Brian D Healy Corey C Phillis Brian Mahardja 20250410 tabular data Flagstaff, AZ U.S. Geological Survey Additional information about Originators: Healy, Brian D, https://orcid.org/0000-0002-4402-638X; Phillis, Corey C., https://orcid.org/0000-0002-8940-3441; Mahardja, Brian, https://orcid.org/0000-0003-0695-3745 https://doi.org/10.5066/P13BBC7D Brian D Healy Corey C Phillis Brian Mahardja Cameron Koizumi Catarina Pien Nancy Parker J. Louise Conrad Julie Ekstrom Julie Leimbach Rafael Silberblatt Tom Fischer Chase Ehlo 2025 publication Open-File Report 2025-1055 NA U.S. Geological Survey https://doi.org/10.3133/ofr20251055 These data were compiled to support water management decision-making by the U.S. Bureau of Reclamation and California Department of Water Resources related to the Central Valley Project (CVP) and State Water Project (SWP) operations in water year 2025. The objectives of the study were to use structured decision making to assess tradeoffs between multiple competing water needs and inform decisions related to summer and fall flow and non-flow actions for delta smelt (Hypomesus transpacificus). These data represent composite utility scores (performance) for each alternative water management strategy considered in the decision analysis, and calculated using objective weights for each participating agency or non-governmental organization. These data were collected during in-person and online workshop sessions between February 13 and March 25, 2025, in Sacramento California. These data were collected and generated by the U.S. Geological Survey, Metropolitan Water District of Southern California, and the U.S. Bureau of Reclamation using established linear value models. The data can be used to rank alternative water management strategies to inform decisions related to water releases from CVP and SWP reservoirs and pumping stations. The purpose of these data are to support water management decision making by the U.S. Bureau of Reclamation and California Department of Water Resources in water year 2025. These data were created to allow these agencies to rank alternative management strategies and consider tradeoffs between associated objectives according to their importance to the agency decision makers, while also gathering input from other management agencies and nongovernmental organizations that have interests or are affected by water management decisions. These data are meant to be applied only to this specific decision context in 2025 in the Central Valley of California, USA. These data were generated from a structured decision making process to inform decision-making by the US Bureau of Reclamation Bay-Delta Office and California Department of Water Resources in February and March 2025. The data can be used by the decision-makers, workshop participants, and public to understand the consequences of water management decisions to water supply exports, endangered Delta Smelt, water quality and human health, and operational flexibility of reservoirs and dams for meeting habitat requirements for salmon and steelhead in the Central Valley of California, USA. The results are specific to the 2025 decision-making process related to water management alternatives considered for implementation between May and October 2025, but consequences to future water operations are reflected in storage volumes in Folsom Lake in December 2025 and predicted for Shasta Lake in April 2026, while incorporating uncertainties in winter 2025-2026 hydrology. All predicted consequences are subject to uncertainty related to hydrological and climatic variation, and actual outcomes of management actions considered may be influenced by other water management or fish and wildlife management actions unrelated to this decision-making process. Lastly, these scientific data offer valuable information to the public that directly improves the Nation by delivering reliable and current information to address challenging water and endangered species management issues that can affect habitat requirements for fish while promoting productive ecosystems for the nearby communities in the Central Valley of California that depend on them, thereby contributing to their health, safety, and prosperity. Data users should read the entire metadata record and acquire the manuscript identified as the ‘Larger Work Citation’ to have a complete understanding of how these data were created and used. These data are specific to the uses identified above, as described in the ‘Larger Work Citation’, and any other use of these data would be inappropriate. See 'Distribution liability' statements for more information. 20250213 20250325 observed Complete None planned -122.5827 -121.0277 40.9576 37.3889 ISO 19115 Topic Category biota environment inlandWaters USGS Thesaurus agriculture decision support methods ecosystem management estuarine processes environmental health (human) fish hydrology modeling public supply water use salinity water budget water quality water supply and demand USGS Metadata Identifier USGS:67d30fb0d34e1acf3979ce70 USGS information products data release None Chinook salmon delta smelt composite utility scores estuary multicriteria decision analysis municipal water predicted performance metric scores steelhead water management Geographic Names Information System (GNIS) American River California Emmaton Folsom Lake Jersey Point Keswick Lake Oroville Oroville Rock Slough Sacramento Sacramento River Sacramento Valley Sacramento-San Joaquin Delta San Francisco Bay San Joaquin River Shasta Lake None Folsom Dam Keswick dam Oroville Dam Shasta Dam Suisun Marsh No access constraints No use constraints. License, Creative Commons Zero v1.0 Universal. Brian D Healy U.S. Geological Survey Research Biologist mailing and physical

2255 North Gemini Drive

Flagstaff AZ 86001 United States 928-556-7094 bhealy@usgs.gov Data collection and analysis supported by the U.S. Bureau of Reclamation, Bay-Delta Office, Sacramento California, Metropolitan Water District of Southern California, and California Department of Water Resources. Terry D. Beacham Clyde B. Murray 1990 Wiley Online Library Transactions of the American Fisheries Society https://doi.org/10.1577/1548-8659(1990)119<0927:TESADO>2.3.CO;2 California Department of Water Resources 2023 Sacramento, CA California Department of Water Resources https://deltacouncil.ca.gov/pdf/science-program/2023-04-19-climate-adjusted-historical-documentation.pdf California Department of Water Resources 2021 California Department of Water Resources (online) California Department of Water Resources https://water.ca.gov/Library/Modeling-and-Analysis/Bay-Delta-Region-models-and-tools/Delta-Simulation-Model-II Michael A. McCarthy Colin Thompson 2021 Zoological Society of London (online) Animal Conservation https://doi.org/10.1017/S136794300100141X Kenneth A. Rose Wim J. Kimmerer Karen P. Edwards William A. Bennett 2013 Wiley Online Library Transactions of the American Fisheries Society https://doi.org/10.1080/00028487.2013.799518 Michael C. Runge Ellen Bean David R. Smith Sonja Kokos 2011 USGS Publications Warehouse (online) U.S. Geological Survey https://pubs.usgs.gov/of/2011/1012/ N. Sandhu D. Wilson R. Finch F. Chung 1999 Sacramento, CA California Department of Water Resources NA S. Seneviratne S. Wu 2007 Sacramento, CA California Department of Water Resources https://sitesreservoirproject.riptideweb.com/references/REF21/Appx_28A_ClimateChange/Seneviratne_Wu_2007_%E2%80%9CChapter%203%20%E2%80%93%20Enhanced%20Development%20of%20Flow-Salinity%20Relationships%20in%20the%20Delta%20Using%20Artificial%20Neural%20Networks%20Incorporating%20Tidal%20Influence%E2%80%9D..pdf D.R. Smith M.C. Runge D.M. Martin S.J. Converse 2023 Sheperdstown, WV U.S. Fish and Wildlife Service NA U.S. Bureau of Reclamation 2024 U.S. Bureau of Reclamation (online) U.S. Bureau of Reclamation, California Great Basin Region https://www.usbr.gov/mp/nepa/nepa_project_details.php?Project_ID=54661 U.S. Fish and Wildlife Service 2006 Sacramento, CA U.S. Fish and Wildlife Service, Energy Planning and Instream flow Branch https://www.noaa.gov/sites/default/files/legacy/document/2020/Oct/07354626849.pdf The unique values for each attribute field (column labels) were reviewed and checked for accuracy, adherence to controlled vocabularies, character encoding, completeness, consistency of terms and spelling. The data values are valid and the units are aligned with the measurement being represented. Capitalization and punctuation are used consistently and, where applicable, follow standard conventions. “No data”, “null”, or blank values are represented using codes appropriate for the data format and content definitions of the data field. Attribute values are within expected ranges for a particular field and do not contain any duplicate records or features. Data values were queried to confirm that numerical values were not outside a reasonable range for a particular field. Outlier checks were performed by plotting numerical values bounded within a range. Data value unit abbreviation codes were compared with code names were for consistency. Data set is considered complete for the information presented, as described in the abstract. Data do not contain any proprietary or sensitive information. Users are advised to read the rest of the metadata record carefully for additional details. Development of the Performance metric scores data table (DeltaSmeltSDM_table1_2025.csv): Workflow for water supply and Delta Smelt objectives (March 2025) We used a series of models to estimate Delta Smelt population parameters and water supply objectives for the alternate water management strategies. To predict the water operation necessary to conduct the various actions and calculate their respective water supply costs, including for the State Water Project (SWP) and Central Valley Project (CVP objectives, we used CalSim 3, a water management simulation model for California’s Central Valley Project and State Water Project (https://water.ca.gov/Library/Modeling-and-Analysis/Central-Valley-models-and-tools/CalSim-3). The primary input to CalSim 3 consists of a 100-year (1922-2021) historical sequence of empirical monthly flows in California’s Central Valley, which can then be adjusted to represent various user-defined water demand and management scenarios from which the model will generate a large number of hydrologic outputs. Some of these user-defined variables include agricultural, urban, and managed wetland water demands, return flows, and groundwater recharge rates. Based on this array of inputs, CalSim 3 calculates river and stream flows, water diversions and return flows, reservoir storage, deliveries to water users, salinity at Suisun Marsh and its corollary (X2-location of low salinity zone by km from the Golden Gate upstream), Delta outflow. We used the 2024 set of environmental regulations as a baseline for our analysis, which also included climate change and sea level rise adjustments corresponding to 2022 median values (California Dept. of Water Resources, 2023; USBR, 2024). A detailed description of CalSim 3 is available at: https://data.cnra.ca.gov/dataset/calsim-3-0-release. For exports for the CVP and SWP, in thousand acre-feet (volume), we used the following parameters from CalSim3: C_CAA003_SWP (SWP pumping at Banks) and C_DMC003+C_CAA003_CVP (pumping at Jones and CVP pumping at Banks). Volumes were converted from monthly cubic feet per second to thousand acre feet, and then summed up by water contract year: January to December for SWP and March to February for CVP. Data was then subsetted to just action years (wet and above normal Sacramento Valley water year index type) and years that follow an action year, with each year only counted once. Annual total export volume values were then averaged over the subsetted contract years for CVP and SWP. To estimate the consequences of the alternative actions on Delta Smelt population dynamics, we leveraged an individual-based life cycle model configured in the R version 4.2.2 (R Core Team 2022, https://github.com/BDO-Science/ds-ibmr-2025) programming language (hereafter referred to as IBMR). The IBMR is a modification of the Delta Smelt individual-based model developed by Rose et al. (2013). The model was calibrated to abundances and growth rates estimated from the wild Delta Smelt population for years 1995 to 2014, and it provides a mechanistic description of the Delta Smelt life cycle as affected by temperature, turbidity, prey density, and entrainment of Delta Smelt individuals into water export facilities. The IBMR simulates reproduction, growth, mortality, and movement of the Delta Smelt population across 12 regions and can simulate abundance. To translate hydrologic information into ecological phenomena that could be represented in the IBMR, the CalSim 3 outputs were fed into three sub-models, which were used to dynamically adjust 1) the Delta Smelt spatial distribution, 2) regional salinity, and 3) zooplankton abundance and distribution. The Delta Smelt distribution sub-model was used to estimate changes in the species’ distribution based on monthly average X2 predicted by CalSim 3. The Delta Smelt distribution sub-model predicted the proportions of Delta Smelt in the 12 IBMR regions based on a given X2, using historical fish survey data and a nearest neighbor approach. When a change in X2 was predicted, Delta Smelt spatial distributions were adjusted by substituting the observed spatial distributions with the historical spatial distribution at X2 closest to the predicted X2. Although we extrapolated changes in distribution based on X2 for every month, X2 and therefore distributions did not vary across alternatives/scenarios outside of summer-fall months. The second sub-model was an X2-salinity sub-model - a generalized linear model that predicts the monthly mean salinity at the 12 IBMR regions based on X2 location. Changes in salinity, due to manipulation of X2 for the alternative water management strategies, were only expected to occur in the Confluence region and further downstream; therefore, zooplankton variation as a function of change in salinity were only estimated for the Confluence and downstream regions. Salinity information produced from the X2-salinity sub-model was then fed into the third sub-model, which governed the connection between salinity and zooplankton. The salinity-zooplankton sub-model consisted of a set of generalized additive models that predict zooplankton density for the downstream IBMR regions based on a given salinity and day of the year. Predictions from the salinity-zooplankton sub-model were converted to scalars representing the change in zooplankton density from observed densities, which were then applied to the original IBMR prey data for each taxonomic group, alternative action, downstream IBMR region, and month between 1995 and 2014. For each alternative, IBMR was run for 330 iterations using the relevant data input (e.g., zooplankton scalar values, X2, etc.). For each 20-year time series iteration, the smallest adult cohort size was retrieved and then averaged across the 330 iterations for every alternative. This expected minimum population size serves as an indicator for the propensity for decline (McCarthy and Thompson 2001). References: California Department of Water Resources (2023), McCarthy and Thompson (2001), Rose and others (2013), U.S. Bureau of Reclamation (2024) Workflow for winter-run Chinook Salmon objective (March 2025) Down-ramping of late-summer or fall flows from the summer peak, which coincides with winter-run Chinook Salmon redd construction and egg-laying, can lead to redd dewatering and complete egg mortality within dewatered redds. The proportion of redds dewatered was calculated using predicted summer and fall (June through September) Sacramento River flow below Keswick generated using CalSim3 for each alternative management strategy, and the Gard model (USFWS 2006). Redd dewatering for winter-run Chinook Salmon was estimated using CalSim monthly hydrology data, historic carcass surveys, and the ‘Gard’ model (USFWS 2006). This instream flow model predicts the proportion of redds dewatered under varying flow conditions based on a "Spawning" flow (the flow at spawning onset) and a "Dewater" flow (the lowest flow experienced before emergence). Spawning timing was determined from annual carcass surveys, with raw counts standardized by calculating the monthly proportion of female carcasses. Emergence timing was estimated using Beacham and Murray’s (1990) formula, which relates incubation duration to water temperature. For each management action, spawning and dewater flows were assigned to historical spawning proportions by month, and emergence month was calculated by adding incubation days to the spawning month. The "Spawning" flow was derived from CalSim-modeled flows corresponding to each spawning month, while the "Dewater" flow represented the lowest monthly flow experienced between estimated spawning and emergence. These values were cross-referenced with lookup tables from USFWS (2006) to estimate the proportion of redds expected to be dewatered under each flow condition. Monthly dewatering was calculated by multiplying the proportion of spawners in each month by the estimated proportion of redds dewatered, and these values were summed across each management action to determine total dewatering for each hydrologic year. Absolute estimates of redd dewatering proportions from this modeling approach are known to be unreliable and biased and should be viewed as relative differences in redd dewatering proportions across alternatives. References: Beacham and Murray (1990), U.S. Fish and Wildlife Service (2006) Workflow for Steelhead objective (March 2025) Operations of Folsom Lake reservoir focus on maintaining a minimum of 300 thousand acre-feet (taf) in December to minimize operational effects to steelhead in the American River downstream. The probability 300 taf would be in storage at Folsom Lake in the December following an action was calculated for each alternative using CalSim3. Workflow for Coldwater pool objective –SWP (March 2025) The River Valve Outlet System (RVOS) at Oroville is not working this year. Without this outlet system, the ability to withdraw cooler water at lower depths in Lake Oroville is severely limited. Based on discussions with experts from State Water Contractors it was determined Oroville needs to target an end of September storage of at least 1.85 million acre-feet so the functioning outlet facilities (i.e., not the RVOS) can withdraw water at low enough temperatures during the summer to meet Feather River temperature compliance. The probability of 1.85 million acre-feet of storage in Lake Oroville at the end of September was calculated for each alternative using CalSim3 output action years (i.e., “wet” or “above normal” water years based on the Sacramento Valley water year index). Workflow for Coldwater pool objective –CVP (March 2025) Lake Shasta water storage is managed to provide cold water discharge in the summer in the Sacramento River mainstem below the Keswick dam to minimize egg-to-fry mortality to winter-run Chinook Salmon. Shasta Lake must have sufficient storage volume to release cold water for the summer and is managed with an objective of maintaining 3.7 million acre-feet (maf) in April. Winter hydrology has an important effect on the April coldwater pool volume in Shasta Lake and cannot be predicted the previous year. However, management during the summer of one year can affect volume the following April (depending on uncertain winter hydrology). To address this uncertainty and address the risk of flow actions in one year on the following April’s volume, the probability of achieving 3.7 maf or greater in April was predicted using CalSim3 exceedance curve outputs for the Shasta Storage parameter (for all water year types). Workflow for human health objectives (March 2025) CalSim 3 uses an artificial neural network (ANN) algorithm developed by California Department of Water Resources (Sandhu et al. 1999) to translate water quality standards into flow equivalents that are to be met through SWP and CVP simulated operations. The ANN constructs flow-salinity relationships and provides a rapid transformation of this information into a form usable by CalSim 3 operations. In short, the ANN utilizes model runs from DSM2, a one-dimensional hydrodynamic and water quality simulation model used to simulate tidal flows, water quality, and particle tracking in the Delta (DWR 2021). The ANN statistically correlates the salinity results from a particular DSM2 model run to the various peripheral flows (Delta inflows, exports, and diversions), Delta channel depletions, Delta Cross Channel gate operations, and Suisun Marsh Salinity Control Gates (SMSCG). The ANN is calibrated or trained on DSM2 results that may represent historical or future conditions using a full-circle analysis (Seneviratne and Wu 2007). A total of 148 days of values for key parameters is included in the correlation, representing an estimate of the length of memory of antecedent conditions in the Delta. The ANN model approximates DSM2 model-generated salinity at the following key locations for the purpose of modeling Delta water quality standards: X2, Sacramento River at Emmaton, San Joaquin River at Jersey Point, Sacramento River at Collinsville, and Old River at Rock Slough. Jersey Point conductivity values from CalSim3 were used to assess potential impacts to human health in the Sacramento-San Joaquin Delta. Based on discussion with experts from Contra Costa Water District and State Water Contractors, it was decided that values below 700 µmho/cm represent a range where human health concerns are not likely to occur, and that values above 1,800 µmho/cm are what water operations would try to avoid when possible due to human health concerns. Between 700 and 1800 µmho/cm there was concern some Municipal and Industrial water source may require additional treatment to avoid human health concerns. A constructed scale was used where the conductivity at Jersey Point in the 12 months following action years (i.e. June to May in Above Normal or Wet water year types) were converted to the following values and then averaged: <700 µmho/cm = 1, 700-1800 µmho/cm = 0.5, > 1800 µmho/cm = 0. References: California Department of Water Resources (2021), Sandhu and others (1999), Seneviratne and Wu (2007) 2025 Development of the Composite utility scores data table (DeltaSmeltSDM_table2_2025.csv): Composite utility scores were calculated using multi-criteria decision analysis methods. The calculations are completed using a linear (additive) value function: Vj = ∑(wi*xi,j) where V is the composite utility score for alternative j, w is the weight on objective i, and x is the predicted performance score for each objective i and alternative j (Converse 2020). Objective weights were first elicited from each participating agency or organization using swing weighting techniques, and then composite utility scores were calculated for each individual group, for each alternative water management strategy, in March 2025. 2025 Data Quality Assessment and Quality Control (QAQC): Quality Assurance (QA) - Quality assurance procedures followed established processes used in previous water planning analyses completed by the Bureau of Reclamation (Bureau of Reclamation, 2024). We followed established swing weighting methods (Runge and others, 2011) to elicit objective weights. The data (objective weights) used in the calculations of composite utility scores were provided by the participants in an established swing weighting score sheet that was created by USGS and USFWS for an NCTC decision tools course (Smith and others, 2023) to ensure objective weights represented the values each participant placed on the objectives while considering the range of consequences for each objective and performance metric. Quality Control (QC) - The Bureau of Reclamation CalSim3 modelers followed methodology used in previous water planning procedures used for decision making for the Central Valley Project’s Long Term Operations (Bureau of Reclamation 2024). Modelers also shared all output with the California Department of Water Resources technical staff to ensure the results were logical and reasonable. To ensure objective weight data, elicited through swing weighting, used to calculate composite utility scores accurately represented the values of each participant, we met one on one with each participant that submitted objective weights to discuss their results, their rationale and logic used to rank and score the objectives, and the composite utility scores calculated from their objective weights. For the composite utility score calculations in Table 2, three analysts separately checked all data for transcription errors in table 1 and made the composite utility score calculations separately and independently and then compared results to ensure they were consistent and that no mistakes in the calculations were made. 2025 Finalize Data for Dissemination: Data sent to the Southwest Biological Science Center Data Steward for dissemination and preservation per USGS Data Management Policies SM 502.6, SM 502.7, SM 502.8 and SM 502.9 (1 October 2016). 2025 DeltaSmeltSDM_table1_2025.csv This data table represents predicted performance metric scores by alternative water management strategy, for each fundamental objective identified by participants during structured decision-making workshops held between February and March 2025. The purpose of this data table is to inform weighting of objectives by participating agencies and non-governmental organizations, by displaying the range of consequences for each objective. Producer defined Alternatives This attribute in the data table identifies alternative water management strategies. The attribute values represent a unique identifier for each alternative water management strategy considered in a structured decision making process. Producer defined 1 Status quo – 2024 record of decision flows, fall X2 at 80km Producer defined 2 Maximize Delta Smelt- evenly distributed summer flow with fall X2 at 74 km Producer defined 3 Maximize Delta Smelt- summer flows similar to historic, June peak flow and summer recession with fall X2 at 74 km Producer defined 4 Summer-Fall even flow – Evenly distributed summer flow and fall X2 at 80 km Producer defined 5 Summer-Fall historic – summer flow with peak in June, and fall X2 at 80 km Producer defined 6 Summer even flow – Evenly distributed summer flow, no fall X2 Producer defined 7 Summer historic – June peak flow and summer recession, no fall X2 Producer defined 8 Summer even flow/modified salinity control gate action, no fall X2 Producer defined 9 June action – minimize duration of summer flow, no fall X2 Producer defined 10 Maximize water supply – minimum flows for water quality standards, incl. salinity control gate Producer defined 11 Maximize water supply, no salinity control gate action – minimum flows for water quality standards Producer defined DeltaSmelt_MinN This attribute in the data table represents the minimum abundance of delta smelt. The attribute values represent the minimum abundance of delta smelt estimated using an established model for each alternative water management strategy (desired direction of this attribute is to maximize the value). Producer defined 39297.20473 1834144.412 decimal number CVP_water This attribute in the data table represents the predicted water supply deliveries from the federal Central Valley Project in thousand acre-feet. The attribute values represent the water supply delivered from the federal Central Valley Project for each alternative water management strategy (desired direction of this attribute is to maximize the value). Producer defined 2104 2298.5 thousand acre-feet SWP_water This attribute in the data table represents the predicted water supply delivered from the California State Water Project in thousand acre-feet. The attribute values represent the water supply delivered from the California State Water Project for each alternative water management strategy (desired direction of this attribute is to maximize the value). Producer defined 2511 2999 thousand acre-feet Steelhead_effect This attribute in the data table represents the degree to which fall-run Chinook Salmon and steelhead would be affected. The attribute values represent the probability of Folsom Lake volume being >300 thousand acre-feet in December of a flow action year which represents the degree to which each alternative water management strategy will allow cold water releases to avoid negative consequences to fall-run Chinook Salmon and steelhead (desired direction of this attribute is to maximize the value). Producer defined 0.8375 1 probability WinterRun_effect This attribute in the data table represents the proportion of winter-run Chinook Salmon redds dewatered. The attribute values are proportions of winter-run Chinook Salmon redds dewatered in the Sacramento River downstream of Shasta Dam as a results of alternative water management strategies(desired direction of this attribute is to minimize the value). Producer defined 0.058444678 0.189530949 proportions ColdpoolSWP This attribute in the data table represents the probability of September Oroville Lake storage >1.85 million acre-feet. The attribute values are probabilities of Oroville Lake storage volume being at or above 1.85 million-acre feet in September 2025, which represents the degree to which each alternative water management strategy will allow for maximal flexibility in Oroville Dam operations to meet habitat requirements for salmonids (desired direction of this attribute is to maximize the value). Producer defined 0.3902439 0.95121955 probability Human_health This attribute in the data table represents a constructed scale as a measure of salinity standards representing water quality and human health. The attribute values represent a constructed scale of salinity values predicted for each alternative water management strategy where values closer to zero represent poor water quality and values closer to 1 represents good water quality and human health (desired direction of values is to maximize the value). Producer defined 0.7775 0.948 constructed scale ColdpoolCVP This attribute in the data table represents the probability of April Shasta Lake storage >3.699 million acre-feet. The attribute values are probabilities of Shasta Lake storage volume being at or above 3.7 million-acre feet in April, which represents the degree to which each alternative water management strategy will allow for maximal flexibility in Shasta Dam operations to meet habitat requirements for winter- and spring-run Chinook Salmon (desired direction of this attribute is to maximize the value). Producer defined 0.686868687 0.838383838 probability DeltaSmeltSDM_table2_2025.csv This data table represents composite utility scores by alternative water management strategy, for each participating agency or non-governmental agency, according to their individual objective weights. The purpose of this data table is to inform ranking of alternative management strategies considered during structured decision making workshops between February and March 2025. Producer defined Alternatives This attribute in the data table represents alternative water management strategies. The attribute values represent a unique identifier for each alternative water management strategy considered in a structured decision making process. Producer defined 1 11 integer number BoR This attribute in the data table represents composite utility scores based on objective weights provided by the U.S. Bureau of Reclamation. The attribute values represent composite utility scores for each alternative water management strategy calculated using objective weights provided by the U.S. Bureau of Reclamation, with a range of 0-1, where 0 is the worst and 1 is the best-performing alternative. Producer defined 0.373 0.658 composite utility score DWR This attribute in the data table represents composite utility scores based on objective weights provided by the California Department of Water Resources. The attribute values represent composite utility scores for each alternative water management strategy calculated using objective weights provided by the California Department of Water Resources, with a range of 0-1, where 0 is the worst and 1 is the best-performing alternative. Producer defined 0.306 0.747 composite utility score ParticipantB This attribute in the data table represents composite utility scores based on objective weights provided by an anonymous participant in the process. The attribute values represent composite utility scores for each alternative water management strategy calculated using objective weights provided by the California Department of Fish and Wildlife, with a range of 0-1, where 0 is the worst and 1 is the best-performing alternative. Producer defined 0.364 0.735 composite utility score ParticipantC This attribute in the data table represents composite utility scores based on objective weights provided by an anonymous participant in the projects. The attribute values represent composite utility scores for each alternative water management strategy calculated using objective weights provided by the U.S. Fish and Wildlife Service, with a range of 0-1, where 0 is the worst and 1 is the best-performing alternative. Producer defined 0.257 0.803 composite utility score GS ScienceBase U.S. Geological Survey mailing and physical

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Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. This file contains data available in comma-separated values (csv) format. The user must have software capable of displaying the data table. 20251210 Brian D Healy U.S. Geological Survey Research Biologist mailing and physical

2255 North Gemini Drive

Flagstaff AZ 86001 United States 928-556-7094 bhealy@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998