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1.
Snowpack dynamics through October 2014–June 2017 were described for a forested, sub‐alpine field site in southeastern Wyoming. Point measurements of wetness and density were combined with numerical modeling and continuous time series of snow depth, snow temperature, and snowpack outflow to identify 5 major classes of distinct snowpack conditions. Class (i) is characterized by no snowpack outflow and variable average snowpack temperature and density. Class (ii) is characterized by short durations of liquid water in the upper snowpack, snowpack outflow values of 0.0008–0.005 cm hr?1, an increase in snowpack temperature, and average snow density between 0.25–0.35 g cm?3. Class (iii) is characterized by a partially saturated wetness profile, snowpack outflow values of 0.005–0.25 cm hr?1, snowpack temperature near 0 °C, and average snow density between 0.25–0.40 g cm?3. Class (iv) is characterized by strong diurnal snowpack outflow pattern with values as high as 0.75 cm hr?1, stable snowpack temperature near 0 °C, and stable average snow density between 0.35–0.45 g cm?3. Class (v) occurs intermittently between Classes (ii)–(iv) and displays low snowpack outflow values between 0.0008–0.04 cm hr?1, a slight decrease in temperature relative to the preceding class, and similar densities to the preceding class. Numerical modeling of snowpack properties with SNOWPACK using both the Storage Threshold scheme and Richards' equation was used to quantify the effect of snowpack capillarity on predictions of snowpack outflow and other snowpack properties. Results indicate that both simulations are able to predict snow depth, snow temperature, and snow density reasonably well with little difference between the 2 water transport schemes. Richards' equation more accurately simulates the timing of snowpack outflow over the Storage Threshold scheme, especially early in the melt season and at diurnal timescales.  相似文献   

2.
This study demonstrates the potential value of a combined unmanned aerial vehicle (UAV) Photogrammetry and ground penetrating radar (GPR) approach to map snow water equivalent (SWE) over large scales. SWE estimation requires two different physical parameters (snow depth and density), which are currently difficult to measure with the spatial and temporal resolution desired for basin-wide studies. UAV photogrammetry can provide very high-resolution spatially continuous snow depths (SD) at the basin scale, but does not measure snow densities. GPR allows nondestructive quantitative snow investigation if the radar velocity is known. Using photogrammetric snow depths and GPR two-way travel times (TWT) of reflections at the snow-ground interface, radar velocities in snowpack can be determined. Snow density (RSN) is then estimated from the radar propagation velocity (which is related to electrical permittivity of snow) via empirical formulas. A Phantom-4 Pro UAV and a MALA GX450 HDR model GPR mounted on a ski mobile were used to determine snow parameters. A snow-free digital surface model (DSM) was obtained from the photogrammetric survey conducted in September 2017. Then, another survey in synchronization with a GPR survey was conducted in February 2019 whilst the snowpack was approximately at its maximum thickness. Spatially continuous snow depths were calculated by subtracting the snow-free DSM from the snow-covered DSM. Radar velocities in the snowpack along GPR survey lines were computed by using UAV-based snow depths and GPR reflections to obtain snow densities and SWEs. The root mean square error of the obtained SWEs (384 mm average) is 63 mm, indicating good agreement with independent SWE observations and the error lies within acceptable uncertainty limits.  相似文献   

3.
An increase of the spatial and temporal resolution of snowpack measurements in Alpine or Arctic regions will improve the predictability of flood and avalanche hazards and increase the spatial validity of snowpack simulation models. In the winter season 2009, we installed a ground‐penetrating radar (GPR) system beneath the snowpack to measure snowpack conditions above the antennas. In comparison with modulated frequency systems, GPR systems consist of a much simpler technology, are commercially available and therefore are cheaper. The radar observed the temporal alternation of the snow height over more than 2·5 months. The presented data showed that with moved antennas, it is possible to record the snow height with an uncertainty of less than 8% in comparison with the probed snow depth. Three persistent melt crusts, which formed at the snow surface and were buried by further new snow events, were used as reflecting tracers to follow the snow cover evolution and to determine the strain rates of underlaying layers between adjacent measurements. The height in two‐way travel time of each layer changed over time, which is a cumulative effect of settlement and variation of wave speed in response to densification and liquid water content. The infiltration of liquid water with depth during melt processes was clearly observed during one event. All recorded reflections appeared in concordance with the physical principles (e.g. in phase structure), and one can assume that distinct density steps above a certain threshold result in reflections in the radargram. The accuracy of the used impulse radar system in determining the snow water equivalent is in good agreement with previous studies, which used continuous wave radar systems. The results of this pilot study encourage further investigations with radar measurements using the described test arrangement on a daily basis for continuous destruction‐free monitoring of the snow cover. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

4.
Reliable hydrological forecasts of snowmelt runoff are of major importance for many areas. Ground‐penetrating radar (GPR) measurements are used to assess snowpack water equivalent for planning of hydropower production in northern Sweden. The travel time of the radar pulse through the snow cover is recorded and converted to snow water equivalent (SWE) using a constant snowpack mean density from the drainage basin studied. In this paper we improve the method to estimate SWE by introducing a depth‐dependent snowpack density. We used 6 years measurements of peak snow depth and snowpack mean density at 11 locations in the Swedish mountains. The original method systematically overestimates the SWE at shallow depths (+25% for 0·5 m) and underestimates the SWE at large depths (?35% for 2·0 m). A large improvement was obtained by introducing a depth–density relation based on average conditions for several years, whereas refining this by using separate relations for individual years yielded a smaller improvement. The SWE estimates were substantially improved for thick snow covers, reducing the average error from 162 ± 23 mm to 53 ± 10 mm for depth range 1·2–2·0 m. Consequently, the introduction of a depth‐dependent snow density yields substantial improvements of the accuracy in SWE values calculated from GPR data. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

5.
The upper 30 cm of the soil profile, which hosts the majority of the root biomass, can be considered as the shallow agricultural root zone of most temperate crops. The electromagnetic wave velocity in the soil obtained from reflection hyperbolas in ground-penetrating radar (GPR) data can be used to estimate soil moisture (SM). Finding shallow hyperbolas in a radargram and minimizing the subjective error associated with the hyperbola fitting are the main challenges in this approach. Nevertheless, we were motivated by the recent improvements of hyperbola fitting algorithms, which can reduce the subjective error and processing time. To overcome the difficulty of finding very shallow hyperbolas, we applied the hyperbola fitting method to reflections ranging from 27- to 50-cm depth using a 500-MHz centre-frequency GPR and compared the estimated moisture with vertically installed, 30-cm-long time-domain reflectometry (TDR) sensors. We also compared TDR and GPR sample areas in a 2-D plane using different GPR survey types and different hyperbola depths. SM measured with TDR and GPR were not significantly different according to Mann–Whitney's test. Our analyses showed that a root mean square error of 0.03 m3 m−3 was found between the two methods. In conclusion, the proposed method might be suitable to estimate SM with an acceptable accuracy within the root zone if the soil profile is fairly uniform within the application depth range.  相似文献   

6.
Two ground penetrating radar (GPR) techniques were used to estimate the shallow soil water content at the field scale. The first technique is based on the ground wave velocity measured with a bistatic impulse radar connected to 450 MHz ground-coupled antennas. The second technique is based on inverse modeling of an off-ground monostatic TEM horn antenna in the 0.8–1.6 GHz frequency range. Data were collected on a 8 by 9 m partially irrigated intensive research plot and along four 148.5 m transects. Time domain reflectometry, capacitance sensors, and volumetric soil samples were used as reference measurements. The aim of the study was to test the applicability of the ground wave method and the off-ground inverse modeling approach at the field scale for a soil with a silt loam texture. The results for the ground wave technique were difficult to interpret due to the strong attenuation of the GPR signal, which is related to the silt loam texture at the test site. The root mean square error of the ground wave technique was 0.076 m3 m−3 when compared to the TDR measurements and 0.102 m3 m−3 when compared with the volumetric soil samples. The off-ground monostatic GPR measured less within-field soil water content variability than the reference measurements, resulting in a root mean square error of 0.053 m3 m−3 when compared with the TDR measurements and an error of 0.051 m3 m−3 when compared with the volumetric soil samples. The variability between the two GPR measurements was even larger with a RSME of 0.115 m3 m−3. In summary, both GPR methods did not provide adequate spatial information on soil water content variation at the field scale. The main reason for the deviating results of the ground wave method was the poor data quality due to high silt and clay content at the test site. Additional reasons were shallow reflections and the dry upper soil layer that cannot be detected by the ground wave method. In the case of off-ground GPR, the high sensitivity to the dry surface layer is the most likely reason for the observed deviations. The off-ground GPR results might be improved by using a different antenna that allows data acquisition in a lower frequency range.  相似文献   

7.
Snowpack water equivalent (SWE) is a key variable for water resource management in snow-dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long-term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra- and inter-year variability in meteorological conditions on densification through a cumulative degree-day index derived from North American Regional Reanalysis products. A non-linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross-validation, the predicted densities had a root-mean-square error of 47.1 kg m−3, a mean bias of −0.039 kg m−3, and a Nash-Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression-based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure-from-Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.  相似文献   

8.
Snow is a critical storage component in the hydrologic cycle, but current measurement networks are sparse. In addition, the heterogeneity of snow requires surveying larger areas to measure the areal average. We presented snow measurements using GPS interferometric reflectometry (GPS‐IR). GPS‐IR measures a large area (~100 m2), and existing GPS installations around the world have the potential to expand existing snow measurement networks. GPS‐IR uses a standard, geodetic GPS installation to measure the snow surface via the reflected component of the signal. We reported GPS‐IR snow depth measurements made at Niwot Ridge, Colorado, from October 2009 through June 2010. This site is in a topographic saddle at 3500 m elevation with a peak snow depth of 1.7 m near the GPS antenna. GPS‐IR measurements are compared with biweekly snow surveys, a continuously operating scanning laser system and an airborne light detection and ranging (LIDAR) measurement. The GPS‐IR measurement of peak snowpack (1.36–1.76 m) matches manual measurements (0.95–1.7 m) and the scanning laser (1.16 m). GPS‐IR has RMS error of 13 cm (bias = 10 cm) compared with the laser, although differences between the measurement locations make comparison imprecise. Over the melt season, when the snowpack is more homogenous, the difference between the GPS‐IR and the laser is reduced (RMS = 9 cm, bias = 6 cm). In other locations, the GPS and the LIDAR agree on which areas have more or less snow, but the GPS estimates more snow on the ground on tracks to the west (1.58 m) than the LIDAR (1.14 m). Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

9.
In this paper, a new snow density estimation methodology is proposed for full-polarimetric Synthetic Aperture Radar (SAR) data. The generalized four component polarimetric decomposition with unitary transformation (G4U) based generalized volume parameter is utilized to invert snowpack dielectric constant using the Fresnel transmission coefficients. The snow density is then estimated using an empirical relationship. Six Radarsat-2 fine resolution full-polarimetric C-band datasets were acquired over Himachal Pradesh, India. The near-real time in-situ measurements were collected with the satellite pass to validate the proposed method. The mean absolute error (MAE) of the proposed method is 0.027 g cm−3 and the root mean square error (RMSE) is 0.032 g cm−3. The snow density variation within a season were also analyzed using multi-temporal Radarsat-2 data.  相似文献   

10.
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11.
Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.  相似文献   

12.
Ground‐penetrating radar (GPR) has become a promising technique in the field of snow hydrological research. It is commonly used to measure snow depth, density, and water equivalent over large distances or along gridded snow courses. Having built and tested a mobile lightweight set‐up, we demonstrate that GPR is capable of accurately measuring snow ablation rates in complex alpine terrain. Our set‐up was optimized for efficient measurements and consisted of a multioffset radar with four pairs of antennas mounted to a plastic sled, which was small enough to permit safe and convenient operations. Repeated measurements at intervals of 2 to 7 days were taken during the 2014/2015 winter season along 10 profiles of 50 to 200 m length within two valleys located in the eastern Swiss Alps. Resulting GPR‐based data of snow depth, density, and water equivalent, as well as their respective change over time, were in good agreement with concurrent manual measurements, in particular if accurate alignment between repeated overpasses could be achieved. Corresponding root‐mean‐square error (RMSE) values amounted to 4.2 cm for snow depth, 17 mm for snow water equivalent, and 22 kg/m3 for snow density, with similar RMSE values for corresponding differential data. With this performance, the presented radar set‐up has the potential to provide exciting new and extensive datasets to validate snowmelt models or to complement lidar‐based snow surveys.  相似文献   

13.
Pathways and fate of road deicing salt (NaCl) applied during the 1994–1995 winter were studied for a 14-km section of a major highway that crosses the Oak Ridges Moraine in southern Ontario. Total salt applications over the winter ranged from 29 to 74 kg m−1 of highway, and NaCl concentrations in snow banks adjacent to the roadway reached 9400 mg l−1 during the later stages of snow cover development. This salt was released to the ground surface during snowmelt. Sodium chloride (NaCl) loadings to soil from snow cover during the final melt phase were relatively uniform along the study section (3–5 kg NaCl m−1 of highway). However, the snowpack at all transects retained <50% of applied NaCl, and this shortfall probably reflected direct runoff and infiltration of saline meltwater from the road surface into the adjacent shoulder and right-of-way. Cation exchange with Ca2+ in near-surface soils most likely resulted in preferential retention of Na+ relative to Cl−, although total storage of NaCl in upper soil horizons by winter's end was <15% of deicing salt applications. An environmental tracer (18O) was used to trace movement of saline meltwater through the unsaturated zone underlying the highway. Average meltwater particle velocities at a site underlain by loam soils were 0.02 m d−1, and ca. 280 mm of water was displaced below a depth of 1.86 m over a 78-day period in the spring and summer of 1995. Sodium ion and chloride ion concentrations in water sampled in late summer 1995 at depths >2 m exceeded 500 mg l−1 and 1000 mg l−1, respectively. Approximately 75% of the net flux of NaCl below the upper soil was retained in the 0–2.8 m depth interval at this site, and results from more permeable soils traversed by the highway indicate an even greater penetration of the annual NaCl application into the unsaturated zone along the moraine. This saline water likely recharges groundwater in this portion of the Oak Ridges Moraine.  相似文献   

14.
The Gurbantonggut Desert, China, is an ideal site for study of sublimation from the snowpack because there are sparse vegetation and simple topography, and the wind speed is not large enough to blow snow into the atmosphere from the snowpack. Daily sublimation was measured by manual snow lysimeters at 8:00, and an automatic weather station was deployed at the top of a stout longitudinal dune chain at the southeastern edge of the desert. It is shown that on a daily scale, there was an extremely significant no‐intercept linear relationship between the measured sublimation and that calculated by the bulk aerodynamic method, although the former was only 83.8% of the latter. It is also demonstrated that ?10°C and 2 m/s were the thresholds where the sublimation varied with the air temperature and the wind speed. When these two thresholds were exceeded, the sublimation accelerated. However, the air temperature and the wind speed at 2 m above the ground averaged ?17.2°C and 1.3 m/s, respectively, and the percentages of the time when the air temperature was below ?10 °C and the wind speed was below 2 m/s were 76.9% and 85.1%, respectively. As a result, the rate of sublimation was quite low most of the time, and the thin snowpack remained in a quasi‐static state until the melt stage started. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

15.
Time sequences of tracer release from an alpine snowpack were investigated at Mammoth Mountain, California in 1989. Lysimeter discharge and conductivity were recorded at 30 minute intervals. Three separate applications of chemical tracers were added to the snow surface to provide an ionic signal with known origins in the snowpack. Grab samples of meltwater and snow from snow pits were analysed for chemical composition. There were three distinct discharge periods, each characterized by diurnal fluctuations in discharge and conductivity. An inverse relation between discharge and conductivity was interpreted as the combination of a concentrated signal from regions in the pack less subject to leaching and a relatively dilute signal from near the snow surface where the snow was actively melting Conductivity peaks were highest and diurnal changes greatest immediately following periods of freezing. Grab samples showed little correlation with either 30 minute or daily average conductivity. Relative concentrations of individual ions in meltwater were similar between samples. Non-systematic grab sampling of snowpack meltwater is shown to be potentially misleading because of multiple ionic pulses over the ablation season and strong diurnal fluctuations in chemical concentrations. Continuous measurements of discharge conductivity are a good indicator of diurnal and seasonal changes in the rate of ion release from the snowpack, and should be used to guide sampling. Composite, or time-integrated samples rather than grab samples may be required to estimate daily and weekly rates of ion release in melting snow.  相似文献   

16.
Summary The microwave emissivity of relatively low-loss media such as snow, ice, frozen ground, and lunar soil is strongly influenced by fine-scale layering and by internal scattering. Radiometric data, however, are commonly interpreted using a model of emission from a homogeneous, dielectric halfspace whose emissivity derives exclusively from dielectric properties. Conclusions based upon these simple interpretations can be erroneous. Examples are presented showing that the emission from fresh or hardpacked snow over either frozen or moist soil is governed dominantly by the size distribution of ice grains in the snowpack. Similarly, the thickness of seasonally frozen soil and the concentration of rock clasts in lunar soil noticeably affect, respectively, the emissivities of northern latitude soils in winter and of the lunar regolith. Petrophysical data accumulated in support of the geophysical interpretation of microwave data must include measurements of not only dielectric properties, but also of geometric factors such as finescale layering and size distributions of grains, inclusions, and voids.  相似文献   

17.
Seasonal snowpack dynamics are described through field measurements under contrasting canopy conditions for a mountainous catchment in the Japan Sea region. Microclimatic data, snow accumulation, albedo and lysimeter runoff are given through the complete winter season 2002–03 in (1) a mature cedar stand, (2) a larch stand, and (3) a regenerating cedar stand or opening. The accumulation and melt of seasonal snowpack strongly influences streamflow runoff during December to May, including winter baseflow, mid‐winter melt, rain on snow, and diurnal peaks driven by radiation melt in spring. Lysimeter runoff at all sites is characterized by constant ground melt of 0·8–1·0 mm day−1. Rapid response to mid‐winter melt or rainfall shows that the snowpack remains in a ripe or near‐ripe condition throughout the snow‐cover season. Hourly and daily lysimeter discharge was greatest during rain on snow (e.g. 7 mm h−1 and 53 mm day−1 on 17 December) with the majority of runoff due to rainfall passing through the snowpack as opposed to snowmelt. For both rain‐on‐snow and radiation melt events lysimeter discharge was generally greatest at the open site, although there were exceptions such as during interception melt events. During radiation melt instantaneous discharge was up to 4·0 times greater in the opening compared with the mature cedar, and 48 h discharge was up to 2·5 times greater. Perhaps characteristic of maritime climates, forest interception melt is shown to be important in addition to sublimation in reducing snow accumulation beneath dense canopies. While sublimation represents a loss from the catchment water balance, interception melt percolates through the snowpack and contributes to soil moisture during the winter season. Strong differences in microclimate and snowpack albedo persisted between cedar, larch and open sites, and it is suggested further work is needed to account for this in hydrological simulation models. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

18.
Albert Rango 《水文研究》1993,7(2):121-138
In the last 20 years remote sensing research has led to significant progress in monitoring and measuring certain snow hydrology processes. Snow distribution in a drainage basin can be adequately assessed by visible sensors. Although there are still some interpretation problems, the NOAA-AVHRR sensor can provide frequent views of the areal snow cover in a basin, and snow cover maps are produced operationally by the National Weather Service on about 3000 drainage basins in North America. Measurement of snow accumulation or snow water equivalent with microwave remote sensing has great potential because of the capabilities for depth penetration, all-weather observation and night-time viewing. Several critical areas of research remain, namely, the acquisition of snow grain size information for input to microwave models and improvement in passive microwave resolution from space. Methods that combine both airborne gamma ray and visible satellite remote sensing of the snowpack with field measurements also hold promise for determining areal snow water equivalent. Some remote sensing techniques can also be used to detect different stages of snow metamorphism. Various aspects of snowpack ripening can be detected using microwave and thermal infra-red capabilities. The capabilities for measurement of snow albedo and surface temperature have direct application in both snow metamorphism and snowpack energy balance studies. The potentially most profitable research area here is the study of the bidirectional reflectance distribution function to improve snow albedo measurements. Most of the remote sensing capabilities in snow hydrology have been developed for improving snowmelt-run-off forecasting. Most applications have used the input of snow cover extent to deterministic models, both of the degree day and energy balance types. Snowmelt-run-off forecasts using satellite derived snow cover depletion curves and the models have been successfully made. As the extraction of additional snow cover characteristics becomes possible, remote sensing will have an even greater impact on snow hydrology. Important remote sensing capabilities will become available in the next 20 years through space platform observing systems that will improve our capability to observe the snowpack on an operational basis.  相似文献   

19.
Vertical fractures with openings of less than one centimetre and irregular karst cause abundant diffractions in Ground‐Penetrating Radar (GPR) records. GPR data acquired with half‐wavelength trace spacing are uninterpretable as they are dominated by spatially undersampled scattered energy. To evaluate the potential of high‐density 3D GPR diffraction imaging a 200 MHz survey with less than a quarter wavelength grid spacing (0.05 m × 0.1 m) was acquired at a fractured and karstified limestone quarry near the village of Cassis in Southern France. After 3D migration processing, diffraction apices line up in sub‐vertical fracture planes and cluster in locations of karstic dissolution features. The majority of karst is developed at intersections of two or more fractures and is limited in depth by a stratigraphic boundary. Such high‐resolution 3D GPR imaging offers an unprecedented internal view of a complex fractured carbonate reservoir model analogue. As seismic and GPR wave kinematics are similar, improvements in the imaging of steep fractures and irregular voids at the resolution limit can also be expected from high‐density seismic diffraction imaging.  相似文献   

20.
This paper describes a data assimilation method that uses observations of snow covered area (SCA) to update hydrologic model states in a mountainous catchment in Colorado. The assimilation method uses SCA information as part of an ensemble Kalman filter to alter the sub-basin distribution of snow as well as the basin water balance. This method permits an optimal combination of model simulations and observations, as well as propagation of information across model states. Sensitivity experiments are conducted with a fairly simple snowpack/water-balance model to evaluate effects of the data assimilation scheme on simulations of streamflow. The assimilation of SCA information results in minor improvements in the accuracy of streamflow simulations near the end of the snowmelt season. The small effect from SCA assimilation is initially surprising. It can be explained both because a substantial portion of snowmelts before any bare ground is exposed, and because the transition from 100% to 0% snow coverage occurs fairly quickly. Both of these factors are basin-dependent. Satellite SCA information is expected to be most useful in basins where snow cover is ephemeral. The data assimilation strategy presented in this study improved the accuracy of the streamflow simulation, indicating that SCA is a useful source of independent information that can be used as part of an integrated data assimilation strategy.  相似文献   

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