Cold regions

Process-based land surface models, such as the Noah-Multiparameterization (Noah-MP) model, are widely used for large-scale hydrologic simulations because of their flexibility in selecting multiple parameterization schemes. However, limited guidance on choosing appropriate configurations constrains their reliability in representing runoff and snowmelt dynamics across diverse land-cover and snow conditions. This study evaluates the default and four alternative parameterization schemes in the Noah-MP land surface model, including Runoff and Groundwater (RUN), Surface Exchange Coefficient for Heat (SFC), Frozen Soil Permeability (INF), and Snow/Soil Temperature Time Scheme (STC), across 50 Hydro-Climate Data Networks (HCDNs) in the Missouri River Basin. Model performance was evaluated using USGS streamflow observations and snow water equivalent (SWE) estimates from the University of Arizona dataset for 2014–2023. Results showed that the alternative schemes improved runoff simulation compared to the default scheme through different approaches representing key hydrological and thermodynamic processes. Specifically, the RUN, SFC, INF, and STC experiments improved the Kling–Gupta Efficiency (KGE) by 0.19, 0.38, 0.44, and 0.14, respectively in representative subbasins, through enhanced groundwater dynamics, reduced evapotranspiration bias, improved rapid runoff response, and more accurate SWE evolution. SWE evaluation further indicates that the STC experiment most effectively reduced SWE bias in snow-dominated subbasins, though improvements in SWE did not universally translate into better runoff simulation. These results highlight the importance of basin-specific parameterization schemes within Noah-MP to improve hydrological prediction and water management across diverse hydroclimatic regions. The findings further indicate optimal parameterization schemes for different climates, land cover, and snow regimes.

Baseflow constitutes over half of streamflow in mountainous headwaters of the western United States (US), making accurate quantification critical for water resource management. However, traditional baseflow separation methods are often arbitrary and ambiguous, complicating validation. Here, we developed an integrated hydrologic model coupling surface and subsurface flow to quantify baseflow in the Coal Creek watershed, a snow‐dominated catchment in Colorado, US. A model‐agnostic approach was implemented to estimate baseflow from simulated hydrologic exchange fluxes and these estimates were used as a reference to evaluate four common baseflow separation methods: the Pettyjohn and Henning (PH) graphical, United Kingdom Institute of Hydrology (UKIH) graphical, Eckhardt digital filter, and conductance mass balance (CMB) methods. Both the UKIH and Eckhardt methods performed relatively well, with modified Kling‐Gupta Efficiency values of 0.73 and 0.68 and Nash‐Sutcliffe Efficiency values of 0.59 and 0.71, respectively. However, the UKIH method underperformed during average and dry years, when stream hydrographs exhibit unimodal peaks typical of snow‐dominated catchments. The PH and CMB methods showed poor performance overall: the PH method substantially overestimated baseflow (average baseflow index (BFI) of 85%) owing to its short moving window, while the CMB method underestimated it (average BFI of 38%), potentially due to the exclusion of interflow contributions, compared to a numerically derived BFI of 58%. These findings highlight the utility of integrated hydrologic models as a reference for evaluating baseflow separation methods, particularly in snow‐dominated catchments where graphical and tracer‐based approaches may misrepresent baseflow contributions.

Catchment hydrologic function describes how meteorological conditions are transformed into streamflow. Whether this transformation is stable or varies from event to event, and what controls that variability, remains unknown at the global scale, limiting confidence in flood-risk and water-security assessments. Here, we analyze about 1.7 million runoff-producing events across 4838 catchments on six continents. We classify each event by its water-input type (rain, snowmelt, or rain-on-snow) and by its antecedent soil moisture (dry, moderate, or wet). These two descriptors together define the event’s hydro-meteorological conditions. We summarize each catchment’s dominant hydro-meteorological event type in the dormant and growing seasons separately, and find a wide diversity of dominant types across the global catchment population. We then quantify, for each catchment, the extent of event-to-event time-variance in the precipitation–streamflow transfer function that represents catchment hydrologic function. Time-variant catchment hydrologic function is pervasive: 95.6% of catchments in the dormant season and 99.0% in the growing season show moderate to strong event-to-event variation, and even catchments experiencing highly similar hydro-meteorological conditions across events remain functionally time-variant. Within-catchment spatial heterogeneity in hydro-meteorological conditions modulates this variation. Greater time-variance in hydrologic function is associated with greater event-to-event variation in a catchment’s runoff response.

Seasonal snow is a critical component of the water cycle in the Southern Andes of Chile and Argentina. Quantitative assessments of snow accumulation remain constrained by the scarcity, heterogeneity, and inconsistency of in situ observations, resulting in substantial uncertainties in mountain hydrological modeling. To address this gap, we compiled and quality-controlled snow depth observations to produce a consistent daily dataset from 81 stations between 21 and 54° S covering 2010–2024. Our quality-control procedure involved data compilation and preprocessing, followed by harmonization of snow depth observations, primarily by correcting the ground reference level relative to the soil surface during snow-free periods and removing anomalous spikes and observations outside physically plausible ranges. This process improved data reliability, increasing the Physical Consistency Index (PCI) from 87 % to 95 % at selected stations in the Maipo River Basin, while reducing the median number of days with data across stations by 23 % (from 1392 to 1074 d). The snow depth data availability increased from one station in 2010 to 57 stations in 2024, largely driven by the expansion of the monitoring network operated by the General Directorate of Water (DGA), Chile. However, this expansion remains spatially uneven across the Andean zones. The Mediterranean Andes have the largest number of stations (39), as well as the largest number of stations with highly complete records with 17 stations reaching 80 %–100 % data coverage, compared with only nine stations each in the Arid and Wet Andes, highlighting persistent spatial and temporal gaps. Using this newly quality-controlled dataset, we find that snow depth generally increases from the Arid to the Wet Andes in association with increasing precipitation, whereas its relationship with elevation is not consistently positive. The snow depth-elevation relationship is nonlinear in the Arid and Mediterranean Andes, with maximum observed snow depths at 4300 m a.s.l. in the Elqui River Basin and 3300 m a.s.l. in the Maipo River Basin. In contrast, the available observations indicate a positive snow depth-elevation relationship in the Wet Andes (Maule–Itata River Basin). However, these relationships should be interpreted in the context of the specific spatial and temporal conditions represented by the available observations. This…

The Rio Grande is the main source of surface water supply for south‐central Colorado and much of New Mexico, with obligations via interstate and international treaties to Texas and Mexico. Recent drought conditions exacerbated by a warming climate have increased demand for this limited water supply. Like other snowmelt‐dominated watersheds in the western US, this water resource is vulnerable to declining snowpack and earlier melt‐off, and the drought conditions over the past two decades have challenged the management of this river system. An examination of key hydroclimatic variables shows that in spite of warm, dry conditions and decreasing snowpack, the Rio Grande headwaters show no significant trend in annual flow, and runoff efficiency shows a positive trend over recent decades. Given this unexpected result, it is important to understand underlying causes of the apparent increase in runoff efficiency to anticipate changes in Rio Grande water supply in the future. Two possible factors are hypothesized: the shift in timing of runoff and the impacts of widespread high‐elevation tree mortality due to a spruce beetle epidemic, and both may be playing a role. The increase in runoff efficiency may be short‐lived, however, and could already be declining. The Rio Grande headwaters exemplifies the convergence of conditions –including natural climate variability, forest disturbance, and anthropogenic climate change– which may be affecting runoff efficiency in other critical headwaters.

In alpine permafrost basins, dry‐season runoff largely depends on subpermafrost groundwater (BPG, deep groundwater), yet cryohydrogeological conditions are complicated by time‐varying hydrothermal regimes. Combined with the storage and regulation effects of permafrost on runoff, hydrograph‐based estimates of the fractional contribution of BPG to streamflow ( f BPG ) are limited in validity and accuracy, and f BPG has not been fully quantified—particularly intra‐annually. Here we leverage ensemble end‐member mixing analysis (EMMA) and extend it to a non‐negative inequality‐constrained least squares (LSEI) framework suited to sparse‐sample estimation with incomplete end members. Using the covariance structure of δ 18 O time series in precipitation, stream water, glacier and snow meltwater, and suprapermafrost groundwater, the approach resolves the contribution of the unknown end member, that is, BPG. The δ 18 O series undergo temporal harmonization, state‐space smoothing, and collinearity screening prior to regression. Applied to the Three‐River headwaters Region—a representative mid‐latitude permafrost region—the method estimated ablation‐season f BPG of 0.10 ± 0.02 to 0.17 ± 0.06, about 0.05 lower than the annual frozen‐season flow fraction Q frozen / Q annual , consistent with additional shoulder‐season recharge. Long‐term (1961–2020) discharge records show slightly increasing Q frozen / Q annual , highlighting a gradually strengthening slow‐reservoir role. The inferred BPG δ 18 O provided an isotopic baseline: winter stream water δ 18 O converged to it, diverged during ablation, and re‐approached after events. Although ablation‐season f BPG was modest, BPG sustained dry‐season baseflow, underscoring the need to track f BPG for future water‐security and climate‐adaptation planning in degrading permafrost regions.

Ground‐based Global Navigation Satellite System (GNSS) vertical displacements are increasingly used to infer terrestrial water storage (TWS) variations, but their applicability remains uncertain where weak hydrological signals coexist with non‐hydrological deformation processes. This study evaluates the potential of GNSS‐derived vertical displacements to capture TWS variability in Northeast China (NEC), a representative cold region characterized by weak hydrologic variability, a sparse GNSS network, and widespread freeze‐thaw processes. Vertical displacements from GNSS, GRACE, and the Noah land surface model during 2012–2023 were compared. At the regional scale, network‐mean GNSS displacements calculated using all examined stations were moderately correlated with GRACE‐ and Noah‐based estimates ( R = 0.61 and 0.68, respectively). At the station scale, GNSS captured long‐term and interannual TWS variations at most stations, but its seasonal cycle differed markedly from GRACE and Noah. Agreement between GNSS and GRACE degraded substantially in winter ( R = 0.30), possibly due in part to non‐elastic deformation associated with freeze‐thaw processes. Regional‐average snow water equivalent was weakly correlated with station‐mean vertical displacements from GNSS ( R = 0.10), GRACE ( R = 0.28), and Noah ( R = −0.21), while correlations between TWS anomalies and vertical displacements were −0.60, −0.88, and −0.91, respectively. These results suggest that snow mass alone does not dominate seasonal TWS variability in NEC. Overall, GNSS vertical displacements capture regional and interannual TWS variations in cold regions, highlighting their potential for large‐scale hydrological monitoring. However, possible non‐elastic deformation associated with freeze‐thaw processes requires further investigation to improve GNSS‐based hydrological applications in complex cold‐region environments.

Accurate estimation of snowpack dynamics is essential for predicting snowmelt‐driven hydrological processes in mountainous regions. Although data assimilation techniques can integrate remote sensing observations into snow models to reduce uncertainties, their operational implementation remains constrained by sparse and irregular coverage of satellite data, complexity of mountainous terrain, and the computational demands of high‐dimensional assimilation. In this study, an Observation‐Masked Localized Ensemble Kalman Filter (OML‐EnKF) framework was developed to restrict analysis updates to observed regions while preserving localized spatial covariance among nearby observations. This framework assimilated daily Copernicus snow cover fraction (SCF) observations into the Wflow_sbm distributed hydrological model across the Rhône River basin, France, for 2017–2019. We compared its performance against an open‐loop run, a direct insertion scheme, and a one‐dimensional EnKF. Results demonstrate that incorporating a snow module in the Wflow_sbm model improved seasonal discharge patterns. The OML‐EnKF produced spatially coherent snow water equivalent patterns consistent with topography and improved snow depth estimates in mid‐elevation regions. Both EnKF schemes refined SCF and improved discharge predictions across multiple gauged stations. Furthermore, temporal shifts in snowpack accumulation and ablation modulated runoff timing, with increased snowmelt contributions during winter (October–March) and diminished discharge in late spring (April–June). Sensitivity analyses showed that a moderate localization radius (0.025–0.1, approximately 2.8–11.1 km) effectively captured spatial correlations within the OML‐EnKF, whereas excessively restrictive radius values degraded performance. These findings show that the OML‐EnKF enhances snowpack characterization and discharge prediction in the Rhône River basin, offering a promising tool for water resource management in snow‐dominated basins.

In a new application of GRACE and GRACE‐FO satellite observations, we estimate basin‐scale net groundwater recharge rates for the combined Sacramento, San Joaquin, and Tulare basins encompassing California's Central Valley aquifer. Net recharge is calculated as the temporal derivative of groundwater storage anomalies and compared with precipitation and snow water equivalent over a fixed study area. Net positive recharge is rare, occurring in only six water years since 2004, and is consistently associated with years of above‐average Sierra Nevada snowpack. Across the observational record, net positive recharge is observed only in years when snow water equivalent exceeds approximately 21 km 3 (17 MAF; ∼121% of the 20‐year mean value), with the largest recharge event coinciding with the exceptionally wet winter of water year 2023. These results suggest that above‐average snowpack conditions, rather than rainfall alone, are an important enabling condition for basin‐scale groundwater recharge under a warming climate.

Snow droughts, defined as periods with below-normal Snow Water Equivalent, have recently received substantial attention as an emerging hazard in a warming world, but their impacts are still poorly understood. Here, we shed light on these impacts across the socio-ecohydrologic spectrum, by leveraging heterogeneous data sources from 38 catchments in Italy: 13 years of snow and runoff data, remote-sensing and in-situ measurements of Gross Primary Production, an inventory of emergency water restrictions obtained via a web-scraping tool and direct consultation of national to local regulations, and a survey among 113 mountain huts. We found that the majority of snow droughts in our sample were warm-dry (53 %), that is, the combination of higher-than-usual temperatures and low precipitation, followed by warm-wet snow droughts (22 %). These events resulted in the snow season being shortened by as much as one month across all elevations, more melt-out events compared to non-snow-drought years, approximately −50 % summer runoff, and a decline in runoff ratio. Notably, growing-season Gross Primary Production of vegetation after a snow drought was up to 10 % higher than after a non-snow-drought winter, particularly above 1500 m, which ground-based data suggest was due to an earlier meltout of snow leading to an earlier-than-usual greening date. By focusing on the recent 2022 and 2023 snow droughts, we also found that water-supply restrictions were issued at all elevations, but particularly across foothills rather than floodplain regions. Meanwhile, about 70 % of hut managers at elevations above 2000 m reported water-supply impacts, with 28 % of them even reporting earlier closing dates. Overall, snow droughts emerge as a deeply multi-sectorial and multi-elevation risk, interconnecting the cryosphere with hydrology, ecology, and society.

Rainfall during the snow season plays an increasingly important role in flood risk as climate warms and extreme events become more frequent. However, a given sized rain-on-snow (ROS) event can yield outcomes ranging from flooding to no runoff, depending partly on the snowpack's antecedent cold content and capillary retention forces. Here, we analyze the seasonal evolution of the snowpack's physical state over a 72-year period to assess long-term changes in its capacity to buffer runoff from liquid water input. We use ERA-5 Land data to force Alpine3D, a distributed snowpack model that tracks the layer-by-layer development of heat, mass, and structural framework of the snowpack throughout the snow season. We test our approach in a large Columbia River headwaters basin in NW Montana, USA. We evaluate cold content and total capillary retention of the snowpack to determine long term trends in Liquid Water Buffering Capacity (LW bc ) as it evolves throughout the snow season. The LW bc of the snowpack exhibited robust long-term declines across all elevation bands, that is driven by reduced SWE along with reduced cold content in late November through late January across all elevation bands, despite high intra- and interannual variability. The largest declines occurred during the Spring period, trending downward across the historical period by 43 % to 80 % depending on the elevation band. The core five weeks of mid-winter showed no trending change of LW bc , and in fact demonstrated an increase in cold content over the 72 years. Our findings demonstrate that changes in the snowpack's ability to buffer runoff, including dependencies on local basin factors related to snowpack seasonality and elevation, are a key component of evolving ROS risk.

Interferometric Synthetic Aperture Radar (InSAR) offers a potential pathway for measuring seasonal snow water equivalent (SWE) and snow depth at spatial and temporal scales needed for basin‐scale hydrology. First proposed for seasonal snow more than 25 years ago, InSAR uses repeat‐pass radar phase differences to estimate changes in SWE or depth caused by microwave propagation through snow. This review synthesizes InSAR‐based snow monitoring from early theoretical and satellite demonstrations through recent tower, airborne, and spaceborne studies. We summarize the retrieval physics, evaluate published validation results, compare performance across wavelengths and snow environments, and identify barriers to operational implementation. The literature shows that InSAR can provide high‐resolution, spatially distributed information on snow accumulation and ablation, especially under dry‐snow conditions when temporal coherence is maintained and snow phase can be separated from atmospheric, vegetation, soil‐moisture, and other non‐snow contributions. Longer wavelengths, particularly L‐band, help maintain coherence, reduce phase‐wrapping ambiguity, and support retrievals in some vegetated environments, making the NASA–ISRO Synthetic Aperture Radar (NISAR) mission a timely opportunity for basin‐scale testing. Remaining challenges include correcting ionospheric and tropospheric delays; defining stable reference phases; identifying, correcting, or flagging coherence loss and phase‐unwrapping errors; accounting for forest‐canopy interactions; collecting spatially distributed validation data; and scaling site demonstrations to regional or global products. Operational use will require algorithmic maturation, robust validation, and integration with other remote sensing and modeling approaches. If these challenges are addressed, InSAR‐capable satellites could become an important component of an integrated global snow observation suite.

Forest disturbance effects on peak flows have been debated for decades. Paired‐catchment analysis (PCA) has been criticized in relation to the long‐term stability of pre‐harvest regressions and the inability of chronological pairing to characterize changes in event frequency. Non‐stationary frequency analysis (NFA) avoids some concerns with PCA. However, a fundamental problem is that the concept of return period is ambiguous under non‐stationary conditions. This study re‐examined peak flow response to forestry at Camp Creek, a snow‐dominated 34‐km 2 catchment in British Columbia, using five approaches: (a) chronologically paired PCA using adjacent Greata Creek as a control; (b) frequency‐paired PCA using maintenance‐of‐variance regression to fit the pre‐harvest relationship; (c) time‐series regression to relate Camp Creek annual maximum flows to annual peak snowpack water equivalent (SWE) and equivalent clearcut area (ECA); (d) NFA using SWE and ECA as covariates; (e) the design‐life‐level (DLL) approach. While all approaches reveal aspects of forestry effects on peak flows, only DLL provides a conceptually meaningful probabilistic approach to risk analysis under non‐stationary forest cover. However, the DLL‐based approach involves the application of NFA using ECA as a covariate. ECA may not provide a robust indicator of the effects of forest disturbance on peak flows in snow‐dominated catchments, particularly for situations in which logging occurs progressively over extended periods of time or where disturbance also occurs due to fire, insect infestation or disease. Further research should focus on developing alternative indices of forestry effects on peak flow, and on testing process‐based models using rigorous evaluation procedures.

Reported values of average annual peak snow water equivalent (SWE) in the mountains of the western United States differ by more than a factor of five, from not enough water to fill Lake Mead, the largest reservoir in the region, to enough water to fill Lake Mead five times. These products are not created equal. Many vastly underestimate snow water storage, and guidelines regarding which products are more accurate are crucial for using these products in any scientific analysis. Here, we review existing products and compare them to each other and to SWE derived from 26 aerial LiDAR acquisitions in Colorado, California, and Washington mountains of the western U.S. Products that match well with observations include (a) resolution of 9 km or finer, (b) reliance on observations of either fractional snow‐covered area or in situ SWE measurements, and (c) a well‐calibrated model framework that includes both quality precipitation estimates and accurate snowmelt. Many of the energy balance models reviewed had too much melt, likely from too much incoming radiation. Over all three regions, for the 14 flights that occurred close to peak SWE (March–April), the Western U.S. SWE Reanalysis (WUS‐SR) is least biased in reproducing near‐peak SWE (bias of 1.6%) and also best represented the spatial distribution of SWE (smallest mean absolute error). SNODAS and both of the University of Arizona products provide reasonable estimates (biases of 7.8%–10.2%). At the basin scale, ERA5‐Land also has small bias (−17.5%), but due to its coarser resolution, less accurate spatial representations.

Seasonal snowfall is declining globally, yet the relative roles of precipitation variability versus warming‐induced phase transitions remain poorly quantified. We develop a physically based attribution framework that decomposes snowfall trends (1983–2023) into hydrological (precipitation‐driven) and thermodynamic (phase‐driven) components, constrained by a wet‐bulb‐temperature‐based phase relationship. Across most seasonal snow regions, snowfall decline is primarily driven by thermodynamic phase shifts rather than changes in precipitation amount. Regions near the rain–snow transition exhibit the strongest sensitivity to warming, consistent with systematic snowline retreat. Results are robust across independent data sets, including ERA5 reanalysis and the satellite‐based GPCP v3.3 products. These findings indicate that precipitation‐phase transitions exert pivotal control on contemporary snowfall loss, suggesting that projected precipitation increases are unlikely to offset warming‐driven reductions in snowfall. This has critical implications for future hydrological regimes and cryosphere‐climate feedbacks.

Wildfires fundamentally alter snow accumulation and melt dynamics by modifying forest canopy structure, snow albedo, and snowpack physical properties. Despite the growing body of work on fire impacts on snow, few studies have used physically based models to simulate post‐fire changes, and none have examined the global parameter sensitivities that govern these dynamics. In this study, we apply the Structure for Unifying Multiple Modeling Alternatives in a high elevation Colorado watershed to identify and evaluate the model parameters that influence post‐fire snowpack evolution. We conduct a global sensitivity analysis, then sample sensitive parameters to identify behavioral parameter sets with respect to airborne LiDAR‐based snow depth in burned and unburned forests. Among behavioral parameter sets, we identify 4 parameters that differ significantly in burned versus unburned forests. These include the albedo decay rate (45% faster in burn than unburned forest), maximum canopy interception for rain (60% lower), minimum temperature for snow unloading (3.9°C warmer), and snow surface roughness (902% higher). Model simulations using behavioral parameter sets for burned and unburned conditions indicate 17% lower peak absolute cold content on average and 17% lower peak snow‐water equivalent (SWE) in burned conditions on average, with differences in depth, SWE, and cold content increasing throughout the snow season. These findings highlight the combined influence of canopy loss and accelerated albedo decay on snow energy balance and reveal additional post‐fire snow processes that are not typically represented in existing modeling frameworks. This study therefore provides a process‐level foundation for improving the simulated representation of post‐fire snow dynamics.

Hydrologic models applied in snow‐dominated basins are typically evaluated by comparing simulated outputs, such as snow water equivalent and streamflow, with observations. However, this approach may overlook the energy balance aspect of snow modeling. Snowmelt initiation and rate are primarily determined by the net energy balance, which varies across snow models due to diverse model inputs, parameterizations, and layering structures. Here, we discuss how decisions regarding model parameters must be made in conjunction with energy budget considerations and model layer structure to model snowmelt effectively, and highlight the importance of incorporating multiple validation data sources, including snow surface temperature. We use the two‐layer Distributed Hydrology Soil Vegetation Model in California's upper Tuolumne River watershed as a case study and explore the influence of model top layer thickness (TLT) on simulating snowmelt, focusing on its significant effects on modeled snow surface temperature ( T ss ), energy propagation through the snowpack, and the timing of snowmelt. Additionally, we examine the interplay between model forcing and parameters, specifically incoming longwave radiation and albedo, to reveal how variations in these factors interact with TLT and shape snowmelt simulations. Our results reveal that TLT has a significant impact on diurnal variations in T ss , snowpack energy distribution, and the overall timing of snowmelt. Lower albedo enhances melting rates more strongly in the thinner upper layers, and biases in incoming longwave radiation can either amplify or mitigate these effects. Snow surface temperature was a key variable to investigate, and we recommend considering it in snowmelt investigations.

Snow plays a critical role in cold-region environments, from climate regulation to water storage and from spring freshet to ecosystem services. However, quantification of snow accumulation and redistribution across spatio-temporal scales is challenging, owing to non-uniform snowfall, topographic attributes, wind redistribution, and interception and sublimation by vegetation. Manual snow surveys are often conducted to improve understanding of snow processes across scales and to support societal needs (e.g., seasonal water supply projections, flood forecasting, reservoir filling, etc.). Nevertheless, manual snow surveys are time-consuming and expensive, which limits their temporal frequency and are rarely conducted in remote locations. This manuscript presents snow data from manual snow surveys conducted from 2009 to 2024 across four small watersheds in high-latitude boreal plains of northern Alberta in Canada. The dataset includes various snow properties, including depth, density and snow water equivalent across different watersheds and land cover types (i.e., Open, Flat Low Lying, Coniferous and Mixed Deciduous). The dataset is expected to be useful in understanding snow processes in high-latitude boreal plains, to help calibrate snow models and to validate airborne lidar or satellite-derived snow depth measurements.

As part of NASA’s Global Precipitation Measurement (GPM) ground validation program, a multi-year winter field study in Storrs, Connecticut brought a unique opportunity to study the long-lasting problem of precipitation phase determination within retrieval algorithms. This study evaluates the phase algorithms of NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset, the flagship GPM global precipitation product, and NOAA’s Multi-Radar Multi-Sensor (MRMS) dataset, which is frequently used to validate IMERG precipitation rates over the continental United States. The study selected ten long-lasting phase transition events where rain and snow were recorded for more than one hour with the presence of mixed precipitation and focused on two diverse events. Using the PARSIVEL disdrometer phase as a reference, phase transition occurs at around wet-bulb temperature of 0.5˚C, which agrees with prior studies. IMERG and MRMS phase algorithms performed better when using High Resolution Rapid Refresh (HRRR) and European Center for Medium-Range Weather Forecast Reanalysis (ERA5) temperatures than Modern-Era Retrospective Analysis for Research and Applications reanalysis (MERRA2) temperatures. Overall, IMERG Version 07 performed better than its predecessor Version 06. This study also evaluated HRRR deterministic and probabilistic phase algorithms, which performed better than HRRR temperature-based MRMS and IMERG algorithms, respectively.

As the atmosphere warms, atmospheric evaporative demand is expected to increase across many high-latitude ecosystems, while the duration of seasonal snow cover is projected to change. In Norway, a typically moisture-rich region, improved understanding of the controls on evaporation is needed to assess how these changes may affect ecosystem hydrology. In this study, we used year-round evaporation estimates from four eddy-covariance wetland sites in Norway to quantify evaporation and identify its main controls. To estimate monthly, seasonal, and annual evaporation, eddy-covariance data were gap-filled using a random forest model. The sites cover a latitudinal gradient from 60 to 78° N, a precipitation gradient from 218 to 968 mm per year and a gradient in mean temperature from −3.9 to 2.7 °C. To identify evaporation controls, we performed a factor analysis on observed time series in the snow-free and snow-covered season, separately. In addition, we compared the observed evaporation with the results of a Penman-Monteith model. We found that ecosystem evaporation was mainly controlled by atmospheric evaporative demand, both in the snow-free and the snow-covered season, whereas soil moisture likely never decreased to a level where it restricted evaporation. However, the sensitivity of the Bowen ratio to the vapour pressure deficit varied between sites, showing a decrease in the Bowen ratio beyond a vapour pressure deficit of 1 kPa at sites with a larger cover of open water and non-vascular vegetation compared to a site with a higher cover of vascular plants. Annual evaporation ranged from 80 to 208 mm, equivalent to 9 % to 30 % of the total precipitation. In the warm season, evaporation was typically around 50 % of the seasonal precipitation, reaching a maximum of 72 %. Sites and years with long-duration snow-cover had lower annual evaporation. Compared to other northern latitude sites in the FLUXNET2015 data set, evaporation was lower than expected from the mean temperature of the warm season. Our results show that evaporation is an important part of the northern latitude water balance, especially during the warm season and in regions with low precipitation. Furthermore, our results indicate that earlier snow-cover melt-out and increased vapour pressure deficit have the potential to increase annual evaporation.

In June 2023, an extreme hydrometeorological event caused widespread flooding across central Chile (34°–39°S), abruptly interrupting a 13-yr dry period. This study provides a comprehensive analysis of the event, characterizing its atmospheric drivers, hydrological impacts, and the role of rain on snow (ROS) in a representative catchment. A category-4 zonal atmospheric river persisted for 4 days in a warm environment, resulting in a steep orographic precipitation gradient over the foothills of the Andes with rainfall accumulations of up to 600 mm and the freezing level above 3000 m MSL. Many rivers reached their highest flows on record, with peak discharges associated with return periods ranging from 9 to 199 years, resulting in significant damage to critical infrastructure and the general population. We also present a quantitative diagnosis of a ROS event in the Andes Cordillera using a physically based and semidistributed hydrological model in a representative catchment. Simulations reveal that snowmelt plus rainfall from snow-covered areas contributed ∼ 15% to the total flood volume, of which snowmelt was the secondary source driven by net longwave radiation and heat from the warm rainfall. This event serves as an example of a compound hydrometeorological extreme event, where intense and prolonged precipitation, combined with high temperatures, converged to produce a major flood in central Chile. Our findings highlight the region’s vulnerability to extreme events and underscore the need for future studies to assess the potential of an integrated hydrological forecasting system to mitigate the impacts of extreme events. Significance Statement The June 2023 flood in central Chile was a historic event, interrupting a 13-yr-long megadrought and producing significant damage to critical infrastructure. Our study provides a detailed hydrometeorological description of the event. Key features include its persistence over 4 days, rainfall accumulations above 600 mm, a steep orographic precipitation gradient, and the freezing level above 3000 m MSL, increasing the pluvial contributing areas and producing rain on snow in some regions, with many rivers reaching their highest flows on record. This study highlights the vulnerability of the region to extreme warm storms.

Hydrologic models are often calibrated using streamflow alone, but increasing availability of in situ and satellite-based observations provide numerous opportunities to constrain model outputs and improve process representation. However, as new observation data emerges, it is often unclear whether calibration with additional data would inform or misinform streamflow prediction. Here, we carry out a multi-observational sensitivity and uncertainty analysis using the U.S. Geological Survey's National Hydrologic Model (NHM) in four headwater catchments in the Upper Colorado River Basin. We use seven different observational data products that pertain to discharge, snow water equivalent, snow-covered area, soil moisture, and evapotranspiration. Informative model parameters are identified using the Morris screening method across all data sets, followed by parameter estimation and streamflow performance assessment using a Latin Hypercube Sample Monte-Carlo filtering approach. Results show that an increased number of informative parameters are determined through the screening process with the use of observation data representing terms beyond streamflow, and that forcing corrections and rain-snow partitioning parameters are particularly impactful to the model fit to observations. Multi-objective Monte Carlo filtering reduces the number of behavioral parameter sets, and estimated parameter values can depend strongly on the observation data criteria. Evapotranspiration is informative for streamflow prediction across all catchments included in this study, but snow and soil moisture datasets are only informative in some. These results provide new insight into the variable value of alternative observation data for streamflow prediction and highlight challenges related to model/observation scale mismatches, compensating errors, and misinformative data.

Flowing stream networks expand and contract in response to dynamic groundwater levels. Field studies generally associate greater flowing network length ( L ) with higher streamflow ( Q ), but this neglects potential hysteresis caused by nonequilibrium groundwater flow after rain and snowmelt. Using a new version of the Distributed Hydrology Soil Vegetation Model (DHSVM), we predict that groundwater hysteresis may decouple L from Q across large (> 100 %) variations in Q . Groundwater hysteresis contributes to the spatial reconfiguration of active flowpaths and changes to hillslope-riparian hydrological connectivity, which can manifest as a network length scaling anomaly relative to the best-fit power law. In a 27 km 2 snowy volcanic watershed, seasonal anomalies in measured stream ionic concentration indicate an outsized contribution from longer subsurface flowpaths during recession, supporting our L - Q hysteresis hypothesis and refining our model calibration. The model can reproduce observed stream network elasticity (from field surveys), and the predicted network length anomaly mirrors seasonal anomalies in measured stream ionic concentration (r=-0.92), suggesting that the model can capture seasonal changes in the spatial configuration of groundwater convergence and streamflow generation. A warmer climate is expected to cause a partial transition from snow to rain resulting in flashier streamflow, but our simulations predict that seasonal groundwater hysteresis would dampen storm-scale stream network elasticity, thereby significantly increasing L - Q hysteresis on daily to monthly timescales ( p < 0.01). Conceptual models of stream networks should consider the potential effects of groundwater hysteresis, especially in a changing environment. More broadly, our investigation highlights how spatially distributed process-based hydrological modeling can reveal emergent hydrological behaviors that are not necessarily apparent from sparse field data.

Snowmelt runoff onset timing represents a critical hydrological parameter, particularly in mountainous regions where seasonal snow serves as a natural reservoir for downstream water resources. Despite this importance, high-resolution observations of snowmelt runoff onset across complex terrain are limited, due to challenges from sparse in situ monitoring networks, intermittent optical remote sensing data, and coarse passive microwave remote sensing data. To address this gap, we prepared a global snowmelt runoff onset timing dataset (https://doi.org/10.5281/zenodo.16953614, Gagliano et al., 2026) for the 10-year period spanning 2015–2024, with 80 m spatial resolution and 9.5 d average temporal resolution. We created this dataset by identifying backscatter minima empirically associated with runoff onset in a time series of Sentinel-1 C-band SAR images, with detection constrained by a custom MODIS-derived snow phenology dataset. We evaluated our dataset using in situ snow pillow estimates of runoff onset from 1116 automated weather stations across the Western United States, British Columbia, Norway, and Nepal, finding a median timing difference of −2.0 d and a median absolute deviation of 10.0 d. The local agreement between our runoff onset estimates and snow pillow runoff onset estimates varies with site-specific variables like forest cover fraction, SWE, and dataset temporal resolution. We characterized these dependencies to provide empirically-derived thresholds for quality filtering as well as guidance for interpretation and use of our products. The dataset includes global annual runoff onset products for each water year, annual local temporal resolution products for each water year, and 10-year composites of median runoff onset, median absolute deviation, and local temporal resolution. This unique combination of high spatial resolution, global coverage, and decade-long temporal coverage provides unprecedented detail for the study of snowmelt runoff onset across snow-covered regions. Our snowmelt runoff onset dataset enables an improved understanding of mountain hydrological processes and informs water resource management in snow-dominated watersheds.

Abrupt snowmelt, triggered by rain-on-snow events or “snow-eater heat waves,” can cause flooding, initiate or accelerate snow drought, and affect water availability. However, the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heat waves have received little attention. To address this gap, we developed a method to identify snow-eater heat waves and estimate their melt potential using 20th Century Reanalysis version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850–2015. Melt season snow-eater heat waves typically last 3 to 5 days, with three to five events, doubling snowmelt rates. Seven of 11 spring superfloods are shown to coincide with snow-eater heat waves. Since the 1850s, snow-eater heat waves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heat-wave impacts into SNOW-17 enhances extreme melt estimates, improving water management support tools.

Soil freezing is a major cold region process that influences hydrological response of northern catchments, in particular during winter rainfall and snowmelt events. In land surface models, frozen soil infiltration is difficult to represent because soil structure and hydropedological processes vary at scales finer than the model grid. This is particularly true in operational modeling, where physical process integration must balance performance improvements against computational efficiency and complexity. In this study, we propose a new configuration of the Soil, Vegetation, and Snow (SVS) model used within the operational prediction systems of Environment and Climate Change Canada (ECCC) that enhances frozen soil infiltration by reducing both surface runoff and sub-surface lateral flow. We assessed the effects of this new configuration (Fr-Inf) on streamflow simulations at more than 580 hydrometric stations located in the Great-Lakes and Saint-Lawrence domain over a five-year period. Fr-Inf significantly improves the Kling-Gupta Efficiency (KGE) compared to the default soil freezing configuration (Fr; ΔKGE = 0.28) but slightly underperforms the configuration of SVS without frozen soil (noFr; ΔKGE = −0.07). Strong degradations relative to the no freezing configuration (ΔKGE < −0.5) are observed only at 4 stations with Fr-Inf (< 1 %) as opposed to 172 stations under the Fr configuration (33 %), highlighting the robustness of the approach. To ensure that the proposed change is also acceptable in the context of operational numerical weather prediction, an evaluation of its impact on soil freezing depth as well as screen-level temperature and dew point temperature predictions is performed against in-situ observations. These results support the potential operational implementation of soil freezing at ECCC for numerical weather and streamflow prediction.

Accurate estimates of snow water equivalent (SWE) are critical for hydrologic forecasting and water resource management in snow-dominated regions. While in situ observations, such as those from the U.S. Department of Agriculture Snowpack Telemetry (SNOTEL) network, provide direct measurements, their sparse spatial coverage limits their broader applicability. Numerical models such as Noah LSM with multiparameterization options (Noah-MP) provide spatially continuous SWE estimates but are computationally demanding. This study develops a deep learning–based emulator to reproduce SWE simulated by Noah-MP using a convolutional long short-term memory (ConvLSTM) network trained on meteorological forcings from the Weather Research and Forecasting Model and augmented with static physiographic variables and remotely sensed snow cover and leaf area index data. Incorporating spatial coupling substantially improves performance relative to a pixelwise long short-term memory baseline, increasing the fraction of grid cells with nonnegative Nash–Sutcliffe efficiency (NSE) by 15%–27% during the test period. The emulator reproduces large-scale SWE patterns consistent with both Noah-MP and Snow Data Assimilation System (SNODAS) and maintains competitive NSE and root-mean-square error performance relative to SNOTEL observations. Seasonal sensitivity experiments yield high R 2 values up to 0.96 during accumulation and 0.97 during melt. Once trained, the emulator generates SWE predictions faster than the parent physics-based simulation, enabling rapid, high-resolution estimates that support real-time monitoring and ensemble forecasting. Significance Statement Communities across the western United States rely on mountain snow as a significant source of freshwater, yet it remains difficult to track how much snow is stored at any given time. Ground measurements are sparse, and the numerical models used to estimate snowpack can take hours to run. We show that a data-driven approach can mimic the behavior of these complex models while producing results almost instantly. By capturing how weather and landscape features shape snowpack across the Colorado Rockies, the system achieves performance comparable to that of a widely used numerical model, with improved agreement with ground measurements at many locations. This capability provides a faster, more practical tool for water planning,…

A 40-year gridded meteorological forcing dataset spanning the water years from 1 October 1983 to 30 September 2023 was compiled for the Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho, USA. This Reynolds Creek Long-Term (RCLT) dataset consists of hourly, 10 m resolution grids of air temperature, vapor pressure, precipitation mass and phase, incoming shortwave and longwave radiation, visible and infrared snow albedo, and wind speed and direction. These variables were interpolated and calculated from hourly measurements from the dense meteorological station network within the mountainous 239 km 2 RCEW, which contains elevations that span the historical winter rain-to-snow transition. The observations are foundational for many ecological and hydrological Land Surface Models (LSMs) used in research and operational applications. Additionally, an example use case is presented in which we show how the snow-dominated area of the basin has evolved over the data record. This 13 TB dataset, stored in cloud-optimized Zarr format, enables future model development, benchmarking, uncertainty analyses of existing models, independent validation of gridded atmospheric reanalysis datasets, and novel investigations of hydroclimatic variability across snow-dominated semi-arid environments. Data access is available via the following repository: https://doi.org/10.15482/USDA.ADC/30199954 (Hedrick et al., 2025).