(Near) Real-Time Snow Water Equivalent Observation Using GNSS Refractometry and RTKLIB.

Global navigation satellite system (GNSS) refractometry enables automated and continuous in situ snow water equivalent (SWE) observations. Such accurate and reliable in situ data are needed for calibration and validation of remote sensing data and could enhance snow hydrological monitoring and modeling. In contrast to previous studies which relied on post-processing with the highly sophisticated Bernese GNSS processing software, the feasibility of in situ SWE determination in post-processing and (near) real time using the open-source GNSS processing software RTKLIB and GNSS refractometry based on the biased coordinate Up component is investigated here. Available GNSS observations from a fixed, high-end GNSS refractometry snow monitoring setup in the Swiss Alps are reprocessed for the season 2016/17 to investigate the applicability of RTKLIB in post-processing. A fixed, low-cost setup provides continuous SWE estimates in near real time at a low cost for the complete 2021/22 season. Additionally, a mobile, (near) real-time and low-cost setup was designed and evaluated in March 2020. The fixed and mobile multi-frequency GNSS setups demonstrate the feasibility of (near) real-time SWE estimation using GNSS refractometry. Compared to state-of-the-art manual SWE observations, a mean relative bias below 5% is achieved for (near) real-time and post-processed SWE estimation using RTKLIB.

Winter snow and spring temperature have differential effects on vegetation phenology and productivity across Arctic plant communities.

Tundra dominates two-thirds of the unglaciated, terrestrial Arctic. Although this region has experienced rapid and widespread changes in vegetation phenology and productivity over the last several decades, the specific climatic drivers responsible for this change remain poorly understood. Here we quantified the effect of winter snowpack and early spring temperature conditions on growing season vegetation phenology (timing of the start, peak, and end of the growing season) and productivity of the dominant tundra vegetation communities of Arctic Alaska. We used daily remotely sensed normalized difference vegetation index (NDVI), and daily snowpack and temperature variables produced by SnowModel and MicroMet, coupled physically based snow and meteorological modeling tools, to (1) determine the most important snowpack and thermal controls on tundra vegetation phenology and productivity and (2) describe the direction of these relationships within each vegetation community. Our results show that soil temperature under the snowpack, snowmelt timing, and air temperature following snowmelt are the most important drivers of growing season timing and productivity among Arctic vegetation communities. Air temperature after snowmelt was the most important control on timing of season start and end, with warmer conditions contributing to earlier phenology in all vegetation communities. In contrast, the controls on the timing of peak season and productivity also included snowmelt timing and soil temperature under the snowpack, dictated in part by the snow insulating capacity. The results of this novel analysis suggest that while future warming effects on phenology may be consistent across communities of the tundra biome, warming may result in divergent, community-specific productivity responses if coupled with reduced snow insulating capacity lowers winter soil temperature and potential nutrient cycling in the soil.

Solving Challenges of Assimilating Microwave Remote Sensing Signatures With a Physical Model to Estimate Snow Water Equivalent.

Global monitoring of seasonal snow water equivalent (SWE) has advanced significantly over the past decades. However, challenges remain when estimating SWE from passive and active microwave signatures, because a priori characterization of snow properties is required for SWE retrievals. Numerical experiments have shown that utilizing physical snow models to acquire snowpack characterization can potentially improve microwave-based SWE retrievals. This study aims to identify the challenges of assimilating active and passive microwave signatures with physical snow models, and to examine solutions to those challenges. Guided by observations from a point-based study, we designed a sensitivity experiment to quantify the effects of changes in the physically modeled SWE-and of corresponding changes to other snowpack properties-to the microwave-based SWE retrievals. The results indicate that assimilating microwave signatures with physical snow models face some critical challenges associated with the physical relationship between SWE and snow microstructure. We demonstrate these challenges can be overcome if the microwave algorithms account for these relationships.

The Value of Long-Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation-Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications.

Observation-based long-term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long-term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow-2 SWE, and University of Arizona [UA] SWE) were evaluated by seasonal snow cover and land cover classifications over the conterminous United States from 1982 to 2017, using the historical airborne gamma radiation SWE observations (20,738 measurements). We found that there are similar patterns in SSMI/S and GlobSnow-2 SWE when compared against the gamma SWE. However, GlobSnow-2 SWE had better agreement with gamma SWE than SSMI/S SWE in some forested-type classes and maritime and prairie snow classes. As compared to SSMI/S and GlobSnow-2 SWE, UA SWE has much better agreement with gamma SWE in all land cover types and snow classes. Tree cover and topographic heterogeneity affect the agreement between the gamma and gridded SWE and accuracy of gamma SWE itself with the largest differences typically occurring when the percent tree cover was 80% or higher, the terrain slope was steeper than 2.5°, and the elevation range exceeded 100 m. The results demonstrate the reliability of the UA SWE products and the benefits of the gamma radiation approach to measure SWE, especially in forested regions.

Low Cost and Compact FMCW 24 GHz Radar Applications for Snowpack and Ice Thickness Measurements.

Monitoring the evolution of snow on the ground and lake ice-two of the most important components of the changing northern environment-is essential. In this paper, we describe a lightweight, compact and autonomous 24 GHz frequency-modulated continuous-wave (FMCW) radar system for freshwater ice thickness and snow mass (snow water equivalent, SWE) measurements. Although FMCW radars have a long-established history, the novelty of this research lies in that we take advantage the availability of a new generation of low cost and low power requirement units that facilitates the monitoring of snow and ice at remote locations. Test performance (accuracy and limitations) is presented for five different applications, all using an automatic operating mode with improved signal processing: (1) In situ lake ice thickness measurements giving 2 cm accuracy up to ≈1 m ice thickness and a radar resolution of 4 cm; (2) remotely piloted aircraft-based lake ice thickness from low-altitude flight at 5 m; (3) in situ dry SWE measurements based on known snow depth, giving 13% accuracy (RMSE 20%) over boreal forest, subarctic taiga and Arctic tundra, with a measurement capability of up to 3 m in snowpack thickness; (4) continuous monitoring of surface snow density under particular Antarctic conditions; (5) continuous SWE monitoring through the winter with a synchronized and collocated snow depth sensor (ultrasonic or LiDAR sensor), giving 13.5% bias and 25 mm root mean square difference (RMSD) (10%) for dry snow. The need for detection processing for wet snow, which strongly absorbs radar signals, is discussed. An appendix provides 24 GHz simulated effective refractive index and penetration depth as a function of a wide range of density, temperature and wetness for ice and snow.

Inter-Sensor Calibration between HY-2B and AMSR2 Passive Microwave Data in Land Surface and First Result for Snow Water Equivalent Retrieval.

The self-designed HaiYang-2B (HY-2B) satellite was launched on 24 October 2018 in China at 22:57 UT in a 99.34° inclination sun-synchronous orbit. The Scanning Microwave Radiometer (SMR) on the core observatory has the capability to provide near-real-time multi-channel brightness temperature (Tb) observations, which are designed mainly for improving the level of marine forecasting and monitoring, serving the development and utilization of marine resources. After internal calibration and ocean calibration, the first effort to retrieve land surface snow parameters was performed in this study, which obtained extremely low accuracy both in snow extent and snow mass. Accordingly, land inter-sensor calibration was carried out between SMR and the Advanced Microwave Scanning Radiometer 2 (AMSR2) in order to broaden the research and application of SMR data on the Earth's land surface. Finally, we evaluated the consistency of the snow extent and snow mass derived from the initial and land-calibrated SMR data. The results indicated that a systematic SMR cold deviation whose magnitude depends on the channel is present for all the compared channels. After intercalibration, the conformity of the snow extent and snow mass were substantially improved compared to before; the relative bias of the snow extent and snow mass decreased from -49.97% to 2.97% and from -51.71% to 3.01%, respectively.

Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems.

Although seasonal snow is recognized as an important component in the global climate system, the ability of snow to affect plant production remains an important unknown for assessing climate change impacts on vegetation dynamics at high-latitude ecosystems. Here, we compile data on satellite observation of vegetation greenness and spring onset date, satellite-based soil moisture, passive microwave snow water equivalent (SWE) and climate data to show that winter SWE can significantly influence vegetation greenness during the early growing season (the period between spring onset date and peak photosynthesis timing) over nearly one-fifth of the land surface in the region north of 30 degrees, but the magnitude and sign of correlation exhibits large spatial heterogeneity. We then apply an assembled path model to disentangle the two main processes (via changing early growing-season soil moisture, and via changing the growth period) in controlling the impact of winter SWE on vegetation greenness, and suggest that the "moisture" and "growth period" effect, to a larger extent, result in positive and negative snow-productivity associations, respectively. The magnitude and sign of snow-productivity association is then dependent upon the relative dominance of these two processes, with the "moisture" effect and positive association predominating in Central, western North America and Greater Himalaya, and the "growth period" effect and negative association in Central Europe. We also indicate that current state-of-the-art models in general reproduce satellite-based snow-productivity relationship in the region north of 30 degrees, and do a relatively better job of capturing the "moisture" effect than the "growth period" effect. Our results therefore work towards an improved understanding of winter snow impact on vegetation greenness in northern ecosystems, and provide a mechanistic basis for more realistic terrestrial carbon cycle models that consider the impacts of winter snow processes.

Summer and winter drought drive the initiation and spread of spruce beetle outbreak.

This study used Landsat-based detection of spruce beetle (Dendroctonus rufipennis) outbreak over the years 2000-2014 across the Southern Rocky Mountain Ecoregion to examine the spatiotemporal patterns of outbreak and assess the influence of temperature, drought, forest characteristics, and previous spruce beetle activity on outbreak development. During the 1999-2013 period, time series of spruce beetle activity were highly spatially correlated (r > 0.5) at distances <5 km, but remained weakly correlated (r = 0.08) at distances >400 km. Furthermore, cluster analysis on time series of outbreak activity revealed the outbreak developed at multiple incipient locations and spread to unaffected forest, highlighting the importance of both local-scale dispersal and regional-scale drivers in synchronizing spruce beetle outbreak. Spatial overlay analysis and Random Forest modeling of outbreak development show that outbreaks initiate in areas characterized by summer, winter, and multi-year drought and that outbreak spread is strongly linked to the proximity and extent of nearby outbreak, but remains associated with drought. Notably, we find that spruce beetle outbreak is associated with low peak snow water equivalent, not just summer drought. As such, future alterations to both winter and summer precipitation regimes are likely to drive important changes in subalpine forests.

Winter storm Uri and temporary drought relief in the western climate divisions of Texas.

Texas ranks first in the United States in the variety and frequency of most natural disasters, such as flooding, wildfires, hurricanes, winter storms, and droughts. In February 2021, the winter storm named Uri caused an abnormal decline in the air temperature in the southcentral United States, notably in Texas. Right before Uri, most of Texas was going through a drought spell. Thus, this study analyzed how Uri influenced the drought severity, soil profile moisture content, and vegetation cover (Normalized Difference Vegetation Index, NDVI) across Texas. Data used in this analysis was obtained from the web-based geospatial applications gridMET and Crop-CASMA. The collected datasets include the Palmer Drought Severity Index (PDSI), Snow Water Equivalent (SWE), soil moisture, and NDVI at different spatial resolutions. These datasets were aggregated to the county scale using the zonal statistics analysis. The strength of the correlation between SWE and soil moisture was quantified based on the Pearson correlation coefficient. The percentage change in live vegetation cover due to the impact of the frigid temperature and snow coverage across the state was quantified by analyzing the average weekly NDVI before and after the winter storm. There was a reasonably strong correlation between the SWE contribution of Uri and the increase of the rootzone soil moisture (Pearson's r = 0.42). Similarly, the SWE showed a higher correlation with daily rootzone soil moisture with a Pearson's correlation coefficient of 0.49 on March 1. Furthermore, our results revealed a reduction in the NDVI values to less than 0.60 across Texas during the third week of February. Overall, Texas NDVI values seriously decreased due to Uri. Despite its disruptive effects on the state infrastructures and the economy, Uri snow lessened the drought conditions relatively for a short time.

Assimilation of blended in situ-satellite snow water equivalent into the National Water Model for improving hydrologic simulation in two US river basins.

This study investigates the potential of assimilating a 1/8° blended in situ-satellite snow water equivalent (SWE) product for improving snow and streamflow predictions of the National Water Model (NWM). The blended product is assimilated into the NWM via a three-dimensional variational (3DVAR) scheme and a direct insertion (DI) scheme, with a daily (1d) and a every 5 days (5d) assimilation frequencies. The experiments are for the Upper Colorado River Basin (UCRB) and Susquehanna River Basin (SRB), which feature seasonal and ephemeral snow covers, respectively. Results indicate that 3DVAR with a 5d assimilation frequency generally outperforms the other scenarios. The assimilation of the blended SWE product mitigates the underestimation of SWE evident in the open-loop simulations for both basins and its impacts are more pronounced for UCRB than for SRB since snowfall is the main source of precipitation in the former. Assimilation leads to improved streamflow over a majority of SRB subbasins, but over a minority of UCRB subbasins. The degradations in streamflow for UCRB subbasins are mainly caused by the overestimated SWE. In addition, the open-loop simulation often produces an earlier streamflow peak in UCRB, and this error is mitigated to a limited extent by assimilation. These findings in aggregate suggest that the efficacy of snow assimilation is strongly dependent upon the types of snowpack and differential assimilation methods and frequencies.

Snow as a contributor to spring flooding in Belarus.

There are some peculiarities in instrumental observation over snow cover characteristics in Belarus and those of neighboring countries. Maximum snow water equivalent varies around Belarus from 107 mm in Brest to 207 mm in Novogrudok. It differs significantly in terms of years, which is proved by high values of variation coefficients (Cv). Maximums are observed in the south and south-west of Belarus. Minimum values are typical for central and north-eastern parts of Belarus with a stable snow cover. There is a distinct correlation between snow water equivalent and the stations' altitude. We observe a space-time variability of SWE in Belarus' river catchments. Changes in SWE are of cyclic nature. They correlate with current climate fluctuations. In certain parts of Belarus, there is a trend in reduction of SWE up to 8-10 mm in 10 years. This research determines the amount of water that forms spring flood runoff in the catchments of Belarus' big rivers. Possible daily snow melting is calculated in the research as well. It reaches 26 mm in its maximum and 5-6 mm on average. The amount of river runoff water, which is formed within Belarus, is 58 km3. The amount of melt water is 11 km3, which accounts for 19%. In particularly extreme years, melt water reaches 29 km3, which is over a half of all annual river runoff.

Winter melt trends portend widespread declines in snow water resources.

In many mountainous regions, winter precipitation accumulates as snow that melts in spring and summer, providing water to one billion people globally. Climate warming and earlier snowmelt compromises this natural water storage. While snowpack trend analyses commonly focus on snow water equivalent (SWE), we propose that trends in accumulation season snowmelt serve as a critical indicator of hydrologic change. Here we compare long-term changes in snowmelt and SWE from snow monitoring stations in western North America and find 34% of stations exhibit increasing winter snowmelt trends (p < 0.05), a factor of three larger than the 11% showing SWE declines (p < 0.05). Snowmelt trends are highly sensitive to temperature and an underlying warming signal, while SWE trends are more sensitive to precipitation variability. Thus, continental-scale snow water resources are in steeper decline than inferred from SWE trends alone. More winter snowmelt will complicate future water resource planning and management.

Shortened duration and reduced area of frozen soil in the Northern Hemisphere.

The changes in near-surface soil freeze-thaw cycles (FTCs) are crucial to understanding the related hydrological and biological processes in terrestrial ecosystems under a changing climate. However, long-term dynamics of soil FTCs at the hemisphere scale and the underlying mechanisms are not well understood. In this study, the spatiotemporal patterns and main driving factors of soil FTCs across the Northern Hemisphere (NH) during 1979-2017 were analyzed using multisource data fusion and attribution approaches. Our results showed that the duration and the annual mean area of frozen soil in the NH decreased significantly at rates of 0.13 ± 0.04 days/year and 4.9 × 104 km2/year, respectively, over the past 40 years. These were mainly because the date of frozen soil onset was significantly delayed by 0.1 ± 0.02 days/year, while the end of freezing and onset of thawing were substantially advanced by 0.21 ± 0.02 and 0.15 ± 0.03 days/year, respectively. Moreover, the interannual FTC changes were more drastic in Eurasia than in North America, especially at mid-latitudes (30°-45° N) and in Arctic regions (>75° N). More importantly, our results highlighted that near-surface air temperature (T a ) and snowpack are the main driving factors of the spatiotemporal variations in soil FTCs. Furthermore, our results suggested that the long-term dynamics of soil FTCs at the hemisphere scale should be considered in terrestrial biosphere models to reduce uncertainties in future simulations.

Identifying climate change impacts on surface water supply in the southern Central Valley, California.

Mountain regions in arid and semi-arid climates, such as California, are considered particularly sensitive to climate change because global warming is expected to alter snowpack storage and related surface water supply. It is therefore important to accurately capture snowmelt processes in watershed models for climate change impact assessment. In this study we use the Soil and Water Assessment Tool (SWAT) to estimate projected changes in snowpack and streamflow in four alpine tributaries to the agriculturally important but less studied southern Central Valley, California. Watershed responses are evaluated for four CMIP5 climate models (HadGEM_ES, CNRM-CM5, CanESM2 and MIROC5) and two emission scenarios (RCP 4.5 and RCP 8.5) for 2020-2099. SWAT models are calibrated following a dual-objective, lumped calibration approach with an automatic calibration against observed streamflow (stage 1) and a manual calibration against reconstructed Parallel Energy Balance (ParBal) snow water equivalent (SWE) data (stage 2). Results indicate that under a warming climate, peak streamflow is expected to increase 0.5-4 times in magnitude in the coming decades and to arrive 2-4 months earlier in the year because of earlier snowmelt. In the foreseeable future, snow cover will reduce gradually in the lower elevations and diminish at higher rates at higher elevation towards the end of the 21st century. Surface water supply is predicted to increase in the southern Central Valley under the evaluated scenarios but increased temporal variability (wetter wet seasons and drier dry seasons) will create new challenges for managing supply. The study further highlights that the use of remote sensing based, reconstructed SWE data could fill the current gap of limited in-situ SWE observations to improve the snow calibration of SWAT to better predict climate change impacts in semi-arid, snow-dominated watersheds.

Introducing intense rainfall and snowmelt variables to implement a process-related non-stationary shallow landslide susceptibility analysis.

The study objective was to derive a susceptibility model for shallow landslides that could include process-related non-stationary variables, to be adaptable to climate changes. We selected the territory of the Mont-Emilius and Mont-Cervin Mountain Communities (northern Italy) as the study area. To define summary variables related to landslide predisposing and triggering processes, we investigated the relationships between landslide occurrences and intense rainfall and snowmelt events (period 1991-2020). For landslide susceptibility mapping, we set up a Generalized Additive Model. We defined a reference model through variable penalization (relief, NDVI, land cover and geology predictors). Similarly, we optimized a model including the climate variables, checking their smooth functions to ensure physical plausibility. Finally, we validated the optimized model through a k-fold cross-validation and performed an evaluation based on contingency tables, area under the receiver operating characteristic curve (AUROC) and variable importance (decrease in explained variance). The climate variables that resulted as being statistically and physically significant are the effective annual number of rainfall events with intensity-duration characteristics above a defined threshold (EATean) and the average number of melting events occurring in a hydrological year (MEn). In the optimized model, EATean and MEn accounted for 5% of the explained deviance. Compared to the reference model, their introduction led to an increase in true positive rate and AUROC of 2.4% and 0.8%, respectively. Also, their inclusion caused a transition of the vulnerability class in 11.0% of the study area. The k-fold validation confirmed the statistical significance and physical plausibility of the meteorological variables in 74% (EATean) and 93% (MEn) of the fitted models. Our results demonstrate the validity of the proposed approach to introduce process-related, non-stationary, physically-plausible climate variables within a shallow landslide susceptibility analysis. Not only do the variables improve the model performance, but they make it adaptable to map the future evolution of landslide susceptibility including climate changes.

Land surface phenology and greenness in Alpine grasslands driven by seasonal snow and meteorological factors.

Snow accumulation and melt have multiple impacts on Land Surface Phenology (LSP) and greenness in Alpine grasslands. Our understanding of these impacts and their interactions with meteorological factors are still limited. In this study, we investigate this topic by analyzing LSP dynamics together with potential drivers, using satellite imagery and other data sources. LSP (start and end of season) and greenness metrics were extracted from time series of vegetation and leaf area index. As explanatory variables we used snow accumulation, snow cover melt date and meteorological factors. We tested for inter-annual co-variation of LSP and greenness metrics with seasonal snow and meteorological metrics across elevations and for four sub-regions of natural grasslands in the Swiss Alps over the period 2003-2014. We found strong positive correlations of snow cover melt date and snow accumulation with the start of season, especially at higher elevation. Autumn temperature was found to be important at the end of season below 2000 m above sea level (m asl), while autumn precipitation was relevant above 2000 m asl, indicating climatic growth limiting factors to be elevation dependent. The effects of snow and meteorological factors on greenness revealed that this metric tends to be influenced by temperatures at high elevations, and by snow melt date at low elevations. Given the high sensitivity of alpine grassland ecosystems, these results suggest that alpine grasslands may be particularly affected by future changes in seasonal snow, to varying degree depending on elevation.

Winter precipitation - not summer temperature - is still the main driver for Alpine shrub growth.

High latitude and altitude environments are universally recognized as particularly sensitive to environmental changes and the current climate warming is inducing remarkable transformations on vegetation assemblage in these temperature-limited regions. However, next to the wealth of studies describing the effect of rising growing season temperature on trees, much less is known about the concurrent effects of precipitation and snowpack dynamics on the other key component of alpine vegetation represented by prostrate life forms. Selecting the most widespread shrub species in the North Hemisphere, we assembled a monospecific (Juniperus communis L.) network of 7 sites overarching the European Alps, measured the annual growth on >330 individuals and assessed the climate-growth associations for the last century (1910-2010) adopting a new model estimating the solid fraction of precipitation from unique highly-resolved daily climate records. Despite the high space-time variability of the yearly precipitation amount and distribution across the region, our analysis found a prominent, consistent and negative role of winter precipitation for shrub growth. Moreover, this crucial role of snow is maintained even in recent years, despite the persistent and significant warming trend. The presence of this underrated key factor for Alpine long-lived vegetation will require a thorough consideration. For the prostrate life form, not only temperature but also the solid fraction of winter precipitation should be considered to improve the projections of future growth trajectories.

Impacts of climate change on snow accumulation and melting processes over mountainous regions in Northern California during the 21st century.

A point-location-based analysis of future climate change impacts on snow accumulation and melting processes was conducted over three study watersheds in Northern California during a 90-year future period by means of snow regime projections. The snow regime projections were obtained by means of a physically-based snow model with dynamically downscaled future climate projections. Then, atmospheric and snow-related variables, and their interrelations during the 21st century were investigated to reveal future climate change impacts on snow accumulation and melting processes. The analysis shows large reductions in snow water equivalent (SWE), snowfall to precipitation (S/P) ratio, and snowmelt through the 21st century. Timing of the peak of the SWE and snowmelt will also change in the future. Meanwhile, the analysis in this study shows that air temperature rise will affect, but will not dominate the future change in snowmelt over the study watersheds. This result implies the importance of considering atmospheric variables other than air temperature, such as precipitation, shortwave radiation, relative humidity, and wind speed even if these variables will not clearly change during the 21st century.

Evaluation of Simulated Snow and Snowmelt Timing in the Community Land Model Using Satellite-based Products and Streamflow Observations.

The purpose of this study was to evaluate snow and snowmelt simulated by version 4 of the Community Land Model (CLM4). We performed uncoupled CLM4 simulations, forced by Modem-Era Retrospective Analysis for Research and Applications Land-only (MERRA-Land) meteorological fields. GlobSnow snow cover fraction (SCF), snow water equivalent (SWE) and satellite-based passive microwave (PMW) snowmelt-off day of year (MoD) data were used to evaluate SCF, SWE, and snowmelt simulations. Simulated runoff was then fed into a river routing scheme and evaluation was performed at 408 snow-dominated catchments using gauge observations. CLM4 and GlobSnow snow cover extent showed a strong agreement, especially during the peak snow cover months. Overall there was a good correlation between simulated and observed SWE (correlation coefficient, R = 0.6). Simulated and observed SWE were similar over areas with relatively flat terrain and moderate forest density. The simulated MoD agreed (MoD differences (CLM4-PMW) = +/-7 days) with observations over 39.4% of the study domain. Snowmelt-off occurred earlier in the model compared to the observations over 39.5 % of the domain and later over 21.1% of the domain. Large differences of MoD were seen in the areas with complex terrain and dense forest cover. We also found that, although streamflow seasonal phase was accurately modeled (R=0.9), the peaks controlled by snowmelt were underestimated. Routed CLM4 streamflow tended to occur early (by 10 days on average).

Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model.

The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE) through the assimilation of MODIS SCF observations. Results show that data assimilation (DA) improved SCF estimates compared to the open-loop model without assimilation (OL), especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL). A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from -0.017 m for OL to -0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD) (0.095 m for OL, 0.093 m for DA). The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from -0.111 m for OL to -0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA.