Century-Scale Changes in Dissolved Oxygen, Temperature, and Salinity in Puget Sound

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Leeson, Kathryn M. Hewett, Alexander R. Horner-Devine, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8565389/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Over the last century, many coastal ocean regions have experienced a decrease in dissolved oxygen (DO) concentrations. In this work we consider long-term changes in DO in Puget Sound, a temperate, fjordal, urbanized estuary in Washington State, USA based on nearly 100 years of water column profile data. We observe warming of 1.5°C/century, consistent with warming found throughout the Salish Sea region and similar to coastal ocean and local atmospheric warming. We observe that bottom salinity is increasing at a low rate. Finally, we find that bottom DO is declining at a rate of about 0.6 mg/L/century in Main basin, the largest, central section of Puget Sound. Changes in DO solubility associated with the observed increase in water temperature can account for approximately 50% of the observed DO loss. In Puget Sound’s distal terminal inlets where hypoxic conditions more commonly occur, trends in DO are generally small and the variability in DO is high, obscuring trends there. Documenting long-term changes in estuary water properties is imperative to inform management decisions for ecosystem health. Puget Sound dissolved oxygen estuaries long-term trends Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Dissolved oxygen (DO) loss in marine environments is a global concern (Breitburg et al., 2018 ; Diaz & Rosenburg, 1995 ; Fennel & Testa, 2019 ). Decreasing DO can lead to hypoxia, nominally defined as DO concentration less than 2 mg/L, which can harm species and ecosystems. Even if the hypoxia threshold is not crossed, some species may be negatively impacted by the duration and extent of low oxygen conditions (Vaquer-Sunyer & Duarte, 2008 ). DO loss in estuaries is particularly concerning given their economic and environmental importance and connection to both human civilization and the open ocean (Barbier et al., 2011 ; Levin & Breitburg, 2015 ). More concerning, estuaries may be losing oxygen at a faster rate than the open ocean, fueling the need for a more robust understanding of DO change in these systems (Gilbert et al., 2010 ). DO concentration in estuaries is influenced by both physical and biogeochemical processes. Air-sea gas exchange establishes an equilibrium DO concentration in surface waters that varies given wind and water properties at the surface, such as its temperature (Garcia & Gordon, 1992 ; Kanwisher, 1963 ). Vertical mixing, often driven by tides or wind, distributes DO deeper into the water column (Geyer et al., 2008 ; Scully, 2010a , 2010b ). Primary productivity in the photic zone produces DO via photosynthesis. Respiration of organic matter depletes DO as organic matter sinks through the water column (Howarth et al., 2011 ). The duration over which these biological processes persist in a given water mass depends on the residence time of the system. In estuaries, the residence time is primarily determined by the estuarine exchange flow, wherein density gradients between the landward and seaward ends of the estuary and the movement of tides establish a deep, landward flow and a surface, seaward flow (Hansen & Rattray, 1965 ; MacCready & Geyer, 2010 ; MacCready & Geyer, 2024 ; MacCready et al., 2021 ; Sutherland et al., 2011 ). Ultimately, the oxygen concentration in an estuary and the processes that may impact it have natural variability on diurnal to decadal timescales (Alin et al., 2024 ; Babson et al., 2010 ; Deppe et al., 2017 ; Ebbesmeyer et al., 1989 ; Janzen et al., 1991 ; Lee & Lwiza, 2008 ; Masson, 2010 ; Moore, Mantua, Newton, et al., 2008 ; O'Donnell et al., 2008 ; Panigrahi et al., 2013 ; Scully, 2010b ; Scully et al., 2022 ). Anthropogenic impacts on DO decline include eutrophication and warming atmospheric temperatures. Eutrophication has been observed, for example, in northern temperate estuaries such as Chesapeake Bay, Long Island Sound, and the Baltic Sea (Carstensen et al., 2014 ; Fennel & Testa, 2019 ; Howarth et al., 2011 ; Kemp et al., 2009 ; Mackas & Harrison, 1997 ; Parker & O’Reilly, 1991 ). Warming due to anthropogenic greenhouse gas emissions may have long-term ramifications on estuaries (Du et al., 2018 ; IPCC, 2023 ; Ni et al., 2019 ; Steffen et al., 2011 ) because increasing ocean surface temperatures reduce the solubility of oxygen at the surface, suppress mixing via enhanced stratification, and may increase the rate of cellular respiration (Deutsch et al., 2011 ; Keeling et al., 2010 ). In the Baltic Sea, warming and decreasing solubility is thought to be partially responsible for increasing hypoxic extent (Carstensen et al., 2014 ). In the Chesapeake Bay, the same effect is projected under future climate scenarios (Irby et al., 2018 ; Najjar et al., 2010 ), accounting for about 50% of projected decline in DO (Ni et al., 2019 ). Puget Sound is a fjordal, urbanized estuary in Washington State, USA and the southernmost arm of the Salish Sea, connecting to the Northeast Pacific Ocean via the Strait of Juan de Fuca (Fig. 1 a). The Sound is deep and narrow with shallow entrance sills, many branching terminal inlets, diverse topography including headlands and islands, and large energetic tides (Bretschneider et al., 1985 ; Cannon, 1983 ; Ebbesmeyer & Barnes, 1980 ; Geyer & Cannon, 1982 ; MacCready et al., 2021 ). It is commonly divided into four subregions. Main Basin is the largest and deepest basin and is the central portion of Puget Sound, connected to all other basins, and to the Strait of Juan de Fuca via the glacial sill Admiralty Inlet (Fig. 1 b). To its south connected via the Tacoma Narrows sill, South Sound is the shallowest basin and has many branching inlets (Fig. 1 b). Whidbey Basin and Hood Canal both connect to Main Basin near its northern end. Whidbey Basin is characterized by significant freshwater input from the Skagit River (Fig. 1 b). Hood Canal is long, narrow, and deep, terminating in a shallow inlet called Lynch Cove (Alin et al., 2024 ) (Fig. 1 b). Like other northern temperate estuaries, Puget Sound has experienced a surge of urbanization and development in the last century, raising concerns about DO loss fueled by anthropogenic nutrient loading from wastewater treatment plant effluent or agricultural runoff (Fennel & Testa, 2019 ; Howarth et al., 2011 ; Takesue, 2011 ). Evidence for this mechanism in Puget Sound is unclear, though modeling has shown that a reduction of anthropogenic land-based loads may decrease hypoxia occurrence (Khangaonkar et al., 2018 ). However, historical analyses, most recently conducted in the 1970s, did not find long-term DO loss nor system-wide correlation with anthropogenic nutrient pollution (Collias & Lincoln, 1977 ; Duxbury, 1975 ). Mackas & Harrison ( 1997 ) used a nitrogenous nutrient budget to show that the Salish Sea is unlikely to experience large-scale eutrophication due to the high ambient nitrogen concentration and the fact that the largest source of limiting nitrogen species by far is the Pacific Ocean, accounting for approximately 85% of the total dissolved inorganic nitrogen inputs in Puget Sound. Xiong et al. ( 2025 ) and Beutel et al. ( 2025 ) modeled Salish Sea nitrate influx and found that the major drivers of nitrate variability stemmed from offshore variability. Steinberg et al. ( 2010 ) focused on the effect of watershed nutrients at Hood Canal, which has recurrent hypoxia. They identified that in the euphotic zone, entrainment of marine water accounts for approximately 98% of the nitrogen loading, demonstrating again the dominant influence of natural nitrogen sources compared to anthropogenic nutrient pollution. Although Puget Sound consists of a complex network of basins and inlets, large tides and freshwater inflows contribute to transport processes that redistribute nutrients, salt, heat and DO through the system (MacCready et al., 2021 ). Changes in temperature and salinity may also modify DO directly; importantly, temperature strongly influences air-sea gas exchange (Garcia & Gordon, 1992 ). Several studies link variability in water temperature and salinity to offshore climate fluctuations, river flow, tide stage, and local surface temperatures (Ebbesmeyer et al., 1989 ; Janzen et al., 1991 ; Moore, Mantua, Kellogg, et al., 2008). Salt and heat transport are both primarily modulated by estuarine exchange flow (MacCready & Geyer, 2010 ; Xiong et al., 2025 ). Long-term warming trends are observed in the Straits of Georgia and Juan de Fuca within the Salish Sea, and on the Washington continental shelf (Masson & Cummins, 2007 ; Riche et al., 2014 ; Whitney et al., 2007 ). However, trend analyses with longer than decadal-scale records of DO have yet to be conducted. Here, we assess century-scale trends of DO in Puget Sound alongside temperature and salinity, exploring potential mechanisms for change over the last 100 years. 2 Methods 2.1 Profile data To analyze long term trends of water properties in Puget Sound, we used 12,000 + profiles measuring DO, temperature, and/or salinity from 1932 through 2024. We curated data from four separate sampling agencies: University of Washington (referred to as the Collias dataset), King County, Washington State Department of Ecology, and National Oceanic and Atmospheric Administration National Center for Environmental Information (NOAA NCEI) (Alin et al., 2021; Collias, 1970; Collias & Lincoln, 1977; King County, 2024a, 2024b, 2024c; WA Dept. of Ecology, 2024) (Fig. 2a). Throughout this paper we refer to these as “century-scale trends” recognizing, however, that the data only cover 93 years at most. Observations were made primarily with two sampling modes: bottle samples and CTD profiles. Bottle samples involve water collected at specific depths and analyzed in the lab. CTD (conductivity, temperature, and depth) profiles use CTD and DO sensors to make in situ measurements. Modern profiling often uses rosette systems, which include multiple bottles and CTD + DO sensors. While we focus on just DO, temperature, and salinity data, we note that other water property data were concurrently collected that are not discussed here. The oldest dataset is a collection of sampling for various purposes compiled by Eugene Collias at the University of Washington, with later sampling collected by Collias personally. From 1932 to 1942, many regions of the Sound were sampled by research vessels from Oceanographic Laboratories at the University of Washington. While no systematic surveying was conducted, several Main Basin stations were monitored monthly (Collias & Lincoln, 1977). After a break during World War II, sampling resumed in 1946 occurring at irregular intervals and spacing, with the exception of targeted surveys from 1952 to 1954 (Collias, 1970; Collias et al., 1974). During 1970 and 1971, Collias led the collection of data for a study for the City of Seattle’s Department of Lighting (Collias et al., 1974). In 1974 and 1975, Collias collected data for the Municipality of Metropolitan Seattle (METRO) to begin to understand the effects of sewage discharge in Main Basin. Ultimately, no conclusive evidence of oxygen depletion as a result of wastewater discharge was found (Collias & Lincoln, 1977). King County’s Puget Sound Marine Monitoring group has conducted shipboard sampling in Main Basin near Seattle since 1965 at 31 total stations, primarily monthly (King County, 2024a). Some stations have been monitored continuously throughout this time, while others were discontinued between 1984 and 1986. More sampling stations began monitoring around 1997–1998, with more stations near Tacoma added in 2002 (King County, 2024b). In 2022, fortnightly-to-monthly sampling began at 11 stations in Whidbey Basin (King County, 2024c). Similarly, the Washington State Department of Ecology’s Marine Waters Monitoring Program has collected monthly samples at 22 stations in Puget Sound. Sampling at several sites began in 1973, with more sites being added over time (WA Dept. of Ecology, 2024). Since 1999, these sites have been monitored via float plane (Krembs & Sackman, 2015). Finally, NOAA NCEI has archived data from 2008 to 2018, sampled during three cruises per year working with various sampling programs. These cruises sample approximately 25 sites throughout Puget Sound (Alin et al., 2021). Sampling methods for DO, temperature, and salinity have evolved over the period of the record considered here. Measurements for these variables are almost always taken concurrently. Bottle casts have been taken throughout the dataset, while CTD profiles began in the 1970s. Temperature was initially measured using reversing thermometers on bottles (Collias, 1970), but digital thermistors on CTDs have been used since the 1980s (WA Dept. of Ecology, 2024). Salinity was measured initially using titrations; modern methods determine salinity based on electrical conductivity (Collias, 1970; Collias & Lincoln, 1977). Oxygen concentrations from bottle samples throughout the dataset are calculated using Winkler titrations (Barnes & Collias, 1958). Modern in situ DO measurements use oxygen-sensing optodes or membrane-based voltage meters attached to CTDs and are calibrated against Winkler titrations (King County, 2024c). We note that, due to the historical nature of this dataset, the exact instruments used for in situ profiling are not always known. Though the sampling method is similar to using CTDs, we refer to these samples as taken by unknown sondes, since we cannot verify if CTDs specifically were used (Fig. 2b). Specific description of methods and instrumentation, as discerned from historical and modern resources, are summarized in Appendix A. Also note that due to the varying precision of measurements across time, we report all trends, averages, and confidence intervals to the nearest 0.1. 2.2 River, atmospheric, and offshore data and data products To investigate the mechanisms behind temperature change and its effect on DO over the last century in Puget Sound in Section 4.2, we used atmospheric daily maximum and minimum temperature data since 1948 at Seattle-Tacoma International Airport, averaged monthly (NOAA GHCN, 2018). We selected this site because of its proximity to Puget Sound’s Main Basin and its long time record. We used offshore temperature change rates from 1956–2006 at Line P Station P4 on the 26.7 kg/m 3 potential density surface from Whitney et al. (2007) (Fig. 1a). We chose this station because it is located near the edge of the continental shelf offshore of the Strait of Juan de Fuca, representing offshore changes without significant modification by processes occurring on the shelf. To investigate freshwater change in Puget Sound in Section 4.3, we used monthly mean flow data at the Skagit River near Mount Vernon since 1941 (USGS, 2024). The Skagit River is the largest river in Puget Sound, approximately representing bulk freshwater properties for the region since it accounts for approximately one-third of the total freshwater influx to the Sound (Banas et al., 2014). 2.3 Data quality assurance and conversions from raw data Initial data quality was established via quality assurance and quality control methods performed by the agencies that made the measurements and compiled the data. We performed additional filtering to remove unrealistic values in all DO, temperature, and salinity data including negative values and measurements at erroneous depths. We converted all observed temperature and salinity into conservative temperature and absolute salinity using the Gibbs Seawater Routines (McDougall & Barker, 2011). 2.4 Calculating DO saturation To calculate the expected DO saturation for a given water mass, we used the empirical fitting formula for the solubility of oxygen in water given salinity and temperature as described in Garcia & Gordon (1992). We calculated the expected DO saturation for a water mass using its temperature and salinity, then calculated a trend using methods discussed in Section 2.9. These trends were then compared to the trends in DO measurements in Section 4.2. This process is similar to the concept of apparent oxygen utilization (AOU) since we apply this to both surface and bottom water masses, despite the fact that bottom water is not in immediate contact with the surface for oxygen exchange (Garcia et al., 2013). 2.5 Controlling for spatiotemporal variation in long time histories There is significant spatial and temporal sampling variability within the dataset due to the length of time, number of collecting agencies, goals for data collection, and change of collection methods over time (Fig. 2). Importantly, the spatial variation in this time series is due both to variability in the sampling location (Fig. 2a), as well as due to sampling depth changes within the water column at a given sampling site (Fig. 2c). For example, maximum cast depths around 200m have become more common at Point Jefferson and in the past 40 years (Fig. 3a). Variation in sampling depth is especially important since DO typically varies from higher concentration at the surface to lower concentration at the bottom; as a result, significant changes in sampling depth over time could bias trends in observed DO concentration. Finally, sampling frequency has varied significantly over the record, from an average annual cast count around 100 in the 1930s and 1940s to more than 1000 annually in the last decade (Fig. 2d). We avoided biases that might be introduced by these issues by selecting data groupings based on the following site selection criteria: Criterion 1: Location consistency To prevent spatial bias in sampling location inconsistency over time, sites were required to have tightly-clustered cast locations (latitude and longitude). This ensured that the data represented a consistent volume over time. After inspection of the available data, each site was described with a bounding polygon (Fig. 1b); casts within this polygon represent the respective site. The area and centroid of each polygon is given in Table 1. Criterion 2: Depth consistency To prevent spatial bias due to sampling depth inconsistencies over time, casts included at each site were required to maintain maximum depths within a site-specific percentage of the maximum depth of the site region. These depths were informed by the available data and are shown in Table 1. The maximum depth for each site was defined using gridded bathymetry from LiveOcean, a realistic, regional, ROMS-based numerical model with approximately 500m rectangular grid resolution (MacCready et al., 2021; NOAA NCEI, 2014). Criterion 3: Sufficient time range To prevent aliasing due to system variability on decadal or shorter timescales, sites meeting the above criteria were required to have been sampled for a span of six decades or more. These ranges are shown in Table 1. This criterion was maintained when data were filtered to specific seasons or for specific variables, though the specific year ranges for each season and variable may change. Table 1 Site-specific characteristics: centroid of site-bounding polygon, area of site-bounding polygon, maximum depth at site from LiveOcean grid (MacCready et al., 2021), percentage of maximum depth used to define top bound of bottom water bin, and range of data covered at each site Site Centroid (lon., lat.) Area (km 2 ) Maximum Depth (m) Bottom Depth Percentage Year Range* Point Jefferson (PJ) -122.418, 47.741 8.3 258.8 20% 1933–2024 Near Seattle (NS) -122.448, 47.600 55.0 246.9 25% 1932–2024 Saratoga Passage (SP) -122.498, 48.112 27.5 138.0 20% 1952–2023 Carr Inlet (CI) -122.721, 47.277 23.1 101.5 20% 1935–2023 Lynch Cove (LC) -122.936, 47.393 16.0 27.7 48% 1932–2023 *range for all data; range filtering to specific seasons and/or variables (i.e., DO, temperature, or salinity) may vary To illustrate the site-selection criteria , cast locations that meet Criterion 1 for one selected site, Point Jefferson (PJ) are shown in Fig. 1b, enclosed in the corresponding site polygon. Throughout the time series, there have been consistent casts exceeding 200m in depth (Fig. 3a). Profiles since the late 1980s consistently sample at 200m unlike before, so to avoid potential oversampling bias at 200m and meet Criterion 2 , we define the bottom depth bin to be 20% of the maximum depth of the water column within the site polygon, or ~ 206m and deeper. Sampling meeting Criteria 1 & 2 at PJ began in the 1930s and continues into present day, confirming that we have at least six decades of data that meet Criterion 3 (Fig. 3b). Five sites meet all three criteria, which are shown in Fig. 1b. Two sites are within Puget Sound’s central, deep Main Basin: Point Jefferson (PJ) and Near Seattle (NS). The other three are in primary Sub-Basins: Lynch Cove (LC) in Hood Canal, Saratoga Passage (SP) in Whidbey Basin, and Carr Inlet (CI) in South Sound. These sub-basins are hydrodynamically distinct and include many of Puget Sound’s terminal inlets, which may be hotspots for hypoxia (Khangaonkar et al., 2018). Notably, LC is a shallow, terminal inlet and the only one of the five sites with observed hypoxia. These sites are not necessarily representative of water properties throughout their respective sub-basins because they are selected based on the limitations of data availability; however, they do span much of Puget Sound physical parameter space. 2.6 Partitioning for data analysis by depth At each site, we assessed trends at both the surface and the bottom of the water column. For each individual cast meeting the site-selection criteria described in Section 2.5, the individual measurements taken during the specific cast were averaged over both a surface and bottom depth bin. We defined the surface depth bin as 5m and shallower, and the bottom depth bin as in Table 1. Thus, for a given cast, this binned depth-averaging yields one value to represent each of the surface and bottom water properties sampled by that specific cast. 2.7 Partitioning for seasons For each site and for each depth bin, we assessed multi-year trends occurring during specific seasons to control for inherent seasonal variability in water properties. We divided the year into three seasonal trimesters which are: August-November (late summer/early fall), December-March (winter), and April-July (spring/early summer). We refer to these seasons as the Low-DO season, Winter, and Spring, respectively. These trimesters arise from the identification of the lowest DO time period (Low-DO season). We justify this period selection in Section 2.8. Spring corresponds to the season with the most expected primary productivity and photosynthesis, while Winter corresponds to the season with increased storm activity and rainfall. 2.8 Identifying DO minimum location and time period While we performed analyses for all sites, depth bins, and seasons discussed in Sections 2.6 & 2.7, we focused on understanding the mechanisms driving change during the time period and at the location in the water column where DO is lowest throughout the year. The lowest annual DO occurs at the bottom of water columns and during August-November (Low-DO). This intent is different from studies investigating specific deoxygenation rates (e.g., Pasquier et al., 2024), and instead places emphasis on the potential for long term changes in hypoxic risk in Puget Sound. To assess where in the water column DO minima occur, we considered profiles at these sites that reach the bottom depth bin as defined in Table 1. We found that approximately 65% of profile DO minima occur within the deepest 25% of the profile (Fig. 4a); thus, the highest hypoxia risk exists generally near the bottom of the water column. To assess when during the year the DO minima occur, we analyzed profiles occurring throughout the year. We found that 75% of annual DO minima occur from August to November, with minima outside this time range distributed throughout the year (Fig. 4b); thus, the highest hypoxic risk exists between August-November. We note that there are minima that occur earlier in the year, particularly during the early part of the time series. These are likely due to sampling variation between years. Water column reoxygenation timing varies interannually and we controlled for potential bias by limiting our analysis to the lowest DO measurements. August-November DO minimum timing is variable since late fall/early winter reoxygenation is driven by wind events and winter storms (Scully, 2010b), but the sparse temporal resolution of sampling does not allow us to resolve and separate the semi-independent process of deoxygenation and reoxygenation throughout all years in our dataset. Temporal sampling bias of DO during August-November may occur, depending on the timing of sampling in relation to reoxygenation events. To account for this, we only included casts whose bottom DO falls at or below the median (50th percentile) bottom DO at that specific site and during that specific season, with the median calculated for each year separately. This is an approximate but effective way to avoid averaging DO values that may have occurred after reoxygenation and thus miss the true annual DO minimum. We illustrate this process again at Point Jefferson (PJ), where the median and below of August-November bottom DO values are selected (Fig. 5). We implemented the same process for all seasons in this analysis, ensuring that we identified trends in the DO minima in a given season and minimized bias due to interannual variability of synoptic events that may impact DO. We note that no median filtering was conducted for temperature and salinity in a given season since we are primarily concerned about the DO minima as opposed to salinity or temperature minima. We conducted the same trend analysis with all casts without filtering and found that the trends were similar from both methods and had overlapping 95% confidence intervals; thus, the results of this paper are not sensitive to differences in temporal data filtering. 2.9 Trend analysis methods We used non-parametric methods to assess trends over time, since the time series data contain gaps and are not necessarily normally distributed with normal residuals to a linear regression of the data against time. We used the Mann-Kendall test for monotonic trends to first assess if the data are increasing or decreasing over time (Gilbert, 1987; Kendall, 1975; Mann, 1945), which provides a probability, or significance, value (p-value). Then we calculated a Theil-Sen slope for each time series (Sen, 1968; Theil, 1950). This test allowed us to calculate a trend line slope that is robust to the outliers inherent in observational data and is not reliant on assumptions of normality, as is the case with linear regressions. We calculated a 95% confidence interval and reported sample sizes for all slopes. To understand the sensitivity of trends to the statistical method chosen, we conducted the same analysis of long-term trends with linear regressions. Corresponding slopes from both analysis types had overlapping 95% confidence intervals and thus had no significant difference between the two values. To assess correlation between two independent, non-time variables, we used simple linear regressions. We specifically used this method to assess the predictive capability of river flow on observed salinity, and this will be discussed in Section 4.5. 3 Results 3.1 Time series Between 1932 and 2024, time series of bottom August-November (Low-DO) temperature, salinity, and DO at Point Jefferson (PJ) and Lynch Cove (LC) show both temporal trends and spatial variability in water quality parameters (Fig. 6 ). Recall that while we focus on DO, we concurrently investigated temperature and salinity to understand mechanisms for DO change. We show these time series because, of the three seasonal trimesters we evaluated, the lowest DO occurred seasonally during the late summer/early fall, or the Low-DO season, in bottom water (Fig. 4 ). These two sites represent a range of DO conditions in Puget Sound: PJ, in Main Basin, has much higher DO than LC, a distal, terminal inlet of the Hood Canal Sub-Basin which is generally hypoxic at the bottom during the Low-DO season (Fig. 6 c). During the Low-DO season, Point Jefferson (PJ) had an average bottom temperature of 11.2 ± 0.1°C while Lynch Cove (LC) had a colder average bottom temperature of 10.4 ± 0.1°C (Fig. 6 a; sample sizes shown in Table 2). From the time series, we see that these sites are warming over the last century and at similar rates, with PJ warming at 1.4 + 0.4/-0.3°C/century and LC warming at 1.2 + 0.5/-0.6°C/century (Fig. 6 a; sample sizes and p-values shown in Table 3). PJ had saltier bottom water with an average of 30.9 ± 0.0 g/kg compared to LC’s 30.0 ± 0.1 g/kg. Salinity trends are similar between the two sites but have large uncertainties, with PJ’s trend of 0.1 ± 0.1 g/kg/century and LC’s 0.2 ± 0.4 g/kg/century (Fig. 6 b; Table 3). LC had lower bottom DO than PJ during the Low-DO season, with an average over the time series of 0.9 ± 0.2 mg/L (see Section 2.8 for selection of DO time series values). PJ averaged 5.6 ± 0.1 mg/L (Fig. 6 c; Table 2). Importantly, the rates of change of DO at both sites differ as well. Despite the high ambient oxygen concentration observed at PJ, it experiences bottom DO decline at a rate of -0.3 ± 0.2 mg/L/century. LC has a slight, uncertain increase of DO of 0.2 ± 0.6 mg/L/century (Fig. 6 c; Table 3). We calculated time series averages to reveal the variation in water properties for each season, site, and depth bin (Fig. 7 ; Table 2). The highest temperatures generally occurred at the surface during the Low-DO season with an average across all sites of 13.5°C, while the coldest temperatures of 8.6°C averaged across all sites occurred at the surface during Winter (Fig. 7 a,c; confidence intervals and sample sizes for individual values shown in Table 2). The highest average temperature 16.0 ± 0.6°C was observed at a terminal inlet, Lynch Cove (LC), at the surface during Spring (Fig. 7 b; Table 2). Salinity was highest during the Low-DO season with an average across sites and both depth bins of 29.3 g/kg, while Winter and Spring had salinities of 28.1 and 28.2 g/kg, respectively (Fig. 7 d-f). When comparing surface and bottom depth bins, we observe variation in the difference between surface and bottom salinity. Carr Inlet (CI) had the smallest top-bottom salinity difference with an average of 0.3 g/kg across seasons, indicating weaker stratification than at other sites (Fig. 7 d-f). The largest top-bottom salinity differences were observed at Lynch Cove (LC) and Saratoga Passage (SP), where the bottom salinity exceeded surface salinity by 5.8 g/kg and 5.7 g/kg, respectively, when averaged across seasons (Fig. 7 d-f). This is likely the result of proximity to river inflows from the Skagit River near SP and smaller inflows, such as the Union River, near LC, providing freshwater at the surface near these sites. We note that measurements of surface and bottom salinity do not necessarily represent full water column density stratification. For DO concentrations, all sites had higher DO at the surface than at the bottom during any given season, with Lynch Cove (LC) having the largest difference between surface and bottom values of DO at 7.3 mg/L averaged across seasons (Fig. 7 g-i). The smallest difference between surface and bottom DO occurred during Winter and the largest difference occurred during the Spring, at 2.7 mg/L and 10.7 mg/L, respectively, averaged across sites (Fig. 7 g,h). Across the five long term sites evaluated, LC had the lowest DO, especially at the bottom during the Low-DO season (Fig. 7 g-i). The average bottom DO value across all sites for the Low-DO season was 4.5 mg/L, while the average bottom DO value at Lynch Cove (LC) was 0.9 ± 0.2 mg/L (Fig. 7 i). Out of the five long term sites, LC was the only site with observed hypoxia, or DO less than or equal to 2 mg/L, and the presence of hypoxia was observed multiple times throughout the time series (Fig. 6 c; Fig. 7 i). 3.2 Century-scale trends We summarize century-scale trends based on Theil-Sen slopes in DO, temperature, and salinity in Fig. 8 and Table 3. All sites are warming at an average rate of 1.5°C/century across all sites, depths, and seasons (Figs. 8 a-c; confidence intervals, sample sizes, and p-values for individual values shown in Table 3). On average the surface is warming at 1.7°C/century, slightly faster than the average bottom warming of 1.2°C/century (Figs. 8 a-c). There is more variability among surface trends than at the bottom, with the largest surface trend occurring during Spring at the surface of LC at a rate 4.6 ± 3.3°C/century (Fig. 8 b; Table 3). Salinity trends vary between sites, seasons, and depths (Figs. 8 d-f). At the bottom, salinity is generally increasing very weakly across all sites and seasons at an average rate of 0.2 g/kg/century (Fig. 8 d-f). Meanwhile, the surface salinity averaged across all sites and seasons decreases, or freshens, at a rate of 0.3 g/kg/century (Fig. 8 d-f). However, there is more variability across surface trends than bottom trends, and large uncertainty in the largest surface freshening trends, such as at SP during Winter with a decrease of 3.7 + 2.8/-3.1 g/kg/century (Fig. 8 d; Table 3). Unlike SP, Main Basin sites (PJ and NS) have increasing surface salinity of 0.5 g/kg/century averaged across all seasons (Fig. 8 e,f). We note that Main Basin is deep, wide, comprises approximately 65% of the total volume of Puget Sound, and is connected to all other basins in the Sound (Banas et al., 2014 ); thus, we consider trends here to approximate a system average, or background, trend. Distal regions may be more prone to local variability, especially from local freshwater sources such as the Skagit River, nearest to the site SP. DO trends highlight distinctions between Main Basin and Sub-Basin sites, especially LC and SP (Fig. 8 g-i). During all seasons, bottom DO is decreasing at both Main Basin sites (PJ and NS) at a rate between 0.3 and 0.9 mg/L/century (Fig. 8 g-i; Table 3). Although Sub-Basin sites across all seasons generally have decreasing bottom DO as well, the trend uncertainties captured in the 95% confidence intervals tend to also include increasing slopes, unlike Main Basin sites. For example, SP during Spring has a slope of -0.3 + 0.8/-0.7 mg/L/century (Fig. 8 g; Table 3). Here, the trend uncertainty exceeds the DO trend on this scale. We note that surface DO trends vary more than bottom trends across sites and seasons. Now we focus on the data at the bottom of the water column during the Low-DO season, which corresponds to highest hypoxia risk (Figs. 8 c,f,i). All sites are warming at a rate of approximately 1.5°C/century, averaged across sites (Fig. 8 c). Salinity is increasing at all sites at an average rate of 0.3 g/kg/century but is variable between the sites (Fig. 8 f). Both Main Basin sites’ DO are decreasing during the Low-DO season at the bottom of the water column (Fig. 8 i). PJ has a slightly slower decline of 0.3 ± 0.2 mg/L/century, while NS has a faster decline of 0.9 + 0.3/-0.2 mg/L/century. At Sub-Basin sites, only CI’s DO is declining at a rate of 0.6 ± 0.6 mg/L/century, similar to the Main Basin sites. The other two Sub-Basin sites, SP and LC, have rates close to 0 (Fig. 8 i; Table 3). We note that the site with highest ambient oxygen concentrations (PJ, NS, and CI) have the fastest DO decrease. Conversely, LC has the lowest DO concentration, and yet no DO decrease is observed during the Low-DO season (Fig. 7 i; Fig. 8 i). We discuss this further in Section 4.1 . 4 Discussion We analyzed nearly a century of data in Puget Sound, which revealed the following long-term trends. All sites are warming at an average rate of approximately 1.5°C/century across all seasons and depths. Salinity change is generally small and has significant variability. Finally and most importantly, central Main Basin bottom water DO is decreasing at a rate of 0.3–0.9 mg/L/century. These results motivate the following questions: How do these trends affect hypoxic risk in Puget Sound? What mechanisms may explain these trends? How much DO change is explained by these mechanisms? 4.1 Implications of DO trends for bottom hypoxia during the Low-DO season During the Low-DO season, Point Jefferson (PJ), Near Seattle (NS), and Carr Inlet (CI) had the highest rates of DO decline of 0.6 mg/L/century, averaged between these sites (Fig. 8 i; confidence intervals, sample sizes, and p-values for individual values shown in Table 3). However, these sites also had the highest bottom DO concentration, ranging 5.5-6.0 mg/L (Fig. 7 i; confidence intervals and sample sizes for individual values shown in Table 2). Conversely, at Lynch Cove (LC), which is the only site among the five long-term sites with measured hypoxia, no clear trend is resolved; the trend is smaller than the variability (Fig. 7 i; Fig. 8 i). For the two Main Basin sites (PJ and NS) and CI, the small rates of decrease relative to the ambient oxygen concentrations do not suggest a risk of increasing hypoxic occurrence in the near future at these sites. Meanwhile in LC, the low average bottom concentration is near the zero-concentration limit, which prevents a negative trend. It is worth noting that other hypoxia metrics such as extent or duration may show trends not apparent in our analysis. These metrics could not be reliably computed with our data due to the low temporal sampling frequency. Recall from Section 3.2 that the Main Basin sites (PJ and NS) are considered to be representative of background trends. The changes in background water properties observable in Main Basin may influence terminal inlets such as Lynch Cove (LC) via exchange flow (MacCready et al., 2021 ). Water exchanged from Main Basin to terminal inlets sets the initial state of the DO coming into the deep layer. Whether or not hypoxia develops at a site is influenced by a balance between local respiration and residence time (Fennel & Testa, 2019 ). This balance is influenced by changing conditions outside of, but connected to, these inlets themselves. In other words, lower DO in Main Basin, exchanged into LC, may set a lower baseline DO and result in more persistent low DO conditions at LC. Although DO at LC is low enough during the low-DO season to make continued reduction hard to detect, it is instructive to consider changes during other seasons. LC has decreasing DO during both Winter and Spring of 1.3 + 1.1/-1.4 mg/L/century and 1.3 ± 1.5 mg/L/century, respectively (Fig. 8 g-i; Table 3). These declining trends may set a lower baseline for DO going into the Low-DO season, and thus again increase the hypoxic risk in LC. 4.2 Warming accounts for a large portion of DO loss In Puget Sound’s Main Basin, we observe a background decline of DO in bottom water over the last century. To explain this trend, we consider a DO-limiting process in estuaries that is well-resolved with our available century-scale data: the change in solubility of oxygen at the air-sea interface. The solubility of DO at the surface, or the equilibrium exchange between atmospheric oxygen and water surface DO, depends on temperature and, to a much lesser extent, salinity (Garcia & Gordon, 1992 ). We expect surface DO solubility changes to be reflected in bottom water on century scales because of reflux and surface entrainment, similar to the concept of apparent oxygen utilization (AOU) (Garcia et al., 2013 ). We directly compare trends in observed DO to trends in DO predictions based on DO saturation, calculated using our temperature and salinity time series, as described in Section 2.9 (Fig. 9 ). Averaged across all sites, we find an average predicted bottom water DO decline based on DO saturation of 0.3 mg/L/century (Fig. 9 a-c; confidence intervals, sample sizes, and p-values for individual values shown in Table 3). The vast majority of this reduction (approximately 90% averaged across all sites) is due to warming, with salinity having a secondary influence. At Main Basin sites PJ and NS, the DO saturation trends vary between 20–100% of the observed trends, depending on the season and site (Fig. 9 a-c). Averaged across all seasons, the trend in saturation-based DO predictions in Main Basin is equivalent to approximately 50% of the observed DO trend. In other words, observed warming predicts a DO reduction here that can account for nearly half of the observed reduction in bottom DO. While other mechanisms are also necessary to generate the trends in Main Basin DO, warming appears to have a dominant influence on long-term DO reduction. A similar magnitude of DO decline accounted for by decreased solubility due to warming was found in the Chesapeake Bay (Dreiss et al., 2024 ; Ni et al., 2019 ; Ni et al., 2020 ). The Baltic Sea has also experienced long-term reduction of bottom DO, which may be worsened by long-term warming (Carstensen et al., 2014 ). Such similar findings show that warming temperatures can reduce DO saturation in diverse estuarine environments (IPCC, 2023 ), and suggests that this process may be important in estuarine systems worldwide. At Sub-Basin sites, there is more variability in the observed trends, often exceeding the trend in the saturation-based prediction (Fig. 9 a-c). Where the trend is small, such as at SP during the Low-DO season, the DO saturation trend is approximately 4 times the size of the observed trend (Fig. 9 c). At LC during the Low-DO season, the observed DO trend is positive, while the DO saturation trend suggests DO loss (Fig. 9 c). These locations demonstrate that while warming is ubiquitous in Puget Sound, DO is influenced by many spatially-variable processes that may influence century-scale trends. Further, the rates of DO decline are different between PJ and NS, despite their close proximity to each other in Main Basin, representing another measure of the uncertainty in these trends. Trends in DO near the surface show more variability than those at depth, and thus they are not as easily explained by the predicted change in DO saturation over the last century (Fig. 8 g-i; Table 3). This variability is likely due to several processes that impact surface DO. First, the surface photic zone is only about 25m deep and is where most primary productivity occurs (e.g., Khangaonkar & Yun, 2023 ). The amount of surface DO produced via photosynthesis varies spatially and temporally; thus, the spatial distribution of estuary gas exchange is non-uniform (Scully et al., 2022 ; Winter et al., 1975 ; Xiong et al., 2025 ). Second, wind varies spatially and temporally in estuaries, driving variable rates of surface oxygen solubility and mixing (Ho et al., 2016 ; Kremer et al., 2003 ; Scully, 2016 ). Third, the advection of water masses, such as river plumes, and sharp interfaces between water masses can cause further variability at our sites (Baschek et al., 2006 ). Overall, the observation that variability is lower at the bottom than at the surface suggests that the influence of near-surface processes such as those listed above has a reduced influence on the dynamics of the bottom layer. Thus, on long timescales, DO saturation change is more coherent at the bottom than on the surface given the variability and complex influences of surface processes on surface oxygen. 4.3 Other potential mechanisms for DO change Using available observational data, we have determined that the change in DO saturation, largely due to warming, accounts for approximately 50% of the observed DO decline in Puget Sound. Determining what other mechanisms also impact long-term change in DO is important to understanding long-term change in Puget Sound, yet is limited by the availability of century-scale data. While we cannot analyze the impacts of these mechanisms in this scope, we discuss the potential influence of these other processes, including anthropogenic eutrophication, the influence of increasing coastal hypoxia, changes in exchange flow dynamics, and shifts in Puget Sound ecosystem dynamics. First, anthropogenic eutrophication due to loading of limited nutrients has been linked to oxygen decline and hypoxia in many urban estuaries (Fennel & Testa, 2019 ). Both the Chesapeake Bay and the Baltic Sea have been shown to have positive correlations between anthropogenic nitrogen loading and algal biomass leading to declines in DO (Boynton & Kemp, 2000 ; Larsson et al., 1985 ). In Puget Sound, Khangaonkar et al. ( 2018 ) used a hydrodynamic and biogeochemical numerical model to compare a year with and without anthropogenic, land-based nutrients. They found that reducing land-based nutrient loading reduces hypoxic occurrence and extent. However, as discussed in Section 1 , Puget Sound’s total nitrogen and fluctuations in nitrogen are dominated by marine sources. We would thus expect that the impacts of nutrient change on DO are similarly small (Beutel et al., 2025 ; Mackas & Harrison, 1997 ; Steinberg et al., 2010 ; Xiong et al., 2025 ). Unfortunately, historical water quality monitoring of nitrogen species and wastewater treatment plant effluent (Mohamedali, 2020 ; Wasielewski et al., 2024 ) lacks sufficient spatiotemporal resolution for century-scale trend analysis. Anthropogenic nutrients from other land-based sources, such as agricultural runoff or deforestation-driven increases in watershed nitrogen-fixing terrestrial plants, may also play a role in nutrient loading and eutrophication (Anderson et al., 2008 ), but are historically poorly monitored. Pilot field surveys to assess the effects of urbanization on small bays have demonstrated the need for decades-long data collection to further understand the ecological impacts of urbanization, including anthropogenic nutrient loading (Takesue, 2011 ). Where, how, and on what scales anthropogenic nutrients drive changes in system DO are the subject of ongoing modeling experiments and field data collection. Second, growing hypoxic zones and changing water properties on the Washington shelf may set a lower baseline for the oxygen concentration in water masses entering the Salish Sea. Observations of bottom oxygen on the Washington shelf have shown evidence of increasingly widespread hypoxic zones and declining DO since 1950, due to a combination of high-nutrient, low-DO water upwelling to the shelf, which is then further depleted by shelf respiration (Barth et al., 2024 ; Chan et al., 2008 ; Pierce et al., 2012 ). During upwelling conditions, the water that flows into Puget Sound is often denser and deeper with lower DO (Brasseale & MacCready, 2025 ). In Eastern Boundary Upwelling Systems, such as the California Current System off the Washington coast, warming due to climate change may drive an increase in upwelling-favorable winds (Bakun, 1990 ; García-Reyes & Largier, 2010 ). While climate modeling scenarios are uncertain with regard to duration and intensity of upwelling, enhanced stratification on the shelf may also contribute to DO decline (Bograd et al., 2023 ; García-Reyes et al., 2015 ). While establishing a linkage between the century-scale decline of DO in Puget Sound and changes in offshore water masses is outside of the scope of the available observational data, the variability of offshore source waters has been shown to be the primary driver of variation of DO in the Salish Sea (Beutel et al., 2025 ). Third, changing freshwater and offshore conditions could lead to changes in estuarine exchange flow, impacting residence time in Puget Sound and thus the amount of time that biological processes can modulate DO in an estuary. The exchange flow is primarily driven by the along-channel salinity gradient and estuarine mixing, which are modified by physical processes, including freshwater discharge into the estuary, the density of offshore water masses, tidal mixing, and the seasonal variation of wind (Babson et al., 2010 ; Deppe et al., 2017 ; Geyer & MacCready, 2014 ; Sutherland et al., 2011 ). Long-term changes in exchange flow due to changing environmental conditions are difficult to quantify, however, and the impact of these changes on DO in Puget Sound are even more so. For example, while exchange flow varies seasonally and tends to be smaller when river flow has been low for several months (MacCready & Geyer, 2024 ), results modeled over several years showed that a low river-flow did not have significantly different exchange flow than a high-flow year, despite increased salinity and decreased stratification (MacCready et al., 2021 ). Furthermore, variations in exchange flow can have bidirectional effects on the DO observed in Puget Sound. Increased exchange flow may reduce residence time, but may enhance the intrusion of offshore water, which may have reduced DO during upwelling conditions (Brasseale & MacCready, 2025 ). In modeled budgets of Puget Sound, exchange flow tends to export DO out of Puget Sound, but can occasionally import DO, especially during downwelling-favorable wind conditions offshore (Xiong et al., 2025 ). Ultimately, while we cannot attribute trends in Puget Sound DO to changes in exchange flow over time using observational data, exchange flow modification is very important when considering DO change in the system. Finally, shifting environmental conditions may modify respiration and the rate at which DO depletion occurs in a given water mass. Increased rates of respiration driven by warming may result in faster DO decline while also enhancing ocean acidification (Gobler & Baumann, 2016 ; Panigrahi et al., 2013 ). Together, warming and ocean acidification may contribute to ecological changes in the system, modifying the quantity, rate, and timing of photosynthesis and respiration. Finally, ecosystem composition changes are likely given different species thresholds’ for low DO conditions, potentially modifying the balance of biological processes that modify DO (Levin et al., 2009 ; Vaquer-Sunyer & Duarte, 2008 ). While the effects of changes to biological processes on DO itself are not quantified here, this is an important consideration in oxygen cycling in Puget Sound. 4.4 Puget Sound bottom water is warming at a rate similar to the local atmosphere and offshore We found that Puget Sound bottom waters are warming at approximately 1.5°C per century (Section 3.2 ) and this can account for a significant portion of the observed DO decline (Section 4.2 ). Here we consider whether this warming results from changes in offshore temperature from the Northeast Pacific Ocean or local atmospheric temperatures. Puget Sound bottom water is generally warming at a similar rate to that observed at Ocean Station Papa in the Northeast Pacific Ocean and the Salish Sea’s northernmost basin, the Strait of Georgia (Riche et al., 2014 ; Whitney et al., 2007 ). The Oregon shelf region near the Newport Hydrographic Line and the Strait of Juan de Fuca are also getting warmer, indicating that warming is ubiquitous in this region (Huyer et al., 2007 ; Masson & Cummins, 2007 ). Local warming is also documented in increasing atmospheric temperatures at Seattle-Tacoma International Airport since 1948 (Fig. 10 a). Although urban development has occurred around the sampling location, previous regional studies showed any potential urban heat island effect to be negligible (Arhonditsis et al., 2004 ). Figure 10 a shows a comparison of local air temperature to the surface and deep temperature at Point Jefferson (PJ) in Main Basin during the Low-DO season. Atmospheric temperatures are increasing at a rate of 2.7 ± 1.7°C/century, higher than in either surface or bottom water, while the warming rate offshore at Line P Station P4, outside of the Strait of Juan de Fuca, is 0.84°C/century (Whitney et al., 2007 ), lower than the rate at PJ in Puget Sound (Fig. 10 b). A warmer ocean offshore indicates that warmer water is available to be exchanged into Puget Sound. However, local environmental conditions such as air temperature and river flow have been shown to be more influential than offshore variations in determining sea surface temperature and salinity in Puget Sound (Moore, Mantua, Kellogg, et al., 2008). Vertical mixing entrains surface waters deeper into the water column, which propagates the effects of local atmospheric warming to bottom water. At Admiralty Inlet, Puget Sound’s fjordal entrance sill, around 40% of outflowing surface water mixes into deeper inflow and is retained by the estuary in a process called reflux (MacCready et al., 2021 ). This process is thought to account for higher rates of warming at depth in the nearby Strait of Juan de Fuca as compared to directly offshore (Masson & Cummins, 2007 ). The above comparison suggests that ocean and atmospheric warming contribute to the warming of Puget Sound waters: while warming of the coastal ocean contributes to increasingly warm waters to inflowing deep water, local warming resulting from a warming atmosphere and vertical mixing is necessary to explain the observed warming rates. On a global scale, ocean warming is well-correlated with atmospheric temperature trends and greenhouse gas emissions (Levitus et al., 2009 ). Thus, Puget Sound’s warming is likely linked to climate change and our results suggest that it is outpacing ocean warming, presumably due to the effects of local atmospheric temperature on the water temperature within the estuary and the fact that it is shallow compared to the global ocean. 4.5 Salinity changes are small and are likely forced by multiple mechanisms Bottom water salinity during the Low-DO season increased at most sites over the last century, but the observed increases are small. Large trends are only observed at SP at the surface and indicate significant freshening. Like warming, salinity change mechanisms are either forced by local freshwater sources or by remote offshore sources. First, we consider rivers, which are the primary source of freshwater to Puget Sound and tend to be more impactful to observed salinity than offshore changes (Moore, Mantua, Kellogg, et al., 2008). As previously mentioned in relation to exchange flow in Section 4.3 , modeling studies have shown that salinity increased during dry years while stratification decreased (MacCready et al., 2021 ). Long-term changes in river flow may be tied to climatological changes in precipitation and temperature, reducing snowpack and shifting peak flow timing (Barnett et al., 2008 ). Evidence of this change is seen in earlier peak flows at the Fraser River, the largest freshwater source in the Salish Sea entering the Strait of Georgia (Riche et al., 2014 ). The Skagit River, which comprises about one third of the Puget Sound freshwater influx (Banas et al., 2014 ), shows evidence of similar timing shifts. Analysis of Skagit discharge since 1942 (not shown here), shows that, while the annual discharge has not changed significantly, Winter discharge has increased by approximately 25% and Spring discharge has decreased by approximately the same amount. These trends are consistent with modeling of climate change impacts on river systems in snow-dominated watersheds in Washington State (Elsner et al., 2010 ). It is possible that changes in river flow timing may explain the small observed increases in salinity, especially during the Low-DO season when river flows are lower. However, linear regression analyses did not yield significant correlation between the Skagit River timing changes and salinity across the sites and seasons. This mechanistic link is likely impossible to resolve due to the small magnitude and large uncertainty of most salinity trends. The large surface freshening trends at SP may be explained by the local influence of the Skagit River plume itself, especially the influence that changing wind may have on the direction, advection, and mixing of the plume (Sutherland et al., 2011 ). However, we do not test this hypothesis here. The most likely driver of higher salinity from the ocean is changes in the coastal upwelling regime. Bograd et al. ( 2023 ) evaluate expected climate shifts in coastal upwelling to influence stratification. Brassaele & MacCready (2025) find that the salinity of water entering the Salish Sea varies with coastal upwelling. Although we lack the data to test this hypothesis, changing offshore conditions are a plausible mechanism for changing Puget Sound salinity. 5 Conclusions We identified five sites in Puget Sound with sufficient temperature, salinity, and DO data to conduct century-scale trend analyses. All sites are warming at a rate of approximately 1.5°C/century, consistent with warming waters throughout the Salish Sea region; this warming is driven by both offshore and local atmospheric warming likely linked to climate change. Salinity changes are generally small; we observe an increase in bottom salinity of less than 0.2 g/kg/century. In Puget Sound’s central Main Basin, we observe bottom DO loss at a rate of about 0.6 mg/L/century. In Sub-Basins and regions with distal, terminal inlets, the century-scale variability of DO exceeds measured trends. We observe that the highest rates of DO loss occur at the sites with the highest ambient oxygen concentrations. Conversely, the trends where DO is lowest are variable across seasons and have high uncertainty relative to the measured slopes, obscuring century-scale trends. In particular, Lynch Cove, a distal terminal inlet with average ambient DO less than 1mg/L during fall, does not have a clear trend during this time period. However, the combination of DO decline earlier in the season along with the decreasing DO background trend in Main Basin indicate that both antecedent and inflowing DO concentrations in Lynch Cove are decreasing, which suggest that low DO conditions are expected to persist. We find that approximately 50% of background bottom DO loss can be explained by surface oxygen solubility reduction, which is primarily driven by warming. This work documents persistent changes in Puget Sound water properties over nearly a century in hopes that it can be used to inform and guide management decisions in support of ecosystem health. Declarations Ethics approval and consent to participate No human participants were involved in this study. Consent for publication All authors consent to the publication of this manuscript. Availability of data and material Data from various agencies were used in the creation of this manuscript. Data were compiled and collected by Eugene Collias at the University of Washington (Collias, 1970; Collias & Lincoln, 1977). The Washington State Department of Ecology (WA Dept. of Ecology, 2024) and King County (King County, 2024a, 2024b, 2024c) historical and modern monitoring datasets were used. NOAA National Center for Environmental Information (NCEI)’s Salish cruise datasets were also used, as part of the Ocean Carbon and Acidification Data System (OCADS) (Alin et al., 2021). Further, air temperature data from NOAA Global Historical Climate Network (GHCN) (NOAA GHCN, 2018) and river discharge data from USGS (USGS, 2024) were used. Data processing and figure creation was conducted using Python 3.11.11 and the following packages: Matplotlib 3.10.1 (Hunter, 2007; The Matplotlib Development Team, 2025), Numpy 2.1.3 (Harris et al., 2020), Scipy 1.15.25 (The pandas development team, 2020; Virtanen et al., 2020), Pandas 2.2.3 (The pandas development team, 2020), Pickle 4.0 (Van Rossum, 2020), and TEOS 10/Gibbs Seawater (GSW) Oceanographic Toolbox (McDougall & Barker, 2011). Grid and plotting tools adapted in this work from LiveOcean (MacCready et al., 2021) can be found https://github.com/parkermac/LO.git. Raw and processed data and data processing and plotting scripts (Mascarenas et al., 2026) can be found at https://doi.org/10.5061/dryad.v6wwpzh9n. Competing Interests The authors have no relevant financial nor non-financial interests to disclose. Funding This study was funded by the King County Wastewater Treatment Division. Authors’ contributions Dakota Mascarenas is the lead investigator and corresponding author, and has performed all data analysis, created all figures, and written the main manuscript text. Aurora J. Leeson and Kathryn M. Hewett assisted to analyze and interpret the data and to write and revise the manuscript. Alexander R. Horner-Devine and Parker MacCready assisted to conceive of the ideas of the study, to analyze and interpret the data, and to write and revise the manuscript. Acknowledgments The authors thank T. Martin, J. Newton, and G. Ikeda for their support curating datasets and verifying data methodologies and M. Brett, B. Roberts, J. Xiong, S. 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Marine Biology 29(2):139–176. https://doi.org/10.1007/bf00388986 Xiong, J., P. MacCready, and A. Leeson. 2025. Impact of Estuarine Exchange Flow on Multiple Tracer Budgets in the Salish Sea. Journal of Geophysical Research: Oceans 130(11). https://doi.org/10.1029/2024jc021645 Table 2 and 3 Table 2 and 3 are available in the Supplementary Files section. Supplementary Files Table23.docx AppendixA.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 22 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor invited by journal 12 Jan, 2026 Editor assigned by journal 10 Jan, 2026 First submitted to journal 09 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8565389","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578491102,"identity":"200b8fb0-156e-4cfd-b6ae-59f9d0640c7a","order_by":0,"name":"Dakota Mascarenas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYDCCA2wQmh9MshGr5QCQlmwgWYvBAWK18B0/lvj4Q822aOMbOQYMH8oOE9YieSbtsMGBY7dztwG1MM44R4QWgwPpbRIH2EBacjcw87YRo+X8c6CWf7dzN88AavlLlJYbacckDrbdzt0gAdTCSIwWyRvPkg3O9t3OnXHm/YeDPefSCWvhO59m+KDi2+3c/va0xAc/yqwJa0EBB0hUPwpGwSgYBaMAFwAAztZHWlLDkjgAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0007-2510-9642","institution":"University of Washington","correspondingAuthor":true,"prefix":"","firstName":"Dakota","middleName":"","lastName":"Mascarenas","suffix":""},{"id":578491103,"identity":"95bb3002-a732-4b46-a6bc-2af0668cb49d","order_by":1,"name":"Aurora J. 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1","display":"","copyAsset":false,"role":"figure","size":1293935,"visible":true,"origin":"","legend":"\u003cp\u003eMap (a) of the Salish Sea region; the Salish Sea’s two basins are the Strait of Georgia and Puget Sound, connected to the Pacific mainly through the Strait of Juan de Fuca. Line P Station P4 is shown, as discussed in Whitney et al. \u003ca href=\"https://web.endnote.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%2BMTc8L3JlZi10eXBlPjxjb250cmlidXRvcnM%2BPGF1dGhvcnM%2BPGF1dGhvcj5XaGl0bmV5LCBGcmFuayBBLjwvYXV0aG9yPjxhdXRob3I%2BRnJlZWxhbmQsIEhvd2FyZCBKLjwvYXV0aG9yPjxhdXRob3I%2BUm9iZXJ0LCBNYXJpZTwvYXV0aG9yPjwvYXV0aG9ycz48L2NvbnRyaWJ1dG9ycz48dGl0bGVzPjx0aXRsZT5QZXJzaXN0ZW50bHkgZGVjbGluaW5nIG94eWdlbiBsZXZlbHMgaW4gdGhlIGludGVyaW9yIHdhdGVycyBvZiB0aGUgZWFzdGVybiBzdWJhcmN0aWMgUGFjaWZpYzwvdGl0bGU%2BPHNlY29uZGFyeS10aXRsZT5Qcm9ncmVzcyBpbiBPY2Vhbm9ncmFwaHk8L3NlY29uZGFyeS10aXRsZT48L3RpdGxlcz48ZGF0ZXM%2BPHllYXI%2BMjAwNzwveWVhcj48L2RhdGVzPjxwYWdlcz4xNzktMTk5PC9wYWdlcz48dm9sdW1lPjc1PC92b2x1bWU%2BPHNlY3Rpb24%2BMTc5PC9zZWN0aW9uPjxpc2JuPjAwNzk2NjExPC9pc2JuPjxlbGVjdHJvbmljLXJlc291cmNlLW51bT5kb2k6MTAuMTAxNi9qLnBvY2Vhbi4yMDA3LjA4LjAwNzwvZWxlY3Ryb25pYy1yZXNvdXJjZS1udW0%2BPG51bWJlcj4yPC9udW1iZXI%2BPHJlYy1ndWlkPmVjYzJlNDNjLTYyYzItNGJlZi04YzE2LTE4NTcyNjI1ZDc0MDwvcmVjLWd1aWQ%2BPHJlYy11c24%2BMTcwPC9yZWMtdXNuPjwvcmVjb3JkPiIsInZvbHVtZSI6Ijc1IiwiZWxlY3Ryb25pY1Jlc291cmNlTnVtYmVyIjoiZG9pOjEwLjEwMTYvai5wb2NlYW4uMjAwNy4wOC4wMDciLCJwYWdlcyI6IjE3OS0xOTkiLCJyZWNvcmRTdGF0dXMiOiJhY3RpdmUiLCJzZWNvbmRhcnlUaXRsZSI6IlByb2dyZXNzIGluIE9jZWFub2dyYXBoeSIsImlzYm4iOiIwMDc5NjYxMSIsIm51bWJlciI6IjIiLCJzZWN0aW9uIjoiMTc5IiwiYXV0aG9ycyI6WyJXaGl0bmV5LCBGcmFuayBBLiIsIkZyZWVsYW5kLCBIb3dhcmQgSi4iLCJSb2JlcnQsIE1hcmllIl19XSwiZXhjbHVkZUF1dGhvciI6dHJ1ZX1dfQ%3D%3D\"\u003e(2007)\u003c/a\u003e. Puget Sound is boxed in (a) and shown in more detail in (b). Detailed map (b) of Puget Sound including all dataset cast locations, sites with sufficient data for century-scale trend analysis, Puget Sound sub-basin delineation, and important regional features. Puget Sound’s four major basins are Main Basin, South Sound, Whidbey Basin, and Hood Canal; the latter three are referred to as Sub-Basins. Of the sites with sufficient data for century-scale trend analysis, two are in Main Basin: Point Jefferson (PJ) and (NS), and the other three are in sub-basins: Carr Inlet (CI), Saratoga Passage (SP), and Lynch Cove (LC)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/d5c9faba16701758e1899644.png"},{"id":101018592,"identity":"1b48d477-744f-476a-9f6c-771918ea2212","added_by":"auto","created_at":"2026-01-24 00:33:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1150147,"visible":true,"origin":"","legend":"\u003cp\u003eMap (a) of unique cast locations for all datasets. Sampling types (b) per sampling agency over time, with specific methodological information shown in Appendix 1; hatches and shading indicate sampling type and can overlap (e.g., both bottles and CTD+DO often profile concurrently). Time series (c) of annual average cast depth. Time series (d) of annual cast count. All plots are colored by sampling agency \u003ca href=\"https://web.endnote.com/citations/eyJkaXNwbGF5VGV4dCI6IihBbGluIGV0IGFsLiwgMjAyMTsgQ29sbGlhcywgMTk3MDsgQ29sbGlhcyAmIExpbmNvbG4sIDE5Nzc7IEtpbmcgQ291bnR5LCAyMDI0YSwgMjAyNGIsIDIwMjRjOyBXQSBEZXB0LiBvZiBFY29sb2d5LCAyMDI0KSIsImNpdGF0aW9ucyI6W3siZ3VpZCI6IjQ1NzkwMGIyLTBiOTQtNDBiNC1iNTM3LWIwM2YwNjI0ODM0ZCIsInJlY29yZCI6eyJ1cmxzIjp7InJlbGF0ZWQtdXJscyI6eyJ1cmwiOiJodHRwczovL2FwcHMuZWNvbG9neS53YS5nb3YvZWltL3NlYXJjaC9EZXRhaWwvRGV0YWlsLmFzcHg%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%2BPHJlZi10eXBlPjEyPC9yZWYtdHlwZT48Y29udHJpYnV0b3JzPjxhdXRob3JzPjxhdXRob3I%2BQ29sbGlhcywgRS4gRS48L2F1dGhvcj48L2F1dGhvcnM%2BPC9jb250cmlidXRvcnM%2BPHRpdGxlcz48dGl0bGU%2BUGh5c2ljYWwgYW5kIENoZW1pY2FsIE9jZWFub2dyYXBoaWMgRGF0YSBvZiBQdWdldCBTb3VuZCBhbmQgSXRzIEFwcHJvYWNoZXMsIDE5MzItMTk2NjwvdGl0bGU%2BPC90aXRsZXM%2BPGRhdGVzPjx5ZWFyPjE5NzA8L3llYXI%2BPC9kYXRlcz48cHVibGlzaGVyPldhc2hpbmd0b24gU2VhIEdyYW50PC9wdWJsaXNoZXI%2BPHVybHM%2BPHJlbGF0ZWQtdXJscz48dXJsPmh0dHBzOi8vYXBwcy5lY29sb2d5LndhLmdvdi9laW0vc2VhcmNoL0RldGFpbC9EZXRhaWwuYXNweD9EZXRhaWxUeXBlPVN0dWR5JmFtcDtTeXN0ZW1Qcm9qZWN0SWQ9OTk5NzE4ODU8L3VybD48L3JlbGF0ZWQtdXJscz48L3VybHM%2BPGN1c3RvbTE%2BMTkzMi0xOTY2PC9jdXN0b20xPjxyZWMtZ3VpZD40NTc5MDBiMi0wYjk0LTQwYjQtYjUzNy1iMDNmMDYyNDgzNGQ8L3JlYy1ndWlkPjxyZWMtdXNuPjMzMDwvcmVjLXVzbj48L3JlY29yZD4iLCJ0aXRsZSI6IlBoeXNpY2FsIGFuZCBDaGVtaWNhbCBPY2Vhbm9ncmFwaGljIERhdGEgb2YgUHVnZXQgU291bmQgYW5kIEl0cyBBcHByb2FjaGVzLCAxOTMyLTE5NjYifV19LHsicmVjb3JkIjp7InJlZi10eXBlIjoiMTciLCJ0aXRsZXMiOnsidGl0bGUiOiJBIFN0dWR5IG9mIHRoZSBOdXRyaWVudHMgaW4gdGhlIE1haW4gQmFzaW4gb2YgUHVnZXQgU291bmQ6IEZpbmFsIFJlcG9ydCJ9LCJjb250cmlidXRvcnMiOnsiYXV0aG9ycyI6eyJhdXRob3IiOlsiQ29sbGlhcywgRS4gRS4iLCJMaW5jb2xuLCBKLiBILiJdfX0sInVybHMiOnsicmVsYXRlZC11cmxzIjp7InVybCI6Imh0dHBzOi8vYXBwcy5lY29sb2d5LndhLmdvdi9laW0vc2VhcmNoL0RldGFpbC9EZXRhaWwuYXNweD9EZXRhaWxUeXBlPVN0dWR5JlN5c3RlbVByb2plY3RJZD05OTk3MTg4NSJ9fSwicmVjLXVzbiI6IjI2NiIsImRhdGVzIjp7InllYXIiOiIxOTc3In0sInJlYy1ndWlkIjoiNWVlNmY2ZDgtYzVkZC00MzdiLWEzN2MtOTkwNWJiNmI4NWY1In0sImJpYmxpb0NvbnRlbnQiOlt7Imdyb3VwR3VpZHMiOltdLCJyc3htbCI6IjxyZWNvcmQ%2BPHJlZi10eXBlPjE3PC9yZWYtdHlwZT48Y29udHJpYnV0b3JzPjxhdXRob3JzPjxhdXRob3I%2BQ29sbGlhcywgRS4gRS48L2F1dGhvcj48YXV0aG9yPkxpbmNvbG4sIEouIEguPC9hdXRob3I%2BPC9hdXRob3JzPjwvY29udHJpYnV0b3JzPjx0aXRsZXM%2BPHRpdGxlPkEgU3R1ZHkgb2YgdGhlIE51dHJpZW50cyBpbiB0aGUgTWFpbiBCYXNpbiBvZiBQdWdldCBTb3VuZDogRmluYWwgUmVwb3J0PC90aXRsZT48L3RpdGxlcz48ZGF0ZXM%2BPHllYXI%2BMTk3NzwveWVhcj48L2RhdGVzPjx1cmxzPjxyZWxhdGVkLXVybHM%2BPHVybD5odHRwczovL2FwcHMuZWNvbG9neS53YS5nb3YvZWltL3NlYXJjaC9EZXRhaWwvRGV0YWlsLmFzcHg%2FRGV0YWlsVHlwZT1TdHVkeSZhbXA7U3lzdGVtUHJvamVjdElkPTk5OTcxODg1PC91cmw%2BPC9yZWxhdGVkLXVybHM%2BPC91cmxzPjxyZWMtZ3VpZD41ZWU2ZjZkOC1jNWRkLTQzN2ItYTM3Yy05OTA1YmI2Yjg1ZjU8L3JlYy1ndWlkPjxyZWMtdXNuPjI2NjwvcmVjLXVzbj48L3JlY29yZD4iLCJ0aXRsZSI6IkEgU3R1ZHkgb2YgdGhlIE51dHJpZW50cyBpbiB0aGUgTWFpbiBCYXNpbiBvZiBQdWdldCBTb3VuZDogRmluYWwgUmVwb3J0IiwiZ3VpZCI6IjVlZTZmNmQ4LWM1ZGQtNDM3Yi1hMzdjLTk5MDViYjZiODVmNSIsInJlZmVyZW5jZVR5cGUiOiIxNyIsInJlY29yZFN0YXR1cyI6ImFjdGl2ZSIsInVybCI6WyJodHRwczovL2FwcHMuZWNvbG9neS53YS5nb3YvZWltL3NlYXJjaC9EZXRhaWwvRGV0YWlsLmFzcHg%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%2BMTI8L3JlZi10eXBlPjxjb250cmlidXRvcnM%2BPGF1dGhvcnM%2BPGF1dGhvcj5LaW5nIENvdW50eSw8L2F1dGhvcj48L2F1dGhvcnM%2BPHNlY29uZGFyeS1hdXRob3JzPjxhdXRob3I%2BS2luZyBDb3VudHkgTWFyaW5lICZhbXA7IFNlZGltZW50IEFzc2Vzc21lbnQgR3JvdXA8L2F1dGhvcj48L3NlY29uZGFyeS1hdXRob3JzPjwvY29udHJpYnV0b3JzPjx0aXRsZXM%2BPHRpdGxlPkNlbnRyYWwgUHVnZXQgU291bmQgZGF0YSwgMTk2NS0yMDIzPC90aXRsZT48L3RpdGxlcz48ZGF0ZXM%2BPHllYXI%2BMjAyNDwveWVhcj48L2RhdGVzPjx1cmxzPjxyZWxhdGVkLXVybHM%2BPHVybD5odHRwczovL2dyZWVuMi5raW5nY291bnR5Lmdvdi9tYXJpbmUvPC91cmw%2BPC9yZWxhdGVkLXVybHM%2BPC91cmxzPjxjdXN0b20xPjE5NjUtMjAyMzwvY3VzdG9tMT48cmVjLWd1aWQ%2BOTgxODJjNmQtMjhmMy00Y2RlLTk0OTgtMjYwZjI2MTYzYzhmPC9yZWMtZ3VpZD48cmVjLXVzbj4zMzU8L3JlYy11c24%2BPC9yZWNvcmQ%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%2BPGNvbnRyaWJ1dG9ycz48YXV0aG9ycz48YXV0aG9yPktpbmcgQ291bnR5LDwvYXV0aG9yPjwvYXV0aG9ycz48L2NvbnRyaWJ1dG9ycz48dGl0bGVzPjx0aXRsZT5IaXN0b3JpY2FsIFB1Z2V0IFNvdW5kIGRhdGEsIDE5NjUtMjAwMDwvdGl0bGU%2BPC90aXRsZXM%2BPGRhdGVzPjx5ZWFyPjIwMjQ8L3llYXI%2BPC9kYXRlcz48Y3VzdG9tMT4xOTY1LTIwMDA8L2N1c3RvbTE%2BPHJlYy1ndWlkPjIyYmM1YjY0LTQyNjctNDU0OS1hODdkLWQ1YTI2MTVhYmVmYzwvcmVjLWd1aWQ%2BPHJlYy11c24%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%2BPHJlZi10eXBlPjEyPC9yZWYtdHlwZT48Y29udHJpYnV0b3JzPjxhdXRob3JzPjxhdXRob3I%2BS2luZyBDb3VudHksPC9hdXRob3I%2BPC9hdXRob3JzPjxzZWNvbmRhcnktYXV0aG9ycz48YXV0aG9yPktpbmcgQ291bnR5IFdhdGVyICZhbXA7IExhbmQgUmVzb3VyY2VzIERpdmlzaW9uPC9hdXRob3I%2BPC9zZWNvbmRhcnktYXV0aG9ycz48L2NvbnRyaWJ1dG9ycz48dGl0bGVzPjx0aXRsZT5QdWdldCBTb3VuZCB3YXRlciBxdWFsaXR5IG1vbml0b3JpbmcgZGF0YSwgMTk2NS0yMDI0PC90aXRsZT48L3RpdGxlcz48ZGF0ZXM%2BPHllYXI%2BMjAyNDwveWVhcj48L2RhdGVzPjx1cmxzPjxyZWxhdGVkLXVybHM%2BPHVybD5odHRwczovL2RhdGEua2luZ2NvdW50eS5nb3YvRW52aXJvbm1lbnQtV2FzdGUtTWFuYWdlbWVudC9XYXRlci1RdWFsaXR5L3Z3bXQtcHZqdzwvdXJsPjwvcmVsYXRlZC11cmxzPjwvdXJscz48Y3VzdG9tMT4xOTY1LTIwMjQ8L2N1c3RvbTE%2BPHJlYy1ndWlkPmFhYzE3YWY1LWY4ZDMtNGQ4Yi05MzcyLWZjOGM4MDk5ZWY5NzwvcmVjLWd1aWQ%2BPHJlYy11c24%2BMzM2PC9yZWMtdXNuPjwvcmVjb3Jk\"\u003e(Alin et al., 2021; Collias, 1970; Collias \u0026amp; Lincoln, 1977; King County, 2024a, 2024b, 2024c; WA Dept. of Ecology, 2024)\u003c/a\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/7a40fde4c35c39e9bd392751.png"},{"id":101018581,"identity":"022dca68-bd8a-4b85-8751-74c5b373fa19","added_by":"auto","created_at":"2026-01-24 00:33:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":536522,"visible":true,"origin":"","legend":"\u003cp\u003eTime series (a) of individual cast maximum depth at Point Jefferson (PJ); site extents for PJ are shown in Figure 1b. Time series (b) of annual cast count at PJ\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/b687c5d75efe0e47b2959df0.png"},{"id":101018583,"identity":"09e48bfa-a953-466d-8a48-01187700e47c","added_by":"auto","created_at":"2026-01-24 00:33:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":420422,"visible":true,"origin":"","legend":"\u003cp\u003eFor all site casts that samples the full water column: scatter plot (a) of DO measured at the bottom of the cast vs. the minimum oxygen measured by that cast, where unity indicates that the bottom-most cast measurement is the minimum DO measured in the specific cast; (b) yearday occurrence of the annual DO minimum for all five sites. Note: hypoxic occurrence appears to be clustered near later ends of the time series due to Lynch Cove’s time record beginning in the 1950s\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/80466bb3b8a920005a3626a0.png"},{"id":101203709,"identity":"5be4b961-c684-4cfd-84e1-a333283cb7c1","added_by":"auto","created_at":"2026-01-27 09:40:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":776224,"visible":true,"origin":"","legend":"\u003cp\u003eTime series (a) of the yearday on which a cast measuring DO was taken at Point Jefferson (PJ); gray shading indicates yeardays corresponding to the Low-DO (August-November). PJ site extends are indicated in Figure 1b. Time series (b) of the bottom water oxygen values for each cast; gray shading indicates hypoxia (DO less than 2mg/L). The two time series show all cast values in gray, the cast values measured during the Low-DO season in black, and finally cast values measured during the Low-DO season that represent the 50th percentile and lower for bottom DO values at PJ for each individual year in red. This last set of values are those which are used in trend analysis for DO; see Section 2.8 for further description of this method\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/c5c9b55c1d1bd7408194fd19.png"},{"id":101204086,"identity":"473b9059-2c61-4b90-9c91-55fb90cdda90","added_by":"auto","created_at":"2026-01-27 09:41:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":951153,"visible":true,"origin":"","legend":"\u003cp\u003eBottom August-November time series for Point Jefferson (PJ) and Lynch Cove (NS) for (a) temperature, (b) salinity, and (c) DO. Site extents for PJ and NS shown in Figure 1b. Each point represents an individual cast, with profile values averaged over the site-specific bottom depth bin indicated in Table 1. Theil-Sen slopes are shown in colored lines overlaying data. 95% confidence intervals are shown in shading around Theil-Sen slopes. The slopes with confidence intervals, p-values, and sample sizes are shown in Table 3. A dotted line of zero slope at each time series’ average value is shown for comparison to 95% confidence intervals\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/ab8fd7cc1f651f08ccc4bf37.png"},{"id":101018590,"identity":"2eb081c8-1fc9-413d-99f5-9eb32af27298","added_by":"auto","created_at":"2026-01-24 00:33:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":771940,"visible":true,"origin":"","legend":"\u003cp\u003eTime series averages for (a) temperature, (b) salinity, and (c) DO for each site, season, and depth with 95% confidence intervals. All average values with confidence intervals and sample sizes are shown in Table 2\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/ce8b370e617d1833f13f53bf.png"},{"id":101204413,"identity":"c773d006-b055-4c6c-a5cb-ffab66f9d4bd","added_by":"auto","created_at":"2026-01-27 09:43:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":833934,"visible":true,"origin":"","legend":"\u003cp\u003eTheil-Sen slopes with 95% confidence intervals of each variable (row) and season (column) at all sites, for both surface and bottom values. The slopes with confidence intervals, p-values, and sample sizes for each trend analysis are shown in Table 3\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/b920bd59fd8fda9b550b2759.png"},{"id":101204300,"identity":"41a35acd-0636-46bb-baf5-cb931a50b50d","added_by":"auto","created_at":"2026-01-27 09:42:34","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":332083,"visible":true,"origin":"","legend":"\u003cp\u003eBottom solubility-based DO trends (see Section 2.4) and bottom observed DO trends at all seasons (columns). 95% confidence intervals are shown for all trends. Confidence intervals, p-values, and sample sizes are shown for all trends in Table 3\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/fd94dca2372b833d77c96965.png"},{"id":101204924,"identity":"0ec16051-3413-4ed5-8ae4-784d6a576bcf","added_by":"auto","created_at":"2026-01-27 09:45:04","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":414798,"visible":true,"origin":"","legend":"\u003cp\u003eTime series (a) of annual average temperatures with 95% confidence intervals for: 1) Seattle-Tacoma International Airport (Sea-Tac) air temperature for all months during the year, 2) Point Jefferson (PJ) August-November surface temperatures, and 3) PJ August-November bottom (deep) water temperatures. Temperature trends (Theil-Sen slopes) with vertical bars indicating 95% confidence intervals (b) for: 1) offshore 1956-2006 the 26.7σ isopycnal at Line P Station P4 (shown in Figure 1a) from Whitney et al. \u003ca href=\"https://web.endnote.com/citations/eyJkaXNwbGF5VGV4dCI6IigyMDA3KSIsImNpdGF0aW9ucyI6W3sicmVjb3JkIjp7ImRhdGVzIjp7InllYXIiOiIyMDA3In0sInNlY3Rpb24iOiIxNzkiLCJ0aXRsZXMiOnsidGl0bGUiOiJQZXJzaXN0ZW50bHkgZGVjbGluaW5nIG94eWdlbiBsZXZlbHMgaW4gdGhlIGludGVyaW9yIHdhdGVycyBvZiB0aGUgZWFzdGVybiBzdWJhcmN0aWMgUGFjaWZpYyIsInNlY29uZGFyeS10aXRsZSI6IlByb2dyZXNzIGluIE9jZWFub2dyYXBoeSJ9LCJyZWMtZ3VpZCI6ImVjYzJlNDNjLTYyYzItNGJlZi04YzE2LTE4NTcyNjI1ZDc0MCIsInJlYy11c24iOiIxNzAiLCJpc2JuIjoiMDA3OTY2MTEiLCJwYWdlcyI6IjE3OS0xOTkiLCJ2b2x1bWUiOiI3NSIsImNvbnRyaWJ1dG9ycyI6eyJhdXRob3JzIjp7ImF1dGhvciI6WyJXaGl0bmV5LCBGcmFuayBBLiIsIkZyZWVsYW5kLCBIb3dhcmQgSi4iLCJSb2JlcnQsIE1hcmllIl19fSwibnVtYmVyIjoiMiIsImVsZWN0cm9uaWMtcmVzb3VyY2UtbnVtIjoiZG9pOjEwLjEwMTYvai5wb2NlYW4uMjAwNy4wOC4wMDciLCJyZWYtdHlwZSI6IjE3In0sImd1aWQiOiJlY2MyZTQzYy02MmMyLTRiZWYtOGMxNi0xODU3MjYyNWQ3NDAiLCJleGNsdWRlQXV0aG9yIjp0cnVlLCJiaWJsaW9Db250ZW50IjpbeyJlbGVjdHJvbmljUmVzb3VyY2VOdW1iZXIiOiJkb2k6MTAuMTAxNi9qLnBvY2Vhbi4yMDA3LjA4LjAwNyIsImF1dGhvcnMiOlsiV2hpdG5leSwgRnJhbmsgQS4iLCJGcmVlbGFuZCwgSG93YXJkIEouIiwiUm9iZXJ0LCBNYXJpZSJdLCJpc2JuIjoiMDA3OTY2MTEiLCJ5ZWFyIjoiMjAwNyIsInNlY29uZGFyeVRpdGxlIjoiUHJvZ3Jlc3MgaW4gT2NlYW5vZ3JhcGh5IiwicnN4bWwiOiI8cmVjb3JkPjxyZWYtdHlwZT4xNzwvcmVmLXR5cGU%2BPGNvbnRyaWJ1dG9ycz48YXV0aG9ycz48YXV0aG9yPldoaXRuZXksIEZyYW5rIEEuPC9hdXRob3I%2BPGF1dGhvcj5GcmVlbGFuZCwgSG93YXJkIEouPC9hdXRob3I%2BPGF1dGhvcj5Sb2JlcnQsIE1hcmllPC9hdXRob3I%2BPC9hdXRob3JzPjwvY29udHJpYnV0b3JzPjx0aXRsZXM%2BPHRpdGxlPlBlcnNpc3RlbnRseSBkZWNsaW5pbmcgb3h5Z2VuIGxldmVscyBpbiB0aGUgaW50ZXJpb3Igd2F0ZXJzIG9mIHRoZSBlYXN0ZXJuIHN1YmFyY3RpYyBQYWNpZmljPC90aXRsZT48c2Vjb25kYXJ5LXRpdGxlPlByb2dyZXNzIGluIE9jZWFub2dyYXBoeTwvc2Vjb25kYXJ5LXRpdGxlPjwvdGl0bGVzPjxkYXRlcz48eWVhcj4yMDA3PC95ZWFyPjwvZGF0ZXM%2BPHBhZ2VzPjE3OS0xOTk8L3BhZ2VzPjx2b2x1bWU%2BNzU8L3ZvbHVtZT48c2VjdGlvbj4xNzk8L3NlY3Rpb24%2BPGlzYm4%2BMDA3OTY2MTE8L2lzYm4%2BPGVsZWN0cm9uaWMtcmVzb3VyY2UtbnVtPmRvaToxMC4xMDE2L2oucG9jZWFuLjIwMDcuMDguMDA3PC9lbGVjdHJvbmljLXJlc291cmNlLW51bT48bnVtYmVyPjI8L251bWJlcj48cmVjLWd1aWQ%2BZWNjMmU0M2MtNjJjMi00YmVmLThjMTYtMTg1NzI2MjVkNzQwPC9yZWMtZ3VpZD48cmVjLXVzbj4xNzA8L3JlYy11c24%2BPC9yZWNvcmQ%2BIiwiZ3JvdXBHdWlkcyI6W10sInBhZ2VzIjoiMTc5LTE5OSIsInJlY29yZFN0YXR1cyI6ImFjdGl2ZSIsInZvbHVtZSI6Ijc1IiwidGl0bGUiOiJQZXJzaXN0ZW50bHkgZGVjbGluaW5nIG94eWdlbiBsZXZlbHMgaW4gdGhlIGludGVyaW9yIHdhdGVycyBvZiB0aGUgZWFzdGVybiBzdWJhcmN0aWMgUGFjaWZpYyIsImd1aWQiOiJlY2MyZTQzYy02MmMyLTRiZWYtOGMxNi0xODU3MjYyNWQ3NDAiLCJzZWN0aW9uIjoiMTc5IiwibnVtYmVyIjoiMiIsInJlZmVyZW5jZVR5cGUiOiIxNyJ9XX1dfQ%3D%3D\"\u003e(2007)\u003c/a\u003e, 2) PJ August-November deep water, 3) PJ August-November surface water, and 4) Sea-Tac air monthly averages using all months of the year. PJ surface and deep trends are quantified in Table 3\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/ce6464b984f9e0726db69759.png"},{"id":101399101,"identity":"5e0b15bd-86da-4c54-94a6-f997ef9cbbdd","added_by":"auto","created_at":"2026-01-29 09:51:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8540952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/57f8cbd8-2f28-49b9-b879-2101b1a492de.pdf"},{"id":101204343,"identity":"6d6fa29f-2754-466d-86a2-35bd3a5356bc","added_by":"auto","created_at":"2026-01-27 09:42:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":340553,"visible":true,"origin":"","legend":"","description":"","filename":"Table23.docx","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/adf03fd38016a52d35e878f1.docx"},{"id":101204179,"identity":"ad239163-1686-42d3-9666-797bcedfc0db","added_by":"auto","created_at":"2026-01-27 09:41:53","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26297,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-8565389/v1/9a4614a07419a349347faadb.docx"}],"financialInterests":"","formattedTitle":"Century-Scale Changes in Dissolved Oxygen, Temperature, and Salinity in Puget Sound","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDissolved oxygen (DO) loss in marine environments is a global concern (Breitburg et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Diaz \u0026amp; Rosenburg, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Fennel \u0026amp; Testa, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Decreasing DO can lead to hypoxia, nominally defined as DO concentration less than 2 mg/L, which can harm species and ecosystems. Even if the hypoxia threshold is not crossed, some species may be negatively impacted by the duration and extent of low oxygen conditions (Vaquer-Sunyer \u0026amp; Duarte, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). DO loss in estuaries is particularly concerning given their economic and environmental importance and connection to both human civilization and the open ocean (Barbier et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Levin \u0026amp; Breitburg, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). More concerning, estuaries may be losing oxygen at a faster rate than the open ocean, fueling the need for a more robust understanding of DO change in these systems (Gilbert et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDO concentration in estuaries is influenced by both physical and biogeochemical processes. Air-sea gas exchange establishes an equilibrium DO concentration in surface waters that varies given wind and water properties at the surface, such as its temperature (Garcia \u0026amp; Gordon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Kanwisher, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1963\u003c/span\u003e). Vertical mixing, often driven by tides or wind, distributes DO deeper into the water column (Geyer et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Scully, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e). Primary productivity in the photic zone produces DO via photosynthesis. Respiration of organic matter depletes DO as organic matter sinks through the water column (Howarth et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The duration over which these biological processes persist in a given water mass depends on the residence time of the system. In estuaries, the residence time is primarily determined by the estuarine exchange flow, wherein density gradients between the landward and seaward ends of the estuary and the movement of tides establish a deep, landward flow and a surface, seaward flow (Hansen \u0026amp; Rattray, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; MacCready \u0026amp; Geyer, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; MacCready \u0026amp; Geyer, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; MacCready et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sutherland et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Ultimately, the oxygen concentration in an estuary and the processes that may impact it have natural variability on diurnal to decadal timescales (Alin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Babson et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Deppe et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ebbesmeyer et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Janzen et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Lee \u0026amp; Lwiza, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Masson, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Moore, Mantua, Newton, et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; O'Donnell et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Panigrahi et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Scully, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e; Scully et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnthropogenic impacts on DO decline include eutrophication and warming atmospheric temperatures. Eutrophication has been observed, for example, in northern temperate estuaries such as Chesapeake Bay, Long Island Sound, and the Baltic Sea (Carstensen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fennel \u0026amp; Testa, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Howarth et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kemp et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mackas \u0026amp; Harrison, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Parker \u0026amp; O\u0026rsquo;Reilly, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Warming due to anthropogenic greenhouse gas emissions may have long-term ramifications on estuaries (Du et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ni et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Steffen et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) because increasing ocean surface temperatures reduce the solubility of oxygen at the surface, suppress mixing via enhanced stratification, and may increase the rate of cellular respiration (Deutsch et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Keeling et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In the Baltic Sea, warming and decreasing solubility is thought to be partially responsible for increasing hypoxic extent (Carstensen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the Chesapeake Bay, the same effect is projected under future climate scenarios (Irby et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Najjar et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), accounting for about 50% of projected decline in DO (Ni et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePuget Sound is a fjordal, urbanized estuary in Washington State, USA and the southernmost arm of the Salish Sea, connecting to the Northeast Pacific Ocean via the Strait of Juan de Fuca (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The Sound is deep and narrow with shallow entrance sills, many branching terminal inlets, diverse topography including headlands and islands, and large energetic tides (Bretschneider et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Cannon, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Ebbesmeyer \u0026amp; Barnes, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Geyer \u0026amp; Cannon, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; MacCready et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is commonly divided into four subregions. Main Basin is the largest and deepest basin and is the central portion of Puget Sound, connected to all other basins, and to the Strait of Juan de Fuca via the glacial sill Admiralty Inlet (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). To its south connected via the Tacoma Narrows sill, South Sound is the shallowest basin and has many branching inlets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Whidbey Basin and Hood Canal both connect to Main Basin near its northern end. Whidbey Basin is characterized by significant freshwater input from the Skagit River (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Hood Canal is long, narrow, and deep, terminating in a shallow inlet called Lynch Cove (Alin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLike other northern temperate estuaries, Puget Sound has experienced a surge of urbanization and development in the last century, raising concerns about DO loss fueled by anthropogenic nutrient loading from wastewater treatment plant effluent or agricultural runoff (Fennel \u0026amp; Testa, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Howarth et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Takesue, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Evidence for this mechanism in Puget Sound is unclear, though modeling has shown that a reduction of anthropogenic land-based loads may decrease hypoxia occurrence (Khangaonkar et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, historical analyses, most recently conducted in the 1970s, did not find long-term DO loss nor system-wide correlation with anthropogenic nutrient pollution (Collias \u0026amp; Lincoln, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Duxbury, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). Mackas \u0026amp; Harrison (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) used a nitrogenous nutrient budget to show that the Salish Sea is unlikely to experience large-scale eutrophication due to the high ambient nitrogen concentration and the fact that the largest source of limiting nitrogen species by far is the Pacific Ocean, accounting for approximately 85% of the total dissolved inorganic nitrogen inputs in Puget Sound. Xiong et al. (\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Beutel et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) modeled Salish Sea nitrate influx and found that the major drivers of nitrate variability stemmed from offshore variability. Steinberg et al. (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) focused on the effect of watershed nutrients at Hood Canal, which has recurrent hypoxia. They identified that in the euphotic zone, entrainment of marine water accounts for approximately 98% of the nitrogen loading, demonstrating again the dominant influence of natural nitrogen sources compared to anthropogenic nutrient pollution.\u003c/p\u003e \u003cp\u003eAlthough Puget Sound consists of a complex network of basins and inlets, large tides and freshwater inflows contribute to transport processes that redistribute nutrients, salt, heat and DO through the system (MacCready et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Changes in temperature and salinity may also modify DO directly; importantly, temperature strongly influences air-sea gas exchange (Garcia \u0026amp; Gordon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Several studies link variability in water temperature and salinity to offshore climate fluctuations, river flow, tide stage, and local surface temperatures (Ebbesmeyer et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Janzen et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Moore, Mantua, Kellogg, et al., 2008). Salt and heat transport are both primarily modulated by estuarine exchange flow (MacCready \u0026amp; Geyer, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Long-term warming trends are observed in the Straits of Georgia and Juan de Fuca within the Salish Sea, and on the Washington continental shelf (Masson \u0026amp; Cummins, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Riche et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Whitney et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, trend analyses with longer than decadal-scale records of DO have yet to be conducted. Here, we assess century-scale trends of DO in Puget Sound alongside temperature and salinity, exploring potential mechanisms for change over the last 100 years.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Profile data\u003c/h2\u003e\n \u003cp\u003eTo analyze long term trends of water properties in Puget Sound, we used 12,000 + profiles measuring DO, temperature, and/or salinity from 1932 through 2024. We curated data from four separate sampling agencies: University of Washington (referred to as the Collias dataset), King County, Washington State Department of Ecology, and National Oceanic and Atmospheric Administration National Center for Environmental Information (NOAA NCEI) (Alin et al., 2021; Collias, 1970; Collias \u0026amp; Lincoln, 1977; King County, 2024a, 2024b, 2024c; WA Dept. of Ecology, 2024) (Fig.\u0026nbsp;2a). Throughout this paper we refer to these as “century-scale trends” recognizing, however, that the data only cover 93 years at most. Observations were made primarily with two sampling modes: bottle samples and CTD profiles. Bottle samples involve water collected at specific depths and analyzed in the lab. CTD (conductivity, temperature, and depth) profiles use CTD and DO sensors to make \u003cem\u003ein situ\u003c/em\u003e measurements. Modern profiling often uses rosette systems, which include multiple bottles and CTD + DO sensors. While we focus on just DO, temperature, and salinity data, we note that other water property data were concurrently collected that are not discussed here.\u003c/p\u003e\n \u003cp\u003eThe oldest dataset is a collection of sampling for various purposes compiled by Eugene Collias at the University of Washington, with later sampling collected by Collias personally. From 1932 to 1942, many regions of the Sound were sampled by research vessels from Oceanographic Laboratories at the University of Washington. While no systematic surveying was conducted, several Main Basin stations were monitored monthly (Collias \u0026amp; Lincoln, 1977). After a break during World War II, sampling resumed in 1946 occurring at irregular intervals and spacing, with the exception of targeted surveys from 1952 to 1954 (Collias, 1970; Collias et al., 1974). During 1970 and 1971, Collias led the collection of data for a study for the City of Seattle’s Department of Lighting (Collias et al., 1974). In 1974 and 1975, Collias collected data for the Municipality of Metropolitan Seattle (METRO) to begin to understand the effects of sewage discharge in Main Basin. Ultimately, no conclusive evidence of oxygen depletion as a result of wastewater discharge was found (Collias \u0026amp; Lincoln, 1977).\u003c/p\u003e\n \u003cp\u003eKing County’s Puget Sound Marine Monitoring group has conducted shipboard sampling in Main Basin near Seattle since 1965 at 31 total stations, primarily monthly (King County, 2024a). Some stations have been monitored continuously throughout this time, while others were discontinued between 1984 and 1986. More sampling stations began monitoring around 1997–1998, with more stations near Tacoma added in 2002 (King County, 2024b). In 2022, fortnightly-to-monthly sampling began at 11 stations in Whidbey Basin (King County, 2024c). Similarly, the Washington State Department of Ecology’s Marine Waters Monitoring Program has collected monthly samples at 22 stations in Puget Sound. Sampling at several sites began in 1973, with more sites being added over time (WA Dept. of Ecology, 2024). Since 1999, these sites have been monitored via float plane (Krembs \u0026amp; Sackman, 2015). Finally, NOAA NCEI has archived data from 2008 to 2018, sampled during three cruises per year working with various sampling programs. These cruises sample approximately 25 sites throughout Puget Sound (Alin et al., 2021).\u003c/p\u003e\n \u003cp\u003eSampling methods for DO, temperature, and salinity have evolved over the period of the record considered here. Measurements for these variables are almost always taken concurrently. Bottle casts have been taken throughout the dataset, while CTD profiles began in the 1970s. Temperature was initially measured using reversing thermometers on bottles (Collias, 1970), but digital thermistors on CTDs have been used since the 1980s (WA Dept. of Ecology, 2024). Salinity was measured initially using titrations; modern methods determine salinity based on electrical conductivity (Collias, 1970; Collias \u0026amp; Lincoln, 1977). Oxygen concentrations from bottle samples throughout the dataset are calculated using Winkler titrations (Barnes \u0026amp; Collias, 1958). Modern \u003cem\u003ein situ\u003c/em\u003e DO measurements use oxygen-sensing optodes or membrane-based voltage meters attached to CTDs and are calibrated against Winkler titrations (King County, 2024c). We note that, due to the historical nature of this dataset, the exact instruments used for \u003cem\u003ein situ\u003c/em\u003e profiling are not always known. Though the sampling method is similar to using CTDs, we refer to these samples as taken by unknown sondes, since we cannot verify if CTDs specifically were used (Fig.\u0026nbsp;2b). Specific description of methods and instrumentation, as discerned from historical and modern resources, are summarized in Appendix A. Also note that due to the varying precision of measurements across time, we report all trends, averages, and confidence intervals to the nearest 0.1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 River, atmospheric, and offshore data and data products\u003c/h2\u003e\n \u003cp\u003eTo investigate the mechanisms behind temperature change and its effect on DO over the last century in Puget Sound in Section 4.2, we used atmospheric daily maximum and minimum temperature data since 1948 at Seattle-Tacoma International Airport, averaged monthly (NOAA GHCN, 2018). We selected this site because of its proximity to Puget Sound’s Main Basin and its long time record. We used offshore temperature change rates from 1956–2006 at Line P Station P4 on the 26.7 kg/m\u003csup\u003e3\u003c/sup\u003e potential density surface from Whitney et al. (2007) (Fig.\u0026nbsp;1a). We chose this station because it is located near the edge of the continental shelf offshore of the Strait of Juan de Fuca, representing offshore changes without significant modification by processes occurring on the shelf. To investigate freshwater change in Puget Sound in Section 4.3, we used monthly mean flow data at the Skagit River near Mount Vernon since 1941 (USGS, 2024). The Skagit River is the largest river in Puget Sound, approximately representing bulk freshwater properties for the region since it accounts for approximately one-third of the total freshwater influx to the Sound (Banas et al., 2014).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Data quality assurance and conversions from raw data\u003c/h2\u003e\n \u003cp\u003eInitial data quality was established via quality assurance and quality control methods performed by the agencies that made the measurements and compiled the data. We performed additional filtering to remove unrealistic values in all DO, temperature, and salinity data including negative values and measurements at erroneous depths. We converted all observed temperature and salinity into conservative temperature and absolute salinity using the Gibbs Seawater Routines (McDougall \u0026amp; Barker, 2011).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Calculating DO saturation\u003c/h2\u003e\n \u003cp\u003eTo calculate the expected DO saturation for a given water mass, we used the empirical fitting formula for the solubility of oxygen in water given salinity and temperature as described in Garcia \u0026amp; Gordon (1992). We calculated the expected DO saturation for a water mass using its temperature and salinity, then calculated a trend using methods discussed in Section 2.9. These trends were then compared to the trends in DO measurements in Section 4.2. This process is similar to the concept of apparent oxygen utilization (AOU) since we apply this to both surface and bottom water masses, despite the fact that bottom water is not in immediate contact with the surface for oxygen exchange (Garcia et al., 2013).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.5 Controlling for spatiotemporal variation in long time histories\u003c/h2\u003e\n \u003cp\u003eThere is significant spatial and temporal sampling variability within the dataset due to the length of time, number of collecting agencies, goals for data collection, and change of collection methods over time (Fig.\u0026nbsp;2). Importantly, the spatial variation in this time series is due both to variability in the sampling location (Fig.\u0026nbsp;2a), as well as due to sampling depth changes within the water column at a given sampling site (Fig.\u0026nbsp;2c). For example, maximum cast depths around 200m have become more common at Point Jefferson and in the past 40 years (Fig.\u0026nbsp;3a). Variation in sampling depth is especially important since DO typically varies from higher concentration at the surface to lower concentration at the bottom; as a result, significant changes in sampling depth over time could bias trends in observed DO concentration. Finally, sampling frequency has varied significantly over the record, from an average annual cast count around 100 in the 1930s and 1940s to more than 1000 annually in the last decade (Fig.\u0026nbsp;2d). We avoided biases that might be introduced by these issues by selecting data groupings based on the following site selection criteria:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eCriterion 1: Location consistency\u003c/em\u003e To prevent spatial bias in sampling location inconsistency over time, sites were required to have tightly-clustered cast locations (latitude and longitude). This ensured that the data represented a consistent volume over time. After inspection of the available data, each site was described with a bounding polygon (Fig.\u0026nbsp;1b); casts within this polygon represent the respective site. The area and centroid of each polygon is given in Table\u0026nbsp;1.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eCriterion 2: Depth consistency\u003c/em\u003e To prevent spatial bias due to sampling depth inconsistencies over time, casts included at each site were required to maintain maximum depths within a site-specific percentage of the maximum depth of the site region. These depths were informed by the available data and are shown in Table\u0026nbsp;1. The maximum depth for each site was defined using gridded bathymetry from LiveOcean, a realistic, regional, ROMS-based numerical model with approximately 500m rectangular grid resolution (MacCready et al., 2021; NOAA NCEI, 2014).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eCriterion 3: Sufficient time range\u003c/em\u003e To prevent aliasing due to system variability on decadal or shorter timescales, sites meeting the above criteria were required to have been sampled for a span of six decades or more. These ranges are shown in Table\u0026nbsp;1. This criterion was maintained when data were filtered to specific seasons or for specific variables, though the specific year ranges for each season and variable may change.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSite-specific characteristics: centroid of site-bounding polygon, area of site-bounding polygon, maximum depth at site from LiveOcean grid (MacCready et al., 2021), percentage of maximum depth used to define top bound of bottom water bin, and range of data covered at each site\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCentroid (lon., lat.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum Depth (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBottom Depth Percentage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear Range*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoint Jefferson (PJ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-122.418, 47.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e258.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1933–2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNear Seattle (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-122.448, 47.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1932–2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaratoga Passage (SP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-122.498, 48.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1952–2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarr Inlet (CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-122.721, 47.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1935–2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLynch Cove (LC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-122.936, 47.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1932–2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003e*range for all data; range filtering to specific seasons and/or variables (i.e., DO, temperature, or salinity) may vary\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cp\u003eTo illustrate the site-selection \u003cem\u003ecriteria\u003c/em\u003e, cast locations that meet \u003cem\u003eCriterion 1\u003c/em\u003e for one selected site, Point Jefferson (PJ) are shown in Fig.\u0026nbsp;1b, enclosed in the corresponding site polygon. Throughout the time series, there have been consistent casts exceeding 200m in depth (Fig.\u0026nbsp;3a). Profiles since the late 1980s consistently sample at 200m unlike before, so to avoid potential oversampling bias at 200m and meet \u003cem\u003eCriterion 2\u003c/em\u003e, we define the bottom depth bin to be 20% of the maximum depth of the water column within the site polygon, or ~ 206m and deeper. Sampling meeting \u003cem\u003eCriteria 1 \u0026amp; 2\u003c/em\u003e at PJ began in the 1930s and continues into present day, confirming that we have at least six decades of data that meet \u003cem\u003eCriterion 3\u003c/em\u003e (Fig.\u0026nbsp;3b).\u003c/p\u003e\n \u003cp\u003eFive sites meet all three criteria, which are shown in Fig.\u0026nbsp;1b. Two sites are within Puget Sound’s central, deep Main Basin: Point Jefferson (PJ) and Near Seattle (NS). The other three are in primary Sub-Basins: Lynch Cove (LC) in Hood Canal, Saratoga Passage (SP) in Whidbey Basin, and Carr Inlet (CI) in South Sound. These sub-basins are hydrodynamically distinct and include many of Puget Sound’s terminal inlets, which may be hotspots for hypoxia (Khangaonkar et al., 2018). Notably, LC is a shallow, terminal inlet and the only one of the five sites with observed hypoxia. These sites are not necessarily representative of water properties throughout their respective sub-basins because they are selected based on the limitations of data availability; however, they do span much of Puget Sound physical parameter space.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.6 Partitioning for data analysis by depth\u003c/h2\u003e\n \u003cp\u003eAt each site, we assessed trends at both the surface and the bottom of the water column. For each individual cast meeting the site-selection criteria described in Section 2.5, the individual measurements taken during the specific cast were averaged over both a surface and bottom depth bin. We defined the surface depth bin as 5m and shallower, and the bottom depth bin as in Table\u0026nbsp;1. Thus, for a given cast, this binned depth-averaging yields one value to represent each of the surface and bottom water properties sampled by that specific cast.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.7 Partitioning for seasons\u003c/h2\u003e\n \u003cp\u003eFor each site and for each depth bin, we assessed multi-year trends occurring during specific seasons to control for inherent seasonal variability in water properties. We divided the year into three seasonal trimesters which are: August-November (late summer/early fall), December-March (winter), and April-July (spring/early summer). We refer to these seasons as the Low-DO season, Winter, and Spring, respectively. These trimesters arise from the identification of the lowest DO time period (Low-DO season). We justify this period selection in Section 2.8. Spring corresponds to the season with the most expected primary productivity and photosynthesis, while Winter corresponds to the season with increased storm activity and rainfall.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e2.8 Identifying DO minimum location and time period\u003c/h2\u003e\n \u003cp\u003eWhile we performed analyses for all sites, depth bins, and seasons discussed in Sections 2.6 \u0026amp; 2.7, we focused on understanding the mechanisms driving change during the time period and at the location in the water column where DO is lowest throughout the year. The lowest annual DO occurs at the bottom of water columns and during August-November (Low-DO). This intent is different from studies investigating specific deoxygenation rates (e.g., Pasquier et al., 2024), and instead places emphasis on the potential for long term changes in hypoxic risk in Puget Sound. To assess where in the water column DO minima occur, we considered profiles at these sites that reach the bottom depth bin as defined in Table\u0026nbsp;1. We found that approximately 65% of profile DO minima occur within the deepest 25% of the profile (Fig.\u0026nbsp;4a); thus, the highest hypoxia risk exists generally near the bottom of the water column. To assess when during the year the DO minima occur, we analyzed profiles occurring throughout the year. We found that 75% of annual DO minima occur from August to November, with minima outside this time range distributed throughout the year (Fig.\u0026nbsp;4b); thus, the highest hypoxic risk exists between August-November. We note that there are minima that occur earlier in the year, particularly during the early part of the time series. These are likely due to sampling variation between years.\u003c/p\u003e\n \u003cp\u003eWater column reoxygenation timing varies interannually and we controlled for potential bias by limiting our analysis to the lowest DO measurements. August-November DO minimum timing is variable since late fall/early winter reoxygenation is driven by wind events and winter storms (Scully, 2010b), but the sparse temporal resolution of sampling does not allow us to resolve and separate the semi-independent process of deoxygenation and reoxygenation throughout all years in our dataset. Temporal sampling bias of DO during August-November may occur, depending on the timing of sampling in relation to reoxygenation events. To account for this, we only included casts whose bottom DO falls at or below the median (50th percentile) bottom DO at that specific site and during that specific season, with the median calculated for each year separately. This is an approximate but effective way to avoid averaging DO values that may have occurred after reoxygenation and thus miss the true annual DO minimum. We illustrate this process again at Point Jefferson (PJ), where the median and below of August-November bottom DO values are selected (Fig.\u0026nbsp;5). We implemented the same process for all seasons in this analysis, ensuring that we identified trends in the DO minima in a given season and minimized bias due to interannual variability of synoptic events that may impact DO. We note that no median filtering was conducted for temperature and salinity in a given season since we are primarily concerned about the DO minima as opposed to salinity or temperature minima. We conducted the same trend analysis with all casts without filtering and found that the trends were similar from both methods and had overlapping 95% confidence intervals; thus, the results of this paper are not sensitive to differences in temporal data filtering.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e2.9 Trend analysis methods\u003c/h2\u003e\n \u003cp\u003eWe used non-parametric methods to assess trends over time, since the time series data contain gaps and are not necessarily normally distributed with normal residuals to a linear regression of the data against time. We used the Mann-Kendall test for monotonic trends to first assess if the data are increasing or decreasing over time (Gilbert, 1987; Kendall, 1975; Mann, 1945), which provides a probability, or significance, value (p-value). Then we calculated a Theil-Sen slope for each time series (Sen, 1968; Theil, 1950). This test allowed us to calculate a trend line slope that is robust to the outliers inherent in observational data and is not reliant on assumptions of normality, as is the case with linear regressions. We calculated a 95% confidence interval and reported sample sizes for all slopes. To understand the sensitivity of trends to the statistical method chosen, we conducted the same analysis of long-term trends with linear regressions. Corresponding slopes from both analysis types had overlapping 95% confidence intervals and thus had no significant difference between the two values.\u003c/p\u003e\n \u003cp\u003eTo assess correlation between two independent, non-time variables, we used simple linear regressions. We specifically used this method to assess the predictive capability of river flow on observed salinity, and this will be discussed in Section 4.5.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Time series\u003c/h2\u003e \u003cp\u003eBetween 1932 and 2024, time series of bottom August-November (Low-DO) temperature, salinity, and DO at Point Jefferson (PJ) and Lynch Cove (LC) show both temporal trends and spatial variability in water quality parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Recall that while we focus on DO, we concurrently investigated temperature and salinity to understand mechanisms for DO change. We show these time series because, of the three seasonal trimesters we evaluated, the lowest DO occurred seasonally during the late summer/early fall, or the Low-DO season, in bottom water (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These two sites represent a range of DO conditions in Puget Sound: PJ, in Main Basin, has much higher DO than LC, a distal, terminal inlet of the Hood Canal Sub-Basin which is generally hypoxic at the bottom during the Low-DO season (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the Low-DO season, Point Jefferson (PJ) had an average bottom temperature of 11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u0026deg;C while Lynch Cove (LC) had a colder average bottom temperature of 10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea; sample sizes shown in Table\u0026nbsp;2). From the time series, we see that these sites are warming over the last century and at similar rates, with PJ warming at 1.4\u0026thinsp;+\u0026thinsp;0.4/-0.3\u0026deg;C/century and LC warming at 1.2\u0026thinsp;+\u0026thinsp;0.5/-0.6\u0026deg;C/century (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea; sample sizes and p-values shown in Table\u0026nbsp;3). PJ had saltier bottom water with an average of 30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0 g/kg compared to LC\u0026rsquo;s 30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 g/kg. Salinity trends are similar between the two sites but have large uncertainties, with PJ\u0026rsquo;s trend of 0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 g/kg/century and LC\u0026rsquo;s 0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 g/kg/century (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb; Table\u0026nbsp;3). LC had lower bottom DO than PJ during the Low-DO season, with an average over the time series of 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 mg/L (see Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e2.8\u003c/span\u003e for selection of DO time series values). PJ averaged 5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 mg/L (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec; Table\u0026nbsp;2). Importantly, the rates of change of DO at both sites differ as well. Despite the high ambient oxygen concentration observed at PJ, it experiences bottom DO decline at a rate of -0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 mg/L/century. LC has a slight, uncertain increase of DO of 0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 mg/L/century (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec; Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eWe calculated time series averages to reveal the variation in water properties for each season, site, and depth bin (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e; Table\u0026nbsp;2). The highest temperatures generally occurred at the surface during the Low-DO season with an average across all sites of 13.5\u0026deg;C, while the coldest temperatures of 8.6\u0026deg;C averaged across all sites occurred at the surface during Winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea,c; confidence intervals and sample sizes for individual values shown in Table\u0026nbsp;2). The highest average temperature 16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u0026deg;C was observed at a terminal inlet, Lynch Cove (LC), at the surface during Spring (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb; Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSalinity was highest during the Low-DO season with an average across sites and both depth bins of 29.3 g/kg, while Winter and Spring had salinities of 28.1 and 28.2 g/kg, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed-f). When comparing surface and bottom depth bins, we observe variation in the difference between surface and bottom salinity. Carr Inlet (CI) had the smallest top-bottom salinity difference with an average of 0.3 g/kg across seasons, indicating weaker stratification than at other sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed-f). The largest top-bottom salinity differences were observed at Lynch Cove (LC) and Saratoga Passage (SP), where the bottom salinity exceeded surface salinity by 5.8 g/kg and 5.7 g/kg, respectively, when averaged across seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed-f). This is likely the result of proximity to river inflows from the Skagit River near SP and smaller inflows, such as the Union River, near LC, providing freshwater at the surface near these sites. We note that measurements of surface and bottom salinity do not necessarily represent full water column density stratification.\u003c/p\u003e \u003cp\u003eFor DO concentrations, all sites had higher DO at the surface than at the bottom during any given season, with Lynch Cove (LC) having the largest difference between surface and bottom values of DO at 7.3 mg/L averaged across seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg-i). The smallest difference between surface and bottom DO occurred during Winter and the largest difference occurred during the Spring, at 2.7 mg/L and 10.7 mg/L, respectively, averaged across sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg,h). Across the five long term sites evaluated, LC had the lowest DO, especially at the bottom during the Low-DO season (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg-i). The average bottom DO value across all sites for the Low-DO season was 4.5 mg/L, while the average bottom DO value at Lynch Cove (LC) was 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 mg/L (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei). Out of the five long term sites, LC was the only site with observed hypoxia, or DO less than or equal to 2 mg/L, and the presence of hypoxia was observed multiple times throughout the time series (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Century-scale trends\u003c/h2\u003e \u003cp\u003eWe summarize century-scale trends based on Theil-Sen slopes in DO, temperature, and salinity in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Table\u0026nbsp;3. All sites are warming at an average rate of 1.5\u0026deg;C/century across all sites, depths, and seasons (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-c; confidence intervals, sample sizes, and p-values for individual values shown in Table\u0026nbsp;3). On average the surface is warming at 1.7\u0026deg;C/century, slightly faster than the average bottom warming of 1.2\u0026deg;C/century (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-c). There is more variability among surface trends than at the bottom, with the largest surface trend occurring during Spring at the surface of LC at a rate 4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u0026deg;C/century (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb; Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSalinity trends vary between sites, seasons, and depths (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed-f). At the bottom, salinity is generally increasing very weakly across all sites and seasons at an average rate of 0.2 g/kg/century (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed-f). Meanwhile, the surface salinity averaged across all sites and seasons decreases, or freshens, at a rate of 0.3 g/kg/century (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed-f). However, there is more variability across surface trends than bottom trends, and large uncertainty in the largest surface freshening trends, such as at SP during Winter with a decrease of 3.7\u0026thinsp;+\u0026thinsp;2.8/-3.1 g/kg/century (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed; Table\u0026nbsp;3). Unlike SP, Main Basin sites (PJ and NS) have increasing surface salinity of 0.5 g/kg/century averaged across all seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee,f). We note that Main Basin is deep, wide, comprises approximately 65% of the total volume of Puget Sound, and is connected to all other basins in the Sound (Banas et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); thus, we consider trends here to approximate a system average, or background, trend. Distal regions may be more prone to local variability, especially from local freshwater sources such as the Skagit River, nearest to the site SP.\u003c/p\u003e \u003cp\u003eDO trends highlight distinctions between Main Basin and Sub-Basin sites, especially LC and SP (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg-i). During all seasons, bottom DO is decreasing at both Main Basin sites (PJ and NS) at a rate between 0.3 and 0.9 mg/L/century (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg-i; Table\u0026nbsp;3). Although Sub-Basin sites across all seasons generally have decreasing bottom DO as well, the trend uncertainties captured in the 95% confidence intervals tend to also include increasing slopes, unlike Main Basin sites. For example, SP during Spring has a slope of -0.3\u0026thinsp;+\u0026thinsp;0.8/-0.7 mg/L/century (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg; Table\u0026nbsp;3). Here, the trend uncertainty exceeds the DO trend on this scale. We note that surface DO trends vary more than bottom trends across sites and seasons.\u003c/p\u003e \u003cp\u003eNow we focus on the data at the bottom of the water column during the Low-DO season, which corresponds to highest hypoxia risk (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec,f,i). All sites are warming at a rate of approximately 1.5\u0026deg;C/century, averaged across sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). Salinity is increasing at all sites at an average rate of 0.3 g/kg/century but is variable between the sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef). Both Main Basin sites\u0026rsquo; DO are decreasing during the Low-DO season at the bottom of the water column (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ei). PJ has a slightly slower decline of 0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 mg/L/century, while NS has a faster decline of 0.9\u0026thinsp;+\u0026thinsp;0.3/-0.2 mg/L/century. At Sub-Basin sites, only CI\u0026rsquo;s DO is declining at a rate of 0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 mg/L/century, similar to the Main Basin sites. The other two Sub-Basin sites, SP and LC, have rates close to 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ei; Table\u0026nbsp;3). We note that the site with highest ambient oxygen concentrations (PJ, NS, and CI) have the fastest DO decrease. Conversely, LC has the lowest DO concentration, and yet no DO decrease is observed during the Low-DO season (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ei). We discuss this further in Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eWe analyzed nearly a century of data in Puget Sound, which revealed the following long-term trends. All sites are warming at an average rate of approximately 1.5\u0026deg;C/century across all seasons and depths. Salinity change is generally small and has significant variability. Finally and most importantly, central Main Basin bottom water DO is decreasing at a rate of 0.3\u0026ndash;0.9 mg/L/century. These results motivate the following questions: How do these trends affect hypoxic risk in Puget Sound? What mechanisms may explain these trends? How much DO change is explained by these mechanisms?\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Implications of DO trends for bottom hypoxia during the Low-DO season\u003c/h2\u003e \u003cp\u003eDuring the Low-DO season, Point Jefferson (PJ), Near Seattle (NS), and Carr Inlet (CI) had the highest rates of DO decline of 0.6 mg/L/century, averaged between these sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ei; confidence intervals, sample sizes, and p-values for individual values shown in Table\u0026nbsp;3). However, these sites also had the highest bottom DO concentration, ranging 5.5-6.0 mg/L (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei; confidence intervals and sample sizes for individual values shown in Table\u0026nbsp;2). Conversely, at Lynch Cove (LC), which is the only site among the five long-term sites with measured hypoxia, no clear trend is resolved; the trend is smaller than the variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ei). For the two Main Basin sites (PJ and NS) and CI, the small rates of decrease relative to the ambient oxygen concentrations do not suggest a risk of increasing hypoxic occurrence in the near future at these sites. Meanwhile in LC, the low average bottom concentration is near the zero-concentration limit, which prevents a negative trend. It is worth noting that other hypoxia metrics such as extent or duration may show trends not apparent in our analysis. These metrics could not be reliably computed with our data due to the low temporal sampling frequency.\u003c/p\u003e \u003cp\u003eRecall from Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e that the Main Basin sites (PJ and NS) are considered to be representative of background trends. The changes in background water properties observable in Main Basin may influence terminal inlets such as Lynch Cove (LC) via exchange flow (MacCready et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Water exchanged from Main Basin to terminal inlets sets the initial state of the DO coming into the deep layer. Whether or not hypoxia develops at a site is influenced by a balance between local respiration and residence time (Fennel \u0026amp; Testa, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This balance is influenced by changing conditions outside of, but connected to, these inlets themselves. In other words, lower DO in Main Basin, exchanged into LC, may set a lower baseline DO and result in more persistent low DO conditions at LC.\u003c/p\u003e \u003cp\u003eAlthough DO at LC is low enough during the low-DO season to make continued reduction hard to detect, it is instructive to consider changes during other seasons. LC has decreasing DO during both Winter and Spring of 1.3\u0026thinsp;+\u0026thinsp;1.1/-1.4 mg/L/century and 1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 mg/L/century, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg-i; Table\u0026nbsp;3). These declining trends may set a lower baseline for DO going into the Low-DO season, and thus again increase the hypoxic risk in LC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Warming accounts for a large portion of DO loss\u003c/h2\u003e \u003cp\u003eIn Puget Sound\u0026rsquo;s Main Basin, we observe a background decline of DO in bottom water over the last century. To explain this trend, we consider a DO-limiting process in estuaries that is well-resolved with our available century-scale data: the change in solubility of oxygen at the air-sea interface. The solubility of DO at the surface, or the equilibrium exchange between atmospheric oxygen and water surface DO, depends on temperature and, to a much lesser extent, salinity (Garcia \u0026amp; Gordon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). We expect surface DO solubility changes to be reflected in bottom water on century scales because of reflux and surface entrainment, similar to the concept of apparent oxygen utilization (AOU) (Garcia et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe directly compare trends in observed DO to trends in DO predictions based on DO saturation, calculated using our temperature and salinity time series, as described in Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e2.9\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Averaged across all sites, we find an average predicted bottom water DO decline based on DO saturation of 0.3 mg/L/century (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea-c; confidence intervals, sample sizes, and p-values for individual values shown in Table\u0026nbsp;3). The vast majority of this reduction (approximately 90% averaged across all sites) is due to warming, with salinity having a secondary influence. At Main Basin sites PJ and NS, the DO saturation trends vary between 20\u0026ndash;100% of the observed trends, depending on the season and site (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea-c). Averaged across all seasons, the trend in saturation-based DO predictions in Main Basin is equivalent to approximately 50% of the observed DO trend. In other words, observed warming predicts a DO reduction here that can account for nearly half of the observed reduction in bottom DO. While other mechanisms are also necessary to generate the trends in Main Basin DO, warming appears to have a dominant influence on long-term DO reduction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA similar magnitude of DO decline accounted for by decreased solubility due to warming was found in the Chesapeake Bay (Dreiss et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ni et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ni et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Baltic Sea has also experienced long-term reduction of bottom DO, which may be worsened by long-term warming (Carstensen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Such similar findings show that warming temperatures can reduce DO saturation in diverse estuarine environments (IPCC, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and suggests that this process may be important in estuarine systems worldwide.\u003c/p\u003e \u003cp\u003eAt Sub-Basin sites, there is more variability in the observed trends, often exceeding the trend in the saturation-based prediction (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea-c). Where the trend is small, such as at SP during the Low-DO season, the DO saturation trend is approximately 4 times the size of the observed trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). At LC during the Low-DO season, the observed DO trend is positive, while the DO saturation trend suggests DO loss (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). These locations demonstrate that while warming is ubiquitous in Puget Sound, DO is influenced by many spatially-variable processes that may influence century-scale trends. Further, the rates of DO decline are different between PJ and NS, despite their close proximity to each other in Main Basin, representing another measure of the uncertainty in these trends.\u003c/p\u003e \u003cp\u003eTrends in DO near the surface show more variability than those at depth, and thus they are not as easily explained by the predicted change in DO saturation over the last century (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg-i; Table\u0026nbsp;3). This variability is likely due to several processes that impact surface DO. First, the surface photic zone is only about 25m deep and is where most primary productivity occurs (e.g., Khangaonkar \u0026amp; Yun, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The amount of surface DO produced via photosynthesis varies spatially and temporally; thus, the spatial distribution of estuary gas exchange is non-uniform (Scully et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Winter et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Second, wind varies spatially and temporally in estuaries, driving variable rates of surface oxygen solubility and mixing (Ho et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kremer et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Scully, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Third, the advection of water masses, such as river plumes, and sharp interfaces between water masses can cause further variability at our sites (Baschek et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Overall, the observation that variability is lower at the bottom than at the surface suggests that the influence of near-surface processes such as those listed above has a reduced influence on the dynamics of the bottom layer. Thus, on long timescales, DO saturation change is more coherent at the bottom than on the surface given the variability and complex influences of surface processes on surface oxygen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Other potential mechanisms for DO change\u003c/h2\u003e \u003cp\u003eUsing available observational data, we have determined that the change in DO saturation, largely due to warming, accounts for approximately 50% of the observed DO decline in Puget Sound. Determining what other mechanisms also impact long-term change in DO is important to understanding long-term change in Puget Sound, yet is limited by the availability of century-scale data. While we cannot analyze the impacts of these mechanisms in this scope, we discuss the potential influence of these other processes, including anthropogenic eutrophication, the influence of increasing coastal hypoxia, changes in exchange flow dynamics, and shifts in Puget Sound ecosystem dynamics.\u003c/p\u003e \u003cp\u003eFirst, anthropogenic eutrophication due to loading of limited nutrients has been linked to oxygen decline and hypoxia in many urban estuaries (Fennel \u0026amp; Testa, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Both the Chesapeake Bay and the Baltic Sea have been shown to have positive correlations between anthropogenic nitrogen loading and algal biomass leading to declines in DO (Boynton \u0026amp; Kemp, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Larsson et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). In Puget Sound, Khangaonkar et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) used a hydrodynamic and biogeochemical numerical model to compare a year with and without anthropogenic, land-based nutrients. They found that reducing land-based nutrient loading reduces hypoxic occurrence and extent. However, as discussed in Section \u003cspan refid=\"Sec1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Puget Sound\u0026rsquo;s total nitrogen and fluctuations in nitrogen are dominated by marine sources. We would thus expect that the impacts of nutrient change on DO are similarly small (Beutel et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mackas \u0026amp; Harrison, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Steinberg et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Unfortunately, historical water quality monitoring of nitrogen species and wastewater treatment plant effluent (Mohamedali, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wasielewski et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) lacks sufficient spatiotemporal resolution for century-scale trend analysis. Anthropogenic nutrients from other land-based sources, such as agricultural runoff or deforestation-driven increases in watershed nitrogen-fixing terrestrial plants, may also play a role in nutrient loading and eutrophication (Anderson et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), but are historically poorly monitored. Pilot field surveys to assess the effects of urbanization on small bays have demonstrated the need for decades-long data collection to further understand the ecological impacts of urbanization, including anthropogenic nutrient loading (Takesue, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Where, how, and on what scales anthropogenic nutrients drive changes in system DO are the subject of ongoing modeling experiments and field data collection.\u003c/p\u003e \u003cp\u003eSecond, growing hypoxic zones and changing water properties on the Washington shelf may set a lower baseline for the oxygen concentration in water masses entering the Salish Sea. Observations of bottom oxygen on the Washington shelf have shown evidence of increasingly widespread hypoxic zones and declining DO since 1950, due to a combination of high-nutrient, low-DO water upwelling to the shelf, which is then further depleted by shelf respiration (Barth et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pierce et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). During upwelling conditions, the water that flows into Puget Sound is often denser and deeper with lower DO (Brasseale \u0026amp; MacCready, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In Eastern Boundary Upwelling Systems, such as the California Current System off the Washington coast, warming due to climate change may drive an increase in upwelling-favorable winds (Bakun, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Garc\u0026iacute;a-Reyes \u0026amp; Largier, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). While climate modeling scenarios are uncertain with regard to duration and intensity of upwelling, enhanced stratification on the shelf may also contribute to DO decline (Bograd et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Garc\u0026iacute;a-Reyes et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While establishing a linkage between the century-scale decline of DO in Puget Sound and changes in offshore water masses is outside of the scope of the available observational data, the variability of offshore source waters has been shown to be the primary driver of variation of DO in the Salish Sea (Beutel et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, changing freshwater and offshore conditions could lead to changes in estuarine exchange flow, impacting residence time in Puget Sound and thus the amount of time that biological processes can modulate DO in an estuary. The exchange flow is primarily driven by the along-channel salinity gradient and estuarine mixing, which are modified by physical processes, including freshwater discharge into the estuary, the density of offshore water masses, tidal mixing, and the seasonal variation of wind (Babson et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Deppe et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Geyer \u0026amp; MacCready, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sutherland et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Long-term changes in exchange flow due to changing environmental conditions are difficult to quantify, however, and the impact of these changes on DO in Puget Sound are even more so. For example, while exchange flow varies seasonally and tends to be smaller when river flow has been low for several months (MacCready \u0026amp; Geyer, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), results modeled over several years showed that a low river-flow did not have significantly different exchange flow than a high-flow year, despite increased salinity and decreased stratification (MacCready et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, variations in exchange flow can have bidirectional effects on the DO observed in Puget Sound. Increased exchange flow may reduce residence time, but may enhance the intrusion of offshore water, which may have reduced DO during upwelling conditions (Brasseale \u0026amp; MacCready, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In modeled budgets of Puget Sound, exchange flow tends to export DO out of Puget Sound, but can occasionally import DO, especially during downwelling-favorable wind conditions offshore (Xiong et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Ultimately, while we cannot attribute trends in Puget Sound DO to changes in exchange flow over time using observational data, exchange flow modification is very important when considering DO change in the system.\u003c/p\u003e \u003cp\u003eFinally, shifting environmental conditions may modify respiration and the rate at which DO depletion occurs in a given water mass. Increased rates of respiration driven by warming may result in faster DO decline while also enhancing ocean acidification (Gobler \u0026amp; Baumann, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Panigrahi et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Together, warming and ocean acidification may contribute to ecological changes in the system, modifying the quantity, rate, and timing of photosynthesis and respiration. Finally, ecosystem composition changes are likely given different species thresholds\u0026rsquo; for low DO conditions, potentially modifying the balance of biological processes that modify DO (Levin et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Vaquer-Sunyer \u0026amp; Duarte, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). While the effects of changes to biological processes on DO itself are not quantified here, this is an important consideration in oxygen cycling in Puget Sound.\u003c/p\u003e \u003cp\u003e \u003cem\u003e4.4 Puget Sound bottom water is warming at a rate similar to the local atmosphere and offshore\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe found that Puget Sound bottom waters are warming at approximately 1.5\u0026deg;C per century (Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e) and this can account for a significant portion of the observed DO decline (Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e). Here we consider whether this warming results from changes in offshore temperature from the Northeast Pacific Ocean or local atmospheric temperatures. Puget Sound bottom water is generally warming at a similar rate to that observed at Ocean Station Papa in the Northeast Pacific Ocean and the Salish Sea\u0026rsquo;s northernmost basin, the Strait of Georgia (Riche et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Whitney et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The Oregon shelf region near the Newport Hydrographic Line and the Strait of Juan de Fuca are also getting warmer, indicating that warming is ubiquitous in this region (Huyer et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Masson \u0026amp; Cummins, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLocal warming is also documented in increasing atmospheric temperatures at Seattle-Tacoma International Airport since 1948 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea). Although urban development has occurred around the sampling location, previous regional studies showed any potential urban heat island effect to be negligible (Arhonditsis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea shows a comparison of local air temperature to the surface and deep temperature at Point Jefferson (PJ) in Main Basin during the Low-DO season. Atmospheric temperatures are increasing at a rate of 2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u0026deg;C/century, higher than in either surface or bottom water, while the warming rate offshore at Line P Station P4, outside of the Strait of Juan de Fuca, is 0.84\u0026deg;C/century (Whitney et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), lower than the rate at PJ in Puget Sound (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb). A warmer ocean offshore indicates that warmer water is available to be exchanged into Puget Sound. However, local environmental conditions such as air temperature and river flow have been shown to be more influential than offshore variations in determining sea surface temperature and salinity in Puget Sound (Moore, Mantua, Kellogg, et al., 2008). Vertical mixing entrains surface waters deeper into the water column, which propagates the effects of local atmospheric warming to bottom water. At Admiralty Inlet, Puget Sound\u0026rsquo;s fjordal entrance sill, around 40% of outflowing surface water mixes into deeper inflow and is retained by the estuary in a process called reflux (MacCready et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This process is thought to account for higher rates of warming at depth in the nearby Strait of Juan de Fuca as compared to directly offshore (Masson \u0026amp; Cummins, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The above comparison suggests that ocean and atmospheric warming contribute to the warming of Puget Sound waters: while warming of the coastal ocean contributes to increasingly warm waters to inflowing deep water, local warming resulting from a warming atmosphere and vertical mixing is necessary to explain the observed warming rates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn a global scale, ocean warming is well-correlated with atmospheric temperature trends and greenhouse gas emissions (Levitus et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Thus, Puget Sound\u0026rsquo;s warming is likely linked to climate change and our results suggest that it is outpacing ocean warming, presumably due to the effects of local atmospheric temperature on the water temperature within the estuary and the fact that it is shallow compared to the global ocean.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Salinity changes are small and are likely forced by multiple mechanisms\u003c/h2\u003e \u003cp\u003eBottom water salinity during the Low-DO season increased at most sites over the last century, but the observed increases are small. Large trends are only observed at SP at the surface and indicate significant freshening. Like warming, salinity change mechanisms are either forced by local freshwater sources or by remote offshore sources. First, we consider rivers, which are the primary source of freshwater to Puget Sound and tend to be more impactful to observed salinity than offshore changes (Moore, Mantua, Kellogg, et al., 2008). As previously mentioned in relation to exchange flow in Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e, modeling studies have shown that salinity increased during dry years while stratification decreased (MacCready et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Long-term changes in river flow may be tied to climatological changes in precipitation and temperature, reducing snowpack and shifting peak flow timing (Barnett et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Evidence of this change is seen in earlier peak flows at the Fraser River, the largest freshwater source in the Salish Sea entering the Strait of Georgia (Riche et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Skagit River, which comprises about one third of the Puget Sound freshwater influx (Banas et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), shows evidence of similar timing shifts. Analysis of Skagit discharge since 1942 (not shown here), shows that, while the annual discharge has not changed significantly, Winter discharge has increased by approximately 25% and Spring discharge has decreased by approximately the same amount. These trends are consistent with modeling of climate change impacts on river systems in snow-dominated watersheds in Washington State (Elsner et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It is possible that changes in river flow timing may explain the small observed increases in salinity, especially during the Low-DO season when river flows are lower. However, linear regression analyses did not yield significant correlation between the Skagit River timing changes and salinity across the sites and seasons. This mechanistic link is likely impossible to resolve due to the small magnitude and large uncertainty of most salinity trends. The large surface freshening trends at SP may be explained by the local influence of the Skagit River plume itself, especially the influence that changing wind may have on the direction, advection, and mixing of the plume (Sutherland et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, we do not test this hypothesis here.\u003c/p\u003e \u003cp\u003eThe most likely driver of higher salinity from the ocean is changes in the coastal upwelling regime. Bograd et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) evaluate expected climate shifts in coastal upwelling to influence stratification. Brassaele \u0026amp; MacCready (2025) find that the salinity of water entering the Salish Sea varies with coastal upwelling. Although we lack the data to test this hypothesis, changing offshore conditions are a plausible mechanism for changing Puget Sound salinity.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eWe identified five sites in Puget Sound with sufficient temperature, salinity, and DO data to conduct century-scale trend analyses. All sites are warming at a rate of approximately 1.5\u0026deg;C/century, consistent with warming waters throughout the Salish Sea region; this warming is driven by both offshore and local atmospheric warming likely linked to climate change. Salinity changes are generally small; we observe an increase in bottom salinity of less than 0.2 g/kg/century. In Puget Sound\u0026rsquo;s central Main Basin, we observe bottom DO loss at a rate of about 0.6 mg/L/century. In Sub-Basins and regions with distal, terminal inlets, the century-scale variability of DO exceeds measured trends.\u003c/p\u003e \u003cp\u003eWe observe that the highest rates of DO loss occur at the sites with the highest ambient oxygen concentrations. Conversely, the trends where DO is lowest are variable across seasons and have high uncertainty relative to the measured slopes, obscuring century-scale trends. In particular, Lynch Cove, a distal terminal inlet with average ambient DO less than 1mg/L during fall, does not have a clear trend during this time period. However, the combination of DO decline earlier in the season along with the decreasing DO background trend in Main Basin indicate that both antecedent and inflowing DO concentrations in Lynch Cove are decreasing, which suggest that low DO conditions are expected to persist.\u003c/p\u003e \u003cp\u003eWe find that approximately 50% of background bottom DO loss can be explained by surface oxygen solubility reduction, which is primarily driven by warming. This work documents persistent changes in Puget Sound water properties over nearly a century in hopes that it can be used to inform and guide management decisions in support of ecosystem health.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNo human participants were involved in this study.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and material\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData from various agencies were used in the creation of this manuscript. Data were compiled and collected by Eugene Collias at the University of Washington (Collias, 1970; Collias \u0026amp; Lincoln, 1977). The Washington State Department of Ecology (WA Dept. of Ecology, 2024) and King County (King County, 2024a, 2024b, 2024c) historical and modern monitoring datasets were used. NOAA National Center for Environmental Information (NCEI)’s Salish cruise datasets were also used, as part of the Ocean Carbon and Acidification Data System (OCADS) (Alin et al., 2021). Further, air temperature data from NOAA Global Historical Climate Network (GHCN) (NOAA GHCN, 2018) and river discharge data from USGS (USGS, 2024) were used. Data processing and figure creation was conducted using Python 3.11.11 and the following packages: Matplotlib 3.10.1 (Hunter, 2007; The Matplotlib Development Team, 2025), Numpy 2.1.3 (Harris et al., 2020), Scipy 1.15.25 (The pandas development team, 2020; Virtanen et al., 2020), Pandas 2.2.3 (The pandas development team, 2020), Pickle 4.0 (Van Rossum, 2020), and TEOS 10/Gibbs Seawater (GSW) Oceanographic Toolbox (McDougall \u0026amp; Barker, 2011). Grid and plotting tools adapted in this work from LiveOcean (MacCready et al., 2021) can be found https://github.com/parkermac/LO.git. Raw and processed data and data processing and plotting scripts (Mascarenas et al., 2026) can be found at https://doi.org/10.5061/dryad.v6wwpzh9n.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial nor non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was funded by the King County Wastewater Treatment Division.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors’ contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDakota Mascarenas is the lead investigator and corresponding author, and has performed all data analysis, created all figures, and written the main manuscript text. Aurora J. Leeson and Kathryn M. Hewett assisted to analyze and interpret the data and to write and revise the manuscript. Alexander R. Horner-Devine and Parker MacCready assisted to conceive of the ideas of the study, to analyze and interpret the data, and to write and revise the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003eThe authors thank T. Martin, J. Newton, and G. Ikeda for their support curating datasets and verifying data methodologies and M. Brett, B. Roberts, J. Xiong, S. Mazilli, and M. Kanojia for their collaboration in refining this work. The authors are grateful for funding from the King County Wastewater Treatment Division.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlin, S. R., J. Newton, D. Greeley, B. Curry, J. Herndon, A. Kozyr, and R. A. 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Impact of Estuarine Exchange Flow on Multiple Tracer Budgets in the Salish Sea. \u003cem\u003eJournal of Geophysical Research: Oceans\u003c/em\u003e 130(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2024jc021645\u003c/span\u003e\u003cspan address=\"10.1029/2024jc021645\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 2 and 3","content":"\u003cp\u003eTable 2 and 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Puget Sound, dissolved oxygen, estuaries, long-term trends","lastPublishedDoi":"10.21203/rs.3.rs-8565389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8565389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOver the last century, many coastal ocean regions have experienced a decrease in dissolved oxygen (DO) concentrations. In this work we consider long-term changes in DO in Puget Sound, a temperate, fjordal, urbanized estuary in Washington State, USA based on nearly 100 years of water column profile data. We observe warming of 1.5\u0026deg;C/century, consistent with warming found throughout the Salish Sea region and similar to coastal ocean and local atmospheric warming. We observe that bottom salinity is increasing at a low rate. Finally, we find that bottom DO is declining at a rate of about 0.6 mg/L/century in Main basin, the largest, central section of Puget Sound. Changes in DO solubility associated with the observed increase in water temperature can account for approximately 50% of the observed DO loss. In Puget Sound\u0026rsquo;s distal terminal inlets where hypoxic conditions more commonly occur, trends in DO are generally small and the variability in DO is high, obscuring trends there. Documenting long-term changes in estuary water properties is imperative to inform management decisions for ecosystem health.\u003c/p\u003e","manuscriptTitle":"Century-Scale Changes in Dissolved Oxygen, Temperature, and Salinity in Puget Sound","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-24 00:33:16","doi":"10.21203/rs.3.rs-8565389/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-01-22T22:02:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T05:17:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Estuaries and Coasts","date":"2026-01-12T21:30:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-10T05:32:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Estuaries and Coasts","date":"2026-01-09T22:58:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"23fe68e5-6aad-4e37-a940-b981a92281f9","owner":[],"postedDate":"January 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-24T00:33:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-24 00:33:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8565389","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8565389","identity":"rs-8565389","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}