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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Ultra-Wideband Radar Measurements of Thickness of Snow Over Sea Ice

An accurate knowledge of snow thickness and its variability over sea ice is crucial for determining the overall polar heat and freshwater budget, which influences the global climate. Recently, algorithms have been developed to extract snow thicknesses from passive microwave satellite data. However, validation of these data over the large footprint of the passive microwave sensor has been a challenge. The only method used thus far has been with meter sticks during ship cruises. To address this problem, we developed an ultra wideband frequency-modulated continuous-wave (FM-CW) radar to measure snow thickness over sea ice. We made snow-thickness measurements over Antarctic sea ice by operating the radar from a sled during September and October, 2003. We performed radar measurements over 11 stations with varying snow thickness between 4 and 85 cm. We observed excellent agreement between radar estimates of snow thickness with physical measurements, achieving a correlation coefficient of 0.95 and a vertical resolution of about 3 cm.

Kanagaratnam, P.↗
NASA NTRS2005

Spatial Scaling of Snow Observations and Microwave Emission Modeling During CLPX and Appropriate Satellite Sensor Resolution

Accurate estimates of snow water equivalent and other properties play an important role in weather, natural hazard, and hydrological forecasting and climate modeling over a range of scales in space and time. Remote sensing-derived estimates have traditionally been of the "snapshot" type, but techniques involving models with assimilation are also being explored. In both cases, forward emission models are useful to understand the observed passive microwave signatures and developing retrieval algorithms. However, mismatches between passive microwave sensor resolutions and the scales of processes controlling subpixel heterogeneity can affect the accuracy of the estimates. Improving the spatial resolution of new passive microwave satellite sensors is a major desire in order to (literally) resolve such subpixel heterogeneity, but limited spacecraft and mission resources impose severe constraints and tradeoffs. In order to maximize science return while mitigating risk for a satellite concept, it is essential to understand the scaling behavior of snow in terms of what the sensor sees (brightness temperature) as well as in terms of the actual variability of snow. NASA's Cold Land Processes Experiment-1 (CLPX-1: Colorado, 2002 and 2003) was designed to provide data to measure these scaling behaviors for varying snow conditions in areas with forested, alpine, and meadow/pasture land cover. We will use observations from CLPX-1 ground, airborne, and satellite passive microwave sensors to examine and evaluate the scaling behavior of observed and modeled brightness temperatures and observed and retrieved snow parameters across scales from meters to 10's of kilometers. The conclusions will provide direct examples of the appropriate spatial sampling scales of new sensors for snow remote sensing. The analyses will also illustrate the effects and spatial scales of the underlying phenomena (e.g., land cover) that control subpixel heterogeneity.

Kim, Edward J.↗
NASA NTRS2010

Enhanced Surface Warming and Accelerated Snow Melt in the Himalayas and Tibetan Plateau Induced by Absorbing Aerosols

Numerical experiments with the NASA finite-volume general circulation model show that heating of the atmosphere by dust and black carbon can lead to widespread enhanced warming over the Tibetan Plateau (TP) and accelerated snow melt in the western TP and Himalayas. During the boreal spring, a thick aerosol layer, composed mainly of dust transported from adjacent deserts and black carbon from local emissions, builds up over the Indo-Gangetic Plain, against the foothills of the Himalaya and the TP. The aerosol layer, which extends from the surface to high elevation (approx.5 km), heats the mid-troposphere by absorbing solar radiation. The heating produces an atmospheric dynamical feedback the so-called elevated-heat-pump (EHP) effect, which increases moisture, cloudiness, and deep convection over northern India, as well as enhancing the rate of snow melt in the Himalayas and TP. The accelerated melting of snow is mostly confined to the western TP, first slowly in early April and then rapidly from early to mid-May. The snow cover remains reduced from mid-May through early June. The accelerated snow melt is accompanied by similar phases of enhanced warming of the atmosphere-land system of the TP, with the atmospheric warming leading the surface warming by several days. Surface energy balance analysis shows that the short-wave and long-wave surface radiative fluxes strongly offset each other, and are largely regulated by the changes in cloudiness and moisture over the TP. The slow melting phase in April is initiated by an effective transfer of sensible heat from a warmer atmosphere to land. The rapid melting phase in May is due to an evaporation-snow-land feedback coupled to an increase in atmospheric moisture over the TP induced by the EHP effect.

Lau, William K.↗
NASA NTRS2013

Evaluation of MODIS Albedo Product (MCD43A) over Grassland, Agriculture and Forest Surface Types During Dormant and Snow-Covered Periods

This study assesses the Moderate-resolution Imaging Spectroradiometer (MODIS) BRDF/albedo 8 day standard product and products from the daily Direct Broadcast BRDF/albedo algorithm, and shows that these products agree well with ground-based albedo measurements during the more difficult periods of vegetation dormancy and snow cover. Cropland, grassland, deciduous and coniferous forests are considered. Using an integrated validation strategy, analyses of the representativeness of the surface heterogeneity under both dormant and snow-covered situations are performed to decide whether direct comparisons between ground measurements and 500-m satellite observations can be made or whether finer spatial resolution airborne or spaceborne data are required to scale the results at each location. Landsat Enhanced Thematic Mapper Plus (ETM +) data are used to generate finer scale representations of albedo at each location to fully link ground data with satellite data. In general, results indicate the root mean square errors (RMSEs) are less than 0.030 over spatially representative sites of agriculture/grassland during the dormant periods and less than 0.050 during the snow-covered periods for MCD43A albedo products. For forest, the RMSEs are less than 0.020 during the dormant period and 0.025 during the snow-covered periods. However, a daily retrieval strategy is necessary to capture ephemeral snow events or rapidly changing situations such as the spring snow melt.

MODIS↗
NASA NTRS2016

Ensemble Mean Density and its Connection to Other Microphysical Properties of Falling Snow as Observed in Southern Finland

In this study measurements collected during winters 2013/2014 and 2014/2015 at the University of Helsinki measurement station in Hyytiala are used to investigate connections between ensemble mean snow density, particle fall velocity and parameters of the particle size distribution (PSD). The density of snow is derived from measurements of particle fall velocity and PSD, provided by a particle video imager, and weighing gauge measurements of precipitation rate. Validity of the retrieved density values is checked against snow depth measurements. A relation retrieved for the ensemble mean snow density and median volume diameter is in general agreement with previous studies, but it is observed to vary significantly from one winter to the other. From these observations, characteristic mass- dimensional relations of snow are retrieved. For snow rates more than 0.2mm/h, a correlation between the intercept parameter of normalized gamma PSD and median volume diameter was observed.

Tiira, Jussi↗
NASA NTRS2016

Snow Process Estimation Over the Extratropical Andes Using a Data Assimilation Framework Integrating MERRA Data and Landsat Imagery

A data assimilation framework was implemented with the objective of obtaining high resolution retrospective snow water equivalent (SWE) estimates over several Andean study basins. The framework integrates Landsat fractional snow covered area (fSCA) images, a land surface and snow depletion model, and the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis as a forcing data set. The outputs are SWE and fSCA fields (1985-2015) at a resolution of 90 m that are consistent with the observed depletion record. Verification using in-situ snow surveys showed significant improvements in the accuracy of the SWE estimates relative to forward model estimates, with increases in correlation (0.49-0.87) and reductions in root mean square error (0.316 m to 0.129 m) and mean error (-0.221 m to 0.009 m). A sensitivity analysis showed that the framework is robust to variations in physiography, fSCA data availability and a priori precipitation biases. Results from the application to the headwater basin of the Aconcagua River showed how the forward model versus the fSCA-conditioned estimate resulted in different quantifications of the relationship between runoff and SWE, and different correlation patterns between pixel-wise SWE and ENSO. The illustrative results confirm the influence that ENSO has on snow accumulation for Andean basins draining into the Pacific, with ENSO explaining approximately 25% of the variability in near-peak (1 September) SWE values. Our results show how the assimilation of fSCA data results in a significant improvement upon MERRA-forced modeled SWE estimates, further increasing the utility of the MERRA data for high-resolution snow modeling applications.

SWE↗
NASA NTRS2015

The Potential for Snow to Supply Human Water Demand in the Present and Future

Runoff from snowmelt is regarded as a vital water source for people and ecosystems throughout the Northern Hemisphere (NH). Numerous studies point to the threat global warming poses to the timing and magnitude of snow accumulation and melt. But analyses focused on snow supply do not show where changes to snowmelt runoff are likely to present the most pressing adaptation challenges, given sub-annual patterns of human water consumption and water availability from rainfall. We identify the NH basins where present spring and summer snowmelt has the greatest potential to supply the human water demand that would otherwise be unmet by instantaneous rainfall runoff. Using a multi-model ensemble of climate change projections, we find that these basins - which together have a present population of approx. 2 billion people - are exposed to a 67% risk of decreased snow supply this coming century. Further, in the multi-model mean, 68 basins (with a present population of more than 300 million people) transition from having sufficient rainfall runoff to meet all present human water demand to having insufficient rainfall runoff. However, internal climate variability creates irreducible uncertainty in the projected future trends in snow resource potential, with about 90% of snow-sensitive basins showing potential for either increases or decreases over the near-term decades. Our results emphasize the importance of snow for fulfilling human water demand in many NH basins, and highlight the need to account for the full range of internal climate variability in developing robust climate risk management decisions.

Mankin, Justin S.↗
NASA NTRS2017

A Method for Snow Reanalysis: The Sierra Nevada (USA) Example

This work presents a state-of-the art methodology for constructing snow water equivalent (SWE) reanalysis. The method is comprised of two main components: (1) a coupled land surface model and snow depletion curve model, which is used to generate an ensemble of predictions of SWE and snow cover area for a given set of (uncertain) inputs, and (2) a reanalysis step, which updates estimation variables to be consistent with the satellite observed depletion of the fractional snow cover time series. This method was applied over the Sierra Nevada (USA) based on the assimilation of remotely sensed fractional snow covered area data from the Landsat 5-8 record (1985-2016). The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlation greater than 0.95 compared with in situ SWE observations. The method (fully Bayesian), resolution (daily, 90-meter), temporal extent (31 years), and accuracy provide a unique dataset for investigating snow processes. This presentation illustrates how the reanalysis dataset was used to provide a basic accounting of the stored snowpack water in the Sierra Nevada over the last 31 years and ultimately improve real-time streamflow predictions.

Bayesian↗
NASA NTRS2019

Snow Depth Variability in the Northern Hemisphere Mountains Observed from Space

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map the snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4,000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

Hans Lievens↗
NASA NTRS2019

Evaluation of MODIS and VIIRS Cloud-Gap-Filled Snow-Cover Products for Production of an Earth Science Data Record

MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products have been available since 2000 – following the 1999 launch of the Terra MODIS and the 2002 launch of the Aqua MODIS – and include global snow-cover extent (SCE) (swath, daily, and 8 d composites) at 500 m and ∼5 km spatial resolutions. These products are used extensively in hydrological modeling and climate studies. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has provided improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products at a 375 m spatial resolution have been available since 2011 and are currently being reprocessed for Collection 2 (C2). Both the MODIS C6.1 and the VIIRS C2 products will be available for download from the National Snow and Ice Data Center beginning in early 2020 with the complete time series available in 2020. To address the need for a cloud-reduced or cloud-free daily SCE product for both MODIS and VIIRS, a daily cloud-gap-filled (CGF) snow-cover algorithm was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500 m resolution CGF SCE map products from MODIS. VNP10A1F is the daily, 375 m resolution CGF SCE map product from VIIRS. These CGF products include quality-assurance data such as cloud-persistence statistics showing the age of the observation in each pixel. The objective of this paper is to introduce the new MODIS and VIIRS standard CGF daily SCE products and to provide a preliminary evaluation of uncertainties in the gap-filling methodology so that the products can be used as the basis for a moderate-resolution Earth science data record (ESDR) of SCE. Time series of the MODIS and VIIRS CGF products have been developed and evaluated at selected study sites in the US and southern Canada. Observed differences, although small, are largely attributed to cloud masking and differences in the time of day of image acquisition. A nearly 3-month time-series comparison of Terra MODIS and S-NPP VIIRS CGF snow-cover maps for a large study area covering all or parts of 11 states in the western US and part of southwestern Canada reveals excellent correspondence between the Terra MODIS and S-NPP VIIRS products, with a mean difference of 11 070 sqkm, which is ∼0.45 % of the study area. According to our preliminary validation of the Terra and Aqua MODIS CGF SCE products in the western US study area, we found higher accuracy of the Terra product compared with the Aqua product. The MODIS CGF SCE data record beginning in 2000 has been extended into the VIIRS era, which should last at least through the early 2030s.

Hall, Dorothy K.↗
NASA NTRS2020

Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)

Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and cold/bright surfaces; cloud mask algorithms often heavily rely on shortwave infrared (IR) channels over such surfaces. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) does not have infrared channels, which makes cloud detection over snow and ice surfaces even more challenging. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios in the A band (764, 780 nm) and B band (688, 680 nm) for cloud detection over the snow and ice surfaces. We develop a novel elevation and zenith-angle-dependent threshold scheme based on radiative transfer model simulations that achieves significant improvements over the existing algorithm. When compared against a composite cloud mask based on geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) sensors, the positive detection rate over snow and ice surfaces increased from around 36 % to 65 % while the false detection rate dropped from 50 % to 10 % for observations of January 2016 and 2017. The improvement in July is less substantial due to relatively better performance in the current algorithm. The new algorithm is applicable for all snow and ice surfaces including Antarctic, sea ice, high-latitude snow, and high-altitude glacier regions. This method is less reliable when clouds are optically thin or below 3 km because the sensitivity is low in oxygen band ratios for these cases.

EPIC↗
NASA NTRS2018

Impacts of Snow and Cloud Covers on Satellite-Derived PM 2.5 Levels

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM 2.5 ) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD. As a result, the fully covered and unbiased PM 2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM 2.5 and made full-coverage PM 2.5 predictions with the consideration of these impacts. To estimate the missing AOD, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The“out-of-bag” R 2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM 2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM 2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R 2 of 0.82. In the gap-filling models, the snow fraction was of higher significance in the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM 2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM 2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM 2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD for predicting PM 2.5 levels with high resolutions and complete coverage.

AOD↗
NASA NTRS2021

Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤100 kg/sq. m) and low values of cloud liquid water path (LWP 100–150 g/sq. m), the scattering of light snowfall (intensities ≤0.5 mm/ h) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg/sq. m and the LWP is greater than 100–150 g/sq. m, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg/sq. m and LWP is lower than 100–150 g/sq. m, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

Zeinab Takbiri↗
NASA NTRSDate not supplied

Understanding the Vertical Slope of Mesoscale Snow Band Reflectivity Signatures

Mesoscale snow bands are frequently responsible for dumping large amounts of snow over a relatively small region and pose a considerable forecasting challenge. The ongoing NASA Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign observed several instances of mesoscale snow bands during January and February of 2020 and 2022. These snow bands were sampled by a combination of airborne (NASA ER-2 and P-3) and ground-based remote sensing and in-situ instruments. Along-band cross sections of these snow bands reveal that many of them are vertically sloped. Our preliminary analysis into one of these snow bands suggests that the vertical slope is not maintained by isentropic lift and does not appear to be tied to horizontal deformation. A combined analysis of the reflectivity and wind retrievals from the ER-2 radars raises the possibility that these features could be produced by vertical wind shear layers deforming fall streaks into bands that extend hundreds of kilometers. The present study will focus on extending our preliminary analysis to additional cases and understanding the mechanisms that support these features using a combination of radar data, in-situ aircraft measurements, and numerical modeling.

Charles Helms↗
NASA NTRSDate not supplied

AMSR-2 Daily Snow Depth Data Product Using a Neural Network Algorithm Trained by Collocated ICESat-2 Measurements

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu 1 et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu 2 et al. (2022,2024) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, a neural network (NN) algorithm, employing several channels from AMSR-2 and the humidity vertical profiles GMAO GEOS-IT, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained NN snow-depth was applied to 2014-2020 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. This paper used AMSR-2 data, a passive microwave instrument to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
NASA NTRS2024

Quantifying Volumetric Scattering Bias in ICESat-2 and Operation IceBridge Altimetry Over Greenland Firn and Aged Snow

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has collected surface elevation measurements for over 5 years. ICESat-2 carries an instrument that emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat-2 acquisitions during the melting season. We performed two case studies over the Greenland Ice Sheet to quantify bias in ICESat-2 signals over snow: one to validate Airborne Topographic Mapper (ATM) data against Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) grain sizes, and a second to estimate ICESat-2 bias relative to ATM. We used snow optical grain sizes derived from ATM and AVIRIS-NG to attribute altimetry bias to snowpack properties. For the first case study, the mean and standard deviation of optical grain sizes were 340 ± 65 µm (AVIRIS-NG) and 670 ± 420 µm (ATM). A mean altimetry bias of 4.81 ± 1.76 cm was found for ATM, with larger biases linked to increases in grain size. In the second case study, we found a mean grain size of 910 ± 381 µm and biases of 6.42 ± 1.77 cm (ICESat-2) and 9.82 ± 0.97 cm (ATM). The grain sizes and densities needed to recreate biases with a model are uncommon in nature, so we propose that additional surface attributes must be considered to characterize ICESat-2 bias over snow. The altimetry biases are within the accuracy requirements of the ICESat-2 mission, but we cannot rule out more significant errors over coarse-grained snow.

Zachary Fair↗
NASA NTRS2024

Sentinel-1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps

Seasonal snow is an important water source and contributor to river discharge in mountainous regions. Therefore the amount of snow and its distribution are necessary inputs for hydrological modeling. Recent research has shown the potential of the Sentinel-1 radar satellite to map snow depth (SD) at sub-kilometer resolution in mountainous regions. In this study we assimilate these new SD retrievals into the Noah-Multiparameterization land surface model using an ensemble Kalman filter for the western European Alps. The land surface model was coupled to the Hydrological Modeling and Analysis Platform (HyMAP), a global flow routing scheme that provides simulations of routed river discharge. The performance with different precipitation forcing inputs, namely MERRA-2 (with and without gauge based correction) and ERA5, was compared based on in situ precipitation and SD stations, with ERA5 leading to the best SD performance. The Sentinel-1 based data assimilation (DA) results show small but systematic improvements for SD estimates, with the mean absolute error reducing from 36.4 cm for the open loop (OL) to 35.6 cm for the DA across all stations and timesteps, improving 318 out of 516 in situ sites. The DA updates in SD also result in enhanced snow water equivalent and discharge simulations. The median temporal correlation between discharge simulations and measurements increases from 0.73 to 0.78 for the DA. This study demonstrates the utility of the Sentinel-1 SD retrievals to improve not only the representation of snow in mountain ranges, but also the snow melt contribution to river discharge, and hydrological modeling in general.

Isis Brangers↗
DOE OSTI2022

The impact of forest-controlled snow variability on late-season streamflow varies by climatic region and forest structure

Previous studies have documented how forests influence snow at fine spatial scales, but none have documented the influence that existing forest-snow variability has on streamflow. Here to test how much forest-controlled snow variability influences streamflow, a tiling parameterization based on classifications from high-resolution (1–3 m) vegetation maps was incorporated into the Distributed Hydrology and Soil Vegetation Model (DHSVM). Within each grid cell (90–150 m), the tiling parameterization simulated forest-snow variability with four independently evolving snowpacks. Each tile had unique radiation conditions to represent conditions underneath the canopy, in exposed areas, and along north- and south-facing forest edges. This tiled parameterization was used to test where and when detailed forest-snow modelling should be considered further and where and when the impacts are too small to be worth the effort. To test this, tiled model simulations of streamflow were compared to non-tiled model simulations in the Sierra Nevada, CA, the Jemez Mountains, NM, and the Eastern Cascades, WA. In Tuolumne, CA, the tiled model simulated little difference in grid cell average SWE, and late-season streamflow decreased by only 3%–4% compared to the non-tiled model. In Jemez, NM, the tiled model decreased late-season streamflow by 18% due to increased sublimation. In Chiwawa, WA, the tiled model increased late-season streamflow by 15% due to high shortwave radiation attenuation and less longwave radiation enhancement from the forest. Furthermore, within the Chiwawa, a substantial silvicultural practice was synthetically implemented to increase the north-facing edge's fractional area. This silvicultural experiment, which used the same fractional forest area in all simulations increased late-season streamflow by 35% compared to tiled model simulations that did not represent forest edges. In conclusion, representing forest-SWE variability had an effect on late-season streamflow in some watersheds but not in others based on the fractional area of the forest edges, forest characteristics, and climate conditions.

54 ENVIRONMENTAL SCIENCES↗