Geology topics

Research about Central Valley

Source-linked reports with geographic coverage including Central Valley.

At least 19 recordsLinked to original sources

USGS2026

Satellite embeddings for crop type classification: A comparative examination

Embedding datasets encode complex relationships among multiple sources of Earth observation data into a compact format. Here, we evaluated the utility of a 10-m global Satellite Embedding product (SE) for classifying crop types in central California for the year 2020. We compared the classification accuracy of a random forest model based exclusively on the SE layer to an existing random forest model with multiple imagery inputs. Our results showed the SE-based classification had higher agreement with the reference dataset (California Department of Water Resources crop map) than the classification based on Landsat and National Agricultural Imagery Program inputs (94.7% versus. 91.9% overall accuracy, respectively). The performance of individual crop types was consistent across models, ranging from high agreement for rice (98.4% versus 98% accuracy) to lower agreement for pasture, grain, and fallow/young perennial classes (< 65% accuracy in both models). The SE-based workflow used three times less cloud-based computational resources and represented substantial savings of predictor development time. Geospatial embedding products can aid classification efforts by reducing predictor development time and processing demands while maintaining classification accuracy.

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USGS2026

Comparing DESIS hyperspectral and Landsat 10 simulated superspectral data for crop type classification in California's Central Valley

To advance crop type mapping in support of global food and water security, this study compared three spectral configurations: (A) the full 60-band DLR Earth Sensing Imaging Spectrometer (DESIS) hyperspectral narrowband (HNB) dataset, (B) a 14-band subset of DESIS-derived HNBs aligned with the planned Landsat 10 (formerly Landsat Next) spectral configuration (400–1000 nm), and (C) DESIS-based simulations of Landsat 10 superspectral broadbands. The analysis was conducted in California’s Central Valley, hereafter referred to as “the Central Valley”, during the peak growing month of August. DESIS imagery from August 2021, 2022, and 2023 was used sequentially for model development, testing, and independent validation. Over these three years, DESIS provided extensive hyperspectral coverage of much of the 4 million hectares in the Central Valley’s. Analyses were performed on Google Earth Engine using two pixel-based supervised classifiers, Random Forest (RF) and Support Vector Machine (SVM), to differentiate three major crop classes: row crops, grapes and tree crops, and winter wheat/fallow/other. The highest overall accuracy (86%) was achieved using SVM in combination with either the full DESIS hyperspectral dataset or the 14 DESIS narrowbands corresponding to Landsat 10. This finding aligns with earlier studies showing a small number of strategically positioned narrowbands can be optimal for crop type classification. Use of the narrowband datasets resulted in substantially higher accuracy (overall accuracy of 86%) compared to the simulated Landsat 10 broadbands (overall accuracy of 75%), supporting previous studies highlighting the utility of narrowbands. Despite the high accuracy using August imagery, the study indicates more granular crop type classification will require multi-temporal observations spanning the full phenological cycle (June–October), especially for a large number of crop classes. Acquiring task-based hyperspectral imagery over such large areas throughout the growing season remains operationally challenging. In contrast, Landsat 10 superspectral imagery could provide routine coverage across seasons and years that is practical and scalable for future large area crop type mapping and agricultural monitoring.

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USGS2026

Fatty acid profiles recorded in ocean prey and California salmonine eggs reveal maternal ocean diets linked to thiamine deficiency

Following the principle ‘you are what you eat’, fatty acid signatures (FASs) of adult fish tissues, when compared with those of potential prey, can link diet to nutritional status. In 2020, thiamine (vitamin B 1 ) deficiency was diagnosed for the first time in multiple anadromous salmon populations in California. However, direct information linking ocean diet to thiamine status in marine-feeding salmonines is typically unavailable. This research aimed to link diet composition and egg total thiamine concentration of anadromous Chinook salmon, coho salmon and steelhead using FAS analysis. From 2020 to 2022, unfertilized eggs from these California salmonine species were collected alongside key forage species from the Pacific Ocean. FASs in eggs and prey were quantified using gas chromatography/mass spectrometry and total thiamine concentrations were quantified using high-performance liquid chromatography. Eggs rich in oleic acid (18:1n-9) reflected a diet dominated by Pacific herring and exhibited higher total thiamine concentrations. Conversely, eggs rich in eicosapentaenoic acid (20:5n-3) indicated a diet dominated by northern anchovy or euphausiid krill and were more likely to have low total thiamine concentrations. Polyunsaturated fatty acid proportions and the egg unsaturation index were strongly negatively correlated with egg total thiamine concentration, emphasizing the importance of lipid quality over quantity as a driver of egg thiamine concentration. This study highlights the utility of FASs for tracking the impacts of a changing ocean prey base on diet and underscores how dietary shifts can directly impact egg thiamine status in anadromous salmonines.

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USGS2026

Oak masting breaks down in a highly fragmented, pollen limited landscape

Masting, the production of variable and synchronous seed crops, is common among wind pollinated trees, and periodic pollen limitation is thought to be an important component of the process of masting. However, little is known either about the dynamics of mast seed production or pollen limitation in isolated trees. We tracked seed production of valley oak ( Quercus lobata ) over 11 years in a highly fragmented landscape in California’s Central Valley in which many trees lacked conspecific neighbors within 500 m. We found that oaks with closer neighbors produced more acorns on average than oaks with fewer neighbors. Consistent with theories linking mast seeding and pollination, the isolation effect was most apparent in the highest seed production years. While individual trees showed substantial interannual variation in seed production, we observed little synchrony in seed production among individuals compared to intact oak populations throughout California. Our results indicate that highly fragmented populations suffer signs of pollen limitation and do not produce synchronous seed crops. Fragmentation, alongside climate change, is a dimension of global change that interrupts the typical seed production dynamics of trees.

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USGS2026

Modeling future groundwater depletion to evaluate sustainability goals set under the Sustainable Groundwater Management Act in the critically overdrafted basins of the Central Valley, California, USA (2020–2070)

In 2014, California's Sustainable Groundwater Management Act (SGMA) mandated local agencies to devise and implement groundwater sustainability plans to address critically overdrafted conditions throughout the state's aquifers. However, the feasibility of these agencies' sustainability goals has not previously been assessed through a regional-scale, integrative lens. Here, we develop and analyze a novel, basin-wide database of 936 sustainability indicator wells located within Central Valley subbasins designated as critically overdrafted, most of which lie in the San Joaquin Valley. Our database shows 2040 groundwater elevation goals vary widely from 60 m above to 80 m below 2020 levels, with variability within and between adjacent subbasins. To evaluate the feasibility of achieving these goals, we coupled the database with a regional hydrologic model (Central Valley Hydrologic Model version 2) and simulated multiple future pumping scenarios. Results show that under increased groundwater demand, 60%–70% of indicator wells may fail to meet their 2040 goals. Even a 50% reduction from 2020 demand levels leaves nearly 40% of wells failing to meet their sustainability thresholds by 2040. Baseline models show that by 2070, up to 70% of wells could fail to meet their goals due to large-scale, spatially connected regions of groundwater depletion. This integrated framework, linking the first region-wide compilation of SGMA indicator wells with a regional groundwater model, demonstrates that many local sustainability goals may be unattainable with substantial (up to 50%) reductions in pumping. Additional management interventions, such as expanded recharge or coordinated demand reductions, may help achieve sustainability goals.

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USGS2025

Assessing causes and consequences of winter surface water dynamics in California’s Central Valley using satellite remote sensing

California's Central Valley is increasingly vulnerable to winter floods. A comprehensive spatial baseline of flood extents is critical for inundation analyses that can enhance future flood predictions, but cloud cover has prevented the regular observation of surface water extents with optical satellite imagery. In this study, we leveraged the daily resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to create a continuous series of monthly Dynamic Surface Water Extent (DSWEmod) images across the Central Valley from January 2003 to January 2023. We used the timeseries to assess the climatic driving forces of winter (Oct–April) surface water variability at sub-basin and pixel scales. At the sub-basin scale, we evaluated the influences of winter precipitation, occurrence of atmospheric rivers, and antecedent soil moisture on monthly surface water extents and found that the greatest correspondence occurs in mid-winter (Dec–Feb); in contrast, non-precipitation drivers such as water management play a stronger role in autumn and spring. The pixel-level analysis identified the probabilities of precipitation-driven surface water occurrences in the Sacramento basin are highest along rivers, conveyance channels, and floodways, with higher probabilities under wetter antecedent soil moisture conditions. Precipitation-driven surface water occurrences are also common in leveed areas and outside flood boundaries designated by state and federal agencies where exposure of structures to inundation was larger in terms of their value. Finally, areas with more frequent precipitation-driven flooding have poor recharge potential but are commonly within 5 km of areas classified as having good potential. This study demonstrates a novel approach for exploring the utility of MODIS for understanding surface water dynamics in mid-winter, a period characterized by peak precipitation, flood risk, and surface water extent. This information can provide valuable insights for (1) assessing flood risks for infrastructure and populations, (2) identifying areas most suited to strategic water management investments to increase recharge, and (3) analyzing precipitation thresholds that trigger flooding to allow proactive water management strategies to minimize damage and maximize recharge.

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USGS2024

An evaluation of tradeoffs in restoring ephemeral vs. perennial habitats to conserve animal populations

Introduction: Habitat loss and degradation pose significant threats to global fish and wildlife populations, prompting substantial investments in habitat creation and restoration efforts. Not all habitats provide equal benefits, leading to challenges in prioritizing restoration actions. For example, juvenile anadromous salmonids require high quality rearing aquatic habitats to achieve the physiological requirements needed to successfully migrate to the ocean. However, there are profound disagreements among anadromous salmon restoration managers whether it is best to focus efforts on restoring in-channel habitats that are available for the entire rearing period or floodplain habitats that, while facilitating greater growth and survival than in-channel habitats, are only available for a few weeks at a time and are typically only activated every two-to-three years. Methods: We used an existing fall-run Chinook salmon decision-support model to evaluate under what conditions floodplain restoration would provide greater benefits than in-channel habitat restoration. The simulations included a wide range of floodplain inundation frequencies and durations and floodplain benefits in the form of increased survival and growth relative to in-channel habitats. Results: The simulations results indicated that in-channel habitat restoration was always the best habitat restoration action when there was no existing in-channel habitat despite simulating a wide range of flood frequency, duration, and growth and survival benefits. Floodplain restoration was generally best when there was sufficient in-channel habitat available to successfully rear most of the juveniles produced by the returning adult salmon. Discussion: We hypothesize that in-channel and floodplain habitats have different roles in salmon population maintenance with in-channel habitats regulating the overall population size and floodplains acting as recurrent resource pulses. Our study provides a quantitative framework to evaluate the benefit of these two habitat types and provides generalizable rulesets that can be used by managers when implementing habitat restoration strategies for species that inhabit both in-channel and floodplain habitats.

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USGS2024

Groundwater sustainability and land subsidence in California’s Central Valley

The Central Valley of California is one of the most prolific agricultural regions in the world. Agriculture is reliant on the conjunctive use of surface-water and groundwater. The lack of available surface-water and land-use changes have led to pumping-induced groundwater-level and storage declines, land subsidence, changes to streamflow and the environment, and the degradation of water quality. As a result, in part, the Sustainable Groundwater Management Act (SGMA) was developed. An examination of the components of SGMA and contextualizing regional model applications within the SGMA framework was undertaken to better understand and quantify many of the components of SGMA. Specifically, the U.S. Geological Survey (USGS) updated the Central Valley Hydrologic Model (CVHM) to assess hydrologic system responses to climatic variation, surface-water availability, land-use changes, and groundwater pumping. MODFLOW-OWHM has been enhanced to simulate the timing of land subsidence and attribute its inelastic and elastic portions. In addition to extending CVHM through 2019, the new version, CVHM2, includes several enhancements as follows: managed aquifer recharge (MAR), pumping with multi-aquifer wells, inflows from ungauged watersheds, and more detailed water-balance subregions, streamflow network, diversions, tile drains, land use, aquifer properties, and groundwater level and land subsidence observations. Combined with historical approximations, CVHM2 estimates approximately 158 km 3 of storage loss in the Central Valley from pre-development to 2019. About 15% of the total storage loss is permanent loss of storage from subsidence that has caused damage to infrastructure. Climate extremes will likely complicate the efforts of water managers to store more water in the ground. CVHM2 can provide data in the form of aggregated input datasets, simulate climatic variations and changes, land-use changes or water management scenarios, and resulting changes in groundwater levels, storage, and land subsidence to assist decision-makers in the conjunctive management of water supplies.

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USGS2024

Potential use of poultry farms by wild waterfowl in California's Central Valley varies across space, times of day, and species: implications for influenza transmission risk

Interactions between wildlife and livestock can lead to cross-species disease transmission, which incurs economic costs and threatens wildlife conservation. Wild waterfowl are natural hosts of avian influenza viruses (AIVs), are often abundant near poultry farms, and have been linked to outbreaks of AIVs in poultry. Interspecific and seasonal variation in waterfowl movement and habitat use means that the risk of disease transmission between wild birds and poultry inevitably varies across species, space, and time. Here, we used GPS telemetry data from 10 waterfowl species in and near California's Central Valley, a region where both wild waterfowl and domestic poultry are abundant, to study selection of poultry farms by waterfowl across diel, seasonal, and annual cycles. We found that waterfowl selected for wetlands, open water, protected areas, and croplands, which meant that they generally avoided habitats that were likely to be used for poultry farming. These selection patterns were linked to species' ecology and diel behavioral patterns, such that avoidance of poultry habitats was stronger for local or partial migrants than for long-distance migrants, and stronger during daytime than at night. We then combined these habitat selection results with data on poultry farm locations to map risk of waterfowl–poultry contact across the Central Valley. Average selection strength at poultry farms was low, suggesting that current placement of poultry farms is generally effective for limiting risk of contact with wild birds. When we combined these habitat selection results with data on species' abundances and AIV infection prevalence, we found dramatic variation in potential AIV transmission risk among species. These results could be used to prioritize surveillance and biosecurity efforts for regions and times of relatively high risk. More generally, these results highlight that fine-scale movement data can help identify interspecific, seasonal, and diel patterns in animal behaviors that affect wildlife and poultry health.

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USGS2024

Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California's Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer’s accuracies of 94% to 100%, and user's accuracies of 95% to 100%.

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USGS2023

Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California

In arid and Mediterranean regions, landscape-scale wetland conservation requires understanding how wildlife responds to dynamic freshwater availability and conservation actions to enhance wetland habitat. Taking advantage of Landsat satellite data and structured and community science bird survey data, we built species distribution models to describe how three duck species, the Northern Pintail ( Anas acuta ), Green-winged Teal ( Anas crecca ), and Northern Shoveler ( Anas clypeata ), respond to freshwater supply and food resources on different flooded land cover types in the Central Valley of California. Specifically, our models compared duck habitat suitability between the wettest and driest conditions in each month from September through April. Using abundance-weighted boosted regression trees, we created three sets of species occurrence models based on different covariates: (1) near real-time (hereafter “real-time”) covariates in which duck observations were matched to the water availability within the 16-day window of a Landsat observation, (2) a combination of real-time covariates and waterfowl food resource covariates describing annual corn and rice biomass and managed wetland moist soil seed yield estimates derived from Landsat data, and (3) long-term average covariates—the most common approach to species distribution modeling—in which long-term average surface water availability was used. We modeled the monthly occurrence of three duck species as a function of surface water availability, land cover type, road density, temperature, and bird data source. We found that dry conditions result in reduced habitat suitability, with the biggest reductions in November through January and in agricultural fields; in contrast, suitability of flooded wetland habitat was relatively robust to surface water availability. When models of habitat suitability based on long-term average climate conditions were compared to models based on real-time conditions, the highest long-term suitability values occurred in areas where suitability was high regardless of whether it was a wet or a dry year. While all models performed well, the inclusion of crop and wetland plant yield covariates resulted in slightly higher model performance. Overall, species distribution models created using data on the environmental conditions present at the time of bird observations can aid conservation efforts under extreme conditions over large spatial scales.

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USGS2023

Modeling the dynamic penetration depth of post-1950s water in unconfined aquifers using environmental tracers: Central Valley, California

The penetration depth of post-1950s recharge (D-1950) in aquifers is a marker that is frequently used to identify groundwater that is susceptible to anthropogenic contamination. Here, we compute D-1950 values at wells, interpolate them in space, and project them across time to map the moving front of modern recharge in four dimensions in the Central Valley aquifer system, California, USA. Tracers of groundwater age (tritium, carbon-14, noble gases, sulfur hexafluoride, and chlorofluorocarbons) were collected at 650 wells spatially distributed throughout the Central Valley and were fit to a lumped-parameter model that assumes a logarithmic age-depth profile in the aquifer. For samples where tritium was present (>0.3 tritium units), the model was used to predict D-1950 at wells screened above or across the modern-premodern interface (n = 484). Wells with samples where tritium was absent (≤0.3 tritium units) were used to define the depth beyond which groundwater is completely premodern (n = 166). Predicted D-1950 values were below the depth of screen bottoms for wells where groundwater is completely modern, and above the depth of screen tops for wells where groundwater is completely premodern. The interpolated surface of D-1950 is dynamic, less prone to extreme values, and produces maps with lower interpolation errors due to a higher spatial density of wells than maps based on the depth of premodern groundwater. Between 2005 and 2025, D-1950 is expected to deepen by 11 and 12 m in the northern and southern parts of the Central Valley, respectively. Areas where D-1950 increases rapidly are likely to see increases in nitrate and other anthropogenic contaminants associated with the downward moving front of modern water.

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USGS2023

Knowledge coproduction on the impact of decisions for waterbird habitat in a changing climate

Scientists, resource managers, and decision-makers increasingly use knowledge co-production to guide the stewardship of future landscapes under climate change. This process was applied in the California Central Valley, USA to solve complex conservation problems, where managed wetlands and croplands are flooded between fall and spring to support some of the largest concentrations of shorebirds and waterfowl in the world. We co-produced scenario narratives, spatially-explicit flooded waterbird habitat models, data products, and new knowledge about climate adaptation potential. We document our co-production process, and using the co-produced models, we ask: “when and where do management actions make a difference?” and “when does climate override these actions?” The outcomes of this process provide lessons learned on how to co-create usable information and how to increase climate adaptive capacity in a highly managed landscape. We found that: 1) actions to restore wetlands and prioritize their water supply create habitat outcomes resilient to climate change impacts particularly in March, when habitat is most limited, 2) land protection combined with management can increase the ecosystem's resilience to climate change, and 3) the uptake and use of this information was influenced by the roles of different stakeholders, plus rapidly changing water policies, discrepancies in decision-making time frames, and immediate crises of extreme drought. While a broad stakeholder group contributed knowledge to scenario narratives and model development, to co-produce usable information, data products were tailored to a small set of decision contexts, leading to fewer stakeholder participants over time. A boundary organization convened stakeholders across a large landscape, and early adopters helped to build legitimacy, yet broad-scale use of climate adaptation knowledge will depend on state and local policies, engagement with decision-makers that have legislative and budgetary authority, and the capacity to fit data products to specific decision needs.

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USGS2022

Climate and land change impacts on future managed wetland habitat: A case study from California’s Central Valley

Concept California’s Central Valley provides critical habitat for migratory waterbirds, yet only 10% of naturally occurring wetlands remain. Competition for limited water supplies and climate change will impact the long-term viability of these intensively managed habitats. Objectives Forecast the distribution, abundance, and connectivity of surface water and managed wetland habitats, using 5 spatially explicit (270 m 2 ) climate/land use/water prioritization scenarios. Mapping potential future dynamic flooded habitat used by waterbirds and other wetland-dependent wildlife to inform management decisions. Methods We integrated a climate-driven hydrologic water use model with a spatially explicit land change model, to examine stakeholder-driven scenarios of future land change, climate, and water use and their impacts on future habitat availability. Results Declining water availability is the dominant driver of habitat loss across scenarios. The hot/dry scenarios showed the greatest declines in January flooded area by 2101—an important month for overwintering waterbirds. In contrast, higher water supplies in wet climates drive perennial cropland conversion and loss of potential habitat. Potential flooded cropland declined (25 and 33%) under warmer/wetter climate conditions due to this conversion to perennial crops, exposing habitat vulnerability. Conclusion Climate-driven loss of water availability had a greater impact on flooded habitat availability than land-use change. When combined, climate change and the conversion of potentially flooded cropland to perennial cropland will threaten future waterbird habitat particularly in January, the peak of the migratory bird season, even when habitat restoration goals are met. Stakeholder-informed scenario analysis can identify target areas for potential habitat change, vulnerability, and conservation.

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USGS2021

Post-drought groundwater storage recovery in California’s Central Valley

Groundwater depletion is a major threat to agricultural and municipal water supply in California's Central Valley. Recent droughts during 2007–2009 and 2012–2016 exacerbated chronic groundwater depletion. However, it is unclear how much groundwater storage recovered from drought-related overdrafts during post-drought years, and how climatic conditions and water management affected recovery times. We estimated groundwater storage change in the Central Valley for April 2002 through September 2019 using four methods: GRACE satellite data, a water balance approach, a hydrologic simulation model, and monitoring wells. We also evaluated the sensitivity of drought recovery to different climate scenarios (recent climate ± droughts and future climate change scenarios: 20 GCMs and 2 RCPs) using water balance method and statistical sampling of historical climate data. Estimated Central Valley groundwater loss during the two droughts ranged from 19 km 3 (2007–2009) to 28 km 3 (2012–2016) (median of four methods). Median aquifer storage recovery was 34% and 19% of the overdraft during the 2010–2011 and 2017–2019 post-drought years, respectively. Numerical experiments show that recovery times are sensitive to climate forcing, with longer recovery times for a future climate scenario that replicate historical climatology relative to historical forcing with no droughts. Overdraft recovery times decrease by ∼2× with implementation of pumping restrictions (30th to 50th percentiles of historical groundwater depletion) to constrain groundwater depletion relative to no restrictions with a no-drought future climatology. This study highlights the importance of considering water management implications for future drought recoveries within the context of climate change scenarios.

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USGS2021

Critical aquifer overdraft accelerates degradation of groundwater quality in California’s Central Valley during drought

Drought-induced pumpage has precipitated dramatic groundwater-level declines in California’s Central Valley over the past 30 years, but the impacts of aquifer overdraft on water quality are poorly understood. This study coupled over 160,000 measurements of nitrate from ∼6,000 public-supply wells with a 30-year reconstruction of groundwater levels throughout the Central Valley to evaluate dynamic relations between aquifer exploitation and resource quality. We find that long-term rates of groundwater-level decline and water-quality degradation in critically overdrafted basins accelerate by respective factors of 2–3 and 3–5 during drought, followed by brief reversals during wetter periods. Episodic water-quality degradation can occur during drought where increased pumpage draws shallow, contaminated groundwater down to depth zones tapped by long-screened production wells. These data show, for the first time, a direct linkage between climate-mediated aquifer pumpage and groundwater quality on a regional scale.

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USGS2021

Using high resolution satellite and telemetry data to track flooded habitats, their use by waterfowl, and evaluate effects of drought on waterfowl and shorebird bioenergetics in California

Wetland managers in the Central Valley of California, a dynamic hydrological landscape, require information regarding the amount and location of existing wetland habitat to make decisions on how to best use water resources to support multiple wildlife objectives, particularly during drought. Scientists from the U.S. Geological Survey Western Ecological Research Center (WERC), Point Blue Conservation Science (Point Blue), and the U.S. Fish and Wildlife Service (USFWS) partnered to learn how wetland and flooded agricultural habitats used by waterfowl and shorebirds change during the non-breeding season (July–April) particularly during drought. During extreme drought conditions, the ability to provide sufficient water for wildlife often depends on the timing of water deliveries to managed wetlands and winter-flooded crop fields and decisions on whether to fallow croplands. Waterfowl and shorebirds could be particularly affected by these decisions because they typically rest and feed in flooded habitats. Poor habitat conditions resulting from spatially or temporally suboptimal water deliveries (that is, mismatch between waterfowl habitat needs and timing and location of flooded habitats) could reduce waterfowl hunting opportunities and body condition. Point Blue scientists developed a system for near real-time tracking of habitats used by waterfowl, shorebirds, and some other wetland-dependent “waterbirds” ( www.pointblue.org/watertracker ) and to assess the impacts of drought on habitat availability and on waterfowl and shorebird bioenergetics. The WERC researchers linked these data with near real-time tracking (telemetry) data of duck locations throughout the Valley. The team used these two datasets to relate duck locations to open-water characteristics and to learn how waterfowl use habitats under spatially and temporally changing conditions during drought and non-drought periods. We found that recent extreme drought (2013–15) significantly changed the timing and magnitude of flooding and consequently reduced the availability of habitats used by waterfowl and shorebirds more than other recent historic droughts 2000–11. Drought reduced irrigations of moist soil seed plants and thus there was lower food energy available for waterfowl. Analyses using bioenergetics models indicated that, overall, extreme drought increased food energy deficits (total number of deficit days) for shorebirds and waterfowl. Our analysis indicated a strong direct relationship between duck locations and classified habitat derived from open-water data during the wintering period (October–March). This result helps confirm the application of dynamic water data to identify flooded areas that provide waterfowl habitat. Presence of open water at a 1-hectare resolution can be used effectively to identify flooded landscape areas available as habitat for ducks. Our discoveries from evaluating use of space by ducks also indicated that nighttime feeding locations of ducks were concentrated nearby primary roosts and that foraging distances could depend on hydrologic dynamics of location (Suisun Marsh versus California excluding Suisun Marsh) and time of season (early, middle, late). Other results indicated that some areas on the California landscape with extremely reliable water supplies could receive consistent use by ducks year after year (in essence, almost drought proof). The Water Tracker is set up to automatically track wetland habitat and food availability each year and is making these data available to water and wetland managers. Results from this research are a significant step toward understanding how waterfowl and shorebird habitats can be optimally managed on the landscape to support desired populations of these migratory birds during extreme drought.

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USGS2020

Disease in Central Valley salmon: Status and lessons from other systems

Chinook Salmon ( Oncorhynchus tshawytscha ) are increasingly vulnerable to anthropogenic activities and climate change, especially at their most southern species range in California’s Central Valley. There is considerable interest in understanding stressors that contribute to population decline and in identifying management actions that reduce the impacts of those stressors. Along the west coast of North America, disease has been linked to declining numbers of salmonids and identified as a key stressor resulting in mortality. In the Central Valley, targeted studies have revealed extremely high prevalence of infectious agents and disease. However, there has been insufficient monitoring to understand the effect that disease may have on salmon populations. In order to inform future research, monitoring, and management efforts, a two-day workshop on salmon disease was held at UC Davis on March 14-15, 2018. This paper summarizes the science presented at this workshop, including the current state of knowledge of salmonid disease in the Central Valley and current and emerging tools to better understand its impacts on salmon. We highlight case studies from other systems where successful monitoring programs have been implemented. First, in the Klamath River where the integration of several data collection and modeling approaches led to the development of successful management actions, and second in British Columbia where investment in researching novel technologies led to breakthroughs in the understanding of salmon disease dynamics. Finally, we identify key information and knowledge gaps necessary to guide research and management of disease in Central Valley salmon populations.

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