Science Stories: Adventures in Bay-Delta Data

Protecting Outmigrating Juvenile Spring-run Chinook—Part 3

Pete Nelson, PhD
Department of Water Resources

First… it’s been a while! I wrote two previous posts (Science for Salmon Part 1 and Part 2) about developing our annual forecast for the number of juvenile Spring-run Chinook salmon (“spring-run”) entering the Delta. Since then, the team developing this Juvenile Production Estimate (JPE) has been focused mainly on the modeling that uses various fish counts and environmental conditions to predict the number and timing of outmigrating spring-run. The modeling component is crucial because this is how we finally arrive at a forecast, but it’s a challenge to explain because it requires a lot of advanced mathematics.

Sometimes, scientists will point to a model into which the data are dumped and treat it as a bit of a “black box” from which emerges, sans further explanation, the answer (Figure 1). Our question—how many juvenile spring-run enter the Delta each year?—is a tough one, and we want to make the process used to determine our answer less opaque. So, this article is going to be a balancing act between that “black box” approach and an attempt to explain the beauty behind the math.

Spring-run Chinook salmon have gone from being one of the most abundant Chinook runs in California to being one of the scarcest; consequently, they are listed as threatened under both the California and Federal Endangered Species Acts. The Sacramento-San Joaquin Delta is the Chicago O’Hare International for California Chinook, the hub for adult fish returning from the ocean to their natal streams to spawn, and for the juveniles migrating downstream back to the ocean. Salmon biologists, water managers, and fishermen worry about the gauntlet of threats these anxious travelers experience, including warm water, predators, and taking the “wrong turn” into water diversions. Managing the risk posed to the spring-run population by these diversions is the responsibility of the Department of Water Resources(DWR), and to do this we need to know when and how many salmon youngsters are entering the Delta each year.

The Modeling Architecture: More Than Just a Guess

A spring-run JPE isn't a single number pulled from thin air (or a fish pulled from a barrel); it is generated by a multi-model framework that combines several sophisticated statistical sub-models. To understand how we get to the final answer, we’re going to look at the four main challenges: counting out-migrating juvenile salmon, identifying which of those fish are actually spring-run, estimating their probability of surviving their journey to the Delta, and finally forecasting how many will actually reach that stage in their life-long journey.

Challenge 1: Counting the Invisible (BT-SPAS-X)

The first step is estimating the total number of outmigrating juvenile salmon passing by sampling locations at sites like Knights Landing on the mainstem Sacramento River and on various tributaries. Figure 2, above, shows the expected abundance of outmigrating juvenile Chinook and their weekly capture probability using an example from Lower Clear Creek, 2008. There’s a lot going on in these figures: The upper graph shows weekly abundance estimates, and the lower graph shows weekly capture probabilities in a rotary screw trap. Both figures show the flow or discharge rate in green, the abundance estimates from a modified Peterson model in blue circles and the predicted fish numbers from the BT-SPAS-X model using vertical bars, gray when the estimates are based on trap captures and pink where no data were available. The thin black bars show the range around each weekly estimate within which we’re quite certain the “true” number lies; where that span is narrow, we think we’re close to reality, but where that span is broad our confidence is much reduced. There’s a bit more to the figure, but I’m going to leave it there. If you’re curious about every last detail (nice!), get in touch and I’ll help you out.

Anyway, all of this depends on Rotary Screw Traps which are floating funnels placed in the current, facing the outmigrating salmon as they travel downstream to catch a portion of the passing fish (see Figure 5 for a simplified illustration). We mark a bunch of the fish we catch and then release them upstream of the trap so they have to pass the trap again. We expect, then, to catch a mixture of a few those marked fish and more unmarked fish (i.e., not previously caught). The ratio of marked to unmarked fish tells us the trap “efficiency.” But trap efficiency is strongly affected by both the trap placement and stream conditions, giving results that are sometimes distressingly poor, catching as few as 5 in 2000 marked fish moving down the big mainstem river. These methods traditionally only look at one rotary screw trap at a time, and rely on regular sampling and efficiency tests, but the accuracy can be really poor if there are any interruptions in sampling.

To improve our abundance estimates, we’ve started using a model with penalized splines—mathematical curves that act like a flexible ruler—to smooth out the trends in outmigration, allowing us to predict the trap efficiency when the trap wasn't even in the water, or when efficiency tests didn’t happen. The model "borrows" information from all the other years at that site when the traps were running, and across other sites, as well as using current environmental conditions like stream flow to help fill in the gaps. It also accounts for "process error," recognizing that there will always be some unexplained variation in efficiency.

Challenge 2: Who is Who? (The PLAD Model)

The next challenge is that multiple runs of Chinook salmon—fall, late-fall, winter, and spring—all use the Sacramento River watershed and, based on appearance, the juveniles are indistinguishable. Traditionally, biologists used Length-at-Date (LAD) criteria, which basically assumed that if a fish was a certain size on a certain day, it belonged to a specific run. However, using length-at-date for run assignment is notoriously inaccurate, often misidentifying half of the fish it tries to categorize.

To fix this, our colleague, Noble Hendrix, developed the Probabilistic Length-at-Date (PLAD) model. Instead of a "yes or no" assignment, PLAD uses genetic data from a subset of fish to build a "mixture model.” This model looks at the distribution of fish sizes from a genetically-determined sample of juvenile salmon and calculates the probability that a juvenile is a spring-run based on its length, when it was caught, and where it was caught (Figure 3). It’s like a more accurate version of a growth chart that acknowledges that some "late bloomers" or "early birds" from different runs might overlap in size, and allows us to assign each fish captured a probability regarding its run-type (e.g., 60% likelihood spring run and 40% winter run). By using this probabilistic approach, we can assign run types even to fish that weren't genetically tested, while being honest about how certain we are in that assignment.

Challenge 3: Estimating Survival (Smolt Survival)

The previous models help us to estimate the abundance and timing of juvenile spring-run produced each year in the Sacramento River watershed, but before these fish reach the Delta they must survive something of a gauntlet as they migrate from smaller, higher elevation streams to the confluence of the Sacramento and the San Joaquin rivers. This passage is fraught with danger, ranging from potentially poor water conditions to hungry predators, and estimating the probability of their survival is critical to developing our ultimate estimate (Figure 4).

Our survival model uses fish detection data from acoustic telemetry studies to estimate survival rates for juvenile salmon migrating from their natal tributaries, down the Sacramento River or the Feather River, and eventually to the Delta. Fish tagged with tiny sound-emitting devices are detected as they pass a series of underwater listening devices along the migration path, and each detection is logged as an individual fish of known origin at a specific date and time. So, yes, the government is listening. Just not to you. A sequence of detections provides a measure of downstream passage speed, and a failure of detection suggests the likely demise of that fish somewhere upstream. The primary determinants of survival in the model are fish size (bigger fish have an improved probability of success) and environmental conditions, such as flow or water temperature. For this reason, survival probabilities vary from year to year and from fish to fish, and the model integrates newly acquired data with the previous results each year.

Challenge 4: Predicting the Future (Stock-Recruit and In-Season Models)

Now that we have a decent idea of how many juvenile salmon are moving from their rearing habitat high in the watershed and we have an estimate of their survival probabilities for different flow conditions and fish sizes, we can use models to forecast how many of those are likely to make it to the Delta. We have two general ways of doing this:

First, we can use Spawner-Outmigrant Stock-Recruit models to predict how many outmigrating juveniles that today’s parents will produce over the coming year. This is like trying to guess how many apples an orchard will produce based on how many trees were planted and what the weather is expected to be like. We include environmental conditions in our forecast models, like water temperature and peak flows during spawning and rearing. Our findings show that cooler water and higher flows generally lead to more productive salmon populations.

As the migration season begins, we switch to the other way of forecasting the expected number of outmigrants, using an In-Season Outmigrant Model. This is similar to how a GPS updates your expected arrival time as you drive. Basically, at any given date during the outmigration season, the In-season Outmigrant Model counts how many outmigrants have passed a rotary screw trap and then uses the outmigration timing recorded from previous years to forecast how many more outmigrants we expect to pass by season’s end. Early in the season (say, late December), very few outmigrants have passed most of the rotary screw trap sites, so the forecast is broad and uncertain. But as more fish pass the traps, say by the end of February or March, the model "learns" more about the current season’s migration and environmental conditions, and the forecast becomes much more precise.

Bringing it All Together: The Integrated JPE

The final "answer" comes from the integrated JPE model, which stitches all these pieces together. It takes the spawner counts, expands the rotary screw trap catch counts to estimate outmigrant abundances with BT-SPAS-X, identifies the spring-run among those outmigrants with PLAD, forecasts total season outmigrant numbers with Stock-Recruit and In-season Outmigrant models, and then uses survival models to forecast how many of those expected outmigrants will make it as they swim from their natal creeks down the mainstem Sacramento River to the legal boundary of the Delta where the river flows under the I-Street bridge in the city of Sacramento (Figure 5).

 

This modeling process relies on a massive "field-to-cloud" effort that pipes data from over 40 different information streams overseen by dozens of data stewards to ensure that the data we put into the models are reliable and updated on a weekly basis for real time management decisions. Well…that’s the system that’s being built and should be in place by the end of 2026. Because each step—from counting a single egg to tracking a juvenile past a trap—has some uncertainty, our models propagate that error throughout the entire process. This means we won’t just give management teams a single JPE number wrought in stone (the answer!); we will give them a distribution of possible JPEs centered on a highest probability estimate that expresses our degree of confidence in the JPE forecast. And we can update that forecast through the season as more data roll in and our confidence in the forecast increases.

Patience truly does lead to beauty. By moving away from simple guesses and toward these sophisticated, probabilistic tools, we are creating a more transparent and robust way to protect our threatened spring-run Chinook salmon. For us, the answer to how many juvenile spring-run are entering the Delta is going to be different every year, each time generating additional, challenging questions: What was the effect of rainfall this time? How did ocean conditions affect the health of returning adults? And how should we ensure that there are sufficient water resources for this incredible fish every year?

Table 1. This table displays each of the icons used in Figure 5, a schematic of the spring run JPE, accompanied by text describing what each icon represents.

Image	Explanation
	Sacramento River and its tributaries
	Sierra Nevada mountains with snowpack
	Dams and other diversions give us the means to store and regulate water use
	Chinook salmon are an anadromous fish, juveniles hatching and rearing in freshwater but completing most of their growth in the ocean; adults return to freshwater to spawn
	Pacific Ocean offers salmon abundant food, vast space and rapid growth potential
	Data: numbers, size, genetics and more; this information is crucial to understanding salmon ecology, determining fisheries policy, and making water use decisions
	Rotary Screw Trap: these capture juvenile salmon as they migrate downstream, holding them so that we can count and measure them before releasing them back into the river
	Scientists collect data in the field, analyze samples in the lab, enter those data into the system, and develop and run the models to understand the patterns
	Cloud storage for the data provides a central repository accessible to all
	Models, including the response of salmon production to positive environmental conditions, help to make sense of lots of data
	State capital where policy is developed, such as that determining how water use decisions are made
	Decisions like a mandatory reduction in water exports to prevent drawing migrating juvenile salmon into pumping plants, for example

Note: Dr. Josh Korman, who led the technical team developing these models, died on 25 May 2026. Josh was one of the rare quantitative ecologists who also spent major amounts of time in the field—with a mask and snorkel, pulling nets and counting fish—and his keen enthusiasm for the challenge of understanding threatened fish populations was inspiring. We lost Josh before the completion of this project, but we were lucky to work with him. As we finish, I trust that we’d have made Josh proud.

By Peter Nelson

The Challenge

Spring-run Chinook salmon (“spring-run”) are listed as threatened under both the California Endangered Species Act and the Federal Endangered Species Act. Like most salmon, these fish are anadromous: The adults, having grown and matured in the ocean, return to their natal stream to spawn, and the juveniles, after rearing in freshwater, eventually migrate downstream to the ocean (see Figure 1). During that downstream migration, juvenile salmon are exposed to a gauntlet of threats, including warm water temperatures, predators of all sorts, and “taking the wrong turn” through water diversions and getting lost on their way to the ocean. Managing or reducing the risk posed by water diversions is a responsibility of the Department of Water Resources, and to do that water managers need to know the number and timing of those outmigrating juvenile spring-run as they enter the Delta. Coming up with an accurate prediction of this—what’s termed a Juvenile Production Estimate (PDF) or JPE—is not simple. This is the first of a two part piece about our efforts to develop a JPE, both what’s been accomplished and what’s planned, as well as a timeline.

Figure 1. Spring-run chinook have a complex life cycle. The adults migrate upstream in January through March, but instead of spawning right away like most salmon they hold in coldwater pools all summer and spawn in the fall. Diagram by Rosemary Hartman, Department of Water Resources. Click to enlarge.

The Approach

We know when to expect adult spring-run to return to their natal streams to spawn based on past experience: Humans, beginning with the indigenous peoples of the West Coast, have been observing these runs for generations, and we might reasonably expect that the numbers of returning adult salmon are a decent predictor of the juvenile fish those returning salmon will eventually produce. Observations by multiple teams of biologists of adult salmon throughout the Central Valley allow us to predict the likely numbers of juvenile spring-run expected to migrate downstream and enter the Delta on the way to the Pacific Ocean each year. “Hold on a minute,” you might say, “What about the water in those streams? If the creeks are low and the water is warm, surely those baby salmon won’t do as well as they might when conditions are good.” You’d be right! The number of reproducing salmon—the parents—isn’t a perfect predictor of the number of offspring: There are many environmental factors that affect juvenile production, but, based on past studies of salmon ecology, we can include factors like flow in our analysis of the likely number of juveniles that will be produced by the annual return of adult salmon (for example, see Michel 2019; Singer et al. 2020).

These estimates, however, are just that—we can’t know exactly how the varying amount of water will affect the survival of juvenile salmon as they grow and migrate, but we should get reasonably close, and we have another source of information to improve our estimates, the number of outmigrating juveniles that we observe directly as they swim towards the Delta: The streams where spring-run spawn regularly have rotary screw traps (Video) (RSTs, Figure 2) on them. These devices divert migrating juveniles into a holding pen where biologists count and measure them each day before releasing them back into the stream to continue their journey to the sea. Data from these RSTs give us another check on our estimates based on spawner production, and are themselves an alternative means for estimating spring -run juvenile production.

Figure 2. A rotary screw trap floating in the Yolo Bypass Toe Drain with its cone out of the water (not sampling). Photo courtesy of the Department of Water resources.

One last point: In order for water managers to use these predictions for how many (and when) spring-run are expected to reach the Delta, these estimates need to happen each year before spring-run are expected to enter the Delta when water managers need to make decisions about their operations. This is especially tricky for estimates that rely on that RST data because it only takes a few weeks for juvenile salmon to travel from the RSTs to the Delta. This means that the process of counting adult salmon and (especially) juvenile salmon in the RSTs, entering those data into a shared database, and crunching the numbers to produce a JPE must be fast, efficient and accurate.

Gathering Information

This is a collaborative, interagency effort, which we began by holding a broad-based, public workshop in September 2020 with the Department of Fish and Wildlife (see Nelson et al. 2022 for details) and writing a science plan (PDF) with our agency partners to determine what monitoring data were needed to develop a spring-run JPE. Estimating an annual spring-run JPE is complicated by (1) the broad geographic and geologic range of Central Valley streams that support spring-run, (2) the challenge of developing a holistic, coordinated multi-agency monitoring framework for generating quantitative estimates of juvenile spring-run across their range, (3) the variable life history displayed across the spring-run streams, and (4) the difficulty of distinguishing juvenile spring-run from other run types (fall run, late-fall run, and winter run) found in the same streams (we will talk more about distinguishing salmon run type in our next blog post).

Monitoring

Most of the monitoring in spring-run streams is conducted by the staff of several governmental agencies (e.g., Deer Creek), gathering data on the numbers and timing of returning adults and of migrating juveniles, and tracking the changes in these metrics from year-to-year, but monitoring historically was designed to focus on local management needs, employed multiple methods and focused on different life stages across the watershed. Some work has been done to integrate data on number of returning adults (CDFW's GrandTab dataset, which produced the graph of returning adults, Figure 3 below). However, a spring-run JPE will require more a coordinated approach with the means of combining data from more than 40 monitoring programs from eight regions, several governmental agencies, and nearly two dozen data stewards and managers, using diverse methods and having large discrepancies in monitoring histories. These are significant challenges, but they can be met as long as we’re aware of the limitations (see below).

Figure 3. Total escapement (number of returning adults) by tributary for 2000-2022. Click for an enlarged version broken out by tributary.

In addition to gathering data on the number and timing of returning adults and departing juveniles, we’ll also need data on year-to-year salmon spawning success and on the survival of those outmigrating juveniles as they move from higher elevation habitats through lower, slower and warmer tributaries, and as they migrate down the mainstem of the Sacramento River to finally reach the Delta (streams with major spring-run spawning are shown in Figure 4).

Environmental conditions too are crucial: Preeminent are the quantity of water in the system and water temperature; we know that these have strong effects on salmon survivorship and behavior. The number and location of predators also vary from year to year and can affect the number of juvenile spring-run reaching the Delta.

Figure 4. Map of the Sacramento River watershed highlighting the rivers and streams where data is being collected for the spring-run JPE. Some spring-run also spawn in the San Joaquin watershed, but they have not been added to the spring-run JPE dataset yet. Click to enlarge.

Data Management

You may have heard the expression, “garbage in, garbage out”? Wherever the phrase originated, it certainly applies to ecology! Quality data and metadata (how, when, where, and by whom the data are collected) are critical to an accurate spring-run JPE and its application to salmon conservation and water management. DWR led the formation of a team to design a data management system. This team conducted extensive outreach to the various monitoring programs for the seven spring-run spawning streams identified as most important to the JPE.

This data management system is now a reality, and is designed to provide timely access to machine-readable monitoring data and metadata. To meet the annual deadlines for calculating a spring-run JPE, new RST data must be compatible across programs and reported rapidly. Building the initial dataset took over a year because of historical inconsistencies in data reporting across monitoring programs, but state and federal agencies are collaborating to make newly collected data compatible from the moment of data entry. Data from some monitoring programs are now acquired automatically from digital entry and uploads are occurring directly from the field daily; the rest of the monitoring programs will move to this “field-to-cloud” data entry system over the next several years, improving data quality and the greatly facilitating the ease of access. All historical RST data are now publicly available from the Environmental Data Initiative (use search term “JPE”), and new RST data will be added to this repository on a weekly basis. Indeed, one of the most exciting and novel aspects of the spring-run JPE effort is that it has unified much of the existing data reporting from multiple agencies monitoring along with new monitoring under a common goal and purpose.

The spring-run JPE data management program

• has now standardized data collection methodologies, schemas, encodings, and processing protocols;
• produces machine-readable data for all RST monitoring programs (adult data will follow soon);
• uploads data in near real-time to a shared data management system; and
• makes data publicly accessible in a simple format.

This system allows us to look at all the different data sources at once to learn new things! For example, if we plot the catch of salmon from the rotary screw traps at Mill Creek, the Feather River, Knights Landing, and Delta Entry from upstream to downstream (Figure 5) we see that the most upstream site (Mill Creek) catches salmon earlier than the downstream sites and catches a lot more of them. Moving downstream the catch gets smaller and smaller as juvenile salmon get lost, eaten, or die along the way. Mill Creek also has juvenile salmon leaving the stream as late as May or June, but very few of these fish make it all the way down to the Delta, indicating that later migrants might have a harder time surviving.

Figure 5. Plot of rotary screw trap catch over time for the spring of 2023 at several locations in the Central Valley. Click to enlarge.

In our next post on the spring-run JPE, we’ll describe the cutting-edge genetic tools we’re using to distinguish spring-run from the other Central Valley Chinook, the quantitative modeling we’re developing that pulls in all of the salmon and environmental data and actually produced a juvenile production estimate along with an indication of our confidence in that estimate, the peer-review process that will critique our program and recommend improvements, and where we expect to take this spring -run JPE program next.

Further Reading

• Atlas WI, Ban NC, Moore JW, Tuohy AM, Greening S, Reid AJ, Morven N, White E, Housty WG, Housty JA et al. 2020. Indigenous Systems of Management for Culturally and Ecologically Resilient Pacific Salmon (Oncorhynchus spp.) Fisheries. BioScience. 71(2):186-204.
• Nelson PA, Baerwald M, Burgess O, Bush E, Collins A, Cordoleani F, DeBey H, Gille D, Goertler PAL, Harvey B et al. 2022. Considerations for the Development of a Juvenile Production Estimate for Central Valley Spring-Run Chinook Salmon. San Francisco Estuary and Watershed Science. 20(2).
• Cordoleani F, Satterthwaite WH, Daniels ME, Johnson MR. 2020. Using Life-Cycle Models to Identify Monitoring Gaps for Central Valley Spring-Run Chinook Salmon. San Francisco Estuary and Watershed Science. 18(4).
• Harvey BN, Jacobson DP, Banks MA. 2014. Quantifying the Uncertainty of a Juvenile Chinook Salmon Race Identification Method for a Mixed-Race Stock. North American Journal of Fisheries Management. 34(6):1177-1186.
• Michel CJ. 2019. Decoupling outmigration from marine survival indicates outsized influence of streamflow on cohort success for California’s Chinook salmon populations. Canadian Journal of Fisheries and Aquatic Sciences. 76(8):1398-1410.
• Satterthwaite WH, Cordoleani F, O'Farrell MR, Kormos B, Mohr MS. 2018. Central Valley Spring-Run Chinook Salmon and Ocean Fisheries: Data Availability and Management Possibilities. San Francisco Estuary and Watershed Science. 16(1).
• Singer GP, Chapman ED, Ammann AJ, Klimley AP, Rypel AL, Fangue NA. 2020. Historic drought influences outmigration dynamics of juvenile fall and spring-run Chinook Salmon. Environmental Biology of Fishes. 103(5):543-559.

We all know climate change is going to be rough. We expect increases in temperature, changes in rainfall (where, when, and how much), and local extinctions or migration of plants and wildlife as the climate shifts. Climate change can sound abstract and is often spoken of as a phenomenon of the future, despite the changes we are already seeing in our surroundings. These changes affect the San Francisco Estuary and will eventually make it necessary to adjust the way we manage our water in California if we want to lessen the impact on those ecosystems. To better understand the impacts of climate change and to better inform management strategies, a group of Interagency Ecological Program (IEP) scientists wanted to find out how much is known about climate change in the Sacramento-San Joaquin Delta, Suisun Bay and Suisun Marsh and how management actions can lessen these effects. To do this, they gathered scientists with broad expertise – from zooplankton to aquatic vegetation – and created the Climate Change Project Work Team.

The team decided to start by creating a conceptual model (similar to the Baylands Goals model created for the San Francisco Bay) and synthesize already published research in a technical report. A conceptual model is an organized way of thinking through a particular problem, system, or idea in a visual way to make it easier to see and understand connections. Conceptual models are especially helpful when working in groups as while it is developed everyone participates and has to think through the problem and understands why the model looks like it does when it’s done. The climate change conceptual model made by the group let them see how the Estuary responds to different environmental drivers and that in turn showed what subjects to read about to find the answers they were looking for. The Climate Change conceptual model (Figure 1) started with global-scale changes in the top box, which impact landscape-scale environmental conditions in the Estuary. Those landscape-scale conditions influence site-level environmental change. For example, increases in global air temperature cause increases in water temperature in the rivers and bays, which in turn impact the temperatures experienced by each critter in the rivers. These climate-change effects also interact with landscape management (such as levee construction or wetland restoration) to impact the aquatic environment at a site.

Figure 1. The Climate Change Project Work Team's conceptual model.

Putting together the conceptual model and writing a synthesis of what we know so far is useful in other ways as well. It allowed the team to find out where there are things we need to study more if we want to be able to give better answers about what will happen in our aquatic ecosystems. The model highlighted three aquatic ecosystems in the estuary where organisms will experience different effects from climate change. The largest ecosystem in the Estuary today is open water. Marshes and floodplains make up a much smaller proportion of the habitat, but are still highly important to native species. Three different teams of scientists went on to review literature on the different ecosystems, diving into the current status of fish, benthic invertebrates, plankton and aquatic vegetation, and trying to predict changes and risks.

So, what did the teams find?

Out of the three, the open water ecosystem will be most impacted by drought and warmer temperatures. The changes brought by this will make this ecosystem more suitable for many of the invasive fish, invertebrates and aquatic vegetation, though higher salinity conditions during droughts may also favor some native fishes and aquatic vegetation (Figure 2). Predictions of future Delta temperatures have found that Delta Smelt's spawning window may be greatly restricted, further stressing this endangered fish (Brown et al. 2016).

Figure 2. Impacts of climate change in open water ecosystems include harmful algal blooms, increased invasive clams, increased aquatic weeds, and increased invasive fishes, such as largemouth bass and Mississippi silversides.

Floodplains will experience major changes in timing and magnitude of inundation. Precipitation will become more variable with more frequent extreme floods and droughts. The larger storms we have seen lately benefit floodplains and the native fish that use them to spawn and feed, but only if they occur at the right time. Floods will shift to earlier in the season as more precipitation falls as rain instead of snow, keeping migratory species from being able to use the floodplain when they need it. More frequent droughts will mean the floodplain may not be available at all for years at a time (Figure 3). Management actions that increase the frequency or duration of floodplain inundation, such as the Yolo Big Notch Project, may become more important if floodplains are to be sustainable in the future.

Figure 3. Floodplains, which are important habitat for spawning Sacramento Splittail and juvenile Chinook Salmon will not be inundated as frequently as droughts become more frequent, and may experience earlier flooding as more precipitation falls as rain instead of snow.

Tidal marshes are relatively scarce, but very important habitats. They provide food and nursery habitat for many fish and waterbird species. Whether they will continue to exist where they are will depend on the amount of sediment that will deposit in the marshes to keep up with sea level rise. Some models show that the larger storms will bring more sediment to the Delta which will help the marshes remain, but other models show that much of our tidal marsh will drown, especially if they do not have gentle, sloping transitions to uplands. Restoration planners may need to prioritize areas with adequate transition zones if they want restoration sites to be sustainable in the long-term.

Figure 4. Tidal marshes may drown as sea levels rise unless they have gentle transitions to upland areas. They may also experience the same increases to invasive species and increased temperature as open water ecosystems.

Other members of the Climate Change PWT have been working on looking for temperature trends from our monitoring record. They have found evidence for increased temperatures over the past 50 years (Bashevkin et. al., 2021), lower temperatures during wetter years (Bashevkin and Mahardja, 2022), differences in temperatures at the top and bottom of the water (Mahardja et. al., 2022), and hotter temperatures in the South Delta (Pien et. al., draft manuscript).

For a young adult audience interested to learn more about the San Francisco Estuary, the Sacramento-San Joaquin Delta in general and how climate change will affect it and the species living there check out a collection called Where the river meets the ocean – Stories from San Francisco Estuary . Many of the scientists that are on the team who wrote the Climate Change Technical Report also wrote for this collection, published by Frontiers for Young Minds.

Further Reading:

Bashevkin, S. M., and B. Mahardja. in press. Seasonally variable relationships between surface water temperature and inflow in the upper San Francisco Estuary. Limnology and Oceanography

Bashevkin, S. M., B. Mahardja, and L. R. Brown. 2021. Warming in the upper San Francisco Estuary: Patterns of water temperature change from 5 decades of data.

Brown, L. R., L. M. Komoroske, R. W. Wagner, T. Morgan-King, J. T. May, R. E. Connon, and N. A. Fangue. 2016. Coupled downscaled climate models and ecophysiological metrics forecast habitat compression for an endangered estuarine fish. Plos ONE 11(1):e0146724. 

Colombano, D. D., S. Y. Litvin, S. L. Ziegler, S. B. Alford, R. Baker, M. A. Barbeau, J. Cebrián, R. M. Connolly, C. A. Currin, L. A. Deegan, J. S. Lesser, C. W. Martin, A. E. McDonald, C. McLuckie, B. H. Morrison, J. W. Pahl, L. M. Risse, J. A. M. Smith, L. W. Staver, R. E. Turner, and N. J. Waltham. 2021. Climate Change Implications for Tidal Marshes and Food Web Linkages to Estuarine and Coastal Nekton. Estuaries and Coasts.

Dettinger, M., J. Anderson, M. Anderson, L. Brown, D. Cayan, and E. Maurer. 2016. Climate change and the Delta. San Francisco Estuary and Watershed Science 14(3).

Knowles, N., C. Cronkite-Ratcliff, D. W. Pierce, and D. R. Cayan. 2018. Responses of Unimpaired Flows, Storage, and Managed Flows to Scenarios of Climate Change in the San Francisco Bay-Delta Watershed. Water Resources Research 54(10):7631-7650. 2

Mann, M. E., and P. H. Gleick. 2015. Climate change and California drought in the 21st century. Proceedings of the National Academy of Sciences 112(13):3858-3859.