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Seasonal snowpack in the Western United States (WUS) is vital for meeting summer hydrological demands, reducing the intensity and frequency of wildfires, and supporting snow‐tourism economies. While the frequency and severity of snow droughts (SD), that is, anomalously low snowpacks, are expected to increase under continued global warming, the uncertainty from internal climate variability remains challenging to quantify with observations alone. Using a 30‐member large ensemble from a state‐of‐the‐art global climate model, the Seamless System for Prediction and EArth System Research (SPEAR), and an observations‐based data set, we find WUS SD changes are already significant. By 2100, SPEAR projects SDs to be nearly 9 times more frequent under shared socioeconomic pathway 5‐8.5 (SSP5‐8.5) and 5 times more frequent under SSP2‐4.5, compared to a 1921–2011 average. By investigating the influence of the two primary drivers of SD, temperature and precipitation amount, we find the average WUS SD will become warmer and wetter. To assess how these changes affect future summer water availability, we track late winter and spring snowpack across WUS watersheds, finding differences in the onset time of a "no‐snow" threshold between regions and large internal variability within the ensemble that are both on the order of decades. We attribute the inter‐regional variability to differences in the regions' mean winter temperature and the intra‐regional variability to irreducible internal climate variability which is not well‐explained by temperature variations alone. Despite strong scenario forcing, internal climate variability will continue to drive variations in SD and no‐snow conditions through 2100. Plain Language Summary: Snow drought (SD) occurs when there is significantly less snow on the ground than normal. SDs can intensify water shortages, accelerate wildfires, and harm snow‐based tourism economies. For the Western United States, whose water supply is already limited, a recent increase in snow drought frequency is particularly concerning. Here, we use observational data and a new climate model to examine snow drought changes across the region between 1921 and 2100. We find SDs are already more common and could increase almost nine times under a high emissions scenario or five times under moderate emissions cuts by 2100. To better understand the increase, we tracked the evolution of the two main snow drought drivers: warmer temperatures and decreased precipitation. We find the average snow drought will become warmer and wetter, indicating warming temperatures are driving the increase. As the model consists of multiple simulations of future climate, or ensemble members, that differ only in the realization of random climate variability, we can determine when Western regions are expected to lose most of their spring snowpack. We find that loss timing varies dramatically between regions and ensemble members, suggesting random climate variability will shape the West's future water availability. Key Points: Excluding the Pacific Northwest, observed increases of Western U.S. severe snow drought by 4%–46% are supported by the Seamless System for Prediction and EArth System Research (SPEAR) climate model21st century increases in Western U.S. severe snow drought in SPEAR are driven by rising temperatures and not decreased precipitationBy 2100 under SSP5‐8.5, SPEAR projects persistent Western U.S. spring no‐snow with timing sensitive to internal climate variability [ABSTRACT FROM AUTHOR]

Wintertime precipitation poses many observational and forecasting challenges, especially in the complex topography of the western United States where radar beam blockage and difficulty siting in situ observations yields more sparse observations than in the eastern United States. Uncertainty in western U.S. winter precipitation is known to be high, so much so that some studies have found model simulated precipitation to produce similar or better large-scale estimates of annual precipitation than gridded observational products during climatologically anomalous years. This study evaluates high-resolution gridded precipitation estimates from Multi-Radar Multi-Sensor (MRMS) and Stage IV as well as forecasts from NOAA's High-Resolution Rapid Refresh (HRRR) model in the Colorado Rocky Mountains. Gridded precipitation estimates and forecasts are compared with in situ SNOTEL measurements for two seasons of wintertime precipitation. The influence of forecast length, lead time, and model elevation on seasonal precipitation predictions from the HRRR are investigated. Additional comparisons are made with the relatively dense network of observations deployed in Colorado's East River Watershed during the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign. Gridded products and forecasts are found to underestimate cold-season precipitation by 25%–65% relative to in situ and aircraft measurements, with longer forecast periods and lead times (6–24 h) having smaller biases (25%–30%) than shorter forecast periods and lead times (55%–65%). The assessment of multiple years of observations indicates that these biases are related more to the data and methods used to create the gridded products and forecasts than to precipitation characteristics. Significance Statement: In the mountainous western United States, it is very challenging to both observe and forecast wintertime precipitation, yet snowfall plays an important role in providing the region's annual water supply. This study aims to increase our understanding of the biases in observations and forecasts of snowfall in the Colorado Rocky Mountains, which can in turn impact forecasts of water availability for the ensuing warm season. In this study we find high-resolution gridded precipitation estimates and forecasts to underestimate cold-season precipitation when compared with in situ observing stations, with longer-range forecasts (e.g., daily) being the least biased. These findings were consistent over two years of study and have broad implications for the hydrologic modeling and water management communities. [ABSTRACT FROM AUTHOR]

Gridded spatiotemporal maps of precipitation are essential for hydrometeorological and ecological analyses. In the United States, most of these datasets are developed using the Cooperative Observer (COOP) network of ground-based precipitation measurements, interpolation, and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) to map these measurements to places where data are not available. Here, we evaluate two daily datasets gridded at 1/16° resolution against independent daily observations from over 100 snow pillows in California's Sierra Nevada from 1990 to 2010. Over the entire period, the gridded datasets performed reasonably well, with median total water-year errors generally falling within ±10%. However, errors in individual storm events sometimes exceeded 50% for the median difference across all stations, and in many cases, the same underpredicted storms appear in both datasets. Synoptic analysis reveals that these underpredicted storms coincide with 700-hPa winds from the west or northwest, which are associated with post-cold-frontal flow and disproportionately small precipitation rates in low-elevation valley locations, where the COOP stations are primarily located. This atmospheric circulation leads to a stronger than normal valley-to-mountain precipitation gradient and underestimation of actual mountain precipitation. Because of the small average number of storms (

The atmospheric conditions that lead to strong offshore surface winds in Southern California, commonly referred to as Santa Ana winds, are investigated using the North American Regional Reanalysis and a 12-year, 6-km resolution regional climate simulation of Southern California. We first construct an index to characterize Santa Ana events based on offshore wind strength. This index is then used to identify the average synoptic conditions associated with Santa Ana events—a high pressure anomaly over the Great Basin. This pressure anomaly causes offshore geostrophic winds roughly perpendicular to the region’s mountain ranges, which in turn cause surface flow as the offshore momentum is transferred to the surface. We find, however, that there are large variations in the synoptic conditions during Santa Ana conditions, and that there are many days with strong offshore flow and weak synoptic forcing. This is due to local thermodynamic forcing that also causes strong offshore surface flow: a large temperature gradient between the cold desert surface and the warm ocean air at the same altitude creates an offshore pressure gradient at that altitude, in turn causing katabatic-like offshore flow in a thin layer near the surface. We quantify the contribution of “synoptic” and “local thermodynamic” mechanisms using a bivariate linear regression model, and find that, unless synoptic conditions force strongly onshore winds, the local thermodynamic forcing is the primary control on Santa Ana variability.

SIGNIFICANCE STATEMENT: Water is a limited and critical resource in the western United States. To protect water access for millions of people and ecosystems, and to preserve our ability to irrigate millions of acres of cropland, policymakers and water managers require advanced weather and water prediction systems. To improve these forecast systems in high-altitude complex terrain, the National Oceanic and Atmospheric Administration (NOAA) has deployed instrumentation in the East River watershed of the Upper Colorado River basin as part of the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH). SPLASH observations are being used to advance fundamental understanding of mountain weather and water and to advance predictive capabilities across weather and climate time scales. Water is a critical resource that causes significant challenges to inhabitants of the western United States. These challenges are likely to intensify as the result of expanding population and climate-related changes that act to reduce runoff in areas of complex terrain. To better understand the physical processes that drive the transition of mountain precipitation to streamflow, the National Oceanic and Atmospheric Administration has deployed suites of environmental sensors throughout the East River watershed of Colorado as part of the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH). This includes surface-based sensors over a network of five different observing sites, airborne platforms, and sophisticated remote sensors to provide detailed information on spatiotemporal variability of key parameters. With a 2-yr deployment, these sensors offer detailed insight into precipitation, the lower atmosphere, and the surface, and support the development of datasets targeting improved prediction of weather and water. Initial datasets have been published and are laying a foundation for improved characterization of physical processes and their interactions driving mountain hydrology, evaluation and improvement of numerical prediction tools, and educational activities. SPLASH observations contain a depth and breadth of information that enables a variety of atmospheric and hydrological science analyses over the coming years that leverage collaborations between national laboratories, academia, and stakeholders, including industry. [ABSTRACT FROM AUTHOR]

Snow influences land–atmosphere interactions in snow-dominated areas, and snow melt contributes to basin streamflows. However, estimating snowpack properties such as the snow depth (SD) and snow water equivalent (SWE) from land surface model simulations remains a challenge. This is, in part, due to uncertainties in the atmospheric forcing variables, which propagate into hydrological model predictions. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) land surface model in the NOAA's National Water Model (NWM) version 2.0 configuration to estimate snow in a single column and subsequently the streamflow across the Aroostook River's sub-basins in Maine for water years (WY) 2014–2016. This study evaluates how differences between two atmospheric forcing datasets, the North American Land Data Assimilation version 2 (NLDAS-2) and in situ (Station), translate into differences in the simulation of snow. NLDAS-2 was used as the meteorological forcing in the retrospective NWM 2.0 simulations. The results from the single-column study showed that differences in the simulated SWE and SD were linked to differences in the 2 m air temperature (T2m), which influenced the precipitation partitioning of rain and snow, as parameterized in Noah-MP. The negative mean bias of −0.7 K (during the accumulation period) in T2m for NLDAS-2, compared to the Station forcing, was a major factor that contributed to the positive mean bias of +52 mm on average in the peak SWE in the NLDAS-2-forced Noah-MP simulation during the study period. The higher T2m values at the Station led to higher sensible heat fluxes towards the snowpack, which led to a higher amount of net energy at the snow's surface and melt events during the accumulation season in Station-forced Noah-MP simulations. The results from the retrospective NWM version 2.0′s simulation in the basin showed that the streamflow estimates were closer to the United States Geological Survey gage observations at the two larger sub-basins (NSE = 0.9), which were mostly forested, compared to the two smaller sub-basins (NSE ≥ 0.4), which had more agricultural land-use. This study also showed that the spring snowmelt timing was captured quite well by the timing of the decline in the simulated SWE and SD, providing an early indication of melt in most sub-basins. The simulated fractional snow cover area (fSCA) however provided less information about the changes in snow or onset of snowmelt as it was mostly binary (full snow cover in winter), which differed from the more realistic fSCA values shown by the Moderate Resolution Imaging Spectroradiometer. [ABSTRACT FROM AUTHOR]

Using a 6-km-resolution regional climate simulation of Southern California, the effect of orographic blocking on the precipitation climatology is examined. To diagnose whether blocking occurs, precipitating hours are categorized by a bulk Froude number. The precipitation distribution becomes much more spatially homogeneous as the Froude number decreases, and an inspection of winds confirms that this results from the increasing prevalence of orographic blocking. Low Froude (Froude approximately less than 1), blocked cases account for a large fraction of climatological precipitation, particularly at the coastline where more than half is attributable to blocked cases. Thus, the climatological precipitation--slope relationship seen in observations and in the simulation is a hybrid of blocked and unblocked cases. Simulated precipitation distributions are compared to those predicted by a simple linear model that includes only rainfall arising from direct forced topographic ascent. The agreement is nearly perfect for high Froude (Froude substantially larger than 1) cases but degrades dramatically as the index decreases; as blocking becomes more prevalent, the precipitation slope relationship becomes continuously weaker than that predicted by the linear model. Because of its high fidelity during unblocked cases, it is surmised that blocking effects are the primary limitation preventing the linear model from accurately representing precipitation climatology and that the representation would be significantly improved during low Froude hours by the addition of a term to reduce the effective slope of the topography. These results suggest orographic blocking may substantially affect climatological precipitation distributions in similarly configured coastal areas.

Accurate quantitative precipitation estimates (QPEs) at high spatial and temporal resolution are difficult to obtain in regions of complex terrain due to the large spatial heterogeneity of orographically enhanced precipitation, sparsity of gauges, precipitation phase variations, and terrain effects that impact the quality of remotely sensed estimates. The large uncertainty of QPE in these regions also makes the evaluation of high-resolution quantitative precipitation forecasts (QPFs) challenging, as it can be difficult to choose a reference QPE that is reliable at both high and low elevations. In this paper we demonstrate a methodology to combine information from multiple high-resolution hourly QPE products to evaluate QPFs from NOAA's High-Resolution Rapid Refresh (HRRR) model in a region of Northern California. The methodology uses the quantiles of monthly QPE distributions to determine a range of hourly precipitation that correspond to "good," "possible," "underestimated," or "overestimated" QPFs. In this manuscript, we illustrate the use of the methodology to evaluate QPFs for seven atmospheric river events that occurred during the 2016–17 wet season in Northern California. Because the presence of frozen precipitation is often not captured by traditional QPE products, we evaluate QPFs both for all precipitation, and with likely frozen precipitation excluded. The methodology is shown to provide useful information to evaluate model performance while taking into account the uncertainty of available QPE at various temporal and spatial scales. The potential of the technique to evaluate changes between model versions is also shown. [ABSTRACT FROM AUTHOR]

A series of precipitation events impacted the Pacific Northwest during the first two weeks of November 2006. This sequence was punctuated by a particularly potent inland-penetrating atmospheric river (AR) that produced record-breaking precipitation across the region during 5-7 November. The precipitation caused destructive flooding as far inland as Montana's Glacier National Park, 800 km from the Pacific Ocean. This study investigates the inland penetration of moisture during the event using a 4-1.33-km grid spacing configuration of the Weather Research and Forecasting (WRF) modeling system. A high-resolution simulation allowed an analysis of interactions between the strong AR and terrain features such as the Cascade Mountains and the Columbia River Gorge (CR Gorge). Moisture transport in the vicinity of the Cascades is assessed using various metrics. The most efficient pathway for moisture penetration was through the gap (i.e., CR Gap) between Mt. Adams and Mt. Hood, which includes the CR Gorge. While the CR Gap is a path of least resistance through the Cascades, most of the total moisture transport that survived transit past the Cascades overtopped the mountain barrier itself. This is due to the disparity between the length of the ridge (~800 km) and relatively narrow width of the CR Gap (~93 km). Moisture transport reductions were larger across the Washington Cascades and the southern-central Oregon Cascades than through the CR Gap. During the simulation, drying ratios through the CR Gap (9.3%) were notably less than over adjacent terrain (19.6%-30.6%). Drying ratios decreased as moisture transport intensity increased. [ABSTRACT FROM AUTHOR]

The near-surface climate, including the atmosphere, ocean, sea ice, and land state and fluxes, in the initial version of the Regional Arctic System Model (RASM) are presented. The sensitivity of the RASM near-surface climate to changes in atmosphere, ocean, and sea ice parameters and physics is evaluated in four simulations. The near-surface atmospheric circulation is well simulated in all four RASM simulations but biases in surface temperature are caused by biases in downward surface radiative fluxes. Errors in radiative fluxes are due to biases in simulated clouds with different versions of RASM simulating either too much or too little cloud radiative impact over open ocean regions and all versions simulating too little cloud radiative impact over land areas. Cold surface temperature biases in the central Arctic in winter are likely due to too few or too radiatively thin clouds. The precipitation simulated by RASM is sensitive to changes in evaporation that were linked to sea surface temperature biases. Future work will explore changes in model microphysics aimed at minimizing the cloud and radiation biases identified in this work. [ABSTRACT FROM AUTHOR]

An analysis of atmospheric rivers (ARs) as defined by an automated AR detection tool based on integrated water vapor transport (IVT) and the connection to heavy precipitation in the southeast United States (SEUS) is performed. Climatological water vapor and water vapor transport fields are compared between the U.S. West Coast (WCUS) and the SEUS, highlighting stronger seasonal variation in integrated water vapor in the SEUS and stronger seasonal variation in IVT in the WCUS. The climatological analysis suggests that IVT values above ~500 kg m−1 s−1 (as incorporated into an objective identification tool such as the AR detection tool used here) may serve as a sensible threshold for defining ARs in the SEUS. Atmospheric river impacts on heavy precipitation in the SEUS are shown to vary on an annual cycle, and a connection between ARs and heavy precipitation during the nonsummer months is demonstrated. When identified ARs are matched to heavy precipitation days (>100 mm day−1), an average match rate of ~41% is found. Results suggest that some aspects of an AR identification framework in the SEUS may offer benefit in forecasting heavy precipitation, particularly at medium- to longer-range forecast lead times. However, the relatively high frequency of SEUS heavy precipitation cases in which an AR is not identified necessitates additional careful consideration and incorporation of other critical aspects of heavy precipitation environments such that significant predictive skill might eventually result. [ABSTRACT FROM AUTHOR]

This work evaluates the fidelity of the polar marine Ekman layer in the Regional Arctic System Model (RASM) and Community Earth System Model (CESM) using sea-ice inertial oscillations as a proxy for ice-ocean Ekman transport. A case study is presented that demonstrates that RASM replicates inertial oscillations in close agreement with motion derived using the GPS. This result is obtained from a year-long case study pre-dating the recent decline in perennial Arctic sea ice, using RASM with sub-hourly coupling between the atmosphere, sea-ice and ocean components. To place this work in context, the RASM coupling method is applied to CESM, increasing the frequency of oceanic flux exchange from once per day in the standard CESM configuration, to every 30 min. For a single year simulation, this change causes a considerable increase in the median inertial ice speed across large areas of the Southern Ocean and parts of the Arctic sea-ice zone. The result suggests that processes associated with the passage of storms over sea ice (e.g. oceanic mixing, sea-ice deformation and surface energy exchange) are underestimated in Earth System Models that do not resolve inertial frequencies in their marine coupling cycle. [ABSTRACT FROM AUTHOR]

ABSTRACT Using output from a high-resolution meteorological simulation, we evaluate the sensitivity of southern California wind energy generation to variations in key characteristics of current wind turbines. These characteristics include hub height, rotor diameter and rated power, and depend on turbine make and model. They shape the turbine's power curve and thus have large implications for the energy generation capacity of wind farms. For each characteristic, we find complex and substantial geographical variations in the sensitivity of energy generation. However, the sensitivity associated with each characteristic can be predicted by a single corresponding climate statistic, greatly simplifying understanding of the relationship between climate and turbine optimization for energy production. In the case of the sensitivity to rotor diameter, the change in energy output per unit change in rotor diameter at any location is directly proportional to the weighted average wind speed between the cut-in speed and the rated speed. The sensitivity to rated power variations is likewise captured by the percent of the wind speed distribution between the turbines rated and cut-out speeds. Finally, the sensitivity to hub height is proportional to lower atmospheric wind shear. Using a wind turbine component cost model, we also evaluate energy output increase per dollar investment in each turbine characteristic. We find that rotor diameter increases typically provide a much larger wind energy boost per dollar invested, although there are some zones where investment in the other two characteristics is competitive. Our study underscores the need for joint analysis of regional climate, turbine engineering and economic modeling to optimize wind energy production. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Five 915-MHz wind profilers and GPS receivers across California's northern Central Valley (CV) and adjacent Sierra foothills and coastal zone, in tandem with a 6-km-resolution gridded reanalysis dataset generated from the Weather Research and Forecasting Model, document key spatiotemporal characteristics of Sierra barrier jets (SBJs), landfalling atmospheric rivers (ARs), and their interactions. Composite kinematic and thermodynamic analyses are based on the 13 strongest SBJ cases observed by the Sloughhouse profiler between 2009 and 2011. The analyses show shallow, cool, south-southeasterly (i.e., Sierra parallel) flow and associated water vapor transport strengthening with time early in the 24-h compositing period, culminating in an SBJ core at <1 km above ground over the eastern CV. The SBJ core increases in altitude up the Sierra's windward slope and poleward toward the north end of the CV, but it does not reach the westernmost CV. Above the developing SBJ, strengthening southwesterly flow descends temporally in response to the landfalling AR. The moistening SBJ reaches maximum intensity during the strongest AR flow aloft, at which time the core of the AR-parallel vapor transport slopes over the SBJ. The inland penetration of the AR through the San Francisco Bay gap in the coastal mountains contributes to SBJ moistening and deepening. The SBJ subsequently weakens with the initial cold-frontal period aloft, during which the shallow flow shifts to southwesterly and the heaviest precipitation falls in the Sierra foothills. An orographic precipitation analysis quantitatively links the Sierra-perpendicular (nearly AR parallel) vapor fluxes to enhanced precipitation along the Sierra's windward slope and the SBJ-parallel fluxes to heavy precipitation at the north end of the CV. [ABSTRACT FROM AUTHOR]

This study characterizes kinematic and thermodynamic structures of Sierra barrier jets (SBJs), atmospheric rivers (ARs), and their interaction over the period 14-16 February 2011 when a winter storm made landfall in northern California. A suite of scanning and profiling Doppler radars, rawinsondes, and GPS receivers is used to document these structures across the Central Valley and up the western Sierra slope to the crest along an ~200-km segment of the Sierra. The winter storm is grouped into two episodes, each having an AR that made landfall. Low-level winds in the SBJ observed during episode 1 were southeasterly and embedded in a stably stratified air mass. Along-barrier wind speeds U340 reached maximum values of 25-30 m s−1, as low as ~0.2 km MSL over the Central Valley, and as high as ~1.5 km MSL over the western Sierra slope. Southwesterly winds associated with the AR overlaid the SBJ along an interface that sloped upward from southwest to northeast with a southwestern extent at the western edge of the Central Valley. In contrast, low-level winds in the SBJ observed during episode 2 were more southerly and embedded in a less stable air mass. The U340 reached maximum values that were slightly weaker (~20-25 m s−1) and spread over a thicker layer that extended to higher levels over the western Sierra (~2.5 km MSL). Southwesterly winds associated with the AR overlaying the SBJ tilted upward from southwest to northeast with a steeper slope but did not extend as far southwest. [ABSTRACT FROM AUTHOR]

This wind profiler–based study highlights key characteristics of the barrier jet along the windward slope of California’s Sierra Nevada. Between 2000 and 2007 roughly 10% of 100 000 hourly wind profiles, recorded at two sites, satisfied the sierra barrier jet (SBJ) threshold criteria described in the text. The mean magnitude of the terrain-parallel flow in the SBJ core (i.e., Vmax) was similar at both sites (∼17.5 m s−1) and at a comparable altitude, 500–1000 m above the surface. The cross-mountain wind speed was weak at the altitude of Vmax, consistent with blocked conditions. The seasonal cycle of SBJ occurrences showed a maximum during the cooler months and a minimum in summer. Additionally, the SBJ was stronger in winter than in summer. Because the warm-season (May–September) SBJs were different than their cool-season (October–April) counterparts and occurred during California’s dry season, they were not discussed in detail. An inventory of ∼250 cool-season SBJ cases from the two sites was generated (a case contains ≥8 consecutive SBJ profiles). Up to 60% of the nearby cool-season precipitation fell during SBJ cases, and these cases shifted the precipitation down the sierra’s windward slope and enhanced precipitation at the north end of the Central Valley (relative to non-SBJ conditions). The large number of cool-season SBJ cases was stratified by the mean strength and altitude of Vmax and by the case duration. Composite profiles of the along-barrier component for the top- and bottom-20 ranked cases in each of these three SBJ classes reveal stark differences in the magnitude and vertical positioning of the barrier jet. The three SBJ classes yielded uniquely different local precipitation characteristics in proximity to the wind profilers, with the strongest and longest-lived SBJs yielding the greatest precipitation. North American Regional Reanalysis plan-view composites were generated to explore the synoptic conditions responsible for, and to showcase the precipitation distributions associated with, the top- and bottom-20 ranked cases in each of the three classes of SBJs. The composite analyses yielded large contrasts between the SBJ classes that could prove useful in forecasting SBJs and their precipitation impacts. All SBJ classes occurred, on average, in the pre-cold-frontal environment of landfalling winter storms. [ABSTRACT FROM AUTHOR]

This work investigates the utility of the National Oceanic and Atmospheric Administration's National Water Model (NWM) for water management operations by assessing the total inflow into a select number of reservoirs across the Central and Western U.S. Total inflow is generally an unmeasured quantity, though critically important for anticipating both floods and shortages in supply over a short-term (hourly) to sub-seasonal (monthly) time horizon. The NWM offers such information at over 5000 reservoirs across the U.S., however, its skill at representing inflow processes is largely unknown. The goal of this work is to understand the drivers for both well performing and poor performing NWM inflows such that managers can get a sense of the capability of NWM to capture natural hydrologic processes and in some cases, the effects of upstream management. We analyzed the inflows for a subset of Bureau of Reclamation (BoR) reservoirs within the NWM over the long-term simulations (retrospectively, seven years) and for short, medium and long-range operational forecast cycles over a one-year period. We utilize ancillary reservoir characteristics (e.g., physical and operational) to explain variation in inflow performance across the selected reservoirs. In general, we find that NWM inflows in snow-driven basins outperform those in rain-driven, and that assimilated basin area, upstream management, and calibrated basin area all influence the NWM's ability to reproduce daily reservoir inflows. The final outcome of this work proposes a framework for how the NWM reservoir inflows can be useful for reservoir management, linking reservoir purposes with the forecast cycles and retrospective simulations. [ABSTRACT FROM AUTHOR]