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Floods caused by atmospheric rivers and wildfires fanned by Santa Ana winds are common occurrences in California with devastating societal impacts. In this work, we show that winter weather variability in California, including the occurrence of extreme and impactful events, is linked to four atmospheric circulation regimes over the North Pacific Ocean previously named and identified as the “NP4 modes”. These modes come in and out of phase with each other during the season, resulting in distinct weather patterns that recur throughout the historical record. Some phase combinations favor atmospheric river landfalls and extreme daily or multi-day precipitation, while other phase combinations favor anomalously hot weather and drying Santa Ana wind conditions over Southern California. This historical perspective of atmospheric circulation and impacts over 70 years reveals that weather patterns are changing in a way that enhances wildfire hazard in California, while the frequency of weather patterns linked to historical floods is not diminishing. These changes highlight the rising hazards of cascading weather extremes in California’s present and future.

Though fine particulate matter (PM

This cross-sectional study quantifies exposure to wildfire particulate matter less than 2.5 μm among schoolchildren in California.

Epidemiological studies on the detrimental health impacts of exposure to fine particulate matter (PM2.5) from different sources of emission can inform regulatory policy and identify vulnerable communities. Though PM2.5 has decreased in the U.S. in the two past decades, the increasing frequency and severity of wildfires contribute to episodically impair air quality in wildfire-prone regions and beyond. Monitoring air quality extensively is challenging. Since government-operated monitors are sparsely located across California and the U.S., several regions and populations remain unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on gathering large amounts of data, such as satellite-derived aerosol properties and meteorological variables. and direct use of low-cost air sensor measurements that may be associated with substantial uncertainty Furthermore, modelling wildfire-specific PM2.5 is often based on chemical transport model predictions, which results in highly computationally intensive efforts. Our study used an ensemble model that integrated multiple machine learning algorithms and a large set of predictor variables to estimate daily PM2.5 at the ZIP code level, a relevant spatio-temporal resolution for epidemiological and public health studies. Our models achieved comparable results to previous machine learning studies for PM2.5 prediction, but avoided processing larger, computationally intensive datasets. In addition, we use machine learning to estimate the wildfire-specific PM2.5 concentrations through a novel multiple imputation approach.

Rationale: There is significant evidence of increased healthcare utilization from cardiopulmonary causes in adults from exposure to wildfire smoke, but evidence in pediatric age groups is limited.Objectives: To quantify and examine the healthcare utilization effects of the December 2017 Lilac Fire in San Diego County among pediatric patients at the Rady Children's Hospital (RCH) emergency department and urgent care (UC) clinics.Methods: Using data from 2011 to 2017, including data on daily particulate matter

Precipitation in California is projected to become more volatile: less frequent but more extreme as global warming pushes midlatitude frontal cyclones further poleward while bolstering the atmospheric rivers (ARs), which tend to produce the region's extreme rainfall. Pollutant accumulation and delivery to coastal waters can be expected to increase, as lengthening dry spells will be increasingly punctuated by more extreme precipitation events. Coastal pollution exposes human populations to high levels of fecal bacteria and associated pathogens, which can cause a variety of health impacts. Consequently, studying the impact of atmospheric rivers as the mechanism generating pulses of water pollution in coastal areas is relevant for public health and in the context of climate change. We aimed to quantify the links between precipitation events and water quality in order to explore meteorological causes as first steps toward effective early warning systems for the benefit of population health in California and beyond. We used historical gridded daily precipitation and weekly multiple fecal bacteria indicators at ~500 monitoring locations in California's coastal waters to identify weekly associations between precipitation and water quality during 2003-09 using canonical correlation analysis to account for the nested/clustered nature of longitudinal data. We then quantified, using a recently published catalog of atmospheric rivers, the proportion of coastal pollution events attributable to ARs. Association between precipitation and fecal bacteria was strongest in Southern California. Over two-thirds of coastal water pollution spikes exceeding one standard deviation were associated with ARs. This work highlights the importance of skillful AR landfall predictions in reducing vulnerability to extreme weather improving resilience of human populations in a varying and changing climate. Quantifying the impacts of ARs on waterborne diseases is important for planning effective preventive strategies for public health.

BACKGROUND AND AIM: The 2020 wildfire season broke records for the most active fire year on the West Coast, resulting in the worst air quality observed in decades. Concurrently, the public health t...

BACKGROUND AND OBJECTIVES: Exposure to airborne fine particles with diameters ≤2.5 μm (PM2.5) pollution is a well-established cause of respiratory diseases in children; whether wildfire-specific PM2.5 causes more damage, however, remains uncertain. We examine the associations between wildfire-specific PM2.5 and pediatric respiratory health during the period 2011–2017 in San Diego County, California, and compare these results with other sources of PM2.5. METHODS: Visits to emergency and urgent care facilities of Rady’s Children Hospital network in San Diego County, California, by individuals (aged ≤19 years) with ≥1 of the following respiratory conditions: difficulty breathing, respiratory distress, wheezing, asthma, or cough were regressed on daily, community-level exposure to wildfire-specific PM2.5 and PM2.5 from ambient sources (eg, traffic emissions). RESULTS: A 10-unit increase in PM2.5 (from nonsmoke sources) was estimated to increase the number of admissions by 3.7% (95% confidence interval: 1.2% to 6.1%). In contrast, the effect of PM2.5 attributable to wildfire was estimated to be a 30.0% (95% confidence interval: 26.6% to 33.4%) increase in visits. CONCLUSIONS: Wildfire-specific PM2.5 was found to be ∼10 times more harmful on children’s respiratory health than PM2.5 from other sources, particularly for children aged 0 to 5 years. Even relatively modest wildfires and associated PM2.5 resolved on our record produced major health impacts, particularly for younger children, in comparison with ambient PM2.5.

Santa Ana winds (SAWs) are associated with anomalous temperatures in coastal Southern California (SoCal). As dry air flows over SoCal's coastal ranges on its way from the elevated Great Basin down to sea level, all SAWs warm adiabatically. Many but not all SAWs produce coastal heat events. The strongest regionally averaged SAWs tend to be cold. In fact, some of the hottest and coldest observed temperatures in coastal SoCal are linked to SAWs. We show that hot and cold SAWs are produced by distinct synoptic dynamics. High-amplitude anticyclonic flow around a blocking high pressure aloft anchored at the California coast produces hot SAWs. Cold SAWs result from anticyclonic Rossby wave breaking over the northwestern U.S. Hot SAWs are preceded by warming in the Great Basin and dry conditions across the Southwestern U.S. Precipitation over the Southwest, including SoCal, and snow accumulation in the Great Basin usually precede cold SAWs. Both SAW flavors, but especially the hot SAWs, yield low relative humidity at the coast. Although cold SAWs tend to be associated with the strongest winds, hot SAWs tend to last longer and preferentially favor wildfire growth. Historically, out of large (> 100 acres) SAW-spread wildfires, 90% were associated with hot SAWs, accounting for 95% of burned area. As health impacts of SAW-driven coastal fall, winter and spring heat waves and impacts of smoke from wildfires have been recently identified, our results have implications for designing early warning systems. The long-term warming trend in coastal temperatures associated with SAWs is focused on January-March, when hot and cold SAW frequency and temperature intensity have been increasing and decreasing, respectively, over our 71-year record.Supplementary informationThe online version contains supplementary material available at 10.1007/s00382-021-05802-z.

Wildfires are becoming more frequent and destructive in a changing climate. Fine particulate matter, PM2.5, in wildfire smoke adversely impacts human health. Recent toxicological studies suggest that wildfire particulate matter may be more toxic than equal doses of ambient PM2.5. Air quality regulations however assume that the toxicity of PM2.5 does not vary across different sources of emission. Assessing whether PM2.5 from wildfires is more or less harmful than PM2.5 from other sources is a pressing public health concern. Here, we isolate the wildfire-specific PM2.5 using a series of statistical approaches and exposure definitions. We found increases in respiratory hospitalizations ranging from 1.3 to up to 10% with a 10 μg m−3 increase in wildfire-specific PM2.5, compared to 0.67 to 1.3% associated with non-wildfire PM2.5. Our conclusions point to the need for air quality policies to consider the variability in PM2.5 impacts on human health according to the sources of emission., Recent toxicological studies suggest that wildfire particulate matter may be more toxic than equal doses of ambient PM2.5. Here, the authors show that even for similar exposure levels, PM2.5 from wildfires is considerably more dangerous for respiratory health at the population level.

Supplemental Digital Content is available in the text., Background: Wildfire smoke adversely impacts respiratory health as fine particles can penetrate deeply into the lungs. Epidemiological studies of differential impacts typically target population subgroups in terms of vulnerability to wildfire smoke. Such information is useful to customize smoke warnings and mitigation actions for specific groups of individuals. In addition to individual vulnerability, it is also important to assess spatial patterns of health impacts to identify vulnerable communities and tailor public health actions during wildfire smoke events. Methods: We assess the spatiotemporal variation in respiratory hospitalizations in San Diego County during a set of major wildfires in 2007, which led to a substantial public health burden. We propose a spatial within-community matched design analysis, adapted to the study of wildfire impacts, coupled with a Bayesian Hierarchical Model, that explicitly considers the spatial variation of respiratory health associated with smoke exposure, compared to reference periods before and after wildfires. We estimate the signal-to-noise ratio to ultimately gauge the precision of the Bayesian model output. Results: We find the highest excess hospitalizations in areas covered by smoke, mainly ZIP codes contained by and immediately downwind of wildfire perimeters, and that excess hospitalizations tend to follow the distribution of smoke plumes across space (ZIP codes) and time (days). Conclusions: Analyzing the spatiotemporal evolution of exposure to wildfire smoke is necessary due to variations in smoke plume extent, particularly in this region where the most damaging wildfires are associated with strong wind conditions.

Wildfires alter nitrogen (N) cycling in Mediterranean-type ecosystems, resetting plant and soil microbial growth, combusting plant biomass to ash, and enhancing N availability in the upper soil layer. This ash and soil N pool (that is, wildfire N) is susceptible to loss from watersheds via runoff and leaching during post-fire rains. Plant and soil microbial recovery may mitigate these losses by sequestering N compounds in new biomass, thereby promoting landscape N retention in N-limited chaparral ecosystems. We investigated the relative balance between wildfire N loss, and plant and soil microbial N uptake and stream N export for an upland chaparral watershed in southern California that burned (61%) in a high-intensity wildfire in 2009 by using a combination of stream, vegetation, soil microbial, and remote sensing analyses. Soil N in the burn scar was 440% higher than unburned soil N in the beginning of the first post-fire wet season and returned within 66 days to pre-fire levels. Stream N export was 1480% higher than pre-fire export during the first post-fire rain and returned within 106 days over the course of the following three rainstorms to pre-fire levels. A watershed-scale N mass balance revealed that 52% of wildfire N could be accounted for in plant and soil microbial growth, whereas 1% could be accounted for in stream export of dissolved nitrogen.

Key Points Santa Ana winds have a predominant ventilation effect as background PM2.5 is transported offshore from highly polluted areasA polluting effect occurs when SAWs spread smoke PM2.5 from wildfires inland toward the coastal regionStatistical approaches that relate surface wind and PM2.5 over space and time can help in identifying wildfire PM2.5, Fine particulate matter (PM2.5) raises human health concerns since it can deeply penetrate the respiratory system and enter the bloodstream, thus potentially impacting vital organs. Strong winds transport and disperse PM2.5, which can travel over long distances. Smoke from wildfires is a major episodic and seasonal hazard in Southern California (SoCal), where the onset of Santa Ana winds (SAWs) in early fall before the first rains of winter is associated with the region's most damaging wildfires. However, SAWs also tend to improve visibility as they sweep haze particles from highly polluted areas far out to sea. Previous studies characterizing PM2.5 in the region are limited in time span and spatial extent, and have either addressed only a single event in time or short time series at a limited set of sites. Here we study the space‐time relationship between daily levels of PM2.5 in SoCal and SAWs spanning 1999–2012 and also further identify the impact of wildfire smoke on this relationship. We used a rolling correlation approach to characterize the spatial‐temporal variability of daily SAW and PM2.5. SAWs tend to lower PM2.5 levels, particularly along the coast and in urban areas, in the absence of wildfires upwind. On the other hand, SAWs markedly increase PM2.5 in zip codes downwind of wildfires. These empirical relationships can be used to identify windows of vulnerability for public health and orient preventive measures.

Supplemental Digital Content is available in the text., Background: Wildfire smoke harms health. We add to this literature by evaluating the health effects of California’s 2018 Carr Fire and preceding wildfire seasons in Shasta County. Methods: With data from the Shasta County Health and Human Services Agency, we examined the link between weekly wildfire fine particulate matter (PM2.5) exposure estimated using a spatiotemporal multiple imputation approach and emergency department (ED) visits and mortality using time-series models that controlled for temporal trends and temperature. Results: Between 2013 and 2018, Shasta County experienced 19 weeks with average wildfire PM2.5 ≥5.5 μg/m3 (hereafter, “high wildfire PM2.5 concentration”). Among all Shasta County Zip Code Tabulation Areas (ZCTAs; n = 36), we detected no association between high wildfire PM2.5 concentrations and respiratory or circulatory disease-related ED visits or mortality. Subsequent analyses were confined to valley ZCTAs (n = 11, lower elevation, majority of population, worse air quality in general). In valley ZCTAs, high wildfire PM2.5 was associated with a 14.6% (95% confidence interval [CI] = 4.2, 24.9) increase in same-week respiratory disease-related ED visits but no increase in the subsequent 2 weeks nor on circulatory disease-related mortality or ED visits or all-cause mortality. Two weeks after high wildfire PM2.5 weeks, respiratory disease-related deaths decreased (–31.5%, 95% CI = –64.4, 1.5). The 2018 Carr Fire appeared to increase respiratory disease-related ED visits by 27.0% (95% CI = 4.0, 50.0) over expectation and possibly reduce circulatory disease-related deaths (–18.2%, 95% CI = –39.4, 2.9). Conclusions: As climate change fuels wildfire seasons, studies must continue to evaluate their health effects, particularly in highly exposed populations.

Currently, many water quality variables can be automatically monitored at subdaily frequencies, but most environmental datasets still rely heavily on comparatively low-frequency (e.g., monthly) sampling campaigns. Taking advantage of the long-term ecological data available from polymictic Lake Muggelsee (Germany) at weekly temporal scales, we tested whether dynamic factor analysis (DFA), a dimension-reduction technique especially designed for time series with data gaps, was able to reproduce the same underlying patterns if the resolution of the observations was artificially reduced from weekly to once every 2 or 4 weeks (2-weekly or 4-weekly, respectively) and whether the same results were obtained for different variables (water temperature, water chemistry, and plankton). As expected, the reduction in data resolution and the addition of gaps increased the amount of variance in our case studies; however, water temperature patterns, which generally vary only slowly if considered at broader temporal scales, were represented well in the 4-weekly data model. By contrast, total algal biomass and nutrient dynamics, which usually fluctuate more rapidly, were not. We further assessed the representativeness of spot samples taken by selection at 4-weekly and seasonal intervals compared to samples obtained by averaging the weekly data over these 2 intervals. In this case, the resulting observation variance was more pronounced in DFA models of the samples taken by selection than of those obtained by averaging. The introduction of additional artificial gaps in one univariate and one multivariate model decreased the amount of information that could be explained by the pattern(s) extracted by DFA. Overall, we found that a too low sampling frequency could be a limiting factor when studying interactions and responses to changing conditions in aquatic systems, particularly when biological variables are involved

In-stream nutrient retention results from the interaction between hydrological and biogeochemical processes involved in downstream transport. While hydrological processes set the opportunity for nutrient retention to occur, metabolic activity and abiotic processes determine the potential biogeochemical reactivity of streams. Yet, a comprehensive assessment of the relevance of hydrological opportunity versus biogeochemical reactivity on the variability of nutrient retention across streams is still missing. We compiled an extensive data set of existing studies on nutrient retention for ammonium, nitrate, and soluble reactive phosphorus to explore how variability in hydrological opportunity and biogeochemical reactivity explain nutrient retention. We quantified the relative contribution of hydrological opportunity and biogeochemical reactivity to the observed variability in stream nutrient retention using a linearization of the retention equation, which allows for an exact partitioning of the variance associated with residence time and nutrient uptake rate. Finally, we explored potential patterns of nutrient retention along the river network resulting from the interaction between hydrological opportunity and biogeochemical reactivity. Our results show that biogeochemical reactivity has a more relevant role on nutrient retention variability than previously thought, explaining over 66% of the variability in nutrient retention. Among the studied nutrients, retention variability of ammonium was the most subjected to biogeochemical reactivity controls. Furthermore, our results provide insights on controls of longitudinal patterns of nutrient retention along river networks, indicating that retention per unit length for the three nutrients will most likely decrease from headwaters to river mouth because of a decrease in water residence time., R.M. participation was funded by MINECO through the project REMEDIATION (CGL2014-57215-C4-2-R). S.B. work was funded by MINECO through the project NICUS (CGL-2014-55234-JIN). E.M. participation was funded through the project MEDSOUL (CGL2014-59977-C3-2-R). D.v.S. was funded by MINECO through the project DIVERSION (CGL2016-77487-R) and by the Basque Government through a Grant for Research Groups of the Basque University System (IT-951-16).

The authors would like to thank the Spanish Ministry of Economy and Competitiveness for its financial support through the project SCARCE (Consolider-Ingenio 2010 CSD2009-00065)

Attributing changes in river water quality to specific factors is challenging because multiple factors act at different temporal and spatial scales, and it often requires examining long-term series of continuous data. But data consistency is sometimes hindered by the lack of observations of relevant water quality variables and the low and uneven sampling frequency that characterize many water quality monitoring schemes. Nitrate and dissolved phosphate concentration time-series (1980–2011) from 50 sampling stations across a large Mediterranean river basin were analyzed to disentangle the role of hydrology, land-use practices, and global climatic phenomena on the observed nutrient patterns, with the final aim of understanding how the different aspects of global change affected nutrient dynamics in the basin. Dynamic Factor Analysis (DFA) provided the methodological framework to extract underlying common patterns in nutrient time-series with missing observations. Using complementary methods such as frequency and trend analyses, we sought to further characterize the common patterns and identify the drivers behind their variability across time and space. Seasonal and other cyclic patterns were identified, as well as trends of increase or decrease of nutrient concentration in particular areas of the basin. Overall, the impact of global change, which includes both climate change and anthropogenic impacts, on the dynamics of nitrate concentration across the study basin was found to be a multifaceted process including regional and global factors, such as climatic oscillations and agricultural irrigation practices, whereas impacts on phosphate concentration seemed to depend more on local impacts, such as urban and industrial activities, and less on large-scale factors This work was supported by Spanish Ministry of Economy and Competitiveness through the project SCARCE (Consolider-Ingenio 2010 CSD2009-00065) and by a Doctoral Grant (FI-DGR 2012) from the Catalan Government

The Llobregat as well as other streams and rivers elsewhere transports and transforms nutrients, regulating nutrient export downstream. The most common approach is to study nutrient in-stream processes at the reach level, but a correct understanding of their role on nutrient cycling in the watershed requires taking the perspective of the entire river network. Considering the complexity and the spatial scale of the Llobregat River network, modeling tools are useful to determine the sources and the processes by which nutrients are transported at the basin scale. In this chapter, we apply a hybrid process-based and statistical model (SPARROW, SPAtially Referenced Regression On Watershed attributes) to the Llobregat River basin in order to estimate the annual nitrate and phosphate loads that reach the drainage network. The model allows emphasizing the description of the in-stream processes that affect nutrient transport and retention in the Llobregat, and it might be useful in other impaired rivers as well. Although forests and grasslands are the predominant land cover types in the Llobregat basin, activities related to the presence of agricultural land and human agglomerations cause impairment in water quality and alter the natural flow. Increased exported nutrient load and decreased in-stream nutrient removal coincide in the downstream direction, probably due to the significant chemical and geomorphological changes in the lower Llobregat River basin.

The significance of nutrient inputs at the watershed scale is best expressed in terms of in-stream processes, compared to evaluating simple field measurements of nutrient inputs. Modeling tools are necessary to consider the complexity of river networks in the determination of the sources and processes by which nutrients are transported at the watershed scale. Mediterranean rivers are potentially vulnerable to climate change (decrease in precipitation and increase of extreme events), and identifying and quantifying nutrient pollution sources and their spatial distribution can improve water resource management at the watershed scale. We apply a hybrid process-based and statistical model (SPARROW, spatially referenced regression on watershed attributes) to a largely disturbed Mediterranean watershed in NE Spain in order to estimate the annual nitrate and phosphate loads reaching the drainage network. The model emphasized the contribution of in-stream processes in nutrient transport and retention, and the inter-annual (7 years) effects of hydrological variability on the export of nutrients from the landscape to water bodies. Although forest and grassland land cover types predominate, agricultural activities and human agglomerations were significant sources of nutrient enrichment. Nutrient flux apportionment was also linked to inter-annual hydrological variability. Exported nutrient load increased in the downstream direction and coincided with decreased in-stream nutrient removal, probably worsened by the significant chemical and geomorphological impairment found in the lower parts of the watershed Authors would like to thank the Spanish Ministry of Economy and Competitiveness for its financial support through the projects SCARCE (Consolider-Ingenio 2010 CSD2009-00065) and CARBONET (CGL2011-30474-C02-01), as well as two anonymous reviewers for their valuable comments on an earlier version of the manuscript. R.A. benefited from a grant (FI-DGR 2012) from the Catalan Government