Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception

Forest canopies exert significant controls over the spatial distribution of snow cover. Canopy snow interception efficiency is controlled by intrinsic processes (e.g., canopy structure), extrinsic processes (e.g., meteorological conditions), and the interaction of intrinsic-extrinsic factors (i.e., air temperature and branch stiffness). In hydrological models, intrinsic processes governing snow interception are typically represented by two-dimensional metrics like the leaf area index (LAI). To improve snow interception estimates and their scalability, new approaches are needed for better characterizing the three-dimensional distribution of canopy elements. Airborne laser scanning (ALS) provides a potential means of achieving this, with recent research focused on using ALS-derived metrics that describe forest spacing to predict interception storage. A wide range of canopy structural metrics that describe individual trees can also be extracted from ALS, although relatively little is known about which of them, and in what combination, best describes intrinsic canopy properties known to affect snow interception. The overarching goal of this study was to identify important ALS-derived canopy structural metrics that could help to further improve our ability to characterize intrinsic factors affecting snow interception. Specifically, we sought to determine how much variance in canopy intercepted snow volume can be explained by ALS-derived crown metrics, and what suite of existing and novel crown metrics most strongly affects canopy intercepted snow volume. To achieve this, we first used terrestrial laser scanning (TLS) to quantify snow interception on 14 trees. We then used these snow interception measurements to fit a random forest model with ALS-derived crown metrics as predictors. Next, we bootstrapped 1000 calculations of variable importance (percent increase in mean squared error when a given explanatory variable is removed), keeping nine canopy metrics for the final model that exceeded a variable importance threshold of 0.2. ALS-derived canopy metrics describing intrinsic tree structure explained approximately two-thirds of the snow interception variability (R2 ≥ 0.65, RMSE ≤ 0.52 m3, relative RMSE ≤ 48%) in our study when extrinsic factors were kept as constant as possible. For comparison, a generalized linear mixed-effects model predicting snow interception volume from LAI alone had a marginal R2 = 0.01. The three most important predictor variables were canopy length, whole-tree volume, and unobstructed returns (a novel metric). These results suggest that a suite of intrinsic variables may be used to map interception potential across larger areas and provide an improvement to interception estimates based on LAI.

Snow water scarcity induced by record-breaking warm winter in 2020 in Japan

This study highlights the severity of the low snow water equivalent (SWE) and remarkably high temperatures in 2020 in Japan, where reductions in SWE have significant impacts on society due to its importance for water resources. A continuous 60-year land surface simulation forced by reanalysis data revealed that the low SWE in many river basins in the southern snowy region of mainland Japan are the most severe on record. The impact of the remarkably high temperatures in 2020 on the low SWE was investigated by considering the relationships among SWE, temperature, and precipitation. The main difference between the 2020 case and prior periods of low SWE is the record-breaking high temperatures. Despite the fact that SWE was the lowest in 2020, precipitation was much higher than that in 2019, which was one of the lowest SWE on record pre-2020. The results indicate the possibility that even more serious low-SWE periods will be caused if lower precipitation and higher temperatures occur simultaneously.

Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016

Snow cover is a key parameter of the climate system and its significant seasonal and annual variability have significant impacts on the surface energy balance and global water circulation. However, current snow depth datasets show large inconsistencies and uncertainties, which limit their applications in climate change projections and hydrological processes simulations. In this study, a comprehensive assessment of five hemispheric snow depth datasets was carried out against ground observations from 43,391 stations. The five snow depth datasets included three remote sensing datasets, i.e., Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), and two reanalysis datasets, i.e., ERA-Interim and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Assessment results imply that the spatial distribution of GlobSnow and ERA-Interim exhibit overall better agreements with ground observations than other datasets. GlobSnow and ERA-Interim exhibit less uncertainty during the snow accumulation and ablation periods, respectively. In plain and forested regions, GlobSnow, ERA-Interim and MERRA-2 show better performances, while in mountain and forested mountain areas, GlobSnow exhibits the best performance. AMSR-E and AMSR2 agree better with ground observations in shallow snow condition (0–10 cm), while MERRA-2 shows more satisfying performance when snow depth exceeds 50 cm. These systematic and integrated understanding of the five representative snow depth datasets provides information on data selection and data refinement, as well as data fusion, which is our next work of interest.

Does Data Availability Constrain Temperature-Index Snow Models? A Case Study in a Humid Boreal Forest

Temperature-index (TI) models are commonly used to simulate the volume and occurrence of meltwater in snow-fed catchments. TI models have varying levels of complexity but are all based on air temperature observations. The quality and availability of data that drive these models affect their predictive ability, particularly given that they are frequently applied in remote environments. This study investigates the performance of non-calibrated TI models in simulating the subcanopy snow water equivalent (SWE) of a small watershed located in Eastern Canada, for which some distinctive observations were collected. Among three relatively simple TI algorithms, the model that performed the best was selected based on the average percent bias (Pbias of 24%) and root mean square error (RMSE of 100 mm w.e.), and was designated as the base TI model. Then, a series of supplemental tests were conducted in order to quantify the performance gain that resulted from including the following inputs/processes to the base TI model: subcanopy incoming radiation, canopy interception, snow surface temperature, sublimation, and cold content. As a final test, all the above modifications were performed simultaneously. Our results reveal that, with the exception of snow sublimation (Pbias of 5.4%) and snow surface temperature, the variables mentioned above were unable to improve TI models within our sites. It is therefore worth exploring other feasible alternatives to existing TI models in complex forested environments.

Evaluation of Snowmelt Estimation Techniques for Enhanced Spring Peak Flow Prediction

Water resources management and planning requires accurate and reliable spring flood forecasts. In cold and snowy countries, particularly in snow-dominated watersheds, enhanced flood prediction requires adequate snowmelt estimation techniques. Whereas the majority of the studies on snow modeling have focused on comparing the performance of empirical techniques and physically based methods, very few studies have investigated empirical models and conceptual models for improving spring peak flow prediction. The objective of this study is to investigate the potential of empirical degree-day method (DDM) to effectively and accurately predict peak flows compared to sophisticated and conceptual SNOW-17 model at two watersheds in Canada: the La-Grande River Basin (LGRB) and the Upper Assiniboine river at Shellmouth Reservoir (UASR). Additional insightful contributions include the evaluation of a seasonal model calibration approach, an annual model calibration method, and two hydrological models: McMaster University Hydrologiska Byrans Vattenbalansavdelning (MAC-HBV) and Sacramento Soil Moisture Accounting model (SAC-SMA). A total of eight model scenarios were considered for each watershed. Results indicate that DDM was very competitive with SNOW-17 at both the study sites, whereas it showed significant improvement in prediction accuracy at UASR. Moreover, the seasonally calibrated model appears to be an effective alternative to an annual model calibration approach, while the SAC-SMA model outperformed the MAC-HBV model, no matter which snowmelt computation method, calibration approach, or study basin is used. Conclusively, the DDM and seasonal model calibration approach coupled with the SAC-SMA hydrologic model appears to be a robust model combination for spring peak flow estimation.

Improving SWE Estimation by Fusion of Snow Models with Topographic and Remotely Sensed Data

This paper presents a new concept to derive the snow water equivalent (SWE) based on the joint use of snow model (AMUNDSEN) simulation, ground data, and auxiliary products derived from remote sensing. The main objective is to characterize the spatial-temporal distribution of the model-derived SWE deviation with respect to the real SWE values derived from ground measurements. This deviation is due to the intrinsic uncertainty of any theoretical model, related to the approximations in the analytical formulation. The method, based on the k-NN algorithm, computes the deviation for some labeled samples, i.e., samples for which ground measurements are available, in order to characterize and model the deviations associated to unlabeled samples (no ground measurements available), by assuming that the deviations of samples vary depending on the location within the feature space. Obtained results indicate an improved performance with respect to AMUNDSEN model, by decreasing the RMSE and the MAE with ground data, on average, from 154 to 75 mm and from 99 to 45 mm, respectively. Furthermore, the slope of regression line between estimated SWE and ground reference samples reaches 0.9 from 0.6 of AMUNDSEN simulations, by reducing the data spread and the number of outliers.

Surface-Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest

Tropical South America plays a central role in global climate. Bowen ratio teleconnects to circulation and precipitation processes far afield, and the global CO2 growth rate is strongly influenced by carbon cycle processes in South America. However, quantification of basin-wide seasonality of flux partitioning between latent and sensible heat, the response to anomalies around climatic norms, and understanding of the processes and mechanisms that control the carbon cycle remains elusive. Here, we investigate simulated surface-atmosphere interaction at a single site in Brazil, using models with different representations of precipitation and cloud processes, as well as differences in scale of coupling between the surface and atmosphere. We find that the model with parameterized clouds/precipitation has a tendency toward unrealistic perpetual light precipitation, while models with explicit treatment of clouds produce more intense and less frequent rain. Models that couple the surface to the atmosphere on the scale of kilometers, as opposed to tens or hundreds of kilometers, produce even more realistic distributions of rainfall. Rainfall intensity has direct consequences for the "fate of water," or the pathway that a hydrometeor follows once it interacts with the surface. We find that the model with explicit treatment of cloud processes, coupled to the surface at small scales, is the most realistic when compared to observations. These results have implications for simulations of global climate, as the use of models with explicit (as opposed to parameterized) cloud representations becomes more widespread.

Exploration of the Snow Ablation Process in the Semiarid Region in China by Combining Site-Based Measurements and the Utah Energy Balance Model—A Case Study of the Manas River Basin

Understanding the snow accumulation and melting process is of great significance for the assessment and regulation of water resources and the prevention of meltwater flooding, especially for the semiarid region in the Manas River Basin. However, the lack of long snow measurement time series in this semiarid region prevents a full understanding of the detailed local-scale snow ablation process. Additionally, the modeling of snow accumulation and melting is challenging due to parameter uncertainty. In this study, the snow ablation process in the Manas River Basin was quantitatively explored with long time-series of 3-h measurements of snow depth, snow density and snow water equivalent (SWE) at the Wulanwusu (WLWS), Hanqiazi (HQZ), and Baiyanggou (BYG) sites. This study explored the ability of the Utah energy balance (UEB) snow accumulation and melt model to simulate SWE, energy flux and water loss in the study area. Furthermore, the uncertainty in the ground surface aerodynamic roughness index zos in the UEB model was also analyzed. The results showed that: (1) noticeable variations in snow depth, SWE and snow density occurred on seasonal and interannual time scales, and variations in melting time and melting ratios occurred on short time scales; (2) a rapid decrease in snow depth did not influence the variations in SWE, and snow melting occurred during all time periods, even winter, which is a typical characteristic of snow accumulation in arid environments; (3) the UEB model accurately simulated the snow ablation processes, including SWE, snow surface temperature, and energy flux, at WLWS, HQZ, and BYG sites; (4) the lowest contribution of net radiation to melting occurred in the piedmont clinoplain, followed by the mountain desert grassland belt and mountain forest belt, whereas the contributions of net turbulence exhibited the opposite pattern; (5) the optimal zos in the UEB model was experimentally determined to be 0.01 m, and the UEB model-simulated SWE based on this value was the most consistent with the measured SWE; and (6) the results may provide theoretical and data foundations for research on the snow accumulation process at the watershed scale.

Combining MODIS and National Land Resource Products to Model Land Cover-Dependent Surface Albedo for Norway

Surface albedo is an important physical attribute of the climate system and satellite retrievals are useful for understanding how it varies in time and space. Surface albedo is sensitive to land cover and structure, which can vary considerably within the area comprising the effective spatial resolution of the satellite-based retrieval. This is particularly true for MODIS products and for topographically complex regions, such as Norway, which makes it difficult to separate the environmental drivers (e.g., temperature and snow) from those related to land cover and vegetation structure. In the present study, we employ high resolution datasets of Norwegian land cover and structure to spectrally unmix MODIS surface albedo retrievals (MCD43A3 v6) to study how surface albedo varies with land cover and structure. Such insights are useful for constraining land cover-dependent albedo parameterizations in models employed for regional climate or hydrological research and for developing new empirical models. At the scale of individual land cover types, we found that the monthly surface albedo can be predicted at a high accuracy when given additional information about forest structure, snow cover, and near surface air temperature. Such predictions can provide useful empirical benchmarks for climate model predictions made at the land cover level, which is critical for instilling greater confidence in the albedo-related climate impacts of anthropogenic land use/land cover change (LULCC).

Generating Observation-Based Snow Depletion Curves for Use in Snow Cover Data Assimilation

Snow depletion curves (SDC) are functions that are used to show the relationship between snow covered area and snow depth or water equivalent. Previous snow cover data assimilation (DA) studies have used theoretical SDC models as observation operators to map snow depth to snow cover fraction (SCF). In this study, a new approach is introduced that uses snow water equivalent (SWE) observations and satellite-based SCF retrievals to derive SDC relationships for use in an Ensemble Kalman filter (EnKF) to assimilate snow cover estimates. A histogram analysis is used to bin the SWE observations, which the corresponding SCF observations are then averaged within, helping to constrain the amount of data dispersion across different temporal and regional conditions. Logarithmic functions are linearly regressed with the binned average values, for two U.S. mountainous states: Colorado and Washington. The SDC-based logarithmic functions are used as EnKF observation operators, and the satellite-based SCF estimates are assimilated into a land surface model. Assimilating satellite-based SCF estimates with the observation-based SDC shows a reduction in SWE-related RMSE values compared to the model-based SDC functions. In addition, observation-based SDC functions were derived for different intra-annual and physiographic conditions, and landcover and elevation bands. Lower SWE-based RMSE values are also found with many of these categorical observation-based SDC EnKF experiments. All assimilation experiments perform better than the open-loop runs, except for the Washington region’s 2004–2005 snow season, which was a major drought year that was difficult to capture with the ensembles and observations.

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.

The Kühtai data set: 25 years of lysimetric, snow pillow, and meteorological measurements

Snow measurements at the Kühtai station in Tirol, Austria, (1920 m.a.s.l.) are described. The data set includes snow water equivalent from a 10 m² snow pillow, snow melt outflow from a 10 m² snow lysimeter placed at the same location as the pillow, meteorological data (precipitation, incoming shortwave radiation, reflected shortwave radiation, air temperature, relative air humidity, and wind speed), and other data (snow depths, snow temperatures at seven heights) from the period October 1990 to May 2015. All data have been quality checked, and gaps in the meteorological data have been filled in. The data set is unique in that all data are available at a temporal resolution of 15 min over a period of 25 years with minimal changes in the experimental setup. The data set can therefore be used to analyze snow pack processes over a long-time period, including their extremes and long-term changes, in an Alpine climate. Analyses may benefit from the combined measurement of snow water equivalent, lysimeter outflow, and precipitation at a wind-sheltered alpine site. An example use of data shows the temporal variability of daily and 1 April snow water equivalent observed at the Kühtai site. The results indicate that the snow water equivalent maximum varies between 200 and more than 500 mm w.e., but there is no statistically significant temporal trend in the period 1990-2015.

Intercomparison of global river discharge simulations focusing on dam operation - Part II: Multiple models analysis in two case-study river basins, Missouri-Mississippi and Green-Colorado

We performed a twofold intercomparison of river discharge regulated by dams under multiple meteorological forcings among multiple global hydrological models for a historical period by simulation. Paper II provides an intercomparison of river discharge simulated by five hydrological models under four meteorological forcings. This is the first global multimodel intercomparison study on dam-regulated river flow. Although the simulations were conducted globally, the Missouri-Mississippi and Green- Colorado Rivers were chosen as case-study sites in this study. The hydrological models incorporate generic schemes of dam operation, not specific to a certain dam. We examined river discharge on a longitudinal section of river channels to investigate the effects of dams on simulated discharge, especially at the seasonal time scale. We found that the magnitude of dam regulation differed considerably among the hydrological models. The difference was attributable not only to dam operation schemes but also to the magnitude of simulated river discharge flowing into dams. That is, although a similar algorithm of dam operation schemes was incorporated in different hydrological models, the magnitude of dam regulation substantially differed among the models. Intermodel discrepancies tended to decrease toward the lower reaches of these river basins, which means model dependence is less significant toward lower reaches. These case-study results imply that, intermodel comparisons of river discharge should be made at different locations along the river's course to critically examine the performance of hydrological models because the performance can vary with the locations.

Comparison of commonly-used microwave radiative transfer models for snow remote sensing

This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (T_B): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated T_B at 11, 19 and 37 GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (D_max) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (D_max), assuming non-sticky spheres for the DMRT models. Simulated T_B sensitivity analysis using the same inputs shows relatively consistent T_B behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18 K and 27 K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground-based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated T_B. Results using in-situ grain size measurements (SSA or D_max, depending on the model) give a mean T_B RMSE (19 and 37 GHz) of the order of 16-26 K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than D_max measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor.

Effects of the duration of cold stratification on early life stages of the Mediterranean alpine plant Silene ciliata

Cold stratification provided by snow cover is essential to break seed dormancy in many alpine plant species. The forecast reduction in snow precipitation and snow cover duration in most temperate mountains as a result of global warming could threaten alpine plant populations, especially those at the edge of their species distribution, by altering the dynamics of early life stages. We simulated some effects of a reduction in the snow cover period by manipulating the duration of cold stratification in seeds of Silene ciliata, a Mediterranean alpine specialist. Seeds from three populations distributed along an altitudinal gradient were exposed to different periods of cold stratification (2, 4 and 6 months) in the laboratory and then moved to common garden conditions in a greenhouse. The duration of the cold stratification treatment and population origin significantly affected seed emergence percentage, emergence rate and seedling size, but not the number of seedling leaves. The 6-month and 4-month cold stratification treatments produced higher emergence percentages and faster emergence rates than seeds without cold stratification treatment. No significant cold stratification duration x seed population origin interactions were found, thus differential sensitivity to cold stratification along elevation is not supported.

Influence of the physical terrestrial Arctic in the eco-climate system

This synthesis paper provides a summary of the major components of the physical terrestrial Arctic and the influences of their changes upon the larger eco-climate system. Foci here are snow cover, permafrost, and land hydrology. During the last century, snow cover duration has shortened in a large portion of the circum-Arctic, mainly because of its early northward retreat in spring due to warming. Winter precipitation has generally increased, resulting in an increase in maximum snow depth over large areas. This is consistent with the increase in river discharge over large Russian watersheds. Soil temperature has also increased, and the active layer has deepened in most of the permafrost regions, whereas thinning of the seasonally frozen layer has been observed in areas not underlain by permafrost. These active components are mutually interrelated, conditioned by ambient micro- to landscape-level topography and local surface and subsurface conditions, and they are closely related with vegetation and ecology, as evidenced by evolution in the late Quaternary. Further, we provide examples and arguments for discussions on the pathways through which changes in the Arctic terrestrial system can affect or propagate to remote areas beyond the Arctic, reaching to the extratropics in the larger climate system. These considerations include dynamical and thermodynamical responses and feedbacks,'modification of hemisphere-scale atmospheric circulation associated with troposphere-stratosphere couplings, and moisture intrusion at a continental scale.

Performance of complex snow cover descriptions in a distributed hydrological model system: A case study for the high Alpine terrain of the Berchtesgaden Alps

[1] Runoff generation in Alpine regions is typically affected by snow processes. Snow accumulation, storage, redistribution, and ablation control the availability of water. In this study, several robust parameterizations describing snow processes in Alpine environments were implemented in a fully distributed, physically based hydrological model. Snow cover development is simulated using different methods from a simple temperature index approach, followed by an energy balance scheme, to additionally accounting for gravitational and wind-driven lateral snow redistribution. Test site for the study is the Berchtesgaden National Park (Bavarian Alps, Germany) which is characterized by extreme topography and climate conditions. The performance of the model system in reproducing snow cover dynamics and resulting discharge generation is analyzed and validated via measurements of snow water equivalent and snow depth, satellite-based remote sensing data, and runoff gauge data. Model efficiency (the Nash-Sutcliffe coefficient) for simulated runoff increases from 0.57 to 0.68 in a high Alpine headwater catchment and from 0.62 to 0.64 in total with increasing snow model complexity. In particular, the results show that the introduction of the energy balance scheme reproduces daily fluctuations in the snowmelt rates that trace down to the channel stream. These daily cycles measured in snowmelt and resulting runoff rates could not be reproduced by using the temperature index approach. In addition, accounting for lateral snow transport changes the seasonal distribution of modeled snowmelt amounts, which leads to a higher accuracy in modeling runoff characteristics.