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DOE OSTI2024

Understanding Observed Precipitation Change and the New Climate Normal from the Perspective of Daily Weather Types in the Southeast United States

Abstract Observed precipitation changes in the Southeast United States (SEUS) are spatially heterogeneous. Most of the inland SEUS and eastern Gulf Coast become drier, and the East Coast north of Charleston, South Carolina, and southern Florida become wetter from the old 30-yr period of 1961–90 to the recent period of 1991–2020. The observed climate change is examined from the perspective of daily weather types (WTs). A k -means clustering analysis has been conducted using daily 850-hPa circulation for 1948–2021. The obtained 10 WTs peak in different seasons, respectively. The frequencies and precipitation intensity of the WTs have been analyzed. A winter WT characterized by a western Appalachian trough (WAT) and a summer WT featuring North Atlantic subtropical high (NASH) have a rising trend of annual frequency from 1948 to 2021. An Appalachian high in the autumn has a decreasing frequency but becomes drier and stronger. Some precipitation intensity change and small location shift have also been observed. The drying up on the eastern Gulf Coast and the inland area of the SEUS is mainly caused by the weakened southwesterly low-level jet (LLJ) on the western flank of the NASH that reduces rain in the spring, the less frequent but stronger and drier Appalachian high in the summer and autumn, and the weaker and more western located Plains trough (PT) in the winter, spring, and autumn. The precipitation increase in the East Coast and southern Florida is majorly due to more frequent, stronger, and rainier troughs along the western Appalachian as well as the East Coast.

Qian, Jian-Hua↗

DOE OSTI2020

Wind ramp events validation in NWP forecast models during the second Wind Forecast Improvement Project (WFIP2) using the Ramp Tool and Metric (RT&M)

The second Wind Forecast Improvement Project (WFIP2) is a multi-agency field campaign held in the Columbia Gorge area (October 2015 - March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well-known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011 - August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-height) wind speeds measured by 19 sodars and 3 lidars, and to forecasts from the High Resolution Rapid Refresh (HRRR), 3-km, and from the High Resolution Rapid Refresh Nest (HRRRNEST), 750-m horizontal grid spacing, models. The diurnal and seasonal distribution of ramp events are analyzed, finding a noticeable diurnal variability for spring and summer but less for fall and especially winter. Also, winter has fewer ramps compared to the other seasons. The model skill at forecasting ramp events, including the impact of the modification to the model physical parameterizations, was finally investigated.

Djalalova, Irina V.↗

DOE OSTI2015

Winter rye as a bioenergy feedstock: impact of crop maturity on composition, biological solubilization and potential revenue

We report that winter annual crops such as winter rye (Secale cereale L) can produce biomass feedstock on seasonally fallow land that continues to provide high-value food and feed from summer annuals such as corn and soybeans. As energy double crops, winter grasses are likely to be harvested while still immature and thus structurally different from the fully senesced plant material typically used for biofuels. This study investigates the dynamic trends in biomass yield, composition, and biological solubilization over the course of a spring harvest season. The water soluble fraction decreased with increasing maturity while total carbohydrate content stayed roughly constant at about 65%. The protein mass fraction decreased with increasing maturity, but was counterbalanced by increasing harvest yield resulting in similar total protein across harvest dates. Winter rye was ground and autoclaved then fermented at 15 g/L total solids by either (1) Clostridium thermocellum or (2) simultaneous saccharification and cofermentation (SSCF) using commercial cellulases (CTec2 and HTec2) and a xylose-fermenting Saccharomyces cerevisiae strain. Solubilization of total carbohydrate dropped significantly as winter rye matured for both C. thermocellum (from approximately 80% to approximately 50%) and SSCF (from approximately 60% to approximately 30%). C. thermocellum achieved total solubilization 33% higher than that of SSCF for the earliest harvest date and 50% higher for the latest harvest date. Potential revenue from protein and bioethanol was stable over a range of different harvest dates, with most of the revenue due to ethanol. In a crop rotation with soybean, recovery of the soluble protein from winter rye could increase per hectare protein production by 20 to 35%. Double-cropping winter rye can produce significant biomass for biofuel production and feed protein as coproduct without competing with the main summer crop. During a 24-day harvest window, the total carbohydrate content remained relatively constant while the early-harvest yielded much higher carbohydrate solubilization for both C. thermocellum fermentation and SSCF. C. thermocellum fermentation achieved higher carbohydrate solubilization than SSCF across all growth stages tested. In conclusion, although winter rye’s yield, composition, and biological reactivity change rapidly in the spring, it offers a substantial and stable income across the harvest season and thus flexibility for the farmer.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Permanent draft genome of Thermithiobacillus tepidarius DSM 3134T, a moderately thermophilic, obligately chemolithoautotrophic member of the Acidithiobacillia

Abstract Thermithiobacillus tepidarius DSM 3134 T was originally isolated (1983) from the waters of a sulfidic spring entering the Roman Baths (Temple of Sulis-Minerva) at Bath, United Kingdom and is an obligate chemolithoautotroph growing at the expense of reduced sulfur species. This strain has a genome size of 2,958,498 bp. Here we report the genome sequence, annotation and characteristics. The genome comprises 2,902 protein coding and 66 RNA coding genes. Genes responsible for the transaldolase variant of the Calvin-Benson-Bassham cycle were identified along with a biosynthetic horseshoe in lieu of Krebs’ cycle sensu stricto . Terminal oxidases were identified, viz. cytochrome c oxidase ( cbb 3 , EC 1.9.3.1) and ubiquinol oxidase ( bd , EC 1.10.3.10). Metalloresistance genes involved in pathways of arsenic and cadmium resistance were found. Evidence of horizontal gene transfer accounting for 5.9 % of the protein-coding genes was found, including transfer from Thiobacillus spp. and Methylococcus capsulatus Bath, isolated from the same spring. A sox gene cluster was found, similar in structure to those from other Acidithiobacillia – by comparison with Thiobacillus thioparus and Paracoccus denitrificans , an additional gene between soxA and soxB was found, annotated as a DUF302-family protein of unknown function. As the Kelly-Friedrich pathway of thiosulfate oxidation (encoded by sox ) is not used in Thermithiobacillus spp., the role of the operon (if any) in this species remains unknown. We speculate that DUF302 and sox genes may have a role in periplasmic trithionate oxidation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Data for Soil Oxygen Dynamics: A Key Mediator of Tile Drainage Impacts on Coupled Hydrological, Biogeochemical, and Crop Systems

Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the US Midwest and throughout the world. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although examining the complex interactions and feedbacks in an integrated hydrology–biogeochemistry–crop system remains elusive. Oxygen dynamics are critical to unraveling these interactions and have been ignored or oversimplified in existing models. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the US Midwest. We used the ecosys model that uniquely incorporates first-principle soil oxygen dynamics and crop oxygen uptake mechanisms to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth in corn–soybean rotation fields. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for the corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Plant oxygen stress turns out as an emergent property of the equilibrium between the soil oxygen supply and biological demand. The impact of tile drainage on the system is achieved through a series of coupled feedback mechanisms. The model results show that tile drainage reduces the soil water content and enhances soil oxygenation. It additionally increases the subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviates crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigates water stress during dry summers, leading to an overall increase in the crop yield by ∼6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the underlying causal mechanisms that drive the agroecosystem response to drainage on the coupled hydrology, biogeochemistry, and crop system dynamics.

Modeling↗

DOE OSTI2013

A multifactor analysis of fungal and bacterial community structure of the root microbiome of mature Populus deltoides trees

Bacterial and fungal communities associated with plant roots are central to the host- health, survival and growth. However, a robust understanding of root-microbiome and the factors that drive host associated microbial community structure have remained elusive, especially in mature perennial plants from natural settings. Here, we investigated relationships of bacterial and fungal communities in the rhizosphere and root endosphere of the riparian tree species Populus deltoides, and the influence of soil parameters, environmental properties (host phenotype and aboveground environmental settings), host plant genotype (Simple Sequence Repeat (SSR) markers), season (Spring vs. Fall) and geographic setting (at scales from regional watersheds to local riparian zones) on microbial community structure. Each of the trees sampled displayed unique aspects to it s associated community structure with high numbers of Operational Taxonomic Units (OTUs) specific to an individual trees (bacteria >90%, fungi >60%). Over the diverse conditions surveyed only a small number of OTUs were common to all samples within rhizosphere (35 bacterial and 4 fungal) and endosphere (1 bacterial and 1 fungal) microbiomes. As expected, Proteobacteria and Ascomycota were dominant in root communities (>50%) while other higher-level phylogenetic groups (Chytridiomycota, Acidobacteria) displayed greatly reduced abundance in endosphere compared to the rhizosphere. Variance partitioning partially explained differences in microbiome composition between all sampled roots on the basis of seasonal and soil properties (4% to 23%). While most variation remains unattributed, we observed significant differences in the microbiota between watersheds (Tennessee vs. North Carolina) and seasons (Spring vs. Fall). SSR markers clearly delineated two host populations associated with the samples taken in TN vs. NC, but overall genotypic distances did not have a significant effect on corresponding communities that could be separated from other measured effects.

Bacteria↗

DOE OSTI2017

Multi-scale temporal patterns in fish presence in a high-velocity tidal channel

The natural variation of fish presence in high-velocity tidal channels is not well understood. A better understanding of fish use of these areas would aid in predicting fish interactions with marine hydrokinetic (MHK) devices, the effects of which are uncertain but of high concern. To characterize the patterns in fish presence at a tidal energy site in Cobscook Bay, Maine, we examined two years of hydroacoustic data continuously collected at the proposed depth of an MHK turbine with a bottom-mounted, side-looking echosounder. The maximum number of fish counted per hour ranged from hundreds in the early spring to over 1,000 in the fall. Counts varied greatly with tidal and diel cycles in a seasonally changing relationship, likely linked to the seasonally changing fish community of the bay. In the winter and spring, higher hourly counts were generally confined to ebb tides and low slack tides near sunrise and sunset. In summer and fall of each year, the highest fish counts shifted to night and occurred during ebb, low slack, and flood tides. Fish counts were not linked to current speed, and did not decrease as current speed increased, contrary to observations at other tidal power sites. As fish counts may be proportional to the encounter rate of fish with an MHK turbine at the same depth, highly variable counts indicate that the risk to fish is similarly variable. The links between fish presence and environmental cycles at this site will likely be present at other locations with similar environmental forcing, making these observations useful in predicting potential fish interactions at tidal energy sites worldwide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

CUORE and CUORE-0 experiments

Neutrino oscillation experiments proved that neutrinos have mass and this enhanced the interest in neutrinoless double-beta decay (0vββ). The observation of this very rare hypothetical decay would prove the leptonic number violation and would give us indications about neutrinos mass hierarchy and absolute mass scale. CUORE (Cryogenic Underground Observatory for Rare Events) is an array of 988 crystals of TeO 2 , for a total sensitive mass of 741 kg. Its goal is the observation of 0vββ of 130 Te. The crystals, placed into the a dilution cryostat, are operated as bolometers at a temperature close to 10 mK. CUORE commissioning phase has been concluded recently in Gran Sasso National Laboratory, Italy, and data taking is expected to start in spring 2017. If target background rate is reached (0.01counts/day/keV/kg), the sensibility of CUORE will be, in five years of data taking, T 1/2 10 26 years (1σ CL). In order to test the quality of materials and optimize the construction procedures, the collaboration realized CUORE-0, that took data from spring of 2013 to summer 2015. Here, after a brief description of CUORE, I report its commissioning status and CUORE-0 results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Accelerated Springtime Melt of Snow on Tundra Downwind from Northern Alaska River Systems Resulting from Niveo-aeolian Deposition Events

It is well known that light-absorbing particulate matter (PM) enhances absorption of sunlight when deposited on ice and snow. Such increased absorption is due to a reduction in surface albedo, resulting in accelerated melt of frozen surfaces. In isolation, earlier melt enhances Arctic warming since dark surfaces underlying snow and ice are exposed and absorb additional solar energy. Here, we combine various observational tools to demonstrate that aeolian deposition of PM along fluvial features on the North Slope of Alaska resulted in a notable reduction of surface albedo in the spring of 2016, from values typical for snow (~0.8) to around 0.35 on average. This reduction resulted in accelerated snow and ice melt by up to three weeks compared to unaffected areas. This phenomenon was observed to some degree in 12 other years dating back to 2003. Deposition generally was found to occur near particular sections of the rivers, with several areas affected by events in multiple years. In all years, the deposition is attributed to high wind events. The extreme case in 2016 is linked to unusually strong and extraordinarily persistent winds during April. The deposited material is thought to be the natural sediment carried by the rivers, resulting in a seasonally replenished source of PM. These findings indicate a previously unreported impact of both fluvial and atmospheric processes on the seasonal melt of northern Alaska rivers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Areas with Surface Thermal Anomalies as Detected by ASTER and LANDSAT Data in Northern Saguache County, Colorado

This map shows areas of anomalous surface temperature in northern Saguache Counties identified from ASTER and LANDSAT thermal data and spatial based insolation model. The temperature for the ASTER data was calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas having anomalous temperature in the ASTER data are shown in blue diagonal hatch, while areas having anomalous temperature in the LANDSAT data are shown in magenta on the map. Thermal springs and areas with favorable geochemistry are also shown. Springs or wells having non-favorable geochemistry are shown as blue dots.

15 GEOTHERMAL ENERGY↗

DOE OSTI2012

Areas with Surface Thermal Anomalies as Detected by ASTER and LANDSAT Data in Northwest Delta, Colorado

15 GEOTHERMAL ENERGY↗

DOE OSTI2012

Areas with Surface Thermal Anomalies as Detected by ASTER and LANDSAT Data in Ouray, Colorado

This map shows areas of anomalous surface temperature in Ouray identified from ASTER and LANDSAT thermal data and spatial based insolation model. The temperature for the ASTER data was calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas having anomalous temperature in the ASTER data are shown in blue diagonal hatch, while areas having anomalous temperature in the LANDSAT data are shown in magenta on the map. Thermal springs and areas with favorable geochemistry are also shown. Springs or wells having non-favorable geochemistry are shown as blue dots.

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

Critical Elements in Produced Fluids from Nevada and Utah

Critical elements and related analytical data for produced fluids from geothermal fields in Nevada and Utah, Sevier thermal belt hot springs, Utah, and Uinta basin oil-gas wells, Utah are reported. Analytical results include pH, major species, trace elements, transition metals, other metals, metalloids and REEs. Gas samples were collected and analyzed from Beowawe, Dixie Valley, Roosevelt Hot Springs, and Thermo. Helium gases and helium isotopes were analyzed on samples collected at Patua, San Emido and two wells in the Uinta basin.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Warm air intrusions reaching the MOSAiC expedition in April 2020—The YOPP targeted observing period (TOP)

In the spring period of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, an initiative was in place to increase the radiosounding frequency during warm air intrusions in the Atlantic Arctic sector. Two episodes with increased surface temperatures were captured during April 12–22, 2020, during a targeted observing period (TOP). The large-scale circulation efficiently guided the pulses of warm air into the Arctic and the observed surface temperature increased from -30°C to near melting conditions marking the transition to spring, as the temperatures did not return to values below -20°C. Back-trajectory analysis identifies 3 pathways for the transport. For the first temperature maximum, the circulation guided the airmass over the Atlantic to the northern Norwegian coast and then to the MOSAiC site. The second pathway was from the south, and it passed over the Greenland ice sheet and arrived at the observational site as a warm but dry airmass due to precipitation on the windward side. The third pathway was along the Greenland coast and the arriving airmass was both warm and moist. The back trajectories originating from pressure levels between 700 and 900 hPa line up vertically, which is somewhat surprising in this dynamically active environment. The processes acting along the trajectory originating from 800 hPa at the MOSAIC site are analyzed. Vertical profiles and surface energy exchange are presented to depict the airmass transformation based on ERA5 reanalysis fields. The TOP could be used for model evaluation and Lagrangian model studies to improve the representation of the small-scale physical processes that are important for airmass transformation. A comparison between MOSAiC observations and ERA5 reanalysis demonstrates challenges in the representation of small-scale processes, such as turbulence and the contributions to various terms of the surface energy budget, that are often misrepresented in numerical weather prediction and climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

MASK4 Test Campaign for Sandia WaveBot Device

This data and report details the findings from a wave tank test focused on production of useful work of a wave energy converter (WEC) device. The experimental system and test were specifically designed to validate models for power transmission throughout the WEC system. Additionally, the validity of co-design informed changes to the power take-off (PTO) were assessed and shown to provide the expected improvements in system performance. These data describe the "MASK4" wave tank test of the Sandia WaveBot device. The WaveBot device has been tested a number of times in different permutations at the US Navy's Maneuvering and Sea Keeping (MASK) basin. Each test in this series is referred to as MASK1, MASK2, etc. The WaveBot device was first tested in one degree of freedom (heave) in 2016. This MASK1 test focused primarily on system identification and modeling. After MASK1, major modifications were performed to improve the overall real-time control and measurement system, improve the heave drive train, and add surge and pitch degrees of freedom. The second set of testing, which was broken up in to two stages: MASK2A and MASK2B, focused on bench testing and closed-loop control performance as well as nonlinear modeling. MASK3 then focused on multi-input, multi-output modeling and control for maximization of electrical power. The attached report presents the results from MASK4, which focuses on detailed modeling of the power conversion chain and validation co-design principles by way of the introduction of a magnetic spring. The test log, report, and data from the MASK4 test of the WaveBot augmented with a tunable magnetic spring. Processing codes can be found at the Github link below.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

HERO WEC Belt Test Data

The following submission includes raw and processed data from the 2024 Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC) belt tests conducted using NREL's Large Amplitude Motion Platform (LAMP). A description of the motion profiles run during testing can be found in the run log document. Data was collected using NREL's Modular Ocean Data AcQuisition (MODAQ) system in the form of TDMS files. Data was then processed using Python and MATLAB and converted to MATLAB workspace, parquet, and csv file formats. During Data processing, a low pass filter was applied to each array and the arrays were then resampled to common 10Hz timestamps. A MATLAB data viewer script is provided to quickly visualize these data sets. The following arrays are contained in each test data file: - Time: Unix seconds timestamp - Test_Time: Time in seconds since beginning of test - POS_OS_1001: Encoder position in degrees (the encoder is located on the secondary shaft of the spring return and is driven by the winch after a 4.5:1 gear reduction) - LC_ST_1001: Anchor load cell data in lbf - PRESS_OS_2002: Air spring pressure in psi This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

Maps of land surface phenology derived from PlanetScope data, 2018-2022, Teller, Kougarok, and Council, Seward Peninsula

Remote sensing maps of land surface phenology derived from PlanetScope (Planet Team, 2017) normalized differential greenness index (NDGI; Yang et al., 2019) time series data. These maps include four phenological timing metric - start of spring (SOS), end of spring (EOS), start of fall (SOF), and end of fall (EOF), corresponding NDGI value at the four phenological timings, and annual maximum and minimum NDGI. This package includes maps for Next-Generation Ecosystem Experiment Arctic (NGEE Arctic)’s Teller Mile Marker (MM) 27, Kougarok MM64, and Council MM 71 watersheds. Maps of 5 years from 2018 to 2022 are included in this dataset. The map data and metadata are provided as image (ENVI) and text (*.txt, *hdr) formats. Additional supporting map quicklooks are provided as GIS *.kml files. These datasets are provided in support of Yang et al., (In revision) “Fine-scale Landscape Characteristics, Vegetation Composition, and Snowmelt Timing Control Phenological Heterogeneity across Arctic Tundra Landscapes”The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Mountain Basin Controls on the Snow-to-Streamflow Signal: An AIC-Weighted Multiple Linear Regression Framework

A regression-based analysis quantifies how basin characteristics modulate the snow-to-streamflow signal. First, we use the ERA5-Land reanalysis gridded product (European Centre for Medium Range Weather Forecasts reanalysis 5 -Land component) for 4,655 hydrologic unit code - 10 (HUC10) mountain basins across the western United States (US) for water years 1987–2024. Linear regressions are performed for peak snow water equivalent (SWE) and annual streamflow for each mountain basin. Models use ordinary least squares in Python’s statsmodels package. After which, an Akaike Information Criterion (AIC)–weighted ensemble multiple linear regression (MLR) framework with 47 watershed traits is used to predict the linear regression coefficient of determination (r-squared) defining the ability of peak SWE to predict annual streamflow across all mountain basin. Predictor sets are constrained to avoid multicollinearity by excluding models with variance inflation factors (VIF) greater than 5. Mountain basin traits included in the MLR include seasonal climate, topography, vegetation type and structure, and bedrock geology. Accepted models are considered if their AIC is within 2.0 of the model with the minimum AIC, or best model. To compare predictor influence across acceptable models, we computed standardized regression coefficients. To evaluate structural redundancy among models, we constructed binary inclusion vectors for each acceptable model, denoting whether a predictor was present (1) or absent (0). Core predictor variables are defined as occurring in at least 67% of the acceptable models. For this regional analysis, only one model was found acceptable, with higher snow-to-streamflow translation (higher r-squared) occurring in colder mountain basins with higher relative winter precipitation, more snow accumulation and a lower fraction of annual precipitation that falls in the spring and summer. The second component of the data package uses previously published, high-resolution output from an integrated hydrological model of the East River watershed using the U.S. Geological Survey Groundwater and Surface water Flow model (GSFLOW, doi:10.15485/1998576). East River MLR expands upon the approach described above to explore the response of five streamflow metrics—annual streamflow, runoff efficiency, 7-day minimum flow, low-flow duration, and non-perennial stream fraction to snow system indicators including peak SWE, snow-covered area, snow disappearance date, and the fraction of basin area characterized by low-to-no snow, as well as seasonal precipitation and temperature, and annual hydrologic variables representing soil moisture, evapotranspiration (ET), the partitioning of incoming precipitation to evapotranspiration (ET/P), groundwater storage, and groundwater inflow to streams. MLR was done on all water years (P0: 1987-2024) and for each period as determined in the split analysis using pooled regression techniques (P1: 1987-2011 and P2: 2012-2024) to evaluate shifting predictor variable emphasis on streamflow generation. Results indicate that since 2012, peak SWE has lost statistical strength in its prediction of annual streamflow and runoff efficiency, and the indirect influence of spring temperature has emerged as critically important. Low-flow metrics remain largely influenced by soil moisture, vegetation water use and groundwater inflows with summer precipitation becoming a direct influence on minimum summer flow. Together, these data and Python-based analysis tools provide a framework for identifying the key watershed characteristics that control how streamflow responds to snow from year to year. The package also helps quantify uncertainty in statistical models and assess how snow–streamflow relationships vary across regions and over time. This dataset contains comma-separated values files (.csv), text files (.txt), python code files (.py), figure files (.png), and shapefiles (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .xml). Further details on file contents and MLR execution can be found in the readme file and the FLMD files. Work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗