common contaminants, (2) school-level factors associated with exceedances of various water quality standards, and (3) recommendations. Methods: We collected and analyzed drinking water samples from at least 3 sources in 83 schools from a representative sample of California public schools from 2017 through 2022. We used multivariate logistic regression to examine school-level factors associated with lead in drinking water exceedances at the American Academy of Pediatrics (AAP) recommendation level (1 part per billion [ppb]) and state action-level exceedances of other contaminants (lead, copper, arsenic, nitrate, and hexavalent chromium). Results: No schools had state action-level violations for arsenic or nitrate; however, 4% had ≥1 tap that exceeded either the proposed 10 ppb action level for hexavalent chromium or the 1300 ppb action level for copper. Of first-draw lead samples, 4% of schools had ≥1 tap that exceeded the California action level of 15 ppb, 18% exceeded the US Food and Drug Administration (FDA) bottled water standard of 5 ppb, and 75% exceeded the AAP 1 ppb recommendation. After turning on the tap and flushing water for 45 seconds, 2%, 10%, and 33% of schools exceeded the same standards, respectively. We found no significant differences in demographic characteristics between schools with and without FDA or AAP exceedances. Conclusions: Enforcing stricter lead action levels (<5 ppb) will markedly increase remediation costs. Continued sampling, testing, and remediation efforts are necessary to ensure drinking water meets safety standards in US schools. [ABSTRACT FROM AUTHOR]

Canopy temperature provides valuable information for efficient irrigation management and for detecting drought injury in a fast and non-destructive way. It also helps quantify the trade-offs between water conservation and the cooling benefit of the irrigated urban landscape, a critical issue in semiarid inland southern California with limited available water resources. Two adjacent hybrid bermudagrass and tall fescue irrigation trials were conducted to determine canopy temperature changes under a wide range of irrigation treatments from 2017 to 2019 in Riverside, California, USA. The canopy temperature data were also used to develop an empirical crop water stress index (CWSI) for each species. The CWSI values ranged from -0.15 to 0.71 for tall fescue and from -0.28 to 0.54 for hybrid bermudagrass. We observed a moderate correlation between visual rating scores (VR) and CWSI values for tall fescue (r = -0.68) and hybrid bermudagrass (r = -0.61). The fitted linear regression between VR and CWSI data suggested CWSI thresholds close to zero for both species to maintain their acceptable visual quality. The irrigation level consistently showed a significant effect on canopy temperature for both species. On average, a 10% decrease in irrigation application increased the canopy temperature of tall fescue and hybrid bermudagrass by 1.3°C and 0.9°C, respectively. Therefore, deficit irrigation might reduce the cooling benefits of irrigated urban lawns in the semiarid climate of inland southern California. [ABSTRACT FROM AUTHOR]

Aim: As climate change increases the frequency and severity of droughts in many regions, conservation during drought is becoming a major challenge for ecologists. Droughts are multidimensional climate events whose impacts may be moderated by changes in temperature, water availability or food availability, or some combination of these. Simultaneously, other stressors such as extensive anthropogenic landscape modification may synergize with drought. Useful observational models for guiding conservation decision‐making during drought require multidimensional, dynamic representations to disentangle possible drought impacts, and consequently, they will require large, highly resolved data sets. In this paper, we develop a two‐stage predictive framework for assessing how drought impacts vary with species, habitats and climate pathways. Location: Central Valley, California, USA. Methods: We used a two‐stage counterfactual analysis combining predictive linear mixed models and N‐mixture models to characterize the multidimensional impacts of drought on 66 bird species. We analysed counts from the eBird participatory science data set between 2010 and 2019 and produced species‐ and habitat‐specific estimates of the impact of drought on relative abundance. Results: We found that while fewer than a quarter (16/66) of species experienced abundance declines during drought, nearly half of all species (27/66) changed their habitat associations during drought. Among species that shifted their habitat associations, the use of natural habitats declined during drought while use of developed habitat and perennial agricultural habitat increased. Main Conclusions: Our findings suggest that birds take advantage of agricultural and developed land with artificial irrigation and heat‐buffering microhabitat structure, such as in orchards or parks, to buffer drought impacts. A working lands approach that promotes biodiversity and mitigates stressors across a human‐induced water gradient will be critical for conserving birds during drought. [ABSTRACT FROM AUTHOR]

BACKGROUND: There is a lack of research on the relationship between water fluoridation and pregnancy outcomes. OBJECTIVES: We assessed whether hypothetical interventions to reduce fluoride levels would improve birth outcomes in California. METHODS: We linked California birth records from 2000 to 2018 to annual average fluoride levels by community water system. Fluoride levels were collected from consumer confidence reports using publicly available data and public record requests. We estimated the effects of a hypothetical intervention reducing water fluoride levels to 0.7 ppm (the current level recommended by the US Department of Health and Human Services) and 0.5 ppm (below the current recommendation) on birth weight, birth-weight-for-gestational age 푧-scores, gestational age, preterm birth, small-for-gestational age, large-for-gestational age, and macrosomia using linear regression with natural cubic splines and G-computation. Inference was calculated using a clustered bootstrap with Wald-type confidence intervals. We evaluated race/ethnicity, health insurance type, fetal sex, and arsenic levels as potential effect modifiers. RESULTS: Fluoride levels ranged from 0 to 2.5 ppm, with a median of 0.51 ppm. There was a small negative association on birth weight with the hypothetical intervention to reduce fluoride levels to 0.7 ppm [-2.2 g; 95% confidence interval (CI): -4.4, 0.0] and to 0.5 ppm (-5.8 g; 95% CI: -10.0, -1.6). There were small negative associations with birth-weight-for-gestational-age z-scores for both hypothetical interventions (0.7 ppm: -0.004; 95% CI: -0.007, 0.000 and 0.5 ppm: -0.006; 95% CI: -0.013, 0.000). We also observed small negative associations for risk of large-for-gestational age for both the hypothetical interventions to 0.7 ppm [risk difference (RD)= -0.001; 95% CI: -0.002, 0.000 and 0.5 ppm (-0.001; 95% CI: -0.003, 0.000)]. We did not observe any associations with preterm birth or with being small for gestational age for either hypothetical intervention. We did not observe any associations with risk of preterm birth or small-for-gestational age for either hypothetical intervention. CONCLUSION: We estimated that a reduction in water fluoride levels would modestly decrease birth weight and birth-weight-for-gestational-age 푧-scores in California. [ABSTRACT FROM AUTHOR]

Coordinating management of groundwater, surface water, and irrigated crops is fundamental economically for many arid and semi-arid regions. This paper examines conjunctive water management for agriculture using hydro-economic optimization modeling. The analysis is integrated across two timescales: a two-stage stochastic decadal model for managing annual and perennial crops spanning dry and wet years and a far-horizon dynamic program embedding the decadal model into a longer groundwater policy setting. The modeling loosely represents California's San Joaquin Valley and has insights for many irrigated arid and semi-arid regions relying on groundwater with variable annual hydrology. Results show how conjunctive water management can stabilize crop decisions and improve agricultural profitability across different water years by pumping more in dry years and increasing recharging groundwater in wetter years. Using groundwater as a buffer for droughts allows growing more higher-value perennial crops, which maximizes profit even with water-scarce conditions. Nevertheless, ending overdraft in basins with declining groundwater for profit-maximizing farming reduces annual crops to maintain more profitable perennial crops through droughts. Results are affected by economic discount rates and future climates. Operating and opportunity costs from forgone annual crops can reduce aquifer recharge early in regulatory periods. [ABSTRACT FROM AUTHOR]

Dynamic planning of water infrastructure requires identifying signals for adaptation, including measures of system performance linked to vulnerabilities. However, it remains a challenge to detect projected changes in performance outside the envelope of natural variability, and to identify whether such detections can be attributed to one or more uncertain drivers. This study investigated these questions using a combination of ensemble simulation, nonparametric tests, and variance decomposition, which were demonstrated for a case study of the Sacramento–San Joaquin River Basin, California. We trained a logistic regression classifier to predict future detections given observed trends in performance over time. The scenario ensemble includes coupled climate and land-use change through the end of the century, evaluated using a multireservoir simulation model to determine changes in water supply reliability and flooding metrics relative to the historical period (1951–2000). The results show that the reliability metric is far more likely to exhibit a significant change within the century, with the most severe scenarios tending to be detected earlier, reflecting long-term trends. Changes in flooding often are not detected due to natural variability despite severe events in some scenarios. We found that the variance in detection times is attributable largely to the choice of climate model, and also to the emissions scenario and its interaction with the choice of climate model. Finally, in the prediction model for both cases, reliability and flooding, the model learns to associate more-recent observations of system performance with nonstationarity detection. These findings underscore the importance of differentiating between long-term change and natural variability in identifying signals for adaptation. [ABSTRACT FROM AUTHOR]

Water system management models represent different purposes, such as water supply, flood control, recreation, and hydropower. When building large-scale system models to represent these diverse objectives, their most appropriate time steps for each purpose often do not coincide. A monthly time step is usually sufficient for water supply modeling, but it can be too coarse for flood control, hydropower, and energy operations, where hourly time steps are preferred. Large-scale water management and planning models mostly employ monthly time steps, but using monthly average energy prices underestimates hydropower revenue and overestimates pumping energy cost because these plants tend to operate during times with above- or below-average energy prices within any month. The approach developed here uses hourly varying prices depending on the percent of monthly operating hours. This paper examines an approach that approximately incorporates hourly energy price variations for hydropower and pumping into large-scale monthly time-step water system model operations without affecting water delivery results. Results from including hourly varying energy prices in a large-scale monthly water supply model of California (CALVIN) are presented. CALVIN is a hydroeconomic linear programming optimization model that allocates water to agricultural and urban users with an objective to minimize total scarcity costs, operating costs, and hydropower revenue loss. Thirteen hydropower plants are modeled with hourly varying prices, and their revenue increased by 25 to 58% compared to revenue calculated with monthly average constant energy prices. Hydropower revenue improvements are greater in critically dry years. For pumping plants modeled with hourly varying prices, the energy use cost decreased by 10 to 59%. This study improves system representation and results for large-scale modeling. [ABSTRACT FROM AUTHOR]

Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values dmax were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies. [ABSTRACT FROM AUTHOR]

Water reuse with Advanced Water Treatment (AWT) is increasingly appealing for urban areas seeking water supply reliability. In cities facing water scarcity, how can large-scale reuse support reliability and how do reuse operations affect water quality, energy use, and water conservation? This paper presents a systems analysis of metropolitan-scale water reuse and its effects on water supply and quality. For the case study of Los Angeles County, California, USA, hydroeconomic modeling is used to evaluate reuse as a contributor to water supply given urban water conservation, drought-induced water scarcity, and costs and benefits for supply and demand. Results indicate that AWT can be a viable source of supply especially when coupled with conservation. Across modeled scenarios, reuse provides as much as 30% of regional supplies. New water reuse with AWT becomes viable when imported water availability is 50% or less of historic values. Existing indirect potable reuse operations in the county remain important. Systemwide energy intensity of operations increases with greater reuse in the absence of water conservation. Modeled influent flow rates to wastewater treatment plants resemble historical values, but extreme flow events could pose risks. The paper offers a holistic framework to evaluate water reuse as a component of urban water management. [ABSTRACT FROM AUTHOR]

Changes in land use and land cover, water systems, and climate are inextricably linked, and their combined stresses have had severe impacts in many regions worldwide. Integrated adaptation planning can support adaptive capacity by helping institutions manage land and water resources at regional to local scales. Linkages between these stressors mean that planners are often faced with potential trade-offs, and how to couple social and environmental sustainability remains a key question. We explore these questions in California's Central Coast, a region that is already experiencing serious water shortages, housing shortages, rapid expansion of perennial agriculture, and severe droughts that are projected to become worse with climate change. Linked models of land use change (the Land Use and Carbon + Water Simulator [LUCAS-W]), water resources (LUCAS-W), and climate (the Basin Characterization Model [BCM]) produced forecasts of exposure to regional changes at 270-m resolution. We worked with regional stakeholders to develop a matrix of nine vulnerability measures that assessed key sensitivities to these changes. Each vulnerability measure combined one of the three exposure projections with spatial datasets representing one of three sensitivity communities (agricultural, domestic, or ecological). We assessed how five scenarios of land-use and water management strategies under consideration by regional planners could provide institutional, top-down adaptive capacity, and whether there were trade-offs in sustainable development goals for these communities. We found that specific land and water management strategies could greatly reduce regional vulnerability, particularly programs to cap water extractions to sustainable levels. The most dramatic trade-off was between the strategy of water demand caps that increased risk of habitat loss and ecosystem preservation that increased water vulnerability. However, trade-offs were usually limited and spatially localized, suggesting local tailoring of the strategies we assessed could reduce them. Trade-offs were more frequent across exposure classes (land use vs. water vs. climate changes) rather than sensitivity classes (agricultural vs. domestic vs. ecological communities), suggesting win-win opportunities for natural resource management. Our vulnerability maps can inform prioritization efforts for local adaptation planning. [ABSTRACT FROM AUTHOR]

The article explores the impact of climate change on California's water resources, specifically in the headwaters. It predicts warmer temperatures, less snow, more rain, and drier summers. While future precipitation levels are uncertain, models agree that there will be less medium-to-light rainfall and more dry days, with occasional intense storms. The article proposes strategies to address these challenges, such as managing water in the headwaters by reducing flood flows, aridification, and restoring natural hydrologies. It also suggests reintroducing beavers to retain water and implementing forecast-informed reservoir operations to make better decisions about water release. These strategies could help mitigate the effects of climate change on California's water resources. [Extracted from the article]

Facing pressures to contend with continual changes in physical availability, to balance water supply with environmental and social impacts, and to build resilience to environmental hazards such as droughts and climate change, water managers increasingly use management plans as a blueprint for managing water. We apply qualitative content analysis to evaluate water management plans from diverse water and land use organizations in California's Central Valley. To understand whether plans are working toward holistic, multi-dimensional management, we assess plans' coverage of water supply, environmental, and socioeconomic dimensions of water use, as well as the quality and implementability of the plans. The plans provide a strong assessment of water supplies and indicate progression toward integrated water resource management. However, we identify gaps in managing water for the environment, considering socioeconomic and distributional impacts, planning for future drought and climate change, and effective coordination with other water agencies and the public. [ABSTRACT FROM AUTHOR]

Objectives. To evaluate the potential for drinking water contamination in Los Angeles (LA) County, California, based on the proximity of supply wells to oil and gas wells, and characterize risk with respect to race/ethnicity and measures of structural racism. Methods. We identified at-risk community water systems (CWSs) as those with supply wells within 1 kilometer of an oil or gas well. We characterized sociodemographics of the populations served by each CWS by using the 2013–2017 American Community Survey. We estimated the degree of redlining in each CWS service area by using 1930s Home Owners' Loan Corporation security maps, and characterized segregation by using the Index of Concentration at the Extremes. Multivariable regression models estimated associations between these variables and CWS contamination risk. Results. A quarter of LA County CWSs serving more than 7 million residents have supply wells within 1 kilometer of an oil or gas well. Higher percentages of Hispanic, Black, and Asian/Pacific Islander residents and a greater degree of redlining and residential segregation were associated with higher contamination risk. Conclusions. Redlining and segregation predict drinking water contamination risks from oil development in LA County, with people of color at greater risk. (Am J Public Health. 2023;113(11):1191–1200. https://doi.org/10.2105/AJPH.2023.307374) [ABSTRACT FROM AUTHOR]

This study presented a novel paradigm for forecasting 12-step-ahead monthly precipitation at 126 California gauge stations. First, the satellite-based precipitation time series from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), TerraClimate, ECMWF Reanalysis V5 (ERA5), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) products were bias-corrected using historical precipitation data. Four methods were tested, and quantile mapping (QM) was the best. After pre-processing data, 19 machine-learning models were developed. random forest, Extreme Gradient Boosting (XGBoost), extreme gradient boosting, support vector machine, multi-layer perceptron, and K-nearest-neighbours were chosen as the best models based on Complex Proportional Assessment (COPRAS) measurement. After hyperparameter adjustment, the Bayesian back-propagation regularization algorithm fused the results. The superior models' predictions were considered inputs, and the target's initial step was labeled. The next 11 steps at each station followed this approach, and the fusion models accurately predicted all steps. The 12th step's average Nash-Sutcliffe efficiency (NSE), mean square error (MSE), coefficient of determination (R2), correlation coefficient (R) were 0.937, 52.136, 0.880, and 0.869, respectively, demonstrating the framework's effectiveness at high forecasting horizons to help policymakers manage water resources. [ABSTRACT FROM AUTHOR]

The responses of rare plants to environmental stressors will determine their potential to adapt to a rapidly changing climate. We used a common garden approach to evaluate how six populations of the annual San Diego thornmint (Acanthomintha ilicifolia Lamiaceae; listed as endangered in the state of California and as threatened by the US Fish and Wildlife Service) from across the species range respond in terms of growth (biomass, height, and width) and reproduction (seed production, floral production, and next generation seed viability) to experimental differences in water availability. We found a significant irrigation-by-population interaction on the aboveground growth, wherein the differences in the magnitude and direction of treatment did not correlate directly with climate variables in natural populations. With respect to reproduction, the low-irrigation treatment produced more seeds per plant, more reproductive individuals, and a larger proportion of viable seed in most, but not all, populations. The seed production and the effect of irrigation on seed production correlated positively with rainfall at wild source populations. These results suggest that Acanthomintha ilicifolia responds to water limitation by creating more and higher-quality seed, and that plants locally adapted to a higher annual rainfall show a greater plasticity to differences in water availability than plants adapted to a lower annual rainfall, a finding that can inform the in situ demographic management and ex situ collection strategy for Acanthomintha ilicifolia and other rare California annuals. [ABSTRACT FROM AUTHOR]

Gradual loss of water resource in the Salton Sea has got much attention from researchers recently for its damage to the local environment and ecosystems for human beings, animals and plants. To monitor the water resource of the lake, researchers usually obtain certain water resource indices manually from databases such as the satellite images of the region. In this paper, we develop a new method to monitor the area of the Salton Sea automatically. By this method, the lake boundary is first segmented from each satellite image by an image segmentation procedure, and then its area is computed by a numerical algorithm. The sequence of lake areas computed from satellite images taken at different time points is then monitored by a control chart from the statistical process control literature. Because the lake area changes gradually over time, the control chart designed for detecting process mean drifts is used here for the water resource surveillance application. [ABSTRACT FROM AUTHOR]

Accurate observation of hillslope groundwater storage and instantaneous recharge remains difficult due to limited monitoring and the complexity of mountainous landscapes. We introduce a novel storage‐discharge method to estimate hillslope recharge and the recharge ratio—the fraction of precipitation that recharges groundwater. The method, which relies on streamflow data, is corroborated by independent measurements of water storage dynamics inside the Rivendell experimental hillslope at the Eel River Critical Zone Observatory, California, USA. We find that along‐hillslope patterns in bedrock weathering and plant‐driven storage dynamics govern the seasonal evolution of recharge ratios. Thinner weathering profiles and smaller root‐zone storage deficits near‐channel are replenished before larger ridge‐top deficits. Consequently, precipitation progressively activates groundwater from channel to divide, with an attendant increase in recharge ratios throughout the wet season. Our novel approach and process observations offer valuable insights into controls on groundwater recharge, enhancing our understanding of a critical flux in the hydrologic cycle. Plain Language Summary: Groundwater in hilly areas is an important source of water. The amount of rainfall that replenishes groundwater storage is known as groundwater recharge. Because groundwater recharge is challenging to measure directly, we applied a technique that makes it possible to use a more readily observable variable—streamflow, or the water flow in rivers and streams—to calculate how much water is stored in the hillslope as groundwater. This made it possible to use streamflow to estimate how much rainfall becomes groundwater recharge. By understanding the structure of the ground and how moisture is distributed, we were able to determine how the amount of recharge changes over the wet season. Our work improves understanding of how rainfall and plant water use affect groundwater recharge, which is important for managing water resources in mountain landscapes. Key Points: Increases in hillslope groundwater storage can be quantified from storage‐discharge relationsField measurements of groundwater and vadose zone (VZ) storage corroborate seasonality in recharge ratios (recharge per precipitation input)Recharge ratio increases with decreasing plant‐driven VZ (soil and rock) storage deficits, reflecting spatial variations in storage [ABSTRACT FROM AUTHOR]

Key Takeaways: Facing an increase in the size and complexity of wildland interface fires, water utilities must pay more attention to protecting vital infrastructures during potentially catastrophic wildfires. Water utilities can mitigate the impacts of wildfires by using a three‐pronged approach that includes collaborating with fire agencies, protecting infrastructure, and supporting air assets like firefighting helicopters. Gaining situational awareness at the onset of a wildfire helps minimize its impacts. [ABSTRACT FROM AUTHOR]

Well‐dated lacustrine records are essential to establish the timing and drivers of regional hydroclimate change. Searles Basin, California, records the depositional history of a fluctuating saline‐alkaline lake in the terminal basin of the Owens River system draining the eastern Sierra Nevada. Here, we establish a U‐Th chronology for the ∼76‐m‐long SLAPP‐SLRS17 core collected in 2017 based on dating of evaporite minerals. Ninety‐eight dated samples comprising nine different minerals were evaluated based on stratigraphic, mineralogic, textural, chemical, and reproducibility criteria. After the application of these criteria, a total of 37 dated samples remained as constraints for the age model. A lack of dateable minerals between 145 and 110 ka left the age model unconstrained over the penultimate glacial termination (Termination II). We thus established a tie point between plant wax δD values in the core and a nearby speleothem δ18O record at the beginning of the Last Interglacial. We construct a Bayesian age model allowing stratigraphy to inform sedimentation rate inflections. We find that the >210 ka SLAPP‐SRLS17 record contains five major units that correspond with prior work. The new dating is broadly consistent with previous efforts but provides more precise age estimates and enables a detailed evaluation of evaporite depositional history. We also offer a substantial revision of the age of the Bottom Mud‐Mixed Layer contact, shifting it from ∼130 ka to 178 ± 3 ka. The new U‐Th chronology documents the timing of mud and salt layers and lays the foundation for climate reconstructions. Plain Language Summary: Searles Lake, California, is currently a dry saltpan; however, in the past, it was a large and deep lake (>200 m). Lake levels have varied with changes in climate. These changes influenced the sediments deposited. Thus, changes in the lake sediments can be applied to understand the past environments of the region. Here, we developed a chronology for a 76‐m‐long sediment core using isotopic dating methods. The lake sediments contain many different minerals which can be dated. However, some of these minerals formed after the lake sediments were deposited and others have been chemically altered in the time since their formation. We developed criteria for the selection of samples that are the most likely to reflect the age of sediment deposition. We used the selected ages along with statistical modeling to determine the ages of the sediments with depth. We find that the core contains a record that spans over 200,000 years, including the last two glacial cycles. The age model presented here lays the foundation for the exploration of how past climate changes impacted water availability and vegetation in southeastern California. Key Points: >200 ka‐long sediment record of southwestern North American paleoenvironmentEstablishes general criteria for selection of evaporite minerals for U‐Th datingExplores a wide range of parameters for Bayesian age‐depth modeling [ABSTRACT FROM AUTHOR]

The article discusses a study by Berberian and colleagues which assessed the vulnerability of ground water in Los Angeles County, California from nearby oil wells. Topics include structural racism and groundwater vulnerability, groups that bear a disproportionate burden of hazardous facility siting, and implication of the challenges posed by oil extraction nearby community water system (CWS).

Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite‐based fire detections with snow seasonality classifications to examine fire activity in California's seasonal and ephemeral snow zones. We find a nearly tenfold increase in fire activity during 2020–2021 versus 2001–2019. Accumulation season broadband snow albedo declined 25%–71% at two burned sites (2021 and 2022) according to in‐situ data relative to un‐burned conditions, with greater declines associated with increased burn severity. By enhancing snowpack susceptibility to melt, both decreased snow albedo and canopy drove midwinter melt during a multi‐week dry spell in 2022. Despite similar meteorological conditions in December–February 2013 and 2022–linked to persistent high pressure weather regimes–minimal melt occurred in 2013. Post‐fire snowpack differences are confirmed with satellite measurements. With growing geographical overlap between wildfire and snow, our findings suggest California's snowpack is increasingly vulnerable to the compounding effects of dry spells and wildfire. Plain Language Summary: Satellite fire detections indicate substantial increases in wildfire activity in California's snow‐covered landscapes during 2020 and 2021, suggesting wildfire is increasingly altering mountain hydrology. During 2022, a multi‐week mid‐winter drought, or dry spell, occurred. A meteorologically‐similar dry spell occurred in 2013, and the 2022 event provides a test case to examine how post‐fire changes (canopy loss and deposition of burned dark material on snowpack) alter snowmelt patterns. Using field observations, weather station data, and satellite remote sensing of snow, we find large reductions in snow albedo and canopy cover drove rapid melt during the 2022 dry spell in burned areas whereas during 2013, minimal melt occurred. The societal connection between mountains and humans will be strained as mountains face increasing climate‐related stressors. Midwinter drought, snow loss, and increasing wildfire are expectations of a warming world. Addressing these challenges requires innovative water and forest management paradigms. Our findings motivate additional research into assessing and planning for post‐fire hydrologic changes in snow‐dominated landscapes as both wildfire and dry spells will increase in frequency with climate warming. Key Points: A 9.8x increase in satellite fire detections in California's snow zones in 2020–2021 versus 2001–2019 implies growing overlap in fire and snowPost‐fire accumulation season broadband snow albedo declined 25%–71%, driving fewer snow‐covered days and lower snow‐cover fractionCompared with the meteorologically similar 2013 dry spell, albedo and canopy declines led to rapid midwinter melt in 2022 [ABSTRACT FROM AUTHOR]

Assessments of large-scale water resources projects in two regions of the United States provide lessons about long-term outcomes. In the humid East, the Tennessee Valley Authority (TVA) serves multiple objectives and is the largest public power enterprise in the nation. In the West, California's State Water Project serves nearly 27 million people and some 750,000 acres of farmland with extensive infrastructure. Assessing the outcomes of such large-scale projects requires integrated analysis and involves different value propositions. The major lessons are about government actions in river basin and public power systems development and their impacts on agriculture, urban development, and a vulnerable and changing delta ecosystem. [ABSTRACT FROM AUTHOR]

Groundwater management planning requires balancing the interests of different stakeholder groups because (1) water supply development causes changes to groundwater systems that include declines in groundwater levels and (2) benefits and costs from pumping the common‐pool resource often do not occur such that they are shared proportionate to use. Quantifying impacts from declining groundwater levels among user groups can be useful for evaluating management strategies. In California, considering impacts to supply well operations is proving important for acceptance of management plans that have been required by law. A case study is presented to illustrate the types of results that may contribute to the planning process. The results indicate that significant differences in impact may occur between groundwater pumpers. Suggestions are provided for using this information to mitigate impacts to the most vulnerable stakeholders. [ABSTRACT FROM AUTHOR]

Groundwater provides nearly half of irrigation water supply, and it enables resilience during drought, but in many regions of the world, it remains poorly, if at all managed. In heavily agricultural regions like California's Central Valley, where groundwater management is being slowly implemented over a 27-year period that began in 2015, groundwater provides two–thirds or more of irrigation water during drought, which has led to falling water tables, drying wells, subsiding land, and its long-term disappearance. Here we use nearly two decades of observations from NASA's GRACE satellite missions and show that the rate of groundwater depletion in the Central Valley has been accelerating since 2003 (1.86 km3/yr, 1961–2021; 2.41 km3/yr, 2003–2021; 8.58 km3/yr, 2019–2021), a period of megadrought in southwestern North America. Results suggest the need for expedited implementation of groundwater management in the Central Valley to ensure its availability during the increasingly intense droughts of the future. Liu et al. used the NASA GRACE/FO missions to show that since 2019, groundwater depletion in California's Central Valley has accelerated by 31% compared to recent droughts, and has increased by a nearly a factor of 5 compared to the 60-year average. [ABSTRACT FROM AUTHOR]

Key Takeaways: Greenhouse gas mitigation and adaptation to climate change vulnerabilities should be incorporated into existing utility business and project management processes. A comprehensive climate action plan can meet and exceed legal requirements, reduce risk, achieve business process efficiencies, apply best available science, and demonstrate social responsibility. Focused internal workgroups, expert consultation, inspired management, feedback loops, breadth of application, and quantitative detail are critical to success. [ABSTRACT FROM AUTHOR]

Using treated wastewater for crop irrigation could help mitigate water scarcity. We examine the feasibility of this strategy focusing on the role of local conditions, such as the costs and benefits of alternative wastewater discharge options, and adaptability of agricultural production to water quantity and quality changes. Our approach accounts for uncertainties in the availability of natural water resources and regulatory constraints concerning wastewater discharge. Our analysis of a region in Southern California finds reuse for crop irrigation unwarranted; however, utilizing that practice to support agriculture in the region is economically inexpensive. A sensitivity analysis reveals that diversified agriculture and limitations on the safe and remote discharge of treated wastewater are strong incentives for reuse in agricultural irrigation. [ABSTRACT FROM AUTHOR]

In California, it is essential to understand the evolution of water resources in response to a changing climate to sustain its economy and agriculture and to build resilient communities. Although extreme conditions have characterized the historical hydroclimate of California, climate change will likely intensify hydroclimatic extremes by the end of the century (EoC). However, few studies have investigated the impacts of EoC extremes on watershed hydrology. We use cutting-edge global climate and integrated hydrologic models to simulate EoC extremes and their effects on the water-energy balance. We assess the impacts of projected driest, median, and wettest water years under Representative Concentration Pathway (RCP) 8.5 on the hydrodynamics of the Cosumnes River basin. Substantial changes to annual average temperature (>+2.5 ∘ C) and precipitation (>+38 %) will characterize the EoC extreme water years compared to their historical counterparts. A shift in the dominant form of precipitation, mostly in the form of rain, is projected to fall earlier. These changes reduce snowpack by more than 90 %, increase peak surface water and groundwater storages up to 75 % and 23 %, respectively, and drive the timing of peak storage to occur earlier in the year. Because EoC temperatures and soil moisture are high, both potential and actual evapotranspiration (ET) increase. The latter, along with the lack of snowmelt in the warm EoC, causes surface water and groundwater storages to significantly decrease in summer, with groundwater showing the highest rates of decrease. These changes result in more ephemeral EoC streams with more focused flow and increased storage in the mainstem of the river network during the summer. [ABSTRACT FROM AUTHOR]

Water resources management in estuarine environments for water supply and environmental protection typically requires estimates of salinity for various flow and operational conditions. This study develops and applies two novel deep learning (DL) models, a residual long short-term memory (Res-LSTM) network, and a residual gated recurrent unit (Res-GRU) model, in estimating the spatial and temporal variations of salinity. Four other machine learning (ML) models, previously developed and reported, consisting of multi-layer perceptron (MLP), residual network (ResNet), LSTM, and GRU are utilized as the baseline models to benchmark the performance of the two novel models. All six models are applied at 23 study locations in the Sacramento–San Joaquin Delta (Delta), the hub of California's water supply system. Model input features include observed or calculated tidal stage (water level), flow and salinity at model upstream boundaries, salinity control gate operations, crop consumptive use, and pumping for the period of 2001–2019. Meanwhile, field observations of salinity at the study locations during the same period are also utilized for the development of the predictive use of the models. Results indicate that the proposed DL models generally outperform the baseline models in simulating and predicting salinity on both daily and hourly scales at the study locations. The absolute bias is generally less than 5%. The correlation coefficients and Nash–Sutcliffe efficiency values are close to 1. Particularly, Res-LSTM has slightly superior performance over Res-GRU. Moreover, the study investigates the overfitting issues of both the DL and baseline models. The investigation indicates that overfitting is not notable. Finally, the study compares the performance of Res-LSTM against that of an operational process-based salinity model. It is shown Res-LSTM outperforms the process-based model consistently across all study locations. Overall, the study demonstrates the feasibility of DL-based models in supplementing the existing operational models in providing accurate and real-time estimates of salinity to inform water management decision making. [ABSTRACT FROM AUTHOR]

Energy use forecasting is crucial in balancing the electricity supply and demand to reduce the uncertainty inherent in the inter-basin water transfer project. Energy use prediction supports the reliable water-energy supply and encourages cost-effective operation by improving generation scheduling. The objectives are to develop subsequent monthly energy use predictive models for the Mokelumne River Aqueduct in California, US. Partial objectives are to (a) compare the model performance of a baseline model (multiple linear regression (MLR)) to three machine learning-based models (random forest (RF), deep neural network (DNN), support vector regression (SVR)), (b) compare the model performance of the whole system to three subsystems (conveyance, treatment, distribution), and (c) conduct sensitivity analysis. We simulate a total of 64 cases (4 algorithms (MLR, RF, DNN, SVR) x 4 systems (whole, conveyance, treatment, distribution) x 4 scenarios (different combinations of independent variables). We concluded that the three machine learning algorithms showed better model performance than the baseline model as they reflected non-linear energy use characteristics for water transfer systems. Among the three machine learning algorithms, DNN models yielded higher model performance than RF and SVR models. Subsystems performed better than the whole system as the models more closely reflected the unique energy use characteristics of the subsystems. The best case was having water supply (t), water supply (t-1), precipitation (t), temperature (t), and population (y) as independent variables. These models can help water and energy utility managers to understand energy performance better and enhance the energy efficiency of their water transfer systems. [ABSTRACT FROM AUTHOR]

The article presents a study which analyzed the arsenic concentrations in community and prison water systems (CWS) in the U.S.' Southwestern region. Other topics include access to safe drinking water as a universal human right, the trends in compliance, water quality and treatment after the adoption of the arsenic maximum contaminant level (MCL) for arsenic in the region, and the effectiveness of arsenic remediation.

Assessment of water consumption is a crucial task for irrigation management in grapevines, especially in areas with limited water resources, which is the case of California Central Valley. This study evaluated the utility of the Simple Algorithm for Evapotranspiration Retrievement (SAFER) model to estimate daily and seasonal actual evapotranspiration (ETa) using Sentinel 2 images at 10-m spatial resolution and 5-day revisit time in 3 vineyards located at two sites in California. A unique characteristic of this model is the estimation of "synthetic" temperature maps, which are used as part of the estimation of ET and energy balance. The SAFER energy balance results were validated with six eddy covariance (EC) flux towers as part of the Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). The estimated surface temperature derived from upwelling longwave radiation measurements was closely correlated with the observed sensor surface temperature with R2 higher than 0.86 for the analyzed EC towers. After performing an internal calibration, SAFER root mean square error (RMSE) values on daily ETa were between 0.64 and 0.75 mm day−1. Additionally, the seasonal ETa was estimated and compared with the EC observations showing an average R2 ranging from 0.64 to 0.52 mm/season. Spatial patterns of ETa showed variability between sites and producer management activities. The results found indicate both limitations and potential utility of SAFER for irrigation management in vineyards using daily or seasonal ETa under different irrigation treatments. [ABSTRACT FROM AUTHOR]

The potential for machine learning to answer questions of environmental science, monitoring, and regulatory enforcement is evident, but there is cause for concern regarding potential embedded bias: algorithms can codify discrimination and exacerbate systematic gaps. This paper, organized into two halves, underscores the importance of vetting algorithms for bias when used for questions of environmental science and justice. In the first half, we present a case study of using machine learning for environmental justice-motivated research: prediction of drinking water quality. While performance varied across models and contaminants, some performed well. Multiple models had overall accuracy rates at or above 90 % and F2 scores above 0.60 on their respective test sets. In the second half, we dissect this algorithmic approach to examine how modeling decisions affect modeling outcomes - and not only how these decisions change whether the model is correct or incorrect, but for whom. We find that multiple decision points in the modeling process can lead to different predictive outcomes. More importantly, we find that these choices can result in significant differences in demographic characteristics of false negatives. We conclude by proposing a set of practices for researchers and policy makers to follow (and improve upon) when applying machine learning to questions of environmental science, management, and justice., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Climate change in California is expected to alter future water availability, impacting water supplies needed to support future housing growth and agriculture demand. In groundwater-dependent regions like California's Central Coast, new land-use related water demand and decreasing recharge is already stressing depleted groundwater basins. We developed a spatially explicit state-and-transition simulation model that integrates climate, land-use change, water demand, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand in five counties along the Central Coast from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future from a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows. Results indicate that future changes in climate-driven groundwater recharge, coupled with cumulative increases in agricultural water demand, result in overall declines in future groundwater balance, with a Hot/Dry future resulting in cumulative groundwater decline in all but Santa Cruz County. Cumulative declines by 2060 are especially prominent in San Luis Obispo (-2.9 to -5.1 Bm 3 ) and Monterey counties (-6.5 to -8.7 Bm 3 ), despite limited changes in agricultural water demand over the model period. These two counties show declining groundwater reserves in a Warm/Wet future as well, while San Benito and Santa Barbara County barely reach equilibrium. These results suggest future groundwater supplies may not be able to keep pace with regional demand and declining climate-driven recharge, resulting in a potential reduction in water security in the region. However, our county-scale projections showed new housing and associated water demand does not conflict with California's groundwater sustainability goals. Rather, future climate coupled with increasing agricultural groundwater demand may reduce water security in some counties, potentially limiting available groundwater supplies for new housing., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Tamara Wilson reports financial support was provided by California Strategic Growth Council. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

In California, after three decades of water market development, volume of water traded in the market remains lower than desired. Total volume of water traded annually accounts for just around 2%–5% of total water used. Vast difference in the marginal values of water in agriculture and urban or environmental uses suggest that significant economic gains can be achieved by easing the barriers to trading water across sectors. This paper draws attention to the role of state and local policies in restricting water transfers. Using novel, purpose-built, satellite-mapped data of a sample of water-trading irrigation districts in California, this paper shows how districts' water transfer decisions are made in response to conflicting pressures of drought-induced high-water demand against the need to support the agricultural economy and the environment in water-exporting areas. The time period under study is 1991–2019, which spans the entire history of water market activity in California from inception to maturity, and helps illustrate the role of changing local and state policies on irrigation districts' water market participation. Despite policy-induced restrictions, this paper shows that sample irrigation districts are highly responsive to demand in water-buying areas, have increased the volume and frequency of water transfers, particularly in the last decade. This paper highlights important research and policy questions about the distribution of economic and environmental effects of water transfers in source area communities that could affect the future potential of water markets in easing structural and drought-induced water scarcity in California. [ABSTRACT FROM AUTHOR]

Highly mobile species, such as migratory birds, respond to seasonal and interannual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species‐distribution models typically rely on long‐term average habitat conditions, mostly because large‐extent, temporally resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real‐time environmental data increases the predictive ability of distribution models, relative to using long‐term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16 days), and 17‐year long‐term average surface water data. Using abundance‐weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. Although modeling with both real‐time and long‐term average data provided good fit to withheld validation data (the area under the receiver operating characteristic curve, or AUC, averaged between 0.79 and 0.89 for all taxa), there were small differences in model performance. The best models incorporated long‐term average conditions and spatial pattern information for real‐time flooding (e.g., perimeter‐area ratio of real‐time water bodies). There was not a substantial difference in the performance of real‐time and long‐term average data models within time periods when real‐time surface water differed substantially from the long‐term average (specifically during drought years 2013–2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real‐time data may ultimately be best for guiding dynamic, adaptive conservation actions, whereas models based on long‐term averages may be more helpful for guiding permanent wetland protection and restoration. [ABSTRACT FROM AUTHOR]

Currently, there are over 62,000 petroleum‐contaminated sites undergoing remediation in the United States. Understanding attenuation rates for dissolved contaminant concentrations and factors that influence these rates is important to effectively manage these sites and move them towards closure. The GeoTracker and California Department of Public Health databases provide more than 15 years of groundwater monitoring results for tens of thousands of monitoring wells and public water supply wells in California. First‐order maximum concentration attenuation rate constants (kc‐max) were determined for sites with 5 or more years of monitoring data over the time period of 2002 to 2017. kc‐max values vary by constituent; across more than 5000 sites, the median kc‐max value ranged from 0.30 year−1 for methyl tert‐butyl ether (MTBE) (half‐life of 2.3 years) to 0.076 year−1 for naphthalene (half‐life of 9.1 years). A focused evaluation of remediation technologies at 50 petroleum release sites indicates that dissolved contaminant attenuation typically increased during the time period of remediation. Further, the analysis indicates that biodegradation is an important attenuation process even during periods without active remediation (i.e., periods of natural attenuation). There was little evidence of petroleum and fuel oxygenate impacts to public water supply wells. Overall, the analyses indicate that both remediation and natural biodegradation contribute to site remediation and that these attenuation processes have been sufficient to prevent widespread impacts to public water supply wells. [ABSTRACT FROM AUTHOR]

As the world's fifth‐largest economy entity, California (CA) is vulnerable to climate changes especially the magnitude of winter (wet season) precipitation that is closely linked to regional drought severity, vegetation growth, and wildfire activities. However, the fate of CA precipitation in the future remains highly uncertain, for example, the state‐of‐the‐art Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) projected −3% to +42% and −27% to +63% precipitation changes in northern and central‐southern CA, respectively, under the high greenhouse gas (GHG) emission scenario (SSP585). In this work, we applied the Pareto optimality concept and used observed teleconnection patterns (causal relationships between ocean regions and CA precipitation) to mechanistically constrain CMIP6 projected CA precipitations. We estimated that precipitation will robustly increase by 0.4–1.3 mm d−1 (10–34%) over northern CA and increase by 0.1–0.5 mm d−1 (7–32%) over central‐southern CA by the end of the 21st century compared with present‐day. Up to 71% of ESM projection uncertainties were reduced mainly due to the strong and consistent causal relationship between North American west coast sea level pressure and CA precipitation in both observations and CMIP6 models. Our results suggest that teleconnection patterns are powerful mechanistic constraints that can help explain and reduce uncertainties in ESM projections. Plain Language Summary: California (CA) has surpassed the United Kingdom and achieved the world's fifth‐largest economy since 2018. However, ongoing climate change poses unprecedented challenges for California's economic growth through declining water resources, intensifying regional drought, and exacerbating wildfire activities. Winter precipitation (CA wet season) will play an increasingly important role in combating the regional dryout. Therefore, it is critically important to understand the future fate of CA winter precipitation. This study relied on the state‐of‐the‐art Earth system model projections and observed teleconnection patterns to estimate the future CA precipitation under a high emission scenario. We found that CA winter precipitation will robustly increase by up to ∼34% till the end of 21st century. The enhanced precipitation will provide necessary water resources and more importantly will provide natural solutions to the environmental disturbances (e.g., wildfire). Key Points: Coupled Model Intercomparison Project Phase 6 models projected wide ranges of precipitation changes over California, which do not even agree on the sign of changesUp to 71% of Earth system model projection uncertainties could be robustly reduced by using observed teleconnection patternsCalifornia precipitation will increase by 10–34% and 7–32% over northern and southern regions by the end of the 21st century [ABSTRACT FROM AUTHOR]

Water scarcity is a growing problem around the world, and regions such as California are working to develop diversified, interconnected, flexible, and resilient water supply portfolios. To meet these goals, water utilities, irrigation districts, and other organizations will need to cooperate across scales to finance, build, and operate shared water supply infrastructure. However, planning studies to date have generally focused on partnership‐level outcomes (i.e., highly aggregated cost‐benefit analyses), while ignoring the heterogeneity of benefits, costs, and risks across the individual partners. This study contributes an exploratory modeling analysis that tests thousands of alternative infrastructure partnerships in the Central Valley of California, using a daily scale simulation model (CALFEWS) to evaluate the effects of new infrastructure on individual water providers. The viability of conveyance and groundwater banking investments are as strongly shaped by partnership design choices (i.e., which water providers are participating, and how is the project's debt distributed?) as by extreme hydrologic conditions (i.e., floods and droughts). Importantly, most of the analyzed partnerships yield highly unequal distributions of water supply and financial risks across the partners, so that only 8% of the partnerships explored are capable of providing water to each partner for under $200/ML. Partnership viability is especially rare in the absence of groundwater banking facilities (1%), or under dry hydrologic conditions (1%), even under explicitly optimistic assumptions regarding climate change. Given these results, we outline several major policy implications for institutionally complex regions such as California, which are currently investing heavily in cooperative approaches to resilient water portfolio design. Key Points: Water portfolio planning frameworks need to account for heterogeneity across participating partners, not just "average" performanceExploratory modeling shows how most infrastructure partnerships are highly unequal in their water deliveries and financial risksViable partnership design is shown to be especially difficult for dry hydrologic scenarios, or when building conveyance without storage [ABSTRACT FROM AUTHOR]

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

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An inconsistent relationship between precipitation and runoff has been observed between drought and non-drought periods, with less runoff usually observed during droughts than would be expected based solely on precipitation deficit. Predictability of these shifts in the precipitation–runoff relationship is still challenging, largely because the underlying hydrologic mechanisms are poorly constrained. Using 30 years of data for 14 basins in California, we show how the Budyko framework can be leveraged to decompose shifts in precipitation versus runoff during droughts into "regime" shifts, which result from changes in the aridity index along the same Budyko curve, and "partitioning shifts", which imply a change in the Budyko parameter ω and thus in the relationship among water balance components that governs partitioning of available water. Regime shifts are primarily due to measurable interannual changes in precipitation or temperature, making them predictable based on drought conditions. Partitioning shifts involve further nonlinear and indirect catchment feedbacks to drought conditions and are thus harder to predict a priori. We show that regime shifts dominate changes in absolute runoff during droughts but that gains or losses due to partitioning shifts are still significant. Low aridity, high baseflow, a shift from snow to rain, and resilience of high-elevation runoff correlate with higher annual runoff during droughts than would be predicted by the precipitation–runoff ratio during non-drought years. Differentiating between these shifts in the precipitation–runoff relationship using a Budyko approach will help water resource managers, particularly in arid, drought-prone regions, to better project runoff magnitudes during droughts based on available climate data and, furthermore, understand under what circumstances and to what extent their forecasts may be less reliable due to nonlinear basin–climate feedbacks. [ABSTRACT FROM AUTHOR]